This repository is built with nbdev, which means that the repo structure has a few differences compared to a standard python repo.
from pymemri.data.schema import * from pymemri.pod.client import * class Dog(Item): def __init__(self, name, age, id=None, deleted=None): super().__init__(id=id, deleted=deleted) self.name = name self.age = age @classmethod def from_json(cls, json): id = json.get("id", None) name = json.get("name", None) age = json.get("age", None) return cls(id=id,name=name,age=age) client = PodClient() example_dog = Dog("max", 2) client.add_to_schema(example_dog) dog = Dog("bob", 3) client.create(dog)
After installation, users can use the plugin CLI to manually run a plugin. For more information, see
run_plugin --pod_full_address=<pod_address> --plugin_run_id=<plugin_run_id> --owner_key=<owner_key> \ --database_key=<dabase_key>
The Python integrators are written in nbdev (video). With nbdev, it is encouraged to write code in
Jupyter Notebooks. Nbdev syncs all the notebooks in
/nbs with the python code in
/pymemri. Tests are written side by side with the code in the notebooks, and documentation is automatically generated from the code and markdown in the notebooks and exported into the
/docs folder. Check out the nbdev quickstart for an introduction, watch the video linked above, or see the nbdev documentation for a all functionalities and tutorials.