{ "cells": [ { "cell_type": "markdown", "id": "e9de210d-a426-44b9-b4ad-a211680db15d", "metadata": {}, "source": [ "# Statistics of neighbors\n", "When characterizing tissues, it may be useful to summarize statistics such as the average distance labels to their neighbors. \n", "\n", "Note: When measuring distances, the centroid-to-centroid distance is called _distance_ for simplicity unless mentioned otherwise. Furthermore, keep in mind that most of the shown measurements only work correctly when using label images with isotropic pixels / voxels." ] }, { "cell_type": "code", "execution_count": 1, "id": "768bb5ea-2e1d-4c88-8f73-515fd450c1ff", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pyclesperanto_prototype as cle\n", "from skimage.io import imread\n", "import matplotlib.pyplot as plt\n", "import pandas as pd" ] }, { "cell_type": "markdown", "id": "939779d2-54c4-4cbb-9b29-cf9753dc9d87", "metadata": {}, "source": [ "We use this example label image to show what the different measurements mean. In the following, most measures will be explained for the object number `7` in the center of the image." ] }, { "cell_type": "code", "execution_count": 2, "id": "74b80485-c5da-493e-886b-78231634b637", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
\n", "\n", "\n", "cle._ image
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" ], "text/plain": [ "cl.OCLArray([[1, 1, 1, ..., 3, 3, 3],\n", " [1, 1, 1, ..., 3, 3, 3],\n", " [1, 1, 1, ..., 3, 3, 3],\n", " ...,\n", " [5, 5, 5, ..., 4, 4, 4],\n", " [5, 5, 5, ..., 4, 4, 4],\n", " [5, 5, 5, ..., 4, 4, 4]], dtype=uint32)" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "labels = cle.scale(cle.asarray([\n", " [1, 1, 2, 2, 3, 3, 3, 3],\n", " [1, 1, 2, 2, 3, 3, 3, 3],\n", " [1, 1, 7, 7, 7, 7, 3, 3],\n", " [1, 1, 7, 7, 7, 7, 3, 3],\n", " [6, 6, 7, 7, 7, 7, 4, 4],\n", " [6, 6, 7, 7, 7, 7, 4, 4],\n", " [5, 5, 5, 5, 5, 5, 4, 4],\n", " [5, 5, 5, 5, 5, 5, 4, 4],\n", "]), factor_x=10, factor_y=10, auto_size=True).astype(np.uint32)\n", "\n", "labels" ] }, { "cell_type": "markdown", "id": "15916c9f-dfd0-432b-8cf7-7d4392048341", "metadata": {}, "source": [ "# Distance meshes\n", "Before diving into details we should first have a look at neighborhood relationships and distances between neighbors. A distance mesh visualizes the distances between centroids in colour." ] }, { "cell_type": "code", "execution_count": 3, "id": "113e2acd-0d92-47ad-ba67-ba5b8bfe569b", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "distance_mesh = cle.draw_distance_mesh_between_touching_labels(labels)\n", "cle.imshow(distance_mesh, colorbar=True, colormap=\"rainbow\")" ] }, { "cell_type": "markdown", "id": "62080990-6ca8-4900-a419-088955684f38", "metadata": {}, "source": [ "Simple statistics such as the longest distance between direct neighbors can be measured from that image." ] }, { "cell_type": "code", "execution_count": 4, "id": "c5745e43-7d9d-4104-af0a-2efbe71f47f7", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "43.843155" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "distance_mesh.max()" ] }, { "cell_type": "markdown", "id": "65196c9f-c988-42d5-996e-7d4ee8ea6892", "metadata": {}, "source": [ "For more detailed statistics we use a table / pandas DataFrame." ] }, { "cell_type": "code", "execution_count": 5, "id": "ed8124f6-61c2-4add-98f8-28efda93b23f", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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labeltouching_neighbor_countminimum_distance_of_touching_neighborsaverage_distance_of_touching_neighborsmaximum_distance_of_touching_neighborsmax_min_distance_ratio_of_touching_neighborsproximal_neighbor_count_d10proximal_neighbor_count_d20proximal_neighbor_count_d40proximal_neighbor_count_d80...touch_portion_above_0.2_neighbor_counttouch_portion_above_0.33_neighbor_counttouch_portion_above_0.5_neighbor_counttouch_portion_above_0.75_neighbor_counttouch_count_summinimum_touch_countmaximum_touch_countminimum_touch_portionmaximum_touch_portionstandard_deviation_touch_portion
013.022.36068029.47206536.0555111.6124520.00.03.06.0...3.03.00.00.060.020.020.00.3333330.3333332.980232e-08
123.022.36068029.32564233.9934651.5202340.00.03.06.0...3.03.00.00.060.020.020.00.3333330.3333332.980232e-08
233.032.99831836.94498443.8431551.3286480.00.02.06.0...3.02.00.00.080.020.040.00.2500000.5000001.111111e-01
343.036.05551140.37657543.8431551.2159900.00.01.06.0...3.02.00.00.060.020.020.00.3333330.3333332.980232e-08
453.028.28427333.71270441.2310561.4577380.00.02.06.0...3.02.00.00.080.020.040.00.2500000.5000001.111111e-01
563.028.28427329.96901531.6227761.1180340.00.03.06.0...3.02.00.00.060.020.020.00.3333330.3333332.980232e-08
676.031.62277633.32961336.0555111.1401750.00.06.06.0...6.06.00.00.0160.020.040.00.1250000.2500005.555556e-02
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7 rows × 44 columns

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" ], "text/plain": [ " label touching_neighbor_count minimum_distance_of_touching_neighbors \\\n", "0 1 3.0 22.360680 \n", "1 2 3.0 22.360680 \n", "2 3 3.0 32.998318 \n", "3 4 3.0 36.055511 \n", "4 5 3.0 28.284273 \n", "5 6 3.0 28.284273 \n", "6 7 6.0 31.622776 \n", "\n", " average_distance_of_touching_neighbors \\\n", "0 29.472065 \n", "1 29.325642 \n", "2 36.944984 \n", "3 40.376575 \n", "4 33.712704 \n", "5 29.969015 \n", "6 33.329613 \n", "\n", " maximum_distance_of_touching_neighbors \\\n", "0 36.055511 \n", "1 33.993465 \n", "2 43.843155 \n", "3 43.843155 \n", "4 41.231056 \n", "5 31.622776 \n", "6 36.055511 \n", "\n", " max_min_distance_ratio_of_touching_neighbors proximal_neighbor_count_d10 \\\n", "0 1.612452 0.0 \n", "1 1.520234 0.0 \n", "2 1.328648 0.0 \n", "3 1.215990 0.0 \n", "4 1.457738 0.0 \n", "5 1.118034 0.0 \n", "6 1.140175 0.0 \n", "\n", " proximal_neighbor_count_d20 proximal_neighbor_count_d40 \\\n", "0 0.0 3.0 \n", "1 0.0 3.0 \n", "2 0.0 2.0 \n", "3 0.0 1.0 \n", "4 0.0 2.0 \n", "5 0.0 3.0 \n", "6 0.0 6.0 \n", "\n", " proximal_neighbor_count_d80 ... touch_portion_above_0.2_neighbor_count \\\n", "0 6.0 ... 3.0 \n", "1 6.0 ... 3.0 \n", "2 6.0 ... 3.0 \n", "3 6.0 ... 3.0 \n", "4 6.0 ... 3.0 \n", "5 6.0 ... 3.0 \n", "6 6.0 ... 6.0 \n", "\n", " touch_portion_above_0.33_neighbor_count \\\n", "0 3.0 \n", "1 3.0 \n", "2 2.0 \n", "3 2.0 \n", "4 2.0 \n", "5 2.0 \n", "6 6.0 \n", "\n", " touch_portion_above_0.5_neighbor_count \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "5 0.0 \n", "6 0.0 \n", "\n", " touch_portion_above_0.75_neighbor_count touch_count_sum \\\n", "0 0.0 60.0 \n", "1 0.0 60.0 \n", "2 0.0 80.0 \n", "3 0.0 60.0 \n", "4 0.0 80.0 \n", "5 0.0 60.0 \n", "6 0.0 160.0 \n", "\n", " minimum_touch_count maximum_touch_count minimum_touch_portion \\\n", "0 20.0 20.0 0.333333 \n", "1 20.0 20.0 0.333333 \n", "2 20.0 40.0 0.250000 \n", "3 20.0 20.0 0.333333 \n", "4 20.0 40.0 0.250000 \n", "5 20.0 20.0 0.333333 \n", "6 20.0 40.0 0.125000 \n", "\n", " maximum_touch_portion standard_deviation_touch_portion \n", "0 0.333333 2.980232e-08 \n", "1 0.333333 2.980232e-08 \n", "2 0.500000 1.111111e-01 \n", "3 0.333333 2.980232e-08 \n", "4 0.500000 1.111111e-01 \n", "5 0.333333 2.980232e-08 \n", "6 0.250000 5.555556e-02 \n", "\n", "[7 rows x 44 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stats = pd.DataFrame(cle.statistics_of_labelled_neighbors(labels))\n", "stats" ] }, { "cell_type": "markdown", "id": "c2382f75-f475-44a8-9f21-ab6e17f1b2f2", "metadata": {}, "source": [ "This table contains these columns:" ] }, { "cell_type": "code", "execution_count": 6, "id": "5546c297-98e5-4fd9-b28a-f7bc0196779c", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countmeanstdmin25%50%75%max
label7.04.0000002.1602471.000000e+002.500000e+004.000000e+005.5000007.000000
touching_neighbor_count7.03.4285711.1338933.000000e+003.000000e+003.000000e+003.0000006.000000
minimum_distance_of_touching_neighbors7.028.8523585.1909992.236068e+012.532248e+012.828427e+0132.31054736.055511
average_distance_of_touching_neighbors7.033.3043714.1848732.932564e+012.972054e+013.332961e+0135.32884440.376575
maximum_distance_of_touching_neighbors7.038.0920914.8810633.162278e+013.502449e+013.605551e+0142.53710643.843155
max_min_distance_ratio_of_touching_neighbors7.01.3418960.1937601.118034e+001.178083e+001.328648e+001.4889861.612452
proximal_neighbor_count_d107.00.0000000.0000000.000000e+000.000000e+000.000000e+000.0000000.000000
proximal_neighbor_count_d207.00.0000000.0000000.000000e+000.000000e+000.000000e+000.0000000.000000
proximal_neighbor_count_d407.02.8571431.5735921.000000e+002.000000e+003.000000e+003.0000006.000000
proximal_neighbor_count_d807.06.0000000.0000006.000000e+006.000000e+006.000000e+006.0000006.000000
proximal_neighbor_count_d1607.06.0000000.0000006.000000e+006.000000e+006.000000e+006.0000006.000000
maximum_distance_of_n1_nearest_neighbors7.028.8523585.1910002.236068e+012.532248e+012.828427e+0132.31054736.055511
average_distance_of_n1_nearest_neighbors7.028.8523585.1910002.236068e+012.532248e+012.828427e+0132.31054736.055511
maximum_distance_of_n2_nearest_neighbors7.032.8704033.9223703.000000e+013.081139e+013.162278e+0132.80812141.231056
average_distance_of_n2_nearest_neighbors7.030.8613804.2572442.618034e+012.806693e+012.995353e+0132.55933438.643284
maximum_distance_of_n3_nearest_neighbors7.037.4588395.4443543.162278e+013.280812e+013.605551e+0142.53710443.843155
average_distance_of_n3_nearest_neighbors7.033.0605354.2326082.932564e+012.972054e+013.162278e+0135.32884240.376572
maximum_distance_of_n4_nearest_neighbors7.049.1421559.0860523.299832e+014.472136e+015.343740e+0153.64452460.827621
average_distance_of_n4_nearest_neighbors7.037.0809404.9181693.196666e+013.341583e+013.546340e+0139.90776145.489334
maximum_distance_of_n5_nearest_neighbors7.056.8084649.7052043.605551e+015.692582e+016.000000e+0161.86041664.031242
average_distance_of_n5_nearest_neighbors7.041.0264475.3461823.278444e+013.881543e+013.914105e+0144.21553049.197720
maximum_distance_of_n6_nearest_neighbors7.061.85549512.1377883.605551e+016.289321e+016.289321e+0168.07113372.111023
average_distance_of_n6_nearest_neighbors7.044.4979556.0607613.332961e+014.292323e+014.463604e+0147.32847653.016602
maximum_distance_of_n7_nearest_neighbors7.061.85549512.1377883.605551e+016.289321e+016.289321e+0168.07113372.111023
average_distance_of_n7_nearest_neighbors7.048.5855335.1949383.901267e+014.723577e+014.870390e+0151.01169455.887230
maximum_distance_of_n8_nearest_neighbors7.061.85549512.1377883.605551e+016.289321e+016.289321e+0168.07113372.111023
average_distance_of_n8_nearest_neighbors7.048.5855335.1949383.901267e+014.723577e+014.870390e+0151.01169455.887230
maximum_distance_of_n10_nearest_neighbors7.061.85549512.1377883.605551e+016.289321e+016.289321e+0168.07113372.111023
average_distance_of_n10_nearest_neighbors7.048.5855335.1949383.901267e+014.723577e+014.870390e+0151.01169455.887230
maximum_distance_of_n20_nearest_neighbors7.061.85549512.1377883.605551e+016.289321e+016.289321e+0168.07113372.111023
average_distance_of_n20_nearest_neighbors7.048.5855335.1949383.901267e+014.723577e+014.870390e+0151.01169455.887230
distance_to_most_distant_other7.061.85549512.1377883.605551e+016.289321e+016.289321e+0168.07113372.111023
touch_portion_above_0_neighbor_count7.03.4285711.1338933.000000e+003.000000e+003.000000e+003.0000006.000000
touch_portion_above_0.16_neighbor_count7.03.4285711.1338933.000000e+003.000000e+003.000000e+003.0000006.000000
touch_portion_above_0.2_neighbor_count7.03.4285711.1338933.000000e+003.000000e+003.000000e+003.0000006.000000
touch_portion_above_0.33_neighbor_count7.02.8571431.4638502.000000e+002.000000e+002.000000e+003.0000006.000000
touch_portion_above_0.5_neighbor_count7.00.0000000.0000000.000000e+000.000000e+000.000000e+000.0000000.000000
touch_portion_above_0.75_neighbor_count7.00.0000000.0000000.000000e+000.000000e+000.000000e+000.0000000.000000
touch_count_sum7.080.00000036.5148396.000000e+016.000000e+016.000000e+0180.000000160.000000
minimum_touch_count7.020.0000000.0000002.000000e+012.000000e+012.000000e+0120.00000020.000000
maximum_touch_count7.028.57142810.6904502.000000e+012.000000e+012.000000e+0140.00000040.000000
minimum_touch_portion7.00.2797620.0787431.250000e-012.500000e-013.333333e-010.3333330.333333
maximum_touch_portion7.00.3690480.0944912.500000e-013.333333e-013.333333e-010.4166670.500000
standard_deviation_touch_portion7.00.0396830.0528442.980232e-082.980232e-082.980232e-080.0833330.111111
\n", "
" ], "text/plain": [ " count mean std \\\n", "label 7.0 4.000000 2.160247 \n", "touching_neighbor_count 7.0 3.428571 1.133893 \n", "minimum_distance_of_touching_neighbors 7.0 28.852358 5.190999 \n", "average_distance_of_touching_neighbors 7.0 33.304371 4.184873 \n", "maximum_distance_of_touching_neighbors 7.0 38.092091 4.881063 \n", "max_min_distance_ratio_of_touching_neighbors 7.0 1.341896 0.193760 \n", "proximal_neighbor_count_d10 7.0 0.000000 0.000000 \n", "proximal_neighbor_count_d20 7.0 0.000000 0.000000 \n", "proximal_neighbor_count_d40 7.0 2.857143 1.573592 \n", "proximal_neighbor_count_d80 7.0 6.000000 0.000000 \n", "proximal_neighbor_count_d160 7.0 6.000000 0.000000 \n", "maximum_distance_of_n1_nearest_neighbors 7.0 28.852358 5.191000 \n", "average_distance_of_n1_nearest_neighbors 7.0 28.852358 5.191000 \n", "maximum_distance_of_n2_nearest_neighbors 7.0 32.870403 3.922370 \n", "average_distance_of_n2_nearest_neighbors 7.0 30.861380 4.257244 \n", "maximum_distance_of_n3_nearest_neighbors 7.0 37.458839 5.444354 \n", "average_distance_of_n3_nearest_neighbors 7.0 33.060535 4.232608 \n", "maximum_distance_of_n4_nearest_neighbors 7.0 49.142155 9.086052 \n", "average_distance_of_n4_nearest_neighbors 7.0 37.080940 4.918169 \n", "maximum_distance_of_n5_nearest_neighbors 7.0 56.808464 9.705204 \n", "average_distance_of_n5_nearest_neighbors 7.0 41.026447 5.346182 \n", "maximum_distance_of_n6_nearest_neighbors 7.0 61.855495 12.137788 \n", "average_distance_of_n6_nearest_neighbors 7.0 44.497955 6.060761 \n", "maximum_distance_of_n7_nearest_neighbors 7.0 61.855495 12.137788 \n", "average_distance_of_n7_nearest_neighbors 7.0 48.585533 5.194938 \n", "maximum_distance_of_n8_nearest_neighbors 7.0 61.855495 12.137788 \n", "average_distance_of_n8_nearest_neighbors 7.0 48.585533 5.194938 \n", "maximum_distance_of_n10_nearest_neighbors 7.0 61.855495 12.137788 \n", "average_distance_of_n10_nearest_neighbors 7.0 48.585533 5.194938 \n", "maximum_distance_of_n20_nearest_neighbors 7.0 61.855495 12.137788 \n", "average_distance_of_n20_nearest_neighbors 7.0 48.585533 5.194938 \n", "distance_to_most_distant_other 7.0 61.855495 12.137788 \n", "touch_portion_above_0_neighbor_count 7.0 3.428571 1.133893 \n", "touch_portion_above_0.16_neighbor_count 7.0 3.428571 1.133893 \n", "touch_portion_above_0.2_neighbor_count 7.0 3.428571 1.133893 \n", "touch_portion_above_0.33_neighbor_count 7.0 2.857143 1.463850 \n", "touch_portion_above_0.5_neighbor_count 7.0 0.000000 0.000000 \n", "touch_portion_above_0.75_neighbor_count 7.0 0.000000 0.000000 \n", "touch_count_sum 7.0 80.000000 36.514839 \n", "minimum_touch_count 7.0 20.000000 0.000000 \n", "maximum_touch_count 7.0 28.571428 10.690450 \n", "minimum_touch_portion 7.0 0.279762 0.078743 \n", "maximum_touch_portion 7.0 0.369048 0.094491 \n", "standard_deviation_touch_portion 7.0 0.039683 0.052844 \n", "\n", " min 25% \\\n", "label 1.000000e+00 2.500000e+00 \n", "touching_neighbor_count 3.000000e+00 3.000000e+00 \n", "minimum_distance_of_touching_neighbors 2.236068e+01 2.532248e+01 \n", "average_distance_of_touching_neighbors 2.932564e+01 2.972054e+01 \n", "maximum_distance_of_touching_neighbors 3.162278e+01 3.502449e+01 \n", "max_min_distance_ratio_of_touching_neighbors 1.118034e+00 1.178083e+00 \n", "proximal_neighbor_count_d10 0.000000e+00 0.000000e+00 \n", "proximal_neighbor_count_d20 0.000000e+00 0.000000e+00 \n", "proximal_neighbor_count_d40 1.000000e+00 2.000000e+00 \n", "proximal_neighbor_count_d80 6.000000e+00 6.000000e+00 \n", "proximal_neighbor_count_d160 6.000000e+00 6.000000e+00 \n", "maximum_distance_of_n1_nearest_neighbors 2.236068e+01 2.532248e+01 \n", "average_distance_of_n1_nearest_neighbors 2.236068e+01 2.532248e+01 \n", "maximum_distance_of_n2_nearest_neighbors 3.000000e+01 3.081139e+01 \n", "average_distance_of_n2_nearest_neighbors 2.618034e+01 2.806693e+01 \n", "maximum_distance_of_n3_nearest_neighbors 3.162278e+01 3.280812e+01 \n", "average_distance_of_n3_nearest_neighbors 2.932564e+01 2.972054e+01 \n", "maximum_distance_of_n4_nearest_neighbors 3.299832e+01 4.472136e+01 \n", "average_distance_of_n4_nearest_neighbors 3.196666e+01 3.341583e+01 \n", "maximum_distance_of_n5_nearest_neighbors 3.605551e+01 5.692582e+01 \n", "average_distance_of_n5_nearest_neighbors 3.278444e+01 3.881543e+01 \n", "maximum_distance_of_n6_nearest_neighbors 3.605551e+01 6.289321e+01 \n", "average_distance_of_n6_nearest_neighbors 3.332961e+01 4.292323e+01 \n", "maximum_distance_of_n7_nearest_neighbors 3.605551e+01 6.289321e+01 \n", "average_distance_of_n7_nearest_neighbors 3.901267e+01 4.723577e+01 \n", "maximum_distance_of_n8_nearest_neighbors 3.605551e+01 6.289321e+01 \n", "average_distance_of_n8_nearest_neighbors 3.901267e+01 4.723577e+01 \n", "maximum_distance_of_n10_nearest_neighbors 3.605551e+01 6.289321e+01 \n", "average_distance_of_n10_nearest_neighbors 3.901267e+01 4.723577e+01 \n", "maximum_distance_of_n20_nearest_neighbors 3.605551e+01 6.289321e+01 \n", "average_distance_of_n20_nearest_neighbors 3.901267e+01 4.723577e+01 \n", "distance_to_most_distant_other 3.605551e+01 6.289321e+01 \n", "touch_portion_above_0_neighbor_count 3.000000e+00 3.000000e+00 \n", "touch_portion_above_0.16_neighbor_count 3.000000e+00 3.000000e+00 \n", "touch_portion_above_0.2_neighbor_count 3.000000e+00 3.000000e+00 \n", "touch_portion_above_0.33_neighbor_count 2.000000e+00 2.000000e+00 \n", "touch_portion_above_0.5_neighbor_count 0.000000e+00 0.000000e+00 \n", "touch_portion_above_0.75_neighbor_count 0.000000e+00 0.000000e+00 \n", "touch_count_sum 6.000000e+01 6.000000e+01 \n", "minimum_touch_count 2.000000e+01 2.000000e+01 \n", "maximum_touch_count 2.000000e+01 2.000000e+01 \n", "minimum_touch_portion 1.250000e-01 2.500000e-01 \n", "maximum_touch_portion 2.500000e-01 3.333333e-01 \n", "standard_deviation_touch_portion 2.980232e-08 2.980232e-08 \n", "\n", " 50% 75% \\\n", "label 4.000000e+00 5.500000 \n", "touching_neighbor_count 3.000000e+00 3.000000 \n", "minimum_distance_of_touching_neighbors 2.828427e+01 32.310547 \n", "average_distance_of_touching_neighbors 3.332961e+01 35.328844 \n", "maximum_distance_of_touching_neighbors 3.605551e+01 42.537106 \n", "max_min_distance_ratio_of_touching_neighbors 1.328648e+00 1.488986 \n", "proximal_neighbor_count_d10 0.000000e+00 0.000000 \n", "proximal_neighbor_count_d20 0.000000e+00 0.000000 \n", "proximal_neighbor_count_d40 3.000000e+00 3.000000 \n", "proximal_neighbor_count_d80 6.000000e+00 6.000000 \n", "proximal_neighbor_count_d160 6.000000e+00 6.000000 \n", "maximum_distance_of_n1_nearest_neighbors 2.828427e+01 32.310547 \n", "average_distance_of_n1_nearest_neighbors 2.828427e+01 32.310547 \n", "maximum_distance_of_n2_nearest_neighbors 3.162278e+01 32.808121 \n", "average_distance_of_n2_nearest_neighbors 2.995353e+01 32.559334 \n", "maximum_distance_of_n3_nearest_neighbors 3.605551e+01 42.537104 \n", "average_distance_of_n3_nearest_neighbors 3.162278e+01 35.328842 \n", "maximum_distance_of_n4_nearest_neighbors 5.343740e+01 53.644524 \n", "average_distance_of_n4_nearest_neighbors 3.546340e+01 39.907761 \n", "maximum_distance_of_n5_nearest_neighbors 6.000000e+01 61.860416 \n", "average_distance_of_n5_nearest_neighbors 3.914105e+01 44.215530 \n", "maximum_distance_of_n6_nearest_neighbors 6.289321e+01 68.071133 \n", "average_distance_of_n6_nearest_neighbors 4.463604e+01 47.328476 \n", "maximum_distance_of_n7_nearest_neighbors 6.289321e+01 68.071133 \n", "average_distance_of_n7_nearest_neighbors 4.870390e+01 51.011694 \n", "maximum_distance_of_n8_nearest_neighbors 6.289321e+01 68.071133 \n", "average_distance_of_n8_nearest_neighbors 4.870390e+01 51.011694 \n", "maximum_distance_of_n10_nearest_neighbors 6.289321e+01 68.071133 \n", "average_distance_of_n10_nearest_neighbors 4.870390e+01 51.011694 \n", "maximum_distance_of_n20_nearest_neighbors 6.289321e+01 68.071133 \n", "average_distance_of_n20_nearest_neighbors 4.870390e+01 51.011694 \n", "distance_to_most_distant_other 6.289321e+01 68.071133 \n", "touch_portion_above_0_neighbor_count 3.000000e+00 3.000000 \n", "touch_portion_above_0.16_neighbor_count 3.000000e+00 3.000000 \n", "touch_portion_above_0.2_neighbor_count 3.000000e+00 3.000000 \n", "touch_portion_above_0.33_neighbor_count 2.000000e+00 3.000000 \n", "touch_portion_above_0.5_neighbor_count 0.000000e+00 0.000000 \n", "touch_portion_above_0.75_neighbor_count 0.000000e+00 0.000000 \n", "touch_count_sum 6.000000e+01 80.000000 \n", "minimum_touch_count 2.000000e+01 20.000000 \n", "maximum_touch_count 2.000000e+01 40.000000 \n", "minimum_touch_portion 3.333333e-01 0.333333 \n", "maximum_touch_portion 3.333333e-01 0.416667 \n", "standard_deviation_touch_portion 2.980232e-08 0.083333 \n", "\n", " max \n", "label 7.000000 \n", "touching_neighbor_count 6.000000 \n", "minimum_distance_of_touching_neighbors 36.055511 \n", "average_distance_of_touching_neighbors 40.376575 \n", "maximum_distance_of_touching_neighbors 43.843155 \n", "max_min_distance_ratio_of_touching_neighbors 1.612452 \n", "proximal_neighbor_count_d10 0.000000 \n", "proximal_neighbor_count_d20 0.000000 \n", "proximal_neighbor_count_d40 6.000000 \n", "proximal_neighbor_count_d80 6.000000 \n", "proximal_neighbor_count_d160 6.000000 \n", "maximum_distance_of_n1_nearest_neighbors 36.055511 \n", "average_distance_of_n1_nearest_neighbors 36.055511 \n", "maximum_distance_of_n2_nearest_neighbors 41.231056 \n", "average_distance_of_n2_nearest_neighbors 38.643284 \n", "maximum_distance_of_n3_nearest_neighbors 43.843155 \n", "average_distance_of_n3_nearest_neighbors 40.376572 \n", "maximum_distance_of_n4_nearest_neighbors 60.827621 \n", "average_distance_of_n4_nearest_neighbors 45.489334 \n", "maximum_distance_of_n5_nearest_neighbors 64.031242 \n", "average_distance_of_n5_nearest_neighbors 49.197720 \n", "maximum_distance_of_n6_nearest_neighbors 72.111023 \n", "average_distance_of_n6_nearest_neighbors 53.016602 \n", "maximum_distance_of_n7_nearest_neighbors 72.111023 \n", "average_distance_of_n7_nearest_neighbors 55.887230 \n", "maximum_distance_of_n8_nearest_neighbors 72.111023 \n", "average_distance_of_n8_nearest_neighbors 55.887230 \n", "maximum_distance_of_n10_nearest_neighbors 72.111023 \n", "average_distance_of_n10_nearest_neighbors 55.887230 \n", "maximum_distance_of_n20_nearest_neighbors 72.111023 \n", "average_distance_of_n20_nearest_neighbors 55.887230 \n", "distance_to_most_distant_other 72.111023 \n", "touch_portion_above_0_neighbor_count 6.000000 \n", "touch_portion_above_0.16_neighbor_count 6.000000 \n", "touch_portion_above_0.2_neighbor_count 6.000000 \n", "touch_portion_above_0.33_neighbor_count 6.000000 \n", "touch_portion_above_0.5_neighbor_count 0.000000 \n", "touch_portion_above_0.75_neighbor_count 0.000000 \n", "touch_count_sum 160.000000 \n", "minimum_touch_count 20.000000 \n", "maximum_touch_count 40.000000 \n", "minimum_touch_portion 0.333333 \n", "maximum_touch_portion 0.500000 \n", "standard_deviation_touch_portion 0.111111 " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stats.describe().T" ] }, { "cell_type": "markdown", "id": "734c41d1-32b4-4ea2-9535-a88fa263db71", "metadata": {}, "source": [ "The following code snippets show how we can interpret that table. All examples refer to the labeled object `7` in the center of the label image shown above." ] }, { "cell_type": "code", "execution_count": 7, "id": "e19217a5-d478-464d-b6e7-51582176c985", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The label of the last object is 7\n" ] } ], "source": [ "print(\"The label of the last object is\", \n", " stats[\"label\"].tolist()[-1])" ] }, { "cell_type": "code", "execution_count": 8, "id": "52f9eded-4d92-406e-888b-01f7bdd21ae3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The last object has 6.0 touching neighbors\n" ] } ], "source": [ "print(\"The last object has\", \n", " stats[\"touching_neighbor_count\"].tolist()[-1],\n", " \"touching neighbors\")" ] }, { "cell_type": "code", "execution_count": 9, "id": "b17dbf55-a7d8-44b7-a03a-afd871ea2aec", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The maximum distance of any label centroid to any other is 43.843155\n" ] } ], "source": [ "print(\"The maximum distance of any label centroid to any other is\", \n", " stats[\"maximum_distance_of_touching_neighbors\"].max())" ] }, { "cell_type": "code", "execution_count": 10, "id": "73567a74-b83e-4a9b-b8e0-9898cdc1846a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The last object has an average distance to its touching neighbors of 33.329612731933594\n" ] } ], "source": [ "print(\"The last object has an average distance to its touching neighbors of\",\n", " stats[\"average_distance_of_touching_neighbors\"].tolist()[-1]\n", " )" ] }, { "cell_type": "code", "execution_count": 11, "id": "3c187b25-c606-4260-84d8-ab5b521f68ed", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The last object has an minimum distance to its touching neighbors of 31.62277603149414\n" ] } ], "source": [ "print(\"The last object has an minimum distance to its touching neighbors of\",\n", " stats[\"minimum_distance_of_touching_neighbors\"].tolist()[-1]\n", " )" ] }, { "cell_type": "code", "execution_count": 12, "id": "f9787526-fbc5-41d0-b30a-ec8f4792e2c3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The last object has an maximum distance to its touching neighbors of 36.055511474609375\n" ] } ], "source": [ "print(\"The last object has an maximum distance to its touching neighbors of\",\n", " stats[\"maximum_distance_of_touching_neighbors\"].tolist()[-1]\n", " )" ] }, { "cell_type": "code", "execution_count": 13, "id": "c6e78678-d763-455d-aec7-af09963a29b2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "There are 0.0 objects around the last labeled object within a radius of 10 pixels\n", "There are 0.0 objects around the last labeled object within a radius of 20 pixels\n", "There are 6.0 objects around the last labeled object within a radius of 40 pixels\n", "There are 6.0 objects around the last labeled object within a radius of 80 pixels\n" ] } ], "source": [ "for d in [10, 20, 40, 80]:\n", " print(\"There are\",\n", " stats[\"proximal_neighbor_count_d\" + str(d)].tolist()[-1],\n", " \"objects around the last labeled object within a radius of\",\n", " d,\n", " \"pixels\"\n", " )" ] }, { "cell_type": "code", "execution_count": 14, "id": "7544c773-d918-4c8e-a989-21b40bfccd23", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The 1. neighbor of the last label is 31.62277603149414 pixels away.\n", "The 2. neighbor of the last label is 31.62277603149414 pixels away.\n", "The 3. neighbor of the last label is 31.62277603149414 pixels away.\n", "The 4. neighbor of the last label is 32.99831771850586 pixels away.\n", "The 5. neighbor of the last label is 36.055511474609375 pixels away.\n", "The 6. neighbor of the last label is 36.055511474609375 pixels away.\n" ] } ], "source": [ "for n in [1,2,3,4,5,6]:\n", " print(\"The \" + str(n) + \". neighbor of the last label is\",\n", " stats[\"maximum_distance_of_n\" + str(n) + \"_nearest_neighbors\"].tolist()[-1],\n", " \"pixels away.\"\n", " )" ] }, { "cell_type": "code", "execution_count": 15, "id": "d9ff5a1a-2e04-41b1-954b-70f7e4b28f8f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The average distance to the 1 neighbors of the last label is 31.62277603149414 pixels.\n", "The average distance to the 2 neighbors of the last label is 31.62277603149414 pixels.\n", "The average distance to the 3 neighbors of the last label is 31.622777938842773 pixels.\n", "The average distance to the 4 neighbors of the last label is 31.966663360595703 pixels.\n", "The average distance to the 5 neighbors of the last label is 32.7844352722168 pixels.\n", "The average distance to the 6 neighbors of the last label is 33.329612731933594 pixels.\n" ] } ], "source": [ "for n in [1,2,3,4,5,6]:\n", " print(\"The average distance to the \" + str(n) + \" neighbors of the last label is\",\n", " stats[\"average_distance_of_n\" + str(n) + \"_nearest_neighbors\"].tolist()[-1],\n", " \"pixels.\"\n", " )" ] }, { "cell_type": "markdown", "id": "1bdc1f51-6908-4457-a8b0-0b12311d6fb2", "metadata": {}, "source": [ "## Tocuh count and touch portion\n", "Just for reader's convenience we visualize the label image again." ] }, { "cell_type": "code", "execution_count": 16, "id": "a52ec48f-c03b-4f4f-8d0f-ed5fc599419f", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
\n", "\n", "\n", "cle._ image
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shape(80, 80)
dtypeuint32
size25.0 kB
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" ], "text/plain": [ "cl.OCLArray([[1, 1, 1, ..., 3, 3, 3],\n", " [1, 1, 1, ..., 3, 3, 3],\n", " [1, 1, 1, ..., 3, 3, 3],\n", " ...,\n", " [5, 5, 5, ..., 4, 4, 4],\n", " [5, 5, 5, ..., 4, 4, 4],\n", " [5, 5, 5, ..., 4, 4, 4]], dtype=uint32)" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "labels" ] }, { "cell_type": "code", "execution_count": 17, "id": "07b6bb22-d99f-43dc-b6bb-122d6b8eeda0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The last labelled object has 160.0 pixels where it touches others\n" ] } ], "source": [ "print(\"The last labelled object has\", \n", " stats[\"touch_count_sum\"].tolist()[-1],\n", " \"pixels where it touches others\")" ] }, { "cell_type": "code", "execution_count": 18, "id": "c3948bdf-fbf3-453f-917d-bb6f442cc1b8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The last labelled object touches an other object with at least 20.0 pixels\n" ] } ], "source": [ "print(\"The last labelled object touches an other object with at least\", \n", " stats[\"minimum_touch_count\"].tolist()[-1],\n", " \"pixels\")" ] }, { "cell_type": "code", "execution_count": 19, "id": "c35a35a1-3f23-4395-8a90-00b72ab17fae", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The last labelled object touches an other object with up to 40.0 pixels\n" ] } ], "source": [ "print(\"The last labelled object touches an other object with up to\", \n", " stats[\"maximum_touch_count\"].tolist()[-1],\n", " \"pixels\")" ] }, { "cell_type": "code", "execution_count": 20, "id": "22d28dbb-c13a-45be-a542-07a1d52f1873", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The last labelled object touches an other object with at least 12.5 percent of its border\n" ] } ], "source": [ "print(\"The last labelled object touches an other object with at least\", \n", " stats[\"minimum_touch_portion\"].tolist()[-1] * 100,\n", " \"percent of its border\")" ] }, { "cell_type": "code", "execution_count": 21, "id": "68498ed8-664a-414b-8145-9d0968aae32f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The last labelled object touches an other object with up to 25.0 percent of its border\n" ] } ], "source": [ "print(\"The last labelled object touches an other object with up to\", \n", " stats[\"maximum_touch_portion\"].tolist()[-1] * 100,\n", " \"percent of its border\")" ] }, { "cell_type": "markdown", "id": "e0534b4f-2784-4c53-b4ca-f4e8b980e2a4", "metadata": {}, "source": [ "## Visualization of statistics\n", "We can visualize those measurements in parametric map images.\n", "\n", "For visualization of the table columns as maps, we typically need to prefix the measurements with a `0`. This `0` represents the measurement of the background." ] }, { "cell_type": "code", "execution_count": 22, "id": "27f65f82-e567-4d44-aeed-72911698b61c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 6.0]" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stats[\"touching_neighbor_count\"].tolist()" ] }, { "cell_type": "code", "execution_count": 23, "id": "2011c972-feeb-4588-af55-7d03b78e79e8", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
cle.array([[0. 3. 3. 3. 3. 3. 3. 6.]], dtype=float32)
" ], "text/plain": [ "cl.OCLArray([[0., 3., 3., 3., 3., 3., 3., 6.]], dtype=float32)" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "list_of_measurements = cle.prefix_in_x([stats[\"touching_neighbor_count\"].tolist()])\n", "list_of_measurements" ] }, { "cell_type": "code", "execution_count": 24, "id": "e6d09d49-a594-4acf-ae6c-36218276dd5f", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
\n", "\n", "\n", "cle._ image
\n", "\n", "\n", "\n", "\n", "\n", "
shape(80, 80)
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" ], "text/plain": [ "cl.OCLArray([[3., 3., 3., ..., 3., 3., 3.],\n", " [3., 3., 3., ..., 3., 3., 3.],\n", " [3., 3., 3., ..., 3., 3., 3.],\n", " ...,\n", " [3., 3., 3., ..., 3., 3., 3.],\n", " [3., 3., 3., ..., 3., 3., 3.],\n", " [3., 3., 3., ..., 3., 3., 3.]], dtype=float32)" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cle.replace_intensities(labels, list_of_measurements)" ] }, { "cell_type": "markdown", "id": "f47a73e6-1f9f-4fb8-98c2-70dd6fda943c", "metadata": {}, "source": [ "As more realistic example, we visualize the measurements in an image stack of a Tribolium embryo. The dataset, curtesy of Daniela Vorkel / Myers lab / MPI-CBG / CSBD can be [downloaded here](https://github.com/clEsperanto/clesperanto_example_data/blob/main/Lund-100MB.tif). " ] }, { "cell_type": "code", "execution_count": 25, "id": "ed12c5ef-9cde-4b1e-a7e1-eeabe8439130", "metadata": {}, "outputs": [], "source": [ "embryo = imread(\"../../../clesperanto_example_data/Lund-100MB.tif\")[3]\n", "\n", "bg_subtracted = cle.top_hat_box(embryo, radius_x=5, radius_y=5)\n", "\n", "nuclei_labels = cle.voronoi_otsu_labeling(bg_subtracted, spot_sigma=0.5, outline_sigma=1)\n", "\n", "cell_estimation = cle.dilate_labels(nuclei_labels, radius=12)" ] }, { "cell_type": "code", "execution_count": 26, "id": "21bfab9d-baee-4b6e-8320-a981b7b7e1b9", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, axs = plt.subplots(1, 4, figsize=(10,10))\n", "\n", "axs[0].set_title(\"Raw\")\n", "cle.imshow(embryo, plot=axs[0])\n", "axs[1].set_title(\"Background subtracted\")\n", "cle.imshow(bg_subtracted, plot=axs[1])\n", "axs[2].set_title(\"Nuclei segmentation\")\n", "cle.imshow(nuclei_labels, plot=axs[2], labels=True)\n", "axs[3].set_title(\"Cell estimation\")\n", "cle.imshow(cell_estimation, plot=axs[3], labels=True)" ] }, { "cell_type": "code", "execution_count": 27, "id": "0620605a-9b59-42d3-bfe9-fa7d2f540569", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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labeltouching_neighbor_countminimum_distance_of_touching_neighborsaverage_distance_of_touching_neighborsmaximum_distance_of_touching_neighborsproximal_neighbor_count_d10proximal_neighbor_count_d20proximal_neighbor_count_d40proximal_neighbor_count_d80proximal_neighbor_count_d160...average_distance_of_n10_nearest_neighborsmaximum_distance_of_n20_nearest_neighborsaverage_distance_of_n20_nearest_neighborsdistance_to_most_distant_othertouch_count_summinimum_touch_countmaximum_touch_countminimum_touch_portionmaximum_touch_portionstandard_deviation_touch_portion
0111.010.34856815.76386325.4928780.09.042.0195.0665.0...15.16447727.04611420.066113482.9016421586.012.0373.00.0075660.2351830.065240
124.09.68011011.96857615.2065671.05.026.0149.0568.0...19.31597935.63835125.482178505.4353641077.038.0486.00.0352830.4512530.117224
237.07.61234313.00779015.4584111.07.044.0201.0690.0...16.43660428.81181021.466221473.300323632.04.0308.00.0063290.4873420.100555
344.09.38296411.71130013.7442531.09.039.0190.0648.0...15.70533928.55045520.369110486.618164584.048.0214.00.0821920.3664380.085616
458.09.38296413.52785117.9165731.08.037.0180.0620.0...15.66967229.88317521.231649494.4498291499.015.0420.00.0100070.2801870.067357
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" ], "text/plain": [ " label touching_neighbor_count minimum_distance_of_touching_neighbors \\\n", "0 1 11.0 10.348568 \n", "1 2 4.0 9.680110 \n", "2 3 7.0 7.612343 \n", "3 4 4.0 9.382964 \n", "4 5 8.0 9.382964 \n", "\n", " average_distance_of_touching_neighbors \\\n", "0 15.763863 \n", "1 11.968576 \n", "2 13.007790 \n", "3 11.711300 \n", "4 13.527851 \n", "\n", " maximum_distance_of_touching_neighbors proximal_neighbor_count_d10 \\\n", "0 25.492878 0.0 \n", "1 15.206567 1.0 \n", "2 15.458411 1.0 \n", "3 13.744253 1.0 \n", "4 17.916573 1.0 \n", "\n", " proximal_neighbor_count_d20 proximal_neighbor_count_d40 \\\n", "0 9.0 42.0 \n", "1 5.0 26.0 \n", "2 7.0 44.0 \n", "3 9.0 39.0 \n", "4 8.0 37.0 \n", "\n", " proximal_neighbor_count_d80 proximal_neighbor_count_d160 ... \\\n", "0 195.0 665.0 ... \n", "1 149.0 568.0 ... \n", "2 201.0 690.0 ... \n", "3 190.0 648.0 ... \n", "4 180.0 620.0 ... \n", "\n", " average_distance_of_n10_nearest_neighbors \\\n", "0 15.164477 \n", "1 19.315979 \n", "2 16.436604 \n", "3 15.705339 \n", "4 15.669672 \n", "\n", " maximum_distance_of_n20_nearest_neighbors \\\n", "0 27.046114 \n", "1 35.638351 \n", "2 28.811810 \n", "3 28.550455 \n", "4 29.883175 \n", "\n", " average_distance_of_n20_nearest_neighbors distance_to_most_distant_other \\\n", "0 20.066113 482.901642 \n", "1 25.482178 505.435364 \n", "2 21.466221 473.300323 \n", "3 20.369110 486.618164 \n", "4 21.231649 494.449829 \n", "\n", " touch_count_sum minimum_touch_count maximum_touch_count \\\n", "0 1586.0 12.0 373.0 \n", "1 1077.0 38.0 486.0 \n", "2 632.0 4.0 308.0 \n", "3 584.0 48.0 214.0 \n", "4 1499.0 15.0 420.0 \n", "\n", " minimum_touch_portion maximum_touch_portion \\\n", "0 0.007566 0.235183 \n", "1 0.035283 0.451253 \n", "2 0.006329 0.487342 \n", "3 0.082192 0.366438 \n", "4 0.010007 0.280187 \n", "\n", " standard_deviation_touch_portion \n", "0 0.065240 \n", "1 0.117224 \n", "2 0.100555 \n", "3 0.085616 \n", "4 0.067357 \n", "\n", "[5 rows x 37 columns]" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tribolium_statistics = pd.DataFrame(cle.statistics_of_labelled_neighbors(cell_estimation))\n", "tribolium_statistics.head()" ] }, { "cell_type": "code", "execution_count": 28, "id": "cc2ec4f6-02c5-4600-8d9b-42517ff87e94", "metadata": {}, "outputs": [], "source": [ "def visualize(label_image, statistics, column):\n", " list_of_measurements = cle.prefix_in_x([statistics[column].tolist()])\n", " \n", " return cle.replace_intensities(label_image, list_of_measurements)" ] }, { "cell_type": "code", "execution_count": 29, "id": "ea87ecbd-4757-467e-aa0d-548eceffa58d", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
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