semvec/pypi-packages huggingface dataset:
retinawhitenoise 1.0.1
Recreate white-noise stimuli
License: MIT
Requires Python: >=3.7
Keywords: retina,stimulus,white noise

# RecreateWhiteNoiseStimuliWithPython

[![Build status](https://github.com/gollischlab/RecreateWhiteNoiseStimuliWithPython/actions/workflows/pypi.yml/badge.svg)](https://github.com/gollischlab/RecreateWhiteNoiseStimuliWithPython/deployments/PyPI)
[![PyPI version](https://img.shields.io/pypi/v/retinawhitenoise.svg)](https://pypi.python.org/pypi/retinawhitenoise)

Code for regenerating the random-number-based contrast values applied in experiments with white-noise stimulation in the Gollisch Lab.
Used for both temporal and spatiotemporal stimuli, and for both binary and Gaussian white noise.

The random-number sequences are needed for analyzing the spike responses of recorded cells under white-noise stimulation.

### Installation
A full installation of the package is done from command-line using `pip` with
```
pip install retinawhitenoise
```

#### Manual installation
If the `pip` package is not available for a given operating system or Python version, the package and Cython code can be compiled manually instead.
```
pip install https://github.com/gollischlab/RecreateWhiteNoiseStimuliWithPython/archive/main.tar.gz
```

Windows might require the [Microsoft C++ Build Tools](https://visualstudio.microsoft.com/visual-cpp-build-tools) to be installed (including *"Windows SDK"* and *"MSVC ... C++ x64/x86 build tools"*). For details, see [here](https://www.scivision.dev/python-windows-visual-c-14-required).

### Usage
The Python package `retinawhitenoise` offers two modules `binarystimulus` and `gaussianstimulus`,
each offering routines to `recreate`, `save` (to disk), and `load` stimulus sequences.

Instructions on how to use them with example code and explanations is included in the jupyter notebook `examples.ipynb`.
gicha 1.2.0
Operating Chalice from Github
License: MIT

Gicha CLI
========

The Python CLI for maintaining a [Gicha stack](https://github.com/hei-school/gicha)... or dozens of them!

## Use cases

### Install gicha and list all possible parameters

```
pip install gicha
python -m gicha --help
```

### Create a completely new project

```
python -m gicha --app-name=gicha-base
```

Those configurations will be automatically saved in [gicha.yml](https://github.com/hei-school/gicha-cli/blob/main/tests/oracles/oracle-gicha-base/gicha.yml) at the end of the creation.

### Upgrade an already existing project

```
pip install gicha --upgrade
python -m gicha --app-name=gicha-base --output-dir=folder-already-created
```
Note the `--upgrade` and the `--output-dir=folder-already-created` flags. The Gicha configuration that was used for the previous generation is saved in `gicha.yml`: it will be updated after the new upgrade.

If you want to do an upgrade without re-specifying each parameter (there are a lot of them!), then gicha conveniently provides de `--gicha-conf` parameter.
Just modify the existing `gicha.yml`
(modify mandatorily the `version` parameter so that it reflects the newly upgraded version,
modify optionally any other parameter depending on your needs), then:
```
pip install gicha --upgrade
cd folder-already-created
python -m gicha --gicha-conf=gicha.yml --output-dir=.
```

### Activate Function URL

If your calls last longer than 30 seconds (but less than 15 minutes),
then you need to do the invocations through [Function URL](https://docs.aws.amazon.com/lambda/latest/dg/lambda-urls.html).

1. Call Gicha `--with-function-url=true`. This will just save the configuration in `gicha.yml` for now.
2. Activate Function URL. You can use either AWS Console or CLI.
3. Set handler to app.minimalistic_handler. You can use either AWS Console or CLI.

```diff
- TODO: automate step 2 and 3
``````
fuzzy-secret-stdout 0.3.1b37
Small utility to fuzzy search from a secret store and print the value to stdout
License: MIT
Requires Python: >=3.9,<4.0
Keywords: aws,secrets,ssm,parameter-store

# `fuzzy-secret-stdout`

> Small utility to fuzzy search from a secret store and print the value to stdout

[![main](https://github.com/kiran94/fuzzy-secret-stdout/actions/workflows/main.yml/badge.svg)](https://github.com/kiran94/fuzzy-secret-stdout/actions/workflows/main.yml)
![GitHub License](https://img.shields.io/github/license/kiran94/fuzzy-secret-stdout)
![PyPI - Version](https://img.shields.io/pypi/v/fuzzy-secret-stdout)

## Install

```bash
python -m pip install fuzzy-secret-stdout
```

Dependencies:

* Python 3.9+
* [`fzf`](https://github.com/junegunn/fzf?tab=readme-ov-file#installation)
* Valid [AWS Credentials](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html) available your terminal context

## Usage

```bash
# fuzzy search from secrets from aws parameter store
fuzzy-secret-stdout

# alias for the above
fss

# fuzzy search and explicitly specify the secret store to search
fss -i AWS_SECRET_MAN

# fuzzy search aws secret manager and pipe into jq
fss -i AWS_SECRET_MAN | jq .
```

## Integrations

`fuzzy-secret-stdout` supports the following secret stores:

| Secret Store                                                                                                                             | Command Line Argument  |
| -------------                                                                                                                            | ---------------------- |
| [AWS Systems Manager Parameter Store](https://docs.aws.amazon.com/systems-manager/latest/userguide/systems-manager-parameter-store.html) | `AWS_SSM`              |
| [AWS Secrets Manager](https://docs.aws.amazon.com/secretsmanager/latest/userguide/intro.html)                                            | `AWS_SECRET_MAN`       |

The *Command Line Argument* above is passed as the `-i` flag. `AWS_SSM` is the default.

## Environment Variables

| Environment Variables   | Description                                                                                                                                                                                                                       | Default  |
| ---------------------   | ----------                                                                                                                                                                                                                       | -------- |
| `FSS_MAX_BATCH_RESULTS` | The maximum number of results to request from the underlying secret service per batch. Note that this value might be rejected by the underlying secret service. For example `boto3` validates this value to be <= 50 for AWS SSM | `50`     |