MODULE

PYTEST_HELM_CHARTS.GIANTSWARM_APP_PLATFORM.CATALOG

</>
Classes
Functions
  • catalog_factory_func(kube_client, objects, namespace_factory) (CatalogFactoryFunc) — Return a factory object, that can be used to configure new Catalog CRs for the 'app-operator' running in the cluster</>
class

pytest_helm_charts.giantswarm_app_platform.catalog.CatalogCR(api, obj)

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Bases
pykube.objects.NamespacedAPIObject pykube.objects.APIObject

Baseclass for all Kubernetes API objects

Parameters
  • api (HTTPClient) —
  • obj (dict) —
Attributes
  • _original_obj —
  • annotations (dict) — Annotations of the Kubernetes resource (metadata.annotations)
    Annotations is an unstructured key value map stored with a resource that may be set by external tools to store and retrieve arbitrary metadata. They are not queryable and should be preserved when modifying objects. More info: http://kubernetes.io/docs/user-guide/annotations</>
  • api —
  • labels (dict) — Labels of the Kubernetes resource (metadata.labels)
    Map of string keys and values that can be used to organize and categorize (scope and select) objects. May match selectors of replication controllers and services. More info: http://kubernetes.io/docs/user-guide/labels</>
  • name (str) — Name of the Kubernetes resource (metadata.name)
    Name must be unique within a namespace. Is required when creating resources, although some resources may allow a client to request the generation of an appropriate name automatically. Name is primarily intended for creation idempotence and configuration definition. Cannot be updated. More info: http://kubernetes.io/docs/user-guide/identifiers#names</>
  • namespace (str) — Namespace scope of the Kubernetes resource (metadata.namespace)
    Namespace defines the space within each name must be unique. Cannot be updated. More info: http://kubernetes.io/docs/user-guide/namespaces</>
  • obj —
Methods
  • delete(propagation_policy) — Delete the Kubernetes resource by calling the API.</>
  • patch(strategic_merge_patch, subresource) — Patch the Kubernetes resource by calling the API with a "strategic merge" patch.</>
  • update(is_strategic, subresource) — Update the Kubernetes resource by calling the API (patch)</>
method

patch(strategic_merge_patch, subresource=None)

</>

Patch the Kubernetes resource by calling the API with a "strategic merge" patch.

Parameters
  • strategic_merge_patch —
  • subresource (optional) —
method

update(is_strategic=True, subresource=None)

</>

Update the Kubernetes resource by calling the API (patch)

Parameters
  • is_strategic (optional) —
  • subresource (optional) —
method

delete(propagation_policy=None)

</>

Delete the Kubernetes resource by calling the API.

The parameter propagation_policy defines whether to cascade the delete. It can be "Foreground", "Background" or "Orphan". See https://kubernetes.io/docs/concepts/workloads/controllers/garbage-collection/#setting-the-cascading-deletion-policy

Parameters
  • propagation_policy (str, optional) —
class

pytest_helm_charts.giantswarm_app_platform.catalog.CatalogFactoryFunc(*args, **kwds)

</>
Bases
typing.Protocol typing.Generic

Base class for protocol classes.

Protocol classes are defined as::

class Proto(Protocol):
    def meth(self) -> int:
        ...

Such classes are primarily used with static type checkers that recognize structural subtyping (static duck-typing), for example::

class C:
    def meth(self) -> int:
        return 0

def func(x: Proto) -> int:
    return x.meth()

func(C())  # Passes static type check

See PEP 544 for details. Protocol classes decorated with @typing.runtime_checkable act as simple-minded runtime protocols that check only the presence of given attributes, ignoring their type signatures. Protocol classes can be generic, they are defined as::

class GenProto(Protocol[T]):
    def meth(self) -> T:
        ...
Parameters
  • *args —
  • **kwds —
Classes
Methods
class

typing._ProtocolMeta(name, bases, namespace, **kwargs)

</>
Bases
abc.ABCMeta

Metaclass for defining Abstract Base Classes (ABCs).

Use this metaclass to create an ABC. An ABC can be subclassed directly, and then acts as a mix-in class. You can also register unrelated concrete classes (even built-in classes) and unrelated ABCs as 'virtual subclasses' -- these and their descendants will be considered subclasses of the registering ABC by the built-in issubclass() function, but the registering ABC won't show up in their MRO (Method Resolution Order) nor will method implementations defined by the registering ABC be callable (not even via super()).

Parameters
  • name —
  • bases —
  • namespace —
  • **kwargs —
Methods
staticmethod
register(cls, subclass)
</>

Register a virtual subclass of an ABC.

Returns the subclass, to allow usage as a class decorator.

Parameters
  • cls —
  • subclass —
staticmethod
__subclasscheck__(cls, subclass)
</>

Override for issubclass(subclass, cls).

Parameters
  • cls —
  • subclass —
staticmethod
__instancecheck__(cls, instance)
</>

Override for isinstance(instance, cls).

Parameters
  • cls —
  • instance —
classmethod

__init_subclass__(*args, **kwargs)

</>

This method is called when a class is subclassed.

The default implementation does nothing. It may be overridden to extend subclasses.

Parameters
  • *args —
  • **kwargs —
function

pytest_helm_charts.giantswarm_app_platform.catalog.catalog_factory_func(kube_client, objects, namespace_factory) → CatalogFactoryFunc

</>

Return a factory object, that can be used to configure new Catalog CRs for the 'app-operator' running in the cluster

Parameters