measurement-error-analysis
Analyze measurement errors, uncertainties, and statistical variations in experimental data for quality control.
来源信息
- 仓库
- InternScience/DrClaw
- 最近来源活动
- 2026年3月13日 15:08
- 检测到的 SKILL.md 语言
- 英语
- 星标
- 172
- 分支
- 10
安装方式
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
检查来源文件
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
正在显示 SKILL.md
SKILL.md
来源说明 · 只读预览- name
- measurement-error-analysis
- description
- Analyze measurement errors, uncertainties, and statistical variations in experimental data for quality control.
- license
- MIT license
- metadata
- {"skill-author":"PJLab"}
- i18n
- {"zh":{"description":"分析测量误差与不确定性。"}}
# Measurement Error Analysis
## Usage
```python
import asyncio
import json
from mcp.client.streamable_http import streamablehttp_client
from mcp import ClientSession
import numpy as np
class AnalysisClient:
def __init__(self, server_url: str, api_key: str):
self.server_url = server_url
self.api_key = api_key
self.session = None
async def connect(self):
try:
self.transport = streamablehttp_client(url=self.server_url, headers={"SCP-HUB-API-KEY": self.api_key})
self.read, self.write, self.get_session_id = await self.transport.__aenter__()
self.session_ctx = ClientSession(self.read, self.write)
self.session = await self.session_ctx.__aenter__()
await self.session.initialize()
return True
except:
return False
async def disconnect(self):
if self.session:
await self.session_ctx.__aexit__(None, None, None)
if hasattr(self, 'transport'):
await self.transport.__aexit__(None, None, None)
def parse_result(self, result):
try:
if hasattr(result, 'content') and result.content:
return json.loads(result.content[0].text)
return str(result)
except:
return {"error": "parse error"}
## Initialize and use
client = AnalysisClient("https://scp.intern-ai.org.cn/api/v1/mcp/26/Data_processing_and_statistical_analysis", "<your-api-key>")
await client.connect()
# Analyze measurement errors
measurements = [10.2, 10.5, 10.1, 10.4, 10.3]
mean = np.mean(measurements)
std_dev = np.std(measurements, ddof=1)
std_error = std_dev / np.sqrt(len(measurements))
print(f"Mean: {mean:.2f}")
print(f"Standard deviation: {std_dev:.3f}")
print(f"Standard error: {std_error:.3f}")
print(f"Result: {mean:.2f} ± {std_error:.3f}")
await client.disconnect()
```
### Use Cases
- Experimental physics, quality control, calibration, uncertainty quantification
在 GitHub 查看