metocean-visualizer

name	metocean-visualizer
description	Create interactive metocean visualizations including time series plots, wave roses, scatter plots, geographic maps, and dashboards. Use for data exploration, reporting, and operational monitoring.

Metocean Visualizer Skill

Interactive visualization toolkit for metocean data analysis using Plotly

When to Use This Skill

Use this skill when you need to:

• Plot wave data or create metocean dashboards
• Visualize buoy observations or hindcast data
• Generate wave roses or wind roses
• Create time series charts for Hs, Tp, wind speed
• Build scatter plots (Hs vs Tp, wind vs waves)
• Map metocean stations with interactive overlays
• Compare forecasts vs observations
• Generate joint distribution contour plots
• Create operational monitoring dashboards

Trigger phrases:

• "Plot wave data", "Create metocean dashboard"
• "Visualize buoy observations", "Generate wave rose"
• "Time series chart", "Scatter plot Hs vs Tp"
• "Map metocean stations", "Interactive plot"
• "Compare forecasts vs observations"

Visualization Types

Time Series Plots

• Single parameter trends (Hs, Tp, wind speed)
• Multi-parameter comparison on shared x-axis
• Quality flag indicators with color coding
• Forecast vs observation overlays

Directional Roses

• Wave roses (Hs by direction)
• Wind roses (speed by direction)
• Current roses (velocity by direction)
• Customizable sectors (8, 12, 16, 36)

Scatter Plots

• Hs vs Tp (wave height vs period)
• Wind speed vs wave height correlations
• Joint distribution contours
• Color-coded by time/quality/source

Geographic Maps

• Station locations with Scattermapbox
• Data overlays (latest values)
• Regional coverage visualization
• Interactive hover details

Joint Distribution Charts

• 2D histograms (Histogram2d)
• Contour plots for environmental design
• Conditional distributions
• Return period contours

Statistical Charts

• Histograms with kernel density
• CDFs (cumulative distribution functions)
• QQ plots for distribution comparison
• Box plots by month/season

Core Patterns

"""
ABOUTME: Interactive visualization toolkit for metocean data analysis
ABOUTME: Provides chart templates for waves, wind, currents, and mapping
"""

import plotly.graph_objects as go
from plotly.subplots import make_subplots
import pandas as pd
import numpy as np
from typing import Optional


class MetoceanChartBuilder:
    """Build interactive metocean charts with Plotly."""

    def time_series(
        self,
        df: pd.DataFrame,
        output_path: Optional[str] = None
    ) -> go.Figure:
        """Create interactive time series of wave parameters."""
        fig = make_subplots(
            rows=3, cols=1,
            shared_xaxes=True,
            vertical_spacing=0.05,
            subplot_titles=('Wave Height', 'Wave Period', 'Wind Speed')
        )

        # Wave height
        fig.add_trace(
            go.Scatter(
                x=df['time'], y=df['wave_height_m'],
                name='Hs', line=dict(color='#1f77b4')
            ),
            row=1, col=1
        )

        # Wave period
        fig.add_trace(
            go.Scatter(
                x=df['time'], y=df['wave_period_s'],
                name='Tp', line=dict(color=)
            ),
            row=, col=
        )

        
        fig.add_trace(
            go.Scatter(
                x=df[], y=df[],
                name=, line=(color=)
            ),
            row=, col=
        )

        fig.update_layout(
            height=,
            title=,
            hovermode=
        )
        fig.update_yaxes(title_text=, row=, col=)
        fig.update_yaxes(title_text=, row=, col=)
        fig.update_yaxes(title_text=, row=, col=)

         output_path:
            fig.write_html(output_path)
         fig

     () -> go.Figure:
        
        sector_width =  / n_sectors
        bins = np.arange(,  + sector_width, sector_width)

        df[] = pd.cut(
            df[direction_col],
            bins=bins,
            labels=bins[:-] + sector_width / 
        )

        stats = df.groupby().agg({
            height_col: [, ]
        }).reset_index()
        stats.columns = [, , ]
        total = stats[].()
        stats[] =  * stats[] / total

        fig = go.Figure()
        fig.add_trace(go.Barpolar(
            r=stats[],
            theta=stats[],
            width=sector_width * ,
            marker_color=stats[],
            marker_colorscale=,
            marker_colorbar=(title=),
            hovertemplate=(
                
                
                
            )
        ))

        fig.update_layout(
            polar=(
                radialaxis=(visible=, =[, stats[].() * ]),
                angularaxis=(direction=, rotation=)
            ),
            title=,
            showlegend=
        )

         output_path:
            fig.write_html(output_path)
         fig

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

        
        fig.add_trace(go.Histogram2d(
            x=df[],
            y=df[],
            colorscale=,
            showscale=,
            colorbar=(title=),
            nbinsx=,
            nbinsy=
        ))

        
        fig.add_trace(go.Scatter(
            x=df[],
            y=df[],
            mode=,
            marker=(size=, color=, opacity=),
            hovertemplate=
        ))

        fig.update_layout(
            xaxis_title=,
            yaxis_title=,
            title=
        )

         output_path:
            fig.write_html(output_path)
         fig

     () -> go.Figure:
        
        lats = [s[]  s  stations]
        lons = [s[]  s  stations]
        names = [s[]  s  stations]

        fig = go.Figure(go.Scattermapbox(
            lat=lats,
            lon=lons,
            mode=,
            marker=(size=, color=),
            text=names,
            hovertemplate=
        ))

        fig.update_layout(
            mapbox=(
                style=,
                center=(lat=(lats) / (lats), lon=(lons) / (lons)),
                zoom=
            ),
            title=,
            margin=(l=, r=, t=, b=)
        )

         output_path:
            fig.write_html(output_path)
         fig

Dashboard Template

def create_metocean_dashboard(
    df: pd.DataFrame,
    stations: list,
    output_path: str = 'reports/metocean_dashboard.html'
) -> go.Figure:
    """Create comprehensive metocean dashboard."""
    fig = make_subplots(
        rows=2, cols=2,
        specs=[
            [{"type": "scatter"}, {"type": "polar"}],
            [{"type": "scatter"}, {"type": "scattermapbox"}]
        ],
        subplot_titles=('Time Series', 'Wave Rose', 'Hs vs Tp', 'Station Map'),
        vertical_spacing=0.12,
        horizontal_spacing=0.1
    )

    # Time series (row 1, col 1)
    fig.add_trace(
        go.Scatter(
            x=df['time'], y=df['wave_height_m'],
            name='Hs', line=dict(color='#1f77b4')
        ),
        row=1, col=1
    )

    # Wave rose (row 1, col 2)
    dir_stats = calculate_directional_stats(df)
    fig.add_trace(
        go.Barpolar(
            r=dir_stats['occurrence_pct'],
            theta=dir_stats['direction'],
            marker_color=dir_stats['mean_hs'],
            marker_colorscale='Viridis'
        ),
        row=1, col=2
    )

    # Scatter plot (row 2, col 1)
    fig.add_trace(
        go.Scatter(
            x=df['wave_height_m'], y=df['wave_period_s'],
            mode=, marker=(size=, opacity=),
            name=
        ),
        row=, col=
    )

    
    fig.add_trace(
        go.Scattermapbox(
            lat=[s[]  s  stations],
            lon=[s[]  s  stations],
            mode=,
            marker=(size=),
            text=[s[]  s  stations]
        ),
        row=, col=
    )

    fig.update_layout(
        height=,
        title=,
        mapbox=(style=, zoom=)
    )
    fig.write_html(output_path)
     fig


 () -> pd.DataFrame:
    
    sector_width =  / n_sectors
    bins = np.arange(,  + sector_width, sector_width)

    df_copy = df.copy()
    df_copy[] = pd.cut(
        df_copy[],
        bins=bins,
        labels=bins[:-] + sector_width / 
    )

    stats = df_copy.groupby().agg({
        : [, ]
    }).reset_index()
    stats.columns = [, , ]
    total = stats[].()
    stats[] =  * stats[] / total

     stats

Wind Rose with Matplotlib (windrose package)

from windrose import WindroseAxes
import matplotlib.pyplot as plt
import numpy as np


def plot_wind_rose_matplotlib(
    speeds: np.ndarray,
    directions: np.ndarray,
    output_path: Optional[str] = None,
    title: str = 'Wind Rose'
) -> plt.Figure:
    """Create wind rose diagram using windrose package."""
    fig = plt.figure(figsize=(10, 10))
    ax = WindroseAxes.from_ax(fig=fig)
    ax.bar(
        directions, speeds,
        normed=True, opening=0.8,
        bins=np.arange(0, 25, 5),
        cmap=plt.cm.viridis
    )
    ax.set_legend(title='Speed (m/s)')
    ax.set_title(title)

    if output_path:
        plt.savefig(output_path, dpi=150, bbox_inches='tight')
    return fig

Forecast vs Observation Comparison

def plot_forecast_comparison(
    obs_df: pd.DataFrame,
    fcst_df: pd.DataFrame,
    param: str = 'wave_height_m',
    output_path: Optional[str] = None
) -> go.Figure:
    """Compare forecast vs observation time series."""
    fig = go.Figure()

    fig.add_trace(go.Scatter(
        x=obs_df['time'], y=obs_df[param],
        name='Observation',
        mode='lines+markers',
        marker=dict(size=4),
        line=dict(color='#1f77b4')
    ))

    fig.add_trace(go.Scatter(
        x=fcst_df['time'], y=fcst_df[param],
        name='Forecast',
        mode='lines',
        line=dict(color='#ff7f0e', dash='dash')
    ))

    fig.update_layout(
        title=f'{param} - Forecast vs Observation',
        xaxis_title='Time',
        yaxis_title=param,
        hovermode='x unified',
        legend=dict(yanchor='top', y=0.99, xanchor='left', x=0.01)
    )

    if output_path:
        fig.write_html(output_path)
    return fig

HTML Report Generation

import plotly.io as pio


def generate_metocean_report(
    df: pd.DataFrame,
    station_info: dict,
    output_path: str = 'reports/metocean_report.html'
) -> str:
    """Generate comprehensive HTML metocean report."""
    builder = MetoceanChartBuilder()

    ts_fig = builder.time_series(df)
    rose_fig = builder.wave_rose(df)
    scatter_fig = builder.scatter_hs_tp(df)

    html_content = f'''<!DOCTYPE html>
<html>
<head>
    <title>Metocean Report - {station_info["id"]}</title>
    <script src="https://cdn.plot.ly/plotly-latest.min.js"></script>
    <style>
        body {{ font-family: Arial, sans-serif; margin: 20px; }}
        .plot-container {{ margin: 20px 0; }}
        h1 {{ color: #333; }}
        h2 {{ color: #555; border-bottom: 1px solid #ddd; padding-bottom: 5px; }}
        .metadata {{ background: #f5f5f5; padding: 15px; border-radius: 5px; }}
        .metadata p {{ margin: 5px 0; }}
    </style>
</head>
<body>
    <h1>Metocean Report: Station {station_info["id"]}</h1>
    <div class="metadata">
        <p><strong>Location:</strong> {station_info["lat"]:.3f} N, {station_info["lon"]:.3f} W</p>
        <p><strong>Period:</strong> {df["time"].min()} to {df["time"].max()}</p>
        <p><strong>Records:</strong> {len(df):,}</p>
        <p><strong>Data Source:</strong> {station_info.get("source", "N/A")}</p>
    </div>

    <h2>Time Series</h2>
    <div class="plot-container">
        {pio.to_html(ts_fig, include_plotlyjs=False, full_html=False)}
    </div>

    <h2>Wave Rose</h2>
    <div class="plot-container">
        
    </div>

    <h2>Joint Distribution</h2>
    <div class="plot-container">
        
    </div>
</body>
</html>'''

     (output_path, )  f:
        f.write(html_content)

     output_path

Usage Examples

from worldenergydata.metocean.visualize import MetoceanChartBuilder
import pandas as pd

# Load data
df = pd.read_csv('data/processed/buoy_data.csv', parse_dates=['time'])

# Create individual charts
builder = MetoceanChartBuilder()

# Time series
fig_ts = builder.time_series(df)
fig_ts.write_html('reports/time_series.html')

# Wave rose
fig_rose = builder.wave_rose(df)
fig_rose.write_html('reports/wave_rose.html')

# Scatter plot
fig_scatter = builder.scatter_hs_tp(df)
fig_scatter.write_html('reports/scatter_hs_tp.html')

# Station map
stations = [
    {'station_id': 'NDBC-41001', 'latitude': 34.68, 'longitude': -72.66},
    {'station_id': 'NDBC-41002', 'latitude': 31.76, 'longitude': -74.84}
]
fig_map = builder.station_map(stations)
fig_map.write_html('reports/station_map.html')

# Generate full report
generate_metocean_report(df, {'id': 'NDBC-41001', 'lat': 34.68, 'lon': -72.66})

External Tool Integration

windrose package (for matplotlib roses):

pip install windrose

MetOceanViewer Patterns (Desktop Reference):

• Map-based station selection
• Time series with multiple y-axes
• Model-observation comparison panels
• Geographic data overlays

Best Practices

1. Always use interactive plots - Plotly preferred for web-based reports
2. Include hover information - Provide relevant data on all points
3. Use relative paths - Store data in /data/raw/ or /data/processed/
4. Consistent color schemes - Use same colors across related plots
5. Include metadata - Station info, time range, data source
6. Export standalone HTML - Embed Plotly.js for portability
7. Optimize for both screen and print - Consider responsive layouts

Output Formats

Format	Use Case	Method
Interactive HTML	Web dashboards, reports	fig.write_html()
PNG/SVG	Static reports, publications	fig.write_image()
JSON	Data interchange	fig.to_json()
Dashboard HTML	Multi-panel views	make_subplots()

Common Workflows

1. Station Overview: Fetch data -> Time series + Rose + Map -> HTML report
2. Comparison Dashboard: Multiple stations -> Side-by-side plots -> Export
3. Operational Monitoring: Real-time fetch -> Update dashboard -> Alert thresholds
4. Design Analysis: Joint distributions -> Environmental contours -> Export

Related Skills

• energy-data-visualizer - General energy visualization patterns
• metocean-data-fetcher - Data source for visualization
• metocean-statistics - Statistical analysis for contours