semvec/pypi-packages huggingface dataset:
flask-logger-decorator 0.1.1
Flask logger decorator

DDD Api Gateway Framework
=========================

|Build Status| |Pypi Status| |Coveralls Status|

Installation
------------

From source code:

::

    python setup.py install

From pypi:

::

    pip install flask-logger-decorator

Usage
-----

::

    from flask_logger_decorator.logger_extension import LoggerExtension


    logger = LoggerExtension()
    logger.init_app(app)

    #examples
    2019-08-28T10:33:34 flask-request-logger      DEBUG: trace_uuid=befc1448-8f6d-4d1e-88ca-d3e04cd16f8e endpoint=/api/v1/user method=GET  query_args: post_values:
    2019-08-28T10:33:34 flask-request-logger      DEBUG: trace_uuid=befc1448-8f6d-4d1e-88ca-d3e04cd16f8e ip=127.0.0.1 method=GET  path:/api/v1/user status_code:401



License
-------

This software is licensed under the `MIT license <http://en.wikipedia.org/wiki/MIT_License>`_

See `License file <https://github.com/sunwei/ddd-base/blob/master/LICENSE>`_

.. |Build Status| image:: https://travis-ci.com/sunwei/flask-logger-decorator.svg?branch=master
   :target: https://travis-ci.com/sunwei/flask-logger-decorator
.. |Pypi Status| image:: https://badge.fury.io/py/flask-logger-decorator.svg
   :target: https://badge.fury.io/py/flask-logger-decorator
.. |Coveralls Status| image:: https://coveralls.io/repos/github/sunwei/flask-logger-decorator/badge.svg?branch=master
   :target: https://coveralls.io/github/sunwei/flask-logger-decorator?branch=master

Changelog
=========

0.1.1
-----

- Update readme with usage

0.1.0
-----

- Release Alpha version with basic log for all requests
clinphen 1.28
An automatic phenotype extractor

#ClinPhen is freely available for academic, nonprofit, and personal use.
#Licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

#ClinPhen is a tool that automatically extracts phenotypes from clinical notes.
#PIP VERSION INSTRUCTIONS
##To extract phenotypes:
clinphen <path_to_clinincal_note_file>

##To see how to use other features:
clinphen -h

##We also have a version that parses phenotypes from a large table of notes. You can run it with:
clinphen_bulk <path_to_table_file> <path_to_output_file>

##The input file for clinphen_bulk, by default, is a 2-column table delimited by a "|" character. The first column is the patient identifier, the second is the note. It should have the following header:
"MRN"|"NOTE"

##To learn more, run:
clinphen_bulk -h

#STANDARD (NON-PIP) VERSION INSTRUCTIONS
#Running the ClinPhen GUI on a mac:

#Running the ClinPhen GUI on a non-mac:

#Running the ClinPhen GUI on the command line:
##Open the terminal
##cd to the ClinPhen directory
##Run the following command:
./ClinPhenApp

#Running ClinPhen directly on the command line:
##Open the terminal
##cd to the ClinPhen directory
##Run the following command:
./clinphen input.txt
##Replace "input.txt" with the path to the free-text clinical note file that you want to parse.
##You can also run the command line with a custom thesaurus:
./clinphen input.txt thesaurus.txt
##Replace "thesaurus.txt" with the path to your custom thesaurus, which should be in the format: HPO_ID<tab>Synonym
accessdb 0.0.1
Fast way to create Access Database
Keywords: python,accessdb,text

Why I created this package ?
----------------------------
1) One by one insertion into Access DB, But it takes too much time to insert 100k
records,
```python
    cursor.execute("INSERT STATEMENT")
    cursor.commit()
```

2) Bulk way insertion into Access DB and it is fast if you compare with above one,
But even it is very slow from https://github.com/mkleehammer/pyodbc/issues/120
```python
    cursor.executemany("Bulk INSERT STATEMENT")
    cursor.commit()
```
But think if you want to insert 1000k records into AccessDB, how much time you have to
wait?

What the package will do ?
---------------------------
  1) Imports the data from text file to Access Database.
  2) Creating Access Database from pandas dataframe very quickly.
  3) Primary Key support.
  4) Can create many tables in Access Database
  5) Data Types support

How to Use:
-----------
1) If you have pandas dataframe you can follow bellow example
```python
    import accessdb
    # your dataframe
    # df.to_accessdb(<DB_PATH>, <TABLE_NAME>)
    df.to_accessdb(r'C:\Users\<user>\Desktop\test.accdb', 'SAMPLE')
```
2) If you have text file you can follow bellow example
```python
    from accessdb import create_accessdb
    # create_accessdb(<DB_PATH>, <TEXT_FILE_PATH>, <TABLE_NAME>)
    create_accessdb(r'C:\Users\<user>\Desktop\test.accdb', r'C:\Users\<user>\Documents\test.text', 'SAMPLE')
```

Installation:
-------------
    pip install accessdb

Note:
-----------
1) It will create text file if you are using pandas dataframe to create Access Database,
   But the file will be deleted after completion of process.
2) It supports only for Windows.
PyECCArithmetic 1.1.0
Basic arithmetic operations for points on elliptic curves.
Requires Python: >=3.0

# PyECCArithmetic

This package provides basic arithmethic point operations on elliptic curves. The following operations for points are available:
* Addition
* Subtraction
* Multiplication
* Division
* Order of point (for fair points)
* Inversion of a point
* Is the point on the curve?

The following curves are already implemented:
* secp224r1
* secp256r1
* secp384r1
* secp521r1
* brainpoolP160r1
* brainpoolP192r1
* brainpoolP224r1
* brainpoolP256r1
* brainpoolP320r1
* brainpoolP384r1
* brainpoolP512r1

It is also possible to define your own curve.

## Installation
```
pip install PyECCArithmetic
```

## Addition
```python
from PyECCArithmetic import *

p = Point(x_1, y_1, curve=Curve.secp256r1())
q = Point(x_2, y_2, curve=Curve.secp256r1())

z = p + q # z is a new point
```

## Subtraction
```python
from PyECCArithmetic import *

p = Point(x_1, y_1) # curve defaults to Curve.secp256r1()
q = Point(x_2, y_2)

z = p - q # z = p + (-q), z is a new point
```

## Multiplication
Multiplication is realised with the double and add algorithm.
```python
from PyECCArithmetic import *

p = Point(x_1, y_1) # curve defaults to Curve.secp256r1()

z = p * 3 # z is a new point
```

## Division
```python
from PyECCArithmetic import *

p = Point(x_1, y_1) # curve defaults to Curve.secp256r1()
q = Point(x_2, y_2)

z = p / q # z is int, such that z * q == p
```

## Order calculation
```python
from PyECCArithmetic import *

p = Point(x_1, y_1) # curve defaults to Curve.secp256r1()
order = p.calcOrder(timeout=5) # tries to calculate the order for maximal timeout seconds 
```

## Custom curve definition
```python
from PyECCArithmetic import *

# Only curves defined as Weierstrass equation are supported
# y^2 = x^3 + a * x + b mod p
c = Curve(a, b, p, name='optional')
```

## Is the point on the curve?
```python
from PyECCArithmetic import *

onCurve = Point(1,1).isOnCurve # true or false 
```
girder-geobrowser 0.5.7
Map-oriented custom Girder appfor geospatial metadata browsing

# Girder GeoBrowser

## Requirements
* The Girder [Geospatial](https://github.com/OpenGeoscience/girder_geospatial) Plugin

## Plugin Installation
```
pip install girder-geobrowser
```

## Custom Python Commands
`setup.py` includes 2 custom commands:

* `build_ui`: This command builds the frontend from the `gui` folder, and places the build into `geobrowser_plugin/external_web_client`.
* `clean_build`: This command cleans the project of the files generated by the previous command.

**WARNING**

The standalone frontend will be served at `/geobrowser` if the built front-end path exists (i.e. `python setup.py build_ui` has been run), or if the mode is not `development`. So, if running `girder serve` in `development` mode, you should see no errors if the frontend is not built. However, if running `girder serve` in `production` mode, it will attempt to serve the files, regardless of if they've been built or not, throwing an error through cherrypy if they don't exist.

## Custom Girder CLI Commands
This plugin adds custom Girder CLI commands:

### `extract-geospatial`
This command manually runs the [Geospatial](https://github.com/OpenGeoscience/girder_geospatial) plugin on any Girder paths specified (defaults to '/'). To specify one or more path, use the `-p` argument. For example, to run this command on the collections `collection1` and `collection2`, you would run:
```
girder extract-geospatial -p collection/collection1 -p collection/collection2
```

### `populate-collection-meta`
This command populates the specified collection(s) with the provided metadata. The provided metadata must be in JSON format. The rules for setting metadata follow that of the [setMetadata](https://girder.readthedocs.io/en/stable/api-docs.html?highlight=setMetadata#girder.models.folder.Folder.setMetadata) function. This script requires that you specify 1 metadata JSON file and one or more collection IDs. For example, to populate the meta field of collections `5d3201b4dbdd758d55819007` and `5d3201c54731b3d3a1350823`, with the file `metadata.json`, you would run:
```
girder populate-collection-meta -i 5d3201b4dbdd758d55819007 -i 5d3201c54731b3d3a1350823 -d metadata.json
```
To use a Girder path instead of a collection ID, use the -p/--path option (also works with multiple):
```
girder populate-collection-meta -p /collection/foo -d metadata.json
```
When using this option, if the specified path doesn't exist, it will be created.

## Standalone Frontend Setup

### Install Yarn
```
npm install -g yarn
```

### Install Frontend packages
```
cd gui
yarn install
```

### Compiles and hot-reloads for development
```
yarn run serve
```

### Compiles and minifies for production
```
yarn run build
```

### Run your tests
```
yarn run test
```

### Lints and fixes files
```
yarn run lint
```

### Customize configuration
See [Configuration Reference](https://cli.vuejs.org/config/).