diff --git a/docs/examples/C2.h5 b/docs/examples/C2.h5
index 443672aa..dc0632d1 100644
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index e0c0d5c9..6ce6a47b 100644
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" 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" 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+       <use xlink:href="#m591ace2aef" x="811.905882" y="755.003697" style="stroke: #000000; stroke-width: 0.8"/>
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diff --git a/docs/examples/clusters.h5 b/docs/examples/clusters.h5
index a20691a0..029f3341 100644
Binary files a/docs/examples/clusters.h5 and b/docs/examples/clusters.h5 differ
diff --git a/docs/examples/clusters.py b/docs/examples/clusters.py
index de2f0962..5f3a1952 100644
--- a/docs/examples/clusters.py
+++ b/docs/examples/clusters.py
@@ -22,6 +22,7 @@
     parser.add_argument("--save", action="store_true")
     parser.add_argument("--check", action="store_true")
     parser.add_argument("--plot", action="store_true")
+    parser.add_argument("--show", action="store_true")
     args = parser.parse_args()
 
     if args.save:
@@ -44,6 +45,7 @@
         import matplotlib.pyplot as plt
         import matplotlib as mpl
         import matplotlib.cm as cm
+        import prrng
         from mpl_toolkits.axes_grid1 import make_axes_locatable
 
         # color-scheme: modify such that the background is white
@@ -52,12 +54,30 @@
         cmap[0, :3] = 1.0
         cmap = mpl.colors.ListedColormap(cmap)
 
+        # reshuffle for better visualisation
+        assert np.all(np.diff(np.unique(clusters)) == 1)
+        assert np.all(np.diff(np.unique(clusters_periodic)) == 1)
+        assert np.unique(clusters).size >= np.unique(clusters_periodic).size
+        rng = prrng.pcg32()
+        lab = np.unique(clusters)
+        lab = lab[lab != 0]
+        new = np.copy(lab).astype(np.int64)
+        rng.shuffle(new)
+        rename = np.array(([0] + list(lab), [0] + list(new))).T
+        clusters = GooseEYE.labels_rename(clusters, rename)
+        lmap = GooseEYE.labels_map(clusters_periodic, clusters)
+        unq, unq_idx, unq_cnt = np.unique(lmap[:, 1], return_inverse=True, return_counts=True)
+        assert np.all(np.in1d(np.unique(clusters_periodic), lmap[:, 0]))
+        assert np.all(np.equal(np.sort(lmap[:, 0]), np.unique(lmap[:, 0])))
+        assert np.all(np.equal(np.sort(lmap[:, 1]), np.unique(lmap[:, 1])))
+        clusters_periodic = GooseEYE.labels_rename(clusters_periodic, lmap)
+
         try:
             plt.style.use(["goose", "goose-latex"])
         except OSError:
             pass
 
-        fig, axes = plt.subplots(figsize=(18, 6), nrows=1, ncols=3)
+        fig, axes = plt.subplots(figsize=(8 * 4, 6), nrows=1, ncols=4)
 
         ax = axes[0]
         im = ax.imshow(img, clim=(0, 1), cmap=mpl.colors.ListedColormap(cm.gray([0, 255])))
@@ -82,13 +102,9 @@
         ax.set_xlabel(r"$x$")
         ax.set_ylabel(r"$y$")
         ax.set_title(r"clusters")
-        div = make_axes_locatable(ax)
-        cax = div.append_axes("right", size="5%", pad=0.1)
-        cbar = plt.colorbar(im, cax=cax)
-        cbar.set_ticks([])
 
         ax = axes[2]
-        im = ax.imshow(clusters_periodic, clim=(0, np.max(clusters) + 1), cmap=cmap)
+        im = ax.imshow(clusters_periodic, clim=(0, np.max(clusters_periodic) + 1), cmap=cmap)
         ax.xaxis.set_ticks([0, 500])
         ax.yaxis.set_ticks([0, 500])
         ax.set_xlim([0, 500])
@@ -96,10 +112,23 @@
         ax.set_xlabel(r"$x$")
         ax.set_ylabel(r"$y$")
         ax.set_title(r"clusters (periodic)")
-        div = make_axes_locatable(ax)
-        cax = div.append_axes("right", size="5%", pad=0.1)
-        cbar = plt.colorbar(im, cax=cax)
-        cbar.set_ticks([])
 
-        fig.savefig(root / "clusters.svg")
+        ax = axes[3]
+        im = ax.imshow(
+            np.where(clusters_periodic != clusters, clusters_periodic, 0),
+            clim=(0, np.max(clusters_periodic) + 1),
+            cmap=cmap,
+        )
+        ax.xaxis.set_ticks([0, 500])
+        ax.yaxis.set_ticks([0, 500])
+        ax.set_xlim([0, 500])
+        ax.set_ylim([0, 500])
+        ax.set_xlabel(r"$x$")
+        ax.set_ylabel(r"$y$")
+        ax.set_title(r"difference")
+
+        if args.show:
+            plt.show()
+        else:
+            fig.savefig(root / "clusters.svg")
         plt.close(fig)
diff --git a/docs/examples/clusters.svg b/docs/examples/clusters.svg
index 810b33d7..c8b7b301 100644
--- a/docs/examples/clusters.svg
+++ b/docs/examples/clusters.svg
@@ -1,12 +1,12 @@
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+<svg xmlns:xlink="http://www.w3.org/1999/xlink" width="2304pt" height="432pt" viewBox="0 0 2304 432" xmlns="http://www.w3.org/2000/svg" version="1.1">
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    <cc:Work>
     <dc:type rdf:resource="http://purl.org/dc/dcmitype/StillImage"/>
-    <dc:date>2023-11-23T11:19:40.956742</dc:date>
+    <dc:date>2023-11-25T15:34:51.371528</dc:date>
     <dc:format>image/svg+xml</dc:format>
     <dc:creator>
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@@ -22,8 +22,8 @@
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-L 436.487395 80.916303
-L 162 80.916303
-L 162 355.403697
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Bc8aQPIQ2POFxEB592P9E9P0bW7Yx+sfxvxIJp1KP/50ZKzeWJR7nR5rXQn5IUeCeNWlAeb9EY89V5upGnzOgkYPvGTrZPKM1DtLreDw07r4ztruaT/rufggLjt34EcW0i0/fk1VfrYbPFfnswgNuJMMK0tpM3Gs1jQZTn34rYgzSyKGGH8lgdf3Gj95N8/hl9C2zUbXv0zSooOv3NZx0DC2NaAvCNTxAcGn/cO94HOJY3AcNzVA8aUhjJ/zcY1Vym3sPtLDlKx5tqx7Oiejm+3vGC+9euQ4VqQyrjEHEw8tybmQgIru1krxRdx9/j6OOYWhqE2X4GxvuEtSqyHslb8YieOY2eupQeHNW80yllUs1vDJiEcpsIx9cV5sjWhn2WvcbrwnxjtyqT/vjqNWXakgqFXLvcxz1jNsMTl93kQkK4Rm5nbHXRHKXoW15rx0YK/c2WIQb/SdaYWLwCt2LDhMc2cUgN9h4zAdYo9+TUBekCpxdTrg2151Tw8NDZrBGH4nMRolbLbInaqNB6xEurINciwPXVg47VqpFVhgPxMCAaJm/1e+PIFomVZiN7Z0cIaJ30GkFzANawg+14uZuOvYml6cqpJWR9JXzW7J7p1qUDULNEUQ8ZOFdTjiKLGXGn7AaF8S+7gTZ6Hdd7FygT6w+7PuN9dvPOLJGMfjoejyCnAR3wnLv9ATNg8SH6+VuqKxPqMYqUubo+qjBOO/e5uFdv8bfa9PRO80vskywO3bakVbtV3bGXeyZ9Pf031c8wpm3PcjtCTMn0+pqRLtuTlY5VCLbODy1FaEPXXBtE66TxjpRK9sEvaJRA1+6Ossquze0V60V5eRRC6rdO5twVZyZElG/NzJGQlWOjLJMcKtoyKyoqFsebOveybwa1eDN8GuGG+4ua0t2l61H/z2T/e4OgtXvcWzm3vGubV55Unq8ctATTbeXVegeehmN2fyyWpl7v93L+p7WtHvHCG7Nm+aZjLKzMArU7N4o3sp+ZDSUFaPBtjL6nFo0ksmzUvMmG5KYa+r3PM8IqicNPaHVD2pZhSaedu8ctO0n91BT4zqUayPXlHli1ubIEtDa90a/nibcqCfvSJ6o3Ya1f36FrVb6x2Ff98VyxVOt/tFItdX2Dq8LXN19afaZci3rt6LN7nv9XwREBdeyYjkQb9ek3hNZ3tK2ofbNSh/Gf+PfKaA9EDVcbVwZZAWtf68hm9eBzXwgM4LYh1kKNkp7z2gkbfdOpros0vut/J4r86iDZIRrNnN+rfS9DnYqs5KCjS6v8VC1KrOV5w6r0hGtsdZ80Dcyfhj9cVKvRqF0qOJxu5V/ghqKFxU+tuMYcsYPDY22aoYc727w0fo6de88bSkzTQAOFv3VltXZRq8x8IiyQJsUV6rq+hOaY+EhO9Tx8XDVrtqBXyGbdxfihglahRUiZrdKM/a4kwwxFI+KlXsLTSc8sFqMZRjvt2tHYn3uohEOTo7T5xhcC6O/8qS0TqHWSomnKkpmg3/y1Ic2+u9E6zOHqDIVkViHrnJzIZ4QvS7x7buavlC0eFf0lHgNLOLMs8ukkSPNVdn5rFCDkhm51itnC5eM1q5BG87qQrq1jU7yQcdype/ty0c5O9Byk3nsVLXs2nYZuaigGi9qsplG8hBXBqgys2AmXw0DupMsT1ajE5GhlLpoow/ESny/B4jtipaJN7P+Iskioi0rRrpygbi3cNttX6KCCtIEvuLVrtnOAVU21nhFrXGyfq/j5JFhHAFatCBFZm+7l8/n0yv9Ro6khMITnslRqMYnQxY0Ug7LDmjIrFf6jZjVVRtFgb2MPVpdKY14bCs076+14peElGfjbpdF7dP3+60ZvXOCEO0QPUG9yBR5oz12mtmWaHoV4eLw0CULOUfOAc69272zSPQqtWk8aB3Gh/uwKW30T1/w+E/z+iufWRK1dc0Sbqktn1mNlSY31jqtnbhKpbTR92B8mFgfOs6Irm6646pw7LOn26UqHsaTmn+ifV3p9TUo7dPfCaQQR9RDSEufq5bvG82nH41Xpvps/KRj63EuwtEbktFHi1dtfoJqZI8DT3esqyBKaaP/E2TdRoKjN6/uncrbyCogZsx6t6GKniKMmSUrdavu3KezVXkVXeDACSx5XemjxS83z1i9x6AaWVbTSC47Cb1aj+Fpl/260u9ByQPCYXKjBydAYMfVLTrRY/LoATj6IDctaP7yTFgdEFISArV2z1lW0FnaqQX6jruNfhKyuCSOI9/DSLO9XgYumz48gdA+bdDdcl17pxgIBjf6/lyytTfabdDgR4HN6OSsBbwnncYLShoZKK/HbF2IJ7tMe6VPZJZuj1j2F2HFnx3umGeoER/FXWVINN7ahlRUUCLHXuk3zQ27G2+t/o/vA0A0+G9vmkJD2r42+k0zYOHK8SzRIPmdpgHMUIzu7U1TVtfXuN7qtUPdO+in3NEgTpJV0CM4tNwOln2xfFnNccwNoFa9emT3TgY03GRhK33rJ+wKaEbW0h/oydv9orbU531XV7Zo+oIEcmmQE4peoiKR4/bunaeJjzzgErx9l6hGVONeleoKZTaAq1DGbzyTWAFNduWN/mxHwVH0DCsXNCz8mXf/qjIuRo7Drta8RX35qHcNaCPVMbR+pwzZpKS6z/4mXX16DqK1HxeNJ18l2sFaVNnlswYPpR8IOoDQhicqLxhmlF/pN01GpGdemm+TQjbcDZ/tjH7Gp7vmlr5iJU7vMfWQ26oBlo4rp7JnZrx0BlF+YvcOx9Xy9HmGMK7o9l1Dtd6+Q72WJdKDL04bpSGJmvewhKID2qGnq/O7wQVipd8KRIf7JqEopKtNC1avGyXbcZy9eXu4IIHWnuPAtWsioy9VCrR4cavfWoGqVBbs1NeRu1r8MzR0FXH+PqERVqkJ4iLsSkj0zphKbBWGdve7VcVAHsRd6DGg6bBETtz5geCa9b5/dH+lLK/0JSuBq9CyC7DRxVofvBYYTYPKstGnTAZKFEnDI5OfFRWkA+8Vel41EkSvS7TcZlqSJdkpIhPQIhnNMrZcglUROPTicjN2cn9624HVrGptxO/IzRjSxR3sqL541oexfoCjG0It33SWTNknsiyItOD0V3MhECmzlGUYpHAOdDMbfM17SY0iihwRsk4RDj8tydQ/SXDH6n2iZSNe6VdCU1kl19JWwrd2eK12vPHchVZZIa/2A30nxyHTQ2sFiOSs48A4hNTa3qPFDe8KtQAfEtE6o3UOxPn8+j2E6qnoOiIl3OiPRjJa6bVB6A9CG2agt8+a0dBFG76IEhh3n++uFyNa8gjz6VNCD6s/cb2YyTEyGWdM0jsO/THPrkNRrgbrJLAoMrpu7ubJcazLPzwjNwNSg4SsaNyDLAuDf/d3VHlpwnF7HAdm5mmmuXzdSWXRLwv5/rG6sAYo7dIoVxsFonJnquvSzEHUrztGfdpZv/5U9qcjkGVSUKjUlwYL60P38VoVdHnFXn+/3+PzffillVA0HywVBu4J72xcL/9t1fDQ47BfPaLKI1NCWibXznHoJjWe13KP3tEUeO9MfrPqipr9znLFVQnrvqHq+1u/kcYcqS2eXHUnPGSzuWelGqRGqYTrddDrgnuCsENFHguETOeKWMjv0b1jdcPjsFmxVFYui2zH6DIU6OGAWu17cidUyeBt8Bl17TFks4qCZfPh3XFtv0Z/OAYnQn7R46VZiyi6L42MjAUlR8aw7BD3jofAqmb6ZizxfAeqO4D7QFwlup/NnDebkdmmqEfvnCsgrkuC8jstdp5wq/LdRWYRbhd0V9duVHW9TaN3pH5iSp2N8TvXw8MVMghdStaVhTeV5LSDXu9KlJ7+Mvqnknk0KKrTWY1CG4A56C69mX/4Lmqqx9sfL52J0NNTn/6MK+zVan9oE6zZi7daPpJraNKunCaSz+fz7yUqXGV/UkxNfzE3c7OqH04CQmy5B5oZoa1HdViJPPM683pyb3vwB3WFThXC6llA9YmKOq5NY83oreD8TnLPLHRGbkEyKWDToOBZSTcy632pnr62UXnaho1/00oUqrzKb4Mvg+MqrKxHO+I9d6L0B2Klz3HltFHDJ/sYdabtnuwyphBGnwLFT7fLoHlzt+O6Iypk0qLKY+tSDBo6M4a/cn9bnWnBNbdGEAR910zq73YYyBOriJ23DGuLonCr9KIgF0i6cxz8OZRNr8JX+tYZuNkGxBuNxCCAdQOJLO3UBrnfiG3j6L6mffGSRbjRtwRRoZDJVEp79V476UTWDGXudyx4W/hoRt14j1Ooe8fCyKBtFSPQjj6Rqoin3LNE3lgl51iOvbbMsoyVFZpJhRzCSit7G3zK51XQjj6pOuksuJYu4ZQxQWRst2Y/0MpjeBPZpz8eE/rqJ442IBUV6I63ranX/RBX+RGgLUiiZdWLjjj+HodvZc1mDYo7YIxU8pos0rLYmu1Artw6iySLll2DiUX04Y+M3DFEjxqyhwB6+7Q5x2e2BV9Rllm4424ytuS60PL251+/H/mwoejU7g9DV5/+eDPr0EopkcqBvLrUxKPmu0efkAyJZlu0E5G0bICkDY0+pUM2KUiNjEeo1ep1V1Z+lM+sJ6pEjhbZuW9UPpSMdN31A8GGz3EcbC2MCjV6g9MujazN1SzhFazDJlHC5852aLo8PHQRTX7R7WhoRISYL630I1ZTFDzbRak/06wR6fJoGk8iDvaXVvoj0QdCI28rcK2na8RTevWBMmuPtNYI2vhHgXIoiboTb+Z4zSMVo58RraJc1sW9tKKoLNw7nu6tLEQXe7szHP1Q9ge5MsC2Rv849FLhLYzf7Jorhr+Nfm2iHzZUdngAoe+0/jP62gdoWUBTQqvzAEofOe4J5JWMBDR9oJBhLDIvEDg6wZm/YWefx8YrfUQijf7s/k8ZwKv3QQLFF78KcvvRV70UqEbfYgduwdI7chsbog3+yne9IpWsVuDS8gmND5GeCCuDf/7Ou08io9+TAR/r8TkNv1csPHp0lTfIDy2qIaS0r8p4IaCWnNWDImN1a7iDYeNgufNAlafEvWP5QEDyb1vbLMsscm3avQMC11Vylx7fO6+GC4LBP7/v7b5DCV7xnrfsjNxZJmoTS7TyNnigZsdLv69JtO2CX+k/rUjb4MihrvZb1vdET15P2q33zk76wEGlymZ2JWvlaDKRRV+pdiG7/cjGcsG1yJKrWlyLo3WhtGYGip5To3UaX1b1I0KvRCt9jZowUaCeTVwfqJzPdid63JrfRI6J5rskqiHKyM3qV+ya4zWRFKPLcE4lrYbqTbR9iAoZRT+XW17p7/RkbOozuiyjDWZmKK7SyvYDPdFsKU4fOQuwaSRU0tm3OWgxRznG3NpGaJQJWa1eipw/08lZTRk8awFlgFIk7/rfUaUakAzicfAPyy1W9paJY6YvRu8J2KCDZnAygzjfPVx1Fv22bLOp0e8J1Vih8dKQbPpZuW/WvBl/1AeWBVu6d7pQWU6orols48d5gFFcWNn678lbpBai8ddmOWSzguK9helleQXdDqCHwa0gCR1e1U2rxY7UWHqP3dheqdHPpHvLK/0Kh2bXgR4H7a06X6ZBbmKY6Yk0ygVN/yT2IOrFKLvOY5F7RyMsCcHNspJZPDtd1070QXsJBupEsQpBROivVjuQQwm92bXvage52gb//DzbbkK7vENU/yl1yNGwmMSSa2qs8i3Z1ejtjmn0zgzu9nZXemI2WWhdzUGI0d/ZiK/Qk6l5oudSw0VUcG0VzQOfp1P4FTTahXBGoYFFdJaGDzlDYbQZ1kYaQRbZCxqujBFqX+4Ic+8gsjJwmQY7kut7CyREFEbTNNQZs0O5UMal0ryRhMlGUC45a/eIBFQsQuSsV/3X67de8ckSKTS2jRN+SjX4SP13d+9oJ3Founfursdtz3gtlIFeATFk09N1YPVQ8Vj5ZdY7D6q4YVcwMfqUkq4reBjYbC+qyMbKOEUk/VgbhR18+6hUqCYgQdWnf42r946x1xqoLmplw6puRPlDn8Y2y5ij+ZEbHMQr/ZXtdpZ0bUT3BgIcuUhq5kTXc9F2Hb5dXxOttkZmg1vcN3tkkQZpDnKj6nM0dMaJKjFqCCvVWW0m7esjMkZcec0Hy8PPrlLqaPSzH2o2+bCoV2PB6g6Jcz0uqA+iRk5InD43vrofFutoT14LY2BlYLLqDarBR3wQVE+ksuCHT5/rw9YQ+E6hU9Y+4tn9rOPj7+6z6j/VMi6Z9edNxp45ChbXp96buwuittHivC6LN4Pk3nnqDCeJ4fz+7G9ZhKZNHxj/hKtXO2J93hDJUyIfRSeo8tC0O2MS33j9u+/N2mMNaaV/HDqr/WrKyeVupe8RD+4VdaIZvRMduUPB2uAi1BlCeaiguXEyuyT/8+lXTBbp1aJdjXlqDZyIGiye9Xiswy4pMrYEweDvhIfN+s/oVxvcqCSxGVdjSZE3SrtncLfS1N+u6qOXHl+N8RsZdi3IrMovw/yJQByy2f5XOypNdksfdJSc3u5L9fU2zQztOUMy+is3bOXOifWh8kqoLsoBGIW3BVAb//xkX+T+MPorivgW04t6GIlC75Te2THCK+KQ+DjwHkaIc8N7zqrvjA9B7R3LOhZasbga9VfQJoIVu6enayCNZvPK5dA2+pbt1kxW05rPmQNfln36nE5zBG0Vi7sKiqFrt8CeZFh03M3Za7tREr1mf0OXsSZQr0vkPkh24e5A0PIeku/sDFc+4/e9yh/MrqcVD/92HtO69Iz1AwjK6HPZRXGuSsCJGFm9h+Q7WThDecd/ErjyGb+/muvAxSJUdgVL9zAX7thnngtpSitL4Pqqrf2qK9vLDg/ckyxjaN1Oj8NTrivt6bzgzbXlwawvqVf6FNDcFsjbXZQV4A7MVvWcxC9k0OaeNdcH0+rOUfOQ+en+MEbfYvBXzgioBz9IID9IENH2aVvgsXK++6d9D43vcAwo2oNSsnOQ3vNxYXEAhWxqPxlXr2fl3lktPrZ6bbQJgIRHnHrUmCBF/EjDgFd+H1n2wivkVoLIp0/xsyF2+g2rNr/JS3LfTvL6HyhGz7L0xMgsEghBFndoBCVoyxZVVpqI3TsU3+RIGycbrtu6HZT3CbS+exv8u8+j5pxXRBIKGeagSvQOdUWjHWGi7dqpAKqioVFFThn03HPHgwB6H0U+fQ4ct4aWyyjq9JzKLpMgCoRzDit33tu1re7pieTMsLpsJMBE71zZRfi79PPEa1U6c2d4ujos3S7ev2vqAJGcdbfivf53r4hrgJhZaQVKO3aFG9iwk32BdO943derDRZ4hBxmBGGie7gWJA+VTDqiYTeibI8X7Ezio7jRf7u3x/21qdYfTaKNvpcxjoxFt+Ipxl3zbKa9Bo4+/cjQrUrlBXZLbW/0yabzY2kKq4i/XXD16b+dslsOSIXB5paVQOozWnuaPJy6k0F/ECLG3giN3pllETa1QFN8RFr/f3KNbopMMKOSpQYWZMhm0+yIR3Eu5IcvpfAbmgE9oYTnenI+JO8elm30g0BV3l2YHRIiME7auwnMNeARBh9VvlWgVgW+/q2NfgBv9a6bdThhfKjjwFk1alaE1GbVNYO2akZk5vq604nr30KN/iwhKwutgHjMXATWB4JSt4tFjP/YZw9fuWXoakY7oYn07CA8IzfrAN4VjzsO+wikqslmFkTJYDUbdGU1fA1lpP6Gcj0JM6PUeunDTM7t3kkGZdL0xIon6xhorPw1cnKylWT2apeG66uN/gJak8IiAgN1UqAxJvxY3YPaBmu4OosQIqmZlFUFDddXuHsnI9zt+9M1In/f/MNajrsWDtSYI9drNXqQa+94+a0z8VQrpOHTsrQr1Ba9QMkMNcPW+6xNkvmbzr0TveW80lvPdWbJI5lliaSfJ1J5UvuE2PdVZu6tVYOrqdcS1xfJ6KOUS0BUqswGKgKKrxhxnClkKhmgSbZyCW9kKmq4Yn9SuXc0w8oaX6xcFyhoFdrSKg19d36gHTtPud6bbx9xjCVjkKGv5IPcc/AiG655OITKU/9QFKb5TURcOsUIay7UVvtC2dVV0u0nG4XUR5ZPH6HhCG3ISrYHZpb2asaUr+q3tcus5x0d9FDTDtkE4i1jkqs8lGJMq9dEU+RVtFaaliGKb23M8nB8IlqnrORn3Z9V3W2jvzkcxRlLT2Q1/FZuECsXaGWDH01G+d2VgFGP3mns0Y4YsFDmq2JxDvYkbdHsh0fkUNYH4YyKfcrIWxgpOVfg+/1+e1AxeBu0lRU5FQtf8piNuoJlMovXvS2xXql6JH9x76dFtge8RgLY9/v9t9KPXkE2TfOPjHNR6+Ayw0P2BNXgU67x5zhsV5ANnS6kZkMmvdWoREn5nUUxuEw6qp2zkIGz3X8RO0CJVrBqd3QSGuJ4RNGysGVn+a5EW2WJLprZx8/ngxWnP6Zzv32eaQW3OzsbmB2pvEvwRluW7OidqIMWjVNrVLK3n4qH62IEJXLICmTjSr2XpGqlhAh9HInQsRRx+tRwRs0kG4/JIY23RYCbdn7+PUO5Ce8FjvX9kGR7h2UZCTQi5z654JoH2UKoVqEWqvK4j9a9kIkOF31CuzhXhmJf0kJwb/1YzTj3WoRI5r7WfIZZ6WfYSlcExRhYgapXFoXIxrwIxLGVGPzz+xb9QpTVCpR+qGbkrk4w1ImZHUpYXmV21qvqY3tH9fGehdFyQmxVVvoS/1T1gYoGYfJncDuMWLQLdfUdhWSReCdHbhhm1vHgurjG38G4d6zJcHCYgdUCbRrX88LSj9uLHFuogRhjaDiaDloidu9YFNlqMLEaU4vrWh7eZiyVsBM7GfAVxCt9bsheBLOJp73SpFxvFraIJLcRTtuiD+g4W/0VQ08JJ9wx1b+ZgzD3Vdw7KynN2lgn80gSTd6+q2HwkNwk2r7aaLx1G1EGjS4rc5/63Tt7fP2dmk/fM6lpvO+MDH5laajd1T/ZBoMHgv5EzZ2dQZE5d5dJTVibfnYEJ2d5bIHf7vGWJcq93yoriSWILiI0t4ZERhIXkVcSnhYRRnBFV5BkZolVcmV49M6qa6jawEsOHpHS1aPdfHesutF2CP/bqfRB848tXpdI2QZRt/FWVJlgGmcTVkgyXFe+i54cR8kK1rzeHZZjkhnLCDEIo48w8NQDEkTOdqG2j8v3m79y6sksg9KKU37jv/E72vfTvu5IFf2OJtSnz1WQyAJYHvevgvbEj5Z7Jr2gZD9rr8gtzpWyuss0sN4xQaz077iuXjl1JTTuyf2s+YllRmsEPfbPWCVnUoMYnnYymbHWN1ijfxw92RocItw02lgYxii53PWlkuG3JDx6B41MkxgZhIQ9bZ7CMrPojGRMOP2MlEem8ZihnVF+RWz0qwi50UfiQ0Ymc5ijRYi0Zma5FOsDcu97WrBs9O+yxLJ1vtHhbTLMMo6zPRDQ2xu5w9p1/ls97ChjuXLfpegdi4YgrBJ2QVPWGrqQJTqGY0xR2nxHJ0PyQdBRrXnLNvpWfqbGHosQWWujj6JH1QxllQeYFwhGXwvo6J3mHerkRXZLVIiMqUqEjxyN6Gx9bVg+fcSOaRyiRR3ErRRYu/utlWvMy+WGWDQuGssxpWI1BtnOA61861GwVvpoHdNILY96kEnijCvFKFsl92ji2ZaxpIF2uYQnIpIhqSDpQgW2d+8gKTcVbnEvyaTpCeenI5EPwMhdHeXv0XpYyc0FE72zQma3gFbML3WyriqW5Npvv/XQIy1jZnmQ6xUcoXXobtGOp8Q3yvcyEhm8oJ6RW2VQsoAg79U2WMaUZ/MbW0M9jKTsGKWGfxx3qsHXuHez6N5BibYY75dJGbgumgjQ2kNh5geX+Mi5skCTHaU9HBehVkE1ri3xqOkT7UpahVqAbtmnfz3siTz4eVMeKzQUw1N+aEYoEutJ7RH1UgE0nbzbHVrfJ4Iln/7uZKq1kgnp1t0rHJHrj6W6Td6Q+r0lfuRI/3pFl46FT58aAt5VNhsYskzsWS2h69+f/tu6BMZTm57OUNDljt6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" 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wTQS8eEv68pMtZ6UPxPHlKnyf8HWUAGPEnxvZ1JB+6Pf7xN9L3/wJHa47ZMb+o5SlIF6y6+9w994pTWvh68hbePTdryF8NYxN/KG++Vzkr3Y6k1lspeGq8CmFL04CkFfTja8V+ra3Vukr5kZIg3jFrr/yysBwKcJHRj0lTsrG/LtE1Pgif//ab0de0NNa/C7hK+Y+xwdxkv/znhoB23Y3L84qTAnhm9+Tt2w9bK+p4XIjttZX2U5oEKe2noMtQsLHDkNBqeUy452ChIRfZJoddLfkCvbIbATEqwHknzaeh1Ptz+VH688LQD2Zh7AtF8plkix9cTqAvJRuRkohXr35XuuNfZJPbJXf6oZek01CFWwJ0YWIFX6prN+1bMSpdMsjKd4Rp6//bY1N7KH3XZ/1RGo0NAKxAscML69cRpUPMAsfjtSKuaKlb4q+pfh1eZsix0i9d/Fj+65PJlyIERunrH8k1v+JSqAHgUvwtaMen6x7F/mohC46i71GAWBX9d7LyVz5ydllkyNccvxSqG1L3/aiMn15qV38LuGLMwHkxTFTmCwR1f8+5+KsHli63DlV7qPL3kQvOvaQn9z1RokNcgo/TKrIehegDd9vcn2GyurnCdwkxFm042vVK6cX5Efdy4hi2SnPR/feMav9lFhHnOkYd4NGgkPXtckWKY2ZK+aZsk9Dl704C0C+r928LBEleMoeOzrVb7jmEr4CI37K3J6T8FNy7V7oJbpxVbdLEV+N34+NeUodGVBEO71clStet/le1StyQ8LHDjMqIfHJT/OR42QT2w7WGza5L6XBw9KL8F3sMcKGOhJK7Eru5utR4X7NASbflu8pPx8jgKnguVb5vQsfoGK8E1vBh2IeX8SjIptQDMQp2lkCPUQ8reMd8T/dn8n/XWceauCLeEqf8I0Rf++SbxrvxJykxQzrErb+vm+YKfz66EcttiMaDsx4oz2cunb2ju3os+sbrukVfagRSOmlI94IIN+RPn8THFyEr5iSb0vpSl9evax++qb4q57IRVXwCd02MTJPEb76q55PJpPl0Hu046L6/fR9Uud0MZde4bes9meDMxmR1hdqyavdUvd9NgJdxzs1aCF8U/T66xk3TUahxEVI4s0A8o8j5mG33Ln+W0TxZvv7Mb9xY5y1L86abKKfO8BU9qXEL94GIM8tM+7JhBqXEE1yBNka329M/V1T+gUQ56+/ludZhsmMbWLFL97mGM+U/KRDsMJX9Cr+EtKf/yOXGFP4tvcocnqqrN/VGIyKuKD1HExaENtIcKDUPE/pV2aemJ1M8uhR4CmUOjpZrPRbVHythL+0at6H3NZ6DiaTtiyq944pev11jzLAXjwmz53in0xK9IRpxey9gwBT2VOJ33Uil7rSjzmZ65P+PJm7XMS7AOTrW89FHCXinR7Fn8oiKn1slCMuiBO/eNfme/L1ZXrrlGIKf1nYtln9vd4agCUjLtp8T56N+B5FpS8+ACB/N3csZUjJ7jHit+08q+87dpyWlf5k4ttmdbiLf1b6duErQuInOZHLVfilCO082J0rhyn8SSlqbL/cWEqPIIAF997pmSn8SSyxIucs/hJVeU+Vvq/KxzC09FO7ZVJ057Tm/S3u4/OB+tPEIC5pPQeTSZ9gcnsfQ0s/tTcORS8eVy5aS/ziA1vC159zQAlfXDLlP6lDT5V8afYC2LXjyTNaz8pykO/IO6mrvmtrQHxyV5+1PAczJW9HfBhA/vfWc9EPusSx96ehEr+4HEC+imZcqciz03vv7KVXXQC08hcftr/f+8YtX5/We2dtGE3YXLtzlkCesUzxiz8DkL/l+OzDW3973zdaYDYAJat6cXm5cceiBC8uwkc+4hJLvEO1Q7qEX5vYqAY7vDO+SejuRhX5YCMcTlHPyIg/23ror9eG+bD/9SSO0jFO6wrfRozwAQAEfNDeTz+n4sduuDWrmppX5MaSU+mvHTFEyJxLN1txFYA8mWhchlBdlXXUOD8BIF+WPy8m8rf8+0mJfSOmRw5lX31xlTFuovU9iUNJf+gTuTohobe8905qpZ8q/JThS2DKgBPiE7se5nNKXGJvHfFwvzhrksdipA+wS+zqYXvdM7GVO4dKn7LiC1XWUeNyCL6E+GuCOtdELHxbw865sR8VPbYnl35MJtkyv6wl+t5FURoq8edGOZTrCdMAqWHMqr50lV+7iret3xnv1EeP6xeT6dfEJxBfPhyT7bsiIUxsw6HKL4Wvl8zGsImix2T82CMPivMOkwkGb6Y/++y3AZvtz9swuOEg/EldxGdbz0FfbEifQviYCn7UKn8yAeBfwYvP2h9N5uU6gnF0In6K35qKcvtavENd4Y96cVaI1HjHOi7kf8dyfp/xba9bklLpR687RMRTs3GIFaN8fpn50DElKE8oP82aYCVf83fvpa6QLBHpjC73GuRGOaMIX9wAII+rO035svR++iMgPltW/DYhiuvGEX/Lqt7HXgAzw5/0QSvxT8ogTxi30o8Vfm5jJ260vy+PsQy7lP+R24olV4rcoYzhvNOxxDy1M//UzLtWxDOK7AHyKvyU5eAS/mqchvhZ/I9c8eX11/JZbeajBFP4fFHxje190unsFnxMd9LeEV/G78cjCb8Hml+Rawrf9d5kUgL5si3J68+LTGtBwtf/TnjRXPou5gbTBnHzrket6dSYFoZ5RDbhQI2Tv2ylP1LEwwFxK35YeVS5+QDYFD0X8U9oUPvu3IfjqRF1sZU+J2KEyQ1x69b8689dlBb+ZBlM4fMl6kSu+Nr6a/mM/BmYG0c5XIL3iV8eUWZeaiK+sf5aPr3NfOiY86RTY/7k8/ldtSr+wf2ZfEq9+egdeUxcl00WvXe4M4IIl4JNruIb7cTvk70+DIeGqSY+4eufL0n+OdGOTe4u0PGOWeW73usF8Q/rj8kuakRZZoQ0aqSEEX7KsLUo1Ud/7m9tQUvfFuVQxDuTMnA/D6FEP6rwU4gVv/jOg1HDx0ici/B7ayBSe9/UvFZhsVfkmhvTiIeROeLvNdJyibN2fJJaucfMp/jOgyCfsHfahBqQI/Ce9k9d/LZbTejYZK835iXW78z0Jxv0KnyAXdLkeCK3BD0Jf0ko0Suh62IP3XLCPHor0bAvVvo9VQ4tELf2K39KyYu7HwB50D50I5wkIf6hr33WJXYOt5xYbLyzNHSJR12olSD+nkUp7n4gOEzot+WelB31yIQ6n++pEcBiO09DXenPi7MWgi5vn8ixw5mIux9YPWyvRyL0m3KkParwS9DbSV4uLDbeedjO++A/Dzyg9Ww0IyT+mKOBkAR7rvy5s+8Pvw8/ecSjWs9GM2rEPnvec//a64ceu1+xackn7F38RO7i4p2H7bxv472S8t///nvhR/s9utj4W5JSxXOWf8zvKRXxhCr9fX/4fe/nrRsA2/ypeSpVmZeUvil8RUnxl2bGO4XY//57Yf/77914Pomnx2VXIqYJCR87TClc01bv95jB2+Tes/ABFiZ9W5Xvev+R//GvydNxSapHeblIzeqx31MNpdlwllqGsb9nlHMVOdu5TqixadkYTdaZ8Q5sxTu+HeAHD38ceho+MeVGPQc+dPfa6517HpQ1vlRypOeLRmKkTh2bUcY7AHERD+bIIEactWMezLzp81S7J89j4J8AAOB78HO0Ew6gptti2i4WVekDbOb31Hl+SFo5laopfNd7vRK7bLgfOWEjnp577Oz7w+8nVfGUUQ9mXN+Dn2siXTVNLsIHiJD+4+GOkvNRFSX6EidwQ9UntjrlLPPcaINrNFLiJLN8+ubDfB9DrFhLxympsteRT9l6jAwn4QMgumzqslfP/wWeXG6OKsGxu6Ypev11qxjHRHW/pI53Uqv2Fr2jchuHlMr+J494VPN4p0RDMvva12ej0qeq6A+GHSTj6RGXhHxy4lzZA8DGhVejIg/axyl132ejQiV6WyM0hd+GNekr4Zt/TUINgxL+wbBjsfL/0X6PXklef24DI/wDH7rbWu3XOAIYWfIudMH3JnuKKl+Pb3KjnKULn5sDvfHOv8CTrYIfId6x8YOHP87agyem544JdfSwc8+DnA1ACaiFTxnt1KAn2VNAGeHU6EHE9XyAKXr99V1wCMk0ngi3w7fhqdHf2+iy+Xi4Y0PqpvhD0jd/MNWP/Hn4Bvwj8Orq8PNg75eHnc/YWKd2tk8pfaqumiajXvHsw3flK/V4U/HNT26l34vsfaR48Ylwu/MzbAOA7qdvawxclJC+LlesUA8F+/9z/BbQ/Msvl/AV1OLvWfgKl/in9MO4hExVUdes8lOlz1X2AGkxTowbfcJXYMSP7rIZE+moH3IXHEJW5SuBcqn0Q8LHDsMZbLQRE4Es8fzAZJ2cKn/+X+t8hrwi11Xhm6RW/LEyxzRUoWqf85W3Svq5V7SmVPtLqfIBylb6tbN8SmnnVP82V6R4IedkLbYwxlT6AOFqf3FX5HKFSz98k1AVnyJ81/CxAp/CD3+GpfXdOVvgKg6xRSNHdOG7GokpfUa4xN+6QfD1W2/FkoQP4JcyJ2G3mJeUo4aQ2HsW/xPh9pXw9eeKxf4TFa60FryLWoJXMrdFPUsTvaLWP0qJuepXzU/qvMmn8M/lD4WvkXX6oODb8NRgxOP6XL3/bXjqrPQn+ZS4zXLMxW25PA3+pti4czAvkOJITmPEuScOV1L65ZvMSn+STeq9eDBHD7Vkr55/E3652PRi8Em+dZxDOX2qij/m3yb2HN0AbIlfvzgLe5L3iXD7mL13FCX76WN78HDpYlqanv51IrayxzYA+vhyG43YeKU0Lf4Hb0ojEHvUUKKHX+l++j6w0geYJ3KTwch8KcKfjEvro4pJmBjhAwzaT78mubdhoOYwuAVugyObTJv6P0+VICa/x1TttvHlVPvcKv0WcKn0S/fXp6ryAWalX5V/hKevHvrrFhwGt6z+quc1WdrNyQA2BV/rnADXE7sUxAo85YRwSOipETBW5JTCj2VW+oNgkzzHir9lw5DSSycm16cQ/qz01wlV/VQ9gHzdMw+Hr8LfwTOTx30w7Cgu+ZirdVHSz/3Rk7L4qvpW4leo/7TFBep4h5op/XV80p9dPreIkX4w3jkcvpo9Q5OyuMTeWvgA9Sr7I+CmKtMpzegyPwpujBre/D+6of+rewTcNMy2EAOm/74aJljpK+nPSp8/ZsXPQfocoe6yWYpS98yvjU/0N8MxFedkWbj+yUqw0p+y7wcl+dvgyCn8AfjJIx61krz+3MbRcH2t2Zogab1OXNX/PJHbMcfCF2A7PLf1bHRJqNpPrfJ/Fb5off8r8JyNYfT3UvBJ5UtwfNa4KVlapW+uF07rAmBKvxuOhS+ghuPQCBwHn4Eb4DdazwYaqp43AG7pA+RLXgdbRXIQztKlD8BjPSjmvXcGg0P135PwAdpn97G0jg0mceuAk/AB5sVZSZwAn6o6PWyVnzr8JMyJcE1wGF+VT0Ws8GcDQU/vy3TGO0h8or8OXlRsujkCb13x945P9J+Dl1rfLx3vpAindaV5FNy4inH059w4Gq73LitOy/4FcDV8Gk5K+u6s9CcTC5jKfmReAFeTjEfl+eovR+EfDdevhK4/50ruupnSn0wMMMKPbRRaVfmx33sBXL2Siv48BfMEbuyFWTVwLRsq8VM3IPr6SF03TU/kvhiu3HjvL+AVDeaEJyNl8y+HK5yffRxOqTgndk6Ea5yRTQyUPXRMvgTHF40YXBJJiRJcgucc7/SAWg9dxjs24fver0HtE7QhSmXyPgG3mN7L4Yrq8wSwS/Tqob+O+f7EjkvsnIQfakA5xzypwgeY8Q6cAJ9aPWyvR0PJtZZoY6ZRU/wUwqY4MsBwMlxefBqhqIAq4+cE5ghIz/pT4dZ4DC/9V8JHrO9jxK5/7uqhU7LnTu9QS/xkuNz6GBX996m/sb1BsMOHKseUytKs6jlV+THkSrt17ymTLi/Ocon8Y/DbqPdiqnjbsDVFvx2em5Tt99pd8+VwhTXjp5I7VSRTq8rXORkuh6vgVehsn4NsOIueWwVei+Er/cmkR1yNHLbxSxG+q5rPyY8n/Fic9HvM6mOrdt/wqor+OJzCotcMBbUjnhpV/lXwqqj3FV+C47Mq/E/DSSvJ689Ho1aVz+FoyyQ73jkVLrW+/1E43fs9vWvmi+FK9l01W2f3SuS+qAfbONSQfU6e74p4XIREqMiJdlrEOSbqd/qEFbqqFIuS/WlwsfXzy+DM7GlM2sCi0ucufE5sh+euHrbXXMhpWEo1SqnibiX8q+BVK9FjGzZKXMIPfdYCbvMDwLPKB8iUvqvKD32meA28P2fy0eREOxxjIW6iL0kL6XFB/+2YWIIiusBIlIto1XxwmZ/ciK00WTdcC4ndFfGYsv8QvDZ1FqJJlXfreKdHUiOekvFTSsTDIdrRCUk9Rzgp4uwx6slpGDkLHUNypY+p5F3D6JKvKfwJf0qfb4gVODfhTya5JEs/dKI2NMyUfRpnwEVJn022wIqcq/B9lWbvVeikPE3indakRDwt4p1ciV8CZ0d/5yy4cOO998E5WfOBjXlqdyFNuV8+BtsyVOQuy9Kk5uJLiXhyGtXXwTsAAOA98MbkcVCQ/U9UUrtstiRW+q3yfIrKHSt+n6gAyoqfw/UCVHfZDC1HAN7iX0qmr6j1bw+V8BUtxd/lbRhyuQ5ehBZ/z8LHghHVWXBhlqxaiR27s9WMcnKXJXdOg4u7aQhSb1cdy3vgjeNU+iNxAnyKTPJvgLfCO+EPk75LLfxQtY+RvqInWZnCV5TY6WKWoYLjskyt9H3f66UBUFBd4MYVFhdnAQBsg/NbzwKJ8N8Ab4U3wFtXzyftsMm9dZUVIqXxoCRF0KGGAtuQqH1H34daMLLwARhIfxucvxK+/nwUOIife6+eN8FbWs9CU86CC1cP2+vaxIgfO2xI/Lb9hMO+wwmq/aRZpu+Tu/rsAjiv1uwMjS/eiRULZR6tb8Tq+dvhj0jGreBe2Ydolf+HIhs1TG+8Gd4Efwxvbz0bUdj2E4D0faWJ9Hur5t8MbwIASN5YcvL90rwPzmmS6buqllLy5wh2uavhassfI3Uutz7AoPbjnsRf4ih4DwCA82Eb+Ygp4NI4/DG8PWsjaSn8lL76pcFsyEuPfHqhl2pfCd/1eknsoYR//u5EfUIL1wo/BY69TSZjYNtPKPcds2jrpdL3kVIYnQ/bQPwveY61y+Z5cEH2TNmgrN5HzvxrX41b+uIsRcyG2lvEU/rEK9dGFxPxcDki6CnaAfDvL7H7hyrqq/feoRQ1l/iHE5fA2UmRzvvgHKdUKGXTm8hjKC3l1t05XYSEzkX4AGNU+CnoKU7zLpsTO7HiTpW9juopouTlawiWxlvhDWTjSl2mnNeFS+ychN8jJYokZ7wD0EfEo1hq1JMqemzVWEo0oZin9RFBSPJ/CO9EjcfV5XKUK3gn/DHP1Tq7bJYSfm22wfndNwi62M+Ai0gq+phha8uGu/DVMBjxT1H3zTvgddb33wjvqTwn6ZwHF7SPd2pIeNQrfVt0wSyRJbvE3oPwU4Y1mY0Bb94Br3MKH/M5Z8h775wP2zZaFsy4VUVeQ869V/45pAp8KZJKETk26nFRq+fUBEeKzHuo/L29d3KEr//1DWcOo0ScKuQliLzXyqI2F8JZrWeBjCn8cWm1nW5IXwm/xoVarS4G6zXq6aGaaIm6TZn5nAvvhv9hfV/1kjJ7TU3h16dWYdViO1Vu30M90WVvXqWLkfO8mnfSEteOE7NDUXbLdOESv2KKflKS8+CCXf9EJVbYrvgnVfyujB9L6rmA0SMhyhOwnGWEEfs58D7UuFpk+pMyXARnwNlwSdR3Uiv92KNwc5vFbp8U7MG1QscKOfVcwBT+ZDImF8EZcBGcsXqOJSfa6el827wid0Cm8CeTeHLOmcV+V1X26uxNTZLup099dKC6eZqY1TjVhVYjV/lT+Hn8IbwTHfHMWGcsalfrtWWvYFHpY7uI1urHP8kj5pCaIxiZT+GPx1J6x7GQPgbzClsbI1fwLTFP4rqkbmapteQfqphSKqop9Ta8H16TPY6zd99+UD2PYQni995wrRaYSt8meozkR7j3Tgyleuz4BH42XBL8vCau3jytDqcnfnyify18qOKc7CI25umtoWhe6ade/Rvbu2dih+LCoF7iHG4Xa9XiYjit9Sw4oajsqYmROKXwa62n5tIvyaVwautZ6IpSffFrNgohsS9J/BfDaSuR6M+5gBF+q0bhjfAer9BDn8dQez01jXdK3L7ZJ/rT4aPk0+MI9b3yc6VdM97BSr111PMReOXa69+Gj5GMFyuMM+Ey1HD6fFLNowIr9BYRTw0w6wq7nmJoUumfBxdUFz7m81HAyDymqq+dyffM5XCy9dEjZsNkvs4hpoLnGAHl0vKoa48a/yxFSb6U7GOY4i8T47gaBo5VfgtC4qcUKobWUU9M9T5qpd+KvQDq3llzksYV8PK116fAx63D6O/Xul9Oavc4as6B9zUTP6aavxxOhlfBVdbPqKOTyRhcDKeRRzxrV+TqVbh5lSz3BmEpFbxCCd5sDPTXtoYBM14bZ8PHu+ml0wNK8h+BV1bP8/XhS2TGWF4LHwpGN0uv8qtm+mYMkxrL1IpzWp6kvRJe3GS6LkGXwrzoRX9NxdXwAtLx2Wh9EleHssKPFURoeHPeShyNLF3qLRi6yyaGXMko4V8JLy4m/1S5x37PN7z+WekYJ2edlLg6N0TMidreTuoq0beIn2aDUAYhpYzusomJelqcsMVEPOqIwCWWk+DT6OnZJP8K+Av092PIrepDUQ92/CmRUQxqvcSsBx8XwllVKnuszF2ZPhXUXTYnZfGtr1LrKKnSDwm9VQ+dUMRDGQG5qvqSFf9SoBI+AK8oZzIxcYm9ZKOcVOmbuG6N3Apbxa8L3xcf5Fb6Cm4Vv69Cjx2nOa5r4ER4KXwuab5GIlTtl67yFa0u+pnkUevEOon0e4Qi3gFwi59K+mY3zBLSjxmvPp5r4MSNz5cuf5f4awn/U3ACvAiuW3uvdS+dJWLbNxSt95HFSh9gU/ypsUKJbN8mYSXcFPFP6Y/Np+AE6/tmA9Caz8Bx8BtwQ+vZKIpP+IqW+8lK+mqj4baRlOZqeAFphpxLqW6YmBOwoWmHhK/oWfxfgGPhubC99WxE4RI+AJ/9+TNw3MZ7vcj/ejgajocvoYbFCF/Raj9ZdKXPkZbS903f9v1RKv0vwLHez7k3Aj7pA7QVv032Jtzlj5V+jPAVLfaXKX1GtBZ+CuaGXmoj/iL8KjwHvkI+3pDwFZzFvwTpXw9HAwCgK+7apAhfUVv8Sf8YXVFqR5zQUbpf/Uvhc9V678Rubz6hc5Z4DCHhq2FaiB8jfDVcSPxcZd8jSZX+F+FXN96b8s8j9eSs73ulhc8RbOWeAteGIqfSvwmOgGfDrdSzBAB46QOUj3huhKM23jsGbiYb/2Iq/QkdIYHbhtf/Amx275xMQnAQvhq+hPhtsjc/o5R/CrWvc4m+ItdW5fven9RjCn9i0irPjxV4y5O5voahBuwr/efAV2a8UwhstT/lbqdktMONHrpptqa1zLlCcpfN3oV/ExzRehYmEzSYk7ccwFbv3LtsjkaS9J8DX1mJvmfh3wRHrISvP59MTLicxMX21pnUJTWiadFPP6vS14X/VTg8d16q4hJ8a/GfAh93xje+z5bOkqKdXog9mUtJbLSzpCgo6+Isn+ifCX+XOtriYMVeqmfDpAyp4n8ubLd+l0t1r4it4Ftn+z7p14h0YkReu/tmyyvXkyv93ir7ycTHc2H7SvL680k8n4HjglV+y6OA0oSE3vpWJUmVPlb4XKv9WemPSU6l3wMpWb2v2v8beBr8MnwzZ5Y2iJV5i4uybLgq/VvgMOv7R8Jt6Hng9v8m5sVZk2FwxTRLxSb8v4GnOV9TNwAYON5q2SV61zChBiBF+Gq9lFgnRf8x+oyAJtzppcrvAY6RzTFwc/ErbjGNRCwlG+Ci0uca70z652twqPX9GIn3JvyYE7OtT+JyIyR/jr13Sol/kfHOs+FWb64/s3yemKLXXz8DvrV63pvMXQ2Y/psUL4LrWN9KmTvHwM3em6+VqNq5kVTpYyp47lW+S+z6+9+An7c+JvVxiRH7OUe+Bod659v1uU/qrYSfE+3UjoVa32CtNYvsp29iu71sSO5Ph38sOUsTA4zUbZVxS26HJ8JT4dvWz2IbKarfZp7I1cmNE1Ll3epE7o1w1EYDkFrpx/TmaU1WvKOL/atweJLod8DBzs8OgbsS5ioes7rHoIazyd/2m3J+yx3weHgy/Evy91NxLQuuDd7X4FBy8bda9iZUv00Xe4kumz2x1Iqf7EQutfDV56FhuOGa39TfcQc8Pmd2kvE1flwjrhKVfo7wqar8UixZ+EumaO8dHzES7E38lHCoMicTDNz620/sNJH+kiWewhT/xAWXo4ZJP2SdyE0lVfq2XPw78Ji110+A7yWNGyA9ttAzbg7nKCgocSL7O/CYrPUDYJcctxO4PkpLmsOywJ7Q5XpkkHIyt6cTuc3iHY6kiIzrSU1uqMbZbKRjeQZ8ayU2/XlJ7oYDycZVen45VP6/ATcEhc5V+CmEhP+v8Mg6M4JkuIuzKKrJCT1PgO+Rr5vSVb8u+7vhQDgIdpKNewnoYud4jx3Fv8Ij4XHwg9XrI+E2dLWPFb45jZZUj3dy83wzIqGMdwDiIx5fpb8DDu4q0jHh2GUTW8lSyN9W4VOIv0Y1ziHm4Yyv+uYi51IUkf69sD88Gn7k/Jwy01dQVZGU0p9sEto2bKRKMkd8vkinB/FP6bsJxS2jS580078X9od7Yf+N5zWgig1iJD6Fjyd122iVUbvE3kvEwyHbn/Aku9LH7rx6dVei0i8Bx3iDA9+Hfa3vPwp+svEeZvtwVf654sqtds1qn1r4JcVMVel/H/a1rtfS3PfQw+CAPf+TfLzYk6ojV/vdnMhtkY0vXe6xmILIOdLjUKkqyZc6iavEzOG32lCNu/pbS/73PfSw1V9q8T8OfrD4eKea9FOy3MkkB6r71ZSOdGzzmNMQUPxm19HcpH+a9NM/BO6Kqtx77gHTmgfuF6TjKyGDUud+ej2ZyVX4HBuClO179Eo+xJr07/+PPa0PF7E7qzl8SOaxjQN3sMuVCrVDUIq/xCG+eQTINe6oRUjcrs+pGjnXOm6R7dt44H6xethe55DTIDz4Q9oCqxSoeOf+/9gT9nv4QxvvPxp+FCV+W7yjS733fu2puDaWvR9R/Q4ZLHgGfGvx4g+hnw/o9YjGxQF7/qc1z8dI/YH7BeyzX3i/0eWee+GUvv+q57Z919co1NzX13rv+KpPm/QB4qr9pWf65vLd7+EPFd8QsDtBDDV677TuuYNBfl+AeFS5nZXDfYZa9d4xSaniqbd7nVJVfQ35ryr90nFDE+F/TwA8ZpnVsqLEhq9LICSF0NFgie2ilvDV31Li51DNcxD+knjwh6K4+KtV+tWl/z2jJWYq/1DFMErEY9tOQttESrVfU45o4c/iI4ucrL5UtV8yvy+9z2d32YzN9Sd4RhE+wJbgS3TdbVUFB4WvFx7q+ZT/JJZ/FgD/hW67QUnfVeX7WHp+3y3/7KhgiDa6mO0Cc/ESu5OY5hGm6/Mp/27ppZeOizXpp8jdVeWr90nkf6cAeNKYO8nej5Ddb0Sl0cU+Ym8VK6W3+bsc29zBvPYz6utMKKi+zxJW+QCZ994pmuff6Vmo2J2BIkvdIQAO4bUjFMNV5SuIN74hCVX6OrZt09zuS4mfWvo7jPER7jOUmT5Vb7bS0i8Z7SZn+jE5flSO65O9PgxmZ6A4hOYi/Nt3L5enMpmfSR16OMo1ha/eU/vO7ZbPK23HtgbD9l7J7p3c4PXvEjHCTxm2d/SdxrYDURCq8rHDLJmYKt82vGubpt7WXVV+6DMXtsLIJ3zf+5PiHTh4ST+WpYhfr4pCFdLfJS4TTHQzUrzzf4X9kUPskaU5vKuip670fRFOzUwfKf4aVXhshNRzz7pubq2cRUiEhxsr8K+N4f8b8Qo2x4+ZRozs1XPzd036g3u0oygcg+6znyx+Ujc27zfFry6s0i+watZJ42YBcJT9t/Rd6WPAVL6p1XEKNuH73q+Jr5IfqcrngOuo4ElyS/S9CN8FppIfKObRe/U8+EORJHySI4ibxfpfAz7SLxHVxMhcDWuTLwch+3D9zpqNWU/4YpzciIeK0sI/WNoflGBO1iKGibmDJrcTsjESJ4uMVIXvqPR5ddlMEb9v50iR3uGyXLyDaTxSp2X7rTPeceOS+38lXGa2E7s1Lsr6sgB4FpN1H6rkA9IPyd4m+dwYKKfhMKt7jtl/VqaPuQVDl1fmUmf4+nh94s+Z7uFyVvaKGwXAMQx2NiX4Gvfe+bJwv+bSAJhkCl8NQ1ndcztSKEF2vOOS+qPhR27h3zjlVARV2R8ul13lcxC+Tm3h2z4PDVMKl9gHvd5EVfZ7P0KyrPIBiHrvoG+mpctePc/ZQamjnRFYsuxjoIxxWtJK5jEowd8uhpW9DlfZK7Iy/Sh81b0p/lC2jz3JlZrp1+KvRbkoaQLwRcv6f07l5W2bB0XuvKQIn2vUYyHn5C23f7rCCT69d3R676qGZWnC/8tKVekXhVu2vs9KzEfo89R5Sa3wezgymBSFx8VZthNvuvh7uP/IJMyvV1iHtYQegst8LJTYi7mWUuUDcI13KMFGPL1l4a6quYZYORMj21JRT4rwY+clp2IfJOLBippiHKy5VgC8EP87eMY7lGBkPorwQ59NypNa4dc8MuAa8XxOrD/ALeUYWe+zn1w9bK+7J0L4ADWl76rma3Sv80l9JOHHDDNZLh1V+gBgFXbOuJZO3UzfJvhrNUFFtlhR9CZ3GzEy/0vBK+r5pAB4CaP5mfTD5wTAiZ1U5p+07KPMtvu28c61wv96Mg7MNnyWzJO/62gxz9pzrtiE73u/ETx670wmk7iTuc+S4/XTP1GbN5fgd1f97AiJvfaR7tXG/Jy0Ne3xT+Ry5Uperf/iUILlWl1fLdwPRazAWwj/CqbLdxRsy9cUvvHelH4LlPCn+OnBVsv6hVHcxG/baV2f54j8ssK/WwnpChEn/1CMwz3mqYG+TM3le5Jlm2BT6ZsnbkueyC3Fh+YGyI7nSLf8fZ9RTTvneyHh2wiJ/1lyfZjLxJbw9eeU5FT4ofiGY7xTE9eyRS7z9pl+j6IHWJe9/vw1BX/Pr0t8Dx5OPXdaUfs+O/p0Uy4SixG+GlZVcJiK3yd39dlpBMvMJ6VT5nZZBVu1v5sZ7/QGRuZT+O1p1eDkQlH5u8QeI3xXNc+1yq91kjZUzSOq/Sn9FHIjnVfI9b+x+KQ+hY9Dj3lKCTo03tJRk06syEvFPjGcKLckrz9fMqGGE9Gwto93euQ1kk78qUy501Bauvr4vyj6PQKI5RRJ13Nnyp4U/A3XLtFW4BlzJQDApvhL5vmjM5dl2klcT3a7Irdip8j5e+UDlmX3u5blgb0AiyoGsjWoyPisv3jHthJaocT0GrlMSeXwIbH+0Ol5WV7EaPtU5Eob22hw/O2pfEC4XWN7HyNzytz/FLklef05Apz0LxH+17XgJHxFz4JqgU3ytmF6REnvIjGWAEPov3eE347xDBcXJfSG6iveUQvadng14U2KyHtqUG2iOzth/mMiHl+0c5nYrPBzYh7b0QJG7mdL/3Apy6g0MUI3XeSLeZjcfwp/IvcMuUv8LfP835V8WthSvMvx+17PY4OZWHBJ7SJRTmo24ZtS119TRDwp4wg1DCWXUQteItnfaTMu0+dwAndW+elc2FmD2UvM45JWiswwJ2ZthKr4eTK3Hi+RW5LXnzNhdtnkhKvKV5/FVvs2yZvvnZM4ztjvceXtAuBNBL8lFGPEYIr/ahGOcnpGLbdWFX+p9KC07BO33Sn9pXOhwAtcbzBivseNtwv365wGQImfWl4jC781PcbF+vaqnkdst/112RwVX5UfM4yiRJSjS94m/D9yTDMnpqGMeN4uNoVvGyaHkfJpxYx2eODbfjHb9m6EPB8kvGWuVBaEpI6Nd1KEn1q1u0QPAGvbVaq8qXrwpMicIvYpTelKHyN8yi6aNRvN3Cq/9vlF7Dbs227/SOyu9H07rsl5HR4OTSa9cG7E/sUl2jlbbslaf54ynl7gKnwfuz2/S/rYSn8Kvyy+Sn522UyHYoepxdsi1nNq7HKaDH8XM4xJT9LOqfJ77UG42/N7sYx2tgmACzzzFfo8B73SitkBqZhy36Kni7N6ZMlZfco1P61kH1u0+Hr1vEVGnsg9v/CP3iZ2PdTz0Oe2YSY8mQJfFrFVf09HCbWJPbcUGD6+905p8evoUncJfgT59z7/WGLE3/IELsV3axFbrdes7rEitw33hgrLPqZyL1Xl1/idBn3008dIkSryeZvcFfHUiHZsjVqp2KoUb5H2jgCu2FDJ3NWbh9MRQc3eO78vAP6k8PS4xzmmAPXX72Q+77Hov009r/Qb8Tdcq0FuxduLMDG/k+K3YLtu9nqRFZbUir209H/fM18pDYCvNw8X4dsuXoupdkNifK0xrvcjf7cr36eu8DG/1fUbKbpsAqdKfykRBzem8NvgE776PFb8uthTb5BWmhzhq+FLVMS99sgxQRQqtFfkmq0slin8MoSEPoU/LhyFX5pU//TCm6Rb6r7PDGgqfX1hq+fYw6op/LJwEPvveNbxBxnMn40S0U6N3L4nUk9iuqr998s48Z8uAC7tcH2Etk3X/vZB1U9/KZzuWBA9rvSWvFoA/Clymflkrw/DTfzUwtejnFCsM8lDiT9UdCofqL8L8kB+vONqWUc/1Foiry60TjGNQywlT96eGjHuKfn6YFOGhZJf6bsOqTgteFeVrz6jbOVfKQA+Fhif6plji7Y490DCVvgA8SKnrvjfJMl6OwDApuj11x91fD9H+DMGGhMVS9mirUpdNmnindgsrQRUsnRVca4d2yQkfB1d/rnzj2lsapFauXOMegDiKnsKpvDHR5c/Vva/KQD+HDHsB+XmPqjtV3SZPjZLoyYkS1+Vrw/TMtPTfwN2xeq8Umz95SL+XghV+RjhnyrwRYGNP5HzJG9tLpXt93sAnPB/U9if+zzhKZ7aX5yV03sHWx2HxK9WfGgHz9mxMfymMX2M/F9pmefW4s/J6EtU+raYB3uyFlvl27YNTLzDSfQvFQDXVJ6flB48o12d68L0gYvIIrF9750LZJr4OWffKWBXsMnH5Lr4exZ+KZTgY/+naEysk1vtt+Klwv26dgMwqcIy/l2i7xAOW+Vjh0klNtLhSm6lXrLRiO2KGSNx27ChKr51lW8KP/ZzkxdWqNpnlZ83LHCRfmzVXqLKx+zgXCs5Vd23rvKp+B3B84ghBZfYSwr/hcL+0IkVOmZ66nkpliL8wrTN9GNjndKRTutMfxSohd26R09P24VPutfuns8U4fuiHts0r81cJqXusdMDKVFvRFLAo9K3oQR/gdx6lMa383LasblDLenWVf9c925cjUxuxY8R/vPF+mMUCke9fKUPMN7J2km/uMTfU4NQInpxVfS5lX4Im+RHEn9B2vfe4UZPOzFnbBeI9I5t23i+APhsJ9vMtTI9y4/pzlla+D56Wh8+/lwy7rJ5nAC4YYCFPKFHxTyjyd+sKPXX3IVzTaL4fcJXkn+haCt8gLLL/wTHcruO+To3SJf+cWLz+ZT/MjnOsTOo7UHP+M1bLfTWIHCPEK6VZXvQhKa9RE4QZcSPqfYT8v+03juunVyRIn+q/3E7CXOsANhOtKwptoWQ+Fv33lHECJ9zxU9d6S8BV5WvqFHtp9yixUK89EM7uWJW/fw4NnJnxzQMpaXfo/AVo4h/6cIH4CF9Inj33pmEwYo8Vvg1cYmdi/CXTEnhP5vxNqkTEj52GCbEZfrYKr8m+oZzU+IG+kxtHF+tKJojjeV5S8S0dYlTxjXmNEqM18RW6S9d+EeKuO0BC4cKX99n1fPUfbcG18kFV/rcIhuzUkipHJ7ZqCEzhe96z4ataudcyftwRTucTvDWPHl7pNjaDvTnFPiErwR/jdx6cOGXGG0LAzDjnZqVPRW26ttXkec0CL02JpTUyuZdgqcUv4tat1V2FWau95XwW4u/ZCVf+bfFd9m8QZbpvdOSnsWPiWC2y3R5h8bt2x5C20GNav4wAXAb8/WLkXqpuCeW3OV5k7QL3hbvmDL8JQHwtw2XAaX4bb9NUfg30l+R25vwe6dG5h4idZ2XvGr3MLH5nLv8S4LJ8kPVvlqO1OLHCF9/v6X4ByAt3nHt5LWFb24snE8GmdiqNg6VnA6HBiWWw8S68LGfhYiNeLh118RENxjhu17HovZV1z7rEntp4f+C2PXokUPF+sNBeqZ/g9ySvP68NqGNpxSehYpGSf4WWV74PQq8FLnCClFK+DWy/ZpwK9J02ZcUf+PzE+3/R26P6ML/1lx8ZJi3aIglRuY58YSvN49N+E8WAHd4poeVua0weLLlu65pYXrvuLAt21px2YiRjk/8qb/VLEQdbpp32ZzwoZe++brYXXd1NGWsv/Y1ADHYhK/et03DdbM1Tt0zbYwm/MaUr/R/TgD804Ar7VAxq3xu1Kr0fdu0S8QmppQx1b5Z6fumFWpYUrpo6st3ySfFKXBV+hUauBnvcOMgAXD3XCVJtJY+Vvg6GPljY53QuCcTmPEOLw4S63+n/HlS8shVCT7UL/8OmVfpTxYLrvfOY8Wux4Q1QlzQehaWS0qV7/ueS/hPFluPJUPoI3ngspblYip934oVO/urilyCN9+XcluN2anOg48UsPcPjPV2m8zqjvngIze/uzGNlqSeL5hY0Z0gDxRdeiAFXKV/j9z16JRQSy4PFF219jEV/UjV/4OPFKuH7TUaS57vGkf0uFugJH+HXI7wCXykS34pwgdYwA3XYmTeXPwqw79bkub5I4g/JN/V57dJ90lax2focbvgELUsRfbELEn2iqF776RIPHcj+PFPC/ipfy+3SFMF3nPME1Ntp8Qx2PF7x50j/hhhm9OZsp9EMkSmT5nHpY7rxz8tNp6XlP9SiI1XrFn/SEzJTzLpNt5RObyq5s3XNdGFj3l/wocuMvvC3LfPXAax9LzMuot3WuXuvurfJ3fKaj83m+8x4kmRcmylTxLvAKRHPI2r9/v2EXDAA11poDpYyfewHLuq9JufaLUQquYpq/0caVMIf+dedZd/ahXerHpPlXfjE8E9iKolMVV9yhFA7f2qK+lzJFTJj5Lrqw2z5gbKKZsvPi8cegAV4h7R72+rEeMc+P/qbucr6ddeMd+NnB7HKn8ERujOaYOyx0+U8JfUVx6B8krP4h+NVaZ/jxDw2Irx/neFgJ+NmF5L6WN689hinFJVfoqopdyG/p4rCtq5l6helVBGNVh5F+kBNNjVtHcKAU8K7L820dd0DAWplT7nyKybE7m1pM+9nz5Aneo89hwARgKptBB/EULi70D4d1pE7lvvuvh7Ez5AP9LfIQQcgly+M9MnhmOGn3ISF9uw3CnESgT6c0ooRT27aKbjWrcl1vkExw4hYMfu5a8/9zHExVlU9HJJti5xn5zVcNRHBqGdXH1OWfnv/QNJJmy2F3CV+O9ajXmslDPPL4RL8Op9V+VvlX7MoUIPiJ1yuLtsKswGoHRf/NZVnZJ1bgPQRPyhe+DrhP6vLjN88d4UPy/W4p2UQ4VeEDvl6mF7PQLchE/ZQOh31qSghPC/KQR8M/SbGYr86wTrKXRU12OePyp7AbgPE/TPVOXvG7bk0UGoWvd9D/NeLW61LL8jSi63jGjHPHJQO3brah8gv+JvFvEw6Y9vil5//YtT0Flw7rkDEHEiF1P5lz4yiJW1Gp5LH3+b8H3vU0B9FW+ral8J2hR1qrhLCP9pUsLTBhCmq/J3VfOlem1NykDee4eT+M0bsrUkJPaS4qckdgenPpnbNbFVfqGjAkyc4xO/Wqf681HhXrWnUKT3TukTwab4XbdDNkW/pH+JNmFIzIlcNTxTRpe9yQEPSG+f/dTGISfuPURKa5Edcq+QUsoS1Xnr3j/ceutgK/lS+X7qVbw2sJFNTTHEZvvNjhoaSz/2pG1uvn+zY3pHJY73ZiGSv0tJ7p1JMT6IccG8OAvcYudc6Zc8oTs6MRJvGhM1rt5jJF5K+KHPbMOqh+11C2rEPjGRb0yRvUfsF7hxUydZeG/4TgBzPbTHyLz78wKdgBEyxTAtxd8r1nvvqEOF3NjH1Zhs18Z7bIZATOE/2zEuLlm+r+VOqfJtkU2ot04o5knt7VPy3jupsL3yVmGLeiodCYRintQqP0XCtriGajwciangSxz9W+MdJeucIwCM8G2vscRU+ByED+BegVTCD71f8kZtqcJPXf8YWAsfYP02zIxuyTz76Y9Nt5m+q6rnzhFSriSvP48hJO/cC7JqsF2IlfD154ukgexdYucg/NTIpoeoJ7Zrdomu3MFbK6dGPNhKHyA/4um1AUgl9r74lD13cgnJPWdbGBEh3uv8TMrfqzgnOFLFq0czOfLuIeJhGe/ocD/JuzThx5JauZeo+DHV/KIrfgOf8DGf90IPoh6J6vGOWcnNyi6OUf+94SSNUcRPRcpRwvWVC43WXbNR0o+p9g+RMjj8sVKuHpM4St9JsxQxFfzSq30h3hsl8yn+LVKOGo63fKd2Q2CjVOOArvQxMgcof++dCR5ORwUxDTzXYmDKNcwIUY0Sfknx+zpxpHbwwBJ97x1d/Cn3fZjUodR/zVoapuj11xxPpHLgKCnRMcsIjYQPIS61vi/l6QCwVc3fKkS12IfsH6NT32RNiLc5P5PyXLLp9IpL5mb8kyP9ElFSL713Yqp6SvnnHE1wbIRS772Te3HW53d//3mJ29P1QlhjHywu2Zso+deE7ERuLeHXoPX0Q/hE3qKyF+Ld6GG5SN1HrHgpY59UcecIX4iLk78b4igprQ/M92Kno5Mqe0WO8LnD7uIsjHBLSlmNm6v4MVLXh0mt1rHfU8IX4t1o+bvE30ODMBJCXLwSvv681byYYMXvGi5X/Klgq/zYYakgi3coSBEtZdRjmz6nKCm2ijf/aXrK9/zzsyl5Kf8APR2OcIhXUuYhZtpYuUt5ZvR8pGDOj2+6XG6t7CNW5LUjHnaVfitcDQ7Xir8UMUcGvQueK7GNB8csH0vs0QUn4VNV6bWr/f8P6S/4xYOP9DkAAAAASUVORK5CYII=" 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diff --git a/environment.yaml b/environment.yaml
index 5d354713..291e0a3c 100644
--- a/environment.yaml
+++ b/environment.yaml
@@ -3,7 +3,6 @@ channels:
 dependencies:
 - catch2
 - cmake
-- docopt
 - doxygen
 - enstat >=0.9.7
 - h5py
@@ -16,6 +15,7 @@ dependencies:
 - python
 - python-prrng
 - scikit-build
+- scipy
 - setuptools_scm
 - xsimd
 - xtensor
diff --git a/include/GooseEYE/Ensemble.hpp b/include/GooseEYE/Ensemble.hpp
index 185d1e1c..3ef1162b 100644
--- a/include/GooseEYE/Ensemble.hpp
+++ b/include/GooseEYE/Ensemble.hpp
@@ -14,7 +14,7 @@ namespace GooseEYE {
 inline Ensemble::Ensemble(const std::vector<size_t>& roi, bool periodic, bool variance)
     : m_periodic(periodic), m_variance(variance), m_shape_orig(roi)
 {
-    GOOSEEYE_ASSERT(m_shape_orig.size() <= MAX_DIM);
+    GOOSEEYE_ASSERT(m_shape_orig.size() <= MAX_DIM, std::out_of_range);
 
     m_first = xt::atleast_3d(xt::zeros<double>(m_shape_orig));
     m_second = zeros_like(m_first);
@@ -70,7 +70,7 @@ inline array_type::array<double> Ensemble::norm() const
 
 inline array_type::array<double> Ensemble::distance(size_t axis) const
 {
-    GOOSEEYE_ASSERT(axis < m_shape_orig.size());
+    GOOSEEYE_ASSERT(axis < m_shape_orig.size(), std::out_of_range);
     axis = detail::atleast_3d_axis(m_shape_orig.size(), axis);
 
     array_type::tensor<double, 3> dist = xt::empty<double>(m_shape);
@@ -113,7 +113,7 @@ inline array_type::array<double> Ensemble::distance() const
 
 inline array_type::array<double> Ensemble::distance(const std::vector<double>& h) const
 {
-    GOOSEEYE_ASSERT(m_shape_orig.size() == h.size());
+    GOOSEEYE_ASSERT(m_shape_orig.size() == h.size(), std::out_of_range);
 
     array_type::array<double> ret = xt::zeros<double>(m_shape_orig);
 
@@ -126,7 +126,7 @@ inline array_type::array<double> Ensemble::distance(const std::vector<double>& h
 
 inline array_type::array<double> Ensemble::distance(const std::vector<double>& h, size_t axis) const
 {
-    GOOSEEYE_ASSERT(m_shape_orig.size() == h.size());
+    GOOSEEYE_ASSERT(m_shape_orig.size() == h.size(), std::out_of_range);
     return this->distance(axis) * h[axis];
 }
 
diff --git a/include/GooseEYE/Ensemble_C2.hpp b/include/GooseEYE/Ensemble_C2.hpp
index 285d7864..94b73e1c 100644
--- a/include/GooseEYE/Ensemble_C2.hpp
+++ b/include/GooseEYE/Ensemble_C2.hpp
@@ -20,13 +20,13 @@ inline void Ensemble::C2(const T& f, const T& g, const M& fmask, const M& gmask)
     static_assert(std::is_integral<value_type>::value, "Integral image required.");
     static_assert(std::is_integral<mask_type>::value, "Integral mask required.");
 
-    GOOSEEYE_ASSERT(xt::has_shape(f, g.shape()));
-    GOOSEEYE_ASSERT(xt::has_shape(f, fmask.shape()));
-    GOOSEEYE_ASSERT(xt::has_shape(f, gmask.shape()));
-    GOOSEEYE_ASSERT(f.dimension() == m_shape_orig.size());
-    GOOSEEYE_ASSERT(xt::all(xt::equal(fmask, 0) || xt::equal(fmask, 1)));
-    GOOSEEYE_ASSERT(xt::all(xt::equal(gmask, 0) || xt::equal(gmask, 1)));
-    GOOSEEYE_ASSERT(m_stat == Type::C2 || m_stat == Type::Unset);
+    GOOSEEYE_ASSERT(xt::has_shape(f, g.shape()), std::out_of_range);
+    GOOSEEYE_ASSERT(xt::has_shape(f, fmask.shape()), std::out_of_range);
+    GOOSEEYE_ASSERT(xt::has_shape(f, gmask.shape()), std::out_of_range);
+    GOOSEEYE_ASSERT(f.dimension() == m_shape_orig.size(), std::out_of_range);
+    GOOSEEYE_ASSERT(xt::all(xt::equal(fmask, 0) || xt::equal(fmask, 1)), std::out_of_range);
+    GOOSEEYE_ASSERT(xt::all(xt::equal(gmask, 0) || xt::equal(gmask, 1)), std::out_of_range);
+    GOOSEEYE_ASSERT(m_stat == Type::C2 || m_stat == Type::Unset, std::out_of_range);
 
     // lock statistic
     m_stat = Type::C2;
diff --git a/include/GooseEYE/Ensemble_L.hpp b/include/GooseEYE/Ensemble_L.hpp
index fdff9847..c7786e03 100644
--- a/include/GooseEYE/Ensemble_L.hpp
+++ b/include/GooseEYE/Ensemble_L.hpp
@@ -18,8 +18,8 @@ inline void Ensemble::L(const T& f, path_mode mode)
 
     static_assert(std::is_integral<value_type>::value, "Integral image required.");
 
-    GOOSEEYE_ASSERT(f.dimension() == m_shape_orig.size());
-    GOOSEEYE_ASSERT(m_stat == Type::L || m_stat == Type::Unset);
+    GOOSEEYE_ASSERT(f.dimension() == m_shape_orig.size(), std::out_of_range);
+    GOOSEEYE_ASSERT(m_stat == Type::L || m_stat == Type::Unset, std::out_of_range);
 
     // lock statistics
     m_stat = Type::L;
diff --git a/include/GooseEYE/Ensemble_S2.hpp b/include/GooseEYE/Ensemble_S2.hpp
index 0355e75a..a350471a 100644
--- a/include/GooseEYE/Ensemble_S2.hpp
+++ b/include/GooseEYE/Ensemble_S2.hpp
@@ -19,13 +19,13 @@ inline void Ensemble::S2(const T& f, const T& g, const M& fmask, const M& gmask)
 
     static_assert(std::is_integral<mask_type>::value, "Integral mask required.");
 
-    GOOSEEYE_ASSERT(xt::has_shape(f, g.shape()));
-    GOOSEEYE_ASSERT(xt::has_shape(f, fmask.shape()));
-    GOOSEEYE_ASSERT(xt::has_shape(f, gmask.shape()));
-    GOOSEEYE_ASSERT(f.dimension() == m_shape_orig.size());
-    GOOSEEYE_ASSERT(xt::all(xt::equal(fmask, 0) || xt::equal(fmask, 1)));
-    GOOSEEYE_ASSERT(xt::all(xt::equal(gmask, 0) || xt::equal(gmask, 1)));
-    GOOSEEYE_ASSERT(m_stat == Type::S2 || m_stat == Type::Unset);
+    GOOSEEYE_ASSERT(xt::has_shape(f, g.shape()), std::out_of_range);
+    GOOSEEYE_ASSERT(xt::has_shape(f, fmask.shape()), std::out_of_range);
+    GOOSEEYE_ASSERT(xt::has_shape(f, gmask.shape()), std::out_of_range);
+    GOOSEEYE_ASSERT(f.dimension() == m_shape_orig.size(), std::out_of_range);
+    GOOSEEYE_ASSERT(xt::all(xt::equal(fmask, 0) || xt::equal(fmask, 1)), std::out_of_range);
+    GOOSEEYE_ASSERT(xt::all(xt::equal(gmask, 0) || xt::equal(gmask, 1)), std::out_of_range);
+    GOOSEEYE_ASSERT(m_stat == Type::S2 || m_stat == Type::Unset, std::out_of_range);
 
     // lock statistic
     m_stat = Type::S2;
diff --git a/include/GooseEYE/Ensemble_W2.hpp b/include/GooseEYE/Ensemble_W2.hpp
index 29f86cf2..4354dfe5 100644
--- a/include/GooseEYE/Ensemble_W2.hpp
+++ b/include/GooseEYE/Ensemble_W2.hpp
@@ -19,11 +19,11 @@ inline void Ensemble::W2(const T& f, const T& g, const M& gmask)
 
     static_assert(std::is_integral<mask_type>::value, "Integral mask required.");
 
-    GOOSEEYE_ASSERT(xt::has_shape(f, g.shape()));
-    GOOSEEYE_ASSERT(xt::has_shape(f, gmask.shape()));
-    GOOSEEYE_ASSERT(f.dimension() == m_shape_orig.size());
-    GOOSEEYE_ASSERT(xt::all(xt::equal(gmask, 0) || xt::equal(gmask, 1)));
-    GOOSEEYE_ASSERT(m_stat == Type::W2 || m_stat == Type::Unset);
+    GOOSEEYE_ASSERT(xt::has_shape(f, g.shape()), std::out_of_range);
+    GOOSEEYE_ASSERT(xt::has_shape(f, gmask.shape()), std::out_of_range);
+    GOOSEEYE_ASSERT(f.dimension() == m_shape_orig.size(), std::out_of_range);
+    GOOSEEYE_ASSERT(xt::all(xt::equal(gmask, 0) || xt::equal(gmask, 1)), std::out_of_range);
+    GOOSEEYE_ASSERT(m_stat == Type::W2 || m_stat == Type::Unset, std::out_of_range);
 
     // lock statistic
     m_stat = Type::W2;
diff --git a/include/GooseEYE/Ensemble_W2c.hpp b/include/GooseEYE/Ensemble_W2c.hpp
index 922467f0..1b7c5bff 100644
--- a/include/GooseEYE/Ensemble_W2c.hpp
+++ b/include/GooseEYE/Ensemble_W2c.hpp
@@ -22,12 +22,12 @@ Ensemble::W2c(const C& clusters, const C& centers, const T& f, const M& fmask, p
     static_assert(std::is_integral<cluster_type>::value, "Integral clusters required.");
     static_assert(std::is_integral<mask_type>::value, "Integral mask required.");
 
-    GOOSEEYE_ASSERT(f.shape() == clusters.shape());
-    GOOSEEYE_ASSERT(f.shape() == centers.shape());
-    GOOSEEYE_ASSERT(xt::has_shape(f, fmask.shape()));
-    GOOSEEYE_ASSERT(f.dimension() == m_shape_orig.size());
-    GOOSEEYE_ASSERT(xt::all(xt::equal(fmask, 0) || xt::equal(fmask, 1)));
-    GOOSEEYE_ASSERT(m_stat == Type::W2c || m_stat == Type::Unset);
+    GOOSEEYE_ASSERT(f.shape() == clusters.shape(), std::out_of_range);
+    GOOSEEYE_ASSERT(f.shape() == centers.shape(), std::out_of_range);
+    GOOSEEYE_ASSERT(xt::has_shape(f, fmask.shape()), std::out_of_range);
+    GOOSEEYE_ASSERT(f.dimension() == m_shape_orig.size(), std::out_of_range);
+    GOOSEEYE_ASSERT(xt::all(xt::equal(fmask, 0) || xt::equal(fmask, 1)), std::out_of_range);
+    GOOSEEYE_ASSERT(m_stat == Type::W2c || m_stat == Type::Unset, std::out_of_range);
 
     // lock statistic
     m_stat = Type::W2c;
diff --git a/include/GooseEYE/Ensemble_heightheight.hpp b/include/GooseEYE/Ensemble_heightheight.hpp
index e5b8d6ca..319709d5 100644
--- a/include/GooseEYE/Ensemble_heightheight.hpp
+++ b/include/GooseEYE/Ensemble_heightheight.hpp
@@ -18,10 +18,10 @@ inline void Ensemble::heightheight(const T& f, const M& fmask)
 
     static_assert(std::is_integral<mask_type>::value, "Integral mask required.");
 
-    GOOSEEYE_ASSERT(xt::has_shape(f, fmask.shape()));
-    GOOSEEYE_ASSERT(f.dimension() == m_shape_orig.size());
-    GOOSEEYE_ASSERT(xt::all(xt::equal(fmask, 0) || xt::equal(fmask, 1)));
-    GOOSEEYE_ASSERT(m_stat == Type::heightheight || m_stat == Type::Unset);
+    GOOSEEYE_ASSERT(xt::has_shape(f, fmask.shape()), std::out_of_range);
+    GOOSEEYE_ASSERT(f.dimension() == m_shape_orig.size(), std::out_of_range);
+    GOOSEEYE_ASSERT(xt::all(xt::equal(fmask, 0) || xt::equal(fmask, 1)), std::out_of_range);
+    GOOSEEYE_ASSERT(m_stat == Type::heightheight || m_stat == Type::Unset, std::out_of_range);
 
     // lock statistic
     m_stat = Type::heightheight;
diff --git a/include/GooseEYE/Ensemble_mean.hpp b/include/GooseEYE/Ensemble_mean.hpp
index e0ca2d02..d5582c1f 100644
--- a/include/GooseEYE/Ensemble_mean.hpp
+++ b/include/GooseEYE/Ensemble_mean.hpp
@@ -14,8 +14,8 @@ namespace GooseEYE {
 template <class T>
 void Ensemble::mean(const T& f)
 {
-    GOOSEEYE_ASSERT(m_shape == std::vector<size_t>(MAX_DIM, 1));
-    GOOSEEYE_ASSERT(m_stat == Type::mean || m_stat == Type::Unset);
+    GOOSEEYE_ASSERT(m_shape == std::vector<size_t>(MAX_DIM, 1), std::out_of_range);
+    GOOSEEYE_ASSERT(m_stat == Type::mean || m_stat == Type::Unset, std::out_of_range);
 
     m_stat = Type::mean;
 
@@ -29,10 +29,10 @@ void Ensemble::mean(const T& f, const M& fmask)
 {
     static_assert(std::is_integral<typename M::value_type>::value, "Integral mask required.");
 
-    GOOSEEYE_ASSERT(xt::has_shape(f, fmask.shape()));
-    GOOSEEYE_ASSERT(xt::all(xt::equal(fmask, 0) || xt::equal(fmask, 1)));
-    GOOSEEYE_ASSERT(m_shape == std::vector<size_t>(MAX_DIM, 1));
-    GOOSEEYE_ASSERT(m_stat == Type::mean || m_stat == Type::Unset);
+    GOOSEEYE_ASSERT(xt::has_shape(f, fmask.shape()), std::out_of_range);
+    GOOSEEYE_ASSERT(xt::all(xt::equal(fmask, 0) || xt::equal(fmask, 1)), std::out_of_range);
+    GOOSEEYE_ASSERT(m_shape == std::vector<size_t>(MAX_DIM, 1), std::out_of_range);
+    GOOSEEYE_ASSERT(m_stat == Type::mean || m_stat == Type::Unset, std::out_of_range);
 
     m_stat = Type::mean;
 
diff --git a/include/GooseEYE/GooseEYE.h b/include/GooseEYE/GooseEYE.h
index 483e4484..ba207944 100644
--- a/include/GooseEYE/GooseEYE.h
+++ b/include/GooseEYE/GooseEYE.h
@@ -14,6 +14,41 @@
 
 namespace GooseEYE {
 
+namespace detail {
+
+/**
+ * @brief Get identifier of the namespace.
+ */
+inline std::string get_namespace()
+{
+    std::string ret = "GooseEYE";
+#ifdef GOOSEEYE_USE_XTENSOR_PYTHON
+    return ret + ".";
+#else
+    return ret + "::";
+#endif
+}
+
+/**
+ * @brief Convert shape to string.
+ * @param shape Shape.
+ */
+template <class T>
+inline std::string shape_to_string(const T& shape)
+{
+    std::string ret = "[";
+    for (size_t i = 0; i < shape.size(); ++i) {
+        ret += std::to_string(shape[i]);
+        if (i < shape.size() - 1) {
+            ret += ", ";
+        }
+    }
+    ret += "]";
+    return ret;
+}
+
+} // namespace detail
+
 /**
  * Collect kernels.
  */
@@ -65,9 +100,9 @@ inline array_type::array<int> dummy_circles(
     const array_type::tensor<int, 1>& r,
     bool periodic = true)
 {
-    GOOSEEYE_ASSERT(row.shape() == col.shape());
-    GOOSEEYE_ASSERT(row.shape() == r.shape());
-    GOOSEEYE_ASSERT(shape.size() == 2);
+    GOOSEEYE_ASSERT(row.shape() == col.shape(), std::out_of_range);
+    GOOSEEYE_ASSERT(row.shape() == r.shape(), std::out_of_range);
+    GOOSEEYE_ASSERT(shape.size() == 2, std::out_of_range);
 
     array_type::array<int> out = xt::zeros<int>(shape);
 
@@ -112,7 +147,7 @@ inline array_type::array<int> dummy_circles(
 inline array_type::array<int>
 dummy_circles(const std::vector<size_t>& shape, bool periodic = true, uint64_t seed = 0)
 {
-    GOOSEEYE_ASSERT(shape.size() == 2);
+    GOOSEEYE_ASSERT(shape.size() == 2, std::out_of_range);
     prrng::pcg32 rng(seed);
 
     // set default: number of circles in both directions and (constant) radius
@@ -204,9 +239,10 @@ inline T dilate(const T& f, size_t iterations = 1, bool periodic = true);
  * @return List of length `max(a) + 1` with per label in `a` the corresponding label in `b`.
  */
 template <class T, class S>
-array_type::tensor<size_t, 1> relabel_map(const T& a, const S& b)
+[[deprecated]] array_type::tensor<size_t, 1> relabel_map(const T& a, const S& b)
 {
-    GOOSEEYE_ASSERT(xt::has_shape(a, b.shape()));
+    GOOSEEYE_WARNING_PYTHON("relabel_map is deprecated, use labels_map instead (new API) instead");
+    GOOSEEYE_ASSERT(xt::has_shape(a, b.shape()), std::out_of_range);
 
     array_type::tensor<size_t, 1> ret = xt::zeros<size_t>({static_cast<size_t>(xt::amax(a)() + 1)});
 
@@ -217,10 +253,553 @@ array_type::tensor<size_t, 1> relabel_map(const T& a, const S& b)
     return ret;
 }
 
+/**
+ * @brief Get a map to relabel from `a` to `b`.
+ * @param a Image with labels.
+ * @param b Image with labels.
+ * @return Array with each row the pair (old_label, new_label).
+ */
+template <class T>
+inline array_type::tensor<typename T::value_type, 2> labels_map(const T& a, const T& b)
+{
+    using value_type = typename T::value_type;
+    std::map<value_type, value_type> map;
+
+    for (size_t i = 0; i < a.size(); ++i) {
+        map.try_emplace(a.flat(i), b.flat(i));
+    }
+
+    size_t i = 0;
+    array_type::tensor<typename T::value_type, 2> ret =
+        xt::empty<typename T::value_type>(std::array<size_t, 2>{map.size(), 2});
+
+    for (auto const& [key, val] : map) {
+        ret(i, 0) = key;
+        ret(i, 1) = val;
+        ++i;
+    }
+
+    return ret;
+}
+
+/**
+ * @brief Rename labels.
+ * @param labels Image with labels.
+ * @param rename Array with each row the pair (old_label, new_label).
+ * @return Image with reordered labels.
+ */
+template <class L, class A>
+inline L labels_rename(const L& labels, const A& rename)
+{
+    GOOSEEYE_ASSERT(rename.dimension() == 2, std::out_of_range);
+    GOOSEEYE_ASSERT(rename.shape(1) == 2, std::out_of_range);
+    using value_type = typename A::value_type;
+    std::map<value_type, value_type> map;
+    for (size_t i = 0; i < rename.shape(0); ++i) {
+        map.emplace(rename(i, 0), rename(i, 1));
+    }
+
+#ifdef GOOSEEYE_ENABLE_ASSERT
+    auto l = xt::unique(labels);
+    for (size_t i = 0; i < l.size(); ++i) {
+        GOOSEEYE_ASSERT(map.count(l(i)) > 0, std::out_of_range);
+    }
+#endif
+
+    L ret = xt::empty_like(labels);
+    for (size_t i = 0; i < labels.size(); ++i) {
+        ret.flat(i) = map[labels.flat(i)];
+    }
+
+    return ret;
+}
+
+/**
+ * @brief Reorder labels.
+ * @param labels Image with labels.
+ * @param order List of new order of labels (`unique(labels)` in desired order).
+ * @return Image with reordered labels.
+ */
+template <class L, class A>
+inline L labels_reorder(const L& labels, const A& order)
+{
+#ifdef GOOSEEYE_ENABLE_ASSERT
+    auto a = xt::unique(labels);
+    auto b = xt::unique(order);
+    GOOSEEYE_ASSERT(a.size() == b.size(), std::out_of_range);
+    GOOSEEYE_ASSERT(xt::all(xt::equal(a, b)), std::out_of_range);
+#endif
+
+    auto maxlab = *std::max_element(order.begin(), order.end());
+    std::vector<typename A::value_type> renum(maxlab + 1);
+
+    for (size_t i = 0; i < order.size(); ++i) {
+        renum[order[i]] = i;
+    }
+
+    L ret = xt::empty_like(labels);
+    for (size_t i = 0; i < labels.size(); ++i) {
+        ret.flat(i) = renum[labels.flat(i)];
+    }
+
+    return ret;
+}
+
+/**
+ * @brief Size per label.
+ * @param labels Image with labels (0..n).
+ * @return List of size n + 1 with the size per label.
+ */
+template <class T>
+array_type::tensor<typename T::value_type, 2> labels_sizes(const T& labels)
+{
+    using value_type = typename T::value_type;
+    std::map<value_type, value_type> map;
+
+    for (size_t i = 0; i < labels.size(); ++i) {
+        if (map.count(labels.flat(i)) == 0) {
+            map.emplace(labels.flat(i), 1);
+        }
+        else {
+            map[labels.flat(i)]++;
+        }
+    }
+
+    size_t i = 0;
+    array_type::tensor<value_type, 2> ret =
+        xt::empty<value_type>(std::array<size_t, 2>{map.size(), 2});
+
+    for (auto const& [key, val] : map) {
+        ret(i, 0) = key;
+        ret(i, 1) = val;
+        ++i;
+    }
+
+    return ret;
+}
+
+namespace detail {
+
+/**
+ * @brief Unravel index.
+ * @param idx Flat index to unravel.
+ * @param strides Strides.
+ * @param indices Output: array indices.
+ */
+inline void unravel_index(
+    ptrdiff_t idx,
+    const std::array<ptrdiff_t, 2>& strides,
+    std::array<ptrdiff_t, 2>& indices)
+{
+    indices[0] = idx / strides[0];
+    indices[1] = idx % strides[0];
+}
+
+/**
+ * @brief Convert kernel to array of distances and remove zero distance.
+ * @param kernel Kernel.
+ * @return Array of distances.
+ */
+template <size_t Dim, class T>
+inline array_type::tensor<ptrdiff_t, Dim> kernel_to_dx(T kernel)
+{
+#ifdef GOOSEEYE_ENABLE_ASSERT
+    for (size_t i = 0; i < Dim; ++i) {
+        GOOSEEYE_ASSERT(kernel.shape(i) % 2 == 1, std::out_of_range);
+    }
+#endif
+
+    std::array<size_t, Dim> mid;
+    for (size_t i = 0; i < Dim; ++i) {
+        mid[i] = (kernel.shape(i) - 1) / 2;
+    }
+    size_t idx = 0;
+    for (size_t i = 0; i < Dim; ++i) {
+        idx += mid[i] * kernel.strides()[i];
+    }
+    GOOSEEYE_ASSERT(kernel.flat(idx) == 1, std::out_of_range);
+    kernel.flat(idx) = 0;
+
+    if constexpr (Dim == 1) {
+        return xt::flatten_indices(xt::argwhere(kernel)) - mid[0];
+    }
+
+    auto ret = xt::from_indices(xt::argwhere(kernel));
+    for (size_t i = 0; i < Dim; ++i) {
+        xt::view(ret, xt::all(), i) -= mid[i];
+    }
+    return ret;
+}
+
+} // namespace detail
+
+/**
+ * @brief (Incrementally) Label clusters (0 as background, 1..n as labels).
+ * @tparam Dimension The rank (a.k.a. `dimension`) of the image.
+ * @note The default kernel is `GooseEYE::kernel::nearest()`.
+ */
+template <size_t Dimension, bool Periodicity = true>
+class ClusterLabeller {
+public:
+    static constexpr size_t Dim = Dimension; ///< Dimensionality of the system.
+    static constexpr bool Periodic = Periodicity; ///< Periodicity of the system.
+
+private:
+    std::array<size_t, Dim> m_shape; ///< Shape of the system.
+    array_type::tensor<ptrdiff_t, Dim> m_dx; ///< Kernel (in distances along each dimension).
+    array_type::tensor<ptrdiff_t, Dim> m_label; ///< Per block, the label (`0` for background).
+    ptrdiff_t m_new_label = 1; ///< The next label number to assign.
+    size_t m_nmerge = 0; ///< Number of times that clusters have been merged.
+    std::array<ptrdiff_t, Dim> m_index; ///< Array index (reused).
+    std::array<ptrdiff_t, Dim> m_strides; ///< Strides of the array.
+
+    /**
+     * @brief Memory used for relabeling.
+     * @note
+     *      -   The maximum label is `maxlab = prod(shape) + 1`.
+     *      -   `m_renum` is allocated to `arange(maxlab)`.
+     */
+    std::vector<ptrdiff_t> m_renum;
+
+    /**
+     * @brief Linked list of labels connected to a certain label.
+     * @warning Each label can only be connected to one other label.
+     * @note To get the labels connected to label `i`:
+     *
+     *      labels = []
+     *      while m_next[i] != -1:
+     *          labels.append(m_next[i])
+     *          i = m_next[i]
+     */
+    std::vector<ptrdiff_t> m_next;
+    std::vector<ptrdiff_t> m_connected; ///< List of labels connected to the current block.
+
+public:
+    /**
+     * @param shape @copydoc ClusterLabeller::m_shape
+     */
+    template <class T>
+    ClusterLabeller(const T& shape)
+    {
+        if constexpr (Dim == 1) {
+            // kernel = {1, 1, 1}
+            m_dx = {-1, 1};
+        }
+        else if constexpr (Dim == 2) {
+            // kernel = {{0, 1, 0}, {1, 1, 1}, {0, 1, 0}};
+            m_dx = {{-1, 0}, {0, -1}, {0, 1}, {1, 0}};
+        }
+        this->init(shape);
+    }
+
+    /**
+     * @param shape @copydoc ClusterLabeller::m_shape
+     * @param kernel Kernel (e.g. GooseEYE::kernel::nearest()).
+     */
+    template <class T, class K>
+    ClusterLabeller(const T& shape, const K& kernel)
+    {
+        m_dx = detail::kernel_to_dx<Dim>(kernel);
+        this->init(shape);
+    }
+
+private:
+    template <class T>
+    void init(const T& shape)
+    {
+        static_assert(Dim == 1 || Dim == 2, "WIP: 1d and 2d supported.");
+        m_label = xt::empty<ptrdiff_t>(shape);
+        m_renum.resize(m_label.size() + 1);
+        m_next.resize(m_label.size() + 1);
+        for (size_t i = 0; i < Dim; ++i) {
+            m_shape[i] = static_cast<ptrdiff_t>(shape[i]);
+            m_strides[i] = static_cast<ptrdiff_t>(m_label.strides()[i]);
+        }
+        GOOSEEYE_ASSERT(m_strides.back() == 1, std::out_of_range);
+        this->reset();
+        m_connected.resize(m_dx.shape(0));
+    }
+
+public:
+    /**
+     * @brief Reset labels to zero.
+     */
+    void reset()
+    {
+        std::fill(m_label.begin(), m_label.end(), 0);
+        std::iota(m_renum.begin(), m_renum.end(), 0);
+        m_new_label = 1;
+        this->clean_next();
+    }
+
+    /**
+     * @brief Prune: renumber labels to lowest possible label, see also AvalancheSegmenter::nlabels.
+     * @note This might change all labels.
+     */
+    void prune()
+    {
+        ptrdiff_t n = static_cast<ptrdiff_t>(m_new_label);
+        m_new_label = 1;
+        m_renum[0] = 0;
+        for (ptrdiff_t i = 1; i < n; ++i) {
+            if (m_renum[i] == i) {
+                m_renum[i] = m_new_label;
+                ++m_new_label;
+            }
+        }
+        this->private_renumber(m_renum);
+        std::iota(m_renum.begin(), m_renum.begin() + n, 0);
+    }
+
+private:
+    /**
+     * @brief Clean linked list.
+     */
+    void clean_next()
+    {
+        std::fill(m_next.begin(), m_next.end(), -1);
+    }
+
+    /**
+     * @brief Apply renumbering without assertions.
+     * @param renum List of new label for each label in used.
+     */
+    template <class T>
+    void private_renumber(const T& renum)
+    {
+        for (size_t i = 0; i < m_label.size(); ++i) {
+            m_label.flat(i) = renum[m_label.flat(i)];
+        }
+    }
+
+    /**
+     * @brief Link `b` to `head[a]`.
+     * @details
+     *      -   `head[list(b)] = head[a]`
+     *      -   `list(head[a]).append(list(b))`
+     * @param a Target label.
+     * @param b Label to merge into `a`.
+     */
+    void merge_detail(ptrdiff_t a, ptrdiff_t b)
+    {
+        // -> head[list(b)] = head[a]
+        ptrdiff_t i = m_renum[b];
+        ptrdiff_t target = m_renum[a];
+        m_renum[b] = target;
+        while (true) {
+            i = m_next[i];
+            if (i == -1) {
+                break;
+            }
+            m_renum[i] = target;
+        }
+        // -> list(head[a]).append(list(b))
+        while (m_next[a] != -1) {
+            a = m_next[a];
+        }
+        m_next[a] = b;
+    }
+
+    /**
+     * @brief Mark list of labels as merged.
+     * @note Link all labels to the lowest label in the list.
+     * @warning `m_labels` is not updated.
+     * @param labels List of labels to merge.
+     * @param nlabels Number of labels in the list.
+     */
+    ptrdiff_t merge(ptrdiff_t* labels, size_t nlabels)
+    {
+        std::sort(labels, labels + nlabels);
+        nlabels = std::unique(labels, labels + nlabels) - labels;
+        ptrdiff_t target = labels[0];
+        for (size_t i = 1; i < nlabels; ++i) {
+            this->merge_detail(target, labels[i]);
+        }
+        return target;
+    }
+
+    void apply_merge()
+    {
+        if (m_nmerge == 0) {
+            return;
+        }
+
+        this->private_renumber(m_renum);
+        this->clean_next();
+        m_nmerge = 0;
+    }
+
+    void label_impl(size_t idx)
+    {
+        static_assert(Dim == 1 || Dim == 2, "WIP: 1d and 2d supported.");
+
+        ptrdiff_t compare;
+        size_t nconnected = 0;
+
+        for (size_t j = 0; j < m_dx.shape(0); ++j) {
+            if constexpr (Dim == 1 && Periodic) {
+                ptrdiff_t nn = m_shape[0];
+                ptrdiff_t ii = (nn + idx + m_dx(j)) % nn; // index corrected for periodicity
+                compare = ii;
+            }
+            else if constexpr (Dim == 1 && !Periodic) {
+                ptrdiff_t nn = m_shape[0];
+                ptrdiff_t ii = idx + m_dx(j);
+                if (ii < 0 || ii >= nn) {
+                    continue;
+                }
+                compare = ii;
+            }
+            else if constexpr (Dim == 2 && Periodic) {
+                detail::unravel_index(idx, m_strides, m_index);
+                ptrdiff_t nn = m_shape[0];
+                ptrdiff_t mm = m_shape[1];
+                ptrdiff_t ii = (nn + m_index[0] + m_dx(j, 0)) % nn;
+                ptrdiff_t jj = (mm + m_index[1] + m_dx(j, 1)) % mm;
+                compare = ii * mm + jj;
+            }
+            else if constexpr (Dim == 2 && !Periodic) {
+                detail::unravel_index(idx, m_strides, m_index);
+                ptrdiff_t nn = m_shape[0];
+                ptrdiff_t mm = m_shape[1];
+                ptrdiff_t ii = m_index[0] + m_dx(j, 0);
+                ptrdiff_t jj = m_index[1] + m_dx(j, 1);
+                if (ii < 0 || ii >= nn || jj < 0 || jj >= mm) {
+                    continue;
+                }
+                compare = ii * mm + jj;
+            }
+
+            if (m_label.flat(compare) != 0) {
+                m_connected[nconnected] = m_renum[m_label.flat(compare)];
+                nconnected++;
+            }
+        }
+
+        if (nconnected == 0) {
+            m_label.flat(idx) = m_new_label;
+            m_new_label += 1;
+            return;
+        }
+
+        if (nconnected == 1) {
+            m_label.flat(idx) = m_connected[0];
+            return;
+        }
+
+        // mark all labels in the list for merging
+        // `m_label` is not yet updated to avoid looping over all blocks too frequently
+        // the new label can be read by `m_renum[lab]` (as done above)
+        m_label.flat(idx) = this->merge(&m_connected[0], nconnected);
+        m_nmerge++;
+
+        // every so often: apply the renumbering to `m_label`
+        // the linked labels in `m_next` can be released
+        if (m_nmerge > 100) {
+            this->apply_merge();
+        }
+    }
+
+public:
+    /**
+     * @brief Add image. Previous labels are not overwritten.
+     * @param img Image (can be incremental only).
+     */
+    template <class T>
+    void add_image(const T& img)
+    {
+        GOOSEEYE_ASSERT(xt::has_shape(img, m_label.shape()), std::out_of_range);
+
+        for (size_t idx = 0; idx < img.size(); ++idx) {
+            if (img.flat(idx) == 0) {
+                continue;
+            }
+            if (m_label.flat(idx) != 0) {
+                continue;
+            }
+            this->label_impl(idx);
+        }
+        this->apply_merge();
+    }
+
+    /**
+     * @brief Add sequence of points.
+     * @param begin Iterator to first point.
+     * @param end Iterator to last point.
+     */
+    template <class T>
+    void add_points(const T& begin, const T& end)
+    {
+#ifdef GOOSEEYE_ENABLE_ASSERT
+        size_t n = m_label.size();
+        GOOSEEYE_ASSERT(
+            !std::any_of(begin, end, [n](size_t i) { return i < 0 || i >= n; }), std::out_of_range);
+#endif
+
+        for (auto it = begin; it != end; ++it) {
+            if (m_label.flat(*it) != 0) {
+                continue;
+            }
+            this->label_impl(*it);
+        }
+        this->apply_merge();
+    }
+
+    /**
+     * @brief Add sequence of points.
+     * @param idx List of points.
+     */
+    template <class T>
+    void add_points(const T& idx)
+    {
+        GOOSEEYE_ASSERT(idx.dimension() == 1, std::out_of_range);
+        return this->add_points(idx.begin(), idx.end());
+    }
+
+    /**
+     * @brief Basic class info.
+     * @return std::string
+     */
+    std::string repr() const
+    {
+        return detail::get_namespace() + "ClusterLabeller" + std::to_string(Dim) + " " +
+               detail::shape_to_string(m_shape);
+    }
+
+    /**
+     * @brief Shape of ClusterLabeller::s and ClusterLabeller::labels.
+     * @return Shape
+     */
+    const auto& shape() const
+    {
+        return m_label.shape();
+    }
+
+    /**
+     * @brief Size of ClusterLabeller::s and ClusterLabeller::labels (`== prod(shape)`).
+     * @return Size
+     */
+    auto size() const
+    {
+        return m_label.size();
+    }
+
+    // todo: allow resetting a cluster map ?
+
+    /**
+     * @brief @copydoc ClusterLabeller::m_label
+     * @return array of signed integers.
+     */
+    const auto& labels() const
+    {
+        return m_label;
+    }
+};
+
 /**
  * Compute clusters and obtain certain characteristic about them.
  */
-class Clusters {
+class [[deprecated]] Clusters {
 public:
     Clusters() = default;
 
@@ -246,12 +825,15 @@ class Clusters {
     template <class T, class S>
     Clusters(const T& f, const S& kernel, bool periodic = true) : m_periodic(periodic)
     {
+        GOOSEEYE_WARNING_PYTHON("Clusters is deprecated, use ClusterLabeller (new API) instead "
+                                "(please open a PR for missing functions)");
+
         static_assert(std::is_integral<typename T::value_type>::value, "Integral labels required.");
         static_assert(std::is_integral<typename S::value_type>::value, "Integral kernel required.");
 
-        GOOSEEYE_ASSERT(xt::all(xt::equal(f, 0) || xt::equal(f, 1)));
-        GOOSEEYE_ASSERT(xt::all(xt::equal(kernel, 0) || xt::equal(kernel, 1)));
-        GOOSEEYE_ASSERT(f.dimension() == kernel.dimension());
+        GOOSEEYE_ASSERT(xt::all(xt::equal(f, 0) || xt::equal(f, 1)), std::out_of_range);
+        GOOSEEYE_ASSERT(xt::all(xt::equal(kernel, 0) || xt::equal(kernel, 1)), std::out_of_range);
+        GOOSEEYE_ASSERT(f.dimension() == kernel.dimension(), std::out_of_range);
 
         m_shape = detail::shape(f);
         m_kernel = xt::atleast_3d(kernel);
@@ -330,8 +912,9 @@ class Clusters {
      * Return size per cluster
      * @return List.
      */
-    array_type::tensor<size_t, 1> sizes() const
+    [[deprecated]] array_type::tensor<size_t, 1> sizes() const
     {
+        GOOSEEYE_WARNING_PYTHON("Clusters.sizes() is deprecated, use labels_sizes() (new API)");
         array_type::tensor<size_t, 1> ret = xt::zeros<size_t>({xt::amax(m_l)() + size_t(1)});
 
         for (size_t h = 0; h < m_l.shape(0); ++h) {
@@ -526,14 +1109,50 @@ class Clusters {
     array_type::tensor<int, 3> m_l_np; // labels before applying periodicity
 };
 
+namespace detail {
+
+template <size_t Dimension, bool Periodicity>
+class ClusterLabellerOverload : public ClusterLabeller<Dimension, Periodicity> {
+public:
+    template <class T>
+    ClusterLabellerOverload(const T& img) : ClusterLabeller<Dimension, Periodicity>(img.shape())
+    {
+        this->add_image(img);
+        this->prune();
+    }
+
+    auto get() const
+    {
+        return this->labels();
+    }
+};
+
+} // namespace detail
+
 /**
- * Compute clusters. Wraps GooseEYE::Clusters::labels().
+ * @brief Compute clusters.
  * @param f Image.
  * @param periodic Interpret image as periodic.
+ * @return 'Image' with labels (1..n) for labels, 0 for background.
  */
 template <class T>
 array_type::array<int> clusters(const T& f, bool periodic = true)
 {
+    auto n = f.dimension();
+    if (n == 1 && periodic) {
+        return detail::ClusterLabellerOverload<1, true>(f).get();
+    }
+    if (n == 1 && !periodic) {
+        return detail::ClusterLabellerOverload<1, false>(f).get();
+    }
+    if (n == 2 && periodic) {
+        return detail::ClusterLabellerOverload<2, true>(f).get();
+    }
+    if (n == 2 && !periodic) {
+        return detail::ClusterLabellerOverload<2, false>(f).get();
+    }
+
+    GOOSEEYE_WARNING("WIP: updated 3d implementation needs to be completed. Please file a PR.");
     return Clusters(f, kernel::nearest(f.dimension()), periodic).labels();
 }
 
@@ -547,11 +1166,11 @@ array_type::array<int> clusters(const T& f, bool periodic = true)
 template <typename T, typename U, typename V>
 inline T pos2img(const T& img, const U& positions, const V& labels)
 {
-    GOOSEEYE_ASSERT(img.dimension() > 0);
-    GOOSEEYE_ASSERT(img.dimension() <= 3);
-    GOOSEEYE_ASSERT(img.dimension() == positions.shape(1));
-    GOOSEEYE_ASSERT(positions.shape(0) == labels.size());
-    GOOSEEYE_ASSERT(labels.dimension() == 1);
+    GOOSEEYE_ASSERT(img.dimension() > 0, std::out_of_range);
+    GOOSEEYE_ASSERT(img.dimension() <= 3, std::out_of_range);
+    GOOSEEYE_ASSERT(img.dimension() == positions.shape(1), std::out_of_range);
+    GOOSEEYE_ASSERT(positions.shape(0) == labels.size(), std::out_of_range);
+    GOOSEEYE_ASSERT(labels.dimension() == 1, std::out_of_range);
 
     using value_type = typename T::value_type;
     T res = img;
@@ -590,8 +1209,8 @@ array_type::tensor<double, 2> center_of_mass(const T& labels, bool periodic = tr
 {
     static_assert(std::is_integral<typename T::value_type>::value, "Integral labels required.");
 
-    GOOSEEYE_ASSERT(labels.dimension() > 0);
-    GOOSEEYE_ASSERT(xt::all(labels >= 0));
+    GOOSEEYE_ASSERT(labels.dimension() > 0, std::out_of_range);
+    GOOSEEYE_ASSERT(xt::all(labels >= 0), std::out_of_range);
 
     double pi = xt::numeric_constants<double>::PI;
     size_t N = static_cast<size_t>(xt::amax(labels)(0)) + 1ul;
diff --git a/include/GooseEYE/config.h b/include/GooseEYE/config.h
index 133cb9b6..2e7e42b5 100644
--- a/include/GooseEYE/config.h
+++ b/include/GooseEYE/config.h
@@ -4,8 +4,8 @@
  * @license This project is released under the GPLv3 License.
  */
 
-#ifndef GOOSEEYE_INCLUDE_H
-#define GOOSEEYE_INCLUDE_H
+#ifndef GOOSEEYE_CONFIG_H
+#define GOOSEEYE_CONFIG_H
 
 /**
  * @cond
@@ -18,9 +18,11 @@
 #include <algorithm>
 #include <assert.h>
 #include <cstdlib>
+#include <exception>
 #include <iomanip>
 #include <iostream>
 #include <limits>
+#include <map>
 #include <math.h>
 #include <memory>
 #include <numeric>
@@ -46,9 +48,9 @@
     std::cout << std::string(file) + ":" + std::to_string(line) + " (" + std::string(function) + \
                      ")" + ": " message ") \n\t";
 
-#define GOOSEEYE_ASSERT_IMPL(expr, file, line, function) \
+#define GOOSEEYE_ASSERT_IMPL(expr, assertion, file, line, function) \
     if (!(expr)) { \
-        throw std::runtime_error( \
+        throw assertion( \
             std::string(file) + ":" + std::to_string(line) + " (" + std::string(function) + ")" + \
             ": assertion failed (" #expr ") \n\t"); \
     }
@@ -76,9 +78,41 @@
  * \throw std::runtime_error
  */
 #ifdef GOOSEEYE_ENABLE_ASSERT
-#define GOOSEEYE_ASSERT(expr) GOOSEEYE_ASSERT_IMPL(expr, __FILE__, __LINE__, __FUNCTION__)
+#define GOOSEEYE_ASSERT(expr, assertion) \
+    GOOSEEYE_ASSERT_IMPL(expr, assertion, __FILE__, __LINE__, __FUNCTION__)
 #else
-#define GOOSEEYE_ASSERT(expr)
+#define GOOSEEYE_ASSERT(expr, assertion)
+#endif
+
+/**
+ * Warnings are implemented as:
+ *
+ *      GOOSEEYE_WARNING(...)
+ *
+ * They can be disables by:
+ *
+ *      #define GOOSEEYE_DISABLE_WARNING
+ */
+#ifndef GOOSEEYE_DISABLE_WARNING
+#define GOOSEEYE_WARNING(message) GOOSEEYE_WARNING_IMPL(message, __FILE__, __LINE__, __FUNCTION__)
+#else
+#define GOOSEEYE_WARNING(message)
+#endif
+
+/**
+ * Warnings specific to the Python API are implemented as:
+ *
+ *      GOOSEEYE_WARNING_PYTHON(...)
+ *
+ * They can be enabled by:
+ *
+ *      #define GOOSEEYE_ENABLE_WARNING_PYTHON
+ */
+#ifdef GOOSEEYE_ENABLE_WARNING_PYTHON
+#define GOOSEEYE_WARNING_PYTHON(message) \
+    GOOSEEYE_WARNING_IMPL(message, __FILE__, __LINE__, __FUNCTION__)
+#else
+#define GOOSEEYE_WARNING_PYTHON(message)
 #endif
 
 /**
diff --git a/include/GooseEYE/dilate.hpp b/include/GooseEYE/dilate.hpp
index f7f3a4dd..774cc902 100644
--- a/include/GooseEYE/dilate.hpp
+++ b/include/GooseEYE/dilate.hpp
@@ -96,10 +96,10 @@ inline T
 dilate(const T& f, const S& kernel, const array_type::tensor<size_t, 1>& iterations, bool periodic)
 {
     using value_type = typename T::value_type;
-    GOOSEEYE_ASSERT(f.dimension() <= 3);
-    GOOSEEYE_ASSERT(f.dimension() == kernel.dimension());
-    GOOSEEYE_ASSERT(xt::all(xt::equal(kernel, 0) || xt::equal(kernel, 1)));
-    GOOSEEYE_ASSERT(static_cast<size_t>(xt::amax(f)()) <= iterations.size() + 1);
+    GOOSEEYE_ASSERT(f.dimension() <= 3, std::out_of_range);
+    GOOSEEYE_ASSERT(f.dimension() == kernel.dimension(), std::out_of_range);
+    GOOSEEYE_ASSERT(xt::all(xt::equal(kernel, 0) || xt::equal(kernel, 1)), std::out_of_range);
+    GOOSEEYE_ASSERT(static_cast<size_t>(xt::amax(f)()) <= iterations.size() + 1, std::out_of_range);
 
     xt::pad_mode pad_mode = xt::pad_mode::constant;
     int pad_value = 0;
diff --git a/include/GooseEYE/kernel.hpp b/include/GooseEYE/kernel.hpp
index 96359e08..7ded8b17 100644
--- a/include/GooseEYE/kernel.hpp
+++ b/include/GooseEYE/kernel.hpp
@@ -14,7 +14,7 @@ namespace kernel {
 
 inline array_type::array<int> nearest(size_t ndim)
 {
-    GOOSEEYE_ASSERT(ndim > 0 && ndim <= 3);
+    GOOSEEYE_ASSERT(ndim > 0 && ndim <= 3, std::out_of_range);
 
     std::vector<size_t> shape(ndim, 3);
 
diff --git a/python/GooseEYE/__init__.py b/python/GooseEYE/__init__.py
index 4072ceea..0bae7bc9 100644
--- a/python/GooseEYE/__init__.py
+++ b/python/GooseEYE/__init__.py
@@ -8,6 +8,24 @@
 from ._GooseEYE import *  # noqa: F401, F403
 
 
+def ClusterLabeller(shape, periodic=True, **kwargs):
+    """
+    Allocate a cluster labeller.
+    :param shape: The shape of the image.
+    :param periodic: Whether the image is periodic.
+    :return: A cluster labeller.
+    """
+    if len(shape) == 1:
+        if periodic:
+            return ClusterLabeller1p(shape, **kwargs)
+        return ClusterLabeller1(shape, **kwargs)
+    if len(shape) == 2:
+        if periodic:
+            return ClusterLabeller2p(shape, **kwargs)
+        return ClusterLabeller2(shape, **kwargs)
+    raise NotImplementedError("3d extension needed, please open a PR")
+
+
 class Structure(enstat.static):
     r"""
     Compute the ensemble average structure factor:
diff --git a/python/main.cpp b/python/main.cpp
index 006054f4..670da66d 100644
--- a/python/main.cpp
+++ b/python/main.cpp
@@ -16,6 +16,50 @@
 
 namespace py = pybind11;
 
+template <size_t First, size_t Last, typename Lambda>
+inline void static_for(Lambda const& f)
+{
+    if constexpr (First < Last) {
+        f(std::integral_constant<size_t, First>{});
+        static_for<First + 1, Last>(f);
+    }
+}
+
+template <class Class>
+void allocate_ClusterLabeller(py::module& mod)
+{
+    const size_t Dim = Class::Dim;
+    std::string name = "ClusterLabeller" + std::to_string(Dim);
+    if (Class::Periodic) {
+        name += "p";
+    }
+    py::class_<Class> cls(mod, name.c_str());
+    cls.def(py::init<const std::template array<size_t, Dim>&>(), name.c_str(), py::arg("shape"));
+    cls.def(
+        py::init<const std::template array<size_t, Dim>&, const xt::template pytensor<int, Dim>&>(),
+        name.c_str(),
+        py::arg("shape"),
+        py::arg("kernel"));
+
+    cls.def("__repr__", &Class::repr);
+    cls.def_property_readonly("shape", &Class::shape, "Shape of system.");
+    cls.def_property_readonly("size", &Class::shape, "Size of system.");
+    cls.def_property_readonly("labels", &Class::labels, "Cluster of each block.");
+    cls.def("prune", &Class::prune, "Prune: renumber to smallest index.");
+    cls.def("reset", &Class::reset, "Reset labels to zero.");
+    cls.def(
+        "add_image",
+        &Class::template add_image<xt::pytensor<int, Dim>>,
+        "Add image",
+        py::arg("img"));
+
+    cls.def(
+        "add_points",
+        static_cast<void (Class::*)(const xt::pytensor<size_t, 1>&)>(&Class::add_points),
+        "Add points",
+        py::arg("idx"));
+}
+
 PYBIND11_MODULE(_GooseEYE, m)
 {
     xt::import_numpy();
@@ -99,6 +143,11 @@ PYBIND11_MODULE(_GooseEYE, m)
         py::arg("iterations") = 1,
         py::arg("periodic") = true);
 
+    static_for<1, 3>(
+        [&](auto i) { allocate_ClusterLabeller<GooseEYE::ClusterLabeller<i, true>>(m); });
+    static_for<1, 3>(
+        [&](auto i) { allocate_ClusterLabeller<GooseEYE::ClusterLabeller<i, false>>(m); });
+
     py::class_<GooseEYE::Clusters>(m, "Clusters")
 
         .def(
@@ -130,6 +179,22 @@ PYBIND11_MODULE(_GooseEYE, m)
         py::arg("f"),
         py::arg("periodic") = true);
 
+    m.def("labels_map", &GooseEYE::labels_map<xt::pyarray<int>>, py::arg("a"), py::arg("b"));
+
+    m.def(
+        "labels_rename",
+        &GooseEYE::labels_rename<xt::pyarray<int>, xt::xtensor<int, 2>>,
+        py::arg("labels"),
+        py::arg("rename"));
+
+    m.def(
+        "labels_reorder",
+        &GooseEYE::labels_reorder<xt::pyarray<int>, xt::xtensor<int, 1>>,
+        py::arg("labels"),
+        py::arg("order"));
+
+    m.def("labels_sizes", &GooseEYE::labels_sizes<xt::pyarray<int>>, py::arg("labels"));
+
     m.def(
         "relabel_map",
         &GooseEYE::relabel_map<xt::pyarray<int>, xt::pyarray<int>>,
diff --git a/tests/miscellaneous.cpp b/tests/miscellaneous.cpp
deleted file mode 100644
index f093645e..00000000
--- a/tests/miscellaneous.cpp
+++ /dev/null
@@ -1,33 +0,0 @@
-#include <GooseEYE/GooseEYE.h>
-#include <catch2/catch_all.hpp>
-
-TEST_CASE("GooseFEM::Clusters", "clusters.hpp")
-{
-
-    SECTION("relabel_map")
-    {
-        xt::xarray<size_t> a = xt::zeros<size_t>({5, 5});
-        xt::view(a, xt::range(0, 2), xt::range(0, 2)) = 1;
-        xt::view(a, xt::range(3, 4), xt::range(3, 4)) = 2;
-
-        xt::xarray<size_t> b = xt::zeros<size_t>({5, 5});
-        xt::view(b, xt::range(0, 2), xt::range(0, 2)) = 3;
-        xt::view(b, xt::range(3, 4), xt::range(3, 4)) = 4;
-
-        REQUIRE(xt::all(xt::equal(GooseEYE::relabel_map(a, b), xt::xtensor<size_t, 1>{0, 3, 4})));
-        REQUIRE(
-            xt::all(xt::equal(GooseEYE::relabel_map(b, a), xt::xtensor<size_t, 1>{0, 0, 0, 1, 2})));
-    }
-
-    SECTION("path")
-    {
-        xt::xtensor<int, 2> path = {{0, 0}, {0, 1}, {0, 2}};
-        REQUIRE(xt::all(xt::equal(GooseEYE::path({0, 0}, {0, 2}), path)));
-        REQUIRE(xt::all(
-            xt::equal(GooseEYE::path({0, 0}, {0, 2}, GooseEYE::path_mode::Bresenham), path)));
-        REQUIRE(
-            xt::all(xt::equal(GooseEYE::path({0, 0}, {0, 2}, GooseEYE::path_mode::actual), path)));
-        REQUIRE(
-            xt::all(xt::equal(GooseEYE::path({0, 0}, {0, 2}, GooseEYE::path_mode::full), path)));
-    }
-}
diff --git a/tests/test_cluster.py b/tests/test_cluster.py
deleted file mode 100644
index 09737a0e..00000000
--- a/tests/test_cluster.py
+++ /dev/null
@@ -1,20 +0,0 @@
-import GooseEYE as eye
-import numpy as np
-
-
-def test_relabel_map():
-    a = np.array(
-        [
-            [1, 1, 0, 0, 1],
-            [1, 0, 0, 0, 0],
-            [0, 0, 3, 3, 0],
-            [0, 0, 3, 0, 0],
-            [1, 0, 0, 0, 1],
-        ]
-    )
-
-    b = a.copy()
-    b = np.where(b == 1, 4, b)
-    b = np.where(b == 3, 7, b)
-
-    assert list(eye.relabel_map(a, b)) == [0, 4, 0, 7]
diff --git a/tests/test_clusters.py b/tests/test_clusters.py
new file mode 100644
index 00000000..d35861bc
--- /dev/null
+++ b/tests/test_clusters.py
@@ -0,0 +1,281 @@
+import faulthandler
+
+import GooseEYE as eye
+import numpy as np
+import pytest
+import scipy.ndimage
+
+faulthandler.enable()
+
+
+def test_init():
+    s = np.zeros([4, 4], dtype=int)
+    assert np.all(eye.clusters(s) == s)
+
+
+def test_labels_map():
+    a = np.array([[1, 1, 0, 0], [0, 0, 3, 3], [2, 2, 0, 0], [0, 0, 4, 4]])
+    b = np.array([[-3, -3, 0, 0], [0, 0, 1, 1], [5, 5, 0, 0], [0, 0, 7, 7]])
+    lmap = np.array([[0, 0], [1, -3], [2, 5], [3, 1], [4, 7]])
+    imap = lmap[:, [1, 0]]
+    imap = imap[np.argsort(imap[:, 0])]
+    assert np.all(np.equal(eye.labels_map(a, b), lmap))
+    assert np.all(np.equal(eye.labels_map(b, a), imap))
+    assert np.all(np.equal(eye.labels_rename(a, lmap), b))
+    assert np.all(np.equal(eye.labels_rename(b, imap), a))
+
+    a = np.array(
+        [
+            [1, 1, 0, 0, 1],
+            [1, 0, 0, 0, 0],
+            [0, 0, 3, 3, 0],
+            [0, 0, 3, 0, 0],
+            [1, 0, 0, 0, 1],
+        ]
+    )
+    lmap = [[0, 0], [1, 4], [3, 7]]
+
+    b = a.copy()
+    for i, j in lmap:
+        b = np.where(b == i, j, b)
+
+    assert np.all(np.equal(eye.labels_map(a, b), lmap))
+
+
+def test_clusters_simple():
+    tests = [
+        [
+            np.array([[1, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]),
+            np.array([[1, 1, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]),
+            np.array([[1, 1, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]),
+            np.array([[1, 1, 0, 0], [1, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]),
+            np.array([[1, 1, 0, 0], [1, 0, 0, 0], [0, 0, 2, 0], [0, 0, 0, 0]]),
+            np.array([[1, 1, 0, 0], [1, 0, 0, 0], [0, 0, 2, 2], [0, 0, 0, 0]]),
+            np.array([[1, 1, 0, 0], [1, 0, 2, 0], [0, 0, 2, 2], [0, 0, 0, 0]]),
+            np.array([[1, 1, 0, 0], [1, 1, 1, 0], [0, 0, 1, 1], [0, 0, 0, 0]]),
+            np.array([[1, 1, 0, 0], [1, 1, 1, 0], [0, 0, 1, 1], [0, 0, 0, 0]]),
+            np.array([[1, 1, 0, 0], [1, 1, 1, 0], [0, 0, 1, 1], [0, 0, 0, 1]]),
+            np.array([[1, 1, 0, 0], [1, 1, 1, 0], [0, 0, 1, 1], [0, 0, 1, 1]]),
+        ],
+        [
+            np.array([[1, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]),
+            np.array([[1, 1, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]),
+            np.array([[1, 1, 0, 0], [0, 0, 0, 0], [2, 0, 0, 0], [0, 0, 0, 0]]),
+            np.array([[1, 1, 0, 0], [0, 0, 0, 0], [2, 2, 0, 0], [0, 0, 0, 0]]),
+            np.array([[1, 1, 0, 0], [0, 0, 3, 0], [2, 2, 0, 0], [0, 0, 0, 0]]),
+            np.array([[1, 1, 0, 0], [0, 0, 3, 3], [2, 2, 0, 0], [0, 0, 0, 0]]),
+            np.array([[1, 1, 0, 0], [0, 0, 3, 3], [2, 2, 0, 0], [0, 0, 4, 0]]),
+            np.array([[1, 1, 0, 0], [0, 0, 3, 3], [2, 2, 0, 0], [0, 0, 4, 4]]),
+            np.array([[1, 1, 0, 1], [0, 0, 1, 1], [2, 2, 0, 0], [0, 0, 1, 1]]),
+            np.array([[1, 1, 0, 1], [1, 0, 1, 1], [1, 1, 0, 0], [0, 0, 1, 1]]),
+        ],
+        [
+            np.array([[1, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]),
+            np.array([[1, 0, 2, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]),
+            np.array([[1, 0, 2, 0], [0, 3, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]),
+            np.array([[1, 0, 2, 0], [0, 3, 0, 4], [0, 0, 0, 0], [0, 0, 0, 0]]),
+            np.array([[1, 0, 2, 0], [0, 3, 0, 4], [5, 0, 0, 0], [0, 0, 0, 0]]),
+            np.array([[1, 0, 2, 0], [0, 3, 0, 4], [5, 0, 6, 0], [0, 0, 0, 0]]),
+            np.array([[1, 0, 2, 0], [1, 1, 0, 1], [1, 0, 6, 0], [0, 0, 0, 0]]),
+            np.array([[1, 0, 1, 0], [1, 1, 1, 1], [1, 0, 1, 0], [0, 0, 0, 0]]),
+        ],
+    ]
+
+    for test in [i for test in tests for i in test]:
+        labels = eye.clusters(np.where(test > 0, 1, 0), periodic=True)
+        assert np.all(np.equal(labels, eye.labels_rename(test, eye.labels_map(test, labels))))
+
+    segmenter = eye.ClusterLabeller(shape=(4, 4))
+    for test in tests:
+        segmenter.reset()
+        for i in test:
+            segmenter.add_image(np.where(i > 0, 1, 0))
+            assert np.all(np.equal(segmenter.labels, i))
+
+
+def test_clusters_simple2():
+    img = np.array(
+        [
+            [1, 0, 0, 1, 1],
+            [0, 0, 0, 0, 0],
+            [0, 0, 0, 0, 0],
+            [1, 0, 0, 1, 1],
+            [0, 0, 0, 0, 0],
+        ]
+    )
+    labels = np.array(
+        [
+            [1, 0, 0, 1, 1],
+            [0, 0, 0, 0, 0],
+            [0, 0, 0, 0, 0],
+            [2, 0, 0, 2, 2],
+            [0, 0, 0, 0, 0],
+        ]
+    )
+    assert np.all(np.equal(eye.clusters(img), labels))
+
+
+def test_clusters_simple3():
+    labels = np.array(
+        [
+            [0, 0, 0, 0, 0, 0, 1, 0, 0, 0],
+            [0, 1, 0, 0, 1, 1, 1, 0, 2, 0],
+            [0, 1, 1, 1, 0, 1, 1, 0, 2, 2],
+            [0, 1, 1, 1, 1, 1, 1, 0, 0, 2],
+            [0, 1, 1, 1, 0, 1, 1, 0, 0, 0],
+            [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        ]
+    )
+    assert np.all(np.equal(eye.clusters(np.where(labels > 0, 1, 0)), labels))
+
+
+def test_clusters_scipy():
+    img = eye.dummy_circles((500, 500), periodic=False)
+    clusters = eye.clusters(img, periodic=False)
+    scp, num = scipy.ndimage.label(img)
+    assert np.unique(clusters).size == num + 1
+    lmap = eye.labels_map(scp, clusters)
+    scp = eye.labels_rename(scp, lmap)
+    assert np.all(np.equal(scp, clusters))
+
+
+def test_labels_reorder():
+    labels = np.array([[1, 0, 2, 0], [0, 0, 0, 0], [3, 0, 4, 0], [0, 0, 0, 0]])
+    assert np.all(np.equal(eye.clusters(np.where(labels > 0, 1, 0)), labels))
+
+    labels = eye.labels_reorder(labels, [0, 4, 1, 2, 3])
+    assert np.all(
+        np.equal(labels, np.array([[2, 0, 3, 0], [0, 0, 0, 0], [4, 0, 1, 0], [0, 0, 0, 0]]))
+    )
+
+    with pytest.raises(IndexError):
+        eye.labels_reorder(labels, [0, 4, 1, 2])
+
+
+def test_labels_rename():
+    labels = np.array([[1, 0, 2, 0], [0, 0, 0, 0], [3, 0, 4, 0], [0, 0, 0, 0]])
+    assert np.all(np.equal(eye.clusters(np.where(labels > 0, 1, 0)), labels))
+
+    rename = [[0, 0], [1, 4], [2, 1], [3, 2], [4, 3]]
+    labels = eye.labels_rename(labels, rename)
+    assert np.all(
+        np.equal(labels, np.array([[4, 0, 1, 0], [0, 0, 0, 0], [2, 0, 3, 0], [0, 0, 0, 0]]))
+    )
+
+    with pytest.raises(IndexError):
+        eye.labels_rename(labels, [[0, 0], [3, 2]])
+
+
+def test_labels_rename2():
+    segmenter = eye.ClusterLabeller(shape=(4, 4))
+    segmenter.add_points([0, 2, 8, 10])
+    lab = segmenter.labels
+    assert np.all(np.equal(lab, np.array([[1, 0, 2, 0], [0, 0, 0, 0], [3, 0, 4, 0], [0, 0, 0, 0]])))
+
+    rename = [[0, 0], [1, 6], [2, 1], [3, 2], [4, 3]]
+    lab = eye.labels_rename(lab, rename)
+    assert np.all(np.equal(lab, np.array([[6, 0, 1, 0], [0, 0, 0, 0], [2, 0, 3, 0], [0, 0, 0, 0]])))
+
+
+def test_labels_sizes():
+    labels = np.array([[1, 1, 0, 0], [1, 0, 0, 0], [0, 0, 2, 0], [0, 0, 0, 0]])
+    assert np.all(
+        np.equal(
+            eye.labels_sizes(labels),
+            [[i, s] for i, s in zip(*np.unique(labels, return_counts=True))],
+        )
+    )
+
+
+def test_prune():
+    segmenter = eye.ClusterLabeller(shape=(4, 4))
+    segmenter.add_points([0, 2, 8, 10, 1])
+    lab = segmenter.labels
+    assert np.all(np.equal(lab, np.array([[1, 1, 1, 0], [0, 0, 0, 0], [3, 0, 4, 0], [0, 0, 0, 0]])))
+    segmenter.prune()
+    lab = segmenter.labels
+    assert np.all(np.equal(lab, np.array([[1, 1, 1, 0], [0, 0, 0, 0], [2, 0, 3, 0], [0, 0, 0, 0]])))
+
+
+def test_bug_a():
+    shape = (10, 10)
+    actions = [
+        dict(idx=19, label=1),
+        dict(idx=5, label=2),
+        dict(idx=10, label=1),
+        dict(idx=69, label=3),
+        dict(idx=68, label=3),
+        dict(idx=24, label=4),
+        dict(idx=22, label=5),
+        dict(idx=87, label=6),
+        dict(idx=47, label=7),
+        dict(idx=8, label=8),
+        dict(idx=35, label=9),
+        dict(idx=7, label=8),
+        dict(idx=66, label=10),
+        dict(idx=92, label=11),
+        dict(idx=56, label=10),
+        dict(idx=27, label=12),
+        dict(idx=46, label=10, merge=[7, 10]),
+        dict(idx=55, label=7),
+        dict(idx=27, label=12),
+        dict(idx=37, label=7, merge=[7, 12]),
+        dict(idx=45, label=7, merge=[7, 9]),
+        dict(idx=9, label=1, merge=[1, 8]),
+        dict(idx=46, label=7),
+        dict(idx=59, label=3),
+        dict(idx=58, label=3),
+        dict(idx=48, label=3, merge=[3, 7]),
+        dict(idx=67, label=3),
+        dict(idx=36, label=3),
+        dict(idx=26, label=3),
+        dict(idx=97, label=1, merge=[1, 6]),
+        dict(idx=38, label=3),
+        dict(idx=17, label=1, merge=[1, 3]),
+        dict(idx=98, label=1),
+        dict(idx=49, label=1),
+        dict(idx=12, label=5),
+        dict(idx=99, label=1),
+        dict(idx=8, label=1),
+        dict(idx=6, label=1, merge=[1, 2]),
+        dict(idx=2, label=5, merge=[5, 11]),
+        dict(idx=94, label=13),
+        dict(idx=57, label=1),
+        dict(idx=3, label=5),
+        dict(idx=16, label=1),
+        dict(idx=0, label=1),
+        dict(idx=18, label=1),
+        dict(idx=1, label=1, merge=[1, 5]),
+        dict(idx=25, label=1, merge=[1, 4]),
+        dict(idx=64, label=14),
+        dict(idx=91, label=1),
+        dict(idx=4, label=1, merge=[1, 13]),
+        dict(idx=13, label=1),
+        dict(idx=84, label=1),
+        dict(idx=44, label=1),
+        dict(idx=65, label=1, merge=[1, 14]),
+    ]
+
+    idx = [action["idx"] for action in actions]
+    sizes = [np.zeros(shape, dtype=int) for _ in range(len(actions))]
+    labels = [np.zeros(shape, dtype=int) for _ in range(len(actions))]
+
+    for i, action in enumerate(actions):
+        if i > 0:
+            sizes[i] = sizes[i - 1].copy()
+            labels[i] = labels[i - 1].copy()
+        sizes[i].flat[action["idx"]] += 1
+        labels[i].flat[action["idx"]] = action["label"]
+        if "merge" in action:
+            for label in action["merge"][1:]:
+                labels[i] = np.where(labels[i] == label, action["merge"][0], labels[i])
+
+    n = len(idx)
+    for steps in range(1, n):
+        segmenter = eye.ClusterLabeller(shape=shape)
+        start = 0
+        for i in range(n // steps + 1):
+            step = min((i + 1) * steps, n)
+            segmenter.add_points(idx[start:step])
+            start = step
+            assert np.all(segmenter.labels == labels[step - 1])
diff --git a/tests/test_clusters_example.py b/tests/test_clusters_example.py
new file mode 100644
index 00000000..4af8925b
--- /dev/null
+++ b/tests/test_clusters_example.py
@@ -0,0 +1,266 @@
+import GooseEYE as eye
+import numpy as np
+
+img = np.array(
+    [
+        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+        [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],
+        [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],
+        [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],
+        [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],
+        [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1],
+        [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1],
+        [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1],
+        [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1],
+        [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1],
+        [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1],
+        [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1],
+        [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1],
+        [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],
+        [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],
+        [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],
+        [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],
+    ]
+)
+
+labels = np.array(
+    [
+        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2],
+        [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2],
+        [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2],
+        [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2],
+        [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2],
+        [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2],
+        [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2],
+        [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2],
+        [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2],
+        [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5],
+        [4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5],
+        [4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5],
+        [4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5],
+        [4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5],
+        [4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5],
+        [4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5],
+        [4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5],
+    ]
+)
+
+labels_periodic = np.array(
+    [
+        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+        [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],
+        [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],
+        [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],
+        [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],
+        [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1],
+        [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1],
+        [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1],
+        [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1],
+        [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1],
+        [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1],
+        [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1],
+        [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1],
+        [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],
+        [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],
+        [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],
+        [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],
+    ]
+)
+
+centers = np.array(
+    [
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+    ]
+)
+
+centers_periodic = np.array(
+    [
+        [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+    ]
+)
+
+labels_dilate = np.array(
+    [
+        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],
+        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2],
+        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2],
+        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2],
+        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2],
+        [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2],
+        [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2],
+        [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2],
+        [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2],
+        [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2],
+        [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5],
+        [4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5],
+        [4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5],
+        [4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5],
+        [4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5],
+        [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5],
+        [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5],
+        [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5],
+        [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5],
+    ]
+)
+
+labels_periodic_dilate = np.array(
+    [
+        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+        [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],
+        [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1],
+        [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1],
+        [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1],
+        [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1],
+        [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1],
+        [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1],
+        [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1],
+        [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1],
+        [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],
+        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+    ]
+)
+
+
+def test_main():
+    assert np.all(np.equal(eye.dummy_circles([30, 30], [0, 15], [0, 15], [10, 5]), img))
+
+    assert np.all(np.equal(eye.clusters(img, periodic=False), labels))
+
+    test = eye.clusters(img, periodic=True)
+    assert np.all(
+        np.equal(labels_periodic, eye.labels_rename(test, eye.labels_map(test, labels_periodic)))
+    )
+
+    assert np.all(np.equal(eye.dilate(labels, iterations=1, periodic=False), labels_dilate))
+    assert np.all(
+        np.equal(eye.dilate(labels_periodic, iterations=1, periodic=True), labels_periodic_dilate)
+    )
+
+    assert np.all(np.equal(eye.Clusters(img, periodic=False).centers(), centers))
+    assert np.all(np.equal(eye.Clusters(img, periodic=True).centers(), centers_periodic))
diff --git a/tests/test_historic.py b/tests/test_historic.py
index 9ec7c919..ce46c408 100644
--- a/tests/test_historic.py
+++ b/tests/test_historic.py
@@ -156,3 +156,6 @@ def test_pixel_path():
         [[0, 0], [-1, 0], [-1, 1], [-2, 1], [-3, 1], [-3, 2], [-4, 2], [-5, 2], [-5, 3], [-6, 3]]
     )
     assert np.all(np.equal(eye.path([0, 0], [-6, 3], eye.path_mode.full), path))
+
+    for mode in [eye.path_mode.Bresenham, eye.path_mode.full, eye.path_mode.actual]:
+        assert np.all(np.equal(eye.path([0, 0], [0, 2], mode), [[0, 0], [0, 1], [0, 2]]))