codes/reconstruction/transientnerf/loaders/loader_captured_ours.py

import json
import os
from typing import Sequence, Tuple

# import h5py
import numpy as np
import scipy
import torch
import torch.nn.functional as F
from tqdm import tqdm

from .utils import Rays


C = 299792458.0


def _normalize_index(idx: torch.Tensor, size: int) -> torch.Tensor:
    if size <= 1:
        return torch.zeros_like(idx)
    return 2.0 * idx / float(size - 1) - 1.0


def _load_numpy_coo_npz(npz_path: str) -> np.ndarray:
    pack = np.load(npz_path, allow_pickle=False)
    try:
        if "format" not in pack:
            raise ValueError("missing 'format' key in npz")
        fmt = str(np.asarray(pack["format"]).item())
        if fmt != "coo_numpy":
            raise ValueError(f"unsupported npz format tag: {fmt}")

        shape = tuple(np.asarray(pack["shape"], dtype=np.int64).tolist())
        row = np.asarray(pack["row"], dtype=np.int64)
        col = np.asarray(pack["col"], dtype=np.int64)
        data = np.asarray(pack["data"])

        dense = np.zeros(shape, dtype=data.dtype)
        np.add.at(dense, (row, col), data)
        return dense
    finally:
        pack.close()


def _load_histogram_2d(path: str, dtype=np.float32) -> np.ndarray:
    ext = os.path.splitext(path)[1].lower()
    if ext == ".txt":
        arr = np.loadtxt(path, dtype=dtype)
    elif ext == ".npz":
        try:
            from scipy import sparse  # type: ignore

            arr = sparse.load_npz(path).toarray()
        except Exception:
            arr = _load_numpy_coo_npz(path)
            if dtype is not None:
                arr = np.asarray(arr, dtype=dtype)
    else:
        raise ValueError(f"unsupported histogram extension for 2D loader: {ext}")

    arr = np.asarray(arr, dtype=dtype)
    if arr.ndim == 1:
        arr = arr.reshape(1, -1)
    return arr


def _reshape_histogram_to_hwt(arr: np.ndarray, hw: Tuple[int, int]) -> torch.Tensor:
    h, w = int(hw[0]), int(hw[1])
    if arr.ndim == 3:
        if arr.shape[0] == h and arr.shape[1] == w:
            out = arr
        elif arr.shape[0] == w and arr.shape[1] == h:
            out = arr.transpose(1, 0, 2)
        else:
            raise ValueError(f"3D histogram shape {arr.shape} incompatible with target ({h}, {w}, T)")
        return torch.from_numpy(np.asarray(out, dtype=np.float32))

    if arr.ndim != 2:
        raise ValueError(f"expected 2D/3D histogram array, got shape={arr.shape}")

    if arr.shape[0] == h * w:
        out = arr.reshape(h, w, arr.shape[1])
    elif arr.shape[1] == h * w:
        out = arr.T.reshape(h, w, arr.shape[0])
    else:
        raise ValueError(
            f"2D histogram shape {arr.shape} does not match H*W={h*w}; "
            "expected [H*W, bins] or [bins, H*W]."
        )
    return torch.from_numpy(np.asarray(out, dtype=np.float32))


def _load_valid_mask_from_offset(
    offset_path: str,
    source_hw: Tuple[int, int],
    target_hw: Tuple[int, int],
    invalid_gt: float = 10.0,
) -> torch.Tensor:
    ext = os.path.splitext(offset_path)[1].lower()
    if ext == ".npy":
        arr = np.load(offset_path).astype(np.float32)
    else:
        arr = np.loadtxt(offset_path, dtype=np.float32)
    arr = np.asarray(arr, dtype=np.float32).squeeze()

    src_h, src_w = int(source_hw[0]), int(source_hw[1])
    if arr.ndim == 1:
        if arr.size != src_h * src_w:
            raise ValueError(
                f"offset map length {arr.size} does not match source H*W={src_h*src_w}"
            )
        arr = arr.reshape(src_h, src_w)
    elif arr.ndim == 2:
        if arr.shape == (src_h, src_w):
            pass
        elif arr.shape == (src_w, src_h):
            arr = arr.T
        elif arr.size == src_h * src_w:
            arr = arr.reshape(src_h, src_w)
        else:
            raise ValueError(
                f"offset map shape {arr.shape} incompatible with source shape ({src_h}, {src_w})"
            )
    else:
        raise ValueError(f"offset map must be 1D/2D, got shape={arr.shape}")

    valid = (arr <= float(invalid_gt)).astype(np.float32)
    valid_t = torch.from_numpy(valid)[None, None, ...]

    dst_h, dst_w = int(target_hw[0]), int(target_hw[1])
    if (src_h, src_w) != (dst_h, dst_w):
        valid_t = F.interpolate(
            valid_t,
            size=(dst_h, dst_w),
            mode="nearest",
        )
    return valid_t.squeeze(0).squeeze(0) > 0.5


def _load_measurement_histogram(path: str, hw: Tuple[int, int]) -> torch.Tensor:
    ext = os.path.splitext(path)[1].lower()
    if ext in (".txt", ".npz"):
        arr = _load_histogram_2d(path, dtype=np.float32)
        hist = _reshape_histogram_to_hwt(arr, hw)
    elif ext == ".pt":
        raw = torch.load(path, map_location="cpu")
        if not isinstance(raw, torch.Tensor):
            raise ValueError(f".pt file must contain a tensor: {path}")
        hist = raw.to_dense() if raw.is_sparse else raw
        hist = hist.to(torch.float32).cpu()
        if hist.ndim == 2:
            hist = _reshape_histogram_to_hwt(hist.numpy(), hw)
        elif hist.ndim == 3:
            pass
        else:
            raise ValueError(f"unsupported tensor shape in {path}: {tuple(hist.shape)}")
    # elif ext in (".h5", ".hdf5"):
    #     with h5py.File(path, "r") as f:
    #         if "data" not in f:
    #             raise ValueError(f"h5 file missing key 'data': {path}")
    #         arr = np.asarray(f["data"])
    #     hist = _reshape_histogram_to_hwt(arr, hw)
    else:
        raise ValueError(f"unsupported measurement extension: {ext}")

    if hist.ndim == 4 and hist.shape[-1] == 1:
        hist = hist[..., 0]
    if hist.ndim == 4 and hist.shape[-1] == 3:
        hist = hist[..., 0]
    if hist.ndim != 3:
        raise ValueError(f"expected histogram shape [H,W,T], got {tuple(hist.shape)} from {path}")
    return hist


def _parse_shift_for_grid(shift, hw: Tuple[int, int]):
    h, w = int(hw[0]), int(hw[1])
    arr = np.asarray(shift, dtype=np.float32)
    if arr.ndim == 0:
        return float(arr.item()), None

    arr = arr.squeeze()
    if arr.ndim == 0 or arr.size == 1:
        return float(arr.reshape(-1)[0]), None

    if arr.ndim == 1 and arr.size == h * w:
        return 0.0, torch.from_numpy(arr.reshape(h, w))

    if arr.ndim == 2:
        shift_map = torch.from_numpy(arr.astype(np.float32))
        if tuple(shift_map.shape) != (h, w):
            shift_map = F.interpolate(
                shift_map[None, None, ...],
                size=(h, w),
                mode="bilinear",
                align_corners=True,
            ).squeeze(0).squeeze(0)
        return 0.0, shift_map

    return float(arr.reshape(-1)[0]), None


def _build_shift_grid(
    hw: Tuple[int, int],
    n_bins: int,
    shift,
    bin_width_s: float,
    device: str = "cpu",
) -> torch.Tensor:
    h, w = int(hw[0]), int(hw[1])
    exposure_time = C * float(bin_width_s)
    shift_scalar, shift_map = _parse_shift_for_grid(shift, hw)

    z = torch.arange(n_bins, device=device, dtype=torch.float32)[:, None, None]
    z = z * exposure_time / 2.0
    if shift_map is not None:
        z = z - shift_map.to(device)[None, ...]
    else:
        z = z - float(shift_scalar)
    z = z * 2.0 / exposure_time
    z = _normalize_index(z, n_bins)

    x = _normalize_index(torch.arange(w, device=device, dtype=torch.float32), w)
    y = _normalize_index(torch.arange(h, device=device, dtype=torch.float32), h)

    x = x[None, None, :].expand(n_bins, h, w)
    y = y[None, :, None].expand(n_bins, h, w)
    z = z.expand(n_bins, h, w)

    # grid_sample 5D grid order is (x, y, z) for input shaped [N,C,D,H,W].
    grid = torch.stack((x, y, z), dim=-1)[None, ...]
    return grid


def _apply_shift(hist: torch.Tensor, shift, bin_width_s: float) -> torch.Tensor:
    h, w, t = hist.shape
    grid = _build_shift_grid((h, w), t, shift, bin_width_s, device=hist.device)
    # [H, W, T] -> [N=1, C=1, D=T, H, W]
    hist_dhw = hist.permute(2, 0, 1)[None, None, ...]
    shifted = F.grid_sample(hist_dhw, grid, align_corners=True)
    # back to [H, W, T]
    return shifted.squeeze(0).squeeze(0).permute(1, 2, 0)


def _resize_hist_spatial(hist: torch.Tensor, target_hw: Tuple[int, int]) -> torch.Tensor:
    target_h, target_w = int(target_hw[0]), int(target_hw[1])
    h, w, _ = hist.shape
    if (h, w) == (target_h, target_w):
        return hist
    # [H,W,T] -> [1,T,H,W] for spatial resize only.
    hist_chw = hist.permute(2, 0, 1)[None, ...]
    resized = F.interpolate(hist_chw, size=(target_h, target_w), mode="area")
    return resized.squeeze(0).permute(1, 2, 0)


def _resolve_measurement_path(
    data_dir: str,
    split: str,
    frame_file_path: str,
    measurement_root: str = None,
    data_exts: Sequence[str] = (".npz", ".txt", ".pt", ".h5", ".hdf5"),
) -> str:
    raw = str(frame_file_path)
    if os.path.isabs(raw) and os.path.isfile(raw):
        return raw

    rel = raw.replace("\\", "/").lstrip("./")
    base = os.path.basename(rel)
    stem, ext = os.path.splitext(base)
    rel_stem = os.path.splitext(rel)[0]

    roots = []
    if measurement_root:
        roots.extend([measurement_root, os.path.join(measurement_root, split)])
    roots.extend([data_dir, os.path.join(data_dir, split)])

    seen_roots = set()
    unique_roots = []
    for r in roots:
        rr = os.path.normpath(r)
        if rr not in seen_roots:
            unique_roots.append(rr)
            seen_roots.add(rr)

    # Prefer exact path if extension already exists.
    if ext:
        for root in unique_roots:
            for rel_name in (rel, base):
                cand = os.path.normpath(os.path.join(root, rel_name))
                if os.path.isfile(cand):
                    return cand

    # Then resolve by stem and supported extensions.
    for root in unique_roots:
        for data_ext in data_exts:
            for rel_name in (rel_stem + data_ext, stem + data_ext):
                cand = os.path.normpath(os.path.join(root, rel_name))
                if os.path.isfile(cand):
                    return cand

    raise FileNotFoundError(
        f"Cannot resolve measurement file for frame '{frame_file_path}'. "
        f"Searched roots={unique_roots}, exts={tuple(data_exts)}."
    )


def _load_renderings_transient_real_ours(
    root_fp: str,
    split: str,
    have_images=True,
    img_shape=(256, 256),
    source_img_shape=None,
    n_bins=4096,
    shift=0,
    bin_width_s=4e-12,
    measurement_root=None,
    data_exts=(".npz", ".txt", ".pt", ".h5", ".hdf5"),
):
    data_dir = root_fp
    with open(os.path.join(data_dir, f"transforms_{split}.json"), "r", encoding="utf-8") as fp:
        meta = json.load(fp)

    images = []
    camtoworlds = []

    if have_images:
        tqdm.write("Loading data")
        reshape_hw = tuple(source_img_shape) if source_img_shape is not None else tuple(img_shape)
        for i in tqdm(range(len(meta["frames"]))):
            frame = meta["frames"][i]
            file_key = frame.get("file_path", frame.get("filepath"))
            if file_key is None:
                raise KeyError("Each frame in transforms json must contain 'file_path' or 'filepath'.")
            measurement_path = _resolve_measurement_path(
                data_dir=data_dir,
                split=split,
                frame_file_path=file_key,
                measurement_root=measurement_root,
                data_exts=data_exts,
            )

            hist = _load_measurement_histogram(measurement_path, reshape_hw).to(torch.float32).cpu()
            hist = torch.clip(hist, min=0.0)
            hist = _apply_shift(hist, shift=shift, bin_width_s=bin_width_s)

            if hist.shape[-1] != int(n_bins):
                raise ValueError(
                    f"Histogram bin count mismatch for '{measurement_path}': "
                    f"loaded {hist.shape[-1]} vs config n_bins={n_bins}. "
                    "This pipeline does not truncate or adjacent-bin average."
                )

            hist = _resize_hist_spatial(hist, img_shape)
            hist_rgb = hist[..., None].repeat(1, 1, 1, 3)

            camtoworlds.append(frame["transform_matrix"])
            images.append(hist_rgb)

        images = torch.stack(images, axis=0)
        max_value = torch.max(images).clamp_min(1e-8)
        images = images / max_value
        camtoworlds = np.stack(camtoworlds, axis=0)
    else:
        for frame in meta["frames"]:
            camtoworlds.append(frame["transform_matrix"])
        camtoworlds = np.stack(camtoworlds, axis=0)
        max_value = torch.tensor(1.0)

    return images, camtoworlds, max_value


class SubjectLoaderTransientRealOurs(torch.utils.data.Dataset):
    """Captured-data loader for custom txt/npz/pt histograms without bin truncation/averaging."""

    SPLITS = ["train", "val", "trainval", "test"]
    NEAR, FAR = 0, 6
    OPENGL_CAMERA = True

    def __init__(
        self,
        subject_id: str,
        root_fp: str,
        split: str,
        color_bkgd_aug: str = "black",
        num_rays: int = None,
        near: float = None,
        far: float = None,
        batch_over_images: bool = True,
        have_images=True,
        img_shape=(256, 256),
        source_img_shape=None,
        n_bins=10000,
        rfilter_sigma=0.15,
        sample_as_per_distribution=True,
        shift=0.0,
        testing=False,
        bin_width_s=4e-12,
        measurement_root=None,
        data_exts=(".npz", ".txt", ".pt", ".h5", ".hdf5"),
        invalid_mask_path=None,
        invalid_mask_invalid_gt=10.0,
    ):
        super().__init__()
        assert color_bkgd_aug in ["white", "black", "random"]
        self.sample_as_per_distribution = sample_as_per_distribution
        self.rfilter_sigma = rfilter_sigma
        self.HEIGHT, self.WIDTH = int(img_shape[0]), int(img_shape[1])
        self.split = split
        self.testing = testing
        self.num_rays = num_rays
        self.near = self.NEAR if near is None else near
        self.far = self.FAR if far is None else far
        self.training = (num_rays is not None) and (split in ["train", "trainval"])
        self.shift = shift
        self.rep = 1
        self.color_bkgd_aug = color_bkgd_aug
        self.batch_over_images = batch_over_images
        self.have_images = have_images
        self.n_bins = int(n_bins)
        self.bin_width_s = float(bin_width_s)
        self.measurement_root = measurement_root
        self.data_exts = tuple(data_exts)
        self.source_img_shape = tuple(source_img_shape) if source_img_shape is not None else None
        self.invalid_mask_path = invalid_mask_path
        self.invalid_mask_invalid_gt = float(invalid_mask_invalid_gt)

        if have_images:
            self.images, self.camtoworlds, self.max = _load_renderings_transient_real_ours(
                root_fp=root_fp,
                split=split,
                n_bins=self.n_bins,
                shift=shift,
                img_shape=(self.HEIGHT, self.WIDTH),
                source_img_shape=self.source_img_shape,
                bin_width_s=self.bin_width_s,
                measurement_root=self.measurement_root,
                data_exts=self.data_exts,
            )
            self.images = self.images.to(torch.float32)
            assert self.images.shape[1:3] == (self.HEIGHT, self.WIDTH)
        else:
            raise ValueError("have_images=False is not supported in SubjectLoaderTransientRealOurs.")

        self.camtoworlds = torch.from_numpy(self.camtoworlds).to(torch.float32)
        if self.invalid_mask_path:
            source_hw = (
                tuple(self.source_img_shape)
                if self.source_img_shape is not None
                else (self.HEIGHT, self.WIDTH)
            )
            self.valid_mask = _load_valid_mask_from_offset(
                self.invalid_mask_path,
                source_hw=source_hw,
                target_hw=(self.HEIGHT, self.WIDTH),
                invalid_gt=self.invalid_mask_invalid_gt,
            ).to(torch.bool)
        else:
            self.valid_mask = torch.ones((self.HEIGHT, self.WIDTH), dtype=torch.bool)

    def __len__(self):
        return len(self.camtoworlds)

    def __getitem__(self, index):
        data = self.fetch_data(index)
        data = self.preprocess(data)
        return data

    def preprocess(self, data):
        rgba, rays = data["rgba"], data["rays"]

        if rgba is not None:
            pixels = rgba.to(self.camtoworlds.device)
        else:
            pixels = rgba

        if self.color_bkgd_aug == "random":
            color_bkgd = torch.rand(3, device=self.camtoworlds.device)
        elif self.color_bkgd_aug == "white":
            color_bkgd = torch.ones(3, device=self.camtoworlds.device)
        else:
            color_bkgd = torch.zeros(3, device=self.camtoworlds.device)

        return {
            "pixels": pixels,
            "rays": rays,
            "color_bkgd": color_bkgd,
            **{k: v for k, v in data.items() if k not in ["rgba", "rays"]},
        }

    def update_num_rays(self, num_rays):
        self.num_rays = num_rays

    def fetch_data(self, index, rep=None, num_rays=None):
        if num_rays is None:
            num_rays = self.num_rays
        if rep is None:
            rep = self.rep

        if self.training:
            if self.batch_over_images:
                image_id = torch.randint(
                    0,
                    len(self.images),
                    size=(num_rays,),
                    device=self.images.device,
                )
            else:
                image_id = [index]
            x = torch.randint(0, self.WIDTH, size=(num_rays,), device="cpu")
            y = torch.randint(0, self.HEIGHT, size=(num_rays,), device="cpu")
            x = x.repeat(rep)
            y = y.repeat(rep)
            image_id = image_id.repeat(rep)
            rgba = self.images[image_id, y, x]
        elif self.testing:
            image_id = [index]
            x, y = torch.meshgrid(
                torch.arange(self.WIDTH, device="cpu"),
                torch.arange(self.HEIGHT, device="cpu"),
                indexing="xy",
            )
            x = x.flatten().repeat(rep)
            y = y.flatten().repeat(rep)
            rgba = self.images[image_id, y, x] if self.have_images else None
        else:
            image_id = [index]
            x, y = torch.meshgrid(
                torch.arange(self.WIDTH, device=self.camtoworlds.device),
                torch.arange(self.HEIGHT, device=self.camtoworlds.device),
                indexing="xy",
            )
            x = x.flatten()
            y = y.flatten()
            rgba = self.images[image_id, y, x] if self.have_images else None

        x_cpu_long = x.detach().cpu().long()
        y_cpu_long = y.detach().cpu().long()
        valid_mask = self.valid_mask[y_cpu_long, x_cpu_long]

        c2w = self.camtoworlds[image_id]
        scale = self.rfilter_sigma

        if self.training or self.testing:
            s_x, s_y, weights = spatial_filter(
                x,
                y,
                sigma=scale,
                rep=self.rep,
                prob_dithering=self.sample_as_per_distribution,
            )
            s_x = torch.clip(x + torch.from_numpy(s_x), 0, self.WIDTH - 1).to(self.camtoworlds.device).to(torch.float32)
            s_y = torch.clip(y + torch.from_numpy(s_y), 0, self.HEIGHT - 1).to(self.camtoworlds.device).to(torch.float32)
            weights = torch.tensor(weights, device=self.camtoworlds.device, dtype=torch.float32)
        else:
            s_x = x
            s_y = y
            weights = None

        camera_dirs = self.K(s_x, s_y)

        directions = (camera_dirs[:, None, :] * c2w[:, :3, :3]).sum(dim=-1)
        origins = torch.broadcast_to(c2w[:, :3, -1], directions.shape)
        viewdirs = directions / torch.linalg.norm(directions, dim=-1, keepdims=True)

        if self.training:
            origins = torch.reshape(origins, (-1, 3))
            viewdirs = torch.reshape(viewdirs, (-1, 3))
            rgba = torch.reshape(rgba, (-1, self.n_bins * 3))
            valid_mask = torch.reshape(valid_mask, (-1,))
        elif self.testing:
            origins = torch.reshape(origins, (-1, 3))
            viewdirs = torch.reshape(viewdirs, (-1, 3))
            if self.have_images:
                rgba = torch.reshape(rgba, (-1, self.n_bins * 3))
            valid_mask = torch.reshape(valid_mask, (-1,))
        elif self.have_images:
            origins = torch.reshape(origins, (self.HEIGHT, self.WIDTH, 3))
            viewdirs = torch.reshape(viewdirs, (self.HEIGHT, self.WIDTH, 3))
            rgba = torch.reshape(rgba, (self.HEIGHT, self.WIDTH, self.n_bins * 3))
            valid_mask = torch.reshape(valid_mask, (self.HEIGHT, self.WIDTH))
        else:
            origins = torch.reshape(origins, (self.HEIGHT, self.WIDTH, 3))
            viewdirs = torch.reshape(viewdirs, (self.HEIGHT, self.WIDTH, 3))
            rgba = None
            valid_mask = torch.reshape(valid_mask, (self.HEIGHT, self.WIDTH))

        rays = Rays(origins=origins, viewdirs=viewdirs)
        if self.training or self.testing:
            return {"rgba": rgba, "rays": rays, "weights": weights, "valid_mask": valid_mask}
        return {"rgba": rgba, "rays": rays, "valid_mask": valid_mask}


class LearnRays(torch.nn.Module):
    """Interpolation-based ray lookup from per-pixel ray table (H,W,3)."""

    def __init__(self, rays, device="cuda:0", img_shape=(256, 256)):
        super().__init__()
        self.device = device
        self.img_shape = (int(img_shape[0]), int(img_shape[1]))
        self.height = self.img_shape[0]
        self.width = self.img_shape[1]

        rays = np.asarray(rays, dtype=np.float32)
        if rays.ndim != 3 or rays.shape[-1] != 3:
            raise ValueError(f"rays must be [H,W,3], got shape={rays.shape}")

        rays_t = torch.from_numpy(rays).to(self.device)
        if tuple(rays_t.shape[:2]) != self.img_shape:
            rays_t = F.interpolate(
                rays_t.permute(2, 0, 1)[None, ...],
                size=self.img_shape,
                mode="bilinear",
                align_corners=True,
            ).squeeze(0).permute(1, 2, 0)

        rays_t = rays_t / torch.linalg.norm(rays_t, dim=-1, keepdims=True).clamp_min(1e-8)
        self.rays = rays_t

    def forward(self, x0, y0):
        rays = self.rays
        x0 = x0.to(rays.device).float()
        y0 = y0.to(rays.device).float()

        x1 = torch.floor(x0)
        y1 = torch.floor(y0)
        x2 = x1 + 1
        y2 = y1 + 1

        x1 = torch.clip(x1, 0, self.width - 1)
        y1 = torch.clip(y1, 0, self.height - 1)
        x2 = torch.clip(x2, 0, self.width - 1)
        y2 = torch.clip(y2, 0, self.height - 1)

        wx2 = ((x0 - x1) / (x2 - x1 + 1e-8))[:, None]
        wx1 = 1.0 - wx2
        wy2 = ((y0 - y1) / (y2 - y1 + 1e-8))[:, None]
        wy1 = 1.0 - wy2

        x1 = x1.long()
        y1 = y1.long()
        x2 = x2.long()
        y2 = y2.long()

        out = (
            wx1 * wy1 * rays[y1, x1]
            + wx1 * wy2 * rays[y2, x1]
            + wx2 * wy1 * rays[y1, x2]
            + wx2 * wy2 * rays[y2, x2]
        )
        out = out / torch.linalg.norm(out, dim=-1, keepdims=True).clamp_min(1e-8)
        return out.float()


def spatial_filter(x, y, sigma, rep, prob_dithering=True):
    pdf_fn = lambda z: np.exp(-z / (2 * sigma**2)) - np.exp(-16)
    if prob_dithering:
        bounds_max = [4 * sigma] * x.shape[0]
        loc = 0
        s_x = scipy.stats.truncnorm.rvs(
            (-np.array(bounds_max) - loc) / sigma,
            (np.array(bounds_max) - loc) / sigma,
            loc=loc,
            scale=sigma,
        )
        s_y = scipy.stats.truncnorm.rvs(
            (-np.array(bounds_max) - loc) / sigma,
            (np.array(bounds_max) - loc) / sigma,
            loc=loc,
            scale=sigma,
        )
        weights = np.ones_like(s_x) * 1 / rep
    else:
        s_x = np.random.uniform(low=-4 * sigma, high=4 * sigma, size=(rep, x.shape[0] // rep))
        s_y = np.random.uniform(low=-4 * sigma, high=4 * sigma, size=(rep, x.shape[0] // rep))
        dists = s_x**2 + s_y**2
        weights = pdf_fn(dists)
        weights = weights / weights.sum(0)[None, :]
        s_x = s_x.flatten()
        s_y = s_y.flatten()
        weights = weights.flatten()
    return s_x, s_y, weights