semvec/pypi-packages huggingface dataset:
nmap-query-tool 1.1.8
A tool for parsing and dynamically querying nmap scan data

# NMAP Query Tool
Post-processor for nmap output that allows fast, simple filtering of data both interactively and inline (i.e. via a bash pipe).

```bash
Usage: python nmap-query.py [-h] [-p PORTS] [-a IPS] [-os OS] [-d DEVICE_TYPE] [-c] [-i NMAP_SCAN_RESULTS_FILE] [-o OUTPUT_FILENAME] [-q]

        Note: the nmap scan results can be provided either through an input file or from STDIN (through a pipe)

        Example: nmap -sV 10.0.0.0/24 | python nmap-query.py -q



[-h | --help]: Will print the help/usage message

[-p | --ports] PORTS: specify one or more (open) ports to filter on. The following input formats are accepted:

        Single port number: -p 22
        Set of port numbers: -p 22,80,443

[-a | --ip-addrs] IPS: specify one or more IP addresses on which to filter. The following input formats are accepted:

        Single IP: -a 10.0.0.1
        Set of IPs: -a 10.0.0.1,10.0.0.2,10.0.0.3
        Single CIDR block: -a 10.0.0.0/24
        Set of CIDR blocks: -a 10.0.0.0/24,10.0.1.0/24
        Range of IPs: -a 10.0.0.1-4

[-os | --operating-system] OS: specify one or more operating systems on which to filter. The following input formats are accepted:

        Single operating system: -os Windws
        Set of operating systems: -os Windows,Linux

[-d | --device-type] DEVICE_TYPE: specify one or more device types on which to filter. The following input formats are accepted:

        Single device type: -d router
        Set of device types: -d router,switch

[-c | --output-csv]: use CSV as the output format

[-i | --input-file] NMAP_SCAN_RESULTS_FILE: specify the name of the input file (i.e. the file which contains the results of the Nmap scan)

[-o | --output-file] OUTPUT_FILENAME: specify the name of the output file

[-q | --query-mode]: enter the interactive query mode
```

# Locate script install location

1. `pip3 show nmap-query-tool`; will show the install directory of the package
1. `cd <INSTALL DIRECTORY>/NmapQueryTool`
1. `python3 nmap_query_tool.py`

You can also setup an alias for running the script: `alias nmap-query="python3 ${pwd}/nmap_query.py"`
rmo 0.1.0
Stateful rpc for Python
Requires Python: >=3.7,<4.0

A stored event queue solution for building event based systems easily. Fountainhead will essentially do two things store events sequentially by topic and notify any subscriber in real time. This is meant to be used as the unique source of truth feeding data to arbitrary number of dependant systems (database, datalake, monitoring dashboard, data cube, etc...) in highly reliable way.

<img src="./General.svg">

One can easily subscribe to those topics to process events starting from any point in time and receive new events from then onward. This allows any subscriber to catch up from where it previously stopped. It is safe by desgin thanks to its append only nature (think Git), although can still rewrite the past if need be.

## How to use it

One can save events as follows.

``` python
with create_sync_client(server_host, server_port, name="producer") as client:
        topic = "uploads/client_1"
        payload = {"value": 1}
        time_stamp = client.write_event(topic, payload)
```

This will save the payload under *uploads/client_1* topic. Events can be read starting from any point in time.

``` python
with create_sync_client(server_host, server_port, name="consumer") as client:
    with client.read_events("uploads/client_1") as events:
        for time_stamp, value in events:
            print(time_stamp, value)
```

This will display all the events saved under *uploads/client_1* topic from the beginning then will return newly saved ones. You can specify the beginning and the end of the iteration using *start* and *end* arguments.

##
rpyo 0.1.0
Stateful rpc for Python
Requires Python: >=3.7,<4.0

A stored event queue solution for building event based systems easily. Fountainhead will essentially do two things store events sequentially by topic and notify any subscriber in real time. This is meant to be used as the unique source of truth feeding data to arbitrary number of dependant systems (database, datalake, monitoring dashboard, data cube, etc...) in highly reliable way.

<img src="./General.svg">

One can easily subscribe to those topics to process events starting from any point in time and receive new events from then onward. This allows any subscriber to catch up from where it previously stopped. It is safe by desgin thanks to its append only nature (think Git), although can still rewrite the past if need be.

## How to use it

One can save events as follows.

``` python
with create_sync_client(server_host, server_port, name="producer") as client:
        topic = "uploads/client_1"
        payload = {"value": 1}
        time_stamp = client.write_event(topic, payload)
```

This will save the payload under *uploads/client_1* topic. Events can be read starting from any point in time.

``` python
with create_sync_client(server_host, server_port, name="consumer") as client:
    with client.read_events("uploads/client_1") as events:
        for time_stamp, value in events:
            print(time_stamp, value)
```

This will display all the events saved under *uploads/client_1* topic from the beginning then will return newly saved ones. You can specify the beginning and the end of the iteration using *start* and *end* arguments.

##
bigquery-operator 2.0
Wrapper for usual operations on a fixed BigQuery dataset.

bigquery-operator
=================

.. image:: https://img.shields.io/pypi/v/bigquery-operator
    :target: https://pypi.org/project/bigquery-operator

.. image:: https://img.shields.io/pypi/l/bigquery-operator.svg
    :target: https://pypi.org/project/bigquery-operator

.. image:: https://img.shields.io/pypi/pyversions/bigquery-operator.svg
    :target: https://pypi.org/project/bigquery-operator

.. image:: https://codecov.io/gh/augustin-barillec/bigquery-operator/branch/main/graph/badge.svg
    :target: https://codecov.io/gh/augustin-barillec/bigquery-operator

.. image:: https://pepy.tech/badge/bigquery-operator
    :target: https://pepy.tech/project/bigquery-operator

Wrapper for usual operations on a fixed BigQuery dataset.

Documentation is available at https://bigquery-operator.readthedocs.io/en/latest/.