semvec/pypi-packages huggingface dataset:
SquareCalcLib 0.0.5
A module for calculating the areas of an ellipse and a triangle.
Requires Python: >=3.10
Keywords: square,triangle,ellipse,calcutation

# SquareCalcLib
## A project to confirm knowledge on the platform hh.ru
### Date: 08.03.2024

A project to confirm knowledge on the platform **hh.ru**.

Library for calculating the areas of an ellipse and a triangle.

Stack of technologies used:
- _Python 3.10_

> [!NOTE]
> All modules are located in ***["SquareCalcLib"](/SquareCalcLib)***

> [!NOTE]
> Input values for the module **"find_square_triangle"**:
> 
> _To find the area of a triangle, you can enter both 3 sides and 2 sides and the angle **BETWEEN THEM**_
> - sides: list[float] - list of sides of the triangle
> - angle: float = None - the value in degrees of the angle between the two sides of the triangle

> [!NOTE]
> Input values for the module **"find_square_ellipse"**:
>
> _If you are sure that your shape is a circle, then you can not enter the "small_semi_axis" parameter, but represent "large_semi_axis" as the radius of the circle_
> - large_semi_axis: float - the value of the length of the large semi-axis of the ellipse 
> - small_semi_axis: float = None - the value of the length of the small semi-axis of the ellipse

> [!NOTE]
> NumberVariablesError 
> The error class of the entered values
> 
> Types of errror:
> - ZeroOrNegativeSemiAxis - semi-axis cannot be negative or equal to zero
> - IncorrectValuesSidesTriangle - the values of the sides of a triangle cannot be negative
> - SumSidesTriangle - the value of any one side cannot be greater than the sum of the values of the other sides
> - ValueAngleError - The angle cannot be less than or equal to zero or the angle cannot be more than 180 degrees
nbodyx 0.0.1rc56.post1
A JAX simulator for N body problems.
Requires Python: >=3.10.0

# NBODYX: Simulating N-Body Systems with Jax

This repository derives ODEs for N-Body systems and simulates them using Jax. The main goal is to provide a simple and efficient way to simulate N-Body systems using Jax.
It is heavily inspired by the amazing [ThreeBodyBot](https://github.com/kirklong/ThreeBodyBot) and specifically the [NumericsTutorial](https://github.com/kirklong/ThreeBodyBot/blob/master/NumericsTutorial/NumericalDynamicsTutorial.ipynb).

![3-body problem](assets/three_body.gif)

## Installation

You can install the package using `pip`:
```bash
pip install nbodyx
```
or locally in editable mode:
```bash
pip install -e .
```

For animations, we require `ffmpeg` to be installed. On Ubuntu, you can install it using:

```bash
sudo apt-get install ffmpeg
```
or using `conda`:
```bash
conda install -c conda-forge ffmpeg
```

## Usage

The following example simulates a 3-body system:

```python
from diffrax import diffeqsolve, Dopri5, ODETerm, SaveAt
import jax.numpy as jnp
from nbodyx.constants import G
from nbodyx.ode import make_n_body_ode
from nbodyx.rendering.opencv import animate_n_body, render_n_body

if __name__ == "__main__":
    body_masses = jnp.array([1.0, 1.0, 1.0]) / G
    ode_fn = make_n_body_ode(body_masses)

    # initial positions
    x0 = jnp.array(
        [
            [-0.97000436, 0.24208753],
            [0.0, 0.0],
            [0.97000436, -0.24208753],
        ]
    )
    # initial velocities
    v0 = jnp.array(
        [
            [0.4662036850, 0.4323657300],
            [-0.93240737, -0.86473146],
            [0.4662036850, 0.4323657300],
        ]
    )

    # initial state
    y0 = jnp.stack([x0, v0], axis=0).reshape(-1)
    # external torques
    tau = jnp.zeros((6,))

    # state bounds
    x_min, x_max = -1.5 * jnp.ones((1,)), 1.5 * jnp.ones((1,))

    # simulation settings
    duration = 6.3259  # duration of the simulation [s]
    ts = jnp.linspace(0.0, duration, 1001)
    dt = 2e-4  # time step [s]

    # solve the ODE
    ode_term = ODETerm(ode_fn)
    sol = diffeqsolve(ode_term, Dopri5(), ts[0], ts[-1], dt, y0, tau, saveat=SaveAt(ts=ts), max_steps=None)
    # extract the solution
    y_bds_ts = sol.ys.reshape((len(ts), 2, 3, 2))  # shape: (timesteps, 2, num_bodies, num_dims)

    # animate the solution
    animate_n_body(
        ts,
        x_bds_ts=y_bds_ts[:, 0, ...],
        width=500,
        height=500,
        video_path="examples/outputs/three_body.mp4",
        speed_up=ts[-1] / 10,
        x_min=x_min,
        x_max=x_max,
        timestamp_unit="s",
    )
```
django-cloud-storage 1.0.5
Custom storage backend tailored for a proprietary cloud storage service.
Requires Python: >=3.10

# Django Cloud Storage


Django Cloud Storage is a Python package that provides a custom storage backend designed for seamless integration with 
proprietary cloud storage service.

## Installation

```bash
pip install django-cloud-storage
```

## Configuration

In your Django settings file, set the following values:

```python
CLOUD_STORAGE_URL = 'https://your-cloud-service.com'
CLOUD_STORAGE_API_TOKEN = 'your-api-token'
CLOUD_STORAGE_PROJECT_ALIAS = 'myproject'
```

## Usage
To integrate the Cloud Storage with a Django model, follow these steps:

* Import the required models and CloudStorage:
    ```python
    from django_cloud_storage.storage import CloudStorage
    ```
* Define your Django model, using `CloudStorage` as the storage backend for a `FileField`:

    ```python
    class MyModel(models.Model):
        file_field = models.FileField(storage=CloudStorage)

        def __str__(self):
            return self.file_field.name
    ```
django-gcp-iam-auth 0.0.4
Allow Django's database wrappers to work with Google Cloud SQL's IAM Auth
Requires Python: >=3.7

# django_gcp_iam_auth

django db engine for using IAM Authentication with Google Cloud SQL.

Available on pypi at https://pypi.org/project/django-gcp-iam-auth

## Installation

```
pip install django_gcp_iam_auth
```

## Usage

Update the DATABASES entry to replace the ENGINE for your connection.

```
DATABASES["default"]["ENGINE"]  = "django_gcp_iam_auth.postgresql"
```
vnpy-qmt 0.3.3
vnpy qmt gateway
Keywords: vnpy gateway qmt

# vnpy_qmt
QMT Gateway for vnpy

# 实现功能
- 连接mini客户端实现普通买卖

# 安装方式
- 源码安装： 下载源码, 解压, 切换vnpy环境, 在cmd里执行
```commandline
(vnpyO) D:\work\my>cd vnpy_qmt
(vnpyO) D:\work\my\vnpy_qmt>pip install .
```
# 启用通道
1. 需要使用脚本启动方式启动vnpy, 下面是一个示例(文件名如：run.py)
2. 切换到vnpy环境(或者使用vnpy对应的python绝对路径)，运行 `python run.py`
```python

# -*- coding:utf-8 -*-

from vnpy.event import EventEngine
from vnpy.trader.engine import MainEngine
from vnpy.trader.ui import MainWindow, create_qapp
# 导入QMT gateway
from vnpy_qmt.qmt_gateway import QmtGateway
from vnpy_ctastrategy import CtaEngine, CtaStrategyApp


def main():
    """Start VN Trader"""
    qapp = create_qapp()

    event_engine = EventEngine()
    main_engine = MainEngine(event_engine)
    main_engine.add_app(CtaStrategyApp)
    # 添加gateway
    main_engine.add_gateway(QmtGateway, gateway_name="QMT")

    main_window = MainWindow(main_engine, event_engine)
    main_window.showMaximized()

    qapp.exec()


if __name__ == "__main__":
    main()
```



## 依赖项目
[迅投xtquant官网下载](https://dict.thinktrader.net/nativeApi/download_xtquant.html)
[迅投xtquant介绍](https://dict.thinktrader.net/nativeApi/start_now.html)
# 使用
1. 启动、登录QMT mini客户端
2. 在vn.py连接QMT
![login_qmt.png](doc%2Flogin_qmt.png)