semvec/pypi-packages huggingface dataset:
amlengine 2.0.5.4
Easy access to the .Net implementation of the AML.Engine (https://github.com/AutomationML/AMLEngine2.1)
Requires Python: >=3.7

# Python AML.Engine

This provides access to the .Net implementation of the [AML.Engine](https://github.com/AutomationML/AMLEngine2.1) - an API for simple access to AutoamtionML files.

Access to the functionalities of the .Net dlls is provided via the [pythonnet](https://pythonnet.github.io/) package.

Besides the access to the native API of the AML.Engine, the package also contains some additional helper functions implemented in Python that are available via the *amlhelper* module.

## Usage

```python
# ensure the package is loaded correctly and the required DLLs are registered with pythonnet
import amlengine

# depending on what functionalities you want to use
from Aml.Engine import *
from Aml.Engine.CAEX import *
from Aml.Engine.CAEX.Extensions import *
from Aml.Engine.AmlObjects import *
from Aml.Engine.AmlObjects.Extensions import *
from Aml.Engine.Services import *
from Aml.Engine.Adapter import *

# access to the native .Net API
aml_file = CAEXDocument.New_CAEXDocument(CAEXDocument.CAEXSchema.CAEX2_15)

```

### .Net version

By default, the DLLs compiled for .Net Framework 4.8 are used. However, using another version provided by the AML.Engine is also possible. Therefore, set the environment variable *AML_ENGINE_DOTNET_VERSION* to one of the following values before importing the *amlengine*:
 - net5.0
 - net6.0
 - net48 (default)
 - netcoreapp3.1
tirf-trajectory 0.0.6
TIRF Trajectory
Requires Python: >=3.6

# TIRF Trajectory

## 介绍
该软件用来处理tirf实验采集的带噪声的FRET强度信号，目前可以自动对进行FRET强度等级进行分类和截断。

## 安装
1. [安装miniconda](https://docs.conda.io/en/main/miniconda.html). 国内用户可以使用[清华镜像](https://mirrors.tuna.tsinghua.edu.cn/anaconda/miniconda/?C=M&O=D)下载安装文件, 国内软件源镜像的设置可以参考[清华tuna的说明](https://mirrors.tuna.tsinghua.edu.cn/help/anaconda/).
   
1. 以Linux为例，假设miniconda安装在默认目录，打开命令行界面，按照以下命令行安装运行环境（只需运行一次，即完成安装）。
    ```bash
    source ~/miniconda3/bin/activate
    cd path/to/proj/dir/contains/environment_rt.yml
    conda env create -f environment_rt.yml
    ```

## 运行

1. 以后每次使用之前，先自行下面的操作
    ```bash
    source ~/miniconda3/bin/activate
    cd path/to/proj/dir/notebooks
    conda activate tirf_traj
    jupyter lab --ip=0.0.0.0
    ```

1. 打开浏览器输入 http://10.0.0.3:8888/lab (假设运行上一步命令机器的ip为10.0.0.3)，或 http://127.0.0.1:8888/lab (假设运行上一步命令的机器就是打开浏览器的机器)。处于安全考虑，不建议再未做进一步设置的前提下在公网可访问的情况下开启jupyter。

1. 在浏览器中打开tirfdata.ipynb,依次执行即可，请特别注意文本标注的cell中的提示。此外需要注意的事项，目前版本如需正常显示图像，需要保证你的浏览器可以正常连接以下网址。
   * [unpkg.com](https://unpkg.com/@holoviz/panel@0.14.1/dist/panel.min.js)
   * [cdn.bokeh.org](https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.3.min.js)
   * [cdn.holoviz.org](https://cdn.holoviz.org/panel/0.14.1/dist/bundled/datatabulator/tabulator-tables@5.3.2/dist/js/tabulator.js)

2. 在浏览器中打开tdp.ipynb,可画TDP图