diff --git a/src/solarspatialtools/cloudfield.py b/src/solarspatialtools/cloudfield.py
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+++ b/src/solarspatialtools/cloudfield.py
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+import numpy as np
+from scipy.interpolate import RegularGridInterpolator
+
+import matplotlib.pyplot as plt
+from scipy.ndimage import sobel, uniform_filter
+
+from solarspatialtools.stats import variability_score
+
+
+def _random_at_scale(rand_size, final_size, plot=False):
+    """
+    Generate a 2D array of random. Generate initially at the size of rand_size
+    and then linearly interpolate to the size of final_size.
+
+    Parameters
+    ----------
+    rand_size : tuple
+        The size of the random array to generate (rows, cols)
+    final_size : tuple
+        The size of the final array to return (rows, cols)
+
+    Returns
+    -------
+    np.ndarray
+        A 2D array of random values
+    """
+
+    # Generate random values at the scale of rand_size
+    random = np.random.rand(rand_size[0], rand_size[1])
+
+    # Linearly interpolate to the final size
+    x = np.linspace(0, 1, rand_size[0])
+    y = np.linspace(0, 1, rand_size[1])
+
+    xnew = np.linspace(0, 1, final_size[0])
+    ynew = np.linspace(0, 1, final_size[1])
+
+    interp_f = RegularGridInterpolator((x, y), random, method='linear')
+    Xnew, Ynew = np.meshgrid(xnew, ynew, indexing='ij')
+    random_new = interp_f((Xnew, Ynew))
+
+
+
+    if plot:
+        # generate side by side subplots to compare
+        fig, axs = plt.subplots(1, 2, figsize=(10, 5))
+        axs[0].imshow(random, extent=(0, rand_size[1], 0, rand_size[0]))
+        axs[0].set_title('Original Random')
+        axs[1].imshow(random_new, extent=(0, final_size[1], 0, final_size[0]))
+        axs[1].set_title('Interpolated Random')
+        plt.show()
+
+    return random, random_new
+
+
+def _calc_vs_weights(scales, vs):
+    """
+    Calculate the weight for each scale
+
+    Parameters
+    ----------
+    scales : int
+        The space scale
+    vs : float
+        The variability score
+
+    Returns
+    -------
+
+    """
+    VS1 = -np.log(1-vs/180)/0.6
+    weight = scales ** (1/VS1)
+    return weight / np.sum(weight)
+
+
+def _calc_wavelet_weights(waves):
+    """
+    Calculate the weights for each wavelet
+
+    Parameters
+    ----------
+    waves : np.ndarray
+        The wavelet coefficients
+
+    Returns
+    -------
+
+    """
+    scales = np.nanmean(waves**2, axis=1)
+    return scales / np.sum(scales)
+
+
+
+def space_to_time(pixres=1, cloud_speed=50):
+    pixjump = cloud_speed / pixres
+    # n space
+    # dx space
+    # velocity
+    # dt = dx / velocity
+    # max space = n * dx
+    # max time = max space / velocity
+
+
+def stacked_field(vs, size, weights=None, scales=(1, 2, 3, 4, 5, 6, 7), plot=False):
+
+    field = np.zeros(size, dtype=float)
+
+    if weights is None:
+        weights = _calc_vs_weights(scales, vs)
+    else:
+        assert len(weights) == len(scales)
+
+    for scale, weight in zip(scales, weights):
+        prop = 2**(-scale+1)  # proportion for this scale
+        _, i_field = _random_at_scale((int(size[0]*prop), int(size[1]*prop)), size)
+        field += i_field * weight
+
+    # Scale it zero to 1??
+    field = (field - np.min(field))
+    field = field / np.max(field)
+    assert np.min(field) == 0
+    assert np.max(field) == 1
+
+    if plot:
+        # Plot the field
+        plt.imshow(field, extent=(0, size[1], 0, size[0]))
+        plt.show()
+
+    return field
+
+def _clip_field(field, kt=0.5, plot=False):
+    # Zero where clouds, 1 where clear
+
+    # clipping needs to be based on pixel fraction, which thus needs to be
+    # done on quantile because the field has a normal distribution
+    quant = np.quantile(field, kt)
+
+    # Find that quantile and cap it
+    field_out = np.ones_like(field)
+    field_out[field > quant] = 0
+
+    assert (np.isclose(kt, np.sum(field_out) / field.size, rtol=1e-3))
+
+    if plot:
+        plt.imshow(field_out, extent=(0, field.shape[1], 0, field.shape[0]))
+        plt.show()
+
+    return field_out
+
+def _find_edges(size, plot=False):
+    edges = np.abs(sobel(out_field))
+    smoothed = uniform_filter(edges, size=size)
+
+    # We want to binarize it
+    smoothed[smoothed < 1e-5] = 0  # Zero out the small floating point values
+    # Calculate a threshold based on quantile, because otherwise we get the whole clouds
+    baseline = np.quantile(smoothed[smoothed>0], 0.5)
+    smoothed = smoothed > baseline
+
+    if plot:
+        # Compare the edges and uniform filtered edges side by side
+        fig, axs = plt.subplots(1, 2, figsize=(10, 5))
+        axs[0].imshow(edges, extent=(0, ysiz, 0, xsiz))
+        axs[0].set_title('Edges')
+        axs[1].imshow(smoothed, extent=(0, ysiz, 0, xsiz))
+        axs[1].set_title('Uniform Filtered Edges')
+        plt.show()
+
+    return edges, smoothed
+
+def shift_mean_lave(field, ktmean, max_overshoot=1.4, ktmin=0.2, min_quant=0.005, max_quant=0.995, plot=True):
+
+    # ##### Shift values of kt to range from 0.2 - 1
+
+    # Calc the "max" and "min", excluding clear values
+    field_max = np.quantile(field[field < 1], max_quant)
+    field_min = np.quantile(field[field < 1], min_quant)
+
+    # Scale it between ktmin and max_overshoot
+    field_out = (field - field_min) / (field_max - field_min) * (1-ktmin) + ktmin
+
+    # # Clip limits to sensible boundaries
+    field_out[field_out > 1] = 1
+    field_out[field_out < 0] = 0
+
+    # ##### Apply multiplier to shift mean to ktmean #####
+
+    # Rescale the mean
+    tgtsum = np.prod(np.shape(field_out)) * ktmean  # Mean scaled over whole field
+    diff_sum = tgtsum - np.sum(field_out == 1)  # Shifting to exclude fully clear values
+    tgt_mean = diff_sum / np.sum(field_out < 1)  # Recalculating the expected mean of the cloudy-only aareas
+    current_cloud_mean = np.mean(field_out[field_out < 1]) # Actual cloud mean
+
+    if diff_sum > 0:
+        field_out[field_out!=1] = tgt_mean / current_cloud_mean * field_out[field_out!=1]
+
+    # print(diff_sum)
+    # print(current_cloud_mean)
+    print(f"Desired Mean: {ktmean}, actual global mean {np.mean(field_out)}.")
+
+
+    if plot:
+        plt.hist(field_out[field_out<1].flatten(), bins=100)
+        plt.show()
+
+        # plot field and field_out side by side
+        fig, axs = plt.subplots(1, 2, figsize=(10, 5))
+        axs[0].imshow(field, extent=(0, ysiz, 0, xsiz))
+        axs[0].set_title('Original Field')
+        axs[1].imshow(field_out, extent=(0, ysiz, 0, xsiz))
+        axs[1].set_title('Shifted Field')
+        plt.show()
+    return field_out
+
+
+def lave_scaling_exact(field, ktmean, max_overshoot=1.4, ktmin=0.2, min_quant=0.005, max_quant=0.995, plot=True):
+
+    # ##### Shift values of kt to range from 0.2 - 1
+
+    # Calc the "max" and "min", excluding clear values
+    field_min = np.quantile(field[field < 1], .99)
+
+    # Scale it between ktmin and max_overshoot
+    clouds3 = 1 - field*0.8/field_min
+
+
+    # # Clip limits to sensible boundaries
+    clouds3[clouds3 > 1] = 1
+    clouds3[clouds3 < 0] = 0
+
+    # ##### Apply multiplier to shift mean to ktmean #####
+    mn = np.mean(clouds3)
+    minmn = np.min(clouds3)/mn
+    maxmn = np.max(clouds3/mn-minmn)
+
+    ce = 1+ (clouds3/mn-minmn)/maxmn*(1.4-1)
+
+    # Rescale the mean
+    tgtsum = np.prod(np.shape(field_out)) * ktmean  # Mean scaled over whole field
+    diff_sum = tgtsum - np.sum(field_out == 1)  # Shifting to exclude fully clear values
+    tgt_mean = diff_sum / np.sum(field_out < 1)  # Recalculating the expected mean of the cloudy-only aareas
+    current_cloud_mean = np.mean(field_out[field_out < 1]) # Actual cloud mean
+
+    if diff_sum > 0:
+        field_out[field_out!=1] = tgt_mean / current_cloud_mean * field_out[field_out!=1]
+
+    # print(diff_sum)
+    # print(current_cloud_mean)
+    print(f"Desired Mean: {ktmean}, actual global mean {np.mean(field_out)}.")
+
+
+    if plot:
+        plt.hist(field_out[field_out<1].flatten(), bins=100)
+        plt.show()
+
+        # plot field and field_out side by side
+        fig, axs = plt.subplots(1, 2, figsize=(10, 5))
+        axs[0].imshow(field, extent=(0, ysiz, 0, xsiz))
+        axs[0].set_title('Original Field')
+        axs[1].imshow(field_out, extent=(0, ysiz, 0, xsiz))
+        axs[1].set_title('Shifted Field')
+        plt.show()
+    return field_out
+
+
+def get_settings_from_timeseries(kt_ts, plot=True):
+    # Get the mean and standard deviation of the time series
+    ktmean = np.mean(kt_ts)
+    ktstd = np.std(kt_ts)
+    ktmax = np.max(kt_ts)
+    ktmin = np.min(kt_ts)
+
+    # Get the fraction of clear sky
+    frac_clear = np.sum(kt_ts > 0.95) / np.prod(np.shape(kt_ts))
+
+    vs = variability_score(kt) * 1e4
+
+    # Compute the wavelet weights
+    # should be the mean(wavelet squared) for all modes except the steady mode
+    waves, tmscales = pvlib.scaling._compute_wavelet(kt_ts)
+
+    if plot:
+        # create a plot where each of the timeseries in waves is aligned vertically in an individual subplot
+        fig, axs = plt.subplots(len(waves), 1, figsize=(10, 2 * len(waves)), sharex=True)
+        for i, wave in enumerate(waves):
+            axs[i].plot(wave)
+            axs[i].set_title(f'Wavelet {i+1}')
+        plt.show()
+
+    waves = waves[:-1, :]  # remove the steady mode
+    tmscales = [i+1 for i, _ in enumerate(tmscales[:-1])]
+    weights = _calc_wavelet_weights(waves)
+
+    return ktmean, ktstd, ktmin, ktmax, frac_clear, vs, weights, tmscales
+
+
+
+
+if __name__ == '__main__':
+
+    import pandas as pd
+    import pvlib
+
+    datafn = "../../demos/data/hope_melpitz_1s.h5"
+    twin = pd.date_range('2013-09-08 9:15:00', '2013-09-08 10:15:00', freq='1s', tz='UTC')
+    data = pd.read_hdf(datafn, mode="r", key="data")
+    data = data[40]
+    plt.plot(data)
+    plt.show()
+
+    pos = pd.read_hdf(datafn, mode="r", key="latlon")
+    loc = pvlib.location.Location(np.mean(pos['lat']), np.mean(pos['lon']))
+    cs_ghi = loc.get_clearsky(data.index, model='simplified_solis')['ghi']
+    cs_ghi = 1000/max(cs_ghi) * cs_ghi
+    kt = pvlib.irradiance.clearsky_index(data, cs_ghi, 2)
+
+    plt.plot(data)
+    plt.plot(cs_ghi)
+    plt.show()
+
+    plt.plot(kt)
+    plt.show()
+
+    ktmean, ktstd, ktmin, ktmax, frac_clear, vs, weights, scales = get_settings_from_timeseries(kt, plot=False)
+
+    print(f"Clear Fraction is: {frac_clear}")
+
+    np.random.seed(42)  # seed it for repeatability
+
+    xsiz = 2**12
+    ysiz = 2**14
+
+    cfield = stacked_field(vs, (xsiz, ysiz), weights, scales)
+
+    mask_field = stacked_field(vs, (xsiz, ysiz), weights, scales)
+    mask_field = _clip_field(mask_field, frac_clear, plot=False)
+
+    # Clear Everywhere
+    out_field = np.ones_like(cfield)
+    # Where it's cloudy, mask in the clouds
+    out_field[mask_field == 0] = cfield[mask_field == 0]
+
+    # plt.imshow(out_field, extent=(0, ysiz, 0, xsiz))
+    # plt.show()
+
+    edges, smoothed = _find_edges(3)
+
+    # field_final = shift_mean_lave(out_field, ktmean)
+    lave_scaling_exact(out_field, ktmean)
+
+    plt.plot(field_final[1,:])
+    plt.show()
+
+    # assert np.all(r == rnew)
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