semvec/pypi-packages huggingface dataset:
jsondler 0.0.4
JSON handler for Python

# jsondler

JSON handler for Python

## Installation

To install the package from PyPI type:

```
pip install jsondler
```

Or from the repo:

```
pip install git+https://github.com/loven-doo/jsondler.git
```

For 'dev' branch version:

```
pip install git+https://github.com/loven-doo/jsondler.git@dev
```

## Package reference

### DictsList

### json_tools
bottle-cors 0.1.5
CORS plugin for Bottle framework

# bottle-cors

Simple plugin to easily enable [CORS](https://www.moesif.com/blog/technical/cors/Authoritative-Guide-to-CORS-Cross-Origin-Resource-Sharing-for-REST-APIs/) 
support in [Bottle](https://bottlepy.org/) routes.

### Example
```python
from bottle import Bottle, request, run
from truckpad.bottle.cors import CorsPlugin, enable_cors

app = Bottle()

@app.get('/')
def index():
    """
    CORS is disabled for this route
    """
    return "cors is disabled here"

@enable_cors
@app.get('/endpoint_1')
def endpoint_1():
    """
    CORS is enabled for this route. 
    OPTIONS requests will be handled by the plugin itself
    """
    return "cors is enabled, OPTIONS handled by plugin"

@enable_cors
@app.route('/endpoint_2', method=['GET', 'POST', 'OPTIONS'])
def endpoint_2():
    """
    CORS is enabled for this route. 
    OPTIONS requests will be handled by *you*
    """
    if request.method == 'OPTIONS':
        # do something here?
        pass
    return "cors is enabled, OPTIONS handled by you!"

app.install(CorsPlugin(origins=['http://list.of.allowed.domains.com', 'https://another.domain.org']))

run(app)

```
BinHexDecOctConversions 1.2
A package that converts between binary, hexadecimal, decimal and octal.

# Binary/Hexadecimal/Decimal/Octal Conversion
This module allows you to convert binary, hexadecimal, decimal and octal (base16, base2, base10 and base8) numbers between one another.

The functions setup within this module are as follows:
#### Conversion from binary
```python
binHex(number)
binDec(number)
binOct(number)
```

#### Conversion from hexadecimal
```python
hexBin(number)
hexDec(number)
hexOct(number)
```

#### Conversion from decimal
```python
decBin(number)
decHex(number)
decOct(number)
```

#### Conversion from octal
```python
octBin(number)
octHex(number)
octDec(number)
```
###
##### How to use the functions properly
1. **Conversion from binary** - You do not need to have at least 8 bits of information and you can have more than 8.
2. **Conversion from hexadecimal** - You do not need to have "0x" before the hexadecimal value.
3. **Conversion from decimal** - You can enter any integer you want.
4. **Conversion from octal** - You do not have to have "0o" before the octal value.
projest 0.0.7
A small example package

# Projest

Made by [Esther Vendrell]

Last updated: July 2019

## Introduction
Projest is my first program. Consist in introduce the name of a protein and obtain information about that as the most similar protein, the differents models it has or the atoms. All the information you can obtain comes from: https://www.rcsb.org/

## Description
The main file is protein.py. In spite of main functions are in extract_atoms_information.py - load protein pdb or load protein fasta - the rest of functions are in protein. 
In this way we will have functions directly linked to protein like:
Protein("2ki5").get_similar_protein() -> We will obtain fasta file, pdb file, all the information of protein in mongodb (in the init) and the similar protein of each chain. 

## Functions
There are 7 functions:
  - get_sequence_aminoacids: in Protein. Returns a list with chain and the sequence of aminoacids (1 letter). From fasta file.
  - get_chain_list: in Protein. Return a list with the differents chains there are.
  - get_aminoacid_list: in Protein. Return a list with the differents aminoacids (3 letters) with the differents sequence numbers classified in chains
  - get_similar_protein: in Protein. Return a dictionary with differents chains and the most similar protein of that chain of the protein.
  - general_dictionary: in Protein. Any return. It inserts in mongodb all the information classified. It is not callable.
  - load_protein_pdb: in extract_atoms_information. Returns the pdb file of the protein. Download the pdb file of the protein in the same directory of the files. 
  - load_protein_fasta: in extract_atoms_information. Returns the fasta file of the protein.Download the fasta file of the protein in the same directory of the files. 

## MongoDB
I could have used an other structure to upload all the information to MongoDB:
Protein - Model - residue_sequence_number - atom_number - information atom. 

Class Model:
    def __init__(self, model_identifier, model_dict):
        self.model_identifier = model_identifier
        self.chain_list = []
        for chain_identifier, value in model_dict:
            self.chain_list.append(Chain(chain_identifier))

In this way we obtain information from mongodb. This would be easier but I haven't had enough time since I have realized.

The structure of my project is:
Portein - Model - residue_name - residue_sequence_number - element_name - atom_name - information atom. 

This is more easy to understand the information we are getting and classified it. This is why I have used this structure. Visually is better but for obtaining this information internally it is worse.