This visualization was completed for my thesis on measuring the dissapation of transverse sound waves in aqueous media under adiabatic conditions. Jk, its just a cool UV playground
Try it out here!
This visualization was completed for my thesis on measuring the dissapation of transverse sound waves in aqueous media under adiabatic conditions. Jk, its just a cool UV playground
Try it out here!
| async def __call__( | |
| self, | |
| *, | |
| action: Action, | |
| text: str | None = None, | |
| coordinate: tuple[int, int] | None = None, | |
| **kwargs, | |
| ): | |
| if action in ("mouse_move", "left_click_drag"): | |
| if coordinate is None: |
This is a start. It doesn't work atm.
$ python open1.py --epochs 3 --use_mcts --multi_agent --progressive_training --visualize
2024-09-16 07:51:04.977649: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.Objective:
We are building a SaaS application where common features like CRUD operations, authentication, access control, background tasks, real-time communication, and templated entities are required. Below is a general framework you can use for building SaaS applications.
CRUDBase:| I am attesting that this GitHub handle JacobFV is linked to the Tezos account tz1Zc5iZyfdtoVcMwG1dHY7F11FH7DWVTJic for tzprofiles | |
| sig:edsigtjzq5fN911g9oxkn4WLMg21D1BaY6VeaALTGx1dAEsDSQisqTgXy5MaVkShYWmeABhZiNhw8YocR5GD72fiPvvqpBVC1YE |
| def polymorphic(fn): | |
| """ | |
| A decorator for creating polymorphic functions. | |
| This decorator allows you to define a base function and register multiple | |
| implementations for different conditions. When the decorated function is called, | |
| it will execute the appropriate implementation based on the registered conditions. | |
| The decorator adds a 'register' method to the wrapped function, which can be used | |
| to register new implementations with their corresponding condition functions. |
| def call_with_appropriate_args(fn, *args, **kwargs): | |
| """ | |
| Call a function with only the arguments it can accept. | |
| This function inspects the signature of the given function and calls it with | |
| only the arguments that match its parameters. It filters out any excess | |
| arguments that are not part of the function's signature. | |
| Args: | |
| fn (callable): The function to be called. |
| class HasPermissions(SQLBaseModel, table=False): | |
| """ | |
| A base class to handle permission levels for viewing and updating fields in SQLModel entities. | |
| This class allows setting permissions at the field level and automatically applies these permissions | |
| as SQL policies on the database. | |
| Attributes: | |
| __owner_field_name__ (ClassVar[Optional[str]]): The name of the field that identifies the owner of the record. | |
| Usage: |
| import traceback | |
| class DontRaiseUntilDone: | |
| def __init__(self): | |
| self.exceptions = [] | |
| def __enter__(self): | |
| return self | |
| def __exit__(self, exc_type, exc_val, exc_tb): |
| from abc import ABC | |
| from datetime import datetime | |
| from enum import Enum | |
| from typing import ClassVar, Optional, Self | |
| from typing_extensions import Unpack | |
| from fastapi import APIRouter, Depends | |
| from pydantic import UUID4, BaseModel, PrivateAttr | |
| import pydantic | |
| from pydantic.config import ConfigDict |