Simplified Python Script for Optimizing Image Batching in Blockchain Transactions

Introduction

The purpose of this script is to assist healthcare developers in optimizing the number of medical images they inject into a blockchain per transaction batch. By batching images, they aim to reduce transaction costs (gas fees) and improve efficiency.

This script evaluates different batch sizes and estimates the associated costs using provided metrics and weights. With the given inputs and our defined cost function, the script will suggest an optimal number of images per batch to minimize costs.

Simplified Python Script

import numpy as np

def compute_gas_fee_for_batch(n):
    """
    Placeholder function to compute gas fee for a batch of n images.
    Replace with an actual function to compute gas fees from your blockchain network.
    """
    return n * 10  # Sample logic, adjust as required

def compute_cost_per_image(n):
    """
    Compute the cost efficiency for a given batch size.
    """
    total_fee = compute_gas_fee_for_batch(n)
    return total_fee / n

def find_optimal_batch_size(min_batch, max_batch):
    """
    Evaluate different batch sizes to find the most cost-efficient one.
    """
    costs = [compute_cost_per_image(n) for n in range(min_batch, max_batch+1)]
    optimal_batch_size = np.argmin(costs) + min_batch
    return optimal_batch_size

# Sample usage:
min_images = 1
max_images = 100  # Just a sample value; adjust as necessary based on the use case

optimal_size = find_optimal_batch_size(min_images, max_images)
print(f"Optimal number of images per transaction batch: {optimal_size}")

Explanation

• compute_gas_fee_for_batch(n): This function is a placeholder. It's supposed to compute the gas fee for a batch of n images. The current logic is just a sample (every image costs 10 units of gas), and this function should be replaced with one that fetches actual gas fees from your blockchain network.

• compute_cost_per_image(n): This function computes the average gas fee for each image in a batch of size n.

• find_optimal_batch_size(min_batch, max_batch): This function evaluates different batch sizes in the provided range (from min_batch to max_batch) and finds the size that has the lowest average cost per image.

The sample usage at the end runs the script, evaluates batch sizes from 1 to 100, and then prints out the optimal batch size. Adjust the range (min_images and max_images) as needed.

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