semvec/pypi-packages huggingface dataset:
ultimaker-printer-api 0.0.6
An Ultimaker Printer API client implementation in Python

# python-ultimaker-printer-api

An Ultimaker Printer API Client implementation in Python derived from Swagger documentation (see http://printer_ip/docs/api/) and request testing

[![Build Status](https://travis-ci.org/vanderbilt-design-studio/python-ultimaker-printer-api.svg?branch=master)](https://travis-ci.org/vanderbilt-design-studio/python-ultimaker-printer-api)

[![Coverage Status](https://coveralls.io/repos/github/vanderbilt-design-studio/python-ultimaker-printer-api/badge.svg?branch=master)](https://coveralls.io/github/vanderbilt-design-studio/python-ultimaker-printer-api?branch=master)

## Usage
```python
from ultimaker import Printer, Identity, CredentialsDict
IDENTITY = Identity('Application', 'Anonymous')
IP = '192.168.1.18'
PORT = 80

credentials_dict = CredentialsDict('credentials.json')

printer = Printer(IP, PORT, IDENTITY)
printer.save_credentials(credentials_dict)

credentials_dict.save()

printer.put_system_display_message("It's over, Anakin!", "Acquire high ground")

```

## mDNS

If your local network supports mDNS (some school/corproate networks disable it), printers can be automatically discovered with the zeroconf package.
ancli 0.1.3
Small utility to convert functions into CLI

ancli
=====

.. image:: https://i.imgur.com/5hCKc9u.png
    :target: https://i.imgur.com/5hCKc9u.png
    :align: center

Building argument parser from a function annotation. A simple utility inspired by
``Fire`` and ``docopt``. Ad-hoc solution for someone who often writes scripts with a
single entry point.

How?
----

The process of building CLI with ``ancli`` is very simple.

1. Write a plain Python function with annotated parameters.
2. Wrap it with ``make_cli``.
3. Run your script.


Examples
--------

1. Function with annotated parameters
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

The function ``run`` has explicitly annotated parameters and its signature is used
to instantiate ``argparse.ArgumentParser`` instance that accepts parameters with
specific types and default (if any) parameters. If default value is not provided,
then the parameter is considered to be required.

.. code:: python

    from ancli import make_cli

    def run(path: str, flag: bool = True, iterations: int = 1):
        print(f'run: path={path}, flag={flag}, iterations={iterations}')

    if __name__ == '__main__':
        make_cli(run)

Now this snippet can be used as follows.

.. code:: bash

    $ python script.py --path file.txt --flag 0
    run: path=file.txt, flag=False, iterations=1


2. Function without annotations
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

The functions without type annotations try to infer the parameters types based
on their default values.

.. code:: python

    from ancli import make_cli

    def run(a, b=2, c=3.0):
        for param in (a, b, c):
            print(type(param))

    if __name__ == '__main__':
        make_cli(run)

The parameters without default values are considered as strings.

.. code:: bash

    $ python script.py --a 1 --b 2 --c 3.0
    <type 'str'>
    <type 'int'>
    <type 'float'>

3. Running ``ancli`` as a module
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Running package as a module allows to dynamically build a CLI from some function. 
You just need to specify a path to the module, and function which should be 
treated as an entry point.

.. code:: bash

    $ python -m ancli examples.functions:compute --a 2 --b 6
    42
squat 1.0.3
SQUAT

# **S**pend **Q**uality and **U**sage **A**nalysis **T**ool (**SQUAT**)

This Project is a tool to analyse Bankstatements transactions
to give a comprehensive report on the spend, earning and usage of an user.
It does the following job:

* Creates and trains a Machine learning model to classify transactions based on the narration.
* All the training and other repeatative work is already done for you.
* Once the package is installed with pip, the developer just need to pass the bankstatement dataframe
to get the report.

<br><br>

## Project Components

SQUAT contains the packages or libraries required for supporting and running the whole process.

1. spacy
2. Core ENG package for spacy
3. pandas
4. jupyter notebook

**Source**:<br>
https://github.com/binayr/SQUAT

## About the ML model

The model is created based on most common keyword observed from the bankstatements of singapore.
This project has a large scope of improving the accuracy and adding more classifications in future
depending on the type of dataset available to us.

Everytime we update the model a new version of SQUAT is supposed to get released.

## Create and use whl file

* with and updated setup.py execute the following command to create a whl file,
    ```python setup.py bdist_wheel```

* Please make sure you have pre-installed pandas, spacy and jupyter from standard chartered artifactory in your
 virtualenv

* Also make sure once spacy is installed the eng core library is also pre-installed in the virtualenv using pip.

* Now you can pip install squat using the whl file or from standard chartered artifactory if it is hosted.

## API

* You can import the utility by typing the following,
```from squat.Classifier.ClassifierUtil import ClassifierUtil```

* Read any csv or excel using pandas and create a dataframe. Please make sure the df has the following header atleast,
date, description, debit, credit, runningbalance (irrespective of the order)

* The ```ClassifierUtil``` can be initialized using the above df.

* Once initialized please make sure to call ```obj.evaluate()``` to evaluate each transaction.

* Once evaluated you can call ```get_analysis``` method to get the comprehensive analysis or call
```show_stat``` to get the statistics.

OR

* You can import the utility by typing the following,
```from squat.Classifier.ClassifierUtil import ClassifierUtilRaw```

* Read any csv or excel using pandas and create a dataframe. Please make sure the df has the following header atleast,
date, description, debit, credit, runningbalance (irrespective of the order)

* The ```ClassifierUtilRaw``` can be initialized to get the category.

* Once initialized please make sure to call ```obj.get_cat(text)``` to evaluate the category of the text.

* For Example,
	```
	obj.get_cat('paytm transaction gurgaon')
	Out: ('Digital', 0.9632782936096191)
	```
flaskErrorHandler 0.3
A flask package to handle exception and notify on gmail
Requires Python: >=3.6

This package can be used to notify the developers about the unhandled exceptions in the code.

When the user of the application faces an error, the developers will be notified and also provided with stacktrace, local variables content needed to debug the issues faster

USAGE: 

	To install: pip install flaskErrorHandler

	Import and initialize with flask app instance

	from flaskErrorHandler import SafeRun

	SafeRun(<<Flask APP Instance>>,{'from_addr':<<FROM ADDRESS>>,'to_addr':<<TO ADDRESS>>,'password':<<FROM ADDRESS GMAIL PASSWORD>>})