semvec/pypi-packages huggingface dataset:
grn-dazzle 0.0.2
Improving single-cell GRN Inference using Dropout Augmentation
Requires Python: >=3.7

# DAZZLE

This repository include code and documentation for our manuscript "Improving Gene Regulatory Network Inference using Dropout Augmentation". 

## Install

This package is available on pip 

```
pip install grn-dazzle
```

## Basic Usage

The core function `runDAZZLE` requires the following two things to get started:

- **Single cell gene expression table**. We suggest you use log transformation to normalize the data
- **Experiment Configs**. We also provide two sets of default configs with this package, namely `DEFAULT_DAZZLE_CONFIGS` and `DEFAULT_DEEPSEM_CONFIGS`. They are just two python dictionaries. If you need to make modifications, just save them to a variable and adjust the values. 

## Quick Example

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1kwRG0dsqJAHxsOXF9zFeyNxpuh_TWSGg?usp=sharing)

```
from dazzle import load_beeline, runDAZZLE, get_metrics, DEFAULT_DAZZLE_CONFIGS


bl_data, bl_ground_truth = load_beeline(
    data_dir='data', 
    benchmark_data="hESC", 
    benchmark_setting="500_STRING"
)

model, adjs = runDAZZLE(bl_data.X, DEFAULT_DAZZLE_CONFIGS)

get_metrics(model.get_adj(), bl_ground_truth)
```
vipes 0.0.5
The most reliable script manager for crossplatform projects
Requires Python: >=3.7
Keywords: script,manager,crossplatform,configuration

# Vipes

## What is Vipes?

Vipes is a complete script manager, it helps you to manage your development workflow by providing you with the ability to add, remove and update custom scripts. Viper is crossplatform, that means you can use it on any operating system that supports python as long as you configure it to do so.

## How to install Vipes?

To install Vipes, you need to use the following command:

```bash
pip install vipes
```
composition_based_feature_vector 1.0.6
Tool for quickly creating a composition-based feature vector.
Requires Python: >=3

# CBFV Package
Tool to quickly create a composition-based feature vectors from materials datafiles.

# Installation
The source code is currently hosted on GitHub at: https://github.com/kaaiian/CBFV

Binary installers for the latest released version are available at the <a href="https://pypi.org/project/cbfv/">Python Package Index (PyPI)</a>
```
# PyPI
pip install CBFV
```

## Making the composition-based feature vector
The CBFV package assumes your data is stored in a pandas dataframe of the following structure:

formula | target
---|---
Tc1V1 | 248.539
Cu1Dy1 | 66.8444
Cd3N2 | 91.5034

To featurize this data, the `generate_features` function can be called as follows:

```
from CBFV import composition
X, y, formulae, skipped = composition.generate_features(df)
```

## Extended Functionality

The featurization scheme can be adjusted by calling the the `elem_prop` parameter. The following featurization schemes are included within CBFV:
- jarvis
- magpie
- mat2vec
- oliynyk (default)
- onehot
- random_200

Duplicate formula handeling is controlled by the `drop_duplicates` parameter. It is set to `False` by default to preserve datapoints containing variation outside of their formula. For example, heat capacity measurements performed for the same material at different temperatures.

The `extend_features` parameter is used to specify whether columns outside of `['formula', 'target']` should be considered during featurization. It is set to `False` by default to exclude nuisance information from consideration. Setting `extend_features=True` would allow additional information (i.e. `['temperature', 'pressure']`) to be preserved.

The `sum_feat` parameter specifies whether to calculate the sum features when generating the CBFVs for the chemical formulae. It is set to `False` by default.

Calling `generate_features` with these parameters can be implemented as follows:

formula | target | temp
---|---|---
Tc1V1 | 248.539 | 373
Tc1V1 | 66.8444 | 473
Cd3N2 | 91.5034 | 273

```
from CBFV import composition
X, y, formulae, skipped = composition.generate_features(df,
                                                        elem_prop='magpie',
                                                        drop_duplicates=False,
                                                        extend_features=True,
                                                        sum_feat=True)
```
dbt-converter 0.1.0
Easily convert out of date, or unsported metrics spec into the latest suported metrics spec in dbt
Requires Python: >=3.9,<3.10

dbt converter helps you easily migrate from the dbt metrics spec supported in dbt <=1.5.xx to the new dbt metrics spec supported in dbt >=1.6. 

To migrate your metrics to the new spec:
1. install the package `pip install dbt-converter`
2. Run the converter `dbt-converter --dbt-project-path path/to/dbt/project`