energy-data-visualizer

name	energy-data-visualizer
description	Interactive visualization for oil and gas production data analysis using Plotly dashboards
type	reference
capabilities	[]
requires	[]
see_also	[]
tags	[]
category	data
version	1.0.0

Energy Data Visualizer Skill

Interactive visualization for oil & gas data analysis using Plotly

When to Use This Skill

Use this skill when you need to:

• Create production time series charts
• Visualize decline curves and forecasts
• Build economic scenario comparison charts
• Generate field/block comparison visualizations
• Create interactive HTML dashboards

Core Pattern

"""
ABOUTME: Interactive visualization toolkit for energy data analysis
ABOUTME: Provides chart templates for production, economics, and mapping
"""

import plotly.graph_objects as go
from plotly.subplots import make_subplots
import pandas as pd


class ProductionChartBuilder:
    """Build interactive production charts."""

    def production_time_series(
        self,
        df: pd.DataFrame,
        date_col: str = "date",
        rate_cols: list = ["oil_bopd", "gas_mcfd"]
    ) -> go.Figure:
        """Create production rate vs time chart."""
        fig = make_subplots(rows=2, cols=1, shared_xaxes=True)

        colors = {"oil_bopd": "#2E7D32", "gas_mcfd": "#D32F2F"}

        for col in rate_cols:
            if col in df.columns:
                fig.add_trace(
                    go.Scatter(x=df[date_col], y=df[col], name=col),
                    row=1, col=1
                )

        fig.update_layout(title="Production History", hovermode=)
         fig

     () -> go.Figure:
        
        fig = go.Figure()

        fig.add_trace(go.Scatter(
            x=actual_df[],
            y=actual_df[],
            mode=,
            name=
        ))

         forecast_df   :
            fig.add_trace(go.Scatter(
                x=forecast_df[],
                y=forecast_df[],
                mode=,
                name=
            ))

         log_scale:
            fig.update_yaxes(=)

         fig


 :
    

     () -> go.Figure:
        
        names = (components.keys()) + []
        values = (components.values())
        values.append((values))

        fig = go.Figure(go.Waterfall(
            x=names,
            y=values,
            measure=[] * ((values)-) + []
        ))

        fig.update_layout(title=)
         fig

     () -> go.Figure:
        
        params = (sensitivities.keys())
        lows = [s[] - base_npv  s  sensitivities.values()]
        highs = [s[] - base_npv  s  sensitivities.values()]

        fig = go.Figure()
        fig.add_trace(go.Bar(y=params, x=lows, orientation=, name=))
        fig.add_trace(go.Bar(y=params, x=highs, orientation=, name=))

        fig.update_layout(barmode=, title=)
         fig