semvec/pypi-packages huggingface dataset:
nickffs-transliteration 0.0.9
Hinglish --> Hindi transliteration
Requires Python: >=3.6
Keywords: Artificial Intelligence,Transliteration,PyTorch,Hindi,Hinglish

# hindi-transliteration
A transformer model that transliterates Hinglish words to hindi. 


## Usage

```python
from transliterate import Translit
transliterator = Translit()
transliterator.transliterate("Abhilash")
```
BBN 0.0.1
Big Bang Nucleosynthesis
Requires Python: >=3.6

# BBN: Big Bang Nucleosynthesis
```
BBN(eta, index, n_step=256, **kw):
input:
  eta = baryon to photon ratio (Kolb and Turner eq.3.104)
  index = list of element's name (string)
          (available indices are given in nuclear.py)
  n_step = number of steps at which data are evaluated
           with logarithmic interval in T
  kw = keyword arguments passed to solve_ivp, e.g.
    atol = tolerance for absolute error (default 1e-6)
    rtol = tolerance for relative error (default 1e-3)
return T,X where
  T = temperature / MeV (shape(n_step,)) in [T_init, T_final]
  X = mass fraction of elements (shape(len(index),n_step))
reference:
  E. W. Kolb and M. S. Turner
    "The Early Universe" chapter 4
-------------------------------------------------------------

initialize(T_init=1e1, T_final=1e-2, N_nu=3, tau_n=885.7):
input:
  T_init,T_final = temperature / MeV
  N_nu = number of neutrino generation
  tau_n = neutron mean lifetime / sec
```
# example code:
```
import matplotlib.pyplot as plt
from BBN import BBN

# all available indices
index = ['neutron', 'proton', 'deutron', 'tritium',
         'helium3', 'helium4', 'lithium7', 'beryllium7']
label = ['n', 'p', 'd', 't', r'He$^3$',
         r'He$^4$', r'Li$^7$', r'Be$^7$']

# initialize() has been executed with default arguments
T,X = BBN(5e-10, index, atol=1e-13)
plt.axis([2, 1e-2, 1e-13, 2])
plt.loglog(T, X.T)
plt.xlabel('T = temperature / MeV')
plt.ylabel('X = mass fraction')
plt.legend(label)
plt.show()
```
# example code:
```
import numpy as np
import matplotlib.pyplot as plt
from BBN import BBN,initialize

index = ['helium4']
label = [r'$N_{\nu}$ = 2', '3', '4']

eta = np.geomspace(3e-11, 1e-8, 50)
X = []
for N_nu in [2,3,4]:# number of neutrino generagion
    initialize(N_nu=N_nu)
    X1 = []
    for e in eta:
        T,X2 = BBN(e, index, 2, rtol=1e-6, atol=1e-9)
        X1.append(X2[0,-1])
        print(e, X1[-1])

    X.append(X1)

plt.axis([eta[0], eta[-1], 0.17, 0.27])
plt.semilogx(eta, np.asarray(X).T)
plt.xlabel(r'$\eta$ = baryon to photon ratio')
plt.ylabel(r'$X_4$ = mass fraction of He$^4$')
plt.legend(label, markerfirst=False)
plt.show()
```
transcription-burato 0.0.2
A simples program that transcribes dna
Requires Python: >=3.8

# transcription-burato

transcription
The package transcription-burato is used to:

- create RNA from DNA

## What is a transcription?

During the transcription process, the information encoded in the DNA sequence of one or more genes, adenine (A), cytosine (C), guanine (G) and thymine (T), is transcribed on an RNA strand, also called RNA transcription. The resulting single-stranded RNA molecule, composed of ribonucleotides containing the bases uracil (U),guanine (G), cytosine (C) and adenine (A)

![image](https://s3-us-west-2.amazonaws.com/courses-images/wp-content/uploads/sites/1094/2016/11/03164619/OSC_Microbio_11_03_TxnElong.jpg)

fonte [link](https://courses.lumenlearning.com/suny-microbiology/chapter/rna-transcription/)

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install transcription-burato

```bash
pip install transcription-burato
```

## Usage

``` python
from transcription_burato import io, process

gen = io.read(path_to_file)
result_gen = process.gen_from(gen)
io.plot(result_gen)
```

## Test with

you can find a genome to test the package at [ncbi](www.ncbi.nlm.nih.gov) or find by [organism](https://www.ncbi.nlm.nih.gov/assembly/organism/)

## Author

Otavio Burato de Oliveira

## License
[MIT](https://choosealicense.com/licenses/mit/)

## Project -> Making package creation uncomplicated

Inspired by a project by [dio](https://digitalinnovation.one)
conclusion [716EEBD3](https://certificates.digitalinnovation.one/716EEBD3)