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images

Image visualization tools.

cmat(arr: np.ndarray, labels: Iterable[str] | None = None, annot: bool = True, cmap: str = 'gist_heat_r', cbar: bool = False, fmt: str = '0.0%', dark_color: str = '#222222', light_color: str = '#dddddd', grid_color: str = cast(str, c.gray[9]), theta: float = 0.5, label_fontsize: float = 10.0, fontsize: float = 10.0, vmin: float = 0.0, vmax: float = 1.0, **kwargs: Any) -> tuple[AxesImage, Axes]

Plot confusion matrix.

Source code in src/jetplot/images.py 
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@plotwrapper
def cmat(
    arr: np.ndarray,
    labels: Iterable[str] | None = None,
    annot: bool = True,
    cmap: str = "gist_heat_r",
    cbar: bool = False,
    fmt: str = "0.0%",
    dark_color: str = "#222222",
    light_color: str = "#dddddd",
    grid_color: str = cast(str, c.gray[9]),
    theta: float = 0.5,
    label_fontsize: float = 10.0,
    fontsize: float = 10.0,
    vmin: float = 0.0,
    vmax: float = 1.0,
    **kwargs: Any,
) -> tuple[AxesImage, Axes]:
    """Plot confusion matrix."""
    num_rows, num_cols = arr.shape

    ax = kwargs.pop("ax")
    cb = imv(arr, ax=ax, vmin=vmin, vmax=vmax, cmap=cmap, cbar=cbar)

    xs, ys = np.meshgrid(np.arange(num_cols), np.arange(num_rows), indexing="xy")

    for x, y, value in zip(xs.flat, ys.flat, arr.flat, strict=True):  # pyrefly: ignore
        color = dark_color if (value <= theta) else light_color
        label = f"{{:{fmt}}}".format(value)
        ax.text(x, y, label, ha="center", va="center", color=color, fontsize=fontsize)

    if labels is not None:
        ax.set_xticks(np.arange(num_cols))
        ax.set_xticklabels(labels, rotation=90, fontsize=label_fontsize)
        ax.set_yticks(np.arange(num_rows))
        ax.set_yticklabels(labels, fontsize=label_fontsize)

    ax.xaxis.set_minor_locator(FixedLocator((np.arange(num_cols) - 0.5).tolist()))

    ax.yaxis.set_minor_locator(FixedLocator((np.arange(num_rows) - 0.5).tolist()))

    ax.grid(
        visible=True,
        which="minor",
        axis="both",
        linewidth=1.0,
        color=grid_color,
        linestyle="-",
        alpha=1.0,
    )

    return cb, ax

fsurface(func: Callable[..., np.ndarray], xrng: tuple[float, float] | None = None, yrng: tuple[float, float] | None = None, n: int = 100, nargs: int = 2, **kwargs: Any) -> None

Plot a 2‑D function as a filled surface.

Source code in src/jetplot/images.py 
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@plotwrapper
def fsurface(
    func: Callable[..., np.ndarray],
    xrng: tuple[float, float] | None = None,
    yrng: tuple[float, float] | None = None,
    n: int = 100,
    nargs: int = 2,
    **kwargs: Any,
) -> None:
    """Plot a 2‑D function as a filled surface."""
    xrng = (-1, 1) if xrng is None else xrng
    yrng = xrng if yrng is None else yrng

    xs = np.linspace(xrng[0], xrng[1], n)
    ys = np.linspace(yrng[0], yrng[1], n)

    xm, ym = np.meshgrid(xs, ys)

    if nargs == 1:
        zz = np.vstack([xm.ravel(), ym.ravel()])
        args = (zz,)
    elif nargs == 2:
        args = (xm.ravel(), ym.ravel())
    else:
        raise ValueError(f"Invalid value for nargs ({nargs})")

    zm = func(*args).reshape(xm.shape)

    kwargs["ax"].contourf(xm, ym, zm)

img(data: np.ndarray, mode: str = 'div', cmap: str | None = None, aspect: str = 'equal', vmin: float | None = None, vmax: float | None = None, cbar: bool = True, interpolation: str = 'none', **kwargs: Any) -> AxesImage

Visualize a matrix as an image.

Parameters:

Name Type Description Default
img

array_like, The array to visualize.

 required
mode str

string, One of 'div' for a diverging image, 'seq' for sequential, 'cov' for covariance matrices, or 'corr' for correlation matrices (default: 'div').

 'div'
cmap str | None

string, Colormap to use.

 None
aspect str

string, Either 'equal' or 'auto'

 'equal'
Source code in src/jetplot/images.py 
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@plotwrapper
def img(
    data: np.ndarray,
    mode: str = "div",
    cmap: str | None = None,
    aspect: str = "equal",
    vmin: float | None = None,
    vmax: float | None = None,
    cbar: bool = True,
    interpolation: str = "none",
    **kwargs: Any,
) -> AxesImage:
    """Visualize a matrix as an image.

    Args:
      img: array_like, The array to visualize.
      mode: string, One of 'div' for a diverging image, 'seq' for
        sequential, 'cov' for covariance matrices, or 'corr' for
        correlation matrices (default: 'div').
      cmap: string, Colormap to use.
      aspect: string, Either 'equal' or 'auto'
    """
    # work with a copy of the original image data
    img = np.squeeze(data.copy())

    # image bounds
    img_min = np.min(img)
    img_max = np.max(img)
    abs_max = np.max(np.abs(img))

    if mode == "div":
        if vmin is None:
            vmin = -abs_max
        if vmax is None:
            vmax = abs_max
        if cmap is None:
            cmap = "seismic"
    elif mode == "seq":
        if vmin is None:
            vmin = img_min
        if vmax is None:
            vmax = img_max
        if cmap is None:
            cmap = "viridis"
    elif mode == "cov":
        vmin, vmax, cmap, cbar = 0, 1, "viridis", True
    elif mode == "corr":
        vmin, vmax, cmap, cbar = -1, 1, "seismic", True
    else:
        raise ValueError("Unrecognized mode: '" + mode + "'")

    # make the image
    im = kwargs["ax"].imshow(
        img, cmap=cmap, interpolation=interpolation, vmin=vmin, vmax=vmax, aspect=aspect
    )

    # colorbar
    if cbar:
        plt.colorbar(im)

    # clear ticks
    noticks(ax=kwargs["ax"])

    return im