diff-diff/diff_diff/visualization/_power.py at main · UtilityTools/diff-diff · GitHub

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"""Power analysis visualization functions."""

from typing import TYPE_CHECKING, Any, List, Optional, Tuple, Union

import numpy as np

import pandas as pd

if TYPE_CHECKING:

from diff_diff.power import PowerResults, SimulationPowerResults

from diff_diff.pretrends import PreTrendsPowerCurve, PreTrendsPowerResults

def plot_power_curve(

results: Optional[Union["PowerResults", "SimulationPowerResults", pd.DataFrame]] = None,

effect_sizes: Optional[List[float]] = None,

powers: Optional[List[float]] = None,

mde: Optional[float] = None,

target_power: float = 0.80,

plot_type: str = "effect",

figsize: Tuple[float, float] = (10, 6),

title: Optional[str] = None,

xlabel: Optional[str] = None,

ylabel: str = "Power",

color: str = "#2563eb",

mde_color: str = "#dc2626",

target_color: str = "#22c55e",

linewidth: float = 2.0,

show_mde_line: bool = True,

show_target_line: bool = True,

show_grid: bool = True,

ax: Optional[Any] = None,

show: bool = True,

backend: str = "matplotlib",

) -> Any:

"""

Create a power curve visualization.

Shows how statistical power changes with effect size or sample size,

helping researchers understand the trade-offs in study design.

Parameters

----------

results : PowerResults, SimulationPowerResults, or DataFrame, optional

Results object from PowerAnalysis or simulate_power(), or a DataFrame

with columns 'effect_size' and 'power' (or 'sample_size' and 'power').

If None, must provide effect_sizes and powers directly.

effect_sizes : list of float, optional

Effect sizes (x-axis values). Required if results is None.

powers : list of float, optional

Power values (y-axis values). Required if results is None.

mde : float, optional

Minimum detectable effect to mark on the plot.

target_power : float, default=0.80

Target power level to show as horizontal line.

plot_type : str, default="effect"

Type of power curve: "effect" (power vs effect size) or

"sample" (power vs sample size).

figsize : tuple, default=(10, 6)

Figure size (width, height) in inches.

title : str, optional

Plot title. If None, uses a sensible default.

xlabel : str, optional

X-axis label. If None, uses a sensible default.

ylabel : str, default="Power"

Y-axis label.

color : str, default="#2563eb"

Color for the power curve line.

mde_color : str, default="#dc2626"

Color for the MDE vertical line.

target_color : str, default="#22c55e"

Color for the target power horizontal line.

linewidth : float, default=2.0

Line width for the power curve.

show_mde_line : bool, default=True

Whether to show vertical line at MDE.

show_target_line : bool, default=True

Whether to show horizontal line at target power.

show_grid : bool, default=True

Whether to show grid lines.

ax : matplotlib.axes.Axes, optional

Axes to plot on. If None, creates new figure.

show : bool, default=True

Whether to call plt.show() at the end.

backend : str, default="matplotlib"

Plotting backend: ``"matplotlib"`` or ``"plotly"``.

Returns

-------

matplotlib.axes.Axes or plotly.graph_objects.Figure

The axes object (matplotlib) or figure (plotly).

Examples

--------

From PowerAnalysis results:

>>> from diff_diff import PowerAnalysis, plot_power_curve

>>> pa = PowerAnalysis(power=0.80)

>>> curve_df = pa.power_curve(n_treated=50, n_control=50, sigma=5.0)

>>> mde_result = pa.mde(n_treated=50, n_control=50, sigma=5.0)

>>> plot_power_curve(curve_df, mde=mde_result.mde)

From simulation results:

>>> from diff_diff import simulate_power, DifferenceInDifferences

>>> results = simulate_power(

... DifferenceInDifferences(),

... effect_sizes=[1, 2, 3, 5, 7, 10],

... n_simulations=200

... )

>>> plot_power_curve(results)

Manual data:

>>> plot_power_curve(

... effect_sizes=[1, 2, 3, 4, 5],

... powers=[0.2, 0.5, 0.75, 0.90, 0.97],

... mde=2.5,

... target_power=0.80

... )

"""

# Extract data from results if provided

if results is not None:

if isinstance(results, pd.DataFrame):

if "effect_size" in results.columns:

effect_sizes = results["effect_size"].tolist()

plot_type = "effect"

elif "sample_size" in results.columns:

effect_sizes = results["sample_size"].tolist()

plot_type = "sample"

else:

raise ValueError("DataFrame must have 'effect_size' or 'sample_size' column")

powers = results["power"].tolist()

elif hasattr(results, "effect_sizes") and hasattr(results, "powers"):

# SimulationPowerResults

effect_sizes = results.effect_sizes

powers = results.powers

if mde is None and hasattr(results, "true_effect"):

mde = results.true_effect

elif hasattr(results, "mde"):

raise ValueError(

"PowerResults should be used to get mde value, not as direct input. "

"Use PowerAnalysis.power_curve() to generate curve data."

)

else:

raise TypeError(f"Cannot extract power curve data from {type(results).__name__}")

elif effect_sizes is None or powers is None:

raise ValueError("Must provide either 'results' or both 'effect_sizes' and 'powers'")

# Default titles and labels

if title is None:

title = "Power Curve" if plot_type == "effect" else "Power vs Sample Size"

if xlabel is None:

xlabel = "Effect Size" if plot_type == "effect" else "Sample Size"

if backend == "plotly":

return _render_power_curve_plotly(

effect_sizes=effect_sizes,

powers=powers,

mde=mde,

target_power=target_power,

title=title,

xlabel=xlabel,

ylabel=ylabel,

color=color,

mde_color=mde_color,

target_color=target_color,

linewidth=linewidth,

show_mde_line=show_mde_line,

show_target_line=show_target_line,

show_grid=show_grid,

show=show,

)

return _render_power_curve_mpl(

effect_sizes=effect_sizes,

powers=powers,

mde=mde,

target_power=target_power,

figsize=figsize,

title=title,

xlabel=xlabel,

ylabel=ylabel,

color=color,

mde_color=mde_color,

target_color=target_color,

linewidth=linewidth,

show_mde_line=show_mde_line,

show_target_line=show_target_line,

show_grid=show_grid,

ax=ax,

show=show,

)

def _render_power_curve_mpl(

effect_sizes,

powers,

mde,

target_power,

figsize,

title,

xlabel,

ylabel,

color,

mde_color,

target_color,

linewidth,

show_mde_line,

show_target_line,

show_grid,

ax,

show,

"""Render power curve with matplotlib."""

from diff_diff.visualization._common import _require_matplotlib

plt = _require_matplotlib()

if ax is None:

fig, ax = plt.subplots(figsize=figsize)

else:

fig = ax.get_figure()

# Plot power curve

ax.plot(effect_sizes, powers, color=color, linewidth=linewidth, label="Power")

# Add target power line

if show_target_line:

ax.axhline(

y=target_power,

color=target_color,

linestyle="--",

linewidth=1.5,

alpha=0.7,

label=f"Target power ({target_power:.0%})",

)

# Add MDE line

if show_mde_line and mde is not None:

ax.axvline(

x=mde,

color=mde_color,

linestyle=":",

linewidth=1.5,

alpha=0.7,

label=f"MDE = {mde:.3f}",

)

# Mark intersection point

if mde in effect_sizes:

idx = effect_sizes.index(mde)

power_at_mde = powers[idx]

else:

effect_arr = np.array(effect_sizes)

power_arr = np.array(powers)

if effect_arr.min() <= mde <= effect_arr.max():

power_at_mde = np.interp(mde, effect_arr, power_arr)

else:

power_at_mde = None

if power_at_mde is not None:

ax.scatter([mde], [power_at_mde], color=mde_color, s=50, zorder=5)

ax.set_xlabel(xlabel)

ax.set_ylabel(ylabel)

ax.set_title(title)

ax.set_ylim(0, 1.05)

ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda y, _: f"{y:.0%}"))

if show_grid:

ax.grid(True, alpha=0.3)

ax.legend(loc="lower right")

fig.tight_layout()

if show:

plt.show()

return ax

def _render_power_curve_plotly(

effect_sizes,

powers,

mde,

target_power,

title,

xlabel,

ylabel,

color,

mde_color,

target_color,

linewidth,

show_mde_line,

show_target_line,

show_grid,

show,

"""Render power curve with plotly."""

from diff_diff.visualization._common import _plotly_default_layout, _require_plotly

go = _require_plotly()

fig = go.Figure()

fig.add_trace(

go.Scatter(

x=effect_sizes,

y=powers,

mode="lines",

line=dict(color=color, width=linewidth),

name="Power",

)

if show_target_line:

fig.add_hline(

y=target_power,

line_dash="dash",

line_color=target_color,

opacity=0.7,

annotation_text=f"Target ({target_power:.0%})",

)

if show_mde_line and mde is not None:

fig.add_vline(

x=mde,

line_dash="dot",

line_color=mde_color,

opacity=0.7,

annotation_text=f"MDE = {mde:.3f}",

)

_plotly_default_layout(fig, title=title, xlabel=xlabel, ylabel=ylabel)

fig.update_xaxes(showgrid=show_grid)

fig.update_yaxes(range=[0, 1.05], tickformat=".0%", showgrid=show_grid)

if show:

fig.show()

return fig

def plot_pretrends_power(

results: Optional[Union["PreTrendsPowerResults", "PreTrendsPowerCurve", pd.DataFrame]] = None,

M_values: Optional[List[float]] = None,

powers: Optional[List[float]] = None,

mdv: Optional[float] = None,

target_power: float = 0.80,

figsize: Tuple[float, float] = (10, 6),

title: str = "Pre-Trends Test Power Curve",

xlabel: str = "Violation Magnitude (M)",

ylabel: str = "Power",

color: str = "#2563eb",

mdv_color: str = "#dc2626",

target_color: str = "#22c55e",

linewidth: float = 2.0,

show_mdv_line: bool = True,

show_target_line: bool = True,

show_grid: bool = True,

ax: Optional[Any] = None,

show: bool = True,

backend: str = "matplotlib",

) -> Any:

"""

Plot pre-trends test power curve.

Visualizes how the power to detect parallel trends violations changes

with the violation magnitude (M). This helps understand what violations

your pre-trends test is capable of detecting.

Parameters

----------

results : PreTrendsPowerResults, PreTrendsPowerCurve, or DataFrame, optional

Results from PreTrendsPower.fit() or power_curve(), or a DataFrame

with columns 'M' and 'power'. If None, must provide M_values and powers.

M_values : list of float, optional

Violation magnitudes (x-axis). Required if results is None.

powers : list of float, optional

Power values (y-axis). Required if results is None.

mdv : float, optional

Minimum detectable violation to mark on the plot.

target_power : float, default=0.80

Target power level to show as horizontal line.

figsize : tuple, default=(10, 6)

Figure size (width, height) in inches.

title : str

Plot title.

xlabel : str

X-axis label.

ylabel : str

Y-axis label.

color : str, default="#2563eb"

Color for the power curve line.

mdv_color : str, default="#dc2626"

Color for the MDV vertical line.

target_color : str, default="#22c55e"

Color for the target power horizontal line.

linewidth : float, default=2.0

Line width for the power curve.

show_mdv_line : bool, default=True

Whether to show vertical line at MDV.

show_target_line : bool, default=True

Whether to show horizontal line at target power.

show_grid : bool, default=True

Whether to show grid lines.

ax : matplotlib.axes.Axes, optional

Axes to plot on. If None, creates new figure.

show : bool, default=True

Whether to call plt.show() at the end.

backend : str, default="matplotlib"

Plotting backend: ``"matplotlib"`` or ``"plotly"``.

Returns

-------

matplotlib.axes.Axes or plotly.graph_objects.Figure

The axes object (matplotlib) or figure (plotly).

Examples

--------

From PreTrendsPower results:

>>> from diff_diff import MultiPeriodDiD

>>> from diff_diff.pretrends import PreTrendsPower

>>> from diff_diff.visualization import plot_pretrends_power

>>>

>>> mp_did = MultiPeriodDiD()

>>> event_results = mp_did.fit(data, outcome='y', treatment='treated',

... time='period', post_periods=[4, 5, 6, 7])

>>>

>>> pt = PreTrendsPower()

>>> curve = pt.power_curve(event_results)

>>> plot_pretrends_power(curve)

Notes

-----

The power curve shows how likely you are to reject the null hypothesis

of parallel trends given a true violation of magnitude M.

diff-diff/diff_diff/visualization/_power.py at main · UtilityTools/diff-diff · GitHub

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