143 lines
4.7 KiB
Python
143 lines
4.7 KiB
Python
import json
|
||
import math
|
||
import pandas as pd
|
||
import matplotlib.pyplot as plt
|
||
from matplotlib import rcParams
|
||
|
||
# 设置字体为 SimHei（黑体）或其他支持中文的字体
|
||
rcParams['font.sans-serif'] = ['SimHei'] # 或者 ['Microsoft YaHei']
|
||
rcParams['axes.unicode_minus'] = False # 解决负号显示问题
|
||
|
||
# 指定 JSON 文件的路径
|
||
file_path = r"C:\Users\10561\Desktop\2025-01-06_应用日志.json"
|
||
|
||
# 打开并读取 JSON 文件
|
||
with open(file_path, 'r', encoding='utf-8') as file:
|
||
data = json.load(file)
|
||
|
||
# 提取指定设备和类型的日志片段
|
||
log_ones = []
|
||
start_idx = -1
|
||
for idx, one in enumerate(data):
|
||
if 'Acar' == one['设备'] and '2' == one['类型']:
|
||
if 1 == one['位置'] and 1 == one['方向']:
|
||
start_idx = idx
|
||
continue
|
||
if 8 == one['位置'] and 2 == one['方向']:
|
||
if start_idx == -1:
|
||
continue
|
||
log_ones.append(data[start_idx:idx+1])
|
||
start_idx = -1
|
||
print(idx)
|
||
|
||
# 筛选出只有 "Acar" 的日志片段
|
||
log_ones_new = []
|
||
for one in log_ones:
|
||
one_only_car = [one_one for one_one in one if 'Acar' == one_one['设备']]
|
||
log_ones_new.append(one_only_car)
|
||
[print(o) for o in log_ones_new]
|
||
|
||
# 计算时间差
|
||
time_diffs = []
|
||
for one_one in log_ones_new:
|
||
df = pd.DataFrame(one_one)
|
||
df['时间戳'] = pd.to_numeric(df['时间戳'])
|
||
df['时间差'] = df['时间戳'].diff()
|
||
time_diff = df[['位置', '方向', '时间差']].dropna().reset_index(drop=True)
|
||
time_diffs.append(time_diff)
|
||
|
||
# 找出行数最多的时间差数据集，以它的 (位置-方向) 组合作为标准 X 轴标签
|
||
max_len_dataset = max(time_diffs, key=len)
|
||
all_x_labels = max_len_dataset['位置'].astype(str) + '-' + max_len_dataset['方向'].astype(str)
|
||
|
||
# 标准化所有数据集（没有对应的“位置-方向”就补 0）
|
||
standardized_time_diffs = []
|
||
for time_diff in time_diffs:
|
||
time_diff['位置-方向'] = time_diff['位置'].astype(str) + '-' + time_diff['方向'].astype(str)
|
||
standardized_time_diff = []
|
||
|
||
data_idx = 0
|
||
last_is_null = 0
|
||
for idx_label, label in enumerate(all_x_labels):
|
||
row = time_diff.iloc[data_idx] if data_idx < len(time_diff) else None
|
||
if row is not None and row['位置-方向'] == label:
|
||
# 命中当前 label
|
||
if last_is_null == 1:
|
||
last_is_null = 0
|
||
standardized_time_diff.append(0) # 这里保持你原先的逻辑，先补 0，后面再统一填充
|
||
else:
|
||
standardized_time_diff.append(row['时间差'])
|
||
data_idx += 1
|
||
else:
|
||
# 没有命中当前 label
|
||
standardized_time_diff.append(0)
|
||
last_is_null = 1
|
||
|
||
standardized_time_diffs.append(standardized_time_diff)
|
||
|
||
# -------- 新增：填充 0 的函数 --------
|
||
def fill_zeros_with_nearest(values):
|
||
"""
|
||
将列表中的 0 用最近的非 0 值进行填充
|
||
（先前向填充，再后向填充）
|
||
"""
|
||
# 前向填充
|
||
for i in range(1, len(values)):
|
||
if values[i] == 0 and values[i-1] != 0:
|
||
values[i] = values[i-1]
|
||
# 后向填充
|
||
for i in range(len(values) - 2, -1, -1):
|
||
if values[i] == 0 and values[i+1] != 0:
|
||
values[i] = values[i+1]
|
||
return values
|
||
|
||
# 创建子图的行列数（自动计算）
|
||
num_plots = len(all_x_labels)
|
||
rows = math.ceil(math.sqrt(num_plots)) # 行数
|
||
cols = math.ceil(num_plots / rows) # 列数
|
||
|
||
# 创建大画布
|
||
fig, axes = plt.subplots(rows, cols, figsize=(16, 12))
|
||
axes = axes.flatten() # 将子图数组展平，方便迭代
|
||
|
||
# 在每个子图中绘制折线
|
||
for idx, label in enumerate(all_x_labels):
|
||
# 获取当前 "位置-方向" 对应的 y 值
|
||
y_values = []
|
||
for time_diff in standardized_time_diffs:
|
||
# 如果下标越界就补 0
|
||
y_values.append(time_diff[idx] if idx < len(time_diff) else 0)
|
||
|
||
# -------- 在绘图前，对 y_values 做一次非 0 值填充 --------
|
||
y_values_filled = fill_zeros_with_nearest(y_values)
|
||
|
||
# 绘制当前子图
|
||
ax = axes[idx]
|
||
ax.plot(
|
||
range(len(y_values_filled)),
|
||
y_values_filled,
|
||
marker='o',
|
||
)
|
||
ax.set_ylim(0, 10000)
|
||
|
||
|
||
# 设置标题和轴标签
|
||
ax.set_title(f'{label} 的时间差折线图', fontsize=10)
|
||
ax.set_xlabel('标准化时间差数据集', fontsize=8)
|
||
ax.set_ylabel('时间差 (ms)', fontsize=8)
|
||
ax.set_xticks(range(len(y_values_filled)))
|
||
ax.set_xticklabels(
|
||
[f'TimeDiff {i+1}' for i in range(len(y_values_filled))],
|
||
fontsize=6,
|
||
rotation=45
|
||
)
|
||
ax.grid(axis='y', linestyle='--', alpha=0.7)
|
||
|
||
# 删除多余的子图（如果子图数量多于折线图数量）
|
||
for ax in axes[num_plots:]:
|
||
fig.delaxes(ax)
|
||
|
||
# 调整布局
|
||
plt.tight_layout()
|
||
plt.show()
|