### Install AMBD-MC and Dependencies

Source: https://context7.com/autombd/ambd-mc/llms.txt

Clone the repository, set up a virtual environment, and install runtime and development dependencies using pip. Verify the installation and run tests.

```bash
# Clone the repository
git clone https://github.com/your-username/your-project.git
cd your-project

# (Recommended) Create and activate a virtual environment
python -m venv venv
source venv/bin/activate        # Linux/macOS
# venv\Scripts\activate         # Windows

# Install runtime dependencies
pip install -r requirements.txt

# Install development dependencies (for contributors)
pip install -e ".[dev]"

# Verify the installation
python -c "import your_project; print('Installation successful!')"

# Run the test suite
python -m pytest tests/

# Start the development server
python app.py
```

--------------------------------

### Project Attribution Example

Source: https://github.com/autombd/ambd-mc/blob/main/docs/doc-lic.md

An example of how to attribute the project's use in your documentation. This is a recommended practice when using the software.

```plaintext
本软件使用了 [项目名称]，该软件基于 MIT 许可证。
版权所有 (c) 2024 [版权所有者姓名]
```

--------------------------------

### Multi-Environment Configuration Files

Source: https://context7.com/autombd/ambd-mc/llms.txt

Example YAML configuration files for production and development environments. The active environment is determined by the APP_ENV environment variable.

```yaml
# config.production.yaml
database:
  host: "prod-db.example.com"
  port: 5432
  ssl: true
cache:
  redis:
    host: "redis.example.com"
    port: 6379

# config.development.yaml
database:
  host: "localhost"
  port: 5432
  ssl: false
cache:
  redis:
    host: "localhost"
    port: 6379
```

--------------------------------

### Alerting and Notifications Setup

Source: https://context7.com/autombd/ambd-mc/llms.txt

Evaluates metric conditions against rules and dispatches alerts via Slack or SMTP email. Configure webhook URLs and SMTP settings.

```python
from your_project.alerting import AlertManager

alert_manager = AlertManager(
    slack_webhook="https://hooks.slack.com/services/YOUR/WEBHOOK/URL",
    email_settings={"smtp_server": "smtp.example.com"}
)

alert_manager.add_rule(
    name="high_error_rate",
    condition=lambda m: m.error_rate > 0.1,
    action=alert_manager.send_slack_alert
)

```

--------------------------------

### Create an Adapter for a Third-Party Library in Python

Source: https://github.com/autombd/ambd-mc/blob/main/docs/faq.md

Wrap a third-party library's functionality within an adapter class to integrate it smoothly. Assumes the library is installed and importable.

```python
class ThirdPartyAdapter:
    def __init__(self, config):
        import third_party_lib
        self.lib = third_party_lib
    
    def adapt_method(self, *args, **kwargs):
        # 适配第三方库接口
        return self.lib.original_method(*args, **kwargs)
```

--------------------------------

### Unit, Integration, and Performance Tests with Pytest

Source: https://context7.com/autombd/ambd-mc/llms.txt

Examples of unit, integration, and performance tests using the pytest framework. Includes testing normal and edge cases, database interactions, and performance benchmarks. Ensure 'your_project.module' is available.

```python
import pytest
import timeit
from your_project.module import your_function

# Unit test — normal and edge cases
def test_your_function_normal_case():
    assert your_function(1, 2) == 3

def test_your_function_edge_case():
    with pytest.raises(ValueError):
        your_function(-1, 2)

# Integration test — database round-trip
def test_integration_with_database(db_connection):
    setup_test_data(db_connection)
    result = query_database(db_connection)
    assert result == expected_data
    cleanup_test_data(db_connection)

# Performance test — 1000 executions under 1 second
def test_performance():
    t = timeit.timeit(
        "your_function(1000)",
        setup="from your_project.module import your_function",
        number=1000
    )
    assert t < 1.0

# Run the full suite with coverage
# pytest --cov=your_project tests/
# flake8 your_project/
# black --check your_project/
# mypy your_project/
```

--------------------------------

### Check Third-Party Dependencies Licenses

Source: https://github.com/autombd/ambd-mc/blob/main/docs/doc-lic.md

Use this command to list the licenses of Python dependencies installed via pip. Each dependency typically has its own open-source license.

```bash
# 通过 pip 安装的依赖项
# 每个依赖项都有自己的许可证，通常是开源许可证
# 运行以下命令查看依赖项的许可证信息：
# pip-licenses
```

--------------------------------

### Start Interactive Debugging with PDB

Source: https://github.com/autombd/ambd-mc/blob/main/docs/faq.md

Launch a Python script under the control of the Python Debugger (PDB) for interactive debugging.

```bash
python -m pdb your_script.py
```

--------------------------------

### Prometheus Monitoring with MetricsCollector

Source: https://context7.com/autombd/ambd-mc/llms.txt

Instruments Python functions for timing and tracks custom gauges/counters, exporting metrics to a Prometheus endpoint. Requires Prometheus setup.

```python
from your_project.monitoring import MetricsCollector

metrics = MetricsCollector(
    prometheus_url="http://localhost:9090",
    application_name="myapp"
)

@metrics.timer("function_execution_time")
def business_logic():
    pass   # Execution time recorded automatically

# Custom metrics
metrics.gauge("active_users", 150)
metrics.increment("requests_processed")
```

--------------------------------

### Initialize Project with Configuration

Source: https://context7.com/autombd/ambd-mc/llms.txt

Initialize the project by setting up logging and loading configuration from a YAML file. This configures project metadata, database connections, and logging settings.

```yaml
# config.yaml
# project:
#   name: "My Motor Control Project"
#   version: "1.0.0"
# database:
#   host: "localhost"
#   port: 5432
#   name: "mydb"
#   user: "admin"
# logging:
#   level: "INFO"
#   file: "logs/app.log"
```

```python
from your_project import Project
from your_project.utils import setup_logging
import logging

# Configure logging
setup_logging(level=logging.INFO)

# Initialize and load project configuration
project = Project(config_path="config.yaml", log_level="INFO")
project.load_config()
```

--------------------------------

### 克隆和设置远程仓库

Source: https://github.com/autombd/ambd-mc/blob/main/docs/doc-contributing.md

Fork 项目仓库后，克隆您的 fork 并设置上游远程指向原始仓库，以便同步更新。

```bash
git clone https://github.com/your-username/your-project.git
cd your-project
git remote add upstream https://github.com/original-owner/your-project.git
```

--------------------------------

### 提交代码更改

Source: https://github.com/autombd/ambd-mc/blob/main/docs/doc-contributing.md

添加所有更改的文件并使用符合规范的提交信息提交您的代码。

```bash
git add .
git commit -m "类型(范围): 描述性信息

详细说明（可选）

解决: #issue-number"
```

--------------------------------

### 推送更改到远程仓库

Source: https://github.com/autombd/ambd-mc/blob/main/docs/doc-contributing.md

将本地分支的更改推送到您的 GitHub fork。

```bash
git push origin feature/your-feature-name
```

--------------------------------

### Source File Header License Notice

Source: https://github.com/autombd/ambd-mc/blob/main/docs/doc-lic.md

A recommended short license notice to include at the beginning of each source code file. This helps in identifying the license and copyright.

```python
# 版权所有 (c) 2024 [版权所有者姓名]
# MIT 许可证
# 更多信息请参见 LICENSE 文件
```

--------------------------------

### 安装开发依赖

Source: https://github.com/autombd/ambd-mc/blob/main/docs/doc-contributing.md

安装项目所需的开发依赖，通常使用 pip 的可编辑模式。

```bash
pip install -e ".[dev]"
```

--------------------------------

### 创建新分支

Source: https://github.com/autombd/ambd-mc/blob/main/docs/doc-contributing.md

在开始新功能或修复之前，先同步主分支并基于其创建一个新的功能或修复分支。

```bash
git fetch upstream
git checkout main
git merge upstream/main
git checkout -b feature/your-feature-name
```

--------------------------------

### Custom Plugin System Implementation

Source: https://context7.com/autombd/ambd-mc/llms.txt

Defines and registers custom plugins that can be executed within the application pipeline. Ensure BasePlugin is imported.

```python
from your_project.plugin import PluginManager, BasePlugin

class CustomPlugin(BasePlugin):
    def __init__(self, config):
        super().__init__(config)
        self.name = "CustomPlugin"

    def execute(self, context):
        return self._process_context(context)

    def _process_context(self, context):
        return {"processed": True, "data": context}

manager = PluginManager()
manager.register_plugin(CustomPlugin)
manager.load_plugins()

results = manager.execute_all({"test": "data"})
print(results)
# Output: [{"processed": True, "data": {"test": "data"}}]
```

--------------------------------

### 运行代码质量检查

Source: https://github.com/autombd/ambd-mc/blob/main/docs/doc-contributing.md

使用 flake8, black, 和 mypy 等工具检查代码质量、格式和类型提示。

```bash
flake8 your_project/
black --check your_project/
mypy your_project/
```

--------------------------------

### Evaluate Rules Against Live Metrics

Source: https://context7.com/autombd/ambd-mc/llms.txt

This snippet shows how to use the alert_manager to check and alert based on current metrics. Ensure alert_manager and current_metrics are properly initialized.

```python
alert_manager.check_and_alert(current_metrics)
```

--------------------------------

### Execute Analysis with Project.analyze

Source: https://context7.com/autombd/ambd-mc/llms.txt

Run the core analysis pipeline on transformed data. The result is a dictionary containing a summary, metrics, distribution, and prediction arrays.

```python
from your_project import Project

project = Project(config_path="config.yaml", log_level="INFO")
project.load_config()

data = project.load_data("data/input/sample.csv")
transformed_data = project.transform_data(data)

analysis_result = project.analyze(transformed_data)
print(f"Analysis complete: {analysis_result['summary']}")
# Output: Analysis complete: Analysis successful, 5 metrics computed
```

--------------------------------

### 运行测试

Source: https://github.com/autombd/ambd-mc/blob/main/docs/doc-contributing.md

安装依赖后，运行测试以确保开发环境设置正确。

```bash
pytest tests/
```

--------------------------------

### BibTeX Citation for Project

Source: https://github.com/autombd/ambd-mc/blob/main/docs/faq.md

Provides the BibTeX format for citing the project in academic work. Includes author, title, year, publisher, and URL.

```bibtex
@software{your_project_2024,
  author = {作者},
  title = {项目名称},
  year = {2024},
  publisher = {GitHub},
  url = {https://github.com/your-username/your-project}
}
```

--------------------------------

### Enable Detailed Logging in Python

Source: https://github.com/autombd/ambd-mc/blob/main/docs/faq.md

Configure Python's logging module to display DEBUG level messages. Useful for detailed troubleshooting.

```python
import logging
logging.basicConfig(level=logging.DEBUG)
```

--------------------------------

### 文档目录结构

Source: https://github.com/autombd/ambd-mc/blob/main/docs/doc-contributing.md

展示了项目文档的推荐目录结构，包括首页、快速开始、教程、API文档、示例、架构文档和贡献指南。

```tree
docs/
├── index.md              # 首页
├── getting-started.md    # 快速开始
├── tutorials/           # 教程
├── api/                # API 文档
├── examples/           # 示例
├── architecture.md     # 架构文档
└── contributing.md     # 本文档
```

--------------------------------

### Define and Register Custom Extension Module

Source: https://context7.com/autombd/ambd-mc/llms.txt

This Python code defines a custom extension module 'MyExtension' inheriting from 'BaseExtension' and registers it with the 'ExtensionRegistry'. Ensure 'your_project.base' and 'your_project.registry' are available.

```python
from your_project.base import BaseExtension
from your_project.registry import ExtensionRegistry

class MyExtension(BaseExtension):
    def __init__(self, config):
        super().__init__(config)
        self.version = "1.0.0"

    def setup(self):
        self.logger.info("Setting up MyExtension")

    def teardown(self):
        self.logger.info("Tearing down MyExtension")

    def custom_method(self, data):
        return {"processed": data, "extension": "my_module"}

registry = ExtensionRegistry()
registry.register("my_extension", MyExtension)
```

--------------------------------

### Set Application Environment

Source: https://context7.com/autombd/ambd-mc/llms.txt

This bash command sets the APP_ENV environment variable to 'production', which will be used by the application to select the appropriate configuration file.

```bash
# Select environment at runtime
export APP_ENV=production
python app.py
```

--------------------------------

### Implement a Custom Module in Python

Source: https://github.com/autombd/ambd-mc/blob/main/docs/faq.md

Define a new module by inheriting from BaseModule and implementing the process method. Requires importing BaseModule from the project's base module.

```python
from your_project.base import BaseModule

class CustomModule(BaseModule):
    def __init__(self, config):
        super().__init__(config)
    
    def process(self, input_data):
        # 实现你的逻辑
        return processed_data
```

--------------------------------

### Enable Profiling and Generate Performance Report

Source: https://context7.com/autombd/ambd-mc/llms.txt

This Python code enables profiling using the Debugger, profiles a critical section of code, and generates an HTML performance report. Ensure 'your_project.debug' is available and 'perform_critical_operation' is defined.

```python
import logging
from your_project.debug import Debugger

logging.basicConfig(level=logging.DEBUG)

debugger = Debugger()
debugger.enable_profiling()

with debugger.profile("critical_section"):
    perform_critical_operation()

debugger.generate_report("performance_report.html")
# Output: performance_report.html with flamegraph and timing breakdown
```

--------------------------------

### Python 函数文档字符串示例

Source: https://github.com/autombd/ambd-mc/blob/main/docs/doc-contributing.md

遵循 Google 风格的 Python 函数文档字符串示例，包含简要描述、详细描述、参数、返回值和异常。

```python
def function_name(param1: str, param2: int) -> bool:
    """函数简要描述。
    
    详细描述（可选）。
    
    Args:
        param1: 参数1的描述
        param2: 参数2的描述
    
    Returns:
        返回值的描述
    
    Raises:
        ValueError: 当参数无效时
    """
```

--------------------------------

### Register a Custom Module in Python

Source: https://github.com/autombd/ambd-mc/blob/main/docs/faq.md

Register a custom module with a given name using the register_module function. Requires importing register_module from the project's registry.

```python
from your_project.registry import register_module
register_module('custom', CustomModule)
```

--------------------------------

### LRU and Redis Caching

Source: https://context7.com/autombd/ambd-mc/llms.txt

Provides in-process LRU memoization and distributed Redis-backed caching with configurable Time-To-Live (TTL).

```python
import time
from your_project.cache import LRUCache, RedisCache

# In-process LRU cache
cache = LRUCache(maxsize=1000)

@cache.memoize(ttl=300)   # Cache for 5 minutes
def expensive_operation(x, y):
    time.sleep(2)
    return x * y

print(expensive_operation(3, 4))   # First call: 2 s delay → 12
print(expensive_operation(3, 4))   # Cached: instant → 12

# Distributed Redis cache
redis_cache = RedisCache(host="localhost", port=6379, db=0, ttl=3600)
```

--------------------------------

### 保持分支更新

Source: https://github.com/autombd/ambd-mc/blob/main/docs/doc-contributing.md

在开发过程中，定期从上游主分支拉取更新并合并或变基到您的当前分支，以避免冲突。

```bash
git fetch upstream
git merge upstream/main
# 或使用变基
git rebase upstream/main
```

--------------------------------

### Train and Save a Model

Source: https://context7.com/autombd/ambd-mc/llms.txt

Trains a model using provided training and validation data for a specified number of epochs and saves the serialized model.

```python
from your_project.trainer import ModelTrainer

trainer = ModelTrainer()
model = trainer.train(
    training_data="train.csv",
    validation_data="val.csv",
    epochs=10
)
trainer.save_model("model.pkl")
print("Model saved to model.pkl")
```

--------------------------------

### Full MIT License Text

Source: https://github.com/autombd/ambd-mc/blob/main/docs/doc-lic.md

The complete text of the MIT License, including copyright notice and disclaimers. This should be included in all distributions of the software.

```plaintext
MIT 许可证

版权所有 (c) 2024 [版权所有者姓名]

特此免费授予任何获得本软件及相关文档文件（以下简称"软件"）副本的人士，
允许不受限制地处理本软件，包括但不限于使用、复制、修改、合并、发布、分发、再许可和/或销售本软件的副本，
并允许向其提供本软件的人士这样做，但须符合以下条件：

上述版权声明和本许可声明应包含在本软件的所有副本或重要部分中。

本软件按"原样"提供，不提供任何形式的明示或暗示保证，包括但不限于适销性、
特定用途适用性和非侵权性保证。在任何情况下，作者或版权持有人均不对
因本软件或本软件的使用或其他交易而产生的任何索赔、损害赔偿或其他责任
承担责任，无论是在合同诉讼、侵权诉讼还是其他诉讼中。
```

--------------------------------

### 性能测试示例

Source: https://github.com/autombd/ambd-mc/blob/main/docs/doc-contributing.md

使用 timeit 模块进行性能测试，测量函数执行时间并设置阈值。

```python
import timeit

def test_performance():
    """性能测试"""
    execution_time = timeit.timeit(
        'your_function(1000)',
        setup='from your_project.module import your_function',
        number=1000
    )
    assert execution_time < 1.0  # 1秒内完成1000次执行
```

--------------------------------

### PluginManager

Source: https://context7.com/autombd/ambd-mc/llms.txt

Allows users to define, register, and execute custom processing plugins that hook into the application pipeline.

```APIDOC
## `PluginManager` — Custom Plugin System

Allows users to define, register, and execute custom processing plugins that hook into the application pipeline.

### Usage
```python
from your_project.plugin import PluginManager, BasePlugin

class CustomPlugin(BasePlugin):
    def __init__(self, config):
        super().__init__(config)
        self.name = "CustomPlugin"

    def execute(self, context):
        return self._process_context(context)

    def _process_context(self, context):
        return {"processed": True, "data": context}

manager = PluginManager()
manager.register_plugin(CustomPlugin)
manager.load_plugins()

results = manager.execute_all({"test": "data"})
print(results)
# Output: [{"processed": True, "data": {"test": "data"}}]
```
```

--------------------------------

### MessageQueue

Source: https://context7.com/autombd/ambd-mc/llms.txt

Connects to a RabbitMQ broker, publishes task messages, and registers callback consumers for asynchronous task processing.

```APIDOC
## `MessageQueue` — RabbitMQ Integration

Connects to a RabbitMQ broker, publishes task messages, and registers callback consumers for asynchronous task processing.

### Usage
```python
from your_project.integrations import MessageQueue

mq = MessageQueue(
    host="localhost",
    port=5672,
    username="guest",
    password="guest",
    queue="task_queue"
)

# Publish a task
mq.publish({"task": "process", "data": "example"})

# Consume messages
def callback(message):
    print(f"Received: {message}")
    return True   # Acknowledge message

mq.consume(callback)
```
```

--------------------------------

### RabbitMQ Message Queue Integration

Source: https://context7.com/autombd/ambd-mc/llms.txt

Connects to a RabbitMQ broker for publishing messages and consuming them with a callback function. Ensure RabbitMQ is running.

```python
from your_project.integrations import MessageQueue

mq = MessageQueue(
    host="localhost",
    port=5672,
    username="guest",
    password="guest",
    queue="task_queue"
)

# Publish a task
mq.publish({"task": "process", "data": "example"})

# Consume messages
def callback(message):
    print(f"Received: {message}")
    return True   # Acknowledge message

mq.consume(callback)
```

--------------------------------

### Project.load_data

Source: https://context7.com/autombd/ambd-mc/llms.txt

Loads structured input data from CSV or JSON files into the project's processing pipeline. It returns a list of records that are ready for subsequent transformations.

```APIDOC
## Project.load_data — Load CSV/JSON Input Data

Reads structured input files (CSV or JSON) into the project pipeline. Returns a list of records ready for transformation.

```python
from your_project import Project

project = Project(config_path="config.yaml", log_level="INFO")
project.load_config()

# Load a CSV data file
data = project.load_data("data/input/sample.csv")
print(f"Loaded {len(data)} records")
# Output: Loaded 5 records
```
```

--------------------------------

### 集成测试示例

Source: https://github.com/autombd/ambd-mc/blob/main/docs/doc-contributing.md

一个数据库集成测试示例，展示了如何设置测试数据、执行查询、验证结果和进行清理。

```python
def test_integration_with_database(db_connection):
    """数据库集成测试"""
    # 设置测试数据
    setup_test_data(db_connection)
    
    # 执行测试
    result = query_database(db_connection)
    
    # 验证结果
    assert result == expected_data
    
    # 清理
    cleanup_test_data(db_connection)
```

--------------------------------

### 提交信息示例

Source: https://github.com/autombd/ambd-mc/blob/main/docs/doc-contributing.md

一个符合规范的提交信息示例，包含类型、范围、描述、详细说明和关联的 Issue。

```gitcommit
feat(api): 添加用户认证端点

- 添加 POST /auth/login 端点
- 实现 JWT 令牌生成
- 添加相关测试用例

解决: #42
```

--------------------------------

### Inspect Intermediate Results in Python

Source: https://github.com/autombd/ambd-mc/blob/main/docs/faq.md

Add print statements to check variable values and use pickle to save data structures for later inspection. Useful for debugging complex states.

```python
# 在关键位置添加检查点
print(f"变量值: {variable}")
import pickle
pickle.dump(data, open('debug.pkl', 'wb'))
```

--------------------------------

### Project.analyze

Source: https://context7.com/autombd/ambd-mc/llms.txt

Executes the core analysis pipeline on the transformed data. It returns a dictionary containing analysis results such as a summary, metrics, distributions, and predictions.

```APIDOC
## Project.analyze — Execute Core Analysis

Runs the configured analysis pipeline on transformed data and returns a result dictionary containing a summary, metrics, distribution, and prediction arrays.

```python
from your_project import Project

project = Project(config_path="config.yaml", log_level="INFO")
project.load_config()

data = project.load_data("data/input/sample.csv")
transformed_data = project.transform_data(data)

analysis_result = project.analyze(transformed_data)
print(f"Analysis complete: {analysis_result['summary']}")
# Output: Analysis complete: Analysis successful, 5 metrics computed
```
```

--------------------------------

### LRUCache / RedisCache

Source: https://context7.com/autombd/ambd-mc/llms.txt

Provides in-process LRU memoisation and distributed Redis-backed caching with configurable TTLs to avoid redundant computation.

```APIDOC
## `LRUCache` / `RedisCache` — Caching Layer

Provides in-process LRU memoisation and distributed Redis-backed caching with configurable TTLs to avoid redundant computation.

### Usage
```python
import time
from your_project.cache import LRUCache, RedisCache

# In-process LRU cache
cache = LRUCache(maxsize=1000)

@cache.memoize(ttl=300)   # Cache for 5 minutes
def expensive_operation(x, y):
    time.sleep(2)
    return x * y

print(expensive_operation(3, 4))   # First call: 2 s delay → 12
print(expensive_operation(3, 4))   # Cached: instant → 12

# Distributed Redis cache
redis_cache = RedisCache(host="localhost", port=6379, db=0, ttl=3600)
```
```

--------------------------------

### AlertManager

Source: https://context7.com/autombd/ambd-mc/llms.txt

Evaluates metric conditions against configurable rules and dispatches alerts via Slack webhooks or SMTP email.

```APIDOC
## `AlertManager` — Alerting and Notifications

Evaluates metric conditions against configurable rules and dispatches alerts via Slack webhooks or SMTP email.

### Usage
```python
from your_project.alerting import AlertManager

alert_manager = AlertManager(
    slack_webhook="https://hooks.slack.com/services/YOUR/WEBHOOK/URL",
    email_settings={"smtp_server": "smtp.example.com"}
)

alert_manager.add_rule(
    name="high_error_rate",
    condition=lambda m: m.error_rate > 0.1,
    action=alert_manager.send_slack_alert
)
```
```

--------------------------------

### ModelTrainer

Source: https://context7.com/autombd/ambd-mc/llms.txt

Trains a model from CSV training/validation splits for a fixed number of epochs and persists the serialised model to disk.

```APIDOC
## `ModelTrainer` — Train and Persist a Model

Trains a model from CSV training/validation splits for a fixed number of epochs and persists the serialised model to disk.

### Usage
```python
from your_project.trainer import ModelTrainer

trainer = ModelTrainer()
model = trainer.train(
    training_data="train.csv",
    validation_data="val.csv",
    epochs=10
)
trainer.save_model("model.pkl")
print("Model saved to model.pkl")
```
```

--------------------------------

### Load Input Data with Project.load_data

Source: https://context7.com/autombd/ambd-mc/llms.txt

Load structured input data from CSV or JSON files into the project pipeline. This function returns a list of records ready for further processing.

```python
from your_project import Project

project = Project(config_path="config.yaml", log_level="INFO")
project.load_config()

# Load a CSV data file
data = project.load_data("data/input/sample.csv")
print(f"Loaded {len(data)} records")
# Output: Loaded 5 records
```

--------------------------------

### Project.save_results

Source: https://context7.com/autombd/ambd-mc/llms.txt

Serialises the analysis result dictionary to a JSON file at the specified output path. Creates parent directories automatically if they do not exist.

```APIDOC
## `Project.save_results` — Persist Analysis Output

Serialises the analysis result dictionary to a JSON file at the specified output path. Creates parent directories automatically if they do not exist.

### Usage
```python
from pathlib import Path
from your_project import Project

project = Project(config_path="config.yaml", log_level="INFO")
project.load_config()

data = project.load_data("data/input/sample.csv")
transformed_data = project.transform_data(data)
analysis_result = project.analyze(transformed_data)

Path("data/output").mkdir(parents=True, exist_ok=True)
project.save_results(analysis_result, output_path="data/output/results.json")
# Output file: data/output/results.json
```
```

--------------------------------

### 单元测试示例

Source: https://github.com/autombd/ambd-mc/blob/main/docs/doc-contributing.md

使用 pytest 编写单元测试，包括正常情况、边界情况和使用 fixture 的测试。

```python
import pytest
from your_project.module import your_function

def test_your_function_normal_case():
    """测试正常情况"""
    result = your_function(1, 2)
    assert result == 3

def test_your_function_edge_case():
    """测试边界情况"""
    with pytest.raises(ValueError):
        your_function(-1, 2)

def test_your_function_with_fixture(sample_fixture):
    """使用 fixture 的测试"""
    result = your_function(sample_fixture, 2)
    assert result == expected_value
```

--------------------------------

### Concurrent Batch Processing with Asyncio

Source: https://context7.com/autombd/ambd-mc/llms.txt

Processes a batch of items concurrently using asyncio.gather, separating successful results from exceptions. Requires asyncio.

```python
import asyncio
from your_project.async_processor import AsyncProcessor

async def process_batch(items):
    processor = AsyncProcessor()
    tasks = [processor.process(item) for item in items]
    results = await asyncio.gather(*tasks, return_exceptions=True)

    successful = [r for r in results if not isinstance(r, Exception)]
    failed     = [r for r in results if isinstance(r, Exception)]
    return successful, failed

async def main():
    items = [1, 2, 3, 4, 5]
    successful, failed = await process_batch(items)
    print(f"Succeeded: {len(successful)}, Failed: {len(failed)}")
    # Output: Succeeded: 5, Failed: 0

asyncio.run(main())
```

--------------------------------

### Transform Data with Project.transform_data

Source: https://context7.com/autombd/ambd-mc/llms.txt

Apply data cleaning, normalization, and feature engineering transformations. This can be done via the Project interface or directly using the DataProcessor class.

```python
from your_project import Project
from your_project.data_processor import DataProcessor

project = Project(config_path="config.yaml", log_level="INFO")
project.load_config()

data = project.load_data("data/input/sample.csv")

# Transform via the Project interface
transformed_data = project.transform_data(data)
print("Transformation complete")

# Alternatively, use DataProcessor directly
processor = DataProcessor()
processed_data = processor.clean_and_transform("raw_data.csv")
processor.save("processed_data.csv")
```

--------------------------------

### Save Analysis Results to JSON

Source: https://context7.com/autombd/ambd-mc/llms.txt

Persists analysis results to a JSON file. Parent directories are created automatically if they don't exist.

```python
from pathlib import Path
from your_project import Project

project = Project(config_path="config.yaml", log_level="INFO")
project.load_config()

data = project.load_data("data/input/sample.csv")
transformed_data = project.transform_data(data)
analysis_result = project.analyze(transformed_data)

Path("data/output").mkdir(parents=True, exist_ok=True)
project.save_results(analysis_result, output_path="data/output/results.json")
# Output file: data/output/results.json
```

--------------------------------

### AsyncProcessor

Source: https://context7.com/autombd/ambd-mc/llms.txt

Processes a batch of items concurrently using `asyncio.gather`, separating successful results from exceptions.

```APIDOC
## `AsyncProcessor` — Concurrent Batch Processing

Processes a batch of items concurrently using `asyncio.gather`, separating successful results from exceptions.

### Usage
```python
import asyncio
from your_project.async_processor import AsyncProcessor

async def process_batch(items):
    processor = AsyncProcessor()
    tasks = [processor.process(item) for item in items]
    results = await asyncio.gather(*tasks, return_exceptions=True)

    successful = [r for r in results if not isinstance(r, Exception)]
    failed     = [r for r in results if isinstance(r, Exception)]
    return successful, failed

async def main():
    items = [1, 2, 3, 4, 5]
    successful, failed = await process_batch(items)
    print(f"Succeeded: {len(successful)}, Failed: {len(failed)}")
    # Output: Succeeded: 5, Failed: 0

asyncio.run(main())
```
```

--------------------------------

### MetricsCollector

Source: https://context7.com/autombd/ambd-mc/llms.txt

Instruments Python functions and tracks custom gauges/counters, exporting metrics to a Prometheus endpoint.

```APIDOC
## `MetricsCollector` — Prometheus Monitoring

Instruments Python functions and tracks custom gauges/counters, exporting metrics to a Prometheus endpoint.

### Usage
```python
from your_project.monitoring import MetricsCollector

metrics = MetricsCollector(
    prometheus_url="http://localhost:9090",
    application_name="myapp"
)

@metrics.timer("function_execution_time")
def business_logic():
    pass   # Execution time recorded automatically

# Custom metrics
metrics.gauge("active_users", 150)
metrics.increment("requests_processed")
```
```

--------------------------------

### Project.transform_data

Source: https://context7.com/autombd/ambd-mc/llms.txt

Applies a series of data transformations, including cleaning and normalization, to the raw data. This method can be called directly on the Project object or by instantiating the DataProcessor class.

```APIDOC
## Project.transform_data — Data Transformation Pipeline

Applies cleaning, normalisation, and feature engineering transformations to raw data loaded from disk.

```python
from your_project import Project
from your_project.data_processor import DataProcessor

project = Project(config_path="config.yaml", log_level="INFO")
project.load_config()

data = project.load_data("data/input/sample.csv")

# Transform via the Project interface
transformed_data = project.transform_data(data)
print("Transformation complete")

# Alternatively, use DataProcessor directly
processor = DataProcessor()
processed_data = processor.clean_and_transform("raw_data.csv")
processor.save("processed_data.csv")
```
```

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