Cluster MIBs part 2 (python) by dabiri
Related: Fishing
Description: This is part 2 of the design to cluster SOSs geographically- python not razor
#This is python not razor! It expects your journal file in a folder named 'journal' and it will return a razor script that can be run once you're back in game to target the box of all SOS's, then target each individual bag, then it moves each mib into its cluster appropriate bag. Enjoy and good luck! Depending on your programmin experience this is perhaps not for the faint of heart :) import os import re import glob import numpy as np from sklearn.metrics import pairwise_distances batch_size = 10 def parse_log_file(file_path): sos_data = [] coordinates_pattern = r'\[Razor\]: a waterstained SOS message \(located at (\d+), (\d+)\)' serial_pattern = r'\[Razor\]: Added (\d+) to ignore list' with open(file_path, 'r') as file: lines = file.readlines() i = 0 while i < len(lines): coord_match = re.search(coordinates_pattern, lines[i]) if coord_match: x, y = coord_match.groups() # Look for the serial number in the next few lines for j in range(i+1, min(i+5, len(lines))): serial_match = re.search(serial_pattern, lines[j]) if serial_match: serial = serial_match.group(1) sos_data.append({ 'coordinates': (int(x), int(y)), 'serial': serial }) break i += 1 return sos_data def is_in_big_blue(coord): x, y = coord return 1600 <= x <= 4000 and 750 <= y <= 3000 def ball_clustering(sos_data, batch_size): coordinates = np.array([entry['coordinates'] for entry in sos_data]) # Separate points into Big Blue and outside big_blue_indices = [i for i, coord in enumerate(coordinates) if is_in_big_blue(coord)] outside_indices = [i for i, coord in enumerate(coordinates) if not is_in_big_blue(coord)] # Function to cluster a set of indices def cluster_indices(indices): clusters = [] unclustered = indices.copy() while unclustered: start_point = unclustered.pop(0) current_cluster = [start_point] if unclustered: distances = pairwise_distances([coordinates[start_point]], coordinates[unclustered])[0] closest_indices = np.argsort(distances)[:min(batch_size - 1, len(distances))] for idx in sorted(closest_indices, reverse=True): point_idx = unclustered.pop(idx) current_cluster.append(point_idx) clusters.append([sos_data[i] for i in current_cluster]) return clusters # Cluster Big Blue and outside separately big_blue_clusters = cluster_indices(big_blue_indices) outside_clusters = cluster_indices(outside_indices) # Combine and return all clusters return big_blue_clusters + outside_clusters def cluster_coordinates(sos_data, batch_size): return ball_clustering(sos_data, batch_size) def generate_razor_script(batches): script = [] # Add header script.append("// You will be targeting {} bags".format(len(batches))) script.append("") script.append("overhead 'Target main box'") script.append("@setvar main_box") script.append("") # Add bag targeting instructions for i in range(len(batches)): script.append(f"overhead 'Target bag {i+1}'") script.append(f"@setvar bag_{i+1}") script.append("") # Process each batch for i, batch in enumerate(batches): script.append(f"//Process bag_{i+1}") for entry in batch: script.append(f"lift {entry['serial']}") script.append(f"drop bag_{i+1} -1 -1 0") script.append("pause 650") script.append("") # Join all lines and return return "\n".join(script) def generate_map_pin_files(batches): output_folder = './output' os.makedirs(output_folder, exist_ok=True) for i, batch in enumerate(batches): filename = os.path.join(output_folder, f'bag_{i+1}.txt') with open(filename, 'w') as f: for entry in batch: x, y = entry['coordinates'] name = f"{x}/{y}" line = f"+\t{name}\t{x}\t{y}\t7\tfalse\n" f.write(line) print(f"Map pin file for bag {i+1} has been written to {filename}") # Find the most recent log file in the 'journal' folder journal_folder = './journal' list_of_files = glob.glob(os.path.join(journal_folder, '*_journal.txt')) if not list_of_files: print("No journal files found.") exit() latest_file = max(list_of_files, key=os.path.getctime) # Parse the log file sos_data = parse_log_file(latest_file) batches = cluster_coordinates(sos_data, batch_size) # Print the results for i, batch in enumerate(batches): print(f"Batch {i+1}:") for entry in batch: print(f" Coordinates: {entry['coordinates']}, Serial: {entry['serial']}") print() # After clustering razor_script = generate_razor_script(batches) # Write to file output_folder = './output' os.makedirs(output_folder, exist_ok=True) output_file = os.path.join(output_folder, 'process_bags.razor') with open(output_file, 'w') as f: f.write(razor_script) print(f"Razor script has been written to {output_file}") # After clustering and generating the Razor script generate_map_pin_files(batches) print('\n\n~~~Map pin files have been generated~~~\n\n') print ('\n~~~All done~~~\n')
