experimental_data_processing
$
npx mdskill add InternScience/scp/experimental_data_processing**Discipline**: Experimental Physics | **Tools Used**: 5 | **Servers**: 1
SKILL.md
.github/skills/experimental_data_processingView on GitHub ↗
---
name: experimental_data_processing
description: "Experimental Data Processing - Process experimental data: absolute error, mean square, max value, scientific notation formatting. Use this skill for experimental physics tasks involving calculate absolute error calculate mean square calculate max value format scientific notation convert to scientific notation. Combines 5 tools from 1 SCP server(s)."
---
# Experimental Data Processing
**Discipline**: Experimental Physics | **Tools Used**: 5 | **Servers**: 1
## Description
Process experimental data: absolute error, mean square, max value, scientific notation formatting.
## Tools Used
- **`calculate_absolute_error`** from `server-26` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/26/Data_processing_and_statistical_analysis`
- **`calculate_mean_square`** from `server-26` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/26/Data_processing_and_statistical_analysis`
- **`calculate_max_value`** from `server-26` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/26/Data_processing_and_statistical_analysis`
- **`format_scientific_notation`** from `server-26` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/26/Data_processing_and_statistical_analysis`
- **`convert_to_scientific_notation`** from `server-26` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/26/Data_processing_and_statistical_analysis`
## Workflow
1. Calculate absolute errors
2. Compute mean square
3. Find maximum
4. Format in scientific notation
5. Summarize results
## Test Case
### Input
```json
{
"measurements": [
9.78,
9.81,
9.83,
9.79,
9.8
],
"true_value": 9.81
}
```
### Expected Steps
1. Calculate absolute errors
2. Compute mean square
3. Find maximum
4. Format in scientific notation
5. Summarize results
## Usage Example
> **Note:** Replace `<YOUR_SCP_HUB_API_KEY>` with your own SCP Hub API Key. You can obtain one from the [SCP Platform](https://scphub.intern-ai.org.cn).
```python
import asyncio
import json
from mcp import ClientSession
from mcp.client.streamable_http import streamablehttp_client
from mcp.client.sse import sse_client
SERVERS = {
"server-26": "https://scp.intern-ai.org.cn/api/v1/mcp/26/Data_processing_and_statistical_analysis"
}
async def connect(url, transport_type):
transport = streamablehttp_client(url=url, headers={"SCP-HUB-API-KEY": "<YOUR_SCP_HUB_API_KEY>"})
read, write, _ = await transport.__aenter__()
ctx = ClientSession(read, write)
session = await ctx.__aenter__()
await session.initialize()
return session, ctx, transport
def parse(result):
try:
if hasattr(result, 'content') and result.content:
c = result.content[0]
if hasattr(c, 'text'):
try: return json.loads(c.text)
except: return c.text
return str(result)
except: return str(result)
async def main():
# Connect to required servers
sessions = {}
sessions["server-26"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/26/Data_processing_and_statistical_analysis", "streamable-http")
# Execute workflow steps
# Step 1: Calculate absolute errors
result_1 = await sessions["server-26"].call_tool("calculate_absolute_error", arguments={})
data_1 = parse(result_1)
print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}")
# Step 2: Compute mean square
result_2 = await sessions["server-26"].call_tool("calculate_mean_square", arguments={})
data_2 = parse(result_2)
print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}")
# Step 3: Find maximum
result_3 = await sessions["server-26"].call_tool("calculate_max_value", arguments={})
data_3 = parse(result_3)
print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}")
# Step 4: Format in scientific notation
result_4 = await sessions["server-26"].call_tool("format_scientific_notation", arguments={})
data_4 = parse(result_4)
print(f"Step 4 result: {json.dumps(data_4, indent=2, ensure_ascii=False)[:500]}")
# Step 5: Summarize results
result_5 = await sessions["server-26"].call_tool("convert_to_scientific_notation", arguments={})
data_5 = parse(result_5)
print(f"Step 5 result: {json.dumps(data_5, indent=2, ensure_ascii=False)[:500]}")
# Cleanup
print("Workflow complete!")
if __name__ == "__main__":
asyncio.run(main())
```