diff --git a/multiwell align count dec-jan 2022.ipynb b/multiwell align count dec-jan 2022.ipynb
index 2773e4ce01731ac8e6691c6b5de81628dc0a6ee7..9db4bb941041e10672691a20d429eec22159d257 100644
--- a/multiwell align count dec-jan 2022.ipynb	
+++ b/multiwell align count dec-jan 2022.ipynb	
@@ -2,19 +2,10 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 1,
    "id": "53a4f1b5-8cc1-4f9a-82db-be5b7d3a19cd",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import asyncio\n",
     "from aicsimageio import imread, imread_dask\n",
@@ -30,37 +21,6 @@
     "%autoreload 2"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "75cf35eb-e0e9-470e-894d-1b1d2d144d92",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "^C\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING: Retrying (Retry(total=4, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ConnectTimeoutError(<pip._vendor.urllib3.connection.HTTPSConnection object at 0x0000017569913250>, 'Connection to pypi.org timed out. (connect timeout=15)')': /simple/tables/\n",
-      "WARNING: Retrying (Retry(total=3, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ConnectTimeoutError(<pip._vendor.urllib3.connection.HTTPSConnection object at 0x0000017569913790>, 'Connection to pypi.org timed out. (connect timeout=15)')': /simple/tables/\n",
-      "WARNING: Retrying (Retry(total=2, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ConnectTimeoutError(<pip._vendor.urllib3.connection.HTTPSConnection object at 0x0000017569913910>, 'Connection to pypi.org timed out. (connect timeout=15)')': /simple/tables/\n",
-      "WARNING: Retrying (Retry(total=1, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ConnectTimeoutError(<pip._vendor.urllib3.connection.HTTPSConnection object at 0x0000017569913A90>, 'Connection to pypi.org timed out. (connect timeout=15)')': /simple/tables/\n",
-      "WARNING: Retrying (Retry(total=0, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ConnectTimeoutError(<pip._vendor.urllib3.connection.HTTPSConnection object at 0x0000017569913C10>, 'Connection to pypi.org timed out. (connect timeout=15)')': /simple/tables/\n",
-      "ERROR: Could not find a version that satisfies the requirement tables (from versions: none)\n",
-      "ERROR: No matching distribution found for tables\n"
-     ]
-    }
-   ],
-   "source": [
-    "!pip install tables"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 2,
@@ -116,7 +76,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "e17feda0-0cf8-4f4c-8f48-383048572f4b",
    "metadata": {},
    "outputs": [],
@@ -143,7 +103,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 37,
    "id": "40d21c33-f77d-4396-9a8d-4e0df7c6891a",
    "metadata": {},
    "outputs": [],
@@ -163,59 +123,155 @@
     "        tf.imwrite(path_to_save, aligned, dtype='uint16',  imagej=True, metadata=register.META_ALIGNED)\n",
     "    else:\n",
     "        print('Already aligned')\n",
-    "        aligned = imread(path_to_save)[0,:,0]\n",
-    "    print(aligned.shape)\n",
+    "        aligned = tf.imread(path_to_save)\n",
+    "    print('Counting ', aligned.shape)\n",
     "    counts = mic.get_cell_numbers(aligned[1], aligned[2], threshold_abs=2, plot=False, bf=aligned[0])\n",
     "    \n",
     "    counts.loc[:,'ng'] = int(ng)\n",
-    "    l = poisson.fit(counts.query('n_cells < 10').n_cells, title=f'automatic {ng}ug')\n",
-    "    poisson.plt.show()\n",
+    "    l = poisson.fit(counts.query('n_cells < 10').n_cells, title=f'automatic {ng}ug', save_fig_path=path_to_save.replace('.tif', '-hist.png'))\n",
     "    counts.loc[:, 'poisson fit'] = l\n",
     "    \n",
     "    counts.to_csv((cp := path_to_save.replace('.tif', '-counts.csv')), index=None)\n",
     "    print(f'Saving count to {cp}')\n",
+    "    \n",
+    "    # poisson.plt.savefig((sf := path_to_save.replace('.tif', '-hist.png')))\n",
+    "    # print(f'Saving histogram to {sf}')\n",
+    "    poisson.plt.show()\n",
+    "    \n",
     "    return aligned"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
-   "id": "eb5e9886-28f7-43b7-be93-d53d89b6284c",
+   "execution_count": 9,
+   "id": "f22c8bd3-9439-4f20-a2d7-0d63c12d1706",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "img = imread_dask('E:/Andrey/20220124-MIC-cipro-resistant/day1/raw/000ng-BF.nd2')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "87e5eb33-f5c6-4dd2-a871-e1a8a5d6f598",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "img._path  = 'E:/Andrey/20220124-MIC-cipro-resistant/day1/raw/000ng-BF.nd2'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "96c22c7a-cbb4-4541-8509-7cf5ccafbee0",
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "img.path = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "a64ec66e-fea9-4ef7-a0f5-a11ecab428d9",
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "64 ng\n"
+      "0 ng\n",
+      "Already aligned\n",
+      "(3, 6544, 20896)\n"
      ]
     },
     {
-     "name": "stderr",
+     "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": [
-      "C:\\Users\\nikon\\AppData\\Local\\Temp/ipykernel_14660/2211652258.py:12: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
-      "  aligned, tvec = register.align_stack_nd(stack=np.array((BF, fluo)), path=None, template16=template16, mask2=big_labels, binnings=(2,16,2))\n"
+      "Saving count to E:/Andrey/20220124-MIC-cipro-resistant/day1/composites/000ng-counts.csv\n"
      ]
     },
+    {
+     "data": {
+      "text/plain": [
+       "array([[[15690, 15639, 15678, ..., 15647, 15634, 15565],\n",
+       "        [15659, 15536, 15627, ..., 15552, 15513, 15505],\n",
+       "        [15599, 15537, 15656, ..., 15584, 15544, 15503],\n",
+       "        ...,\n",
+       "        [15480, 15480, 15480, ..., 15507, 15348, 15400],\n",
+       "        [15480, 15480, 15480, ..., 15532, 15364, 15421],\n",
+       "        [15480, 15480, 15479, ..., 15490, 15412, 15418]],\n",
+       "\n",
+       "       [[  416,   412,   410, ...,   415,   416,   418],\n",
+       "        [  416,   413,   411, ...,   412,   413,   414],\n",
+       "        [  413,   412,   412, ...,   411,   411,   412],\n",
+       "        ...,\n",
+       "        [  415,   415,   415, ...,   416,   418,   413],\n",
+       "        [  415,   415,   415, ...,   414,   417,   413],\n",
+       "        [  415,   415,   415, ...,   414,   416,   411]],\n",
+       "\n",
+       "       [[    0,     0,     0, ...,     0,     0,     0],\n",
+       "        [    0,     0,     0, ...,     0,     0,     0],\n",
+       "        [    0,     0,     0, ...,     0,     0,     0],\n",
+       "        ...,\n",
+       "        [    0,     0,     0, ...,     0,     0,     0],\n",
+       "        [    0,     0,     0, ...,     0,     0,     0],\n",
+       "        [    0,     0,     0, ...,     0,     0,     0]]], dtype=uint16)"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ng=0\n",
+    "align3D(f'E:/Andrey/20220124-MIC-cipro-resistant/day1/raw/{ng:03d}ng-BF.nd2',\n",
+    "                      f'E:/Andrey/20220124-MIC-cipro-resistant/day1/raw/{ng:03d}ng-TRITC.nd2',\n",
+    "                      f'E:/Andrey/20220124-MIC-cipro-resistant/day1/composites/{ng:03d}ng.tif',)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "eb5e9886-28f7-43b7-be93-d53d89b6284c",
+   "metadata": {},
+   "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "0 ng\n",
       "Aligning None: \n",
       " bf: (7383, 22392),  2\n",
       " tmp: (818, 2612),  16\n",
       " mask: (6544, 20896),  2\n",
       "\n",
-      "{'tvec': array([143.16637983,  63.18095375]), 'success': 0.045464008929561664, 'angle': -5.556934827875381, 'scale': 0.9993398785133398, 'Dscale': 0.0004774761996094207, 'Dangle': 0.013400833829660513, 'Dt': 0.25, 'timg': None}\n",
-      "transform (8878, 22386)\n",
+      "{'tvec': array([-70.9758149 ,  21.14565473]), 'success': 0.026467361485992724, 'angle': -1.8841896353838763, 'scale': 0.9963997090738338, 'Dscale': 0.0004760714113483222, 'Dangle': 0.013400833829660513, 'Dt': 0.25, 'timg': None}\n",
+      "transform (7383, 22392)\n",
       "transform (7383, 22392)\n",
       "(3, 6544, 20896)\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -229,124 +285,62 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Saving count to E:/Andrey/20220118-MIC-cipro-resistant/day1/composites/064ng-counts.csv\n"
+      "Saving count to E:/Andrey/20220127-W3110_WT_RFP+/day1/000ng-counts.csv\n"
      ]
     }
    ],
    "source": [
-    "ng=64\n",
-    "Thread(target=align3D, args=(f'E:/Andrey/20220118-MIC-cipro-resistant/day1/raw/{ng:03d}ng-BF.nd2',\n",
-    "                      f'E:/Andrey/20220118-MIC-cipro-resistant/day1/raw/{ng:03d}ng-TRITC.nd2',\n",
-    "                      f'E:/Andrey/20220118-MIC-cipro-resistant/day1/composites/{ng:03d}ng.tif',)).start()"
+    "ng=0\n",
+    "Thread(target=align3D, args=(f'E:/Andrey/20220127-W3110_WT_RFP+/day1/{ng:03d}ng-BF.nd2',\n",
+    "                      f'E:/Andrey/20220127-W3110_WT_RFP+/day1/{ng:03d}ng-TRITC.nd2',\n",
+    "                      f'E:/Andrey/20220127-W3110_WT_RFP+/day1/{ng:03d}ng.tif',)).start()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "647e28a9-7630-4ed7-84fd-3701a15d89a7",
+   "execution_count": 35,
+   "id": "1cc15709-8899-4004-b5f4-b24ec1caa91d",
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "['E:Andrey/20220118-MIC-cipro-resistant/day2/raw\\\\000ng-BF-TRITC.nd2',\n",
-       " 'E:Andrey/20220118-MIC-cipro-resistant/day2/raw\\\\032ng-BF-TRITC.nd2',\n",
-       " 'E:Andrey/20220118-MIC-cipro-resistant/day2/raw\\\\096ng-BF-TRITC.nd2',\n",
-       " 'E:Andrey/20220118-MIC-cipro-resistant/day2/raw\\\\128ng-BF-TRITC.nd2',\n",
-       " 'E:Andrey/20220118-MIC-cipro-resistant/day2/raw\\\\192ng-BF-TRITC.nd2',\n",
-       " 'E:Andrey/20220118-MIC-cipro-resistant/day2/raw\\\\256ng-BF-TRITC.nd2']"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 ng\n"
+     ]
     }
    ],
    "source": [
-    "paths = glob('E:Andrey/20220118-MIC-cipro-resistant/day2/raw/*ng-BF-TRITC.nd2')\n",
-    "paths"
+    "ng=0\n",
+    "Thread(target=align3D, args=(f'E:/Andrey/20220127-W3110_WT_RFP+/day1/000ng-BF_after4h.nd2',\n",
+    "                      f'E:/Andrey/20220127-W3110_WT_RFP+/day1/000ng-TRITC_after4h.nd2',\n",
+    "                      f'E:/Andrey/20220127-W3110_WT_RFP+/day1/000ng_after4h.tif',)).start()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "abf620ed-b02a-4b8a-8568-9a263d1c0987",
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true
-    },
-    "tags": []
-   },
+   "execution_count": 36,
+   "id": "5d735546-ad2b-4f42-b2ae-5a5d26001e42",
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 ug\n",
-      "32 96 ug\n",
-      "ug\n",
-      "128 ug\n",
-      "192 ug\n",
-      "256 ug\n",
-      "Aligning E:Andrey/20220118-MIC-cipro-resistant/day2/raw\\032ng-BF-TRITC.nd2: \n",
-      " bf: (8878, 22386),  2\n",
-      " tmp: (818, 2612),  16\n",
-      " mask: (6544, 20896),  2\n",
-      "\n",
-      "Aligning E:Andrey/20220118-MIC-cipro-resistant/day2/raw\\000ng-BF-TRITC.nd2: \n",
-      " bf: (8878, 22386),  2\n",
-      " tmp: (818, 2612),  16\n",
-      " mask: (6544, 20896),  2\n",
-      "\n",
-      "Aligning E:Andrey/20220118-MIC-cipro-resistant/day2/raw\\192ng-BF-TRITC.nd2: \n",
-      " bf: (8878, 22386),  2\n",
-      " tmp: (818, 2612),  16\n",
-      " mask: (6544, 20896),  2\n",
-      "\n",
-      "Aligning E:Andrey/20220118-MIC-cipro-resistant/day2/raw\\128ng-BF-TRITC.nd2: \n",
-      " bf: (8878, 22386),  2\n",
-      " tmp: (818, 2612),  16\n",
-      " mask: (6544, 20896),  2\n",
-      "\n",
-      "Aligning E:Andrey/20220118-MIC-cipro-resistant/day2/raw\\096ng-BF-TRITC.nd2: \n",
-      " bf: (8878, 22386),  2\n",
-      " tmp: (818, 2612),  16\n",
-      " mask: (6544, 20896),  2\n",
-      "\n",
-      "Aligning E:Andrey/20220118-MIC-cipro-resistant/day2/raw\\256ng-BF-TRITC.nd2: \n",
-      " bf: (8878, 22386),  2\n",
+      "Aligning None: \n",
+      " bf: (7383, 22392),  2\n",
       " tmp: (818, 2612),  16\n",
       " mask: (6544, 20896),  2\n",
       "\n",
-      "{'tvec': array([ 17.74043206, -81.78606124]), 'success': 0.021730222150385524, 'angle': -2.6898205797898243, 'scale': 0.9996702506742179, 'Dscale': 0.0004914313620703765, 'Dangle': 0.013400833829660513, 'Dt': 0.25, 'timg': None}\n",
-      "transform (8878, 22386)\n",
-      "{'tvec': array([151.20316411, -88.14456159]), 'success': 0.038535965124869044, 'angle': -0.8439093592218683, 'scale': 0.9998521433399404, 'Dscale': 0.0004915207793161196, 'Dangle': 0.013400833829660513, 'Dt': 0.25, 'timg': None}\n",
-      "transform (8878, 22386)\n",
-      "{'tvec': array([ 149.9188894 , -576.18459112]), 'success': 0.07547106651366141, 'angle': -2.2890127735244334, 'scale': 0.9994160772606592, 'Dscale': 0.0004913064120813746, 'Dangle': 0.013400833829660513, 'Dt': 0.25, 'timg': None}\n",
-      "transform (8878, 22386)\n",
-      "{'tvec': array([160.12202253,  48.61542111]), 'success': 0.030041858892629166, 'angle': -1.865457029903979, 'scale': 0.9998117388158432, 'Dscale': 0.0004915009167161297, 'Dangle': 0.013400833829660513, 'Dt': 0.25, 'timg': None}\n",
-      "transform (8878, 22386)\n",
-      "{'tvec': array([64.2188064, 79.7229307]), 'success': 0.0387483283605787, 'angle': -0.9722580233374742, 'scale': 0.9997681458385859, 'Dscale': 0.0004914794866934039, 'Dangle': 0.013400833829660513, 'Dt': 0.25, 'timg': None}\n",
-      "transform (8878, 22386)\n",
-      "{'tvec': array([ 128.04585963, -159.70608637]), 'success': 0.022292114173627232, 'angle': -4.010284823024364, 'scale': 0.9950235952890625, 'Dscale': 0.0004891470966503957, 'Dangle': 0.013400833829660513, 'Dt': 0.25, 'timg': None}\n",
-      "transform (8878, 22386)\n",
-      "transform (8878, 22386)\n",
-      "transform (8878, 22386)\n",
-      "transform (8878, 22386)\n",
-      "transform (8878, 22386)transform (8878, 22386)\n",
-      "\n",
-      "transform (8878, 22386)\n",
-      "Saved aligned stack E:Andrey/20220118-MIC-cipro-resistant/day2/raw\\032ng-BF-TRITC.aligned.tif\n",
-      "Saved aligned stack E:Andrey/20220118-MIC-cipro-resistant/day2/raw\\256ng-BF-TRITC.aligned.tif\n",
-      "Saved aligned stack E:Andrey/20220118-MIC-cipro-resistant/day2/raw\\128ng-BF-TRITC.aligned.tif\n",
-      "Saved aligned stack E:Andrey/20220118-MIC-cipro-resistant/day2/raw\\000ng-BF-TRITC.aligned.tif\n",
-      "Saved aligned stack E:Andrey/20220118-MIC-cipro-resistant/day2/raw\\192ng-BF-TRITC.aligned.tif\n"
+      "0 ng\n",
+      "Already aligned\n",
+      "(3, 6544, 20896)\n",
+      "Save histogram E:/Andrey/20220127-W3110_WT_RFP+/day1/000ng-hist.png\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -360,12 +354,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Saved aligned stack E:Andrey/20220118-MIC-cipro-resistant/day2/raw\\096ng-BF-TRITC.aligned.tif\n"
+      "Saving count to E:/Andrey/20220127-W3110_WT_RFP+/day1/000ng-counts.csv\n",
+      "{'tvec': array([80.16638158, -7.25601293]), 'success': 0.02273800055195475, 'angle': -1.5424193658767535, 'scale': 0.9962311252041538, 'Dscale': 0.00047599086339147493, 'Dangle': 0.013400833829660513, 'Dt': 0.25, 'timg': None}\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "(3, 6544, 20896)\n",
+      "Save histogram E:/Andrey/20220127-W3110_WT_RFP+/day1/000ng_after4h-hist.png\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -376,28 +375,39 @@
      "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": [
+      "Saving count to E:/Andrey/20220127-W3110_WT_RFP+/day1/000ng_after4h-counts.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "ng=0\n",
+    "Thread(target=align3D, args=(f'E:/Andrey/20220127-W3110_WT_RFP+/day1/000ng-BF.nd2',\n",
+    "                      f'E:/Andrey/20220127-W3110_WT_RFP+/day1/000ng-TRITC.nd2',\n",
+    "                      f'E:/Andrey/20220127-W3110_WT_RFP+/day1/000ng.tif',)).start()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "d510b1de-1aa4-489f-8aaf-bfd58d68339f",
+   "metadata": {},
+   "outputs": [
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\nikon\\miniconda3\\envs\\nd2\\lib\\site-packages\\scipy\\optimize\\minpack.py:833: OptimizeWarning: Covariance of the parameters could not be estimated\n",
-      "  warnings.warn('Covariance of the parameters could not be estimated',\n"
+      "256 ng\n",
+      "Already aligned\n",
+      "(3, 6544, 20896)\n",
+      "Save histogram E:/Andrey/20220127-W3110_ciproR_RFP+/day1/256ng-hist.png\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -408,58 +418,391 @@
      "output_type": "display_data"
     },
     {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 375.2x278.84 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving count to E:/Andrey/20220127-W3110_ciproR_RFP+/day1/256ng-counts.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "ng=256\n",
+    "Thread(target=align3D, args=(f'E:/Andrey/20220127-W3110_ciproR_RFP+//day1/{ng:03d}ng-BF.nd2',\n",
+    "                      f'E:/Andrey/20220127-W3110_ciproR_RFP+/day1/{ng:03d}ng-TRITC.nd2',\n",
+    "                      f'E:/Andrey/20220127-W3110_ciproR_RFP+/day1/{ng:03d}ng.tif',)).start()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "6dc55412-bd85-4575-87b4-b85aae859e54",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from aicsimageio import imread_xarray_dask"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "f244f34c-6a9c-4a2a-9228-ccd501556506",
+   "metadata": {},
+   "outputs": [
     {
-     "data": {
-      "image/png": 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44F3A4HD5NUD3PiQxRWJjx2IFBWp3F5EeqUM3Mbn788Dz4fQ6YEor29QC56WgtkhZQQGxceN0p6qI9EgafuAAYuUJales1AiRItLjKNwPIJ4oD0aIDJ+6LiLSUyjcD0CdqiLSUyncDyC/pITcgQPVqSoiPY7C/QDMjHgioU5VEelxFO5tiJUnqFu7jsaqqqhLERFpN4V7G+KJ8mCEyGXLoi5FRKTdFO5tiCc0QqSI9DwK9zbk9utHwciR6lQVkR5F4d4OQafqUo0QKSI9hsK9HWLlCRq3b6dh8+aoSxERaReFezvEE+UAapoRkR5D4d4OsbFHYoWF6lQVkR5D4d4Olp9P7OijdeYuIj2Gwr2d4okEtStW4PX1UZciItImhXs7xcsT+N691L72WtSliIi0SeHeTrGwU7VWTTMi0gMo3Nspf/gwcgcNUqeqiPQICvd2em+ESIW7iGQ+hXsHxMsT1K1bR+Pu3VGXIiJyQAr3DoglwiczaYRIEclwCvcOiE8IRohUp6qIZDqFewfk9utHwahR6lQVkYyncO8gjRApIj2Bwr2D4uUJGnfsoH7TpqhLERHZL4V7BzV3qqrdXUQymcK9g2JHaoRIEcl8CvcOsvx8YuPH62YmEcloCvdOiCcS1K5cqREiRSRjKdw7Yd8Ika9qhEgRyUwK906IN9+puvSViCsREWmdwr0T8oYNI3fIEGrVqSoiGUrh3gkaIVJEMp3CvZPiiQR1b7xB465dUZciIvIBCvdOipc3jxC5POJKREQ+SOHeSbEJE8BMnaoikpEU7p2UW1xMwRGj1KkqIhlJ4d4F8US5RogUkYzUZribWczMXjKzV8xshZl9L1w+0sxeNLM1ZvagmRWEywvD+TXh+tI0f4bIxBMJGt95h/qKiqhLERF5n/acue8FTnH3cmAicKqZHQ/cDPzU3UcD7wAzw+1nAu+Ey38abpeV9nWqqmlGRDJMm+HugapwNj98OXAK8Ei4fA5wVjh9ZjhPuP6jZmapKjiTFI4Zg8Xj6lQVkYzTrjZ3M8s1syXA28AzwFpgp7s3hJtUAMPD6eHARoBw/S5gcAprzhiWl0ds/NHqVBWRjNOucHf3RnefCJQAU4CjuvrGZna5mS00s4Xbtm3r6uEiE0+UU7tqFV5XF3UpIiL7dOhqGXffCTwHnAAMMLO8cFUJ0PzcuU3AYQDh+v5AZSvHut3dJ7v75KFDh3au+gwQT0zA6+qoffXVqEsREdmnPVfLDDWzAeF0HPgYsIog5M8NN5sOPB5OPxHOE66f71l8reC+ESLVNCMiGaQ9Z+6HAs+Z2VLgZeAZd/8D8A3gGjNbQ9Cmfle4/V3A4HD5NcCs1JedOfIOPZTcoUPUqSoiGSWvrQ3cfSkwqZXl6wja31surwXOS0l1PUAwQmS5OlVFJKPoDtUUiCcS1G3YQOPOnVGXIiICKNxT4r0RIpdFXImISEDhngKxsrJghEg1zYhIhlC4p0BucTGFo49Qp6qIZAyFe4rEEglqly7TCJEikhEU7ikST5TTuHMn9W++GXUpIiIK91TZ16mqh2aLSAZQuKdI4ejR4QiRumJGRKKncE8Ry8sjPn68OlVFJCMo3FMoVp5g78pVNGmESBGJmMI9heKJcry+nr2rV0ddioj0cgr3FNJj90QkUyjcUyj/kEPIO+ggXTEjIpFTuKdYvDyhTlURiZzCPcViiQT1G96kf45+tSISHSVQisUT5QAk4vGIKxGR3kzhnmLxsvGQk0MipnAXkego3FMsp6iIwtGjScRiUZciIr2Ywj0N4uUJJsTjGiFSRCKjcE+DWCLBgNxc6jdsiLoUEemlFO5p0NypquvdRSQqCvc0KBx9BNVNTbpTVUQio3BPA8vNZXltrc7cRSQyCvc0WVpTQ+3q1TTt3Rt1KSLSCync02RpbQ3U17N31aqoSxGRXkjhniZLa2sBdaqKSDQU7mnydkMDeQcfrE5VEYmEwj2N4omEztxFJBIK9zSKlyeo37iRhh07oi5FRHoZhXsaxRLhk5l09i4i3Uzhnkbx8cEIkbUKdxHpZgr3NMopKqJwzBh1qopIt1O4p1k8kaBm2TK8qSnqUkSkF1G4p1m8PEHT7t3UrdcIkSLSfRTuafZep6oemi0i3UfhnmaFRxxBTp8+6lQVkW6lcE8zy80lNmECNUuXRV2KiPQiCvduEE8kqH31VY0QKSLdRuHeDeLlCaivp3blyqhLEZFeos1wN7PDzOw5M1tpZivM7Kpw+SAze8bMXg9/DgyXm5n93MzWmNlSMzsm3R8i0zV3qqrdXUS6S3vO3BuAf3f3o4HjgS+b2dHALOBZdx8DPBvOA5wGjAlflwO/SnnVPUz+QQeRd+ihuplJRLpNm+Hu7m+5+z/C6XeBVcBw4ExgTrjZHOCscPpM4Dce+DswwMwOTXXhPY1GiBSR7tShNnczKwUmAS8CB7v7W+GqLcDB4fRwYGPSbhXhspbHutzMFprZwm3btnW07h4nnkhQX1GhESJFpFu0O9zNrBiYB3zV3Xcnr3N3B7wjb+zut7v7ZHefPHTo0I7s2iPFy8ObmV7RzUwikn7tCnczyycI9rnu/vtw8dbm5pbw59vh8k3AYUm7l4TLerXY+PGQm6umGRHpFu25WsaAu4BV7n5L0qongOnh9HTg8aTll4RXzRwP7Epqvum1cuJxCo88klp1qopIN8hrxzYnAhcDy8xsSbjsW8BNwENmNhPYAJwfrnsKOB1YA1QDM1JZcE8WTyTY/dRTeFMTlqNbDEQkfdoMd3d/AbD9rP5oK9s78OUu1pWV4okEOx98kLr16ykcNSrqckQki+n0sRvFExMAdL27iKSdwr0bFYwaRU5RkYb/FZG0U7h3o+YRItWpKiLppnDvZvFEgtrXXqOptjbqUkQkiyncu1m8PAENDRohUkTSSuHezeLNj91T04yIpJHCvZvlDR1K3rBD1akqImmlcI9APFGuTlURSSuFewTiiQT1mzfTsH171KWISJZSuEdg3wiRGkRMRNJE4R6B2NFHByNEqmlGRNJE4R6BnHicwrFHqlNVRNJG4R6ReCJB7bLleFNT1KWISBZSuEckniinqaqKunXroi5FRLKQwj0i7z12T+3uIpJ6CveIFIwcSU7fvrpiRkTSQuEeEcvJIT6hTOEuImmhcI9QLJFg72uv0VRTE3UpIpJlFO4RiifKobFRI0SKSMop3COkTlURSReFe4TyBg8mf/hwtbuLSMop3CMWL0/oTlURSTmFe8RiiQQNm9+iYdu2qEsRkSyicI9YPFEOaIRIEUkthXvEYkePg7w8daqKSEop3COWE4sRGztWZ+4iklIK9wwQL09Qu2wZ3tgYdSkikiUU7hkgNiFB0549GiFSRFJG4Z4B9Ng9EUk1hXsGKCgtDUaIVKeqiKSIwj0DBCNETtCZu4ikjMI9Q8TKwxEiq6ujLkVEsoDCPUPEEwloaqJ2xYqoSxGRLKBwzxDxct2pKiKpo3DPEHmDBpFfUqJOVRFJCYV7BoknEjpzF5GUULhnkHh5goYtW6jf+nbUpYhID6dwzyCxRPPNTBrfXUS6RuGeQWJHHw35+dSqaUZEuqjNcDezu83sbTNbnrRskJk9Y2avhz8HhsvNzH5uZmvMbKmZHZPO4rNNTmFhMEKkOlVFpIvac+Z+L3Bqi2WzgGfdfQzwbDgPcBowJnxdDvwqNWX2HvFEgtrlyzVCpIh0SZvh7u7/C+xosfhMYE44PQc4K2n5bzzwd2CAmR2aolp7hXh5gqbqavauWRt1KSLSg3W2zf1gd38rnN4CHBxODwc2Jm1XES77ADO73MwWmtnCbXp+6D7qVBWRVOhyh6q7O+Cd2O92d5/s7pOHDh3a1TKyRkFpKTn9+6tTVUS6pLPhvrW5uSX82Xxh9ibgsKTtSsJl0k5mFowQqU5VEemCzob7E8D0cHo68HjS8kvCq2aOB3YlNd9IO8UTCfauWUPTnj1RlyIiPVR7LoW8H/gbMNbMKsxsJnAT8DEzex3453Ae4ClgHbAGuAP4t7RUneXi5cEIkTXLNUKkiHROXlsbuPun97Pqo61s68CXu1pUb9fcqVq7bClFU6dEXI2I9ES6QzUD5Q0cSP7hh6vdXUQ6TeGeoTRCpIh0hcI9Q8UTCRq2bqV+69aoSxGRHkjhnqHi5eHNTK/oZiYR6TiFe4YqHDdOI0SKSKcp3DNUTkEBsaOOUqeqiHSKwj2DxRMJalas0AiRItJhCvcMFi9P4NXV7F2zJupSRKSHUbhnsHhCnaoi0jkK9wyWP2IEuf3763p3EekwhXsGMzPyS0upeu55qhcvjrocEelBFO4ZrHrxYmpXrKCxspI3p1+qgBeRdlO4Z7Dql16GpiYAvK6O7bf9Cg/nRUQOROGewfpMOQ4rKIDcHMjJYc+CBWy8/As0bN8edWkikuHaHPJXotNn0iQOv+duql96mT7HHcfeV1ez9T9uYt2ZZzHs5pspPunEqEsUkQxlwRDs0Zo8ebIvXLiwU/uaGZnwGVpKV121r77Gpn+/hro1axk083McdNVVwdm9iPQ6ZrbI3Se3tk7NMmlkZil/xY8aS9lTT/HgznfYcdfdPDTmSA4vKGj3/qWlpVH/WkSkG6hZJo3S/RfF7j/+iZzvfpdnhgzhkBtuoP+nPtnmPmaW1ppEJDPozL0H63fqvzDq0d9TOHYsm6+9ls2zvqmHaosIoHDv8fKHD2fEb+Yw5N++xK7HH+eNfz2X2pUroy5LRCKmcM8ClpfH0Cuv5PB776Wpupr1F1zIjjlzMrKjWUS6h8I9ixRNncLIxx+j6KST2PofN1HxxS/RsGNH1GWJSAQU7lkmb+BASm77JQd/+9vs+etfeePMs9jz979HXZaIdDOFexYyMwZd/FlKH36InOJi3pzxOd6+5ad4fX3UpYlIN9GlkFksdtRRjJz3CFt+9CMqb7+d6hdfZHh+ftRliUg30Jl7lsvp04dhP/gBw2/5CXvXruX3I0rZ/fTTUZclImmmcO8l+p1+OiMfe5R1dXVsuvoa3rruOpqqq6MuS0TSROHeixSUlHDxmxsYfPnl7HxkHm+cex61r74adVkikgYK916mATjomqs5/O67aHx3N+vPO58dc+fqmniRLKNw76WKTjiBUY8/Tp8Tjmfr939AxZevoOGdd6IuS0RSROHei+UNGsRhv/oVB39zFlULFvDGWWez56WXoi5LRFJA4d7LWU4Og6ZPp/SB+8mJxXjz0hls+/l/4w0NUZcmIl2gcBcA4uPHUzpvHv3POIPtt93GhkumU795c9RliUgnKdxln9ziIobd9B8M+6//ZO/q1aw762x2//nPUZclIp2gcJcP6P+pTzHysUcpOPxwNl15FW/dcANNtbVRlyUiHaBw74Xa8zi+whEjGDvvEe7aUcnOBx7kD+OOZkxhYVoeHajH/4mknsaW6YU6ek171YIXyJ01i//p14+DvzmLARdckPLH9enxfyKppTN3aVPxtJMY9fhj9Jk8mS03fI9NV32Vxl27oi5LRA5A4S7tkjdkCIfdcTsHXfs13p0/n7Wnf4LN3/kOVX/9G97UFHV5ItJCWpplzOxU4GdALnCnu9+UjveR7mU5OQyeOZOc/gPYct117HpkHrsemQdATnExOUVFwc/iInKLij+4rLiYnKLmZeF8+BqYm0tTXR05BQURf0qR7JDycDezXOCXwMeACuBlM3vC3fXU5izRWFkJZuAOZvSZMoXCsUfSVLWHpj17aKqqoqmqivq3t75vGQdo6/+/0WN4NVGO5efvC/x9XwJF730J5BQXkVOU9EWx78sk+BLZu2EDe1esID55MvFEORCUSnObvlmr00br24w64gjWb9jQ5d9ZeSzGlD59eKm6mldSdOXRiBEjWL9+fZePU714MdUvvUyfKcfRZ9KkrheWIqqra9Jx5j4FWOPu6wDM7AHgTEDhniX6TDkOKyjA6+ux/HyGfvWqNv+Re1MTXlNDY9UemvZU7Qv8xqoqmqr2cMXnPkdRTg5FOTkU5+RQnJuTNJ9LcdK6WE73tSY+FYvD2KPev7CDXxTuDnV17+1fWIjl5na5tj1VVaw+5tguHcMbG2Hv3n3zlqLausobG/FMrssMKyzk8HvuztiAT0e4Dwc2Js1XAFNbbmRmlwOXh7NVZtbpsWdTeKXFEGB7qg6WzXUV5eQU9TE7pNp9y55jjtmTgrK67OC8vEMG5+YNN8Bxr2xs3Ly1oWGL6tq/TK2tx9Q1ZUrUdY3Y34rILoV099uB26N6/9aY2UJ3nxx1HS2pro5RXR2XqbWprs5Lx9+3m4DDkuZLwmUiItJN0hHuLwNjzGykmRUAFwJPpOF9RERkP1LeLOPuDWZ2BfAngksh73b3Fal+nzTJqGaiJKqrY1RXx2Vqbaqrk0yPVxMRyT66Q1VEJAsp3EVEspDCPWRmp5rZq2a2xsxmRV0PgJndbWZvm9nyqGtJZmaHmdlzZrbSzFaY2VVR1wRgZjEze8nMXgnr+l7UNSUzs1wzW2xmf4i6lmZmtt7MlpnZEjNbGHU9zcxsgJk9YmarzWyVmZ2QATWNDX9Pza/dZvbVqOvaH7W5s2/IhNdIGjIB+HTUQyaY2clAFfAbdy+LspZkZnYocKi7/8PM+gKLgLMy4PdlQJG7V5lZPvACcJW7/z3KupqZ2TXAZKCfu38y6nogCHdgsrun7Ca5VDCzOcACd78zvOquj7vvjLisfcLM2ARMdfeuj0+RBjpzD+wbMsHd64DmIRMi5e7/C+yIuo6W3P0td/9HOP0usIrgzuRIeaAqnM0PXxlx9mJmJcAngDujriXTmVl/4GTgLgB3r8ukYA99FFibqcEOCvdmrQ2ZEHlY9QRmVgpMAl6MuBRgX9PHEuBt4Bl3z4i6gFuBrwOZNj6yA382s0XhkCCZYCSwDbgnbMa608yKoi6qhQuB+6Mu4kAU7tJpZlYMzAO+6u67o64HwN0b3X0iwZ3RU8ws8uYsM/sk8La7L4q6llac5O7HAKcBXw6bAqOWBxwD/MrdJwF7gIzoBwMIm4nOAB6OupYDUbgHNGRCB4Vt2vOAue7++6jraSn8M/454NSISwE4ETgjbN9+ADjFzO6LtqSAu28Kf74NPErQRBm1CqAi6a+uRwjCPlOcBvzD3bdGXciBKNwDGjKhA8KOy7uAVe5+S9T1NDOzoWY2IJyOE3SQr460KMDdv+nuJe5eSvBva767fzbisjCzorBDnLDZ4+NA5FdmufsWYKOZjQ0XfZTMGjL802R4kwzoAdlA5g6ZYGb3Ax8GhphZBXC9u98VbVVAcCZ6MbAsbN8G+Ja7PxVdSQAcCswJr2TIAR5y94y57DADHQw8Gg4BnQf8zt3/GG1J+3wFmBuebK0DZkRcD7DvS/BjwBeirqUtuhRSRCQLqVlGRCQLKdxFRLKQwl1EJAsp3EVEspDCXUQkCyncJWXMrKod29xpZkeH099qse6vqXiPVDKz580s7Q9CNrMrw9EP53bxOPea2bnhdLfULplJ4S7dyt0vSxo98lst1n0ogpLSxsw6ch/JvwEfc/fPpKse6V0U7pJyZvbh8KyxeTzuueFdrfvOJs3sJiAejos9N1xXFf4sNrNnzewf4VjjBxyh08xKw7PeO8Jx3P8c3qH6vrNXMxsSDgGAmV1qZo+Z2TPhmOZXmNk14UBVfzezQUlvcXFY53IzmxLuX2TBePsvhfucmXTcJ8xsPvBsK7VeEx5nefNY4GY2GxgFPG1mV7fYPtfMfhxuv9TMvhIuP9bM/hIO+PUnC4Zh3t/vJzc8o18e/j6v3t+2kkXcXS+9UvICqsKfHwZ2EYzRkwP8jWCAKoDnCcYP37d9K/vnEYx5DjAEWMN7N9xVtfK+pUADMDGcfwj4bCvvNwRYH05fGh63LzA0rPeL4bqfEgyG1rz/HeH0ycDycPpHSe8xgOB5AEXhcSuAQa3UeSywLNyuGFgBTArXrQeGtLLPlwjGVskL5wcRDGX8V2BouOwCgruqAe4Fzk3+7OH7PpN0zAFR/1vRK/0vDT8g6fKSu1cAhEMUlBI8PKM9DPhROEJhE8HwywcDWw6wzxvuviScXhS+X1ue82A8+nfNbBfwP+HyZUAiabv7IRhf38z6hePXfJxgMLCvhdvEgMPD6WfcvbVx+E8CHnX3PQBm9ntgGrD4ADX+MzDb3RvCGnaEI12WAc+EfxDlAm8d4BjrgFFm9t/Ak8CfD7CtZAmFu6TL3qTpRjr2b+0zBGfTx7p7fdiUEuvg+8XD6Qbea35seYzkfZqS5pta1NtyjA4n+AL6V3d/NXmFmU0lGKI2nQxY4e7tevScu79jZuXAvwBfBM4HPpfG+iQDqM1dolQfDh3cUn+C8c/rzewjwIguvMd6gmYJgHM7eYwLAMzsJGCXu+8iGGTuK0l9CZPacZwFwFlm1iccgOrscNmBPAN8oblzNuwLeBUYauFzRc0s38zG7+8AZjYEyHH3ecB3yKzhcyVNFO4SpduBpa1c/jcXmGxmy4BL6NqwvT8GvmRmiwna3DujNtx/NjAzXPZ9grbvpWa2Ipw/IA8eTXgv8BLBk6vudPcDNclA8Fi+N8P3eQW4yINHQZ4L3BwuWwIc6Eqj4cDzYfPYfcA326pVej6NCikikoV05i4ikoUU7iIiWUjhLiKShRTuIiJZSOEuIpKFFO4iIllI4S4ikoX+PzeU4+RQV2/gAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
+     "ename": "KeyError",
+     "evalue": "'aicsimageio.readers.nd2_reader.ND2Reader'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_10296/3996781571.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mimg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mimread_xarray_dask\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'E:/Andrey/20220124-MIC-cipro-resistant//day1/raw/{ng:03d}ng-BF001.nd2'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32m~\\miniconda3\\envs\\nd2\\lib\\site-packages\\aicsimageio\\aics_image.py\u001b[0m in \u001b[0;36mimread_xarray_dask\u001b[1;34m(image, scene_id, **kwargs)\u001b[0m\n\u001b[0;32m    881\u001b[0m         \u001b[0mxarray\u001b[0m \u001b[0mDataArray\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    882\u001b[0m     \"\"\"\n\u001b[1;32m--> 883\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0m_construct_img\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscene_id\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxarray_dask_data\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    884\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    885\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\miniconda3\\envs\\nd2\\lib\\site-packages\\aicsimageio\\aics_image.py\u001b[0m in \u001b[0;36m_construct_img\u001b[1;34m(image, scene_id, **kwargs)\u001b[0m\n\u001b[0;32m    848\u001b[0m ) -> AICSImage:\n\u001b[0;32m    849\u001b[0m     \u001b[1;31m# Construct image\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 850\u001b[1;33m     \u001b[0mimg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mAICSImage\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    851\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    852\u001b[0m     \u001b[1;31m# Select scene\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\miniconda3\\envs\\nd2\\lib\\site-packages\\aicsimageio\\aics_image.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, image, reader, reconstruct_mosaic, **kwargs)\u001b[0m\n\u001b[0;32m    224\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mreader\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    225\u001b[0m             \u001b[1;31m# Determine reader class and create dask delayed array\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 226\u001b[1;33m             \u001b[0mReaderClass\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdetermine_reader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    227\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    228\u001b[0m             \u001b[1;31m# Init reader\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\miniconda3\\envs\\nd2\\lib\\site-packages\\aicsimageio\\aics_image.py\u001b[0m in \u001b[0;36mdetermine_reader\u001b[1;34m(image, **kwargs)\u001b[0m\n\u001b[0;32m    189\u001b[0m             \u001b[1;32mfor\u001b[0m \u001b[0mformat_ext\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreaders\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mFORMAT_IMPLEMENTATIONS\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    190\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mendswith\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\".{format_ext}\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 191\u001b[1;33m                     \u001b[0minstaller\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mREADER_TO_INSTALL\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mreaders\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    192\u001b[0m                     raise exceptions.UnsupportedFileFormatError(\n\u001b[0;32m    193\u001b[0m                         \u001b[1;34m\"AICSImage\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mKeyError\u001b[0m: 'aicsimageio.readers.nd2_reader.ND2Reader'"
+     ]
     }
    ],
    "source": [
-    "_ = [Thread(target=align2D, args=(p,)).start() for p in paths]"
+    "img = imread_xarray_dask('E:/Andrey/20220124-MIC-cipro-resistant//day1/raw/{ng:03d}ng-BF001.nd2')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "2f595774-7b96-45bd-8aeb-8a567b691245",
+   "execution_count": 12,
+   "id": "647e28a9-7630-4ed7-84fd-3701a15d89a7",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[None, None, None, None, None, None]"
+       "['E:Andrey/20220125-test-novec\\\\10min\\\\0ng-BF.nd2',\n",
+       " 'E:Andrey/20220125-test-novec\\\\10min\\\\0ng-TRITC.nd2',\n",
+       " 'E:Andrey/20220125-test-novec\\\\24h\\\\0ng-BF.nd2',\n",
+       " 'E:Andrey/20220125-test-novec\\\\24h\\\\0ng-TRITC.nd2']"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "_"
+    "paths = glob('E:Andrey/20220125-test-novec/*/*ng*.nd2')\n",
+    "paths"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 13,
+   "id": "23b81185-99b8-46c9-a3f4-d6461ff044f0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 ng\n"
+     ]
+    }
+   ],
+   "source": [
+    "Thread(target=align3D, args=('E:Andrey/20220125-test-novec\\\\10min\\\\0ng-BF.nd2',\n",
+    "                             'E:Andrey/20220125-test-novec\\\\10min\\\\0ng-TRITC.nd2',\n",
+    "                             'E:Andrey/20220125-test-novec\\\\10min\\\\0ng-composite.tif',)).start()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "0817c20f-04cf-4cb5-9976-1fdfa51e96a0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 ng\n",
+      "Aligning None: \n",
+      " bf: (7383, 22392),  2\n",
+      " tmp: (818, 2612),  16\n",
+      " mask: (6544, 20896),  2\n",
+      "\n",
+      "{'tvec': array([62.01352349,  7.40486583]), 'success': 0.03637142480762208, 'angle': 0.4724671833309344, 'scale': 0.9998402492512599, 'Dscale': 0.00047771527254494015, 'Dangle': 0.013400833829660513, 'Dt': 0.25, 'timg': None}\n",
+      "transform (7383, 22392)\n",
+      "Aligning None: \n",
+      " bf: (8878, 22386),  2\n",
+      " tmp: (818, 2612),  16\n",
+      " mask: (6544, 20896),  2\n",
+      "\n",
+      "transform (7383, 22392)\n",
+      "{'tvec': array([ 94.47931348, 104.56988561]), 'success': 0.021573970518913133, 'angle': -4.950760768229145, 'scale': 0.9952701484456418, 'Dscale': 0.0004892683005708673, 'Dangle': 0.013400833829660513, 'Dt': 0.25, 'timg': None}\n",
+      "transform (8878, 22386)\n",
+      "(3, 6544, 20896)\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": [
+      "Saving count to E:Andrey/20220125-test-novec\\10min\\0ng-composite-counts.csv\n",
+      "transform (8878, 22386)\n",
+      "(3, 6544, 20896)\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": [
+      "Saving count to E:Andrey/20220125-test-novec\\24h\\0ng-composite-counts.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "Thread(target=align3D, args=('E:Andrey/20220125-test-novec\\\\24h\\\\0ng-BF.nd2',\n",
+    "                             'E:Andrey/20220125-test-novec\\\\24h\\\\0ng-TRITC.nd2',\n",
+    "                             'E:Andrey/20220125-test-novec\\\\24h\\\\0ng-composite.tif',)).start()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "26faaacc-5aad-435e-9225-d5100449564d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 ug\n",
+      "Aligning E:Andrey/20220124-MIC-cipro-resistant//day2/raw\\000ng-BF-TRITC-cf-wf.nd2: \n",
+      " bf: (7383, 22392),  2\n",
+      " tmp: (818, 2612),  16\n",
+      " mask: (6544, 20896),  2\n",
+      "\n",
+      "{'tvec': array([ 22.48108866, -69.29357712]), 'success': 0.049799268940690006, 'angle': -1.8991117647310602, 'scale': 0.9947105964170997, 'Dscale': 0.00047526436750929387, 'Dangle': 0.013400833829660513, 'Dt': 0.25, 'timg': None}\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "Saved aligned stack E:Andrey/20220124-MIC-cipro-resistant//day2/raw\\000ng-BF-TRITC-cf-wf.aligned.tif\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"
+    }
+   ],
+   "source": [
+    "Thread(target=align2D, args=(\"E:Andrey/20220124-MIC-cipro-resistant//day2/raw\\\\000ng-BF-TRITC-cf-wf.nd2\",)).start()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "abf620ed-b02a-4b8a-8568-9a263d1c0987",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3296 ug\n",
+      " ug\n",
+      "128 ug\n",
+      "192 256ug \n",
+      "64 ug\n",
+      "ug\n",
+      "Aligning E:Andrey/20220124-MIC-cipro-resistant//day2/raw\\032ng-BF-TRITC.nd2: \n",
+      " bf: (7383, 22392),  2\n",
+      " tmp: (818, 2612),  16\n",
+      " mask: (6544, 20896),  2\n",
+      "\n",
+      "Aligning E:Andrey/20220124-MIC-cipro-resistant//day2/raw\\192ng-BF-TRITC.nd2: \n",
+      " bf: (7383, 22392),  2\n",
+      " tmp: (818, 2612),  16\n",
+      " mask: (6544, 20896),  2\n",
+      "\n",
+      "Aligning E:Andrey/20220124-MIC-cipro-resistant//day2/raw\\096ng-BF-TRITC.nd2: \n",
+      " bf: (7383, 22392),  2\n",
+      " tmp: (818, 2612),  16\n",
+      " mask: (6544, 20896),  2\n",
+      "\n",
+      "Aligning E:Andrey/20220124-MIC-cipro-resistant//day2/raw\\128ng-BF-TRITC.nd2: \n",
+      " bf: (7383, 22392),  2\n",
+      " tmp: (818, 2612),  16\n",
+      " mask: (6544, 20896),  2\n",
+      "\n",
+      "Aligning E:Andrey/20220124-MIC-cipro-resistant//day2/raw\\64ng-BF-TRITC.nd2: \n",
+      " bf: (7383, 22392),  2\n",
+      " tmp: (818, 2612),  16\n",
+      " mask: (6544, 20896),  2\n",
+      "\n",
+      "Aligning E:Andrey/20220124-MIC-cipro-resistant//day2/raw\\256ng-BF-TRITC.nd2: \n",
+      " bf: (7383, 22392),  2\n",
+      " tmp: (818, 2612),  16\n",
+      " mask: (6544, 20896),  2\n",
+      "\n",
+      "{'tvec': array([147.11614181, 103.01484957]), 'success': 0.07189963945107272, 'angle': 0.20269948898882717, 'scale': 0.9945409070208068, 'Dscale': 0.0004751832913411172, 'Dangle': 0.013400833829660513, 'Dt': 0.25, 'timg': None}\n",
+      "transform (7383, 22392)\n",
+      "{'tvec': array([-97.76961665,  81.05622809]), 'success': 0.03007398614601033, 'angle': -1.7707895012592019, 'scale': 0.9967012147445565, 'Dscale': 0.0004762154682251793, 'Dangle': 0.013400833829660513, 'Dt': 0.25, 'timg': None}\n",
+      "transform (7383, 22392)\n",
+      "{'tvec': array([116.07226221,  56.35645877]), 'success': 0.03402017283993729, 'angle': -0.4860527503272749, 'scale': 0.996142191379233, 'Dscale': 0.0004759483715569621, 'Dangle': 0.013400833829660513, 'Dt': 0.25, 'timg': None}{'tvec': array([17.78810891, -0.17171515]), 'success': 0.04070191232057892, 'angle': 0.506930402049079, 'scale': 0.9949575363307372, 'Dscale': 0.00047538235332576193, 'Dangle': 0.013400833829660513, 'Dt': 0.25, 'timg': None}{'tvec': array([-30.2451639 ,  39.68105326]), 'success': 0.04349219799013625, 'angle': -0.5254391385828399, 'scale': 0.9971468486743948, 'Dscale': 0.00047642838837358046, 'Dangle': 0.013400833829660513, 'Dt': 0.25, 'timg': None}\n",
+      "\n",
+      "transform (7383, 22392)\n",
+      "\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "{'tvec': array([112.46861282, 205.87009387]), 'success': 0.04443055988413198, 'angle': -2.382327045168239, 'scale': 0.9954625018283763, 'Dscale': 0.0004756236215988796, 'Dangle': 0.013400833829660513, 'Dt': 0.25, 'timg': None}\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "Saved aligned stack E:Andrey/20220124-MIC-cipro-resistant//day2/raw\\032ng-BF-TRITC.aligned.tif\n",
+      "Saved aligned stack E:Andrey/20220124-MIC-cipro-resistant//day2/raw\\256ng-BF-TRITC.aligned.tif\n",
+      "Saved aligned stack E:Andrey/20220124-MIC-cipro-resistant//day2/raw\\192ng-BF-TRITC.aligned.tifSaved aligned stack E:Andrey/20220124-MIC-cipro-resistant//day2/raw\\128ng-BF-TRITC.aligned.tif\n",
+      "\n",
+      "Saved aligned stack E:Andrey/20220124-MIC-cipro-resistant//day2/raw\\64ng-BF-TRITC.aligned.tif\n",
+      "Saved aligned stack E:Andrey/20220124-MIC-cipro-resistant//day2/raw\\096ng-BF-TRITC.aligned.tif\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"
+    }
+   ],
+   "source": [
+    "_ = [Thread(target=align2D, args=(p,)).start() for p in paths]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "2f595774-7b96-45bd-8aeb-8a567b691245",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[None, None, None, None, None, None]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
    "id": "12dd3437-238e-4491-b326-8d4cef1c6692",
    "metadata": {},
    "outputs": [],
@@ -1734,252 +2077,609 @@
     }
    ],
    "source": [
-    "timelapse"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "2ef73cc9-f0fa-4f4f-92ec-f2e079c5808b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "max_proj = timelapse.max(axis=2).compute()"
+    "timelapse"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "2ef73cc9-f0fa-4f4f-92ec-f2e079c5808b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "max_proj = timelapse.max(axis=2).compute()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "7503d34a-4b6f-45a7-9e02-5e3c1c693d3d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(39, 1, 7383, 22392)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "max_proj.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "697b9070-09a7-4b54-8ae2-02eb83e13591",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "0b9e35dd-7cdc-47fe-8e28-b3d502abaeb5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "mean_proj = timelapse.mean(axis=2, dtype='float32').compute()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "004aaaa5-220c-4eb8-94be-002581381441",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tf.imwrite('E:Andrey/20220111-MIC-resistant/timelapse-30min/0ng-TRITC-19h_maxIP.tif', max_proj)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "6837db08-bb45-47ee-9864-7f3459f07752",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tf.imwrite('Y:Lena/Data//20220111-MIC-resistant/timelapse-30min/0ng-TRITC-19h_meanIP.tif', mean_proj)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "3028c8e0-a242-44f8-9cf4-f46509b4cc55",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mean_proj = tf.imread('Y:Lena/Data//20220111-MIC-resistant/timelapse-30min/0ng-TRITC-19h_meanIP.tif')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "df9f1fe8-9da8-40f0-953e-36c3992c043d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import napari"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "65a7b489-8bb3-4e0a-9c84-87f9901c2abf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "v = napari.Viewer()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "625f2ef1-23b4-4258-8997-2d458f43916d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1, 1, 1, 7383, 22392)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# fluo = tf.imread('E:Andrey/20220111-MIC-resistant/timelapse-30min/0ng-TRITC-19h_maxIP.tif')\n",
+    "bf = imread('Y:Lena/Data/20220111-MIC-resistant/timelapse-30min/0ng-BF.nd2')\n",
+    "bf.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "ca44c0dd-d47b-43bd-bd24-09ebe6d3bb5c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(39, 1, 7383, 22392)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean_proj.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "fa8e6661-463f-454c-870b-88ac57797b75",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'tvec': array([-15.72473317,  20.36454919]), 'success': 0.03702985214889686, 'angle': -2.7811090559223715, 'scale': 0.9929738758061613, 'Dscale': 0.00047443457698964815, 'Dangle': 0.013400833829660513, 'Dt': 0.25, 'timg': None}\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n"
+     ]
+    }
+   ],
+   "source": [
+    "aligned_maxIP = register.align_timelapse(bf[0,0,0], fluo[:,0], template16=template16, mask2=big_labels, binnings=(2,16,2))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "id": "7503d34a-4b6f-45a7-9e02-5e3c1c693d3d",
-   "metadata": {},
+   "execution_count": 7,
+   "id": "c5fe5614-bcdb-41ad-b44a-8b6d1085c0fa",
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "(39, 1, 7383, 22392)"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'tvec': array([-15.72473317,  20.36454919]), 'success': 0.03702985214889686, 'angle': -2.7811090559223715, 'scale': 0.9929738758061613, 'Dscale': 0.00047443457698964815, 'Dangle': 0.013400833829660513, 'Dt': 0.25, 'timg': None}\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n"
+     ]
     }
    ],
    "source": [
-    "max_proj.shape"
+    "aligned_meanIP = register.align_timelapse(bf[0,0,0], mean_proj[:,0], template16=template16, mask2=big_labels, binnings=(2,16,2))"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "697b9070-09a7-4b54-8ae2-02eb83e13591",
+   "id": "0dd9f5c3-e5a3-4209-a4f4-fbb437d1ffce",
    "metadata": {},
    "outputs": [],
    "source": []
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "0b9e35dd-7cdc-47fe-8e28-b3d502abaeb5",
-   "metadata": {
-    "tags": []
-   },
+   "execution_count": 32,
+   "id": "d15ad928-85d2-44ee-966c-78f6596002b1",
+   "metadata": {},
    "outputs": [],
    "source": [
-    "mean_proj = timelapse.mean(axis=2, dtype='float32').compute()"
+    "tf.imwrite('Y:Lena/Data/20220111-MIC-resistant/timelapse-30min/0ng-fluo-aligned-end.tif', aligned_meanIP[1][38])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "id": "004aaaa5-220c-4eb8-94be-002581381441",
+   "execution_count": 8,
+   "id": "6d356482-babe-4d4d-8e4f-51649c5cd698",
    "metadata": {},
    "outputs": [],
    "source": [
-    "tf.imwrite('E:Andrey/20220111-MIC-resistant/timelapse-30min/0ng-TRITC-19h_maxIP.tif', max_proj)"
+    "fluo = np.array(aligned_meanIP[1]).reshape((39, 1, 1, 6544, 20896))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "6837db08-bb45-47ee-9864-7f3459f07752",
+   "execution_count": 34,
+   "id": "eb75199b-6c80-431b-a99b-5e75b3ea37fc",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(39, 1, 1, 6544, 20896)"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "tf.imwrite('Y:Lena/Data//20220111-MIC-resistant/timelapse-30min/0ng-TRITC-19h_meanIP.tif', mean_proj)"
+    "fluo.shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
-   "id": "3028c8e0-a242-44f8-9cf4-f46509b4cc55",
+   "execution_count": 36,
+   "id": "60579e7f-dee4-4ee1-995d-b01cb26190a9",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(39, 22392)"
+       "(39, 1, 1, 10, 10)"
       ]
      },
-     "execution_count": 91,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "max_proj.shape"
+    "fluo[:,:,:,:10,:10].shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "df9f1fe8-9da8-40f0-953e-36c3992c043d",
+   "execution_count": 37,
+   "id": "6cd86282-4047-4665-89bd-198a17a43a60",
    "metadata": {},
    "outputs": [],
    "source": [
-    "import napari"
+    "tf.imwrite('Y:Lena/Data/20220111-MIC-resistant/timelapse-30min/0ng-fluo-aligned-bin10.tif', fluo[:,:,:,::10,::10])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "65a7b489-8bb3-4e0a-9c84-87f9901c2abf",
+   "execution_count": 11,
+   "id": "041e24ea-2fa8-4af7-84ee-49f32ac81829",
    "metadata": {},
    "outputs": [],
    "source": [
-    "v = napari.Viewer()"
+    "from droplet_growth import multiwell"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "625f2ef1-23b4-4258-8997-2d458f43916d",
+   "execution_count": 21,
+   "id": "e0b833e5-1c93-4b50-beb7-df1365e38bee",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "labels = tf.imread('Y:/Lena/Data/20220111-MIC-resistant/timelapse-30min/lables.tif')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "51daa291-bdfc-4b28-ac07-47e430d6f4c8",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(1, 1, 1, 7383, 22392)"
+       "array([[0, 0, 0, ..., 0, 0, 0],\n",
+       "       [0, 0, 0, ..., 0, 0, 0],\n",
+       "       [0, 0, 0, ..., 0, 0, 0],\n",
+       "       ...,\n",
+       "       [0, 0, 0, ..., 0, 0, 0],\n",
+       "       [0, 0, 0, ..., 0, 0, 0],\n",
+       "       [0, 0, 0, ..., 0, 0, 0]], dtype=uint16)"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "fluo = tf.imread('E:Andrey/20220111-MIC-resistant/timelapse-30min/0ng-TRITC-19h_maxIP.tif')\n",
-    "bf = imread('E:Andrey/20220111-MIC-resistant/timelapse-30min/0ng-BF.nd2')\n",
-    "bf.shape"
+    "labels"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "ca44c0dd-d47b-43bd-bd24-09ebe6d3bb5c",
+   "execution_count": 22,
+   "id": "f4ba38f6-388c-4a66-aa43-2113e272a313",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(39, 7383, 22392)"
+       "(6544, 20896)"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "fluo[:,0].shape"
+    "labels.shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "fa8e6661-463f-454c-870b-88ac57797b75",
+   "execution_count": 19,
+   "id": "aeb052db-15dd-498a-b2ec-31d7f546dd2b",
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
+     "data": {
+      "text/plain": [
+       "(39, 6544, 20896)"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fluo[:,0,0].shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "827695fb-76ee-45fb-b633-0670f18b6d74",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "{'tvec': array([-15.72473317,  20.36454919]), 'success': 0.03702985214889686, 'angle': -2.7811090559223715, 'scale': 0.9929738758061613, 'Dscale': 0.00047443457698964815, 'Dangle': 0.013400833829660513, 'Dt': 0.25, 'timg': None}\n",
-      "transform (7383, 22392)\n",
-      "transform (7383, 22392)\n",
-      "transform (7383, 22392)\n",
-      "transform (7383, 22392)\n",
-      "transform (7383, 22392)\n",
-      "transform (7383, 22392)\n",
-      "transform (7383, 22392)\n",
-      "transform (7383, 22392)\n",
-      "transform (7383, 22392)\n",
-      "transform (7383, 22392)\n",
-      "transform (7383, 22392)\n",
-      "transform (7383, 22392)\n",
-      "transform (7383, 22392)\n",
-      "transform (7383, 22392)\n",
-      "transform (7383, 22392)\n",
-      "transform (7383, 22392)\n",
-      "transform (7383, 22392)\n",
-      "transform (7383, 22392)\n",
-      "transform (7383, 22392)\n",
-      "transform (7383, 22392)\n",
-      "transform (7383, 22392)\n",
-      "transform (7383, 22392)\n",
-      "transform (7383, 22392)\n",
-      "transform (7383, 22392)\n",
-      "transform (7383, 22392)\n",
-      "transform (7383, 22392)\n",
-      "transform (7383, 22392)\n",
-      "transform (7383, 22392)\n",
-      "transform (7383, 22392)\n",
-      "transform (7383, 22392)\n",
-      "transform (7383, 22392)\n",
-      "transform (7383, 22392)\n",
-      "transform (7383, 22392)\n",
-      "transform (7383, 22392)\n",
-      "transform (7383, 22392)\n",
-      "transform (7383, 22392)\n",
-      "transform (7383, 22392)\n",
-      "transform (7383, 22392)\n",
-      "transform (7383, 22392)\n",
-      "transform (7383, 22392)\n"
+      "C:\\Users\\nikon\\miniconda3\\envs\\nd2\\lib\\site-packages\\seaborn\\categorical.py:1296: UserWarning: 68.7% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
+      "  warnings.warn(msg, UserWarning)\n",
+      "C:\\Users\\nikon\\miniconda3\\envs\\nd2\\lib\\site-packages\\seaborn\\categorical.py:1296: UserWarning: 69.7% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
+      "  warnings.warn(msg, UserWarning)\n",
+      "C:\\Users\\nikon\\miniconda3\\envs\\nd2\\lib\\site-packages\\seaborn\\categorical.py:1296: UserWarning: 69.3% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
+      "  warnings.warn(msg, UserWarning)\n",
+      "C:\\Users\\nikon\\miniconda3\\envs\\nd2\\lib\\site-packages\\seaborn\\categorical.py:1296: UserWarning: 71.5% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
+      "  warnings.warn(msg, UserWarning)\n",
+      "C:\\Users\\nikon\\miniconda3\\envs\\nd2\\lib\\site-packages\\seaborn\\categorical.py:1296: UserWarning: 68.9% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
+      "  warnings.warn(msg, UserWarning)\n",
+      "C:\\Users\\nikon\\miniconda3\\envs\\nd2\\lib\\site-packages\\seaborn\\categorical.py:1296: UserWarning: 71.3% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
+      "  warnings.warn(msg, UserWarning)\n",
+      "C:\\Users\\nikon\\miniconda3\\envs\\nd2\\lib\\site-packages\\seaborn\\categorical.py:1296: UserWarning: 70.1% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
+      "  warnings.warn(msg, UserWarning)\n",
+      "C:\\Users\\nikon\\miniconda3\\envs\\nd2\\lib\\site-packages\\seaborn\\categorical.py:1296: UserWarning: 72.5% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
+      "  warnings.warn(msg, UserWarning)\n",
+      "C:\\Users\\nikon\\miniconda3\\envs\\nd2\\lib\\site-packages\\seaborn\\categorical.py:1296: UserWarning: 70.3% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
+      "  warnings.warn(msg, UserWarning)\n",
+      "C:\\Users\\nikon\\miniconda3\\envs\\nd2\\lib\\site-packages\\seaborn\\categorical.py:1296: UserWarning: 69.1% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
+      "  warnings.warn(msg, UserWarning)\n",
+      "C:\\Users\\nikon\\miniconda3\\envs\\nd2\\lib\\site-packages\\seaborn\\categorical.py:1296: UserWarning: 69.9% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
+      "  warnings.warn(msg, UserWarning)\n",
+      "C:\\Users\\nikon\\miniconda3\\envs\\nd2\\lib\\site-packages\\seaborn\\categorical.py:1296: UserWarning: 67.9% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
+      "  warnings.warn(msg, UserWarning)\n",
+      "C:\\Users\\nikon\\miniconda3\\envs\\nd2\\lib\\site-packages\\seaborn\\categorical.py:1296: UserWarning: 66.1% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
+      "  warnings.warn(msg, UserWarning)\n",
+      "C:\\Users\\nikon\\miniconda3\\envs\\nd2\\lib\\site-packages\\seaborn\\categorical.py:1296: UserWarning: 65.5% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
+      "  warnings.warn(msg, UserWarning)\n",
+      "C:\\Users\\nikon\\miniconda3\\envs\\nd2\\lib\\site-packages\\seaborn\\categorical.py:1296: UserWarning: 63.9% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
+      "  warnings.warn(msg, UserWarning)\n",
+      "C:\\Users\\nikon\\miniconda3\\envs\\nd2\\lib\\site-packages\\seaborn\\categorical.py:1296: UserWarning: 61.1% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
+      "  warnings.warn(msg, UserWarning)\n",
+      "C:\\Users\\nikon\\miniconda3\\envs\\nd2\\lib\\site-packages\\seaborn\\categorical.py:1296: UserWarning: 60.1% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
+      "  warnings.warn(msg, UserWarning)\n",
+      "C:\\Users\\nikon\\miniconda3\\envs\\nd2\\lib\\site-packages\\seaborn\\categorical.py:1296: UserWarning: 58.3% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
+      "  warnings.warn(msg, UserWarning)\n",
+      "C:\\Users\\nikon\\miniconda3\\envs\\nd2\\lib\\site-packages\\seaborn\\categorical.py:1296: UserWarning: 54.3% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
+      "  warnings.warn(msg, UserWarning)\n",
+      "C:\\Users\\nikon\\miniconda3\\envs\\nd2\\lib\\site-packages\\seaborn\\categorical.py:1296: UserWarning: 56.1% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
+      "  warnings.warn(msg, UserWarning)\n",
+      "C:\\Users\\nikon\\miniconda3\\envs\\nd2\\lib\\site-packages\\seaborn\\categorical.py:1296: UserWarning: 53.9% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
+      "  warnings.warn(msg, UserWarning)\n",
+      "C:\\Users\\nikon\\miniconda3\\envs\\nd2\\lib\\site-packages\\seaborn\\categorical.py:1296: UserWarning: 52.5% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
+      "  warnings.warn(msg, UserWarning)\n",
+      "C:\\Users\\nikon\\miniconda3\\envs\\nd2\\lib\\site-packages\\seaborn\\categorical.py:1296: UserWarning: 52.1% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
+      "  warnings.warn(msg, UserWarning)\n",
+      "C:\\Users\\nikon\\miniconda3\\envs\\nd2\\lib\\site-packages\\seaborn\\categorical.py:1296: UserWarning: 53.3% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
+      "  warnings.warn(msg, UserWarning)\n",
+      "C:\\Users\\nikon\\miniconda3\\envs\\nd2\\lib\\site-packages\\seaborn\\categorical.py:1296: UserWarning: 53.1% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
+      "  warnings.warn(msg, UserWarning)\n",
+      "C:\\Users\\nikon\\miniconda3\\envs\\nd2\\lib\\site-packages\\seaborn\\categorical.py:1296: UserWarning: 52.3% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
+      "  warnings.warn(msg, UserWarning)\n",
+      "C:\\Users\\nikon\\miniconda3\\envs\\nd2\\lib\\site-packages\\seaborn\\categorical.py:1296: UserWarning: 51.3% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
+      "  warnings.warn(msg, UserWarning)\n",
+      "C:\\Users\\nikon\\miniconda3\\envs\\nd2\\lib\\site-packages\\seaborn\\categorical.py:1296: UserWarning: 54.5% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
+      "  warnings.warn(msg, UserWarning)\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"
     }
    ],
    "source": [
-    "aligned_maxIP = register.align_timelapse(bf[0,0,0], fluo[:,0], template16=template16, mask2=big_labels, binnings=(2,16,2))"
+    "intensities = multiwell.get_intensity_table(labelled_mask=labels.astype('int'), intensity_image_sequence=fluo[:,0,0])intensities = multiwell.get_intensity_table(labelled_mask=labels.astype('int'), intensity_image_sequence=fluo[:,0,0])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "id": "6d356482-babe-4d4d-8e4f-51649c5cd698",
+   "execution_count": 26,
+   "id": "764b27ee-bc5f-4e1c-b8ba-d7e7111ba974",
    "metadata": {},
    "outputs": [],
    "source": [
-    "fluo = np.array(aligned_maxIP[1]).reshape((39, 1, 1, 6544, 20896))"
+    "intensities.loc[:, 'h'] = intensities.time * .5"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "eb75199b-6c80-431b-a99b-5e75b3ea37fc",
+   "execution_count": 27,
+   "id": "b3c4da51-7f18-438d-8ee4-78ffc215b63e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "intensities.to_csv(\"Y:Lena/Data/20220111-MIC-resistant/timelapse-30min/intesities.csv\", index=None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "aa1a35cb-4ac0-47a1-bd20-3d49a319b721",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "785f19ce-a696-46a3-a267-9bdde38edcc0",
    "metadata": {},
    "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "cannot reindex from a duplicate axis",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_2668/1750120335.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlineplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mintensities\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'h'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'I'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'label'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mestimator\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0munits\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'label'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32m~\\miniconda3\\envs\\nd2\\lib\\site-packages\\seaborn\\_decorators.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     44\u001b[0m             )\n\u001b[0;32m     45\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 46\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     47\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     48\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\miniconda3\\envs\\nd2\\lib\\site-packages\\seaborn\\relational.py\u001b[0m in \u001b[0;36mlineplot\u001b[1;34m(x, y, hue, size, style, data, palette, hue_order, hue_norm, sizes, size_order, size_norm, dashes, markers, style_order, units, estimator, ci, n_boot, seed, sort, err_style, err_kws, legend, ax, **kwargs)\u001b[0m\n\u001b[0;32m    708\u001b[0m     \u001b[0mp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_attach\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    709\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 710\u001b[1;33m     \u001b[0mp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    711\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    712\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\miniconda3\\envs\\nd2\\lib\\site-packages\\seaborn\\relational.py\u001b[0m in \u001b[0;36mplot\u001b[1;34m(self, ax, kws)\u001b[0m\n\u001b[0;32m    469\u001b[0m         \u001b[1;31m# Loop over the semantic subsets and add to the plot\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    470\u001b[0m         \u001b[0mgrouping_vars\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"hue\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"size\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"style\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 471\u001b[1;33m         \u001b[1;32mfor\u001b[0m \u001b[0msub_vars\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msub_data\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miter_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgrouping_vars\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfrom_comp_data\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    472\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    473\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msort\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\miniconda3\\envs\\nd2\\lib\\site-packages\\seaborn\\_core.py\u001b[0m in \u001b[0;36miter_data\u001b[1;34m(self, grouping_vars, reverse, from_comp_data)\u001b[0m\n\u001b[0;32m    981\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    982\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mfrom_comp_data\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 983\u001b[1;33m             \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcomp_data\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    984\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    985\u001b[0m             \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot_data\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\miniconda3\\envs\\nd2\\lib\\site-packages\\seaborn\\_core.py\u001b[0m in \u001b[0;36mcomp_data\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1055\u001b[0m                     \u001b[0morig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mvar\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdropna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1056\u001b[0m                 \u001b[0mcomp_col\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfloat\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvar\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1057\u001b[1;33m                 \u001b[0mcomp_col\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0morig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_numeric\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconvert_units\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0morig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1058\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1059\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_scale\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"log\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\miniconda3\\envs\\nd2\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m__setitem__\u001b[1;34m(self, key, value)\u001b[0m\n\u001b[0;32m    721\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    722\u001b[0m         \u001b[0miloc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"iloc\"\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 723\u001b[1;33m         \u001b[0miloc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_setitem_with_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    724\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    725\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_validate_key\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\miniconda3\\envs\\nd2\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_setitem_with_indexer\u001b[1;34m(self, indexer, value, name)\u001b[0m\n\u001b[0;32m   1730\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_setitem_with_indexer_split_path\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1731\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1732\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_setitem_single_block\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1733\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1734\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_setitem_with_indexer_split_path\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\miniconda3\\envs\\nd2\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_setitem_single_block\u001b[1;34m(self, indexer, value, name)\u001b[0m\n\u001b[0;32m   1957\u001b[0m             \u001b[1;31m# setting for extensionarrays that store dicts. Need to decide\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1958\u001b[0m             \u001b[1;31m# if it's worth supporting that.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1959\u001b[1;33m             \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_align_series\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mSeries\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1960\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1961\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mABCDataFrame\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mname\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;34m\"iloc\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\miniconda3\\envs\\nd2\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_align_series\u001b[1;34m(self, indexer, ser, multiindex_indexer)\u001b[0m\n\u001b[0;32m   2094\u001b[0m             \u001b[1;31m# series, so need to broadcast (see GH5206)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2095\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0msum_aligners\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mall\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mis_sequence\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2096\u001b[1;33m                 \u001b[0mser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mser\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2097\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2098\u001b[0m                 \u001b[1;31m# single indexer\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\miniconda3\\envs\\nd2\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36mreindex\u001b[1;34m(self, index, **kwargs)\u001b[0m\n\u001b[0;32m   4578\u001b[0m     )\n\u001b[0;32m   4579\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mreindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4580\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4581\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4582\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mdeprecate_nonkeyword_arguments\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mversion\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mallowed_args\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"self\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"labels\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\miniconda3\\envs\\nd2\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mreindex\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   4816\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4817\u001b[0m         \u001b[1;31m# perform the reindex on the axes\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4818\u001b[1;33m         return self._reindex_axes(\n\u001b[0m\u001b[0;32m   4819\u001b[0m             \u001b[0maxes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlimit\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfill_value\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4820\u001b[0m         ).__finalize__(self, method=\"reindex\")\n",
+      "\u001b[1;32m~\\miniconda3\\envs\\nd2\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m_reindex_axes\u001b[1;34m(self, axes, level, limit, tolerance, method, fill_value, copy)\u001b[0m\n\u001b[0;32m   4837\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4838\u001b[0m             \u001b[0maxis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_axis_number\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4839\u001b[1;33m             obj = obj._reindex_with_indexers(\n\u001b[0m\u001b[0;32m   4840\u001b[0m                 \u001b[1;33m{\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mnew_index\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4841\u001b[0m                 \u001b[0mfill_value\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfill_value\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\miniconda3\\envs\\nd2\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m_reindex_with_indexers\u001b[1;34m(self, reindexers, fill_value, copy, allow_dups)\u001b[0m\n\u001b[0;32m   4881\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4882\u001b[0m             \u001b[1;31m# TODO: speed up on homogeneous DataFrame objects\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4883\u001b[1;33m             new_data = new_data.reindex_indexer(\n\u001b[0m\u001b[0;32m   4884\u001b[0m                 \u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4885\u001b[0m                 \u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\miniconda3\\envs\\nd2\\lib\\site-packages\\pandas\\core\\internals\\managers.py\u001b[0m in \u001b[0;36mreindex_indexer\u001b[1;34m(self, new_axis, indexer, axis, fill_value, allow_dups, copy, consolidate, only_slice)\u001b[0m\n\u001b[0;32m    668\u001b[0m         \u001b[1;31m# some axes don't allow reindexing with dups\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    669\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mallow_dups\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 670\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_can_reindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    671\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    672\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0maxis\u001b[0m \u001b[1;33m>=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\miniconda3\\envs\\nd2\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36m_validate_can_reindex\u001b[1;34m(self, indexer)\u001b[0m\n\u001b[0;32m   3783\u001b[0m         \u001b[1;31m# trying to reindex on an axis with duplicates\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3784\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_index_as_unique\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3785\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"cannot reindex from a duplicate axis\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3786\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3787\u001b[0m     def reindex(\n",
+      "\u001b[1;31mValueError\u001b[0m: cannot reindex from a duplicate axis"
+     ]
+    },
     {
      "data": {
+      "image/png": "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\n",
       "text/plain": [
-       "(39, 1, 1, 6544, 20896)"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "fluo.shape"
+    "sns.lineplot(data=intensities, x='h', y='I', hue='label', estimator=None, units='label')"
    ]
   },
   {
@@ -2077,6 +2777,16 @@
     "tf.imwrite('Y:Lena/Data/20220111-MIC-resistant//timelapse-30min/0ng-TRITC-19h_maxIP.aligned.tif', fluo)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "67477499-930b-464a-bf9e-da0a3353628e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tf.imwrite('Y:Lena/Data/20220111-MIC-resistant//timelapse-30min/0ng-TRITC-19h_meanIP.aligned.tif', fluo)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 14,