diff --git a/src/dolphin/filtering.py b/src/dolphin/filtering.py
index 56242082..e23b19af 100644
--- a/src/dolphin/filtering.py
+++ b/src/dolphin/filtering.py
@@ -1,348 +1,348 @@
-import multiprocessing as mp
-from concurrent.futures import ProcessPoolExecutor
-from itertools import repeat
-from pathlib import Path
-from typing import Sequence
-
-import numpy as np
-from numpy.typing import ArrayLike, NDArray
-from scipy import fft, ndimage
-
-
-def filter_long_wavelength(
-    unwrapped_phase: ArrayLike,
-    bad_pixel_mask: ArrayLike,
-    wavelength_cutoff: float = 50 * 1e3,
-    pixel_spacing: float = 30,
-    workers: int = 1,
-) -> np.ndarray:
-    """Filter out signals with spatial wavelength longer than a threshold.
-
-    Parameters
-    ----------
-    unwrapped_phase : ArrayLike
-        Unwrapped interferogram phase to filter.
-    bad_pixel_mask : ArrayLike
-        Boolean array with same shape as `unwrapped_phase` where `True` indicates a
-        pixel to ignore during ramp fitting
-    wavelength_cutoff : float
-        Spatial wavelength threshold to filter the unwrapped phase.
-        Signals with wavelength longer than 'wavelength_cutoff' are filtered out.
-        The default is 50*1e3 (m).
-    pixel_spacing : float
-        Pixel spatial spacing. Assume same spacing for x, y axes.
-        The default is 30 (m).
-    workers : int
-        Number of `fft` workers to use for `scipy.fft.fft2`.
-        Default is 1.
-
-    Returns
-    -------
-    filtered_ifg : 2D complex array
-        filtered interferogram that does not contain signals with spatial wavelength
-        longer than a threshold.
-
-    Raises
-    ------
-    ValueError
-        If wavelength_cutoff too large for image size/pixel spacing.
-
-    """
-    good_pixel_mask = ~bad_pixel_mask
-
-    rows, cols = unwrapped_phase.shape
-    unw0 = np.nan_to_num(unwrapped_phase)
-    # Take either nan or 0 pixels in `unwrapped_phase` to be nodata
-    nodata_mask = unw0 == 0
-    in_bounds_pixels = ~nodata_mask
-
-    total_valid_mask = in_bounds_pixels & good_pixel_mask
-
-    plane = fit_ramp_plane(unw0, total_valid_mask)
-    # Remove the plane, setting to 0 where we had no data for the plane fit:
-    unw_ifg_interp = np.where((~nodata_mask & good_pixel_mask), unw0, plane)
-
-    # Fill the boundary area by reflecting pixel values
-    reflect_fill = _fill_boundary_area(unw_ifg_interp, in_bounds_pixels)
-
-    # Find the filter `sigma` which gives the correct cutoff in meters
-    sigma = _compute_filter_sigma(wavelength_cutoff, pixel_spacing, cutoff_value=0.5)
-
-    if sigma > unw0.shape[0] or sigma > unw0.shape[0]:
-        msg = f"{wavelength_cutoff = } too large for image."
-        msg += f"Shape = {(rows, cols)}, and {pixel_spacing = }"
-        raise ValueError(msg)
-    # Pad the array with edge values
-    # The padding extends further than the default "radius = 2*sigma + 1",
-    # which given specified in `gaussian_filter`
-    # https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.gaussian_filter.html#scipy.ndimage.gaussian_filter
-    pad_rows = pad_cols = int(3 * sigma)
-    # See here for illustration of `mode="reflect"`
-    # https://scikit-image.org/docs/stable/auto_examples/transform/plot_edge_modes.html#interpolation-edge-modes
-    padded = np.pad(
-        reflect_fill, ((pad_rows, pad_rows), (pad_cols, pad_cols)), mode="reflect"
-    )
-
-    # Apply Gaussian filter
-    result = fft.fft2(padded, workers=workers)
-    result = ndimage.fourier_gaussian(result, sigma=sigma)
-    # Make sure to only take the real part (ifft returns complex)
-    result = fft.ifft2(result, workers=workers).real.astype(unwrapped_phase.dtype)
-
-    # Crop back to original size
-    lowpass_filtered = result[pad_rows:-pad_rows, pad_cols:-pad_cols]
-
-    filtered_ifg = unw_ifg_interp - lowpass_filtered * in_bounds_pixels
-    return np.where(in_bounds_pixels, filtered_ifg, 0)
-
-
-def _fill_boundary_area(unw_ifg_interp: np.ndarray, mask: np.ndarray) -> np.ndarray:
-    """Fill the boundary area by reflecting pixel values."""
-    # Rotate the data to align the frame
-    rt_unw_ifg = ndimage.rotate(unw_ifg_interp, 8.6, reshape=False)
-
-    rt_pad = 700  # Apply padding for rotating back
-    # Fill boundary area using np.pad for each horizontal step and
-    reflect_filled = np.pad(
-        rt_unw_ifg[846:6059, :],
-        ((846, rt_unw_ifg.shape[0] - 6059), (0, 0)),
-        mode="reflect",
-    )
-    reflect_filled = np.pad(
-        reflect_filled[618:6241, :],
-        ((618, rt_unw_ifg.shape[0] - 6241), (0, 0)),
-        mode="reflect",
-    )
-    reflect_filled = np.pad(
-        reflect_filled[399:6447, :],
-        ((399 + rt_pad, rt_unw_ifg.shape[0] - 6447 + rt_pad), (0, 0)),
-        mode="reflect",
-    )
-    # Fill vertical boundary area using np.pad
-    reflect_filled = np.pad(
-        reflect_filled[:, 591:8861],
-        ((0, 0), (591 + rt_pad, rt_unw_ifg.shape[1] - 8861 + rt_pad)),
-        mode="reflect",
-    )
-
-    # Rotate back to original angle
-    reflect_filled = ndimage.rotate(reflect_filled, -8.6, reshape=False)
-    reflect_filled = reflect_filled[rt_pad:-rt_pad, rt_pad:-rt_pad]
-    # Copy original in-bound pixels
-    reflect_filled[mask] = unw_ifg_interp[mask]
-
-    return reflect_filled
-
-
-def _compute_filter_sigma(
-    wavelength_cutoff: float, pixel_spacing: float, cutoff_value: float = 0.5
-) -> float:
-    sigma_f = 1 / wavelength_cutoff / np.sqrt(np.log(1 / cutoff_value))
-    sigma_x = 1 / np.pi / 2 / sigma_f
-    sigma = sigma_x / pixel_spacing
-    return sigma
-
-
-def fit_ramp_plane(unw_ifg: ArrayLike, mask: ArrayLike) -> np.ndarray:
-    """Fit a ramp plane to the given data.
-
-    Parameters
-    ----------
-    unw_ifg : ArrayLike
-        2D array where the unwrapped interferogram data is stored.
-    mask : ArrayLike
-        2D boolean array indicating the valid (non-NaN) pixels.
-
-    Returns
-    -------
-    np.ndarray
-        2D array of the fitted ramp plane.
-
-    """
-    # Extract data for non-NaN & masked pixels
-    Y = unw_ifg[mask]
-    Xdata = np.argwhere(mask)  # Get indices of non-NaN & masked pixels
-
-    # Include the intercept term (bias) in the model
-    X = np.c_[np.ones((len(Xdata))), Xdata]
-
-    # Compute the parameter vector theta using the least squares solution
-    theta = np.linalg.pinv(X.T @ X) @ X.T @ Y
-
-    # Prepare grid for the entire image
-    nrow, ncol = unw_ifg.shape
-    X1_, X2_ = np.mgrid[:nrow, :ncol]
-    X_ = np.hstack(
-        (np.reshape(X1_, (nrow * ncol, 1)), np.reshape(X2_, (nrow * ncol, 1)))
-    )
-    X_ = np.hstack((np.ones((nrow * ncol, 1)), X_))
-
-    # Compute the fitted plane
-    plane = np.reshape(X_ @ theta, (nrow, ncol))
-
-    return plane
-
-
-def filter_rasters(
-    unw_filenames: list[Path],
-    cor_filenames: list[Path] | None = None,
-    conncomp_filenames: list[Path] | None = None,
-    temporal_coherence_filename: Path | None = None,
-    wavelength_cutoff: float = 50_000,
-    correlation_cutoff: float = 0.5,
-    output_dir: Path | None = None,
-    max_workers: int = 4,
-) -> list[Path]:
-    """Filter a list of unwrapped interferogram files using a long-wavelength filter.
-
-    Remove long-wavelength components from each unwrapped interferogram.
-    It can optionally use temporal coherence, correlation, and connected component
-    information for masking.
-
-    Parameters
-    ----------
-    unw_filenames : list[Path]
-        List of paths to unwrapped interferogram files to be filtered.
-    cor_filenames : list[Path] | None
-        List of paths to correlation files
-        Passing None skips filtering on correlation.
-    conncomp_filenames : list[Path] | None
-        List of paths to connected component files, filters any 0 labeled pixels.
-        Passing None skips filtering on connected component labels.
-    temporal_coherence_filename : Path | None
-        Path to the temporal coherence file for masking.
-        Passing None skips filtering on temporal coherence.
-    wavelength_cutoff : float, optional
-        Spatial wavelength cutoff (in meters) for the filter. Default is 50,000 meters.
-    correlation_cutoff : float, optional
-        Threshold of correlation (if passing `cor_filenames`) to use to ignore pixels
-        during filtering.
-    output_dir : Path | None, optional
-        Directory to save the filtered results.
-        If None, saves in the same location as inputs with .filt.tif extension.
-    max_workers : int, optional
-        Number of parallel images to process. Default is 4.
-
-    Returns
-    -------
-    list[Path]
-        Output filtered rasters.
-
-    Notes
-    -----
-    - If temporal_coherence_filename is provided, pixels with coherence < 0.5 are masked
-
-    """
-    from dolphin import io
-
-    bad_pixel_mask = np.zeros(
-        io.get_raster_xysize(unw_filenames[0])[::-1], dtype="bool"
-    )
-    if temporal_coherence_filename:
-        bad_pixel_mask = bad_pixel_mask | (
-            io.load_gdal(temporal_coherence_filename) < 0.5
-        )
-
-    if output_dir is None:
-        assert unw_filenames
-        output_dir = unw_filenames[0].parent
-    output_dir.mkdir(exist_ok=True)
-    ctx = mp.get_context("spawn")
-
-    with ProcessPoolExecutor(max_workers, mp_context=ctx) as pool:
-        return list(
-            pool.map(
-                _filter_and_save,
-                unw_filenames,
-                cor_filenames or repeat(None),
-                conncomp_filenames or repeat(None),
-                repeat(output_dir),
-                repeat(wavelength_cutoff),
-                repeat(bad_pixel_mask),
-                repeat(correlation_cutoff),
-            )
-        )
-
-
-def _filter_and_save(
-    unw_filename: Path,
-    cor_path: Path | None,
-    conncomp_path: Path | None,
-    output_dir: Path,
-    wavelength_cutoff: float,
-    bad_pixel_mask: NDArray[np.bool_],
-    correlation_cutoff: float = 0.5,
-) -> Path:
-    """Filter one interferogram (wrapper for multiprocessing)."""
-    from dolphin import io
-    from dolphin._overviews import Resampling, create_image_overviews
-
-    # Average for the pixel spacing for filtering
-    _, x_res, _, _, _, y_res = io.get_raster_gt(unw_filename)
-    pixel_spacing = (abs(x_res) + abs(y_res)) / 2
-
-    if cor_path is not None:
-        bad_pixel_mask |= io.load_gdal(cor_path) < correlation_cutoff
-    if conncomp_path is not None:
-        bad_pixel_mask |= io.load_gdal(conncomp_path, masked=True).astype(bool) == 0
-
-    unw = io.load_gdal(unw_filename)
-    filt_arr = filter_long_wavelength(
-        unwrapped_phase=unw,
-        wavelength_cutoff=wavelength_cutoff,
-        bad_pixel_mask=bad_pixel_mask,
-        pixel_spacing=pixel_spacing,
-        workers=1,
-    )
-    io.round_mantissa(filt_arr, keep_bits=9)
-    output_name = output_dir / Path(unw_filename).with_suffix(".filt.tif").name
-    io.write_arr(arr=filt_arr, like_filename=unw_filename, output_name=output_name)
-
-    create_image_overviews(output_name, resampling=Resampling.AVERAGE)
-
-    return output_name
-
-
-def gaussian_filter_nan(
-    image: ArrayLike, sigma: float | Sequence[float], mode="constant", **kwargs
-) -> np.ndarray:
-    """Apply a gaussian filter to an image with NaNs (avoiding all nans).
-
-    The scipy.ndimage `gaussian_filter` will make the output all NaNs if
-    any of the pixels in the input that touches the kernel is NaN
-
-    Source:
-    https://stackoverflow.com/a/36307291
-
-    Parameters
-    ----------
-    image : ndarray
-        Image with nans to filter
-    sigma : float
-        Size of filter kernel. passed into `gaussian_filter`
-    mode : str, default = "constant"
-        Boundary mode for `[scipy.ndimage.gaussian_filter][]`
-    **kwargs : Any
-        Passed into `[scipy.ndimage.gaussian_filter][]`
-
-    Returns
-    -------
-    ndarray
-        Filtered version of `image`.
-
-    """
-    from scipy.ndimage import gaussian_filter
-
-    if np.sum(np.isnan(image)) == 0:
-        return gaussian_filter(image, sigma=sigma, mode=mode, **kwargs)
-
-    V = image.copy()
-    nan_idxs = np.isnan(image)
-    V[nan_idxs] = 0
-    V_filt = gaussian_filter(V, sigma, **kwargs)
-
-    W = np.ones(image.shape)
-    W[nan_idxs] = 0
-    W_filt = gaussian_filter(W, sigma, **kwargs)
-
-    return V_filt / W_filt
+import multiprocessing as mp
+from concurrent.futures import ProcessPoolExecutor
+from itertools import repeat
+from pathlib import Path
+from typing import Sequence
+
+import numpy as np
+from numpy.typing import ArrayLike, NDArray
+from scipy import fft, ndimage
+
+
+def filter_long_wavelength(
+    unwrapped_phase: ArrayLike,
+    bad_pixel_mask: ArrayLike,
+    wavelength_cutoff: float = 50 * 1e3,
+    pixel_spacing: float = 30,
+    workers: int = 1,
+) -> np.ndarray:
+    """Filter out signals with spatial wavelength longer than a threshold.
+
+    Parameters
+    ----------
+    unwrapped_phase : ArrayLike
+        Unwrapped interferogram phase to filter.
+    bad_pixel_mask : ArrayLike
+        Boolean array with same shape as `unwrapped_phase` where `True` indicates a
+        pixel to ignore during ramp fitting
+    wavelength_cutoff : float
+        Spatial wavelength threshold to filter the unwrapped phase.
+        Signals with wavelength longer than 'wavelength_cutoff' are filtered out.
+        The default is 50*1e3 (m).
+    pixel_spacing : float
+        Pixel spatial spacing. Assume same spacing for x, y axes.
+        The default is 30 (m).
+    workers : int
+        Number of `fft` workers to use for `scipy.fft.fft2`.
+        Default is 1.
+
+    Returns
+    -------
+    filtered_ifg : 2D complex array
+        filtered interferogram that does not contain signals with spatial wavelength
+        longer than a threshold.
+
+    Raises
+    ------
+    ValueError
+        If wavelength_cutoff too large for image size/pixel spacing.
+
+    """
+    good_pixel_mask = ~bad_pixel_mask
+
+    rows, cols = unwrapped_phase.shape
+    unw0 = np.nan_to_num(unwrapped_phase)
+    # Take either nan or 0 pixels in `unwrapped_phase` to be nodata
+    nodata_mask = unw0 == 0
+    in_bounds_pixels = ~nodata_mask
+
+    total_valid_mask = in_bounds_pixels & good_pixel_mask
+
+    plane = fit_ramp_plane(unw0, total_valid_mask)
+    # Remove the plane, setting to 0 where we had no data for the plane fit:
+    unw_ifg_interp = np.where((~nodata_mask & good_pixel_mask), unw0, plane)
+
+    # Fill the boundary area by reflecting pixel values
+    reflect_fill = _fill_boundary_area(unw_ifg_interp, in_bounds_pixels)
+
+    # Find the filter `sigma` which gives the correct cutoff in meters
+    sigma = _compute_filter_sigma(wavelength_cutoff, pixel_spacing, cutoff_value=0.5)
+
+    if sigma > unw0.shape[0] or sigma > unw0.shape[0]:
+        msg = f"{wavelength_cutoff = } too large for image."
+        msg += f"Shape = {(rows, cols)}, and {pixel_spacing = }"
+        raise ValueError(msg)
+    # Pad the array with edge values
+    # The padding extends further than the default "radius = 2*sigma + 1",
+    # which given specified in `gaussian_filter`
+    # https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.gaussian_filter.html#scipy.ndimage.gaussian_filter
+    pad_rows = pad_cols = int(3 * sigma)
+    # See here for illustration of `mode="reflect"`
+    # https://scikit-image.org/docs/stable/auto_examples/transform/plot_edge_modes.html#interpolation-edge-modes
+    padded = np.pad(
+        reflect_fill, ((pad_rows, pad_rows), (pad_cols, pad_cols)), mode="reflect"
+    )
+
+    # Apply Gaussian filter
+    result = fft.fft2(padded, workers=workers)
+    result = ndimage.fourier_gaussian(result, sigma=sigma)
+    # Make sure to only take the real part (ifft returns complex)
+    result = fft.ifft2(result, workers=workers).real.astype(unwrapped_phase.dtype)
+
+    # Crop back to original size
+    lowpass_filtered = result[pad_rows:-pad_rows, pad_cols:-pad_cols]
+
+    filtered_ifg = unw_ifg_interp - lowpass_filtered * in_bounds_pixels
+    return np.where(in_bounds_pixels, filtered_ifg, 0)
+
+
+def _fill_boundary_area(unw_ifg_interp: np.ndarray, mask: np.ndarray) -> np.ndarray:
+    """Fill the boundary area by reflecting pixel values."""
+    # Rotate the data to align the frame
+    rt_unw_ifg = ndimage.rotate(unw_ifg_interp, 8.6, reshape=False)
+
+    rt_pad = 700  # Apply padding for rotating back
+    # Fill boundary area using np.pad for each horizontal step and
+    reflect_filled = np.pad(
+        rt_unw_ifg[846:6059, :],
+        ((846, rt_unw_ifg.shape[0] - 6059), (0, 0)),
+        mode="reflect",
+    )
+    reflect_filled = np.pad(
+        reflect_filled[618:6241, :],
+        ((618, rt_unw_ifg.shape[0] - 6241), (0, 0)),
+        mode="reflect",
+    )
+    reflect_filled = np.pad(
+        reflect_filled[399:6447, :],
+        ((399 + rt_pad, rt_unw_ifg.shape[0] - 6447 + rt_pad), (0, 0)),
+        mode="reflect",
+    )
+    # Fill vertical boundary area using np.pad
+    reflect_filled = np.pad(
+        reflect_filled[:, 591:8861],
+        ((0, 0), (591 + rt_pad, rt_unw_ifg.shape[1] - 8861 + rt_pad)),
+        mode="reflect",
+    )
+
+    # Rotate back to original angle
+    reflect_filled = ndimage.rotate(reflect_filled, -8.6, reshape=False)
+    reflect_filled = reflect_filled[rt_pad:-rt_pad, rt_pad:-rt_pad]
+    # Copy original in-bound pixels
+    reflect_filled[mask] = unw_ifg_interp[mask]
+
+    return reflect_filled
+
+
+def _compute_filter_sigma(
+    wavelength_cutoff: float, pixel_spacing: float, cutoff_value: float = 0.5
+) -> float:
+    sigma_f = 1 / wavelength_cutoff / np.sqrt(np.log(1 / cutoff_value))
+    sigma_x = 1 / np.pi / 2 / sigma_f
+    sigma = sigma_x / pixel_spacing
+    return sigma
+
+
+def fit_ramp_plane(unw_ifg: ArrayLike, mask: ArrayLike) -> np.ndarray:
+    """Fit a ramp plane to the given data.
+
+    Parameters
+    ----------
+    unw_ifg : ArrayLike
+        2D array where the unwrapped interferogram data is stored.
+    mask : ArrayLike
+        2D boolean array indicating the valid (non-NaN) pixels.
+
+    Returns
+    -------
+    np.ndarray
+        2D array of the fitted ramp plane.
+
+    """
+    # Extract data for non-NaN & masked pixels
+    Y = unw_ifg[mask]
+    Xdata = np.argwhere(mask)  # Get indices of non-NaN & masked pixels
+
+    # Include the intercept term (bias) in the model
+    X = np.c_[np.ones((len(Xdata))), Xdata]
+
+    # Compute the parameter vector theta using the least squares solution
+    theta = np.linalg.pinv(X.T @ X) @ X.T @ Y
+
+    # Prepare grid for the entire image
+    nrow, ncol = unw_ifg.shape
+    X1_, X2_ = np.mgrid[:nrow, :ncol]
+    X_ = np.hstack(
+        (np.reshape(X1_, (nrow * ncol, 1)), np.reshape(X2_, (nrow * ncol, 1)))
+    )
+    X_ = np.hstack((np.ones((nrow * ncol, 1)), X_))
+
+    # Compute the fitted plane
+    plane = np.reshape(X_ @ theta, (nrow, ncol))
+
+    return plane
+
+
+def filter_rasters(
+    unw_filenames: list[Path],
+    cor_filenames: list[Path] | None = None,
+    conncomp_filenames: list[Path] | None = None,
+    temporal_coherence_filename: Path | None = None,
+    wavelength_cutoff: float = 50_000,
+    correlation_cutoff: float = 0.5,
+    output_dir: Path | None = None,
+    max_workers: int = 4,
+) -> list[Path]:
+    """Filter a list of unwrapped interferogram files using a long-wavelength filter.
+
+    Remove long-wavelength components from each unwrapped interferogram.
+    It can optionally use temporal coherence, correlation, and connected component
+    information for masking.
+
+    Parameters
+    ----------
+    unw_filenames : list[Path]
+        List of paths to unwrapped interferogram files to be filtered.
+    cor_filenames : list[Path] | None
+        List of paths to correlation files
+        Passing None skips filtering on correlation.
+    conncomp_filenames : list[Path] | None
+        List of paths to connected component files, filters any 0 labeled pixels.
+        Passing None skips filtering on connected component labels.
+    temporal_coherence_filename : Path | None
+        Path to the temporal coherence file for masking.
+        Passing None skips filtering on temporal coherence.
+    wavelength_cutoff : float, optional
+        Spatial wavelength cutoff (in meters) for the filter. Default is 50,000 meters.
+    correlation_cutoff : float, optional
+        Threshold of correlation (if passing `cor_filenames`) to use to ignore pixels
+        during filtering.
+    output_dir : Path | None, optional
+        Directory to save the filtered results.
+        If None, saves in the same location as inputs with .filt.tif extension.
+    max_workers : int, optional
+        Number of parallel images to process. Default is 4.
+
+    Returns
+    -------
+    list[Path]
+        Output filtered rasters.
+
+    Notes
+    -----
+    - If temporal_coherence_filename is provided, pixels with coherence < 0.5 are masked
+
+    """
+    from dolphin import io
+
+    bad_pixel_mask = np.zeros(
+        io.get_raster_xysize(unw_filenames[0])[::-1], dtype="bool"
+    )
+    if temporal_coherence_filename:
+        bad_pixel_mask = bad_pixel_mask | (
+            io.load_gdal(temporal_coherence_filename) < 0.5
+        )
+
+    if output_dir is None:
+        assert unw_filenames
+        output_dir = unw_filenames[0].parent
+    output_dir.mkdir(exist_ok=True)
+    ctx = mp.get_context("spawn")
+
+    with ProcessPoolExecutor(max_workers, mp_context=ctx) as pool:
+        return list(
+            pool.map(
+                _filter_and_save,
+                unw_filenames,
+                cor_filenames or repeat(None),
+                conncomp_filenames or repeat(None),
+                repeat(output_dir),
+                repeat(wavelength_cutoff),
+                repeat(bad_pixel_mask),
+                repeat(correlation_cutoff),
+            )
+        )
+
+
+def _filter_and_save(
+    unw_filename: Path,
+    cor_path: Path | None,
+    conncomp_path: Path | None,
+    output_dir: Path,
+    wavelength_cutoff: float,
+    bad_pixel_mask: NDArray[np.bool_],
+    correlation_cutoff: float = 0.5,
+) -> Path:
+    """Filter one interferogram (wrapper for multiprocessing)."""
+    from dolphin import io
+    from dolphin._overviews import Resampling, create_image_overviews
+
+    # Average for the pixel spacing for filtering
+    _, x_res, _, _, _, y_res = io.get_raster_gt(unw_filename)
+    pixel_spacing = (abs(x_res) + abs(y_res)) / 2
+
+    if cor_path is not None:
+        bad_pixel_mask |= io.load_gdal(cor_path) < correlation_cutoff
+    if conncomp_path is not None:
+        bad_pixel_mask |= io.load_gdal(conncomp_path, masked=True).astype(bool) == 0
+
+    unw = io.load_gdal(unw_filename)
+    filt_arr = filter_long_wavelength(
+        unwrapped_phase=unw,
+        wavelength_cutoff=wavelength_cutoff,
+        bad_pixel_mask=bad_pixel_mask,
+        pixel_spacing=pixel_spacing,
+        workers=1,
+    )
+    io.round_mantissa(filt_arr, keep_bits=9)
+    output_name = output_dir / Path(unw_filename).with_suffix(".filt.tif").name
+    io.write_arr(arr=filt_arr, like_filename=unw_filename, output_name=output_name)
+
+    create_image_overviews(output_name, resampling=Resampling.AVERAGE)
+
+    return output_name
+
+
+def gaussian_filter_nan(
+    image: ArrayLike, sigma: float | Sequence[float], mode="constant", **kwargs
+) -> np.ndarray:
+    """Apply a gaussian filter to an image with NaNs (avoiding all nans).
+
+    The scipy.ndimage `gaussian_filter` will make the output all NaNs if
+    any of the pixels in the input that touches the kernel is NaN
+
+    Source:
+    https://stackoverflow.com/a/36307291
+
+    Parameters
+    ----------
+    image : ndarray
+        Image with nans to filter
+    sigma : float
+        Size of filter kernel. passed into `gaussian_filter`
+    mode : str, default = "constant"
+        Boundary mode for `[scipy.ndimage.gaussian_filter][]`
+    **kwargs : Any
+        Passed into `[scipy.ndimage.gaussian_filter][]`
+
+    Returns
+    -------
+    ndarray
+        Filtered version of `image`.
+
+    """
+    from scipy.ndimage import gaussian_filter
+
+    if np.sum(np.isnan(image)) == 0:
+        return gaussian_filter(image, sigma=sigma, mode=mode, **kwargs)
+
+    V = image.copy()
+    nan_idxs = np.isnan(image)
+    V[nan_idxs] = 0
+    V_filt = gaussian_filter(V, sigma, **kwargs)
+
+    W = np.ones(image.shape)
+    W[nan_idxs] = 0
+    W_filt = gaussian_filter(W, sigma, **kwargs)
+
+    return V_filt / W_filt