semvec/pypi-packages huggingface dataset:
aws-cdk-billing-alarm 1.1.0
aws-cdk-billing-alarm
License: MIT
Requires Python: >=3.6

# aws-cdk-billing-alarm

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[![Mentioned in Awesome CDK](https://awesome.re/mentioned-badge.svg)](https://github.com/kolomied/awesome-cdk)

A CDK construct that sets up email notification for when you exceed a given AWS estimated charges amount.

Create this construct in any stack you find appropriate **with only a few lines**. This construct is an implementation of the manual
setup described on [AWS Estimated Charges Monitoring](https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/gs_monitor_estimated_charges_with_cloudwatch.html).

## Get Started

### Pre-Requisites

> **IMPORTANT!** Only complete ***Step 1: Enable Billing Alerts*** of the following documentation link. This construct will take
> care of creating the rest of the resources for you.

You must first enable billing alerts from the AWS Console as per [documentation](https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/gs_monitor_estimated_charges_with_cloudwatch.html#gs_turning_on_billing_metrics).

Billing alerts will allow your AWS account to start collecting billing metrics (`EstimatedCharges`) on a periodic 6-hour basis.

### Installation

#### [Node documentation](https://www.npmjs.com/package/aws-cdk-billing-alarm)

```shell
npm install --save aws-cdk-billing-alarm
```

#### [Python documentation](https://pypi.org/project/aws-cdk-billing-alarm/)

```shell
pip install aws-cdk-billing-alarm
```

### Usage

```python
# Example automatically generated from non-compiling source. May contain errors.
import { Stack, StackProps } from 'aws-cdk-lib';
import { Construct } from 'constructs';
import { BillingAlarm } from 'aws-cdk-billing-alarm';

class CdkStack extends Stack {
  constructor(scope: Construct, id: string, props?: StackProps) {
    super(scope, id, props);

    // Create an alarm that emails `admin@example.com`
    // if estimated charges exceed 50 USD
    new BillingAlarm(this, 'AWSAccountBillingAlarm', {
      monthlyThreshold: 50,
      emails: ['admin@example.com'],
    });
  }
}
```

### Post-Deployment

Confirm the subscription to the newly created topic for the emails you specified as endpoints in `BillingAlarmProps`.
You can do so by clicking on the `SubscribeURL` of the JSON email you received.

> **Note**: If you did not receive the email, you can fire a **Request confirmation** for the subscription from the AWS SNS Console.

## Limitations

* [USD currency](https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/monitor_estimated_charges_with_cloudwatch.html#creating_billing_alarm_with_wizard)
resume-parser 0.8.4
A resume parser used for extracting information from resumes
License: MIT
Keywords: resume,parser,cv

Resume parser
=============
Premium resume parsing services have been moved to [Resume-Parser](https://www.resume-parser.com/application/resumes). Please try the demo for free and give us your [feedback](https://www.resume-parser.com)

Premium resume parsing services have been moved to `Resume-Parser <https://www.resume-parser.com/application/resumes>`__. 
Please try the demo for free and give us your `feedback <https://www.resume-parser.com>`__
::

    A resume parser used for extracting information from resumes

Built with ❤︎ and :coffee: by `Kumar
Rajwani <https://github.com/kbrajwani>`__ and `Brian
Njoroge <https://github.com/Brianjoroge>`__

--------------

Features
========

-  Extract name
-  Extract email
-  Extract mobile numbers
-  Extract skills
-  Extract total experience
-  Extract college name
-  Extract degree
-  Extract designation
-  Extract company names

Installation
============

-  You can install this package using

.. code:: bash

    pip install resume-parser

-  For NLP operations we use spacy and nltk. Install them using below
   commands:

.. code:: bash

    # spaCy
    python -m spacy download en_core_web_sm

    # nltk
    python -m nltk.downloader stopwords
    python -m nltk.downloader punkt
    python -m nltk.downloader averaged_perceptron_tagger
    python -m nltk.downloader universal_tagset
    python -m nltk.downloader wordnet
    python -m nltk.downloader brown
    python -m nltk.downloader maxent_ne_chunker

Supported File Formats
======================

-  PDF and DOCx and TXT files are supported on all Operating Systems

Usage
=====

-  Import it in your Python project

.. code:: python

    from resume_parser import resumeparse

    data = resumeparse.read_file('/path/to/resume/file')

Result
======

The module would return a dictionary with result as follows:

::

    {'degree': ['BSc','MSc'],
         'designition': [
             'content writer',
             'data scientist',
             'systems administrator',
         ],
         'email': 'maunarokguy@gmail.com',
         'name': 'Brian Njoroge',
         'phone': '+918511593595',
         'skills': [
             'Python',
             ' C++',
             'Power BI',
             'Tensorflow',
             'Keras',
             'Pytorch',
             'Scikit-Learn',
             'Pandas',
             'NLTK',
             'OpenCv',
             'Numpy',
             'Matplotlib',
             'Seaborn',
             'Django',
             'Linux',
             'Docker'],
         'total_exp': 3,
         'university': ['gujarat university', 'wuhan university', 'egerton university']}