diff --git a/active_plugins/calculategini.py b/active_plugins/calculategini.py
new file mode 100644
index 0000000..3605d3a
--- /dev/null
+++ b/active_plugins/calculategini.py
@@ -0,0 +1,370 @@
+#################################
+#
+# Imports from useful Python libraries
+#
+#################################
+
+import numpy as np
+
+#################################
+#
+# Imports from CellProfiler
+#
+##################################
+
+import cellprofiler_core.module as cpm
+import cellprofiler_core.setting as cps
+from cellprofiler_core.constants.measurement import COLTYPE_FLOAT
+from cellprofiler_core.setting.do_something import DoSomething
+from cellprofiler_core.setting.subscriber import ImageSubscriber, LabelSubscriber
+from cellprofiler_core.utilities.core.object import size_similarly
+from centrosome.cpmorphology import fixup_scipy_ndimage_result as fix
+
+
+__doc__ = """\
+CalculateGini
+================
+
+**CalculateGini** extracts the Gini coefficient from a given distribution of pixel values.
+
+The user can use all pixels to compute the gini or can restrict to pixels within objects.
+If the image has a mask, only unmasked pixels will be used.
+
+Code borrows almost entirely from calculatemoments.py
+
+Available measurements:
+- Gini
+
+============ ============ ===============
+Supports 2D? Supports 3D? Respects masks?
+============ ============ ===============
+YES          NO            YES
+============ ============ ===============
+
+"""
+
+
+def get_gini(pixels, labels):
+    """For each label, find the corresponding pixels and compute the gini"""
+    labs = np.unique(labels)
+    gini = np.zeros(np.max(labs) + 1)
+    for lab in labs:
+        if lab != 0:
+            px = pixels[np.where(labels == lab)]
+            gini[lab] = get_gini_on_pixels(px)
+    return gini[1:]  # skip the 0th value
+
+
+def get_gini_on_pixels(pixels):
+    """Given an array of pixels, get the Gini coefficient
+
+    This entails generating a histogram of pixel values, calculating the CDF,
+    and then calculating the Gini coefficient from the CDF.
+
+    Assumes intensties are always positive.
+    """
+    flattened = np.sort(np.ravel(pixels))
+    npix = np.size(flattened)
+    normalization = np.abs(np.mean(flattened)) * npix * (npix - 1)
+    kernel = (2.0 * np.arange(1, npix + 1) - npix - 1) * np.abs(flattened)
+
+    return np.sum(kernel) / normalization
+
+
+GINI = "GINI"
+
+
+class CalculateGini(cpm.Module):
+
+    module_name = "CalculateGini"
+    category = "Measurement"
+    variable_revision_number = 1
+
+    def create_settings(self):
+        """Create the settings for the module at startup."""
+        self.image_groups = []
+        self.image_count = cps.HiddenCount(self.image_groups)
+        self.add_image_cb(can_remove=False)
+        self.add_images = DoSomething("", "Add another image", self.add_image_cb)
+        self.image_divider = cps.Divider()
+
+        self.object_groups = []
+        self.object_count = cps.HiddenCount(self.object_groups)
+        self.add_object_cb(can_remove=True)
+        self.add_objects = DoSomething("", "Add another object", self.add_object_cb)
+        self.object_divider = cps.Divider()
+
+    def settings(self):
+        """The settings as they appear in the save file."""
+        result = [self.image_count, self.object_count]
+        for groups, elements in [
+            (self.image_groups, ["image_name"]),
+            (self.object_groups, ["object_name"]),
+        ]:
+            for group in groups:
+                for element in elements:
+                    result += [getattr(group, element)]
+        return result
+
+    def prepare_settings(self, setting_values):
+        """Adjust the number of groups based on the number of
+        setting_values"""
+        for count, sequence, fn in (
+            (int(setting_values[0]), self.image_groups, self.add_image_cb),
+            (int(setting_values[1]), self.object_groups, self.add_object_cb),
+        ):
+            del sequence[count:]
+            while len(sequence) < count:
+                fn()
+
+    def visible_settings(self):
+        """The settings as they appear in the module viewer"""
+        result = []
+        for groups, add_button, div in [
+            (self.image_groups, self.add_images, self.image_divider),
+            (self.object_groups, self.add_objects, self.object_divider),
+        ]:
+            for group in groups:
+                result += group.visible_settings()
+            result += [add_button, div]
+
+        return result
+
+    def add_image_cb(self, can_remove=True):
+        """Add an image to the image_groups collection
+
+        can_delete - set this to False to keep from showing the "remove"
+                     button for images that must be present.
+        """
+        group = cps.SettingsGroup()
+        if can_remove:
+            group.append("divider", cps.Divider(line=False))
+        group.append(
+            "image_name",
+            ImageSubscriber(
+                "Select an image to measure",
+                "None",
+                doc="""What did you call the grayscale images whose Gini you want to calculate?""",
+            ),
+        )
+        if can_remove:
+            group.append(
+                "remover",
+                cps.do_something.RemoveSettingButton(
+                    "", "Remove this image", self.image_groups, group
+                ),
+            )
+        self.image_groups.append(group)
+
+    def add_object_cb(self, can_remove=True):
+        """Add an object to the object_groups collection
+
+        can_delete - set this to False to keep from showing the "remove"
+        button for objects that must be present.
+        """
+        group = cps.SettingsGroup()
+        if can_remove:
+            group.append("divider", cps.Divider(line=False))
+        group.append(
+            "object_name",
+            LabelSubscriber(
+                "Select objects to measure",
+                "None",
+                doc="""
+                What did you call the objects from which you want to calculate the Gini?
+                If you only want to calculate the Gini of the image overall, you can remove
+                all objects using the "Remove this object" button. Objects specified here
+                will have Gini computed against *all* images specified above, which
+                may lead to image-object combinations that are unnecessary. If you
+                do not want this behavior, use multiple CalculateGini modules to specify
+                the particular image-object measures that you want.""",
+            ),
+        )
+        if can_remove:
+            group.append(
+                "remover",
+                cps.do_something.RemoveSettingButton(
+                    "", "Remove this object", self.object_groups, group
+                ),
+            )
+        self.object_groups.append(group)
+
+    def validate_module(self, pipeline):
+        """Make sure chosen images are selected only once"""
+        images = set()
+        for group in self.image_groups:
+            if group.image_name.value in images:
+                raise cps.ValidationError(
+                    "%s has already been selected" % group.image_name.value,
+                    group.image_name,
+                )
+            images.add(group.image_name.value)
+
+        objects = set()
+        for group in self.object_groups:
+            if group.object_name.value in objects:
+                raise cps.ValidationError(
+                    "%s has already been selected" % group.object_name.value,
+                    group.object_name,
+                )
+            objects.add(group.object_name.value)
+
+    def run(self, workspace):
+        """Run, computing the measurements"""
+        statistics = [["Image", "Object", "Measurement", "Value"]]
+
+        for image_group in self.image_groups:
+            image_name = image_group.image_name.value
+            statistics += self.run_image(image_name, workspace)
+            for object_group in self.object_groups:
+                object_name = object_group.object_name.value
+                statistics += self.run_object(image_name, object_name, workspace)
+
+        if workspace.frame is not None:
+            workspace.display_data.statistics = statistics
+
+    def run_image(self, image_name, workspace):
+        """Run measurements on image"""
+        statistics = []
+        input_image = workspace.image_set.get_image(image_name, must_be_grayscale=True)
+        pixels = input_image.pixel_data
+        gini = get_gini_on_pixels(pixels)
+        statistics += self.record_image_measurement(workspace, image_name, "Gini", gini)
+        return statistics
+
+    def run_object(self, image_name, object_name, workspace):
+        statistics = []
+        input_image = workspace.image_set.get_image(image_name, must_be_grayscale=True)
+        objects = workspace.get_objects(object_name)
+        pixels = input_image.pixel_data
+        if input_image.has_mask:
+            mask = input_image.mask
+        else:
+            mask = None
+        labels = objects.segmented
+        try:
+            pixels = objects.crop_image_similarly(pixels)
+        except ValueError:
+            #
+            # Recover by cropping the image to the labels
+            #
+            pixels, m1 = size_similarly(labels, pixels)
+            if np.any(~m1):
+                if mask is None:
+                    mask = m1
+                else:
+                    mask, m2 = size_similarly(labels, mask)
+                    mask[~m2] = False
+
+        if mask is not None:
+            labels = labels.copy()
+            labels[~mask] = 0
+
+        # the good stuff
+        gini = get_gini(pixels, labels)
+        statistics += self.record_measurement(
+            workspace, image_name, object_name, "Gini", gini
+        )
+        return statistics
+
+    def is_interactive(self):
+        return False
+
+    def display(self, workspace, figure):
+        statistics = workspace.display_data.statistics
+        figure.set_subplots((1, 1))
+        figure.subplot_table(0, 0, statistics, ratio=(0.25, 0.25, 0.25, 0.25))
+
+    def get_features(self):
+        """Return a measurement feature name"""
+        return ["Gini"]
+
+    def get_measurement_columns(self, pipeline):
+        """Get column names output for each measurement."""
+        cols = []
+        for im in self.image_groups:
+            for feature in self.get_features():
+                cols += [
+                    (
+                        "Image",
+                        "%s_%s_%s" % (GINI, feature, im.image_name.value),
+                        COLTYPE_FLOAT,
+                    )
+                ]
+
+        for ob in self.object_groups:
+            for im in self.image_groups:
+                for feature in self.get_features():
+                    cols += [
+                        (
+                            ob.object_name.value,
+                            "%s_%s_%s" % (GINI, feature, im.image_name.value),
+                            COLTYPE_FLOAT,
+                        )
+                    ]
+
+        return cols
+
+    def get_categories(self, pipeline, object_name):
+        """Get the measurement categories.
+
+        pipeline - pipeline being run
+        image_name - name of images in question
+        returns a list of category names
+        """
+        if any([object_name == og.object_name for og in self.object_groups]):
+            return [GINI]
+        elif object_name == "Image":
+            return [GINI]
+        else:
+            return []
+
+    def get_measurements(self, pipeline, object_name, category):
+        """Get the measurements made on the given image in the given category
+
+        pipeline - pipeline being run
+        image_name - name of image being measured
+        category - measurement category
+        """
+        if category in self.get_categories(pipeline, object_name):
+            return self.get_features()
+        return []
+
+    def get_measurement_images(self, pipeline, object_name, category, measurement):
+        """Get the list of images measured
+
+        pipeline - pipeline being run
+        image_name - name of objects being measured
+        category - measurement category
+        measurement - measurement made on images
+        """
+        if measurement in self.get_measurements(pipeline, object_name, category):
+            return [x.image_name.value for x in self.image_groups]
+        return []
+
+    def record_measurement(
+        self, workspace, image_name, object_name, feature_name, result
+    ):
+        """Record the result of a measurement in the workspace's
+        measurements"""
+        data = fix(result)
+        data[~np.isfinite(data)] = 0
+        workspace.add_measurement(
+            object_name, "%s_%s_%s" % (GINI, feature_name, image_name), data
+        )
+        statistics = [
+            [image_name, object_name, feature_name, "%f" % (d) if len(data) else "-"]
+            for d in data
+        ]
+        return statistics
+
+    def record_image_measurement(self, workspace, image_name, feature_name, result):
+        """Record the result of a measurement in the workspace's
+        measurements"""
+        if not np.isfinite(result):
+            result = 0
+        workspace.measurements.add_image_measurement(
+            "%s_%s_%s" % (GINI, feature_name, image_name), result
+        )
+        statistics = [[image_name, "-", feature_name, "%f" % (result)]]
+        return statistics