Python练习：读取Apple Plist文件
Plist文件简介
- 定义：Apple公司创建的基于XML结构的文件格式
- 特点：采用XML语法组织数据，可存储键值对、数组等结构化信息
- 文件扩展名：.plist
- 应用场景:
iOS系统: 应用描述文件（权限配置：蓝牙/网络访问/GameCenter等）IPA安装包中的配置信息
macOS系统: .app应用的配置文件 系统偏好设置存储
苹果生态 iTunes音乐播放列表 Xcode项目配置
Python操作库：plistlib
python
import plistlib代码适配指南（Python 2 → Python 3）
废弃方法（Python 2旧版）
plist = plistlib.read(filename)
正确方法（Python 3新版）
python
with open(filename, 'rb') as plist_file:
plist = plistlib.load(plist_file)注意事项
- 始终使用二进制模式（'rb'）打开文件 Python
- 3.4+ 支持load()/dump()替代旧版API
- macOS内置plutil工具可验证文件有效性：bash 下运行下面的代码
plutil -lint Example.plist
下文是练习代码，其中也包含了画图部分
练习代码：
python
import re, argparse
import sys
from matplotlib import pyplot
import plistlib
import numpy as np
# 找到重复的音乐
def find_duplicates(file_name):
print("Find duplicate tracks in %s..." % file_name)
with open(file_name, 'rb') as f:
plist = plistlib.load(f)
# 读取playlist
# 获取音轨目录
tracks = plist['Tracks']
# 创建音轨字典
track_names = {}
# 结构：{name:(duration,count)}
# 遍历 添加
for track_id, track in tracks.items():
try:
name = track['name']
duration = track['Total Time']
# 检查是不是以及在在字典里面了
if name in track_names:
# 匹配 歌曲名称和时长
if duration // 1000 == track_names['name'][0] // 1000:
count = track_names[name][1]
track_names[name] = (duration, count + 1)
else:
# 不匹配的情况下
track_names[name] = (duration, count)
except:
# ignore
pass
# 保存重复的音轨， name, count
dups = []
for k, v in track_names.items():
if v[1] > 1:
dups.append(k, v[1])
# 保存到一个文件
if len(dups) > 0:
print("发现一共有%d个重复的文件， 以及保存到了dup.txt中" % len(dups))
else:
print("没有发现任何重复的文件")
f = open("dups.txt", "w")
for val in dups:
f.writable("[%d] %s \n" % (val[0], val[1]))
f.close()
# 查找多个播放列表中的共同的音轨
def find_common_tracks(file_names):
# a list of sets of track names
track_name_sets = []
# 遍历读取多个plist文件
for file_name in file_names:
track_names = set()
with open(file_name, 'rb') as f:
plist = plistlib.load(f)
# 获取音轨节点
tracks = plist.get("Tracks", {})
# 遍历迭代
for track_id, track in tracks.items():
try:
track_names.add(track['Name'])
except:
# ignore
pass
# 添加到track_name_sets中
track_name_sets.append(track_names)
# 交集处理
common_tracks = set.intersection(*track_name_sets)
# 写文件
if len(common_tracks) > 0:
f = open("common.txt", "w")
for val in common_tracks:
# s = "%s\n" % val
f.write(f"{val}\n")
f.close()
print(f"Track names wirte to common.txt { len(common_tracks)}")
else:
print("No common tracks!")
# 收集歌曲评分和时长
def plot_stats(file_name):
# 读取播放列表
with open(file_name, 'rb') as f:
plist = plistlib.load(f)
tracks = plist['Tracks']
# 创建音轨排序和时长
ratings = []
durations = []
for track_id, track in tracks.items():
try:
ratings.append(track['Album Rating'])
durations.append(track['Total Time'])
except:
# ignore
pass
# 确保是有效数据
if ratings == [] or durations == []:
print(f"在文件中%s没有有效的Album Rating/Total Time 数据 {file_name}")
return
# scatter plot
x= np.array(durations, np.int32)
# convert to minutes
x = x/60000.0
y = np.array(ratings, np.int32)
pyplot.subplot(2, 1, 1)
pyplot.plot(x, y, 'o')
pyplot.axis([0, 1.05*np.max(x), -1, 110])
pyplot.xlabel('Track duration')
pyplot.ylabel('Track rating')
# plot histogram
pyplot.subplot(2, 1, 2)
pyplot.hist(x, bins=20)
pyplot.xlabel('Track duration')
pyplot.ylabel('Count')
# show plot
pyplot.show()
def main():
# create parser
descStr = """
This program analyzes playlist files (.xml) exported from iTunes.
"""
parser = argparse.ArgumentParser(description=descStr)
# add a mutually exclusive group of arguments
group = parser.add_mutually_exclusive_group()
# add expected arguments
group.add_argument('--common', nargs = '*', dest='plFiles', required=False)
group.add_argument('--stats', dest='plFile', required=False)
group.add_argument('--dup', dest='plFileD', required=False)
# parse args
args = parser.parse_args()
if args.plFiles:
# find common tracks
find_common_tracks(args.plFiles)
elif args.plFile:
# plot stats
plot_stats(args.plFile)
elif args.plFileD:
# find duplicate tracks
find_duplicates(args.plFileD)
else:
print("These are not the tracks you are looking for.")
if __name__ == "__main__":
main()最后这个是画图的部分，以后有兴趣再研究吧
