🚀 Complete VS Code Jupyter Notebook Guide

# ClydeEnergy VS Code Setup Steps:
# 1. Press Ctrl+Shift+X (or Cmd+Shift+X on Mac)
# 2. Search for "Jupyter"
# 3. Install the official Jupyter extension by Microsoft
# 4. This enables native Jupyter notebook support

# ClydeEnergy Python Extension:
# 1. Search for "Python" in VS Code Extensions
# 2. Install the official Python extension by Microsoft
# 3. Provides IntelliSense and debugging support
# 4. Essential for ClydeEnergy development workflow

# ClydeEnergy Python Environment Check
python --version
# or
python3 --version

# Expected: Python 3.8+ for ClydeEnergy projects

# Create ClydeEnergy virtual environment
python -m venv clydeenergy_jupyter

# Activate on Windows
clydeenergy_jupyter\Scripts\activate

# Activate on macOS/Linux
source clydeenergy_jupyter/bin/activate

# Install essential packages for ClydeEnergy
pip install jupyter pandas numpy matplotlib seaborn

# ClydeEnergy Notebook Creation:
# Method 1: Command Palette
# Press Ctrl+Shift+P → Type "Create: New Jupyter Notebook"

# Method 2: File Menu
# File → New File → Jupyter Notebook

# Method 3: File Explorer
# Right-click → New File → name.ipynb

# ClydeEnergy Naming Convention:
# Format: YYYY-MM-DD_ClydeEnergy_ProjectType_Description.ipynb

# Examples:
# 2025-08-03_ClydeEnergy_DataAnalysis_EnergyMetrics.ipynb
# 2025-08-03_ClydeEnergy_MachineLearning_Forecasting.ipynb
# 2025-08-03_ClydeEnergy_Visualization_Dashboard.ipynb

# ClydeEnergy Kernel Connection Steps:
# 1. Click "Select Kernel" in top-right of notebook
# 2. Choose "Python Environment" from dropdown
# 3. Select your ClydeEnergy virtual environment
# 4. Wait for kernel to initialize

# ClydeEnergy Kernel Verification
import sys
import datetime

print("🏢 ClydeEnergy Development Environment")
print(f"🐍 Python: {sys.version}")
print(f"📅 Time: {datetime.datetime.now()}")
print("✅ Kernel connected successfully!")

# ClydeEnergy Hello World
print("🏢 Hello from ClydeEnergy!")
print("🚀 Welcome to VS Code Jupyter Notebooks")

# ClydeEnergy metrics example
efficiency = 95.5
savings = 150000
print(f"⚡ Efficiency: {efficiency}%")
print(f"💰 Savings: ${savings:,}")

# ClydeEnergy Execution Methods:
# 1. Single cell: Ctrl+Alt+Enter or play button
# 2. Run all: Click "Run All" in toolbar
# 3. Run above: Right-click → "Execute Cells Above"
# 4. Run below: Right-click → "Execute Cells Below"
# 5. Run selection: Highlight code → F8

# ClydeEnergy Analysis Report

## Executive Summary
Energy efficiency analysis for Q3 2025.

### Key Findings:
- **Energy Savings**: 25% improvement
- **Cost Reduction**: $150,000 annually
- **ROI Timeline**: 18 months

### ClydeEnergy Recommendations:
1. Implement Phase 2 upgrades
2. Expand to international facilities
3. Develop predictive models

# ClydeEnergy Data Analysis
import pandas as pd
import matplotlib.pyplot as plt

# Sample ClydeEnergy data
data = {
    'Product': ['Solar Panel', 'Wind Turbine', 'Battery'],
    'Efficiency': [95.5, 98.2, 96.8],
    'Cost': [25000, 45000, 15000]
}

df = pd.DataFrame(data)
print("📊 ClydeEnergy Product Analysis:")
print(df)

# Create visualization
plt.figure(figsize=(10, 6))
plt.bar(df['Product'], df['Efficiency'])
plt.title('ClydeEnergy: Product Efficiency')
plt.ylabel('Efficiency (%)')
plt.show()

# ClydeEnergy Energy Calculation
def calculate_savings(usage, efficiency):
    """Calculate energy savings for ClydeEnergy"""
    baseline = usage
    optimized = usage * (1 - efficiency/100)
    savings = baseline - optimized
    return savings, optimized

# Test with ClydeEnergy data
current_usage = 10000  # kWh
improvement = 25  # 25% efficiency gain

savings, new_usage = calculate_savings(current_usage, improvement)
print(f"🔋 Current: {current_usage} kWh")
print(f"⚡ New: {new_usage:.0f} kWh")
print(f"💚 Savings: {savings:.0f} kWh")

# ClydeEnergy ROI Calculation
def calculate_roi(investment, monthly_savings, years=5):
    """Calculate ROI for ClydeEnergy projects"""
    annual_savings = monthly_savings * 12
    total_savings = annual_savings * years
    
    # Set breakpoint here to inspect variables
    roi = ((total_savings - investment) / investment) * 100
    payback = investment / annual_savings
    
    return roi, payback

# ClydeEnergy example
roi, payback = calculate_roi(50000, 2500)
print(f"📈 ROI: {roi:.1f}%")
print(f"⏳ Payback: {payback:.1f} years")

# ClydeEnergy Advanced Analysis
import pandas as pd
import numpy as np

# Generate ClydeEnergy dataset
np.random.seed(42)
dates = pd.date_range('2025-01-01', periods=365)
energy_data = {
    'date': dates,
    'solar': np.random.normal(850, 150, 365),
    'wind': np.random.normal(620, 200, 365),
    'consumption': np.random.normal(1200, 100, 365)
}

clydeenergy_df = pd.DataFrame(energy_data)
clydeenergy_df['net_generation'] = clydeenergy_df['solar'] + clydeenergy_df['wind']
clydeenergy_df['balance'] = clydeenergy_df['net_generation'] - clydeenergy_df['consumption']

print("🔋 ClydeEnergy Annual Summary:")
print(f"☀️ Solar: {clydeenergy_df['solar'].sum():,.0f} kWh")
print(f"💨 Wind: {clydeenergy_df['wind'].sum():,.0f} kWh")
print(f"⚡ Consumption: {clydeenergy_df['consumption'].sum():,.0f} kWh")

# ClydeEnergy Data Wrangler Usage:
# 1. Click "Variables" in notebook toolbar
# 2. Find 'clydeenergy_df' in the variable list
# 3. Click the data viewer icon
# 4. Install Data Wrangler extension if prompted

# Sample operations you can do:
monthly_summary = clydeenergy_df.groupby(clydeenergy_df['date'].dt.month).agg({
    'solar': 'mean',
    'wind': 'mean',
    'consumption': 'mean'
}).round(2)

print("📅 ClydeEnergy Monthly Analysis:")
print(monthly_summary.head())