semvec/pypi-packages huggingface dataset:
xycmap 1.0.1
Bivariate colormap solutions
License: MIT
Keywords: visualization,colormap,color,bivariate,two-dimensional

# xycmap
> Bivariate colormap solutions.

This package makes it easy to create custom two-dimensional colormaps, apply them to your series, and add bivariate color legends to your plots.

![example](https://user-images.githubusercontent.com/31345940/109506935-7b7ad100-7a9e-11eb-868f-899804e05bf6.png)

## Installation

`pip install xycmap`

## Usage

Make a custom interpolated colormap by specifying four corner colors (see recognized formats [here](https://matplotlib.org/stable/api/colors_api.html)), and dimensions `n`:

```python
import xycmap
corner_colors = ("lightgrey", "green", "blue", "red")
n = (5, 5)  # x, y
cmap = xycmap.custom_xycmap(corner_colors=corner_colors, n=n)
```

![custom_xycmap](https://user-images.githubusercontent.com/31345940/109507925-8c781200-7a9f-11eb-9a2d-32c19b07a1c0.png)

Or make a colormap by mixing two matplotlib colormaps, and specifying dimensions `n`:

```python
import matplotlib.pyplot as plt
xcmap = plt.cm.rainbow
ycmap = plt.cm.Greys
n = (5, 5)  # x, y
cmap = xycmap.mean_xycmap(xcmap=xcmap, ycmap=ycmap, n=n)
```

![mean_xycmap](https://user-images.githubusercontent.com/31345940/109420855-d647f600-79d4-11eb-8b3a-f50505fcc44a.png)

With that in place, apply the colormap to two series that are numeric or categorical:

```python
colors = xycmap.bivariate_color(sx=sx, sy=sy, cmap=cmap)
```

Note that you can apply limits to the axes, as well as pass custom bins for the axes (if numerical). See the docstring for details.

Then simply pass `colors` to your plot. To add a legend, create a new ax and run `bivariate_legend()` into the ax with the same parameters as `bivariate_color()`, e.g.:

```python
cax = fig.add_axes([1, 0.25, 0.5, 0.5])
cax = xycmap.bivariate_legend(ax=cax, sx=sx, sy=sy, cmap=cmap)
```

## Meta

Remco Bastiaan Jansen – r.b.jansen.uu@gmail.com - [https://github.com/rbjansen](https://github.com/rbjansen)

Distributed under the MIT license. See `LICENSE` for more information.
pipfreeze 2.0.3
Tool to produce better formatted pip freeze.
License: MIT
Requires Python: >=2.7

# Pip Freeze

Tool to produce better formatted pip freeze.

Instead of a flat list of requirements, this indents requirements which are dependencies and those that are primary installs. Dependencies shared by multiple packages are commented out. 

This format can be re-read by the typical `pip install -r requirements.txt` and requires not adjustment to other code. 

## Installation

`pip install pipfreeze`

## Basic Usage

`pipfreeze > requirements.txt`

## Motivation

Typical `pip freeze` flattens all requirements, regardless of the dependency structure. 

``` bash
cycler==0.10.0
kiwisolver==1.3.1
matplotlib==3.3.4
numpy==1.20.1
pandas==1.2.2
Pillow==8.1.0
pyparsing==2.4.7
python-dateutil==2.8.1
pytz==2021.1
six==1.15.0
```

This `pipfreeze` command produces output that is valid requirements.txt but is nested

``` bash
matplotlib==3.3.4
    cycler==0.10.0
        six==1.15.0
    kiwisolver==1.3.1
    numpy==1.20.1
    Pillow==8.1.0
    pyparsing==2.4.7
    python-dateutil==2.8.1
        # six==1.15.0
pandas==1.2.2
    # numpy==1.20.1
    # python-dateutil==2.8.1
        # six==1.15.0
    pytz==2021.1
```

## Contributing

Local development is controlled through docker. `docker-compose.yml` contains
different python environment containers to test this code out in.

`make test` will run the docker tests.

Makefile contains commands for development.
apex-legends 0.1.7
Python wrapper for https://apex.tracker.gg/ api.
License: MIT

[![PyPI version](https://badge.fury.io/py/apex-legends.svg)](https://badge.fury.io/py/apex-legends)

# apex-legends
Python wrapper for https://apex.tracker.gg/ api.

## Installation

You can install it via setup.py

```
python setup.py install
```

or from pip:

```
pip install apex-legends
```


## Usage

You need to register for an api key at https://apex.tracker.gg/

Then it's just easy as:

```
from apex_legends import ApexLegends

apex = ApexLegends("apex_api_key")

player = apex.player('NRG_dizzy')

print(player)

for legend in player.legends:
    print(legend.legend_name)
    print(legend.icon)
    print(legend.damage)
```

## Asynchronous Calls

For those who wish to use this API wrapper for their asynchronous applications, you may do so by calling the `AsyncLegends` class.

**WARNING**: This portion of the wrapper is for use with Python version 3.5+. [PEP 492](https://www.python.org/dev/peps/pep-0492/) released the keywords `async` and `await`,  as well as the magic methods `__aenter__` and `__aexit__`, which this portion of the wrapper takes advantage of. This results in the asynchronous class not being compatible with Python versions 3.4 and lower.

```py
import asyncio

from apex_legends import AsyncLegends
from apex_legends.domain import Platform

my_api_key = 'https://apex.tracker.gg api key here'


async def main(api_key, player_name, platform=None):
    async with AsyncLegends(api_key) as apex:
        player = await apex.player(player_name, platform=platform if platform else Platform.PC)
    return player

loop = asyncio.get_event_loop()
result = loop.run_until_complete(main(my_api_key, player_name='NRG_dizzy'))

print(result)

for legend in result.legends:
    print(legend.legend_name)
    print(legend.icon)
    print(getattr(legend, 'damage', 'Damage not found.'))
```
sqlalchemy-mptt-stubs 0.0.4
Stubs for sqlalchemy_mptt
License: MIT
Requires Python: >=3.8,<4.0

sqlalchemy_mptt-stubs
========================
[![PyPI](https://img.shields.io/badge/pypi-0.0.4-orange.svg)](https://pypi.org/project/sqlalchemy-mptt-stubs/0.0.4) [![PyPI](https://img.shields.io/badge/python-3-blue.svg)](https://pypi.org/project/sqlalchemy-mptt-stubs/0.0.4)

Most annotations generated by py pyannotate. It's still unstable.
france-naf 20210302
Python accessors to French NAF/APE codes
License: MIT
Keywords: naf ape

# france-naf

Python accessors to NAF/APE codes.

From https://www.insee.fr/fr/information/2406147

## Installation

    pip install france-naf


## Usage

    from naf import DB

    # Get one NAF object
    my_naf = DB["02.30Z"]
    print(my_naf)

    # "Récolte de produits forestiers non ligneux poussant à l'état sauvage"
    "02.30Z" in DB
    # True
    print(my_naf.section) or print(my_naf.section.code)
    # 'A'
    print(my_naf.section.description)
    # 'AGRICULTURE, SYLVICULTURE ET PÊCHE'

    dict(my_naf)
    # {'code': '02.30Z',
    # 'description': "Récolte de produits forestiers non ligneux poussant à l'état sauvage",
    # 'classe': {'code': '02.30',
    #  'description': "Récolte de produits forestiers non ligneux poussant à l'état sauvage"},
    # 'groupe': {'code': '02.3',
    #  'description': "Récolte de produits forestiers non ligneux poussant à l'état sauvage"},
    # 'division': {'code': '02',
    #  'description': 'Sylviculture et exploitation forestière'},
    # 'section': {'code': 'A', 'description': 'AGRICULTURE, SYLVICULTURE ET PÊCHE'}}

    # Iter over NAF objects
    for naf in DB:
        do_something_with(naf)
    # Iter over code, description
    for code, description in DB.pairs():
        pass
    # Get the whole DB as a json string
    str(DB)