semvec/pypi-packages huggingface dataset:
cehuibaibaoxiang-no-gdal-version 0.1.0
（自用）本人写的测绘专业课作业代码合集
License: MIT
Requires Python: >=3.8

﻿# 测绘百宝箱

该程序是本人在测绘专业课上写的作业的一个合集，主要用于自用，不保证代码运行准确性，仅供参考和测绘学习使用，由于使用该库引发的各种问题本人概不负责，如果能帮助到各位还请大家给个star，也欢迎大家对库内容进行修改。
## 请注意，由于GDAL库存在奇怪的问题，如果安装不上去，请使用管理员权限或者用conda安装
## 主要功能

### 测绘计算

- **高斯投影**：实现高斯正算和反算，支持将经纬度坐标转换为高斯平面坐标，以及将高斯平面坐标转换回经纬度坐标。
- **交会测量**：包含测边交会、测角交会和边角交会等多种交会测量方法，用于确定未知点的平面位置。
- **坐标变换**：支持大地坐标、平面坐标等多种坐标系统的变换，满足不同应用场景下的坐标转换需求。


### 地理信息处理

- **断面计算**：提供纵断面和横断面的计算功能，能够根据给定的高程点数据生成断面图并计算面积。
- **图幅编号**：实现图幅编号的计算和生成，支持多种比例尺的图幅编号。
- **泰森多边形**：基于给定的点数据生成泰森多边形，可用于空间分析和区域划分。（该功能目前暂时没开发完全）


### 图像处理

- **点云去噪**：采用统计方法对点云数据进行去噪处理，提高数据质量。
- **后方交会**：利用像片控制点进行后方交会计算，确定像片的外方位元素。
- **图像配准**：提供图像配准功能，能够将不同来源的图像进行精确配准。

###磁场计算，计算带电正N边形磁场强度
## 使用指南

### 安装

您可以通过以下命令安装本库：

```bash
pip install 测绘百宝箱
```
### 帮助
在引入库后，运行print(cehuibaibaoxiang.help)即可得到帮助

### 示例代码

#### 高斯正算

```python
from 测绘百宝箱 import GaussForward

# 创建高斯正算对象
gauss_forward = GaussForward(B1=30.0, L1=110.0, s12=1000.0, A12=90.0)

# 执行高斯正算
x, y = gauss_forward.forward_gauss()

print(f"高斯平面坐标: x = {x}, y = {y}")
```


#### 测边交会

```python
from 测绘百宝箱 import SideIntersection

# 创建测边交会对象
side_intersection = SideIntersection(x1=100.0, y1=200.0, x2=300.0, y2=400.0, sa=250.0, sb=300.0)

# 执行测边交会计算
xp, yp = side_intersection.calculate()

print(f"未知点坐标: xp = {xp}, yp = {yp}")
```


## 项目依赖

本项目依赖以下库：

- NumPy
- Matplotlib
- GDAL
- SciPy
- Pandas
- OpenCV-Python


## 贡献指南

欢迎广大开发者参与本项目的开发和改进。如需贡献代码，请按照以下步骤进行：

1. 叉取本项目仓库。
2. 在本地克隆叉取的仓库。
3. 创建新的分支进行开发。
4. 提交代码并发起 Pull Request。

## 许可证

本项目采用 MIT License 许可证，具体内容详见 [LICENSE](LICENSE) 文件。
param-sweeps 0.2.1
Parameter sweeper for ui.json powered applications.
License: MIT
Requires Python: <4.0,>=3.10
Keywords: geology, geophysics, earth sciences

Params-sweep
============

The **params-sweep** package lets users generate and run ui.json applications over a suite of values.


Installation
^^^^^^^^^^^^
**params-sweep** is currently written for Python 3.10 or higher.

Install **params-sweep** from PyPI::

    $ pip install params-sweep


Feedback
^^^^^^^^
Have comments or suggestions? Submit feedback.
All the content can be found on the github_ repository.

.. _github: https://github.com/MiraGeoscience/params-sweep


Visit `Mira Geoscience website <https://mirageoscience.com/>`_ to learn more about our products
and services.


License
^^^^^^^
MIT License

Copyright (c) 2020-2025 Mira Geoscience

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

Third Party Software
^^^^^^^^^^^^^^^^^^^^
The params-sweep Software may provide links to third party libraries or code (collectively “Third Party Software”)
to implement various functions. Third Party Software does not comprise part of the Software.
The use of Third Party Software is governed by the terms of such software license(s).
Third Party Software notices and/or additional terms and conditions are located in the
`THIRD_PARTY_SOFTWARE.rst`_ file.

.. _THIRD_PARTY_SOFTWARE.rst: ./THIRD_PARTY_SOFTWARE.rst

Copyright
^^^^^^^^^
Copyright (c) 2024-2025 Mira Geoscience Ltd.
epgm-model 0.1.1
Implementation of the EPGM model for time series forecasting.
License: MIT

# EPGM (2,1, Tau)

[![PyPI version](https://img.shields.io/pypi/v/epgm_model.svg)](https://pypi.org/project/epgm_model/)

An open-source Python implementation of the EPGM(2,1,Tau) model from the paper:  
*A novel dynamic time-delay grey model of energy prices and its application in crude oil price forecasting*  
https://doi.org/10.1016/j.energy.2022.123968

---

## Overview

EPGM is a second-order grey forecasting model designed to improve traditional grey models by incorporating a time delay parameter **Tau**.

This implementation allows fitting and forecasting with the EPGM(2,1,Tau) model, supporting parameter optimization using simulated annealing, regularized regression, and multi-step ahead prediction.

---

## Features

- Fit EPGM(2,1,Tau) model on univariate time series data  
- Optimize model parameters (`r1`, `tau`, regularization alpha) via simulated annealing  
- Support both unregularized least squares and Ridge regression solvers  
- Predict future values with option to constrain predictions to positive values  
- Handles data normalization internally  
- Built-in evaluation using Mean Absolute Percentage Error (MAPE)  
- Plotting utilities for visualizing test set performance  

---

## Installation

```bash
pip install epgm_model