blackcurve 0.3.1

An easy to use python interface for BlackCurve's API

BlackCurve Python API Library

API Documentation

Documentation for the BlackCurve endpoints can be found here.

Requirements

• Python 2.7+
• Requests HTTP library for python v2.0 or above.

Installation

$ pip install blackcurve

Basic Usage

Initiate a Connection

You just need your subdomain and a access token to get started

   from blackcurve.api import BlackCurveAPI	
   bc = BlackCurveAPI({{ subdomain }}, {{ access_token }})	

Reload a Access Token

Need a new access token, or just misplaced the old one?

   bc = BlackCurveAPI({{ subdomain }})
   token = bc.get_access_token({{ client_key }}, {{ client_secret }})
   print(token)	

This will also update your BlackCurveAPI instance with the new token so you can immediately carry on with requests.

Get Prices

Get a list of current Prices

   # get all the prices
   bc.prices().all()

   # get all prices
   prices = bc.prices().all()
   print('You have {} prices'.format(len(prices)))

   # get a price for a single product by Product ID
   bc.prices().find('UK42')

   # filter specific product columns
   bc.prices(columns=['Price', 'Product ID']).all()

   # filter geography
   bc.prices(geography='UK').all()

   # filter by column value -- price >= 5
   bc.prices(price_gte=5).all()

Data Sources Info

Get column and data type information about your data sources

   # get all the data sources
   data_sources = bc.data_sources_info().all()
   print(data_sources)

   # get a single data source
   sales_history = bc.data_sources_info().find('Sales History')
   print(sales_history)

   # create a new column 
   sales_history['New Order Column'] = 'Integer'
   sales_history.save()

   # delete a column
   del sales_history['New Order Column']
   sales_history.save()

Data Sources

Get a list of all of the data in a given source

   # get all of the data from sales history
   bc.data_sources('Sales History').all()

   # get just the volume and product id columns in sales history
   bc.data_sources('Sales History', columns=['Volume', 'Product ID']).all()

   # filter by column value -- price >= 5
   bc.data_sources('Sales History', price_gte=5).all()

   # get a generator for all the pages returned in sales history (lazy requests)
   sales_history = bc.data_sources('Sales History').all()
   page = 1
   for x in sales_history:
   	print('Page %s of Sales History: %s' % (page, x))
   	page += 1

   # get a Transactions system id
   sales_history = bc.data_sources('Sales History').all()
   first_sale = sales_history[0]
   first_sale_id = first_sale.id

   # find a transaction by a system id
   first_sale = bc.data_sources('Sales History').find(first_sale_id)

   # edit a column on a transaction
   first_sale['Price'] = 42.00
   first_sale.save()

   # create a new transaction
   sales_history = bc.data_sources('Sales History')
   sales_history.create({
       'Product ID': 'UK54321',
       'Profit': 7.77,
       'Revenue': 6.66,
       'Volume': 1,
       'Price': 3.33,
       'Transaction Date': datetime.date.today()
   })

   # get all transactions for a given product id
   transactions = bc.data_sources('Sales History', product_id='UK54321').all()
   print('There are {} transactions for product - UK54321'.format(len(transactions)))

   # change the price of a product [must use the kwarg product_id as .find() is only for system id] 
   product = bc.data_sources('Product Inventory', product_id='UK54321').all()[0]
   product['Price'] = 55.99
   product.save()

Geographies & Currencies

Get a list of associated data for Geographies and Currencies

    # get a list of all of the geography data
    all_geographies = bc.geographies().all()

    # get a specific geography
    website_uk = bc.geographies('Website UK').all()

    # get a list of all currencies
    all_currencies = bc.currencies().all()