Repository: haesleinhuepf/napari-skimage-regionprops (language: python)

Issue:
Parametric images do not account tables with background measurements
Hi Robert @haesleinhuepf ,

The parametric images from table does not support tables where you have a label `0` meaning measurements from the background.
It wrongly assigns values to labels shifted by one. The problem is [here](https://github.com/haesleinhuepf/napari-skimage-regionprops/blob/59ff289fa08c621acfff8e87e32cf0f6ea10c7d0/napari_skimage_regionprops/_parametric_images.py#L88). It always insert a 0, no matter the situation.

![ezgif com-video-to-gif](https://user-images.githubusercontent.com/26173597/223662162-4a629565-c84a-4135-8f99-2ce8456092e6.gif)



Discussion / hints:

Fix patch:
diff --git a/README.md b/README.md
index 75667a9..a812d39 100644
--- a/README.md
+++ b/README.md
@@ -70,7 +70,7 @@ Also, take a look at [this example notebook](https://github.com/haesleinhuepf/na
 
 You can also control the tables programmatically. See this 
 [example notebook](https://github.com/haesleinhuepf/napari-skimage-regionprops/blob/master/demo/tables.ipynb) for details on regionprops and
-[this example notebook](https://github.com/haesleinhuepf/napari-skimage-regionprops/blob/master/demo/measure_points.ipynb) for details on measuring intensity at point coordinates.
+[this example notebook](https://github.com/haesleinhuepf/napari-skimage-regionprops/blob/master/demo/measure_points.ipynb) for details on measuring intensity at point coordinates. For creating parametric map images, see [this notebook](https://github.com/haesleinhuepf/napari-skimage-regionprops/blob/master/demo/map_measurements.ipynb).
 
 
 ## Features
diff --git a/demo/map_measurements.ipynb b/demo/map_measurements.ipynb
new file mode 100644
index 0000000..796503e
--- /dev/null
+++ b/demo/map_measurements.ipynb
@@ -0,0 +1,299 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0871c5b3-6073-4cb5-8d16-ce1cd67df3ca",
+   "metadata": {},
+   "source": [
+    "# Map measurements on labels\n",
+    "In this notebook we demonstrate how [unsorted] measurements can be mapped on label images. The result is a parametric map where pixels contain the measurement from the corresponding label."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "625bd0a9-f4ab-42cc-85c3-00b7b1c0ab89",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import napari\n",
+    "import napari_skimage_regionprops as nsr\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "669f06cb-60d1-4c3f-bca5-31831ec0e7aa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "image = np.asarray([[0,1,2,3]])\n",
+    "labels = np.asarray([[0,1,2,3]]).astype(int)\n",
+    "\n",
+    "measurements = pd.DataFrame({\n",
+    "    'label': [2, 3, 1, 0],\n",
+    "    'values': [5, 6, 7, 8]\n",
+    "})\n",
+    "\n",
+    "# reference:\n",
+    "reference_map = np.asarray([[8, 7, 5, 6]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "bf8d0182-e9dd-4ce3-b355-2a533c889dfd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "viewer = napari.Viewer()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "a89c74d1-5343-4f2a-a983-3e7b20cf2cfe",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Napari status bar display of label properties disabled because https://github.com/napari/napari/issues/5417 and https://github.com/napari/napari/issues/4342\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
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\" alt=\"\"></img>"
+      ],
+      "text/plain": [
+       "<napari.utils.notebook_display.NotebookScreenshot at 0x1f78e9d8760>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "viewer.add_image(image)\n",
+    "labels_layer = viewer.add_labels(labels)\n",
+    "labels_layer.features = measurements\n",
+    "\n",
+    "nsr.add_table(labels_layer, viewer)\n",
+    "\n",
+    "napari.utils.nbscreenshot(viewer)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "09fd9bbc-807d-4c30-8ad2-ae470db3fa81",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\" alt=\"\"></img>"
+      ],
+      "text/plain": [
+       "<napari.utils.notebook_display.NotebookScreenshot at 0x1f789b06fd0>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "map_image = nsr.map_measurements_on_labels(labels_layer, 'values')\n",
+    "\n",
+    "viewer.add_image(map_image)\n",
+    "\n",
+    "napari.utils.nbscreenshot(viewer)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "c4be0dfc-07b1-4fc5-9dfc-4a6fcaa8f5f9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[8, 7, 5, 6]])"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "map_image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "49c7a6ee-9a95-429b-a106-13f6e8d04f81",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.array_equal(map_image, reference_map)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a2593294-2a60-4d05-9f40-483394561da8",
+   "metadata": {},
+   "source": [
+    "## Measurements without background\n",
+    "The same procedure also works in case background was not measured. In that case, background will remain `0`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ea8f7a68-3f8c-47d0-a8d2-45d984fda47c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Napari status bar display of label properties disabled because https://github.com/napari/napari/issues/5417 and https://github.com/napari/napari/issues/4342\n"
+     ]
+    }
+   ],
+   "source": [
+    "measurements = pd.DataFrame({\n",
+    "    'label': [2, 3, 1],\n",
+    "    'values': [5, 6, 7]\n",
+    "})\n",
+    "labels_layer.features = measurements\n",
+    "nsr.add_table(labels_layer, viewer)\n",
+    "\n",
+    "# reference:\n",
+    "reference_map = np.asarray([[0, 7, 5, 6]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "b726cac6-a79a-4c5e-9c7a-22666a8b8f9e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\" alt=\"\"></img>"
+      ],
+      "text/plain": [
+       "<napari.utils.notebook_display.NotebookScreenshot at 0x1f78e9b9580>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "map_image = nsr.map_measurements_on_labels(labels_layer, 'values')\n",
+    "\n",
+    "viewer.add_image(map_image)\n",
+    "\n",
+    "napari.utils.nbscreenshot(viewer)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "f4f5a06c-8410-45ac-8831-e3a8481f7e41",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0, 7, 5, 6]])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "map_image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "c09fe5e9-fbb5-4a24-b9d9-83ce2ef82f58",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.array_equal(map_image, reference_map)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e63c837c-ed22-490f-ba6d-aa946233fb26",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/napari_skimage_regionprops/_parametric_images.py b/napari_skimage_regionprops/_parametric_images.py
index 85c6daf..d37ca4b 100644
--- a/napari_skimage_regionprops/_parametric_images.py
+++ b/napari_skimage_regionprops/_parametric_images.py
@@ -59,28 +59,14 @@ def map_measurements_on_labels(labels_layer:"napari.layers.Labels", column:str =
             axis=0)
         return stack
     else:
-        measurements = np.asarray(table[column]).tolist()
-        
-        # Ensure background measurements are present in measurements
-        # if also present in labels
-        if (0 not in table['label'].values) and (0 in labels):
-            measurements.insert(0, 0)
-        return relabel_skimage(labels, measurements)
+        label_list = np.asarray(table['label']).tolist()
+        measurement_list = np.asarray(table[column]).tolist()
+
+        return relabel_with_map_array(labels, label_list, measurement_list)
     
-def relabel_timepoint_with_map_array(labels, table, column, frame_column, timepoint):
-    labels_one_timepoint = labels[timepoint]
-    if frame_column is not None:
-        table_one_timepoint = table[table[frame_column] == timepoint]
-    else:
-        table_one_timepoint = table
-    measurements = np.asarray(table_one_timepoint[column]).tolist()
-    return relabel_skimage(labels_one_timepoint, measurements)
 
-def relabel_skimage(image, measurements):
-    from skimage.util import map_array
-    return map_array(image, np.unique(image), np.array(measurements))
 
-@deprecated("This function is deprecated! To adhere to future behavior and suppress this warning, use 'map_measurements_on_labels' instead")
+@deprecated("visualize_measurement_on_labels() is deprecated. Use map_measurements_on_labels() instead")
 def visualize_measurement_on_labels(labels_layer:"napari.layers.Labels", column:str = "label", viewer:"napari.Viewer" = None) -> "napari.types.ImageData":
     """
     Visualize a quantiative measurement on a label image by replacing the label IDs with specified table colum values.
@@ -140,6 +126,31 @@ def visualize_measurement_on_labels(labels_layer:"napari.layers.Labels", column:
         measurements = np.asarray(table[column]).tolist()
         return relabel(labels, measurements)
 
+
+def relabel_timepoint_with_map_array(labels, table, column, frame_column, timepoint):
+    labels_one_timepoint = labels[timepoint]
+    if frame_column is not None:
+        table_one_timepoint = table[table[frame_column] == timepoint]
+    else:
+        table_one_timepoint = table
+
+    label_list = np.asarray(table_one_timepoint['label']).tolist()
+    measurement_list = np.asarray(table_one_timepoint[column]).tolist()
+    return relabel_with_map_array(labels_one_timepoint, label_list, measurement_list)
+
+def relabel_with_map_array(image, label_list, measurement_list):
+    """
+    Produce parametric map image from a label image, a list of labels and a list of measurements.
+    The two lists must provide labels and corresponding values in the same order.
+
+    See also
+    --------
+    https://scikit-image.org/docs/stable/api/skimage.util.html#skimage.util.map_array
+    """
+    from skimage.util import map_array
+    return map_array(image, np.asarray(label_list), np.array(measurement_list))
+
+@deprecated("relabel_timepoint() is deprecated. Use relabel_timepoint_with_map_array() instead")
 def relabel_timepoint(labels, table, column, frame_column, timepoint):
     labels_one_timepoint = labels[timepoint]
     if frame_column is not None:
@@ -149,6 +160,7 @@ def relabel_timepoint(labels, table, column, frame_column, timepoint):
     measurements = np.asarray(table_one_timepoint[column]).tolist()
     return relabel(labels_one_timepoint, measurements)
 
+@deprecated("relabel() is deprecated. Use skimage.util.map_array() instead")
 def relabel(image, measurements):
     import importlib
     loader = importlib.find_loader("pyclesperanto_prototype")
@@ -159,9 +171,11 @@ def relabel(image, measurements):
     else:
         return relabel_numpy(image, measurements)
 
+@deprecated("relabel_cle() is deprecated. Use skimage.util.map_array() instead")
 def relabel_cle(image, measurements):
     import pyclesperanto_prototype as cle
     return cle.pull(cle.replace_intensities(image, np.insert(np.array(measurements), 0, 0)))
 
+@deprecated("relabel_numpy() is deprecated. Use skimage.util.map_array() instead")
 def relabel_numpy(image, measurements):
     return numpy.take(np.insert(np.array(measurements), 0, 0), image)


Test patch:
diff --git a/napari_skimage_regionprops/_tests/test_function.py b/napari_skimage_regionprops/_tests/test_function.py
index dff34cc..3dcc9f3 100644
--- a/napari_skimage_regionprops/_tests/test_function.py
+++ b/napari_skimage_regionprops/_tests/test_function.py
@@ -466,6 +466,32 @@ def test_map_measurements_WITHOUT_BG_on_NON_sequential_labels_WITHOUT_BG(make_na
     result = map_measurements_on_labels(labels_layer, column='measurements')
     assert np.array_equal(output, result)
 
+
+def test_map_measurements_WITHOUT_BG_on_NON_sequential_labels_WITHOUT_BG_unsorted(make_napari_viewer):
+    import numpy as np
+    from pandas import DataFrame
+    from napari_skimage_regionprops._parametric_images import map_measurements_on_labels
+
+    measurements = [6, 9, 12, 15]
+    labels = np.array(
+        [[2, 4],
+         [6, 8]]
+    )
+    output = np.array(
+        [[9, 6],
+         [12, 15]]
+    )
+
+    table = DataFrame({
+        'label': [4, 2, 6, 8],
+        'measurements': measurements})
+
+    viewer = make_napari_viewer()
+    labels_layer = viewer.add_labels(labels, features=table)
+    result = map_measurements_on_labels(labels_layer, column='measurements')
+    assert np.array_equal(output, result)
+
+
 def test_map_measurements_WITHOUT_BG_on_sequential_labels_WITH_BG(make_napari_viewer):
     import numpy as np
     from pandas import DataFrame