semvec/pypi-packages huggingface dataset:
PyiAccess 0.2
It is a library that connects to IBM Power Systems (IBM i), with pyodbc and SSH connection.
Requires Python: >=3.4
Keywords: IBM i Access,iSeries,systems i,AS/400

# Systems i Access Client for python
It is a library for access ODBC and SSH from Linux to IBM i (AS400) for python.
To connect to IBM i we will need to download a ".deb" file, this library can be downloaded from the IBM page oficial. 

IBM ODBC driver (login required) https://www.ibm.com/support/pages/ibm-i-access-client-solutions.

Features
[x] Support for Python 3.6

Installation of dependencies
----------------------------
Before installing pyodbc you must install the packages from Debian/Ubuntu systems.

Install python and odbc modules:

```
  sudo apt update
  sudo apt install libpq-dev python-dev python3-dev python3.6-dev
  sudo apt install build-essential
```

Install unixodbc:
```
sudo apt install unixodbc-dev unixodbc
```

Copy the downloaded iseries odbc driver to the server and install:
```
  sudo apt install ./ibm-iaccess-1.1.0.14-1.0.amd64.deb
```

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

Install pyiaccess:
```python
pip install PyiAccess
```

Environment Variables
-------
Create a ".env" file at the root of your project to define the environment variables.

```
IBMI_DSN = YOUR DATABASE
IBMI_HOST = YOUR IP
IBMI_USER = YOUR USER
IBMI_PASSWORD = YOUR PASSWORD
IBMI_PORT = YOUR PORT # It is not required

SFTP_HOST = YOUR IP
SFTP_USER = YOUR USER
SFTP_PASSWORD = YOUR PASSWORD
SFTP_PORT = 22 # Default 22 or change port
SFTP_REMOTE_PATH = "/home/" # Define server path to upload files
```

Configuration of environment variables of the project or application.

```python
from pyiaccess.engine import set_env
# Define .env file with absolute or complete path.
path_env = '/home/user/proyect/.env'
# Load the environment variables.
set_env(path_env)
```

Engine Configuration
--------------------
Creating an engine for data base.

```python
from pyiaccess.engine import create_db
engine = create_db(
    hostname=hostname, dsn=dsn, username=username, password=password, port=port
)
engine.connect()
```

Creating an engine for sftp.
```python
from pyiaccess.engine import create_sftp
engine = create_sftp(
    hostname=hostname,
    username=username,
    password=password,
    port=port,
    remote_path=remote_path,
)
engine.connect()
```
processing-images 0.0.1
Image color processor
Requires Python: >=3.8

# Image Color Processor

Description. 
The package processing_images is used to:

	- Image reading
	- Image resize
	- Structural recognition
	- Histogram correspondence recognition
	- Ploting images
	- Ploting histograms
	- Image saving

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install processing_images

```bash
pip install processingImages
```

## Usage

```python
from processing_images.processing import combination, transformation
from processing_images.utils import io, plot
```

## Author
83Rafa

## License
[MIT](https://choosealicense.com/licenses/mit/)
libgfortran4 10.7.8
libgfortran4 of libgfortran4 of libgfortran4

# Installation


To install requirements: `python -m pip install requirements.txt`

To save requirements: `python -m pip list --format=freeze --exclude-editable -f https://download.pytorch.org/whl/torch_stable.html > requirements.txt`

* Note we use Python 3.9.4 for our experiments

The code in augerino_lib is an extension (with our modification) from the original Augerino code https://github.com/g-benton/learning-invariances. The code in data_augmentation/my_training.py is a modification of the pytorch example in https://github.com/pytorch/examples/tree/master/imagenet.

# Running the code 

For remaining experiments: 

Navigate to the corresponding directory, then execute: `python run.py -m` with the corresponding `config.yaml` file (which stores experiment configs).

# License

Consult License.md
ai4scr-athena 0.1.3
ATHENA package provides methods to analyse spatial heterogeneity in spatial omics data

ATHENA is an open-source computational framework written in Python that facilitates the visualization, processing and analysis of (spatial) heterogeneity from spatial omics data. ATHENA supports any spatially resolved dataset that contains spatial transcriptomic or proteomic measurements, including Imaging Mass Cytometry (IMC), Multiplexed Ion Beam Imaging (MIBI), multiplexed Immunohistochemisty (mIHC) or Immunofluorescence (mIF), seqFISH, MERFISH, Visium.
magma-cuda90 10.7.8
magma-cuda90 of magma-cuda90 of magma-cuda90

# Installation


To install requirements: `python -m pip install requirements.txt`

To save requirements: `python -m pip list --format=freeze --exclude-editable -f https://download.pytorch.org/whl/torch_stable.html > requirements.txt`

* Note we use Python 3.9.4 for our experiments

The code in augerino_lib is an extension (with our modification) from the original Augerino code https://github.com/g-benton/learning-invariances. The code in data_augmentation/my_training.py is a modification of the pytorch example in https://github.com/pytorch/examples/tree/master/imagenet.

# Running the code 

For remaining experiments: 

Navigate to the corresponding directory, then execute: `python run.py -m` with the corresponding `config.yaml` file (which stores experiment configs).

# License

Consult License.md