semvec/pypi-packages huggingface dataset:
statlab 0.0.4
A python package offering a variety of statistical and machine learning tools.
Requires Python: >=3.8


# statlab
[![PyPi version](https://badgen.net/pypi/v/statlab)](https://pypi.org/project/statlab)

statlab is a new Python package offering a variety of statistical and machine learning tools.


# Products

## "ord" ordinal classification tools
Submodule contains:
+ TreeOrdinalClassifier
+ SubtractionOrdinalClassifier
+ Functions to calculate classification metrics on ordinal data

Submodule developed by: Noam Rotenberg, Andreia Faria, Brian Caffo

Examples coming soon!

## "aled" Adaptive Label Error Detection
Submodule contains: ALED class

Methods details and cite: (details available soon)

Submodule developed by: Zan Chaudhry, Noam Rotenberg, Brian Caffo, Craig Jones, Haris Sair

Example available in examples/aled-medMNIST.py

# Usage

Install: ``!pip install statlab``

Import a single module: ``import statlab.ord`` - imports only ord

Import all modules: ``from statlab.all import *`` - can access any function without prefixes, e.g., ``TreeOrdinalClassifier(base_clf)``

Specific example:
```
!pip install statlab
import statlab.ord
base_clf = # some sklearn-style classifier
clf = statlab.ord.TreeOrdinalClassifier(base_clf)
```

# Future work:

+ Dataset mislabeling detection and classification
+ Naive Bayes classifier using nonparametric statistics
+ Automated nonlinear feature tuning
parserrtm 0.1.1
Fully-featured Python interface for rapid radiative transfer model (RRTM)
Requires Python: >=3.9

# parserrtm
A high-level interface for running the Rapid Radiative Transfer Model (RRTM) in Python, both longwave (RRTM_LW) and shortwave (RRTM_SW) versions. A high-level interface helps because the Fortran text file inputs are unreadable by humans and have a complicated structure. With this library, all you interact with are named input parameters and calculation results. The strength of `parserrtm` is that it supports (almost) every possible configuration of both models (reach out if a configuration you need isn't supported!).

With `parserrtm`, you can simply take an existing example input file and modify the field of interest to you by name. The library takes care of underlying file I/O and just gives you a dataset with the results of your calculation.

# installing parserrtm

Two components are needed: (1) this Python library and (2) executables for the RRTM Fortran codes. The Python library is easy to install and I have a [compilation guide](https://github.com/bertrandclim/parserrtm/blob/main/rrtm_compiling_guide.md) for getting the RRTM executables from `rrtm_lw/` and `rrtm_sw/` in this repository. If you are running anything other than Red Hat Enterprise Linux 8.10, you will probably need to compile RRTM.

I've only ever successfully compiled RRTM on linux with the PGI/NVIDIA compiler, so if you're on another OS I'd reccomend using a virtual machine (like `lima` for macOS).

## from pypi
```
git clone https://github.com/bertrandclim/parserrtm.git
pip install parserrtm
```
## from git repo
```
git clone https://github.com/bertrandclim/parserrtm.git
cd parserrtm
python3 -m build
pip install dist/parserrtm-0.0.1.tar.gz
```

# after installing
First, you should benchmark your RRTM executable. Open `/tests/run_stock_examples.ipynb` in Jupyterlab (or similar) and hit `notebook -> run all cells`. This will run a series of example cases for RRTM_LW and RRTM_SW and validate the output.

# overview
`parserrtm` offers three high-level classes for running this radiative transfer model: `InputLW`, `InputSW`, and `Runner`.

The `InputLW` and `InputSW` classes expose RRTM parameter fields as attributes you can assign to or declare. For explanations of these parameter fields, see `rrtm_lw/rrtm_instructions` and `rrtm_sw/rrtm_sw_instructions`. Each instance of `InputLW` or `InputSW` holds all the data for a single RRTM run.

You can initialize an `InputLW` or `InputSW` object from existing RRTM input text files or a dictionary. Once you have an `InputLW` or `InputSW` object that sets up the kind of calculation you want, you can simply modify the attributes of interest and rerun the calculations.

Calculations are handled by the `Runner` class. `Runner` takes a series of input objects, runs RRTM (in parallel) for each input, and loads the output back into Python.

Coming soon: some usage example tutorials and documentation pages. For now, the docstrings of `Runner`, `Input`, `InputSW`, and `InputLW` should be a good starting point.
django-gbase8s 3.2.0
Django backend for GBase 8s database
Requires Python: >=3.6

# django-gbase8s

#### 介绍
GBase 8s数据库的Django方言，支持GBase 8s V8.8_3.6.2版本及以上。

#### 依赖
- gbase8sdb
- django


#### 安装教程

```python
pip install django-gbase8s
```

#### 使用说明

1.  方言使用python-gbase8sdb驱动连接数据库，安装django-gbase8s时会作为依赖进行安装，您也可以手动安装：
```python
pip install gbase8sdb
```
2.  python-gbase8sdb驱动连接数据库依赖GSDK 1.1版本，所以您需要联系GBase 8s技术支持或通过官方渠道获取相应版本的GSDK，并安装到您的机器上， 并设置如下环境变量：
```bash
GSDK_PATH=/path/to/gsdk
export LD_LIBRARY_PATH=${GSDK_PATH}/lib:$LD_LIBRARY_PATH
export GBASEDBTDIR=${GSDK_PATH}/lib
```


3. 在Django项目的`settings.py`文件中进行如下配置：
```python
DATABASES = {
    "default": {
        "ENGINE": "django_gbase8s",
        'SERVER_NAME': 'ol_gbasedbt1210_1',     # 实例的名称
        'NAME': 'testdb',                     # 数据库的名称
        'HOST': '192.168.xxx.xxx',              # 数据库IP
        'PORT': 9088,                          # 实例端口号
        'DB_LOCALE': 'zh_CN.utf8',              # 数据库字符集
        'USER': 'gbasedbt',                     # 用户名
        'PASSWORD': 'xxxxxxx'                   # 密码
    }
}
```