semvec/pypi-packages huggingface dataset:
pyflies-psychopy 0.2.0
Generator for PsychoPy from pyFlies models
License: MIT
Keywords: experiments,pyflies,behavior,psychology,neuroscience,textX

# pyflies-psychopy

Generator for [PsychoPy](https://www.psychopy.org/) experiments from
[pyFlies](https://github.com/pyflies/pyflies) models.

When this project is installed you will have a textX registered generator that
can generate PsychoPy experiment from pyFlies models (`.pf` files). You can
verify that generator is available by:

```
textx list-generators
```

and you can generate PsychoPy experiment by:

```
textx generate <your pyflies model.pf> --target psychopy --overwrite
```

For the details see [running generators
section](https://pyflies.github.io/pyflies/latest/generators/) of pyFlies
documentation.


# Configuration

PsychoPy can be configured in [userPrefs.cfg
file](https://www.psychopy.org/general/prefs.html#preferences). Its location
[varies by
machine](https://www.psychopy.org/troubleshooting.html#cleaning-preferences-and-app-data).

You can use `Preferences` dialog in the PsychoPy GUI for the configuration.


# Parameters in the `target` configuration block

See `pfpsychopy.default_settings`.

- `resolution: Point` - Screen/window resolution, default is `(1024, 768)`
- `fullScreen: boolean` - should the experiment run in full screen, default is `false`
- `background: string` - background color, default is `black`
- `frameTolerance` - tolerance in frame duration in seconds, default is `0.001`


# Credits

Initial project layout generated with `textx startproject`.
image-features-extract 0.4.19
toolbox for extracting features from an image
License: MIT
Keywords: Image,data processing

## Product Name
Toolbox to extract features from an image.

## Installation
Run the following to install:
```python
pip install image_features_extract
```

## Usage example
**refer to** [image_features_extract-examples](https://github.com/Bertin-fap/image_feature_extract_example).


# Release History
0.O.3 add read_plu_topography to read a binary .plu image,
add fill_gap to replace missing values in .plu image by interpolated ones

# Meta
	- François Bertin– francois.bertin7@wanadoo.fr 
	- Amal Chabli

Distributed under the [MIT license](https://mit-license.org/)

# About the authors
	- François Bertin now retired was an expert senior in nanocharacterization at CEA-LETI
	- Amal Chabli now retired was a Director of Research in nanocharacterization at CEA-LITEN
hirez-helpers 1.0.3
helper functions for hi rez ML engineers
License: MIT

# Hi Rez Helpers

[![N|Solid](https://cldup.com/dTxpPi9lDf.thumb.png)](https://nodesource.com/products/nsolid)

[![Build Status](https://travis-ci.org/joemccann/dillinger.svg?branch=master)](https://travis-ci.org/joemccann/dillinger)

Helper functions for machine learning engineers at Hi Rez studios

  - connect to redshift
  - darkplot
  - more!

#### New Features!

  - add your own credentials
  - more helper functions forthcoming


You can also:
  - contribute
  - do other stuff


### Installation

hirez-helpers requires SQLAlchemy and some other libraries to run. Adding a requirements.txt is forthcoming 

Install the dependencies and install using pip

```sh
$ pip install hirez-helpers
```

### Todos

 - requirements.txt
 - more helper functions

License
----

MIT
total-space 0.2.6
Investigate the total state space of communicating finite state machines
License: MIT
Keywords: sample setuptools development

Investigate the total state space of communicating finite state machines. Specifically,
        given a model of a system comprising of multiple agents, where each agent is a
        non-deterministic state machine, which responds to either time or receiving a message with
        one of some possible actions, where each such action can change the agent state and/or send
        messages to other agents; Then this package will generate the total possible state space of
        the overall system, validate the model for completeness, validate each system state for
        additional arbitrary correctness criteria, and visualize the states and transitions in
        various ways.
yediemin 0.2.0
Bulletproof attachment serving for Django Rest Framework.
License: MIT
Requires Python: >=3.4,<4
Keywords: python,django,django rest framework,file,attachment

# Yediemin

A package for bulletproof attachment serving in Django Rest Framework.

## Getting Started

### Requirements
- Nginx
- Django Rest Framework
- Session Authentication
- Django Storages (S3)

### Installation Steps

1) Install package from [PyPi](https://pypi.org/project/yediemin/).

```sh
pip install yediemin
```

2) Add the view to `urls.py`

```python
from yediemin import YedieminView

urlpatterns = [
    re_path(r'^yediemin/(?P<file_name>\S+)/$', YedieminView.as_view(), name='yediemin'),
]
```

3) Configure Nginx. Place the configuration below under your server.

```
location /yediemin-files/ {
            internal;
            resolver 8.8.8.8;
            set $redirect_uri "$upstream_http_redirect_uri";

            proxy_buffering off;
            proxy_pass $redirect_uri;
}
```

4) Use `YedieminFileField` in serializer for `FileField`.

```python
from yediemin import YedieminFileField

class AttachmentSerializer(serializers.ModelSerializer):
    file = YedieminFileField()

    class Meta:
        model = Attachment
        fields = (
            "id",
            "file",
        )
```

5) Use `PrivateS3Boto3Storage` for the field in `models.py`

```python
from yediemin import PrivateS3Boto3Storage

class Attachment(models.Model):
    file = models.FileField(storage=PrivateS3Boto3Storage())
```

6) Upload files to S3 with `YedieminFileField`. Yediemin requires [presigned object url](https://docs.aws.amazon.com/AmazonS3/latest/dev/ShareObjectPreSignedURL.html).

### Settings

- `YEDIEMIN_HIDDEN_REDIRECT_PATH`

Default: `yediemin-files`.
It should be same with location in nginx configuration.

- `YEDIEMIN_AUTHENTICATION_CLASSES`

Default: `[rest_framework.authentication.SessionAuthentication]`

- `YEDIEMIN_EXPIRE_IN`

Default: `604800` seconds which is 1 week. This is the maximum limit provided by AWS. [Using Query Parameters](https://docs.aws.amazon.com/AmazonS3/latest/API/sigv4-query-string-auth.html)
knotty 0.4.0
Application Performance Monitoring Made Easy
License: MIT
Requires Python: >=3.7

[![Build Status](https://travis-ci.org/kahinton/knotty.svg?branch=master)](https://travis-ci.org/kahinton/knotty)


[![codecov](https://codecov.io/gh/kahinton/knotty/branch/master/graph/badge.svg)](https://codecov.io/gh/kahinton/knotty)



### Welcome to Knotty, the easiest way to tie down your application performance metrics!

NOTE: While all the basic functionality is here, Knotty is still in Alpha development. The project aims to have
more libraries automatically tracked, as well as more export options. APIs and functionality are also apt to 
change with little to no warning.

Modern applications require close monitoring, and Knotty aims to provide everything you need out of the box.
Importing the library will automatically register numerous metrics regarding the performance of the application
and the system it is operating on, including hardware utilization, application logging rates, http request rates,
and many more. Beyond the build in metrics, there is also an extremely powerful meters API that allows you to 
create whatever custom metrics you would like. 

Once you have your meters defined, Knotty also provides a simple API for exporting your metrics to a number of 
commonly used metrics backends (Prometheus, InfluxDB, Graphite, etc). 

Here's a simple example of how you can add a Prometheus endpoint to a Flask application:

```python
from flask import Flask
from knotty.exporters import PrometheusExporter


app = Flask(__name__)
PrometheusExporter(flask_app=app)

@app.route('/hello', allowed_methods=['GET'])
def hello()
    return 'Hello World'
    

app.run()
```