dataaetherscan 1.0.0

Python integration to DataAetherScan

DataAetherScan

dataaetherscan is a Python package that facilitates interaction with the DataAetherScan API. It provides methods for authentication and access to various API resources such as users, channels, jobs, and credits.

Installation

Install the package using pip with the following command:

pip install dataaetherscan

Usage

First, you need to create a DataAetherScan object with your credentials:

from dataaetherscan import DataAetherScan

das = DataAetherScan("[e-mail]", "yourPassword")

Authentication

Authentication is automatically performed upon instantiation:

das.authenticate()

API Methods

After successful authentication, you can access various API endpoints:

Retrieve User Information

user_info = das.get_user()
print(user_info)

Retrieve Channel List

channels = das.get_channels()
print(channels)

Retrieve Credit Information

credit_info = das.get_credit()
print(credit_info)

Retrieve and Manage Jobs

• Retrieve all jobs:

  jobs = das.get_jobs()
  print(jobs)
  

• Retrieve a specific job:

  job = das.get_job(job_id)
  print(job)
  

• Create a new job:

  job_data = {'name': 'Job1', 'description': 'Data processing'}
  new_job = das.create_job(job_data)
  print(new_job)
  

• Delete a job:

  response = das.delete_job(job_id)
  print(response)
  

Error Handling

The methods can raise a ValueError if there are issues with the API request. Make sure to handle your requests within try-except blocks:

try:
    user_info = das.get_user()
except ValueError as e:
    print("An error occurred:", e)

---

Creating a Job with DataAetherScan

This section outlines how to properly configure and send a request to create a job using the dataaetherscan API. A job is a task submitted to the API that involves tracking certain products across different channels and countries.

Job Data Structure

To create a job, you need to specify details about the channels, country codes, and products. Here’s the structure of the data required:

job_data = {
    "channel": "Google",
    "country_code": "DE",
    "products": [
        {
            "sku": 1,
            "gtin_ean": "761856508385"
        }
    ]
}

Parameters

• channel: (string) The name of the channel where the products will be tracked.
• country_code: (string) The ISO country code where the channel is located.
• products: (list of dictionaries) A list of products to be tracked, each with specific attributes.

Product Attributes

Each product dictionary may contain the following fields:

• sku: (string, required) The stock keeping unit or identifier for the product.
• gtin_ean: (string, optional) The global trade item number or European article number of the product.
• title: (string, optional) The title or name of the product.

Validation Rules

The job creation involves several validation steps to ensure the integrity of the data:

1. Product Validation:

   • At least one of gtin_ean or title must be present along with sku.
   • If neither gtin_ean nor title is provided, a validation error will be raised, indicating that at least one of these fields is required.

2. Job Serializer Validation:

   • Products must be provided as a list of dictionaries.
   • An empty list of products is not allowed.
   • Each product's data is validated individually.

Creating a Job

To create a job, use the create_job method of your DataAetherScan instance:

das = DataAetherScan("[e-mail]", "yourPassword")
try:
    new_job = das.create_job(job_data)
    print("Job created successfully:", new_job)
except ValueError as e:
    print("An error occurred while creating the job:", e)

Error Handling

Handle potential errors during the job creation process to manage situations where the input data might not meet the API requirements:

• Use try-except blocks to catch exceptions, particularly ValueError, which indicates a problem with the job data or an API request failure.

This guide provides a comprehensive overview of the requirements and steps to create a job using the dataaetherscan package, ensuring that all data complies with the underlying API's validation rules.

Contributing

Improvements and pull requests are welcome! Please ensure that you follow existing code styles and practices.