semvec/pypi-packages huggingface dataset:
LN2-Monitor 0.0.10
A Project that helps automate the weighing of LN2
Requires Python: >=3.7

# LN2_Monitor
This project will use a USB connection to a dymo scale to help graph and weigh the percentage full of an LN2 trap. The percentage full is based on the maximum weight a LN2 trap can hold(about 28.26kg). The scale used in conjunction with this code is the Dymo Digital Shipping Scale (S100) (https://www.dymo.com/scales/dymo-s100-digital-usb-shipping-scale-100-pound-capacity/SAP_1776111.html)

![LN2_dewar](https://github.com/dav17393/LN2_Monitor/blob/main/LN2_dewar.png)

**Required Software:**
download the software below first:

libusb-win32 (https://sourceforge.net/projects/libusb-win32/)


The easiest way to install it is to download the Zadig installer https://zadig.akeo.ie/

Select your device and select the libusb-win32 driver. Then click replace or install driver

![LN2_dewar](https://github.com/dav17393/LN2_Monitor/blob/main/zadig.png)

to install the code: python3 -m pip install LN2_Monitor
then go into python and input
```
from ln2_monitor import d1 
scale = d1.SCL()
scale.monitor()
```
or 
```
scale.plot()
```

Both plot and monitor will generate text file logs in the current directory

**To turn off "auto-sleep" mode on the scale, hold the kg/lb button (sometimes tare button) and turn the scale on. **

The basic minimum and maximum weights(kg) are set to default as the dewar that comes with a bluefors cryostat. Scale.setweight() can be run to change the minumum and maximum weight of the cryostat.


Enjoy!
starlette-caches 0.4.1
Server-side HTTP caching for ASGI applications, inspired by Django's cache framework
Requires Python: >=3.9

# starlette-caches

[![Build Status](https://github.com/mattmess1221/starlette-caches/actions/workflows/ci.yml/badge.svg)](https://github.com/mattmess1221/starlette-caches/actions/workflows/ci.yml)
[![Coverage](https://codecov.io/gh/mattmess1221/starlette-caches/branch/main/graph/badge.svg)](https://codecov.io/gh/mattmess1221/starlette-caches)
[![Package version](https://badge.fury.io/py/starlette-caches.svg)](https://pypi.org/project/starlette-caches)

`starlette-caches` provides middleware and utilities for adding server-side HTTP caching to ASGI applications. It is powered by [`aiocache`](https://aiocache.aio-libs.org/en/latest/), and inspired by Django's cache framework.

Documentation is available at: https://mattmess1221.github.io/starlette-caches/

**Note**: this project is in an "alpha" status. Several features still need to be implemented, and you should expect breaking API changes across minor versions.

## Features

- Compatibility with any ASGI application (e.g. Starlette, FastAPI, Quart, etc.).
- Support for application-wide or per-endpoint caching.
- Ability to fine-tune the cache behavior (TTL, cache control) down to the endpoint level.
- Clean and explicit API enabled by a loose coupling with `aiocache`.
- Fully type annotated.
- 100% test coverage.

## Installation

```bash
pip install "starlette-caches"
```

To install with redis or memcached support, use:

```bash
pip install "starlette-caches[redis,memcached]"
```

## Quickstart

```python
from aiocache import Cache
from starlette_caches.middleware import CacheMiddleware

cache = Cache(ttl=2 * 60)

async def app(scope, receive, send):
    assert scope["type"] == "http"
    headers = [(b"content-type", "text/plain")]
    await send({"type": "http.response.start", "status": 200, "headers": headers})
    await send({"type": "http.response.body", "body": b"Hello, world!"})

app = CacheMiddleware(app, cache=cache)
```

This example:

- Sets up an in-memory cache (see the [aiocache docs](https://aiocache.aio-libs.org/en/latest/) for specifics).
- Sets up an application (in this case, a raw-ASGI 'Hello, world!' app).
- Applies caching on the entire application.

To learn more, head to the [documentation](https://mattmess1221.github.io/starlette-caches/).

## Credits

Due credit goes to the Django developers and maintainers, as a lot of the API and implementation was directly inspired by the [Django cache framework](https://docs.djangoproject.com/en/2.2/topics/cache/).

## License

MIT
msdbook 0.1.6
Jupyter notebook support for the MSD uncertainty ebook.
Requires Python: >=3.9
Keywords: MSD, MultiSector Dynamics, uncertainty characterization

[![test](https://github.com/IMMM-SFA/msd_uncertainty_ebook/actions/workflows/01_test.yml/badge.svg)](https://github.com/IMMM-SFA/msd_uncertainty_ebook/actions/workflows/01_test.yml) [![deploy](https://github.com/IMMM-SFA/msd_uncertainty_ebook/actions/workflows/02_deploy.yml/badge.svg)](https://github.com/IMMM-SFA/msd_uncertainty_ebook/actions/workflows/02_deploy.yml) [![codecov](https://codecov.io/gh/IMMM-SFA/msd_uncertainty_ebook/branch/dev/graph/badge.svg?token=sc7PduW907)](https://codecov.io/gh/IMMM-SFA/msd_uncertainty_ebook)
[![PyPI version](https://badge.fury.io/py/msdbook.svg)](https://badge.fury.io/py/msdbook)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/msdbook)](https://pypi.org/project/msdbook/)


## Addressing Uncertainty in [MultiSector Dynamics](https://climatemodeling.science.energy.gov/program-area/multisector-dynamics) Research

### [Read the living document here](https://immm-sfa.github.io/msd_uncertainty_ebook/)!

To provide feedback or report a bug, please [open an issue](https://github.com/IMMM-SFA/msd_uncertainty_ebook/issues/new).

Merges to `main` will automatically compile, release, and deploy the ebook.

### Developer Workflow

Follow the [UC eBook Development Workflow](https://uc-ebook.org/dev/docs/html/contributing.html#development-workflow), which broadly follows the [GitHub workflow](https://docs.github.com/en/get-started/using-github/github-flow).