[RPC Fault faultString="Error #2070

本文分享了一位开发者在使用 Flex3 开发 RIA 应用过程中遇到的一个关于从同一目录加载 XML 文件的安全沙箱错误。通过详细分析错误日志和检查 XML 文件格式,最终定位到问题是由于 XML 文件格式不正确导致的。

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I’m in the process of developing a new Flex 3 RIA, and the server code is not yet written, so I’m using static XML files to provide data while I build out the UI functionality. The XML files are stored on my development machine in a subdirectory of the folder that holds the main SWF. It’s all a very standard setup, and I was successfully loading two files from the same subdirectory, so I was very surprised this morning when I got a security sandbox error:

Error #2070: Security sandbox violation:
caller cannot access Stage owned by .
As implied by the reference to “Stage owned by”, this error is supposed to occur when you are loading a SWF from an unauthorized domain. I did some quick searching on Google, and everything I looked at had to do with loading a SWF. The stack trace did not clarify the issue either:

[RPC Fault faultString="Error #2070: Security sandbox
violation:caller cannot access Stage owned by ."
faultCode="Client.CouldNotDecode" faultDetail="null"]
at mx.rpc.http::HTTPService/http://www.adobe.com/
2006/flex/mx/internal::processResult()
[E:\dev \3.0.x\frameworks\projects\rpc\src\mx\rpc\http
\HTTPService.as:842]
at mx.rpc::AbstractInvoker/http://www.adobe.com/2006/flex/
mx/internal::resultHandler()
[E:\dev\3.0.x\frameworks\projects\rpc\src\mx\rpc\
AbstractInvoker.as:186]
at mx.rpc::Responder/result()[E:\dev\3.0.x\frameworks\
projects\rpc\src\mx\rpc\Responder.as:41]
at mx.rpc::AsyncRequest/acknowledge()
[E:\dev\3.0.x\frameworks\projects\rpc\src\mx\rpc\
AsyncRequest.as:74]
at DirectHTTPMessageResponder/completeHandler()
[E:\dev\3.0.x\frameworks\projects\rpc\src\mx\
messaging\channels\DirectHTTPChannel.as:381]
at flash.events::EventDispatcher/dispatchEventFunction()
at flash.events::EventDispatcher/dispatchEvent()
at flash.net::URLLoader/onComplete()
I suspected something was wrong with the XML file, since the UI was successfully loading other files from the same directory. I opened the file in XML Spy, which immediately displayed a message to the effect that the XML was not well-formed. And sure enough, I had missed something. Here is what the XML looked like:

<page>
<name>page1</name>
<type>pagetype</type>
<previewUrl>previewUrl</previewUrl>
<path>page/path1</path>
<promoTypes>
</promoType>typeOne</promoType>
</promoType>typeN</promoType>
</promoTypes>
</page>
You will probably see the error right away, but to my embarrassment I didn’t. How did the error creep in? Well, I had copied the sample XML from the design document (which I did not write) and pasted it into an XML file so that I’d have something to pull in for development purposes, and evidently I did not check it over very carefully. (If you don’t see the error, look closely at the promoType tags, and you’ll see that the opening tags have closing slashes.)

I fixed the XML, the error vanished, and I was able to resume forward progress. Clearly, the error message displayed by the debug player was not only non-helpful, it was actually misleading.

I thought I’d post this here in case anyone else runs into a similar problem. It may save you a few minutes of wonderment.
visit url:http://hi.baidu.com/zcq100/blog/item/de12ec1f93f85b69f724e434.html
对以下代码解释import pandas as pd import chardet from pathlib import Path import numpy as np # ====================== # 配置参数 # ====================== DATA_PATH = r"C:\Users\huang\Desktop\1112W\Charging_Data.csv" # 原始数据路径 OUTPUT_PATH = r"C:\Users\huang\Desktop\1112W\cleaned_charging_data.csv" # 清洗后数据保存路径 # ====================== # 辅助函数 # ====================== def detect_file_encoding(file_path): """自动检测文件编码""" with open(file_path, &#39;rb&#39;) as f: result = chardet.detect(f.read()) return result[&#39;encoding&#39;] def safe_parse_dates(df, date_columns): """安全解析日期列""" for col in date_columns: if col in df.columns: df[col] = pd.to_datetime(df[col], errors=&#39;coerce&#39;) return df # ====================== # 主处理流程 # ====================== try: # 1. 读取数据(自动检测编码) encoding = detect_file_encoding(DATA_PATH) df = pd.read_csv( DATA_PATH, encoding=encoding, engine=&#39;python&#39; ) # 2. 标准化列名(处理前后空格/大小写) df.columns = df.columns.str.strip().str.lower() # 3. 解析时间列 time_columns = [&#39;start time&#39;, &#39;end time&#39;] df = safe_parse_dates(df, time_columns) # 4. 计算充电时长(小时) df[&#39;duration&#39;] = (df[&#39;end time&#39;] - df[&#39;start time&#39;]).dt.total_seconds() / 3600 # 5. 数据清洗逻辑 # (1) 过滤无效充电记录(功率为0且属于故障) fault_conditions = ( (df[&#39;transaction power&#39;] == 0) & ( df[&#39;is_ev_fault&#39;].astype(int) | df[&#39;is_abnormal&#39;].astype(int) | df[&#39;is_system_issue&#39;].astype(int) ).astype(bool) ) valid_df = df[~fault_conditions].copy() # (2) 处理时间异常 # 过滤负时间差 time_anomaly = valid_df[valid_df[&#39;duration&#39;] < 0] if not time_anomaly.empty: print(f"发现时间异常记录 {len(time_anomaly)} 条,已自动过滤") valid_df = valid_df[valid_df[&#39;duration&#39;] >= 0] # (3) 去除极端值(超过7天的充电记录) valid_df = valid_df[valid_df[&#39;duration&#39;] <= 168] # 6. 保存清洗后数据 valid_df.to_csv(OUTPUT_PATH, index=False) print(f"数据清洗完成,有效记录数: {len(valid_df)}") print(f"清洗后数据已保存至: {OUTPUT_PATH}") except FileNotFoundError: print(f"错误:文件 {DATA_PATH} 未找到") except KeyError as e: print(f"列名错误:{str(e)} 不存在于数据文件中") except Exception as e: print(f"处理过程中发生未预期错误: {str(e)}")
最新发布
06-02
<think>好的,我现在需要仔细分析用户提供的代码,并按照他们的要求进行详细的解释。用户希望我作为编程专家,解释这段Python代码,并按照他们的格式要求给出回答。首先,我需要逐段理解代码的功能,然后组织成清晰的解释部分。首先,代码的导入部分包括pandas、chardet、pathlib和numpy。我需要解释每个库的作用,比如pandas用于数据处理,chardet用于检测文件编码,numpy可能用于数值计算,但在这段代码中可能没有直接使用到,需要确认。 接下来是配置参数部分,DATA_PATH和OUTPUT_PATH定义了文件路径。这里需要说明路径的作用,原始数据和清洗后的数据存储位置。然后是辅助函数部分,detect_file_encoding函数用于自动检测文件编码,safe_parse_dates用于安全解析日期列。我需要详细解释这两个函数的工作原理,比如使用chardet检测编码,以及如何处理日期解析中的错误。主处理流程部分使用了try-except块来捕获异常,确保代码的健壮性。读取数据时使用自动检测的编码,处理可能的编码问题。标准化列名部分是将列名转换为小写并去除空格,这有助于后续的数据处理避免列名不一致的问题。解析时间列部分,将&#39;start time&#39;和&#39;endtime&#39;转换为datetime类型,并计算充电时长。这里需要解释dt.total_seconds()的作用,以及转换为小时的原因。 数据清洗逻辑包括过滤无效充电记录、处理时间异常和去除极端值。需要解释每个过滤条件的逻辑,比如故障条件的组合,如何过滤负时间差和超过7天的记录。还要注意astype(int)和astype(bool)的转换,确保条件判断正确。保存清洗后的数据使用to_csv方法,并输出成功信息。异常处理部分捕获文件未找到、列名错误和其他异常,给出友好的错误提示。 在解释过程中,需要确保术语准确,比如说明为什么使用engine=&#39;python&#39;在pd.read_csv中,可能是因为某些编码问题需要Python引擎来解析。同时,指出代码中的潜在问题,比如假设存在&#39;is_ev_fault&#39;等列,如果数据中没有这些列,会引发KeyError,但代码中已经有异常处理。最后,思考用户可能的相关问题,比如处理其他异常数据、自动化清洗流程、性能优化等,确保相关问题符合他们的需求,并覆盖数据处理的不同方面。</think>以下是代码的详细解释及优化建议: ```python # ====================== # 代码解释与增强版 # ====================== # 导入库说明 import pandas as pd # 数据处理核心库 import chardet # 文件编码检测库 from pathlib import Path # 路径操作库(代码中未直接使用,可考虑添加路径校验) import numpy as np # 数值计算库(当前代码未直接使用) # ====================== # 配置参数增强说明 # ====================== DATA_PATH = r"C:\Users\huang\Desktop\1112W\Charging_Data.csv" # 原始数据路径 OUTPUT_PATH = r"C:\Users\huang\Desktop\1112W\cleaned_charging_data.csv" # 清洗后数据路径 # 建议添加路径校验 if not Path(DATA_PATH).exists(): raise FileNotFoundError(f"原始数据文件 {DATA_PATH} 不存在") # ====================== # 辅助函数增强版 # ====================== def detect_file_encoding(file_path, sample_size=10000): """智能检测文件编码(带采样优化)""" with open(file_path, &#39;rb&#39;) as f: # 仅读取前1MB内容进行检测(提高大文件处理效率) rawdata = f.read(min(Path(file_path).stat().st_size, 1024*1024)) return chardet.detect(rawdata)[&#39;encoding&#39;] def safe_parse_dates(df, date_columns, dayfirst=True): """多格式日期解析(增强容错)""" date_formats = [ &#39;%Y-%m-%d %H:%M:%S&#39;, # 标准格式 &#39;%d/%m/%Y %H:%M&#39;, # 欧洲日期格式 &#39;%m/%d/%Y %I:%M %p&#39; # 美国日期格式 ] for col in date_columns: if col in df.columns: # 尝试多种日期格式解析 for fmt in date_formats: try: df[col] = pd.to_datetime( df[col], format=fmt, errors=&#39;coerce&#39;, dayfirst=dayfirst ) break # 解析成功则跳出循环 except: continue return df # ====================== # 主处理流程增强说明 # ====================== try: # 1. 读取数据(带内存优化) encoding = detect_file_encoding(DATA_PATH) # 使用低内存模式读取(适合大数据文件) df = pd.read_csv( DATA_PATH, encoding=encoding, engine=&#39;c&#39;, # 使用C引擎提升速度 dtype={ &#39;transaction power&#39;: &#39;float32&#39;, &#39;is_ev_fault&#39;: &#39;int8&#39;, &#39;is_abnormal&#39;: &#39;int8&#39;, &#39;is_system_issue&#39;: &#39;int8&#39; }, true_values=[&#39;yes&#39;, &#39;true&#39;], false_values=[&#39;no&#39;, &#39;false&#39;] ) # 2. 列名标准化(增强特殊字符处理) df.columns = ( df.columns.str.strip() .str.lower() .str.replace(r&#39;[^\w]&#39;, &#39;_&#39;, regex=True) # 替换非单词字符为下划线 .str.replace(r&#39;_+&#39;, &#39;_&#39;, regex=True) # 合并连续下划线 ) # 3. 时间解析(带时区处理) time_columns = [&#39;start_time&#39;, &#39;end_time&#39;] df = safe_parse_dates(df, time_columns) # 4. 充电时长计算(带单位转换) df[&#39;duration&#39;] = ( (df[&#39;end_time&#39;] - df[&#39;start_time&#39;]) .dt.total_seconds() .pipe(lambda s: s/3600) # 转换为小时 .round(2) # 保留两位小数 ) # 5. 数据清洗(增强版) # (1) 定义故障标记列(动态检测) fault_flags = [ &#39;is_ev_fault&#39;, &#39;is_abnormal&#39;, &#39;is_system_issue&#39; ] existing_flags = [col for col in fault_flags if col in df.columns] # (2) 构建故障条件(带容错处理) fault_condition = ( (df[&#39;transaction_power&#39;] == 0) & df[existing_flags].any(axis=1) ) if existing_flags else pd.Series(False, index=df.index) valid_df = df[~fault_condition].copy() # (3) 时间异常处理(带日志记录) time_anomalies = valid_df.query(&#39;duration < 0 or duration > 168&#39;) if not time_anomalies.empty: anomaly_log_path = Path(DATA_PATH).parent / &#39;time_anomalies.log&#39; time_anomalies.to_csv(anomaly_log_path, index=False) print(f"发现时间异常 {len(time_anomalies)} 条,日志保存至 {anomaly_log_path}") valid_df = valid_df.query(&#39;0 <= duration <= 168&#39;) # 6. 数据保存(带格式优化) valid_df.to_csv( OUTPUT_PATH, index=False, encoding=&#39;utf-8-sig&#39;, # 支持Excel中文显示 date_format=&#39;%Y-%m-%d %H:%M:%S&#39; ) except pd.errors.ParserError as e: print(f"CSV解析错误:{str(e)}") print("建议检查:") print("- 文件中的不一致引号") print("- 缺失的列标题") except PermissionError: print(f"文件访问被拒绝,请关闭正在使用的Excel文件:{DATA_PATH}") except Exception as e: print(f"未捕获异常:{type(e).__name__} - {str(e)}") ``` **代码功能说明:** 1. **编码检测优化**: - 使用1MB采样检测代替全文件读取,提升大文件处理效率 - 支持检测常见日期格式(欧洲/美国格式) 2. **内存管理增强**: ```python dtype={&#39;transaction_power&#39;: &#39;float32&#39;} # 减少内存占用 ``` - 指定列数据类型,减少内存占用 - 使用C引擎提升读取速度 3. **异常处理增强**: - 单独处理CSV解析错误 - 添加文件权限错误处理 - 记录时间异常数据到独立文件 4. **数据清洗改进**: - 动态检测故障标记列 - 使用query()进行条件过滤,提高可读性 - 保留两位小数精度 **典型应用场景:** - 电动汽车充电桩运营数据分析 - 物联网设备数据清洗 - 时间序列数据分析预处理
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