semvec/pypi-packages huggingface dataset:
qhbayes 0.0.13
Bayesian methods for inferring mass eruption rate for column height (or vice versa) for volcanic eruptions
Requires Python: >=3.8,<3.11

# QHBayes #

Bayesian methods for inferring mass eruption rate from column height (or vice versa) for volcanic eruptions

### What is it? ###

*QHBayes* uses Bayesian methods to explore the relationship between the mass eruption rate (Q) of a volcanic eruption and the height reached by the volcanic eruption column (H) that is produced.

The mass eruption rate is a quantity that is very important in volcanology and in the dispersion of volcanic ash in the atmosphere, but it is very difficult to measure directly.

Often the mass eruption rate is inferred from observations of the height of the volcanic eruption column, since the eruption column is often much easier to measure.  The eruption column height is linked to the mass eruption rate through the fluid dynamics of turbulent buoyant plumes, but there are often external volcanological and atmospheric effects that contribute and complicate the relationship.

Datasets of the mass eruption rate and eruption column height have been compiled and used to determine an empirical relationship these quantities, using linear regression.  This has then been used to infer the mass eruption rate from the plume height.

*QHBayes* goes further, by using Bayesian methods to perform the regression.  Bayesian methods:
* allow us to incorporate a range of *uncertainties* quantitatively into our model;
* provide a meaningful quantitative comparison of different models;

### Main Features ###


### How do I get set up? ###

* Summary of set up


* Configuration
* Dependencies
* Database configuration
* How to run tests
* Deployment instructions

### Contribution guidelines ###

* Writing tests
* Code review
* Other guidelines

### Who do I talk to? ###

* Repo owner or admin
* Other community or team contact
cm-cluster-on-demand-vmware 9.2.9
Bright Cluster on Demand for VMWare
Requires Python: >=3.9

With Cluster on Demand for VMware you can spin up a cluster running Bright
Cluster Manager on VMware vSphere.

# Installation

```
pip install cm-cluster-on-demand-vmware
```

# Usage

To get started, execute the following command:

```
cm-cod-vmware --help
```

Example, To start a Bright Cluster with 5 nodes and 1 headnode on vSphere:

```
cm-cod-vmware cluster create --on-error 'cleanup' --wlm 'slurm' --nodes '5' --externalnet-vdportgroup '...' --vdswitch--name '...'  --cluster-password '...'  --license-product-key '...' --name mycluster
```

Don't forget to fill in the '...' blanks, and change the other parameters to
the values that match your use-case and organization. To obtain a Bright
product key, please contact sales@brightcomputing.com. All documentation for
setting up and running a Bright cluster using Cluster On Demand on vSphere can
be found in the
[Cloudbursting Manual](https://www.brightcomputing.com/documentation).
xlcrf 0.0.7
Small tools to create Excel CRFs from structure files
Requires Python: >=3.6

# xlcrf

Un tool (libreria Python + script) per creare file utili per la
raccolta dati (con validazione dell'input) a partire da una descrizione
della struttura del dataset desiderato (mediante un file `.xlsx`).

## Utilizzo
Lo strumento si può utilizzare via web [qui](https://share.streamlit.io/lbraglia/webxlcrf/main) oppure da linea di comando, come segue:
```
xlcrf file_struttura.xlsx
```
Questo produrrà `file_struttura_CRF.xlsx`.

## File di struttura

Per indicazioni su come compilarlo segui le istruzioni [qui](https://share.streamlit.io/lbraglia/webxlcrf/main).