semvec/pypi-packages huggingface dataset:
cmos-noise-map 0.1.4
A tool to create a read noise map for CMOS detectors by modelling Random Telegraph Signal. This was originally created for use by LCOGT's BANZAI pipeline.
Requires Python: >=3.8,<3.12

# CMOS Noise Map
[![Python application](https://github.com/LCOGT/cmos-noise-map/actions/workflows/python-app.yml/badge.svg)](https://github.com/LCOGT/cmos-noise-map/actions/workflows/python-app.yml)

[![Documentation Status](https://readthedocs.org/projects/cmos-noise-map/badge/?version=latest)](https://cmos-noise-map.readthedocs.io/en/latest/?badge=latest)

Code to model random telegraph noise in a CMOS detector. Originally designed for the Las Cumbres Observatory BANZAI pipeline.
Authors: Prerana Kottapalli, Matt Daily, Curtis McCully

Read the docs: https://cmos-noise-map.readthedocs.io/en/latest/index.html

## Installation
### From PyPi
```
pip install cmos-noise-map
```

### From Github
To install the tool, clone this repository and run:

```
pip install poetry
cd cmos-noise-map
poetry install
```
## Tests
To run the unit tests, simply run:

`poetry run pytest`

## Usage
Once you've installed the tool, it can be run simply by:
`rts-maker <path> <options> <write filename>`

```
Usage: rts-maker [OPTIONS] PATH [METHOD] [FILEPATH]

  This script builds a noise map with the chosen method.

  path: Path to input without the .fits at the end

  filepath:OPTIONAL Path to write file

  method: Default method is std. Available methods are std, rts, and param.
  See docs for more information about each method.

Options:
  -r, --data_ext TEXT             Extension of fits file that contains the
                                  image data
  -uq, --upper_quantile FLOAT     Standard deviation cutoff for pixel noise
                                  evaluation
  -t, --tolerance FLOAT           The minimum difference between silhouette
                                  scores. See docs for more information.
  -m, --min_peak_separation FLOAT
                                  Minimum difference between pixel value
                                  cluster centers to be considered separate
                                  clusters
  -o, --out_hdu_name TEXT         Name for the header in which the data will
                                  be stored
  -f, --fpack                     Adding this option will fpack your output
                                  fits file
  -b, --bias_check                Adding this option will skip the check to
                                  see if files used are bias files  [default:
                                  True]
  --help                          Show this message and exit.
```
medspacy-simstring 2.2a
Clone of simstring for medspacy which was cloned from another clone designed to work with QuickUMLS. Original version here: http://chokkan.org/software/simstring/

# NOTE

This is a modified version of a QuickUMLS clone of simstring.  This was modified to ensure continued support on Windows.

# SimString Python Package

Orginal version by [chokkan](https://github.com/chokkan/simstring). New version (compiles under macOS) from [here](https://github.com/blinkhealth/simstring-python-package)

Windows/Anaconda/VisualStudio specific steps:

1. Make sure that iconv is available to your desired conda environment: `conda install -c conda-forge libiconv`
2. Then, open a Visual Studio Developer Prompt (e.g. “VS 2015 x64 Developer Command Prompt” or “VS 2015 x86 Developer Command Prompt”)
3. Now you can any of these commands to build or install: `python setup.py build && python setup.py install`
4. **NOTE**: If there is a failure that `rc.exe` cannot be found, add the appropriate WindowKits binary path to `PATH`. More info on this [here](https://stackoverflow.com/questions/14372706/visual-studio-cant-build-due-to-rc-exe).
grandiorite 1.0.5
Hello World in python.
Requires Python: >=3.6

### Setup

`pip install -r requirements.txt`

### Run

`python main.py`

### Building and Uploading

#### Install tools: pyenv for .python-version, and twine

``` python
python -m pip install --upgrade pip
pip install twin
python3 setup.py sdist bdist_wheel
python3 -m twine upload dist/*
[username: __token__, password: <API TOKEN FROM PYPI.ORG ACCOUNT SETTINGS>]
```