viz_plotly

frequenz.lib.notebooks.reporting.asset_optimization.viz_plotly

Visualization for asset optimization reporting using Plotly.

Functions

frequenz.lib.notebooks.reporting.asset_optimization.viz_plotly.plot_battery_power

plot_battery_power(df: DataFrame) -> Figure

Plot battery power and state of charge (SOC) over time.

Creates a Plotly figure visualizing battery behavior, including available power, charging, discharging, and SOC. The SOC is displayed on a secondary y-axis, while power-related traces share the primary axis.

PARAMETER	DESCRIPTION
df	Input DataFrame containing the columns required by prepare_battery_power_data. TYPE: DataFrame

RETURNS	DESCRIPTION
Figure	A Plotly figure showing battery power flows and SOC.

Source code in src/frequenz/lib/notebooks/reporting/asset_optimization/viz_plotly.py

def plot_battery_power(df: pd.DataFrame) -> go.Figure:
    """Plot battery power and state of charge (SOC) over time.

    Creates a Plotly figure visualizing battery behavior, including available
    power, charging, discharging, and SOC. The SOC is displayed on a secondary
    y-axis, while power-related traces share the primary axis.

    Args:
        df: Input DataFrame containing the columns required by
            ``prepare_battery_power_data``.

    Returns:
        A Plotly figure showing battery power flows and SOC.
    """
    data = prepare_battery_power_data(df)

    fig = go.Figure()

    fig.add_trace(
        go.Scatter(
            x=data.index,
            y=data.soc,
            name="SOC",
            line={"color": SOC, "shape": "hv"},
            fill="tozeroy",
            opacity=0.4,
            yaxis="y2",
            hovertemplate="<b>%{fullData.name}</b>: %{y}%<extra></extra>",
        ),
    )

    fig.add_trace(
        go.Scatter(
            x=data.index,
            y=data.available,
            name="Available power",
            line={"color": AVAILABLE, "shape": "hv"},
            hovertemplate="<b>%{fullData.name}</b>: %{y} kW<extra></extra>",
        ),
    )

    fig.add_trace(
        go.Scatter(
            x=data.index,
            y=[0] * len(data.index),
            name="Zero",
            line={"color": ZERO_LINE, "dash": "dash", "shape": "hv"},
            showlegend=False,
            hoverinfo="skip",
        ),
    )

    fig.add_trace(
        go.Scatter(
            x=data.index,
            y=data.charge,
            name="Charge",
            line={"color": CHARGE, "shape": "hv"},
            opacity=0.9,
            hovertemplate="<b>%{fullData.name}</b>: %{y} kW<extra></extra>",
        ),
    )
    fig.add_trace(
        go.Scatter(
            x=data.index,
            y=data.discharge,
            name="Discharge",
            line={"color": DISCHARGE, "shape": "hv"},
            opacity=0.9,
            hovertemplate="<b>%{fullData.name}</b>: %{y} kW<extra></extra>",
        ),
    )

    fig.update_layout(
        yaxis={
            "title": "Battery Power",
            "range": [-data.max_abs_battery * 1.1, data.max_abs_battery * 1.1],
        },
        yaxis2={
            "title": "Battery SOC",
            "range": [0, 100],
            "overlaying": "y",
            "side": "right",
            "tickfont": {"color": SOC},
        },
    )

    _apply_common_layout(fig)
    _apply_range_slider(fig)
    return fig

frequenz.lib.notebooks.reporting.asset_optimization.viz_plotly.plot_energy_trade

plot_energy_trade(
    df: DataFrame,
    fig: Figure | None = None,
    row: int | None = None,
) -> Figure

Plot the microgrid energy trade as a time-series chart.

Creates a Plotly figure showing bought and sold energy over time based on the processed output of prepare_energy_trade_data. The function adds separate traces for energy buy and sell values and optionally applies the standard layout and range slider when used as a standalone figure.

PARAMETER	DESCRIPTION
df	Input DataFrame containing the columns required by prepare_energy_trade_data. TYPE: DataFrame
fig	Optional existing figure to which traces are added. If not provided, a new go.Figure is created. TYPE: Figure | None DEFAULT: None
row	Optional subplot row index. When provided, the common layout and range slider are not applied, and axis padding is scoped to the specified subplot row. TYPE: int | None DEFAULT: None

RETURNS	DESCRIPTION
Figure	A Plotly figure containing the energy trade traces.

Source code in src/frequenz/lib/notebooks/reporting/asset_optimization/viz_plotly.py

def plot_energy_trade(
    df: pd.DataFrame,
    fig: go.Figure | None = None,
    row: int | None = None,
) -> go.Figure:
    """Plot the microgrid energy trade as a time-series chart.

    Creates a Plotly figure showing bought and sold energy over time based on
    the processed output of ``prepare_energy_trade_data``. The function adds
    separate traces for energy buy and sell values and optionally applies the
    standard layout and range slider when used as a standalone figure.

    Args:
        df: Input DataFrame containing the columns required by
            ``prepare_energy_trade_data``.
        fig: Optional existing figure to which traces are added. If not provided,
            a new ``go.Figure`` is created.
        row: Optional subplot row index. When provided, the common layout and
            range slider are not applied, and axis padding is scoped to the
            specified subplot row.

    Returns:
        A Plotly figure containing the energy trade traces.
    """
    data = prepare_energy_trade_data(df)

    if fig is None:
        fig = go.Figure()

    fig.add_trace(
        go.Scatter(
            x=data.index,
            y=data.buy,
            name="Buy",
            line={"color": BUY, "shape": "hv"},
            fill="tozeroy",
            hovertemplate="<b>Buy</b>: %{y} kWh<extra></extra>",
        )
    )
    fig.add_trace(
        go.Scatter(
            x=data.index,
            y=data.sell,
            name="Sell",
            line={"color": SELL, "shape": "hv"},
            fill="tozeroy",
            hovertemplate="<b>Sell</b>: %{y} kWh<extra></extra>",
        )
    )

    if row is None:
        _apply_common_layout(fig, y_title="Energy (kWh)")
        _apply_range_slider(fig)
    y_all = pd.concat([data.buy, data.sell])
    _apply_axis_padding(
        fig,
        x_index=data.index,
        y_min=float(y_all.min()),
        y_max=float(y_all.max()),
        row=row,
        col=1 if row is not None else None,
    )
    return fig

frequenz.lib.notebooks.reporting.asset_optimization.viz_plotly.plot_monthly

plot_monthly(df: DataFrame) -> tuple[Figure, DataFrame]

Plot monthly aggregated energy data as grouped bar charts.

Builds a Plotly bar chart showing positive and negative monthly energy values returned by prepare_monthly_data. Positive values are plotted as standard bars, while negative values are rendered with reduced opacity for visual distinction.

PARAMETER	DESCRIPTION
df	Input DataFrame containing time-series data required by prepare_monthly_data. TYPE: DataFrame

RETURNS	DESCRIPTION
tuple[Figure, DataFrame]	The monthly aggregated DataFrame used for plotting.

Source code in src/frequenz/lib/notebooks/reporting/asset_optimization/viz_plotly.py

def plot_monthly(df: pd.DataFrame) -> tuple[go.Figure, pd.DataFrame]:
    """Plot monthly aggregated energy data as grouped bar charts.

    Builds a Plotly bar chart showing positive and negative monthly energy
    values returned by ``prepare_monthly_data``. Positive values are plotted
    as standard bars, while negative values are rendered with reduced opacity
    for visual distinction.

    Args:
        df:
            Input DataFrame containing time-series data required by
            ``prepare_monthly_data``.

    Returns:
        The monthly aggregated DataFrame used for plotting.
    """
    data = prepare_monthly_data(df)

    fig = go.Figure()
    x_labels = pd.to_datetime(data.months.index).strftime("%d-%m-%Y")
    for column in data.positive.columns:
        fig.add_trace(
            go.Bar(
                x=[x_labels, [column] * len(x_labels)],
                y=data.positive[column],
                name=column,
                text=data.positive[column].round(3),
                textposition="outside",
            )
        )
    for column in data.negative.columns:
        fig.add_trace(
            go.Bar(
                x=[x_labels, [column] * len(x_labels)],
                y=data.negative[column],
                name=column,
                opacity=0.7,
                text=data.negative[column].round(3),
                textposition="outside",
            )
        )

    _apply_common_layout(fig, y_title="Energy (MWh)")
    fig.update_xaxes(
        tickangle=45, showdividers=False, dividercolor="rgba(0,0,0,0)", automargin=True
    )
    return fig, data.months

frequenz.lib.notebooks.reporting.asset_optimization.viz_plotly.plot_power_flow

plot_power_flow(
    df: DataFrame,
    fig: Figure | None = None,
    row: int | None = None,
) -> Figure

Plot the microgrid power flow as a stacked time-series chart.

Builds a Plotly figure showing the evolution of key power-flow components over time, including on-site production (CHP and/or PV), battery charging and discharging (when available), grid exchange, and total consumption. The input data is normalized via prepare_power_flow_data before traces are added.

If no figure is provided, a new one is created. When row is not provided, the function also applies the common layout and a range slider. Axis padding is applied in all cases based on the combined y-range of the plotted series.

PARAMETER	DESCRIPTION
df	Input DataFrame containing the columns required by prepare_power_flow_data. TYPE: DataFrame
fig	Optional existing figure to add traces to. If not provided, a new go.Figure is created. TYPE: Figure | None DEFAULT: None
row	Optional subplot row index. When provided, common layout and range slider configuration are skipped and axis padding is applied to the specified subplot row. TYPE: int | None DEFAULT: None

RETURNS	DESCRIPTION
Figure	A Plotly figure containing the power-flow traces.

Source code in src/frequenz/lib/notebooks/reporting/asset_optimization/viz_plotly.py

def plot_power_flow(
    df: pd.DataFrame,
    fig: go.Figure | None = None,
    row: int | None = None,
) -> go.Figure:
    """Plot the microgrid power flow as a stacked time-series chart.

    Builds a Plotly figure showing the evolution of key power-flow components
    over time, including on-site production (CHP and/or PV), battery charging and
    discharging (when available), grid exchange, and total consumption. The input
    data is normalized via ``prepare_power_flow_data`` before traces are added.

    If no figure is provided, a new one is created. When ``row`` is not provided,
    the function also applies the common layout and a range slider. Axis padding is
    applied in all cases based on the combined y-range of the plotted series.

    Args:
        df: Input DataFrame containing the columns required by
            ``prepare_power_flow_data``.
        fig: Optional existing figure to add traces to. If not provided, a new
            ``go.Figure`` is created.
        row: Optional subplot row index. When provided, common layout and range
            slider configuration are skipped and axis padding is applied to the
            specified subplot row.

    Returns:
        A Plotly figure containing the power-flow traces.
    """
    data = prepare_power_flow_data(df)

    if fig is None:
        fig = go.Figure()

    if data.has_chp:
        fig.add_trace(
            go.Scatter(
                x=data.index,
                y=data.chp,
                name="CHP",
                line={"color": CHP, "shape": "hv"},
                fill="tozeroy",
                opacity=0.5,
                hovertemplate="<b>CHP</b>: %{y} kW<extra></extra>",
            )
        )

    if data.has_pv:
        pv_label = "PV (on CHP)" if data.has_chp else "PV"
        fig.add_trace(
            go.Scatter(
                x=data.index,
                y=data.production,
                name=pv_label,
                line={"color": PV, "shape": "hv"},
                fill="tonexty" if data.has_chp else "tozeroy",
                opacity=0.7,
                hovertemplate="<b>%{fullData.name}</b>: %{y} kW<extra></extra>",
            )
        )

    if data.charge is not None and data.discharge is not None:
        fig.add_trace(
            go.Scatter(
                x=data.index,
                y=data.consumption,
                name="Consumption (base)",
                line={"color": TRANSPARENT, "shape": "hv"},
                showlegend=False,
                hoverinfo="skip",
            )
        )
        fig.add_trace(
            go.Scatter(
                x=data.index,
                y=data.charge,
                name="Charge",
                line={"color": CHARGE, "shape": "hv"},
                opacity=0.2,
                hovertemplate="<b>Charge</b>: %{y} kW<extra></extra>",
            )
        )
        fig.add_trace(
            go.Scatter(
                x=data.index,
                y=data.consumption,
                name="Consumption (base)",
                line={"color": TRANSPARENT, "shape": "hv"},
                showlegend=False,
                hoverinfo="skip",
            )
        )
        fig.add_trace(
            go.Scatter(
                x=data.index,
                y=data.discharge,
                name="Discharge",
                line={"color": DISCHARGE, "shape": "hv"},
                opacity=0.5,
                hovertemplate="<b>Discharge</b>: %{y} kW<extra></extra>",
            )
        )

    if data.grid is not None:
        fig.add_trace(
            go.Scatter(
                x=data.index,
                y=data.grid,
                name="Grid",
                line={"color": GRID, "shape": "hv"},
                hovertemplate="<b>Grid</b>: %{y} kW<extra></extra>",
            )
        )

    fig.add_trace(
        go.Scatter(
            x=data.index,
            y=data.consumption,
            name="Consumption",
            line={"color": CONSUMPTION, "shape": "hv"},
            hovertemplate="<b>Consumption</b>: %{y} kW<extra></extra>",
        )
    )

    if row is None:
        _apply_common_layout(fig, y_title="Power (kW)")
        _apply_range_slider(fig)

    y_series = [data.consumption, data.chp, data.production]
    if data.grid is not None:
        y_series.append(data.grid)
    if data.charge is not None:
        y_series.append(data.charge)
    if data.discharge is not None:
        y_series.append(data.discharge)
    y_all = pd.concat(y_series)
    _apply_axis_padding(
        fig,
        x_index=data.index,
        y_min=float(y_all.min()),
        y_max=float(y_all.max()),
        row=row,
        col=1 if row is not None else None,
    )
    return fig

frequenz.lib.notebooks.reporting.asset_optimization.viz_plotly.plot_power_flow_trade

plot_power_flow_trade(df: DataFrame) -> Figure

Create a combined subplot showing power flow and energy trade.

Builds a two-row Plotly figure with shared x-axis: - The top subplot renders the power-flow visualization. - The bottom subplot renders the corresponding energy-trade visualization.

Traces from the underlying figures are merged into a single subplot layout and assigned to two separate legends (one per subplot). The figure applies a common layout configuration, axis styling, subplot-specific y-axis titles, and adds a range slider on the lower subplot.

PARAMETER	DESCRIPTION
df	Input DataFrame containing the columns required by plot_power_flow and plot_energy_trade. TYPE: DataFrame

RETURNS	DESCRIPTION
Figure	A Plotly figure containing the stacked power-flow and energy-trade
Figure	subplots with independent legends and shared time navigation.

Source code in src/frequenz/lib/notebooks/reporting/asset_optimization/viz_plotly.py

def plot_power_flow_trade(df: pd.DataFrame) -> go.Figure:
    """Create a combined subplot showing power flow and energy trade.

    Builds a two-row Plotly figure with shared x-axis:
    - The top subplot renders the power-flow visualization.
    - The bottom subplot renders the corresponding energy-trade visualization.

    Traces from the underlying figures are merged into a single subplot layout and
    assigned to two separate legends (one per subplot). The figure applies a common
    layout configuration, axis styling, subplot-specific y-axis titles, and adds a
    range slider on the lower subplot.

    Args:
        df: Input DataFrame containing the columns required by ``plot_power_flow``
            and ``plot_energy_trade``.

    Returns:
        A Plotly figure containing the stacked power-flow and energy-trade
        subplots with independent legends and shared time navigation.
    """
    fig_final = make_subplots(
        rows=2,
        cols=1,
        shared_xaxes=True,
        vertical_spacing=0.1,
        row_heights=[0.75, 0.25],
    )

    # Generate standalone figures
    fig_power = plot_power_flow(df)
    fig_trade = plot_energy_trade(df)

    for trace in fig_power.data:
        trace.legend = "legend"
        fig_final.add_trace(trace, row=1, col=1)

    for trace in fig_trade.data:
        trace.legend = "legend2"
        fig_final.add_trace(trace, row=2, col=1)

    # Apply "Common Layout" Logic manually to the Subplot
    fig_final.update_layout(
        width=FIGURE_SIZE[0],
        height=FIGURE_SIZE[1],
        template="plotly_white",
        paper_bgcolor="white",
        plot_bgcolor="white",
        hovermode="x unified",
        margin={"l": 60, "r": 150, "t": 40, "b": 40},  # Increased 'r' for legends
    )

    # Apply Multi-Legend Formatting
    legend_style = {
        "orientation": "v",
        "xanchor": "left",
        "x": 1.05,
        "bordercolor": "black",
        "borderwidth": 1,
    }

    # Configure the layout for both legends
    fig_final.update_layout(
        legend={**legend_style, "y": 1, "yanchor": "top", "title": "Power Flow"},
        legend2={
            **legend_style,
            "y": 0.05,
            "yanchor": "bottom",
            "title": "Energy Trade",
        },
    )

    fig_final.update_xaxes(showgrid=True, showline=True, mirror=True, linecolor="black")

    fig_final.update_yaxes(showgrid=True, showline=True, mirror=True, linecolor="black")

    # Set Specific Titles
    fig_final.update_yaxes(title_text="Power (kW)", row=1, col=1)
    fig_final.update_yaxes(title_text="Energy (kWh)", row=2, col=1)
    _apply_range_slider(fig_final, row=2, col=1)

    return fig_final