semvec/pypi-packages huggingface dataset:
pypi-timemachine 0.3.1
Run a PyPI server from the past
Requires Python: >=3.11

A PyPI time machine
-------------------

Do you wish you could just install packages with pip as if you were at
some fixed date in the past? If so, the PyPI time machine is for you!

**Disclaimer:** this is alpha-quality software - for now it is a quick hack,
but I'd love to make this more robust/usable with your help!

Installing
~~~~~~~~~~

To install::

   pip install pypi-timemachine

Using
~~~~~

Once installed, you can run a PyPI server with::

   pypi-timemachine 2021-02-03

or if you need to specify a precise time (in UTC)::

   pypi-timemachine 2021-02-03T12:33:02

This will start up a webapp running uvicorn, and will print out a line such as::

    pypi-timemachine server listening at http://localhost:5000  (Press CTRL+C to quit)
      Hint: Setting the environment variable PIP_INDEX_URL="http://localhost:5000" is one way to configure pip to use this timemachine

You can then configure pip to use the time machine, for example::

   pip install --index-url http://127.0.0.1:5000/ astropy

and this will then install the requested packages and all dependencies,
ignoring any releases after the cutoff date specified above.

For convenience, it is also possible to specify the cutoff date as part of the
repository URL, using the pattern `http://{host}/snapshot/{cutoff_date}/`.
Cutoff date can be either an RFC 3339 timestamp (e.g. `2006-12-02T02:07:43Z`)
or a simple date (e.g., 2006-12-02). For example::

   pip install --index-url http://127.0.0.1:5000/snapshot/2024-12-03 astropy

Would install the requested packages and all dependencies from the date given in
the index URL.

It is possible to run the time machine against a custom Python package
repository, provided it includes date metadata as defined in PEP-700,
specifically the `upload-time` field::

  pypi-timemachine 2021-02-03 --index-url https://my-custom-repo/simple/

How it works
~~~~~~~~~~~~

`pypi-timemachine` builds upon the `simple-repository`_ stack, and uses the
standards based PEP-503 repository definition to serve packages.
In order to filter by time, the upstream repository must provide PEP-700
metadata (which PyPI does).
The results are filtered by pypi-timemachine, and then served as HTML or JSON
via the standard PEP-503 interface.


Caveats/warnings
~~~~~~~~~~~~~~~~

If a package is already installed, pip will not try installing it again.
This means that if e.g. you try and run pip as described above, but the
package you are trying to install (or any of its dependencies) is
already installed, no matter how recent the version, it will not be
installed again. Therefore, I recommend using pip with the custom index
URL inside a clean environment (but you can run the ``pypi-timemachine``
command inside your regular environment.)


.. _simple-repository: https://github.com/simple-repository/
ChatBattery 0.1.0
Add your description here
Requires Python: >=3.10

## [ChatBattery] Expert-Guided LLM Reasoning for Battery Discovery: From AI-Driven Hypothesis to Synthesis and Characterization

Authors: Shengchao Liu<sup>\*</sup>, Hannan Xu<sup>\*</sup>, Yan Ai<sup>\*</sup>, Huanxin Li<sup>+</sup>, Yoshua Bengio<sup>+</sup>, Harry Guo<sup>+</sup>


### 1 Environment Setup
```
conda create -n ChatBattery python=3.9
conda activate ChatBattery

pip install pandas
pip install openai==0.28
pip install Levenshtein
pip install pymatgen==2024.4.13
pip install ase
pip install scikit-learn
pip install xgboost
pip install mp-api==0.41.2
pip install Flask
pip install -e .
```


### 2 Data Preprocess

In our case, we download all the chemical formula including Lithium from ICSDN. Then after preprocessing, the file is `data/Li_battery/preprocessed.csv`.

### 3 Run Scripts
You need to set up two keys:
- First set the OpenAI API key, type this in terminal `export OPENAI_API_KEY=xxxxx`.
- Then is the MP API key, you can change it in the file `ChatBattery/search_agent` and reset `MP_api_key = 'xxx'`.

#### 3.1 Exploration Phase

For stage 1 to 4, run this CMD in terminal
```bash
python main.py
```

The default address is `127.0.0.1:5000`. Simply open this address in your browser, and you will see the interface displayed like this:

<p align="center">
  <img src="static/fig/interface.png" /> 
</p>

Then please follow the instructions from stage 1 to stage 4. Enter `Ctrl + C` if you want to stop the program.
The results are collected into a csv file, which will be fed into the next stage.

##### 3.2 Exploitation Phase

- Stage 5, first please get prepared the input file
```bash
cd stage_05_deduplication
python stage_05_deduplication.py --input_file=xxx
```

- Stage 6, then run the following CMD
```bash
cd stage_06_ranking
bash stage_06_ranking.sh
```
PyAibote 1.9.7
A pure code RPA office automation framework, which supports Android, Browser and Windows
Requires Python: <4.0,>=3.5

Support Android native APP and H5 interface elements and color positioning. The speed of element location is 10 times that of Appium framework, and the color location of 2340*1080 image only takes 50 milliseconds, It supports the positioning of window interface elements and colors developed by Windows applications,. NET, WinForm, WPF, QT, Java (GUI libraries such JAVA(Swing and AWT) and Electron. The exclusive xpath algorithm is concise and rapid, and the positioning speed of elements/colors is 3 times and 20 times that of visual RPA, respectively, All browsers and applications that support the chromium kernel. A web automation framework developed by C/C++ language based on browser kernel protocol. Ten times faster than Selenium
adcpasciireader 1.1.3
Code for reading and plotting adcp data from classic ascii output

# ðŸ“¦ ADCP-ASCII-Reader

A lightweight Python package to read and process **ASCII output files from Acoustic Doppler Current Profilers (ADCPs)**.  
This package converts raw ADCP ASCII data into structured arrays or pandas DataFrames, making it easier to analyze current profiles, water velocity data, and instrument metadata.
b2b-saas2saas 1.0.1
A package to generate random SaaS ideas using ChatGPT
Requires Python: >=3.7

# B2B SaaS to SaaS

This is an AI-powered SaaS generator that can be used to make you (or lose you) millions!

https://pypi.org/project/b2b-saas2saas/

## Installation

```bash
pip install b2b_saas2saas
```

Add your OpenAI key to a .env file. 
```
OPENAI_API_KEY=your_key
```

## Usage

```python
from b2b_saas2saas import get_random_saas_idea
import dotenv
dotenv.load_dotenv()

idea = get_random_saas_idea()
print(idea)
```

dependencies:
- openai
- python-dotenv

Use at your own risk - I'm not liable for any damages!

License: MIT