Computational Protocols¶

This page contains computational protocols and analysis pipelines used in the lab.

General Guidelines¶

Environment Setup¶

# Example setup commands
conda create -n lab-env python=3.9
conda activate lab-env
pip install -r requirements.txt

Code Standards¶

• Follow PEP 8 for Python code
• Document all functions
• Use version control
• Write unit tests when possible

Protocol 1: Data Preprocessing¶

Overview¶

This protocol describes the standard data preprocessing pipeline.

Input¶

• Raw data format: [Format description]
• Location: [Data location]

Steps¶

1. Data Loading:

   import pandas as pd
   data = pd.read_csv('input.csv')
   

2. Data Cleaning:

   # Remove missing values
   data = data.dropna()
   

3. Data Transformation:

   # Apply transformations
   data['normalized'] = (data['value'] - data['value'].mean()) / data['value'].std()
   

Output¶

• Processed data format: [Format description]
• Location: [Output location]

Protocol 2: Analysis Pipeline¶

Overview¶

Standard analysis pipeline for [type of analysis].

Dependencies¶

pip install numpy pandas scipy matplotlib seaborn

Workflow¶

# Example workflow
import numpy as np
import pandas as pd

# Step 1: Load data
data = pd.read_csv('processed_data.csv')

# Step 2: Perform analysis
results = perform_analysis(data)

# Step 3: Generate visualizations
create_plots(results)

Output¶

• Results format: [Format description]
• Visualizations: [Location and format]

Additional Protocols¶

[Add more computational protocols as needed]

Resources¶

• Software Tools
• Datasets