diff --git a/Multiwell_MIC_22-June-2021_0-10ng_5x-napari.ipynb b/Multiwell_MIC_22-June-2021_0-10ng_5x-napari.ipynb
index 1015daef768c56f6cccecb82da4e8923b061c657..4ebb6eaa627362945f10a3e136e26166b088b99c 100644
--- a/Multiwell_MIC_22-June-2021_0-10ng_5x-napari.ipynb
+++ b/Multiwell_MIC_22-June-2021_0-10ng_5x-napari.ipynb
@@ -2,15 +2,19 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n"
+      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/napari/_qt/__init__.py:54: UserWarning: \n",
+      "\n",
+      "napari was tested with QT library `>=5.12.3`.\n",
+      "The version installed is 5.9.7. Please report any issues with\n",
+      "this specific QT version at https://github.com/Napari/napari/issues.\n",
+      "  warn(message=warn_message)\n"
      ]
     }
    ],
@@ -45,7 +49,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -54,7 +58,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -175,37 +179,59 @@
     "    if progress is not None:\n",
     "        progress.increment()\n",
     "        progress.tooltip = 'counting cells'\n",
-    "        \n",
-    "    counts = mic.get_cell_numbers(\n",
-    "        fluo_aligned, \n",
-    "        mask_aligned, \n",
-    "        threshold_abs=2,\n",
-    "        plot=False,\n",
-    "        meta={'ng': c}\n",
-    "    )\n",
-    "    l = poisson.fit(counts.query('n_cells < 10').n_cells, title=f'{c} ng')\n",
-    "    counts.loc[:, 'poisson'] = l\n",
-    "    counts.to_csv(path.replace('tif', 'counts.csv'), index=None)\n",
-    "    if progress is not None:\n",
-    "        progress.increment()\n",
-    "        progress.tooltip = 'save table'\n",
-    "        progress.label += ': DONE'\n",
-    "    # return table\n",
-    "    return counts\n"
+    "    \n",
+    "    peaks = count.peak_local_max_labels((f := fluo_aligned), label=mask_aligned, threshold_abs=f.mean() + 5* f.std())\n",
+    "#     counts = mic.get_cell_numbers(\n",
+    "#         fluo_aligned, \n",
+    "#         mask_aligned, \n",
+    "#         threshold_abs=2,\n",
+    "#         plot=False,\n",
+    "#         meta={'ng': c}\n",
+    "#     )\n",
+    "#     l = poisson.fit(counts.query('n_cells < 10').n_cells, title=f'{c} ng')\n",
+    "    return peaks\n",
+    "\n",
+    "#     counts.loc[:, 'poisson'] = l\n",
+    "#     counts.to_csv(path.replace('tif', 'counts.csv'), index=None)\n",
+    "#     if progress is not None:\n",
+    "#         progress.increment()\n",
+    "#         progress.tooltip = 'save table'\n",
+    "#         progress.label += ': DONE'\n",
+    "#     # return table\n",
+    "#     return counts\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 9,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "DEFAULT_PATH = r'/home/aaristov/Anchor/Lena/Data/20210705-MIC-0h/'\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
    "metadata": {
     "scrolled": true
    },
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "counting error\n"
+     ]
+    },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "<ipython-input-5-edf0ca5e5a49>:55: FutureWarning: \n",
+      "<ipython-input-9-881807eea35c>:55: FutureWarning: \n",
       "\n",
       "magicgui 0.4.0 will change the way that callbacks are called.\n",
       "Instead of a single `Event` instance, with an `event.value` attribute,\n",
@@ -220,7 +246,7 @@
       "*other* than `Event`, e.g. `def callback(x: int): ...`\n",
       "For details, see: https://github.com/napari/magicgui/issues/255\n",
       "  def align_wrapper(*args):\n",
-      "<ipython-input-5-edf0ca5e5a49>:67: FutureWarning: \n",
+      "<ipython-input-9-881807eea35c>:81: FutureWarning: \n",
       "\n",
       "magicgui 0.4.0 will change the way that callbacks are called.\n",
       "Instead of a single `Event` instance, with an `event.value` attribute,\n",
@@ -235,7 +261,7 @@
       "*other* than `Event`, e.g. `def callback(x: int): ...`\n",
       "For details, see: https://github.com/napari/magicgui/issues/255\n",
       "  def count_wrapper(*args):\n",
-      "<ipython-input-5-edf0ca5e5a49>:88: FutureWarning: \n",
+      "<ipython-input-9-881807eea35c>:99: FutureWarning: \n",
       "\n",
       "magicgui 0.4.0 will change the way that callbacks are called.\n",
       "Instead of a single `Event` instance, with an `event.value` attribute,\n",
@@ -250,7 +276,7 @@
       "*other* than `Event`, e.g. `def callback(x: int): ...`\n",
       "For details, see: https://github.com/napari/magicgui/issues/255\n",
       "  def update_view(data):\n",
-      "<ipython-input-5-edf0ca5e5a49>:88: FutureWarning: \n",
+      "<ipython-input-9-881807eea35c>:99: FutureWarning: \n",
       "\n",
       "magicgui 0.4.0 will change the way that callbacks are called.\n",
       "Instead of a single `Event` instance, with an `event.value` attribute,\n",
@@ -269,12 +295,12 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Container (update_flist: NoneType = False, live: NoneType = False, align_button: NoneType = False, count_button: NoneType = False)>"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -282,191 +308,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      " chosen ['05ng-24h.aligned.tif']\n",
-      "click (Event(value=False, type='changed', source=PushButton(value=False, annotation=None, name='count_button')),), selections: ['05ng-24h.aligned.tif']\n",
-      "Counting /home/aaristov/Documents/composites-24h/05ng-24h.aligned.tif\n",
-      "5\n",
-      "Concentration 5 ng\n",
-      "5 ng already aligned\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "changed to (PosixPath('/home/aaristov/Anchor/Lena/Data/20210705-MIC-0h/Composites/00ng.aligned-big-labels.tif'), PosixPath('/home/aaristov/Anchor/Lena/Data/20210705-MIC-0h/Composites/02ng.aligned-big-labels.tif'), PosixPath('/home/aaristov/Anchor/Lena/Data/20210705-MIC-0h/Composites/05ng.aligned-big-labels.tif'), PosixPath('/home/aaristov/Anchor/Lena/Data/20210705-MIC-0h/Composites/06ng.aligned-big-labels.tif'), PosixPath('/home/aaristov/Anchor/Lena/Data/20210705-MIC-0h/Composites/07ng.aligned-big-labels.tif'), PosixPath('/home/aaristov/Anchor/Lena/Data/20210705-MIC-0h/Composites/08ng.aligned-big-labels.tif'), PosixPath('/home/aaristov/Anchor/Lena/Data/20210705-MIC-0h/Composites/12ng.aligned-big-labels.tif'), PosixPath('/home/aaristov/Anchor/Lena/Data/20210705-MIC-0h/Composites/15ng.aligned-big-labels.tif'))\n",
-      " chosen []\n",
-      " chosen ['00ng.aligned-big-labels.tif']\n",
-      " chosen ['00ng.aligned-big-labels.tif', '02ng.aligned-big-labels.tif', '05ng.aligned-big-labels.tif', '06ng.aligned-big-labels.tif', '07ng.aligned-big-labels.tif', '08ng.aligned-big-labels.tif', '12ng.aligned-big-labels.tif', '15ng.aligned-big-labels.tif']\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING: QWidget::repaint: Recursive repaint detected\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "click (Event(value=False, type='changed', source=PushButton(value=False, annotation=None, name='count_button')),), selections: ['00ng.aligned-big-labels.tif', '02ng.aligned-big-labels.tif', '05ng.aligned-big-labels.tif', '06ng.aligned-big-labels.tif', '07ng.aligned-big-labels.tif', '08ng.aligned-big-labels.tif', '12ng.aligned-big-labels.tif', '15ng.aligned-big-labels.tif']\n",
-      "Counting /home/aaristov/Anchor/Lena/Data/20210705-MIC-0h/Composites/00ng.aligned-big-labels.tif\n",
-      "Counting /home/aaristov/Anchor/Lena/Data/20210705-MIC-0h/Composites/02ng.aligned-big-labels.tif\n",
-      "Counting /home/aaristov/Anchor/Lena/Data/20210705-MIC-0h/Composites/05ng.aligned-big-labels.tif\n",
-      "Counting /home/aaristov/Anchor/Lena/Data/20210705-MIC-0h/Composites/06ng.aligned-big-labels.tif\n",
-      "Counting /home/aaristov/Anchor/Lena/Data/20210705-MIC-0h/Composites/07ng.aligned-big-labels.tif\n",
-      "Counting /home/aaristov/Anchor/Lena/Data/20210705-MIC-0h/Composites/08ng.aligned-big-labels.tif\n",
-      "Counting /home/aaristov/Anchor/Lena/Data/20210705-MIC-0h/Composites/12ng.aligned-big-labels.tif\n",
-      "Counting /home/aaristov/Anchor/Lena/Data/20210705-MIC-0h/Composites/15ng.aligned-big-labels.tif\n",
-      "0\n",
-      "Concentration 0 ng\n",
-      "2\n",
-      "Concentration 2 ng\n",
-      "0 ng already aligned2 ng already aligned\n",
-      "\n",
-      "5\n",
-      "Concentration 5 ng\n",
-      "5 ng already aligned\n",
-      "6\n",
-      "Concentration 6 ng\n",
-      "6 ng already aligned\n",
-      "7\n",
-      "Concentration 7 ng\n",
-      "7 ng already aligned\n",
-      "8\n",
-      "Concentration 8 ng\n",
-      "128 ng already aligned\n",
-      "\n",
-      "Concentration 12 ng\n",
-      "12 ng already aligned\n",
-      "15\n",
-      "Concentration 15 ng\n",
-      "15 ng already aligned\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "changed to (PosixPath('/home/aaristov/Anchor/Lena/Data/20210705-MIC-0h/Composites/00ng.aligned-big-labels.tif'), PosixPath('/home/aaristov/Anchor/Lena/Data/20210705-MIC-0h/Composites/02ng.aligned-big-labels.tif'))\n",
       " chosen ['00ng.aligned-big-labels.tif']\n",
       " chosen True\n",
-      "Opening /home/aaristov/Anchor/Lena/Data/20210705-MIC-0h/Composites/00ng.aligned-big-labels.tif\n"
+      "Opening /home/aaristov/Anchor/Lena/Data/20210705-MIC-0h/Composites/00ng.aligned-big-labels.tif\n",
+      "click (Event(value=False, type='changed', source=PushButton(value=False, annotation=None, name='count_button')),), selections: ['00ng.aligned-big-labels.tif']\n",
+      "Counting /home/aaristov/Anchor/Lena/Data/20210705-MIC-0h/Composites/00ng.aligned-big-labels.tif\n"
      ]
     }
    ],
    "source": [
-    "DEFAULT_PATH = r'/home/aaristov/Documents/composites-24h/'\n",
-    "    \n",
     "@thread_worker\n",
     "def load_stack(path):\n",
     "    return tf.imread(path), path\n",
@@ -475,17 +326,17 @@
     "    print (exc.args)\n",
     "    raise exc\n",
     "\n",
-    "def get_flist(path):\n",
-    "    return list(map(os.path.basename, sorted(glob(os.path.join(path, '*.???')))))\n",
+    "def get_flist(path, suffix='*.???'):\n",
+    "    return list(map(os.path.basename, sorted(glob(os.path.join(path, suffix)))))\n",
     "\n",
     "\n",
     "\n",
     "dirr = FileEdit(label='Open ', value=DEFAULT_PATH, mode='rm', filter=None, )\n",
     "select = Select(label='file list', choices=get_flist(DEFAULT_PATH))\n",
     "update = PushButton(label='Update flist', name='update_flist')\n",
-    "align = PushButton(label='Align!', name='align_button')\n",
-    "count = PushButton(label='Count!', name='count_button')\n",
-    "live = Checkbox(label='Live preview', name='live')\n",
+    "align_btn = PushButton(label='Align!', name='align_button')\n",
+    "count_btn = PushButton(label='Count!', name='count_button')\n",
+    "live = Checkbox(label='Live preview', name='live', enabled=True)\n",
     "select.data_dir=DEFAULT_PATH\n",
     "\n",
     "@update.clicked.connect\n",
@@ -518,7 +369,7 @@
     "        progress_bar.label  = progress_bar.label.replace('Aligning', 'ERROR!')\n",
     "        progress_bar.tooltip = e.args\n",
     "\n",
-    "@align.clicked.connect\n",
+    "@align_btn.clicked.connect\n",
     "def align_wrapper(*args):\n",
     "    print(f'click {args}, selections: {select.current_choice}')\n",
     "    for item in select.current_choice:\n",
@@ -528,8 +379,22 @@
     "        container.append(ppp := widgets.ProgressBar(max=5, label=(ttt := f'Aligning {os.path.basename(path)}'), name=ttt))\n",
     "        \n",
     "        worker = aligner(path, progress_bar=ppp)\n",
+    "    return \n",
+    "\n",
+    "def show_detections(data):\n",
+    "    viewer.add_points(data[:,:2], name='detections', size=20, edge_width=3, edge_color=\"#ff00ff\",face_color='#ffff0000', opacity=1)\n",
+    "\n",
+    "\n",
+    "        \n",
+    "@thread_worker(connect={\"errored\": print('counting error'), \"returned\": show_detections})\n",
+    "def do_counts():\n",
+    "    fluo = viewer.layers[1].data\n",
+    "    labels = viewer.layers[2].data\n",
+    "    detections = count.peak_local_max_labels(fluo[:], labels=labels, threshold_abs=500, min_distance=5)\n",
+    "    return detections\n",
+    "\n",
     "        \n",
-    "@count.clicked.connect\n",
+    "@count_btn.clicked.connect\n",
     "\n",
     "def count_wrapper(*args):\n",
     "    print(f'click {args}, selections: {select.current_choice}')\n",
@@ -537,10 +402,7 @@
     "        path = os.path.join(select.data_dir, item)\n",
     "        print ('Counting ' + path)\n",
     "        assert os.path.exists(path)\n",
-    "        container.append(ppp := widgets.ProgressBar(max=2, label=(ttt := f'Counting_{os.path.basename(path)}'), name=ttt))\n",
-    "        \n",
-    "        worker = align_n_count_mic(path, progress=ppp)\n",
-    "        worker.start()\n",
+    "        do_counts()\n",
     "        \n",
     "\n",
     "@dirr.changed.connect\n",
@@ -549,7 +411,7 @@
     "    select.choices= sorted(list(map(os.path.basename, data)))\n",
     "    select.data_dir = os.path.dirname(dirr.value[0])\n",
     "    \n",
-    "@thread_worker\n",
+    "# @thread_worker\n",
     "@select.changed.connect\n",
     "@live.changed.connect\n",
     "def update_view(data):\n",
@@ -557,13 +419,14 @@
     "    \n",
     "    try:\n",
     "        if 'aligned' in select.current_choice[0]:\n",
-    "            align.enabled = False\n",
-    "            count.enabled = True\n",
+    "            align_btn.enabled = False\n",
+    "            count_btn.enabled = True\n",
     "        else:\n",
-    "            align.enabled = True\n",
-    "            count.enabled = False\n",
-    "    except IndexError:\n",
-    "        align.enabled = False\n",
+    "            align_btn.enabled = True\n",
+    "            count_btn.enabled = False\n",
+    "    except IndexError as e:\n",
+    "        print(f'Error: {e.args}')\n",
+    "        align_btn.enabled = False\n",
     "    \n",
     "    if len(select.current_choice) == 1 and live.value: \n",
     "        path = os.path.join(select.data_dir, select.current_choice[0])\n",
@@ -573,23 +436,23 @@
     "        worker.start()\n",
     "\n",
     "container = Container(name='Multiwell-Aligner', \n",
-    "                      widgets=[dirr, select, update, live, align, count])\n",
+    "                      widgets=[dirr, select, update, live, align_btn, count_btn])\n",
     "# viewer.window.add_dock_widget(container)\n",
     "container.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<Points layer 'detections' at 0x7ff9df34f850>"
+       "<Points layer 'detections' at 0x7f00f8c18100>"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -598,1869 +461,498 @@
     "fluo = viewer.layers[1].data\n",
     "labels = viewer.layers[2].data\n",
     "\n",
-    "viewer.add_points(detections:=count.peak_local_max(fluo[:], labels=labels, threshold_abs=fluo.mean()+2*fluo.std(), min_distance=5), size=20, edge_width=3, edge_color=\"#ff00ff\",face_color='#ffff0000', opacity=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
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-       "       [ 4722,  2224],\n",
-       "       [ 4615,  2704],\n",
-       "       [ 4681,  4355],\n",
-       "       [ 4709,  5013],\n",
-       "       [ 4675,  5353],\n",
-       "       [ 4680,  6412],\n",
-       "       [ 4581,  6480],\n",
-       "       [ 4727,  7053],\n",
-       "       [ 4672,  8694],\n",
-       "       [ 5180, 13660],\n",
-       "       [ 5121, 20644],\n",
-       "       [ 4995,   479],\n",
-       "       [ 5081,  1495],\n",
-       "       [ 5077,  2038],\n",
-       "       [ 5213,  2412],\n",
-       "       [ 5055,  3513],\n",
-       "       [ 5101, 12507],\n",
-       "       [ 5150, 12695],\n",
-       "       [ 5106, 14125],\n",
-       "       [ 5141, 14627],\n",
-       "       [ 5044, 17337],\n",
-       "       [ 5041, 17441],\n",
-       "       [ 4982, 18907],\n",
-       "       [ 5042,  1045],\n",
-       "       [ 5183,  4167],\n",
-       "       [ 5021,  5107],\n",
-       "       [ 5146,  5810],\n",
-       "       [ 4994,  5771],\n",
-       "       [ 5010,  6334],\n",
-       "       [ 5067,  7737],\n",
-       "       [ 5060,  7735],\n",
-       "       [ 5115,  7922],\n",
-       "       [ 5051, 10917],\n",
-       "       [ 5086, 11591],\n",
-       "       [ 5147, 11565],\n",
-       "       [ 5018, 11636],\n",
-       "       [ 5180, 12026],\n",
-       "       [ 5494,  3954],\n",
-       "       [ 5622,  3832],\n",
-       "       [ 5621,  4945],\n",
-       "       [ 5439,  4894],\n",
-       "       [ 5534,  7656],\n",
-       "       [ 5560,  8043],\n",
-       "       [ 5605,  9174],\n",
-       "       [ 5431,  9141],\n",
-       "       [ 5510,  9191],\n",
-       "       [ 5510,  9581],\n",
-       "       [ 5605, 11362],\n",
-       "       [ 5612, 11838],\n",
-       "       [ 5660, 12404],\n",
-       "       [ 5478, 12396],\n",
-       "       [ 5541, 12954],\n",
-       "       [ 5549, 12949],\n",
-       "       [ 5517, 13484],\n",
-       "       [ 5462, 14019],\n",
-       "       [ 5521, 14505],\n",
-       "       [ 5490, 16557],\n",
-       "       [ 5611, 17703],\n",
-       "       [ 5612, 18666],\n",
-       "       [ 5607, 19710],\n",
-       "       [ 5544, 20221],\n",
-       "       [ 5457,   710],\n",
-       "       [ 5457,  1190],\n",
-       "       [ 5588,  1725],\n",
-       "       [ 5481,  2306],\n",
-       "       [ 5586,  2332],\n",
-       "       [ 5436,  2856],\n",
-       "       [ 5460,  2730],\n",
-       "       [ 5436,  2762],\n",
-       "       [ 5542,  3233],\n",
-       "       [ 5621,  6520],\n",
-       "       [ 6032,  7300],\n",
-       "       [ 5972,  8280],\n",
-       "       [ 5978, 10424],\n",
-       "       [ 6035, 10993],\n",
-       "       [ 5988, 12521],\n",
-       "       [ 6016, 12658],\n",
-       "       [ 5976, 12582],\n",
-       "       [ 5930, 14143],\n",
-       "       [ 5878, 15188],\n",
-       "       [ 5931, 15684],\n",
-       "       [ 5861, 15737],\n",
-       "       [ 5926, 16364],\n",
-       "       [ 5963, 16327],\n",
-       "       [ 5911,   988],\n",
-       "       [ 5968,  1500],\n",
-       "       [ 5923,  1571],\n",
-       "       [ 5890,  3077],\n",
-       "       [ 6062,  4642],\n",
-       "       [ 5881,  4626],\n",
-       "       [ 6016,  4715],\n",
-       "       [ 5880,  4668],\n",
-       "       [ 5980,  6717]])"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ppp"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "props = mic.regionprops(labels)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(slice(505, 820, None), slice(265, 572, None))"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "props[0].slice"
+    "viewer.add_points((detections:=count.peak_local_max_labels(fluo[:], labels=labels, threshold_abs=fluo.mean()+10*fluo.std(), min_distance=5))[:,:2], name='detections', size=20, edge_width=3, edge_color=\"#ff00ff\",face_color='#ffff0000', opacity=1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Container (update_flist: NoneType = False, live: NoneType = False, align_button: NoneType = False, count_button: NoneType = False)>"
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING: QWidget::repaint: Recursive repaint detected\n",
-      "WARNING: QBackingStore::endPaint() called with active painter on backingstore paint device\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "click (Event(value=False, type='changed', source=PushButton(value=False, annotation=None, name='count_button')),), selections: ['00ng-24h.aligned.tif']\n",
-      "Counting /home/aaristov/Documents/composites-24h/00ng-24h.aligned.tif\n",
-      "0\n",
-      "Concentration 0 ng\n",
-      "0 ng already aligned\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/skimage/feature/peak.py:162: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/vispy/app/backends/_qt.py:627: DeprecationWarning: an integer is required (got type KeyboardModifiers).  Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/vispy/app/backends/_qt.py:627: DeprecationWarning: an integer is required (got type KeyboardModifiers).  Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/vispy/app/backends/_qt.py:627: DeprecationWarning: an integer is required (got type KeyboardModifiers).  Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.!\n",
-      "/home/aaristov/miniconda3/envs/nd2/lib/python3.8/site-packages/vispy/app/backends/_qt.py:627: DeprecationWarning: an integer is required (got type KeyboardModifiers).  Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.!\n"
-     ]
-    },
-    {
-     "data": {
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\n",
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>y</th>\n",
+       "      <th>x</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>label</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1.0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2.0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4.0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6.0</th>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8.0</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>492.0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>493.0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>494.0</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>497.0</th>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>501.0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>347 rows × 2 columns</p>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "       y  x\n",
+       "label      \n",
+       "1.0    3  3\n",
+       "2.0    3  3\n",
+       "4.0    3  3\n",
+       "6.0    4  4\n",
+       "8.0    2  2\n",
+       "...   .. ..\n",
+       "492.0  1  1\n",
+       "493.0  1  1\n",
+       "494.0  2  2\n",
+       "497.0  4  4\n",
+       "501.0  1  1\n",
+       "\n",
+       "[347 rows x 2 columns]"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "container"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def \n",
-    "counts = mic.get_cell_numbers(\n",
-    "        fluo_aligned, \n",
-    "        mask_aligned, \n",
-    "        threshold_abs=2,\n",
-    "        plot=False,\n",
-    "        meta={'ng': c}\n",
-    "    )\n",
-    "poisson.fit(counts.query('n_cells < 10').n_cells, title=f'{c} ng')"
+    "counts = pd.DataFrame(data=detections, columns=('y', 'x', 'label')).groupby('label').count()\n",
+    "counts"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
+   "execution_count": 38,
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
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+       " 140,\n",
+       " 141,\n",
+       " 142,\n",
+       " 146,\n",
+       " 147,\n",
+       " 148,\n",
+       " 154,\n",
+       " 155,\n",
+       " 159,\n",
+       " 163,\n",
+       " 165,\n",
+       " 166,\n",
+       " 174,\n",
+       " 176,\n",
+       " 178,\n",
+       " 183,\n",
+       " 184,\n",
+       " 193,\n",
+       " 195,\n",
+       " 196,\n",
+       " 198,\n",
+       " 199,\n",
+       " 203,\n",
+       " 204,\n",
+       " 205,\n",
+       " 208,\n",
+       " 209,\n",
+       " 215,\n",
+       " 218,\n",
+       " 222,\n",
+       " 223,\n",
+       " 226,\n",
+       " 229,\n",
+       " 230,\n",
+       " 234,\n",
+       " 240,\n",
+       " 242,\n",
+       " 243,\n",
+       " 254,\n",
+       " 259,\n",
+       " 263,\n",
+       " 264,\n",
+       " 267,\n",
+       " 271,\n",
+       " 272,\n",
+       " 275,\n",
+       " 287,\n",
+       " 290,\n",
+       " 292,\n",
+       " 294,\n",
+       " 295,\n",
+       " 304,\n",
+       " 306,\n",
+       " 309,\n",
+       " 312,\n",
+       " 313,\n",
+       " 314,\n",
+       " 318,\n",
+       " 320,\n",
+       " 324,\n",
+       " 325,\n",
+       " 330,\n",
+       " 332,\n",
+       " 334,\n",
+       " 335,\n",
+       " 336,\n",
+       " 337,\n",
+       " 340,\n",
+       " 345,\n",
+       " 347,\n",
+       " 348,\n",
+       " 351,\n",
+       " 359,\n",
+       " 360,\n",
+       " 362,\n",
+       " 363,\n",
+       " 374,\n",
+       " 379,\n",
+       " 382,\n",
+       " 384,\n",
+       " 385,\n",
+       " 388,\n",
+       " 389,\n",
+       " 398,\n",
+       " 403,\n",
+       " 407,\n",
+       " 408,\n",
+       " 415,\n",
+       " 416,\n",
+       " 418,\n",
+       " 419,\n",
+       " 420,\n",
+       " 421,\n",
+       " 427,\n",
+       " 428,\n",
+       " 431,\n",
+       " 435,\n",
+       " 445,\n",
+       " 460,\n",
+       " 461,\n",
+       " 463,\n",
+       " 464,\n",
+       " 465,\n",
+       " 466,\n",
+       " 471,\n",
+       " 472,\n",
+       " 476,\n",
+       " 479,\n",
+       " 481,\n",
+       " 485,\n",
+       " 486,\n",
+       " 487,\n",
+       " 495,\n",
+       " 496,\n",
+       " 498,\n",
+       " 499,\n",
+       " 500]"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "'07ng-24h.tif' in select.choices"
+    "a = range(1,502)\n",
+    "empty_labels = [_ for _ in range(1,502) if _ not in counts.index]\n",
+    "empty_labels"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "11\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
-       "['00ng-24h.aligned.tif',\n",
-       " '00ng-24h.tif',\n",
-       " '02ng-24h.aligned.tif',\n",
-       " '02ng-24h.tif',\n",
-       " '04ng-24h.aligned.tif',\n",
-       " '04ng-24h.tif',\n",
-       " '05ng-24h.aligned.tif',\n",
-       " '05ng-24h.tif',\n",
-       " '06ng-24h.aligned.tif',\n",
-       " '06ng-24h.tif',\n",
-       " '07ng-24h.aligned.tif',\n",
-       " '*07ng-24h.tif',\n",
-       " '07ng-24h.tif',\n",
-       " '08ng-24h.aligned.tif',\n",
-       " '08ng-24h.tif',\n",
-       " '10ng-24h.aligned.tif',\n",
-       " '10ng-24h.tif',\n",
-       " '12ng-24h.aligned.tif',\n",
-       " '12ng-24h.tif',\n",
-       " '15ng-24h.aligned.tif',\n",
-       " '15ng-24h.tif']"
+       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f2f9d07b520>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2f9d0131c0>]],\n",
+       "      dtype=object)"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "i = select.choices.index('07ng-24h.tif')\n",
-    "print(i)\n",
-    "flist = list(select.choices)\n",
-    "flist.insert(i, '*07ng-24h.tif')\n",
-    "flist"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "container.append(w := widgets.ProgressBar(label='abc'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "w.close()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
+    },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQAAAAAXCAYAAADk1pHxAAAA2UlEQVR42u3cP0rDcBjH4TfFzeZPSQ9gcwARPIGIuwheUhBnB3EutYi7xrFDGyINmeM1xN/zHOE7fN7tzfr9bvps2xiGIbpDF8D/Vi/rKPMimmYV2dPjw1RVVZxfXFoGEvHxvo0in8fJOI5xe3dvEUjI1fVNvL48x+yn760BCeoOXczMAOkSABAAQAAAAQAEABAAQAAAAQAEABAAQAAAAQAEABAAQACAvxuAarGwAiSoXtYxy0/nsd2srQEJedusoyzKyPr9bvpqv+M4HH0FhkQuf1mU0azO4hdAGzHt0/JL6gAAAABJRU5ErkJggg==\n",
+      "image/png": "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\n",
       "text/plain": [
-       "ProgressBar(value=-1, annotation=None, name='')"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "container.pop(-1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "list(select.choices)"
+    "empty_wells = pd.DataFrame(index=empty_labels, columns=['x', 'y'], data=np.zeros((len(empty_labels), 2)))\n",
+    "counts.append(empty_wells).hist(bins=range(8))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>y</th>\n",
+       "      <th>x</th>\n",
+       "      <th>index</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>343</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>344</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>345</th>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>346</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>347</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>[3, 5, 7, 10, 13, 32, 39, 40, 41, 44, 47, 50, ...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>348 rows × 3 columns</p>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "['a']"
+       "     y  x                                              index\n",
+       "0    3  3                                                NaN\n",
+       "1    3  3                                                NaN\n",
+       "2    3  3                                                NaN\n",
+       "3    4  4                                                NaN\n",
+       "4    2  2                                                NaN\n",
+       "..  .. ..                                                ...\n",
+       "343  1  1                                                NaN\n",
+       "344  2  2                                                NaN\n",
+       "345  4  4                                                NaN\n",
+       "346  1  1                                                NaN\n",
+       "347  0  0  [3, 5, 7, 10, 13, 32, 39, 40, 41, 44, 47, 50, ...\n",
+       "\n",
+       "[348 rows x 3 columns]"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "tmp = []\n",
-    "tmp.insert(i, 'a')\n",
-    "tmp"
+    "counts.append({'index': empty_labels, 'x': 0, 'y': 0}, ignore_index=True)"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 14,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "update_flist(select)"
+    "# show chips one under another"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [],
    "source": [
-    "l = viewer.layers[1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " chosen ['07ng-24h.aligned.tif']\n",
-      "Opening /home/aaristov/Documents/composites-24h/07ng-24h.aligned.tif\n",
-      " chosen ['08ng-24h.aligned.tif']\n",
-      "Opening /home/aaristov/Documents/composites-24h/08ng-24h.aligned.tif\n"
-     ]
-    }
-   ],
-   "source": [
-    "l.multiscale"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['00ng-24h.aligned.tif',\n",
-       " '00ng-24h.tif',\n",
-       " '02ng-24h.aligned.tif',\n",
-       " '02ng-24h.tif',\n",
-       " '04ng-24h.aligned.tif',\n",
-       " '04ng-24h.tif',\n",
-       " '05ng-24h.aligned.tif',\n",
-       " '05ng-24h.tif',\n",
-       " '06ng-24h.aligned.tif',\n",
-       " '06ng-24h.tif',\n",
-       " '07ng-24h.aligned.tif',\n",
-       " '07ng-24h.tif',\n",
-       " '08ng-24h.aligned.tif',\n",
-       " '08ng-24h.tif',\n",
-       " '10ng-24h.aligned.tif',\n",
-       " '10ng-24h.tif',\n",
-       " '12ng-24h.tif',\n",
-       " '15ng-24h.aligned.tif',\n",
-       " '15ng-24h.tif']"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " chosen ['00ng-24h.tif']\n",
-      " chosen ['00ng-24h.aligned.tif']\n",
-      " chosen ['00ng-24h.tif']\n",
-      " chosen ['00ng-24h.aligned.tif']\n",
-      " chosen ['00ng-24h.tif']\n",
-      " chosen ['02ng-24h.tif']\n",
-      " chosen ['00ng-24h.tif']\n",
-      " chosen ['00ng-24h.aligned.tif']\n",
-      " chosen ['02ng-24h.aligned.tif']\n",
-      " chosen ['00ng-24h.aligned.tif']\n",
-      " chosen True\n",
-      "Opening /home/aaristov/Documents/composites-24h/00ng-24h.aligned.tif\n",
-      " chosen ['02ng-24h.aligned.tif']\n",
-      "Opening /home/aaristov/Documents/composites-24h/02ng-24h.aligned.tif\n",
-      " chosen ['04ng-24h.aligned.tif']\n",
-      "Opening /home/aaristov/Documents/composites-24h/04ng-24h.aligned.tif\n",
-      " chosen ['05ng-24h.aligned.tif']\n",
-      "Opening /home/aaristov/Documents/composites-24h/05ng-24h.aligned.tif\n",
-      " chosen ['06ng-24h.aligned.tif']\n",
-      "Opening /home/aaristov/Documents/composites-24h/06ng-24h.aligned.tif\n",
-      " chosen ['00ng-24h.aligned.tif']\n",
-      "Opening /home/aaristov/Documents/composites-24h/00ng-24h.aligned.tif\n",
-      " chosen ['02ng-24h.aligned.tif']\n",
-      "Opening /home/aaristov/Documents/composites-24h/02ng-24h.aligned.tif\n",
-      " chosen ['06ng-24h.aligned.tif']\n",
-      "Opening /home/aaristov/Documents/composites-24h/06ng-24h.aligned.tif\n",
-      " chosen ['07ng-24h.aligned.tif']\n",
-      "Opening /home/aaristov/Documents/composites-24h/07ng-24h.aligned.tif\n",
-      " chosen ['08ng-24h.aligned.tif']\n",
-      "Opening /home/aaristov/Documents/composites-24h/08ng-24h.aligned.tif\n",
-      " chosen ['07ng-24h.aligned.tif']\n",
-      "Opening /home/aaristov/Documents/composites-24h/07ng-24h.aligned.tif\n",
-      " chosen ['06ng-24h.aligned.tif']\n",
-      "Opening /home/aaristov/Documents/composites-24h/06ng-24h.aligned.tif\n",
-      " chosen ['07ng-24h.aligned.tif']\n",
-      "Opening /home/aaristov/Documents/composites-24h/07ng-24h.aligned.tif\n",
-      " chosen ['06ng-24h.aligned.tif']\n",
-      "Opening /home/aaristov/Documents/composites-24h/06ng-24h.aligned.tif\n",
-      " chosen ['07ng-24h.aligned.tif']\n",
-      "Opening /home/aaristov/Documents/composites-24h/07ng-24h.aligned.tif\n",
-      " chosen ['08ng-24h.aligned.tif']\n",
-      "Opening /home/aaristov/Documents/composites-24h/08ng-24h.aligned.tif\n",
-      " chosen ['05ng-24h.tif']\n",
-      "Opening /home/aaristov/Documents/composites-24h/05ng-24h.tif\n",
-      " chosen ['06ng-24h.aligned.tif']\n",
-      "Opening /home/aaristov/Documents/composites-24h/06ng-24h.aligned.tif\n",
-      " chosen ['06ng-24h.tif']\n",
-      "Opening /home/aaristov/Documents/composites-24h/06ng-24h.tif\n",
-      " chosen ['07ng-24h.tif']\n",
-      "Opening /home/aaristov/Documents/composites-24h/07ng-24h.tif\n",
-      " chosen ['05ng-24h.aligned.tif']\n",
-      "Opening /home/aaristov/Documents/composites-24h/05ng-24h.aligned.tif\n",
-      " chosen ['04ng-24h.aligned.tif']\n",
-      "Opening /home/aaristov/Documents/composites-24h/04ng-24h.aligned.tif\n"
-     ]
-    }
-   ],
-   "source": [
-    "get_flist(select.data_dir)"
+    "for path in get_flist(select.data_dir, '*aligned.tif')[::2]:\n",
+    "    bf, fluo, _ = tf.imread(os.path.join(select.data_dir, path))\n",
+    "    ng = get_concentration(path, regex='(\\\\d+)ng-24h.aligned.tif')\n",
+    "    viewer.add_image(fluo, name=ng, contrast_limits=(440,600), colormap='inferno')\n",
+    "\n",
+    "bf, fluo, _ = tf.imread(os.path.join(select.data_dir, path))\n",
+    "\n",
+    "viewer.add_image(bf, name='BF', contrast_limits=(5000,50000), colormap='gray')"
    ]
   },
   {