semvec/pypi-packages huggingface dataset:
concept-xblock 0.4.0
concept XBlock

ConceptXBlock
===========

This is a simple XBlock which will allows one to tag problems with
concepts. To be useful, it requires a concept server. There is one in
the matching concept-tag-server repo.

Usage: 

       <Concept server="http://www.sample-concept-demo-server.org:7000/">

This displays: 

![ConceptXBlock screenshot](learning_objectives.png)

In the bottom left, you can search for learning objectives, and drag
them into one of the top three bins. The bins are: 

* Taught: For concepts which the item introduces. For example, a video
  explaining the quadaratic equation would be tagged as teach the
  quadratic equation. Perhaps should be renamed to 'introduced'? 
* Exercised: For concepts which the problem is explicitly designed to
  practice. For example, "Solve 5x^2+6x=7" would exercise the
  quadratic equation.
* Required: For concepts implicitly required. For example, a physics
  problem on the path of a rocket might have the quadratic equation
  as a prerequisite, but not be explictly designed to exercise it. 

The edit button is a link back to the concept tag server, where we can
edit concept descriptions.

The system has a few serious issues: 

1. Performance. This is trivial to fix, but the system currently makes
   an AJAX request per objective on the page. Bulk requests would
   solve this.
2. 500 errors when e.g. a concept is not on the concept server. 
3. No test cases. 
4. Shown as a student (rather than instructor) view. This is a Studio
   limitation. 
5. Lack of configurability. It works on my taxonomy
   (taught/exercised/required), and not yours.
6. Horrific styling. In desperate need of a cleanup. 

It does have a few nice properties: 

1. Continues to develop edX-as-a-platform, rather than
   edX-as-a-product.
2. Natural path (via concept wiki) to coming up with a concept
   taxonomy. Next steps will be to split and join objectives.
3. Natural extension to other wise of tagging objectives (e.g. Bloom's
   taxonomy, etc.)
csbtest 0.1.0
Crowdsourced bathymetry toolkit
Requires Python: >=3.6

# CSB
Crowdsourced bathymetry toolkit


## Georeference 
Get the XYZ position (in the ECEF frame) of a depth sounding (in the WGS84 frame) 

```

# Example values
# latitude = math.radians(48.4525) //decimal degrees
# longitude = math.radians(-68.5232) //decimal degrees
# ellipsoidal_height = -28 //meters
# heading = math.radians(350)
# pitch = math.radians(0.5)
# roll = math.radians(1.5)
# depth = 5 //meters
# offset_x = 1 //meters
# offset_y = 2 //meters
# offset_z = 3 //meters

import georeference
g = georeference.Georeference()
result = g.compute(latitude,longitude,ellipsoidal_height,heading,pitch,roll,depth,offset_x,offset_y,offset_z)
```
## Uncertainty
Get the uncertainty associated with an XYZ position (in the ECEF frame) of a depth sounding (in the WGS84 frame) 

```
# Example values
# latitude = math.radians(48.4525) //decimal degrees
# longitude = math.radians(-68.5232) //decimal degrees
# ellipsoidal_height = -28 //meters
# heading = math.radians(350)
# pitch = math.radians(0.5)
# roll = math.radians(1.5)
# depth = 5 //meters
# offset_x = 1 //meters
# offset_y = 2 //meters
# offset_z = 3 //meters
# latitude_sigma = 0.00001
# longitude_sigma = 0.0001
# ellipsoidal_height_sigma = 1
# heading_sigma = 1
# pitch_sigma = 1
# roll_sigma = 1
# depth_sigma = 0.5
# offset_x_sigma = 0.0000001
# offset_y_sigma = 0.0000001
# offset_z_sigma = 0.0000001

import uncertainty
u = uncertainty.TotalPropagatedUncertainty()
result = u.compute(latitude,longitude,ellipsoidal_height,heading,pitch,roll,depth,offset_x,offset_y,offset_z,latitude_sigma,longitude_sigma,ellipsoidal_height_sigma,heading_sigma,pitch_sigma,roll_sigma,depth_sigma,offset_x_sigma,offset_y_sigma,offset_z_sigma)
```
crc-shinigami 0.6.1
A stand alone Python application for killing errant processes on Slurm based compute nodes.
Requires Python: <4.0.0,>=3.9.0
Keywords: process, terminate, slurm

# Shinigami
[![](https://app.codacy.com/project/badge/Grade/d5325904cffc4936b24dd6be0d58a1ee)](https://app.codacy.com/gh/pitt-crc/shinigami/dashboard)
[![](https://app.codacy.com/project/badge/Coverage/d5325904cffc4936b24dd6be0d58a1ee)](https://app.codacy.com/gh/pitt-crc/shinigami/dashboard)

Shinigami is a stand alone Python application for killing errant processes on Slurm based compute nodes.
The application scans for and terminates any running processes not associated with a currently running Slurm job.
Processes associated with whitelisted users (root, administrators, service accounts, etc.) are ignored.

## Installation and Setup

The `shinigami` command line utility is installable via the pip (or pipx) package manager:

```bash
pipx install shinigami
```

To be of maximal use, it is recommended to run the utility every half hour.
However, you may find a different cadence more appropriate depending on your cluster size and use case.
Running the utility automatically is accomplished via a simple cron job:

```cron
0,30 * * * * shinigami
```

You may wish to configure the cron job to run under a dedicated service account.
When doing so, ensure the user is added to the admin list and satisfies the following criteria:

- Exists on all compute nodes
- Has appropriate permissions to terminate system processes on compute nodes
- Has established SSH keys for connecting to compute nodes