body_hash stringlengths 64 64 | body stringlengths 23 109k | docstring stringlengths 1 57k | path stringlengths 4 198 | name stringlengths 1 115 | repository_name stringlengths 7 111 | repository_stars float64 0 191k | lang stringclasses 1 value | body_without_docstring stringlengths 14 108k | unified stringlengths 45 133k |
|---|---|---|---|---|---|---|---|---|---|
f0113c27851b3985b63144ce5d653620c1082eebb8bd8bd04c49804bb14e94b5 | @property
def timezone(self):
'Gets the timezone of this V1alpha1CronWorkflowSpec. # noqa: E501\n\n Timezone is the timezone against which the cron schedule will be calculated, e.g. "Asia/Tokyo". Default is machine\'s local time. # noqa: E501\n\n :return: The timezone of this V1alpha1CronWorkflowSpec. # noqa: E501\n :rtype: str\n '
return self._timezone | Gets the timezone of this V1alpha1CronWorkflowSpec. # noqa: E501
Timezone is the timezone against which the cron schedule will be calculated, e.g. "Asia/Tokyo". Default is machine's local time. # noqa: E501
:return: The timezone of this V1alpha1CronWorkflowSpec. # noqa: E501
:rtype: str | argo/workflows/client/models/v1alpha1_cron_workflow_spec.py | timezone | ButterflyNetwork/argo-client-python | 0 | python | @property
def timezone(self):
'Gets the timezone of this V1alpha1CronWorkflowSpec. # noqa: E501\n\n Timezone is the timezone against which the cron schedule will be calculated, e.g. "Asia/Tokyo". Default is machine\'s local time. # noqa: E501\n\n :return: The timezone of this V1alpha1CronWorkflowSpec. # noqa: E501\n :rtype: str\n '
return self._timezone | @property
def timezone(self):
'Gets the timezone of this V1alpha1CronWorkflowSpec. # noqa: E501\n\n Timezone is the timezone against which the cron schedule will be calculated, e.g. "Asia/Tokyo". Default is machine\'s local time. # noqa: E501\n\n :return: The timezone of this V1alpha1CronWorkflowSpec. # noqa: E501\n :rtype: str\n '
return self._timezone<|docstring|>Gets the timezone of this V1alpha1CronWorkflowSpec. # noqa: E501
Timezone is the timezone against which the cron schedule will be calculated, e.g. "Asia/Tokyo". Default is machine's local time. # noqa: E501
:return: The timezone of this V1alpha1CronWorkflowSpec. # noqa: E501
:rtype: str<|endoftext|> |
f937f483e9bb109edde994ff05316680bad268aeebdfa59430d19c1e02ca4886 | @timezone.setter
def timezone(self, timezone):
'Sets the timezone of this V1alpha1CronWorkflowSpec.\n\n Timezone is the timezone against which the cron schedule will be calculated, e.g. "Asia/Tokyo". Default is machine\'s local time. # noqa: E501\n\n :param timezone: The timezone of this V1alpha1CronWorkflowSpec. # noqa: E501\n :type: str\n '
self._timezone = timezone | Sets the timezone of this V1alpha1CronWorkflowSpec.
Timezone is the timezone against which the cron schedule will be calculated, e.g. "Asia/Tokyo". Default is machine's local time. # noqa: E501
:param timezone: The timezone of this V1alpha1CronWorkflowSpec. # noqa: E501
:type: str | argo/workflows/client/models/v1alpha1_cron_workflow_spec.py | timezone | ButterflyNetwork/argo-client-python | 0 | python | @timezone.setter
def timezone(self, timezone):
'Sets the timezone of this V1alpha1CronWorkflowSpec.\n\n Timezone is the timezone against which the cron schedule will be calculated, e.g. "Asia/Tokyo". Default is machine\'s local time. # noqa: E501\n\n :param timezone: The timezone of this V1alpha1CronWorkflowSpec. # noqa: E501\n :type: str\n '
self._timezone = timezone | @timezone.setter
def timezone(self, timezone):
'Sets the timezone of this V1alpha1CronWorkflowSpec.\n\n Timezone is the timezone against which the cron schedule will be calculated, e.g. "Asia/Tokyo". Default is machine\'s local time. # noqa: E501\n\n :param timezone: The timezone of this V1alpha1CronWorkflowSpec. # noqa: E501\n :type: str\n '
self._timezone = timezone<|docstring|>Sets the timezone of this V1alpha1CronWorkflowSpec.
Timezone is the timezone against which the cron schedule will be calculated, e.g. "Asia/Tokyo". Default is machine's local time. # noqa: E501
:param timezone: The timezone of this V1alpha1CronWorkflowSpec. # noqa: E501
:type: str<|endoftext|> |
cf79e6694d3c8428d450d2cdf332aecc6128213f247e7a1983c37a0de327e9e5 | @property
def workflow_metadata(self):
'Gets the workflow_metadata of this V1alpha1CronWorkflowSpec. # noqa: E501\n\n WorkflowMetadata contains some metadata of the workflow to be run # noqa: E501\n\n :return: The workflow_metadata of this V1alpha1CronWorkflowSpec. # noqa: E501\n :rtype: V1ObjectMeta\n '
return self._workflow_metadata | Gets the workflow_metadata of this V1alpha1CronWorkflowSpec. # noqa: E501
WorkflowMetadata contains some metadata of the workflow to be run # noqa: E501
:return: The workflow_metadata of this V1alpha1CronWorkflowSpec. # noqa: E501
:rtype: V1ObjectMeta | argo/workflows/client/models/v1alpha1_cron_workflow_spec.py | workflow_metadata | ButterflyNetwork/argo-client-python | 0 | python | @property
def workflow_metadata(self):
'Gets the workflow_metadata of this V1alpha1CronWorkflowSpec. # noqa: E501\n\n WorkflowMetadata contains some metadata of the workflow to be run # noqa: E501\n\n :return: The workflow_metadata of this V1alpha1CronWorkflowSpec. # noqa: E501\n :rtype: V1ObjectMeta\n '
return self._workflow_metadata | @property
def workflow_metadata(self):
'Gets the workflow_metadata of this V1alpha1CronWorkflowSpec. # noqa: E501\n\n WorkflowMetadata contains some metadata of the workflow to be run # noqa: E501\n\n :return: The workflow_metadata of this V1alpha1CronWorkflowSpec. # noqa: E501\n :rtype: V1ObjectMeta\n '
return self._workflow_metadata<|docstring|>Gets the workflow_metadata of this V1alpha1CronWorkflowSpec. # noqa: E501
WorkflowMetadata contains some metadata of the workflow to be run # noqa: E501
:return: The workflow_metadata of this V1alpha1CronWorkflowSpec. # noqa: E501
:rtype: V1ObjectMeta<|endoftext|> |
09cafb5ff0d5297dbdbba9ce3639e12047459d1a5226544f7efd8ad022778ab9 | @workflow_metadata.setter
def workflow_metadata(self, workflow_metadata):
'Sets the workflow_metadata of this V1alpha1CronWorkflowSpec.\n\n WorkflowMetadata contains some metadata of the workflow to be run # noqa: E501\n\n :param workflow_metadata: The workflow_metadata of this V1alpha1CronWorkflowSpec. # noqa: E501\n :type: V1ObjectMeta\n '
self._workflow_metadata = workflow_metadata | Sets the workflow_metadata of this V1alpha1CronWorkflowSpec.
WorkflowMetadata contains some metadata of the workflow to be run # noqa: E501
:param workflow_metadata: The workflow_metadata of this V1alpha1CronWorkflowSpec. # noqa: E501
:type: V1ObjectMeta | argo/workflows/client/models/v1alpha1_cron_workflow_spec.py | workflow_metadata | ButterflyNetwork/argo-client-python | 0 | python | @workflow_metadata.setter
def workflow_metadata(self, workflow_metadata):
'Sets the workflow_metadata of this V1alpha1CronWorkflowSpec.\n\n WorkflowMetadata contains some metadata of the workflow to be run # noqa: E501\n\n :param workflow_metadata: The workflow_metadata of this V1alpha1CronWorkflowSpec. # noqa: E501\n :type: V1ObjectMeta\n '
self._workflow_metadata = workflow_metadata | @workflow_metadata.setter
def workflow_metadata(self, workflow_metadata):
'Sets the workflow_metadata of this V1alpha1CronWorkflowSpec.\n\n WorkflowMetadata contains some metadata of the workflow to be run # noqa: E501\n\n :param workflow_metadata: The workflow_metadata of this V1alpha1CronWorkflowSpec. # noqa: E501\n :type: V1ObjectMeta\n '
self._workflow_metadata = workflow_metadata<|docstring|>Sets the workflow_metadata of this V1alpha1CronWorkflowSpec.
WorkflowMetadata contains some metadata of the workflow to be run # noqa: E501
:param workflow_metadata: The workflow_metadata of this V1alpha1CronWorkflowSpec. # noqa: E501
:type: V1ObjectMeta<|endoftext|> |
7cbdc680b9bf004a14fadb016d217348c1a657c91fafeccfaf09d35f6ef8e569 | @property
def workflow_spec(self):
'Gets the workflow_spec of this V1alpha1CronWorkflowSpec. # noqa: E501\n\n WorkflowSpec is the spec of the workflow to be run # noqa: E501\n\n :return: The workflow_spec of this V1alpha1CronWorkflowSpec. # noqa: E501\n :rtype: V1alpha1WorkflowSpec\n '
return self._workflow_spec | Gets the workflow_spec of this V1alpha1CronWorkflowSpec. # noqa: E501
WorkflowSpec is the spec of the workflow to be run # noqa: E501
:return: The workflow_spec of this V1alpha1CronWorkflowSpec. # noqa: E501
:rtype: V1alpha1WorkflowSpec | argo/workflows/client/models/v1alpha1_cron_workflow_spec.py | workflow_spec | ButterflyNetwork/argo-client-python | 0 | python | @property
def workflow_spec(self):
'Gets the workflow_spec of this V1alpha1CronWorkflowSpec. # noqa: E501\n\n WorkflowSpec is the spec of the workflow to be run # noqa: E501\n\n :return: The workflow_spec of this V1alpha1CronWorkflowSpec. # noqa: E501\n :rtype: V1alpha1WorkflowSpec\n '
return self._workflow_spec | @property
def workflow_spec(self):
'Gets the workflow_spec of this V1alpha1CronWorkflowSpec. # noqa: E501\n\n WorkflowSpec is the spec of the workflow to be run # noqa: E501\n\n :return: The workflow_spec of this V1alpha1CronWorkflowSpec. # noqa: E501\n :rtype: V1alpha1WorkflowSpec\n '
return self._workflow_spec<|docstring|>Gets the workflow_spec of this V1alpha1CronWorkflowSpec. # noqa: E501
WorkflowSpec is the spec of the workflow to be run # noqa: E501
:return: The workflow_spec of this V1alpha1CronWorkflowSpec. # noqa: E501
:rtype: V1alpha1WorkflowSpec<|endoftext|> |
f6ba94c5b45bc81d350fd8a5fc25474606c6df68459a6622ac3d08559fd97dd0 | @workflow_spec.setter
def workflow_spec(self, workflow_spec):
'Sets the workflow_spec of this V1alpha1CronWorkflowSpec.\n\n WorkflowSpec is the spec of the workflow to be run # noqa: E501\n\n :param workflow_spec: The workflow_spec of this V1alpha1CronWorkflowSpec. # noqa: E501\n :type: V1alpha1WorkflowSpec\n '
if (workflow_spec is None):
raise ValueError('Invalid value for `workflow_spec`, must not be `None`')
self._workflow_spec = workflow_spec | Sets the workflow_spec of this V1alpha1CronWorkflowSpec.
WorkflowSpec is the spec of the workflow to be run # noqa: E501
:param workflow_spec: The workflow_spec of this V1alpha1CronWorkflowSpec. # noqa: E501
:type: V1alpha1WorkflowSpec | argo/workflows/client/models/v1alpha1_cron_workflow_spec.py | workflow_spec | ButterflyNetwork/argo-client-python | 0 | python | @workflow_spec.setter
def workflow_spec(self, workflow_spec):
'Sets the workflow_spec of this V1alpha1CronWorkflowSpec.\n\n WorkflowSpec is the spec of the workflow to be run # noqa: E501\n\n :param workflow_spec: The workflow_spec of this V1alpha1CronWorkflowSpec. # noqa: E501\n :type: V1alpha1WorkflowSpec\n '
if (workflow_spec is None):
raise ValueError('Invalid value for `workflow_spec`, must not be `None`')
self._workflow_spec = workflow_spec | @workflow_spec.setter
def workflow_spec(self, workflow_spec):
'Sets the workflow_spec of this V1alpha1CronWorkflowSpec.\n\n WorkflowSpec is the spec of the workflow to be run # noqa: E501\n\n :param workflow_spec: The workflow_spec of this V1alpha1CronWorkflowSpec. # noqa: E501\n :type: V1alpha1WorkflowSpec\n '
if (workflow_spec is None):
raise ValueError('Invalid value for `workflow_spec`, must not be `None`')
self._workflow_spec = workflow_spec<|docstring|>Sets the workflow_spec of this V1alpha1CronWorkflowSpec.
WorkflowSpec is the spec of the workflow to be run # noqa: E501
:param workflow_spec: The workflow_spec of this V1alpha1CronWorkflowSpec. # noqa: E501
:type: V1alpha1WorkflowSpec<|endoftext|> |
1a13f2940cee21f9114157e93c86a55415af1e0c823f09077439f0eff51428e3 | 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(V1alpha1CronWorkflowSpec, dict):
for (key, value) in self.items():
result[key] = value
return result | Returns the model properties as a dict | argo/workflows/client/models/v1alpha1_cron_workflow_spec.py | to_dict | ButterflyNetwork/argo-client-python | 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(V1alpha1CronWorkflowSpec, 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(V1alpha1CronWorkflowSpec, 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 | argo/workflows/client/models/v1alpha1_cron_workflow_spec.py | to_str | ButterflyNetwork/argo-client-python | 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` | argo/workflows/client/models/v1alpha1_cron_workflow_spec.py | __repr__ | ButterflyNetwork/argo-client-python | 0 | python | def __repr__(self):
return self.to_str() | def __repr__(self):
return self.to_str()<|docstring|>For `print` and `pprint`<|endoftext|> |
f5dcf49a7454405ac7ee56f402144454758d0e96a0990cab4f8a167d28f372ff | def __eq__(self, other):
'Returns true if both objects are equal'
if (not isinstance(other, V1alpha1CronWorkflowSpec)):
return False
return (self.__dict__ == other.__dict__) | Returns true if both objects are equal | argo/workflows/client/models/v1alpha1_cron_workflow_spec.py | __eq__ | ButterflyNetwork/argo-client-python | 0 | python | def __eq__(self, other):
if (not isinstance(other, V1alpha1CronWorkflowSpec)):
return False
return (self.__dict__ == other.__dict__) | def __eq__(self, other):
if (not isinstance(other, V1alpha1CronWorkflowSpec)):
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 | argo/workflows/client/models/v1alpha1_cron_workflow_spec.py | __ne__ | ButterflyNetwork/argo-client-python | 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|> |
74a4bdc93bcae731dc51b21ec65c767d78c95d85de9fd0ed382c7345d9fd58d3 | def cache_file(app_name=APPNAME, app_author=APPAUTHOR, filename=DATABASENAME):
'Returns the filename (including path) for the data cache.\n\n The path will depend on the operating system, certain environmental\n variables and whether it is being run inside a virtual environment.\n See `homebase <https://github.com/dwavesystems/homebase>`_.\n\n Args:\n app_name (str, optional): The application name.\n Default is given by :obj:`.APPNAME`.\n app_author (str, optional): The application author. Default\n is given by :obj:`.APPAUTHOR`.\n filename (str, optional): The name of the database file.\n Default is given by :obj:`DATABASENAME`.\n\n Returns:\n str: The full path to the file that can be used as a cache.\n\n Notes:\n Creates the directory if it does not already exist.\n\n If run inside of a virtual environment, the cache will be stored\n in `/path/to/virtualenv/data/app_name`\n\n '
user_data_dir = homebase.user_data_dir(app_name=app_name, app_author=app_author, create=True)
return os.path.join(user_data_dir, filename) | Returns the filename (including path) for the data cache.
The path will depend on the operating system, certain environmental
variables and whether it is being run inside a virtual environment.
See `homebase <https://github.com/dwavesystems/homebase>`_.
Args:
app_name (str, optional): The application name.
Default is given by :obj:`.APPNAME`.
app_author (str, optional): The application author. Default
is given by :obj:`.APPAUTHOR`.
filename (str, optional): The name of the database file.
Default is given by :obj:`DATABASENAME`.
Returns:
str: The full path to the file that can be used as a cache.
Notes:
Creates the directory if it does not already exist.
If run inside of a virtual environment, the cache will be stored
in `/path/to/virtualenv/data/app_name` | dwave/system/cache/cache_manager.py | cache_file | seatim/dwave-system | 85 | python | def cache_file(app_name=APPNAME, app_author=APPAUTHOR, filename=DATABASENAME):
'Returns the filename (including path) for the data cache.\n\n The path will depend on the operating system, certain environmental\n variables and whether it is being run inside a virtual environment.\n See `homebase <https://github.com/dwavesystems/homebase>`_.\n\n Args:\n app_name (str, optional): The application name.\n Default is given by :obj:`.APPNAME`.\n app_author (str, optional): The application author. Default\n is given by :obj:`.APPAUTHOR`.\n filename (str, optional): The name of the database file.\n Default is given by :obj:`DATABASENAME`.\n\n Returns:\n str: The full path to the file that can be used as a cache.\n\n Notes:\n Creates the directory if it does not already exist.\n\n If run inside of a virtual environment, the cache will be stored\n in `/path/to/virtualenv/data/app_name`\n\n '
user_data_dir = homebase.user_data_dir(app_name=app_name, app_author=app_author, create=True)
return os.path.join(user_data_dir, filename) | def cache_file(app_name=APPNAME, app_author=APPAUTHOR, filename=DATABASENAME):
'Returns the filename (including path) for the data cache.\n\n The path will depend on the operating system, certain environmental\n variables and whether it is being run inside a virtual environment.\n See `homebase <https://github.com/dwavesystems/homebase>`_.\n\n Args:\n app_name (str, optional): The application name.\n Default is given by :obj:`.APPNAME`.\n app_author (str, optional): The application author. Default\n is given by :obj:`.APPAUTHOR`.\n filename (str, optional): The name of the database file.\n Default is given by :obj:`DATABASENAME`.\n\n Returns:\n str: The full path to the file that can be used as a cache.\n\n Notes:\n Creates the directory if it does not already exist.\n\n If run inside of a virtual environment, the cache will be stored\n in `/path/to/virtualenv/data/app_name`\n\n '
user_data_dir = homebase.user_data_dir(app_name=app_name, app_author=app_author, create=True)
return os.path.join(user_data_dir, filename)<|docstring|>Returns the filename (including path) for the data cache.
The path will depend on the operating system, certain environmental
variables and whether it is being run inside a virtual environment.
See `homebase <https://github.com/dwavesystems/homebase>`_.
Args:
app_name (str, optional): The application name.
Default is given by :obj:`.APPNAME`.
app_author (str, optional): The application author. Default
is given by :obj:`.APPAUTHOR`.
filename (str, optional): The name of the database file.
Default is given by :obj:`DATABASENAME`.
Returns:
str: The full path to the file that can be used as a cache.
Notes:
Creates the directory if it does not already exist.
If run inside of a virtual environment, the cache will be stored
in `/path/to/virtualenv/data/app_name`<|endoftext|> |
dc48fbd46d0dcc3d2253c69e121d1d66fd03c811953473fe3d9214103e511eb3 | def data_loader(eigen_data_path):
'\n Loads the eigendata stored in a numpy zip and returns\n the eigenvalues and eigenvectors.\n\n Paramters\n ---------\n eigen_data_path: string\n Path to the eigendata numpy zip (.npz) file.\n\n Returns\n -------\n eigen_vals: numpy array\n Eigenvalues stored in the eigendata zip.\n eigen_vecs: numpy array\n Eigenvectors stored in the eigendata zip.\n '
eigen_data = np.load(eigen_data_path)
(eigen_vals, eigen_vecs) = (eigen_data['eigen_vals'], eigen_data['eigen_vecs'])
return (eigen_vals, eigen_vecs) | Loads the eigendata stored in a numpy zip and returns
the eigenvalues and eigenvectors.
Paramters
---------
eigen_data_path: string
Path to the eigendata numpy zip (.npz) file.
Returns
-------
eigen_vals: numpy array
Eigenvalues stored in the eigendata zip.
eigen_vecs: numpy array
Eigenvectors stored in the eigendata zip. | src/analysis/temporal_anomaly_detection.py | data_loader | quinngroup/ornet-JOSS | 3 | python | def data_loader(eigen_data_path):
'\n Loads the eigendata stored in a numpy zip and returns\n the eigenvalues and eigenvectors.\n\n Paramters\n ---------\n eigen_data_path: string\n Path to the eigendata numpy zip (.npz) file.\n\n Returns\n -------\n eigen_vals: numpy array\n Eigenvalues stored in the eigendata zip.\n eigen_vecs: numpy array\n Eigenvectors stored in the eigendata zip.\n '
eigen_data = np.load(eigen_data_path)
(eigen_vals, eigen_vecs) = (eigen_data['eigen_vals'], eigen_data['eigen_vecs'])
return (eigen_vals, eigen_vecs) | def data_loader(eigen_data_path):
'\n Loads the eigendata stored in a numpy zip and returns\n the eigenvalues and eigenvectors.\n\n Paramters\n ---------\n eigen_data_path: string\n Path to the eigendata numpy zip (.npz) file.\n\n Returns\n -------\n eigen_vals: numpy array\n Eigenvalues stored in the eigendata zip.\n eigen_vecs: numpy array\n Eigenvectors stored in the eigendata zip.\n '
eigen_data = np.load(eigen_data_path)
(eigen_vals, eigen_vecs) = (eigen_data['eigen_vals'], eigen_data['eigen_vecs'])
return (eigen_vals, eigen_vecs)<|docstring|>Loads the eigendata stored in a numpy zip and returns
the eigenvalues and eigenvectors.
Paramters
---------
eigen_data_path: string
Path to the eigendata numpy zip (.npz) file.
Returns
-------
eigen_vals: numpy array
Eigenvalues stored in the eigendata zip.
eigen_vecs: numpy array
Eigenvectors stored in the eigendata zip.<|endoftext|> |
a9fd1bf2e7120db1f479bfc77e84b601384e1881a056d2c3e9748394ee31b29e | def plot(eigen_vals, z_scores, title, save_fig, outdir_path=None):
'\n Plots eigenvalue time-series data, and a\n corresponding z-score curve.\n '
sns.set()
fig = plt.figure()
fig.suptitle(title)
ax = fig.add_subplot(211)
ax.plot(eigen_vals)
ax.set_ylabel('Magnitude')
ax = fig.add_subplot(212)
ax.plot(z_scores)
ax.set_xlabel('Frame')
ax.set_ylabel('Signal')
if save_fig:
file_name = os.path.join(outdir_path, title.split(' ')[0])
plt.savefig(file_name)
else:
plt.show()
plt.close() | Plots eigenvalue time-series data, and a
corresponding z-score curve. | src/analysis/temporal_anomaly_detection.py | plot | quinngroup/ornet-JOSS | 3 | python | def plot(eigen_vals, z_scores, title, save_fig, outdir_path=None):
'\n Plots eigenvalue time-series data, and a\n corresponding z-score curve.\n '
sns.set()
fig = plt.figure()
fig.suptitle(title)
ax = fig.add_subplot(211)
ax.plot(eigen_vals)
ax.set_ylabel('Magnitude')
ax = fig.add_subplot(212)
ax.plot(z_scores)
ax.set_xlabel('Frame')
ax.set_ylabel('Signal')
if save_fig:
file_name = os.path.join(outdir_path, title.split(' ')[0])
plt.savefig(file_name)
else:
plt.show()
plt.close() | def plot(eigen_vals, z_scores, title, save_fig, outdir_path=None):
'\n Plots eigenvalue time-series data, and a\n corresponding z-score curve.\n '
sns.set()
fig = plt.figure()
fig.suptitle(title)
ax = fig.add_subplot(211)
ax.plot(eigen_vals)
ax.set_ylabel('Magnitude')
ax = fig.add_subplot(212)
ax.plot(z_scores)
ax.set_xlabel('Frame')
ax.set_ylabel('Signal')
if save_fig:
file_name = os.path.join(outdir_path, title.split(' ')[0])
plt.savefig(file_name)
else:
plt.show()
plt.close()<|docstring|>Plots eigenvalue time-series data, and a
corresponding z-score curve.<|endoftext|> |
4eb1ac80b858d65b12ecff00decda1cf6d33b70f4b5a8c0ea39df06097a92b56 | def temporal_anomaly_detection(vid_name, eigen_vals, outdir_path, k=10, window=20, threshold=2):
'\n Generates a figure comprised of a time-series plot\n of the eigenvalue vectors, and an outlier detection \n signals plot.\n\n Parameters\n ----------\n vid_name: string\n Name of the microscopy video.\n eigen_vals: NumPy array (NXM)\n Matrix comprised of eigenvalue vectors. \n N represents the number of frames in the\n corresponding video, and M is the number of\n mixture components.\n outdir_path: string\n Path to a directory to save the plots and anomalous\n time points.\n k: int\n The number of leading eigenvalues to display.\n window: int\n The size of the window to be used for anomaly \n detection.\n threshold: float\n Value used to determine whether a signal value\n is anomalous. \n\n Returns\n -------\n NoneType object\n '
eigen_vals_avgs = [np.mean(x) for x in eigen_vals]
moving_avgs = np.empty(shape=(eigen_vals.shape[0],), dtype=np.float)
moving_stds = np.empty(shape=(eigen_vals.shape[0],), dtype=np.float)
z_scores = np.empty(shape=(eigen_vals.shape[0],), dtype=np.float)
signals = np.empty(shape=(eigen_vals.shape[0],), dtype=np.float)
moving_avgs[:window] = 0
moving_stds[:window] = 0
z_scores[:window] = 0
for i in range(window, moving_avgs.shape[0]):
moving_avgs[i] = np.mean(eigen_vals_avgs[(i - window):i])
moving_stds[i] = np.std(eigen_vals_avgs[(i - window):i])
z_scores[i] = ((eigen_vals_avgs[i] - moving_avgs[i]) / moving_stds[i])
plot_title = (vid_name + ' Signals Plot')
timepoint_title = (vid_name + '.txt')
with open(os.path.join(outdir_path, timepoint_title), 'w+') as writer:
for (i, score) in enumerate(z_scores):
if (score > threshold):
signals[i] = 1
writer.write((str(i) + '\n'))
elif (score < (threshold * (- 1))):
signals[i] = (- 1)
writer.write((str(i) + '\n'))
else:
signals[i] = 0
plot(eigen_vals[(:, :k)], signals, plot_title, False, outdir_path) | Generates a figure comprised of a time-series plot
of the eigenvalue vectors, and an outlier detection
signals plot.
Parameters
----------
vid_name: string
Name of the microscopy video.
eigen_vals: NumPy array (NXM)
Matrix comprised of eigenvalue vectors.
N represents the number of frames in the
corresponding video, and M is the number of
mixture components.
outdir_path: string
Path to a directory to save the plots and anomalous
time points.
k: int
The number of leading eigenvalues to display.
window: int
The size of the window to be used for anomaly
detection.
threshold: float
Value used to determine whether a signal value
is anomalous.
Returns
-------
NoneType object | src/analysis/temporal_anomaly_detection.py | temporal_anomaly_detection | quinngroup/ornet-JOSS | 3 | python | def temporal_anomaly_detection(vid_name, eigen_vals, outdir_path, k=10, window=20, threshold=2):
'\n Generates a figure comprised of a time-series plot\n of the eigenvalue vectors, and an outlier detection \n signals plot.\n\n Parameters\n ----------\n vid_name: string\n Name of the microscopy video.\n eigen_vals: NumPy array (NXM)\n Matrix comprised of eigenvalue vectors. \n N represents the number of frames in the\n corresponding video, and M is the number of\n mixture components.\n outdir_path: string\n Path to a directory to save the plots and anomalous\n time points.\n k: int\n The number of leading eigenvalues to display.\n window: int\n The size of the window to be used for anomaly \n detection.\n threshold: float\n Value used to determine whether a signal value\n is anomalous. \n\n Returns\n -------\n NoneType object\n '
eigen_vals_avgs = [np.mean(x) for x in eigen_vals]
moving_avgs = np.empty(shape=(eigen_vals.shape[0],), dtype=np.float)
moving_stds = np.empty(shape=(eigen_vals.shape[0],), dtype=np.float)
z_scores = np.empty(shape=(eigen_vals.shape[0],), dtype=np.float)
signals = np.empty(shape=(eigen_vals.shape[0],), dtype=np.float)
moving_avgs[:window] = 0
moving_stds[:window] = 0
z_scores[:window] = 0
for i in range(window, moving_avgs.shape[0]):
moving_avgs[i] = np.mean(eigen_vals_avgs[(i - window):i])
moving_stds[i] = np.std(eigen_vals_avgs[(i - window):i])
z_scores[i] = ((eigen_vals_avgs[i] - moving_avgs[i]) / moving_stds[i])
plot_title = (vid_name + ' Signals Plot')
timepoint_title = (vid_name + '.txt')
with open(os.path.join(outdir_path, timepoint_title), 'w+') as writer:
for (i, score) in enumerate(z_scores):
if (score > threshold):
signals[i] = 1
writer.write((str(i) + '\n'))
elif (score < (threshold * (- 1))):
signals[i] = (- 1)
writer.write((str(i) + '\n'))
else:
signals[i] = 0
plot(eigen_vals[(:, :k)], signals, plot_title, False, outdir_path) | def temporal_anomaly_detection(vid_name, eigen_vals, outdir_path, k=10, window=20, threshold=2):
'\n Generates a figure comprised of a time-series plot\n of the eigenvalue vectors, and an outlier detection \n signals plot.\n\n Parameters\n ----------\n vid_name: string\n Name of the microscopy video.\n eigen_vals: NumPy array (NXM)\n Matrix comprised of eigenvalue vectors. \n N represents the number of frames in the\n corresponding video, and M is the number of\n mixture components.\n outdir_path: string\n Path to a directory to save the plots and anomalous\n time points.\n k: int\n The number of leading eigenvalues to display.\n window: int\n The size of the window to be used for anomaly \n detection.\n threshold: float\n Value used to determine whether a signal value\n is anomalous. \n\n Returns\n -------\n NoneType object\n '
eigen_vals_avgs = [np.mean(x) for x in eigen_vals]
moving_avgs = np.empty(shape=(eigen_vals.shape[0],), dtype=np.float)
moving_stds = np.empty(shape=(eigen_vals.shape[0],), dtype=np.float)
z_scores = np.empty(shape=(eigen_vals.shape[0],), dtype=np.float)
signals = np.empty(shape=(eigen_vals.shape[0],), dtype=np.float)
moving_avgs[:window] = 0
moving_stds[:window] = 0
z_scores[:window] = 0
for i in range(window, moving_avgs.shape[0]):
moving_avgs[i] = np.mean(eigen_vals_avgs[(i - window):i])
moving_stds[i] = np.std(eigen_vals_avgs[(i - window):i])
z_scores[i] = ((eigen_vals_avgs[i] - moving_avgs[i]) / moving_stds[i])
plot_title = (vid_name + ' Signals Plot')
timepoint_title = (vid_name + '.txt')
with open(os.path.join(outdir_path, timepoint_title), 'w+') as writer:
for (i, score) in enumerate(z_scores):
if (score > threshold):
signals[i] = 1
writer.write((str(i) + '\n'))
elif (score < (threshold * (- 1))):
signals[i] = (- 1)
writer.write((str(i) + '\n'))
else:
signals[i] = 0
plot(eigen_vals[(:, :k)], signals, plot_title, False, outdir_path)<|docstring|>Generates a figure comprised of a time-series plot
of the eigenvalue vectors, and an outlier detection
signals plot.
Parameters
----------
vid_name: string
Name of the microscopy video.
eigen_vals: NumPy array (NXM)
Matrix comprised of eigenvalue vectors.
N represents the number of frames in the
corresponding video, and M is the number of
mixture components.
outdir_path: string
Path to a directory to save the plots and anomalous
time points.
k: int
The number of leading eigenvalues to display.
window: int
The size of the window to be used for anomaly
detection.
threshold: float
Value used to determine whether a signal value
is anomalous.
Returns
-------
NoneType object<|endoftext|> |
dbb77ea38863b550fea47b750655307f6a4ed45a4de236d264334284ebdabfaa | def parse_cli(input_args):
'\n Parses the command line arguments.\n\n Parameters\n ----------\n input_args: list\n Arguments to be parsed.\n\n Returns\n -------\n parsed_args: dict\n Key value pairs of arguments.\n '
parser = argparse.ArgumentParser(description='Anomaly detection of of eigenvalue time-series data.')
parser.add_argument('-i', '--input', required=True, help='Input directory of eigendata file (.npz).')
parser.add_argument('-o', '--outdir', default=os.getcwd(), help='Output directory for plots.')
return vars(parser.parse_args(input_args)) | Parses the command line arguments.
Parameters
----------
input_args: list
Arguments to be parsed.
Returns
-------
parsed_args: dict
Key value pairs of arguments. | src/analysis/temporal_anomaly_detection.py | parse_cli | quinngroup/ornet-JOSS | 3 | python | def parse_cli(input_args):
'\n Parses the command line arguments.\n\n Parameters\n ----------\n input_args: list\n Arguments to be parsed.\n\n Returns\n -------\n parsed_args: dict\n Key value pairs of arguments.\n '
parser = argparse.ArgumentParser(description='Anomaly detection of of eigenvalue time-series data.')
parser.add_argument('-i', '--input', required=True, help='Input directory of eigendata file (.npz).')
parser.add_argument('-o', '--outdir', default=os.getcwd(), help='Output directory for plots.')
return vars(parser.parse_args(input_args)) | def parse_cli(input_args):
'\n Parses the command line arguments.\n\n Parameters\n ----------\n input_args: list\n Arguments to be parsed.\n\n Returns\n -------\n parsed_args: dict\n Key value pairs of arguments.\n '
parser = argparse.ArgumentParser(description='Anomaly detection of of eigenvalue time-series data.')
parser.add_argument('-i', '--input', required=True, help='Input directory of eigendata file (.npz).')
parser.add_argument('-o', '--outdir', default=os.getcwd(), help='Output directory for plots.')
return vars(parser.parse_args(input_args))<|docstring|>Parses the command line arguments.
Parameters
----------
input_args: list
Arguments to be parsed.
Returns
-------
parsed_args: dict
Key value pairs of arguments.<|endoftext|> |
c69158b7f288ceeabdf5458488941e947be7edb105b6e73bf498ac7465dc841e | @staticmethod
def n_dist_param():
'Number of distributional parameter.\n\n '
n_param = 2
return n_param | Number of distributional parameter. | xgboostlss/distributions/Gamma.py | n_dist_param | Cattes/XGBoostLSS | 0 | python | @staticmethod
def n_dist_param():
'\n\n '
n_param = 2
return n_param | @staticmethod
def n_dist_param():
'\n\n '
n_param = 2
return n_param<|docstring|>Number of distributional parameter.<|endoftext|> |
166b7c8eebcc403fda2665da49fc18ddb2e2d5eddd479df023dd118ec793fc9f | @staticmethod
def param_dict():
' Dictionary that holds the name of distributional parameter and their corresponding response functions.\n\n '
param_dict = {'location': soft_plus, 'scale': soft_plus}
return param_dict | Dictionary that holds the name of distributional parameter and their corresponding response functions. | xgboostlss/distributions/Gamma.py | param_dict | Cattes/XGBoostLSS | 0 | python | @staticmethod
def param_dict():
' \n\n '
param_dict = {'location': soft_plus, 'scale': soft_plus}
return param_dict | @staticmethod
def param_dict():
' \n\n '
param_dict = {'location': soft_plus, 'scale': soft_plus}
return param_dict<|docstring|>Dictionary that holds the name of distributional parameter and their corresponding response functions.<|endoftext|> |
1fe57a04bee3b4caacdef8a2719bb07e970223d2439d1e8bde40bc09bc788cd9 | @staticmethod
def param_dict_inv():
' Dictionary that holds the name of distributional parameter and their corresponding link functions.\n\n '
param_dict_inv = {'location_inv': soft_plus_inv, 'scale_inv': soft_plus_inv}
return param_dict_inv | Dictionary that holds the name of distributional parameter and their corresponding link functions. | xgboostlss/distributions/Gamma.py | param_dict_inv | Cattes/XGBoostLSS | 0 | python | @staticmethod
def param_dict_inv():
' \n\n '
param_dict_inv = {'location_inv': soft_plus_inv, 'scale_inv': soft_plus_inv}
return param_dict_inv | @staticmethod
def param_dict_inv():
' \n\n '
param_dict_inv = {'location_inv': soft_plus_inv, 'scale_inv': soft_plus_inv}
return param_dict_inv<|docstring|>Dictionary that holds the name of distributional parameter and their corresponding link functions.<|endoftext|> |
74736eafaad7e25c33c563d490be283bb831433cea1f195ef992d85bd0168cfb | @staticmethod
def initialize(y: np.ndarray):
' Function that calculates the starting values, for each distributional parameter individually.\n\n y: np.ndarray\n Data from which starting values are calculated.\n\n '
(loc_fit, scale_fit) = (np.nanmean(y), np.nanstd(y))
location_init = Gamma.param_dict_inv()['location_inv'](loc_fit)
scale_init = Gamma.param_dict_inv()['scale_inv'](scale_fit)
start_values = np.array([location_init, scale_init])
return start_values | Function that calculates the starting values, for each distributional parameter individually.
y: np.ndarray
Data from which starting values are calculated. | xgboostlss/distributions/Gamma.py | initialize | Cattes/XGBoostLSS | 0 | python | @staticmethod
def initialize(y: np.ndarray):
' Function that calculates the starting values, for each distributional parameter individually.\n\n y: np.ndarray\n Data from which starting values are calculated.\n\n '
(loc_fit, scale_fit) = (np.nanmean(y), np.nanstd(y))
location_init = Gamma.param_dict_inv()['location_inv'](loc_fit)
scale_init = Gamma.param_dict_inv()['scale_inv'](scale_fit)
start_values = np.array([location_init, scale_init])
return start_values | @staticmethod
def initialize(y: np.ndarray):
' Function that calculates the starting values, for each distributional parameter individually.\n\n y: np.ndarray\n Data from which starting values are calculated.\n\n '
(loc_fit, scale_fit) = (np.nanmean(y), np.nanstd(y))
location_init = Gamma.param_dict_inv()['location_inv'](loc_fit)
scale_init = Gamma.param_dict_inv()['scale_inv'](scale_fit)
start_values = np.array([location_init, scale_init])
return start_values<|docstring|>Function that calculates the starting values, for each distributional parameter individually.
y: np.ndarray
Data from which starting values are calculated.<|endoftext|> |
0e8c101ff1bc30bb47d8f08b4e8d8ad135b3b5afdf77cd442336b8e76c4ddf64 | @staticmethod
def dGamma(y: np.ndarray, location: np.ndarray, scale: np.ndarray, log=True):
'Density function.\n\n '
loglik = (((((1 / (scale ** 2)) * np.log((y / (location * (scale ** 2))))) - (y / (location * (scale ** 2)))) - np.log(y)) - loggamma((1 / (scale ** 2))))
loglik = (np.exp(loglik) if (log == False) else loglik)
return loglik | Density function. | xgboostlss/distributions/Gamma.py | dGamma | Cattes/XGBoostLSS | 0 | python | @staticmethod
def dGamma(y: np.ndarray, location: np.ndarray, scale: np.ndarray, log=True):
'\n\n '
loglik = (((((1 / (scale ** 2)) * np.log((y / (location * (scale ** 2))))) - (y / (location * (scale ** 2)))) - np.log(y)) - loggamma((1 / (scale ** 2))))
loglik = (np.exp(loglik) if (log == False) else loglik)
return loglik | @staticmethod
def dGamma(y: np.ndarray, location: np.ndarray, scale: np.ndarray, log=True):
'\n\n '
loglik = (((((1 / (scale ** 2)) * np.log((y / (location * (scale ** 2))))) - (y / (location * (scale ** 2)))) - np.log(y)) - loggamma((1 / (scale ** 2))))
loglik = (np.exp(loglik) if (log == False) else loglik)
return loglik<|docstring|>Density function.<|endoftext|> |
9c90a89dc0f3a910ca79f33c4d435ebe161dfa3dc1c6153b64ca55d930b24603 | @staticmethod
def qGamma(p: float, location: np.ndarray, scale: np.ndarray):
'Quantile function.\n\n '
q = gamma.ppf(p, a=(1 / (scale ** 2)), scale=(location * (scale ** 2)))
return q | Quantile function. | xgboostlss/distributions/Gamma.py | qGamma | Cattes/XGBoostLSS | 0 | python | @staticmethod
def qGamma(p: float, location: np.ndarray, scale: np.ndarray):
'\n\n '
q = gamma.ppf(p, a=(1 / (scale ** 2)), scale=(location * (scale ** 2)))
return q | @staticmethod
def qGamma(p: float, location: np.ndarray, scale: np.ndarray):
'\n\n '
q = gamma.ppf(p, a=(1 / (scale ** 2)), scale=(location * (scale ** 2)))
return q<|docstring|>Quantile function.<|endoftext|> |
bc96c0a2308ab4ae88e0e9c9a59d318e506c9e73fdaf0fb0e2970a441e86f6c7 | def rGamma(n: int, location: np.ndarray, scale: np.ndarray):
'Random variable generation function.\n\n '
n = math.ceil(n)
p = np.random.uniform(0, 1, n)
r = Gamma.qGamma(p, location=location, scale=scale)
return r | Random variable generation function. | xgboostlss/distributions/Gamma.py | rGamma | Cattes/XGBoostLSS | 0 | python | def rGamma(n: int, location: np.ndarray, scale: np.ndarray):
'\n\n '
n = math.ceil(n)
p = np.random.uniform(0, 1, n)
r = Gamma.qGamma(p, location=location, scale=scale)
return r | def rGamma(n: int, location: np.ndarray, scale: np.ndarray):
'\n\n '
n = math.ceil(n)
p = np.random.uniform(0, 1, n)
r = Gamma.qGamma(p, location=location, scale=scale)
return r<|docstring|>Random variable generation function.<|endoftext|> |
96d89a9b018da8d7e27d866746a3cd4c73b6b702644ae2ed069b150f6def9437 | @staticmethod
def gradient_location(y: np.ndarray, location: np.ndarray, scale: np.ndarray, weights: np.ndarray):
'Calculates Gradient of location parameter.\n\n '
grad = ((y - location) / ((scale ** 2) * (location ** 2)))
grad = stabilize_derivative(grad, Gamma.stabilize)
grad = ((grad * (- 1)) * weights)
return grad | Calculates Gradient of location parameter. | xgboostlss/distributions/Gamma.py | gradient_location | Cattes/XGBoostLSS | 0 | python | @staticmethod
def gradient_location(y: np.ndarray, location: np.ndarray, scale: np.ndarray, weights: np.ndarray):
'\n\n '
grad = ((y - location) / ((scale ** 2) * (location ** 2)))
grad = stabilize_derivative(grad, Gamma.stabilize)
grad = ((grad * (- 1)) * weights)
return grad | @staticmethod
def gradient_location(y: np.ndarray, location: np.ndarray, scale: np.ndarray, weights: np.ndarray):
'\n\n '
grad = ((y - location) / ((scale ** 2) * (location ** 2)))
grad = stabilize_derivative(grad, Gamma.stabilize)
grad = ((grad * (- 1)) * weights)
return grad<|docstring|>Calculates Gradient of location parameter.<|endoftext|> |
8ebf9fd61d46d12c041ba0bcaf5e19f40a425268dbc13fa3bba2fc7247c3ab37 | @staticmethod
def hessian_location(location: np.ndarray, scale: np.ndarray, weights: np.ndarray):
'Calculates Hessian of location parameter.\n\n '
hes = ((- 1) / ((scale ** 2) * (location ** 2)))
hes = stabilize_derivative(hes, Gamma.stabilize)
hes = ((hes * (- 1)) * weights)
return hes | Calculates Hessian of location parameter. | xgboostlss/distributions/Gamma.py | hessian_location | Cattes/XGBoostLSS | 0 | python | @staticmethod
def hessian_location(location: np.ndarray, scale: np.ndarray, weights: np.ndarray):
'\n\n '
hes = ((- 1) / ((scale ** 2) * (location ** 2)))
hes = stabilize_derivative(hes, Gamma.stabilize)
hes = ((hes * (- 1)) * weights)
return hes | @staticmethod
def hessian_location(location: np.ndarray, scale: np.ndarray, weights: np.ndarray):
'\n\n '
hes = ((- 1) / ((scale ** 2) * (location ** 2)))
hes = stabilize_derivative(hes, Gamma.stabilize)
hes = ((hes * (- 1)) * weights)
return hes<|docstring|>Calculates Hessian of location parameter.<|endoftext|> |
d71b0b9f394748bb0ac0fbf624f9b1a129ab8200f32d92385a2d9201c2fb20bf | @staticmethod
def gradient_scale(y: np.ndarray, location: np.ndarray, scale: np.ndarray, weights: np.ndarray):
'Calculates Gradient of scale parameter.\n\n '
grad = ((2 / (scale ** 3)) * ((((((y / location) - np.log(y)) + np.log(location)) + np.log((scale ** 2))) - 1) + polygamma(0, (1 / (scale ** 2)))))
grad = stabilize_derivative(grad, Gamma.stabilize)
grad = ((grad * (- 1)) * weights)
return grad | Calculates Gradient of scale parameter. | xgboostlss/distributions/Gamma.py | gradient_scale | Cattes/XGBoostLSS | 0 | python | @staticmethod
def gradient_scale(y: np.ndarray, location: np.ndarray, scale: np.ndarray, weights: np.ndarray):
'\n\n '
grad = ((2 / (scale ** 3)) * ((((((y / location) - np.log(y)) + np.log(location)) + np.log((scale ** 2))) - 1) + polygamma(0, (1 / (scale ** 2)))))
grad = stabilize_derivative(grad, Gamma.stabilize)
grad = ((grad * (- 1)) * weights)
return grad | @staticmethod
def gradient_scale(y: np.ndarray, location: np.ndarray, scale: np.ndarray, weights: np.ndarray):
'\n\n '
grad = ((2 / (scale ** 3)) * ((((((y / location) - np.log(y)) + np.log(location)) + np.log((scale ** 2))) - 1) + polygamma(0, (1 / (scale ** 2)))))
grad = stabilize_derivative(grad, Gamma.stabilize)
grad = ((grad * (- 1)) * weights)
return grad<|docstring|>Calculates Gradient of scale parameter.<|endoftext|> |
add314fd459a6ef71b6b3961bd870afcdda27e14706902e22036c2f134bd4809 | @staticmethod
def hessian_scale(scale: np.ndarray, weights: np.ndarray):
'Calculates Hessian of scale parameter.\n\n '
hes = ((4 / (scale ** 4)) - ((4 / (scale ** 6)) * polygamma(1, (1 / (scale ** 2)))))
hes = stabilize_derivative(hes, Gamma.stabilize)
hes = ((hes * (- 1)) * weights)
return hes | Calculates Hessian of scale parameter. | xgboostlss/distributions/Gamma.py | hessian_scale | Cattes/XGBoostLSS | 0 | python | @staticmethod
def hessian_scale(scale: np.ndarray, weights: np.ndarray):
'\n\n '
hes = ((4 / (scale ** 4)) - ((4 / (scale ** 6)) * polygamma(1, (1 / (scale ** 2)))))
hes = stabilize_derivative(hes, Gamma.stabilize)
hes = ((hes * (- 1)) * weights)
return hes | @staticmethod
def hessian_scale(scale: np.ndarray, weights: np.ndarray):
'\n\n '
hes = ((4 / (scale ** 4)) - ((4 / (scale ** 6)) * polygamma(1, (1 / (scale ** 2)))))
hes = stabilize_derivative(hes, Gamma.stabilize)
hes = ((hes * (- 1)) * weights)
return hes<|docstring|>Calculates Hessian of scale parameter.<|endoftext|> |
d545f63797ccd6d9092813abbc7425c942228857f049f0fd5e64ecedbb558c05 | def Dist_Objective(predt: np.ndarray, data: xgb.DMatrix):
'A customized objective function to train each distributional parameter using custom gradient and hessian.\n\n '
target = data.get_label()
preds_location = Gamma.param_dict()['location'](predt[(:, 0)])
preds_scale = Gamma.param_dict()['scale'](predt[(:, 1)])
if (data.get_weight().size == 0):
weights = np.ones_like(target, dtype=float)
else:
weights = data.get_weight()
grad = np.zeros(shape=(len(target), Gamma.n_dist_param()))
hess = np.zeros(shape=(len(target), Gamma.n_dist_param()))
grad[(:, 0)] = Gamma.gradient_location(y=target, location=preds_location, scale=preds_scale, weights=weights)
hess[(:, 0)] = Gamma.hessian_location(location=preds_location, scale=preds_scale, weights=weights)
grad[(:, 1)] = Gamma.gradient_scale(y=target, location=preds_location, scale=preds_scale, weights=weights)
hess[(:, 1)] = Gamma.hessian_scale(scale=preds_scale, weights=weights)
grad = grad.flatten()
hess = hess.flatten()
return (grad, hess) | A customized objective function to train each distributional parameter using custom gradient and hessian. | xgboostlss/distributions/Gamma.py | Dist_Objective | Cattes/XGBoostLSS | 0 | python | def Dist_Objective(predt: np.ndarray, data: xgb.DMatrix):
'\n\n '
target = data.get_label()
preds_location = Gamma.param_dict()['location'](predt[(:, 0)])
preds_scale = Gamma.param_dict()['scale'](predt[(:, 1)])
if (data.get_weight().size == 0):
weights = np.ones_like(target, dtype=float)
else:
weights = data.get_weight()
grad = np.zeros(shape=(len(target), Gamma.n_dist_param()))
hess = np.zeros(shape=(len(target), Gamma.n_dist_param()))
grad[(:, 0)] = Gamma.gradient_location(y=target, location=preds_location, scale=preds_scale, weights=weights)
hess[(:, 0)] = Gamma.hessian_location(location=preds_location, scale=preds_scale, weights=weights)
grad[(:, 1)] = Gamma.gradient_scale(y=target, location=preds_location, scale=preds_scale, weights=weights)
hess[(:, 1)] = Gamma.hessian_scale(scale=preds_scale, weights=weights)
grad = grad.flatten()
hess = hess.flatten()
return (grad, hess) | def Dist_Objective(predt: np.ndarray, data: xgb.DMatrix):
'\n\n '
target = data.get_label()
preds_location = Gamma.param_dict()['location'](predt[(:, 0)])
preds_scale = Gamma.param_dict()['scale'](predt[(:, 1)])
if (data.get_weight().size == 0):
weights = np.ones_like(target, dtype=float)
else:
weights = data.get_weight()
grad = np.zeros(shape=(len(target), Gamma.n_dist_param()))
hess = np.zeros(shape=(len(target), Gamma.n_dist_param()))
grad[(:, 0)] = Gamma.gradient_location(y=target, location=preds_location, scale=preds_scale, weights=weights)
hess[(:, 0)] = Gamma.hessian_location(location=preds_location, scale=preds_scale, weights=weights)
grad[(:, 1)] = Gamma.gradient_scale(y=target, location=preds_location, scale=preds_scale, weights=weights)
hess[(:, 1)] = Gamma.hessian_scale(scale=preds_scale, weights=weights)
grad = grad.flatten()
hess = hess.flatten()
return (grad, hess)<|docstring|>A customized objective function to train each distributional parameter using custom gradient and hessian.<|endoftext|> |
16fafefd7f25b32e2b16703cbbec6dd9d476c85818cae9c7dde831a6ca57ac4f | def Dist_Metric(predt: np.ndarray, data: xgb.DMatrix):
'A customized evaluation metric that evaluates the predictions using the negative log-likelihood.\n\n '
target = data.get_label()
preds_location = Gamma.param_dict()['location'](predt[(:, 0)])
preds_scale = Gamma.param_dict()['scale'](predt[(:, 1)])
nll = (- np.nansum(Gamma.dGamma(y=target, location=preds_location, scale=preds_scale, log=True)))
return ('NegLogLikelihood', nll) | A customized evaluation metric that evaluates the predictions using the negative log-likelihood. | xgboostlss/distributions/Gamma.py | Dist_Metric | Cattes/XGBoostLSS | 0 | python | def Dist_Metric(predt: np.ndarray, data: xgb.DMatrix):
'\n\n '
target = data.get_label()
preds_location = Gamma.param_dict()['location'](predt[(:, 0)])
preds_scale = Gamma.param_dict()['scale'](predt[(:, 1)])
nll = (- np.nansum(Gamma.dGamma(y=target, location=preds_location, scale=preds_scale, log=True)))
return ('NegLogLikelihood', nll) | def Dist_Metric(predt: np.ndarray, data: xgb.DMatrix):
'\n\n '
target = data.get_label()
preds_location = Gamma.param_dict()['location'](predt[(:, 0)])
preds_scale = Gamma.param_dict()['scale'](predt[(:, 1)])
nll = (- np.nansum(Gamma.dGamma(y=target, location=preds_location, scale=preds_scale, log=True)))
return ('NegLogLikelihood', nll)<|docstring|>A customized evaluation metric that evaluates the predictions using the negative log-likelihood.<|endoftext|> |
dadffea76faecb7bf8123464c5597af1d16f2a00074eda97e085918b7c325efa | def pred_dist_rvs(pred_params: pd.DataFrame, n_samples: int, seed: int):
'\n Function that draws n_samples from a predicted response distribution.\n\n pred_params: pd.DataFrame\n Dataframe with predicted distributional parameters.\n n_samples: int\n Number of sample to draw from predicted response distribution.\n seed: int\n Manual seed.\n Returns\n -------\n pd.DataFrame with n_samples drawn from predicted response distribution.\n\n '
pred_dist_list = []
for i in range(pred_params.shape[0]):
pred_dist_list.append(Gamma.rGamma(n=n_samples, location=np.array([pred_params.loc[(i, 'location')]]), scale=np.array([pred_params.loc[(i, 'scale')]])))
pred_dist = pd.DataFrame(pred_dist_list)
return pred_dist | Function that draws n_samples from a predicted response distribution.
pred_params: pd.DataFrame
Dataframe with predicted distributional parameters.
n_samples: int
Number of sample to draw from predicted response distribution.
seed: int
Manual seed.
Returns
-------
pd.DataFrame with n_samples drawn from predicted response distribution. | xgboostlss/distributions/Gamma.py | pred_dist_rvs | Cattes/XGBoostLSS | 0 | python | def pred_dist_rvs(pred_params: pd.DataFrame, n_samples: int, seed: int):
'\n Function that draws n_samples from a predicted response distribution.\n\n pred_params: pd.DataFrame\n Dataframe with predicted distributional parameters.\n n_samples: int\n Number of sample to draw from predicted response distribution.\n seed: int\n Manual seed.\n Returns\n -------\n pd.DataFrame with n_samples drawn from predicted response distribution.\n\n '
pred_dist_list = []
for i in range(pred_params.shape[0]):
pred_dist_list.append(Gamma.rGamma(n=n_samples, location=np.array([pred_params.loc[(i, 'location')]]), scale=np.array([pred_params.loc[(i, 'scale')]])))
pred_dist = pd.DataFrame(pred_dist_list)
return pred_dist | def pred_dist_rvs(pred_params: pd.DataFrame, n_samples: int, seed: int):
'\n Function that draws n_samples from a predicted response distribution.\n\n pred_params: pd.DataFrame\n Dataframe with predicted distributional parameters.\n n_samples: int\n Number of sample to draw from predicted response distribution.\n seed: int\n Manual seed.\n Returns\n -------\n pd.DataFrame with n_samples drawn from predicted response distribution.\n\n '
pred_dist_list = []
for i in range(pred_params.shape[0]):
pred_dist_list.append(Gamma.rGamma(n=n_samples, location=np.array([pred_params.loc[(i, 'location')]]), scale=np.array([pred_params.loc[(i, 'scale')]])))
pred_dist = pd.DataFrame(pred_dist_list)
return pred_dist<|docstring|>Function that draws n_samples from a predicted response distribution.
pred_params: pd.DataFrame
Dataframe with predicted distributional parameters.
n_samples: int
Number of sample to draw from predicted response distribution.
seed: int
Manual seed.
Returns
-------
pd.DataFrame with n_samples drawn from predicted response distribution.<|endoftext|> |
f3b691ddc79cdb7d709e5eb973c200d331f66065ed1227afa54a8a80493caefa | def pred_dist_quantile(quantiles: list, pred_params: pd.DataFrame):
'\n Function that calculates the quantiles from the predicted response distribution.\n\n quantiles: list\n Which quantiles to calculate\n pred_params: pd.DataFrame\n Dataframe with predicted distributional parameters.\n\n Returns\n -------\n pd.DataFrame with calculated quantiles.\n\n '
pred_quantiles_list = []
for i in range(len(quantiles)):
pred_quantiles_list.append(Gamma.qGamma(p=quantiles[i], location=pred_params['location'], scale=pred_params['scale']))
pred_quantiles = pd.DataFrame(pred_quantiles_list).T
return pred_quantiles | Function that calculates the quantiles from the predicted response distribution.
quantiles: list
Which quantiles to calculate
pred_params: pd.DataFrame
Dataframe with predicted distributional parameters.
Returns
-------
pd.DataFrame with calculated quantiles. | xgboostlss/distributions/Gamma.py | pred_dist_quantile | Cattes/XGBoostLSS | 0 | python | def pred_dist_quantile(quantiles: list, pred_params: pd.DataFrame):
'\n Function that calculates the quantiles from the predicted response distribution.\n\n quantiles: list\n Which quantiles to calculate\n pred_params: pd.DataFrame\n Dataframe with predicted distributional parameters.\n\n Returns\n -------\n pd.DataFrame with calculated quantiles.\n\n '
pred_quantiles_list = []
for i in range(len(quantiles)):
pred_quantiles_list.append(Gamma.qGamma(p=quantiles[i], location=pred_params['location'], scale=pred_params['scale']))
pred_quantiles = pd.DataFrame(pred_quantiles_list).T
return pred_quantiles | def pred_dist_quantile(quantiles: list, pred_params: pd.DataFrame):
'\n Function that calculates the quantiles from the predicted response distribution.\n\n quantiles: list\n Which quantiles to calculate\n pred_params: pd.DataFrame\n Dataframe with predicted distributional parameters.\n\n Returns\n -------\n pd.DataFrame with calculated quantiles.\n\n '
pred_quantiles_list = []
for i in range(len(quantiles)):
pred_quantiles_list.append(Gamma.qGamma(p=quantiles[i], location=pred_params['location'], scale=pred_params['scale']))
pred_quantiles = pd.DataFrame(pred_quantiles_list).T
return pred_quantiles<|docstring|>Function that calculates the quantiles from the predicted response distribution.
quantiles: list
Which quantiles to calculate
pred_params: pd.DataFrame
Dataframe with predicted distributional parameters.
Returns
-------
pd.DataFrame with calculated quantiles.<|endoftext|> |
f52be5c1634e3920647a550127b19b14c7485916178e42ed9df0a211b7322791 | @property
def last_block(self):
'\n The last block in the chain, ie. the most recent block added\n '
return self.chain[(- 1)] | The last block in the chain, ie. the most recent block added | WebBlockChain/Blockchain.py | last_block | Johnson-Su/Certi-Chain | 1 | python | @property
def last_block(self):
'\n \n '
return self.chain[(- 1)] | @property
def last_block(self):
'\n \n '
return self.chain[(- 1)]<|docstring|>The last block in the chain, ie. the most recent block added<|endoftext|> |
71c2c942b1327a3d90c12ab2932092f432bca10dd191fd8b7ece84de0fe91b9c | @staticmethod
def proof_of_work(block):
"\n A proof of work is the process of adding a constraint to a block's\n hash. By adding the constraint, it makes it difficult for a valid \n hash to be computed.\n "
block.nonce = 0
computed_hash = block.compute_hash()
while (not computed_hash.startswith(('0' * Blockchain.DIFFICULTY))):
block.nonce += 1
computed_hash = block.compute_hash()
return computed_hash | A proof of work is the process of adding a constraint to a block's
hash. By adding the constraint, it makes it difficult for a valid
hash to be computed. | WebBlockChain/Blockchain.py | proof_of_work | Johnson-Su/Certi-Chain | 1 | python | @staticmethod
def proof_of_work(block):
"\n A proof of work is the process of adding a constraint to a block's\n hash. By adding the constraint, it makes it difficult for a valid \n hash to be computed.\n "
block.nonce = 0
computed_hash = block.compute_hash()
while (not computed_hash.startswith(('0' * Blockchain.DIFFICULTY))):
block.nonce += 1
computed_hash = block.compute_hash()
return computed_hash | @staticmethod
def proof_of_work(block):
"\n A proof of work is the process of adding a constraint to a block's\n hash. By adding the constraint, it makes it difficult for a valid \n hash to be computed.\n "
block.nonce = 0
computed_hash = block.compute_hash()
while (not computed_hash.startswith(('0' * Blockchain.DIFFICULTY))):
block.nonce += 1
computed_hash = block.compute_hash()
return computed_hash<|docstring|>A proof of work is the process of adding a constraint to a block's
hash. By adding the constraint, it makes it difficult for a valid
hash to be computed.<|endoftext|> |
7ab171b527f7480224cfa701c9ec3d23ce7e9d235807a4f8711cf9de8dd47992 | def add_block(self, block, proof):
'\n To add a block into the blockchain, we must determine if the block \n to be added is in the correct chronological order (no adding \n transactions that occured before the last block), \n and we must determine if the data has not been tampered with. \n '
previous_hash = self.last_block.hash
if (previous_hash != block.previous_hash):
return False
if (not Blockchain.is_valid_proof(block, proof)):
return False
block.hash = proof
self.chain.append(block)
return True | To add a block into the blockchain, we must determine if the block
to be added is in the correct chronological order (no adding
transactions that occured before the last block),
and we must determine if the data has not been tampered with. | WebBlockChain/Blockchain.py | add_block | Johnson-Su/Certi-Chain | 1 | python | def add_block(self, block, proof):
'\n To add a block into the blockchain, we must determine if the block \n to be added is in the correct chronological order (no adding \n transactions that occured before the last block), \n and we must determine if the data has not been tampered with. \n '
previous_hash = self.last_block.hash
if (previous_hash != block.previous_hash):
return False
if (not Blockchain.is_valid_proof(block, proof)):
return False
block.hash = proof
self.chain.append(block)
return True | def add_block(self, block, proof):
'\n To add a block into the blockchain, we must determine if the block \n to be added is in the correct chronological order (no adding \n transactions that occured before the last block), \n and we must determine if the data has not been tampered with. \n '
previous_hash = self.last_block.hash
if (previous_hash != block.previous_hash):
return False
if (not Blockchain.is_valid_proof(block, proof)):
return False
block.hash = proof
self.chain.append(block)
return True<|docstring|>To add a block into the blockchain, we must determine if the block
to be added is in the correct chronological order (no adding
transactions that occured before the last block),
and we must determine if the data has not been tampered with.<|endoftext|> |
fa94d78a5084626e352ad3856ce9806ba011ffddc2fd9eeda880c587d76bb6ed | def __init__(self):
'Constructeur du paramètre'
Parametre.__init__(self, 'supprimer', 'del')
self.schema = '<cle>'
self.aide_courte = 'supprime une auberge'
self.aide_longue = 'Cette commande permet simplement de supprimer une flottante. Vous devez préciser sa clé en paramètre.' | Constructeur du paramètre | src/secondaires/auberge/commandes/auberge/supprimer.py | __init__ | stormi/tsunami | 0 | python | def __init__(self):
Parametre.__init__(self, 'supprimer', 'del')
self.schema = '<cle>'
self.aide_courte = 'supprime une auberge'
self.aide_longue = 'Cette commande permet simplement de supprimer une flottante. Vous devez préciser sa clé en paramètre.' | def __init__(self):
Parametre.__init__(self, 'supprimer', 'del')
self.schema = '<cle>'
self.aide_courte = 'supprime une auberge'
self.aide_longue = 'Cette commande permet simplement de supprimer une flottante. Vous devez préciser sa clé en paramètre.'<|docstring|>Constructeur du paramètre<|endoftext|> |
b3182ca6be824eb7d1f5fe2a2723d6e1a02c9cdf253d6d04b47a92ede52afbdd | def interpreter(self, personnage, dic_masques):
'Interprétation du paramètre'
cle = dic_masques['cle'].cle
if (cle not in importeur.auberge.auberges):
(personnage << "|err|Cette auberge n'existe pas.|ff|")
return
importeur.auberge.supprimer_auberge(cle)
(personnage << "L'auberge '{}' a bien été supprimée".format(cle)) | Interprétation du paramètre | src/secondaires/auberge/commandes/auberge/supprimer.py | interpreter | stormi/tsunami | 0 | python | def interpreter(self, personnage, dic_masques):
cle = dic_masques['cle'].cle
if (cle not in importeur.auberge.auberges):
(personnage << "|err|Cette auberge n'existe pas.|ff|")
return
importeur.auberge.supprimer_auberge(cle)
(personnage << "L'auberge '{}' a bien été supprimée".format(cle)) | def interpreter(self, personnage, dic_masques):
cle = dic_masques['cle'].cle
if (cle not in importeur.auberge.auberges):
(personnage << "|err|Cette auberge n'existe pas.|ff|")
return
importeur.auberge.supprimer_auberge(cle)
(personnage << "L'auberge '{}' a bien été supprimée".format(cle))<|docstring|>Interprétation du paramètre<|endoftext|> |
c5401225c52fb344f8f13e08a132a94bea0967ed3c07c29823269596abe46f5a | def get_teachers_info(language_id, api_url='https://api.italki.com/api/v2/teachers', to_crawl=50):
'\n Queries the API for teachers\n :return: JSON data containing conversations\n '
try:
if (to_crawl <= 100):
print('Querying API for {} in page'.format(language_id))
payload = ('{"teach_language":{"language":"%s"},"page_size":%s,"user_timezone":"Europe/Madrid"}' % (language_id, str(to_crawl)))
headers = {'Content-Type': 'application/json'}
response = requests.request('POST', api_url, headers=headers, data=payload).json()
return response['data']
else:
pages_number = ceil((to_crawl / 20))
crawled = 0
data = []
for page_number in range(1, (pages_number + 1)):
remaining = (to_crawl - crawled)
if (remaining > 20):
page_size = 20
else:
page_size = remaining
print('Querying API for {} in page {}'.format(language_id, page_number))
payload = ('{"teach_language":{"language":"%s"},"page":%s,"page_size":%s,"user_timezone":"Europe/Madrid"}' % (language_id, str(page_number), str(page_size)))
headers = {'Content-Type': 'application/json'}
response = requests.request('POST', api_url, headers=headers, data=payload).json()
data.append(response['data'])
crawled += page_size
time.sleep(uniform(0, 2))
return data
except Exception as e:
print('Error querying {}'.format(language_id))
print(e)
return None | Queries the API for teachers
:return: JSON data containing conversations | data_acquisition/crawlers/italki.py | get_teachers_info | javirandor/online-tutoring-analysis | 0 | python | def get_teachers_info(language_id, api_url='https://api.italki.com/api/v2/teachers', to_crawl=50):
'\n Queries the API for teachers\n :return: JSON data containing conversations\n '
try:
if (to_crawl <= 100):
print('Querying API for {} in page'.format(language_id))
payload = ('{"teach_language":{"language":"%s"},"page_size":%s,"user_timezone":"Europe/Madrid"}' % (language_id, str(to_crawl)))
headers = {'Content-Type': 'application/json'}
response = requests.request('POST', api_url, headers=headers, data=payload).json()
return response['data']
else:
pages_number = ceil((to_crawl / 20))
crawled = 0
data = []
for page_number in range(1, (pages_number + 1)):
remaining = (to_crawl - crawled)
if (remaining > 20):
page_size = 20
else:
page_size = remaining
print('Querying API for {} in page {}'.format(language_id, page_number))
payload = ('{"teach_language":{"language":"%s"},"page":%s,"page_size":%s,"user_timezone":"Europe/Madrid"}' % (language_id, str(page_number), str(page_size)))
headers = {'Content-Type': 'application/json'}
response = requests.request('POST', api_url, headers=headers, data=payload).json()
data.append(response['data'])
crawled += page_size
time.sleep(uniform(0, 2))
return data
except Exception as e:
print('Error querying {}'.format(language_id))
print(e)
return None | def get_teachers_info(language_id, api_url='https://api.italki.com/api/v2/teachers', to_crawl=50):
'\n Queries the API for teachers\n :return: JSON data containing conversations\n '
try:
if (to_crawl <= 100):
print('Querying API for {} in page'.format(language_id))
payload = ('{"teach_language":{"language":"%s"},"page_size":%s,"user_timezone":"Europe/Madrid"}' % (language_id, str(to_crawl)))
headers = {'Content-Type': 'application/json'}
response = requests.request('POST', api_url, headers=headers, data=payload).json()
return response['data']
else:
pages_number = ceil((to_crawl / 20))
crawled = 0
data = []
for page_number in range(1, (pages_number + 1)):
remaining = (to_crawl - crawled)
if (remaining > 20):
page_size = 20
else:
page_size = remaining
print('Querying API for {} in page {}'.format(language_id, page_number))
payload = ('{"teach_language":{"language":"%s"},"page":%s,"page_size":%s,"user_timezone":"Europe/Madrid"}' % (language_id, str(page_number), str(page_size)))
headers = {'Content-Type': 'application/json'}
response = requests.request('POST', api_url, headers=headers, data=payload).json()
data.append(response['data'])
crawled += page_size
time.sleep(uniform(0, 2))
return data
except Exception as e:
print('Error querying {}'.format(language_id))
print(e)
return None<|docstring|>Queries the API for teachers
:return: JSON data containing conversations<|endoftext|> |
e1628ca4039ff4292c0f035d6ccd0d36036930e03d837a97abbfc39e89a3f77f | def xyz_file_to_atoms(filename):
'/\n From an .xyz file get a list of atoms\n\n :param filename: (str) .xyz filename\n :return: (list(Atom))\n '
logger.info(f'Getting atoms from {filename}')
atoms = []
if (not filename.endswith('.xyz')):
raise FileMalformatted
with open(filename, 'r') as xyz_file:
try:
n_atoms = int(xyz_file.readline().split()[0])
except IndexError:
raise FileMalformatted
xyz_lines = xyz_file.readlines()[1:(n_atoms + 1)]
for line in xyz_lines:
try:
(atom_label, x, y, z) = line.split()[:4]
atoms.append(Atom(atomic_symbol=atom_label, x=x, y=y, z=z))
except (IndexError, TypeError, ValueError):
raise FileMalformatted
return atoms | /
From an .xyz file get a list of atoms
:param filename: (str) .xyz filename
:return: (list(Atom)) | cgbind/input_output.py | xyz_file_to_atoms | duartegroup/cgbind | 7 | python | def xyz_file_to_atoms(filename):
'/\n From an .xyz file get a list of atoms\n\n :param filename: (str) .xyz filename\n :return: (list(Atom))\n '
logger.info(f'Getting atoms from {filename}')
atoms = []
if (not filename.endswith('.xyz')):
raise FileMalformatted
with open(filename, 'r') as xyz_file:
try:
n_atoms = int(xyz_file.readline().split()[0])
except IndexError:
raise FileMalformatted
xyz_lines = xyz_file.readlines()[1:(n_atoms + 1)]
for line in xyz_lines:
try:
(atom_label, x, y, z) = line.split()[:4]
atoms.append(Atom(atomic_symbol=atom_label, x=x, y=y, z=z))
except (IndexError, TypeError, ValueError):
raise FileMalformatted
return atoms | def xyz_file_to_atoms(filename):
'/\n From an .xyz file get a list of atoms\n\n :param filename: (str) .xyz filename\n :return: (list(Atom))\n '
logger.info(f'Getting atoms from {filename}')
atoms = []
if (not filename.endswith('.xyz')):
raise FileMalformatted
with open(filename, 'r') as xyz_file:
try:
n_atoms = int(xyz_file.readline().split()[0])
except IndexError:
raise FileMalformatted
xyz_lines = xyz_file.readlines()[1:(n_atoms + 1)]
for line in xyz_lines:
try:
(atom_label, x, y, z) = line.split()[:4]
atoms.append(Atom(atomic_symbol=atom_label, x=x, y=y, z=z))
except (IndexError, TypeError, ValueError):
raise FileMalformatted
return atoms<|docstring|>/
From an .xyz file get a list of atoms
:param filename: (str) .xyz filename
:return: (list(Atom))<|endoftext|> |
ee47cb250954cc4ac1c7724be750620ee60092300388ba552cb8340f8473455d | def atoms_to_xyz_file(atoms, filename, title_line=''):
'\n Print a standard .xyz file from a set of atoms\n\n :param atoms: (list(Atom))\n :param filename: (str)\n :param title_line: (str)\n '
with open(filename, 'w') as xyz_file:
print(len(atoms), title_line, sep='\n', file=xyz_file)
for atom in atoms:
(x, y, z) = atom.coord
print(f'{atom.label:<3} {x:^10.5f} {y:^10.5f} {z:^10.5f}', file=xyz_file)
return None | Print a standard .xyz file from a set of atoms
:param atoms: (list(Atom))
:param filename: (str)
:param title_line: (str) | cgbind/input_output.py | atoms_to_xyz_file | duartegroup/cgbind | 7 | python | def atoms_to_xyz_file(atoms, filename, title_line=):
'\n Print a standard .xyz file from a set of atoms\n\n :param atoms: (list(Atom))\n :param filename: (str)\n :param title_line: (str)\n '
with open(filename, 'w') as xyz_file:
print(len(atoms), title_line, sep='\n', file=xyz_file)
for atom in atoms:
(x, y, z) = atom.coord
print(f'{atom.label:<3} {x:^10.5f} {y:^10.5f} {z:^10.5f}', file=xyz_file)
return None | def atoms_to_xyz_file(atoms, filename, title_line=):
'\n Print a standard .xyz file from a set of atoms\n\n :param atoms: (list(Atom))\n :param filename: (str)\n :param title_line: (str)\n '
with open(filename, 'w') as xyz_file:
print(len(atoms), title_line, sep='\n', file=xyz_file)
for atom in atoms:
(x, y, z) = atom.coord
print(f'{atom.label:<3} {x:^10.5f} {y:^10.5f} {z:^10.5f}', file=xyz_file)
return None<|docstring|>Print a standard .xyz file from a set of atoms
:param atoms: (list(Atom))
:param filename: (str)
:param title_line: (str)<|endoftext|> |
7f3006436bb543940c9b2750ad5275a6cbfc496daf9ca7c908266c448606397f | def get_atoms_from_file(filename):
'Get a list of atoms from a structure file'
if filename.endswith('.xyz'):
return xyzfile_to_atoms(filename)
elif filename.endswith('.mol'):
return molfile_to_atoms(filename)
elif filename.endswith('.mol2'):
return mol2file_to_atoms(filename)
else:
raise CgbindCritical('Unsupported file format. Supported formats are{.xyz, .mol, .mol2}') | Get a list of atoms from a structure file | cgbind/input_output.py | get_atoms_from_file | duartegroup/cgbind | 7 | python | def get_atoms_from_file(filename):
if filename.endswith('.xyz'):
return xyzfile_to_atoms(filename)
elif filename.endswith('.mol'):
return molfile_to_atoms(filename)
elif filename.endswith('.mol2'):
return mol2file_to_atoms(filename)
else:
raise CgbindCritical('Unsupported file format. Supported formats are{.xyz, .mol, .mol2}') | def get_atoms_from_file(filename):
if filename.endswith('.xyz'):
return xyzfile_to_atoms(filename)
elif filename.endswith('.mol'):
return molfile_to_atoms(filename)
elif filename.endswith('.mol2'):
return mol2file_to_atoms(filename)
else:
raise CgbindCritical('Unsupported file format. Supported formats are{.xyz, .mol, .mol2}')<|docstring|>Get a list of atoms from a structure file<|endoftext|> |
87eb87f88768b6c1adf34bbc9d1ecac2797cd6b6575671cccc3b5682aa67bf9c | def xyzfile_to_atoms(filename):
'\n Convert a standard xyz file into a list of atoms\n\n :param filename: (str)\n :return: (list(cgbind.atoms.Atom))\n '
logger.info(f'Converting {filename} to list of atoms')
if (not filename.endswith('.xyz')):
logger.error('Could not read .xyz file')
raise FileMalformatted
atoms = []
with open(filename, 'r') as xyz_file:
xyz_lines = xyz_file.readlines()[2:]
for line in xyz_lines:
(atom_label, x, y, z) = line.split()
atoms.append(Atom(atom_label, float(x), float(y), float(z)))
if (len(atoms) == 0):
logger.error(f'Could not read xyz lines in {filename}')
raise FileMalformatted
return atoms | Convert a standard xyz file into a list of atoms
:param filename: (str)
:return: (list(cgbind.atoms.Atom)) | cgbind/input_output.py | xyzfile_to_atoms | duartegroup/cgbind | 7 | python | def xyzfile_to_atoms(filename):
'\n Convert a standard xyz file into a list of atoms\n\n :param filename: (str)\n :return: (list(cgbind.atoms.Atom))\n '
logger.info(f'Converting {filename} to list of atoms')
if (not filename.endswith('.xyz')):
logger.error('Could not read .xyz file')
raise FileMalformatted
atoms = []
with open(filename, 'r') as xyz_file:
xyz_lines = xyz_file.readlines()[2:]
for line in xyz_lines:
(atom_label, x, y, z) = line.split()
atoms.append(Atom(atom_label, float(x), float(y), float(z)))
if (len(atoms) == 0):
logger.error(f'Could not read xyz lines in {filename}')
raise FileMalformatted
return atoms | def xyzfile_to_atoms(filename):
'\n Convert a standard xyz file into a list of atoms\n\n :param filename: (str)\n :return: (list(cgbind.atoms.Atom))\n '
logger.info(f'Converting {filename} to list of atoms')
if (not filename.endswith('.xyz')):
logger.error('Could not read .xyz file')
raise FileMalformatted
atoms = []
with open(filename, 'r') as xyz_file:
xyz_lines = xyz_file.readlines()[2:]
for line in xyz_lines:
(atom_label, x, y, z) = line.split()
atoms.append(Atom(atom_label, float(x), float(y), float(z)))
if (len(atoms) == 0):
logger.error(f'Could not read xyz lines in {filename}')
raise FileMalformatted
return atoms<|docstring|>Convert a standard xyz file into a list of atoms
:param filename: (str)
:return: (list(cgbind.atoms.Atom))<|endoftext|> |
c61bc1c2be4ff874aed4c8339fba335d2d1d3f8264dd8a6541718d80853dd4ab | def molfile_to_atoms(filename):
'\n Convert a .mol file to a list of atoms\n\n :param filename: (str)\n :return: (list(Atom))\n '
'\n e.g. for methane:\n _____________________\n\n OpenBabel03272015013D\n\n 5 4 0 0 0 0 0 0 0 0999 V2000\n -0.2783 0.0756 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\n 0.7917 0.0756 0.0000 H 0 0 0 0 0 0 0 0 0 0 0 0\n -0.6349 -0.9294 -0.0876 H 0 0 0 0 0 0 0 0 0 0 0 0\n -0.6349 0.6539 -0.8266 H 0 0 0 0 0 0 0 0 0 0 0 0\n -0.6349 0.5022 0.9141 H 0 0 0 0 0 0 0 0 0 0 0 0\n 1 2 1 0 0 0 0\n 1 3 1 0 0 0 0\n 1 4 1 0 0 0 0\n 1 5 1 0 0 0 0\n M END\n _____________________\n '
atoms = []
if (not filename.endswith('.mol')):
logger.error('Could not read .mol file')
raise FileMalformatted
with open(filename, 'r') as mol_file:
mol_lines = mol_file.readlines()[3:]
try:
n_atoms = int(mol_lines[0].split()[0])
except ValueError:
raise FileMalformatted
for line in mol_lines[1:(n_atoms + 1)]:
(x, y, z, atom_label) = line.split()[:4]
atoms.append(Atom(atom_label, float(x), float(y), float(z)))
if (len(atoms) == 0):
logger.error(f'Could not read xyz lines in {filename}')
raise FileMalformatted
return atoms | Convert a .mol file to a list of atoms
:param filename: (str)
:return: (list(Atom)) | cgbind/input_output.py | molfile_to_atoms | duartegroup/cgbind | 7 | python | def molfile_to_atoms(filename):
'\n Convert a .mol file to a list of atoms\n\n :param filename: (str)\n :return: (list(Atom))\n '
'\n e.g. for methane:\n _____________________\n\n OpenBabel03272015013D\n\n 5 4 0 0 0 0 0 0 0 0999 V2000\n -0.2783 0.0756 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\n 0.7917 0.0756 0.0000 H 0 0 0 0 0 0 0 0 0 0 0 0\n -0.6349 -0.9294 -0.0876 H 0 0 0 0 0 0 0 0 0 0 0 0\n -0.6349 0.6539 -0.8266 H 0 0 0 0 0 0 0 0 0 0 0 0\n -0.6349 0.5022 0.9141 H 0 0 0 0 0 0 0 0 0 0 0 0\n 1 2 1 0 0 0 0\n 1 3 1 0 0 0 0\n 1 4 1 0 0 0 0\n 1 5 1 0 0 0 0\n M END\n _____________________\n '
atoms = []
if (not filename.endswith('.mol')):
logger.error('Could not read .mol file')
raise FileMalformatted
with open(filename, 'r') as mol_file:
mol_lines = mol_file.readlines()[3:]
try:
n_atoms = int(mol_lines[0].split()[0])
except ValueError:
raise FileMalformatted
for line in mol_lines[1:(n_atoms + 1)]:
(x, y, z, atom_label) = line.split()[:4]
atoms.append(Atom(atom_label, float(x), float(y), float(z)))
if (len(atoms) == 0):
logger.error(f'Could not read xyz lines in {filename}')
raise FileMalformatted
return atoms | def molfile_to_atoms(filename):
'\n Convert a .mol file to a list of atoms\n\n :param filename: (str)\n :return: (list(Atom))\n '
'\n e.g. for methane:\n _____________________\n\n OpenBabel03272015013D\n\n 5 4 0 0 0 0 0 0 0 0999 V2000\n -0.2783 0.0756 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\n 0.7917 0.0756 0.0000 H 0 0 0 0 0 0 0 0 0 0 0 0\n -0.6349 -0.9294 -0.0876 H 0 0 0 0 0 0 0 0 0 0 0 0\n -0.6349 0.6539 -0.8266 H 0 0 0 0 0 0 0 0 0 0 0 0\n -0.6349 0.5022 0.9141 H 0 0 0 0 0 0 0 0 0 0 0 0\n 1 2 1 0 0 0 0\n 1 3 1 0 0 0 0\n 1 4 1 0 0 0 0\n 1 5 1 0 0 0 0\n M END\n _____________________\n '
atoms = []
if (not filename.endswith('.mol')):
logger.error('Could not read .mol file')
raise FileMalformatted
with open(filename, 'r') as mol_file:
mol_lines = mol_file.readlines()[3:]
try:
n_atoms = int(mol_lines[0].split()[0])
except ValueError:
raise FileMalformatted
for line in mol_lines[1:(n_atoms + 1)]:
(x, y, z, atom_label) = line.split()[:4]
atoms.append(Atom(atom_label, float(x), float(y), float(z)))
if (len(atoms) == 0):
logger.error(f'Could not read xyz lines in {filename}')
raise FileMalformatted
return atoms<|docstring|>Convert a .mol file to a list of atoms
:param filename: (str)
:return: (list(Atom))<|endoftext|> |
aa5502d875ff8ffdf1be66cc06d04235c9fdb488cab6525d07e99394ae6f94bf | def mol2file_to_atoms(filename):
'\n Convert a .mol file into a standard set of atoms\n\n :param filename: (str)\n :return: (lis(Atom))\n '
logger.info('Converting .mol2 file to atoms')
if (not filename.endswith('.mol2')):
logger.error('Could not read .mol2 file')
raise FileMalformatted
mol_file_lines = open(filename, 'r').readlines()
(atoms, xyz_block) = ([], False)
for (n_line, line) in enumerate(mol_file_lines):
if (('@' in line) and xyz_block):
break
if xyz_block:
try:
(atom_label, x, y, z) = line.split()[1:5]
try:
atoms.append(Atom(atom_label, float(x), float(y), float(z)))
except TypeError:
logger.error('There was a problem with the .mol2 file')
raise FileMalformatted
except IndexError:
logger.error('There was a problem with the .mol2 file')
raise FileMalformatted
if (('@' in line) and ('ATOM' in line) and (len(mol_file_lines[(n_line + 1)].split()) == 9)):
xyz_block = True
for atom in atoms:
if (len(atom.label) == 1):
continue
elif (atom.label[0].isalpha() and (not atom.label[1].isalpha())):
atom.label = atom.label[0]
elif (atom.label[0].isalpha() and atom.label[1].isalpha()):
atom.label = atom.label[:2].title()
else:
logger.error('Unrecognised atom type')
raise FileMalformatted
return atoms | Convert a .mol file into a standard set of atoms
:param filename: (str)
:return: (lis(Atom)) | cgbind/input_output.py | mol2file_to_atoms | duartegroup/cgbind | 7 | python | def mol2file_to_atoms(filename):
'\n Convert a .mol file into a standard set of atoms\n\n :param filename: (str)\n :return: (lis(Atom))\n '
logger.info('Converting .mol2 file to atoms')
if (not filename.endswith('.mol2')):
logger.error('Could not read .mol2 file')
raise FileMalformatted
mol_file_lines = open(filename, 'r').readlines()
(atoms, xyz_block) = ([], False)
for (n_line, line) in enumerate(mol_file_lines):
if (('@' in line) and xyz_block):
break
if xyz_block:
try:
(atom_label, x, y, z) = line.split()[1:5]
try:
atoms.append(Atom(atom_label, float(x), float(y), float(z)))
except TypeError:
logger.error('There was a problem with the .mol2 file')
raise FileMalformatted
except IndexError:
logger.error('There was a problem with the .mol2 file')
raise FileMalformatted
if (('@' in line) and ('ATOM' in line) and (len(mol_file_lines[(n_line + 1)].split()) == 9)):
xyz_block = True
for atom in atoms:
if (len(atom.label) == 1):
continue
elif (atom.label[0].isalpha() and (not atom.label[1].isalpha())):
atom.label = atom.label[0]
elif (atom.label[0].isalpha() and atom.label[1].isalpha()):
atom.label = atom.label[:2].title()
else:
logger.error('Unrecognised atom type')
raise FileMalformatted
return atoms | def mol2file_to_atoms(filename):
'\n Convert a .mol file into a standard set of atoms\n\n :param filename: (str)\n :return: (lis(Atom))\n '
logger.info('Converting .mol2 file to atoms')
if (not filename.endswith('.mol2')):
logger.error('Could not read .mol2 file')
raise FileMalformatted
mol_file_lines = open(filename, 'r').readlines()
(atoms, xyz_block) = ([], False)
for (n_line, line) in enumerate(mol_file_lines):
if (('@' in line) and xyz_block):
break
if xyz_block:
try:
(atom_label, x, y, z) = line.split()[1:5]
try:
atoms.append(Atom(atom_label, float(x), float(y), float(z)))
except TypeError:
logger.error('There was a problem with the .mol2 file')
raise FileMalformatted
except IndexError:
logger.error('There was a problem with the .mol2 file')
raise FileMalformatted
if (('@' in line) and ('ATOM' in line) and (len(mol_file_lines[(n_line + 1)].split()) == 9)):
xyz_block = True
for atom in atoms:
if (len(atom.label) == 1):
continue
elif (atom.label[0].isalpha() and (not atom.label[1].isalpha())):
atom.label = atom.label[0]
elif (atom.label[0].isalpha() and atom.label[1].isalpha()):
atom.label = atom.label[:2].title()
else:
logger.error('Unrecognised atom type')
raise FileMalformatted
return atoms<|docstring|>Convert a .mol file into a standard set of atoms
:param filename: (str)
:return: (lis(Atom))<|endoftext|> |
fffe29c376880d80eda1da6d22e2332fad38d28c25ee0fb87ddfde1541fe195b | def create_builder_by_name(builder_name, *, builder_dict, build_context):
'Instantiate a new builder with the given builder_name.'
builders = {'doxygen': DoxygenBuilder, 'sphinx': SphinxBuilder}
builder_class = builders.get(builder_name, None)
if (builder_class is None):
builder_names = ', '.join(list(builders.keys()))
raise RuntimeError(f"Error unknown builder '{builder_name}', supported builders: [{builder_names}]")
return builder_class(builder_name, builder_dict, build_context) | Instantiate a new builder with the given builder_name. | rosdoc2/verbs/build/builders/__init__.py | create_builder_by_name | rkent/rosdoc2 | 3 | python | def create_builder_by_name(builder_name, *, builder_dict, build_context):
builders = {'doxygen': DoxygenBuilder, 'sphinx': SphinxBuilder}
builder_class = builders.get(builder_name, None)
if (builder_class is None):
builder_names = ', '.join(list(builders.keys()))
raise RuntimeError(f"Error unknown builder '{builder_name}', supported builders: [{builder_names}]")
return builder_class(builder_name, builder_dict, build_context) | def create_builder_by_name(builder_name, *, builder_dict, build_context):
builders = {'doxygen': DoxygenBuilder, 'sphinx': SphinxBuilder}
builder_class = builders.get(builder_name, None)
if (builder_class is None):
builder_names = ', '.join(list(builders.keys()))
raise RuntimeError(f"Error unknown builder '{builder_name}', supported builders: [{builder_names}]")
return builder_class(builder_name, builder_dict, build_context)<|docstring|>Instantiate a new builder with the given builder_name.<|endoftext|> |
b55d9d12bbfca214734bf1555a677febd0800b96fd11f9ffebb781592cca4e1b | def fetch_available_indexes() -> IndexList:
'Fetches the available Common Crawl Indexes to search.\n\n Returns:\n A list containing available indexes and information about them.\n\n '
index_list = requests.get('https://index.commoncrawl.org/collinfo.json').json()
indexes = [index['id'].replace('CC-MAIN-', '') for index in index_list]
return indexes | Fetches the available Common Crawl Indexes to search.
Returns:
A list containing available indexes and information about them. | comcrawl/utils/initialization.py | fetch_available_indexes | akozlo/comcrawl | 118 | python | def fetch_available_indexes() -> IndexList:
'Fetches the available Common Crawl Indexes to search.\n\n Returns:\n A list containing available indexes and information about them.\n\n '
index_list = requests.get('https://index.commoncrawl.org/collinfo.json').json()
indexes = [index['id'].replace('CC-MAIN-', ) for index in index_list]
return indexes | def fetch_available_indexes() -> IndexList:
'Fetches the available Common Crawl Indexes to search.\n\n Returns:\n A list containing available indexes and information about them.\n\n '
index_list = requests.get('https://index.commoncrawl.org/collinfo.json').json()
indexes = [index['id'].replace('CC-MAIN-', ) for index in index_list]
return indexes<|docstring|>Fetches the available Common Crawl Indexes to search.
Returns:
A list containing available indexes and information about them.<|endoftext|> |
366315aefa5022b700c9daa1ba0da7ac01cc375840a5ca91c312d85692ba09cc | def get_conf_value(name, valid_types=None, default=None):
'Get ``name`` from ``configuration.py``\n\n Returns ``default`` if not present or not one of types in ``valid_types``\n '
spec = importlib.util.spec_from_file_location('*', conf_path)
configuration = importlib.util.module_from_spec(spec)
spec.loader.exec_module(configuration)
if hasattr(configuration, name):
value = getattr(configuration, name)
if ((valid_types is None) or isinstance(value, valid_types)):
return value
else:
return default
else:
return default | Get ``name`` from ``configuration.py``
Returns ``default`` if not present or not one of types in ``valid_types`` | editor/utils.py | get_conf_value | CrazyIvan359/eos | 0 | python | def get_conf_value(name, valid_types=None, default=None):
'Get ``name`` from ``configuration.py``\n\n Returns ``default`` if not present or not one of types in ``valid_types``\n '
spec = importlib.util.spec_from_file_location('*', conf_path)
configuration = importlib.util.module_from_spec(spec)
spec.loader.exec_module(configuration)
if hasattr(configuration, name):
value = getattr(configuration, name)
if ((valid_types is None) or isinstance(value, valid_types)):
return value
else:
return default
else:
return default | def get_conf_value(name, valid_types=None, default=None):
'Get ``name`` from ``configuration.py``\n\n Returns ``default`` if not present or not one of types in ``valid_types``\n '
spec = importlib.util.spec_from_file_location('*', conf_path)
configuration = importlib.util.module_from_spec(spec)
spec.loader.exec_module(configuration)
if hasattr(configuration, name):
value = getattr(configuration, name)
if ((valid_types is None) or isinstance(value, valid_types)):
return value
else:
return default
else:
return default<|docstring|>Get ``name`` from ``configuration.py``
Returns ``default`` if not present or not one of types in ``valid_types``<|endoftext|> |
89b2d329451ef8ef9fc670f5bc131b6be67e48da514b4829fe5ddc4888acd0ad | def validate_item_name(name, prefix, suffix):
'Verifies that ``name`` starts with ``prefix`` and ends with ``suffix``.\n Returns ``True`` or ``False``'
return ((name[:len(prefix)] == prefix) and (name[(- len(suffix)):] == suffix)) | Verifies that ``name`` starts with ``prefix`` and ends with ``suffix``.
Returns ``True`` or ``False`` | editor/utils.py | validate_item_name | CrazyIvan359/eos | 0 | python | def validate_item_name(name, prefix, suffix):
'Verifies that ``name`` starts with ``prefix`` and ends with ``suffix``.\n Returns ``True`` or ``False``'
return ((name[:len(prefix)] == prefix) and (name[(- len(suffix)):] == suffix)) | def validate_item_name(name, prefix, suffix):
'Verifies that ``name`` starts with ``prefix`` and ends with ``suffix``.\n Returns ``True`` or ``False``'
return ((name[:len(prefix)] == prefix) and (name[(- len(suffix)):] == suffix))<|docstring|>Verifies that ``name`` starts with ``prefix`` and ends with ``suffix``.
Returns ``True`` or ``False``<|endoftext|> |
637d7f9a0bfe4314750f7b951f36a9b9c7b3fd69985d68da5b8738add30f81cf | def get_scene_item(group):
'Finds the scene item in a group.\n\n Returns the scene item or ``None`` if it does not find exactly one match.\n '
items = [item for item in group.get('members', {}) if validate_item_name(item['name'], get_conf_value(CONF_KEY_SCENE_PREFIX, default=''), get_conf_value(CONF_KEY_SCENE_SUFFIX, default=''))]
if (not items):
return None
elif (len(items) > 1):
return None
elif (items[0]['type'] not in itemtypesScene):
return None
else:
return items[0] | Finds the scene item in a group.
Returns the scene item or ``None`` if it does not find exactly one match. | editor/utils.py | get_scene_item | CrazyIvan359/eos | 0 | python | def get_scene_item(group):
'Finds the scene item in a group.\n\n Returns the scene item or ``None`` if it does not find exactly one match.\n '
items = [item for item in group.get('members', {}) if validate_item_name(item['name'], get_conf_value(CONF_KEY_SCENE_PREFIX, default=), get_conf_value(CONF_KEY_SCENE_SUFFIX, default=))]
if (not items):
return None
elif (len(items) > 1):
return None
elif (items[0]['type'] not in itemtypesScene):
return None
else:
return items[0] | def get_scene_item(group):
'Finds the scene item in a group.\n\n Returns the scene item or ``None`` if it does not find exactly one match.\n '
items = [item for item in group.get('members', {}) if validate_item_name(item['name'], get_conf_value(CONF_KEY_SCENE_PREFIX, default=), get_conf_value(CONF_KEY_SCENE_SUFFIX, default=))]
if (not items):
return None
elif (len(items) > 1):
return None
elif (items[0]['type'] not in itemtypesScene):
return None
else:
return items[0]<|docstring|>Finds the scene item in a group.
Returns the scene item or ``None`` if it does not find exactly one match.<|endoftext|> |
51b8f3f3b474bc985239e8a816cf003ec9ac34409067371c9fe5aab75cbbf7c1 | def get_light_items(group, host):
'Finds all light items in a group.\n\n Returns a list of valid Eos lights.\n '
return ([item for item in group['members'] if ((item['type'] not in itemtypesGroup) and (item['type'] in itemtypesLight) and (item['name'] != get_scene_item(group)['name']) and (resolve_type(get_value(item['name'], META_NAME_EOS, host)) is not None))] if ('members' in group) else []) | Finds all light items in a group.
Returns a list of valid Eos lights. | editor/utils.py | get_light_items | CrazyIvan359/eos | 0 | python | def get_light_items(group, host):
'Finds all light items in a group.\n\n Returns a list of valid Eos lights.\n '
return ([item for item in group['members'] if ((item['type'] not in itemtypesGroup) and (item['type'] in itemtypesLight) and (item['name'] != get_scene_item(group)['name']) and (resolve_type(get_value(item['name'], META_NAME_EOS, host)) is not None))] if ('members' in group) else []) | def get_light_items(group, host):
'Finds all light items in a group.\n\n Returns a list of valid Eos lights.\n '
return ([item for item in group['members'] if ((item['type'] not in itemtypesGroup) and (item['type'] in itemtypesLight) and (item['name'] != get_scene_item(group)['name']) and (resolve_type(get_value(item['name'], META_NAME_EOS, host)) is not None))] if ('members' in group) else [])<|docstring|>Finds all light items in a group.
Returns a list of valid Eos lights.<|endoftext|> |
25d44bf3abafd06181ee60e76a59c32841dfb7c53ec146d465d1c55d3ca68d07 | def get_group_items(group):
'Finds all group items in a group.\n\n Returns a list of valid Eos groups.\n '
return ([item for item in group['members'] if ((item['type'] in itemtypesGroup) and (get_scene_item(group) is not None))] if ('members' in group) else []) | Finds all group items in a group.
Returns a list of valid Eos groups. | editor/utils.py | get_group_items | CrazyIvan359/eos | 0 | python | def get_group_items(group):
'Finds all group items in a group.\n\n Returns a list of valid Eos groups.\n '
return ([item for item in group['members'] if ((item['type'] in itemtypesGroup) and (get_scene_item(group) is not None))] if ('members' in group) else []) | def get_group_items(group):
'Finds all group items in a group.\n\n Returns a list of valid Eos groups.\n '
return ([item for item in group['members'] if ((item['type'] in itemtypesGroup) and (get_scene_item(group) is not None))] if ('members' in group) else [])<|docstring|>Finds all group items in a group.
Returns a list of valid Eos groups.<|endoftext|> |
fbf6e94fb6698cb895865b5f12955f7c2d6c19e6751b8aa0d232441dded1a7fa | def resolve_type(value):
'Attempts to resolve the type of ``value``.\n\n It will return ``value`` as the python type if possible, otherwise will\n return value as string.\n '
value = str(value).strip()
if (str(value).lower() == 'true'):
return True
elif (str(value).lower() == 'false'):
return False
elif (str(value).lower() == 'none'):
return None
else:
try:
return literal_eval(value)
except ValueError:
pass
except SyntaxError:
pass
try:
return int(value)
except:
pass
try:
return float(value)
except:
pass
return value | Attempts to resolve the type of ``value``.
It will return ``value`` as the python type if possible, otherwise will
return value as string. | editor/utils.py | resolve_type | CrazyIvan359/eos | 0 | python | def resolve_type(value):
'Attempts to resolve the type of ``value``.\n\n It will return ``value`` as the python type if possible, otherwise will\n return value as string.\n '
value = str(value).strip()
if (str(value).lower() == 'true'):
return True
elif (str(value).lower() == 'false'):
return False
elif (str(value).lower() == 'none'):
return None
else:
try:
return literal_eval(value)
except ValueError:
pass
except SyntaxError:
pass
try:
return int(value)
except:
pass
try:
return float(value)
except:
pass
return value | def resolve_type(value):
'Attempts to resolve the type of ``value``.\n\n It will return ``value`` as the python type if possible, otherwise will\n return value as string.\n '
value = str(value).strip()
if (str(value).lower() == 'true'):
return True
elif (str(value).lower() == 'false'):
return False
elif (str(value).lower() == 'none'):
return None
else:
try:
return literal_eval(value)
except ValueError:
pass
except SyntaxError:
pass
try:
return int(value)
except:
pass
try:
return float(value)
except:
pass
return value<|docstring|>Attempts to resolve the type of ``value``.
It will return ``value`` as the python type if possible, otherwise will
return value as string.<|endoftext|> |
bd5e20ca35cdf187909330a2439377573f4fdb175416947d4a64a30468cc206c | def get_item_eos_group(item, host):
"Gets the Eos group from the item's groups.\n\n Returns the group item or ``None`` if it does not find exactly one match.\n "
groups = [group for group in item['groupNames'] if get_scene_item(validate_item(group, host))]
if (not groups):
return None
elif (len(groups) > 1):
return None
else:
return validate_item(groups[0], host) | Gets the Eos group from the item's groups.
Returns the group item or ``None`` if it does not find exactly one match. | editor/utils.py | get_item_eos_group | CrazyIvan359/eos | 0 | python | def get_item_eos_group(item, host):
"Gets the Eos group from the item's groups.\n\n Returns the group item or ``None`` if it does not find exactly one match.\n "
groups = [group for group in item['groupNames'] if get_scene_item(validate_item(group, host))]
if (not groups):
return None
elif (len(groups) > 1):
return None
else:
return validate_item(groups[0], host) | def get_item_eos_group(item, host):
"Gets the Eos group from the item's groups.\n\n Returns the group item or ``None`` if it does not find exactly one match.\n "
groups = [group for group in item['groupNames'] if get_scene_item(validate_item(group, host))]
if (not groups):
return None
elif (len(groups) > 1):
return None
else:
return validate_item(groups[0], host)<|docstring|>Gets the Eos group from the item's groups.
Returns the group item or ``None`` if it does not find exactly one match.<|endoftext|> |
3031aa579d7d44d77640f37652b649e06de3638d5c7152c06c71b5bc70f34cae | def get_other_items(group, host):
'Finds all non Eos items in a group.\n\n Returns a list of all non Eos items in the group.\n '
others = {item['name']: item for item in group['members']}
for item in get_light_items(group, host):
others.pop(item['name'], None)
for item in get_group_items(group):
others.pop(item['name'], None)
for item in [item for item in group['members'] if (item['name'] == get_conf_value(CONF_KEY_REINIT_ITEM, str))]:
others.pop(item['name'], None)
for item in [others[key] for key in others]:
if ((item['type'] not in itemtypesLight) and (item['type'] != itemtypesGroup)):
others.pop(item['name'], None)
others.pop(get_scene_item(group)['name'], None)
return [others[key] for key in others] | Finds all non Eos items in a group.
Returns a list of all non Eos items in the group. | editor/utils.py | get_other_items | CrazyIvan359/eos | 0 | python | def get_other_items(group, host):
'Finds all non Eos items in a group.\n\n Returns a list of all non Eos items in the group.\n '
others = {item['name']: item for item in group['members']}
for item in get_light_items(group, host):
others.pop(item['name'], None)
for item in get_group_items(group):
others.pop(item['name'], None)
for item in [item for item in group['members'] if (item['name'] == get_conf_value(CONF_KEY_REINIT_ITEM, str))]:
others.pop(item['name'], None)
for item in [others[key] for key in others]:
if ((item['type'] not in itemtypesLight) and (item['type'] != itemtypesGroup)):
others.pop(item['name'], None)
others.pop(get_scene_item(group)['name'], None)
return [others[key] for key in others] | def get_other_items(group, host):
'Finds all non Eos items in a group.\n\n Returns a list of all non Eos items in the group.\n '
others = {item['name']: item for item in group['members']}
for item in get_light_items(group, host):
others.pop(item['name'], None)
for item in get_group_items(group):
others.pop(item['name'], None)
for item in [item for item in group['members'] if (item['name'] == get_conf_value(CONF_KEY_REINIT_ITEM, str))]:
others.pop(item['name'], None)
for item in [others[key] for key in others]:
if ((item['type'] not in itemtypesLight) and (item['type'] != itemtypesGroup)):
others.pop(item['name'], None)
others.pop(get_scene_item(group)['name'], None)
return [others[key] for key in others]<|docstring|>Finds all non Eos items in a group.
Returns a list of all non Eos items in the group.<|endoftext|> |
fbdfdfb1003a48a6a094c37987e4ab6c4881c3aef2d27c01b300251369453eb7 | def update_dict(d, u):
'\n Recursively update dict ``d`` with dict ``u``\n '
for k in u:
dv = d.get(k, {})
if (not isinstance(dv, collections.Mapping)):
d[k] = u[k]
elif isinstance(u[k], collections.Mapping):
d[k] = update_dict(dv, u[k])
else:
d[k] = u[k]
return d | Recursively update dict ``d`` with dict ``u`` | editor/utils.py | update_dict | CrazyIvan359/eos | 0 | python | def update_dict(d, u):
'\n \n '
for k in u:
dv = d.get(k, {})
if (not isinstance(dv, collections.Mapping)):
d[k] = u[k]
elif isinstance(u[k], collections.Mapping):
d[k] = update_dict(dv, u[k])
else:
d[k] = u[k]
return d | def update_dict(d, u):
'\n \n '
for k in u:
dv = d.get(k, {})
if (not isinstance(dv, collections.Mapping)):
d[k] = u[k]
elif isinstance(u[k], collections.Mapping):
d[k] = update_dict(dv, u[k])
else:
d[k] = u[k]
return d<|docstring|>Recursively update dict ``d`` with dict ``u``<|endoftext|> |
3a919d944b765b50ebddd97c08750f9f424ec1d46dd2dca0a4c6e3ca9fe9dec9 | def print_box(message: str, min_length: int=100, print_str: bool=True, col=bcolors.OKBLUE) -> str:
'\n Print a string in a neat box.\n\n :param message: Message to be printed\n :param min_length: Minimum length of box (in characters)\n :param print_str: if False, the generated string will not be printed (just returned)\n :return: str to be printed if return_str is True\n '
strlen = len(message)
padding = (' ' * (min_length - strlen))
message += padding
s = [('=' * (max(min_length, strlen) + 4)), f'* {message} *', ('=' * (max(min_length, strlen) + 4))]
if print_str:
for line in s:
printc(line, col=col)
return '\n'.join(s) | Print a string in a neat box.
:param message: Message to be printed
:param min_length: Minimum length of box (in characters)
:param print_str: if False, the generated string will not be printed (just returned)
:return: str to be printed if return_str is True | fsh_validator/fsh_validator.py | print_box | glichtner/fsh-validator | 0 | python | def print_box(message: str, min_length: int=100, print_str: bool=True, col=bcolors.OKBLUE) -> str:
'\n Print a string in a neat box.\n\n :param message: Message to be printed\n :param min_length: Minimum length of box (in characters)\n :param print_str: if False, the generated string will not be printed (just returned)\n :return: str to be printed if return_str is True\n '
strlen = len(message)
padding = (' ' * (min_length - strlen))
message += padding
s = [('=' * (max(min_length, strlen) + 4)), f'* {message} *', ('=' * (max(min_length, strlen) + 4))]
if print_str:
for line in s:
printc(line, col=col)
return '\n'.join(s) | def print_box(message: str, min_length: int=100, print_str: bool=True, col=bcolors.OKBLUE) -> str:
'\n Print a string in a neat box.\n\n :param message: Message to be printed\n :param min_length: Minimum length of box (in characters)\n :param print_str: if False, the generated string will not be printed (just returned)\n :return: str to be printed if return_str is True\n '
strlen = len(message)
padding = (' ' * (min_length - strlen))
message += padding
s = [('=' * (max(min_length, strlen) + 4)), f'* {message} *', ('=' * (max(min_length, strlen) + 4))]
if print_str:
for line in s:
printc(line, col=col)
return '\n'.join(s)<|docstring|>Print a string in a neat box.
:param message: Message to be printed
:param min_length: Minimum length of box (in characters)
:param print_str: if False, the generated string will not be printed (just returned)
:return: str to be printed if return_str is True<|endoftext|> |
6ffb8ec96c1f86db471af8756561a1c76faedd0512ba8abf3b5b438cb467c33f | def download_validator(fname_validator: Path) -> None:
'\n Download FHIR Java validator.\n\n :param fname_validator: Filename where the validator will be downloaded to\n :return: None\n '
urllib.request.urlretrieve(VALIDATOR_URL, fname_validator) | Download FHIR Java validator.
:param fname_validator: Filename where the validator will be downloaded to
:return: None | fsh_validator/fsh_validator.py | download_validator | glichtner/fsh-validator | 0 | python | def download_validator(fname_validator: Path) -> None:
'\n Download FHIR Java validator.\n\n :param fname_validator: Filename where the validator will be downloaded to\n :return: None\n '
urllib.request.urlretrieve(VALIDATOR_URL, fname_validator) | def download_validator(fname_validator: Path) -> None:
'\n Download FHIR Java validator.\n\n :param fname_validator: Filename where the validator will be downloaded to\n :return: None\n '
urllib.request.urlretrieve(VALIDATOR_URL, fname_validator)<|docstring|>Download FHIR Java validator.
:param fname_validator: Filename where the validator will be downloaded to
:return: None<|endoftext|> |
a9d6e074be75a107b3e1a5514cee52fc2e58692315755efbe947ccf708bc5f01 | def parse_fsh(fname_fsh: Path) -> Tuple[(List[Dict], List[Dict])]:
'\n Parse FSH file to extract profiles and instances that are defined in it.\n\n :param fname_fsh: Filename of the FSH file to parse\n :return: List of defined profiles, List of defined instances\n '
with open(fname_fsh, 'r') as f:
content = f.read()
re_group_capture = '[a-zA-Z0-9_\\-\\$]+'
pattern = re.compile(f'Profile: (?P<profile>{re_group_capture})[^\n]*\nParent: (?P<parent>{re_group_capture})[^\n]*\nId: (?P<id>{re_group_capture})', re.MULTILINE)
fsh_profiles = [m.groupdict() for m in pattern.finditer(content)]
pattern = re.compile(f'Instance: (?P<instance>{re_group_capture})[^\n]*\nInstanceOf: (?P<instanceof>{re_group_capture})', re.MULTILINE)
fsh_instances = [m.groupdict() for m in pattern.finditer(content)]
return (fsh_profiles, fsh_instances) | Parse FSH file to extract profiles and instances that are defined in it.
:param fname_fsh: Filename of the FSH file to parse
:return: List of defined profiles, List of defined instances | fsh_validator/fsh_validator.py | parse_fsh | glichtner/fsh-validator | 0 | python | def parse_fsh(fname_fsh: Path) -> Tuple[(List[Dict], List[Dict])]:
'\n Parse FSH file to extract profiles and instances that are defined in it.\n\n :param fname_fsh: Filename of the FSH file to parse\n :return: List of defined profiles, List of defined instances\n '
with open(fname_fsh, 'r') as f:
content = f.read()
re_group_capture = '[a-zA-Z0-9_\\-\\$]+'
pattern = re.compile(f'Profile: (?P<profile>{re_group_capture})[^\n]*\nParent: (?P<parent>{re_group_capture})[^\n]*\nId: (?P<id>{re_group_capture})', re.MULTILINE)
fsh_profiles = [m.groupdict() for m in pattern.finditer(content)]
pattern = re.compile(f'Instance: (?P<instance>{re_group_capture})[^\n]*\nInstanceOf: (?P<instanceof>{re_group_capture})', re.MULTILINE)
fsh_instances = [m.groupdict() for m in pattern.finditer(content)]
return (fsh_profiles, fsh_instances) | def parse_fsh(fname_fsh: Path) -> Tuple[(List[Dict], List[Dict])]:
'\n Parse FSH file to extract profiles and instances that are defined in it.\n\n :param fname_fsh: Filename of the FSH file to parse\n :return: List of defined profiles, List of defined instances\n '
with open(fname_fsh, 'r') as f:
content = f.read()
re_group_capture = '[a-zA-Z0-9_\\-\\$]+'
pattern = re.compile(f'Profile: (?P<profile>{re_group_capture})[^\n]*\nParent: (?P<parent>{re_group_capture})[^\n]*\nId: (?P<id>{re_group_capture})', re.MULTILINE)
fsh_profiles = [m.groupdict() for m in pattern.finditer(content)]
pattern = re.compile(f'Instance: (?P<instance>{re_group_capture})[^\n]*\nInstanceOf: (?P<instanceof>{re_group_capture})', re.MULTILINE)
fsh_instances = [m.groupdict() for m in pattern.finditer(content)]
return (fsh_profiles, fsh_instances)<|docstring|>Parse FSH file to extract profiles and instances that are defined in it.
:param fname_fsh: Filename of the FSH file to parse
:return: List of defined profiles, List of defined instances<|endoftext|> |
09ff521520b343aac97c6ce95954195928fbb756fd50db88b6b3b8b6fac270b6 | def parse_fsh_generated(path: Path) -> Tuple[(Dict, Dict, Dict, Dict, Dict, Dict)]:
'\n Parse json files generated from FSH through SUSHI.\n\n Goal: Extract structure definitions, instances, value set and dependencies from generated JSON files.\n\n :param path: Path to generated files through SUSHI\n :return: StructureDefinitions, Instances, Dependencies, ValueSets, CodeSystems, Extensions\n '
def parse_structure_definition(fname: Path, json_data: str) -> Tuple[(Dict, str)]:
url = parse('$.url').find(json_data)[0].value
type = parse('$.type').find(json_data)[0].value
base = parse('$.baseDefinition').find(json_data)[0].value
abstract = parse('$.abstract').find(json_data)[0].value
profilesAdditional = [x.value[0] for x in parse('$.differential.element[*].type[*].profile').find(json_data)]
return ({url: {'filename': fname.resolve(), 'id': parse('$.id').find(json_data)[0].value, 'type': type, 'base': base, 'profilesAdditional': profilesAdditional, 'abstract': abstract}}, type)
def parse_instance(fname: Path, json_data: str) -> Dict:
resourceType = parse('$.resourceType').find(json_data)[0].value
codeSystems = set((s.value for s in parse('$.*.coding[*].system').find(json_data)))
profile = parse('$.meta.profile').find(json_data)
if (len(profile) == 0):
profile = resourceType
else:
profile = profile[0].value[0]
profilesAdditional = []
if (resourceType == 'Bundle'):
profilesAdditional = [p.value[0] for p in parse('$.entry[*].resource.meta.profile').find(json_data)]
return {parse('$.id').find(json_data)[0].value: {'filename': fname.resolve(), 'profile': profile, 'resourceType': resourceType, 'codeSystems': codeSystems, 'profilesAdditional': profilesAdditional}}
def parse_ig(fname: Path, json_data: str) -> Dict:
deps = parse('$.dependsOn[*]').find(json_data)
return {v.value['packageId']: v.value for v in deps}
def parse_value_set(fname: Path, json_data: str) -> Dict:
url = parse('$.url').find(json_data)[0].value
return {url: {'filename': fname.resolve(), 'id': parse('$.id').find(json_data)[0].value}}
def parse_code_system(fname: Path, json_data: str) -> Dict:
url = parse('$.url').find(json_data)[0].value
return {url: {'filename': fname.resolve(), 'id': parse('$.id').find(json_data)[0].value}}
sdefs = {}
instances = {}
deps = {}
vs = {}
cs = {}
extensions = {}
for fname in path.glob('*.json'):
json_data = json.load(open(fname))
resourceType = parse('$.resourceType').find(json_data)[0].value
if (resourceType == 'StructureDefinition'):
(sd, type) = parse_structure_definition(fname, json_data)
if (type == 'Extension'):
extensions.update(sd)
else:
sdefs.update(sd)
elif (resourceType == 'ImplementationGuide'):
deps.update(parse_ig(fname, json_data))
elif (resourceType == 'ValueSet'):
vs.update(parse_value_set(fname, json_data))
elif (resourceType == 'CodeSystem'):
cs.update(parse_code_system(fname, json_data))
else:
instances.update(parse_instance(fname, json_data))
return (sdefs, instances, deps, vs, cs, extensions) | Parse json files generated from FSH through SUSHI.
Goal: Extract structure definitions, instances, value set and dependencies from generated JSON files.
:param path: Path to generated files through SUSHI
:return: StructureDefinitions, Instances, Dependencies, ValueSets, CodeSystems, Extensions | fsh_validator/fsh_validator.py | parse_fsh_generated | glichtner/fsh-validator | 0 | python | def parse_fsh_generated(path: Path) -> Tuple[(Dict, Dict, Dict, Dict, Dict, Dict)]:
'\n Parse json files generated from FSH through SUSHI.\n\n Goal: Extract structure definitions, instances, value set and dependencies from generated JSON files.\n\n :param path: Path to generated files through SUSHI\n :return: StructureDefinitions, Instances, Dependencies, ValueSets, CodeSystems, Extensions\n '
def parse_structure_definition(fname: Path, json_data: str) -> Tuple[(Dict, str)]:
url = parse('$.url').find(json_data)[0].value
type = parse('$.type').find(json_data)[0].value
base = parse('$.baseDefinition').find(json_data)[0].value
abstract = parse('$.abstract').find(json_data)[0].value
profilesAdditional = [x.value[0] for x in parse('$.differential.element[*].type[*].profile').find(json_data)]
return ({url: {'filename': fname.resolve(), 'id': parse('$.id').find(json_data)[0].value, 'type': type, 'base': base, 'profilesAdditional': profilesAdditional, 'abstract': abstract}}, type)
def parse_instance(fname: Path, json_data: str) -> Dict:
resourceType = parse('$.resourceType').find(json_data)[0].value
codeSystems = set((s.value for s in parse('$.*.coding[*].system').find(json_data)))
profile = parse('$.meta.profile').find(json_data)
if (len(profile) == 0):
profile = resourceType
else:
profile = profile[0].value[0]
profilesAdditional = []
if (resourceType == 'Bundle'):
profilesAdditional = [p.value[0] for p in parse('$.entry[*].resource.meta.profile').find(json_data)]
return {parse('$.id').find(json_data)[0].value: {'filename': fname.resolve(), 'profile': profile, 'resourceType': resourceType, 'codeSystems': codeSystems, 'profilesAdditional': profilesAdditional}}
def parse_ig(fname: Path, json_data: str) -> Dict:
deps = parse('$.dependsOn[*]').find(json_data)
return {v.value['packageId']: v.value for v in deps}
def parse_value_set(fname: Path, json_data: str) -> Dict:
url = parse('$.url').find(json_data)[0].value
return {url: {'filename': fname.resolve(), 'id': parse('$.id').find(json_data)[0].value}}
def parse_code_system(fname: Path, json_data: str) -> Dict:
url = parse('$.url').find(json_data)[0].value
return {url: {'filename': fname.resolve(), 'id': parse('$.id').find(json_data)[0].value}}
sdefs = {}
instances = {}
deps = {}
vs = {}
cs = {}
extensions = {}
for fname in path.glob('*.json'):
json_data = json.load(open(fname))
resourceType = parse('$.resourceType').find(json_data)[0].value
if (resourceType == 'StructureDefinition'):
(sd, type) = parse_structure_definition(fname, json_data)
if (type == 'Extension'):
extensions.update(sd)
else:
sdefs.update(sd)
elif (resourceType == 'ImplementationGuide'):
deps.update(parse_ig(fname, json_data))
elif (resourceType == 'ValueSet'):
vs.update(parse_value_set(fname, json_data))
elif (resourceType == 'CodeSystem'):
cs.update(parse_code_system(fname, json_data))
else:
instances.update(parse_instance(fname, json_data))
return (sdefs, instances, deps, vs, cs, extensions) | def parse_fsh_generated(path: Path) -> Tuple[(Dict, Dict, Dict, Dict, Dict, Dict)]:
'\n Parse json files generated from FSH through SUSHI.\n\n Goal: Extract structure definitions, instances, value set and dependencies from generated JSON files.\n\n :param path: Path to generated files through SUSHI\n :return: StructureDefinitions, Instances, Dependencies, ValueSets, CodeSystems, Extensions\n '
def parse_structure_definition(fname: Path, json_data: str) -> Tuple[(Dict, str)]:
url = parse('$.url').find(json_data)[0].value
type = parse('$.type').find(json_data)[0].value
base = parse('$.baseDefinition').find(json_data)[0].value
abstract = parse('$.abstract').find(json_data)[0].value
profilesAdditional = [x.value[0] for x in parse('$.differential.element[*].type[*].profile').find(json_data)]
return ({url: {'filename': fname.resolve(), 'id': parse('$.id').find(json_data)[0].value, 'type': type, 'base': base, 'profilesAdditional': profilesAdditional, 'abstract': abstract}}, type)
def parse_instance(fname: Path, json_data: str) -> Dict:
resourceType = parse('$.resourceType').find(json_data)[0].value
codeSystems = set((s.value for s in parse('$.*.coding[*].system').find(json_data)))
profile = parse('$.meta.profile').find(json_data)
if (len(profile) == 0):
profile = resourceType
else:
profile = profile[0].value[0]
profilesAdditional = []
if (resourceType == 'Bundle'):
profilesAdditional = [p.value[0] for p in parse('$.entry[*].resource.meta.profile').find(json_data)]
return {parse('$.id').find(json_data)[0].value: {'filename': fname.resolve(), 'profile': profile, 'resourceType': resourceType, 'codeSystems': codeSystems, 'profilesAdditional': profilesAdditional}}
def parse_ig(fname: Path, json_data: str) -> Dict:
deps = parse('$.dependsOn[*]').find(json_data)
return {v.value['packageId']: v.value for v in deps}
def parse_value_set(fname: Path, json_data: str) -> Dict:
url = parse('$.url').find(json_data)[0].value
return {url: {'filename': fname.resolve(), 'id': parse('$.id').find(json_data)[0].value}}
def parse_code_system(fname: Path, json_data: str) -> Dict:
url = parse('$.url').find(json_data)[0].value
return {url: {'filename': fname.resolve(), 'id': parse('$.id').find(json_data)[0].value}}
sdefs = {}
instances = {}
deps = {}
vs = {}
cs = {}
extensions = {}
for fname in path.glob('*.json'):
json_data = json.load(open(fname))
resourceType = parse('$.resourceType').find(json_data)[0].value
if (resourceType == 'StructureDefinition'):
(sd, type) = parse_structure_definition(fname, json_data)
if (type == 'Extension'):
extensions.update(sd)
else:
sdefs.update(sd)
elif (resourceType == 'ImplementationGuide'):
deps.update(parse_ig(fname, json_data))
elif (resourceType == 'ValueSet'):
vs.update(parse_value_set(fname, json_data))
elif (resourceType == 'CodeSystem'):
cs.update(parse_code_system(fname, json_data))
else:
instances.update(parse_instance(fname, json_data))
return (sdefs, instances, deps, vs, cs, extensions)<|docstring|>Parse json files generated from FSH through SUSHI.
Goal: Extract structure definitions, instances, value set and dependencies from generated JSON files.
:param path: Path to generated files through SUSHI
:return: StructureDefinitions, Instances, Dependencies, ValueSets, CodeSystems, Extensions<|endoftext|> |
b16e2bf22c47c2e64f26e577a13b06a63509e8b9ff4be009046043ab4009b895 | def get_paths(base_path: Union[(str, Path)]) -> Tuple[(Path, Path)]:
'\n Get fsh input and output paths from base path.\n\n :param base_path: Base path\n :return: FSH input path, FSH output path\n '
return (((Path(base_path) / 'input') / 'fsh'), ((Path(base_path) / 'fsh-generated') / 'resources')) | Get fsh input and output paths from base path.
:param base_path: Base path
:return: FSH input path, FSH output path | fsh_validator/fsh_validator.py | get_paths | glichtner/fsh-validator | 0 | python | def get_paths(base_path: Union[(str, Path)]) -> Tuple[(Path, Path)]:
'\n Get fsh input and output paths from base path.\n\n :param base_path: Base path\n :return: FSH input path, FSH output path\n '
return (((Path(base_path) / 'input') / 'fsh'), ((Path(base_path) / 'fsh-generated') / 'resources')) | def get_paths(base_path: Union[(str, Path)]) -> Tuple[(Path, Path)]:
'\n Get fsh input and output paths from base path.\n\n :param base_path: Base path\n :return: FSH input path, FSH output path\n '
return (((Path(base_path) / 'input') / 'fsh'), ((Path(base_path) / 'fsh-generated') / 'resources'))<|docstring|>Get fsh input and output paths from base path.
:param base_path: Base path
:return: FSH input path, FSH output path<|endoftext|> |
1ccf12cd387992ac1326f6261862011fbdd90eccd480e6b8d274d4e1a364f9f3 | def get_fsh_base_path(path: Union[(str, Path)]) -> Path:
'\n Get the base path of an FSH project given a filename or path.\n\n FSH files in sushi projects are located in the subfolder "input/fsh/". This method returns the parent of this base\n path, if available, or throws an exception\n\n :param path: Base to get fsh base path from\n :return: FSH project base path\n '
path = Path(path).absolute()
if ((path / 'input') / 'fsh').exists():
return path
for i in range((len(path.parts) - 1)):
if ((path.parts[i] == 'input') and (path.parts[(i + 1)] == 'fsh')):
return Path(*path.parts[:i]).absolute()
raise ValueError(f'Could not find fsh input path (input/fsh/) in "{path}"') | Get the base path of an FSH project given a filename or path.
FSH files in sushi projects are located in the subfolder "input/fsh/". This method returns the parent of this base
path, if available, or throws an exception
:param path: Base to get fsh base path from
:return: FSH project base path | fsh_validator/fsh_validator.py | get_fsh_base_path | glichtner/fsh-validator | 0 | python | def get_fsh_base_path(path: Union[(str, Path)]) -> Path:
'\n Get the base path of an FSH project given a filename or path.\n\n FSH files in sushi projects are located in the subfolder "input/fsh/". This method returns the parent of this base\n path, if available, or throws an exception\n\n :param path: Base to get fsh base path from\n :return: FSH project base path\n '
path = Path(path).absolute()
if ((path / 'input') / 'fsh').exists():
return path
for i in range((len(path.parts) - 1)):
if ((path.parts[i] == 'input') and (path.parts[(i + 1)] == 'fsh')):
return Path(*path.parts[:i]).absolute()
raise ValueError(f'Could not find fsh input path (input/fsh/) in "{path}"') | def get_fsh_base_path(path: Union[(str, Path)]) -> Path:
'\n Get the base path of an FSH project given a filename or path.\n\n FSH files in sushi projects are located in the subfolder "input/fsh/". This method returns the parent of this base\n path, if available, or throws an exception\n\n :param path: Base to get fsh base path from\n :return: FSH project base path\n '
path = Path(path).absolute()
if ((path / 'input') / 'fsh').exists():
return path
for i in range((len(path.parts) - 1)):
if ((path.parts[i] == 'input') and (path.parts[(i + 1)] == 'fsh')):
return Path(*path.parts[:i]).absolute()
raise ValueError(f'Could not find fsh input path (input/fsh/) in "{path}"')<|docstring|>Get the base path of an FSH project given a filename or path.
FSH files in sushi projects are located in the subfolder "input/fsh/". This method returns the parent of this base
path, if available, or throws an exception
:param path: Base to get fsh base path from
:return: FSH project base path<|endoftext|> |
cd928f00822bfcbaa652e7ae59a18432d2ead3f27d2081670c669ddb50f5befa | def deduplicate_obi_codes(fname: Path) -> None:
'\n Remove duplicate http://terminology.hl7.org/CodeSystem/v2-0203#OBI codes from an instance.\n\n When using the Medizininformatik Initiative Profile LabObservation, SUSHI v2.1.1 inserts the identifier.type code\n for http://terminology.hl7.org/CodeSystem/v2-0203#OBI twice, but it has a cardinality of 1, resulting in an error\n by the FHIR validator. This workaround function actively removes the duplicates.\n\n MII Profile: https://www.medizininformatik-initiative.de/fhir/core/modul-labor/StructureDefinition/ObservationLab\n\n :param fname: Filename of instance to remove duplicates from\n :return: None\n '
def num_obi_codes(json_data: Dict):
jp = parse("$.type.coding[?code = 'OBI' & system='http://terminology.hl7.org/CodeSystem/v2-0203']")
return len(jp.find(json_data))
def del_obi_codes(identifier: Dict):
codings = identifier['type']['coding']
for (i, coding) in enumerate(codings):
if ((coding['system'] == 'http://terminology.hl7.org/CodeSystem/v2-0203') and (coding['code'] == 'OBI')):
del codings[i]
break
json_data = json.load(open(fname))
if ('identifier' not in json_data):
return
for identifier in json_data['identifier']:
if (num_obi_codes(identifier) > 1):
warnings.warn(f'Found multiple OBI codes in {fname}, removing')
del_obi_codes(identifier)
json.dump(json_data, open(fname, 'w'), indent=2) | Remove duplicate http://terminology.hl7.org/CodeSystem/v2-0203#OBI codes from an instance.
When using the Medizininformatik Initiative Profile LabObservation, SUSHI v2.1.1 inserts the identifier.type code
for http://terminology.hl7.org/CodeSystem/v2-0203#OBI twice, but it has a cardinality of 1, resulting in an error
by the FHIR validator. This workaround function actively removes the duplicates.
MII Profile: https://www.medizininformatik-initiative.de/fhir/core/modul-labor/StructureDefinition/ObservationLab
:param fname: Filename of instance to remove duplicates from
:return: None | fsh_validator/fsh_validator.py | deduplicate_obi_codes | glichtner/fsh-validator | 0 | python | def deduplicate_obi_codes(fname: Path) -> None:
'\n Remove duplicate http://terminology.hl7.org/CodeSystem/v2-0203#OBI codes from an instance.\n\n When using the Medizininformatik Initiative Profile LabObservation, SUSHI v2.1.1 inserts the identifier.type code\n for http://terminology.hl7.org/CodeSystem/v2-0203#OBI twice, but it has a cardinality of 1, resulting in an error\n by the FHIR validator. This workaround function actively removes the duplicates.\n\n MII Profile: https://www.medizininformatik-initiative.de/fhir/core/modul-labor/StructureDefinition/ObservationLab\n\n :param fname: Filename of instance to remove duplicates from\n :return: None\n '
def num_obi_codes(json_data: Dict):
jp = parse("$.type.coding[?code = 'OBI' & system='http://terminology.hl7.org/CodeSystem/v2-0203']")
return len(jp.find(json_data))
def del_obi_codes(identifier: Dict):
codings = identifier['type']['coding']
for (i, coding) in enumerate(codings):
if ((coding['system'] == 'http://terminology.hl7.org/CodeSystem/v2-0203') and (coding['code'] == 'OBI')):
del codings[i]
break
json_data = json.load(open(fname))
if ('identifier' not in json_data):
return
for identifier in json_data['identifier']:
if (num_obi_codes(identifier) > 1):
warnings.warn(f'Found multiple OBI codes in {fname}, removing')
del_obi_codes(identifier)
json.dump(json_data, open(fname, 'w'), indent=2) | def deduplicate_obi_codes(fname: Path) -> None:
'\n Remove duplicate http://terminology.hl7.org/CodeSystem/v2-0203#OBI codes from an instance.\n\n When using the Medizininformatik Initiative Profile LabObservation, SUSHI v2.1.1 inserts the identifier.type code\n for http://terminology.hl7.org/CodeSystem/v2-0203#OBI twice, but it has a cardinality of 1, resulting in an error\n by the FHIR validator. This workaround function actively removes the duplicates.\n\n MII Profile: https://www.medizininformatik-initiative.de/fhir/core/modul-labor/StructureDefinition/ObservationLab\n\n :param fname: Filename of instance to remove duplicates from\n :return: None\n '
def num_obi_codes(json_data: Dict):
jp = parse("$.type.coding[?code = 'OBI' & system='http://terminology.hl7.org/CodeSystem/v2-0203']")
return len(jp.find(json_data))
def del_obi_codes(identifier: Dict):
codings = identifier['type']['coding']
for (i, coding) in enumerate(codings):
if ((coding['system'] == 'http://terminology.hl7.org/CodeSystem/v2-0203') and (coding['code'] == 'OBI')):
del codings[i]
break
json_data = json.load(open(fname))
if ('identifier' not in json_data):
return
for identifier in json_data['identifier']:
if (num_obi_codes(identifier) > 1):
warnings.warn(f'Found multiple OBI codes in {fname}, removing')
del_obi_codes(identifier)
json.dump(json_data, open(fname, 'w'), indent=2)<|docstring|>Remove duplicate http://terminology.hl7.org/CodeSystem/v2-0203#OBI codes from an instance.
When using the Medizininformatik Initiative Profile LabObservation, SUSHI v2.1.1 inserts the identifier.type code
for http://terminology.hl7.org/CodeSystem/v2-0203#OBI twice, but it has a cardinality of 1, resulting in an error
by the FHIR validator. This workaround function actively removes the duplicates.
MII Profile: https://www.medizininformatik-initiative.de/fhir/core/modul-labor/StructureDefinition/ObservationLab
:param fname: Filename of instance to remove duplicates from
:return: None<|endoftext|> |
b11d82d58b562cdcf34ee4edf72fb65641cc92e18f20471e487fb3070ff66e96 | def get_abstract_profile_ids(sdefs: Dict[(str, Dict)]) -> Set[str]:
'\n Get all abstract profile IDs from a set of StructureDefinitions.\n\n :param sdefs: StructureDefinitions to get abstract profile IDs from\n :return: Set of abstract profile IDs\n '
return set([v['id'] for v in sdefs.values() if v['abstract']]) | Get all abstract profile IDs from a set of StructureDefinitions.
:param sdefs: StructureDefinitions to get abstract profile IDs from
:return: Set of abstract profile IDs | fsh_validator/fsh_validator.py | get_abstract_profile_ids | glichtner/fsh-validator | 0 | python | def get_abstract_profile_ids(sdefs: Dict[(str, Dict)]) -> Set[str]:
'\n Get all abstract profile IDs from a set of StructureDefinitions.\n\n :param sdefs: StructureDefinitions to get abstract profile IDs from\n :return: Set of abstract profile IDs\n '
return set([v['id'] for v in sdefs.values() if v['abstract']]) | def get_abstract_profile_ids(sdefs: Dict[(str, Dict)]) -> Set[str]:
'\n Get all abstract profile IDs from a set of StructureDefinitions.\n\n :param sdefs: StructureDefinitions to get abstract profile IDs from\n :return: Set of abstract profile IDs\n '
return set([v['id'] for v in sdefs.values() if v['abstract']])<|docstring|>Get all abstract profile IDs from a set of StructureDefinitions.
:param sdefs: StructureDefinitions to get abstract profile IDs from
:return: Set of abstract profile IDs<|endoftext|> |
b2a695e7abfc1379a4f056e607b3396462178a7a5ef52bc548968cc7411b24ef | def _validate_fsh_files(path_output: Path, fnames: List[Path], fname_validator: str, fhir_version: str, exclude_code_systems: Optional[Set]=None, exclude_resource_types: Optional[Set]=None, verbose: bool=False) -> List[ValidatorStatus]:
'\n Validate FSH files.\n\n Process:\n - Extract Profiles and Instances defined in each FSH file\n - Run FHIR Java validator for each instance to validate it against its corresponding profile\n\n :param path_output: output path (of SUSHI project)\n :param fnames: FSH file names to validate (full paths)\n :param fname_validator: full path to FHIR Java validator file\n :param fhir_version: FHIR version to use in validator\n :param exclude_code_systems: Optional set of code systems which prevent instances from being validated\n :param exclude_resource_types: Optional set of resource types which prevent instances from being validated\n :param verbose: Print more information\n :return: ValidatorStatus objects\n '
(sdefs, instances, deps, vs, cs, extensions) = parse_fsh_generated(path_output)
for (k, v) in instances.items():
deduplicate_obi_codes(v['filename'])
results = []
for (i, fname) in enumerate(fnames):
if (not fname.exists()):
raise FileNotFoundError(fname)
(fsh_profiles, fsh_instances) = parse_fsh(fname)
percent = (((i + 1) / len(fnames)) * 100)
print(f'[{percent: 5.1f}%] Processing file {fname} with {len(fsh_profiles)} profiles and {len(fsh_instances)} instances ({(i + 1)}/{len(fnames)})')
profiles_without_instance = check_instances_availability(fsh_profiles, fsh_instances, get_abstract_profile_ids(sdefs))
if len(profiles_without_instance):
for p in profiles_without_instance:
status = ValidatorStatus(status=ValidatorStatus.Status.FAILURE, errors=[f'No instances defined for profile {p}'], profile=p)
status.pretty_print(with_header=True)
results.append(status)
continue
fsh_instances_cleaned = []
for fsh_instance in fsh_instances:
instance = instances[fsh_instance['instance']]
if ((exclude_code_systems is not None) and any(((cs in exclude_code_systems) for cs in instance['codeSystems']))):
status = ValidatorStatus(status=ValidatorStatus.Status.WARNING, warnings=[f"Skipped instance {fsh_instance['instance']} due to excluded code system(s) used in the instance"], profile=fsh_instance['instanceof'])
status.pretty_print(with_header=True)
results.append(status)
elif ((exclude_resource_types is not None) and (instance['resourceType'] in exclude_resource_types)):
status = ValidatorStatus(status=ValidatorStatus.Status.WARNING, warnings=[f"Skipped instance {fsh_instance['instance']} due to excluded resource type {instance['resourceType']}"], profile=fsh_instance['instanceof'])
status.pretty_print(with_header=True)
results.append(status)
else:
fsh_instances_cleaned.append(fsh_instance)
results += run_validation(fname_validator, fsh_instances_cleaned, sdefs, instances, deps, vs, cs, extensions, fhir_version=fhir_version, verbose=verbose)
return results | Validate FSH files.
Process:
- Extract Profiles and Instances defined in each FSH file
- Run FHIR Java validator for each instance to validate it against its corresponding profile
:param path_output: output path (of SUSHI project)
:param fnames: FSH file names to validate (full paths)
:param fname_validator: full path to FHIR Java validator file
:param fhir_version: FHIR version to use in validator
:param exclude_code_systems: Optional set of code systems which prevent instances from being validated
:param exclude_resource_types: Optional set of resource types which prevent instances from being validated
:param verbose: Print more information
:return: ValidatorStatus objects | fsh_validator/fsh_validator.py | _validate_fsh_files | glichtner/fsh-validator | 0 | python | def _validate_fsh_files(path_output: Path, fnames: List[Path], fname_validator: str, fhir_version: str, exclude_code_systems: Optional[Set]=None, exclude_resource_types: Optional[Set]=None, verbose: bool=False) -> List[ValidatorStatus]:
'\n Validate FSH files.\n\n Process:\n - Extract Profiles and Instances defined in each FSH file\n - Run FHIR Java validator for each instance to validate it against its corresponding profile\n\n :param path_output: output path (of SUSHI project)\n :param fnames: FSH file names to validate (full paths)\n :param fname_validator: full path to FHIR Java validator file\n :param fhir_version: FHIR version to use in validator\n :param exclude_code_systems: Optional set of code systems which prevent instances from being validated\n :param exclude_resource_types: Optional set of resource types which prevent instances from being validated\n :param verbose: Print more information\n :return: ValidatorStatus objects\n '
(sdefs, instances, deps, vs, cs, extensions) = parse_fsh_generated(path_output)
for (k, v) in instances.items():
deduplicate_obi_codes(v['filename'])
results = []
for (i, fname) in enumerate(fnames):
if (not fname.exists()):
raise FileNotFoundError(fname)
(fsh_profiles, fsh_instances) = parse_fsh(fname)
percent = (((i + 1) / len(fnames)) * 100)
print(f'[{percent: 5.1f}%] Processing file {fname} with {len(fsh_profiles)} profiles and {len(fsh_instances)} instances ({(i + 1)}/{len(fnames)})')
profiles_without_instance = check_instances_availability(fsh_profiles, fsh_instances, get_abstract_profile_ids(sdefs))
if len(profiles_without_instance):
for p in profiles_without_instance:
status = ValidatorStatus(status=ValidatorStatus.Status.FAILURE, errors=[f'No instances defined for profile {p}'], profile=p)
status.pretty_print(with_header=True)
results.append(status)
continue
fsh_instances_cleaned = []
for fsh_instance in fsh_instances:
instance = instances[fsh_instance['instance']]
if ((exclude_code_systems is not None) and any(((cs in exclude_code_systems) for cs in instance['codeSystems']))):
status = ValidatorStatus(status=ValidatorStatus.Status.WARNING, warnings=[f"Skipped instance {fsh_instance['instance']} due to excluded code system(s) used in the instance"], profile=fsh_instance['instanceof'])
status.pretty_print(with_header=True)
results.append(status)
elif ((exclude_resource_types is not None) and (instance['resourceType'] in exclude_resource_types)):
status = ValidatorStatus(status=ValidatorStatus.Status.WARNING, warnings=[f"Skipped instance {fsh_instance['instance']} due to excluded resource type {instance['resourceType']}"], profile=fsh_instance['instanceof'])
status.pretty_print(with_header=True)
results.append(status)
else:
fsh_instances_cleaned.append(fsh_instance)
results += run_validation(fname_validator, fsh_instances_cleaned, sdefs, instances, deps, vs, cs, extensions, fhir_version=fhir_version, verbose=verbose)
return results | def _validate_fsh_files(path_output: Path, fnames: List[Path], fname_validator: str, fhir_version: str, exclude_code_systems: Optional[Set]=None, exclude_resource_types: Optional[Set]=None, verbose: bool=False) -> List[ValidatorStatus]:
'\n Validate FSH files.\n\n Process:\n - Extract Profiles and Instances defined in each FSH file\n - Run FHIR Java validator for each instance to validate it against its corresponding profile\n\n :param path_output: output path (of SUSHI project)\n :param fnames: FSH file names to validate (full paths)\n :param fname_validator: full path to FHIR Java validator file\n :param fhir_version: FHIR version to use in validator\n :param exclude_code_systems: Optional set of code systems which prevent instances from being validated\n :param exclude_resource_types: Optional set of resource types which prevent instances from being validated\n :param verbose: Print more information\n :return: ValidatorStatus objects\n '
(sdefs, instances, deps, vs, cs, extensions) = parse_fsh_generated(path_output)
for (k, v) in instances.items():
deduplicate_obi_codes(v['filename'])
results = []
for (i, fname) in enumerate(fnames):
if (not fname.exists()):
raise FileNotFoundError(fname)
(fsh_profiles, fsh_instances) = parse_fsh(fname)
percent = (((i + 1) / len(fnames)) * 100)
print(f'[{percent: 5.1f}%] Processing file {fname} with {len(fsh_profiles)} profiles and {len(fsh_instances)} instances ({(i + 1)}/{len(fnames)})')
profiles_without_instance = check_instances_availability(fsh_profiles, fsh_instances, get_abstract_profile_ids(sdefs))
if len(profiles_without_instance):
for p in profiles_without_instance:
status = ValidatorStatus(status=ValidatorStatus.Status.FAILURE, errors=[f'No instances defined for profile {p}'], profile=p)
status.pretty_print(with_header=True)
results.append(status)
continue
fsh_instances_cleaned = []
for fsh_instance in fsh_instances:
instance = instances[fsh_instance['instance']]
if ((exclude_code_systems is not None) and any(((cs in exclude_code_systems) for cs in instance['codeSystems']))):
status = ValidatorStatus(status=ValidatorStatus.Status.WARNING, warnings=[f"Skipped instance {fsh_instance['instance']} due to excluded code system(s) used in the instance"], profile=fsh_instance['instanceof'])
status.pretty_print(with_header=True)
results.append(status)
elif ((exclude_resource_types is not None) and (instance['resourceType'] in exclude_resource_types)):
status = ValidatorStatus(status=ValidatorStatus.Status.WARNING, warnings=[f"Skipped instance {fsh_instance['instance']} due to excluded resource type {instance['resourceType']}"], profile=fsh_instance['instanceof'])
status.pretty_print(with_header=True)
results.append(status)
else:
fsh_instances_cleaned.append(fsh_instance)
results += run_validation(fname_validator, fsh_instances_cleaned, sdefs, instances, deps, vs, cs, extensions, fhir_version=fhir_version, verbose=verbose)
return results<|docstring|>Validate FSH files.
Process:
- Extract Profiles and Instances defined in each FSH file
- Run FHIR Java validator for each instance to validate it against its corresponding profile
:param path_output: output path (of SUSHI project)
:param fnames: FSH file names to validate (full paths)
:param fname_validator: full path to FHIR Java validator file
:param fhir_version: FHIR version to use in validator
:param exclude_code_systems: Optional set of code systems which prevent instances from being validated
:param exclude_resource_types: Optional set of resource types which prevent instances from being validated
:param verbose: Print more information
:return: ValidatorStatus objects<|endoftext|> |
fb73502f1250fae84b88d4b363a202903b05c2dd302fa594e450c6c0823914e7 | def validate_fsh(fsh_filenames: List[FshPath], fname_validator: str, fhir_version: str, exclude_code_systems: Optional[Set]=None, exclude_resource_types: Optional[Set]=None, verbose: bool=False) -> List[ValidatorStatus]:
'\n Validate specific fsh files.\n\n Process:\n - Extract Profiles and Instances defined in FSH file\n - Run FHIR Java validator for each instance to validate it against its corresponding profile\n\n :param fsh_filename: FSH file names\n :param fname_validator: Full path to FHIR Java validator file\n :param fhir_version: FHIR version to use in validator\n :param exclude_code_systems: Optional set of code systems which prevent instances from being validated\n :param exclude_resource_types: Optional set of resource types which prevent instances from being validated\n :param verbose: Print more information\n :return: List of validation status, full output and instance and profile names\n '
(_, path_output) = get_paths(fsh_filenames[0].fsh_base_path())
return _validate_fsh_files(path_output=path_output, fnames=[f.absolute() for f in fsh_filenames], fname_validator=fname_validator, fhir_version=fhir_version, exclude_code_systems=exclude_code_systems, exclude_resource_types=exclude_resource_types, verbose=verbose) | Validate specific fsh files.
Process:
- Extract Profiles and Instances defined in FSH file
- Run FHIR Java validator for each instance to validate it against its corresponding profile
:param fsh_filename: FSH file names
:param fname_validator: Full path to FHIR Java validator file
:param fhir_version: FHIR version to use in validator
:param exclude_code_systems: Optional set of code systems which prevent instances from being validated
:param exclude_resource_types: Optional set of resource types which prevent instances from being validated
:param verbose: Print more information
:return: List of validation status, full output and instance and profile names | fsh_validator/fsh_validator.py | validate_fsh | glichtner/fsh-validator | 0 | python | def validate_fsh(fsh_filenames: List[FshPath], fname_validator: str, fhir_version: str, exclude_code_systems: Optional[Set]=None, exclude_resource_types: Optional[Set]=None, verbose: bool=False) -> List[ValidatorStatus]:
'\n Validate specific fsh files.\n\n Process:\n - Extract Profiles and Instances defined in FSH file\n - Run FHIR Java validator for each instance to validate it against its corresponding profile\n\n :param fsh_filename: FSH file names\n :param fname_validator: Full path to FHIR Java validator file\n :param fhir_version: FHIR version to use in validator\n :param exclude_code_systems: Optional set of code systems which prevent instances from being validated\n :param exclude_resource_types: Optional set of resource types which prevent instances from being validated\n :param verbose: Print more information\n :return: List of validation status, full output and instance and profile names\n '
(_, path_output) = get_paths(fsh_filenames[0].fsh_base_path())
return _validate_fsh_files(path_output=path_output, fnames=[f.absolute() for f in fsh_filenames], fname_validator=fname_validator, fhir_version=fhir_version, exclude_code_systems=exclude_code_systems, exclude_resource_types=exclude_resource_types, verbose=verbose) | def validate_fsh(fsh_filenames: List[FshPath], fname_validator: str, fhir_version: str, exclude_code_systems: Optional[Set]=None, exclude_resource_types: Optional[Set]=None, verbose: bool=False) -> List[ValidatorStatus]:
'\n Validate specific fsh files.\n\n Process:\n - Extract Profiles and Instances defined in FSH file\n - Run FHIR Java validator for each instance to validate it against its corresponding profile\n\n :param fsh_filename: FSH file names\n :param fname_validator: Full path to FHIR Java validator file\n :param fhir_version: FHIR version to use in validator\n :param exclude_code_systems: Optional set of code systems which prevent instances from being validated\n :param exclude_resource_types: Optional set of resource types which prevent instances from being validated\n :param verbose: Print more information\n :return: List of validation status, full output and instance and profile names\n '
(_, path_output) = get_paths(fsh_filenames[0].fsh_base_path())
return _validate_fsh_files(path_output=path_output, fnames=[f.absolute() for f in fsh_filenames], fname_validator=fname_validator, fhir_version=fhir_version, exclude_code_systems=exclude_code_systems, exclude_resource_types=exclude_resource_types, verbose=verbose)<|docstring|>Validate specific fsh files.
Process:
- Extract Profiles and Instances defined in FSH file
- Run FHIR Java validator for each instance to validate it against its corresponding profile
:param fsh_filename: FSH file names
:param fname_validator: Full path to FHIR Java validator file
:param fhir_version: FHIR version to use in validator
:param exclude_code_systems: Optional set of code systems which prevent instances from being validated
:param exclude_resource_types: Optional set of resource types which prevent instances from being validated
:param verbose: Print more information
:return: List of validation status, full output and instance and profile names<|endoftext|> |
3e00277415cabebe6150cc941f0b165bbee0b73e433271677ee90e54b9f9ca9a | def validate_all_fsh(base_path: str, subdir: str, fname_validator: str, fhir_version: str, exclude_code_systems: Optional[Set]=None, exclude_resource_types: Optional[Set]=None, verbose: bool=False) -> List[ValidatorStatus]:
'\n Validate all FSH files in a given subdir.\n\n Process:\n - Extract Profiles and Instances defined in FSH file\n - Run FHIR Java validator for each instance to validate it against its corresponding profile\n\n :param base_path: base path (of SUSHI project)\n :param subdir: subdirectory of profiles\n :param fname_validator: full path to FHIR Java validator file\n :param fhir_version: FHIR version to use in validator\n :param verbose: Print more information\n :param exclude_code_systems: Optional set of code systems which prevent instances from being validated\n :param exclude_resource_types: Optional set of resource types which prevent instances from being validated\n :return: List of validation status, full output and instance and profile names\n '
(path_input, path_output) = get_paths(base_path)
path_full = (path_input / subdir)
if (not path_full.exists()):
raise FileNotFoundError(path_full)
fnames = list(path_full.rglob('*.fsh'))
sys.stdout.flush()
return _validate_fsh_files(path_output=path_output, fnames=fnames, fname_validator=fname_validator, fhir_version=fhir_version, exclude_code_systems=exclude_code_systems, exclude_resource_types=exclude_resource_types, verbose=verbose) | Validate all FSH files in a given subdir.
Process:
- Extract Profiles and Instances defined in FSH file
- Run FHIR Java validator for each instance to validate it against its corresponding profile
:param base_path: base path (of SUSHI project)
:param subdir: subdirectory of profiles
:param fname_validator: full path to FHIR Java validator file
:param fhir_version: FHIR version to use in validator
:param verbose: Print more information
:param exclude_code_systems: Optional set of code systems which prevent instances from being validated
:param exclude_resource_types: Optional set of resource types which prevent instances from being validated
:return: List of validation status, full output and instance and profile names | fsh_validator/fsh_validator.py | validate_all_fsh | glichtner/fsh-validator | 0 | python | def validate_all_fsh(base_path: str, subdir: str, fname_validator: str, fhir_version: str, exclude_code_systems: Optional[Set]=None, exclude_resource_types: Optional[Set]=None, verbose: bool=False) -> List[ValidatorStatus]:
'\n Validate all FSH files in a given subdir.\n\n Process:\n - Extract Profiles and Instances defined in FSH file\n - Run FHIR Java validator for each instance to validate it against its corresponding profile\n\n :param base_path: base path (of SUSHI project)\n :param subdir: subdirectory of profiles\n :param fname_validator: full path to FHIR Java validator file\n :param fhir_version: FHIR version to use in validator\n :param verbose: Print more information\n :param exclude_code_systems: Optional set of code systems which prevent instances from being validated\n :param exclude_resource_types: Optional set of resource types which prevent instances from being validated\n :return: List of validation status, full output and instance and profile names\n '
(path_input, path_output) = get_paths(base_path)
path_full = (path_input / subdir)
if (not path_full.exists()):
raise FileNotFoundError(path_full)
fnames = list(path_full.rglob('*.fsh'))
sys.stdout.flush()
return _validate_fsh_files(path_output=path_output, fnames=fnames, fname_validator=fname_validator, fhir_version=fhir_version, exclude_code_systems=exclude_code_systems, exclude_resource_types=exclude_resource_types, verbose=verbose) | def validate_all_fsh(base_path: str, subdir: str, fname_validator: str, fhir_version: str, exclude_code_systems: Optional[Set]=None, exclude_resource_types: Optional[Set]=None, verbose: bool=False) -> List[ValidatorStatus]:
'\n Validate all FSH files in a given subdir.\n\n Process:\n - Extract Profiles and Instances defined in FSH file\n - Run FHIR Java validator for each instance to validate it against its corresponding profile\n\n :param base_path: base path (of SUSHI project)\n :param subdir: subdirectory of profiles\n :param fname_validator: full path to FHIR Java validator file\n :param fhir_version: FHIR version to use in validator\n :param verbose: Print more information\n :param exclude_code_systems: Optional set of code systems which prevent instances from being validated\n :param exclude_resource_types: Optional set of resource types which prevent instances from being validated\n :return: List of validation status, full output and instance and profile names\n '
(path_input, path_output) = get_paths(base_path)
path_full = (path_input / subdir)
if (not path_full.exists()):
raise FileNotFoundError(path_full)
fnames = list(path_full.rglob('*.fsh'))
sys.stdout.flush()
return _validate_fsh_files(path_output=path_output, fnames=fnames, fname_validator=fname_validator, fhir_version=fhir_version, exclude_code_systems=exclude_code_systems, exclude_resource_types=exclude_resource_types, verbose=verbose)<|docstring|>Validate all FSH files in a given subdir.
Process:
- Extract Profiles and Instances defined in FSH file
- Run FHIR Java validator for each instance to validate it against its corresponding profile
:param base_path: base path (of SUSHI project)
:param subdir: subdirectory of profiles
:param fname_validator: full path to FHIR Java validator file
:param fhir_version: FHIR version to use in validator
:param verbose: Print more information
:param exclude_code_systems: Optional set of code systems which prevent instances from being validated
:param exclude_resource_types: Optional set of resource types which prevent instances from being validated
:return: List of validation status, full output and instance and profile names<|endoftext|> |
4f5ded68d1aa9f3395df69fa06639cc05fc2e8a6490546ee89204f910590a98e | def check_instances_availability(fsh_profiles: List[Dict], fsh_instances: List[Dict], abstract_profiles: Set[str]) -> List[str]:
'\n Check if at least one instance exists for each defined profile extracted from FSH file.\n\n :param fsh_profiles: List of profile defined in FSH file\n :param fsh_instances: List of instances defined in FSH file\n :param abstract_profiles: Set of abstract profiles\n :return: List of profiles without instances\n '
profiles_without_instance = []
for p in fsh_profiles:
if (p['id'] in abstract_profiles):
continue
if (not any(((i['instanceof'] == p['id']) for i in fsh_instances))):
profiles_without_instance.append(p['id'])
return profiles_without_instance | Check if at least one instance exists for each defined profile extracted from FSH file.
:param fsh_profiles: List of profile defined in FSH file
:param fsh_instances: List of instances defined in FSH file
:param abstract_profiles: Set of abstract profiles
:return: List of profiles without instances | fsh_validator/fsh_validator.py | check_instances_availability | glichtner/fsh-validator | 0 | python | def check_instances_availability(fsh_profiles: List[Dict], fsh_instances: List[Dict], abstract_profiles: Set[str]) -> List[str]:
'\n Check if at least one instance exists for each defined profile extracted from FSH file.\n\n :param fsh_profiles: List of profile defined in FSH file\n :param fsh_instances: List of instances defined in FSH file\n :param abstract_profiles: Set of abstract profiles\n :return: List of profiles without instances\n '
profiles_without_instance = []
for p in fsh_profiles:
if (p['id'] in abstract_profiles):
continue
if (not any(((i['instanceof'] == p['id']) for i in fsh_instances))):
profiles_without_instance.append(p['id'])
return profiles_without_instance | def check_instances_availability(fsh_profiles: List[Dict], fsh_instances: List[Dict], abstract_profiles: Set[str]) -> List[str]:
'\n Check if at least one instance exists for each defined profile extracted from FSH file.\n\n :param fsh_profiles: List of profile defined in FSH file\n :param fsh_instances: List of instances defined in FSH file\n :param abstract_profiles: Set of abstract profiles\n :return: List of profiles without instances\n '
profiles_without_instance = []
for p in fsh_profiles:
if (p['id'] in abstract_profiles):
continue
if (not any(((i['instanceof'] == p['id']) for i in fsh_instances))):
profiles_without_instance.append(p['id'])
return profiles_without_instance<|docstring|>Check if at least one instance exists for each defined profile extracted from FSH file.
:param fsh_profiles: List of profile defined in FSH file
:param fsh_instances: List of instances defined in FSH file
:param abstract_profiles: Set of abstract profiles
:return: List of profiles without instances<|endoftext|> |
4fd9add56070724fed4051a68574ebcf815a8b8e2e9eeacd4068a176e338ee3c | def get_profile_chain(sdefs: Dict, profile: str) -> List[str]:
'\n Get a list of all profiles that a specific profile is based on and that are part of this SUSHI project.\n\n The objective of this function to provide a list of all parent profiles of a specific profile for inclusion as\n parameters to the FHIR validator.\n\n :param sdefs: StructureDefinitions from SUSHI output\n :param profile: Profile name to get all parents profiles of\n :return: List of all parent profiles of supplied profile name (including the profile itself)\n '
res: List[str] = []
def _traverse(profile, res):
res.append(profile)
if (sdefs[profile]['base'] in sdefs):
_traverse(sdefs[profile]['base'], res)
_traverse(profile, res)
return res | Get a list of all profiles that a specific profile is based on and that are part of this SUSHI project.
The objective of this function to provide a list of all parent profiles of a specific profile for inclusion as
parameters to the FHIR validator.
:param sdefs: StructureDefinitions from SUSHI output
:param profile: Profile name to get all parents profiles of
:return: List of all parent profiles of supplied profile name (including the profile itself) | fsh_validator/fsh_validator.py | get_profile_chain | glichtner/fsh-validator | 0 | python | def get_profile_chain(sdefs: Dict, profile: str) -> List[str]:
'\n Get a list of all profiles that a specific profile is based on and that are part of this SUSHI project.\n\n The objective of this function to provide a list of all parent profiles of a specific profile for inclusion as\n parameters to the FHIR validator.\n\n :param sdefs: StructureDefinitions from SUSHI output\n :param profile: Profile name to get all parents profiles of\n :return: List of all parent profiles of supplied profile name (including the profile itself)\n '
res: List[str] = []
def _traverse(profile, res):
res.append(profile)
if (sdefs[profile]['base'] in sdefs):
_traverse(sdefs[profile]['base'], res)
_traverse(profile, res)
return res | def get_profile_chain(sdefs: Dict, profile: str) -> List[str]:
'\n Get a list of all profiles that a specific profile is based on and that are part of this SUSHI project.\n\n The objective of this function to provide a list of all parent profiles of a specific profile for inclusion as\n parameters to the FHIR validator.\n\n :param sdefs: StructureDefinitions from SUSHI output\n :param profile: Profile name to get all parents profiles of\n :return: List of all parent profiles of supplied profile name (including the profile itself)\n '
res: List[str] = []
def _traverse(profile, res):
res.append(profile)
if (sdefs[profile]['base'] in sdefs):
_traverse(sdefs[profile]['base'], res)
_traverse(profile, res)
return res<|docstring|>Get a list of all profiles that a specific profile is based on and that are part of this SUSHI project.
The objective of this function to provide a list of all parent profiles of a specific profile for inclusion as
parameters to the FHIR validator.
:param sdefs: StructureDefinitions from SUSHI output
:param profile: Profile name to get all parents profiles of
:return: List of all parent profiles of supplied profile name (including the profile itself)<|endoftext|> |
3bec1412fab541931e16d131412c30f28cc2197bcb5371a698470af7a6551873 | def get_profiles_to_include(sdefs, instance):
'\n Get a list of all profiles that a specific instance is based on and that are part of this SUSHI project.\n\n :param sdefs: StructureDefinitions from SUSHI output\n :param instance: Instance name to get all parents profiles of\n :return: List of all parent profiles of supplied instance name (including the instance itself)\n '
profiles_processed = []
if (instance['resourceType'] == 'Bundle'):
profiles_queue = ([instance['profile']] + instance['profilesAdditional'])
else:
profiles_queue = [instance['profile']]
profiles_to_include = []
for profile in profiles_queue:
if (profile in profiles_processed):
continue
profiles_processed.append(profile)
if (profile in sdefs):
profiles_queue += [p for p in sdefs[profile]['profilesAdditional'] if (p not in profiles_processed)]
profile_chain = get_profile_chain(sdefs, profile)
profiles_to_include += profile_chain
profiles_to_include = set(profiles_to_include)
return profiles_to_include | Get a list of all profiles that a specific instance is based on and that are part of this SUSHI project.
:param sdefs: StructureDefinitions from SUSHI output
:param instance: Instance name to get all parents profiles of
:return: List of all parent profiles of supplied instance name (including the instance itself) | fsh_validator/fsh_validator.py | get_profiles_to_include | glichtner/fsh-validator | 0 | python | def get_profiles_to_include(sdefs, instance):
'\n Get a list of all profiles that a specific instance is based on and that are part of this SUSHI project.\n\n :param sdefs: StructureDefinitions from SUSHI output\n :param instance: Instance name to get all parents profiles of\n :return: List of all parent profiles of supplied instance name (including the instance itself)\n '
profiles_processed = []
if (instance['resourceType'] == 'Bundle'):
profiles_queue = ([instance['profile']] + instance['profilesAdditional'])
else:
profiles_queue = [instance['profile']]
profiles_to_include = []
for profile in profiles_queue:
if (profile in profiles_processed):
continue
profiles_processed.append(profile)
if (profile in sdefs):
profiles_queue += [p for p in sdefs[profile]['profilesAdditional'] if (p not in profiles_processed)]
profile_chain = get_profile_chain(sdefs, profile)
profiles_to_include += profile_chain
profiles_to_include = set(profiles_to_include)
return profiles_to_include | def get_profiles_to_include(sdefs, instance):
'\n Get a list of all profiles that a specific instance is based on and that are part of this SUSHI project.\n\n :param sdefs: StructureDefinitions from SUSHI output\n :param instance: Instance name to get all parents profiles of\n :return: List of all parent profiles of supplied instance name (including the instance itself)\n '
profiles_processed = []
if (instance['resourceType'] == 'Bundle'):
profiles_queue = ([instance['profile']] + instance['profilesAdditional'])
else:
profiles_queue = [instance['profile']]
profiles_to_include = []
for profile in profiles_queue:
if (profile in profiles_processed):
continue
profiles_processed.append(profile)
if (profile in sdefs):
profiles_queue += [p for p in sdefs[profile]['profilesAdditional'] if (p not in profiles_processed)]
profile_chain = get_profile_chain(sdefs, profile)
profiles_to_include += profile_chain
profiles_to_include = set(profiles_to_include)
return profiles_to_include<|docstring|>Get a list of all profiles that a specific instance is based on and that are part of this SUSHI project.
:param sdefs: StructureDefinitions from SUSHI output
:param instance: Instance name to get all parents profiles of
:return: List of all parent profiles of supplied instance name (including the instance itself)<|endoftext|> |
2ead2d1490360fdf31de4950b15075ee2ec55c6556d3194e090a4eeea7ed759c | def run_validation(fname_validator: str, fsh_instances: List[Dict], sdefs: Dict, instances: Dict, deps: Dict, vs: Dict, cs: Dict, extensions: Dict, fhir_version: str, verbose: bool) -> List[ValidatorStatus]:
'\n Run FHIR Java validator for each instance defined in FSH file.\n\n :param fname_validator: full path to FHIR Java validator file\n :param fsh_instances: List of instances defined in FSH file\n :param sdefs: StructureDefinitions from SUSHI output\n :param instances: Instance from SUSHI output\n :param deps: Dependencies from SUSHI output\n :param vs: ValueSets from SUSHI output\n :param cs: CodeSystems from SUSHI output\n :param extensions: Extensions from SUSHI output\n :param fhir_version: FHIR version to use in validator\n :param verbose: Print more information\n :return: List of validation result dicts containing validation status, full output and instance and profile names\n '
cmd_base = ['java', f'-jar {fname_validator}', f'-version {fhir_version}', '-txLog logs/txlog.html']
cmd_base += [f"-ig {dep['packageId']}#{dep['version']}" for dep in deps.values()]
cmds = {}
questionnaires = [i['filename'] for i in instances.values() if (i['resourceType'] == 'Questionnaire')]
for fsh_instance in fsh_instances:
if (not (fsh_instance['instance'] in instances)):
raise Exception(f"Could not find {fsh_instance['instance']} in instances")
instance = instances[fsh_instance['instance']]
profiles_include = get_profiles_to_include(sdefs, instance)
cmd = list(cmd_base)
cmd += [f"-ig {sdefs[profile]['filename']}" for profile in profiles_include]
cmd += [f"-ig {valueset['filename']}" for valueset in vs.values()]
cmd += [f"-ig {codesystem['filename']}" for codesystem in cs.values()]
cmd += [f"-ig {extension['filename']}" for extension in extensions.values()]
cmd += [f'-ig {qs}' for qs in questionnaires if (qs != instance['filename'])]
cmd += [f"-profile {instance['profile']}", instance['filename']]
cmds[fsh_instance['instance']] = cmd
results = []
for fsh_instance_name in cmds:
print_box(f"Validating {fsh_instance_name} against profile {instances[fsh_instance_name]['profile']}")
status = execute_validator(cmds[fsh_instance_name], verbose=verbose)
status.instance = fsh_instance_name
status.profile = instance['profile']
results.append(status)
return results | Run FHIR Java validator for each instance defined in FSH file.
:param fname_validator: full path to FHIR Java validator file
:param fsh_instances: List of instances defined in FSH file
:param sdefs: StructureDefinitions from SUSHI output
:param instances: Instance from SUSHI output
:param deps: Dependencies from SUSHI output
:param vs: ValueSets from SUSHI output
:param cs: CodeSystems from SUSHI output
:param extensions: Extensions from SUSHI output
:param fhir_version: FHIR version to use in validator
:param verbose: Print more information
:return: List of validation result dicts containing validation status, full output and instance and profile names | fsh_validator/fsh_validator.py | run_validation | glichtner/fsh-validator | 0 | python | def run_validation(fname_validator: str, fsh_instances: List[Dict], sdefs: Dict, instances: Dict, deps: Dict, vs: Dict, cs: Dict, extensions: Dict, fhir_version: str, verbose: bool) -> List[ValidatorStatus]:
'\n Run FHIR Java validator for each instance defined in FSH file.\n\n :param fname_validator: full path to FHIR Java validator file\n :param fsh_instances: List of instances defined in FSH file\n :param sdefs: StructureDefinitions from SUSHI output\n :param instances: Instance from SUSHI output\n :param deps: Dependencies from SUSHI output\n :param vs: ValueSets from SUSHI output\n :param cs: CodeSystems from SUSHI output\n :param extensions: Extensions from SUSHI output\n :param fhir_version: FHIR version to use in validator\n :param verbose: Print more information\n :return: List of validation result dicts containing validation status, full output and instance and profile names\n '
cmd_base = ['java', f'-jar {fname_validator}', f'-version {fhir_version}', '-txLog logs/txlog.html']
cmd_base += [f"-ig {dep['packageId']}#{dep['version']}" for dep in deps.values()]
cmds = {}
questionnaires = [i['filename'] for i in instances.values() if (i['resourceType'] == 'Questionnaire')]
for fsh_instance in fsh_instances:
if (not (fsh_instance['instance'] in instances)):
raise Exception(f"Could not find {fsh_instance['instance']} in instances")
instance = instances[fsh_instance['instance']]
profiles_include = get_profiles_to_include(sdefs, instance)
cmd = list(cmd_base)
cmd += [f"-ig {sdefs[profile]['filename']}" for profile in profiles_include]
cmd += [f"-ig {valueset['filename']}" for valueset in vs.values()]
cmd += [f"-ig {codesystem['filename']}" for codesystem in cs.values()]
cmd += [f"-ig {extension['filename']}" for extension in extensions.values()]
cmd += [f'-ig {qs}' for qs in questionnaires if (qs != instance['filename'])]
cmd += [f"-profile {instance['profile']}", instance['filename']]
cmds[fsh_instance['instance']] = cmd
results = []
for fsh_instance_name in cmds:
print_box(f"Validating {fsh_instance_name} against profile {instances[fsh_instance_name]['profile']}")
status = execute_validator(cmds[fsh_instance_name], verbose=verbose)
status.instance = fsh_instance_name
status.profile = instance['profile']
results.append(status)
return results | def run_validation(fname_validator: str, fsh_instances: List[Dict], sdefs: Dict, instances: Dict, deps: Dict, vs: Dict, cs: Dict, extensions: Dict, fhir_version: str, verbose: bool) -> List[ValidatorStatus]:
'\n Run FHIR Java validator for each instance defined in FSH file.\n\n :param fname_validator: full path to FHIR Java validator file\n :param fsh_instances: List of instances defined in FSH file\n :param sdefs: StructureDefinitions from SUSHI output\n :param instances: Instance from SUSHI output\n :param deps: Dependencies from SUSHI output\n :param vs: ValueSets from SUSHI output\n :param cs: CodeSystems from SUSHI output\n :param extensions: Extensions from SUSHI output\n :param fhir_version: FHIR version to use in validator\n :param verbose: Print more information\n :return: List of validation result dicts containing validation status, full output and instance and profile names\n '
cmd_base = ['java', f'-jar {fname_validator}', f'-version {fhir_version}', '-txLog logs/txlog.html']
cmd_base += [f"-ig {dep['packageId']}#{dep['version']}" for dep in deps.values()]
cmds = {}
questionnaires = [i['filename'] for i in instances.values() if (i['resourceType'] == 'Questionnaire')]
for fsh_instance in fsh_instances:
if (not (fsh_instance['instance'] in instances)):
raise Exception(f"Could not find {fsh_instance['instance']} in instances")
instance = instances[fsh_instance['instance']]
profiles_include = get_profiles_to_include(sdefs, instance)
cmd = list(cmd_base)
cmd += [f"-ig {sdefs[profile]['filename']}" for profile in profiles_include]
cmd += [f"-ig {valueset['filename']}" for valueset in vs.values()]
cmd += [f"-ig {codesystem['filename']}" for codesystem in cs.values()]
cmd += [f"-ig {extension['filename']}" for extension in extensions.values()]
cmd += [f'-ig {qs}' for qs in questionnaires if (qs != instance['filename'])]
cmd += [f"-profile {instance['profile']}", instance['filename']]
cmds[fsh_instance['instance']] = cmd
results = []
for fsh_instance_name in cmds:
print_box(f"Validating {fsh_instance_name} against profile {instances[fsh_instance_name]['profile']}")
status = execute_validator(cmds[fsh_instance_name], verbose=verbose)
status.instance = fsh_instance_name
status.profile = instance['profile']
results.append(status)
return results<|docstring|>Run FHIR Java validator for each instance defined in FSH file.
:param fname_validator: full path to FHIR Java validator file
:param fsh_instances: List of instances defined in FSH file
:param sdefs: StructureDefinitions from SUSHI output
:param instances: Instance from SUSHI output
:param deps: Dependencies from SUSHI output
:param vs: ValueSets from SUSHI output
:param cs: CodeSystems from SUSHI output
:param extensions: Extensions from SUSHI output
:param fhir_version: FHIR version to use in validator
:param verbose: Print more information
:return: List of validation result dicts containing validation status, full output and instance and profile names<|endoftext|> |
6f8413b2d0b6ac9543f6276d1332ff32ae9913bb34f2c35826bad7e37e27bb44 | def run_command(cmd: Union[(str, List[str])]) -> None:
'\n Run a shell command.\n\n Raises CommandNotSuccessfulException if the return code of the command is not 0.\n\n :param cmd: Command to run as single string or list of strings\n :return: None\n '
if isinstance(cmd, list):
cmd = ' '.join([str(s) for s in cmd])
c = subprocess.run(cmd, shell=True)
if (c.returncode != 0):
raise CommandNotSuccessfulException() | Run a shell command.
Raises CommandNotSuccessfulException if the return code of the command is not 0.
:param cmd: Command to run as single string or list of strings
:return: None | fsh_validator/fsh_validator.py | run_command | glichtner/fsh-validator | 0 | python | def run_command(cmd: Union[(str, List[str])]) -> None:
'\n Run a shell command.\n\n Raises CommandNotSuccessfulException if the return code of the command is not 0.\n\n :param cmd: Command to run as single string or list of strings\n :return: None\n '
if isinstance(cmd, list):
cmd = ' '.join([str(s) for s in cmd])
c = subprocess.run(cmd, shell=True)
if (c.returncode != 0):
raise CommandNotSuccessfulException() | def run_command(cmd: Union[(str, List[str])]) -> None:
'\n Run a shell command.\n\n Raises CommandNotSuccessfulException if the return code of the command is not 0.\n\n :param cmd: Command to run as single string or list of strings\n :return: None\n '
if isinstance(cmd, list):
cmd = ' '.join([str(s) for s in cmd])
c = subprocess.run(cmd, shell=True)
if (c.returncode != 0):
raise CommandNotSuccessfulException()<|docstring|>Run a shell command.
Raises CommandNotSuccessfulException if the return code of the command is not 0.
:param cmd: Command to run as single string or list of strings
:return: None<|endoftext|> |
6c06a6d86a59137de41149339018588dc2a51cc314ce90865b88e9fa09330ec6 | def printc(msg: str, col: str, end: str='\n') -> None:
'\n Print a message in color to console.\n\n :param msg: Message to print\n :param col: Color (from bcolors)\n :param end: end of line character(s)\n :return: None\n '
print(f'{col}{msg}{bcolors.ENDC}', end=end, flush=True) | Print a message in color to console.
:param msg: Message to print
:param col: Color (from bcolors)
:param end: end of line character(s)
:return: None | fsh_validator/fsh_validator.py | printc | glichtner/fsh-validator | 0 | python | def printc(msg: str, col: str, end: str='\n') -> None:
'\n Print a message in color to console.\n\n :param msg: Message to print\n :param col: Color (from bcolors)\n :param end: end of line character(s)\n :return: None\n '
print(f'{col}{msg}{bcolors.ENDC}', end=end, flush=True) | def printc(msg: str, col: str, end: str='\n') -> None:
'\n Print a message in color to console.\n\n :param msg: Message to print\n :param col: Color (from bcolors)\n :param end: end of line character(s)\n :return: None\n '
print(f'{col}{msg}{bcolors.ENDC}', end=end, flush=True)<|docstring|>Print a message in color to console.
:param msg: Message to print
:param col: Color (from bcolors)
:param end: end of line character(s)
:return: None<|endoftext|> |
d78253f1639cec2a2a6bff7e3fd86f400834b324b1e17c9afebaae51c908af6e | def execute_validator(cmd: Union[(str, List[str])], verbose: bool=False) -> ValidatorStatus:
"\n Execute the Java FHIR validator and parse it's output.\n\n :param cmd: Command to execute\n :param verbose: If true, all output from the validator will be printed to stdout.\n :return: ValidatorStatus object\n "
if isinstance(cmd, list):
cmd = ' '.join([str(s) for s in cmd])
if verbose:
print(cmd)
popen = subprocess.Popen(cmd, stdout=subprocess.PIPE, universal_newlines=True, shell=True)
if (popen.stdout is None):
return ValidatorStatus(status=ValidatorStatus.Status.FAILURE, errors=['popen failed'])
output = []
for line in popen.stdout:
if (verbose or (line.strip() in ['Loading', 'Validating'])):
printc(line, col=bcolors.HEADER, end='')
sys.stdout.flush()
output.append(line)
popen.stdout.close()
popen.wait()
try:
status = ValidatorStatus().parse(output)
status.pretty_print()
except Exception:
print('Could not parse validator output:', flush=True)
print(''.join(output), flush=True)
status = ValidatorStatus(status=ValidatorStatus.Status.FAILURE, errors=['Error during validator execution'], output=output)
return status | Execute the Java FHIR validator and parse it's output.
:param cmd: Command to execute
:param verbose: If true, all output from the validator will be printed to stdout.
:return: ValidatorStatus object | fsh_validator/fsh_validator.py | execute_validator | glichtner/fsh-validator | 0 | python | def execute_validator(cmd: Union[(str, List[str])], verbose: bool=False) -> ValidatorStatus:
"\n Execute the Java FHIR validator and parse it's output.\n\n :param cmd: Command to execute\n :param verbose: If true, all output from the validator will be printed to stdout.\n :return: ValidatorStatus object\n "
if isinstance(cmd, list):
cmd = ' '.join([str(s) for s in cmd])
if verbose:
print(cmd)
popen = subprocess.Popen(cmd, stdout=subprocess.PIPE, universal_newlines=True, shell=True)
if (popen.stdout is None):
return ValidatorStatus(status=ValidatorStatus.Status.FAILURE, errors=['popen failed'])
output = []
for line in popen.stdout:
if (verbose or (line.strip() in ['Loading', 'Validating'])):
printc(line, col=bcolors.HEADER, end=)
sys.stdout.flush()
output.append(line)
popen.stdout.close()
popen.wait()
try:
status = ValidatorStatus().parse(output)
status.pretty_print()
except Exception:
print('Could not parse validator output:', flush=True)
print(.join(output), flush=True)
status = ValidatorStatus(status=ValidatorStatus.Status.FAILURE, errors=['Error during validator execution'], output=output)
return status | def execute_validator(cmd: Union[(str, List[str])], verbose: bool=False) -> ValidatorStatus:
"\n Execute the Java FHIR validator and parse it's output.\n\n :param cmd: Command to execute\n :param verbose: If true, all output from the validator will be printed to stdout.\n :return: ValidatorStatus object\n "
if isinstance(cmd, list):
cmd = ' '.join([str(s) for s in cmd])
if verbose:
print(cmd)
popen = subprocess.Popen(cmd, stdout=subprocess.PIPE, universal_newlines=True, shell=True)
if (popen.stdout is None):
return ValidatorStatus(status=ValidatorStatus.Status.FAILURE, errors=['popen failed'])
output = []
for line in popen.stdout:
if (verbose or (line.strip() in ['Loading', 'Validating'])):
printc(line, col=bcolors.HEADER, end=)
sys.stdout.flush()
output.append(line)
popen.stdout.close()
popen.wait()
try:
status = ValidatorStatus().parse(output)
status.pretty_print()
except Exception:
print('Could not parse validator output:', flush=True)
print(.join(output), flush=True)
status = ValidatorStatus(status=ValidatorStatus.Status.FAILURE, errors=['Error during validator execution'], output=output)
return status<|docstring|>Execute the Java FHIR validator and parse it's output.
:param cmd: Command to execute
:param verbose: If true, all output from the validator will be printed to stdout.
:return: ValidatorStatus object<|endoftext|> |
79fe1a3a052eb5d2d809d52130d3231d448d3b452841e378548db6d329e429a5 | def store_log(results: List[ValidatorStatus], log_path: Path) -> None:
'\n Store parsed and full output from validator run to files.\n\n Parsed output will be saved to an excel file in tabular format, full output to a text file.\n\n :param results: List of ValidatorStatus objects as returned by _validate_fsh_files()\n :param log_path: Path where log files are stored\n :return: None\n '
dfs = []
output = ''
for status in results:
dfs.append(status.to_frame())
if (status.instance != ''):
output += print_box(f'Validating {status.instance} on profile {status.profile}', print_str=False)
else:
output += print_box(f'Profile {status.profile}', print_str=False)
output += ''.join(status.output)
output += '\n\n'
df = pd.concat([s.to_frame() for s in results]).reset_index(drop=True)
log_basename = ('validation_' + datetime.now().strftime('%y%m%dT%H%M%S'))
with open((log_path / (log_basename + '.log')), 'w') as f:
f.write(output)
df.to_excel((log_path / (log_basename + '.xlsx')), index=False)
df.to_markdown((log_path / (log_basename + '.md')), index=False) | Store parsed and full output from validator run to files.
Parsed output will be saved to an excel file in tabular format, full output to a text file.
:param results: List of ValidatorStatus objects as returned by _validate_fsh_files()
:param log_path: Path where log files are stored
:return: None | fsh_validator/fsh_validator.py | store_log | glichtner/fsh-validator | 0 | python | def store_log(results: List[ValidatorStatus], log_path: Path) -> None:
'\n Store parsed and full output from validator run to files.\n\n Parsed output will be saved to an excel file in tabular format, full output to a text file.\n\n :param results: List of ValidatorStatus objects as returned by _validate_fsh_files()\n :param log_path: Path where log files are stored\n :return: None\n '
dfs = []
output =
for status in results:
dfs.append(status.to_frame())
if (status.instance != ):
output += print_box(f'Validating {status.instance} on profile {status.profile}', print_str=False)
else:
output += print_box(f'Profile {status.profile}', print_str=False)
output += .join(status.output)
output += '\n\n'
df = pd.concat([s.to_frame() for s in results]).reset_index(drop=True)
log_basename = ('validation_' + datetime.now().strftime('%y%m%dT%H%M%S'))
with open((log_path / (log_basename + '.log')), 'w') as f:
f.write(output)
df.to_excel((log_path / (log_basename + '.xlsx')), index=False)
df.to_markdown((log_path / (log_basename + '.md')), index=False) | def store_log(results: List[ValidatorStatus], log_path: Path) -> None:
'\n Store parsed and full output from validator run to files.\n\n Parsed output will be saved to an excel file in tabular format, full output to a text file.\n\n :param results: List of ValidatorStatus objects as returned by _validate_fsh_files()\n :param log_path: Path where log files are stored\n :return: None\n '
dfs = []
output =
for status in results:
dfs.append(status.to_frame())
if (status.instance != ):
output += print_box(f'Validating {status.instance} on profile {status.profile}', print_str=False)
else:
output += print_box(f'Profile {status.profile}', print_str=False)
output += .join(status.output)
output += '\n\n'
df = pd.concat([s.to_frame() for s in results]).reset_index(drop=True)
log_basename = ('validation_' + datetime.now().strftime('%y%m%dT%H%M%S'))
with open((log_path / (log_basename + '.log')), 'w') as f:
f.write(output)
df.to_excel((log_path / (log_basename + '.xlsx')), index=False)
df.to_markdown((log_path / (log_basename + '.md')), index=False)<|docstring|>Store parsed and full output from validator run to files.
Parsed output will be saved to an excel file in tabular format, full output to a text file.
:param results: List of ValidatorStatus objects as returned by _validate_fsh_files()
:param log_path: Path where log files are stored
:return: None<|endoftext|> |
13d2fceb6785d07126347604dd2db4e331ebedeac94c5d7731a283e40fa35a80 | def get_fhir_version_from_sushi_config(base_path: Path) -> str:
'\n Get the FHIR version from the SUSHI config file.\n\n :param base_path: Path to the SUSHI config file\n :return: FHIR version string\n '
conf_filename = (base_path / 'sushi-config.yaml')
if (not conf_filename.exists()):
raise FileNotFoundError(f'Could not find {conf_filename}')
with open(conf_filename, 'r') as f:
conf = yaml.safe_load(f)
fhir_version = conf['fhirVersion']
return fhir_version | Get the FHIR version from the SUSHI config file.
:param base_path: Path to the SUSHI config file
:return: FHIR version string | fsh_validator/fsh_validator.py | get_fhir_version_from_sushi_config | glichtner/fsh-validator | 0 | python | def get_fhir_version_from_sushi_config(base_path: Path) -> str:
'\n Get the FHIR version from the SUSHI config file.\n\n :param base_path: Path to the SUSHI config file\n :return: FHIR version string\n '
conf_filename = (base_path / 'sushi-config.yaml')
if (not conf_filename.exists()):
raise FileNotFoundError(f'Could not find {conf_filename}')
with open(conf_filename, 'r') as f:
conf = yaml.safe_load(f)
fhir_version = conf['fhirVersion']
return fhir_version | def get_fhir_version_from_sushi_config(base_path: Path) -> str:
'\n Get the FHIR version from the SUSHI config file.\n\n :param base_path: Path to the SUSHI config file\n :return: FHIR version string\n '
conf_filename = (base_path / 'sushi-config.yaml')
if (not conf_filename.exists()):
raise FileNotFoundError(f'Could not find {conf_filename}')
with open(conf_filename, 'r') as f:
conf = yaml.safe_load(f)
fhir_version = conf['fhirVersion']
return fhir_version<|docstring|>Get the FHIR version from the SUSHI config file.
:param base_path: Path to the SUSHI config file
:return: FHIR version string<|endoftext|> |
e0140543422ce45306f8412dd9cc68c7f7971fea5ad6ed3ebc60f44a358059e2 | def assert_sushi_installed() -> None:
'\n Assert that FSH Sushi is an executable on the system.\n\n :return: None\n '
if (shutil.which('sushi') is None):
raise FileNotFoundError('Could not detect fsh sushi on the system. Install via "npm install -g fsh-sushi".') | Assert that FSH Sushi is an executable on the system.
:return: None | fsh_validator/fsh_validator.py | assert_sushi_installed | glichtner/fsh-validator | 0 | python | def assert_sushi_installed() -> None:
'\n Assert that FSH Sushi is an executable on the system.\n\n :return: None\n '
if (shutil.which('sushi') is None):
raise FileNotFoundError('Could not detect fsh sushi on the system. Install via "npm install -g fsh-sushi".') | def assert_sushi_installed() -> None:
'\n Assert that FSH Sushi is an executable on the system.\n\n :return: None\n '
if (shutil.which('sushi') is None):
raise FileNotFoundError('Could not detect fsh sushi on the system. Install via "npm install -g fsh-sushi".')<|docstring|>Assert that FSH Sushi is an executable on the system.
:return: None<|endoftext|> |
02f3185e90547d0afda78f34a789c87edbfe9450e3f880c8c72e23e5df4120cf | def run_sushi(path: str) -> None:
'\n Run SUSHI to convert FSH files.\n\n :param path: Path to run SUSHI in\n :return: None\n '
assert_sushi_installed()
run_command(f'sushi {path}') | Run SUSHI to convert FSH files.
:param path: Path to run SUSHI in
:return: None | fsh_validator/fsh_validator.py | run_sushi | glichtner/fsh-validator | 0 | python | def run_sushi(path: str) -> None:
'\n Run SUSHI to convert FSH files.\n\n :param path: Path to run SUSHI in\n :return: None\n '
assert_sushi_installed()
run_command(f'sushi {path}') | def run_sushi(path: str) -> None:
'\n Run SUSHI to convert FSH files.\n\n :param path: Path to run SUSHI in\n :return: None\n '
assert_sushi_installed()
run_command(f'sushi {path}')<|docstring|>Run SUSHI to convert FSH files.
:param path: Path to run SUSHI in
:return: None<|endoftext|> |
dcfff34591110f80eb83142bf6b215ef109ad26ac74f3f2322941918e2e9093c | def __init__(self, msg: str='Command execution not successful - see command output for more information', *args):
'\n Shell command not successfully executed.\n\n :param msg: Message to display\n :param args: Other positional arguments for BaseException\n :param kwargs: Other keyword arguments for BaseException\n '
args = tuple(([msg] + list(args)))
super().__init__(*args) | Shell command not successfully executed.
:param msg: Message to display
:param args: Other positional arguments for BaseException
:param kwargs: Other keyword arguments for BaseException | fsh_validator/fsh_validator.py | __init__ | glichtner/fsh-validator | 0 | python | def __init__(self, msg: str='Command execution not successful - see command output for more information', *args):
'\n Shell command not successfully executed.\n\n :param msg: Message to display\n :param args: Other positional arguments for BaseException\n :param kwargs: Other keyword arguments for BaseException\n '
args = tuple(([msg] + list(args)))
super().__init__(*args) | def __init__(self, msg: str='Command execution not successful - see command output for more information', *args):
'\n Shell command not successfully executed.\n\n :param msg: Message to display\n :param args: Other positional arguments for BaseException\n :param kwargs: Other keyword arguments for BaseException\n '
args = tuple(([msg] + list(args)))
super().__init__(*args)<|docstring|>Shell command not successfully executed.
:param msg: Message to display
:param args: Other positional arguments for BaseException
:param kwargs: Other keyword arguments for BaseException<|endoftext|> |
72208b4f6abcf0c1984eeeca9d02300e69a705ec9949073f0f5cd4bbebfde27c | def __init__(self, output: Optional[List[str]]=None, status: Status=Status.NOT_RUN, errors: Optional[List[str]]=None, warnings: Optional[List[str]]=None, profile: str='', instance: str=''):
'\n Status information of FHIR Validator run.\n\n :param output: Full validator output\n :param status: status string\n :param errors: list of errors during parsing\n :param warnings: list of warnings during parsing\n :param profile: name of profile against which validation was performed\n :param instance: name of instance that was validated\n '
def list_if_none(v: Optional[List]) -> List:
return ([] if (v is None) else v)
self.status = status
self.errors = list_if_none(errors)
self.warnings: List[str] = list_if_none(warnings)
self.notes: List[str] = []
self.n_errors = len(self.errors)
self.n_warnings = len(self.warnings)
self.n_notes = len(self.notes)
self.output = list_if_none(output)
self.profile = profile
self.instance = instance | Status information of FHIR Validator run.
:param output: Full validator output
:param status: status string
:param errors: list of errors during parsing
:param warnings: list of warnings during parsing
:param profile: name of profile against which validation was performed
:param instance: name of instance that was validated | fsh_validator/fsh_validator.py | __init__ | glichtner/fsh-validator | 0 | python | def __init__(self, output: Optional[List[str]]=None, status: Status=Status.NOT_RUN, errors: Optional[List[str]]=None, warnings: Optional[List[str]]=None, profile: str=, instance: str=):
'\n Status information of FHIR Validator run.\n\n :param output: Full validator output\n :param status: status string\n :param errors: list of errors during parsing\n :param warnings: list of warnings during parsing\n :param profile: name of profile against which validation was performed\n :param instance: name of instance that was validated\n '
def list_if_none(v: Optional[List]) -> List:
return ([] if (v is None) else v)
self.status = status
self.errors = list_if_none(errors)
self.warnings: List[str] = list_if_none(warnings)
self.notes: List[str] = []
self.n_errors = len(self.errors)
self.n_warnings = len(self.warnings)
self.n_notes = len(self.notes)
self.output = list_if_none(output)
self.profile = profile
self.instance = instance | def __init__(self, output: Optional[List[str]]=None, status: Status=Status.NOT_RUN, errors: Optional[List[str]]=None, warnings: Optional[List[str]]=None, profile: str=, instance: str=):
'\n Status information of FHIR Validator run.\n\n :param output: Full validator output\n :param status: status string\n :param errors: list of errors during parsing\n :param warnings: list of warnings during parsing\n :param profile: name of profile against which validation was performed\n :param instance: name of instance that was validated\n '
def list_if_none(v: Optional[List]) -> List:
return ([] if (v is None) else v)
self.status = status
self.errors = list_if_none(errors)
self.warnings: List[str] = list_if_none(warnings)
self.notes: List[str] = []
self.n_errors = len(self.errors)
self.n_warnings = len(self.warnings)
self.n_notes = len(self.notes)
self.output = list_if_none(output)
self.profile = profile
self.instance = instance<|docstring|>Status information of FHIR Validator run.
:param output: Full validator output
:param status: status string
:param errors: list of errors during parsing
:param warnings: list of warnings during parsing
:param profile: name of profile against which validation was performed
:param instance: name of instance that was validated<|endoftext|> |
b9e125b86c5e096de443f9ca0e441744c36b1514d7d9bd6fcd0a65f4185ba431 | def parse(self, output: List[str]) -> 'ValidatorStatus':
'\n Parse FHIR Validator output.\n\n :param output: Output of a validator run\n :return: None\n '
pattern_status = re.compile('(?P<status>\\*FAILURE\\*|Success): (?P<n_errors>\\d+) errors, (?P<n_warnings>\\d+) warnings, (?P<n_notes>\\d+) notes')
pattern_error = re.compile(' (Error @ .*)')
pattern_warn = re.compile(' (Warning @ .*)')
pattern_note = re.compile(' (Information @ .*)')
self.output = output
output_s = ''.join(output)
m = pattern_status.search(output_s)
status_map = {'Success': ValidatorStatus.Status.SUCCESS, '*FAILURE*': ValidatorStatus.Status.FAILURE}
self.status = status_map[m.group(1)]
(self.n_errors, self.n_warnings, self.n_notes) = (int(m.group((i + 2))) for i in range(3))
self.errors = [m.group().strip() for m in pattern_error.finditer(output_s)]
self.warnings = [m.group().strip() for m in pattern_warn.finditer(output_s)]
self.notes = [m.group().strip() for m in pattern_note.finditer(output_s)]
if ((self.status == ValidatorStatus.Status.SUCCESS) and (len(self.warnings) > 0)):
self.status = ValidatorStatus.Status.WARNING
return self | Parse FHIR Validator output.
:param output: Output of a validator run
:return: None | fsh_validator/fsh_validator.py | parse | glichtner/fsh-validator | 0 | python | def parse(self, output: List[str]) -> 'ValidatorStatus':
'\n Parse FHIR Validator output.\n\n :param output: Output of a validator run\n :return: None\n '
pattern_status = re.compile('(?P<status>\\*FAILURE\\*|Success): (?P<n_errors>\\d+) errors, (?P<n_warnings>\\d+) warnings, (?P<n_notes>\\d+) notes')
pattern_error = re.compile(' (Error @ .*)')
pattern_warn = re.compile(' (Warning @ .*)')
pattern_note = re.compile(' (Information @ .*)')
self.output = output
output_s = .join(output)
m = pattern_status.search(output_s)
status_map = {'Success': ValidatorStatus.Status.SUCCESS, '*FAILURE*': ValidatorStatus.Status.FAILURE}
self.status = status_map[m.group(1)]
(self.n_errors, self.n_warnings, self.n_notes) = (int(m.group((i + 2))) for i in range(3))
self.errors = [m.group().strip() for m in pattern_error.finditer(output_s)]
self.warnings = [m.group().strip() for m in pattern_warn.finditer(output_s)]
self.notes = [m.group().strip() for m in pattern_note.finditer(output_s)]
if ((self.status == ValidatorStatus.Status.SUCCESS) and (len(self.warnings) > 0)):
self.status = ValidatorStatus.Status.WARNING
return self | def parse(self, output: List[str]) -> 'ValidatorStatus':
'\n Parse FHIR Validator output.\n\n :param output: Output of a validator run\n :return: None\n '
pattern_status = re.compile('(?P<status>\\*FAILURE\\*|Success): (?P<n_errors>\\d+) errors, (?P<n_warnings>\\d+) warnings, (?P<n_notes>\\d+) notes')
pattern_error = re.compile(' (Error @ .*)')
pattern_warn = re.compile(' (Warning @ .*)')
pattern_note = re.compile(' (Information @ .*)')
self.output = output
output_s = .join(output)
m = pattern_status.search(output_s)
status_map = {'Success': ValidatorStatus.Status.SUCCESS, '*FAILURE*': ValidatorStatus.Status.FAILURE}
self.status = status_map[m.group(1)]
(self.n_errors, self.n_warnings, self.n_notes) = (int(m.group((i + 2))) for i in range(3))
self.errors = [m.group().strip() for m in pattern_error.finditer(output_s)]
self.warnings = [m.group().strip() for m in pattern_warn.finditer(output_s)]
self.notes = [m.group().strip() for m in pattern_note.finditer(output_s)]
if ((self.status == ValidatorStatus.Status.SUCCESS) and (len(self.warnings) > 0)):
self.status = ValidatorStatus.Status.WARNING
return self<|docstring|>Parse FHIR Validator output.
:param output: Output of a validator run
:return: None<|endoftext|> |
08aa3a925e91caec233a728cb875f5948fb6b337e4b21697015e4cabb4318248 | def pretty_print(self, with_header: bool=False) -> None:
'\n Format and print the parsed output of fhir java validator to console.\n\n :param with_header: If true, print a header with information about the profile being validated\n :return: None\n '
if with_header:
print_box(f'Profile {self.profile}')
if (self.n_errors > 0):
col = bcolors.FAIL
elif (self.n_warnings > 0):
col = bcolors.WARNING
else:
col = bcolors.OKGREEN
printc(f'{bcolors.BOLD}{self.status.value.title()}: {self.n_errors} errors, {self.n_warnings} warnings, {self.n_notes} notes', col)
for msg in self.errors:
printc(f' {msg}', bcolors.FAIL)
for msg in self.warnings:
printc(f' {msg}', bcolors.WARNING)
for msg in self.notes:
print(f' {msg}')
sys.stdout.flush() | Format and print the parsed output of fhir java validator to console.
:param with_header: If true, print a header with information about the profile being validated
:return: None | fsh_validator/fsh_validator.py | pretty_print | glichtner/fsh-validator | 0 | python | def pretty_print(self, with_header: bool=False) -> None:
'\n Format and print the parsed output of fhir java validator to console.\n\n :param with_header: If true, print a header with information about the profile being validated\n :return: None\n '
if with_header:
print_box(f'Profile {self.profile}')
if (self.n_errors > 0):
col = bcolors.FAIL
elif (self.n_warnings > 0):
col = bcolors.WARNING
else:
col = bcolors.OKGREEN
printc(f'{bcolors.BOLD}{self.status.value.title()}: {self.n_errors} errors, {self.n_warnings} warnings, {self.n_notes} notes', col)
for msg in self.errors:
printc(f' {msg}', bcolors.FAIL)
for msg in self.warnings:
printc(f' {msg}', bcolors.WARNING)
for msg in self.notes:
print(f' {msg}')
sys.stdout.flush() | def pretty_print(self, with_header: bool=False) -> None:
'\n Format and print the parsed output of fhir java validator to console.\n\n :param with_header: If true, print a header with information about the profile being validated\n :return: None\n '
if with_header:
print_box(f'Profile {self.profile}')
if (self.n_errors > 0):
col = bcolors.FAIL
elif (self.n_warnings > 0):
col = bcolors.WARNING
else:
col = bcolors.OKGREEN
printc(f'{bcolors.BOLD}{self.status.value.title()}: {self.n_errors} errors, {self.n_warnings} warnings, {self.n_notes} notes', col)
for msg in self.errors:
printc(f' {msg}', bcolors.FAIL)
for msg in self.warnings:
printc(f' {msg}', bcolors.WARNING)
for msg in self.notes:
print(f' {msg}')
sys.stdout.flush()<|docstring|>Format and print the parsed output of fhir java validator to console.
:param with_header: If true, print a header with information about the profile being validated
:return: None<|endoftext|> |
36860a8c47e2a73cfa85c6cb1e4675cc58774182105fa6838e5469282927aec8 | def failed(self):
'\n Check if the validation run failed.\n\n :return: True if the validation run failed, False otherwise\n '
return (self.status == ValidatorStatus.Status.FAILURE) | Check if the validation run failed.
:return: True if the validation run failed, False otherwise | fsh_validator/fsh_validator.py | failed | glichtner/fsh-validator | 0 | python | def failed(self):
'\n Check if the validation run failed.\n\n :return: True if the validation run failed, False otherwise\n '
return (self.status == ValidatorStatus.Status.FAILURE) | def failed(self):
'\n Check if the validation run failed.\n\n :return: True if the validation run failed, False otherwise\n '
return (self.status == ValidatorStatus.Status.FAILURE)<|docstring|>Check if the validation run failed.
:return: True if the validation run failed, False otherwise<|endoftext|> |
95b80a5c50a528168a220b54744ee6de7efa434711f9191e6dd16c659ee8c39d | def to_frame(self) -> pd.DataFrame:
'\n Get status as pandas DataFrame.\n\n :return: Status as DataFrame\n '
return pd.DataFrame(dict(status=self.status, n_errors=self.n_errors, n_warnings=self.n_warnings, n_notes=self.n_notes, instance=self.instance, profile=self.profile), index=[0]) | Get status as pandas DataFrame.
:return: Status as DataFrame | fsh_validator/fsh_validator.py | to_frame | glichtner/fsh-validator | 0 | python | def to_frame(self) -> pd.DataFrame:
'\n Get status as pandas DataFrame.\n\n :return: Status as DataFrame\n '
return pd.DataFrame(dict(status=self.status, n_errors=self.n_errors, n_warnings=self.n_warnings, n_notes=self.n_notes, instance=self.instance, profile=self.profile), index=[0]) | def to_frame(self) -> pd.DataFrame:
'\n Get status as pandas DataFrame.\n\n :return: Status as DataFrame\n '
return pd.DataFrame(dict(status=self.status, n_errors=self.n_errors, n_warnings=self.n_warnings, n_notes=self.n_notes, instance=self.instance, profile=self.profile), index=[0])<|docstring|>Get status as pandas DataFrame.
:return: Status as DataFrame<|endoftext|> |
e6013c667bc98fee4a00e52d71fc4a1d306ba42388478e5c0897b763abd39df8 | def validate(ResultDirectory, DataFile, ValidationRange, CorrectThreshold, DataResolution, num_workers, visualizeErrors, expressedPoints=None, considerUnaccountedFor=True):
' Main script for cell phenotype validation. '
bdir = (lambda f: os.path.join(ResultDirectory, f))
if (expressedPoints is None):
expressionVector = loadPointsInRange(bdir('positive_cells.npy'), ValidationRange)
else:
expressionVector = loadPointsInRange(expressedPoints, ValidationRange)
if os.path.isfile(bdir('ground_truth.npy')):
GroundTruth = np.loadtxt(bdir('Log.txt'))
np.save(bdir('ground_truth.npy'), GroundTruth)
GroundTruth = loadPointsInRange(bdir('ground_truth.npy'), ValidationRange)
else:
GroundTruth = loadPointsInRange(bdir('ground_truth.npy'), ValidationRange)
nucleiVector = loadPointsInRange(bdir('spots_filtered.npy'), ValidationRange)
print('Number of total nuclei detected:', nucleiVector.shape[0])
print('Number of cells in ground truth:', GroundTruth.shape[0])
print('Number of detected cells:', expressionVector.shape[0])
if (num_workers <= 1):
start = time.time()
accuracy = unParallelAccuracy(CorrectThreshold, DataResolution, GroundTruth, expressionVector)
print('Serial time elapsed:', (time.time() - start))
else:
(F1, precision, recall, TP, accuracy) = computeAccuracy(CorrectThreshold, DataResolution, GroundTruth, expressionVector, num_workers, nucleiVector)
print('F1 score:', F1)
print('Precision (TP/(TP + FP)):', precision)
print('Recall (TP/(TP + FN)):', recall)
if considerUnaccountedFor:
if (not os.path.isfile(bdir('nuclei_counted.npy'))):
accountedFor = []
for i in range(GroundTruth.shape[0]):
found = False
j = 0
while ((j < len(nucleiVector)) and (found == False)):
if (distanceBetweenPoints(DataResolution, GroundTruth[i], nucleiVector[j]) <= CorrectThreshold):
found = True
accountedFor.append(nucleiVector[j])
j += 1
np.save(bdir('nuclei_counted.npy'), np.asarray(accountedFor))
numUnaccounted = (GroundTruth.shape[0] - len(accountedFor))
accountedFor = np.asarray(accountedFor)
print('Number of unaccounted for:', numUnaccounted)
else:
accountedFor = loadPointsInRange(bdir('nuclei_counted.npy'), ValidationRange)
F1_mod = ((2 * TP) / (expressionVector.shape[0] + len(accountedFor)))
recall_mod = (TP / len(accountedFor))
print('Modified recall:', recall_mod)
print('Modified F1 score:', F1_mod)
else:
F1_mod = F1
recall_mod = recall
if visualizeErrors:
for i in range(len(ValidationRange)):
Rnge = ValidationRange[i]
overlay = plt.overlayPoints(DataFile, accountedFor, pointColor=None, **Rnge)
overlay2 = plt.overlayPoints(DataFile, expressionVector, pointColor=None, **Rnge)
overlay = np.concatenate((overlay, overlay2[(:, :, :, 1:)]), axis=3)
io.writeData(bdir(('groundtruth_overlaid_%d.tif' % i)), overlay)
return (F1_mod, precision, recall_mod, recall) | Main script for cell phenotype validation. | clarity/CellTypeDetection/phenotypeValidation.py | validate | wjguan/phenocell | 0 | python | def validate(ResultDirectory, DataFile, ValidationRange, CorrectThreshold, DataResolution, num_workers, visualizeErrors, expressedPoints=None, considerUnaccountedFor=True):
' '
bdir = (lambda f: os.path.join(ResultDirectory, f))
if (expressedPoints is None):
expressionVector = loadPointsInRange(bdir('positive_cells.npy'), ValidationRange)
else:
expressionVector = loadPointsInRange(expressedPoints, ValidationRange)
if os.path.isfile(bdir('ground_truth.npy')):
GroundTruth = np.loadtxt(bdir('Log.txt'))
np.save(bdir('ground_truth.npy'), GroundTruth)
GroundTruth = loadPointsInRange(bdir('ground_truth.npy'), ValidationRange)
else:
GroundTruth = loadPointsInRange(bdir('ground_truth.npy'), ValidationRange)
nucleiVector = loadPointsInRange(bdir('spots_filtered.npy'), ValidationRange)
print('Number of total nuclei detected:', nucleiVector.shape[0])
print('Number of cells in ground truth:', GroundTruth.shape[0])
print('Number of detected cells:', expressionVector.shape[0])
if (num_workers <= 1):
start = time.time()
accuracy = unParallelAccuracy(CorrectThreshold, DataResolution, GroundTruth, expressionVector)
print('Serial time elapsed:', (time.time() - start))
else:
(F1, precision, recall, TP, accuracy) = computeAccuracy(CorrectThreshold, DataResolution, GroundTruth, expressionVector, num_workers, nucleiVector)
print('F1 score:', F1)
print('Precision (TP/(TP + FP)):', precision)
print('Recall (TP/(TP + FN)):', recall)
if considerUnaccountedFor:
if (not os.path.isfile(bdir('nuclei_counted.npy'))):
accountedFor = []
for i in range(GroundTruth.shape[0]):
found = False
j = 0
while ((j < len(nucleiVector)) and (found == False)):
if (distanceBetweenPoints(DataResolution, GroundTruth[i], nucleiVector[j]) <= CorrectThreshold):
found = True
accountedFor.append(nucleiVector[j])
j += 1
np.save(bdir('nuclei_counted.npy'), np.asarray(accountedFor))
numUnaccounted = (GroundTruth.shape[0] - len(accountedFor))
accountedFor = np.asarray(accountedFor)
print('Number of unaccounted for:', numUnaccounted)
else:
accountedFor = loadPointsInRange(bdir('nuclei_counted.npy'), ValidationRange)
F1_mod = ((2 * TP) / (expressionVector.shape[0] + len(accountedFor)))
recall_mod = (TP / len(accountedFor))
print('Modified recall:', recall_mod)
print('Modified F1 score:', F1_mod)
else:
F1_mod = F1
recall_mod = recall
if visualizeErrors:
for i in range(len(ValidationRange)):
Rnge = ValidationRange[i]
overlay = plt.overlayPoints(DataFile, accountedFor, pointColor=None, **Rnge)
overlay2 = plt.overlayPoints(DataFile, expressionVector, pointColor=None, **Rnge)
overlay = np.concatenate((overlay, overlay2[(:, :, :, 1:)]), axis=3)
io.writeData(bdir(('groundtruth_overlaid_%d.tif' % i)), overlay)
return (F1_mod, precision, recall_mod, recall) | def validate(ResultDirectory, DataFile, ValidationRange, CorrectThreshold, DataResolution, num_workers, visualizeErrors, expressedPoints=None, considerUnaccountedFor=True):
' '
bdir = (lambda f: os.path.join(ResultDirectory, f))
if (expressedPoints is None):
expressionVector = loadPointsInRange(bdir('positive_cells.npy'), ValidationRange)
else:
expressionVector = loadPointsInRange(expressedPoints, ValidationRange)
if os.path.isfile(bdir('ground_truth.npy')):
GroundTruth = np.loadtxt(bdir('Log.txt'))
np.save(bdir('ground_truth.npy'), GroundTruth)
GroundTruth = loadPointsInRange(bdir('ground_truth.npy'), ValidationRange)
else:
GroundTruth = loadPointsInRange(bdir('ground_truth.npy'), ValidationRange)
nucleiVector = loadPointsInRange(bdir('spots_filtered.npy'), ValidationRange)
print('Number of total nuclei detected:', nucleiVector.shape[0])
print('Number of cells in ground truth:', GroundTruth.shape[0])
print('Number of detected cells:', expressionVector.shape[0])
if (num_workers <= 1):
start = time.time()
accuracy = unParallelAccuracy(CorrectThreshold, DataResolution, GroundTruth, expressionVector)
print('Serial time elapsed:', (time.time() - start))
else:
(F1, precision, recall, TP, accuracy) = computeAccuracy(CorrectThreshold, DataResolution, GroundTruth, expressionVector, num_workers, nucleiVector)
print('F1 score:', F1)
print('Precision (TP/(TP + FP)):', precision)
print('Recall (TP/(TP + FN)):', recall)
if considerUnaccountedFor:
if (not os.path.isfile(bdir('nuclei_counted.npy'))):
accountedFor = []
for i in range(GroundTruth.shape[0]):
found = False
j = 0
while ((j < len(nucleiVector)) and (found == False)):
if (distanceBetweenPoints(DataResolution, GroundTruth[i], nucleiVector[j]) <= CorrectThreshold):
found = True
accountedFor.append(nucleiVector[j])
j += 1
np.save(bdir('nuclei_counted.npy'), np.asarray(accountedFor))
numUnaccounted = (GroundTruth.shape[0] - len(accountedFor))
accountedFor = np.asarray(accountedFor)
print('Number of unaccounted for:', numUnaccounted)
else:
accountedFor = loadPointsInRange(bdir('nuclei_counted.npy'), ValidationRange)
F1_mod = ((2 * TP) / (expressionVector.shape[0] + len(accountedFor)))
recall_mod = (TP / len(accountedFor))
print('Modified recall:', recall_mod)
print('Modified F1 score:', F1_mod)
else:
F1_mod = F1
recall_mod = recall
if visualizeErrors:
for i in range(len(ValidationRange)):
Rnge = ValidationRange[i]
overlay = plt.overlayPoints(DataFile, accountedFor, pointColor=None, **Rnge)
overlay2 = plt.overlayPoints(DataFile, expressionVector, pointColor=None, **Rnge)
overlay = np.concatenate((overlay, overlay2[(:, :, :, 1:)]), axis=3)
io.writeData(bdir(('groundtruth_overlaid_%d.tif' % i)), overlay)
return (F1_mod, precision, recall_mod, recall)<|docstring|>Main script for cell phenotype validation.<|endoftext|> |
ae97d9d9d1bbc0991cc4546938c205bad13f43b1eab5d006aad350dec30581f5 | @classmethod
def strip(cls, rowreader, rowwriter):
'\n Trim leading and trailing spaces from every field of delimited\n text.\n\n rowreader is an interator that provides a list or other\n interable of strings from each iteration (like\n csv.reader).\n\n rowwriter is an object with a write() method that accepts a\n single string.\n '
for row in rowreader:
rowwriter.writerow((r.strip() for r in row)) | Trim leading and trailing spaces from every field of delimited
text.
rowreader is an interator that provides a list or other
interable of strings from each iteration (like
csv.reader).
rowwriter is an object with a write() method that accepts a
single string. | tabletext/fieldstrip.py | strip | iegorman/py-tabletext | 0 | python | @classmethod
def strip(cls, rowreader, rowwriter):
'\n Trim leading and trailing spaces from every field of delimited\n text.\n\n rowreader is an interator that provides a list or other\n interable of strings from each iteration (like\n csv.reader).\n\n rowwriter is an object with a write() method that accepts a\n single string.\n '
for row in rowreader:
rowwriter.writerow((r.strip() for r in row)) | @classmethod
def strip(cls, rowreader, rowwriter):
'\n Trim leading and trailing spaces from every field of delimited\n text.\n\n rowreader is an interator that provides a list or other\n interable of strings from each iteration (like\n csv.reader).\n\n rowwriter is an object with a write() method that accepts a\n single string.\n '
for row in rowreader:
rowwriter.writerow((r.strip() for r in row))<|docstring|>Trim leading and trailing spaces from every field of delimited
text.
rowreader is an interator that provides a list or other
interable of strings from each iteration (like
csv.reader).
rowwriter is an object with a write() method that accepts a
single string.<|endoftext|> |
c276947003bf46bba3fb05a8545e825857d9dc955d164bf5e4f8372f3b5ed96c | def __init__(self, embedding_size: int=100, scoring_fct_norm: int=2, epochs: int=100, batch_size: int=(2 ** 10), training_loop: Union[(str, Type[TrainingLoop])]='Stochastic Local Closed World Assumption', random_state: int=42, enable_cache: bool=False):
'Create new PyKeen TransE model.\n \n Details\n -------------------------\n This is a wrapper of the TransE implementation from the\n PyKeen library. Please refer to the PyKeen library documentation\n for details and posssible errors regarding this model.\n\n Parameters\n -------------------------\n embedding_size: int = 100\n The dimension of the embedding to compute.\n scoring_fct_norm: int = 2\n Norm exponent to use in the loss.\n epochs: int = 100\n The number of epochs to use to train the model for.\n batch_size: int = 2**10\n Size of the training batch.\n device: str = "auto"\n The devide to use to train the model.\n Can either be cpu or cuda.\n training_loop: Union[str, Type[TrainingLoop]\n ] = "Stochastic Local Closed World Assumption"\n The training loop to use to train the model.\n Can either be:\n - Stochastic Local Closed World Assumption\n - Local Closed World Assumption\n random_state: int = 42\n Random seed to use while training the model\n enable_cache: bool = False\n Whether to enable the cache, that is to\n store the computed embedding.\n '
self._scoring_fct_norm = scoring_fct_norm
super().__init__(embedding_size=embedding_size, epochs=epochs, batch_size=batch_size, training_loop=training_loop, random_state=random_state, enable_cache=enable_cache) | Create new PyKeen TransE model.
Details
-------------------------
This is a wrapper of the TransE implementation from the
PyKeen library. Please refer to the PyKeen library documentation
for details and posssible errors regarding this model.
Parameters
-------------------------
embedding_size: int = 100
The dimension of the embedding to compute.
scoring_fct_norm: int = 2
Norm exponent to use in the loss.
epochs: int = 100
The number of epochs to use to train the model for.
batch_size: int = 2**10
Size of the training batch.
device: str = "auto"
The devide to use to train the model.
Can either be cpu or cuda.
training_loop: Union[str, Type[TrainingLoop]
] = "Stochastic Local Closed World Assumption"
The training loop to use to train the model.
Can either be:
- Stochastic Local Closed World Assumption
- Local Closed World Assumption
random_state: int = 42
Random seed to use while training the model
enable_cache: bool = False
Whether to enable the cache, that is to
store the computed embedding. | embiggen/embedders/pykeen_embedders/transe.py | __init__ | monarch-initiative/N2V | 2 | python | def __init__(self, embedding_size: int=100, scoring_fct_norm: int=2, epochs: int=100, batch_size: int=(2 ** 10), training_loop: Union[(str, Type[TrainingLoop])]='Stochastic Local Closed World Assumption', random_state: int=42, enable_cache: bool=False):
'Create new PyKeen TransE model.\n \n Details\n -------------------------\n This is a wrapper of the TransE implementation from the\n PyKeen library. Please refer to the PyKeen library documentation\n for details and posssible errors regarding this model.\n\n Parameters\n -------------------------\n embedding_size: int = 100\n The dimension of the embedding to compute.\n scoring_fct_norm: int = 2\n Norm exponent to use in the loss.\n epochs: int = 100\n The number of epochs to use to train the model for.\n batch_size: int = 2**10\n Size of the training batch.\n device: str = "auto"\n The devide to use to train the model.\n Can either be cpu or cuda.\n training_loop: Union[str, Type[TrainingLoop]\n ] = "Stochastic Local Closed World Assumption"\n The training loop to use to train the model.\n Can either be:\n - Stochastic Local Closed World Assumption\n - Local Closed World Assumption\n random_state: int = 42\n Random seed to use while training the model\n enable_cache: bool = False\n Whether to enable the cache, that is to\n store the computed embedding.\n '
self._scoring_fct_norm = scoring_fct_norm
super().__init__(embedding_size=embedding_size, epochs=epochs, batch_size=batch_size, training_loop=training_loop, random_state=random_state, enable_cache=enable_cache) | def __init__(self, embedding_size: int=100, scoring_fct_norm: int=2, epochs: int=100, batch_size: int=(2 ** 10), training_loop: Union[(str, Type[TrainingLoop])]='Stochastic Local Closed World Assumption', random_state: int=42, enable_cache: bool=False):
'Create new PyKeen TransE model.\n \n Details\n -------------------------\n This is a wrapper of the TransE implementation from the\n PyKeen library. Please refer to the PyKeen library documentation\n for details and posssible errors regarding this model.\n\n Parameters\n -------------------------\n embedding_size: int = 100\n The dimension of the embedding to compute.\n scoring_fct_norm: int = 2\n Norm exponent to use in the loss.\n epochs: int = 100\n The number of epochs to use to train the model for.\n batch_size: int = 2**10\n Size of the training batch.\n device: str = "auto"\n The devide to use to train the model.\n Can either be cpu or cuda.\n training_loop: Union[str, Type[TrainingLoop]\n ] = "Stochastic Local Closed World Assumption"\n The training loop to use to train the model.\n Can either be:\n - Stochastic Local Closed World Assumption\n - Local Closed World Assumption\n random_state: int = 42\n Random seed to use while training the model\n enable_cache: bool = False\n Whether to enable the cache, that is to\n store the computed embedding.\n '
self._scoring_fct_norm = scoring_fct_norm
super().__init__(embedding_size=embedding_size, epochs=epochs, batch_size=batch_size, training_loop=training_loop, random_state=random_state, enable_cache=enable_cache)<|docstring|>Create new PyKeen TransE model.
Details
-------------------------
This is a wrapper of the TransE implementation from the
PyKeen library. Please refer to the PyKeen library documentation
for details and posssible errors regarding this model.
Parameters
-------------------------
embedding_size: int = 100
The dimension of the embedding to compute.
scoring_fct_norm: int = 2
Norm exponent to use in the loss.
epochs: int = 100
The number of epochs to use to train the model for.
batch_size: int = 2**10
Size of the training batch.
device: str = "auto"
The devide to use to train the model.
Can either be cpu or cuda.
training_loop: Union[str, Type[TrainingLoop]
] = "Stochastic Local Closed World Assumption"
The training loop to use to train the model.
Can either be:
- Stochastic Local Closed World Assumption
- Local Closed World Assumption
random_state: int = 42
Random seed to use while training the model
enable_cache: bool = False
Whether to enable the cache, that is to
store the computed embedding.<|endoftext|> |
c1b3ab36bc7d850f8f549055264659291265b41e2192a1f7c08c17403dbabb73 | @staticmethod
def smoke_test_parameters() -> Dict[(str, Any)]:
'Returns parameters for smoke test.'
return dict(**EntityRelationEmbeddingModelPyKeen.smoke_test_parameters(), scoring_fct_norm=1) | Returns parameters for smoke test. | embiggen/embedders/pykeen_embedders/transe.py | smoke_test_parameters | monarch-initiative/N2V | 2 | python | @staticmethod
def smoke_test_parameters() -> Dict[(str, Any)]:
return dict(**EntityRelationEmbeddingModelPyKeen.smoke_test_parameters(), scoring_fct_norm=1) | @staticmethod
def smoke_test_parameters() -> Dict[(str, Any)]:
return dict(**EntityRelationEmbeddingModelPyKeen.smoke_test_parameters(), scoring_fct_norm=1)<|docstring|>Returns parameters for smoke test.<|endoftext|> |
9e389851a82afe09adf65518d5130e00935c33476ca1662ac39dbec1347ab92f | @staticmethod
def model_name() -> str:
'Return name of the model.'
return 'TransE' | Return name of the model. | embiggen/embedders/pykeen_embedders/transe.py | model_name | monarch-initiative/N2V | 2 | python | @staticmethod
def model_name() -> str:
return 'TransE' | @staticmethod
def model_name() -> str:
return 'TransE'<|docstring|>Return name of the model.<|endoftext|> |
09f37d8481826bf067604995af161c6c3d3de88d7fa7013c3a7c26464d08113f | def _build_model(self, triples_factory: CoreTriplesFactory) -> TransE:
'Build new TransE model for embedding.\n\n Parameters\n ------------------\n graph: Graph\n The graph to build the model for.\n '
return TransE(triples_factory=triples_factory, embedding_dim=self._embedding_size, scoring_fct_norm=self._scoring_fct_norm) | Build new TransE model for embedding.
Parameters
------------------
graph: Graph
The graph to build the model for. | embiggen/embedders/pykeen_embedders/transe.py | _build_model | monarch-initiative/N2V | 2 | python | def _build_model(self, triples_factory: CoreTriplesFactory) -> TransE:
'Build new TransE model for embedding.\n\n Parameters\n ------------------\n graph: Graph\n The graph to build the model for.\n '
return TransE(triples_factory=triples_factory, embedding_dim=self._embedding_size, scoring_fct_norm=self._scoring_fct_norm) | def _build_model(self, triples_factory: CoreTriplesFactory) -> TransE:
'Build new TransE model for embedding.\n\n Parameters\n ------------------\n graph: Graph\n The graph to build the model for.\n '
return TransE(triples_factory=triples_factory, embedding_dim=self._embedding_size, scoring_fct_norm=self._scoring_fct_norm)<|docstring|>Build new TransE model for embedding.
Parameters
------------------
graph: Graph
The graph to build the model for.<|endoftext|> |
a657b87b8ce30d0f2ff4fcb3b5ebdfe26cd0480cf45ced9041e83fa402e6b848 | def pattern_registry(pattern_type):
'The class decorator used to register all Algorithm subclasses.\n\n Args:\n cls (class): The class of register.\n pattern_type (str): The pattern registration name\n\n Returns:\n cls: The class of register.\n '
def decorator_pattern(cls):
if (pattern_type in PATTERNS):
raise ValueError('Cannot have two patterns with the same name')
PATTERNS[pattern_type] = cls
return cls
return decorator_pattern | The class decorator used to register all Algorithm subclasses.
Args:
cls (class): The class of register.
pattern_type (str): The pattern registration name
Returns:
cls: The class of register. | engine/compile/sub_graph/pattern.py | pattern_registry | intel/neural-compressor | 172 | python | def pattern_registry(pattern_type):
'The class decorator used to register all Algorithm subclasses.\n\n Args:\n cls (class): The class of register.\n pattern_type (str): The pattern registration name\n\n Returns:\n cls: The class of register.\n '
def decorator_pattern(cls):
if (pattern_type in PATTERNS):
raise ValueError('Cannot have two patterns with the same name')
PATTERNS[pattern_type] = cls
return cls
return decorator_pattern | def pattern_registry(pattern_type):
'The class decorator used to register all Algorithm subclasses.\n\n Args:\n cls (class): The class of register.\n pattern_type (str): The pattern registration name\n\n Returns:\n cls: The class of register.\n '
def decorator_pattern(cls):
if (pattern_type in PATTERNS):
raise ValueError('Cannot have two patterns with the same name')
PATTERNS[pattern_type] = cls
return cls
return decorator_pattern<|docstring|>The class decorator used to register all Algorithm subclasses.
Args:
cls (class): The class of register.
pattern_type (str): The pattern registration name
Returns:
cls: The class of register.<|endoftext|> |
44a7fe51d2532041a0ef6a79963a65a177a428a53138967078129d1ea049e320 | @classmethod
def db_table_name(cls):
'database table name'
return cls._meta.db_table | database table name | backend/db_comments/model_mixins.py | db_table_name | WadeBarnes/tfrs | 18 | python | @classmethod
def db_table_name(cls):
return cls._meta.db_table | @classmethod
def db_table_name(cls):
return cls._meta.db_table<|docstring|>database table name<|endoftext|> |
56abee0714df3c85d1b822f646c77234b9b7f950c114b9caaf915fa93abf0997 | @classmethod
def db_table_comment_or_name(cls):
'database table comment (default to name if unset)'
return (cls.db_table_comment or cls.__name__) | database table comment (default to name if unset) | backend/db_comments/model_mixins.py | db_table_comment_or_name | WadeBarnes/tfrs | 18 | python | @classmethod
def db_table_comment_or_name(cls):
return (cls.db_table_comment or cls.__name__) | @classmethod
def db_table_comment_or_name(cls):
return (cls.db_table_comment or cls.__name__)<|docstring|>database table comment (default to name if unset)<|endoftext|> |
f60c9cc2c2bd86de8ea673ecd20ca8bc5c69d033b85d6070131791140b6534db | @classmethod
def db_column_comments(cls):
'database table column comments, including supplemental overrides'
column_comments = {}
for field in cls._meta.fields:
if hasattr(field, 'db_comment'):
column_comments[field.column] = field.db_comment
inspection_list = [cls]
visited = []
while inspection_list:
current = inspection_list.pop()
if (current in visited):
continue
visited.append(current)
if issubclass(current, DBComments):
if hasattr(current, 'db_column_supplemental_comments'):
for (column, comment) in current.db_column_supplemental_comments.items():
column_comments[column] = comment
inspection_list = (inspection_list + list(current.__bases__))
return column_comments | database table column comments, including supplemental overrides | backend/db_comments/model_mixins.py | db_column_comments | WadeBarnes/tfrs | 18 | python | @classmethod
def db_column_comments(cls):
column_comments = {}
for field in cls._meta.fields:
if hasattr(field, 'db_comment'):
column_comments[field.column] = field.db_comment
inspection_list = [cls]
visited = []
while inspection_list:
current = inspection_list.pop()
if (current in visited):
continue
visited.append(current)
if issubclass(current, DBComments):
if hasattr(current, 'db_column_supplemental_comments'):
for (column, comment) in current.db_column_supplemental_comments.items():
column_comments[column] = comment
inspection_list = (inspection_list + list(current.__bases__))
return column_comments | @classmethod
def db_column_comments(cls):
column_comments = {}
for field in cls._meta.fields:
if hasattr(field, 'db_comment'):
column_comments[field.column] = field.db_comment
inspection_list = [cls]
visited = []
while inspection_list:
current = inspection_list.pop()
if (current in visited):
continue
visited.append(current)
if issubclass(current, DBComments):
if hasattr(current, 'db_column_supplemental_comments'):
for (column, comment) in current.db_column_supplemental_comments.items():
column_comments[column] = comment
inspection_list = (inspection_list + list(current.__bases__))
return column_comments<|docstring|>database table column comments, including supplemental overrides<|endoftext|> |
1bcb2118c3de981a2ec20a0d8fc80d65c85aa0bb96cca958771b44f54da75a16 | def non_max_suppression(self, prediction, num_classes, input_shape, image_shape, letterbox_image, conf_thres=0.5, nms_thres=0.4):
'原图上的box，首先利用conf_thres进行第一轮筛选，再进行非极大值抑制。当然conf_thres=0.5, nms_thres=0.4\n 都是默认值，是可以被修改的。nms_thres的值越小，nms越严格'
box_corner = prediction.new(prediction.shape)
box_corner[(:, :, 0)] = (prediction[(:, :, 0)] - (prediction[(:, :, 2)] / 2))
box_corner[(:, :, 1)] = (prediction[(:, :, 1)] - (prediction[(:, :, 3)] / 2))
box_corner[(:, :, 2)] = (prediction[(:, :, 0)] + (prediction[(:, :, 2)] / 2))
box_corner[(:, :, 3)] = (prediction[(:, :, 1)] + (prediction[(:, :, 3)] / 2))
prediction[(:, :, :4)] = box_corner[(:, :, :4)]
output = [None for _ in range(len(prediction))]
for (i, image_pred) in enumerate(prediction):
(class_conf, class_pred) = torch.max(image_pred[(:, 5:(5 + num_classes))], 1, keepdim=True)
conf_mask = ((image_pred[(:, 4)] * class_conf[(:, 0)]) >= conf_thres).squeeze()
image_pred = image_pred[conf_mask]
class_conf = class_conf[conf_mask]
class_pred = class_pred[conf_mask]
if (not image_pred.size(0)):
continue
detections = torch.cat((image_pred[(:, :5)], class_conf.float(), class_pred.float()), 1)
unique_labels = detections[(:, (- 1))].cpu().unique()
if prediction.is_cuda:
unique_labels = unique_labels.cuda()
detections = detections.cuda()
for c in unique_labels:
detections_class = detections[(detections[(:, (- 1))] == c)]
keep = nms(detections_class[(:, :4)], (detections_class[(:, 4)] * detections_class[(:, 5)]), nms_thres)
max_detections = detections_class[keep]
output[i] = (max_detections if (output[i] is None) else torch.cat((output[i], max_detections)))
if (output[i] is not None):
output[i] = output[i].cpu().numpy()
(box_xy, box_wh) = (((output[i][(:, 0:2)] + output[i][(:, 2:4)]) / 2), (output[i][(:, 2:4)] - output[i][(:, 0:2)]))
output[i][(:, :4)] = self.yolo_correct_boxes(box_xy, box_wh, input_shape, image_shape, letterbox_image)
return output | 原图上的box，首先利用conf_thres进行第一轮筛选，再进行非极大值抑制。当然conf_thres=0.5, nms_thres=0.4
都是默认值，是可以被修改的。nms_thres的值越小，nms越严格 | utils/utils_bbox.py | non_max_suppression | ustber002/yolov3-pytorch | 1 | python | def non_max_suppression(self, prediction, num_classes, input_shape, image_shape, letterbox_image, conf_thres=0.5, nms_thres=0.4):
'原图上的box，首先利用conf_thres进行第一轮筛选，再进行非极大值抑制。当然conf_thres=0.5, nms_thres=0.4\n 都是默认值，是可以被修改的。nms_thres的值越小，nms越严格'
box_corner = prediction.new(prediction.shape)
box_corner[(:, :, 0)] = (prediction[(:, :, 0)] - (prediction[(:, :, 2)] / 2))
box_corner[(:, :, 1)] = (prediction[(:, :, 1)] - (prediction[(:, :, 3)] / 2))
box_corner[(:, :, 2)] = (prediction[(:, :, 0)] + (prediction[(:, :, 2)] / 2))
box_corner[(:, :, 3)] = (prediction[(:, :, 1)] + (prediction[(:, :, 3)] / 2))
prediction[(:, :, :4)] = box_corner[(:, :, :4)]
output = [None for _ in range(len(prediction))]
for (i, image_pred) in enumerate(prediction):
(class_conf, class_pred) = torch.max(image_pred[(:, 5:(5 + num_classes))], 1, keepdim=True)
conf_mask = ((image_pred[(:, 4)] * class_conf[(:, 0)]) >= conf_thres).squeeze()
image_pred = image_pred[conf_mask]
class_conf = class_conf[conf_mask]
class_pred = class_pred[conf_mask]
if (not image_pred.size(0)):
continue
detections = torch.cat((image_pred[(:, :5)], class_conf.float(), class_pred.float()), 1)
unique_labels = detections[(:, (- 1))].cpu().unique()
if prediction.is_cuda:
unique_labels = unique_labels.cuda()
detections = detections.cuda()
for c in unique_labels:
detections_class = detections[(detections[(:, (- 1))] == c)]
keep = nms(detections_class[(:, :4)], (detections_class[(:, 4)] * detections_class[(:, 5)]), nms_thres)
max_detections = detections_class[keep]
output[i] = (max_detections if (output[i] is None) else torch.cat((output[i], max_detections)))
if (output[i] is not None):
output[i] = output[i].cpu().numpy()
(box_xy, box_wh) = (((output[i][(:, 0:2)] + output[i][(:, 2:4)]) / 2), (output[i][(:, 2:4)] - output[i][(:, 0:2)]))
output[i][(:, :4)] = self.yolo_correct_boxes(box_xy, box_wh, input_shape, image_shape, letterbox_image)
return output | def non_max_suppression(self, prediction, num_classes, input_shape, image_shape, letterbox_image, conf_thres=0.5, nms_thres=0.4):
'原图上的box，首先利用conf_thres进行第一轮筛选，再进行非极大值抑制。当然conf_thres=0.5, nms_thres=0.4\n 都是默认值，是可以被修改的。nms_thres的值越小，nms越严格'
box_corner = prediction.new(prediction.shape)
box_corner[(:, :, 0)] = (prediction[(:, :, 0)] - (prediction[(:, :, 2)] / 2))
box_corner[(:, :, 1)] = (prediction[(:, :, 1)] - (prediction[(:, :, 3)] / 2))
box_corner[(:, :, 2)] = (prediction[(:, :, 0)] + (prediction[(:, :, 2)] / 2))
box_corner[(:, :, 3)] = (prediction[(:, :, 1)] + (prediction[(:, :, 3)] / 2))
prediction[(:, :, :4)] = box_corner[(:, :, :4)]
output = [None for _ in range(len(prediction))]
for (i, image_pred) in enumerate(prediction):
(class_conf, class_pred) = torch.max(image_pred[(:, 5:(5 + num_classes))], 1, keepdim=True)
conf_mask = ((image_pred[(:, 4)] * class_conf[(:, 0)]) >= conf_thres).squeeze()
image_pred = image_pred[conf_mask]
class_conf = class_conf[conf_mask]
class_pred = class_pred[conf_mask]
if (not image_pred.size(0)):
continue
detections = torch.cat((image_pred[(:, :5)], class_conf.float(), class_pred.float()), 1)
unique_labels = detections[(:, (- 1))].cpu().unique()
if prediction.is_cuda:
unique_labels = unique_labels.cuda()
detections = detections.cuda()
for c in unique_labels:
detections_class = detections[(detections[(:, (- 1))] == c)]
keep = nms(detections_class[(:, :4)], (detections_class[(:, 4)] * detections_class[(:, 5)]), nms_thres)
max_detections = detections_class[keep]
output[i] = (max_detections if (output[i] is None) else torch.cat((output[i], max_detections)))
if (output[i] is not None):
output[i] = output[i].cpu().numpy()
(box_xy, box_wh) = (((output[i][(:, 0:2)] + output[i][(:, 2:4)]) / 2), (output[i][(:, 2:4)] - output[i][(:, 0:2)]))
output[i][(:, :4)] = self.yolo_correct_boxes(box_xy, box_wh, input_shape, image_shape, letterbox_image)
return output<|docstring|>原图上的box，首先利用conf_thres进行第一轮筛选，再进行非极大值抑制。当然conf_thres=0.5, nms_thres=0.4
都是默认值，是可以被修改的。nms_thres的值越小，nms越严格<|endoftext|> |
8da9188186a9375efa7c353f5c5d0502fe4ea736388c74f4be2381307b718c60 | def read_ref(ref: str, kind='branch') -> str:
'\n get sha1 of commit that a ref (branch/tag) is pointing to e.g "master"\n '
ref_kind = ('heads' if (kind == 'branch') else 'tags')
with open(f'.git/refs/{ref_kind}/{ref}') as f:
return f.read().strip() | get sha1 of commit that a ref (branch/tag) is pointing to e.g "master" | src/git.py | read_ref | nojvek/pygit | 0 | python | def read_ref(ref: str, kind='branch') -> str:
'\n \n '
ref_kind = ('heads' if (kind == 'branch') else 'tags')
with open(f'.git/refs/{ref_kind}/{ref}') as f:
return f.read().strip() | def read_ref(ref: str, kind='branch') -> str:
'\n \n '
ref_kind = ('heads' if (kind == 'branch') else 'tags')
with open(f'.git/refs/{ref_kind}/{ref}') as f:
return f.read().strip()<|docstring|>get sha1 of commit that a ref (branch/tag) is pointing to e.g "master"<|endoftext|> |
38a62c2b5aea68db97348d1eea37d677a058d943415b5d944071b1b9b5eacc12 | def pack_object_data(obj_type: str, data: bytes) -> (str, bytes):
'\n Return a tuple of (sha1(type + len + data), gzipped_data)\n '
data_w_header = b'\x00'.join([f'{obj_type} {len(data)}', data])
sha1 = hashlib.sha1(data_w_header).hexdigest()
gzipped_data = zlib.compress(data_w_header)
return (sha1, gzipped_data) | Return a tuple of (sha1(type + len + data), gzipped_data) | src/git.py | pack_object_data | nojvek/pygit | 0 | python | def pack_object_data(obj_type: str, data: bytes) -> (str, bytes):
'\n \n '
data_w_header = b'\x00'.join([f'{obj_type} {len(data)}', data])
sha1 = hashlib.sha1(data_w_header).hexdigest()
gzipped_data = zlib.compress(data_w_header)
return (sha1, gzipped_data) | def pack_object_data(obj_type: str, data: bytes) -> (str, bytes):
'\n \n '
data_w_header = b'\x00'.join([f'{obj_type} {len(data)}', data])
sha1 = hashlib.sha1(data_w_header).hexdigest()
gzipped_data = zlib.compress(data_w_header)
return (sha1, gzipped_data)<|docstring|>Return a tuple of (sha1(type + len + data), gzipped_data)<|endoftext|> |
22e3d4f4009bf650336f1333828f2e0f5eec51d1e031fb2535e5ee7a0f385de2 | def get_connected_objects(sha1: str, object_graph: Dict=None) -> Dict:
'\n Starting from a sha1 of an object e.g commit/tree\n Return an object_graph\n where object_graph is a dictionary of {sha1: unpacked_object}\n '
if (object_graph is None):
object_graph = {}
obj = read_git_object(sha1)
object_graph[sha1] = obj
if isinstance(obj, Tree):
for entry in obj.entries:
get_connected_objects(entry.sha1, object_graph)
elif isinstance(obj, Commit):
get_connected_objects(obj.tree, object_graph)
for parent in obj.parent:
get_connected_objects(parent, object_graph)
return object_graph | Starting from a sha1 of an object e.g commit/tree
Return an object_graph
where object_graph is a dictionary of {sha1: unpacked_object} | src/git.py | get_connected_objects | nojvek/pygit | 0 | python | def get_connected_objects(sha1: str, object_graph: Dict=None) -> Dict:
'\n Starting from a sha1 of an object e.g commit/tree\n Return an object_graph\n where object_graph is a dictionary of {sha1: unpacked_object}\n '
if (object_graph is None):
object_graph = {}
obj = read_git_object(sha1)
object_graph[sha1] = obj
if isinstance(obj, Tree):
for entry in obj.entries:
get_connected_objects(entry.sha1, object_graph)
elif isinstance(obj, Commit):
get_connected_objects(obj.tree, object_graph)
for parent in obj.parent:
get_connected_objects(parent, object_graph)
return object_graph | def get_connected_objects(sha1: str, object_graph: Dict=None) -> Dict:
'\n Starting from a sha1 of an object e.g commit/tree\n Return an object_graph\n where object_graph is a dictionary of {sha1: unpacked_object}\n '
if (object_graph is None):
object_graph = {}
obj = read_git_object(sha1)
object_graph[sha1] = obj
if isinstance(obj, Tree):
for entry in obj.entries:
get_connected_objects(entry.sha1, object_graph)
elif isinstance(obj, Commit):
get_connected_objects(obj.tree, object_graph)
for parent in obj.parent:
get_connected_objects(parent, object_graph)
return object_graph<|docstring|>Starting from a sha1 of an object e.g commit/tree
Return an object_graph
where object_graph is a dictionary of {sha1: unpacked_object}<|endoftext|> |
43b075b80ea5add3999e1c03ff42720bc9d3a5ed7438257ce9b31299028bc07f | def make_wide(formatter, w=120, h=36):
'Return a wider HelpFormatter, if possible.'
try:
kwargs = {'width': w, 'max_help_position': h}
formatter(None, **kwargs)
return (lambda prog: formatter(prog, **kwargs))
except TypeError:
import warnings
warnings.warn('argparse help formatter failed, falling back.')
return formatter | Return a wider HelpFormatter, if possible. | haven/haven_wizard.py | make_wide | mariatippler/haven-ai | 0 | python | def make_wide(formatter, w=120, h=36):
try:
kwargs = {'width': w, 'max_help_position': h}
formatter(None, **kwargs)
return (lambda prog: formatter(prog, **kwargs))
except TypeError:
import warnings
warnings.warn('argparse help formatter failed, falling back.')
return formatter | def make_wide(formatter, w=120, h=36):
try:
kwargs = {'width': w, 'max_help_position': h}
formatter(None, **kwargs)
return (lambda prog: formatter(prog, **kwargs))
except TypeError:
import warnings
warnings.warn('argparse help formatter failed, falling back.')
return formatter<|docstring|>Return a wider HelpFormatter, if possible.<|endoftext|> |
9508b4bb132899b9d7ce7acaa760186e90afe25024bd012b66bc9a140c2b4301 | def __init__(self, interval=1):
'\n inteval is in seconds\n '
super(Service, self).__init__()
self.interval = (interval * 1000)
self.periodicalCb = None | inteval is in seconds | service.py | __init__ | liangsun/firstblog | 4 | python | def __init__(self, interval=1):
'\n \n '
super(Service, self).__init__()
self.interval = (interval * 1000)
self.periodicalCb = None | def __init__(self, interval=1):
'\n \n '
super(Service, self).__init__()
self.interval = (interval * 1000)
self.periodicalCb = None<|docstring|>inteval is in seconds<|endoftext|> |
a99a5aa73feeeabddc8e3a4078c234d7f06eb84002485b94140089bbae6cccf8 | def main(self):
'\n Subclass this method\n '
logging.error(('Subclass main method... %s' % time.clock())) | Subclass this method | service.py | main | liangsun/firstblog | 4 | python | def main(self):
'\n \n '
logging.error(('Subclass main method... %s' % time.clock())) | def main(self):
'\n \n '
logging.error(('Subclass main method... %s' % time.clock()))<|docstring|>Subclass this method<|endoftext|> |
65919d9497660ee3681985d1ca035305a7bb16ba2016d2c915823a1c62863659 | def begin(self):
'!\n @brief initialization the i2c.\n @return returns the initialization status\n @retval True Initialization succeeded\n @retval False Initialization failed\n '
if (not self.scan()):
return False
else:
self.soft_reset()
self.write_bmx_reg(self._BMX160_COMMAND_REG_ADDR, 17)
time.sleep(0.05)
self.write_bmx_reg(self._BMX160_COMMAND_REG_ADDR, 21)
time.sleep(0.1)
self.write_bmx_reg(self._BMX160_COMMAND_REG_ADDR, 25)
time.sleep(0.01)
self.set_magn_conf()
return True | !
@brief initialization the i2c.
@return returns the initialization status
@retval True Initialization succeeded
@retval False Initialization failed | python/raspberrypi/DFRobot_BMX160.py | begin | DFRobot/DFRobot_BMX160 | 9 | python | def begin(self):
'!\n @brief initialization the i2c.\n @return returns the initialization status\n @retval True Initialization succeeded\n @retval False Initialization failed\n '
if (not self.scan()):
return False
else:
self.soft_reset()
self.write_bmx_reg(self._BMX160_COMMAND_REG_ADDR, 17)
time.sleep(0.05)
self.write_bmx_reg(self._BMX160_COMMAND_REG_ADDR, 21)
time.sleep(0.1)
self.write_bmx_reg(self._BMX160_COMMAND_REG_ADDR, 25)
time.sleep(0.01)
self.set_magn_conf()
return True | def begin(self):
'!\n @brief initialization the i2c.\n @return returns the initialization status\n @retval True Initialization succeeded\n @retval False Initialization failed\n '
if (not self.scan()):
return False
else:
self.soft_reset()
self.write_bmx_reg(self._BMX160_COMMAND_REG_ADDR, 17)
time.sleep(0.05)
self.write_bmx_reg(self._BMX160_COMMAND_REG_ADDR, 21)
time.sleep(0.1)
self.write_bmx_reg(self._BMX160_COMMAND_REG_ADDR, 25)
time.sleep(0.01)
self.set_magn_conf()
return True<|docstring|>!
@brief initialization the i2c.
@return returns the initialization status
@retval True Initialization succeeded
@retval False Initialization failed<|endoftext|> |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.