semvec/pypi-packages huggingface dataset:
x7-geom 0.7.3
X7 Geometry library: Point, Vector, Line...
Requires Python: >=3.8

X7 Geometry
====================

Library providing some basic geometry types and operations:

    * ``Point``
    * ``Vector``
    * ``Line``
    * ``BezierCurve``

A distinguishing feature of this library is that it is designed for
modeling, so there are also classes like ``PointRelative`` which
automatically updates when the underlying point is moved.  This
is used by the morphing and animation capabilities.
TOF-SIMS 1.1.0
Open and process 3D datasets generated by TESCAN FERA Xe plasma FIB-SEM with ToF-SIMS microscopes
Requires Python: >=3.6

# Why TOF-SIMS ?
We needed a convenient way to explore the 3D datasets generated by a TESCAN FERA Xe plasma FIB-SEM with ToF-SIMS.  This package expands on the functions provided by the manufacturer.


# Highlights
## Machine learning
* Multivariate analysis with PCA, kPCA, IPCA
* t-SNE clustering
* Machine learning (KMeans)
* Detailed cluster analysis


## Plots
Available functions so far:
* Read data from TOF-SIMS (.HDF5) files, making the data directly accessible in Python (numpy/pandas/matplotlib).
* Generate top, sides projections, visualise surface before FIB-ing
* Plot detected masses
* Maximal projections for any given axis (x,y,z) and every isotopes/masses (useful to have a quick look at detected species).
* Inspect top, side and depth projections for a given isotope/mass
* Plot (and superpose) the abundance of species over any axis.
* Overlays maximal projections.
* Read in the complete file header. Get access to every setting of the machine recorded at the time of the experiment.
* Export plots.
* 3D interactive plot


# Instructions
Code in TOF-SIMS Notebook.ipynb should be self-explanatory.  If needed feel free to contact us at remi.branco@hotmail.fr or daniel.thomas.oldfield@gmail.com
Help and feedback is very welcome.


# What's next
* More machine learning (DB-scan)
* Better 3D plots (open3D)
* Data reprocessing/peak-refining
* Peak identification
* Denoising with neural network
declarative-iptables 0.1.2
work with iptables in a declarative manner

# simple tables
A simple interface to managing iptables on linux

The hardest part of managing iptables is that they are a central piece of data that's constantly changing from many different directions.  Because its state isn't guaranteed, making a simple modification to it can have undesired consequences.  This project aims to solve this problem by making yet another interface to iptables.

This interface aims to be declarative.  Rather than looking at issuing changes directly to iptables, this package defines the desired state.  The significance is that it matters less what the tables look like at any given time and instead the goal is to end up with what it should look like.

Additionally, because it's unknown what changes other systems have made, any tables and rules that have not been declared are left untouched.


## Example

```
import iptables

plan = iptables.Plan()

# this will delete an existing table and create it from scratch
berto_chain = plan.add_chain('OPENVPN_BERTO')

# rules are added with a default priority of 50
berto_chain.add_rule(iptables.Rule('-o tun0 -s 192.168.2.2 -d 192.168.1.1 -j ACCEPT'))

# this will get an existing table and create it if it does not exist
openvpn_chain = plan.get_chain('OPENVPN')

# rules with higher number priority get added to the chain first
# when updating an existing chain, rules already in the chain get a priority of 70.
# being added is compared to existing rules and will be added if missing or left alone.
openvpn_chain.add_rule(iptables.Rule('-o ! tun0 -j RETURN', priority=100))
openvpn_chain.add_rule(berto_chain.jump)

# make necessary changes to achieve the desired outcome
plan_execute()
```