measurement-error-analysis
Analyze measurement errors, uncertainties, and statistical variations in experimental data for quality control.
Informations de source
- Dépôt
- InternScience/DrClaw
- Dernière activité de la source
- 13 mars 2026 à 15:08
- Langue détectée de SKILL.md
- anglais
- Étoiles
- 172
- Forks
- 10
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Lisez SKILL.md et les fichiers associés affichés par SkillsMP avant de décider de l'installer.
Affichage de SKILL.md
SKILL.md
Instructions source · Aperçu en lecture seule- 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
Voir sur GitHub