semvec/pypi-packages huggingface dataset:
tree-aid 0.0.3
ETL tools for Tree Aid
Requires Python: >=3.6

# Readme

Tree Aid Utilities

Simply call:

'''
python3 -m tree_aid.placettes
'''

In the directory with the tree and plot data (.json), will output a CSV with processed data.

Alternativly, use in a script / jupyter notebook..

'''
from tree_aid.placettes import add_data_to_df, get_clean_tree_df, get_dictionary_from_json
#from tree_aid.placettes import add_data_to_df, get_clean_tree_df, get_dictionary_from_json
%load_ext autoreload
%autoreload 2

config = {
    'name_scientific': 'name_scientific',
    'small_trees': 'small_circle/repeat_small_tree_circle',
    'large_trees': 'group_large_tree/repeat_large_tree',
    'geolocation': '_geolocation',
    'large_tree_stems' : 'group_large_tree/repeat_large_tree/grp_tree_stand_shoots/repeat_circumference_stem',
    'large_tree_circumfrence': 'group_large_tree/repeat_large_tree/grp_tree_stand_shoots/repeat_circumference_stem/grp_circumference_stem/circumference_measure'
}
    
tree_data_loc = './test/Mali_PMP_TREE_Survey_FINAL-2021-10-11-15-15-46.json'
plot_data_loc = './test/Mali_PMP_PLOT_Survey_FINAL-2021-10-11-15-16-08.json'
output_data_loc = './output.csv'

tree_data = get_dictionary_from_json(tree_data_loc)
plot_data = get_dictionary_from_json(plot_data_loc)

tree_plot_df = get_clean_tree_df(tree_data,plot_data, config)
output_df = add_data_to_df(tree_plot_df, config)
#output_df.to_csv(output_data_loc)
'''
lanxinplus-openapi 1.0.0.4
LanXin+ OpenAPI
Requires Python: >=3.6
Keywords: LanXin+ OpenAPI

蓝信 Python SDK
===============

关于
----

    -  此Python
       SDK基于\ `蓝信开放平台API <https://openapi.lanxin.cn/doc/#/server-api/>`__\ 构建
    -  蓝信开放平台，便于企业应用与蓝信集成，让协同与管理更加高效
    -  蓝信开放接口SDK，便捷调用服务端API，例如：认证授权、通讯录、消息通知、角色权限、媒体素材、组织管理、应用管理等具体可以访问
       `蓝信开放平台API <https://openapi.lanxin.cn/doc/#/server-api/>`__
       文档 看看

运行环境
--------

    -  Python >= 3.6

安装方法
--------

    -  通过pip安装官方发布的版本（以Linux系统为例）：

.. code:: sh

    pip install lanxinplus_openapi

    -  也可下载源码包，执行安装命令：

.. code:: sh

    sudo python setup.py install

快速使用
--------

获取 APP\_TOKEN
^^^^^^^^^^^^^^^

.. code:: python

    # -*- coding: utf-8 -*-

    import lanxinplus_openapi
    from lanxinplus_openapi.api import auth_api

    config = lanxinplus_openapi.Configuration(
        host = "https://example.com/open/apigw", app_id="", app_secret=""
    )

    with lanxinplus_openapi.ApiClient(config) as api_client:
        lanxinplus_openapi.Configuration.set_default(config)
        try:
            authApi = auth_api.AuthApi(api_client)
            resp = authApi.v1_app_token_create("client_credential", config.app_id, config.app_secret)
            print(resp)
        except lanxinplus_openapi.ApiException as e:
            print("Exception when calling AuthApi->v1_apptoken_create_get: %s\n" % e)

测试用例使用说明
----------------

运行test
^^^^^^^^

.. code:: bash

    nosetests -s test/test_auth_api.py:TestAuthApi.test_v1_app_token_create

联系我们
--------

-  `蓝信官方网站 <https://www.lanxin.cn/>`__
-  `蓝信开放平台文档中心 <https://openapi.lanxin.cn/doc/#/>`__

License
-------

    -  MIT License, see `license file <LICENSE>`__
PrimeLA 0.1.2
A small linear algebra visualization package
Requires Python: >=3.6

#PrimeLA
### Visualization Package for 'Prime Linear Algebra'
### '으뜸 선형대수'를 위한 가시화 패키지

This is a simple preliminary package 
for visualizing the vectors (2d and 3d) and matrix (2x2, 3x3)

[GitHub Repository](https://github.com/dknife/PrimeLA)

Author: Young-Min Kang (c) 2021
organization-map 1.0.5
Creates an organization map image
Keywords: Python,image,hierarchical,tree,diagram

# Organization Map Generator

## Installation

```sh
pip install organization-map
```

## Dependencies

* PIL (pip install pillow)

## Organization structure
```python
orgs=[
    [{'id':11,'name':'Main Org\nLine 2','childs':[21,22],'fill':'#384891','color':'white'}]
    ,[{'arrowtext':'50%','id':21,'name':'Leaf 21','childs':[31,32,33]},{'id':22,'name':'leaf3','childs':[34]}]
    ,[{'id':31,'name':'leaf4','childs':[40]},{'id':32,'name':'leaf5'},{'id':33,'name':'leaf6','childs':[41,42]},{'id':34,'name':'leaf7'}]
    ,[{'id':40,'name':'leaf40'},{'id':41,'name':'Bottom Leaf','childs':[51],'fill':'red','color':'white'},{'id':42,'name':'Bottom Leaf'}]
    ,[{'id':51,'name':'Bottom Leaf 2\nLine 2\nLong Line 3'}]
]
```

Code example:

```python
from organization_map import generator

generator.generate_image(orgs).show()
```

## Output

![Screenshot](https://raw.githubusercontent.com/snuids/organization_map/master/media/Example.jpg?raw=true "Screenshot")

## Multi Level
```python
orgs=[[{'id': '2',
   'name': 'Father',
   'labels': ['', '', ''],
   'childs': [['22'], ['22', '23'], []],
   'parents': [[], [], []],
   'level': 0}],
 [{'id': '22',
   'name': 'Daughter',
   'labels': ['111 %', '222 %', ''],
   'childs': [[], [], []],
   'parents': [['2'], ['2'], []],
   'level': 1},
  {'id': '23',
   'name': 'Son',
   'labels': ['', '223 %', ''],
   'childs': [[], ['33'], []],
   'parents': [[], ['2'], []],
   'level': 1}],
 [{'id': '33',
   'name': 'Daughter 2',
   'labels': ['', '2333 %', ''],
   'childs': [[], [], []],
   'parents': [[], ['23'], []],
   'level': 2}]]
```

Code example:

```python
from organization_map import generator

generator.generate_image(orgs,multi_level=True).show()
```

## Output

![Screenshot](https://raw.githubusercontent.com/snuids/organization_map/master/media/Example2.jpg?raw=true "Screenshot")
gimmick 1.0
Libraray contains algo to generate images by learning representation from data

# gimmick

## Introduction
Whats Gimmick ? Its a library which allows us to generate images out of nothing, basically you tell gimmick what kind of images you would like to generate and what model to use and it does the rest.
Its easy to use as primary focus is on user friendlyness without exposing to much details, but you can be specific if you wanna be.

What do we do internally ? we trained neuralnets, we add different type of networks making sure that interface always remain same, just plugin different algo and see how it perform.

## Installtion
<pre>pip install gimmick</pre>
just like that, its python everything should be easy

## Dependency
Gimmick autmatically install all the dependency for you, but occationaly you run into one of those bugs, so in a nutshell we use tensorflow, sklearn.
i know torch is much faster, we will get their

## Examples
we have already provided samples with the github repo, you can browse it, read it and run it.

Lets demonstrate

### usecase 1 - I just wanna run as simple as i can.
<pre>
import gimmick
model = gimmick.learn(images, algo='autoencoder_dense')  # Just make sure that images is a numpy array which contains N number 3D or 2D images
images_gen = model.generate(16)  # I need 16 images
</pre>

### usecase 2 - I wanna control how my model is trained
<pre>
import gimmick
model = gimmick.learn(images, algo='autoencoder_dense', epochs=500, batch_size=8,
                      optimizer='adam', learning_rate=0.01, loss_function='mae')
images_gen = model.generate(16, batch_size=8)
</pre>

### usecase 3 - I wanna know everything

Go through the detailed documentation.
VibeITUp 0.0.7
PickUp line package
Keywords: python,first package

PickUp line package will help you to get interesting pickup linesbelow is the sample code how to use this package
    
    
    
    
        import pickup as p
        line = p.pickup()
        print(line)
        
        
        import funnyPickup as q
        line = q.funny_pickupline()
        print(line)
        
        import chessyPickup as r
        line = r.chessy_pickupline()
        print(line)
ExpressLine 0.0.7
PickUp line package
Keywords: python,first package

PickUp line package will help you to get interesting pickup linesbelow is the sample code how to use this package
    
    
    
    
        import pickup as p
        line = p.pickup()
        print(line)
        
        
        import funnyPickup as q
        line = q.funny_pickupline()
        print(line)
        
        import chessyPickup as r
        line = r.chessy_pickupline()
        print(line)