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3f39bdfa40f579218f6997b3c6c388ac8cba5c5b059c8d62181d6f3987c72c0d | def to_dict(self):
'Returns the model properties as a dict'
result = {}
for (attr, _) in six.iteritems(self.swagger_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map((lambda x: (x.to_dict() if hasattr(x, 'to_dict') else x)), value))
elif hasattr(value, 'to_dict'):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map((lambda item: ((item[0], item[1].to_dict()) if hasattr(item[1], 'to_dict') else item)), value.items()))
else:
result[attr] = value
if issubclass(CertificateUsageResource, dict):
for (key, value) in self.items():
result[key] = value
return result | Returns the model properties as a dict | octopus_deploy_swagger_client/models/certificate_usage_resource.py | to_dict | cvent/octopus-deploy-api-client | 0 | python | def to_dict(self):
result = {}
for (attr, _) in six.iteritems(self.swagger_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map((lambda x: (x.to_dict() if hasattr(x, 'to_dict') else x)), value))
elif hasattr(value, 'to_dict'):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map((lambda item: ((item[0], item[1].to_dict()) if hasattr(item[1], 'to_dict') else item)), value.items()))
else:
result[attr] = value
if issubclass(CertificateUsageResource, dict):
for (key, value) in self.items():
result[key] = value
return result | def to_dict(self):
result = {}
for (attr, _) in six.iteritems(self.swagger_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map((lambda x: (x.to_dict() if hasattr(x, 'to_dict') else x)), value))
elif hasattr(value, 'to_dict'):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map((lambda item: ((item[0], item[1].to_dict()) if hasattr(item[1], 'to_dict') else item)), value.items()))
else:
result[attr] = value
if issubclass(CertificateUsageResource, dict):
for (key, value) in self.items():
result[key] = value
return result<|docstring|>Returns the model properties as a dict<|endoftext|> |
cbb19eaa2fc8a113d9e32f924ef280a7e97563f8915f94f65dab438997af2e99 | def to_str(self):
'Returns the string representation of the model'
return pprint.pformat(self.to_dict()) | Returns the string representation of the model | octopus_deploy_swagger_client/models/certificate_usage_resource.py | to_str | cvent/octopus-deploy-api-client | 0 | python | def to_str(self):
return pprint.pformat(self.to_dict()) | def to_str(self):
return pprint.pformat(self.to_dict())<|docstring|>Returns the string representation of the model<|endoftext|> |
772243a2c2b3261a9b954d07aaf295e3c1242a579a495e2d6a5679c677861703 | def __repr__(self):
'For `print` and `pprint`'
return self.to_str() | For `print` and `pprint` | octopus_deploy_swagger_client/models/certificate_usage_resource.py | __repr__ | cvent/octopus-deploy-api-client | 0 | python | def __repr__(self):
return self.to_str() | def __repr__(self):
return self.to_str()<|docstring|>For `print` and `pprint`<|endoftext|> |
d51cf462cb88a2785206d8cc04443be4682f2eab1935e1bff060cfc04b2213d5 | def __eq__(self, other):
'Returns true if both objects are equal'
if (not isinstance(other, CertificateUsageResource)):
return False
return (self.__dict__ == other.__dict__) | Returns true if both objects are equal | octopus_deploy_swagger_client/models/certificate_usage_resource.py | __eq__ | cvent/octopus-deploy-api-client | 0 | python | def __eq__(self, other):
if (not isinstance(other, CertificateUsageResource)):
return False
return (self.__dict__ == other.__dict__) | def __eq__(self, other):
if (not isinstance(other, CertificateUsageResource)):
return False
return (self.__dict__ == other.__dict__)<|docstring|>Returns true if both objects are equal<|endoftext|> |
43dc6740163eb9fc1161d09cb2208a64c7ad0cc8d9c8637ac3264522d3ec7e42 | def __ne__(self, other):
'Returns true if both objects are not equal'
return (not (self == other)) | Returns true if both objects are not equal | octopus_deploy_swagger_client/models/certificate_usage_resource.py | __ne__ | cvent/octopus-deploy-api-client | 0 | python | def __ne__(self, other):
return (not (self == other)) | def __ne__(self, other):
return (not (self == other))<|docstring|>Returns true if both objects are not equal<|endoftext|> |
f7d96aace381a0065dac017fe03f036dcb9874e5b97b366f52fd908b8fdcdbb6 | @requires_scipy_version('0.11')
def test_module_nesting():
'Test that module imports are necessary\n '
proc = Popen([sys.executable, '-c', run_script], stdout=PIPE, stderr=PIPE)
(stdout, stderr) = proc.communicate()
if proc.returncode:
raise AssertionError(stdout) | Test that module imports are necessary | mne/tests/test_import_nesting.py | test_module_nesting | fraimondo/mne-python | 1 | python | @requires_scipy_version('0.11')
def test_module_nesting():
'\n '
proc = Popen([sys.executable, '-c', run_script], stdout=PIPE, stderr=PIPE)
(stdout, stderr) = proc.communicate()
if proc.returncode:
raise AssertionError(stdout) | @requires_scipy_version('0.11')
def test_module_nesting():
'\n '
proc = Popen([sys.executable, '-c', run_script], stdout=PIPE, stderr=PIPE)
(stdout, stderr) = proc.communicate()
if proc.returncode:
raise AssertionError(stdout)<|docstring|>Test that module imports are necessary<|endoftext|> |
709c9475c387102906f242be753c4d414bda8d2fd0f4b65924af64869fdc3591 | def __init__(self, plugin: ProjectPlugin, ancestor: ProjectPlugin):
"Initialize cyclic inheritance error.\n\n Args:\n plugin: A ProjectPlugin\n ancestor: The given ProjectPlugins' ancestor.\n "
super().__init__()
self.plugin = plugin
self.ancestor = ancestor | Initialize cyclic inheritance error.
Args:
plugin: A ProjectPlugin
ancestor: The given ProjectPlugins' ancestor. | src/meltano/core/plugin/project_plugin.py | __init__ | Mu-L/meltano | 0 | python | def __init__(self, plugin: ProjectPlugin, ancestor: ProjectPlugin):
"Initialize cyclic inheritance error.\n\n Args:\n plugin: A ProjectPlugin\n ancestor: The given ProjectPlugins' ancestor.\n "
super().__init__()
self.plugin = plugin
self.ancestor = ancestor | def __init__(self, plugin: ProjectPlugin, ancestor: ProjectPlugin):
"Initialize cyclic inheritance error.\n\n Args:\n plugin: A ProjectPlugin\n ancestor: The given ProjectPlugins' ancestor.\n "
super().__init__()
self.plugin = plugin
self.ancestor = ancestor<|docstring|>Initialize cyclic inheritance error.
Args:
plugin: A ProjectPlugin
ancestor: The given ProjectPlugins' ancestor.<|endoftext|> |
80a7611bc285e6c35d0bbdfb4bff9cf5dfadd5aae3d7dcb555405fe226945752 | def __str__(self):
'Return error message.\n\n Returns:\n A formatted error message string.\n '
return ("{type} '{name}' cannot inherit from '{ancestor}', " + "which itself inherits from '{name}'").format(type=self.plugin.type.descriptor.capitalize(), name=self.plugin.name, ancestor=self.ancestor.name) | Return error message.
Returns:
A formatted error message string. | src/meltano/core/plugin/project_plugin.py | __str__ | Mu-L/meltano | 0 | python | def __str__(self):
'Return error message.\n\n Returns:\n A formatted error message string.\n '
return ("{type} '{name}' cannot inherit from '{ancestor}', " + "which itself inherits from '{name}'").format(type=self.plugin.type.descriptor.capitalize(), name=self.plugin.name, ancestor=self.ancestor.name) | def __str__(self):
'Return error message.\n\n Returns:\n A formatted error message string.\n '
return ("{type} '{name}' cannot inherit from '{ancestor}', " + "which itself inherits from '{name}'").format(type=self.plugin.type.descriptor.capitalize(), name=self.plugin.name, ancestor=self.ancestor.name)<|docstring|>Return error message.
Returns:
A formatted error message string.<|endoftext|> |
563cdd88ee2b003c63192674486c986446474c75238295938ab6e4e1524e0ecf | def __init__(self, plugin_type: PluginType, name: str, inherit_from: (str | None)=None, namespace: (str | None)=None, variant: (str | None)=None, pip_url: (str | None)=None, executable: (str | None)=None, config: (dict | None)=None, commands: (dict | None)=None, default_variant=Variant.ORIGINAL_NAME, **extras):
'ProjectPlugin.\n\n Args:\n plugin_type: PluginType instance.\n name: Plugin name.\n inherit_from: (optional) Name of plugin to inherit from.\n namespace: (optional) Plugin namespace.\n variant: (optional) Plugin variant.\n pip_url: (optional) Plugin install pip url.\n executable: (optional) Executable name.\n config: (optional) Plugin configuration.\n commands: (optional) Plugin commands.\n default_variant: (optional) Default variant for this plugin.\n extras: Extra keyword arguments.\n '
super().__init__(plugin_type, name)
self.inherit_from = (inherit_from if (inherit_from and (inherit_from != name)) else None)
self.custom_definition = None
self._flattened.add('custom_definition')
self._parent = None
if ((not self.inherit_from) and namespace):
self.custom_definition = PluginDefinition(plugin_type, name, namespace, variant=variant, pip_url=pip_url, executable=executable, **extras)
extras = self.custom_definition.extras
self.custom_definition.extras = {}
self.parent = base_plugin_factory(self.custom_definition, variant)
self.namespace = namespace
self.set_presentation_attrs(extras)
self.variant = variant
self.pip_url = pip_url
self.executable = executable
self.commands = Command.parse_all(commands)
self._fallbacks.update(['logo_url', 'description', self.VARIANT_ATTR, 'pip_url', 'executable'])
self._defaults[self.VARIANT_ATTR] = (lambda _: default_variant)
if self.inherit_from:
self._defaults['namespace'] = (lambda plugin: plugin.name.replace('-', '_'))
self._defaults['label'] = (lambda plugin: (f'{plugin.parent.label}: {plugin.name}' if plugin.parent else plugin.name))
else:
self._fallbacks.update(['namespace', 'label'])
self.config = copy.deepcopy((config or {}))
self.extras = extras
if ('profiles' in extras):
logger.warning(('Plugin configuration profiles are no longer supported, ignoring ' + f"`profiles` in '{name}' {plugin_type.descriptor} definition.")) | ProjectPlugin.
Args:
plugin_type: PluginType instance.
name: Plugin name.
inherit_from: (optional) Name of plugin to inherit from.
namespace: (optional) Plugin namespace.
variant: (optional) Plugin variant.
pip_url: (optional) Plugin install pip url.
executable: (optional) Executable name.
config: (optional) Plugin configuration.
commands: (optional) Plugin commands.
default_variant: (optional) Default variant for this plugin.
extras: Extra keyword arguments. | src/meltano/core/plugin/project_plugin.py | __init__ | Mu-L/meltano | 0 | python | def __init__(self, plugin_type: PluginType, name: str, inherit_from: (str | None)=None, namespace: (str | None)=None, variant: (str | None)=None, pip_url: (str | None)=None, executable: (str | None)=None, config: (dict | None)=None, commands: (dict | None)=None, default_variant=Variant.ORIGINAL_NAME, **extras):
'ProjectPlugin.\n\n Args:\n plugin_type: PluginType instance.\n name: Plugin name.\n inherit_from: (optional) Name of plugin to inherit from.\n namespace: (optional) Plugin namespace.\n variant: (optional) Plugin variant.\n pip_url: (optional) Plugin install pip url.\n executable: (optional) Executable name.\n config: (optional) Plugin configuration.\n commands: (optional) Plugin commands.\n default_variant: (optional) Default variant for this plugin.\n extras: Extra keyword arguments.\n '
super().__init__(plugin_type, name)
self.inherit_from = (inherit_from if (inherit_from and (inherit_from != name)) else None)
self.custom_definition = None
self._flattened.add('custom_definition')
self._parent = None
if ((not self.inherit_from) and namespace):
self.custom_definition = PluginDefinition(plugin_type, name, namespace, variant=variant, pip_url=pip_url, executable=executable, **extras)
extras = self.custom_definition.extras
self.custom_definition.extras = {}
self.parent = base_plugin_factory(self.custom_definition, variant)
self.namespace = namespace
self.set_presentation_attrs(extras)
self.variant = variant
self.pip_url = pip_url
self.executable = executable
self.commands = Command.parse_all(commands)
self._fallbacks.update(['logo_url', 'description', self.VARIANT_ATTR, 'pip_url', 'executable'])
self._defaults[self.VARIANT_ATTR] = (lambda _: default_variant)
if self.inherit_from:
self._defaults['namespace'] = (lambda plugin: plugin.name.replace('-', '_'))
self._defaults['label'] = (lambda plugin: (f'{plugin.parent.label}: {plugin.name}' if plugin.parent else plugin.name))
else:
self._fallbacks.update(['namespace', 'label'])
self.config = copy.deepcopy((config or {}))
self.extras = extras
if ('profiles' in extras):
logger.warning(('Plugin configuration profiles are no longer supported, ignoring ' + f"`profiles` in '{name}' {plugin_type.descriptor} definition.")) | def __init__(self, plugin_type: PluginType, name: str, inherit_from: (str | None)=None, namespace: (str | None)=None, variant: (str | None)=None, pip_url: (str | None)=None, executable: (str | None)=None, config: (dict | None)=None, commands: (dict | None)=None, default_variant=Variant.ORIGINAL_NAME, **extras):
'ProjectPlugin.\n\n Args:\n plugin_type: PluginType instance.\n name: Plugin name.\n inherit_from: (optional) Name of plugin to inherit from.\n namespace: (optional) Plugin namespace.\n variant: (optional) Plugin variant.\n pip_url: (optional) Plugin install pip url.\n executable: (optional) Executable name.\n config: (optional) Plugin configuration.\n commands: (optional) Plugin commands.\n default_variant: (optional) Default variant for this plugin.\n extras: Extra keyword arguments.\n '
super().__init__(plugin_type, name)
self.inherit_from = (inherit_from if (inherit_from and (inherit_from != name)) else None)
self.custom_definition = None
self._flattened.add('custom_definition')
self._parent = None
if ((not self.inherit_from) and namespace):
self.custom_definition = PluginDefinition(plugin_type, name, namespace, variant=variant, pip_url=pip_url, executable=executable, **extras)
extras = self.custom_definition.extras
self.custom_definition.extras = {}
self.parent = base_plugin_factory(self.custom_definition, variant)
self.namespace = namespace
self.set_presentation_attrs(extras)
self.variant = variant
self.pip_url = pip_url
self.executable = executable
self.commands = Command.parse_all(commands)
self._fallbacks.update(['logo_url', 'description', self.VARIANT_ATTR, 'pip_url', 'executable'])
self._defaults[self.VARIANT_ATTR] = (lambda _: default_variant)
if self.inherit_from:
self._defaults['namespace'] = (lambda plugin: plugin.name.replace('-', '_'))
self._defaults['label'] = (lambda plugin: (f'{plugin.parent.label}: {plugin.name}' if plugin.parent else plugin.name))
else:
self._fallbacks.update(['namespace', 'label'])
self.config = copy.deepcopy((config or {}))
self.extras = extras
if ('profiles' in extras):
logger.warning(('Plugin configuration profiles are no longer supported, ignoring ' + f"`profiles` in '{name}' {plugin_type.descriptor} definition."))<|docstring|>ProjectPlugin.
Args:
plugin_type: PluginType instance.
name: Plugin name.
inherit_from: (optional) Name of plugin to inherit from.
namespace: (optional) Plugin namespace.
variant: (optional) Plugin variant.
pip_url: (optional) Plugin install pip url.
executable: (optional) Executable name.
config: (optional) Plugin configuration.
commands: (optional) Plugin commands.
default_variant: (optional) Default variant for this plugin.
extras: Extra keyword arguments.<|endoftext|> |
7f63dd78fe98ee7fff1eaee7170c2f4fde56b66728ebcbf45e265b20c51996e6 | @property
def parent(self) -> ProjectPlugin:
'Plugins parent.\n\n Returns:\n Parent ProjectPlugin instance, or None if no parent.\n '
return self._parent | Plugins parent.
Returns:
Parent ProjectPlugin instance, or None if no parent. | src/meltano/core/plugin/project_plugin.py | parent | Mu-L/meltano | 0 | python | @property
def parent(self) -> ProjectPlugin:
'Plugins parent.\n\n Returns:\n Parent ProjectPlugin instance, or None if no parent.\n '
return self._parent | @property
def parent(self) -> ProjectPlugin:
'Plugins parent.\n\n Returns:\n Parent ProjectPlugin instance, or None if no parent.\n '
return self._parent<|docstring|>Plugins parent.
Returns:
Parent ProjectPlugin instance, or None if no parent.<|endoftext|> |
971baefebf1fd7b30e22b0b3045032670a7bd3baefc9cab27141aeee81f20702 | @property
def is_variant_set(self) -> bool:
"Check if variant is set explicitly.\n\n Returns:\n 'True' if variant is set explicitly.\n "
return self.is_attr_set(self.VARIANT_ATTR) | Check if variant is set explicitly.
Returns:
'True' if variant is set explicitly. | src/meltano/core/plugin/project_plugin.py | is_variant_set | Mu-L/meltano | 0 | python | @property
def is_variant_set(self) -> bool:
"Check if variant is set explicitly.\n\n Returns:\n 'True' if variant is set explicitly.\n "
return self.is_attr_set(self.VARIANT_ATTR) | @property
def is_variant_set(self) -> bool:
"Check if variant is set explicitly.\n\n Returns:\n 'True' if variant is set explicitly.\n "
return self.is_attr_set(self.VARIANT_ATTR)<|docstring|>Check if variant is set explicitly.
Returns:
'True' if variant is set explicitly.<|endoftext|> |
1e727e5c4873a591db97b7099074fe232c23a345f585e734e13c173b85ab4ad5 | @property
def info(self) -> dict[(str, str)]:
'Plugin info dict.\n\n Returns:\n Dictionary of plugin info (name, namespace and variant)\n '
return {'name': self.name, 'namespace': self.namespace, 'variant': self.variant} | Plugin info dict.
Returns:
Dictionary of plugin info (name, namespace and variant) | src/meltano/core/plugin/project_plugin.py | info | Mu-L/meltano | 0 | python | @property
def info(self) -> dict[(str, str)]:
'Plugin info dict.\n\n Returns:\n Dictionary of plugin info (name, namespace and variant)\n '
return {'name': self.name, 'namespace': self.namespace, 'variant': self.variant} | @property
def info(self) -> dict[(str, str)]:
'Plugin info dict.\n\n Returns:\n Dictionary of plugin info (name, namespace and variant)\n '
return {'name': self.name, 'namespace': self.namespace, 'variant': self.variant}<|docstring|>Plugin info dict.
Returns:
Dictionary of plugin info (name, namespace and variant)<|endoftext|> |
584e4890dc87abe16ed6f1442cea0a600a1368092b425b0e9f30b97b12215ebd | @property
def info_env(self) -> dict[(str, str)]:
'Plugin environment info.\n\n Returns:\n Dictionary of plugin info formatted as Meltano environment variables.\n '
return flatten({'meltano': {self.type.singular: self.info}}, 'env_var') | Plugin environment info.
Returns:
Dictionary of plugin info formatted as Meltano environment variables. | src/meltano/core/plugin/project_plugin.py | info_env | Mu-L/meltano | 0 | python | @property
def info_env(self) -> dict[(str, str)]:
'Plugin environment info.\n\n Returns:\n Dictionary of plugin info formatted as Meltano environment variables.\n '
return flatten({'meltano': {self.type.singular: self.info}}, 'env_var') | @property
def info_env(self) -> dict[(str, str)]:
'Plugin environment info.\n\n Returns:\n Dictionary of plugin info formatted as Meltano environment variables.\n '
return flatten({'meltano': {self.type.singular: self.info}}, 'env_var')<|docstring|>Plugin environment info.
Returns:
Dictionary of plugin info formatted as Meltano environment variables.<|endoftext|> |
eac08ce2e17c40c31d1411c6d9bfb57ae012be40113297e37c2f2e6e4e999b3f | @property
def all_commands(self) -> dict[(str, Command)]:
'Return all commands for this plugin.\n\n Returns:\n Dictionary of supported commands, including those inherited from the parent plugin.\n '
return {**self._parent.all_commands, **self.commands} | Return all commands for this plugin.
Returns:
Dictionary of supported commands, including those inherited from the parent plugin. | src/meltano/core/plugin/project_plugin.py | all_commands | Mu-L/meltano | 0 | python | @property
def all_commands(self) -> dict[(str, Command)]:
'Return all commands for this plugin.\n\n Returns:\n Dictionary of supported commands, including those inherited from the parent plugin.\n '
return {**self._parent.all_commands, **self.commands} | @property
def all_commands(self) -> dict[(str, Command)]:
'Return all commands for this plugin.\n\n Returns:\n Dictionary of supported commands, including those inherited from the parent plugin.\n '
return {**self._parent.all_commands, **self.commands}<|docstring|>Return all commands for this plugin.
Returns:
Dictionary of supported commands, including those inherited from the parent plugin.<|endoftext|> |
1fa7490c47b4c6f2253b780955852f9e766c89d0f6284f29421e67a0428acb6d | @property
def test_commands(self) -> dict[(str, Command)]:
'Return the test commands for this plugin.\n\n Returns:\n Dictionary of supported test commands, including those inherited from the parent plugin.\n '
return {name: command for (name, command) in self.all_commands.items() if name.startswith('test')} | Return the test commands for this plugin.
Returns:
Dictionary of supported test commands, including those inherited from the parent plugin. | src/meltano/core/plugin/project_plugin.py | test_commands | Mu-L/meltano | 0 | python | @property
def test_commands(self) -> dict[(str, Command)]:
'Return the test commands for this plugin.\n\n Returns:\n Dictionary of supported test commands, including those inherited from the parent plugin.\n '
return {name: command for (name, command) in self.all_commands.items() if name.startswith('test')} | @property
def test_commands(self) -> dict[(str, Command)]:
'Return the test commands for this plugin.\n\n Returns:\n Dictionary of supported test commands, including those inherited from the parent plugin.\n '
return {name: command for (name, command) in self.all_commands.items() if name.startswith('test')}<|docstring|>Return the test commands for this plugin.
Returns:
Dictionary of supported test commands, including those inherited from the parent plugin.<|endoftext|> |
b010a86349ecd00625757519f1f79f72dc1a427e00fdd120fd0f18bf25e24c96 | @property
def supported_commands(self) -> list[str]:
'Return supported command names.\n\n Returns:\n All defined command names for the plugin.\n '
return list(self.all_commands.keys()) | Return supported command names.
Returns:
All defined command names for the plugin. | src/meltano/core/plugin/project_plugin.py | supported_commands | Mu-L/meltano | 0 | python | @property
def supported_commands(self) -> list[str]:
'Return supported command names.\n\n Returns:\n All defined command names for the plugin.\n '
return list(self.all_commands.keys()) | @property
def supported_commands(self) -> list[str]:
'Return supported command names.\n\n Returns:\n All defined command names for the plugin.\n '
return list(self.all_commands.keys())<|docstring|>Return supported command names.
Returns:
All defined command names for the plugin.<|endoftext|> |
6f5520522c22cd7da6a47edc51d09e178e628114930b38bd24974511242e6427 | def env_prefixes(self, for_writing=False) -> list[str]:
'Return environment variable prefixes.\n\n Args:\n for_writing: Include parent prefix (used when writing to env vars)\n\n Returns:\n A list of env prefixes.\n '
prefixes = [self.name, self.namespace]
if for_writing:
prefixes.extend(self._parent.env_prefixes(for_writing=True))
prefixes.append(f'meltano_{self.type.verb}')
return uniques_in(prefixes) | Return environment variable prefixes.
Args:
for_writing: Include parent prefix (used when writing to env vars)
Returns:
A list of env prefixes. | src/meltano/core/plugin/project_plugin.py | env_prefixes | Mu-L/meltano | 0 | python | def env_prefixes(self, for_writing=False) -> list[str]:
'Return environment variable prefixes.\n\n Args:\n for_writing: Include parent prefix (used when writing to env vars)\n\n Returns:\n A list of env prefixes.\n '
prefixes = [self.name, self.namespace]
if for_writing:
prefixes.extend(self._parent.env_prefixes(for_writing=True))
prefixes.append(f'meltano_{self.type.verb}')
return uniques_in(prefixes) | def env_prefixes(self, for_writing=False) -> list[str]:
'Return environment variable prefixes.\n\n Args:\n for_writing: Include parent prefix (used when writing to env vars)\n\n Returns:\n A list of env prefixes.\n '
prefixes = [self.name, self.namespace]
if for_writing:
prefixes.extend(self._parent.env_prefixes(for_writing=True))
prefixes.append(f'meltano_{self.type.verb}')
return uniques_in(prefixes)<|docstring|>Return environment variable prefixes.
Args:
for_writing: Include parent prefix (used when writing to env vars)
Returns:
A list of env prefixes.<|endoftext|> |
6bfecb6dfd5c154f930595825708be1fa58268ec5150b2ee6b2d662bdd020a65 | @property
def extra_config(self) -> list[(str, Any)]:
'Return plugin extra config.\n\n Returns:\n Dictionary of extra config.\n '
return {f'_{key}': value for (key, value) in self.extras.items()} | Return plugin extra config.
Returns:
Dictionary of extra config. | src/meltano/core/plugin/project_plugin.py | extra_config | Mu-L/meltano | 0 | python | @property
def extra_config(self) -> list[(str, Any)]:
'Return plugin extra config.\n\n Returns:\n Dictionary of extra config.\n '
return {f'_{key}': value for (key, value) in self.extras.items()} | @property
def extra_config(self) -> list[(str, Any)]:
'Return plugin extra config.\n\n Returns:\n Dictionary of extra config.\n '
return {f'_{key}': value for (key, value) in self.extras.items()}<|docstring|>Return plugin extra config.
Returns:
Dictionary of extra config.<|endoftext|> |
c83ea5fe5395f87b0ed851d5f63d900538f5f3e5467219306ce98594376dbaa3 | @property
def config_with_extras(self) -> list[(str, Any)]:
'Return config with extras.\n\n Returns:\n Complete config dictionary, including config extras.\n '
return {**self.config, **self.extra_config} | Return config with extras.
Returns:
Complete config dictionary, including config extras. | src/meltano/core/plugin/project_plugin.py | config_with_extras | Mu-L/meltano | 0 | python | @property
def config_with_extras(self) -> list[(str, Any)]:
'Return config with extras.\n\n Returns:\n Complete config dictionary, including config extras.\n '
return {**self.config, **self.extra_config} | @property
def config_with_extras(self) -> list[(str, Any)]:
'Return config with extras.\n\n Returns:\n Complete config dictionary, including config extras.\n '
return {**self.config, **self.extra_config}<|docstring|>Return config with extras.
Returns:
Complete config dictionary, including config extras.<|endoftext|> |
ed765e9208dd701f756a8b2221a390c888b9c7c1b1e4d7388f0e23d6ff78da96 | @property
def settings(self) -> list[SettingDefinition]:
'Return plugin settings.\n\n Returns:\n A list of Plugin settings, including those defined by the parent.\n '
existing_settings = self._parent.settings
return [*existing_settings, *SettingDefinition.from_missing(existing_settings, self.config)] | Return plugin settings.
Returns:
A list of Plugin settings, including those defined by the parent. | src/meltano/core/plugin/project_plugin.py | settings | Mu-L/meltano | 0 | python | @property
def settings(self) -> list[SettingDefinition]:
'Return plugin settings.\n\n Returns:\n A list of Plugin settings, including those defined by the parent.\n '
existing_settings = self._parent.settings
return [*existing_settings, *SettingDefinition.from_missing(existing_settings, self.config)] | @property
def settings(self) -> list[SettingDefinition]:
'Return plugin settings.\n\n Returns:\n A list of Plugin settings, including those defined by the parent.\n '
existing_settings = self._parent.settings
return [*existing_settings, *SettingDefinition.from_missing(existing_settings, self.config)]<|docstring|>Return plugin settings.
Returns:
A list of Plugin settings, including those defined by the parent.<|endoftext|> |
99c48470f080660719dfb70af14f0901bb5d526ad33feedb1de40343821801a2 | @property
def extra_settings(self) -> list[SettingDefinition]:
'Return extra settings.\n\n Returns:\n A list of extra SettingDefinitions, including those defined by the parent.\n '
existing_settings = self._parent.extra_settings
return [*existing_settings, *SettingDefinition.from_missing(existing_settings, self.extra_config)] | Return extra settings.
Returns:
A list of extra SettingDefinitions, including those defined by the parent. | src/meltano/core/plugin/project_plugin.py | extra_settings | Mu-L/meltano | 0 | python | @property
def extra_settings(self) -> list[SettingDefinition]:
'Return extra settings.\n\n Returns:\n A list of extra SettingDefinitions, including those defined by the parent.\n '
existing_settings = self._parent.extra_settings
return [*existing_settings, *SettingDefinition.from_missing(existing_settings, self.extra_config)] | @property
def extra_settings(self) -> list[SettingDefinition]:
'Return extra settings.\n\n Returns:\n A list of extra SettingDefinitions, including those defined by the parent.\n '
existing_settings = self._parent.extra_settings
return [*existing_settings, *SettingDefinition.from_missing(existing_settings, self.extra_config)]<|docstring|>Return extra settings.
Returns:
A list of extra SettingDefinitions, including those defined by the parent.<|endoftext|> |
35f6cadbae7ea574e72e2fa57d2f600aa9a3b54dfde5027dbc56d54785f71558 | @property
def settings_with_extras(self) -> list[SettingDefinition]:
'Return all settings.\n\n Returns:\n A complete list of SettingDefinitions, including extras.\n '
return [*self.settings, *self.extra_settings] | Return all settings.
Returns:
A complete list of SettingDefinitions, including extras. | src/meltano/core/plugin/project_plugin.py | settings_with_extras | Mu-L/meltano | 0 | python | @property
def settings_with_extras(self) -> list[SettingDefinition]:
'Return all settings.\n\n Returns:\n A complete list of SettingDefinitions, including extras.\n '
return [*self.settings, *self.extra_settings] | @property
def settings_with_extras(self) -> list[SettingDefinition]:
'Return all settings.\n\n Returns:\n A complete list of SettingDefinitions, including extras.\n '
return [*self.settings, *self.extra_settings]<|docstring|>Return all settings.
Returns:
A complete list of SettingDefinitions, including extras.<|endoftext|> |
2bc5a790281dc49b97544d0c92b005ea929b02340200c43a2561a06e23705cc7 | def is_custom(self) -> bool:
"Return if plugin is custom.\n\n Returns:\n 'True' is plugin is custom.\n "
return (self.custom_definition is not None) | Return if plugin is custom.
Returns:
'True' is plugin is custom. | src/meltano/core/plugin/project_plugin.py | is_custom | Mu-L/meltano | 0 | python | def is_custom(self) -> bool:
"Return if plugin is custom.\n\n Returns:\n 'True' is plugin is custom.\n "
return (self.custom_definition is not None) | def is_custom(self) -> bool:
"Return if plugin is custom.\n\n Returns:\n 'True' is plugin is custom.\n "
return (self.custom_definition is not None)<|docstring|>Return if plugin is custom.
Returns:
'True' is plugin is custom.<|endoftext|> |
31942189a43564349968e2edfd1d637ee5fb33d30ae20901f32c9fcec6d8654d | @property
def is_shadowing(self) -> bool:
"Return whether this plugin is shadowing a base plugin with the same name.\n\n Returns:\n 'True' if this plugin is shadowing a base plugin with the same name.\n "
return (not self.inherit_from) | Return whether this plugin is shadowing a base plugin with the same name.
Returns:
'True' if this plugin is shadowing a base plugin with the same name. | src/meltano/core/plugin/project_plugin.py | is_shadowing | Mu-L/meltano | 0 | python | @property
def is_shadowing(self) -> bool:
"Return whether this plugin is shadowing a base plugin with the same name.\n\n Returns:\n 'True' if this plugin is shadowing a base plugin with the same name.\n "
return (not self.inherit_from) | @property
def is_shadowing(self) -> bool:
"Return whether this plugin is shadowing a base plugin with the same name.\n\n Returns:\n 'True' if this plugin is shadowing a base plugin with the same name.\n "
return (not self.inherit_from)<|docstring|>Return whether this plugin is shadowing a base plugin with the same name.
Returns:
'True' if this plugin is shadowing a base plugin with the same name.<|endoftext|> |
bc83e2b0730ecb313734cd2359e39e28b1e345858079fb60bd79d1070762c6ee | @property
def formatted_pip_url(self) -> str:
'Return the formatted version of the pip_url.\n\n Expands ${MELTANO__PYTHON_VERSION} to the major.minor version string of the current runtime.\n\n Returns:\n Expanded pip url string.\n '
return expand_env_vars(self.pip_url, {'MELTANO__PYTHON_VERSION': f'{sys.version_info.major}.{sys.version_info.minor}'}) | Return the formatted version of the pip_url.
Expands ${MELTANO__PYTHON_VERSION} to the major.minor version string of the current runtime.
Returns:
Expanded pip url string. | src/meltano/core/plugin/project_plugin.py | formatted_pip_url | Mu-L/meltano | 0 | python | @property
def formatted_pip_url(self) -> str:
'Return the formatted version of the pip_url.\n\n Expands ${MELTANO__PYTHON_VERSION} to the major.minor version string of the current runtime.\n\n Returns:\n Expanded pip url string.\n '
return expand_env_vars(self.pip_url, {'MELTANO__PYTHON_VERSION': f'{sys.version_info.major}.{sys.version_info.minor}'}) | @property
def formatted_pip_url(self) -> str:
'Return the formatted version of the pip_url.\n\n Expands ${MELTANO__PYTHON_VERSION} to the major.minor version string of the current runtime.\n\n Returns:\n Expanded pip url string.\n '
return expand_env_vars(self.pip_url, {'MELTANO__PYTHON_VERSION': f'{sys.version_info.major}.{sys.version_info.minor}'})<|docstring|>Return the formatted version of the pip_url.
Expands ${MELTANO__PYTHON_VERSION} to the major.minor version string of the current runtime.
Returns:
Expanded pip url string.<|endoftext|> |
45019dc77e260f406b70e5d89227d93657a9c7035d0c5e785757772bec0c52a5 | @property
def venv_name(self) -> str:
'Return the venv name this plugin should use.\n\n Returns:\n The name of this plugins parent if both pip urls are the same, else this plugins name.\n '
if (not self.inherit_from):
return self.name
if ((not self.pip_url) or (self.parent.pip_url == self.pip_url)):
return self.parent.name
return self.name | Return the venv name this plugin should use.
Returns:
The name of this plugins parent if both pip urls are the same, else this plugins name. | src/meltano/core/plugin/project_plugin.py | venv_name | Mu-L/meltano | 0 | python | @property
def venv_name(self) -> str:
'Return the venv name this plugin should use.\n\n Returns:\n The name of this plugins parent if both pip urls are the same, else this plugins name.\n '
if (not self.inherit_from):
return self.name
if ((not self.pip_url) or (self.parent.pip_url == self.pip_url)):
return self.parent.name
return self.name | @property
def venv_name(self) -> str:
'Return the venv name this plugin should use.\n\n Returns:\n The name of this plugins parent if both pip urls are the same, else this plugins name.\n '
if (not self.inherit_from):
return self.name
if ((not self.pip_url) or (self.parent.pip_url == self.pip_url)):
return self.parent.name
return self.name<|docstring|>Return the venv name this plugin should use.
Returns:
The name of this plugins parent if both pip urls are the same, else this plugins name.<|endoftext|> |
8b3458f48e86b52a898d5f2a0c468e0faf89bc61abc28eececf5eeae6e9f7279 | def fixed_platt_calibrator(slope=(- 1.0), offset=0.0, **params):
'\n **Description**\n None\n\n :param slope: The slope parameter of f(x) = 1 / (1 + exp(slope *\n x + offset) (settings).\n :param offset: The offset parameter of f(x) = 1 / (1 + exp(slope\n * x + offset) (settings).\n '
entrypoint_name = 'FixedPlattCalibrator'
settings = {}
if (slope is not None):
settings['Slope'] = try_set(obj=slope, none_acceptable=True, is_of_type=numbers.Real)
if (offset is not None):
settings['Offset'] = try_set(obj=offset, none_acceptable=True, is_of_type=numbers.Real)
component = Component(name=entrypoint_name, settings=settings, kind='CalibratorTrainer')
return component | **Description**
None
:param slope: The slope parameter of f(x) = 1 / (1 + exp(slope *
x + offset) (settings).
:param offset: The offset parameter of f(x) = 1 / (1 + exp(slope
* x + offset) (settings). | src/python/nimbusml/internal/entrypoints/_calibratortrainer_fixedplattcalibrator.py | fixed_platt_calibrator | montehoover/NimbusML | 134 | python | def fixed_platt_calibrator(slope=(- 1.0), offset=0.0, **params):
'\n **Description**\n None\n\n :param slope: The slope parameter of f(x) = 1 / (1 + exp(slope *\n x + offset) (settings).\n :param offset: The offset parameter of f(x) = 1 / (1 + exp(slope\n * x + offset) (settings).\n '
entrypoint_name = 'FixedPlattCalibrator'
settings = {}
if (slope is not None):
settings['Slope'] = try_set(obj=slope, none_acceptable=True, is_of_type=numbers.Real)
if (offset is not None):
settings['Offset'] = try_set(obj=offset, none_acceptable=True, is_of_type=numbers.Real)
component = Component(name=entrypoint_name, settings=settings, kind='CalibratorTrainer')
return component | def fixed_platt_calibrator(slope=(- 1.0), offset=0.0, **params):
'\n **Description**\n None\n\n :param slope: The slope parameter of f(x) = 1 / (1 + exp(slope *\n x + offset) (settings).\n :param offset: The offset parameter of f(x) = 1 / (1 + exp(slope\n * x + offset) (settings).\n '
entrypoint_name = 'FixedPlattCalibrator'
settings = {}
if (slope is not None):
settings['Slope'] = try_set(obj=slope, none_acceptable=True, is_of_type=numbers.Real)
if (offset is not None):
settings['Offset'] = try_set(obj=offset, none_acceptable=True, is_of_type=numbers.Real)
component = Component(name=entrypoint_name, settings=settings, kind='CalibratorTrainer')
return component<|docstring|>**Description**
None
:param slope: The slope parameter of f(x) = 1 / (1 + exp(slope *
x + offset) (settings).
:param offset: The offset parameter of f(x) = 1 / (1 + exp(slope
* x + offset) (settings).<|endoftext|> |
9901dff9794872b4d99fbbf40d16d14bc2b7c9709bb9610c244e77c694b792f1 | def conversion_types(val):
'\n 将val的类型转换为字符串并插入array\n '
if isinstance(val, datetime):
val = val.strftime('%Y-%m-%d %H:%M:%S')
return val | 将val的类型转换为字符串并插入array | aestate/libs/utils.py | conversion_types | canotf/aestate | 0 | python | def conversion_types(val):
'\n \n '
if isinstance(val, datetime):
val = val.strftime('%Y-%m-%d %H:%M:%S')
return val | def conversion_types(val):
'\n \n '
if isinstance(val, datetime):
val = val.strftime('%Y-%m-%d %H:%M:%S')
return val<|docstring|>将val的类型转换为字符串并插入array<|endoftext|> |
dc47c6ebbc76a016e4893ad510b8d7432a71d6c60d593cd08b0f67bc2b911d41 | def date_format(time_obj=time, fmt='%Y-%m-%d %H:%M:%S') -> str:
'\n 时间转字符串\n :param time_obj:\n :param fmt:\n :return:\n '
_tm = time_obj.time()
_t = time.localtime(_tm)
return time.strftime(fmt, _t) | 时间转字符串
:param time_obj:
:param fmt:
:return: | aestate/libs/utils.py | date_format | canotf/aestate | 0 | python | def date_format(time_obj=time, fmt='%Y-%m-%d %H:%M:%S') -> str:
'\n 时间转字符串\n :param time_obj:\n :param fmt:\n :return:\n '
_tm = time_obj.time()
_t = time.localtime(_tm)
return time.strftime(fmt, _t) | def date_format(time_obj=time, fmt='%Y-%m-%d %H:%M:%S') -> str:
'\n 时间转字符串\n :param time_obj:\n :param fmt:\n :return:\n '
_tm = time_obj.time()
_t = time.localtime(_tm)
return time.strftime(fmt, _t)<|docstring|>时间转字符串
:param time_obj:
:param fmt:
:return:<|endoftext|> |
6c5b42a89a9532d3adc72f7dc0ae054281eac0488496a8db1a6ef6322153be23 | def time_to_datetime(t_time):
'\n 时间戳转datetime\n '
try:
d_time = datetime.fromtimestamp(t_time)
except OSError as ose:
return None
return d_time | 时间戳转datetime | aestate/libs/utils.py | time_to_datetime | canotf/aestate | 0 | python | def time_to_datetime(t_time):
'\n \n '
try:
d_time = datetime.fromtimestamp(t_time)
except OSError as ose:
return None
return d_time | def time_to_datetime(t_time):
'\n \n '
try:
d_time = datetime.fromtimestamp(t_time)
except OSError as ose:
return None
return d_time<|docstring|>时间戳转datetime<|endoftext|> |
618f5c3b72106281c7cbded92690fd2012658572a4c9fba35d4cd80526783e86 | def get_static_fields(cls):
'\n 获取类的非默认全局变量\n '
retD = list(set(dir(cls)).difference(set(BASE_ATTR)))
return retD | 获取类的非默认全局变量 | aestate/libs/utils.py | get_static_fields | canotf/aestate | 0 | python | def get_static_fields(cls):
'\n \n '
retD = list(set(dir(cls)).difference(set(BASE_ATTR)))
return retD | def get_static_fields(cls):
'\n \n '
retD = list(set(dir(cls)).difference(set(BASE_ATTR)))
return retD<|docstring|>获取类的非默认全局变量<|endoftext|> |
9092526a0ec3a5d31bd84a8e809c7d36eb23a2f3a1969d91933f1ac9ed5076d0 | def fullname(o):
'获取对象的类名'
module = o.__class__.__module__
if ((module is None) or (module == str.__class__.__module__)):
cls_name = o.__class__.__name__
else:
cls_name = ((module + '.') + o.__class__.__name__)
if (cls_name == 'type'):
cls_name = ((o.__base__.__module__ + '.') + o.__base__.__name__)
return cls_name | 获取对象的类名 | aestate/libs/utils.py | fullname | canotf/aestate | 0 | python | def fullname(o):
module = o.__class__.__module__
if ((module is None) or (module == str.__class__.__module__)):
cls_name = o.__class__.__name__
else:
cls_name = ((module + '.') + o.__class__.__name__)
if (cls_name == 'type'):
cls_name = ((o.__base__.__module__ + '.') + o.__base__.__name__)
return cls_name | def fullname(o):
module = o.__class__.__module__
if ((module is None) or (module == str.__class__.__module__)):
cls_name = o.__class__.__name__
else:
cls_name = ((module + '.') + o.__class__.__name__)
if (cls_name == 'type'):
cls_name = ((o.__base__.__module__ + '.') + o.__base__.__name__)
return cls_name<|docstring|>获取对象的类名<|endoftext|> |
b01a65413ac57474517dabb8495775eea28f983262e34434f9f602835f8dc308 | @pytest.fixture
def mock_click() -> CliRunner:
'Fixture for invoking command-line interfaces.'
return CliRunner() | Fixture for invoking command-line interfaces. | tests/cli/conftest.py | mock_click | aidanmelen/website_checker | 1 | python | @pytest.fixture
def mock_click() -> CliRunner:
return CliRunner() | @pytest.fixture
def mock_click() -> CliRunner:
return CliRunner()<|docstring|>Fixture for invoking command-line interfaces.<|endoftext|> |
1d081ba177fd415825cc473c49959ba4f32df6977a7c27fd530803064bc49bbb | @pytest.fixture
def mock_checks_network(mocker: MockFixture):
'Fixture for mocking checks.network.'
return mocker.patch('website_checker.checks.network') | Fixture for mocking checks.network. | tests/cli/conftest.py | mock_checks_network | aidanmelen/website_checker | 1 | python | @pytest.fixture
def mock_checks_network(mocker: MockFixture):
return mocker.patch('website_checker.checks.network') | @pytest.fixture
def mock_checks_network(mocker: MockFixture):
return mocker.patch('website_checker.checks.network')<|docstring|>Fixture for mocking checks.network.<|endoftext|> |
7d6456589febe1e2c49e322e79028ed0b0bfb4298a43087cdd901c28530a8b78 | @pytest.fixture
def mock_checks_health(mocker: MockFixture):
'Fixture for mocking checks.health.'
return mocker.patch('website_checker.checks.health') | Fixture for mocking checks.health. | tests/cli/conftest.py | mock_checks_health | aidanmelen/website_checker | 1 | python | @pytest.fixture
def mock_checks_health(mocker: MockFixture):
return mocker.patch('website_checker.checks.health') | @pytest.fixture
def mock_checks_health(mocker: MockFixture):
return mocker.patch('website_checker.checks.health')<|docstring|>Fixture for mocking checks.health.<|endoftext|> |
c2c883bc2f7bce8153e7e30fd9490946080712c27f80b716fd7c12af66e4789c | @pytest.fixture
def mock_checks_latency(mocker: MockFixture):
'Fixture for mocking checks.latency.'
return mocker.patch('website_checker.checks.latency') | Fixture for mocking checks.latency. | tests/cli/conftest.py | mock_checks_latency | aidanmelen/website_checker | 1 | python | @pytest.fixture
def mock_checks_latency(mocker: MockFixture):
return mocker.patch('website_checker.checks.latency') | @pytest.fixture
def mock_checks_latency(mocker: MockFixture):
return mocker.patch('website_checker.checks.latency')<|docstring|>Fixture for mocking checks.latency.<|endoftext|> |
2c56a029df0ab243a96d62f1d1c1b499551a1bf7f419996ea8343461c75470e2 | @pytest.fixture(autouse=True, scope='session')
def mock_envvars():
'Mock environment variables.'
os.environ['CHECK_NETWORK_URLS'] = 'https://google.com https://wikipedia.org'
os.environ['CHECK_HEALTH_URLS'] = 'https://google.com'
os.environ['CHECK_LATENCY_URLS'] = 'https://google.com'
(yield)
del os.environ['CHECK_NETWORK_URLS']
del os.environ['CHECK_HEALTH_URLS']
del os.environ['CHECK_LATENCY_URLS'] | Mock environment variables. | tests/cli/conftest.py | mock_envvars | aidanmelen/website_checker | 1 | python | @pytest.fixture(autouse=True, scope='session')
def mock_envvars():
os.environ['CHECK_NETWORK_URLS'] = 'https://google.com https://wikipedia.org'
os.environ['CHECK_HEALTH_URLS'] = 'https://google.com'
os.environ['CHECK_LATENCY_URLS'] = 'https://google.com'
(yield)
del os.environ['CHECK_NETWORK_URLS']
del os.environ['CHECK_HEALTH_URLS']
del os.environ['CHECK_LATENCY_URLS'] | @pytest.fixture(autouse=True, scope='session')
def mock_envvars():
os.environ['CHECK_NETWORK_URLS'] = 'https://google.com https://wikipedia.org'
os.environ['CHECK_HEALTH_URLS'] = 'https://google.com'
os.environ['CHECK_LATENCY_URLS'] = 'https://google.com'
(yield)
del os.environ['CHECK_NETWORK_URLS']
del os.environ['CHECK_HEALTH_URLS']
del os.environ['CHECK_LATENCY_URLS']<|docstring|>Mock environment variables.<|endoftext|> |
77f08cee950792237901490a6a9e90bfcafc179c5126f6cf7991b37ffdce286b | def get_fieldsets(self, request, obj=None):
' Remove the is_superuser checkbox from the Admin page, if user is MooringLicensingAdmin and NOT superuser '
fieldsets = super(UserAdmin, self).get_fieldsets(request, obj)
if request.user.is_superuser:
return fieldsets
fieldsets = deepcopy(fieldsets)
for fieldset in fieldsets:
if ('is_superuser' in fieldset[1]['fields']):
if (type(fieldset[1]['fields']) == tuple):
fieldset[1]['fields'] = list(fieldset[1]['fields'])
fieldset[1]['fields'].remove('is_superuser')
break
return fieldsets | Remove the is_superuser checkbox from the Admin page, if user is MooringLicensingAdmin and NOT superuser | mooringlicensing/admin.py | get_fieldsets | jawaidm/mooringlicensing | 0 | python | def get_fieldsets(self, request, obj=None):
' '
fieldsets = super(UserAdmin, self).get_fieldsets(request, obj)
if request.user.is_superuser:
return fieldsets
fieldsets = deepcopy(fieldsets)
for fieldset in fieldsets:
if ('is_superuser' in fieldset[1]['fields']):
if (type(fieldset[1]['fields']) == tuple):
fieldset[1]['fields'] = list(fieldset[1]['fields'])
fieldset[1]['fields'].remove('is_superuser')
break
return fieldsets | def get_fieldsets(self, request, obj=None):
' '
fieldsets = super(UserAdmin, self).get_fieldsets(request, obj)
if request.user.is_superuser:
return fieldsets
fieldsets = deepcopy(fieldsets)
for fieldset in fieldsets:
if ('is_superuser' in fieldset[1]['fields']):
if (type(fieldset[1]['fields']) == tuple):
fieldset[1]['fields'] = list(fieldset[1]['fields'])
fieldset[1]['fields'].remove('is_superuser')
break
return fieldsets<|docstring|>Remove the is_superuser checkbox from the Admin page, if user is MooringLicensingAdmin and NOT superuser<|endoftext|> |
ead55a8103da8457d19c9246968ec780f3d3d10802bc5904515b8335121ea006 | @task
def clean(c):
'Remove generated files'
if os.path.isdir(CONFIG['deploy_path']):
shutil.rmtree(CONFIG['deploy_path'])
os.makedirs(CONFIG['deploy_path']) | Remove generated files | tasks.py | clean | amelrod/forktest | 1 | python | @task
def clean(c):
if os.path.isdir(CONFIG['deploy_path']):
shutil.rmtree(CONFIG['deploy_path'])
os.makedirs(CONFIG['deploy_path']) | @task
def clean(c):
if os.path.isdir(CONFIG['deploy_path']):
shutil.rmtree(CONFIG['deploy_path'])
os.makedirs(CONFIG['deploy_path'])<|docstring|>Remove generated files<|endoftext|> |
24095d77dff402c274dd9f42e19393f029363b5ed89e75467f9ded5057fb59c3 | @task
def build(c):
'Build local version of site'
pelican_run('-s {settings_base}'.format(**CONFIG)) | Build local version of site | tasks.py | build | amelrod/forktest | 1 | python | @task
def build(c):
pelican_run('-s {settings_base}'.format(**CONFIG)) | @task
def build(c):
pelican_run('-s {settings_base}'.format(**CONFIG))<|docstring|>Build local version of site<|endoftext|> |
70230cd6f7b1fecefa178f8fa577cc16439c41ead91f5cd7549146f9370356cb | @task
def rebuild(c):
'`build` with the delete switch'
pelican_run('-d -s {settings_base}'.format(**CONFIG)) | `build` with the delete switch | tasks.py | rebuild | amelrod/forktest | 1 | python | @task
def rebuild(c):
pelican_run('-d -s {settings_base}'.format(**CONFIG)) | @task
def rebuild(c):
pelican_run('-d -s {settings_base}'.format(**CONFIG))<|docstring|>`build` with the delete switch<|endoftext|> |
3926b38e5cb7183e82a12622de5725f80885a9d9a35faf7a42da9aaff6449b10 | @task
def regenerate(c):
'Automatically regenerate site upon file modification'
pelican_run('-r -s {settings_base}'.format(**CONFIG)) | Automatically regenerate site upon file modification | tasks.py | regenerate | amelrod/forktest | 1 | python | @task
def regenerate(c):
pelican_run('-r -s {settings_base}'.format(**CONFIG)) | @task
def regenerate(c):
pelican_run('-r -s {settings_base}'.format(**CONFIG))<|docstring|>Automatically regenerate site upon file modification<|endoftext|> |
56f88a63f3724fa65425588bde81a8ecdd5df76e22b65442a5ee86f5d62aab59 | @task
def serve(c):
'Serve site at http://$HOST:$PORT/ (default is localhost:8000)'
class AddressReuseTCPServer(RootedHTTPServer):
allow_reuse_address = True
server = AddressReuseTCPServer(CONFIG['deploy_path'], (CONFIG['host'], CONFIG['port']), ComplexHTTPRequestHandler)
sys.stderr.write('Serving at {host}:{port} ...\n'.format(**CONFIG))
server.serve_forever() | Serve site at http://$HOST:$PORT/ (default is localhost:8000) | tasks.py | serve | amelrod/forktest | 1 | python | @task
def serve(c):
class AddressReuseTCPServer(RootedHTTPServer):
allow_reuse_address = True
server = AddressReuseTCPServer(CONFIG['deploy_path'], (CONFIG['host'], CONFIG['port']), ComplexHTTPRequestHandler)
sys.stderr.write('Serving at {host}:{port} ...\n'.format(**CONFIG))
server.serve_forever() | @task
def serve(c):
class AddressReuseTCPServer(RootedHTTPServer):
allow_reuse_address = True
server = AddressReuseTCPServer(CONFIG['deploy_path'], (CONFIG['host'], CONFIG['port']), ComplexHTTPRequestHandler)
sys.stderr.write('Serving at {host}:{port} ...\n'.format(**CONFIG))
server.serve_forever()<|docstring|>Serve site at http://$HOST:$PORT/ (default is localhost:8000)<|endoftext|> |
db45921abfa7e68e21e894c185b45d0bca9d4ae4eb35173fd054cb6076195f0b | @task
def reserve(c):
'`build`, then `serve`'
build(c)
serve(c) | `build`, then `serve` | tasks.py | reserve | amelrod/forktest | 1 | python | @task
def reserve(c):
build(c)
serve(c) | @task
def reserve(c):
build(c)
serve(c)<|docstring|>`build`, then `serve`<|endoftext|> |
00b2f99be88ad2ceacd6d908ae324c45da57543b1d16848de014da637b10ec48 | @task
def preview(c):
'Build production version of site'
pelican_run('-s {settings_publish}'.format(**CONFIG)) | Build production version of site | tasks.py | preview | amelrod/forktest | 1 | python | @task
def preview(c):
pelican_run('-s {settings_publish}'.format(**CONFIG)) | @task
def preview(c):
pelican_run('-s {settings_publish}'.format(**CONFIG))<|docstring|>Build production version of site<|endoftext|> |
f21ee4b0be110f0b5fbcb3b0579c6aca1582dd1b959aed057d4ceab9eee386d8 | @task
def livereload(c):
'Automatically reload browser tab upon file modification.'
from livereload import Server
build(c)
server = Server()
server.watch(CONFIG['settings_base'], (lambda : build(c)))
content_file_extensions = ['.md', '.rst']
for extension in content_file_extensions:
content_blob = '{0}/**/*{1}'.format(SETTINGS['PATH'], extension)
server.watch(content_blob, (lambda : build(c)))
theme_path = SETTINGS['THEME']
server.watch('{}/templates/*.html'.format(theme_path), (lambda : build(c)))
static_file_extensions = ['.css', '.js']
for extension in static_file_extensions:
static_file = '{0}/static/**/*{1}'.format(theme_path, extension)
server.watch(static_file, (lambda : build(c)))
server.serve(host=CONFIG['host'], port=CONFIG['port'], root=CONFIG['deploy_path']) | Automatically reload browser tab upon file modification. | tasks.py | livereload | amelrod/forktest | 1 | python | @task
def livereload(c):
from livereload import Server
build(c)
server = Server()
server.watch(CONFIG['settings_base'], (lambda : build(c)))
content_file_extensions = ['.md', '.rst']
for extension in content_file_extensions:
content_blob = '{0}/**/*{1}'.format(SETTINGS['PATH'], extension)
server.watch(content_blob, (lambda : build(c)))
theme_path = SETTINGS['THEME']
server.watch('{}/templates/*.html'.format(theme_path), (lambda : build(c)))
static_file_extensions = ['.css', '.js']
for extension in static_file_extensions:
static_file = '{0}/static/**/*{1}'.format(theme_path, extension)
server.watch(static_file, (lambda : build(c)))
server.serve(host=CONFIG['host'], port=CONFIG['port'], root=CONFIG['deploy_path']) | @task
def livereload(c):
from livereload import Server
build(c)
server = Server()
server.watch(CONFIG['settings_base'], (lambda : build(c)))
content_file_extensions = ['.md', '.rst']
for extension in content_file_extensions:
content_blob = '{0}/**/*{1}'.format(SETTINGS['PATH'], extension)
server.watch(content_blob, (lambda : build(c)))
theme_path = SETTINGS['THEME']
server.watch('{}/templates/*.html'.format(theme_path), (lambda : build(c)))
static_file_extensions = ['.css', '.js']
for extension in static_file_extensions:
static_file = '{0}/static/**/*{1}'.format(theme_path, extension)
server.watch(static_file, (lambda : build(c)))
server.serve(host=CONFIG['host'], port=CONFIG['port'], root=CONFIG['deploy_path'])<|docstring|>Automatically reload browser tab upon file modification.<|endoftext|> |
fb4eab289811558e6db41a1c5537d34396a58246d05721a14b3d4adda8abc3ef | @task
def publish(c):
'Publish to production via rsync'
pelican_run('-s {settings_publish}'.format(**CONFIG))
c.run('rsync --delete --exclude ".DS_Store" -pthrvz -c -e "ssh -p {ssh_port}" {} {ssh_user}@{ssh_host}:{ssh_path}'.format((CONFIG['deploy_path'].rstrip('/') + '/'), **CONFIG)) | Publish to production via rsync | tasks.py | publish | amelrod/forktest | 1 | python | @task
def publish(c):
pelican_run('-s {settings_publish}'.format(**CONFIG))
c.run('rsync --delete --exclude ".DS_Store" -pthrvz -c -e "ssh -p {ssh_port}" {} {ssh_user}@{ssh_host}:{ssh_path}'.format((CONFIG['deploy_path'].rstrip('/') + '/'), **CONFIG)) | @task
def publish(c):
pelican_run('-s {settings_publish}'.format(**CONFIG))
c.run('rsync --delete --exclude ".DS_Store" -pthrvz -c -e "ssh -p {ssh_port}" {} {ssh_user}@{ssh_host}:{ssh_path}'.format((CONFIG['deploy_path'].rstrip('/') + '/'), **CONFIG))<|docstring|>Publish to production via rsync<|endoftext|> |
aa1d6992878bfbdec0b358ecf658a6ad755da836a2e4846ea44aa83738fe9196 | @task
def gh_pages(c):
'Publish to GitHub Pages'
preview(c)
c.run('ghp-import -b {github_pages_branch} -m {commit_message} {deploy_path} -p'.format(**CONFIG)) | Publish to GitHub Pages | tasks.py | gh_pages | amelrod/forktest | 1 | python | @task
def gh_pages(c):
preview(c)
c.run('ghp-import -b {github_pages_branch} -m {commit_message} {deploy_path} -p'.format(**CONFIG)) | @task
def gh_pages(c):
preview(c)
c.run('ghp-import -b {github_pages_branch} -m {commit_message} {deploy_path} -p'.format(**CONFIG))<|docstring|>Publish to GitHub Pages<|endoftext|> |
d55a47322826027d14846301de3009452305fdcbb3709a6cac8755f946bd10f4 | def visitMultiElementOneOf(self, ctx: ShExDocParser.MultiElementOneOfContext):
" multiElementOneOf: groupTripleExpr ('|' groupTripleExpr)+ "
expressions = []
for gs in ctx.groupTripleExpr():
parser = ShexTripleExpressionParser(self.context)
parser.visit(gs)
expressions.append(parser.expression)
self.expression = OneOf(expressions=expressions) | multiElementOneOf: groupTripleExpr ('|' groupTripleExpr)+ | pyshexc/parser_impl/shex_oneofshape_parser.py | visitMultiElementOneOf | ericprud/grammar-python-antlr | 1 | python | def visitMultiElementOneOf(self, ctx: ShExDocParser.MultiElementOneOfContext):
" "
expressions = []
for gs in ctx.groupTripleExpr():
parser = ShexTripleExpressionParser(self.context)
parser.visit(gs)
expressions.append(parser.expression)
self.expression = OneOf(expressions=expressions) | def visitMultiElementOneOf(self, ctx: ShExDocParser.MultiElementOneOfContext):
" "
expressions = []
for gs in ctx.groupTripleExpr():
parser = ShexTripleExpressionParser(self.context)
parser.visit(gs)
expressions.append(parser.expression)
self.expression = OneOf(expressions=expressions)<|docstring|>multiElementOneOf: groupTripleExpr ('|' groupTripleExpr)+<|endoftext|> |
d28c8b5086c9a579acc290ac8578411c2d6b4d59a99b7b215bdeab72fd19dc5d | def visitMultiElementGroup(self, ctx: ShExDocParser.MultiElementGroupContext):
" multiElementGroup: unaryTripleExpr (';' unaryTripleExpr)+ ';'? "
expressions = []
for us in ctx.unaryTripleExpr():
parser = ShexTripleExpressionParser(self.context)
parser.visit(us)
expressions.append(parser.expression)
self.expression = EachOf(expressions=expressions) | multiElementGroup: unaryTripleExpr (';' unaryTripleExpr)+ ';'? | pyshexc/parser_impl/shex_oneofshape_parser.py | visitMultiElementGroup | ericprud/grammar-python-antlr | 1 | python | def visitMultiElementGroup(self, ctx: ShExDocParser.MultiElementGroupContext):
" "
expressions = []
for us in ctx.unaryTripleExpr():
parser = ShexTripleExpressionParser(self.context)
parser.visit(us)
expressions.append(parser.expression)
self.expression = EachOf(expressions=expressions) | def visitMultiElementGroup(self, ctx: ShExDocParser.MultiElementGroupContext):
" "
expressions = []
for us in ctx.unaryTripleExpr():
parser = ShexTripleExpressionParser(self.context)
parser.visit(us)
expressions.append(parser.expression)
self.expression = EachOf(expressions=expressions)<|docstring|>multiElementGroup: unaryTripleExpr (';' unaryTripleExpr)+ ';'?<|endoftext|> |
86e1d6ea2f56e661ab163ac955d7b2a0847916f8995f7e21caf87237d09efd9a | def visitUnaryTripleExpr(self, ctx: ShExDocParser.UnaryTripleExprContext):
" unaryTripleExpr: ('$' tripleExprLabel)? (tripleConstraint | bracketedTripleExpr) | include "
if ctx.include():
self.expression = self.context.tripleexprlabel_to_iriref(ctx.include().tripleExprLabel())
else:
lbl = (self.context.tripleexprlabel_to_iriref(ctx.tripleExprLabel()) if ctx.tripleExprLabel() else None)
if ctx.tripleConstraint():
self.expression = TripleConstraint(lbl)
self.visit(ctx.tripleConstraint())
elif ctx.bracketedTripleExpr():
self.visit(ctx.bracketedTripleExpr())
self.expression.id = lbl | unaryTripleExpr: ('$' tripleExprLabel)? (tripleConstraint | bracketedTripleExpr) | include | pyshexc/parser_impl/shex_oneofshape_parser.py | visitUnaryTripleExpr | ericprud/grammar-python-antlr | 1 | python | def visitUnaryTripleExpr(self, ctx: ShExDocParser.UnaryTripleExprContext):
" "
if ctx.include():
self.expression = self.context.tripleexprlabel_to_iriref(ctx.include().tripleExprLabel())
else:
lbl = (self.context.tripleexprlabel_to_iriref(ctx.tripleExprLabel()) if ctx.tripleExprLabel() else None)
if ctx.tripleConstraint():
self.expression = TripleConstraint(lbl)
self.visit(ctx.tripleConstraint())
elif ctx.bracketedTripleExpr():
self.visit(ctx.bracketedTripleExpr())
self.expression.id = lbl | def visitUnaryTripleExpr(self, ctx: ShExDocParser.UnaryTripleExprContext):
" "
if ctx.include():
self.expression = self.context.tripleexprlabel_to_iriref(ctx.include().tripleExprLabel())
else:
lbl = (self.context.tripleexprlabel_to_iriref(ctx.tripleExprLabel()) if ctx.tripleExprLabel() else None)
if ctx.tripleConstraint():
self.expression = TripleConstraint(lbl)
self.visit(ctx.tripleConstraint())
elif ctx.bracketedTripleExpr():
self.visit(ctx.bracketedTripleExpr())
self.expression.id = lbl<|docstring|>unaryTripleExpr: ('$' tripleExprLabel)? (tripleConstraint | bracketedTripleExpr) | include<|endoftext|> |
0568e77d158dae6f6fda203404f0f708fe177f296de1c11bfe99154440643497 | def visitBracketedTripleExpr(self, ctx: ShExDocParser.BracketedTripleExprContext):
" bracketedTripleExpr: '(' tripleExpression ')' cardinality? /* onShapeExpr?*/ annotation* semanticAction* "
enc_shape = ShexTripleExpressionParser(self.context)
enc_shape.visit(ctx.tripleExpression())
self.expression = enc_shape.expression
self._card_annotations_and_semacts(ctx) | bracketedTripleExpr: '(' tripleExpression ')' cardinality? /* onShapeExpr?*/ annotation* semanticAction* | pyshexc/parser_impl/shex_oneofshape_parser.py | visitBracketedTripleExpr | ericprud/grammar-python-antlr | 1 | python | def visitBracketedTripleExpr(self, ctx: ShExDocParser.BracketedTripleExprContext):
" "
enc_shape = ShexTripleExpressionParser(self.context)
enc_shape.visit(ctx.tripleExpression())
self.expression = enc_shape.expression
self._card_annotations_and_semacts(ctx) | def visitBracketedTripleExpr(self, ctx: ShExDocParser.BracketedTripleExprContext):
" "
enc_shape = ShexTripleExpressionParser(self.context)
enc_shape.visit(ctx.tripleExpression())
self.expression = enc_shape.expression
self._card_annotations_and_semacts(ctx)<|docstring|>bracketedTripleExpr: '(' tripleExpression ')' cardinality? /* onShapeExpr?*/ annotation* semanticAction*<|endoftext|> |
33efb8345cab8d4ee7ddffafd2979915ad25114616c0276202be00673f31532c | def visitTripleConstraint(self, ctx: ShExDocParser.TripleConstraintContext):
' tripleConstraint: senseFlags? predicate inlineShapeExpression cardinality? annotation* semanticAction '
if ctx.senseFlags():
self.visit(ctx.senseFlags())
self.visit(ctx.predicate())
self.visit(ctx.inlineShapeExpression())
self._card_annotations_and_semacts(ctx) | tripleConstraint: senseFlags? predicate inlineShapeExpression cardinality? annotation* semanticAction | pyshexc/parser_impl/shex_oneofshape_parser.py | visitTripleConstraint | ericprud/grammar-python-antlr | 1 | python | def visitTripleConstraint(self, ctx: ShExDocParser.TripleConstraintContext):
' '
if ctx.senseFlags():
self.visit(ctx.senseFlags())
self.visit(ctx.predicate())
self.visit(ctx.inlineShapeExpression())
self._card_annotations_and_semacts(ctx) | def visitTripleConstraint(self, ctx: ShExDocParser.TripleConstraintContext):
' '
if ctx.senseFlags():
self.visit(ctx.senseFlags())
self.visit(ctx.predicate())
self.visit(ctx.inlineShapeExpression())
self._card_annotations_and_semacts(ctx)<|docstring|>tripleConstraint: senseFlags? predicate inlineShapeExpression cardinality? annotation* semanticAction<|endoftext|> |
65f5ee8e5defcc1f6cb66529a670a2e88391adb64281a666dd53945971456f2b | def visitStarCardinality(self, ctx: ShExDocParser.StarCardinalityContext):
" '*' "
self.expression.min = 0
self.expression.max = (- 1) | '*' | pyshexc/parser_impl/shex_oneofshape_parser.py | visitStarCardinality | ericprud/grammar-python-antlr | 1 | python | def visitStarCardinality(self, ctx: ShExDocParser.StarCardinalityContext):
" "
self.expression.min = 0
self.expression.max = (- 1) | def visitStarCardinality(self, ctx: ShExDocParser.StarCardinalityContext):
" "
self.expression.min = 0
self.expression.max = (- 1)<|docstring|>'*'<|endoftext|> |
2637ba541301de7631bb4f05545eed08589bd86972fbed8dd9d0548b4e38467d | def visitPlusCardinality(self, ctx: ShExDocParser.PlusCardinalityContext):
" '+' "
self.expression.min = 1
self.expression.max = (- 1) | '+' | pyshexc/parser_impl/shex_oneofshape_parser.py | visitPlusCardinality | ericprud/grammar-python-antlr | 1 | python | def visitPlusCardinality(self, ctx: ShExDocParser.PlusCardinalityContext):
" "
self.expression.min = 1
self.expression.max = (- 1) | def visitPlusCardinality(self, ctx: ShExDocParser.PlusCardinalityContext):
" "
self.expression.min = 1
self.expression.max = (- 1)<|docstring|>'+'<|endoftext|> |
b6a9af22f40692e5cece05d2a8f1d22b933934e891784564bd159bfa59db82bd | def visitOptionalCardinality(self, ctx: ShExDocParser.OptionalCardinalityContext):
" '?' "
self.expression.min = 0
self.expression.max = 1 | '?' | pyshexc/parser_impl/shex_oneofshape_parser.py | visitOptionalCardinality | ericprud/grammar-python-antlr | 1 | python | def visitOptionalCardinality(self, ctx: ShExDocParser.OptionalCardinalityContext):
" "
self.expression.min = 0
self.expression.max = 1 | def visitOptionalCardinality(self, ctx: ShExDocParser.OptionalCardinalityContext):
" "
self.expression.min = 0
self.expression.max = 1<|docstring|>'?'<|endoftext|> |
0699f9a9a50d639939746785b9cffb3df25896fec65145e6eba16884c294e4ea | def visitExactRange(self, ctx: ShExDocParser.ExactRangeContext):
" repeatRange: '{' INTEGER '}' #exactRange "
self.expression.min = int(ctx.INTEGER().getText())
self.expression.max = self.expression.min | repeatRange: '{' INTEGER '}' #exactRange | pyshexc/parser_impl/shex_oneofshape_parser.py | visitExactRange | ericprud/grammar-python-antlr | 1 | python | def visitExactRange(self, ctx: ShExDocParser.ExactRangeContext):
" "
self.expression.min = int(ctx.INTEGER().getText())
self.expression.max = self.expression.min | def visitExactRange(self, ctx: ShExDocParser.ExactRangeContext):
" "
self.expression.min = int(ctx.INTEGER().getText())
self.expression.max = self.expression.min<|docstring|>repeatRange: '{' INTEGER '}' #exactRange<|endoftext|> |
d92f4f308fcfd0eab37e043dec1271f315f9fd3d2a281e72f91b4ce0b9ac834f | def visitMinMaxRange(self, ctx: ShExDocParser.MinMaxRangeContext):
" repeatRange: '{' INTEGER (',' (INTEGER | UNBOUNDED)? '}' "
self.expression.min = int(ctx.INTEGER(0).getText())
self.expression.max = (int(ctx.INTEGER(1).getText()) if (len(ctx.INTEGER()) > 1) else (- 1)) | repeatRange: '{' INTEGER (',' (INTEGER | UNBOUNDED)? '}' | pyshexc/parser_impl/shex_oneofshape_parser.py | visitMinMaxRange | ericprud/grammar-python-antlr | 1 | python | def visitMinMaxRange(self, ctx: ShExDocParser.MinMaxRangeContext):
" "
self.expression.min = int(ctx.INTEGER(0).getText())
self.expression.max = (int(ctx.INTEGER(1).getText()) if (len(ctx.INTEGER()) > 1) else (- 1)) | def visitMinMaxRange(self, ctx: ShExDocParser.MinMaxRangeContext):
" "
self.expression.min = int(ctx.INTEGER(0).getText())
self.expression.max = (int(ctx.INTEGER(1).getText()) if (len(ctx.INTEGER()) > 1) else (- 1))<|docstring|>repeatRange: '{' INTEGER (',' (INTEGER | UNBOUNDED)? '}'<|endoftext|> |
12f7118967862c78f1ab68e4047a011a50f0eb52c1d4c8de976807cbaa183837 | def visitSenseFlags(self, ctx: ShExDocParser.SenseFlagsContext):
" senseFlags: '^' "
self.expression.inverse = True | senseFlags: '^' | pyshexc/parser_impl/shex_oneofshape_parser.py | visitSenseFlags | ericprud/grammar-python-antlr | 1 | python | def visitSenseFlags(self, ctx: ShExDocParser.SenseFlagsContext):
" "
self.expression.inverse = True | def visitSenseFlags(self, ctx: ShExDocParser.SenseFlagsContext):
" "
self.expression.inverse = True<|docstring|>senseFlags: '^'<|endoftext|> |
1b9e16c0c3c658a507674a17df4472dfca3dd0f69710d70423eb1a7dc6a24d72 | def visitPredicate(self, ctx: ShExDocParser.PredicateContext):
' predicate: iri | rdfType '
self.expression.predicate = self.context.predicate_to_IRI(ctx) | predicate: iri | rdfType | pyshexc/parser_impl/shex_oneofshape_parser.py | visitPredicate | ericprud/grammar-python-antlr | 1 | python | def visitPredicate(self, ctx: ShExDocParser.PredicateContext):
' '
self.expression.predicate = self.context.predicate_to_IRI(ctx) | def visitPredicate(self, ctx: ShExDocParser.PredicateContext):
' '
self.expression.predicate = self.context.predicate_to_IRI(ctx)<|docstring|>predicate: iri | rdfType<|endoftext|> |
8a05d7c8e02347eb3ef8caf56d4c28fb589c33303a7f8a253cbce9a73021faf1 | def visitInlineShapeExpression(self, ctx: ShExDocParser.InlineShapeExpressionContext):
' inlineShapeExpression: inlineShapeOr '
expr_parser = ShexShapeExpressionParser(self.context)
expr_parser.visitChildren(ctx)
self.expression.valueExpr = expr_parser.expr | inlineShapeExpression: inlineShapeOr | pyshexc/parser_impl/shex_oneofshape_parser.py | visitInlineShapeExpression | ericprud/grammar-python-antlr | 1 | python | def visitInlineShapeExpression(self, ctx: ShExDocParser.InlineShapeExpressionContext):
' '
expr_parser = ShexShapeExpressionParser(self.context)
expr_parser.visitChildren(ctx)
self.expression.valueExpr = expr_parser.expr | def visitInlineShapeExpression(self, ctx: ShExDocParser.InlineShapeExpressionContext):
' '
expr_parser = ShexShapeExpressionParser(self.context)
expr_parser.visitChildren(ctx)
self.expression.valueExpr = expr_parser.expr<|docstring|>inlineShapeExpression: inlineShapeOr<|endoftext|> |
c5620ae6c321f70693a6a01c012896cc0e569ffcfeff2805c311eeacd7acaed9 | def plugin_cli_arguments(ctlr, parser, plugin_config):
'\n set up cli arguments for the plugin\n '
plugin_class = ctlr.get_plugin_class(plugin_config.get('type'))
config = copy.deepcopy(plugin_config)
confu.schema.apply_defaults(plugin_class.ConfigSchema(), config)
confu_cli_args = plugin_class.confu_cli_args_cls()(parser, plugin_class.ConfigSchema().config, config.get('config'))
if hasattr(plugin_class, 'add_arguments'):
plugin_class.add_arguments(parser, config.get('config'), confu_cli_args)
if (not confu_cli_args.routes):
confu_cli_args.add(parser) | set up cli arguments for the plugin | src/ctl/__init__.py | plugin_cli_arguments | grizz/ctl | 0 | python | def plugin_cli_arguments(ctlr, parser, plugin_config):
'\n \n '
plugin_class = ctlr.get_plugin_class(plugin_config.get('type'))
config = copy.deepcopy(plugin_config)
confu.schema.apply_defaults(plugin_class.ConfigSchema(), config)
confu_cli_args = plugin_class.confu_cli_args_cls()(parser, plugin_class.ConfigSchema().config, config.get('config'))
if hasattr(plugin_class, 'add_arguments'):
plugin_class.add_arguments(parser, config.get('config'), confu_cli_args)
if (not confu_cli_args.routes):
confu_cli_args.add(parser) | def plugin_cli_arguments(ctlr, parser, plugin_config):
'\n \n '
plugin_class = ctlr.get_plugin_class(plugin_config.get('type'))
config = copy.deepcopy(plugin_config)
confu.schema.apply_defaults(plugin_class.ConfigSchema(), config)
confu_cli_args = plugin_class.confu_cli_args_cls()(parser, plugin_class.ConfigSchema().config, config.get('config'))
if hasattr(plugin_class, 'add_arguments'):
plugin_class.add_arguments(parser, config.get('config'), confu_cli_args)
if (not confu_cli_args.routes):
confu_cli_args.add(parser)<|docstring|>set up cli arguments for the plugin<|endoftext|> |
e99ac50dac46f99bb7ff98759cfeb8657c742b3b7ae516e817894138cdc6da7c | def read_config(schema, config_dir, config_name='config', ctx=None):
'\n read a config file from config_dir\n '
conf_path = os.path.expanduser(config_dir)
if (not os.path.exists(conf_path)):
raise OSError(f'config dir not found at {conf_path}')
for (codec, filename) in munge.find_datafile(config_name, conf_path):
if tmpl:
engine = tmpl.get_engine('jinja2')(search_path=os.path.dirname(filename))
engine.engine.undefined = IgnoreUndefined
engine.engine.filters['escape_regex'] = filter_escape_regex
data = codec().loads(engine._render(src=os.path.basename(filename), env=ctx.tmpl['env']))
ctx.tmpl.update(engine=engine)
else:
with open(filename) as fobj:
data = codec().load(fobj)
meta = dict(config_dir=config_dir, config_file=filename)
return confu.config.Config(schema, data, meta)
raise OSError(f'config dir not found at {conf_path}') | read a config file from config_dir | src/ctl/__init__.py | read_config | grizz/ctl | 0 | python | def read_config(schema, config_dir, config_name='config', ctx=None):
'\n \n '
conf_path = os.path.expanduser(config_dir)
if (not os.path.exists(conf_path)):
raise OSError(f'config dir not found at {conf_path}')
for (codec, filename) in munge.find_datafile(config_name, conf_path):
if tmpl:
engine = tmpl.get_engine('jinja2')(search_path=os.path.dirname(filename))
engine.engine.undefined = IgnoreUndefined
engine.engine.filters['escape_regex'] = filter_escape_regex
data = codec().loads(engine._render(src=os.path.basename(filename), env=ctx.tmpl['env']))
ctx.tmpl.update(engine=engine)
else:
with open(filename) as fobj:
data = codec().load(fobj)
meta = dict(config_dir=config_dir, config_file=filename)
return confu.config.Config(schema, data, meta)
raise OSError(f'config dir not found at {conf_path}') | def read_config(schema, config_dir, config_name='config', ctx=None):
'\n \n '
conf_path = os.path.expanduser(config_dir)
if (not os.path.exists(conf_path)):
raise OSError(f'config dir not found at {conf_path}')
for (codec, filename) in munge.find_datafile(config_name, conf_path):
if tmpl:
engine = tmpl.get_engine('jinja2')(search_path=os.path.dirname(filename))
engine.engine.undefined = IgnoreUndefined
engine.engine.filters['escape_regex'] = filter_escape_regex
data = codec().loads(engine._render(src=os.path.basename(filename), env=ctx.tmpl['env']))
ctx.tmpl.update(engine=engine)
else:
with open(filename) as fobj:
data = codec().load(fobj)
meta = dict(config_dir=config_dir, config_file=filename)
return confu.config.Config(schema, data, meta)
raise OSError(f'config dir not found at {conf_path}')<|docstring|>read a config file from config_dir<|endoftext|> |
1983c600647ea94c5e39871a6841869cd977847554059e337f8d551f6ce3fbf4 | def argv_to_grainy_namespace(operation, args=[]):
'\n create gainy permissioning namespace from argv\n '
namespace = ['ctl']
if operation:
namespace.append(operation)
for arg in args:
if (arg[0] == '-'):
continue
namespace.append(arg)
return grainy.core.Namespace(namespace) | create gainy permissioning namespace from argv | src/ctl/__init__.py | argv_to_grainy_namespace | grizz/ctl | 0 | python | def argv_to_grainy_namespace(operation, args=[]):
'\n \n '
namespace = ['ctl']
if operation:
namespace.append(operation)
for arg in args:
if (arg[0] == '-'):
continue
namespace.append(arg)
return grainy.core.Namespace(namespace) | def argv_to_grainy_namespace(operation, args=[]):
'\n \n '
namespace = ['ctl']
if operation:
namespace.append(operation)
for arg in args:
if (arg[0] == '-'):
continue
namespace.append(arg)
return grainy.core.Namespace(namespace)<|docstring|>create gainy permissioning namespace from argv<|endoftext|> |
080ce971e2189dc913fbfe8369b93aa9bbca25ca98a5f3f2346083c03c5955bb | def update_options(self, kwargs):
"\n updates config based on passed options\n\n too complicated for now, we can just make working_dir as an option and simplify this cluster:\n if config_dir is passed, it will be used to load config from\n if home is passed, it will update config::home\n if home is passed and config_dir isn't set, will try to load config from there\n config_dir and config_file cannot both be passed\n if config_file is passed, home or config::home must be set\n "
opt = self.__class__.pop_options(kwargs)
if (opt.get('debug', None) is not None):
self.debug = opt['debug']
if (opt.get('verbose', None) is not None):
self.verbose = opt['verbose']
if (opt.get('quiet', None) is not None):
self.quiet = opt['quiet']
if opt.get('home', None):
if ('config_dir' in kwargs):
raise ValueError('config_dir and home are mutually exclusive')
self._new_home(opt['home'])
elif kwargs.get('config_dir'):
self._new_home(kwargs['config_dir'])
elif (not self.home):
self.find_home()
self.init() | updates config based on passed options
too complicated for now, we can just make working_dir as an option and simplify this cluster:
if config_dir is passed, it will be used to load config from
if home is passed, it will update config::home
if home is passed and config_dir isn't set, will try to load config from there
config_dir and config_file cannot both be passed
if config_file is passed, home or config::home must be set | src/ctl/__init__.py | update_options | grizz/ctl | 0 | python | def update_options(self, kwargs):
"\n updates config based on passed options\n\n too complicated for now, we can just make working_dir as an option and simplify this cluster:\n if config_dir is passed, it will be used to load config from\n if home is passed, it will update config::home\n if home is passed and config_dir isn't set, will try to load config from there\n config_dir and config_file cannot both be passed\n if config_file is passed, home or config::home must be set\n "
opt = self.__class__.pop_options(kwargs)
if (opt.get('debug', None) is not None):
self.debug = opt['debug']
if (opt.get('verbose', None) is not None):
self.verbose = opt['verbose']
if (opt.get('quiet', None) is not None):
self.quiet = opt['quiet']
if opt.get('home', None):
if ('config_dir' in kwargs):
raise ValueError('config_dir and home are mutually exclusive')
self._new_home(opt['home'])
elif kwargs.get('config_dir'):
self._new_home(kwargs['config_dir'])
elif (not self.home):
self.find_home()
self.init() | def update_options(self, kwargs):
"\n updates config based on passed options\n\n too complicated for now, we can just make working_dir as an option and simplify this cluster:\n if config_dir is passed, it will be used to load config from\n if home is passed, it will update config::home\n if home is passed and config_dir isn't set, will try to load config from there\n config_dir and config_file cannot both be passed\n if config_file is passed, home or config::home must be set\n "
opt = self.__class__.pop_options(kwargs)
if (opt.get('debug', None) is not None):
self.debug = opt['debug']
if (opt.get('verbose', None) is not None):
self.verbose = opt['verbose']
if (opt.get('quiet', None) is not None):
self.quiet = opt['quiet']
if opt.get('home', None):
if ('config_dir' in kwargs):
raise ValueError('config_dir and home are mutually exclusive')
self._new_home(opt['home'])
elif kwargs.get('config_dir'):
self._new_home(kwargs['config_dir'])
elif (not self.home):
self.find_home()
self.init()<|docstring|>updates config based on passed options
too complicated for now, we can just make working_dir as an option and simplify this cluster:
if config_dir is passed, it will be used to load config from
if home is passed, it will update config::home
if home is passed and config_dir isn't set, will try to load config from there
config_dir and config_file cannot both be passed
if config_file is passed, home or config::home must be set<|endoftext|> |
93612fd22cdbd1ae60a5f4362e92442aed7ec1b8d323b4173576596149c7c8e6 | def init(self):
'\n call after updating options\n ' | call after updating options | src/ctl/__init__.py | init | grizz/ctl | 0 | python | def init(self):
'\n \n ' | def init(self):
'\n \n '<|docstring|>call after updating options<|endoftext|> |
ac9af169ff1d36148baf2419ec08a046ab62e7d4f37fee3a579051ceccaf9e28 | def init_plugin_manager(self):
"\n Initialize the plugin manager and\n set it's apropriate search paths\n "
plugin_path = self.ctx.config.get_nested('ctl', 'plugin_path')
if plugin_path:
plugin.searchpath = plugin_path
else:
plugin.searchpath = [os.path.join(self.ctx.home, 'plugins')] | Initialize the plugin manager and
set it's apropriate search paths | src/ctl/__init__.py | init_plugin_manager | grizz/ctl | 0 | python | def init_plugin_manager(self):
"\n Initialize the plugin manager and\n set it's apropriate search paths\n "
plugin_path = self.ctx.config.get_nested('ctl', 'plugin_path')
if plugin_path:
plugin.searchpath = plugin_path
else:
plugin.searchpath = [os.path.join(self.ctx.home, 'plugins')] | def init_plugin_manager(self):
"\n Initialize the plugin manager and\n set it's apropriate search paths\n "
plugin_path = self.ctx.config.get_nested('ctl', 'plugin_path')
if plugin_path:
plugin.searchpath = plugin_path
else:
plugin.searchpath = [os.path.join(self.ctx.home, 'plugins')]<|docstring|>Initialize the plugin manager and
set it's apropriate search paths<|endoftext|> |
24af8853af5e84550a682ddc35e8d852b21aac1b3de19febe881e465666de903 | def init_logging(self):
'\n Apply python logging config and create `log` and `usage_log`\n properties\n '
set_pylogger_config(self.ctx.config.get_nested('ctl', 'log'))
self.log = Log('ctl')
self.usage_log = Log('usage') | Apply python logging config and create `log` and `usage_log`
properties | src/ctl/__init__.py | init_logging | grizz/ctl | 0 | python | def init_logging(self):
'\n Apply python logging config and create `log` and `usage_log`\n properties\n '
set_pylogger_config(self.ctx.config.get_nested('ctl', 'log'))
self.log = Log('ctl')
self.usage_log = Log('usage') | def init_logging(self):
'\n Apply python logging config and create `log` and `usage_log`\n properties\n '
set_pylogger_config(self.ctx.config.get_nested('ctl', 'log'))
self.log = Log('ctl')
self.usage_log = Log('usage')<|docstring|>Apply python logging config and create `log` and `usage_log`
properties<|endoftext|> |
62531e6f333d4156ceac041dc0762dda9578075200122f20a74a1ebab156a38e | def init_permissions(self):
'\n Initialize permissions for ctl usage\n '
self.permissions = PermissionSet({row.get('namespace'): int_flags(row.get('permission')) for row in self.ctx.config.get_nested('ctl', 'permissions')}) | Initialize permissions for ctl usage | src/ctl/__init__.py | init_permissions | grizz/ctl | 0 | python | def init_permissions(self):
'\n \n '
self.permissions = PermissionSet({row.get('namespace'): int_flags(row.get('permission')) for row in self.ctx.config.get_nested('ctl', 'permissions')}) | def init_permissions(self):
'\n \n '
self.permissions = PermissionSet({row.get('namespace'): int_flags(row.get('permission')) for row in self.ctx.config.get_nested('ctl', 'permissions')})<|docstring|>Initialize permissions for ctl usage<|endoftext|> |
f117051aae0df98a07a0fa98343364e5cffb6b080df342c1047dfd8c97ef0c66 | def init_plugins(self):
'\n Instantiate plugins\n '
plugin.import_external()
plugins_config = self.ctx.config.get_nested('ctl', 'plugins')
if plugins_config:
plugin.instantiate(plugins_config, self) | Instantiate plugins | src/ctl/__init__.py | init_plugins | grizz/ctl | 0 | python | def init_plugins(self):
'\n \n '
plugin.import_external()
plugins_config = self.ctx.config.get_nested('ctl', 'plugins')
if plugins_config:
plugin.instantiate(plugins_config, self) | def init_plugins(self):
'\n \n '
plugin.import_external()
plugins_config = self.ctx.config.get_nested('ctl', 'plugins')
if plugins_config:
plugin.instantiate(plugins_config, self)<|docstring|>Instantiate plugins<|endoftext|> |
acd69c6437f458191a6400fb820dc19ad8140412bf77a26ef4770c803c497545 | def expose_plugin_vars(self):
'\n Checks all configured plugins if they have\n the `expose_vars` classmethod.\n\n If they do those vars will be exposed to the context\n template environment\n\n This can be done without having to instantiate the plugins\n '
if ('plugin' not in self.ctx.tmpl['env']):
self.ctx.tmpl['env']['plugin'] = {}
for plugin_config in self.config.get_nested('ctl', 'plugins'):
plugin_class = self.get_plugin_class(plugin_config['type'])
name = plugin_config.get('name')
if hasattr(plugin_class, 'expose_vars'):
env = self.ctx.tmpl['env']['plugin'].get(name, {})
errors = plugin_class.expose_vars(env, plugin_config.get('config', {}))
for (filepath, error) in list(errors.items()):
self.log.debug(f'expose_vars: {filepath}: {error}')
self.ctx.tmpl['env']['plugin'][name] = env | Checks all configured plugins if they have
the `expose_vars` classmethod.
If they do those vars will be exposed to the context
template environment
This can be done without having to instantiate the plugins | src/ctl/__init__.py | expose_plugin_vars | grizz/ctl | 0 | python | def expose_plugin_vars(self):
'\n Checks all configured plugins if they have\n the `expose_vars` classmethod.\n\n If they do those vars will be exposed to the context\n template environment\n\n This can be done without having to instantiate the plugins\n '
if ('plugin' not in self.ctx.tmpl['env']):
self.ctx.tmpl['env']['plugin'] = {}
for plugin_config in self.config.get_nested('ctl', 'plugins'):
plugin_class = self.get_plugin_class(plugin_config['type'])
name = plugin_config.get('name')
if hasattr(plugin_class, 'expose_vars'):
env = self.ctx.tmpl['env']['plugin'].get(name, {})
errors = plugin_class.expose_vars(env, plugin_config.get('config', {}))
for (filepath, error) in list(errors.items()):
self.log.debug(f'expose_vars: {filepath}: {error}')
self.ctx.tmpl['env']['plugin'][name] = env | def expose_plugin_vars(self):
'\n Checks all configured plugins if they have\n the `expose_vars` classmethod.\n\n If they do those vars will be exposed to the context\n template environment\n\n This can be done without having to instantiate the plugins\n '
if ('plugin' not in self.ctx.tmpl['env']):
self.ctx.tmpl['env']['plugin'] = {}
for plugin_config in self.config.get_nested('ctl', 'plugins'):
plugin_class = self.get_plugin_class(plugin_config['type'])
name = plugin_config.get('name')
if hasattr(plugin_class, 'expose_vars'):
env = self.ctx.tmpl['env']['plugin'].get(name, {})
errors = plugin_class.expose_vars(env, plugin_config.get('config', {}))
for (filepath, error) in list(errors.items()):
self.log.debug(f'expose_vars: {filepath}: {error}')
self.ctx.tmpl['env']['plugin'][name] = env<|docstring|>Checks all configured plugins if they have
the `expose_vars` classmethod.
If they do those vars will be exposed to the context
template environment
This can be done without having to instantiate the plugins<|endoftext|> |
07c0706fa9f05f2d4a67ff97f4fbc589da2bc897da54e53b36076f1082b9cb12 | def get_plugin_class(self, name):
'\n get plugin class\n '
self.check_permissions(argv_to_grainy_namespace(name), 'r')
return plugin.get_plugin_class(name) | get plugin class | src/ctl/__init__.py | get_plugin_class | grizz/ctl | 0 | python | def get_plugin_class(self, name):
'\n \n '
self.check_permissions(argv_to_grainy_namespace(name), 'r')
return plugin.get_plugin_class(name) | def get_plugin_class(self, name):
'\n \n '
self.check_permissions(argv_to_grainy_namespace(name), 'r')
return plugin.get_plugin_class(name)<|docstring|>get plugin class<|endoftext|> |
ad197c66360122bd380edf349cbccaffa7008769665b0a1a2f2942e236893c37 | def get_plugin(self, name):
'\n get configured plugin by name\n '
self.check_permissions(argv_to_grainy_namespace(name), 'r')
return plugin.get_instance(name, self) | get configured plugin by name | src/ctl/__init__.py | get_plugin | grizz/ctl | 0 | python | def get_plugin(self, name):
'\n \n '
self.check_permissions(argv_to_grainy_namespace(name), 'r')
return plugin.get_instance(name, self) | def get_plugin(self, name):
'\n \n '
self.check_permissions(argv_to_grainy_namespace(name), 'r')
return plugin.get_instance(name, self)<|docstring|>get configured plugin by name<|endoftext|> |
f72ef9cfce8dc0312f54f8f0f0dc6e267e780f97e0198c3fa1f597edd6a55209 | def __setup_user_group(self):
'\n Creates PXF user with the required groups and bash as default shell\n '
import params
User(params.pxf_user, groups=[params.hdfs_superuser_group, params.user_group, params.tomcat_group], shell='/bin/bash') | Creates PXF user with the required groups and bash as default shell | ambari-server/src/main/resources/common-services/PXF/3.0.0/package/scripts/pxf.py | __setup_user_group | MacgradyHuang/ApacheAmbari | 25 | python | def __setup_user_group(self):
'\n \n '
import params
User(params.pxf_user, groups=[params.hdfs_superuser_group, params.user_group, params.tomcat_group], shell='/bin/bash') | def __setup_user_group(self):
'\n \n '
import params
User(params.pxf_user, groups=[params.hdfs_superuser_group, params.user_group, params.tomcat_group], shell='/bin/bash')<|docstring|>Creates PXF user with the required groups and bash as default shell<|endoftext|> |
c519dfbf3aae21eb40c14bd311ae77d914f05dcb3a19cf468a7f02da8e1cedb4 | def __generate_config_files(self):
'\n Generates pxf-env.sh file from jinja template and sets the classpath for HDP\n '
import params
import shutil
hdp_stack = 'HDP'
File('{0}/pxf-env.sh'.format(params.pxf_conf_dir), content=Template('pxf-env.j2'))
if (params.stack_name == hdp_stack):
shutil.copy2('{0}/pxf-privatehdp.classpath'.format(params.pxf_conf_dir), '{0}/pxf-private.classpath'.format(params.pxf_conf_dir))
File('{0}/pxf-public.classpath'.format(params.pxf_conf_dir), content=params.config['configurations']['pxf-public-classpath']['content'].lstrip())
File('{0}/pxf-profiles.xml'.format(params.pxf_conf_dir), content=params.config['configurations']['pxf-profiles']['content'].lstrip())
XmlConfig('pxf-site.xml', conf_dir=params.pxf_conf_dir, configurations=params.config['configurations']['pxf-site'], configuration_attributes=params.config['configurationAttributes']['pxf-site']) | Generates pxf-env.sh file from jinja template and sets the classpath for HDP | ambari-server/src/main/resources/common-services/PXF/3.0.0/package/scripts/pxf.py | __generate_config_files | MacgradyHuang/ApacheAmbari | 25 | python | def __generate_config_files(self):
'\n \n '
import params
import shutil
hdp_stack = 'HDP'
File('{0}/pxf-env.sh'.format(params.pxf_conf_dir), content=Template('pxf-env.j2'))
if (params.stack_name == hdp_stack):
shutil.copy2('{0}/pxf-privatehdp.classpath'.format(params.pxf_conf_dir), '{0}/pxf-private.classpath'.format(params.pxf_conf_dir))
File('{0}/pxf-public.classpath'.format(params.pxf_conf_dir), content=params.config['configurations']['pxf-public-classpath']['content'].lstrip())
File('{0}/pxf-profiles.xml'.format(params.pxf_conf_dir), content=params.config['configurations']['pxf-profiles']['content'].lstrip())
XmlConfig('pxf-site.xml', conf_dir=params.pxf_conf_dir, configurations=params.config['configurations']['pxf-site'], configuration_attributes=params.config['configurationAttributes']['pxf-site']) | def __generate_config_files(self):
'\n \n '
import params
import shutil
hdp_stack = 'HDP'
File('{0}/pxf-env.sh'.format(params.pxf_conf_dir), content=Template('pxf-env.j2'))
if (params.stack_name == hdp_stack):
shutil.copy2('{0}/pxf-privatehdp.classpath'.format(params.pxf_conf_dir), '{0}/pxf-private.classpath'.format(params.pxf_conf_dir))
File('{0}/pxf-public.classpath'.format(params.pxf_conf_dir), content=params.config['configurations']['pxf-public-classpath']['content'].lstrip())
File('{0}/pxf-profiles.xml'.format(params.pxf_conf_dir), content=params.config['configurations']['pxf-profiles']['content'].lstrip())
XmlConfig('pxf-site.xml', conf_dir=params.pxf_conf_dir, configurations=params.config['configurations']['pxf-site'], configuration_attributes=params.config['configurationAttributes']['pxf-site'])<|docstring|>Generates pxf-env.sh file from jinja template and sets the classpath for HDP<|endoftext|> |
8e41489b8e26e98210b8ed1be1ca4e712d8159cce1df06bf8681f2b1abd256f0 | def __grant_permissions(self):
'\n Grants permission to pxf:pxf for PXF instance directory\n '
import params
Directory(params.pxf_instance_dir, owner=params.pxf_user, group=params.pxf_group, create_parents=True) | Grants permission to pxf:pxf for PXF instance directory | ambari-server/src/main/resources/common-services/PXF/3.0.0/package/scripts/pxf.py | __grant_permissions | MacgradyHuang/ApacheAmbari | 25 | python | def __grant_permissions(self):
'\n \n '
import params
Directory(params.pxf_instance_dir, owner=params.pxf_user, group=params.pxf_group, create_parents=True) | def __grant_permissions(self):
'\n \n '
import params
Directory(params.pxf_instance_dir, owner=params.pxf_user, group=params.pxf_group, create_parents=True)<|docstring|>Grants permission to pxf:pxf for PXF instance directory<|endoftext|> |
b2a30d95efc6e7eef5d6c11853e8536e1d1faa1425c9ce8ba26386bce7030ff3 | def _sample_decode(self, model, global_step, sess, src_placeholder, batch_size_placeholder, eval_data, summary_writer):
'Pick a sentence and decode.'
decode_ids = np.random.randint(low=0, high=(len(eval_data) - 1), size=1)
sample_data = []
for decode_id in decode_ids:
sample_data.append(eval_data[decode_id])
iterator_feed_dict = {src_placeholder: sample_data, batch_size_placeholder: len(decode_ids)}
sess.run(model.iterator.initializer, feed_dict=iterator_feed_dict)
(ncm_outputs, infer_summary) = model.decode(sess)
for (i, decode_id) in enumerate(decode_ids):
log.print_out(' # {}'.format(decode_id))
output = ncm_outputs[i]
if ((self.config.beam_width > 0) and self._consider_beam()):
output = output[0]
translation = ncm_utils.get_translation(output, sent_id=0)
delimited_sample = eval_data[decode_id].split('\t')
(utterances, topic) = (delimited_sample[:(- 1)], delimited_sample[(- 1)])
(sources, target) = (utterances[:(- 1)], utterances[(- 1)])
log.print_out(' sources:')
for (t, src) in enumerate(sources):
log.print_out(' @{} {}'.format((t + 1), src))
log.print_out(' topic: {}'.format(topic))
log.print_out(' resp: {}'.format(target))
log.print_out((b' generated: ' + translation))
if (infer_summary is not None):
summary_writer.add_summary(infer_summary, global_step) | Pick a sentence and decode. | thred/models/thred/thred_wrapper.py | _sample_decode | panxuemiao111/THRED | 102 | python | def _sample_decode(self, model, global_step, sess, src_placeholder, batch_size_placeholder, eval_data, summary_writer):
decode_ids = np.random.randint(low=0, high=(len(eval_data) - 1), size=1)
sample_data = []
for decode_id in decode_ids:
sample_data.append(eval_data[decode_id])
iterator_feed_dict = {src_placeholder: sample_data, batch_size_placeholder: len(decode_ids)}
sess.run(model.iterator.initializer, feed_dict=iterator_feed_dict)
(ncm_outputs, infer_summary) = model.decode(sess)
for (i, decode_id) in enumerate(decode_ids):
log.print_out(' # {}'.format(decode_id))
output = ncm_outputs[i]
if ((self.config.beam_width > 0) and self._consider_beam()):
output = output[0]
translation = ncm_utils.get_translation(output, sent_id=0)
delimited_sample = eval_data[decode_id].split('\t')
(utterances, topic) = (delimited_sample[:(- 1)], delimited_sample[(- 1)])
(sources, target) = (utterances[:(- 1)], utterances[(- 1)])
log.print_out(' sources:')
for (t, src) in enumerate(sources):
log.print_out(' @{} {}'.format((t + 1), src))
log.print_out(' topic: {}'.format(topic))
log.print_out(' resp: {}'.format(target))
log.print_out((b' generated: ' + translation))
if (infer_summary is not None):
summary_writer.add_summary(infer_summary, global_step) | def _sample_decode(self, model, global_step, sess, src_placeholder, batch_size_placeholder, eval_data, summary_writer):
decode_ids = np.random.randint(low=0, high=(len(eval_data) - 1), size=1)
sample_data = []
for decode_id in decode_ids:
sample_data.append(eval_data[decode_id])
iterator_feed_dict = {src_placeholder: sample_data, batch_size_placeholder: len(decode_ids)}
sess.run(model.iterator.initializer, feed_dict=iterator_feed_dict)
(ncm_outputs, infer_summary) = model.decode(sess)
for (i, decode_id) in enumerate(decode_ids):
log.print_out(' # {}'.format(decode_id))
output = ncm_outputs[i]
if ((self.config.beam_width > 0) and self._consider_beam()):
output = output[0]
translation = ncm_utils.get_translation(output, sent_id=0)
delimited_sample = eval_data[decode_id].split('\t')
(utterances, topic) = (delimited_sample[:(- 1)], delimited_sample[(- 1)])
(sources, target) = (utterances[:(- 1)], utterances[(- 1)])
log.print_out(' sources:')
for (t, src) in enumerate(sources):
log.print_out(' @{} {}'.format((t + 1), src))
log.print_out(' topic: {}'.format(topic))
log.print_out(' resp: {}'.format(target))
log.print_out((b' generated: ' + translation))
if (infer_summary is not None):
summary_writer.add_summary(infer_summary, global_step)<|docstring|>Pick a sentence and decode.<|endoftext|> |
7141812f00b1006981af0574f21c6fba62e9e7a1bb45c657ef959b8b3667c4d7 | def getsymbles(jinja_env, extensions=''):
'return a list of :class:`.Symbles` of a :class:`jinja2.Environment`;\n\n '
symbles = []
for name in jinja_env.list_templates():
if (not name.endswith(extensions)):
continue
sourcetuple = jinja_env.loader.get_source(jinja_env, name)
try:
ast = jinja_env.parse(sourcetuple)
except:
(exc_type, exc, traceback) = sys.exc_info()
print('getsymbles: parse ....', file=sys.stderr)
error = '{}, {}'.format(exc_type.__name__, exc.message)
print(' {}: {}'.format(name, error), file=sys.stderr)
continue
symbles.append(Symble(ast, name))
return symbles | return a list of :class:`.Symbles` of a :class:`jinja2.Environment`; | gvjinja.py | getsymbles | VCHui/gvjinja | 1 | python | def getsymbles(jinja_env, extensions=):
'\n\n '
symbles = []
for name in jinja_env.list_templates():
if (not name.endswith(extensions)):
continue
sourcetuple = jinja_env.loader.get_source(jinja_env, name)
try:
ast = jinja_env.parse(sourcetuple)
except:
(exc_type, exc, traceback) = sys.exc_info()
print('getsymbles: parse ....', file=sys.stderr)
error = '{}, {}'.format(exc_type.__name__, exc.message)
print(' {}: {}'.format(name, error), file=sys.stderr)
continue
symbles.append(Symble(ast, name))
return symbles | def getsymbles(jinja_env, extensions=):
'\n\n '
symbles = []
for name in jinja_env.list_templates():
if (not name.endswith(extensions)):
continue
sourcetuple = jinja_env.loader.get_source(jinja_env, name)
try:
ast = jinja_env.parse(sourcetuple)
except:
(exc_type, exc, traceback) = sys.exc_info()
print('getsymbles: parse ....', file=sys.stderr)
error = '{}, {}'.format(exc_type.__name__, exc.message)
print(' {}: {}'.format(name, error), file=sys.stderr)
continue
symbles.append(Symble(ast, name))
return symbles<|docstring|>return a list of :class:`.Symbles` of a :class:`jinja2.Environment`;<|endoftext|> |
7fa06193ce4b6bdba0a43266db977d294a7968c9f6924d0b13689040ad567555 | def usage():
'::\n\n Usage: gvjinja.py [-m [module] [env]] [-b]\n\n # print usage;\n $ gvjinja.py\n\n # render a digraph of gvjinja itself;\n $ gvjinja.py -m gvjinja gvjinja.env |\\\n tee gvjinja.gv | dot -T png > gvjinja.png\n\n # render a basic digraph of gvjinja itself;\n $ gvjinja.py -m gvjinja gvjinja.env -b |\\\n tee gvjinja-basic.gv | dot -T png > gvjinja-basic.png\n\n:\n'
pass | ::
Usage: gvjinja.py [-m [module] [env]] [-b]
# print usage;
$ gvjinja.py
# render a digraph of gvjinja itself;
$ gvjinja.py -m gvjinja gvjinja.env |\
tee gvjinja.gv | dot -T png > gvjinja.png
# render a basic digraph of gvjinja itself;
$ gvjinja.py -m gvjinja gvjinja.env -b |\
tee gvjinja-basic.gv | dot -T png > gvjinja-basic.png
: | gvjinja.py | usage | VCHui/gvjinja | 1 | python | def usage():
'::\n\n Usage: gvjinja.py [-m [module] [env]] [-b]\n\n # print usage;\n $ gvjinja.py\n\n # render a digraph of gvjinja itself;\n $ gvjinja.py -m gvjinja gvjinja.env |\\\n tee gvjinja.gv | dot -T png > gvjinja.png\n\n # render a basic digraph of gvjinja itself;\n $ gvjinja.py -m gvjinja gvjinja.env -b |\\\n tee gvjinja-basic.gv | dot -T png > gvjinja-basic.png\n\n:\n'
pass | def usage():
'::\n\n Usage: gvjinja.py [-m [module] [env]] [-b]\n\n # print usage;\n $ gvjinja.py\n\n # render a digraph of gvjinja itself;\n $ gvjinja.py -m gvjinja gvjinja.env |\\\n tee gvjinja.gv | dot -T png > gvjinja.png\n\n # render a basic digraph of gvjinja itself;\n $ gvjinja.py -m gvjinja gvjinja.env -b |\\\n tee gvjinja-basic.gv | dot -T png > gvjinja-basic.png\n\n:\n'
pass<|docstring|>::
Usage: gvjinja.py [-m [module] [env]] [-b]
# print usage;
$ gvjinja.py
# render a digraph of gvjinja itself;
$ gvjinja.py -m gvjinja gvjinja.env |\
tee gvjinja.gv | dot -T png > gvjinja.png
# render a basic digraph of gvjinja itself;
$ gvjinja.py -m gvjinja gvjinja.env -b |\
tee gvjinja-basic.gv | dot -T png > gvjinja-basic.png
:<|endoftext|> |
ca350db86320940900e4c2cd736ca025af783805d075883fd697e74ea9e15bdd | def getrefs(self, reftype):
'yield a list of references for a reference type.\n\n lifted from :func:`jinja2.meta.find_referenced_templates` to\n perform searching for specific reference types; no report for\n dynamic inheritances or inclusions;\n\n '
for node in self.find_all(reftype):
if (not isinstance(node.template, nodes.Const)):
if isinstance(node.template, (nodes.Tuple, nodes.List)):
for template_name in node.template.items:
if isinstance(template_name.value, self.str_types):
(yield template_name.value)
elif isinstance(node.template.value, self.str_types):
(yield node.template.value)
elif isinstance(node, node.Include):
if isinstance(node.template.value, (tuple, list)):
for template_name in node.template.value:
(yield template_name) | yield a list of references for a reference type.
lifted from :func:`jinja2.meta.find_referenced_templates` to
perform searching for specific reference types; no report for
dynamic inheritances or inclusions; | gvjinja.py | getrefs | VCHui/gvjinja | 1 | python | def getrefs(self, reftype):
'yield a list of references for a reference type.\n\n lifted from :func:`jinja2.meta.find_referenced_templates` to\n perform searching for specific reference types; no report for\n dynamic inheritances or inclusions;\n\n '
for node in self.find_all(reftype):
if (not isinstance(node.template, nodes.Const)):
if isinstance(node.template, (nodes.Tuple, nodes.List)):
for template_name in node.template.items:
if isinstance(template_name.value, self.str_types):
(yield template_name.value)
elif isinstance(node.template.value, self.str_types):
(yield node.template.value)
elif isinstance(node, node.Include):
if isinstance(node.template.value, (tuple, list)):
for template_name in node.template.value:
(yield template_name) | def getrefs(self, reftype):
'yield a list of references for a reference type.\n\n lifted from :func:`jinja2.meta.find_referenced_templates` to\n perform searching for specific reference types; no report for\n dynamic inheritances or inclusions;\n\n '
for node in self.find_all(reftype):
if (not isinstance(node.template, nodes.Const)):
if isinstance(node.template, (nodes.Tuple, nodes.List)):
for template_name in node.template.items:
if isinstance(template_name.value, self.str_types):
(yield template_name.value)
elif isinstance(node.template.value, self.str_types):
(yield node.template.value)
elif isinstance(node, node.Include):
if isinstance(node.template.value, (tuple, list)):
for template_name in node.template.value:
(yield template_name)<|docstring|>yield a list of references for a reference type.
lifted from :func:`jinja2.meta.find_referenced_templates` to
perform searching for specific reference types; no report for
dynamic inheritances or inclusions;<|endoftext|> |
224fd5c7c256d2197797d0a73f9a12a41a5ae323f34a5b80af92addc7c80baab | @classmethod
def digraph(my, symbles, extensions=''):
'``stdout`` a UML graph file of env in details.\n\n .. figure:: ../gvjinja.png\n :alt: gvjinja.digraph\n\n '
if isinstance(symbles, Environment):
symbles = getsymbles(symbles, extensions)
return my.env.get_template('DIGRAPH').render(symbles=symbles) | ``stdout`` a UML graph file of env in details.
.. figure:: ../gvjinja.png
:alt: gvjinja.digraph | gvjinja.py | digraph | VCHui/gvjinja | 1 | python | @classmethod
def digraph(my, symbles, extensions=):
'``stdout`` a UML graph file of env in details.\n\n .. figure:: ../gvjinja.png\n :alt: gvjinja.digraph\n\n '
if isinstance(symbles, Environment):
symbles = getsymbles(symbles, extensions)
return my.env.get_template('DIGRAPH').render(symbles=symbles) | @classmethod
def digraph(my, symbles, extensions=):
'``stdout`` a UML graph file of env in details.\n\n .. figure:: ../gvjinja.png\n :alt: gvjinja.digraph\n\n '
if isinstance(symbles, Environment):
symbles = getsymbles(symbles, extensions)
return my.env.get_template('DIGRAPH').render(symbles=symbles)<|docstring|>``stdout`` a UML graph file of env in details.
.. figure:: ../gvjinja.png
:alt: gvjinja.digraph<|endoftext|> |
24daa7b5552594d416b254a5b1446b1ad352be8876d39365f62233365463555d | @classmethod
def digraphbasic(my, symbles, extensions=''):
'``stdout`` a basic graph file of env, no node and edge details.\n\n .. figure:: ../gvjinja-basic.png\n :alt: gvjinja.digraphbasic\n\n '
if isinstance(symbles, Environment):
symbles = getsymbles(symbles, extensions)
return my.env.get_template('DIGRAPHBASIC').render(symbles=symbles) | ``stdout`` a basic graph file of env, no node and edge details.
.. figure:: ../gvjinja-basic.png
:alt: gvjinja.digraphbasic | gvjinja.py | digraphbasic | VCHui/gvjinja | 1 | python | @classmethod
def digraphbasic(my, symbles, extensions=):
'``stdout`` a basic graph file of env, no node and edge details.\n\n .. figure:: ../gvjinja-basic.png\n :alt: gvjinja.digraphbasic\n\n '
if isinstance(symbles, Environment):
symbles = getsymbles(symbles, extensions)
return my.env.get_template('DIGRAPHBASIC').render(symbles=symbles) | @classmethod
def digraphbasic(my, symbles, extensions=):
'``stdout`` a basic graph file of env, no node and edge details.\n\n .. figure:: ../gvjinja-basic.png\n :alt: gvjinja.digraphbasic\n\n '
if isinstance(symbles, Environment):
symbles = getsymbles(symbles, extensions)
return my.env.get_template('DIGRAPHBASIC').render(symbles=symbles)<|docstring|>``stdout`` a basic graph file of env, no node and edge details.
.. figure:: ../gvjinja-basic.png
:alt: gvjinja.digraphbasic<|endoftext|> |
100a237deafc7f43c908c7358345ab83ab71ec457c887eccb6162a6408cf4db0 | def GTA_antihot(onehot_img, colors: dict, width: int, height: int):
'\n This function takes a onehot encoded input and turns it into a image with\n three channels.\n\n Args:\n onehot_img (tensor): onehot encoded image.\n col (dict): a mapping of the differnt layers to different colors.\n width (int): width of the given image.\n height (int): height of the given image.\n\n Return:\n Tensor: An image with 3 channels.\n '
inv_colors = {v: k for (k, v) in colors.items()}
mat = onehot_img.numpy()
map = torch.empty((3, height, width), dtype=torch.uint8)
for i in range(0, len(set(colors.values()))):
col = inv_colors.get(i)
indicies = np.where((mat[i] == 1))
for (r, c) in zip(*indicies):
map[0][r][c] = col[0]
map[1][r][c] = col[1]
map[2][r][c] = col[2]
return map | This function takes a onehot encoded input and turns it into a image with
three channels.
Args:
onehot_img (tensor): onehot encoded image.
col (dict): a mapping of the differnt layers to different colors.
width (int): width of the given image.
height (int): height of the given image.
Return:
Tensor: An image with 3 channels. | GTApack/GTA_antihot.py | GTA_antihot | marctimjen/Deep_final | 0 | python | def GTA_antihot(onehot_img, colors: dict, width: int, height: int):
'\n This function takes a onehot encoded input and turns it into a image with\n three channels.\n\n Args:\n onehot_img (tensor): onehot encoded image.\n col (dict): a mapping of the differnt layers to different colors.\n width (int): width of the given image.\n height (int): height of the given image.\n\n Return:\n Tensor: An image with 3 channels.\n '
inv_colors = {v: k for (k, v) in colors.items()}
mat = onehot_img.numpy()
map = torch.empty((3, height, width), dtype=torch.uint8)
for i in range(0, len(set(colors.values()))):
col = inv_colors.get(i)
indicies = np.where((mat[i] == 1))
for (r, c) in zip(*indicies):
map[0][r][c] = col[0]
map[1][r][c] = col[1]
map[2][r][c] = col[2]
return map | def GTA_antihot(onehot_img, colors: dict, width: int, height: int):
'\n This function takes a onehot encoded input and turns it into a image with\n three channels.\n\n Args:\n onehot_img (tensor): onehot encoded image.\n col (dict): a mapping of the differnt layers to different colors.\n width (int): width of the given image.\n height (int): height of the given image.\n\n Return:\n Tensor: An image with 3 channels.\n '
inv_colors = {v: k for (k, v) in colors.items()}
mat = onehot_img.numpy()
map = torch.empty((3, height, width), dtype=torch.uint8)
for i in range(0, len(set(colors.values()))):
col = inv_colors.get(i)
indicies = np.where((mat[i] == 1))
for (r, c) in zip(*indicies):
map[0][r][c] = col[0]
map[1][r][c] = col[1]
map[2][r][c] = col[2]
return map<|docstring|>This function takes a onehot encoded input and turns it into a image with
three channels.
Args:
onehot_img (tensor): onehot encoded image.
col (dict): a mapping of the differnt layers to different colors.
width (int): width of the given image.
height (int): height of the given image.
Return:
Tensor: An image with 3 channels.<|endoftext|> |
ac0a83ddec0f19c2b1f74277bffb9adab29419b7496b88fbcde64681601c668d | def _inference(self, images, graph_part, cell_list, train_flag):
'Method for recovering the network model provided by graph_part and cellist.\n Args:\n images: Images returned from Dataset() or inputs().\n graph_part: The topology structure of th network given by adjacency table\n cellist:\n Returns:\n Logits.'
topo_order = self._toposort(graph_part)
nodelen = len(graph_part)
inputs = [images for _ in range(nodelen)]
getinput = [False for _ in range(nodelen)]
getinput[0] = True
for node in topo_order:
layer = self._make_layer(inputs[node], cell_list[node], node, train_flag)
for j in graph_part[node]:
if getinput[j]:
inputs[j] = self._pad(inputs[j], layer)
else:
inputs[j] = layer
getinput[j] = True
last_layer = tf.identity(layer, name=('last_layer' + str(self.block_num)))
return last_layer | Method for recovering the network model provided by graph_part and cellist.
Args:
images: Images returned from Dataset() or inputs().
graph_part: The topology structure of th network given by adjacency table
cellist:
Returns:
Logits. | 1_nas/evaluator_classification_2.py | _inference | nuaa-QK/1_NAS | 0 | python | def _inference(self, images, graph_part, cell_list, train_flag):
'Method for recovering the network model provided by graph_part and cellist.\n Args:\n images: Images returned from Dataset() or inputs().\n graph_part: The topology structure of th network given by adjacency table\n cellist:\n Returns:\n Logits.'
topo_order = self._toposort(graph_part)
nodelen = len(graph_part)
inputs = [images for _ in range(nodelen)]
getinput = [False for _ in range(nodelen)]
getinput[0] = True
for node in topo_order:
layer = self._make_layer(inputs[node], cell_list[node], node, train_flag)
for j in graph_part[node]:
if getinput[j]:
inputs[j] = self._pad(inputs[j], layer)
else:
inputs[j] = layer
getinput[j] = True
last_layer = tf.identity(layer, name=('last_layer' + str(self.block_num)))
return last_layer | def _inference(self, images, graph_part, cell_list, train_flag):
'Method for recovering the network model provided by graph_part and cellist.\n Args:\n images: Images returned from Dataset() or inputs().\n graph_part: The topology structure of th network given by adjacency table\n cellist:\n Returns:\n Logits.'
topo_order = self._toposort(graph_part)
nodelen = len(graph_part)
inputs = [images for _ in range(nodelen)]
getinput = [False for _ in range(nodelen)]
getinput[0] = True
for node in topo_order:
layer = self._make_layer(inputs[node], cell_list[node], node, train_flag)
for j in graph_part[node]:
if getinput[j]:
inputs[j] = self._pad(inputs[j], layer)
else:
inputs[j] = layer
getinput[j] = True
last_layer = tf.identity(layer, name=('last_layer' + str(self.block_num)))
return last_layer<|docstring|>Method for recovering the network model provided by graph_part and cellist.
Args:
images: Images returned from Dataset() or inputs().
graph_part: The topology structure of th network given by adjacency table
cellist:
Returns:
Logits.<|endoftext|> |
f38f9349f555d67749bdbdbb843abe04e9e403e0283c20f77babfbf4a51645ff | def _make_layer(self, inputs, cell, node, train_flag):
'Method for constructing and calculating cell in tensorflow\n Args:\n inputs: the input tensor of this operation\n cell: Class Cell(), hyper parameters for building this layer\n node: int, the index of this operation\n train_flag: boolean, indicating whether this is a training process or not\n Returns:\n layer: tensor.'
if (cell.type == 'conv'):
layer = self._makeconv(inputs, cell, node, train_flag)
elif (cell.type == 'pooling'):
layer = self._makepool(inputs, cell)
elif (cell.type == 'id'):
layer = tf.identity(inputs)
elif (cell.type == 'sep_conv'):
layer = self._makesep_conv(inputs, cell, node, train_flag)
else:
assert False, 'Wrong cell type!'
return layer | Method for constructing and calculating cell in tensorflow
Args:
inputs: the input tensor of this operation
cell: Class Cell(), hyper parameters for building this layer
node: int, the index of this operation
train_flag: boolean, indicating whether this is a training process or not
Returns:
layer: tensor. | 1_nas/evaluator_classification_2.py | _make_layer | nuaa-QK/1_NAS | 0 | python | def _make_layer(self, inputs, cell, node, train_flag):
'Method for constructing and calculating cell in tensorflow\n Args:\n inputs: the input tensor of this operation\n cell: Class Cell(), hyper parameters for building this layer\n node: int, the index of this operation\n train_flag: boolean, indicating whether this is a training process or not\n Returns:\n layer: tensor.'
if (cell.type == 'conv'):
layer = self._makeconv(inputs, cell, node, train_flag)
elif (cell.type == 'pooling'):
layer = self._makepool(inputs, cell)
elif (cell.type == 'id'):
layer = tf.identity(inputs)
elif (cell.type == 'sep_conv'):
layer = self._makesep_conv(inputs, cell, node, train_flag)
else:
assert False, 'Wrong cell type!'
return layer | def _make_layer(self, inputs, cell, node, train_flag):
'Method for constructing and calculating cell in tensorflow\n Args:\n inputs: the input tensor of this operation\n cell: Class Cell(), hyper parameters for building this layer\n node: int, the index of this operation\n train_flag: boolean, indicating whether this is a training process or not\n Returns:\n layer: tensor.'
if (cell.type == 'conv'):
layer = self._makeconv(inputs, cell, node, train_flag)
elif (cell.type == 'pooling'):
layer = self._makepool(inputs, cell)
elif (cell.type == 'id'):
layer = tf.identity(inputs)
elif (cell.type == 'sep_conv'):
layer = self._makesep_conv(inputs, cell, node, train_flag)
else:
assert False, 'Wrong cell type!'
return layer<|docstring|>Method for constructing and calculating cell in tensorflow
Args:
inputs: the input tensor of this operation
cell: Class Cell(), hyper parameters for building this layer
node: int, the index of this operation
train_flag: boolean, indicating whether this is a training process or not
Returns:
layer: tensor.<|endoftext|> |
e67d29a6ec95ad2a9d88d495931d9c764cb6184eb225406eca24e606246e0e08 | def _makeconv(self, x, hplist, node, train_flag):
'Generates a convolutional layer according to information in hplist\n Args:\n x: inputing data.\n hplist: hyperparameters for building this layer\n node: int, the index of this operation\n Returns:\n conv_layer: the output tensor\n '
with tf.variable_scope(((('block' + str(self.block_num)) + 'conv') + str(node))) as scope:
inputdim = x.shape[3]
kernel = self._get_variable('weights', shape=[hplist.kernel_size, hplist.kernel_size, inputdim, hplist.filter_size])
x = self._activation_layer(hplist.activation, x, scope)
x = tf.nn.conv2d(x, kernel, [1, 1, 1, 1], padding='SAME')
biases = self._get_variable('biases', hplist.filter_size)
x = self._batch_norm(tf.nn.bias_add(x, biases), train_flag)
return x | Generates a convolutional layer according to information in hplist
Args:
x: inputing data.
hplist: hyperparameters for building this layer
node: int, the index of this operation
Returns:
conv_layer: the output tensor | 1_nas/evaluator_classification_2.py | _makeconv | nuaa-QK/1_NAS | 0 | python | def _makeconv(self, x, hplist, node, train_flag):
'Generates a convolutional layer according to information in hplist\n Args:\n x: inputing data.\n hplist: hyperparameters for building this layer\n node: int, the index of this operation\n Returns:\n conv_layer: the output tensor\n '
with tf.variable_scope(((('block' + str(self.block_num)) + 'conv') + str(node))) as scope:
inputdim = x.shape[3]
kernel = self._get_variable('weights', shape=[hplist.kernel_size, hplist.kernel_size, inputdim, hplist.filter_size])
x = self._activation_layer(hplist.activation, x, scope)
x = tf.nn.conv2d(x, kernel, [1, 1, 1, 1], padding='SAME')
biases = self._get_variable('biases', hplist.filter_size)
x = self._batch_norm(tf.nn.bias_add(x, biases), train_flag)
return x | def _makeconv(self, x, hplist, node, train_flag):
'Generates a convolutional layer according to information in hplist\n Args:\n x: inputing data.\n hplist: hyperparameters for building this layer\n node: int, the index of this operation\n Returns:\n conv_layer: the output tensor\n '
with tf.variable_scope(((('block' + str(self.block_num)) + 'conv') + str(node))) as scope:
inputdim = x.shape[3]
kernel = self._get_variable('weights', shape=[hplist.kernel_size, hplist.kernel_size, inputdim, hplist.filter_size])
x = self._activation_layer(hplist.activation, x, scope)
x = tf.nn.conv2d(x, kernel, [1, 1, 1, 1], padding='SAME')
biases = self._get_variable('biases', hplist.filter_size)
x = self._batch_norm(tf.nn.bias_add(x, biases), train_flag)
return x<|docstring|>Generates a convolutional layer according to information in hplist
Args:
x: inputing data.
hplist: hyperparameters for building this layer
node: int, the index of this operation
Returns:
conv_layer: the output tensor<|endoftext|> |
5e8f88aaf3f23b30d1845c1381d9716bf0b3ef366f50e23f1840174e19d3579f | def _makepool(self, inputs, hplist):
'Generates a pooling layer according to information in hplist\n Args:\n inputs: inputing data.\n hplist: hyperparameters for building this layer\n Returns:\n tensor.\n '
if (hplist.pooling_type == 'avg'):
return tf.nn.avg_pool(inputs, ksize=[1, hplist.kernel_size, hplist.kernel_size, 1], strides=[1, hplist.kernel_size, hplist.kernel_size, 1], padding='SAME')
elif (hplist.pooling_type == 'max'):
return tf.nn.max_pool(inputs, ksize=[1, hplist.kernel_size, hplist.kernel_size, 1], strides=[1, hplist.kernel_size, hplist.kernel_size, 1], padding='SAME')
elif (hplist.pooling_type == 'global'):
return tf.reduce_mean(inputs, [1, 2], keep_dims=True) | Generates a pooling layer according to information in hplist
Args:
inputs: inputing data.
hplist: hyperparameters for building this layer
Returns:
tensor. | 1_nas/evaluator_classification_2.py | _makepool | nuaa-QK/1_NAS | 0 | python | def _makepool(self, inputs, hplist):
'Generates a pooling layer according to information in hplist\n Args:\n inputs: inputing data.\n hplist: hyperparameters for building this layer\n Returns:\n tensor.\n '
if (hplist.pooling_type == 'avg'):
return tf.nn.avg_pool(inputs, ksize=[1, hplist.kernel_size, hplist.kernel_size, 1], strides=[1, hplist.kernel_size, hplist.kernel_size, 1], padding='SAME')
elif (hplist.pooling_type == 'max'):
return tf.nn.max_pool(inputs, ksize=[1, hplist.kernel_size, hplist.kernel_size, 1], strides=[1, hplist.kernel_size, hplist.kernel_size, 1], padding='SAME')
elif (hplist.pooling_type == 'global'):
return tf.reduce_mean(inputs, [1, 2], keep_dims=True) | def _makepool(self, inputs, hplist):
'Generates a pooling layer according to information in hplist\n Args:\n inputs: inputing data.\n hplist: hyperparameters for building this layer\n Returns:\n tensor.\n '
if (hplist.pooling_type == 'avg'):
return tf.nn.avg_pool(inputs, ksize=[1, hplist.kernel_size, hplist.kernel_size, 1], strides=[1, hplist.kernel_size, hplist.kernel_size, 1], padding='SAME')
elif (hplist.pooling_type == 'max'):
return tf.nn.max_pool(inputs, ksize=[1, hplist.kernel_size, hplist.kernel_size, 1], strides=[1, hplist.kernel_size, hplist.kernel_size, 1], padding='SAME')
elif (hplist.pooling_type == 'global'):
return tf.reduce_mean(inputs, [1, 2], keep_dims=True)<|docstring|>Generates a pooling layer according to information in hplist
Args:
inputs: inputing data.
hplist: hyperparameters for building this layer
Returns:
tensor.<|endoftext|> |
59fbbbbea8cb8f4afcd2d851f597b1e337df2a639d2b3b1a261644f23ee38658 | def _makedense(self, inputs, hplist):
'Generates dense layers according to information in hplist\n Args:\n inputs: inputing data.\n hplist: hyperparameters for building layers\n node: number of this cell\n Returns:\n tensor.\n '
inputs = tf.reshape(inputs, [self.batch_size, (- 1)])
for (i, neural_num) in enumerate(hplist[1]):
with tf.variable_scope(((('block' + str(self.block_num)) + 'dense') + str(i))) as scope:
weights = self._get_variable('weights', shape=[inputs.shape[(- 1)], neural_num])
biases = self._get_variable('biases', [neural_num])
mul = (tf.matmul(inputs, weights) + biases)
if (neural_num == self.output_shape[(- 1)]):
local3 = self._activation_layer('', mul, scope)
else:
local3 = self._activation_layer(hplist[2], mul, scope)
inputs = local3
return inputs | Generates dense layers according to information in hplist
Args:
inputs: inputing data.
hplist: hyperparameters for building layers
node: number of this cell
Returns:
tensor. | 1_nas/evaluator_classification_2.py | _makedense | nuaa-QK/1_NAS | 0 | python | def _makedense(self, inputs, hplist):
'Generates dense layers according to information in hplist\n Args:\n inputs: inputing data.\n hplist: hyperparameters for building layers\n node: number of this cell\n Returns:\n tensor.\n '
inputs = tf.reshape(inputs, [self.batch_size, (- 1)])
for (i, neural_num) in enumerate(hplist[1]):
with tf.variable_scope(((('block' + str(self.block_num)) + 'dense') + str(i))) as scope:
weights = self._get_variable('weights', shape=[inputs.shape[(- 1)], neural_num])
biases = self._get_variable('biases', [neural_num])
mul = (tf.matmul(inputs, weights) + biases)
if (neural_num == self.output_shape[(- 1)]):
local3 = self._activation_layer(, mul, scope)
else:
local3 = self._activation_layer(hplist[2], mul, scope)
inputs = local3
return inputs | def _makedense(self, inputs, hplist):
'Generates dense layers according to information in hplist\n Args:\n inputs: inputing data.\n hplist: hyperparameters for building layers\n node: number of this cell\n Returns:\n tensor.\n '
inputs = tf.reshape(inputs, [self.batch_size, (- 1)])
for (i, neural_num) in enumerate(hplist[1]):
with tf.variable_scope(((('block' + str(self.block_num)) + 'dense') + str(i))) as scope:
weights = self._get_variable('weights', shape=[inputs.shape[(- 1)], neural_num])
biases = self._get_variable('biases', [neural_num])
mul = (tf.matmul(inputs, weights) + biases)
if (neural_num == self.output_shape[(- 1)]):
local3 = self._activation_layer(, mul, scope)
else:
local3 = self._activation_layer(hplist[2], mul, scope)
inputs = local3
return inputs<|docstring|>Generates dense layers according to information in hplist
Args:
inputs: inputing data.
hplist: hyperparameters for building layers
node: number of this cell
Returns:
tensor.<|endoftext|> |
010346d444f3f7456b180044206f13e4416171577103a0e02094cca783afeea5 | def evaluate(self, network, pre_block=[], is_bestNN=False, update_pre_weight=False):
"Method for evaluate the given network.\n \n :param network: NetworkItem()\n :param pre_block: The pre-block structure, every block has two parts 'graph_part' and 'cell_list' of this block.\n :param is_bestNN: Symbol for indicating whether the evaluating network is the best network of this round.\n :param update_pre_weight: Symbol for indicating whether to update previous blocks' weight.\n :return: accuracy, float.\n "
assert (self.train_num >= self.batch_size)
tf.reset_default_graph()
self.block_num = len(pre_block)
self.log = (((('-' * 20) + str(network.id)) + ('-' * 20)) + '\n')
for block in pre_block:
self.log = (((self.log + str(block.graph)) + str(block.cell_list)) + '\n')
self.log = (((self.log + str(network.graph)) + str(network.cell_list)) + '\n')
with tf.Session() as sess:
(data_x, data_y, block_input, train_flag) = self._get_input(sess, pre_block, update_pre_weight)
(graph_full, cell_list) = self._recode(network.graph, network.cell_list, NAS_CONFIG['nas_main']['repeat_num'])
graph_full = (graph_full + [[]])
cell_list = (cell_list + [Cell('pooling', 'max', 2)])
logits = self._inference(block_input, graph_full, cell_list, train_flag)
logits = tf.nn.dropout(logits, keep_prob=1.0)
logits = self._makedense(logits, ('', [self.output_shape[(- 1)]], ''))
(precision, log) = self._eval(sess, logits, data_x, data_y, train_flag)
self.log += log
saver = tf.train.Saver(tf.global_variables())
if is_bestNN:
if (not os.path.exists(os.path.join(self.model_path))):
os.makedirs(os.path.join(self.model_path))
saver.save(sess, os.path.join(self.model_path, ('model' + str(network.id))))
(NAS_LOG << ('eva', self.log))
return precision | Method for evaluate the given network.
:param network: NetworkItem()
:param pre_block: The pre-block structure, every block has two parts 'graph_part' and 'cell_list' of this block.
:param is_bestNN: Symbol for indicating whether the evaluating network is the best network of this round.
:param update_pre_weight: Symbol for indicating whether to update previous blocks' weight.
:return: accuracy, float. | 1_nas/evaluator_classification_2.py | evaluate | nuaa-QK/1_NAS | 0 | python | def evaluate(self, network, pre_block=[], is_bestNN=False, update_pre_weight=False):
"Method for evaluate the given network.\n \n :param network: NetworkItem()\n :param pre_block: The pre-block structure, every block has two parts 'graph_part' and 'cell_list' of this block.\n :param is_bestNN: Symbol for indicating whether the evaluating network is the best network of this round.\n :param update_pre_weight: Symbol for indicating whether to update previous blocks' weight.\n :return: accuracy, float.\n "
assert (self.train_num >= self.batch_size)
tf.reset_default_graph()
self.block_num = len(pre_block)
self.log = (((('-' * 20) + str(network.id)) + ('-' * 20)) + '\n')
for block in pre_block:
self.log = (((self.log + str(block.graph)) + str(block.cell_list)) + '\n')
self.log = (((self.log + str(network.graph)) + str(network.cell_list)) + '\n')
with tf.Session() as sess:
(data_x, data_y, block_input, train_flag) = self._get_input(sess, pre_block, update_pre_weight)
(graph_full, cell_list) = self._recode(network.graph, network.cell_list, NAS_CONFIG['nas_main']['repeat_num'])
graph_full = (graph_full + [[]])
cell_list = (cell_list + [Cell('pooling', 'max', 2)])
logits = self._inference(block_input, graph_full, cell_list, train_flag)
logits = tf.nn.dropout(logits, keep_prob=1.0)
logits = self._makedense(logits, (, [self.output_shape[(- 1)]], ))
(precision, log) = self._eval(sess, logits, data_x, data_y, train_flag)
self.log += log
saver = tf.train.Saver(tf.global_variables())
if is_bestNN:
if (not os.path.exists(os.path.join(self.model_path))):
os.makedirs(os.path.join(self.model_path))
saver.save(sess, os.path.join(self.model_path, ('model' + str(network.id))))
(NAS_LOG << ('eva', self.log))
return precision | def evaluate(self, network, pre_block=[], is_bestNN=False, update_pre_weight=False):
"Method for evaluate the given network.\n \n :param network: NetworkItem()\n :param pre_block: The pre-block structure, every block has two parts 'graph_part' and 'cell_list' of this block.\n :param is_bestNN: Symbol for indicating whether the evaluating network is the best network of this round.\n :param update_pre_weight: Symbol for indicating whether to update previous blocks' weight.\n :return: accuracy, float.\n "
assert (self.train_num >= self.batch_size)
tf.reset_default_graph()
self.block_num = len(pre_block)
self.log = (((('-' * 20) + str(network.id)) + ('-' * 20)) + '\n')
for block in pre_block:
self.log = (((self.log + str(block.graph)) + str(block.cell_list)) + '\n')
self.log = (((self.log + str(network.graph)) + str(network.cell_list)) + '\n')
with tf.Session() as sess:
(data_x, data_y, block_input, train_flag) = self._get_input(sess, pre_block, update_pre_weight)
(graph_full, cell_list) = self._recode(network.graph, network.cell_list, NAS_CONFIG['nas_main']['repeat_num'])
graph_full = (graph_full + [[]])
cell_list = (cell_list + [Cell('pooling', 'max', 2)])
logits = self._inference(block_input, graph_full, cell_list, train_flag)
logits = tf.nn.dropout(logits, keep_prob=1.0)
logits = self._makedense(logits, (, [self.output_shape[(- 1)]], ))
(precision, log) = self._eval(sess, logits, data_x, data_y, train_flag)
self.log += log
saver = tf.train.Saver(tf.global_variables())
if is_bestNN:
if (not os.path.exists(os.path.join(self.model_path))):
os.makedirs(os.path.join(self.model_path))
saver.save(sess, os.path.join(self.model_path, ('model' + str(network.id))))
(NAS_LOG << ('eva', self.log))
return precision<|docstring|>Method for evaluate the given network.
:param network: NetworkItem()
:param pre_block: The pre-block structure, every block has two parts 'graph_part' and 'cell_list' of this block.
:param is_bestNN: Symbol for indicating whether the evaluating network is the best network of this round.
:param update_pre_weight: Symbol for indicating whether to update previous blocks' weight.
:return: accuracy, float.<|endoftext|> |
534a5ce3c0256d03219ef29e03c9fe18cb860a5bfb8731b0d7576781faeb32e2 | def _get_input(self, sess, pre_block, update_pre_weight=False):
'Get input for _inference'
if (len(pre_block) > 0):
assert os.path.exists(os.path.join(self.model_path, (('model' + str(pre_block[(- 1)].id)) + '.meta')))
new_saver = tf.train.import_meta_graph(os.path.join(self.model_path, (('model' + str(pre_block[(- 1)].id)) + '.meta')))
new_saver.restore(sess, os.path.join(self.model_path, ('model' + str(pre_block[(- 1)].id))))
graph = tf.get_default_graph()
data_x = graph.get_tensor_by_name('input:0')
data_y = graph.get_tensor_by_name('label:0')
train_flag = graph.get_tensor_by_name('train_flag:0')
block_input = graph.get_tensor_by_name((('last_layer' + str((self.block_num - 1))) + ':0'))
if (not update_pre_weight):
block_input = tf.stop_gradient(block_input, name='stop_gradient')
else:
data_x = tf.placeholder(tf.float32, self.input_shape, name='input')
data_y = tf.placeholder(tf.int32, self.output_shape, name='label')
train_flag = tf.placeholder(tf.bool, name='train_flag')
block_input = tf.identity(data_x)
return (data_x, data_y, block_input, train_flag) | Get input for _inference | 1_nas/evaluator_classification_2.py | _get_input | nuaa-QK/1_NAS | 0 | python | def _get_input(self, sess, pre_block, update_pre_weight=False):
if (len(pre_block) > 0):
assert os.path.exists(os.path.join(self.model_path, (('model' + str(pre_block[(- 1)].id)) + '.meta')))
new_saver = tf.train.import_meta_graph(os.path.join(self.model_path, (('model' + str(pre_block[(- 1)].id)) + '.meta')))
new_saver.restore(sess, os.path.join(self.model_path, ('model' + str(pre_block[(- 1)].id))))
graph = tf.get_default_graph()
data_x = graph.get_tensor_by_name('input:0')
data_y = graph.get_tensor_by_name('label:0')
train_flag = graph.get_tensor_by_name('train_flag:0')
block_input = graph.get_tensor_by_name((('last_layer' + str((self.block_num - 1))) + ':0'))
if (not update_pre_weight):
block_input = tf.stop_gradient(block_input, name='stop_gradient')
else:
data_x = tf.placeholder(tf.float32, self.input_shape, name='input')
data_y = tf.placeholder(tf.int32, self.output_shape, name='label')
train_flag = tf.placeholder(tf.bool, name='train_flag')
block_input = tf.identity(data_x)
return (data_x, data_y, block_input, train_flag) | def _get_input(self, sess, pre_block, update_pre_weight=False):
if (len(pre_block) > 0):
assert os.path.exists(os.path.join(self.model_path, (('model' + str(pre_block[(- 1)].id)) + '.meta')))
new_saver = tf.train.import_meta_graph(os.path.join(self.model_path, (('model' + str(pre_block[(- 1)].id)) + '.meta')))
new_saver.restore(sess, os.path.join(self.model_path, ('model' + str(pre_block[(- 1)].id))))
graph = tf.get_default_graph()
data_x = graph.get_tensor_by_name('input:0')
data_y = graph.get_tensor_by_name('label:0')
train_flag = graph.get_tensor_by_name('train_flag:0')
block_input = graph.get_tensor_by_name((('last_layer' + str((self.block_num - 1))) + ':0'))
if (not update_pre_weight):
block_input = tf.stop_gradient(block_input, name='stop_gradient')
else:
data_x = tf.placeholder(tf.float32, self.input_shape, name='input')
data_y = tf.placeholder(tf.int32, self.output_shape, name='label')
train_flag = tf.placeholder(tf.bool, name='train_flag')
block_input = tf.identity(data_x)
return (data_x, data_y, block_input, train_flag)<|docstring|>Get input for _inference<|endoftext|> |
838298eb9f3870108e978cf23437e274eb581c085f1e24bbfb9df8e749aa5255 | def _eval(self, sess, logits, data_x, data_y, train_flag, retrain=False):
'\n The actual training process, including the definination of loss and train optimizer\n Args:\n sess: tensorflow session\n logits: output tensor of the model, 2-D tensor of shape [self.batch_size, self.NUM_CLASS]\n data_x: input image\n data_y: input label, 2-D tensor of shape [self.batch_size, self.NUM_CLASS]\n Returns:\n targets: float, the optimization target, could be the accuracy or the combination of both time and accuracy, etc\n saver: Tensorflow Saver class\n log: string, log to be write and saved\n '
global_step = tf.Variable(0, trainable=False, name=('global_step' + str(self.block_num)))
accuracy = self._cal_accuracy(logits, data_y)
loss = self._loss(data_y, logits)
train_op = self._train_op(global_step, loss)
sess.run(tf.global_variables_initializer())
if retrain:
self.train_data = np.concatenate((np.array(self.train_data), np.array(self.valid_data)), axis=0).tolist()
self.train_label = np.concatenate((np.array(self.train_label), np.array(self.valid_label)), axis=0).tolist()
max_steps = (len(list(self.train_label)) // self.batch_size)
test_data = copy.deepcopy(self.test_data)
test_label = copy.deepcopy(self.test_label)
num_iter = (len(test_label) // self.batch_size)
else:
max_steps = (self.train_num // self.batch_size)
test_data = copy.deepcopy(self.valid_data)
test_label = copy.deepcopy(self.valid_label)
num_iter = (len(self.valid_label) // self.batch_size)
log = ''
cost_time = 0
precision = np.zeros([self.epoch])
for ep in range(self.epoch):
print('epoch', ep, ':')
start_time = time.time()
for step in range(max_steps):
batch_x = self.train_data[(step * self.batch_size):((step + 1) * self.batch_size)]
batch_y = self.train_label[(step * self.batch_size):((step + 1) * self.batch_size)]
batch_x = DataSet().process(batch_x)
(_, loss_value, acc) = sess.run([train_op, loss, accuracy], feed_dict={data_x: batch_x, data_y: batch_y, train_flag: True})
if np.isnan(loss_value):
return ((- 1), log)
sys.stdout.write(('\r>> train %d/%d loss %.4f acc %.4f' % (step, max_steps, loss_value, acc)))
sys.stdout.write('\n')
for step in range(num_iter):
batch_x = test_data[(step * self.batch_size):((step + 1) * self.batch_size)]
batch_y = test_label[(step * self.batch_size):((step + 1) * self.batch_size)]
(l, acc_) = sess.run([loss, accuracy], feed_dict={data_x: batch_x, data_y: batch_y, train_flag: False})
precision[ep] += (acc_ / num_iter)
sys.stdout.write(('\r>> valid %d/%d loss %.4f acc %.4f' % (step, num_iter, l, acc_)))
sys.stdout.write('\n')
if ((ep > 5) and (not retrain)):
if ((((2 * precision[ep]) - precision[(ep - 5)]) - precision[(ep - 1)]) < (0.001 / DataSet().NUM_CLASSES)):
precision = precision[:ep]
log += ('early stop at %d epoch\n' % ep)
break
cost_time += (float((time.time() - start_time)) / self.epoch)
log += ('epoch %d: precision = %.3f, cost time %.3f\n' % (ep, precision[ep], float((time.time() - start_time))))
print(('precision = %.3f, cost time %.3f' % (precision[ep], float((time.time() - start_time)))))
return (precision[(- 1)], log) | The actual training process, including the definination of loss and train optimizer
Args:
sess: tensorflow session
logits: output tensor of the model, 2-D tensor of shape [self.batch_size, self.NUM_CLASS]
data_x: input image
data_y: input label, 2-D tensor of shape [self.batch_size, self.NUM_CLASS]
Returns:
targets: float, the optimization target, could be the accuracy or the combination of both time and accuracy, etc
saver: Tensorflow Saver class
log: string, log to be write and saved | 1_nas/evaluator_classification_2.py | _eval | nuaa-QK/1_NAS | 0 | python | def _eval(self, sess, logits, data_x, data_y, train_flag, retrain=False):
'\n The actual training process, including the definination of loss and train optimizer\n Args:\n sess: tensorflow session\n logits: output tensor of the model, 2-D tensor of shape [self.batch_size, self.NUM_CLASS]\n data_x: input image\n data_y: input label, 2-D tensor of shape [self.batch_size, self.NUM_CLASS]\n Returns:\n targets: float, the optimization target, could be the accuracy or the combination of both time and accuracy, etc\n saver: Tensorflow Saver class\n log: string, log to be write and saved\n '
global_step = tf.Variable(0, trainable=False, name=('global_step' + str(self.block_num)))
accuracy = self._cal_accuracy(logits, data_y)
loss = self._loss(data_y, logits)
train_op = self._train_op(global_step, loss)
sess.run(tf.global_variables_initializer())
if retrain:
self.train_data = np.concatenate((np.array(self.train_data), np.array(self.valid_data)), axis=0).tolist()
self.train_label = np.concatenate((np.array(self.train_label), np.array(self.valid_label)), axis=0).tolist()
max_steps = (len(list(self.train_label)) // self.batch_size)
test_data = copy.deepcopy(self.test_data)
test_label = copy.deepcopy(self.test_label)
num_iter = (len(test_label) // self.batch_size)
else:
max_steps = (self.train_num // self.batch_size)
test_data = copy.deepcopy(self.valid_data)
test_label = copy.deepcopy(self.valid_label)
num_iter = (len(self.valid_label) // self.batch_size)
log =
cost_time = 0
precision = np.zeros([self.epoch])
for ep in range(self.epoch):
print('epoch', ep, ':')
start_time = time.time()
for step in range(max_steps):
batch_x = self.train_data[(step * self.batch_size):((step + 1) * self.batch_size)]
batch_y = self.train_label[(step * self.batch_size):((step + 1) * self.batch_size)]
batch_x = DataSet().process(batch_x)
(_, loss_value, acc) = sess.run([train_op, loss, accuracy], feed_dict={data_x: batch_x, data_y: batch_y, train_flag: True})
if np.isnan(loss_value):
return ((- 1), log)
sys.stdout.write(('\r>> train %d/%d loss %.4f acc %.4f' % (step, max_steps, loss_value, acc)))
sys.stdout.write('\n')
for step in range(num_iter):
batch_x = test_data[(step * self.batch_size):((step + 1) * self.batch_size)]
batch_y = test_label[(step * self.batch_size):((step + 1) * self.batch_size)]
(l, acc_) = sess.run([loss, accuracy], feed_dict={data_x: batch_x, data_y: batch_y, train_flag: False})
precision[ep] += (acc_ / num_iter)
sys.stdout.write(('\r>> valid %d/%d loss %.4f acc %.4f' % (step, num_iter, l, acc_)))
sys.stdout.write('\n')
if ((ep > 5) and (not retrain)):
if ((((2 * precision[ep]) - precision[(ep - 5)]) - precision[(ep - 1)]) < (0.001 / DataSet().NUM_CLASSES)):
precision = precision[:ep]
log += ('early stop at %d epoch\n' % ep)
break
cost_time += (float((time.time() - start_time)) / self.epoch)
log += ('epoch %d: precision = %.3f, cost time %.3f\n' % (ep, precision[ep], float((time.time() - start_time))))
print(('precision = %.3f, cost time %.3f' % (precision[ep], float((time.time() - start_time)))))
return (precision[(- 1)], log) | def _eval(self, sess, logits, data_x, data_y, train_flag, retrain=False):
'\n The actual training process, including the definination of loss and train optimizer\n Args:\n sess: tensorflow session\n logits: output tensor of the model, 2-D tensor of shape [self.batch_size, self.NUM_CLASS]\n data_x: input image\n data_y: input label, 2-D tensor of shape [self.batch_size, self.NUM_CLASS]\n Returns:\n targets: float, the optimization target, could be the accuracy or the combination of both time and accuracy, etc\n saver: Tensorflow Saver class\n log: string, log to be write and saved\n '
global_step = tf.Variable(0, trainable=False, name=('global_step' + str(self.block_num)))
accuracy = self._cal_accuracy(logits, data_y)
loss = self._loss(data_y, logits)
train_op = self._train_op(global_step, loss)
sess.run(tf.global_variables_initializer())
if retrain:
self.train_data = np.concatenate((np.array(self.train_data), np.array(self.valid_data)), axis=0).tolist()
self.train_label = np.concatenate((np.array(self.train_label), np.array(self.valid_label)), axis=0).tolist()
max_steps = (len(list(self.train_label)) // self.batch_size)
test_data = copy.deepcopy(self.test_data)
test_label = copy.deepcopy(self.test_label)
num_iter = (len(test_label) // self.batch_size)
else:
max_steps = (self.train_num // self.batch_size)
test_data = copy.deepcopy(self.valid_data)
test_label = copy.deepcopy(self.valid_label)
num_iter = (len(self.valid_label) // self.batch_size)
log =
cost_time = 0
precision = np.zeros([self.epoch])
for ep in range(self.epoch):
print('epoch', ep, ':')
start_time = time.time()
for step in range(max_steps):
batch_x = self.train_data[(step * self.batch_size):((step + 1) * self.batch_size)]
batch_y = self.train_label[(step * self.batch_size):((step + 1) * self.batch_size)]
batch_x = DataSet().process(batch_x)
(_, loss_value, acc) = sess.run([train_op, loss, accuracy], feed_dict={data_x: batch_x, data_y: batch_y, train_flag: True})
if np.isnan(loss_value):
return ((- 1), log)
sys.stdout.write(('\r>> train %d/%d loss %.4f acc %.4f' % (step, max_steps, loss_value, acc)))
sys.stdout.write('\n')
for step in range(num_iter):
batch_x = test_data[(step * self.batch_size):((step + 1) * self.batch_size)]
batch_y = test_label[(step * self.batch_size):((step + 1) * self.batch_size)]
(l, acc_) = sess.run([loss, accuracy], feed_dict={data_x: batch_x, data_y: batch_y, train_flag: False})
precision[ep] += (acc_ / num_iter)
sys.stdout.write(('\r>> valid %d/%d loss %.4f acc %.4f' % (step, num_iter, l, acc_)))
sys.stdout.write('\n')
if ((ep > 5) and (not retrain)):
if ((((2 * precision[ep]) - precision[(ep - 5)]) - precision[(ep - 1)]) < (0.001 / DataSet().NUM_CLASSES)):
precision = precision[:ep]
log += ('early stop at %d epoch\n' % ep)
break
cost_time += (float((time.time() - start_time)) / self.epoch)
log += ('epoch %d: precision = %.3f, cost time %.3f\n' % (ep, precision[ep], float((time.time() - start_time))))
print(('precision = %.3f, cost time %.3f' % (precision[ep], float((time.time() - start_time)))))
return (precision[(- 1)], log)<|docstring|>The actual training process, including the definination of loss and train optimizer
Args:
sess: tensorflow session
logits: output tensor of the model, 2-D tensor of shape [self.batch_size, self.NUM_CLASS]
data_x: input image
data_y: input label, 2-D tensor of shape [self.batch_size, self.NUM_CLASS]
Returns:
targets: float, the optimization target, could be the accuracy or the combination of both time and accuracy, etc
saver: Tensorflow Saver class
log: string, log to be write and saved<|endoftext|> |
53f52ed4813834b8e95cca47ef945753d5a54b98ff94593b394f96d35dc2ffa3 | def _cal_accuracy(self, logits, labels):
'\n calculate the target of this task\n Args:\n logits: Logits from softmax.\n labels: Labels from distorted_inputs or inputs(). 2-D tensor of shape [self.batch_size, self.NUM_CLASS]\n Returns:\n Target tensor of type float.\n '
correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(labels, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
return accuracy | calculate the target of this task
Args:
logits: Logits from softmax.
labels: Labels from distorted_inputs or inputs(). 2-D tensor of shape [self.batch_size, self.NUM_CLASS]
Returns:
Target tensor of type float. | 1_nas/evaluator_classification_2.py | _cal_accuracy | nuaa-QK/1_NAS | 0 | python | def _cal_accuracy(self, logits, labels):
'\n calculate the target of this task\n Args:\n logits: Logits from softmax.\n labels: Labels from distorted_inputs or inputs(). 2-D tensor of shape [self.batch_size, self.NUM_CLASS]\n Returns:\n Target tensor of type float.\n '
correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(labels, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
return accuracy | def _cal_accuracy(self, logits, labels):
'\n calculate the target of this task\n Args:\n logits: Logits from softmax.\n labels: Labels from distorted_inputs or inputs(). 2-D tensor of shape [self.batch_size, self.NUM_CLASS]\n Returns:\n Target tensor of type float.\n '
correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(labels, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
return accuracy<|docstring|>calculate the target of this task
Args:
logits: Logits from softmax.
labels: Labels from distorted_inputs or inputs(). 2-D tensor of shape [self.batch_size, self.NUM_CLASS]
Returns:
Target tensor of type float.<|endoftext|> |
afa2035648b7091f9f28681efb089cfef32f419a48fca9365201066cb3fd8d92 | def _loss(self, labels, logits):
'\n Args:\n logits: Logits from softmax.\n labels: Labels from distorted_inputs or inputs(). 1-D tensor of shape [self.batch_size]\n Returns:\n Loss tensor of type float.\n '
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=labels, logits=logits))
l2 = tf.add_n([tf.nn.l2_loss(var) for var in tf.trainable_variables()])
loss = (cross_entropy + (l2 * self.weight_decay))
return loss | Args:
logits: Logits from softmax.
labels: Labels from distorted_inputs or inputs(). 1-D tensor of shape [self.batch_size]
Returns:
Loss tensor of type float. | 1_nas/evaluator_classification_2.py | _loss | nuaa-QK/1_NAS | 0 | python | def _loss(self, labels, logits):
'\n Args:\n logits: Logits from softmax.\n labels: Labels from distorted_inputs or inputs(). 1-D tensor of shape [self.batch_size]\n Returns:\n Loss tensor of type float.\n '
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=labels, logits=logits))
l2 = tf.add_n([tf.nn.l2_loss(var) for var in tf.trainable_variables()])
loss = (cross_entropy + (l2 * self.weight_decay))
return loss | def _loss(self, labels, logits):
'\n Args:\n logits: Logits from softmax.\n labels: Labels from distorted_inputs or inputs(). 1-D tensor of shape [self.batch_size]\n Returns:\n Loss tensor of type float.\n '
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=labels, logits=logits))
l2 = tf.add_n([tf.nn.l2_loss(var) for var in tf.trainable_variables()])
loss = (cross_entropy + (l2 * self.weight_decay))
return loss<|docstring|>Args:
logits: Logits from softmax.
labels: Labels from distorted_inputs or inputs(). 1-D tensor of shape [self.batch_size]
Returns:
Loss tensor of type float.<|endoftext|> |
d870126f11fd4ace144833669bee0c0bae6d008a2504be32fcfc1893977ad464 | def make_app(conf: Union[(Config, CKANConfig)]) -> CKANApp:
'\n Initialise the Flask app and wrap it in dispatcher middleware.\n '
load_environment(conf)
flask_app = make_flask_stack(conf)
global _internal_test_request_context
_internal_test_request_context = flask_app._wsgi_app.test_request_context()
return flask_app | Initialise the Flask app and wrap it in dispatcher middleware. | ckan/config/middleware/__init__.py | make_app | robin-NEC/ckan | 1 | python | def make_app(conf: Union[(Config, CKANConfig)]) -> CKANApp:
'\n \n '
load_environment(conf)
flask_app = make_flask_stack(conf)
global _internal_test_request_context
_internal_test_request_context = flask_app._wsgi_app.test_request_context()
return flask_app | def make_app(conf: Union[(Config, CKANConfig)]) -> CKANApp:
'\n \n '
load_environment(conf)
flask_app = make_flask_stack(conf)
global _internal_test_request_context
_internal_test_request_context = flask_app._wsgi_app.test_request_context()
return flask_app<|docstring|>Initialise the Flask app and wrap it in dispatcher middleware.<|endoftext|> |
ba33984b341b5bca5e30b8dbdd8915265c91a44b9295b0f1743c2e22e03bc241 | def __init__(self, i2c_addr=56, i2c_dev=None):
'Initialise sensor.\n\n :param i2c_addr: i2c address of sensor\n :param i2c_dev: SMBus-compatible instance\n\n '
self._i2c_addr = i2c_addr
self._i2c_dev = i2c_dev
self._is_setup = False
self._bh1745 = Device(I2C_ADDRESSES, i2c_dev=self._i2c_dev, bit_width=8, registers=(Register('SYSTEM_CONTROL', 64, fields=(BitField('sw_reset', 128), BitField('int_reset', 64), BitField('part_id', 63, read_only=True))), Register('MODE_CONTROL1', 65, fields=(BitField('measurement_time_ms', 7, adapter=LookupAdapter({160: 0, 320: 1, 640: 2, 1280: 3, 2560: 4, 5120: 5})),)), Register('MODE_CONTROL2', 66, fields=(BitField('valid', 128, read_only=True), BitField('rgbc_en', 16), BitField('adc_gain_x', 3, adapter=LookupAdapter({1: 0, 2: 1, 16: 2})))), Register('MODE_CONTROL3', 68, fields=(BitField('on', 255, adapter=LookupAdapter({True: 2, False: 0})),)), Register('COLOUR_DATA', 80, fields=(BitField('red', 18446462598732840960, adapter=U16ByteSwapAdapter()), BitField('green', 281470681743360, adapter=U16ByteSwapAdapter()), BitField('blue', 4294901760, adapter=U16ByteSwapAdapter()), BitField('clear', 65535, adapter=U16ByteSwapAdapter())), bit_width=64, read_only=True), Register('DINT_DATA', 88, fields=(BitField('data', 65535, adapter=U16ByteSwapAdapter()),), bit_width=16), Register('INTERRUPT', 96, fields=(BitField('status', 128, read_only=True), BitField('latch', 16, adapter=LookupAdapter({0: 1, 1: 0})), BitField('source', 12, read_only=True, adapter=LookupAdapter({'red': 0, 'green': 1, 'blue': 2, 'clear': 3})), BitField('enable', 1))), Register('PERSISTENCE', 97, fields=(BitField('mode', 3, adapter=LookupAdapter({'toggle': 0, 'update': 1, 'update_on_4': 2, 'update_on_8': 3})),)), Register('THRESHOLD', 98, fields=(BitField('high', 4294901760, adapter=U16ByteSwapAdapter()), BitField('low', 65535, adapter=U16ByteSwapAdapter())), bit_width=32), Register('MANUFACTURER', 146, fields=(BitField('id', 255),), read_only=True, volatile=False)))
self._bh1745.select_address(self._i2c_addr)
for register in self._bh1745.registers:
register = self._bh1745.registers[register]
for field in register.fields:
field = register.fields[field]
if isinstance(field.adapter, LookupAdapter):
for key in field.adapter.lookup_table:
name = 'BH1745_{register}_{field}_{key}'.format(register=register.name, field=field.name, key=key).upper()
globals()[name] = key
'\n Approximate compensation for the spectral response performance curves\n '
self._channel_compensation = (2.2, 1.0, 1.8, 10.0)
self._enable_channel_compensation = True | Initialise sensor.
:param i2c_addr: i2c address of sensor
:param i2c_dev: SMBus-compatible instance | library/bh1745/__init__.py | __init__ | pimoroni/bh1745-python | 8 | python | def __init__(self, i2c_addr=56, i2c_dev=None):
'Initialise sensor.\n\n :param i2c_addr: i2c address of sensor\n :param i2c_dev: SMBus-compatible instance\n\n '
self._i2c_addr = i2c_addr
self._i2c_dev = i2c_dev
self._is_setup = False
self._bh1745 = Device(I2C_ADDRESSES, i2c_dev=self._i2c_dev, bit_width=8, registers=(Register('SYSTEM_CONTROL', 64, fields=(BitField('sw_reset', 128), BitField('int_reset', 64), BitField('part_id', 63, read_only=True))), Register('MODE_CONTROL1', 65, fields=(BitField('measurement_time_ms', 7, adapter=LookupAdapter({160: 0, 320: 1, 640: 2, 1280: 3, 2560: 4, 5120: 5})),)), Register('MODE_CONTROL2', 66, fields=(BitField('valid', 128, read_only=True), BitField('rgbc_en', 16), BitField('adc_gain_x', 3, adapter=LookupAdapter({1: 0, 2: 1, 16: 2})))), Register('MODE_CONTROL3', 68, fields=(BitField('on', 255, adapter=LookupAdapter({True: 2, False: 0})),)), Register('COLOUR_DATA', 80, fields=(BitField('red', 18446462598732840960, adapter=U16ByteSwapAdapter()), BitField('green', 281470681743360, adapter=U16ByteSwapAdapter()), BitField('blue', 4294901760, adapter=U16ByteSwapAdapter()), BitField('clear', 65535, adapter=U16ByteSwapAdapter())), bit_width=64, read_only=True), Register('DINT_DATA', 88, fields=(BitField('data', 65535, adapter=U16ByteSwapAdapter()),), bit_width=16), Register('INTERRUPT', 96, fields=(BitField('status', 128, read_only=True), BitField('latch', 16, adapter=LookupAdapter({0: 1, 1: 0})), BitField('source', 12, read_only=True, adapter=LookupAdapter({'red': 0, 'green': 1, 'blue': 2, 'clear': 3})), BitField('enable', 1))), Register('PERSISTENCE', 97, fields=(BitField('mode', 3, adapter=LookupAdapter({'toggle': 0, 'update': 1, 'update_on_4': 2, 'update_on_8': 3})),)), Register('THRESHOLD', 98, fields=(BitField('high', 4294901760, adapter=U16ByteSwapAdapter()), BitField('low', 65535, adapter=U16ByteSwapAdapter())), bit_width=32), Register('MANUFACTURER', 146, fields=(BitField('id', 255),), read_only=True, volatile=False)))
self._bh1745.select_address(self._i2c_addr)
for register in self._bh1745.registers:
register = self._bh1745.registers[register]
for field in register.fields:
field = register.fields[field]
if isinstance(field.adapter, LookupAdapter):
for key in field.adapter.lookup_table:
name = 'BH1745_{register}_{field}_{key}'.format(register=register.name, field=field.name, key=key).upper()
globals()[name] = key
'\n Approximate compensation for the spectral response performance curves\n '
self._channel_compensation = (2.2, 1.0, 1.8, 10.0)
self._enable_channel_compensation = True | def __init__(self, i2c_addr=56, i2c_dev=None):
'Initialise sensor.\n\n :param i2c_addr: i2c address of sensor\n :param i2c_dev: SMBus-compatible instance\n\n '
self._i2c_addr = i2c_addr
self._i2c_dev = i2c_dev
self._is_setup = False
self._bh1745 = Device(I2C_ADDRESSES, i2c_dev=self._i2c_dev, bit_width=8, registers=(Register('SYSTEM_CONTROL', 64, fields=(BitField('sw_reset', 128), BitField('int_reset', 64), BitField('part_id', 63, read_only=True))), Register('MODE_CONTROL1', 65, fields=(BitField('measurement_time_ms', 7, adapter=LookupAdapter({160: 0, 320: 1, 640: 2, 1280: 3, 2560: 4, 5120: 5})),)), Register('MODE_CONTROL2', 66, fields=(BitField('valid', 128, read_only=True), BitField('rgbc_en', 16), BitField('adc_gain_x', 3, adapter=LookupAdapter({1: 0, 2: 1, 16: 2})))), Register('MODE_CONTROL3', 68, fields=(BitField('on', 255, adapter=LookupAdapter({True: 2, False: 0})),)), Register('COLOUR_DATA', 80, fields=(BitField('red', 18446462598732840960, adapter=U16ByteSwapAdapter()), BitField('green', 281470681743360, adapter=U16ByteSwapAdapter()), BitField('blue', 4294901760, adapter=U16ByteSwapAdapter()), BitField('clear', 65535, adapter=U16ByteSwapAdapter())), bit_width=64, read_only=True), Register('DINT_DATA', 88, fields=(BitField('data', 65535, adapter=U16ByteSwapAdapter()),), bit_width=16), Register('INTERRUPT', 96, fields=(BitField('status', 128, read_only=True), BitField('latch', 16, adapter=LookupAdapter({0: 1, 1: 0})), BitField('source', 12, read_only=True, adapter=LookupAdapter({'red': 0, 'green': 1, 'blue': 2, 'clear': 3})), BitField('enable', 1))), Register('PERSISTENCE', 97, fields=(BitField('mode', 3, adapter=LookupAdapter({'toggle': 0, 'update': 1, 'update_on_4': 2, 'update_on_8': 3})),)), Register('THRESHOLD', 98, fields=(BitField('high', 4294901760, adapter=U16ByteSwapAdapter()), BitField('low', 65535, adapter=U16ByteSwapAdapter())), bit_width=32), Register('MANUFACTURER', 146, fields=(BitField('id', 255),), read_only=True, volatile=False)))
self._bh1745.select_address(self._i2c_addr)
for register in self._bh1745.registers:
register = self._bh1745.registers[register]
for field in register.fields:
field = register.fields[field]
if isinstance(field.adapter, LookupAdapter):
for key in field.adapter.lookup_table:
name = 'BH1745_{register}_{field}_{key}'.format(register=register.name, field=field.name, key=key).upper()
globals()[name] = key
'\n Approximate compensation for the spectral response performance curves\n '
self._channel_compensation = (2.2, 1.0, 1.8, 10.0)
self._enable_channel_compensation = True<|docstring|>Initialise sensor.
:param i2c_addr: i2c address of sensor
:param i2c_dev: SMBus-compatible instance<|endoftext|> |
2cc53da0274d8c697939b5d3bbc50ac54aa5d86a97289ae2863f060f9247abb6 | def ready(self):
'Return true if setup has been successful.'
return self._is_setup | Return true if setup has been successful. | library/bh1745/__init__.py | ready | pimoroni/bh1745-python | 8 | python | def ready(self):
return self._is_setup | def ready(self):
return self._is_setup<|docstring|>Return true if setup has been successful.<|endoftext|> |
76a6e7e9f9d13f913dad54b1ddb3db67663ea4db1c300a29843286640d5f7aca | def setup(self, i2c_addr=None, timeout=BH1745_RESET_TIMEOUT_SEC):
'Set up the bh1745 sensor.\n\n :param i2c_addr: Optional i2c_addr to switch to\n\n '
if self._is_setup:
return True
if (timeout <= 0):
raise ValueError('Device timeout period must be greater than 0')
if (i2c_addr is not None):
self._bh1745.select_address(i2c_addr)
try:
self._bh1745.get('SYSTEM_CONTROL')
except IOError:
raise RuntimeError('BH1745 not found: IO error attempting to query device!')
if ((self._bh1745.get('SYSTEM_CONTROL').part_id != 11) or (self._bh1745.get('MANUFACTURER').id != 224)):
raise RuntimeError('BH1745 not found: Manufacturer or Part ID mismatch!')
self._is_setup = True
self._bh1745.set('SYSTEM_CONTROL', sw_reset=1)
t_start = time.time()
pending_reset = True
while ((time.time() - t_start) < timeout):
if (not self._bh1745.get('SYSTEM_CONTROL').sw_reset):
pending_reset = False
break
time.sleep(0.01)
if pending_reset:
raise BH1745TimeoutError('Timeout waiting for BH1745 to reset.')
self._bh1745.set('SYSTEM_CONTROL', int_reset=0)
self._bh1745.set('MODE_CONTROL1', measurement_time_ms=320)
self._bh1745.set('MODE_CONTROL2', adc_gain_x=1, rgbc_en=1)
self._bh1745.set('MODE_CONTROL3', on=1)
self._bh1745.set('THRESHOLD', low=65535, high=0)
self._bh1745.set('INTERRUPT', latch=1)
time.sleep(0.32) | Set up the bh1745 sensor.
:param i2c_addr: Optional i2c_addr to switch to | library/bh1745/__init__.py | setup | pimoroni/bh1745-python | 8 | python | def setup(self, i2c_addr=None, timeout=BH1745_RESET_TIMEOUT_SEC):
'Set up the bh1745 sensor.\n\n :param i2c_addr: Optional i2c_addr to switch to\n\n '
if self._is_setup:
return True
if (timeout <= 0):
raise ValueError('Device timeout period must be greater than 0')
if (i2c_addr is not None):
self._bh1745.select_address(i2c_addr)
try:
self._bh1745.get('SYSTEM_CONTROL')
except IOError:
raise RuntimeError('BH1745 not found: IO error attempting to query device!')
if ((self._bh1745.get('SYSTEM_CONTROL').part_id != 11) or (self._bh1745.get('MANUFACTURER').id != 224)):
raise RuntimeError('BH1745 not found: Manufacturer or Part ID mismatch!')
self._is_setup = True
self._bh1745.set('SYSTEM_CONTROL', sw_reset=1)
t_start = time.time()
pending_reset = True
while ((time.time() - t_start) < timeout):
if (not self._bh1745.get('SYSTEM_CONTROL').sw_reset):
pending_reset = False
break
time.sleep(0.01)
if pending_reset:
raise BH1745TimeoutError('Timeout waiting for BH1745 to reset.')
self._bh1745.set('SYSTEM_CONTROL', int_reset=0)
self._bh1745.set('MODE_CONTROL1', measurement_time_ms=320)
self._bh1745.set('MODE_CONTROL2', adc_gain_x=1, rgbc_en=1)
self._bh1745.set('MODE_CONTROL3', on=1)
self._bh1745.set('THRESHOLD', low=65535, high=0)
self._bh1745.set('INTERRUPT', latch=1)
time.sleep(0.32) | def setup(self, i2c_addr=None, timeout=BH1745_RESET_TIMEOUT_SEC):
'Set up the bh1745 sensor.\n\n :param i2c_addr: Optional i2c_addr to switch to\n\n '
if self._is_setup:
return True
if (timeout <= 0):
raise ValueError('Device timeout period must be greater than 0')
if (i2c_addr is not None):
self._bh1745.select_address(i2c_addr)
try:
self._bh1745.get('SYSTEM_CONTROL')
except IOError:
raise RuntimeError('BH1745 not found: IO error attempting to query device!')
if ((self._bh1745.get('SYSTEM_CONTROL').part_id != 11) or (self._bh1745.get('MANUFACTURER').id != 224)):
raise RuntimeError('BH1745 not found: Manufacturer or Part ID mismatch!')
self._is_setup = True
self._bh1745.set('SYSTEM_CONTROL', sw_reset=1)
t_start = time.time()
pending_reset = True
while ((time.time() - t_start) < timeout):
if (not self._bh1745.get('SYSTEM_CONTROL').sw_reset):
pending_reset = False
break
time.sleep(0.01)
if pending_reset:
raise BH1745TimeoutError('Timeout waiting for BH1745 to reset.')
self._bh1745.set('SYSTEM_CONTROL', int_reset=0)
self._bh1745.set('MODE_CONTROL1', measurement_time_ms=320)
self._bh1745.set('MODE_CONTROL2', adc_gain_x=1, rgbc_en=1)
self._bh1745.set('MODE_CONTROL3', on=1)
self._bh1745.set('THRESHOLD', low=65535, high=0)
self._bh1745.set('INTERRUPT', latch=1)
time.sleep(0.32)<|docstring|>Set up the bh1745 sensor.
:param i2c_addr: Optional i2c_addr to switch to<|endoftext|> |
1f65cc26b87b30c241a0f3e40eae40b499146d53da067521a6de8048568a849d | def set_measurement_time_ms(self, time_ms):
'Set the measurement time in milliseconds.\n\n :param time_ms: The time in milliseconds: 160, 320, 640, 1280, 2560, 5120\n\n '
self.setup()
self._bh1745.set('MODE_CONTROL1', measurement_time_ms=time_ms) | Set the measurement time in milliseconds.
:param time_ms: The time in milliseconds: 160, 320, 640, 1280, 2560, 5120 | library/bh1745/__init__.py | set_measurement_time_ms | pimoroni/bh1745-python | 8 | python | def set_measurement_time_ms(self, time_ms):
'Set the measurement time in milliseconds.\n\n :param time_ms: The time in milliseconds: 160, 320, 640, 1280, 2560, 5120\n\n '
self.setup()
self._bh1745.set('MODE_CONTROL1', measurement_time_ms=time_ms) | def set_measurement_time_ms(self, time_ms):
'Set the measurement time in milliseconds.\n\n :param time_ms: The time in milliseconds: 160, 320, 640, 1280, 2560, 5120\n\n '
self.setup()
self._bh1745.set('MODE_CONTROL1', measurement_time_ms=time_ms)<|docstring|>Set the measurement time in milliseconds.
:param time_ms: The time in milliseconds: 160, 320, 640, 1280, 2560, 5120<|endoftext|> |
4add40c46f3d349f2f5f77df3d4efd5889547b9dd4eed06241c4c3b0043f491b | def set_adc_gain_x(self, gain_x):
'Set the ADC gain multiplier.\n\n :param gain_x: Must be either 1, 2 or 16\n\n '
self.setup()
self._bh1745.set('MODE_CONTROL2', adc_gain_x=gain_x) | Set the ADC gain multiplier.
:param gain_x: Must be either 1, 2 or 16 | library/bh1745/__init__.py | set_adc_gain_x | pimoroni/bh1745-python | 8 | python | def set_adc_gain_x(self, gain_x):
'Set the ADC gain multiplier.\n\n :param gain_x: Must be either 1, 2 or 16\n\n '
self.setup()
self._bh1745.set('MODE_CONTROL2', adc_gain_x=gain_x) | def set_adc_gain_x(self, gain_x):
'Set the ADC gain multiplier.\n\n :param gain_x: Must be either 1, 2 or 16\n\n '
self.setup()
self._bh1745.set('MODE_CONTROL2', adc_gain_x=gain_x)<|docstring|>Set the ADC gain multiplier.
:param gain_x: Must be either 1, 2 or 16<|endoftext|> |
f114c92b65b4584e7c334afa24b96f6c7e4cfbb012fe751543760fef35ffaff1 | def set_leds(self, state):
'Toggle the onboard LEDs.\n\n :param state: Either 1 for on, or 0 for off\n\n '
self.setup()
self._bh1745.set('INTERRUPT', enable=(1 if state else 0)) | Toggle the onboard LEDs.
:param state: Either 1 for on, or 0 for off | library/bh1745/__init__.py | set_leds | pimoroni/bh1745-python | 8 | python | def set_leds(self, state):
'Toggle the onboard LEDs.\n\n :param state: Either 1 for on, or 0 for off\n\n '
self.setup()
self._bh1745.set('INTERRUPT', enable=(1 if state else 0)) | def set_leds(self, state):
'Toggle the onboard LEDs.\n\n :param state: Either 1 for on, or 0 for off\n\n '
self.setup()
self._bh1745.set('INTERRUPT', enable=(1 if state else 0))<|docstring|>Toggle the onboard LEDs.
:param state: Either 1 for on, or 0 for off<|endoftext|> |
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