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Create Voyager_log_MetaGPT_statistical_caliber.py
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向劲宇
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382
Minecraft plot/Voyager_log_MetaGPT_statistical_caliber.py
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382
Minecraft plot/Voyager_log_MetaGPT_statistical_caliber.py
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import re
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import argparse
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import os
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import matplotlib.pyplot as plt
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from mpl_toolkits.mplot3d import Axes3D
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import math
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import numpy as np
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import random
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import seaborn as sns
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import matplotlib as mpl
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import pandas as pd
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mpl.rcParams.update(mpl.rcParamsDefault)
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def extract_logs(filename, start_time=None, end_time=None):
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with open(filename, 'r',encoding='utf-8') as f:
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lines = f.readlines()
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if start_time is None :
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# 如果没有提供时间参数，则返回所有日志
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return lines
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# 截取开始时间和停止时间之间的日志块
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logs_block = []
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capture = False
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for line in lines:
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if start_time in line:
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capture = True
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if capture:
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logs_block.append(line)
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if end_time and end_time in line:
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capture = False
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break
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return logs_block
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def extract_time_from_first_round_zero(log_filename):
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with open(log_filename, 'r',encoding='utf-8') as f:
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lines = f.readlines()
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# 反向遍历文件的每一行
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for line in reversed(lines):
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if "****Recorder message:" in line:
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if "iteration passed****" in line:
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round_number = re.search(r'(\d+) iteration passed', line).group(1)
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return round_number
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return None
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"""def analyze_log_block(logs_block):
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rounds: list[int] = []
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items_collected: list[int] = []
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total_items = 0
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positions:list[(int, int, int)] = []
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completed_tasks: list[int] = []
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failed_tasks: list[int] = []
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items_variety_collected :list[int] = []
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items_collected_dict :list[dict] = []
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line_counter = 0 # 用于计数
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round_counter = 0 # 用于计数轮数
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for line in logs_block:
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if "****Curriculum Agent human message****" in line:
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line_counter = 0 # 当找到新的 "****Curriculum Agent human message****" 时重置计数器
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round_counter += 1 # 每次找到 "****Curriculum Agent human message****"，轮数加1
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rounds.append(round_counter) # 将新的轮数添加到轮数列表中
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continue
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if line_counter < 50:
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if "Position: x=" in line:
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match = re.search(r'Position: x=([\d.-]+), y=([\d.-]+), z=([\d.-]+)', line)
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x, y, z = float(match.group(1)), float(match.group(2)), float(match.group(3))
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positions.append((x, y, z))
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if "Inventory (" in line:
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items = re.search(r'Inventory \(\d+/36\): ({.*?})', line).group(1)
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items_dict = eval(items)
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items_collected_dict.append(items_dict) # 将这一轮结束时的物品存储状态添加到列表中
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total_items = sum(items_dict.values())
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items_collected.append(total_items)
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items_variety_collected.append(len(items_dict)) # 统计物品种类数量
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if "Completed tasks so far:" in line:
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tasks = line.replace("Completed tasks so far:", "").strip().split(", ")
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completed_tasks.append(0 if tasks == ['None'] else len(tasks))
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if "Failed tasks that are too hard:" in line:
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tasks = line.replace("Failed tasks that are too hard:", "").strip().split(", ")
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failed_tasks.append(0 if tasks == ['None'] else len(tasks))
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line_counter += 1 # 每处理一行就将计数器加1"""
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def analyze_log_block(logs_block):
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rounds: list[int] = []
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items_collected: list[int] = []
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total_items = 0
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positions:list[(int, int, int)] = []
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completed_tasks: list[int] = []
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failed_tasks: list[int] = []
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items_variety_collected :list[int] = []
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items_collected_dict :list[dict] = []
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biomes: list[str] = [] # 存储所有出现过的生物群系
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biomes_per_round: list[list[str]] = [] # 存储每轮结束时已经经历过的生物群系
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new_biome_rounds: list[int] = [] # 存储添加新生物群系的轮次
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processing_block = False # 用于标记是否正在处理一个块
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round_counter = 0 # 用于计数轮数
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for line in logs_block:
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if "****Curriculum Agent human message****" in line:
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processing_block = True # 当找到新的 "****Curriculum Agent human message****" 时开始处理块
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round_counter += 1 # 每次找到 "****Curriculum Agent human message****"，轮数加1
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rounds.append(round_counter) # 将新的轮数添加到轮数列表中
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continue
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if "****Curriculum Agent ai message****" in line:
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processing_block = False # 当找到 "****Curriculum Agent ai message****" 时停止处理块
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continue
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if processing_block: # 如果正在处理一个块，则执行以下操作
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if "Position: x=" in line:
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match = re.search(r'Position: x=([\d.-]+), y=([\d.-]+), z=([\d.-]+)', line)
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x, y, z = float(match.group(1)), float(match.group(2)), float(match.group(3))
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positions.append((x, y, z))
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if "Inventory (" in line:
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items = re.search(r'Inventory \(\d+/36\): ({.*?})', line).group(1)
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items_dict = eval(items)
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items_collected_dict.append(items_dict) # 将这一轮结束时的物品存储状态添加到列表中
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total_items = sum(items_dict.values())
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items_collected.append(total_items)
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items_variety_collected.append(len(items_dict)) # 统计物品种类数量
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if "Biome: " in line:
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biome = line.replace("Biome: ", "").strip()
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if biome not in biomes:
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biomes.append(biome)
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new_biome_rounds.append(rounds[-1]) # 记录添加新生物群系的轮次
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biomes_per_round.append(biomes.copy()) # 记录当前已经经历过
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if "Completed tasks so far:" in line:
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tasks = line.replace("Completed tasks so far:", "").strip().split(", ")
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completed_tasks.append(0 if tasks == ['None'] else len(tasks))
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if "Failed tasks that are too hard:" in line:
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tasks = line.replace("Failed tasks that are too hard:", "").strip().split(", ")
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failed_tasks.append(0 if tasks == ['None'] else len(tasks))
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min_len: int = min(len(rounds), len(items_collected), len(positions), len(completed_tasks), len(failed_tasks))
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return rounds[:min_len], items_collected[:min_len], items_variety_collected[:min_len], items_collected_dict[:min_len], positions[:min_len], completed_tasks[:min_len], failed_tasks[:min_len], biomes, biomes_per_round[:min_len], new_biome_rounds
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def save_item_results_png(rounds, items_collected, items_collected_dict, start_time, path_prefix):
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items_collected_total = {}
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items_collected_total_list = []
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for round_index in range(len(rounds)):
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for item, quantity in items_collected_dict[round_index].items():
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if item in items_collected_total:
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items_collected_total[item] = max(items_collected_total[item], quantity)
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else:
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items_collected_total[item] = quantity
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items_collected_total_list.append(sum(items_collected_total.values()))
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print("总数",items_collected_total_list)
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plt.figure(figsize=(10, 5))
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plt.plot(rounds, items_collected_total_list, label="Items Collected")
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plt.xlabel("# of Rounds")
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plt.ylabel("Total Items Collected")
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plt.title("Items Collected Over Rounds")
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plt.grid(True)
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plt.legend()
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ax = plt.gca()
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ax.xaxis.set_major_locator(plt.MaxNLocator(integer=True))
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ax.yaxis.set_major_locator(plt.MaxNLocator(integer=True))
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ax.spines['top'].set_visible(False)
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ax.spines['right'].set_visible(False)
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ax.spines['bottom'].set_visible(False)
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ax.spines['left'].set_visible(False)
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ax.grid(color='gray', linestyle='-', linewidth=0.3, alpha=0.2)
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# 设置x轴和y轴的箭头
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ax.annotate('', xy=(1, 0), xycoords='axes fraction', xytext=(0, 0),
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arrowprops=dict(arrowstyle="->", color='black'))
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ax.annotate('', xy=(0, 1), xycoords='axes fraction', xytext=(0, 0),
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arrowprops=dict(arrowstyle="->", color='black'))
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plt.savefig(f'{path_prefix}/{start_time}_items_collected_over_rounds.png', dpi=300)
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plt.close()
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# 物品种类数量png
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plt.figure(figsize=(10, 5))
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plt.xlabel("# of Rounds")
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plt.ylabel("Total Variety of Items Collected")
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plt.title("Variety of Items Collected Over Rounds")
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plt.grid(True)
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special_items = ['wood', 'crafting_table', 'wooden_pickaxe', 'wooden_sword', 'wooden_axe', 'wooden_shovel', 'wooden_hoe',
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'stone', 'furnace', 'stone_pickaxe', 'stone_sword', 'stone_axe', 'stone_shovel', 'stone_hoe',
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'iron_ingot', 'iron_pickaxe', 'iron_sword', 'iron_axe', 'iron_shovel', 'iron_hoe',
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'diamond', 'diamond_pickaxe', 'diamond_sword', 'diamond_axe', 'diamond_shovel', 'diamond_hoe']
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collected_items_set = set()
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items_variety_collected = []
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for i in range(0, len(items_collected_dict)):
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current_round_items_set = set(items_collected_dict[i])
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diff_items = list(current_round_items_set - collected_items_set)
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collected_items_set.update(current_round_items_set)
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items_variety_collected.append(len(collected_items_set))
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if diff_items:
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for j, item in enumerate(diff_items):
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# 判断物品类型，选择不同的背景颜色
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if item in special_items:
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color = 'black'
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bgcolor = 'orange'
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else:
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color = 'black'
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bgcolor = 'green'
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# 避免文字重叠，通过调整 y 坐标的值
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y = items_variety_collected[i]-1-1*j
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plt.text(rounds[i], y, item, fontsize=4, color=color, ha='center', va='top',
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bbox=dict(boxstyle='round,pad=0.5', fc=bgcolor, alpha=0.5))
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"""for i in range(0, len(items_collected_dict)):
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current_round_items_set = set(items_collected_dict[i])
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diff_items = list(current_round_items_set - collected_items_set)
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collected_items_set.update(current_round_items_set)
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items_variety_collected.append(len(collected_items_set))
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if diff_items:
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for j, item in enumerate(diff_items):
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color = 'red' if item in special_items else 'black'
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plt.text(rounds[i], items_variety_collected[i] - 1 - j * 0.4, item, fontsize=8, color=color, ha='center', va='top',
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bbox=dict(boxstyle='round,pad=0.5', fc='yellow', alpha=0.5))"""
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plt.plot(rounds, items_variety_collected, label="Variety of Items Collected")
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plt.legend()
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ax = plt.gca()
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# 设置坐标显示为整数
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ax.xaxis.set_major_locator(plt.MaxNLocator(integer=True))
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ax.yaxis.set_major_locator(plt.MaxNLocator(integer=True))
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# 移除所有边框
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ax.spines['top'].set_visible(False)
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ax.spines['right'].set_visible(False)
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ax.spines['bottom'].set_visible(False)
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ax.spines['left'].set_visible(False)
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# 设置网格线颜色和透明度
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ax.grid(color='gray', linestyle='-', linewidth=0.3, alpha=0.2)
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# 设置x轴和y轴的箭头
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ax.annotate('', xy=(1, 0), xycoords='axes fraction', xytext=(0, 0),
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arrowprops=dict(arrowstyle="->", color='black'))
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ax.annotate('', xy=(0, 1), xycoords='axes fraction', xytext=(0, 0),
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arrowprops=dict(arrowstyle="->", color='black'))
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plt.savefig(f'{path_prefix}/{start_time}_variety_items_collected_over_rounds.png', dpi=600)
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plt.close()
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def save_path_results_png(positions, start_time, path_prefix):
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x_coords = [pos[0] for pos in positions]
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y_coords = [pos[1] for pos in positions]
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z_coords = [pos[2] for pos in positions]
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# 计算总里程数
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total_distance = 0
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for i in range(1, len(positions)):
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dx = positions[i][0] - positions[i - 1][0]
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dy = positions[i][1] - positions[i - 1][1]
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dz = positions[i][2] - positions[i - 1][2]
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distance = math.sqrt(dx ** 2 + dy ** 2 + dz ** 2)
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total_distance += distance
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# 3D path png
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fig = plt.figure(figsize=(10, 10))
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ax = fig.add_subplot(111, projection='3d')
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ax.plot(x_coords, y_coords, z_coords, '-o', color='blue', markersize=4)
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ax.set_title("Bot Movement Path in 3D")
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ax.set_xlabel("X Coordinate")
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ax.set_ylabel("Y Coordinate")
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ax.set_zlabel("Z Coordinate")
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ax.text(min(x_coords), max(y_coords), max(z_coords), f"Total Distance: {total_distance:.2f} units", fontsize=15, color='red')
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ax.plot(x_coords[0], y_coords[0], z_coords[0], 'ro') # start point with red color
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ax.plot(x_coords[-1], y_coords[-1], z_coords[-1], 'go') # end point with green color
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plt.savefig(f'{path_prefix}/{start_time}_bot_movement_3D_path.png', dpi=300)
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plt.close()
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def save_task_results_png(rounds , completed, failed, start_time, path_prefix):
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plt.plot(rounds, completed, label='Completed Tasks')
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plt.plot(rounds, failed, label='Failed Tasks')
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plt.xlabel("# of Rounds")
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plt.ylabel("Number of Tasks")
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plt.title("Completed vs Failed Tasks per Round")
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plt.legend()
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ax = plt.gca()
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# 设置坐标显示为整数
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ax.xaxis.set_major_locator(plt.MaxNLocator(integer=True))
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ax.yaxis.set_major_locator(plt.MaxNLocator(integer=True))
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# 移除所有边框
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ax.spines['top'].set_visible(False)
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ax.spines['right'].set_visible(False)
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ax.spines['bottom'].set_visible(False)
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ax.spines['left'].set_visible(False)
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# 设置网格线颜色和透明度
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ax.grid(color='gray', linestyle='-', linewidth=0.3, alpha=0.2)
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# 设置x轴和y轴的箭头
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ax.annotate('', xy=(1, 0), xycoords='axes fraction', xytext=(0, 0),
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arrowprops=dict(arrowstyle="->", color='black'))
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ax.annotate('', xy=(0, 1), xycoords='axes fraction', xytext=(0, 0),
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arrowprops=dict(arrowstyle="->", color='black'))
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plt.savefig(f'{path_prefix}/{start_time}_task_results.png', dpi=300)
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plt.close()
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def main():
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# parser = argparse.ArgumentParser(description="Analyze game log file between a start and end time.")
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# parser.add_argument('--start_time', type=str, default=None, nargs='?',
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# help="Start time for analysis in the log file.")
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# parser.add_argument('--end_time', type=str, default=None, nargs='?', help="End time for analysis in the log file.")
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# args = parser.parse_args()
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filename = r"C:\Users\86151\Desktop\数模国赛\新建文件夹\input\VG-2.txt"
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path_prefix = r"C:\Users\86151\Desktop\数模国赛\新建文件夹\input\results_pic"
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# 自动寻找最新的实验开始时间
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filename = r"D:\MG-MC\input\VG-1.txt"
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path_prefix = r"D:\MG-MC\results_pic"
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rounds, items_collected, items_variety_collected, items_collected_dict, positions, completed_tasks, failed_tasks, biomes, biomes_per_round, new_biome_rounds = analyze_log_block(logs_block)
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""" print(positions)
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print(items_collected_dict)
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print(items_variety_collected)
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print(completed_tasks)
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print(failed_tasks)
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print(rounds)
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print(new_biome_rounds)"""
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#print(biomes_per_round)
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#print(biomes)
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collected_items_set = set()
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total_items_variety = []
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for i in range(0, len(items_collected_dict)):
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current_round_items_set = set(items_collected_dict[i])
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collected_items_set.update(current_round_items_set)
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total_items_variety.append(len(collected_items_set))
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print(total_items_variety)
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#save png
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start_time = start_time.replace(":", "_")
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save_item_results_png(rounds, items_collected, items_collected_dict, start_time, path_prefix)
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save_path_results_png(positions, start_time, path_prefix)
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save_task_results_png(rounds, completed_tasks, failed_tasks, start_time, path_prefix)
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#save_item_results_png(rounds, items_collected, items_variety_collected, items_collected_dict, start_time, path_prefix)
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print("种类",total_items_variety)
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print("轮次", rounds)
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print("总数",items_collected)
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print("成功",completed_tasks)
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print("失败",failed_tasks)
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||||
print("位置",positions)
|
||||
|
||||
data = {'rounds': rounds,
|
||||
'items_collected': items_collected,
|
||||
'total_items_variety':total_items_variety,
|
||||
'items_variety_collected': items_collected_dict,
|
||||
'positions': positions,
|
||||
'completed_tasks': completed_tasks,
|
||||
'failed_tasks': failed_tasks}
|
||||
# 创建一个DataFrame
|
||||
df = pd.DataFrame(data)
|
||||
# 创建一个新的DataFrame，存储new_biome_rounds和biomes
|
||||
df_new_biomes = pd.DataFrame({
|
||||
'new_biome_rounds': new_biome_rounds,
|
||||
'biomes': biomes
|
||||
})
|
||||
# 将新的DataFrame和原来的df进行合并
|
||||
df = pd.concat([df, df_new_biomes], axis=1)
|
||||
|
||||
# 写入到Excel文件中
|
||||
df.to_excel(fr'D:\MG-MC\results_pic\{start_time}_results.xlsx', index=False)
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Loading…
Add table
Add a link
Reference in a new issue