semvec/pypi-packages huggingface dataset:
rumbaweu 2.0.5
freeamongus
Requires Python: >=3.6, <4
Keywords: sample,setuptools,development

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config-proxy 0.1.5
Configuration proxy that enables specifying both source json path and / or environmental variable in order to get configuration value
Requires Python: >=3.7,<4.0
Keywords: configuration,env,json,jsonpath

# config-proxy

This is a Python module that enables you to setup paths for both json configuration and configuration from env variables.

It automatically looks into environmental variables and either uses its value, or looks it up in json configuration file or falls back to default.

## Install

Either clone from this repository or use `pip` / `poetry` like so:

### Via `pip`

```console
pip install config-proxy
```

### Using `poetry`

```console
poetry add config-proxy
```

## Usage

### Basic usage

If your configuration file is either called `config.json` and is expected in the current working directory or its location is set using `CONFIG_PATH` environmental variable, the usage is fairly easy:

#### `config.json`

```json
{
  "database": {
    "host": "mydbhost.databases.com"
  }
}
```

#### `main.py`

```python
from config_proxy import StringProperty

database_host = StringProperty(path="database.host", env="DATABASE_HOST", default="localhost")

print(database_host.value)
```

```bash
$ python main.py
mydbhost.databases.com

$ DATABASE_HOST="overridden.database.com" python main.py
overridden.database.com
```

### Advanced usage

If you want to specify configuration file path and customize env variable that stores the path, you have to extend the `ConfigProxy` class and overload attributes you wish to change.

#### `my-special-config.json`

```json
{
    "database": {
        "host": "mydbhost.databases.com"
    }
}
```

#### `custom.py`

```python
from config_proxy import ConfigProxy as _ConfigProxy, StringProperty

class ConfigProxy(_ConfigProxy):
    env_location = "ENV_VARIABLE_THAT_CONTAINS_MY_CONFIG_PATH"
    config_file_names = ["my-special-config.json"]

```

**Please note**, that `StringProperty` now does not know it should use your subclass instead, do not forget to specify it when creating the property:

```python
database_host = StringProperty(path="database.host", env="DATABASE_HOST", default="localhost", proxy=ConfigProxy)

print(database_host.value)
```

If you want to specify a custom config file, you can use:

```bash
$ ENV_VARIABLE_THAT_CONTAINS_MY_CONFIG_PATH="/actual/path/to/my/config.json" python custom.py
mydbhost.databases.com
```
ado2hugo 1.0.2
Azure DevOps Wiki to Hugo

![Upload Python Package](https://github.com/panicoenlaxbox/ADO2Hugo/workflows/Upload%20Python%20Package/badge.svg)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
[![Imports: isort](https://img.shields.io/badge/%20imports-isort-%231674b1?style=flat&labelColor=ef8336)](https://pycqa.github.io/isort/)

# ADO2Hugo

With this program you will be able to export [Azure DevOps](https://azure.microsoft.com/es-es/services/devops/) wikis to [Hugo](https://gohugo.io/)

Azure DevOps offers two kinds of wiki: Project (managed by the platform) or "Wiki as code" (publish directly markdown files from a branch in your repository).

Currently, only wiki projects are supported, and it does not have sense to support the "wiki as code" option.

The program uses the Azure DevOps API to iterate over an organization's projects and, in accordance with Hugo's folder structure, exports all wiki pages with their corresponding attachments.

I have used [Geekdoc theme](https://themes.gohugo.io/hugo-geekdoc/).

## Installation

```pip install ado2hugo```

## Usage

```bash
ado2hugo -h
usage: ado2hugo [-h] [--organization ORGANIZATION] [--pat PAT] [--project PROJECT] [-v] site_dir

positional arguments:
  site_dir              Site directory

optional arguments:
  -h, --help            show this help message and exit
  --organization ORGANIZATION
                        Organization
  --pat PAT             Personal access token
  --project PROJECT     Project name
  -v, --verbose         Verbose
```

`ORGANIZATION` and `PAT` options, can be also an environment variable.

```bash
ado2hugo --organization <YOUR_ORGANIZATION> --pat <YOUR_PAT> <YOUR_SITE_DIRECTORY>
```

> Be carefully because this program deletes before its execution, all files in the /static and /content folders of supplied path

## Development

You must run the following commands to develop localy:
- Create a pipenv local environment with `pipenv install --dev` 
- Install pre-commit hooks with `pre-commit install`

For executing `__main__.py`, you must use `python -m src.ado2hugo`, so relative imports will work, if you use `python __main__.py` you will receive the error `ImportError: attempted relative import with no known parent package`, 
more information in https://napuzba.com/a/import-error-relative-no-parent
dalys 4.1.0
Dalys

# Dalys - library for visualising data analysis algorithms

Dalys - library that allows to visualize many algorithms that need for statistical data processing and machine learning

## Getting Started

Dalys is a wrapper over the library [scikit-learn](scikit-learn.org/) and you can use [scikit-learn datasets](http://scikit-learn.org/stable/datasets/index.html) for visualize desired algorithm.

Example:

```python
from sklearn.datasets import make_circles
from dalys.kernel_pca import KPCATool


X, y = make_circles(n_samples=1000, random_state=123, noise=0.1, factor=0.2)

style = [('red', '.'), ('blue', '.')]
label_names = ['red dotes', 'blue dotes']

kpca = KPCATool(X, y, style=style, labels_unique_name=label_names, n_components=3,
                kernel='rbf', gamma=2, scaled=True)
kpca.projections_plot(grid=22)
```

There is a number of parameters that can be set for the KPCATool class;
the major ones are as follows:

* *style* - set marker styles and colors for data representation, 
example: style = [('red', '.'), ('blue', '.')] or
style = [('red', 'd'), ('blue', 'd')]. If you need random colors and markers - leave this argument with default parameter (default = None).

* *labels_unique_name* - set list with class names, such as labels_unique_name = ['red dotes', 'blue dotes']. If *labels_unique_name* is *None*,
this means that labels will be numbered in ascending order (default = None).

* *preprocessing* - for use data preprocessing, set this parameter to one of the following options (default = 'std'):
    * **std** - [standardize features](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html)
    * **norm_l1** - [L1 normalization](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.normalize.html)
    * **norm_l2** - [L2 normalization](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.normalize.html)
    * **minmax** - [MinMax scaler](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html)
    * **maxabs** - [MaxAbs scaler](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MaxAbsScaler.html)
    

* *scaled* - this flag need to control preprocessing of your data, if your data is allready processed, you must set "True".

### Installing
pip install dalys

## Authors

* **Timothy Tkachenko** - Machine Learning Researcher
tactile-patterns 0.0.1
Generates patterns recognisable by touch, for use in laser-cut materials (for people who are blind or with a poor eyesight, in dark)

# Disclaimer

Pattern here are of pre-alpha quality - not tested, never used on actual laser cutter, not validated in actual use, examples are missing, usage instruction is missing, tested only in LightBurn etc etc.

All of that will change.

# Tactile patterns for laser cutter

Generates patterns recognisable by touch, for use in laser-cut materials.

Used for example on maps for blind people.

Available as tactile_patterns pip package.

If you would use it but "pip package" is too scary - please open an issue to let me know how it can be also published in way that makes it useful.

# Examples

## Square

```
from tactile_patterns_for_laser_cutter.data_format import Rescale
from tactile_patterns_for_laser_cutter.data_format import Point
from tactile_patterns_for_laser_cutter.data_format import Polygon
from tactile_patterns_for_laser_cutter.data_format import Collection
from tactile_patterns_for_laser_cutter.data_format import LinearRing
from tactile_patterns_for_laser_cutter.data_format import pretty_geojson_string


outer = LinearRing([
            Point(x=0, y=0),
            Point(x=1, y=0),
            Point(x=1, y=1),
            Point(x=0, y=1),
            Point(x=0, y=0),
        ])
polygon = Polygon(outer)
collection = Collection([polygon])
print(pretty_geojson_string(collection.to_geojson()))
```

# Sponsors

<a href="https://osmfoundation.org/"><img src="logo_osmf.png" height="100"/></a><br/>

The [OpenStreetMap foundation](https://wiki.osmfoundation.org/wiki/Main_Page) was funding the development of this project in their first round of the [microgrant program](https://wiki.osmfoundation.org/wiki/Microgrants) in 2020. It was done as part of making [tactile maps based on OpenStreetMap data, for blind or visually impaired children](https://wiki.openstreetmap.org/wiki/Microgrants/Microgrants_2020/Proposal/Tactile_maps_for_blind_or_visually_impaired_children).

If anyone else is also interested in supporting this project via funding - [let me know](mailto:osm-messages@etutanota.com) (opening a new issue is also OK) :)
dretrees 2.0.5
freeamongus
Requires Python: >=3.6, <4
Keywords: sample,setuptools,development

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