diff --git a/rubin_sim/maf/maf_contrib/uniform_fom_summary_metric.py b/rubin_sim/maf/maf_contrib/uniform_fom_summary_metric.py
new file mode 100644
index 00000000..b6dd3fe3
--- /dev/null
+++ b/rubin_sim/maf/maf_contrib/uniform_fom_summary_metric.py
@@ -0,0 +1,204 @@
+__all__ = ("UniformFoMMetric",)
+
+import numpy as np
+"""
+Still TODO:
+  - Set year as in Lynne's comment
+  - Exclude DDFs, twilight exposures, and only use low dust regions.
+"""
+import matplotlib.pyplot as plt
+import healpy as hp
+import pandas as pd
+import scipy
+import sklearn
+
+from rubin_sim.maf.metrics.base_metric import BaseMetric
+
+def make_clustering_dataset(map, maskval=0, priority_fac=0.9, nside=64):
+    """This utility takes a map of values across the sky, and puts it in a form that is convenient
+    for running a clustering algorithm on it.
+
+    It assumes masked regions are set to `maskval`, and masks them.  It also rescales the dimensions
+    to be comparable in magnitude, modulo some priority factor that has a reasonable default.
+    """
+    if priority_fac<0 or priority_fac>=1:
+        raise ValueError("priority_fac must lie between 0 and 1")
+
+    # Get RA and Dec
+    npix = hp.nside2npix(nside)
+    ra, dec = hp.pix2ang(hp.npix2nside(npix), range(npix), lonlat=True)
+    
+    # Make a 3D numpy array containing the unmasked regions, including a rescaling factor as 
+    # needed.
+    n_unmasked = len(map[map>maskval])
+    my_data = np.zeros((n_unmasked, 3))
+    my_data[:,0] = ra[map>maskval]*(1-priority_fac)*np.std(map[map>maskval])/np.std(ra[map>maskval])
+    my_data[:,1] = dec[map>maskval]*(1-priority_fac)*np.std(map[map>maskval])/np.std(dec[map>maskval])
+    my_data[:,2] = map[map>maskval]
+    return my_data
+
+def apply_clustering(clustering_data):
+    """This is just a thin wrapper around sklearn clustering routines.
+
+    We are looking for patterns induced by rolling, which generally result in two clusters
+    (deeper vs. shallower).  So we fix the number of clusters to two.
+    """
+    from sklearn.cluster import KMeans
+    clustering = KMeans(n_clusters=2, random_state=0, n_init="auto").fit(clustering_data)
+    # We would like the labels to be 1 and 2.
+    labels = clustering.labels_ + 1
+    return labels
+
+def expand_labels(map, labels, maskval=0):
+    """A utility to apply the labels from a masked version of a depth map back to the entire depth map."""
+    expanded_labels = np.zeros(hp.nside2npix(nside))
+    expanded_labels[map>maskval] = labels
+    return expanded_labels
+
+def is_ngp(ra, dec):
+    """Returns True if the location is in the northern galactic region, False if in the South."""
+    from astropy import units as u
+    from astropy.coordinates import SkyCoord
+    c = SkyCoord(ra=ra*u.degree, dec=dec*u.degree, frame='icrs')
+    lat = c.galactic.b.deg
+    return lat >= 0
+
+def get_stats_by_region(map, nside, maskval=0, region='all'):
+    """Get statistics of the input map -- either the entire thing, the northern galactic region, or
+    the southern galactic region depending on the value of the region parameter.  Options: 'all',
+    'north', 'south'."""
+    if region not in ['all','north','south']:
+        raise ValueError('Invalid region %s'%region)
+    to_use = (map > maskval)
+    
+    if region != 'all':
+        # Find the north/south part of the map as requested
+        npix = hp.nside2npix(nside)
+        ra, dec = hp.pix2ang(hp.npix2nside(npix), range(npix), lonlat=True)
+        ngp_mask = is_ngp(ra, dec)
+        if region=='north':
+            reg_mask = ngp_mask
+        else: 
+            reg_mask = ~ngp_mask
+        to_use = to_use & reg_mask
+    
+    # Calculate the desired stats
+    reg_mad = scipy.stats.median_abs_deviation(map[to_use])
+    reg_median = np.median(map[to_use])
+    reg_std = np.std(map[to_use])
+    
+    # Return the values
+    return(reg_mad, reg_median, reg_std)
+
+def has_stripes(map, nside, threshold=0.1):
+    """
+    A utility to ascertain whether a particular routine has stripe-y features in a map.  It is tuned
+    to identify the striping in the RIZ coadded exposure time based on the implementation of rolling
+    in v3.3 and v3.4 simulations, though should work well unless major changes are made to the
+    rolling patterns.
+    """
+    # Analyze the map to get MAD, median, std in north/south.
+    mad = {}
+    med = {}
+    frac_scatter = {}
+    regions = ['north', 'south']
+    for region in regions:
+        mad[region], med[region], _ = get_stats_by_region(map, nside, region=region)
+        frac_scatter[region] = mad[region]/med[region]
+    test_statistic = np.abs(frac_scatter['north']/frac_scatter['south']-1)
+    if test_statistic < threshold:
+        return False
+    else:
+        return True
+
+class UniformFoMMetric(BaseMetric):
+    """This calculates any impact on the dark energy Figure of Merit (FoM) for combined DESC static
+    3x2pt measurements due to having to eliminate areas with coherent large-scale depth fluctuations
+    due to residual rolling stripes in the coadd.  It is relevant in the limit that the striping is
+    significant, as this is unlikely to be something we can model.
+
+    This summary metric should be run on RIZDetectionCoaddExposureTime().
+
+    A return value of 1 is the best option, as it means no area has to be removed (indicative of a
+    realistically achievable level of uniformity). Return values below 1 indicate a loss of
+    statistical constraining power. Non-rolling strategies generally return 1 for all years, while
+    rolling strategies return 1 for year 1 and some years after that depending on the rolling
+    implementation.
+
+    """
+    def __init__(
+            self, nside=64, year=10, col=None, **kwargs
+    ):
+        self.nside = nside
+        super().__init__(col=col, year=year, **kwargs)
+        if col is None:
+            self.col = "metricdata"
+        self.year = year
+        self.nside = nside
+
+    def run(self, data_slice, slice_point=None):
+        # Get a map that has zeros for the pixels we do not want to use.
+        map = metric_values.data.copy()
+        map[metric_values.mask] = 0
+
+        # Check whether the map has stripe features
+        stripes = has_stripes(map, self.nside)
+
+        # Start to set output.
+        result = np.empty(1, dtype=[("name", np.str_, 20), ("value", float)])
+        result["name"][0] = "UniformFoMFraction"
+
+        # If there are no stripes, we are done here.
+        if not stripes:
+            result["value"][0] = 1.0
+            return result
+        else:
+            # But if there are stripes, we have to do the clustering to identify the
+            # deeper/shallower regions.
+            clustering_data = make_clustering_dataset(map)
+            labels = apply_clustering(clustering_data)
+
+            # Then we figure out which pixels are in each region.
+            expanded_labels = expand_labels(map, labels)
+            hpid_region = {}
+            hpid_region[1] = np.where(labels == 1)[0]
+            hpid_region[2] = np.where(labels == 2)[0]
+
+            # Get 3x2pt FoM for the overall map.  Start with some basic definitions we will always use.
+            surveyAreas = SkyAreaGenerator(nside=nside)
+            map_footprints, map_labels = surveyAreas.return_maps()
+            days = self.year*365.25
+            use_filter = "i"
+            constraint_str='filter="YY" and note not like "DD%" and night <= XX and note not like "twilight_near_sun" '
+            constraint_str = constraint_str.replace('XX','%d'%days)
+            constraint_str = constraint_str.replace('YY','%s'%use_filter)
+            ThreebyTwoSummary = maf.StaticProbesFoMEmulatorMetric(
+                nside=self.nside, metric_name="3x2ptFoM")
+
+            # Now for some specifics - this part is just when using the overall map.
+            use_slicer = maf.HealpixSubsetSlicer(
+                nside=self.nside, use_cache=False, hpid=np.where(map_labels == "lowdust")[0])
+            depth_map_bundle = maf.MetricBundle(
+                metric=maf.ExgalM5(), slicer=use_slicer, constraint=constraint_str, summary_metrics=[ThreebyTwoSummary])
+            bd = maf.metricBundles.make_bundles_dict_from_list([depth_map_bundle])
+            ### Any other magic needed here to actually get the summary?
+            overall_fom = bd[list(bd.keys())[0]].summary_values['3x2ptFoM']
+
+            fom_region = {}
+            for region in [1, 2]:
+                # Need a slicer that selects for that region.
+                use_slicer = maf.HealpixSubsetSlicer(
+                    nside=self.nside, use_cache=False,
+                    hpid=np.intersect1d(np.where(map_labels == "lowdust")[0], hpid_region[region]))
+                depth_map_bundle = maf.MetricBundle(
+                    metric=maf.ExgalM5(), slicer=use_slicer, constraint=constraint_str,
+                    summary_metrics=[ThreebyTwoSummary])
+                bd = maf.metricBundles.make_bundles_dict_from_list([depth_map_bundle])
+                ### Any other magic needed here to actually get the summary?
+                fom_region[region] = bd[list(bd.keys())[0]].summary_values['3x2ptFoM']
+
+            # Now choose the larger FoM, and return the ratio between that and the one without any
+            # area cuts
+            fom = np.max((fom_region[1], fom_region[2]))
+            result["value"][0] = fom / overall_fom
+            return result