--- a
+++ b/tests/helpers.py
@@ -0,0 +1,105 @@
+from tests.data import HEMASK
+from PIL import Image
+import numpy
+from openslide import AbstractSlide
+
+
+class FakeSlide(AbstractSlide):
+
+    """
+    A class to mimic an openslide.OpenSlide object.
+
+    Args:
+        staining: type of staining you wanna mimic.
+    """
+
+    def __init__(self, name="fake_slide", staining="H&E", extension=".mrxs"):
+        """
+        """
+        self._filename = name + extension
+        self.tissue_color = [154, 120, 156, 255]
+
+    @property
+    def level_count(self):
+        return 11
+
+    @property
+    def level_dimensions(self):
+        return (
+            (83968, 71680),
+            (41984, 35840),
+            (20992, 17920),
+            (10496, 8960),
+            (5248, 4480),
+            (2624, 2240),
+            (1312, 1120),
+            (656, 560),
+            (328, 280),
+            (164, 140),
+            (82, 70),
+        )
+
+    @property
+    def level_downsamples(self):
+        return (
+            1.0,
+            2.0,
+            4.0,
+            8.0,
+            16.0,
+            32.0,
+            64.0,
+            128.0,
+            256.0,
+            512.0,
+            1024.0,
+        )
+
+    @property
+    def properties(self):
+        """
+        """
+        return {}
+
+    @property
+    def associated_images(self):
+        return {}
+
+    def get_best_level_for_downsample(self, downsample):
+        for k in range(1, self.level_count):
+            if self.level_downsamples[k] > downsample:
+                return k - 1
+        return self.level_count
+
+    def read_region(self, location, level, size):
+        """
+        """
+        # un pack request coordinates
+        x, y = location
+        dx, dy = size
+        ds = self.level_downsamples[level]
+        tds = self.level_downsamples[-1]
+        dj, di = self.level_dimensions[-1]
+
+        X_indices = numpy.full((dy, dx), x, dtype=float)
+        Y_indices = numpy.full((dy, dx), y, dtype=float)
+        # go through columns of X
+        X_indices += (numpy.arange(dx) * ds)[None]
+        Y_indices += (numpy.arange(dy) * ds)[:, None]
+        # rescale x and y indices
+        J_indices = numpy.floor(X_indices / tds).astype(int)
+        I_indices = numpy.floor(Y_indices / tds).astype(int)
+        # put a flag on out-of-bounds pixels
+        J_indices[J_indices >= dj] = -1
+        I_indices[I_indices >= di] = -1
+        # compute 1D indices
+        Indices = (I_indices * dj + J_indices)
+        hemask = numpy.array(HEMASK)
+        labels = numpy.where(Indices > 0, hemask[Indices], 0)
+        numpy_img = numpy.zeros((dy, dx, 4), dtype=numpy.uint8)
+        # out of bounds regions have val == 0 ^^
+        numpy_img[labels == 0] = (0, 0, 0, 0)
+        numpy_img[labels == 1] = (255, 255, 255, 255)
+        numpy_img[labels == 2] = self.tissue_color
+        # get pil image
+        return Image.fromarray(numpy_img, mode="RGBA")