semvec/pypi-packages huggingface dataset:
herschelhelp-internal 1.0.5
HELP project internal code
License: MIT

Herschel Extragalactic Legacy Project internal python module
============================================================

This python module contains the code used internally within HELP to process our
data.

Installation
------------

The easiest way is to install the module in development mode using conda.
Provided you installed [Anaconda](https://www.continuum.io/) or
[miniconda](http://conda.pydata.org/miniconda.html) here are the commands to
run:

```Shell
$ conda create -n herschelhelp_internal
$ conda activate herschelhelp_internal
$ conda install  -c openastronomy astropy healpy scipy seaborn
$ pip install -r requirements.txt
$ pip install -e .
```

You will need to activate this new environment with `source activate
herschelhelp_internal` when you want to use this module.

Running inside a Jupyter notebook
---------------------------------

If one need to use this code inside a Jupyter notebook, one should install the
notebook stuff:

```Shell
$ conda install notebook
```

and add the environment Python to the list of kernels for Jupyter:

```Shell
$ python -m ipykernel install --user --name helpint --display-name "Python (herschelhelp_internal)"
```

The notebooks must be set to use this kernel.

*Note: maybe it's not mandatory to install the full notebook and only the
ipykernel is required if Jupyter is already installed on the system.*

E(B-V) maps
-----------

To compute the E(B-V) at each source position, we are using the [sfdmap
package](https://github.com/kbarbary/sfdmap) that needs the Schlegel, Finkbeiner
& Davis (1998) dust maps.  The script `get_sfd_maps.sh` will download them and
place them in the directory `herschelhelp_internal/sfd_data/` where they are
expected by our wrapper function.  This must be done only once.
codon-bias 0.3.5
codon usage bias analysis tools
License: MIT
Requires Python: >=3

# codon-bias

This package provides codon usage bias (CUB) analysis tools for genomic sequences, focusing on protein coding regions, translation efficiency and synonymous mutations. These include implementations of popular models from the past four decades of codon usage study, such as:

- [Nucleotide and codon k-mer statistics (GC, GC3, CpG, etc.)](https://codon-bias.readthedocs.io/en/latest/codonbias.html#module-codonbias.stats)
- [Frequency of Optimal Codons (FOP)](https://codon-bias.readthedocs.io/en/latest/codonbias.html#codonbias.scores.FrequencyOfOptimalCodons)
- [Relative Synonymous Codon Usage (RSCU)](https://codon-bias.readthedocs.io/en/latest/codonbias.html#codonbias.scores.RelativeSynonymousCodonUsage)
- [Codon Adaptation Index (CAI)](https://codon-bias.readthedocs.io/en/latest/codonbias.html#codonbias.scores.CodonAdaptationIndex), including extensions:
    - Codon pair (and k-mers) adaptation
- [Effective Number of Codons (ENC)](https://codon-bias.readthedocs.io/en/latest/codonbias.html#codonbias.scores.EffectiveNumberOfCodons), including extensions:
    - Background correction
    - Improved estimation
    - Effective number of codon pairs (and k-mers) (ENcp)
- [tRNA Adaptation Index (tAI)](https://codon-bias.readthedocs.io/en/latest/codonbias.html#codonbias.scores.TrnaAdaptationIndex)
    - Download tRNA gene copy numbers from [GtRNAdb](http://gtrnadb.ucsc.edu/)
    - Train tAI model parameters (s-values) using expression levels
- [Codon Pair Bias (CPB/CPS)](https://codon-bias.readthedocs.io/en/latest/codonbias.html#codonbias.scores.CodonPairBias)
- [Relative Codon Bias Score (RCBS)](https://codon-bias.readthedocs.io/en/latest/codonbias.html#codonbias.scores.RelativeCodonBiasScore)
- [Normalized Translational Efficiency (nTE)](https://codon-bias.readthedocs.io/en/latest/codonbias.html#codonbias.scores.NormalizedTranslationalEfficiency)
- [Directional Codon Bias Score (DCBS)](https://codon-bias.readthedocs.io/en/latest/codonbias.html#codonbias.scores.RelativeCodonBiasScore)
- [Codon Usage Frequency Similarity (CUFS)](https://codon-bias.readthedocs.io/en/latest/codonbias.html#codonbias.pairwise.CodonUsageFrequency)

This package also includes tools for sequence optimization based on these codon usage models, and generators of random sequence permutations that can be used to compute empirical p-values and z-scores.

## installation

```
pip install codon-bias
```

## documentation

Read on [Read the Docs](https://codon-bias.readthedocs.org).

## cite

Diament, A. (2022). codon-bias (python package). https://doi.org/10.5281/zenodo.8039451

## contributing

Contributions of additional models to the package are welcome! Please familiarize yourself with the existing classes, and try to conform to their style.
valo-api 2.1.0
Valorant API Wrapper for https://github.com/Henrik-3/unofficial-valorant-api
License: MIT
Requires Python: <4.0,>=3.9

# valo_api

<div align="center">

[![Build status](https://github.com/raimannma/ValorantAPI/workflows/build/badge.svg?branch=master&event=push)](https://github.com/raimannma/ValorantAPI/actions?query=workflow%3Abuild)
[![Python Version](https://img.shields.io/pypi/pyversions/valo_api.svg)](https://pypi.org/project/valo_api/)
[![Dependencies Status](https://img.shields.io/badge/dependencies-up%20to%20date-brightgreen.svg)](https://github.com/raimannma/ValorantAPI/pulls?utf8=%E2%9C%93&q=is%3Apr%20author%3Aapp%2Fdependabot)

[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
[![Security: bandit](https://img.shields.io/badge/security-bandit-green.svg)](https://github.com/PyCQA/bandit)
[![Pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white)](https://github.com/raimannma/ValorantAPI/blob/master/.pre-commit-config.yaml)
[![Semantic Versions](https://img.shields.io/badge/%20%20%F0%9F%93%A6%F0%9F%9A%80-semantic--versions-e10079.svg)](https://github.com/raimannma/ValorantAPI/releases)
[![License](https://img.shields.io/github/license/raimannma/ValorantAPI)](https://github.com/raimannma/ValorantAPI/blob/master/LICENSE)
[![Codacy Badge](https://app.codacy.com/project/badge/Grade/3b23d2a3b1694356bc95255a2edb83e6)](https://www.codacy.com/gh/raimannma/ValorantAPI/dashboard?utm_source=github.com&amp;utm_medium=referral&amp;utm_content=raimannma/ValorantAPI&amp;utm_campaign=Badge_Grade)
[![Codacy Badge](https://app.codacy.com/project/badge/Coverage/3b23d2a3b1694356bc95255a2edb83e6)](https://www.codacy.com/gh/raimannma/ValorantAPI/dashboard?utm_source=github.com&utm_medium=referral&utm_content=raimannma/ValorantAPI&utm_campaign=Badge_Coverage)
[![Downloads](https://pepy.tech/badge/valo-api)](https://pepy.tech/project/valo-api)

Valorant API Wrapper for https://github.com/Henrik-3/unofficial-valorant-api

</div>

## Installation

    pip install valo-api

If you want to use the async functions, you need to install the `aiohttp` package.

    pip install valo-api[async]

## Documentation

### Hosted

The documentation is hosted here: https://raimannma.github.io/ValorantAPI/

### From Source

After installing the package dependencies `pip install -r requirements.txt`, you can use the following commands to get the documentation:

    cd docs/ && make html

Open the index.html file in the docs/_build/html/ directory.