semvec/pypi-packages huggingface dataset:
pycompliance 0.0.1
Library for working with compliance benchmarks and data.
Requires Python: >=3.7

# pycompliance

A simply library to represent compliance benchmarks as tree structures.

```python
from pycompliance import pycompliance

benchmark = pycompliance.Benchmark("CIS Red Hat OpenShift Container Platform")
benchmark.version = "1.3.0"

section = pycompliance.Section("1")
section.title = "Control Plane Components"
benchmark.add_section(section)

subsection = pycompliance.Section("1.1")
subsection.title = "Master Node Configuration Files"
benchmark.add_section(subsection)

control = pycompliance.Control("1.1.1")
control.title = "Ensure foobar permissions"
benchmark.add_control(control)

print(benchmark.find('1.1.1').title)
```

Outputs:

```console
Ensure foobar permissions
```
slapstack-controls 0.1.1
Storage Strategies for the SLAPStack simulation Framework.
Requires Python: >3.6.8

# SLAPStack-Controls
This package contains several control heuristics associated with the SLAPStack 
block stacking warehouse (BSW) simulation. The code is hosted together with the 
simulation on [github](https://github.com/malerinc/slapstack).
SLAPStack contains two use cases, namely `WEPAStacks` and `Crossstacks`. 
See the linked repository for more information.

For `WEPAStacks`, the following storage location allocation problem (SLAP) 
strategies were implemented and tested (a comparison of these strategies is 
available through [[3]](#pfrommer2022)):
* Closest open pure lane (COPL) and
* Class-based popularity with the following stock keeping unit (SKU) popularity 
measures:
    * SKU turnover time (indirectly proportional to popularity)
    * The historic number of picks per SKU (directly proportional to popularity)
    * The number of future SKU picks over the next planning period, e.g. week 
    (directly proportional to popularity)
    * The historic SKU throughput calculated as the sum of picks and deliveries 
    per SKU (directly proportional to popularity)
    * The future SKU throughput over the next planning period

For `CrossStacks` (publication pending), the implemented strategies are:
* Closest to destination (CTD),
* Closest open location (COL),
* Random location (RND), and
* Two dual command cycle inspired heuristics:
  * Closest to the next delivery order (CTNR)
  * Shortest leg (SL)

Note that the `CrossStacks` SLAP strategies could be applied to the `WEPAStacks` 
use case and vice-versa, however this application has not yet been tested.

The following unit load selection problem (ULSP) policies are implemented:
* Batch Last In First Out (BLIFO)


## Citing the Project
If you use `SLAPStack`, `WEPAStacks` or `CrossStacks` in your research, you can 
cite this repository as follows:

```
@misc{rinciog2023slapstack
    author = {Rinciog, Alexandru and Pfrommer, Jakob and Morrissey Michael 
      and Sohaib Zahid and Vasileva, Anna and Ogorelysheva, Natalia and 
      Rathod, Hardik and Meyer Anne},
    title = {SLAPStack},
    year = {2023},
    publisher = {GitHub},
    journal = {GitHub Repository},
    howpublished = {\url{https://github.com/malerinc/slapstack.git}},
}
```

## References
<a id="pfrommer2020">[1]</a> Pfrommer, J., Meyer, A.: Autonomously organized 
block stacking warehouses: A review of decision problems and major challenges. 
Logistics Journal: Proceedings 2020(12) (2020)

<a id="rinciog2020">[2]</a> Rinciog, A., Meyer, A.: Fabricatio-rl: 
A reinforcement learning simulation framework for production scheduling. 
In: 2021 Winter Simulation Conference (WSC).
pp. 1â€“12. IEEE (2021)

<a id="pfrommer2022">[3]</a> Pfrommer, J.; Rinciog, A.; Zahid, S.; Morrissey, M; 
Meyer A. (2022): SLAPStack: A Simulation Framework and a Large-Scale Benchmark 
Use Case for Autonomous Block Stacking Warehouses. 
International Conference on Computational Logistics (ICCL) 2022.
DOTSAddressValidation3 1.0.3
DOTS Address Validation 3 US (“AV3”) is a new version of our Address Validation and Address Validation 2 Web services. This service utilizes the latest version of python and can be used as a RESTful service. AV3 is designed to take an unstandardized address, validate it against the latest USPS data, and return standardized, deliverable addresses.
Requires Python: >=3.7

# DOTS Address Validation US - 3

This package simplifies making API calls to Address Validation 3 US.  It allows for testing on our trial environments and running against our production environments as well while implementing best practices and failover.


## Examples:

import AV3

### Trial API 
GetBestMatchesTrial = GetBestMatches("", "27 E Cota St", "Suite 500","Santa Barbara", "CA", "93101", "ws72-soa1-dev1", False)
print(GetBestMatchesTrial) 

### Production API 
GetBestMatchesLive = GetBestMatches("", "27 E Cota St", "Suite 500","Santa Barbara", "CA", "93101", "ws72-soa1-dev1", True) 
print(GetBestMatchesLive)

### Trial API 
SecondaryNumbersResponseTrial = GetSecondaryNumbers("27 E Cota ST", "Santa Barbara", "CA", "93101", "ws72-soa1-dev1", False) 
print(SecondaryNumbersResponseTrial)

### Production API 
SecondaryNumbersResponseLive = GetSecondaryNumbers("27 E Cota ST", "Santa Barbara", "CA", "93101", "ws72-soa1-dev1", True) 
print(SecondaryNumbersResponseLive)

### Trial API 
CityStateZipResponseTrial = ValidateCityStateZip("Santa Barbara", "CA", "93101", "ws72-soa1-dev1", False) 
print(CityStateZipResponseTrial)

### Production API 
CityStateZipResponseLive = ValidateCityStateZip("Santa Barbara", "CA", "93101", "ws72-soa1-dev1", True) 
print(CityStateZipResponseLive)

### Trial API 
SingleLineMatchingResponseTrial = GetBestMatchesSingleLine("fooo", "27 E Cota ST STE 500, Santa Barbara, CA 93101", "ws72-soa1-dev1", False) 
print(SingleLineMatchingResponseTrial)

### Production API 
SingleLineMatchingResponseLive = GetBestMatchesSingleLine("fooo", "27 E Cota ST STE 500, Santa Barbara, CA 93101", "ws72-soa1-dev1", True) 
print(SingleLineMatchingResponseLive)

print()
picoolfx 0.1.1
A small package that applies nice effects to images
Requires Python: >=3.7

Simple package used to apply nice effects to images.
Can be installed using:
pip install picoolfx
"""
import picoolfx


I saw Chris Holmes' repo (https://github.com/cj-holmes/photos-on-spirals) of cool effects on pictures and I thought I would try and make a python version of it.

I just started, so if anybody wants to improve it go ahead, feel free to hit me up and/or open a pull request! I might work on it myself in the near future too.

Also, I definitely want to add some more nice effects. An easy start perhaps would be to just make it out of vertical or horizontal lines. Please, check my github, where there should be a link to a webapp to try them!