semvec/pypi-packages huggingface dataset:
hivae2 0.13
HIVAE (Handling incomplete heterogeneous data using VAEs (doi: 10.1016/j.patcog.2020.107501, 2020 and used in Lancet . 2021 (doi: 10.1016/S0140-6736(21)01638-X).Extension of implementations as easy to use Python library/tf2 version (incl. imputation objects for sklearn API) (a.karwath)
License: MIT

# hivae2

This repository contains a modular reimplemenation of the "Heterogeneous Incomplete Variational Autoencoder model (HI-VAE) written by Alfredo Nazabal (anazabal@turing.ac.uk) et al. .written in Python.

The details of this model can be found are included in this [paper](https://doi.org/10.1016/j.patcog.2020.107501). Please cite it if you use this code/library for your own research.
This is an extenstion of implementations as easy to use Python library, upgraded for tensorflow2.




## Examples

See examples directory for usage


## Files description

*(outdated) **HIVAE.py**: The main script of the library, it needs to imported to work with the library and is connected to all the other scripts.
* **loglik_ models_ missing_normalize.py**: In this file, the different likelihood models for the different types of variables considered (real, positive, count, categorical and ordinal) are included.
* **model_ HIVAE_inputDropout.py**: Contains the HI-VAE with input dropout encoder model.
* **model_ HIVAE_factorized.py**: Contains the HI-VAE with factorized encoder model

## Contact

* **For questions regarding algorithm --> Alfredo Nazabal**: anazabal@turing.ac.uk
* **For bugs or suggestion regarding this code --> Andreas Karwath**: a.karwath@bham.ac.uk

## Comments

This version required tf2. For apple silcone users, please follow : https://developer.apple.com/metal/tensorflow-plugin/


## Comments on general_example.py (might be outdated!)


main_directory: where is the project folder

dataset_name: the name of the database (if you want)

types_list_d: a dictionary where the key is the dataset name, which contains a list with tuples that indicates the column names, types, the number of dimensions and classes 

types:

•	count: real values

•	cat: categorical 0 or 1

•	pos: positive real values

•	ordinal: ordinal number

number of dimensions:

•	number of possibilities in the categorical variables or 1 in numerical

number of classes:

•	number of options (same of number of dimensions for categorical variables)

dataset_path: this is the folder of the csv files

results_path: the output folder for results

network_path: where the models are going to be stored

types_list: the specific type for the dataset you are going to use
data_file: the full dataset
train_file/ test_file: if the dataset was already splitted

train_data/test_data: pandas dataframes

dim_y: the depth of the network

dim_s/dim_z: dimensions of the embedding
surpyvor 0.15.0
Manipulate vcf files of structural variants using SURVIVOR
License: MIT
Requires Python: >=3
Keywords: nanopore

# surpyvor
A python wrapper around [SURVIVOR](https://github.com/fritzsedlazeck/SURVIVOR), with additional convenience functions.

## Installation and dependencies
surpyvor requires bcftools, bgzip, tabix and SURVIVOR to be installed and in the $PATH.
Required python modules are cyvcf2, matplotlib, numpy, matplotlib-venn and upsetplot

surpyvor and its dependencies can be installed from [bioconda](https://anaconda.org/bioconda/surpyvor):

`conda install -c bioconda surpyvor`

## USAGE
### sub-commands:
    merge               merging vcf files of SVs
    highsens            get union of SV vcfs
    highconf            get intersection of SV vcfs
    prf                 calculate precision, recall and F-measure
    upset               Make upset plot for multiple SV vcf files
    venn                Make venn diagram for 2 or 3 SV vcf files

Each sub-command has its own help information, accessible by running `surpyvor <command> -h/--help`

### General and common arguments for most sub-commands:
```
-o/--output: output variant file to write. Default: stdout
--plotout: name ouf output plot to write. Default names depending on plot type.
-d/--distance: maximal pairwise distance between coordinates of SVs to be considered concordant. Default: 500
-l/--minlength: minimal SV length to include. Default: 50
--variants: vcf files to combine
```

### Specific arguments

#### surpyvor prf
```
--ignore_chroms: ignore some chromosomes for calculations. Default: chrEBV
--bar: create a stacked bar chart colored by validation status [not created by default]
--matrix: create a confusion matrix [not created by default]
```

## Citation
If you use this tool, please consider citing our [publication](https://genome.cshlp.org/content/early/2019/06/11/gr.244939.118.abstract) and the [citation for SURVIVOR](https://www.nature.com/articles/ncomms14061).
wbpLogConfig 0.2.0
Log Configuration for Workbench applications.
License: MIT
Requires Python: >=3.8
Keywords: workbench,wxPython,GUI

# wbpLogConfig

Logging Configuration for [Workbench](https://pypi.org/project/wbBase/) applications

## Installation

```shell
pip install wbpLogConfig
```

Installing this plugin registers an entry point 
in the group "*wbbase.plugin*" named "*logconfig*".

To use the plugin in your application, 
add it to your *application.yml* file as follows:
```yaml
AppName: myApp
Plugins:
- Name: logconfig
```

## Documentation

For details read the [Documentation](https://workbench2.gitlab.io/workbench-plugins/wbplogconfig/).