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Parse the shopping list generated by makeplace and get prices and best locations to plan your shopping trip efficiently!

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Makeplace in the Sheets: a python script to grab the lowest prices for your makeplace shopping list and help you plot your shopping journey! Choose how frugal you want to be with the Priority datacenter option!

This script parses a shopping-list.txt file from makeplace and outputs the data to a CSV file. It uses the requests library to access the APIs at https://xivapi.com/docs and https://docs.universalis.app/.

Features

  • Parse a list.txt file and output the data to a CSV file
  • Take input from a file or from a pipe
  • Show a help message with all available options and examples
  • Show progress messages in the console using built-in colors
  • Set datacenters for searching the Universalis API (defaulting to Primal, Aether, Crystal, and Dynamis)
  • Set a rate limit for API request speed (defaulting to 10 requests per second on each API)
  • Set a priority datacenter (defaulting to Primal)
  • Set a fuzzy rate (defaulting to 90000)

Usage

You can run this script using the command python script.py [-o] [--options] -f [inputfile.txt] [outputfile.csv]. If you want to take input from a pipe instead of a file, you can use the -p or --pipe option. The -h or --help option will show a help message with all available options and examples. The -v or --verbose option will show progress messages in the console using built-in colors.

Configuration

There is a config section at the beginning of the script where you can specify the datacenters for searching the Universalis API (defaulting to Primal, Aether, Crystal, and Dynamis), a rate limit for API request speed (defaulting to 10 requests per second on each API), a priority datacenter (defaulting to Primal), and a fuzzy rate (defaulting to 90000).

The priority datacenter feature allows you to specify which datacenter should be given priority when searching for market data on the Universalis API. If the lowest listing from the priority datacenter is less than the fuzzy rate above the lowest listing from any other datacenter, then that listing will be used. Otherwise, the listing with the lowest price from any datacenter will be used.

Dependencies

This script requires the requests library. You can install it using the command pip install requests.Makeplace Shopping Trip This script parses a shopping-list.txt file from makeplace and outputs the data along with the lowest price and location on the marketboards to a CSV file. It uses the requests library to access the APIs at https://xivapi.com/docs and https://docs.universalis.app/.

Features

Parse a list.txt file and output the data to a CSV file Take input from a file or from a pipe Show a help message with all available options and examples Show progress messages in the console using built-in colors Set datacenters for searching the Universalis API (defaulting to Primal, Aether, Crystal, and Dynamis) Set a rate limit for API request speed (defaulting to 10 requests per second on each API) Set a priority datacenter (defaulting to Primal) Set a fuzzy rate (defaulting to 90000) Usage

You can run this script using the command python script.py [-o] [--options] -f [inputfile.txt] [outputfile.csv]. If you want to take input from a pipe instead of a file, you can use the -p or --pipe option. The -h or --help option will show a help message with all available options and examples. The -v or --verbose option will show progress messages in the console using built-in colors.

Configuration

There is a config section at the beginning of the script where you can specify the datacenters for searching the Universalis API (defaulting to Primal, Aether, Crystal, and Dynamis), a rate limit for API request speed (defaulting to 10 requests per second on each API), a priority datacenter (defaulting to Primal), and a fuzzy rate (defaulting to 90000).

Dependencies

This script requires the requests library. You can install it using the command pip install requests

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Parse the shopping list generated by makeplace and get prices and best locations to plan your shopping trip efficiently!

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