"""Functions to plot in 2D."""
from typing import Optional, Tuple, List
import numpy as np
import matplotlib.pyplot as plt
from ..classifiers import Classifier
[docs]
def plot_shots_2d(
ax: plt.Axes,
*shots: List[np.ndarray],
labels: Optional[List[str]] = None,
colors: Optional[List[str]] = None,
) -> plt.Axes:
"""
Plots the experimental shots a 2D plane
Parameters
----------
ax:
Matplotlib axis
shots: [np.ndarray(N, 2), np.ndarray(N, 2), ...]
Experimental data for state 0, 1, ...
labels: (label_0, label_1, label_2)
Labels for the state 0, 1, and 2 data
colors: (color_0, color_1, color_2)
Colors for the state 0, 1, and 2 data
Returns
-------
ax:
Matplotlib axis with the data plotted
"""
if labels is None:
labels = [f"{i}" for i, _ in enumerate(shots)]
if colors is None:
all_colors = ["orange", "blue", "green"]
colors = all_colors[: len(shots)]
# calculate the transparency of the points
# based on the number of points on top of each other
max_points = 0
for shot in shots:
counts, _, _ = np.histogram2d(shot[:, 0], shot[:, 1], bins=[50, 50])
counts = np.max(counts)
if counts > max_points:
max_points = counts
for shots, color, label in zip(shots, colors, labels):
ax.plot(
shots[..., 0],
shots[..., 1],
color=color,
linestyle="none",
marker=".",
alpha=np.min([100 / max_points, 0.1]),
)
ax.plot(
[],
[],
label=label,
color=color,
linestyle="none",
marker=".",
)
ax.set_xlabel("I [a.u.]")
ax.set_ylabel("Q [a.u.]")
ax.legend(loc="best")
# same scale in x and y axis
ax.axis("equal")
return ax
[docs]
def plot_boundaries_2d(
ax: plt.Axes,
classifier: Classifier,
xlim: Tuple[float, float] = None,
ylim: Tuple[float, float] = None,
) -> plt.Axes:
"""
Plots the decision boundaries in a 2D plane
Parameters
----------
ax:
Matplotlib axis
xlim: (xmin, xmax)
Range of the X axis.
Default: `ax.get_xlim()`.
ylim: (ymin, ymax)
Range of the Y axis.
Default: `ax.get_ylim()`.
Returns
-------
ax:
Matplotlib axis with the data plotted
"""
if xlim is None:
xlim = ax.get_xlim()
if ylim is None:
ylim = ax.get_ylim()
x, y = np.linspace(*xlim, 1_000), np.linspace(*ylim, 1_000)
xx, yy = np.meshgrid(x, y, indexing="ij")
XX = np.concatenate([xx[..., np.newaxis], yy[..., np.newaxis]], axis=-1)
prediction = classifier.predict(XX)
ax.contour(
xx,
yy,
prediction,
levels=np.unique(prediction),
colors="black",
linestyles="--",
)
ax.set_xlabel("I [a.u.]")
ax.set_ylabel("Q [a.u.]")
# same scale in x and y axis
ax.axis("equal")
return ax
[docs]
def plot_contour_pdf_2d(
ax: plt.Axes,
pdf_func: callable,
xlim: Tuple[float, float] = None,
ylim: Tuple[float, float] = None,
contour_levels: np.ndarray = [1 / np.e],
) -> plt.Axes:
"""
Plots the decision boundaries in a 2D plane
Parameters
----------
ax:
Matplotlib axis.
xlim: (xmin, xmax)
Range of the X axis.
Default: `ax.get_xlim()`.
ylim: (ymin, ymax)
Range of the Y axis.
Default: `ax.get_ylim()`.
contour_levels
Levels of the PDFs to be plotted.
Returns
-------
ax:
Matplotlib axis with the data plotted
"""
if xlim is None:
xlim = ax.get_xlim()
if ylim is None:
ylim = ax.get_ylim()
x, y = np.linspace(*xlim, 1_000), np.linspace(*ylim, 1_000)
xx, yy = np.meshgrid(x, y, indexing="ij")
XX = np.concatenate([xx[..., np.newaxis], yy[..., np.newaxis]], axis=-1)
pdf = pdf_func(XX)
ax.contour(
xx,
yy,
pdf,
levels=contour_levels,
colors="gray",
linestyles="--",
)
ax.set_xlabel("I [a.u.]")
ax.set_ylabel("Q [a.u.]")
# same scale in x and y axis
ax.axis("equal")
return ax
