semvec/pypi-packages huggingface dataset:
ctg-data-helpers 0.0.1
A helpful module for downloading, processing and ingesting data
Requires Python: >=3.6

# CTG-Data-Helpers
This directory contains modules we developed to help with data processing, downloading and ingesting. 

Required dependencies:
- `logging`
- `os`
- `pathlib`
- `hashlib`
- `pkgutil`
- `io`
- `zipfile`
- `tempfile`
- `pandas`
- `requests`
- `sqlalchemy`
- `psycopg2-binary`

## Download Functions
The first set of functions in the module assist with downloading data

`get_path()` - A string representing the path to the file or directory. If a path is provided, will return that path. If a path is not provided, will provide a new path based on a temp directory and the hash of the URL.

`make_path()` - If a path doesn't exist, `get_path()` internally calls this function to build a new path. (Essentially a wrapper function around `os.mkdir` to catch errors)

`hash_url()` - Hashes the given download URL to help build the temp path if none has been given. 

`pure_download()` - Downloads the data from the URL with the capacity to handle zip files if specified. 

`download_data_cache()` - Checks before calling `pure_download()` to see if the data has previously been downloaded. If the data is already in the cache, the function just returns the path to that data, without downloading it a second time. 

## Pandas Functions
The second set of functions in the module assist with setting up the data in Pandas DataFrames for easier processing

`setup_df()` - Given a set of specified parameters, this function ingests the CSV to a DataFrame. Note: `**cli_options` is integrated with our click-based command-line-interfaces. 


## PostgreSQL Functions
These functions help with setting up the database, confirming a successful upload, and uploading the data itself

`get_engine()` - Given the set environmental variables, returns an engine to connect to the database. 

`confirm_table()` - After data has been ingested, confirms that the data was successfully uploaded to the database. 

`upload_data()` - Uploads the data of a specific DataFrame to the database given some predetermined variables

`has_primary_key()` - Checks to see if an existing table has a primary key

`set_primary_keys()` - Given a table name and a list of columns for a primary key, this function creates the primary key. 

## Extension Functions
`build_plugins_map()` - This function helps dynamically build a plugin map when using this code in a command-line environment
apricopt-synthesis 0.0.1a1.dev1
A python library
Requires Python: >=3.6

# APriCOPT#

APriCOPT (Automatic Personalized Combination Therapy Optimizer) is a python library and standalone program for the synthesis of personalized therapies on biological models given as black-boxes, with a focus on SBML models.

It requires little to no programming experience, as it uses [PEtab](https://petab.readthedocs.io/en/latest/) for describing the problem and providing all the needed information.

APriCOPT natively supports [NOMAD](https://www.gerad.ca/nomad/) for black-box optimization, but any solver can be used by implementing a simple API.

### SBML models

If the biological model is described in the SBML format, APriCOPT provides immediate ways to setup the therapy synthesis.

Currently, we support the following simulators of SBML models:

* COPASI. A state-of-the-art simulator for models of biological processes. We support any level and version of SBML.



### Info ###

WIP

* Version 0.0.1a1.dev1


### Who do I talk to? ###

* Marco Esposito (author) - esposito@di.uniroma1.it
* Leonardo Picchiami (author) - picchiami.1643888@studenti.uniroma1.it


Copyright (C) 2020-2021  Marco Esposito, Leonardo Picchiami.

Distributed under GNU General Public License v3.