反其道而用之，把数组写入文件

最新推荐文章于 2024-05-05 00:46:33 发布
最新推荐文章于 2024-05-05 00:46:33 发布
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文章标签： Windows
本文介绍了一种将MIDI铃声资源转换为数组的方法，并提供了将数组还原为MIDI文件的示例代码，便于验证转换正确性或进一步编辑。

前一段时间我曾经写了一篇文章介绍如何编写程序把铃声资源转为数组，其实也可以把转换后的数组再还原成资源以供验证或者做其他用途。几天前有朋友向我索取MID铃声资源，我给了他转化后的资源数组，原本以为他是直接在程序中使用的，但他却似乎不喜欢这种格式，希望我能直接给他铃声，他大概要放到T卡上试听，恰好我也没有原始的音源。后来想了想，其实也是很简单的，只需要一个读写文件的函数，把数组内容写入文件，修改一下扩展名，就变成我们需要的音源了。代码太简单，和朋友们共同学习，希望大伙不要扔砖头。

#include <stdio.h>
#include <stdlib.h>
#include <assert.h>
#include <windows.h>
const unsigned char aa[] ={
/* Data from file 1(默认).mid */
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0x00, 0x77, 0x4b, 0x64, 0x78, 0x4b, 0x00, 0x01, 0x4c, 0x64, 0x78, 0x4c, 0x00, 0x78, 0x24, 0x64,
0x00, 0x4c, 0x64, 0x77, 0x4c, 0x00, 0x7d, 0x24, 0x00, 0x81, 0x6b, 0x2c, 0x64, 0x81, 0x64, 0x2c,
0x00, 0x81, 0x7c, 0x4b, 0x64, 0x78, 0x4b, 0x00, 0x00, 0xff, 0x2f, 0x00
};

int main(void)
{
FILE *pF;


pF=fopen((char *)"D:\\mymid.mid","ab");
if (NULL==pF)
{
printf("open write file error!!");
return 1;
}

for (int i = 0; i < sizeof(aa); i++)
{
putc(aa[i], pF);
}

fclose(pF);
return 0;
}

iteye_20954
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<think>我们正在讨论参数存储方式的选择：数组（内存中） vs 文件（磁盘上）。根据用户的问题，他需要决定在什么情况下使用数组存储参数，什么情况下使用文件存储参数。 首先，我们需要明确两种存储方式的优缺点： 1. **数组（内存存储）**： - 优点：读写速度快（内存访问速度远高于磁盘），使用方便（直接通过变量名访问），适合频繁访问的数据。 - 缺点：数据易失（程序退出后数据丢失），受内存大小限制（不适合存储非常大的数据）。 2. **文件（磁盘存储）**： - 优点：数据持久化（程序退出后仍存在），存储容量大（受磁盘空间限制），适合长期保存或共享数据。 - 缺点：读写速度慢（磁盘I/O是瓶颈），使用相对复杂（需要序列化和反序列化，管理文件路径等）。 接下来，根据引用内容，我们可以得到一些启示： - 引用[1]提到：结构化数组可以用于将数据写成定长记录字节流（即文件），这是C/C++中常见的数据序列化手段。但要注意，使用文件（如`np.fromfile`）需要知道文件格式。 - 引用[3]提到：在MATLAB中，使用文件存储变量（如`.mat`文件）需要先保存到磁盘，再用时加载。这种方式涉及磁盘读写，速度会有影响，一般用于保存和导入变量。 因此，我们可以根据以下场景来选择存储方式： - **选择数组存储**： - 当参数需要被频繁访问（例如在循环中多次使用）时。 - 当参数大小适中，不会导致内存不足时。 - 当参数不需要在程序结束后保留时。 - **选择文件存储**： - 当参数需要长期保存（例如配置参数、模型参数等）时。 - 当参数非常大，内存无法容纳时（此时可以考虑分块读取）。 - 当参数需要在多个程序之间共享时。 此外，还可以考虑折中方案： - 使用内存映射文件（如numpy的`memmap`）：对于非常大的数组，可以部分加载到内存，同时保持文件持久化。 - 使用数据库：对于结构化参数，数据库可能更合适。 总结建议： 1. 如果参数使用频繁且大小适中，优先选择数组（内存存储）。 2. 如果参数需要持久化、共享或太大，则选择文件存储。 3. 在文件存储中，根据数据结构和访问模式选择合适的格式（如二进制、文本、JSON、HDF5等）。 相关问题： 1. 如何选择合适的数据格式（如二进制、文本、JSON等）来存储参数文件？ 2. 在内存有限的情况下，如何处理非常大的参数数组？ 3. 如何安全地管理参数文件的读写以避免数据损坏？</think>### 参数存储方式选择：数组 vs 文件分析 #### 1. 数组存储（内存存储） **适用场景**： - 需要**频繁读写**的参数（如实时计算、迭代算法） - 数据量**小于可用内存**的情况（通常GB级以内） - 需要**高速访问**的场景（内存访问速度比磁盘快10^5倍） - 临时性数据（如程序运行时中间状态） **优势**： - ⚡️ **超高速访问**：直接内存操作，无I/O延迟 - 🔄 **易用性强**：编程语言原生支持（如Python的list/dict, NumPy数组） - 🧩 **灵活修改**：支持随机访问和动态更新 **局限**： - 💾 **易失性存储**：程序退出后数据丢失 - 📏 **容量受限**：受物理内存大小约束 - 🔒 **共享困难**：多进程/多机访问需额外机制 > *示例代码（Python数组存储）：* ```python config_params = { "learning_rate": 0.01, "batch_size": 64, "hidden_layers": [128, 64] } ``` #### 2. 文件存储（磁盘存储） **适用场景**： - 需要**长期持久化**的参数（如模型配置） - **超大容量**数据（超过内存大小） - **跨会话/跨程序**共享数据 - **备份/版本控制**需求 **优势**： - 💽 **持久化存储**：程序重启后数据保留 - 🌐 **共享便捷**：可通过网络/文件系统共享 - 📦 **大容量支持**：仅受磁盘空间限制 - 🔍 **可读性**：文本格式（如JSON）可直接查看 **局限**： - 🐢 **访问速度慢**：磁盘I/O成为瓶颈 - ⚠️ **管理复杂度**：需处理序列化/反序列化 - 🛡️ **安全风险**：需额外权限控制 > *示例代码（文件存储）：* ```python import json # 保存参数 with open("config.json", "w") as f: json.dump({"learning_rate": 0.01}, f) # 读取参数 with open("config.json") as f: params = json.load(f) ``` #### 3. 决策建议表 | 考量维度 | 推荐选择 | 说明 | |-------------------|-------------------|-------------------------------| | **访问频率** | 数组 > 文件 | 高频访问首选内存 | | **数据持久性** | 文件 > 数组 | 长期保存必须用文件 | | **数据规模** | 小:数组/大:文件 | >10GB数据建议文件存储 | | **共享需求** | 文件 > 数组 | 跨进程共享文件更便捷[^1] | | **开发复杂度** | 数组 > 文件 | 内存操作更简单 | | **安全性要求** | 文件 > 数组 | 文件可加密/权限控制[^2] | #### 4. 混合存储策略 对于复杂场景，推荐组合方案： 1. **热数据加载**：启动时从文件加载参数到内存数组 2. **异步持久化**：修改时同步内存，定时/触发保存到文件 3. **内存映射文件**：使用`np.memmap`等实现大文件部分加载 ```python import numpy as np # 创建内存映射文件 data = np.memmap("large_params.dat", dtype=&#39;float32&#39;, mode=&#39;w+&#39;, shape=(1000000,)) ``` > 当参数需要**跨语言使用**（如C++调用Python生成的数据）时，二进制文件存储是最可靠的跨平台方案[^1] ### 📌 最终建议 - **选择数组**：当参数需要频繁修改+数据量小+无需持久化 - **选择文件**：当参数需要长期保存+跨会话共享+数据量超大 - **优先内存**：性能敏感场景默认使用数组，仅在必要时持久化到文件[^3] --- ###
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