semvec/pypi-packages huggingface dataset:
AuthCore 0.1.3
AuthCore

# AuthCore

The simple membership system for cross-platform application.

# Member System
#### SERVER: setup a member system
```
# 啟動系統(則一使用)

import AuthCore as ac

## Using Json File
itf = ac.SimpleMemberSystem(db_file_path="./")

## Using MongoDB Cloud
itf = ac.MongoDBMemberSystem(account_label="<account_label>", user="<user>", pws="<pws>", collection="<collection>")

```

#### APP: sign up for third-part application
```
# 註冊平台
decode_key, label = itf.signup_platform()

```


#### SERVER: signup a user
```
# 註冊會員
account = "root"
pws = "root"
itf.signup_user(account, pws)

## Raise RuntimeError if user exist
```

#### SERVER: update info of the user
```
# update or increase info of user
data = {
            "var1": idx,
            "var2": idx,
            "var3": idx,
        }
itf.update_user(account, pws, **data)
```

#### APP: login as user
```
from AuthCore import DecryptITF

# the label from the step 1 (setup a member system)
# decode_key from the step 1 (setup a member system)

# user login
### Raise RuntimeError if user is did NOT exist
encode_text = itf.login_user(label, "root", "root")  

# decode the secret user info
decode_text = DecryptITF.decrypt(decode_key, encode_text) 
print(f"解析會員資料： decode_text:{decode_text}")
```

#### SERVER: Delete a user
```
# Delete a user
itf.delete_user(account, pws)
```

# Example:
#### without rsa
```python
import AuthCore as ac

# 啟動系統
itf = ac.SimpleMemberSystem(db_file_path="./")


# 註冊會員
account = "root"
pws = "root"
itf.signup_user(account, pws)

# 更新會員資料
data = {
    "var1": 1,
    "var2": 1,
    "var3": 1,
}
itf.update_user(account, pws, **data)


# user login
user_dict = itf.login_user(None, "root", "root",encode=False)

# delete user
itf.delete_user(account, pws)
```

#### rsa login
```python
import AuthCore as ac
import json

# 啟動系統
itf = ac.SimpleMemberSystem(db_file_path="./")

# 註冊平台
decode_key, platform_label = itf.signup_platform()

# 註冊會員
account = "root"
pws = "root"
itf.signup_user(account, pws)

# 更新會員資料
data = {
    "var1": 1,
    "var2": 1,
    "var3": 1,
}
itf.update_user(account, pws, **data)

# the label from the step 1 (setup a member system)
# decode_key from the step 1 (setup a member system)

# user login
encode_text = itf.login_user(platform_label, "root", "root")

# decode the secret user info
decode_text = ac.DecryptITF.decrypt(decode_key, encode_text)
decode_obj = json.loads(decode_text)

itf.delete_user(account, pws)

```


# Other:

#### 連接至DB
```
# jsonfile
from AuthCore.sdb import jsonfile
db = jsonfile.JsonDBInterface("TableName")

# mongo
from AuthCore.sdb import mongo
db = mongo.MongoDBInterface("ooglx")
db.select_table("unittest", "user")
```
#### 新增資料
```
db.__insert__("user1", {"a": 1, "b": 2})
result = db.__select__("user1")
```
#### 搜索資料
```
result = db.__select__("user1")
```
#### 更新資料
```
db.__update__("user1", {"var1": 1, "var2": 2})
result = db.__select__("user1")
```

#### 刪除資料
```
db.__remove__("user1")
result = db.__select__("user1")
```
lauetools-nn 0.1.0
GUI routine for Laue neural network training and prediction
Requires Python: >=3.7

A feed-forward neural network (FFNN) model to predict the HKL in single/multi-grain/multi-phase Laue patterns with high efficiency and accuracy is introduced. Laue micro-diffraction is a X-ray scattering technique for the determination of local structural parameters (strain, stress and orientation) in materials from microstructure mapping (2D and 3D). The use of a polychromatic beam offers many experimental advantages (no rotation of the sample, many diffraction spots, signal that can be used even in the presence of strong orientation disorder). However, in the case of polycrystalline microstructures, the determination of the structural parameters (lattice parameters) from the fine analysis of the relative position of the spots requires identifying unambiguously all the spots forming the Laue pattern corresponding to an individual crystal among all other spots. The indexation step of the data analysis must be reliable to input unambiguous experimental dataset to the structural model refinement (final step in analysis) and rapid since production rate of data on the synchrotron line can amount to several 10000s diffraction images per hour, each image (Laue patterns) being able to contain contribution from multi-grain/ multi-phase present in the probed volume of the material.
pykaiutils 0.0.4
Python wrapper for pykaiutils: python开发中常用的代码结构库

### 1.启动bert-as-service
架构图：
    https://www.processon.com/view/link/610b87805653bb143a2992eb



文件结构：
    data：数据文件
        56nation.txt : 56个民族 （因为有个别县有包括民族）

    

安装环境：
pip install --user python==3.6.10
pip install --user tensorflow==1.10.0
pip install bert-serving-server #服务端
pip install bert-serving-client #客户端

安装包：
    jieba                              0.39
    numpy                              1.14.5
    xpinyin                            0.7.6
    pymongo                            3.12.0
    pandas                             0.23.0


启动：
    bert-serving-start -model_dir E:/chinese_L-12_H-768_A-12/ -num_worker=1
    bert-serving-start -model_dir E:/Python_project/kashgari_ner_863/chinese_L-12_H-768_A-12/ -num_worker=1




部署：
    python qcc_bus_poi_match.py -cityname=佛山市