diff --git a/multiwell align count dec-jan 2022.ipynb b/multiwell align count dec-jan 2022.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..d5d272ba4118cf1947e114810a136c1df7456504
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+++ b/multiwell align count dec-jan 2022.ipynb	
@@ -0,0 +1,3754 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "9cae36df",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import asyncio\n",
+    "from aicsimageio import imread, imread_dask\n",
+    "from dask import delayed\n",
+    "from droplet_growth import mic, register, poisson\n",
+    "from glob import glob\n",
+    "import h5py\n",
+    "import tifffile as tf\n",
+    "import numpy as np\n",
+    "from napari import Viewer\n",
+    "import os\n",
+    "import pandas as pd\n",
+    "from threading import Thread\n",
+    "import re\n",
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "695cbe48",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(818, 2612)"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "big_labels = tf.imread('Y:/Lena/Data/labels_bin2+100.tif')\n",
+    "template16 = tf.imread('Y:/Lena/Data/20210518_control/template_bin16_bf_mask.tif')[0]\n",
+    "template16.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "4a86d0ae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def count(path, regex='(\\d+)ug', fit_poisson=True):\n",
+    "    cp = ''.join(path.split('.')[:-1]) + '-counts.csv'\n",
+    "    if os.path.exists(cp):\n",
+    "        print(f'{path} already counted')\n",
+    "        return\n",
+    "    bf, fluo, mask = tf.imread(path)\n",
+    "    counts = mic.get_cell_numbers(fluo, big_labels, threshold_abs=2, plot=False, bf=bf)\n",
+    "    try:\n",
+    "        ng = re.compile(regex).findall(path)[0]\n",
+    "        print (int(ng), 'ug')\n",
+    "        counts.loc[:,'ng'] = int(ng)\n",
+    "    except IndexError:\n",
+    "        print('concentration not found')\n",
+    "        ng=None\n",
+    "    if fit_poisson:\n",
+    "        l = poisson.fit(counts.query('n_cells < 10').n_cells, title=f'automatic {ng}ng')\n",
+    "        poisson.plt.show()\n",
+    "        counts.loc[:, 'poisson fit'] = l\n",
+    "    \n",
+    "    counts.to_csv(cp, index=None)\n",
+    "    return counts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e17feda0-0cf8-4f4c-8f48-383048572f4b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def align2D(path_nd2_bf_tritc, regex='(\\d+)ng'):\n",
+    "    try:\n",
+    "        ng = re.compile(regex).findall(path_nd2_bf_tritc)[0]\n",
+    "        print (int(ng), 'ug')\n",
+    "    except IndexError:\n",
+    "        print('concentration not found')\n",
+    "        return\n",
+    "    data = imread(path_nd2_bf_tritc)[0,:,0]\n",
+    "    aligned, tvec = register.align_stack_nd(stack=data, path=path_nd2_bf_tritc, template16=template16, mask2=big_labels, binnings=(2,16,2))\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}ng')\n",
+    "    poisson.plt.show()\n",
+    "    counts.loc[:, 'poisson fit'] = l\n",
+    "    \n",
+    "    counts.to_csv((cp := path_nd2_bf_tritc.replace('.nd2', '-counts.csv')), index=None)\n",
+    "    return aligned"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "40d21c33-f77d-4396-9a8d-4e0df7c6891a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def align3D(path_BF, path_TRITC, path_to_save, regex='(\\d+)ng'):\n",
+    "    try:\n",
+    "        ng = re.compile(regex).findall(path_BF)[0]\n",
+    "        print (int(ng), 'ng')\n",
+    "    except IndexError:\n",
+    "        print('concentration not found')\n",
+    "        return\n",
+    "    if not os.path.exists(path_to_save):\n",
+    "        BF = imread(path_BF)[0,0,0]\n",
+    "        fluo = imread(path_TRITC)[0,0].max(axis=0)\n",
+    "\n",
+    "        aligned, tvec = register.align_stack_nd(stack=np.array((BF, fluo)), path=None, template16=template16, mask2=big_labels, binnings=(2,16,2))\n",
+    "        tf.imwrite(path_to_save, aligned, dtype='uint16',  imagej=True, metadata=register.META_ALIGNED)\n",
+    "    else:\n",
+    "        print('Already aligned')\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', 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": 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": [
+      "0 ng\n",
+      "Already aligned\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/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([-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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CW1RUVKNm6jGhzS7LBMHBDz9i368fos0Pf8hJ997r177SJkygqqSEwqWvBSacMSYsWHEPsNLNm9l9113EfO97dHj0USQy0q/9xZ1xBnH9+lm3SGNMo1hxD6CKAwfYdfMtSFwsnWc+TWSbhIDsN3X8eG+3yI8+Csj+jDGhz4p7gESLkH3bNCoOHKDzU08RdfLJAdt30o8uJTIjnbz5CwK2T2NMaLPiHgCqyq/bt+fw+vV0+O1viTvjjIDuX6KjSR0zhkMffkjptm8Cum9jTGiy4h4AuU/+hSuSksn42c9I+tGlQTlG6ujREBVF/gsvBGX/xpjQYsXdT4V/+zu5f/kLSwoLaDt1StCO40lPJ3no5TZapDGmQay4+6Fk7Tr23n8/8WedxS/37TvhYGCBkDp+vLdb5JKlQT2OMab1s+LeRGW7dpE9bRpRHTrQ8Yk/Ud4Mx6zuFpm30LpFGmNOzIp7E1QWFbHr5lvQyko6zXwaT2pqsx07dcIEynfs5NCHHzbbMY0xrY8V90bS8nJ233kXZTt20OmJJ5p9ntOkS4fgycggb8HCZj2uMaZ1qbe4i8hsEflWRDb6tD0oIrtFZL3zGOqz7D4R2SIi/xWRHwUruBtUlX0PPcShjz/m5F/+koRBA5s9g0RHkzLWukUaY06sIWfuc4HLaml/TFUznccbACLSCxgD9Ha2eUpE/Lv/vgXJf/55Cl58ibZTp5Dy45Gu5UgdNQqJiiJ/oZ29G2NqV29xV9UPgLwG7m8E8KKqlqrqN8AWoPlPb4OgeOW77H/ktyQOGULGXXe5msWTnk7S0MspXLrUukUaY2rlzzX3aSKywblsU/2JYkdgl8862U7bcUTkJhFZLSKrW/r400e+/JLdd99NbK9edPjdb5EI9z+qsG6RxpgTaWqVeho4BcgE9gJ/aOwOVPVZVR2gqgMyMjKaGCP4yr/9ll233EpkUhKdnnqKiLg4tyMBTrfIzEzrFmmMqVWTiruq7lfVSlWtAmbx3aWX3UBnn1U7OW2tUtXhw2TfehuVRUV0fvopotqd5HakY6ROGG/dIo0xtWpScRcR3yEPrwaqe9IsA8aISIyIdAd6Aqv8i+gOrapiz70/58imTXR89FFiTz/d7UjHSbr0Um+3SBst0hhTQ73T7InIIuCHQLqIZAMzgB+KSCagwHbgJwCquklEXga+ACqA21S1MijJgyznsccpfvttTpr+cxIvutDtOLWSqChSxo4h94k/U7rtG2J62BRrxhgvaQkzpQ8YMEBXr17tdoyjChYvYe8DD5AyejTtH5zRoDFjRMSVWecrcnPZcuFFpIwaRfv//YVrOYwxzU9E1qjqgNqWud/to4U59Mkq9s6YQcK559L+Fw8EfTAwf3m7RQ71dossLnY7jjGmhbDi7qP0m2/Ivv12ort2pePjjyFRUW5HapDUo5NoW7dIY4yXFXdHZUEB2TffgkREeOc/TUpyO1KDxfXp7e0WuWAhLfvvDGNMc7HiDmhZGdk/vZ3yPXvo9Jcnie7cuf6NWpjUCeMp37mTwQmBmZTbGNO6hX1xL1m7jm/GXkfJp59y8sMPEd+/v9uRmiTp0kvxnHQS45px+GFjTMsVEsW9W7duiEijH5lxcWweM4bSTZsoV+UHo0Y1aT8t4UNXiYoidewYBie0oXTbNrfjGGNcFhLFfceOHahqox//fPxPRHu8Xf2jPB7++fifmrSfltL1MGXUKMqqqsi3sd6NCXshUdybKn7gWUh0NERGIlFRxA88y+1IfvG0bcsbxcUUvPaadYs0JsyFd3HPyqLLnNlk3H47XebMJj4ry+1IfluQn4eWlFC4ZInbUYwxLqp3+IFQF5+VFRJFvdoXpaXEZWWRt/AFUidMaBHDExtjmp/9zw9BaU63yIMffOB2FGOMS6y4h6DEIUPwnHQS+TZapDFhy4p7CKruFnnoX/+ybpHGhCkr7iEqpXoSbesWaUxYsuIeojxt25I0bJh1izQmTFlxD2Gp48dbt0hjwpQV9xAW16e3t1vkgoVoZaucEMsY00RW3ENc2oTxlO/aZd0ijQkzVtxD3NFukfbBqjFhxYp7iJOoKFKvG+vtFrl1q9txjDHNxIp7GEgZNQqJjiZ/oZ29GxMu6i3uIjJbRL4VkY0+bb8Xka9EZIOILBWRFKe9m4gcFpH1zmNmELObBvKkpZE0dCgFr71OZVGR23GMMc2gIWfuc4HLarS9DfRR1b7A18B9Psu2qmqm87g5MDGNv6q7RRZYt0hjwkK9xV1VPwDyarT9Q1UrnJf/AToFIZsJoLg+vYnr35/8hS9Yt0hjwkAgrrnfALzp87q7iKwTkfdFZHBdG4nITSKyWkRW5+TkBCCGqY91izQmfPhV3EXkAaACqP6kbi/QRVWzgJ8BL4hIUm3bquqzqjpAVQdkZGT4E8M0UOIll+Bp185GizQmDDS5uIvIJOAKYJw6k4iqaqmqHnCerwG2AqcGIKcJgKOjRX78MaVbtrgdxxgTRE0q7iJyGXAvMFxVS3zaM0Qk0nneA+gJ2JizLUh1t8g86xZpTEhrSFfIRcC/gdNEJFtEbgSeBBKBt2t0ebwA2CAi64FXgZtVNa+2/Rp3eNLSSBo2jMLXl1m3SGNCWL1zqKrq2Fqan6tj3cXAYn9DmeBKHT+OwqVLKViyhLaTJrkdxxgTBHaHahiK692buDPPtG6RxoQwK+5hKm38OG+3yPetW6QxociKe5g62i1ygXWLNCYUWXEPU95ukWOtW6QxIcqKexhLGXWtdYs0JkRZcQ9jnrQ0kq64gkIbLdKYkGPFPcyljR+HHj5MwWIbLdKYUGLFPczF9url7Rb5gnWLNCaUWHE3340Wad0ijQkZVtwNiRdfjKd9e/IXzHc7ijEmQKy4G2+3yDFjOPTxv61bpDEhwoq7AXy6RdpNTcaEBCvuBvDpFmmjRRoTEqy4m6Oqu0XuvvseStatczuOMcYPVtzNUVWlpSDCoQ8+YOfkG6zAG9OKWXE3R5Ws+vTocy0rO+a1MaZ1seJujoofeBYSE+N9oUr8wLPcDWSMaTIr7uao+KwsusyZTZsLLwRVULcTGWOaqt5p9kzrIyJ+bR8nwspTvsesK6/kzj27m7SPrl27sn37dr9yGGOazs7cQ5Cq+vUoqaqixy23cGlSEqU7djRpHzt27HD7bTAmrDWouIvIbBH5VkQ2+rSlicjbIrLZ+ZrqtIuIPCEiW0Rkg4j0D1Z4Ezyp48aBx0PevOfdjmKMaYKGnrnPBS6r0TYdeEdVewLvOK8BLgd6Oo+bgKf9j2maW1S7k0geNoyCJUuoLChwO44xppEaVNxV9QMgr0bzCGCe83wecJVP+/Pq9R8gRURODkBW08zSJk9CDx8m/6WX3Y5ijGkkf665t1PVvc7zfUA753lHYJfPetlOm2llYk87jYRzzyV/wQK0rMztOMaYRgjIB6qqqjSy45yI3CQiq0VkdU5OTiBimCBImzyZipwcCpe/4XYUY0wj+FPc91dfbnG+fuu07wY6+6zXyWk7hqo+q6oDVHVARkaGHzFMMCWcfx4xPXuSN2cO3t/hxpjWwJ/ivgy43nl+PfC6T/tEp9fM2UChz+Ub08qICGmTJlH69dcc+vhjt+MYYxqooV0hFwH/Bk4TkWwRuRF4BBgiIpuBS5zXAG8A24AtwCzg1oCnNs0q6coriMxIJ2/OXLejGGMaqEF3qKrq2DoWXVzLugrc5k8o07JEREeTNm4cOY//iSNff03sqae6HckYUw+7Q9U0SMro0UhsLHlz59W/sjHGdVbcTYN4UlNJGXk1RX/7GxXWu8mYFs+Ku2mwtOuvRysqyFu40O0oxph6WHE3DRbdtSttLr6IgkUvUlVS4nYcY8wJWHE3jdJ28mQqCwspeO01t6MYY07AirtplLj+/Ynt25e8efPQykq34xhj6mDF3TSKiNB28iTKd+zk4Lvvuh3HGFMHK+6m0RKHDCGqQwcO2E1NxrRYVtxNo4nHQ9r1Ezm8Zg2HN2xwO44xphZW3E2TJP/4GiISEzkwZ47bUYwxtbDibpoksk0CKaOupfitf1CW3bRJtI0xwWPF3TRZ2oQJEBFB/nybZ9WYlsaKu2myqPbtSbr8cgpeeZXKoiK34xhjfFhxN35pO3kSVSUlFLzyqttRjDE+rLgbv8T26kX8oEHkzZ+Plpe7HccY47DibvyWNnkSFfv2UbTiLbejGGMcVtyN39pccAHRPXrYPKvGtCBW3I3fJCKCtOuv58gXX1Cy6lO34xhjsOJuAiR5xHAi09LIs5uajGkRrLibgIiIjSV17FgOvvcepdu2uR3HmLBnxd0ETOp1Y5HoaJtn1ZgWwNPUDUXkNOAln6YewP8BKcBUoHqizftV9Y2mHse0Hp62bUkeMYLC118nNTLS7TjGhLUmn7mr6n9VNVNVM4EzgRJgqbP4seplVtjDS9rkSWhpKWNTUtyOYkxYC9RlmYuBraq6I0D7M61UTI8etPnBDxibkkrVkSNuxzEmbAWquI8BFvm8niYiG0Rktoik1raBiNwkIqtFZHVOTk5tq5hWKm3yZNp6PBQuW+Z2FGPClt/FXUSigeHAK07T08ApQCawF/hDbdup6rOqOkBVB2RkZPgbw7Qg8YMG8sWRI+TNnYdWVbkdx5iwFIgz98uBtaq6H0BV96tqpapWAbOAgQE4hmlFRIS5eXmUbdvGwQ8+cDuOMWEpEMV9LD6XZETkZJ9lVwMbA3AM08qsKC7C0749eTbPqjGu8Ku4i0gCMARY4tP8OxH5XEQ2ABcCd/lzDNM6VQBpE8ZT8sknHPniC7fjGBN2/CruqnpIVduqaqFP2wRVPUNV+6rqcFXd639M0xqlXHstEfHxHLCzd2Oand2haoImMimJlGuvoejNNynft8/tOMaEFSvuJqhSJ0yEqiry5s93O4oxYcWKuwmq6E4dSfzRpRS8/AqVBw+5HceYsGHF3QRd28mTqSoupnCxzbNqTHOx4m6CLq5vX+LOPJO8ec+jFRVuxzEmLFhxN82i7eRJlO/ZQ/Hbb7sdxZiwYMXdNIs2F15IVNcuHJgz1+ZZNaYZWHE3zUIiI73zrG7YwOG1a92OY0zIs+Jumk3K1VcTmZzMAZtn1Zigs+Jumk1EXBwpY8dw8J2VlG3f7nYcY0KaFXfTrNLGjUM8HvKef97tKMaENCvupll5MjJIuvJKCpYspbKgwO04xoQsK+6m2aVNuh49coT8F1+qf2VjTJNYcTfNLvbUU0k4/3zyFi6gqqzM7TjGhCQr7sYVaZMnUZmTS9Hfl7sdxZiQZMXduCLh3HOJOe008ubaTU3GBIMVd+MKESFt0iRKv/6aQ//62O04xoQcK+7GNcnDhuLJyCDPbmoyJuCsuBvXSHQ0qePHc+hf/+LIf792O44xIcWKu3FV6uhRSFwceXPnuh3FmJBixd24KjIlhZSRIyn8+98p//Zbt+MYEzL8Lu4isl1EPheR9SKy2mlLE5G3RWSz8zXV/6gmVKVdPxEqKshf+ILbUYwJGYE6c79QVTNVdYDzejrwjqr2BN5xXhtTq+guXUi85BLyX3yRqpISt+MYExKCdVlmBDDPeT4PuCpIxzEhIm3yZKoKCylYutTtKMaEhEAUdwX+ISJrROQmp62dqu51nu8D2tXcSERuEpHVIrI6JycnADFMaxaXlUlsv77eeVYrK92OY0yrF4jifr6q9gcuB24TkQt8F6r39sPjbkFU1WdVdYCqDsjIyAhADNOaiQhtJ0+mfOdOileudDuOMa2e38VdVXc7X78FlgIDgf0icjKA89W6QZh6JV5yCVEdO5I3Z67bUYxp9fwq7iKSICKJ1c+BS4GNwDLgeme164HX/TmOCQ/i8ZB2/UQOr13L4c8+czuOMa2av2fu7YCPROQzYBWwXFVXAI8AQ0RkM3CJ89qYeiWP/DERiYkcsLN3Y/zi8WdjVd0G9Kul/QBwsT/7NuEpsk0CqaNHceC52ez/3e9JHHIJ8VlZbscyptWxO1RNixOblQWq5M2ezc7JN1Cybp3bkYxpday4mxanbMtWEAFAS0spWfWpy4mMaX2suJsWJ37gWUhMjPeFKhW5ue4GMqYV8uuauzHBEJ+VRZc5syn55BMOffxv8ufPJ7Z3L1KuusrtaMa0GlbcTdCIc2nFH1EiPN2xEwN/Pp2R4yfw7qGDjdq+a9eubN++3e8cxrQ2VtxN0ARqbtSqQ4fYMfkGnoqJofOsWSQMGtjgbQPxC8aY1siuuZsWLyIhgc7PzCSqS2eyb72Vwxs3uR3JmBbPirtpFTypqXR57jkik5PZNXUqpdu+cTuSMS2aFXfTakS1a0eX2c9BRAQ7p9xI+d699W9kTJiy4m5alehu3egy61mqiorZeeMUKvLz3Y5kTItkxd20OrG9etF55tOU797Nrqk3UXnwkNuRjGlxrLibVil+wAA6Pv4YR778kuxp06gqLXU7kjEtihV302olXnghHX7zMCX/+Q977r4brahwO5IxLYYVd9OqJQ8fTrv776f47X+yd8aMgPWtN6a1s5uYTKuXNnEClQUF5D71FJHJKZx0z91285IJe1bcTUhI/+k0KgsLyZs9m8iUFNJvmup2JGNcZcXdhAQRod0D91NZWEjOH/9IZHIyqaNHuR3LGNdYcTchQyIi6PCbh6ksLmLfgw8SmZzkdiRjXGMfqJqQIlFRdHr8ceL692f3Pfdybny825GMcYUVdxNyIuLi6Pz0U8SccgpPdOzE4fXr3Y5kTLOz4m5CUmRSEl1mPUtuRQU7f3IzpZs3ux3JmGbV5OIuIp1F5F0R+UJENonIHU77gyKyW0TWO4+hgYtrTMN5MjK4MXsXETEx7LxxCmXZu92OZEyz8efMvQL4H1XtBZwN3CYivZxlj6lqpvN4w++UxjTR7vJyOv91FlWlpey88Qabj9WEjSYXd1Xdq6prnefFwJdAx0AFMyZQYk89lc4zn6bi2xx2TplKZVGR25GMCbqAXHMXkW5AFvCJ0zRNRDaIyGwRSa1jm5tEZLWIrM7JyQlEDGPqFJ+VRac//5nSrVvZdcutVB0+7HYkY4LK7+IuIm2AxcCdqloEPA2cAmQCe4E/1Ladqj6rqgNUdUBGRoa/MYypV5vzz6Pj737L4bVr2X3nXWh5uduRjAkav4q7iEThLewLVXUJgKruV9VKVa0CZgENn83YmCBLuvxy2j/4IAfff589992PVlW5HcmYoGjyHariHZnpOeBLVf2jT/vJqlo9/9nVwEb/IhoTWKmjR1FZUEDOY48RmZxMu188YAONmZDjz/AD5wETgM9FZL3Tdj8wVkQyAQW2Az/x4xjGBEXbm6ZSWVBA3pw5RKakkPHTaW5HMiagmlzcVfUjoLbTHev6aFo8EeGke++hsrCQ3L/8hcjkZNImTnA7ljEBYwOHmbAlIpz8q19SWVTI/ocfJjIlmeThw92OZUxA2PADJqyJx0PHP/yB+LPPZs9991P87rtuRzImIOzM3YS8hnxYGi8RzOncmSM/uZmp2btYE+B+8F27dmX79u0B3acxJ2LF3YS8hs6rWpGfz47rxrEwKYmu858n9vTTA5bBeuOY5maXZYxxeFJT6TL7OSISE9k5ZSpldqZtWjEr7sb4iDr5ZLo89xxUVbHzhhspemcluc88S8m6dW5HM6ZR7LKMMTXE9OhO51mz2DF+PLunTQMRJDqaLnNmE5+V5XY8YxrEztyNqUVcn94kXXEFqEJVFXrkCHlz5lJ58KDb0YxpECvuxtQh5ccjkZgYEAERiv/xDzafP5jd/3M3Bz/4AK2ocDuiMXWyyzLG1CE+K4suc+dQsupT4s86CwQKly2j6I03KVq+nMj0dJKHDSN5xHBiTj/desSYFkUa2k0smAYMGKCrV69u8vYi0uDubsHSEjJYjubJUVVWxsH336do2TKK33sfysuJ6dmT5BHDSbrySqLatWuWHMaIyBpVHVDrspbwA2fF3XK01hwV+fkUr1hB4evLOLx+PYiQcM7ZJA0fTtKQIUQkJDRLDhOerLg3g5aQwXK4m6Ns+3YKl/2NwmXLKM/ORuLiSBxyCcnDR5A8+HwqW8D7YUKLFfdm0BIyWI6WkUNVObx2LYWvL6NoxQqqior4tqKc79/0E5JHDCf2tNOaNY8JXVbcm0FLyGA5Wl6OqtJSDr73PvNvuIGLUlOhooKY004jecQIkq4YRtRJJ7mWzbR+VtybQUvIYDlado7yvDyK3niDwteXcWTDBoiIIOGcc0i+agSJF19MRHy82zFNK2PFvRm0hAyWo/XkKN32DYV/W0bR68so37OHiPh4EocMIfmqEcQPHIhERrqY1rQWJyru1s/dmGZSWz94AfrHxTEiKZkfLVlC4euvs6+8nL8XFbGsqJAtZWUBO74NOxxerLgb00zq+wui6sgRDr77Lm1eX8aUDz9kStu2xPQ6neThw4nu2pXSrzcTP/CsJo9vYzdZhRe7LBMgLSGD5QidHBUHDlC0/A0Kly3jyMaNvjsipncvYnr0wJOegScjA096Op6M9KPPI5KSav8roYW8FyZw7LKMMa2Mp21b0iZOIG3iBPb95jfkPz/fO4iZKpW5BzicX0BFTg5ay2UbiY7Gk55OpE/B92RkcG1yMsUrV37X1rYtEh3twndnmkPQztxF5DLgT0Ak8FdVfaSude3M3XJYjrqVrFvHzsk3oOXlSFTU0aGHVZWq4mIqcnOpyMmlIieHitwcKnJyqDymLZfK/Pxa951fUUFuZSU5FRXkVlSQW1nhPHfaKr3tRVVV9IuNZWB8PKtKSvjsyJEmvxeRkZFUVlY2eftAaSk5/PkspNnP3EUkEvgLMATIBj4VkWWq+kUwjmdMKIvPyqLLnNneAcx8rrmLCJFJSUQmJRHTo8cJ96Hl5ZwcH883a9d990sgN5eUnBy6+P4iqOOvATweqB4FU4SY738fT2oqRHmQqCjEE4V4PN6H04bH8117VJS33ePhZ/fey2NP/NnZznO0HY/Pvpw28XigRtuRr7/m8GcbiMvKJP6MM46O2nn04bw3x7eL88X7Oj4hgbLqX1IiCBy/jUidn1WUrFt33L9JUwTrs5CgnLmLyDnAg6r6I+f1fQCq+pva1rczd8thOVpGhrr+Gjj4z3co8fk/GtWxI570dLSiwudRjpaXQ7lPW3m5d2jkUBgeucYvEJyzfomN9WsiF39+Nty45t4R2OXzOhsYVCPUTcBNzsuDIvJffw7o52+/dCDXnx0EIEPABCCHvR/HCpn3o6kZEiIiErpGRZ2Kdw6Iqh2bv/76UFXVoYCGa4B2Hk/7tpGejgIoqgcqK/fsr6jY53qOgQP9yuHHz0bXuha49oGqqj4LPOvW8X2JyOq6fvuFI3s/jmXvx3fsvThWS34/gjUT026gs8/rTk6bMcaYZhCs4v4p0FNEuotINDAGWBakYxljjKkhKJdlVLVCRKYBb+HtCjlbVTcF41gB0iIuD7Ug9n4cy96P79h7cawW+360iDtUjTHGBFawLssYY4xxkRV3Y4wJQWFf3EXkMhH5r4hsEZHpbudxk4h0FpF3ReQLEdkkIne4ncltIhIpIutE5O9uZ3GbiKSIyKsi8pWIfOncrBiWROQu5//IRhFZJCKxbmeqKayLu88wCZcDvYCxItLL3VSuqgD+R1V7AWcDt4X5+wFwB/Cl2yFaiD8BK1T1+0A/wvR9EZGOwO3AAFXtg7fTyBh3Ux0vrIs7MBDYoqrbVLUMeBEY4XIm16jqXlVd6zwvxvuft6O7qdwjIp2AYcBf3c7iNhFJBi4AngNQ1TJVLXA1lLs8QJyIeIB4YI/LeY4T7sW9tmESwraY+RKRbkAW8InLUdz0OHAvUOVyjpagO5ADzHEuU/1VRBLcDuUGVd0NPArsBPYChar6D3dTHS/ci7uphYi0ARYDd6pqkdt53CAiVwDfquoat7O0EB6gP/C0qmYBh4Cw/IxKRFLx/oXfHegAJIjIeHdTHS/ci7sNk1CDiEThLewLVXWJ23lcdB4wXES2471cd5GILHA3kquygWxVrf5L7lW8xT4cXQJ8o6o5qloOLAHOdTnTccK9uNswCT7EOzTdc8CXqvpHt/O4SVXvU9VOqtoN78/FSlVtcWdnzUVV9wG7ROQ0p+liIFznZ9gJnC0i8c7/mYtpgR8uh/U0e61wmIRgOw+YAHwuIuudtvtV9Q33IpkW5KfAQudEaBsw2eU8rlDVT0TkVWAt3h5m62iBwxDY8APGGBOCwv2yjDHGhCQr7sYYE4KsuBtjTAiy4m6MMSHIirsxxoQgK+4moETkYAPW+Wv1gGQicn+NZR8H4hiBJCLviUjQJ0EWkdud0RYX+rmfuSJyjfO8WbKblseKu2l2qjpFVatvgLm/xrIWd6efP5yBpRrqVmCIqo4LVh4TPqy4m6AQkR86Z43V438vdO7mO3o2KSKP4B1Zb3312Wr1WbmItBGRd0RkrYh8LiInHK1TRLo5Z72znHG2/yEicb7Hc56nO0MKICKTROQ1EXlbRLaLyDQR+ZkzMNZ/RCTN5xATnJwbRWSgs32CiMwWkVXONiN89rtMRFYC79SS9WfOfjaKyJ1O20ygB/CmiNxVY/1IEXnUWX+DiPzUaT9TRN4XkTUi8paInHyC9yfSOaPf6Lyfd9W1rgkRqmoPewTsARx0vv4QKMQ7Xk8E8G/gfGfZe3jHwj66fi3be4Ak53k6sIXvbro7WMtxu+G9WzDTef0yML6W46UD253nk5z9JgIZTt6bnWWP4R04rXr7Wc7zC4CNzvOHfY6RAnwNJDj7zQbSasl5JvC5s14bYBOQ5SzbDqTXss0teMdy8Tiv04Ao4GMgw2kbjfcOa4C5wDW+37tz3Ld99pni9s+KPYL7COvhB0zQrVLVbABnOINuwEcN3FaAh0XkArxD7nYE2gH7TrDNN6q63nm+xjlefd5V79j1xSJSCPzNaf8c6Ouz3iIAVf1ARJJEJAW4FO/gYnc768QCXZznb6tqXi3HOx9YqqqHAERkCTAY7y3sdbkEmKmqFU6GPBHpA/QB3nb+IIrEO/xsXbYBPUTkz8ByoMUNUWsCy4q7CaZSn+eVNO7nbRzes+kzVbXcuZRS31RmNY8X5zyv4LtLkDX34btNlc/rqhp5a47ToXh/Af1YVf/ru0BEBuEdEjeYBNikqg2a6k5V80WkH/Aj4GZgFHBDEPMZl9k1d+O2cmeY4ZqS8Y6nXi4iFwJd/TjGdryXJQCuaeI+RgOIyPl4J2coxDvg3E99PkvIasB+PgSuckYUTACudtpO5G3gJ9UfzjqfBfwXyBBnHlMRiRKR3nXtQETSgQhVXQz8gvAdrjdsWHE3bnsW2FBL97+FwAAR+RyYCHzlxzEeBW4RkXV4r7k3xRFn+5nAjU7b/8N77XuDiGxyXp+QeqcxnAuswjvL1V9V9USXZMA7zd9O5zifAdepd1rIa4DfOm3rOfGY4h2B95zLYwuA++rLalo3GxXSGGNCkJ25G2NMCLLibowxIciKuzHGhCAr7sYYE4KsuBtjTAiy4m6MMSHIirsxxoSg/w+hVtsL/d5jkAAAAABJRU5ErkJggg==\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-counts.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "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": 35,
+   "id": "1cc15709-8899-4004-b5f4-b24ec1caa91d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 ng\n"
+     ]
+    }
+   ],
+   "source": [
+    "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": 36,
+   "id": "5d735546-ad2b-4f42-b2ae-5a5d26001e42",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Aligning None: \n",
+      " bf: (7383, 22392),  2\n",
+      " tmp: (818, 2612),  16\n",
+      " mask: (6544, 20896),  2\n",
+      "\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",
+      "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-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",
+      "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": "stdout",
+     "output_type": "stream",
+     "text": [
+      "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",
+      "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_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": [
+    {
+     "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": [
+    "img = imread_xarray_dask('E:/Andrey/20220124-MIC-cipro-resistant//day1/raw/{ng:03d}ng-BF001.nd2')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "647e28a9-7630-4ed7-84fd-3701a15d89a7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['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": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "paths = glob('E:Andrey/20220125-test-novec/*/*ng*.nd2')\n",
+    "paths"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "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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0M1trZivM7LjWPohQYJGR3LF5E11HjmTb3Lls+uWvcJWVfscSkSDVkDP3SuBnzrkBwInATWY2AJgIvOecOwJ4z7sNcA5whHe5FnikxVOHKAf0+OUdJI8bx46XXiLvlluoLivzO5aIBKF6y905t8k595l3fSfwBdAbGArs/aTbucAw7/pQ4C8u4FMg0cx6tnTwUGVmpIy7iR533MGuv73H+uuup2pXid+xRCTINGrM3czSgQxgIdDDObd3kpTNQA/vem9gfY3N8rxlBz7WtWa2xMyWaP7pg3UbdQW97ptE6eLFrBujCcdEpHEaXO5mFg+8ANzinCuueZ8LvLyjUS/xcM7Ncs5lOucyU1JSGrNph9Fl6FBSH3yQsq++IveKK6jYssXvSCISJBpU7mYWSaDY5zvnXvQWb9k73OJ93eot3wD0qbF5qrdMmiDhjNPpM3s2lZs2k3v5cMpzcvyOJCJBoCGvljHgMeAL59zkGne9Coz2ro8GXqmxfJT3qpkTgR01hm+kCeKys+g7dy7Vu3eTM/IK9nz5pd+RRKSda8iZ+8nAFcAZZrbcu5wLTAJ+aGZrgDO92wBvAt8Aa4HZwI0tH7vj6TTQm3AsIoLcK0ZR+tlnfkcSkXbM2sO7ITMzM92SJs6OaGYh847OhhxLxcaNrLtqLBWbN5M6bSrxp5zSRulEpL0xs6XOucza7tM7VINMZK9epM2fR9Th/Vh/403seEMTjonIwVTuQSgiKYm0uXOJHTyYjT+/je1PP+13JBFpZ1TuQSo8IYE+j84m/pRT2HzXbymYOStkhqdEpPlU7kEsLCaG1OkP0vm888h/4AG2/vFPKngRASDC7wDSPBYZSa/77yO8c2e2Pf44VTuK6Pnb32IR+qcV6cjUACHAwsLo8etfEZ7YhYKHH6G6eCe9/vwnwqKi/I4mIj7RsEyIMDNSxo+nxy8msvPdd1l/3XVUl2jCMZGOSuUeYrqNHk3Pe++ldNFicq+6ShOOiXRQKvcQlHjhMFKnTqHs8y9YN2oUFVu21r+RiIQUlXuISjjzTPrMnkXFho3kDh9OeW6u35FEpA2p3ENY3Ikn0nfuHKpLSsgZMZI9X33ldyQRaSMq9xDX6ZhjSJv3JBYeTu4Vo9j+9DMUzJxF6bJlfkcTkVakicPakdY8looNG/h2xAiqNm+BsDAsKoq+TzxObEZGq+xPRFqfJg4TInv3JvGCoYEb1dW4igpKFy32N5SItBqVewcSf/pp4L1z1cLDic06wdc8ItJ6VO4dSGxGBn1mzcJiYog+8kgNyYiEMJV7BxN/0vdIuflm9qxYQWkTn+cQkfZP5d4BdR1+OeEpyWydMiVknowWkf2p3DugsE6dSL7+enYvWUrJP//pdxwRaQUq9w4q8Sc/IaJXT/KnTNXZu0gIUrl3UGFRUaTceCN7Vq5k1wcf+B1HRFqYyr0D6zJ0KJFpfcmfOg1XXe13HBFpQSr3DswiI0kZdzNlX33Fzrff9juOiLQglXsH1/ncc4g+oj/50x7EVVb6HUdEWojKvYOz8HCSb76Z8m+/Zcdrr/sdR0RaiMpdSPjhD4kZMICChx7ClZf7HUdEWoDKvZ0xsza/hIWFMfqdt6nIy+Oy7j1a5DHT09P9/laKdGgRfgeQ/fn1mnPnHLnDR/D7jRtZ8M7bhEVHN+vxzKyFkolIU+jMXYBAGadMmEDlli0UPf2033FEpJlU7rJP3InZxH7vRApmzaa6pMTvOCLSDCp32U/K+PFUFRaybd58v6OISDOo3GU/sRkZxJ96KoWPPUZVcbHfcUSkiVTucpCUCeOpLi5m25y5fkcRkSZSuctBYgYMIOGss9g2dy6V27f7HUdEmkDlLrVKuXkc1aWlFD76qN9RRKQJ6i13M3vczLaa2aoay+4ysw1mtty7nFvjvl+Y2Voz+8rMzmqt4NK6ovv3p/P557F9/lNUbN3qdxwRaaSGnLnPAc6uZfkDzrnB3uVNADMbAFwGHO1t87CZhbdUWGlbKTfdhKuooHDWbL+jiEgj1VvuzrmPgW0NfLyhwNPOuTLn3LfAWiCrGfnER1FpaSRedBFFzzxDxcaNfscRkUZozpj7ODNb4Q3bdPWW9QbW11gnz1smQSr5husBKHjkEZ+TiEhjNLXcHwG+AwwGNgF/buwDmNm1ZrbEzJbk5+c3MYa0tshevUi89FKKXnyJ8txcv+OISAM1qdydc1ucc1XOuWpgNv8detkA9Kmxaqq3rLbHmOWcy3TOZaakpDQlhrSR5OuuxSIjyX/oIb+jiEgDNanczaxnjZsXAntfSfMqcJmZRZtZP+AIYFHzIorfIlJS6DZyBMWvvU7ZmjV+xxGRBmjISyEXAP8CvmtmeWY2FrjfzFaa2QrgdOBWAOfcauBZ4HPgLeAm51xVq6WXNtNt7FjCYmPJf3C631FEpAHqnc/dOXd5LYsfq2P9e4B7mhNK2p+Irl3pNno0BQ8/zO7Vq+l09NF+RxKROugdqtJg3cZcSViXLhRMe9DvKCJSD5W7NFh4QgJJY8ey66OPKF22zO84IlIHlbs0SreRIwhPSiJ/6jS/o4hIHVTu0ihhsbEkX3sNpZ9+Ssmnn/odR0QOQeUujZZ42WVE9OhB/tRpvn2gt4jUTeUujRYWHU3yDTewe9kySj7+2O84IlILlbs0SeJFFxKZmsrWqVN19i7SDqncpUksKorkcTdR9vkX7Hz3Xb/jiMgBVO7SZF3OP5+oww8nf9o0XJXeiCzSnqjcpcksPJyUm8dRvvZrit980+84IlKDyl2aJeGss4g+8kjyp0/HVVT4HUdEPCp3aRYLCyNl/Hgqctex45VX/I4jIh6VuzRb/OmnETNoEPkPP0x1ebnfcUQElbu0ADMjZcJ4KjduoujZ5/yOIyKo3KWFxJ10ErGZmRTMnEH17t1+xxHp8FTu0iLMjJRbJlCVX8D2pxb4HUekw1O5S4uJzcwk7vvfp3D2bOLC9KMl4if9D5QWlTJhPFVFRVzRtavfUUQ6NJW7tKhOxxxD/Jk/YEzXblQVFfkdR6TDUrlLi0u5eTxxYWEUPv6E31FEOiyVu7S4mO/+D3/duZNtTz5JZWGh33FEOiSVu7SKhwoKcGVlFM6a7XcUkQ5J5S6tIqeinC7DhrF9wQIqtmzxO45Ih6Nyl1aTfOONOOcoeOQRv6OIdDgqd2k1Uam9Sbz4xxQ9/wLleXl+xxHpUFTu0qqSr78eCw+n4KGH/Y4i0qGo3KVVRfboQdfLL2fHK69Q9s23fscR6TBU7tLqkq65GouJoWD6g35HEekwVO7S6iKSkuh2xRUUv/lX9nz1ld9xRDoElbu0iaSrxhCWkED+NJ29i7QFlbu0ifAuXUi6agy73nuP3StW+B1HJOSp3KXNdL1iFOGJieRPneZ3FJGQp3KXNhMeH0fSNddQ8o9/ULp4sd9xREKayl3aVNfhlxORksLWqVNxzvkdRyRkqdylTYV16kTS9dexe8lSSv7xT7/jiIQslbu0ucSf/ISIXj3J19m7SKupt9zN7HEz22pmq2os62Zm75rZGu9rV2+5mdk0M1trZivM7LjWDC/BKSwqipQbb2TPypXs+uADv+OIhKSGnLnPAc4+YNlE4D3n3BHAe95tgHOAI7zLtYCmA5RadRk2jMi0vuRPnYarrvY7jkjIqbfcnXMfA9sOWDwUmOtdnwsMq7H8Ly7gUyDRzHq2UFYJIRYRQcq4myn76it2vvWW33FEQk5Tx9x7OOc2edc3Az28672B9TXWy/OWHcTMrjWzJWa2JD8/v4kxJJh1Pvccoo/oT/6D03GVlX7HEQkpzX5C1QWeEWv0s2LOuVnOuUznXGZKSkpzY0gQsvBwksePp/zbb9nx2ut+xxEJKU0t9y17h1u8r1u95RuAPjXWS/WWidQq4cwziRkwgILp03Hl5X7HEQkZTS33V4HR3vXRwCs1lo/yXjVzIrCjxvCNyEHMjJRbJlCxYQNFL77odxyRkNGQl0IuAP4FfNfM8sxsLDAJ+KGZrQHO9G4DvAl8A6wFZgM3tkpqCSlxQ4bQKSODgocfoXrPHr/jiISEiPpWcM5dfoi7flDLug64qbmhpGMJnL3fwrrRoyl65hm6jR5d/0YiUie9Q1XahbjsLGK/dyIFM2dRXVLidxyRoKdyl3YjZfx4qrZtY9u8+X5HEQl6KndpN2IzMog/9VQKH3uMquJiv+OIBDWVu7QrKRPGU11czLY5c+tfWUQOSeUu7UrMgAEknHUW2+bMoXL7dr/jiAQtlbu0Oyk3j6N6924KH33U7ygiQUvlLu1OdP/+dLngfLbPf4qKrVvr30BEDqJyl3Yp+aabcBUVFM6c5XcUkaCkcpd2KapvXxIvuojtzz5LxQZNTyTSWCp3abeSb7geAwpmzPA7ikjQUblLuxXZqxeJl11G0YsvUZ6b63cckaCicpd2Lfnaa7DISPKnP+R3FJGgonKXdi0iJYVuI0dQ/PrrlK1Z43cckaChcpd2r9vYsYTFxpL/4HS/o4gEDZW7tHsRXbvS7cor2fnOO+xevdrvOCJBQeUuQaHblaMJ69KF/GnT/I4iEhRU7hIUwhMSSBo7lpKPPqb0s2V+xxFp91TuEjS6jRxBeFKSzt5FGkDlLkEjLDaW5OuupfTTTyn59FO/44i0ayp3CSqJl15KRI8e5E+ZSuAje0WkNip3CSph0dEk33ADu5cvZ9Mv7qB0mcbfRWqjcpegE/2d74AZO15+mXVXjlHBi9RC5S5Bp/Szz/Zdd2Vl7PrwIx/TiLRPKncJOrFZJ2DR0RAW+PEteuVlytev9zmVSPsS4XcACV1m1mqPfWxMDFmxsWyvrOJnVVVsOf0Mrs1bz5dlZS2+r7S0NHJyclr8cUVak8pdWk1bvZqlbO1a1l19DS936ULqQw8Rd2J2iz5+a/6SEmktGpaRoBfdvz/pTy8goudhrL/mGorfesvvSCK+U7lLSIg87DDS580j5phj2HDrT9k2f77fkUR8pXKXkBGemEjfxx4l/rTT2PK737N1qt7oJB2Xyl1CSlinTqQ+OI0uF/+YwkdmsPk3v8FVVvodS6TN6QlVCTkWEUHP3/2OiJQUCh+ZQWXhNnpP/jNhMTF+RxNpMzpzl5BkZnSfMIEev/4Vuz74gHVXjaWqqMjvWCJtRuUuIa3biBH0fmAye1auJGfkSCo2b/Y7kkibULlLyOt89tn0mT2byk2bybl8OGVff+13JJFWp3KXDiHuxGzSnvwLrqKC3OEjNNmYhDyVu3QYMQMGkL7gKcISu7BuzFXs/OADvyOJtJpmlbuZ5ZjZSjNbbmZLvGXdzOxdM1vjfe3aMlFFmi+qTx/Sn3qK6O98h7xxN1P0wot+RxJpFS1x5n66c26wcy7Tuz0ReM85dwTwnndbpN2ISEqi79y5xGVns+mXv6Rg1my92UlCTmsMywwF5nrX5wLDWmEfIs0SHh9HnxmP0Pm888ifPJktf7gXV13tdyyRFtPcNzE54B0zc8BM59wsoIdzbpN3/2agR20bmtm1wLUAffv2bWYMkcazqCh63X8fEUlJbJs7l6rCQnpOupewqCi/o4k0W3PL/fvOuQ1m1h1418y+rHmnc855xX8Q7xfBLIDMzEz9TSy+sLAwuk/8PyK6p7D1j3+icvs2Uh98kPD4eL+jiTRLs4ZlnHMbvK9bgZeALGCLmfUE8L5ubW5IkdZkZiSNHUvPSfdSumgxuaNGUVlQ4HcskWZpcrmbWZyZJey9DvwIWAW8Coz2VhsNvNLckCJtIXHYMPo8/BDl3+aQM3wE5evW+R1JpMmac+beA/i7mf0bWAS84Zx7C5gE/NDM1gBnerdFgkL8qaeS9sTjVO/YQc7lw9m9erXfkUSapMlj7s65b4Bja1leCPygOaFE/NRp8GDSFjzFuquvZt0VozgxNtbvSCKNpneoitQi+vDDSV+wgMjevZmZ2ofiN9/0O5JIo6jcRQ4hskcP0ubP49+7d7PhZz9n25Pz/I4k0mAqd5E6hHfuzDV564n/wRlsuecetk5+QO9mlaCgchepR5lzpE6dSuIll1A4axabfvkrfXSftHv6mD2RBrDwcA777V1EpKRQ8NBDVG3bRu8HJhPWqZPf0URqpTN3kQYyM1JuHsdhd93Jro8+Yt2Yq6jcvt3vWCK1UrmLNFLXyy6j95Qp7Fm9mtyRV1CxaVP9G4m0MZW7SBN0PutH9HnsUSq3bCHnssspW7PG70gi+1G5izRRXFYWafOehOpqckaMpPSzz/yOJLKPyl2kGWKOPJK0BQuI6NYt8NF977/vdyQRQOUu0mxRqb1Je2o+0d/9buCj+55/3u9IIip3kYYwszovkUlJDHz+OT7ZWcymX/2a65OS692mrS/p6el+fxulDancRRrAOVfvpaS6mmu++YbOF5zPhJQUNv32bqorKxu0bVtccnNz/f42ShvSm5hEWpBFRtJr0iQiklPY9vjjVBYW0uuP9+uj+6TNqdxFWpiFhdHj9tuISE5m6/33s76oiNTpDxKekOB3NOlANCwj0kqSrhpDr/vvo3TpUnJHjaYyP9/vSNKBqNxFWlGXCy6gzyMPU56bS87lwynPyfE7knQQKneRVhY/ZAhpc56guqSEnOEj2L1yld+RpANQuYu0gU6DBpH21HzCOnUid/Rodv3jH35HkhCnchdpI9H9+pG24Cmi+vRh/fU3sOP1N/yOJCFM5S7ShiK7dydt3pPEDh7Mxp//nMI5c/yOJCFKL4UUaWPhCQn0eXQ2G2+7na2T7mPP6tVE9+9PbFYWsRkZfseTEKFyF/FBWHQ0vR+YTN4tt1D82uuBhRERHPbLO+jy4x/rTU/SbBqWEfGJhYfTaeBAMAssqKxk82/v5j9Z2eSOGUPBjBmUfrYMV1Hhb1AJSjpzF/FRbFYWFh2Nq6jAIiJIvukmKvPzKV24kPwpUwGw2FhiMzKIzc4mLjuLmKOPxiL0X1fqpp8QER/FZmTQ94nHKV20mNisE/Ybc6/cvp3SRYspXbiQkkULyZ88mXwgLC6OTpnHE5eVRWxWNjEDjsLCw/07CGmXzDnndwYyMzPdkiVLmrStmdEejqEl6Fjap/ZyLJUFBZQuXkzJwoWULlxE+bffAhCWkEBsZiaxWVnEZWcRfeSRWNjBI67t5Tik5ZjZUudcZm336cxdJEhEJCfT+Zxz6HzOOQBUbN2635n9rg8+ACCsSxdiT8gMnNlnZxN9xBG1lr2ENpW7SJCK7N6dLuf9L13O+18AKjZvpnTRon1n9rv+9h4A4YmJxGZlMTwxkbI1a4jq3x/b+ySuhCwNy7QjOpb2KViPpWLDBkpqnNlXbtwEQHhSErFZJ+w7s4/q109lH6TqGpZRubcjOpb2KRSOxTlH3+hoVi5YsO/MvnLLFgAiUlICb6Dyxuwj09JU9kFC5R4kdCztU6gcS83jcM5RkZtLyaJFlC5cRMmihVTlFwAQ0aMHsdlZxGVnE5uVRWRqqsq+ndITqiICUGdJ94uKIqtTLFnFxZywYQPJr74GwMaKChaWlrKotIRFpaVsqqxsq7iHlJaWRo7mxq+Tyl2kA2noXyDOOcrXrqVk0SISFi6iz6JFXFhUBEBkaup/z+yzs4ns0aMVE9dOf0nUT8My7YiOpX0KlWNpznG46mrK1qzZN4RTungJ1Tt2ABCZ1pe4rEDRW6dOlK9d0+qToIXKv0lzacw9SOhY2qdQOZaWPA5XVUXZV1/tG7MvXbKE6p0791snPCmJ8K6JhMfFExYXV88llrC4OMLjD163tnffhsq/SXP5MuZuZmcDU4Fw4FHn3KTW2peItC0LDydmwABiBgwg6corcVVVbL7nDxQtWADOgRkRPXoQlZpK9a5dVJeUUJm/laqSEqpLSqkuKYEGjt1bTMxBvwge6Z3Khp/+rN5fFgf+wrCYmAYN6ZQuW1brlBDBpFXK3czCgYeAHwJ5wGIze9U593lr7E9E/GXh4XQ5/zx2vPhiYBK0yEgO+/WvDlmMzjlceTnVJSWBi/cLYO+lqsb1vb8Maq6THBHBntWrqSoN3O9KSxsWNCzs0H81xMURFhdP1a5dFL/5JlRVYVFR9J3zRFAWfGuduWcBa51z3wCY2dPAUEDlLhKi6poE7UBmhkVHExYdDd26NXpf6Wa4nG/33XZVVVTv3n3QL4laf1nsOvj+ioJCyvauu3MnVFcHHreiInA8Kvd9egPra9zOA7JrrmBm1wLXejd3mdlXTd1ZGz1zngwUtPZOdCyN0ibHAaFzLPr5ql9cWFhcWmTk/xD4vIvq3Btv+E/J9deVtMrOmi/tUHf49lJI59wsYJZf+28sM1tyqCcugk2oHEuoHAfoWNqjYD+O1poqbgPQp8btVG+ZiIi0gdYq98XAEWbWz8yigMuAV1tpXyIicoBWGZZxzlWa2TjgbQIvhXzcObe6NfbVhoJmCKkBQuVYQuU4QMfSHgX1cbSLNzGJiEjL0seziIiEIJW7iEgIUrnXw8zONrOvzGytmU30O09TmdnjZrbVzFb5naW5zKyPmX1gZp+b2Wozm+B3pqYysxgzW2Rm//aO5bd+Z2oOMws3s2Vm9rrfWZrDzHLMbKWZLTezpk185TONudfBm0bhP9SYRgG4PBinUTCzU4BdwF+ccwP9ztMcZtYT6Omc+8zMEoClwLAg/XcxIM45t8vMIoG/AxOcc5/6HK1JzOynQCbQ2Tl3nt95msrMcoBM51ybvEmuNejMvW77plFwzpUDe6dRCDrOuY+BbX7naAnOuU3Ouc+86zuBLwi8KzrouIBd3s1I7xKUZ1xmlgr8L/Co31lE5V6f2qZRCMoSCVVmlg5kAAt9jtJk3lDGcmAr8K5zLliPZQpwO1Dtc46W4IB3zGypN1VK0FG5S9Ays3jgBeAW51yx33mayjlX5ZwbTOCd3FlmFnTDZmZ2HrDVObfU7ywt5PvOueOAc4CbvGHNoKJyr5umUWinvPHpF4D5zrkX/c7TEpxzRcAHwNk+R2mKk4ELvLHqp4EzzGyev5Gazjm3wfu6FXiJwBBtUFG5103TKLRD3pOQjwFfOOcm+52nOcwsxcwSveudCDx5/6WvoZrAOfcL51yqcy6dwP+T951zI32O1SRmFuc9UY+ZxQE/AoLuVWYq9zo45yqBvdMofAE8G6zTKJjZAuBfwHfNLM/MxvqdqRlOBq4gcHa43Luc63eoJuoJfGBmKwicTLzrnAvqlxGGgB7A383s38Ai4A3n3Fs+Z2o0vRRSRCQE6cxdRCQEqdxFREKQyl1EJASp3EVEQpDKXUQkBKncpcWY2a4GrPOomQ3wrt9xwH3/bIl9tCQz+9DMWv1Dks1svJl9YWbzm/k4c8zsYu96m2SX9knlLm3KOXd1jdkb7zjgvpN8iNRqzKwxH2N5I/BD59yI1sojHYvKXVqcmZ3mnTU+b2Zfmtl8712l+84mzWwS0Ml7A9J8775d3td4M3vPzD7z5tSucyZOM0v3znpne3Oiv+O923O/s1czS/beHo+ZXWlmL5vZu97c3ePM7KfeXOSfmlm3Gru4wsu5ysyyvO3jvDnyF3nbDK3xuK+a2fvAe7Vk/an3OKvM7BZv2QzgcOCvZnbrAeuHm9mfvPVXmNnN3vLjzewjb2Krt71pkA/1/Qn3zuhXed/PWw+1roQQ55wuurTIBdjlfT0N2EFgLp4wAu+M/b5334cE5snet34t20cQmA8cIBlYy3/fcLerlv2mA5XAYO/2s8DIWvaXDOR416/0HjcBSPHyXu/d9wCBycj2bj/bu34KsMq7/oca+0gkMO9/nPe4eUC3WnIeD6z01osHVgMZ3n05QHIt29wAPA9EeLe7EZgW+J9AirfsUgIfQg8wB7i45rF7+323xmMm+v2zokvrXxrzZ6NIYyxyzuUBeNPZphP4IIqGMOAP3kx81QSmWe4BbK5jm2+dc8u960u9/dXnAxeYD36nme0AXvOWrwQG1VhvAQTmxDezzt5cMD8iMFHWz711YoC+3vV3nXO1zZ3/feAl51wJgJm9CAwBltWR8UxghgtMhYFzbps3a+RA4F3vD6JwYFMdj/ENcLiZPQi8AbxTx7oSIlTu0lrKalyvonE/ayMInE0f75yr8IZSYhq5v07e9Ur+O/x44GPU3Ka6xu3qA/IeOEeHI/AL6MfOua9q3mFm2UBJPVmby4DVzrnvNWRl59x2MzsWOAu4HrgEuKoV80k7oDF38VOFN3XvgboQmBu8wsxOB9KasY8cAsMSABc38TEuBTCz7wM7nHM7CEwmd3ON5xIyGvA4nwDDzCzWm23wQm9ZXd4Frtv75Kz3XMBXQIqZfc9bFmlmRx/qAcwsGQhzzr0A/Ao4rgFZJcip3MVPs4AVtbz8bz6QaWYrgVE0bwrcPwE3mNkyAmPuTbHH234GsHc2zd8RGPteYWarvdt1coGPBpxDYKbBhcCjzrm6hmQg8JF167z9/BsY7gIf+XgxcJ+3bDlQ1yuNegMfesNj84Bf1JdVgp9mhRQRCUE6cxcRCUEqdxGREKRyFxEJQSp3EZEQpHIXEQlBKncRkRCkchcRCUH/D/z5Bo5ebm0dAAAAAElFTkSuQmCC\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": "a4e37e72",
+   "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": 23,
+   "id": "d4b08bdf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "h5fn = '/home/aaristov/Anchor/Lena/Data/20220118-MIC-cipro-resistant/20220118-MIC-cipro-resistant.h5'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "1a40aab4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with h5py.File(h5fn,'w') as f:\n",
+    "    f.create_dataset('mask', data=big_labels, dtype='uint16')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "ef52343f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['/home/aaristov//Anchor/Lena/Data/20220118-MIC-cipro-resistant/day2/composites/256ng-BF-TRITC.aligned.tif',\n",
+       " '/home/aaristov//Anchor/Lena/Data/20220118-MIC-cipro-resistant/day2/composites/032ng-BF-TRITC.aligned.tif',\n",
+       " '/home/aaristov//Anchor/Lena/Data/20220118-MIC-cipro-resistant/day2/composites/192ng-BF-TRITC.aligned.tif',\n",
+       " '/home/aaristov//Anchor/Lena/Data/20220118-MIC-cipro-resistant/day2/composites/000ng-BF-TRITC.aligned.tif',\n",
+       " '/home/aaristov//Anchor/Lena/Data/20220118-MIC-cipro-resistant/day2/composites/096ng-BF-TRITC.aligned.tif',\n",
+       " '/home/aaristov//Anchor/Lena/Data/20220118-MIC-cipro-resistant/day2/composites/128ng-BF-TRITC.aligned.tif']"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "glob('/home/aaristov//Anchor/Lena/Data/20220118-MIC-cipro-resistant/day2/composites/*ng-BF-TRITC.aligned.tif')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "9e7a4d61",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "256ng\n",
+      "032ng\n",
+      "192ng\n",
+      "000ng\n",
+      "096ng\n",
+      "128ng\n"
+     ]
+    }
+   ],
+   "source": [
+    "with h5py.File(h5fn,'a') as f:\n",
+    "    try:\n",
+    "        d1 = f.create_group('day2')\n",
+    "    except ValueError:\n",
+    "        d1 = f['day2']\n",
+    "    for p in glob('/home/aaristov//Anchor/Lena/Data/20220118-MIC-cipro-resistant/day2/composites/*ng-BF-TRITC.aligned.tif'):\n",
+    "        ng = re.compile('(\\d+ng)').findall(p)[0]\n",
+    "        print(ng)\n",
+    "        bf, fluo, _ = tf.imread(p)\n",
+    "        try:\n",
+    "            c = d1.create_group(ng)\n",
+    "        except ValueError:\n",
+    "            c = d1[ng]\n",
+    "        c.create_dataset('bf', data=bf, dtype=bf.dtype)\n",
+    "        c.create_dataset('fluo', data=fluo, dtype=fluo.dtype)\n",
+    "        \n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "5da4535c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "000ng\n",
+      "032ng\n",
+      "064ng\n",
+      "096ng\n",
+      "128ng\n",
+      "192ng\n",
+      "256ng\n"
+     ]
+    }
+   ],
+   "source": [
+    "with h5py.File('F:/Andrey/20220118-MIC-cipro-resistant.h5','a') as f:\n",
+    "    d1 = f.create_group('day1')\n",
+    "    for p in glob('/home/aaristov/Anchor/Lena/Data/20220118-MIC-cipro-resistant/day1/composites/*ng.tif'):\n",
+    "        ng = re.compile('(\\d+ng)').findall(p)[0]\n",
+    "        print(ng)\n",
+    "        bf, fluo, _ = tf.imread(p)\n",
+    "        c = d1.create_group(ng)\n",
+    "        c.create_dataset('bf', data=bf, dtype=bf.dtype)\n",
+    "        c.create_dataset('fluo', data=fluo, dtype=fluo.dtype)\n",
+    "        \n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "e66e4611",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "096ng\n",
+      "000ng\n",
+      "192ng\n",
+      "128ng\n",
+      "256ng\n",
+      "032ng\n"
+     ]
+    }
+   ],
+   "source": [
+    "for p in glob('/home/aaristov/Anchor/Lena/Data//20220118-MIC-cipro-resistant/day2/composites/*ng-BF-TRITC-counts.csv'):\n",
+    "    ng = re.compile('(\\d+ng)').findall(p)[0]\n",
+    "    print(ng)\n",
+    "    pd.read_csv(p).to_hdf(h5fn, f'day2/{ng}/counts', 'a',)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "ac943399",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "256ng\n",
+      "000ng\n",
+      "192ng\n",
+      "032ng\n",
+      "128ng\n",
+      "096ng\n",
+      "064ng\n"
+     ]
+    }
+   ],
+   "source": [
+    "for p in glob('/home/aaristov/Anchor/Lena/Data//20220118-MIC-cipro-resistant/day1/composites/*counts.csv'):\n",
+    "    ng = re.compile('(\\d+ng)').findall(p)[0]\n",
+    "    print(ng)\n",
+    "    pd.read_csv(p).to_hdf(h5fn, f'day1/{ng}/counts', 'a',)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "26df8e2d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>label</th>\n",
+       "      <th>x</th>\n",
+       "      <th>y</th>\n",
+       "      <th>n_cells</th>\n",
+       "      <th>ng</th>\n",
+       "      <th>poisson fit</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>663.248626</td>\n",
+       "      <td>417.128241</td>\n",
+       "      <td>1</td>\n",
+       "      <td>32</td>\n",
+       "      <td>0.523281</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>664.647675</td>\n",
+       "      <td>948.961450</td>\n",
+       "      <td>0</td>\n",
+       "      <td>32</td>\n",
+       "      <td>0.523281</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>665.043884</td>\n",
+       "      <td>1475.729516</td>\n",
+       "      <td>1</td>\n",
+       "      <td>32</td>\n",
+       "      <td>0.523281</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>661.226322</td>\n",
+       "      <td>16298.040220</td>\n",
+       "      <td>2</td>\n",
+       "      <td>32</td>\n",
+       "      <td>0.523281</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>660.757642</td>\n",
+       "      <td>16826.050217</td>\n",
+       "      <td>1</td>\n",
+       "      <td>32</td>\n",
+       "      <td>0.523281</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>496</th>\n",
+       "      <td>497</td>\n",
+       "      <td>5962.000000</td>\n",
+       "      <td>4646.000000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>32</td>\n",
+       "      <td>0.523281</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>497</th>\n",
+       "      <td>498</td>\n",
+       "      <td>5961.897296</td>\n",
+       "      <td>5177.702880</td>\n",
+       "      <td>1</td>\n",
+       "      <td>32</td>\n",
+       "      <td>0.523281</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>498</th>\n",
+       "      <td>499</td>\n",
+       "      <td>5961.526499</td>\n",
+       "      <td>5706.998255</td>\n",
+       "      <td>0</td>\n",
+       "      <td>32</td>\n",
+       "      <td>0.523281</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>499</th>\n",
+       "      <td>500</td>\n",
+       "      <td>5961.537016</td>\n",
+       "      <td>6236.971642</td>\n",
+       "      <td>0</td>\n",
+       "      <td>32</td>\n",
+       "      <td>0.523281</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>500</th>\n",
+       "      <td>501</td>\n",
+       "      <td>5961.585169</td>\n",
+       "      <td>6765.801022</td>\n",
+       "      <td>0</td>\n",
+       "      <td>32</td>\n",
+       "      <td>0.523281</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>501 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     label            x             y  n_cells  ng  poisson fit\n",
+       "0        1   663.248626    417.128241        1  32     0.523281\n",
+       "1        2   664.647675    948.961450        0  32     0.523281\n",
+       "2        3   665.043884   1475.729516        1  32     0.523281\n",
+       "3        4   661.226322  16298.040220        2  32     0.523281\n",
+       "4        5   660.757642  16826.050217        1  32     0.523281\n",
+       "..     ...          ...           ...      ...  ..          ...\n",
+       "496    497  5962.000000   4646.000000        0  32     0.523281\n",
+       "497    498  5961.897296   5177.702880        1  32     0.523281\n",
+       "498    499  5961.526499   5706.998255        0  32     0.523281\n",
+       "499    500  5961.537016   6236.971642        0  32     0.523281\n",
+       "500    501  5961.585169   6765.801022        0  32     0.523281\n",
+       "\n",
+       "[501 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.read_hdf(h5fn, 'day1/032ng/counts',)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "241e45a3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<KeysViewHDF5 ['axis0', 'axis1', 'bf', 'block0_items', 'block0_values', 'block1_items', 'block1_values', 'counts', 'fluo']>\n"
+     ]
+    }
+   ],
+   "source": [
+    "with h5py.File('/home/aaristov/Anchor/Lena/Data/20220118-MIC-cipro-resistant//20220118-MIC-cipro-resistant.h5','r') as f:\n",
+    "    print(f['day2/032ng'].keys())\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "9f4ae869",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "\"Unable to open object (object 'day1' doesn't exist)\"",
+     "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_13756/2212118693.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mh5py\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mFile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'F:/Andrey/20220118-MIC-cipro-resistant.h5'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'w'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     \u001b[0mf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'day1'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'day2'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mh5py\\_objects.pyx\u001b[0m in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mh5py\\_objects.pyx\u001b[0m in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32m~\\miniconda3\\envs\\nd2\\lib\\site-packages\\h5py\\_hl\\group.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m    262\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Invalid HDF5 object reference\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    263\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--> 264\u001b[1;33m             \u001b[0moid\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mh5o\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mid\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_e\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlapl\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lapl\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    265\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    266\u001b[0m         \u001b[0motype\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mh5i\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moid\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mh5py\\_objects.pyx\u001b[0m in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mh5py\\_objects.pyx\u001b[0m in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mh5py\\h5o.pyx\u001b[0m in \u001b[0;36mh5py.h5o.open\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;31mKeyError\u001b[0m: \"Unable to open object (object 'day1' doesn't exist)\""
+     ]
+    }
+   ],
+   "source": [
+    "with h5py.File('F:/Andrey/20220118-MIC-cipro-resistant.h5','w') as f:\n",
+    "    f['day1'].name = 'day2'\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e0a30cd4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1, 1, 25, 7383, 22392)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "img = imread('E:/Andrey/20220111-MIC-resistant/000ng-TRITC.nd2')\n",
+    "img.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "7a03c07b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "proj = img[0,0].max(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "1e5b7fb0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(7383, 22392)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "proj.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "8cc2a90e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "inputs: \n",
+      " bf: (7383, 22392),  2\n",
+      " tmp: (818, 2612),  16\n",
+      " mask: (6544, 20896),  2\n",
+      "\n",
+      "{'tvec': array([135.19242177, -23.17480826]), 'success': 0.033146561190619483, 'angle': -0.601658085577327, 'scale': 0.9958393722988022, 'Dscale': 0.00047580368714397894, 'Dangle': 0.013400833829660513, 'Dt': 0.25, 'timg': None}\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n"
+     ]
+    }
+   ],
+   "source": [
+    "t = asyncio.create_task(align3D(path_BF='E:/Andrey/20220113-MIC-W8110_RFPplus-amp/day1/020ug-BF.nd2', \n",
+    "                                path_TRITC='E:/Andrey/20220113-MIC-W8110_RFPplus-amp/day1/020ug-TRITC.nd2',\n",
+    "                                path_to_save='E:/Andrey/20220113-MIC-W8110_RFPplus-amp/day1/composites/020ug.tif'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "f198c845",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[[[15366, 15040, 15413, ..., 15393, 15169,     0],\n",
+       "          [15359, 15424, 15270, ..., 15156, 14840,     0],\n",
+       "          [15294, 15485, 15643, ..., 15021, 14518,     0],\n",
+       "          ...,\n",
+       "          [14651, 15195, 15394, ..., 15328, 15013,     0],\n",
+       "          [15156, 15493, 15001, ..., 15424, 14799,     0],\n",
+       "          [15043, 15453, 15148, ..., 15594, 14994,     0]]]]],\n",
+       "      dtype=uint16)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "imread(r'E:Andrey/20220113-MIC-W8110_RFPplus-amp/day1/020ug-BF.nd2')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "39c0e194",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "_ = await asyncio.gather(t)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "5929e798",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[array([[[15680, 15661, 15544, ..., 15302, 15307, 15254],\n",
+       "         [15738, 15662, 15572, ..., 15409, 15347, 15228],\n",
+       "         [15747, 15643, 15581, ..., 15324, 15291, 15150],\n",
+       "         ...,\n",
+       "         [15501, 15545, 15722, ..., 15665, 15737, 15771],\n",
+       "         [15597, 15551, 15658, ..., 15737, 15743, 15714],\n",
+       "         [15549, 15456, 15536, ..., 15923, 15894, 15776]],\n",
+       " \n",
+       "        [[  417,   417,   417, ...,   415,   414,   415],\n",
+       "         [  417,   418,   418, ...,   419,   417,   415],\n",
+       "         [  416,   418,   417, ...,   416,   417,   414],\n",
+       "         ...,\n",
+       "         [  416,   417,   420, ...,   418,   414,   412],\n",
+       "         [  417,   418,   416, ...,   417,   414,   413],\n",
+       "         [  418,   419,   417, ...,   418,   414,   414]],\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": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "ea55f9b1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Thread(target=align3D, args=('E:/Andrey/20220113-MIC-W8110_RFPplus-amp/day1/004ug-BF.nd2', \n",
+    "                               'E:/Andrey/20220113-MIC-W8110_RFPplus-amp/day1/004ug-TRITC.nd2',\n",
+    "                                'E:/Andrey/20220113-MIC-W8110_RFPplus-amp/day1/composites/004ug.tif')).start()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "0a9f0fc2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "inputs: \n",
+      " bf: (7383, 22392),  2\n",
+      " tmp: (818, 2612),  16\n",
+      " mask: (6544, 20896),  2\n",
+      "\n",
+      "{'tvec': array([-66.77887303, -25.3117496 ]), 'success': 0.028951856694324814, 'angle': -1.0841143459401508, 'scale': 0.9965544422496032, 'Dscale': 0.00047614534155996334, 'Dangle': 0.013400833829660513, 'Dt': 0.25, 'timg': None}\n",
+      "transform (7383, 22392)\n"
+     ]
+    }
+   ],
+   "source": [
+    "Thread(target=align3D, args=('E:/Andrey/20220113-MIC-W8110_RFPplus-amp/day1/008ug-BF.nd2', \n",
+    "                               'E:/Andrey/20220113-MIC-W8110_RFPplus-amp/day1/008ug-TRITC.nd2',\n",
+    "                                'E:/Andrey/20220113-MIC-W8110_RFPplus-amp/day1/composites/008ug.tif')).start()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "cb8b7875",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "transform (7383, 22392)\n",
+      "{'tvec': array([-24.23891734, -92.59478271]), 'success': 0.023470809046558613, 'angle': 0.31726302421077435, 'scale': 0.9966054124989634, 'Dscale': 0.0004761696947169632, 'Dangle': 0.013400833829660513, 'Dt': 0.25, 'timg': None}\n",
+      "transform (7383, 22392)\n",
+      "inputs: \n",
+      " bf: (7383, 22392),  2\n",
+      " tmp: (818, 2612),  16\n",
+      " mask: (6544, 20896),  2\n",
+      "\n",
+      "transform (7383, 22392)\n",
+      "{'tvec': array([140.66432787,  40.83300358]), 'success': 0.02248191467350375, 'angle': -2.3751076621607865, 'scale': 0.9959702346310123, 'Dscale': 0.00047586621206707985, 'Dangle': 0.013400833829660513, 'Dt': 0.25, 'timg': None}\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n"
+     ]
+    }
+   ],
+   "source": [
+    "Thread(target=align3D, args=('E:/Andrey/20220113-MIC-W8110_RFPplus-amp/day1/012ug-BF.nd2', \n",
+    "                               'E:/Andrey/20220113-MIC-W8110_RFPplus-amp/day1/012ug-TRITC.nd2',\n",
+    "                                'E:/Andrey/20220113-MIC-W8110_RFPplus-amp/day1/composites/012ug.tif')).start()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "f2b9fa7c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Aligning None: \n",
+      " bf: (7383, 22392),  2\n",
+      " tmp: (818, 2612),  16\n",
+      " mask: (6544, 20896),  2\n",
+      "\n",
+      "{'tvec': array([61.88855134, 94.2086353 ]), 'success': 0.028595003325769044, 'angle': 0.09435223114010682, 'scale': 0.9960829206971011, 'Dscale': 0.0004759200525229066, 'Dangle': 0.013400833829660513, 'Dt': 0.25, 'timg': None}\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n"
+     ]
+    }
+   ],
+   "source": [
+    "Thread(target=align3D, args=('E:/Andrey/20220113-MIC-W8110_RFPplus-amp/day1/002ug-BF.nd2', \n",
+    "                               'E:/Andrey/20220113-MIC-W8110_RFPplus-amp/day1/002ug-TRITC.nd2',\n",
+    "                                'E:/Andrey/20220113-MIC-W8110_RFPplus-amp/day1/composites/002ug.tif')).start()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "818ec41c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "inputs: \n",
+      " bf: (7383, 22392),  2\n",
+      " tmp: (818, 2612),  16\n",
+      " mask: (6544, 20896),  2\n",
+      "\n",
+      "{'tvec': array([128.11465115,  25.4385776 ]), 'success': 0.024166516717813388, 'angle': 0.7525076234866503, 'scale': 0.9952003324694797, 'Dscale': 0.00047549835928139243, 'Dangle': 0.013400833829660513, 'Dt': 0.25, 'timg': None}\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n"
+     ]
+    }
+   ],
+   "source": [
+    "Thread(target=align3D, args=('E:/Andrey/20220113-MIC-W8110_RFPplus-amp/day1/032ug-BF.nd2', \n",
+    "                               'E:/Andrey/20220113-MIC-W8110_RFPplus-amp/day1/032ug-TRITC.nd2',\n",
+    "                                'E:/Andrey/20220113-MIC-W8110_RFPplus-amp/day1/composites/032ug.tif')).start()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "a0650547",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "inputs: \n",
+      " bf: (7383, 22392),  2\n",
+      " tmp: (818, 2612),  16\n",
+      " mask: (6544, 20896),  2\n",
+      "\n",
+      "{'tvec': array([-20.65952623,  46.92321863]), 'success': 0.021491670767319303, 'angle': -2.834182103113733, 'scale': 0.9926008377397054, 'Dscale': 0.000474256342534974, 'Dangle': 0.013400833829660513, 'Dt': 0.25, 'timg': None}\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n"
+     ]
+    }
+   ],
+   "source": [
+    "Thread(target=align3D, args=('E:/Andrey/20220113-MIC-W8110_RFPplus-amp/day1/016ug-BF.nd2', \n",
+    "                               'E:/Andrey/20220113-MIC-W8110_RFPplus-amp/day1/016ug-TRITC.nd2',\n",
+    "                                'E:/Andrey/20220113-MIC-W8110_RFPplus-amp/day1/composites/016ug.tif')).start()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "c6bb5930",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "inputs: \n",
+      " bf: (7383, 22392),  2\n",
+      " tmp: (818, 2612),  16\n",
+      " mask: (6544, 20896),  2\n",
+      "\n",
+      "{'tvec': array([96.16884521, 71.15136805]), 'success': 0.021886719600972267, 'angle': 0.8654943305871825, 'scale': 0.9948733306900598, 'Dscale': 0.0004753421205779643, 'Dangle': 0.013400833829660513, 'Dt': 0.25, 'timg': None}\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n"
+     ]
+    }
+   ],
+   "source": [
+    "Thread(target=align3D, args=('E:/Andrey/20220113-MIC-W8110_RFPplus-amp/day1/064ug-BF.nd2', \n",
+    "                               'E:/Andrey/20220113-MIC-W8110_RFPplus-amp/day1/064ug-TRITC.nd2',\n",
+    "                                'E:/Andrey/20220113-MIC-W8110_RFPplus-amp/day1/composites/064ug.tif')).start()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "9942e18d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "E:/Andrey/20220113-MIC-W8110_RFPplus-amp/day1/composites\\000ug.tif already countedE:/Andrey/20220113-MIC-W8110_RFPplus-amp/day1/composites\\002ug.tif already counted\n",
+      "\n",
+      "E:/Andrey/20220113-MIC-W8110_RFPplus-amp/day1/composites\\004ug.tif already counted\n",
+      "E:/Andrey/20220113-MIC-W8110_RFPplus-amp/day1/composites\\008ug.tif already countedE:/Andrey/20220113-MIC-W8110_RFPplus-amp/day1/composites\\012ug.tif already counted\n",
+      "\n",
+      "E:/Andrey/20220113-MIC-W8110_RFPplus-amp/day1/composites\\016ug.tif already counted\n",
+      "E:/Andrey/20220113-MIC-W8110_RFPplus-amp/day1/composites\\020ug.tif already counted\n",
+      "E:/Andrey/20220113-MIC-W8110_RFPplus-amp/day1/composites\\032ug.tif already counted\n",
+      "E:/Andrey/20220113-MIC-W8110_RFPplus-amp/day1/composites\\064ug.tif already counted\n"
+     ]
+    }
+   ],
+   "source": [
+    "for p in glob('E:/Andrey/20220113-MIC-W8110_RFPplus-amp/day1/composites/*ug.tif'):\n",
+    "    Thread(target=count, args=(p,)).start()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "b983deb5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Y:Lena/Data/20220111-MIC-resistant/composites-0h\\192ng-Composite..aligned.tif already counted\n",
+      "Y:Lena/Data/20220111-MIC-resistant/composites-0h\\128ng-Composite..aligned.tif already counted\n",
+      "Y:Lena/Data/20220111-MIC-resistant/composites-0h\\096ng-Composite..aligned.tif already counted\n",
+      "Y:Lena/Data/20220111-MIC-resistant/composites-0h\\256ng-Composite..aligned.tif already counted\n"
+     ]
+    }
+   ],
+   "source": [
+    "for p in glob('Y:Lena/Data/20220111-MIC-resistant/composites-0h/*ng-Composite..aligned.tif'):\n",
+    "    threading.Thread(target=count, args=(p, '(\\d+)ng')).start() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "b3d88eee",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Y:Lena/Data/20220111-MIC-resistant/composites-0h\\256ng-Composite.tif (2, 7383, 22392)\n",
+      "Y:Lena/Data/20220111-MIC-resistant/composites-0h\\128ng-Composite.tif (2, 7383, 22392)\n",
+      "Y:Lena/Data/20220111-MIC-resistant/composites-0h\\192ng-Composite.tif (2, 7383, 22392)\n",
+      "Y:Lena/Data/20220111-MIC-resistant/composites-0h\\064ng-Composite.tif (2, 7383, 22392)\n",
+      "Y:Lena/Data/20220111-MIC-resistant/composites-0h\\032ng-Composite.tif (2, 7383, 22392)\n",
+      "Y:Lena/Data/20220111-MIC-resistant/composites-0h\\096ng-Composite.tif (2, 7383, 22392)\n",
+      "Y:Lena/Data/20220111-MIC-resistant/composites-0h\\000ng-Composite.tif (2, 7383, 22392)\n",
+      "Y:Lena/Data/20220111-MIC-resistant/composites-0h\\256ng-Composite.tif {'tvec': array([  22.53591166, -801.32214232]), 'success': 0.03253634399730256, 'angle': -1.4864616321270887, 'scale': 0.9944251925086264, 'Dscale': 0.0004751280039191865, 'Dangle': 0.013400833829660513, 'Dt': 0.25, 'timg': None}\n",
+      "transform (7383, 22392)\n",
+      "Y:Lena/Data/20220111-MIC-resistant/composites-0h\\128ng-Composite.tif {'tvec': array([38.24944294, 64.18302877]), 'success': 0.015587203533002615, 'angle': -0.8737013706948744, 'scale': 0.9947225234883397, 'Dscale': 0.0004752700661637462, 'Dangle': 0.013400833829660513, 'Dt': 0.25, 'timg': None}\n",
+      "transform (7383, 22392)\n",
+      "Y:Lena/Data/20220111-MIC-resistant/composites-0h\\192ng-Composite.tif {'tvec': array([55.73085424, -8.94018689]), 'success': 0.03266118034259374, 'angle': -1.8668383321397073, 'scale': 0.9943551426676936, 'Dscale': 0.00047509453469359974, 'Dangle': 0.013400833829660513, 'Dt': 0.25, 'timg': None}\n",
+      "transform (7383, 22392)\n",
+      "Y:Lena/Data/20220111-MIC-resistant/composites-0h\\064ng-Composite.tif {'tvec': array([40.58535973, -3.40578457]), 'success': 0.023432333623856917, 'angle': -2.0120781105788126, 'scale': 0.9960308934814597, 'Dscale': 0.000475895194356296, 'Dangle': 0.013400833829660513, 'Dt': 0.25, 'timg': None}\n",
+      "transform (7383, 22392)\n",
+      "Y:Lena/Data/20220111-MIC-resistant/composites-0h\\032ng-Composite.tif {'tvec': array([47.20258286, 32.28065721]), 'success': 0.023075283534119067, 'angle': 2.3208830225709676, 'scale': 0.9951400924142342, 'Dscale': 0.00047546957708900593, 'Dangle': 0.013400833829660513, 'Dt': 0.25, 'timg': None}\n",
+      "transform (7383, 22392)\n",
+      "Y:Lena/Data/20220111-MIC-resistant/composites-0h\\096ng-Composite.tif {'tvec': array([21.99663196, 56.89971328]), 'success': 0.03821816963812466, 'angle': -2.1766091738274724, 'scale': 0.9948597257927269, 'Dscale': 0.0004753356202722983, 'Dangle': 0.013400833829660513, 'Dt': 0.25, 'timg': None}\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "Y:Lena/Data/20220111-MIC-resistant/composites-0h\\000ng-Composite.tif {'tvec': array([  2.13472876, 265.46871537]), 'success': 0.029433288851305688, 'angle': -3.594155504195953, 'scale': 0.9930340770031477, 'Dscale': 0.0004744633406158849, '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",
+      "Saved aligned stack Y:Lena/Data/20220111-MIC-resistant/composites-0h\\256ng-Composite.aligned.tif\n",
+      "Saved aligned stack Y:Lena/Data/20220111-MIC-resistant/composites-0h\\128ng-Composite.aligned.tif\n",
+      "Saved aligned stack Y:Lena/Data/20220111-MIC-resistant/composites-0h\\192ng-Composite.aligned.tif\n",
+      "Saved aligned stack Y:Lena/Data/20220111-MIC-resistant/composites-0h\\064ng-Composite.aligned.tif\n",
+      "Saved aligned stack Y:Lena/Data/20220111-MIC-resistant/composites-0h\\096ng-Composite.aligned.tif\n",
+      "Saved aligned stack Y:Lena/Data/20220111-MIC-resistant/composites-0h\\032ng-Composite.aligned.tif\n",
+      "Saved aligned stack Y:Lena/Data/20220111-MIC-resistant/composites-0h\\000ng-Composite.aligned.tif\n"
+     ]
+    }
+   ],
+   "source": [
+    "for p in glob('Y:Lena/Data/20220111-MIC-resistant/composites-0h/*ng-Composite.tif'):\n",
+    "    threading.Thread(target=register.align_stack, kwargs=dict(data_or_path=p, template16=template16, mask2=big_labels, binnings=(2,16,2))).start() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "2dd73ce0",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "32 ug\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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8h6s/tcUY475aXS2jqrsO9bxVNQdYB7QBBgKHLlidiSfwcZa/7kxa9j0Q53xANHrBMdG0+stfaP/22wQnJLDj7rtJu2M0xTt3ul2aMSbAVGvMXUTaA6nAEqClqu5y3tqNZ9gGPMG/3WuzNGfZ0fsaKSLLRWR5enp6dev2a5FndqbDu+/Q4v77yVuyhI2XXU7mjBkEu12YMSZgVDncRSQGmAvcrapHzPrjzCtcrfEEVZ2sqt1VtXtiYmJ1Ng0IEhJC/K3D6Pjxx0Sd3Z29459mTnJ7Ctaudbs0Y0wAqFK4i0gonmCfparvO4v3HBpucX7udZbvANp5bd7WWWbKEda2De1efZU2zz1Li5AQtgy+lj1PjacsL8/t0owxfqzScHeufpkKrFPVZ73emgfc4jy/BfjIa/nN4nEOkO01fGPKISI0ueQSLtu8ibjBg9k3cyYbL7+cnC+/dLs0Y4yfqkrPvRfwe+BCEVnlPAYA44F+IvIrcJHzGuATYBOwAZgCjK77sgNTTlkZrR99hOS33iI4Opq0O0aT9oe7Kd6zt/KNjTHGi00c5kO826JFRWROm07GxIlIWBgt/vRH4oYMQYLsvjNjjIdNHOaHJCyMhFG30/HjeUSc2Zndjz7G1htupPCXX9wuzRjjByzcfVxYcjJJ06ZxwtPjKdq6lc2Drmbvs89RZnchGmMqYOHuB0SEpgMH0vGT+TS9/HIyJ09m0+VXkPvNN26XZozxURbufiSkWTNOeOpJkmbMQIKC2D58BDvuu5+SzEy3SzPG+BgLdz8UfU5POsz7iITRd3Dg00/ZNOBSsubODZgTy8aY2rNw91NB4eEk3nUXHT94n7CTTmLXXx9i2823cHDTZrdLM8b4AAt3Pxd+0kkkv/E6rf72GIXr17N54EDSJ7xMWVGR26UZY1xk4R4AJCiIZoMHc+In84nt35+MCRPYfOVV5C9b5nZpxhiXWLgHkJCEBNo88w/aTZmMFhWx9fc3s/OhhyjNynK7NGNMA7NwD0AxffrQ8eN5xI8YTvYHH7Lx0svI/vifdsLVmEbEph/wIfXRlsKff2bXw+MoXL2a6F69iBt8DUVbtxHV42yiUlPr9FjGmIZV0fQDIQ1djGlYEaedRvvZb7F/9hz2/uMf5H3zDQQFIWFhJE2fZgFvTICyYZlGQIKDaX7TjTT7/U2eBWVlaHEx+UvthKsxgcrCvRGJvfBCCPH8sibBwUT1ONvliowx9cXCvRGJSk2l3eTJSEQE4Z062ZCMMQHMwr2RiTn3NySMGU3hjz+Sv3Kl2+UYY+qJnVD1MZ5vNaxfUSIs6HgiX19xBSPT0urlGMnJyWzZsqVe9m2MqZyFu49pqMs6M6dOo/nf/07eihVEde1a5/tviA8pY8zx2bBMI9Xs+usIjo8n/aWX3C7FGFMPLNwbqaCoKOJHjCD/u+/Jr+ENZMYY32Xh3og1u24IwQkJpL80we1SjDF1zMK9EQuKjCRh5G3kL1lC3pKlbpdjjKlDFu6NXNy11xKSmEjGSy8FzBw9xhgL90YvKCKC+JEjyV++nPwlS9wuxxhTRyzcDXHXDiakZUvSX5pgvXdjAoSFuyEoPJz4kbdRsGIF+d9953Y5xpg6YOFuAIgbPJiQVq1If9HG3o0JBBbuBoCgsDASRt1OwapV5H39jdvlGGNqycLdHBY3aBAhJ7QmfYL13o3xdxbu5jAJCyPh9lEU/riavMWL3S7HGFMLFu7mCHFXXUlomzY29m6Mn7NwN0eQsDAS7hhF4Zo15H71ldvlGGNqyMLdHKPpwIGEtmtHxoSXrfdujJ+ycDfHkNBQEkaNonDtWnK//NLtcowxNWDhbsrVdOAVhCYl2V2rxvgpC3dTLgkJIWH0HRxct46czz93uxxjTDVZuJvjanrZZYQlJ3vG3svK3C7HGFMNFu7muCQkhIQxozm4fj05C633bow/sXA3FWpy6aWEdehAxoQJ1ns3xo9UGu4iMk1E9orIGq9lj4jIDhFZ5TwGeL33oIhsEJH1IvK7+ircNAwJDiZhzBgO/vorOZ995nY5xpgqqkrPfQZwcTnLn1PVFOfxCYCInA5cB5zhbDNRRILrqljjjiaXXEzYiSeS/vLLaGmp2+UYY6qg0nBX1f8A+6q4v4HAHFU9qKqbgQ1Aj1rUZ3yABAeTOGY0RRs2cuDTT90uxxhTBbUZcx8rIqudYZtmzrI2wHavddKcZccQkZEislxElqenp9eiDNMQYi++mPCTTyLj5YnWezfGD9Q03F8BTgRSgF3AM9XdgapOVtXuqto9MTGxhmWYhiJBQSSMGUvRpk0c+OQTt8sxxlSiRuGuqntUtVRVy4Ap/G/oZQfQzmvVts4yEwBi+/cj/NRTPb33khK3yzHGVKBG4S4irb1eXgUcupJmHnCdiISLSAfgZGBp7Uo0vsLTex9N0ZYtHJg/3+1yjDEVCKlsBRGZDZwPJIhIGjAOOF9EUgAFtgC3A6jqWhF5B/gJKAHGqKoN0AaQ2IsuIrxTJ9InTqTJpZciIZX+EzLGuKDS/5mqen05i6dWsP4TwBO1Kcr4LgkKInHsGNLGjCV73sfEDbrK7ZKMMeWwO1RNtcVceCERp59OxiuvoMXFbpdjjCmHhbupNhEhYexYirdvJ3vePLfLMcaUw8Ld1EjMBecT0bkzGROt926ML7JwNzUiIiTeOZbiHTvI+uADt8sxxhzFwt3UWHTfvkR0OYuMV19Fi4rcLscY48XC3dSYiJA4diwlO3eR9b713o3xJRbuplaie/cmsksXMiZNosx678b4DAt3UysiQsJdd1KyaxdZ773ndjnGGIeFu6m16HPPJbJrVzInTabs4EG3yzHGYOFu6sChK2dK9uwh613rvRvjCyzcTZ2IOuccIrt3I3PyZMoKC90ux5hGz8Ld1AlP7/0uSvbuJeudd9wux5hGz8Ld1Jnonj2I6tGDjClTCBdxuxxjGjULd1OnEu8cS2l6BkPi4twuxZhGzcLd1Kmos88m6pxzGNE8nrL8fLfLMabRsnA3dS7xzrEkhISwf/Yct0sxptGycDd1LqpbN77JyyNz6lTrvRvjEgt3Uy8mZKRTum8f+996y+1SjGmULNxNvfixsJDo3r3JfG0qpbl5bpdjTKNj4W7qTeKdYynNymL/rFlul2JMo2PhbupNZJcuRJ/Xl33TplGam+t2OcY0Khbupl4ljh1LaXY2+9980+1SjGlULNxNvYo880xizj+fzOkzKM3JcbscYxoNC3dT7xLuHEtZdjb7Xn/d7VKMaTQs3E29izzjDGJ++1v2zZhJ6YEDbpdjTKNg4W4aROLYMZTl5LBvpvXejWkIFu6mQUR06kRsv4vYN3MmpdnZbpdjTMCzcDcNJmHsWMpyc8mcMcPtUowJeBbupsFEnHoqsb/7Hftff4PSrCy3yzEmoFm4mwaVMGY0Zfn5ZE6f4XYpxgQ0C3fToCJOOYXYi3/H/jfeoGT/frfLMSZgWbibBpc4ZgxlBQXsmzbN7VKMCVgW7qbBhZ90Ek0GDGDfrLco2bfP7XKMCUgW7sYVCWNGo4WFZE6d6nYpxgQkC3fjivCOHWly6aXsf2s2JRkZbpdjTMCxcDeuSRh9B3rwIJmvWe/dmLpm4W5cE96hA00vv5z9c+ZQkp7udjnGBBQLd+OqhNF3oMXFZL72mtulGBNQLNyNq8KSk2l6xRXsn/M2xXv2ul2OMQGj0nAXkWkisldE1ngtay4iC0XkV+dnM2e5iMiLIrJBRFaLSNf6LN4EhoQ7RqElJWROmeJ2KcYEjKr03GcAFx+17AHgC1U9GfjCeQ1wCXCy8xgJvFI3ZZpAFpaURNOrriTrnXco3rPH7XKMCQiVhruq/gc4+k6TgcBM5/lM4Eqv5a+rx/dAnIi0rqNaTQBLGHUHWlZG5qTJbpdiTECo6Zh7S1Xd5TzfDbR0nrcBtnutl+YsO4aIjBSR5SKyPN2ulGj0wtq2Ie6qq8h6912Kd+2qfANjTIVqfUJVVRXQGmw3WVW7q2r3xMTE2pZhAkDCqNtRIGPSJLdLMcbv1TTc9xwabnF+HrrMYQfQzmu9ts4yYyoV2qYNcVcPImvu+xTvsH82xtRGTcN9HnCL8/wW4COv5Tc7V82cA2R7Dd8YU6mE229HgAwbezemVqpyKeRs4DvgVBFJE5HhwHign4j8ClzkvAb4BNgEbACmAKPrpWoTsEJbtyZu8DVkvf8+RWnWezempsQzZO6u7t276/Lly2u0rYjgC22oC9YWj+Ldu9nYrz9NBl7BCY8/XseVGRM4RGSFqnYv7z27Q9X4nNBWrYgbMoTsDz6kaPv2yjcwxhzDwt34pPjbbkNCQsh45VW3SzHGL1m4G58U2rIFcUOuJfujjyjautXtcozxOxbuxmfFjxjh6b1PtFksjKkuC3fjs0JbtKDZ9deT/fHHHNy82e1yjPErFu7Gp8WPGI6EhZHxivXejakOC3fj00ISEmh2ww0c+Od8Dm6y3rsxVWXhbnxe/PBbkfBwMiZOdLsUY/yGhbvxeSHx8TS/6UYOzJ/PwQ0b3C7HGL8Q4nYBJnCJSJ3tKy44mIUdOzKh73ncu2tnne23KpKTk9myZUuDHtOY2rKeu6k3qlpnj/0lJSSNuoMBTZtS+Msvdbrvyh5b7Tp744cs3I3faD5sKEFRUaS/bGPvxlTGwt34jZBmzWh28+/J+fRTCtevd7scY3yahbvxK/FDhxIUE0PGhJfdLsUYn2bhbvxKcNOmNL/5ZnIWLmT344+Tv3Kl2yUZ45Ms3I3fiezaFYD9b85i27BbLeCNKYeFu/E7hWvXgnOZpR48SP7SZS5XZIzvsXA3fieqx9lIeLjnhSole/a4W5AxPshuYjJ+Jyo1laTp08hfsoS8JUvY/9ZbhJ3YkeY33uh2acb4DAt345eiUlOJSk0lfvhw0u6+hz1/e5ygiAjirr7a7dKM8Qk2LGP8moSG0ua5Z4nu1YtdD/0/sv853+2SjPEJFu7G7wWFhdF2wktEde/Ozj//mQMLF7pdkjGus3A3ASEoMpK2r7xCZOfO7Pjjn8hdvNjtkoxxlYW7CRjBMdG0mzKZ8JNPIm3sneR9v8TtkoxxjYW7CSjBTZqQNHUqYUnt2D56NPk/2A1OpnGycDcBJ6RZM5KmTSM0MZHtI0dSsGat2yUZ0+As3E1ACklMJGnGdIKbNGH78OEUrv/F7ZKMaVAW7iZghbZuTdLMGUh4ONtuvdW+YNs0KhbuJqCFtWtH0ozpAGwbNoyi7dtdrsiYhmHhbgJeeMeOJE2bihYWsm3oMIp37XK7JGPqnYW7aRQiTj2Vdq+9Rml2NtuG3UpJerrbJRlTryzcTaMReWZn2k2eTPHevWy7dTgl+/e7XZIx9cbC3TQqUV1TaTfxZYq2bWP78BGUHjjgdknG1AsLd9PoRJ9zDm1fepHCX39l+20jKc3Nc7skY+qchbtplGL69qXNs89QsGYNaaNHU1ZQ4HZJxtQpC3fTaDXp148Txo8nf9ky0u68i7KiIrdLMqbOWLibRq3p5ZfR+vG/kff11+y4549ocbHbJRlTJyzcTaMXd/XVtHzoIXK/+IKdf/4zWlrqdknG1Jp9zZ4xQPObbkQPFrL37/9AwiNo/cTjSJD1fYz/qlW4i8gWIAcoBUpUtbuINAfeBtoDW4BrVdUuKDY+L374cMoKCsmYMAGJCKfVww8jIm6XZUyN1EXX5AJVTVHV7s7rB4AvVPVk4AvntTF+IWHMaOJHDCdr9hz2/t/fUVW3SzKmRupjWGYgcL7zfCbwFfDnejiOMXVOREj8058oKyhk3/TpBEVGul2SMTVS23BXYIGIKDBJVScDLVX10MxMu4GW5W0oIiOBkQBJSUm1LMOYuiMitPzrXyg7WEjGxImMaN7c7ZKMqbbaDsv0VtWuwCXAGBHp6/2men6nLff3WlWdrKrdVbV7YmJiLcswpm5JUBCtH32UJpddxh8TW7Dv9TfcLsmYaqlVuKvqDufnXuADoAewR0RaAzg/99a2SGPcIMHBnPDUkyzMyWHPk0+y/5133C7JmCqrcbiLSLSIxB56DvQH1gDzgFuc1W4BPqptkca4RUJDuXfXTqL79mH3uEfInjfP7ZKMqZLa9NxbAl+LyI/AUmC+qn4KjAf6icivwEXOa2P8VrEqbV98kagePdj5wIMc+PQzt0syplI1PqGqqpuALuUszwR+W5uijPE1QRERtJv4MttG3MaOe+9FIsKJPf98t8sy5rjsFjxjqigoOpp2kycRceqp7LjrD+R9+63bJRlzXBbuxlRDcGws7V6bQlj79mwfM5b85cvdLsmYclm4G1NNIc2akTRtKqGtWrH99lEUrF7tdknGHMPC3ZgaCElIIGnGdIKbN2fbbSMp/Plnt0sy5ggW7sbUUGjLliQ5UxRsu3U4BzdudLskYw6zcDemFsLatiFp+jQICmLb0GEUbd3qdknGABbuxtRaeIcOJE2bihYXs3XYMIp37nS7JGMs3I2pCxGnnEK7qa9RlpPL1qHDKN5js24Yd1m4G1NHIs84g6QpkynNyGDbrbdSsm+f2yWZRszC3Zg6FJmSQttXX6E4LY1ttw6nNDvb7ZJMI2Xhbkwdi+7Rg7YTJlC0cSPbbhtJaW6u2yWZRsjC3Zh6ENOnN21eeJ7Cn35i+6hRlOXnu12SaWQs3I2pJ7EXXkib/3uagh9WkjZ2LGUHD7pdkmlELNyNqUdNBgyg9RNPkPftd+z4w91oUZHbJZlGwsLdmHoWd9WVtBr3MLlffcWO+/+MlpS4XZJpBGr7BdnGmCpodv31lBUeZO/TT7MrPIzWTz2FBFnfytQfC3djGkj8sKFoYQHpL7yIhEfQ6tFHEBG3yzIBysLdmAYUP2oUZQWFZE6ejESE0/LBBy3gTb2wcDemAYkIiffcTVlhAftff4OgyCha3HO322WZAGThbkwDExFaPvggWniQzEmTKN2/n9A2bYjqcTZRqalul2cChIW7MS4QEVo9Mo7iXTvJeucdz7KwMJJmzCCqqwW8qT07XW+MSyQoiKiu3Q6/1qIito8cye4nniRv6VK0tNTF6oy/s567MS6KOqcnMikCLS6CoGDCTz2VrLffZv8bbxDcvDmxv/0tsf37E92zBxIW5na5xo9YuBvjoqjUVJKmTyN/6bLDY+5leXnk/uc/5CxcyIH588l6912CmjQh9oLzie3Xj+jevQmKiHC7dOPjRFXdroHu3bvr8uXLa7StiOALbagL1hbf5GZbyg4eJO+bb8lZuJCcRYsoy85GIiOJ6duX2P79iDnvPIJjYlypzbhPRFaoavfy3rOeuzE+LCg8nNgLLyD2wgvQ4mLyly3jwIIF5Hz+BTmffYaEhhLdqxex/foRc+EFhDRr5nbJxkdYuBtTBb52o1EQkBIZyUUxsfRbuJA2X31FiSrL8vNZmJvD5zk5ZBx1QjY5OZktW7a4Uq9peBbuxlSBLw8xqSqFa38iZ8ECzluwgN9s2cLDrVoTmZJCbP/+xPbrR1jbNj73AWXql425+xBri2/yp7aoKkUbNnBg4UJyFn7OwXXrAIg4/XSe/ve/eWHZUsI7dnS5SlNXKhpzt3D3IdYW3+TPbSnato2chZ+Ts2ABBT/+CEDYiScS278fTfr1I7xTJ+vR+zELdz9hbfFNgdKWlqGhrJs+g5yFC8lftgzKyght25bYfv2I7dePyJQuNg2xn7Fw9xPWFt8UKG3xbkfJvn3kLlrEgYULyfv2OyguJiQxkdh+FxHbvz9R3bsjIXZKztdZuPsJa4tvCpS2HK8dpTk55H71b3IWLCB38WK0sJDguDhifnuh56apc88lyO6O9UkW7n7C2uKbAqUtVWlHWUEBuYsXk7Pwc3K//JKy3FyCoqOJOf98Yvv3J6ZPb4KiohqoYlMZC3c/YW3xTYHSluq2Q4uKyPv+e8/dsZ9/Qen+/Uh4ONF9etOkf39izj+f4CZN6rFiUxkLdz9hbfFNgdKW2rRDS0rIX/EDOQsWkPP555Ts2QOhoUSfc45nnP6iiwhp3pz8lSuPmCfH1C8Ldz9hbfFNgdKWurrkUYAzIyLoFxtLv5hYksLCKFVl/cGDnBweThBQosp9u3byXX4+eWVldXJcb3a3rYfNLWOMAer+TltV5eD69eQsWEjY23MozdwHQLAIL7Zp61kpOJjgpk0Jjovz/PR+Huf9PI6gpk0JbhrneR4dddwPJLs2v3IW7saYGhMRIk47jYjTTiO6T2+2DR2GFhcjwcE0Hz6c4CZNKM3OpjQ7i9Isz8/ivXso/GU9ZVnZlOXnH3/nISHH/VC4vXk8++fMOWZ5UNOKPxQak3oblhGRi4EXgGDgNVUdf7x1bVjGw9rimwKlLQ3RjuqOuWtRkRP+2ZRmZTk/vZ5nl/9cK/pQCA39328FTePK/4CIa3rMcon634eCv5w7aPAxdxEJBn4B+gFpwDLgelX9qbz1Ldw9rC2+KVDaEijtAAgLCiJv927Kygv/rAo+FAoKjrtPCQ0lKK4pEhZOyc6doIqEh5M0Y7rPBrwbY+49gA2quskpYA4wECg33I0xpjqKVQlt0QJatKjWdmUHDx4eHjoU+GVH/daQv3IlOB+CWlzs6cH7aLhXpL7CvQ2w3et1GtDTewURGQmMdF7misj6mh6sgcbXEoCM+j6ItaVaGqQdEDhtsX9flYsOCopODg09Bc+0+WVbR9/xS96o2/Pq5WC1l3y8N1w7oaqqk4HJbh2/ukRk+fF+/fE3gdKWQGkHWFt8kb+3o76mgNsBtPN63dZZZowxpgHUV7gvA04WkQ4iEgZcB8yrp2MZY4w5Sr0My6hqiYiMBT7DcynkNFVdWx/HakB+M4RUBYHSlkBpB1hbfJFft8Mnph8wxhhTt+xrV4wxJgBZuBtjTACycK+EiFwsIutFZIOIPOB2PTUlItNEZK+IrHG7ltoSkXYi8qWI/CQia0XkD27XVFMiEiEiS0XkR6ctj7pdU22ISLCIrBSRf7pdS22IyBYR+a+IrBKRmt0+7zIbc69AdadR8GUi0hfIBV5X1c5u11MbItIaaK2qP4hILLACuNJP/14EiFbVXBEJBb4G/qCq37tcWo2IyB+B7kATVb3M7XpqSkS2AN1VtUFukqsP1nOv2OFpFFS1CDg0jYLfUdX/APvcrqMuqOouVf3BeZ4DrMNzV7TfUY9c52Wo8/DLHpeItAUuBV5zuxZj4V6Z8qZR8MsQCVQi0h5IBZa4XEqNOUMZq4C9wEJV9de2PA/cD9T9t3M0PAUWiMgKZ6oUv2PhbvyWiMQAc4G7VfWA2/XUlKqWqmoKnju5e4iI3w2bichlwF5VXeF2LXWkt6p2BS4BxjjDmn7Fwr1iNo2Cj3LGp+cCs1T1fbfrqQuqmgV8CVzscik10Qu4whmrngNcKCJvultSzanqDufnXuADPEO0fsXCvWI2jYIPck5CTgXWqeqzbtdTGyKSKCJxzvNIPCfvf3a1qBpQ1QdVta2qtsfz/2SRqt7kclk1IiLRzol6RCQa6A/43VVmFu4VUNUS4NA0CuuAd/x1GgURmQ18B5wqImkiMtztmmqhF/B7PL3DVc5jgNtF1VBr4EsRWY2nM7FQVf36MsIA0BL4WkR+BJYC81X1U5drqja7FNIYYwKQ9dyNMSYAWbgbY0wAsnA3xpgAZOFujDEByMLdGGMCkIW7qTMikluFdV4TkdOd53856r1v6+IYdUlEvhKRev+SZBG5S0TWicisWu5nhohc4zxvkNqNb7JwNw1KVUd4zd74l6PeO9eFkuqNiFTnayxHA/1U9cb6qsc0Lhbups6JyPlOr/E9EflZRGY5d5Ue7k2KyHgg0rkBaZbzXq7zM0ZEvhCRH5w5tSuciVNE2ju93inOnOgLnLs9j+i9ikiCc3s8IjJURD4UkYXO3N1jReSPzlzk34tIc69D/N6pc42I9HC2j3bmyF/qbDPQa7/zRGQR8EU5tf7R2c8aEbnbWfYq0BH4l4jcc9T6wSLyD2f91SJyp7O8m4j825nY6jNnGuTj/fkEOz36Nc6f5z3HW9cEEFW1hz3q5AHkOj/PB7LxzMUThOfO2N7Oe1/hmSf78PrlbB+CZz5wgARgA/+74S63nOO2B0qAFOf1O8BN5RwvAdjiPB/q7DcWSHTqHeW89xyeycgObT/Fed4XWOM8f9LrGHF45v2PdvabBjQvp85uwH+d9WKAtUCq894WIKGcbe4A3gNCnNfN8UwL/C2Q6CwbgudL6AFmANd4t9057kKvfca5/W/FHvX/qM6vjcZUx1JVTQNwprNtj+eLKKpCgCedmfjK8Eyz3BLYXcE2m1V1lfN8hXO8ynypnvngc0QkG/jYWf5f4Cyv9WaDZ058EWnizAXTH89EWfc660QASc7zhapa3tz5vYEPVDUPQETeB/oAKyuo8SLgVfVMhYGq7nNmjewMLHR+IQoGdlWwj01ARxF5CZgPLKhgXRMgLNxNfTno9byU6v1buxFPb7qbqhY7QykR1TxepPO8hP8NPx69D+9tyrxelx1V79FzdCieD6CrVXW99xsi0hPIq6TW2hJgrar+piorq+p+EekC/A4YBVwL3FqP9RkfYGPuxk3FztS9R2uKZ27wYhG5AEiuxTG24BmWALimhvsYAiAivYFsVc3GM5ncnV7nElKrsJ/FwJUiEuXMNniVs6wiC4HbD52cdc4FrAcSReQ3zrJQETnjeDsQkQQgSFXnAg8BXatQq/FzFu7GTZOB1eVc/jcL6C4i/wVupnZT4P4DuENEVuIZc6+JQmf7V4FDs2n+Dc/Y92oRWeu8rpB6vhpwBp6ZBpcAr6lqRUMy4PnKum3OcX4EblDPVz5eAzztLFsFVHSlURvgK2d47E3gwcpqNf7PZoU0xpgAZD13Y4wJQBbuxhgTgCzcjTEmAFm4G2NMALJwN8aYAGThbowxAcjC3RhjAtD/B6V0la15G3DfAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 ug\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": [
+      "64 ug\n",
+      "96 ug\n",
+      "128 ug\n",
+      "192 ug\n",
+      "256 ug\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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SksjKyqJjx47s3r2biRMnsnbtWpKSkvjjH/9Y6v2ceeaZzJ49u9z3r6p8/vnnnH/++QCMGzeOuXPnHpZPRDh48CA+n4+srCwiIiJo3Lhx0Xnrsywn0f360v7FF1g3egzb776HdtOnWfPbOCo7Jk8mZ9VPR8yTf+AAOT/9BKogQuTxxxMaG1tm/sjux9P6z38uV/0+n4+PPvqIkSNHcs8999C3b1/mzp3L559/zuWXX87SpUuL5b/33nv55JNPaNOmDenp6QBMnTqVm266iUsvvZTc3Fzy8/NJSUnhxRdf5Ntvv0VVGTRoECeffDLx8fGsXr2aOXPmMH36dC688ELeeustfvvb3xbVkZSUxL333svixYt5+umni9X/0EMPsWLFisPs8ufVV18lJSWFffv20bhxY/bv38/QoUNLzfvKK6/QsmVLmjZtSliYk7u2bduydevWw/Kef/75zJs3j8TERDIzM5kyZQrNmjUrOl8vPMuaIqJtW1reeisHv/qKjLcb1CuTjWqkYN8+J5QAqu64imRlZZGUlERycjLt27fnqquu4quvvuKyyy4DXN/fnj17ir3MDWDIkCFcccUVTJ8+nfz8fABOOukkJk+ezMMPP8zGjRuJioriq6++4rzzziMmJobY2Fh+85vfFL1/qFOnTiQlJQHQv3//Yk3ZqrJ8+XIyMjK45JJLeOuttwCIi4tj6dKlpW49evQod9mLFi0iNDSUbdu2sX79eh599NFiL9arF55lTXh4Y5s0YXhsHPP37+fsJk04buJEzhk7ll35vqNfbDRYyuMBZi5ZwqbxV6J5eUh4OMf84+9Vnm1R2GdZUaZOncq3337LBx98QP/+/UlJSeGSSy5h0KBBfPDBB4waNYrnnnvuiGX4vzwtNDT0sGZ4VXj88ce55ZZb6NSpE/fccw/jx48/qmfZvXt30tPT8fl8hIWFsWXLFtq0aVNq3pEjRxIeHk7Lli0ZMmQIixcvpnPnzkA98Syru89y53PTuKd1Ir+KjeVviYmcOfFOmsTEkHL11RQUFFifpVElovu6Lp4WN95YrdPShg4dWtTX98UXX9C8efNifXIAa9euZdCgQdx77720aNGCzZs3s27dOjp37syNN97ImDFjWLZsGUOHDmXu3LlkZmZy8OBB3nnnnTIFqyLExcWxf//+Us/t2rWL//znP4wdO5YhQ4awceNGtm3bdlTPUkQ49dRTefPNNwGYOXNmqa/haN++PZ9//jngXvf8zTffcPzxxxedrxdiWd1k/u9/xY6zUr6nxU03cWDBAva9/36QrDLqE9F9+9L8d9dU6/zdSZMmkZKSQu/evZk4cWKpb4P84x//SK9evejZsyeDBw+mT58+vP766/Ts2ZOkpCRWrFjB5ZdfTr9+/bjiiisYOHAggwYNYsKECfQNgO0JCQkMGTKEnj17HjbA89xzzzFhwgTCw8MBuPjii3n11VfLVe7DDz/MY489RpcuXdizZw9XXXUV4KZN3X333QBcf/31HDhwgBNOOIEBAwYwfvx4evfuXVRGnX4HT3JyMikpKdXu2e197TV23DOp6Dj+qqtodestbLz0t+SuX0/n998jvGVL8zANAFatWkX37t2DbYYRYMyzLAfxY8fS5MILCW3RAoD02bPJ37ePxMkPUJCVxY577wuyhYZhVDcmluXgwMKFZLz+Ovm7dkFoKJqdzfr/O5+IDh1o/ocb2D9/PiPj4oJtpmEY1YiJZTnIXrkKCkfc8/MJb9cO37ZtbL72WhLGj6dRr178pWUrfGlpwTXUMIxqw8SyHEQPHIBERhYJZqM+vQlr1YqDXy5k97RpJD5wP3GhoaTef3+QLTUMo7owsSwHRVM7br6J2BGns//9D2hy7hgkIoLdTz6FLzWVZ3fvZt+HH7Fv/vxgm2sYRjVgYllO3NSO39H2iSdofNZZ7HluGk0vuRhE2Hzd9byfkU5kj+7s+Nu9+PbuDba5hmEEGBPLCiIhIRzz0IPEnDyMvTNnEXfWKMjL45WOnWg9aRL56emklgjtZBg1jYVoK04gQrQddfUK0A5YAKwEfgRu8tKbAfOB1d7feC9dgCeBNcAyoF85VslUiv79+ytujmaNb41EdFa79vpDt+P0lXbtdeVxx+v6iy7W1Cee0JXHHa/7Pv+8srdl1HFWrlwZbBM0JiamaP+SSy7RRx99NIjWHM6cOXP0qquuOix9/fr1esIJJxzx2nPPPVffeeedCtV3wQUX6Jw5c1RV9Xe/+50+++yzh+WZPXu2jh07VlVVDx48qB06dND169cXnS+PZ+kDblPVHsCJwPUi0gOYCHymql2Bz7xjgDOBrt52DfDPctRRJbSalzuWtmUVFHDJjyuIPaEHSfHxpOblkbVkCfkZGUR268aOeyaRH4CACEbDwEK01YMQbaq6Hdju7e8XkVVAG2AMcIqXbSbwBXCnlz5LVRX4RkSaikiiV06hUe2AWUArV2zdXPkSGhdH++nT2Xjpb4lauxaJjCR99is0v/lmdj/1FKkPPcwxkx8ItplGELEQbQ00RJuIdAT6At8CrfwEcAdO+MAJ6Wa/y7Z4af74gP8CmUBORWyobYQlJND+hec5WFBQNL1o91NP0fQ3vyHj7bc54IWtMoyysBBt5adOhGgTkVjgLeBmVd3nHxZNVVVEyu0eeiL7F2+DSq4Nry2EH3MMV23ezCetWhESF0fBvn1kfPwxEZ06sf2vd9P5/feO6CkY9RcL0dbAQrSJSDhOKGer6ttecqqIJHrnE4GdXvpW3KBQIW29tLLK7lgeG2o7G/Jyaf+v6VBQgMTEoPv3o/n5+HbuZOcjfw+2eUYtxkK0HaJOh2gT50I+D6xS1cf8Tr0LjPP2xwHz/NIvF8eJQIZ/f2WJsgu91VrP3ldfY9fTzxyxA75Rjx60m/pP5yFERpK3aRMRnTuT/vrrHCwR5s0w/LEQbY46HaJNRH4FLASWAwVe8p9x/ZavA+2BjcCFqprmievTwFlAc5zHmQVMU9UnRKQZ8BrQEYgHpqjq/arKTTfdxIcffkh0dDQzZsygX79+R7StpkK0Hfjvf9l81QQAJDyc9jNnEt2v+AdDRIrsOPCf/7D5uutdP1RBAaEJCYQ0akTnd+cREhNTrbYawcdCtNVPjupZqupXqiqq2ltVk7ztQ1Xdo6rDVbWrqp6uqmleflXV64GTgGGq2plSphwB/wN+AhoDfPTRR6xevZrVq1czbdo0rr322mq65YqTveJHCHGPSvPy2HbXn8nduLHM/LEnn8wxDz8MBe63JX/PHvK2bWPnY1NqxF7DMAJPta3gUdXtqvq9t78f8J9ytAq4DDex/SaAefPmcfnllyMinHjiiaSnp7N9e6mt9xoneuAAJCICQkMhLAzf9h2sO/scdj35JAVldF43OfssWk+651BCSAh7Z88m87vvashqwzACSY0sdyxlytE8VRXgBCAbYOvWrbRrd2hcqKy5UNOmTSM5OZnk5GR27dpVE+YX64Dv8NIsjv30E+J+/Wt2P/tP1p19Dvu9TuGSxF90ES1uucUd5OdDaCjb/nxXmQJrGEbtpdrFsuSUI/9z3mz0CnU4XnPNNSxevJjFixfTwotcXhP4d8CHt2xJm3/8nfYzZyJRjdhy3fU826YtuZs2HXZdwjVXEz/+CneQn0/e5s3sfPyJGrPbMIzAUK1iWZEpR23atGHz5kNz2cuaC1WbiBk0kM7vvEPLO+5gQHSUa5o/9TQF2dlFeUSEVnfcQeMxo4vS9s6cSeb3gV/WZhhG9VFtYlnRKUejR49m1qxZqCrffPMNTZo0ITExsbrMCxgSHk7CleM5a/164k4/nd3PPOOa5gsWHMojwjGTJxM9ZEhR2tZbbikmqoZh1G6q07McghvEOU1ElnrbKOAhYISIrAZO944ZNWoUnTt3pkuXLlx99dVHDNNUG9np89HmsUdpP2MGEhnJlmuvY/O115G7ZQsAEhpKu38+S0S3bgD4UlPZMXlyME026jEWoq04NRKirYa2SlEYoq02gF/4tnDQ8fHNdHHXbvp91256bUKCRrjloBolol8ce6yuPO54/bHbcTogKjqgoeM6dOgQ7EfR4LEQbUenvoZoM8qJeuKfq8oLaXvo/eV/aHn2WfyheQt+HH46+xYsILOggCHffQeRkYgIs7p0wZeVFbAfno1HmP9p1F4sRFs9CNFmVJ7w1q1p89hjNL3gAnbcdz9bfn8tscOH0+pPf6Lz3HdYN+osNDeXDedfwLHvvxdsc41qwEK0NdAQbUbliDnpJDrPfYeWt9/Gwa+/Zt1ZZ7H/449pM9XFRc5ds4YdDz0cZCuNYGEh2spPnQjRZlQNiYggYcIEGp91FqkPP8KuJ54kvH17Go8Zw75589g7YwbRJ51I45NPDrapRgCxEG0NLESbETjCExNp+/gU2j3/LyQkhH3z5hES3xSArb+/lpz164NroFHjWIi2Q9TpEG1G9RA7ZAid3p1Hi1tvRbO9YPGqrBs9xl6l2wCxEG2OOh2irYaolBE1FaKtPPiHaKsoedu2sf2eSRz0+nwkNpZu//2KEL/mTE3YYQQGC9FWPzHPshYQfswxtJ8+jSYXXQSAHjjAL0N+xb5PPqm26SSGYVQMG+CpRRwz6R5yflpF9tIf0AMH2HrTzYAbHGo/c0a1NtEMwzgy5lnWMto98wzSqFGxNM3NJWPuvDKuMAyjJjCxrGWEJSSQeP99h6X7du8OgjWGYRRiYhlARCQgW9NzzmFXXl7RQI2q8s4775TrWsMwqgcTywASqPXdqkqHU08tEj8R4dyzzirXdYZhVA8mlrWUiBIrDAoOHAiSJUZdxEK0FScQIdpMLGspTc4dA97kW4C8HTuCaI1R1yhc7rhixQoiIiKYOnVqmXlHjx7NxIkTa9A6+Oyzz+jVqxdLlixh6NChfPjhhzRt2vSoYvn5558TGRnJBx98UKH67rzzTm655RbWrFlDfHw8zz///GF53njjDXJycli+fDkpKSk899xzxda1m1jWUqL79qXDrJlIVBQAvu3b8e3ZE2SrjOrCQrRZiDajCkT37UvT889n70svAbDv449pdumlQbbKqAgWos1CtBk1RJOzzyraT3/1tSBaYlQXFqKt/FiINqNMGvXqhURFoVlZ5KxejW/PHsISEoJtllFOLESbhWgzaggJCSH2lFOKjvd98knwjDGqBQvRdggL0WZUiabnHvrHpr/+RhAtMaoLC9HmsBBtR6dBh2g7GgU5Ofzcrz94fUhdv1pIWPPmNW6HUT4sRFv9xDzLABKo5Y4lt9BGjfjGryP+t1272nJHw6hhTCwDSCCXO5bcznvqqaJ6HjxtuC13NIwaxsSyjhB3yqEXmWX/+CO+XbuCaI1hNDxMLANIdTXDRYSw+Hh+zs4uquuybt2sGW4YNYiJZQCpzma4qjL07r8W1TV5xBnWDDeMGsTEsg4RN2JE0X72smXWFDeMGsTEsg4R0a4dYa1aFR3v+/TTIFpj1GYsRFtxyhOiLTc3l/Hjx9OrVy/69OnDF198Uez8UcVSRF4QkZ0issIvbZKIbBWRpd42yu/cn0RkjYj8LCK/rtAdGUel8TlnF+1nvPd+EC0xajMWoq045QnRNn36dMCtP58/fz633XYbBQUFRefL41nOAEaWkj5FVZO87UMAEekBXASc4F3zrIiEVuiujCPS+NeHfn+yly4lb+fOIFpjBAoL0Rb8EG0rV67ktNNOAyiKVLR48eKi80cNpKGqX4pIx3LaNQZ4VVVzgPUisgYYCPzPP5OIvACcDexU1Z7lLNsAGp1wAiGxsUWR0/d/Op9mv7WwbbUVC9FWd0K09enTh3fffZeLL76YzZs3k5KSwubNmxk4cCBQtT7LG0RkmddMj/fS2gCb/fJs8dJKMgOYBhwrIotLOW+UgYSEFBvo2VfB5ohR+7AQbeWnOkO0XXnllbRt25bk5GRuvvlmBg8eTGjooYZxZUO0/RO4D7em+z7gUeDK8l7seaubgHNVNZlKrg1vqDQedSYZ77wDQNaSJeTt3El4y5ZBtsooDQvRVndCtIWFhTFlypSi48GDB9OtW7dD5ytjsKqmFu6LyHSgcKRhK9DOL2tbL80IINGDBkFYGPh8gDXF6zqFIdoyF31H9MAB1R6i7a9//etRQ7QNGjSIjz76iM2bN5ORkVEUom3Tpk0sW7aMYcOGccUVVzBx4sSiVzW/5EX0rwrlCdE2depUwsPDi0K0HXPMMUf9YSgM0XbRRReVGaItMzMTVSUmJob58+cTFhZWzDOtVDNcRBL9Ds8DCkfK3wUuEpFIEXkdOB2Y7ndd0Sg68CFQ1DHz4IMP0qVLF4477jg+sZiNRyQkIoKYwYOLjq0pXvexEG2OYIZo27lzJ/369aN79+48/PDDh4n/UUO0icgc4BSgOZAK3OMdJ+GazxuA36nqdi//XbgmeTjwCPD7wkEcEZkEHFDVf3iDRu+ras+VK1fqxRdfzKJFi9i2bRunn346v/zyS7H+gtJoKCHaSiPj3XfZdsedhZXT5YsvCG/V0kK01QIsRFv95KiepaperKqJqhquqm1V9XlVvUxVe6lqb1UdXSiUXv4HVPVYVW3Poeb5EZk3bx4XXXQRkZGRdOrUiS5durBo0aIq3Fb9J3bYMChcC67KfpugbhjVSjBW8NwgIunASuA4EdmydetW2rU71NVZ1tA+wLRp00hOTiY5OZldDXi5X2jTpjQ64YSi430ffRhEawyj/lPTYvlP4FigGTAFeElV21akgGuuuYbFixezePFiWrRoUR021hkan3NO0X7W90vIS009Qm6jJrGukPpHjYqlqqaqar6qFuAGfgYCtGnThs2bD03PLGto3yhO3PDhxY73f2JN8dpAo0aN2LNnjwlmPaNGX4UrIol+/ZtFo+ijR4/mkksu4dZbb2Xbtm2sXr26aNa8UTYRbdsQ3q4teZu3ALDv44+CbJEBrhtpy5YtDbqbqD5SbWLpP4ouIlvwRtFFJAm/UXSAE044gQsvvJAePXoQFhbGM888c9SRcMPR+Oyz2fNPFyQh6/sltAyzV8EHm/DwcDp16hRsM4wAY293DBDBmrKTteJHNngBAgAmp6by0t60GrfDMOo7Fs+yjtPohB6ENGniDkQY2TguuAYZRj3FxLKOIyKHwrap0i8qmrwdO4JrlGHUQ0ws6wH+UYgA9ttyUcMIOCaW9YDoQQORiAgAfKrs+9jE0jACjYllPSAkIoKYk11U6zARF7Zt+/ajXGUYRkUwsawnND6j+OuO9llT3DACiollPSH25GEQ4v07w8LYb01xwwgoJpb1hNDGjYnq29fN9fT5yFq6lLxt24JtlmHUG0ws6xGNR45ECsO2AftsrbhhBAwTy3pE3GmnFu1LZCT7P/44iNYYRv3CxLIeEd6mDWtzcgDQnByyfvjBmuKGESBMLOsZn5R42ZM1xQ0jMJhY1jM+O3BILCUqysK2GUaAMLGsZ6zKySE0IQEAzc4m+4dl5JXxig7DMMqPiWU9JM4vsAZYU9wwAoGJZT0kbvhpRfshsbHss1Fxw6gyJpb1kJgBA5BGjQAoyMwke9kycrdYU9wwqoKJZT1EIiKIPfUUd1BQAFjYNsOoKiaW9ZS44acX7Yc0aWJNccOoIiaW9ZTYYUOLAmsUHDxI9vLl1hQ3jCpgYllPCW3cmOgBA9yBzwfA/k/MuzSMymJiWY/xf91EaLNm7PvIxNIwKouJZT3GP7BGQWYm2StWkLtlSxAtMoy6i4llPSb8mGOI7NYNcKt5AItEZBiVxMSynhN3xhlF+2EtW1pT3DAqiYllPcd/NU9BVhbZP/5I7ubNQbTIMOomJpb1nMjjjyesZUsACrzwbTbn0jAqjollPUdEio2KhyUm2svMDKMSmFg2APyb4pqb65rimzYF0SLDqHuYWDYAogcMQKKjAcjfsweAfeZdGkaFOKpYisgLIrJTRFb4pTUTkfkistr7G++li4g8KSJrRGSZiPSrTuON8iHh4cSdemjOZXjbtjaFyDAqSHk8yxnAyBJpE4HPVLUr8Jl3DHAm0NXbrgH+GRgzjapSrCleUED2ypXkbtwYRIsMo25xVLFU1S+BtBLJY4CZ3v5M4Fy/9Fnq+AZoKiKJJcsszVs1qpeYYcMgNBQAn/fGR2uKG0b5qWyfZStV3e7t7wBaefttAP9JfFu8tJLMAKYBx4rI4kraYFSA0NhYYgYNKjoO79CBfRZYwzDKTZUHeFRVAS2ZLiIvAEOBV/3SmonIfOB54GRgvaomqyo33ngjXbp0oXfv3nz//fdVNcsohdjThx86CAkhZ+Uqa4obRjmprFimFjavvb87vfStQDtvfwawDfD5Xeff1/k10ALgo48+YvXq1axevZpp06Zx7bXXVtIs40jEnXao3zLPmzpkTXHDKB+VFct3gXHe/jhgnl/65SIiQC6QTnGx9O/rfBNoDDBv3jwuv/xyRIQTTzyR9PR0tm/fjhFYwlu3JrJ7d3eQn0/EsZ1tNY9hlJPyTB2aA/wPOE5EtojIVcBDwAgRWQ2c7h0DfAisA9YA04G/lijOv69zFxAGsHXrVtq1a1eUqW3btmwt413X06ZNIzk5meTkZHbt2lW+uzSKiPNriktEJDmrVpG7YUPwDDKMOkJ5RsMvVtVEVQ1X1baq+ryq7lHV4araVVVPV9U0L6+q6vWqeqyq9gKWB9rga665hsWLF7N48WJatGgR6OLrPXHDD4llzrp1gDXFDaM81PQKnlQRSfS81W+BEBHZ0qZNGzb7RcLZsmULbdqUNohuVJXI444jLNGbzZWTQ2TXrtYUN4xyUNNi+S4wTlUvBqYAf1fVtqNHj2bWrFmoKt988w1NmjQhMfGw6ZlGABAR4k4/9OZHiY0l56efyFm/PohWGUbtp9rEsiJ9naNGjaJz58506dKFq6++mmeffba6zDIovponZ/VqwN4rbhhHQ9w0yaBTKSOSk5NJSUmhNtyDiNQZOzQvj19OGkzBgQMARB5/HCh0nje3Biw0jLqJRR1qgEh4OLGnngoiAITGNyPn55/JWWdNccMoCxPLBkrc8NPA80BzfvkFsPeKG8aRMLFsoMT86lcQFga4GJeNTuhhLzMzjCNgYtlACY2NJebEEw81xVu0JOeXX4rmXhqGURwTywaMf1M8d+1aELE5l4ZRBiaWDZhY/8AamzfTqFdPe5mZYZSBiWU9RETKtUW0bs3yrCwKPO/yna/+S84vv3BsZGS5yyhr69ixY3AfgmEEGBPLeoiqlns7beKdhHj9lmMGDgQRvnnssQqVUdq20eJkGvUME8sGTuxphwJr5P78M1F9+pDxzlx2PzeNzCVLgmiZYdQuTCwbOJHduhJeGLRElZC4WPK2bGHXE0+wafyVJpiG4WFi2cBxgTWGF00hKlrFU1CA5uWRuei7IFpnGLUHE0uD2FMPTSHybdtWNFldQkKIHjggmKYZRq3BxNIgOrk/IY0buwNVml9/HSFNmxDaogVRPXsG1zjDqCWYWBpIWBixp5wMIe7jkP3Tzxwz+UF827aRNnt2kK0zjNqBiaUBQNxpw6GgAIADn31G9k+raNSnD7uffgbfnj1Bts4wgo/FswwQtSmeZWWIlhC+7tqFcNyk8nxV8lQJE+GdjAzuSd1R4TJrw/MwjEBhnqUBQKYW8M3BTBQncqEihIuwNCuL/2vShB6RkcE20TCCiomlUcTnBw4Q4i1XBPfhWJGVRVp+Pne1ahVc4wwjyJhYGkUs8F4z4d987hXViMd37aJvVDTnFI6YG0YDxMSyHlLZ9dw7fXmEt2lT5FmKCCd278EbaXto1LMnj/ZJIv/AgXKVZRj1DRNLoxgxQ4YUO87buJF9771H67/chW/nTnY/Ny1IlhlGcDGxNIohUVElEoSdDz9CRMeONBkzmrQXXyTXIgoZDRATS6MYjUf+GkJD3YEIqJK/dy87H5tCi1tvQ8LDSX34keAaaRhBwMTSKEZ03760f/5fbrljQgJ4U4bSX38d3/ZtJFz7ew58/jkHFn4VZEsNo2YxsTQOI+bEE2n31FPk795NzIBklxgSwvZ7JhF/6aWEd2hP6oMPonl5wTXUMGoQE0ujVKIHDCD+kos5+N+viejUEQoKyPn5ZzLeeINWEyeSu26drRs3GhQmlkaZtLj1NsISW6MFWhTvMnXK40QefzwxQ4e6deO7dwfZSsOoGUwsjTIJjY0h8W/3krdxI1F9+7rEnBx2PvQwrf70Jwqys9n5+ONBtdEwagoTS+OIxA79FU3OPZesH34gtFkzUGX/J5+Qt2UzzS67jIy33iZr+Ypgm2kY1Y6JpXFUWk28k9Bm8YQWLncUYce999LsqisJTUgg9YEHbNWOUe8xsTSOSmjTprS++25yN2wg8rjjQJW8LVvZO3s2LW+5haylS9n33nvBNtMwqhUTS6NcNB4xgriRI8ldt65olc+eadNp1KcPjXr1Yuff/0H+gYNBttIwqo8qiaWIbBCR5SKyVEQWe2nNRGS+iKz2/sYHxlQj2LT+y12ExMQQ1qqlS8jPJ/W++2j15z/j27WLPc89F1wDDaMaCYRneaqqJqmqN3uZicBnqtoV+Mw7NuoBYc2b0+quP5O3YWPRu8Yzv/2WvC2baTJmDGkzZti6caPeUh3N8DHATG9/JnBuNdRhBInGZ59N7Mknu/mV3hryHQ9MJuF317h14w89HGQLDaN6qKpYKvCpiKSIyDVeWitV3e7t7wAOC7FdWvM9LS2NESNG0LVrV0aMGMHevXuraJpRHYgIrf82CQkPJ/yYYwAoSE9n78sv0/y6azmwYAEHFi4MspWGEXiqKpa/UtV+wJnA9SIyzP+kuvkkZc0peRHwFR489NBDDB8+nNWrVzN8+HAeeuihKppmVBfhrVvT8o4/krd5M6FNmwKw95U5RPXr59aNT36Q8OCaaBgBp0piqapbvb87gXeAgUCqiCQCeH93lnH5LFVNLuzrnDdvHuPGjQNg3LhxzJ07tyqmGdVM0wsuIPrEEynIySlK23HvfbS6805y16/nkngb1zPqF5UWSxGJEZG4wn3gDGAF8C4wzss2DphXyuWHNd9TU1NJTEwEoHXr1qSmppZa77Rp00hOTiY5OZldu3ZV1nyjiogIiffdC0BY69YA5Pz0E3lbtxEzbCjXJTS3deNGvaIqnmUr4CsR+QFYBHygqh8DDwEjRGQ1cLp3XJJizfcvv/yy2Enxe8NgSa655hoWL17M4sWLadGiRRXMN6pKRLt2tLzlZnw7diCNGgGQ+thjJFxzDY1CQtg5ZUqQLTSMwFFpsVTVdarax9tOUNUHvPQ9qjpcVbuq6umqmlbKtcWa74sWLaJVq1Zs3+7GhbZv307Lli0ra5pRg8RfeilRSUlIYXT1rCz2zn6Fl/amkfH2O2QtXx5cAw0jQNT4Cp7Smu89e/Zk9OjRzJzpZhzNnDmTMWPG1LRpRiWQ0FASH7gfzc0lNKEZAPs/+oiUzEy3bvz+B9CCgiBbaRhVJxjLHQ9rvo8cOZKJEycyf/58unbtyr///W8mTrS57HWFyGOPpfkNN5C/J61o7uWfWraixY1/IOuHH2zduFEvkFoSLaZSRiQnJ5OSklIrIt6ISIO2Q/PyWD92LHkbN1Fw0K0RT7j+eg4uXIhv+3Y6f/QRobExNW6XYQQKC6RhBAQJD+eYBx6gICeHkNhYAPZMnUrCleO9deNTg2yhYVQNE0sjYDTq3p2ECVdRcOAABaqQn0/aK3NofO65pM2YSe6GDcE20TAqjYmlEVCaX3cdEcceS5Y3qJO1aBHRSX1s3bhR5zGxNAJKSEQExzxwP1EhIYj3zvGdUx6n2YSrOPDFF7Zu3KizmFgaAScqKYlZe/ei3lLIgowMfKk7iejQgdTJD6K5uUG20DAqjomlUS08uXsX4e3bF63sSX/tNZpeegm569eT9rK9b9yoe5hYGtVCtiqJ992HZmcXzb1Mf/NNYoYNZfczz+Czdf1GHcPE0qg2YgYNpOnYseAN9uT+sppGvXpTkJvLzimPB9c4w6ggJpZGtSEidL3/Prbn5pLjCeaWp55izs6dZLz9Nr2iooqCplTX1rFjx+A+BKPeYGJpVBuqyoH8fAa+8DyRIe6jFhUSwoQLzie0eXPeGz2Ggvx8VLXato32TiAjQJhYGtVO7Mkn02TMaPDC7h3892c0GTOGrB9+IOPdd4NsnWGUD1sbHiAa+trw0uzwp0lICO916kyT0FDCgB0+H7t9PlqHh3PmunVkavVFJqoNz8Oo+5hnadQIGQUF3JeaSrgnoonh4azKzqZFWBi/T0gIsnWGcXRMLI1qo2T/4af79xF3xhlFgYIvTEggbsQIJrRuTc769dXSZ2kYgSIs2AYY9ZfSXg3SPDSU9zp1JjYkhJD8fBbOm0eXyEheH3YyXx48wKLMTH7Izg6CtYZxZMyzNGqU3fn5PLgzlVBvak9SdDTfZ2XRPzqaG5u34IV27enjrfoxjNqEiaVRbZTVNJ6bnk7MsKFFo+PDvNfmhooQFRLCp5MnWzPcqHWYWBo1joiQ+Le/ERIV5RLy8tySSE8802bOYvf06cXeSW4YwcbE0ggK4YmJtLzjj4cS8vMJa92aJuf/HzGDB7Pr0cdYd9bZ7PvkU/MQjVqBiaURNJpeeCHRAwcWHfu2byfjzbeIPXkY7V94npCoKLbedBObLrucrB9/DKKlhmFiaQQRCQkh8f77iprfhex5+WViBg+m0ztv03rSJHLWrmXD+Rew7c93kbdzZ5CsNRo6JpZGUIlo356wNm2KpeWtXsPas88hc3EK8ReN5dhPP6HZlePJeO891o48k91Tn6PAphcZNYyJpRF0YgYPLp4QHk7umjVsuuIK1gwfzsH/fk3L22/n2PffI3bIYHY9/jhrR41i34cfWn+mUWOYWBpBp+l55yIREe5AxI2Oh4UR0rgxeVu3sfXmm1n9q19x8Ov/0eaJJ2g/YwahjZuw9dbb2Hjpb8lavjy4N2A0CCyQRoCoTQEs6qIdmUuWkLnoO6IHDiAsPp60l2eT8fbbFGRmEhITQ8HBgwCExMaScNWVNLvySva99x47pzxO/p49NBkzhha33kJ4q1ZVssMwysLEMkDUli9lfbIj/8ABMt5+m7SXZ5O3aRMSGYHm5oEqEhlJ04vG0uzqq0mfOYu0GTMgLIyECVeRcOWVRXM4a8vzMOo+JpYBorZ8KeujHVpQwIH//Ie9L73Mwa+/hpAQUHVbWBhNzhpF/BXj2TN1Kvs/+YSw1q1pedttND77LEJCQmrF8zDqPiaWAaI+ilRttCNnzRrSXn6ZjLnz3MvQRJxoihBz8jCa/uY37Jn6HNkrVxLVpw9j3p3HD1lZAbfDaHiYWAaI+i5Stc2O/IwM0t98i7TZs/Ft23ZINIFG/foRd/Iw9r48G9+uXTQ++2xa3nYr4YmJ1WaPUf8xsQwQDUWkapsd6vOxf8EC0ma9RNZ33xU7F9GtG68uWsSoxo1R4MW0NJ5P20NWEJ5Phw4d2LBhQ43XawQOE8sA0dBEqjbakf3TT6S99BIZ894Fnw9wkY+25uWyLc/HwJgYUvPymLJ7F+/t21e5D10VqA3/F6PymFgGiIYsUrXNDt/evaS/9jppM2eSv3cvqoqIkObzcbCggHYRESzPyuLNjHSahobWWMDhYP9fOnbsWCvedllXvWwTywBhIlX77NC8PEYlJDChWQI9GjUqEs2sgnwKFGJCQ1FVCoBpe/bwdeZBNuXmsTvfVy1eZ7CfR234n9QmOyqKiWWAqC0fALOjdDuyli9nz7Tp7P/3v4sGgsq8plEjwtu2IaJdeyLatyO8bTv3t117wtu2IaRwtVEl7AgmtcGG2mRHRak2sRSRkcATQCjwL1V96AjZTSzNjhqxw7drF3tmziTtX8+XfkFEBBIejoSEQEGBC9iRn+9fIGGtWhHRvj3h7dsR4SekEe3aEtq0aZl21AZq4/+krlAtLywTkVDgGWAEsAX4TkTeVdWVR7ru448/5qabbiI/P58JEyYwceLE6jDPaMCEtWhBq9tvJ3ftOg4sWFCULhEREBKCZmejubll/3qr4tuxA19qKnz33WFeqkQ1IjzxGCI6dCDi2M7ub7v2HBMWxg6fjwLgloTmjIiLY/7+/UzZs7va7tUILNXiWYrIScAkVf21d/wnAFV9sIxLND8/n27dujF//nzatm3LgAEDmDNnDj169CizHvMszY7K2pG5ZAkbL7vcjZqHhdHhpVlE9+2L+nz40tLIT0vDt3sPvp2p5G7aRN7WbfhSU/Gl7SE/PYOCgwfdpPiK3KOIW33k56k26tuXuFNPgfBwQsLCkYhw59mGRyAR4RAWTkhk5KH0iEgkIoKQiHCIjCQkIgKJjHTnQkOLXjNckr2vvcabt93Op/v38+a+jAo+xcpxfuMmnBEXV2qd1fnZ2Pvaa+z/dD5xZ4wgfuzYMvNVdMCrusTyfGCkqk7wji8DBqnqDX55rgGuAYiJienftm1btm/fTteuXQHYvn07AIneROLly5fTrVs3IiIiWLVqFVlZWURHR9O9e/cq27tr1y5atGhRdJySkkL//v2rXG5VMTuq144u+fl0z89nVWgoa8oQmSPaoEo00FiV+IICEguUllpAQoHSVJXGBQXEAo1wTbiaCPFV8vtceOTfCVDA4f1eFVUB/3ok5NCdFaaG4PrfCsn36q0oh9lVuMy1qHIBEbSgAAkJIbREvS9ERPBFRHglaj6coL03XFWnAdMKjwsFdvHixcUEduvWrTcUeqrLly//tSeydwNkZmY2SUlJ+bm08vtX4Fu1e/fuYmJpHP4DUh9ZExrK/9LSKn+fImQCmSLsCAlhVTkuyUtNJX/XoaZ3aIvmh0VKKg8V+eHI3bCRggMHio5DYmOJ6NihwnUCrFq1qpiDkpKSUmq+DuHhxIaE4GRaOVBQwMa8vErVWV769+8f0HstSW1phh/RGy2Pp+qlF3mrwHFAqUJaCt2hXJ/1itAR2BDgMmuSQD+TjtTO51Ed//uj0ZHgPIvuwKoO4eHHxoaENi1MPFCQn74xL29tRcoIhDGdIiK6RktI48LjTC3Ytz43d3UgyqYCdpbXjuryLL8DuopIJ2ArcBFwyVGu2Qq08ztu66WVm5LeankRkcWqmlzR645S5sFAl1mTBPqZ1NbnUR3/+3LUGZRnEYh7rabvSp0os1q6UVTVB9wAfIJT99dV9Wiv5ysSWBGJwAnsu965KgvpUaiwwBpGA6XBfleqrc9SVT8EPqxAfp+IFApsKPCCn8BWxlOtiK0N9gNwBBrKM2ko9wkBuNc69F0JuJ21ZQXPURGRUcDjHBLSB4Jr0ZERkZdU9bJg21FbsOdxCHsWxRGRa+qCCNcZsTQMwwgmDf7tjiIyUkR+FpE1ImJLhgyjDESkqYi8KSI/icgqb9ZLg6FBi6XfsswzgR7AxSJS9pKheo6ItBORBSKyUkR+FJGbgm1TdSMioSKyRETeD7Yt1UmAhO4J4GNVPR7oQ81PuToqInKL99ldISJzRKRRoMpu0GIJDATWqOo6Vc0FXgXGVLVQEblLRHK97aMqW1lz+IDbVLUHcCJwfVV+PERkoIjsFZEcEckWkbcCZmnguIka/tKLSLiIZIpIag1WWyWhE5EmwDDgeQBVzVXV9KoaFUhvVUTaADcCyaraEze+cVFVbSykoYtlG2Cz3/EWL63SiEg4MAkXRCQeOEVEzqlKmTWFqm5X1e+9/f24L1RVnkcOcL2qRgKdgHNq07MQkbbAWcC/arjqN4GdNVVZgISuE7ALeNHzxP8lIjEBMC/Q3moYECUiYUA0sK2K5RXR0MWyOrgCyFDV/6jqQeA/wPXBNaniiEhHoC/wbWXLUNUfVPUVb387sAfX3VFbeBy4g8otW64UIpIMDAWerKk6CYzQhQH9gH+qal/gIFClPv5Ae6uquhX4B7AJ2I77Hn5aFRv9aehiWR2T3Y8D/ONubQCOqWKZNYqIxAJvATer6r4AlfkroAXwciDKqyoicjawU1VLX9xcfcwF/kANCjSBEbotwBZVLfzxfNMrsyoE1FsVkXhcN1on3HcuRkR+W0Ubi2joYnmkVUMNEq8b4S1gtqq+HaAyW+EWGzzq/frXBoYAo0VkA66v+jQRqVYhF5G/AemqOrs66ymFKgudqu4ANovIcV7ScOCI8WnLQaC91dOB9aq6S1XzgLeBwVW0sYgGLZaVXJZ5NH4GmvsddySA/SbVibhw3s8Dq1T1sQCVGQX8CPxbVe8MRJmBQFX/pKptVbUj7kfyc1UNmBdSBiOA40XEBzwKtBSRddVcZyCF7g/AbBFZBiQBk6toWqC91U3AiSIS7X2WhxPAwTublB5gRCQSOACcBnyPa5KPVdVa77F6TeWFwHIONRP/7C1drUx5AqwB9nmeQ61ERE4BblfVs2uwzpuBP6lqxeOzVa6+JNxAVgSwDhivqntrou4jISILgQmq+rOITAJiVPWPVSjvb8BY3MyOJV7ZOQGx1cQy8IjI3cBfcMH8FqjqGUE2KSiIyLXAs4D/e2YfVNV7g2RSraGmxbK2UltFvDRMLA3DMMpBg+6zNAzDKC8mloZhGOXAxNIwDKMcmFgaAUNEDpQjz78K15uLyJ9LnPs6EHUEEhH5wlt1U9313Oitja7SHEwRmSHunVU1ZntDwcTSKDeBEENVnaCqhXP8ip1X1cH1SQy99cmF+0cTw+uAEap66VHKNDEMEiaWRkDx3sDZUkS+AO71osnMFhHxxPALEUkWkYdwAQ+WFgpIoVCKSKyIfCYi34tIvogcMRKUiLwqImtFZLqIpIrIp95keEQko1BQRKS5t2IHEblCROYCvYFFInKDiNzqLbv7RkSa+VVxmWfnChEZ6F0fIyIvisgi75oxfuW+KyKfA5/5lXEdblJ6iogcFBc/9WbvmqlAZ+AjEbmlxL2Fisg/vLqXAcd76f1xE8NfEpFPRCSxlEfTBnhDRF7xRHaFiCwvWcfRMIF2BO294UbdxZvEPQk34b4nkAL81jv3BTAHF+JNcHMszwCGiMjHwGIgChjgFRcKvF6iimzgPFXd5wnoE564foUT2E+BMaqa5dX3KHA+MBu4CkgHHhe3zDIfeFdEJuOiQCWKyDfAS57tPwKDcNFvdgDnARcDE0TkeNyKkp5euXuBt0Xke5zIZqlqdxFpihPcfwO/xsVHXQM85z2TQjFcAMR4z+suYKo3KbvQmw7BTaYuFMPHcCvAInFhx7aIyBzvmT3l2X4T0AX4UkRycAKpuNUwbbznkgo85IUtw7PXqCDmWRqVpS9wMy6KUGfcWmt/FuLEJAn3xe3ody4XJ0pZwKnAoyIifucFmOx5UlFAa6CbV6fihPjfftdMxi0pPc+79lc4weoJNAEuBx7w/oITlwtx4pXvXbMVt5zvBdwKpktxwncAyARewYlTlJeeDeSLyHJcuLVGwEPAubjgENHAnSLyKE6MFbcG/V+41VHnAwk477OvZ/+ZwCAR+Q4XrepfuB+XRcDXnrd6Ki5IRE9cSLPZOOHs5N3bamCBJ9CNcNGNfg10FpGnRGQkcMDfWxWRP4ATaBH5j4ikHMFbxcsbWhVvtS5iYmlUlkWqukVVC4ClFBdDcLEsC8nn8FbMZJzw/BvnAfmvZLkUF6GoP05Qd+MEqatX7mRctCh/gc4F7sOJ0mO4paYLvON9QAbOO9wO3Ask+9nowwnYl0BjnMi0wYlSU69svOvBeZgK/B9OvLapanugmXev73nlxwNtvaWe2ThRLCQamO7dW2cv7RbgY2C8Z890XHCIk4CWOIH9GCfuP+KE9D7v2WzCed7dgVaq+nvP7t+p6mScsH4B/N67z45Akqr2xq33DseJ7vmq2h/3o3GklwImAW1Utaeq9gJePELeeoGJpVFZjiaGR+JMnBjuwzXHU3ECVUgTXPi0PFyTsw1O0BbhRMrnXdPR2y+k5CsEcrz8/XHi8zBOAG/38kaXYZ/iBGmjV+e/cQLbBedl5gP/w3miAIhIoRDuBvJUdZdX/wovPQ8nmOfivnfv4DzhDbimeQhOeCfiPPFQnAhO8fK8qqppuGWB27zn1xgnWu/gPOX53n1e7X8zItIcCFHVt3DLcHsAz3mBZPDKPQ7nrc4XkaVevrZlPB9wSxP9vdWAhPKrzZhYGtVJnuexlCQWJwTTcH17HUqcnw0ke03cMGCtl+4v0Oqd24ATG3CeV0lygWtxX/x4nNDc7aWHenkKhX4QzgPNwjXL47z0bOA2nNC9gBOkF4FwXHfDMTgP7xdc0zpMXFzGKJy3V0g6MAMn3s/gmtlves+jcN3xlbj+0O24gBD3A2nAeSLyA84L9Xn32hnXtfA74CRPDHd4NvjTBvjCE8GXOSTg/gjwo6omeVuvI8U08NZv+3urNR1tvsYxsTQChqrGerspXgSfacAyIF5VZ3h5TsH17SUDo4DPgZ/8r1fV3ap6kte8y8E1RX0l6ijkH7gmqg8XGk9xgzVzD5mlvXEiuNALyXY8zrO8pzAPrmn9MHCVZ+sFOAFJwI1i78YFBVmP86J6qurvcIM427z73Qj8gOsz/RbX37m8xDN6DBdH9RNVfRwXQuygtyUCf8d5oecDT+MGg3p6z7SP37Naiuv+uMWz/Uqvj7e9333tAH7wItb3KxRCnGD+TrypTd7I/89AC/HegSPuPUEnUAaleKtVDQRc67HRcKPc+InZFziPojD9Br/9U/z27wTuLOX63bh+uDLrKJG2QUTG45rPqOo/xL32AlX9SUQW40KsLfY82WXA96p6qYhc4RVzHPCe560u5pDonCIiB0oJIZeDE8FeACJyOxCrqjO80fTXReQa4IMS1y0tfB6F05Q8bsf9QBSSLSJLcN7pWaq6yJvu9LhnfwgukO3Z3j0ke/YW3k/Rs/ZG/a/0kv+kqk+XfIYl+BduwGyZiOQB01X1aW960JPiXvcQ5tlSVnzXNrgI54UO15+OUmedx6IOGYZhlANrhhuGYZQDE0vDMIxyYGJpGIZRDkwsDcMwyoGJpWEYRjn4f17xFlPQSvITAAAAAElFTkSuQmCC\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": [
+    "for p in glob('Y:Lena/Data/20220111-MIC-resistant/composites-0h/*ng-Composite.aligned.tif'):\n",
+    "    threading.Thread(target=count, args=(p, '(\\d+)ng')).start() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "007dfa6e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1, 2, 1, 7383, 22392)"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "img = imread('Y:Lena/Data/20220111-MIC-resistant/day2/032ng-BF-TRIRC-2D.nd2')\n",
+    "img.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "9ebc9d9b",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Aligning: \n",
+      " bf: (7383, 22392),  2\n",
+      " tmp: (818, 2612),  16\n",
+      " mask: (6544, 20896),  2\n",
+      "\n",
+      "Aligning: \n",
+      " bf: (7383, 22392),  2\n",
+      " tmp: (818, 2612),  16\n",
+      " mask: (6544, 20896),  2\n",
+      "\n",
+      "Aligning: \n",
+      " bf: (7383, 22392),  2\n",
+      " tmp: (818, 2612),  16\n",
+      " mask: (6544, 20896),  2\n",
+      "\n",
+      "Aligning: \n",
+      " bf: (7383, 22392),  2\n",
+      " tmp: (818, 2612),  16\n",
+      " mask: (6544, 20896),  2\n",
+      "\n",
+      "Aligning: \n",
+      " bf: (7383, 22392),  2\n",
+      " tmp: (818, 2612),  16\n",
+      " mask: (6544, 20896),  2\n",
+      "\n",
+      "Aligning: \n",
+      " bf: (7383, 22392),  2\n",
+      " tmp: (818, 2612),  16\n",
+      " mask: (6544, 20896),  2\n",
+      "\n",
+      "{'tvec': array([ 47.49869315, 782.22433011]), 'success': 0.0347714760157043, 'angle': 2.365980016714559, 'scale': 0.9953595841399938, 'Dscale': 0.0004755744483918677, 'Dangle': 0.013400833829660513, 'Dt': 0.25, 'timg': None}\n",
+      "transform (7383, 22392)\n",
+      "{'tvec': array([43.24333377, 33.00162748]), 'success': 0.04407896854343055, 'angle': -1.9741001135549254, 'scale': 0.9966525602662759, 'Dscale': 0.0004761922215241491, 'Dangle': 0.013400833829660513, 'Dt': 0.25, 'timg': None}\n",
+      "transform (7383, 22392)\n",
+      "{'tvec': array([18.56428512,  8.19045983]), 'success': 0.054814509251525805, 'angle': -2.8250010125605627, 'scale': 0.9946248719175125, 'Dscale': 0.0004752234090634674, 'Dangle': 0.013400833829660513, 'Dt': 0.25, 'timg': None}\n",
+      "transform (7383, 22392)\n",
+      "{'tvec': array([ 136.92456736, 1240.36744994]), 'success': 0.011818560986849152, 'angle': -0.6314239654889491, 'scale': 0.9941499155456122, 'Dscale': 0.0004749964788985543, 'Dangle': 0.013400833829660513, 'Dt': 0.25, 'timg': None}\n",
+      "transform (7383, 22392)\n",
+      "{'tvec': array([-50.21775788,  72.55194017]), 'success': 0.055208369474825075, 'angle': -1.6114845767652923, 'scale': 0.9945671392788149, 'Dscale': 0.00047519582489364545, 'Dangle': 0.013400833829660513, 'Dt': 0.25, 'timg': None}\n",
+      "transform (7383, 22392)\n",
+      "{'tvec': array([56.46594546, 24.27842932]), 'success': 0.04573243544740337, 'angle': -2.046126365596052, 'scale': 0.9950799186786157, 'Dscale': 0.0004754408265835801, '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)transform (7383, 22392)\n",
+      "\n",
+      "Saved aligned stack Y:Lena/Data/20220111-MIC-resistant/day2\\032ng-BF-TRIRC-2D.aligned.tif\n",
+      "Saved aligned stack Y:Lena/Data/20220111-MIC-resistant/day2\\128ng-BF-TRIRC-2D.aligned.tif\n",
+      "Saved aligned stack Y:Lena/Data/20220111-MIC-resistant/day2\\256ng-BF-TRIRC-2D.aligned.tif\n",
+      "Saved aligned stack Y:Lena/Data/20220111-MIC-resistant/day2\\096ng-BF-TRIRC-2D.aligned.tifSaved aligned stack Y:Lena/Data/20220111-MIC-resistant/day2\\064ng-BF-TRIRC-2D.aligned.tif\n",
+      "\n",
+      "Saved aligned stack Y:Lena/Data/20220111-MIC-resistant/day2\\192ng-BF-TRIRC-2D.aligned.tif\n"
+     ]
+    }
+   ],
+   "source": [
+    "for p in glob('Y:Lena/Data/20220111-MIC-resistant/day2/*ng-BF-TRIRC-2D.nd2'):\n",
+    "    threading.Thread(target=align2D, args=(p,)).start() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "d76d580c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Aligning: \n",
+      " bf: (7383, 22392),  2\n",
+      " tmp: (818, 2612),  16\n",
+      " mask: (6544, 20896),  2\n",
+      "\n",
+      "{'tvec': array([-10.19643603,  16.20111523]), 'success': 0.04205151101878612, 'angle': -0.6368168081930321, 'scale': 0.9941750295785632, 'Dscale': 0.0004750084781725432, 'Dangle': 0.013400833829660513, 'Dt': 0.25, 'timg': None}\n",
+      "transform (7383, 22392)\n"
+     ]
+    }
+   ],
+   "source": [
+    "threading.Thread(target=align2D, args=('Y:Lena/Data/20220111-MIC-resistant/day2/128ng-BF-TRIRC-2D.nd2',)).start()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "d0b0f6f1",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "96 ug\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": [
+      "64 ug\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": [
+      "128 ug\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": [
+      "192 ug\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": [
+      "256 ug\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": [
+      "32 ug\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": [
+    "for p in glob('Y:/Lena/Data//20220111-MIC-resistant/day2/*ng-BF-TRIRC-2D.aligned.tif'):\n",
+    "    threading.Thread(target=count, args=(p, '(\\d+)ng')).start()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "e9a7d9d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "timelapse = imread_dask('Y:Lena/Data//20220111-MIC-resistant/timelapse-30min/0ng-TRITC-19h.nd2')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "6a95eb4c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "timelapse = timelapse.rechunk((1,1,1,1000,1000))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "4f205ff6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 300.23 GiB </td>\n",
+       "                        <td> 1.91 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (39, 1, 25, 7383, 22392) </td>\n",
+       "                        <td> (1, 1, 1, 1000, 1000) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 363675 Tasks </td>\n",
+       "                        <td> 179400 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> uint16 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"374\" height=\"108\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"169.948598\" y=\"78.800194\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >22392</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "dask.array<rechunk-merge, shape=(39, 1, 25, 7383, 22392), dtype=uint16, chunksize=(1, 1, 1, 1000, 1000), chunktype=numpy.ndarray>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "timelapse"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "4cb4c31d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "max_proj = timelapse.max(axis=2).compute()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "24a1cd40",
+   "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": "fdd09646",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "51c31a05",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "mean_proj = timelapse.mean(axis=2, dtype='float32').compute()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "f0de9708",
+   "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": "76eb35d8",
+   "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": "9764bcdc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import napari"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "929c3e50",
+   "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": "f8395792",
+   "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": 12,
+   "id": "f02a07b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "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": "0dd9f5c3-e5a3-4209-a4f4-fbb437d1ffce",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "d15ad928-85d2-44ee-966c-78f6596002b1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tf.imwrite('Y:Lena/Data/20220111-MIC-resistant/timelapse-30min/0ng-fluo-aligned-end.tif', aligned_meanIP[1][38])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "6d356482-babe-4d4d-8e4f-51649c5cd698",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fluo = np.array(aligned_meanIP[1]).reshape((39, 1, 1, 6544, 20896))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "eb75199b-6c80-431b-a99b-5e75b3ea37fc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(39, 1, 1, 6544, 20896)"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fluo.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "60579e7f-dee4-4ee1-995d-b01cb26190a9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(39, 1, 1, 10, 10)"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fluo[:,:,:,:10,:10].shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "6cd86282-4047-4665-89bd-198a17a43a60",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tf.imwrite('Y:Lena/Data/20220111-MIC-resistant/timelapse-30min/0ng-fluo-aligned-bin10.tif', fluo[:,:,:,::10,::10])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "041e24ea-2fa8-4af7-84ee-49f32ac81829",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from droplet_growth import multiwell"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "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": [
+       "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": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "labels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "f4ba38f6-388c-4a66-aa43-2113e272a313",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(6544, 20896)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "labels.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "aeb052db-15dd-498a-b2ec-31d7f546dd2b",
+   "metadata": {},
+   "outputs": [
+    {
+     "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": [
+      "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": [
+    "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": 26,
+   "id": "764b27ee-bc5f-4e1c-b8ba-d7e7111ba974",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "intensities.loc[:, 'h'] = intensities.time * .5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "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": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.lineplot(data=intensities, x='h', y='I', hue='label', estimator=None, units='label')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "d267edc2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Image layer 'fluo' at 0x22856006190>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Failed to parse XML for the provided file.\n",
+      "not well-formed (invalid token): line 1, column 0\n",
+      "Failed to parse XML for the provided file.\n",
+      "not well-formed (invalid token): line 1, column 0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AICSImageIO: Reader will load image in-memory: False\n"
+     ]
+    }
+   ],
+   "source": [
+    "v.add_image(fluo, contrast_limits=(400,600))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c67e057d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "a786bac7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(39, 6544, 20896)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.array(aligned_maxIP[1]).shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "5de4d78e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Failed to parse XML for the provided file.\n",
+      "not well-formed (invalid token): line 1, column 6\n",
+      "Failed to parse XML for the provided file.\n",
+      "not well-formed (invalid token): line 1, column 6\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AICSImageIO: Reader will load image in-memory: False\n"
+     ]
+    }
+   ],
+   "source": [
+    "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,
+   "id": "9ea70e06",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tf.imwrite('E:Andrey/20220111-MIC-resistant/timelapse-30min/0ng-BF.aligned.tif', aligned_maxIP[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "8b655220",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Failed to parse XML for the provided file.\n",
+      "not well-formed (invalid token): line 1, column 0\n",
+      "Failed to parse XML for the provided file.\n",
+      "not well-formed (invalid token): line 1, column 0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AICSImageIO: Reader will load image in-memory: False\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Failed to parse XML for the provided file.\n",
+      "not well-formed (invalid token): line 1, column 0\n",
+      "Failed to parse XML for the provided file.\n",
+      "not well-formed (invalid token): line 1, column 0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AICSImageIO: Reader will load image in-memory: False\n",
+      "AICSImageIO: Reader will load image in-memory: False\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Failed to parse XML for the provided file.\n",
+      "not well-formed (invalid token): line 1, column 0\n",
+      "Failed to parse XML for the provided file.\n",
+      "not well-formed (invalid token): line 1, column 0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AICSImageIO: Reader will load image in-memory: False\n"
+     ]
+    }
+   ],
+   "source": [
+    "tf.imwrite('E:Andrey/20220111-MIC-resistant/timelapse-30min/lables.tif', aligned_maxIP[2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "12791a53",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "02 ug\n",
+      " 4ug \n",
+      "8 ug\n",
+      "ug\n",
+      "12 ug\n",
+      "16 20 ug\n",
+      "32 ug\n",
+      "64ug \n",
+      "ug\n",
+      "Aligning: \n",
+      " bf: (7383, 22392),  2\n",
+      " tmp: (818, 2612),  16\n",
+      " mask: (6544, 20896),  2\n",
+      "\n",
+      "Aligning: \n",
+      " bf: (7383, 22392),  2\n",
+      " tmp: (818, 2612),  16\n",
+      " mask: (6544, 20896),  2\n",
+      "\n",
+      "Aligning: \n",
+      " bf: (7383, 22392),  2\n",
+      " tmp: (818, 2612),  16\n",
+      " mask: (6544, 20896),  2\n",
+      "\n",
+      "Aligning: \n",
+      " bf: (7383, 22392),  2\n",
+      " tmp: (818, 2612),  16\n",
+      " mask: (6544, 20896),  2\n",
+      "\n",
+      "Aligning: \n",
+      " bf: (7383, 22392),  2\n",
+      " tmp: (818, 2612),  16\n",
+      " mask: (6544, 20896),  2\n",
+      "\n",
+      "Aligning: \n",
+      " bf: (7383, 22392),  2\n",
+      " tmp: (818, 2612),  16\n",
+      " mask: (6544, 20896),  2\n",
+      "\n",
+      "Aligning: \n",
+      " bf: (7383, 22392),  2\n",
+      " tmp: (818, 2612),  16\n",
+      " mask: (6544, 20896),  2\n",
+      "\n",
+      "Aligning: \n",
+      " bf: (7383, 22392),  2\n",
+      " tmp: (818, 2612),  16\n",
+      " mask: (6544, 20896),  2\n",
+      "\n",
+      "Aligning: \n",
+      " bf: (7383, 22392),  2\n",
+      " tmp: (818, 2612),  16\n",
+      " mask: (6544, 20896),  2\n",
+      "\n",
+      "{'tvec': array([  6.00595407, -86.43060697]), 'success': 0.05071503990371378, 'angle': -2.574944370882008, 'scale': 0.9958864814019974, 'Dscale': 0.00047582619547777437, 'Dangle': 0.013400833829660513, 'Dt': 0.25, 'timg': None}\n",
+      "transform (7383, 22392)\n",
+      "{'tvec': array([ -2.72199608, -12.88955087]), 'success': 0.04770340244982728, 'angle': 0.0727192434734718, 'scale': 0.9961971927236494, 'Dscale': 0.000475974650737319, 'Dangle': 0.013400833829660513, 'Dt': 0.25, 'timg': None}\n",
+      "transform (7383, 22392)\n",
+      "{'tvec': array([ 4.9740718 , 12.02751284]), 'success': 0.04922607854064676, 'angle': 0.7473142806928763, 'scale': 0.9962216938458605, 'Dscale': 0.0004759863571677048, 'Dangle': 0.013400833829660513, 'Dt': 0.25, 'timg': None}\n",
+      "transform (7383, 22392)\n",
+      "{'tvec': array([87.16192209,  3.60583888]), 'success': 0.05455882974296436, 'angle': -0.633922564162134, 'scale': 0.9958735257302511, 'Dscale': 0.0004758200053666402, 'Dangle': 0.013400833829660513, 'Dt': 0.25, 'timg': None}\n",
+      "transform (7383, 22392)\n",
+      "{'tvec': array([-8.15988749, 80.90227973]), 'success': 0.06470470295298651, 'angle': 0.7463712315575606, 'scale': 0.9951720111094522, 'Dscale': 0.0004754848275734676, 'Dangle': 0.013400833829660513, 'Dt': 0.25, 'timg': None}\n",
+      "transform (7383, 22392)\n",
+      "{'tvec': array([-1.33090234,  2.57526347]), 'success': 0.03748038742971667, 'angle': -1.1437310609691735, 'scale': 0.995911447224145, 'Dscale': 0.0004758381239378864, 'Dangle': 0.013400833829660513, 'Dt': 0.25, 'timg': None}\n",
+      "transform (7383, 22392)\n",
+      "{'tvec': array([ 75.1656801 , 145.87189219]), 'success': 0.04181608751974607, 'angle': -2.8924057828552634, 'scale': 0.9922101658023396, 'Dscale': 0.00047406968276490156, 'Dangle': 0.013400833829660513, 'Dt': 0.25, 'timg': None}\n",
+      "transform (7383, 22392)\n",
+      "{'tvec': array([123.33485801,  22.75342746]), 'success': 0.05831623922610776, 'angle': 1.397738072638191, 'scale': 0.9953222688406427, 'Dscale': 0.00047555661945528207, 'Dangle': 0.013400833829660513, 'Dt': 0.25, 'timg': None}\n",
+      "transform (7383, 22392)\n",
+      "{'tvec': array([   2.53782817, -551.87598408]), 'success': 0.04046757960824941, 'angle': -1.1218255641676933, 'scale': 0.9965979554238714, 'Dscale': 0.00047616613178912284, 'Dangle': 0.013400833829660513, 'Dt': 0.25, 'timg': None}\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)transform (7383, 22392)\n",
+      "\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)transform (7383, 22392)\n",
+      "\n",
+      "transform (7383, 22392)\n",
+      "transform (7383, 22392)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Exception in thread Thread-9:\n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\nikon\\miniconda3\\envs\\nd2\\lib\\threading.py\", line 932, in _bootstrap_inner\n",
+      "    self.run()\n",
+      "  File \"C:\\Users\\nikon\\miniconda3\\envs\\nd2\\lib\\threading.py\", line 870, in run\n",
+      "    self._target(*self._args, **self._kwargs)\n",
+      "  File \"C:\\Users\\nikon\\AppData\\Local\\Temp/ipykernel_11096/1785177906.py\", line 10, in align2D\n",
+      "IndexError: tuple index out of range\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved aligned stack E:Andrey/20220113-MIC-W8110_RFPplus-amp/day2\\002ug-BF-TRITC.aligned.tif\n",
+      "Saved aligned stack E:Andrey/20220113-MIC-W8110_RFPplus-amp/day2\\020ug-BF-TRITC.aligned.tif\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Exception in thread Thread-14:\n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\nikon\\miniconda3\\envs\\nd2\\lib\\threading.py\", line 932, in _bootstrap_inner\n",
+      "    self.run()\n",
+      "  File \"C:\\Users\\nikon\\miniconda3\\envs\\nd2\\lib\\threading.py\", line 870, in run\n",
+      "    self._target(*self._args, **self._kwargs)\n",
+      "  File \"C:\\Users\\nikon\\AppData\\Local\\Temp/ipykernel_11096/1785177906.py\", line 10, in align2D\n",
+      "IndexError: tuple index out of range\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved aligned stack E:Andrey/20220113-MIC-W8110_RFPplus-amp/day2\\004ug-BF-TRITC.aligned.tifSaved aligned stack E:Andrey/20220113-MIC-W8110_RFPplus-amp/day2\\032ug-BF-TRITC.aligned.tif\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Exception in thread Exception in thread Thread-10:\n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\nikon\\miniconda3\\envs\\nd2\\lib\\threading.py\", line 932, in _bootstrap_inner\n",
+      "Thread-15:\n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\nikon\\miniconda3\\envs\\nd2\\lib\\threading.py\", line 932, in _bootstrap_inner\n",
+      "    self.run()\n",
+      "  File \"C:\\Users\\nikon\\miniconda3\\envs\\nd2\\lib\\threading.py\", line 870, in run\n",
+      "    self.run()\n",
+      "  File \"C:\\Users\\nikon\\miniconda3\\envs\\nd2\\lib\\threading.py\", line 870, in run\n",
+      "        self._target(*self._args, **self._kwargs)\n",
+      "  File \"C:\\Users\\nikon\\AppData\\Local\\Temp/ipykernel_11096/1785177906.py\", line 10, in align2D\n",
+      "self._target(*self._args, **self._kwargs)\n",
+      "  File \"C:\\Users\\nikon\\AppData\\Local\\Temp/ipykernel_11096/1785177906.py\", line 10, in align2D\n",
+      "IndexErrorIndexError: tuple index out of range\n",
+      ": tuple index out of range\n",
+      "Exception in thread Thread-8:\n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\nikon\\miniconda3\\envs\\nd2\\lib\\threading.py\", line 932, in _bootstrap_inner\n",
+      "    self.run()\n",
+      "  File \"C:\\Users\\nikon\\miniconda3\\envs\\nd2\\lib\\threading.py\", line 870, in run\n",
+      "    self._target(*self._args, **self._kwargs)\n",
+      "  File \"C:\\Users\\nikon\\AppData\\Local\\Temp/ipykernel_11096/1785177906.py\", line 10, in align2D\n",
+      "IndexError: tuple index out of range\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved aligned stack E:Andrey/20220113-MIC-W8110_RFPplus-amp/day2\\000ug-BF-TRITC.aligned.tif\n",
+      "Saved aligned stack E:Andrey/20220113-MIC-W8110_RFPplus-amp/day2\\064ug-BF-TRITC.aligned.tif\n",
+      "Saved aligned stack E:Andrey/20220113-MIC-W8110_RFPplus-amp/day2\\012ug-BF-TRITC.aligned.tif\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Exception in thread Thread-16:\n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\nikon\\miniconda3\\envs\\nd2\\lib\\threading.py\", line 932, in _bootstrap_inner\n",
+      "Exception in thread Thread-12:\n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\nikon\\miniconda3\\envs\\nd2\\lib\\threading.py\", line 932, in _bootstrap_inner\n",
+      "Exception in thread Thread-11:\n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\nikon\\miniconda3\\envs\\nd2\\lib\\threading.py\", line 932, in _bootstrap_inner\n",
+      "    self.run()\n",
+      "  File \"C:\\Users\\nikon\\miniconda3\\envs\\nd2\\lib\\threading.py\", line 870, in run\n",
+      "    self.run()    Exception in thread \n",
+      "  File \"C:\\Users\\nikon\\miniconda3\\envs\\nd2\\lib\\threading.py\", line 870, in run\n",
+      "self.run()\n",
+      "  File \"C:\\Users\\nikon\\miniconda3\\envs\\nd2\\lib\\threading.py\", line 870, in run\n",
+      "Thread-13:\n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\nikon\\miniconda3\\envs\\nd2\\lib\\threading.py\", line 932, in _bootstrap_inner\n",
+      "        self._target(*self._args, **self._kwargs)\n",
+      "  File \"C:\\Users\\nikon\\AppData\\Local\\Temp/ipykernel_11096/1785177906.py\", line 10, in align2D\n",
+      "self._target(*self._args, **self._kwargs)    self._target(*self._args, **self._kwargs)\n",
+      "  File \"C:\\Users\\nikon\\AppData\\Local\\Temp/ipykernel_11096/1785177906.py\", line 10, in align2D\n",
+      "\n",
+      "  File \"C:\\Users\\nikon\\AppData\\Local\\Temp/ipykernel_11096/1785177906.py\", line 10, in align2D\n",
+      "    self.run()\n",
+      "  File \"C:\\Users\\nikon\\miniconda3\\envs\\nd2\\lib\\threading.py\", line 870, in run\n",
+      "IndexError: tuple index out of range\n",
+      "    self._target(*self._args, **self._kwargs)\n",
+      "  File \"C:\\Users\\nikon\\AppData\\Local\\Temp/ipykernel_11096/1785177906.py\", line 10, in align2D\n",
+      "IndexErrorIndexError: tuple index out of range\n",
+      ": tuple index out of range\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved aligned stack E:Andrey/20220113-MIC-W8110_RFPplus-amp/day2\\008ug-BF-TRITC.aligned.tif\n",
+      "Saved aligned stack E:Andrey/20220113-MIC-W8110_RFPplus-amp/day2\\016ug-BF-TRITC.aligned.tif\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "IndexError: tuple index out of range\n"
+     ]
+    }
+   ],
+   "source": [
+    "for p in glob('E:Andrey/20220113-MIC-W8110_RFPplus-amp/day2/*.nd2'):\n",
+    "    _= Thread(target=align2D, args=(p,)).start()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "238f7e6e",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "E:Andrey/20220113-MIC-W8110_RFPplus-amp/day2\\000ug-BF-TRITCaligned-counts.csv\n",
+      "E:Andrey/20220113-MIC-W8110_RFPplus-amp/day2\\002ug-BF-TRITCaligned-counts.csv\n",
+      "E:Andrey/20220113-MIC-W8110_RFPplus-amp/day2\\004ug-BF-TRITCaligned-counts.csv\n",
+      "E:Andrey/20220113-MIC-W8110_RFPplus-amp/day2\\008ug-BF-TRITCaligned-counts.csv\n",
+      "E:Andrey/20220113-MIC-W8110_RFPplus-amp/day2\\012ug-BF-TRITCaligned-counts.csv\n",
+      "E:Andrey/20220113-MIC-W8110_RFPplus-amp/day2\\016ug-BF-TRITCaligned-counts.csv\n",
+      "E:Andrey/20220113-MIC-W8110_RFPplus-amp/day2\\020ug-BF-TRITCaligned-counts.csv\n",
+      "E:Andrey/20220113-MIC-W8110_RFPplus-amp/day2\\032ug-BF-TRITCaligned-counts.csv\n",
+      "E:Andrey/20220113-MIC-W8110_RFPplus-amp/day2\\064ug-BF-TRITCaligned-counts.csv\n",
+      "20 ug\n",
+      "12 ug\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "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"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4 ug\n",
+      "16 ug\n",
+      "32 ug\n"
+     ]
+    },
+    {
+     "ename": "Done",
+     "evalue": "<matplotlib.backends.backend_agg.RendererAgg object at 0x00000277243B7C40>",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mDone\u001b[0m                                      Traceback (most recent call last)",
+      "\u001b[1;32m~\\miniconda3\\envs\\nd2\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    339\u001b[0m                 \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    340\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--> 341\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\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    342\u001b[0m             \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    343\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\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\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(fig, fmt, bbox_inches, base64, **kwargs)\u001b[0m\n\u001b[0;32m    149\u001b[0m         \u001b[0mFigureCanvasBase\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    150\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 151\u001b[1;33m     \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\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    152\u001b[0m     \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetvalue\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    153\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'svg'\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\\matplotlib\\backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[0;32m   2287\u001b[0m                 \u001b[1;31m# CL works.  \"tight\" also needs a draw to get the right\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2288\u001b[0m                 \u001b[1;31m# locations:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2289\u001b[1;33m                 renderer = _get_renderer(\n\u001b[0m\u001b[0;32m   2290\u001b[0m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2291\u001b[0m                     functools.partial(\n",
+      "\u001b[1;32m~\\miniconda3\\envs\\nd2\\lib\\site-packages\\matplotlib\\backend_bases.py\u001b[0m in \u001b[0;36m_get_renderer\u001b[1;34m(figure, print_method)\u001b[0m\n\u001b[0;32m   1542\u001b[0m                 figure.canvas._get_output_canvas(None, fmt), f\"print_{fmt}\")\n\u001b[0;32m   1543\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1544\u001b[1;33m             \u001b[0mprint_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mio\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mBytesIO\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[0m\u001b[0;32m   1545\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mDone\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1546\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_cachedRenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mexc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\miniconda3\\envs\\nd2\\lib\\site-packages\\matplotlib\\backend_bases.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m   1646\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1647\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1648\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\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   1649\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1650\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\miniconda3\\envs\\nd2\\lib\\site-packages\\matplotlib\\_api\\deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*inner_args, **inner_kwargs)\u001b[0m\n\u001b[0;32m    410\u001b[0m                          \u001b[1;32melse\u001b[0m \u001b[0mdeprecation_addendum\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    411\u001b[0m                 **kwargs)\n\u001b[1;32m--> 412\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minner_args\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0minner_kwargs\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    413\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    414\u001b[0m     \u001b[0mDECORATORS\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mwrapper\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdecorator\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\miniconda3\\envs\\nd2\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[1;34m(self, filename_or_obj, metadata, pil_kwargs, *args)\u001b[0m\n\u001b[0;32m    538\u001b[0m             \u001b[1;33m*\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mincluding\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mdefault\u001b[0m \u001b[1;34m'Software'\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    539\u001b[0m         \"\"\"\n\u001b[1;32m--> 540\u001b[1;33m         \u001b[0mFigureCanvasAgg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\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    541\u001b[0m         mpl.image.imsave(\n\u001b[0;32m    542\u001b[0m             \u001b[0mfilename_or_obj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuffer_rgba\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"png\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morigin\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"upper\"\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\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    434\u001b[0m              (self.toolbar._wait_cursor_for_draw_cm() if self.toolbar\n\u001b[0;32m    435\u001b[0m               else nullcontext()):\n\u001b[1;32m--> 436\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrenderer\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    437\u001b[0m             \u001b[1;31m# A GUI class may be need to update a window using this draw, so\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    438\u001b[0m             \u001b[1;31m# don't forget to call the superclass.\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\\matplotlib\\backend_bases.py\u001b[0m in \u001b[0;36m_draw\u001b[1;34m(renderer)\u001b[0m\n\u001b[0;32m   1531\u001b[0m         \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1532\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1533\u001b[1;33m     \u001b[1;32mdef\u001b[0m \u001b[0m_draw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mDone\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\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   1534\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1535\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0mcbook\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_setattr_cm\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdraw\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0m_draw\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;31mDone\u001b[0m: <matplotlib.backends.backend_agg.RendererAgg object at 0x00000277243B7C40>"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 375.075x278.84 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "8 ug\n",
+      "64 ug\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"
+    },
+    {
+     "ename": "RuntimeError",
+     "evalue": "functools.partial(<bound method FigureCanvasAgg.print_png of <matplotlib.backends.backend_agg.FigureCanvasAgg object at 0x0000027718E65B50>>, orientation='portrait') did not call Figure.draw, so no renderer is available",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
+      "\u001b[1;32m~\\miniconda3\\envs\\nd2\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    339\u001b[0m                 \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    340\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--> 341\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\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    342\u001b[0m             \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    343\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\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\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(fig, fmt, bbox_inches, base64, **kwargs)\u001b[0m\n\u001b[0;32m    149\u001b[0m         \u001b[0mFigureCanvasBase\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    150\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 151\u001b[1;33m     \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\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    152\u001b[0m     \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetvalue\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    153\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'svg'\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\\matplotlib\\backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[0;32m   2287\u001b[0m                 \u001b[1;31m# CL works.  \"tight\" also needs a draw to get the right\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2288\u001b[0m                 \u001b[1;31m# locations:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2289\u001b[1;33m                 renderer = _get_renderer(\n\u001b[0m\u001b[0;32m   2290\u001b[0m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2291\u001b[0m                     functools.partial(\n",
+      "\u001b[1;32m~\\miniconda3\\envs\\nd2\\lib\\site-packages\\matplotlib\\backend_bases.py\u001b[0m in \u001b[0;36m_get_renderer\u001b[1;34m(figure, print_method)\u001b[0m\n\u001b[0;32m   1547\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1548\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-> 1549\u001b[1;33m             raise RuntimeError(f\"{print_method} did not call Figure.draw, so \"\n\u001b[0m\u001b[0;32m   1550\u001b[0m                                f\"no renderer is available\")\n\u001b[0;32m   1551\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mRuntimeError\u001b[0m: functools.partial(<bound method FigureCanvasAgg.print_png of <matplotlib.backends.backend_agg.FigureCanvasAgg object at 0x0000027718E65B50>>, orientation='portrait') did not call Figure.draw, so no renderer is available"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 396x281.04 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "ename": "Done",
+     "evalue": "<matplotlib.backends.backend_agg.RendererAgg object at 0x00000277192369A0>",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mDone\u001b[0m                                      Traceback (most recent call last)",
+      "\u001b[1;32m~\\miniconda3\\envs\\nd2\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    339\u001b[0m                 \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    340\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--> 341\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\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    342\u001b[0m             \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    343\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\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\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(fig, fmt, bbox_inches, base64, **kwargs)\u001b[0m\n\u001b[0;32m    149\u001b[0m         \u001b[0mFigureCanvasBase\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    150\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 151\u001b[1;33m     \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\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    152\u001b[0m     \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetvalue\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    153\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'svg'\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\\matplotlib\\backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[0;32m   2287\u001b[0m                 \u001b[1;31m# CL works.  \"tight\" also needs a draw to get the right\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2288\u001b[0m                 \u001b[1;31m# locations:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2289\u001b[1;33m                 renderer = _get_renderer(\n\u001b[0m\u001b[0;32m   2290\u001b[0m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2291\u001b[0m                     functools.partial(\n",
+      "\u001b[1;32m~\\miniconda3\\envs\\nd2\\lib\\site-packages\\matplotlib\\backend_bases.py\u001b[0m in \u001b[0;36m_get_renderer\u001b[1;34m(figure, print_method)\u001b[0m\n\u001b[0;32m   1542\u001b[0m                 figure.canvas._get_output_canvas(None, fmt), f\"print_{fmt}\")\n\u001b[0;32m   1543\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1544\u001b[1;33m             \u001b[0mprint_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mio\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mBytesIO\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[0m\u001b[0;32m   1545\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mDone\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1546\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_cachedRenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mexc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\miniconda3\\envs\\nd2\\lib\\site-packages\\matplotlib\\backend_bases.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m   1646\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1647\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1648\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\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   1649\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1650\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\miniconda3\\envs\\nd2\\lib\\site-packages\\matplotlib\\_api\\deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*inner_args, **inner_kwargs)\u001b[0m\n\u001b[0;32m    410\u001b[0m                          \u001b[1;32melse\u001b[0m \u001b[0mdeprecation_addendum\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    411\u001b[0m                 **kwargs)\n\u001b[1;32m--> 412\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minner_args\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0minner_kwargs\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    413\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    414\u001b[0m     \u001b[0mDECORATORS\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mwrapper\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdecorator\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\miniconda3\\envs\\nd2\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[1;34m(self, filename_or_obj, metadata, pil_kwargs, *args)\u001b[0m\n\u001b[0;32m    538\u001b[0m             \u001b[1;33m*\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mincluding\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mdefault\u001b[0m \u001b[1;34m'Software'\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    539\u001b[0m         \"\"\"\n\u001b[1;32m--> 540\u001b[1;33m         \u001b[0mFigureCanvasAgg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\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    541\u001b[0m         mpl.image.imsave(\n\u001b[0;32m    542\u001b[0m             \u001b[0mfilename_or_obj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuffer_rgba\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"png\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morigin\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"upper\"\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\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    434\u001b[0m              (self.toolbar._wait_cursor_for_draw_cm() if self.toolbar\n\u001b[0;32m    435\u001b[0m               else nullcontext()):\n\u001b[1;32m--> 436\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrenderer\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    437\u001b[0m             \u001b[1;31m# A GUI class may be need to update a window using this draw, so\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    438\u001b[0m             \u001b[1;31m# don't forget to call the superclass.\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\\matplotlib\\backend_bases.py\u001b[0m in \u001b[0;36m_draw\u001b[1;34m(renderer)\u001b[0m\n\u001b[0;32m   1531\u001b[0m         \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1532\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1533\u001b[1;33m     \u001b[1;32mdef\u001b[0m \u001b[0m_draw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mDone\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\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   1534\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1535\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0mcbook\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_setattr_cm\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdraw\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0m_draw\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;31mDone\u001b[0m: <matplotlib.backends.backend_agg.RendererAgg object at 0x00000277192369A0>"
+     ]
+    },
+    {
+     "data": {
+      "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"
+    },
+    {
+     "ename": "RuntimeError",
+     "evalue": "functools.partial(<bound method FigureCanvasAgg.print_png of <matplotlib.backends.backend_agg.FigureCanvasAgg object at 0x00000277243ABFA0>>, orientation='portrait') did not call Figure.draw, so no renderer is available",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
+      "\u001b[1;32m~\\miniconda3\\envs\\nd2\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    339\u001b[0m                 \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    340\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--> 341\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\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    342\u001b[0m             \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    343\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\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\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(fig, fmt, bbox_inches, base64, **kwargs)\u001b[0m\n\u001b[0;32m    149\u001b[0m         \u001b[0mFigureCanvasBase\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    150\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 151\u001b[1;33m     \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\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    152\u001b[0m     \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetvalue\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    153\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'svg'\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\\matplotlib\\backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[0;32m   2287\u001b[0m                 \u001b[1;31m# CL works.  \"tight\" also needs a draw to get the right\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2288\u001b[0m                 \u001b[1;31m# locations:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2289\u001b[1;33m                 renderer = _get_renderer(\n\u001b[0m\u001b[0;32m   2290\u001b[0m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2291\u001b[0m                     functools.partial(\n",
+      "\u001b[1;32m~\\miniconda3\\envs\\nd2\\lib\\site-packages\\matplotlib\\backend_bases.py\u001b[0m in \u001b[0;36m_get_renderer\u001b[1;34m(figure, print_method)\u001b[0m\n\u001b[0;32m   1547\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1548\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-> 1549\u001b[1;33m             raise RuntimeError(f\"{print_method} did not call Figure.draw, so \"\n\u001b[0m\u001b[0;32m   1550\u001b[0m                                f\"no renderer is available\")\n\u001b[0;32m   1551\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mRuntimeError\u001b[0m: functools.partial(<bound method FigureCanvasAgg.print_png of <matplotlib.backends.backend_agg.FigureCanvasAgg object at 0x00000277243ABFA0>>, orientation='portrait') did not call Figure.draw, so no renderer is available"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2 ug\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": [
+      "0 ug\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": [
+    "for p in glob('E:Andrey/20220113-MIC-W8110_RFPplus-amp/day2/*.tif'):\n",
+    "    print(''.join(p.split('.')[:-1]) + '-counts.csv')\n",
+    "    _= Thread(target=count, args=(p,)).start()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "2087e295",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tvec  = register.get_transform(bf[0,0,0,::8,::8], template16, plot=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "e7632c19",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'tvec': array([-1.96559165,  2.54556865]),\n",
+       " 'success': 0.03702985214889686,\n",
+       " 'angle': -2.7811090559223715,\n",
+       " 'scale': 0.9929738758061613,\n",
+       " 'Dscale': 0.00047443457698964815,\n",
+       " 'Dangle': 0.013400833829660513,\n",
+       " 'Dt': 0.25,\n",
+       " 'timg': array([[15434.7634942 , 15435.0746548 , 15435.33933471, ...,\n",
+       "         15433.53681108, 15433.99571869, 15434.40407532],\n",
+       "        [15434.608721  , 15434.92088445, 15435.18711067, ...,\n",
+       "         15433.3839413 , 15433.84124509, 15434.24903335],\n",
+       "        [15434.46485754, 15434.77747325, 15435.04478226, ...,\n",
+       "         15433.24415882, 15433.69904843, 15434.10554109],\n",
+       "        ...,\n",
+       "        [15435.30903116, 15435.61252563, 15435.86850429, ...,\n",
+       "         15434.09440208, 15434.55157307, 15434.95571302],\n",
+       "        [15435.11226435, 15435.41921834, 15435.67889533, ...,\n",
+       "         15433.89020914, 15434.34921182, 15434.75589249],\n",
+       "        [15434.93089557, 15435.24036336, 15435.50290131, ...,\n",
+       "         15433.70496678, 15434.16449633, 15434.57253473]])}"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tvec"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "866d7701",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tvec_scaled = register.scale_tvec(tvec, 8)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "c496e2cb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'tvec': array([-15.72473317,  20.36454919]),\n",
+       " 'success': 0.03702985214889686,\n",
+       " 'angle': -2.7811090559223715,\n",
+       " 'scale': 0.9929738758061613,\n",
+       " 'Dscale': 0.00047443457698964815,\n",
+       " 'Dangle': 0.013400833829660513,\n",
+       " 'Dt': 0.25,\n",
+       " 'timg': None}"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tvec_scaled"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "d441c98c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.ndimage.interpolation import zoom, rotate, shift"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "d86338c6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(39, 1, 1, 7383, 22392)"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fluo = fluo.reshape((39, 1, 1, 7383, 22392))\n",
+    "fluo.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "32c6e5cb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rfluo = rotate(input=fluo, angle=tvec_scaled['angle'], axes=(4,3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "dba3a4e7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "del fluo"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "ba9da841",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "MemoryError",
+     "evalue": "Unable to allocate 48.0 GiB for an array with shape (39, 1, 1, 7383, 22392) and data type float64",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mMemoryError\u001b[0m                               Traceback (most recent call last)",
+      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_1176/1968532831.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mz\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtvec_scaled\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'scale'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mzfluo\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mzoom\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfluo\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mzoom\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mz\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mz\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[0m",
+      "\u001b[1;32m~\\miniconda3\\envs\\nd2\\lib\\site-packages\\scipy\\ndimage\\interpolation.py\u001b[0m in \u001b[0;36mzoom\u001b[1;34m(input, zoom, output, order, mode, cval, prefilter, grid_mode)\u001b[0m\n\u001b[0;32m    783\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mprefilter\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0morder\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    784\u001b[0m         \u001b[0mpadded\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnpad\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_prepad_for_spline_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcval\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 785\u001b[1;33m         filtered = spline_filter(padded, order, output=numpy.float64,\n\u001b[0m\u001b[0;32m    786\u001b[0m                                  mode=mode)\n\u001b[0;32m    787\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~\\miniconda3\\envs\\nd2\\lib\\site-packages\\scipy\\ndimage\\interpolation.py\u001b[0m in \u001b[0;36mspline_filter\u001b[1;34m(input, order, output, mode)\u001b[0m\n\u001b[0;32m    179\u001b[0m     \u001b[0minput\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    180\u001b[0m     \u001b[0mcomplex_output\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miscomplexobj\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 181\u001b[1;33m     output = _ni_support._get_output(output, input,\n\u001b[0m\u001b[0;32m    182\u001b[0m                                      complex_output=complex_output)\n\u001b[0;32m    183\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mcomplex_output\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\\scipy\\ndimage\\_ni_support.py\u001b[0m in \u001b[0;36m_get_output\u001b[1;34m(output, input, shape, complex_output)\u001b[0m\n\u001b[0;32m     85\u001b[0m             \u001b[0mwarnings\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwarn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"promoting specified output dtype to complex\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     86\u001b[0m             \u001b[0moutput\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpromote_types\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcomplex64\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 87\u001b[1;33m         \u001b[0moutput\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0moutput\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     88\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutput\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[0;32m     89\u001b[0m         \u001b[0moutput\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msctypeDict\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0moutput\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mMemoryError\u001b[0m: Unable to allocate 48.0 GiB for an array with shape (39, 1, 1, 7383, 22392) and data type float64"
+     ]
+    }
+   ],
+   "source": [
+    "z = tvec_scaled['scale']\n",
+    "zfluo = zoom(input=fluo, zoom=(1,1,1,z,z),)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "6be93143",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(39, 1, 1, 8461, 22724)"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rfluo.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "134a1b8b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Image layer 'rfluo' at 0x1a3e9f4b6a0>"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v.add_image(rfluo, contrast_limits=(440,600))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cbf1b797",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/timelapse 11 jan 2022.ipynb b/timelapse 11 jan 2022.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..58e8f7428949734613fad198984c8947543b79bb
--- /dev/null
+++ b/timelapse 11 jan 2022.ipynb	
@@ -0,0 +1,127 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "2ae2bab7-a76d-489a-82db-541b3b35998d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from droplet_growth import multiwell\n",
+    "import tifffile as tf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a31037f7-f7f7-4523-876a-806d0cf4e799",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "big_labels = tf.imread('Y:/Lena/Data/labels_bin2+100.tif')\n",
+    "template16 = tf.imread('Y:/Lena/Data/20210518_control/template_bin16_bf_mask.tif')[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "740a2dde-a75e-402e-b4a3-056d230b56ef",
+   "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": null,
+   "id": "73d33d14-935a-4430-9640-864d25d9d70a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "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": "1797f6de-4e51-4da9-86f2-0e5bf411e491",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fluo = np.array(aligned_meanIP[1]).reshape((39, 1, 1, 6544, 20896))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e21e05be-06b4-4889-814c-e701b9f0180a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fluo.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f52b459a-c3db-423c-a53b-d54a2b64f6a2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "labels = tf.imread('Y:/Lena/Data/20220111-MIC-resistant/timelapse-30min/lables.tif')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "31849d84-0452-4db2-b1d1-873e42ca4353",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "intensities = multiwell.get_intensity_table(labelled_mask=labels.astype('int'), intensity_image_sequence=fluo[:,0,0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "84defb2a-71b0-47b6-b44e-82053d030675",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "intensities.loc[:, 'h'] = intensities.time * .5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "35aca943-50f6-48fb-92fe-55163b6105d7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "intensities.to_csv(\"Y:Lena/Data/20220111-MIC-resistant/timelapse-30min/intesities.csv\", index=None)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/zarr.ipynb b/zarr.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..8dfe28b8bcec6c29efd24fe6d8a8ab00cfd77fdc
--- /dev/null
+++ b/zarr.ipynb
@@ -0,0 +1,1176 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "13ddd473-b4d6-4af2-94d3-c1cfd82022d0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import zarr\n",
+    "import dask\n",
+    "import aicsimageio\n",
+    "import matplotlib.pyplot as plt\n",
+    "from skimage.transform import pyramid_gaussian\n",
+    "import os\n",
+    "import napari"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "2a06e28a-d15d-4804-a343-f49e42b47594",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def convert2zarr(path, chunksize=None, nzooms=4):\n",
+    "    img = aicsimageio.imread_dask(path)\n",
+    "    print(img)\n",
+    "    if chunksize is not None:\n",
+    "        img  = img.rechunk(chunksize)\n",
+    "        print(\"rechunk\",img)\n",
+    "    baseurl = path.replace(path.split('.')[-1], \"zarr\")\n",
+    "    print(baseurl)\n",
+    "    store = zarr.DirectoryStore(baseurl)\n",
+    "    grp = zarr.group(store)\n",
+    "    print(\"start writing base datset\")\n",
+    "    img.to_zarr(url=os.path.join(baseurl, '0'))\n",
+    "    datasets = [{'path':'0'},]\n",
+    "    \n",
+    "    for i in range(nzooms-1):\n",
+    "        j = i + 1\n",
+    "        print(f\"Start writing {j} dataset (bin {2**j})\")\n",
+    "        img = img[:,:,:,::2,::2]\n",
+    "        img.to_zarr(url=os.path.join(baseurl, j))\n",
+    "        datasets.append({'path': f'{j}'})  \n",
+    "\n",
+    "    grp.attrs['multiscales'] = {\n",
+    "        \"multiscales\": [\n",
+    "            {\n",
+    "                \"datasets\": datasets,\n",
+    "                \"name\": os.path.basename(path),\n",
+    "                \"type\": \"skip\",\n",
+    "                \"version\": \"0.1\"\n",
+    "            },\n",
+    "\n",
+    "        ]\n",
+    "    }\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "55e2186a-4c4d-4745-972e-791a25fff26b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Failed to parse XML for the provided file.\n",
+      "not well-formed (invalid token): line 1, column 6\n",
+      "C:\\Users\\nikon\\miniconda3\\envs\\nd2\\lib\\site-packages\\dask\\array\\core.py:1519: RuntimeWarning: overflow encountered in long_scalars\n",
+      "  cbytes = format_bytes(np.prod(self.chunksize) * self.dtype.itemsize)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
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+       "                        <th> Bytes </th>\n",
+       "                        <td> 10.02 GiB </td>\n",
+       "                        <td> -707840896 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 3, 156, 3900, 2948) </td>\n",
+       "                        <td> (1, 1, 156, 3900, 2948) </td>\n",
+       "                    </tr>\n",
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+       "                        <th> Count </th>\n",
+       "                        <td> 21 Tasks </td>\n",
+       "                        <td> 3 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> uint16 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "dask.array<transpose, shape=(1, 3, 156, 3900, 2948), dtype=uint16, chunksize=(1, 1, 156, 3900, 2948), chunktype=numpy.ndarray>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "img = aicsimageio.imread_dask('Y:/Gustave/Peixoto/image_1/210309_5_Cut.tif')\n",
+    "img"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "1e7774cb-2c6e-4920-a767-6f33eaff0544",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "img = aicsimageio.imread_dask('Y:/Lena/Data/20220111-MIC-resistant/timelapse-30min/0ng-TRITC-19h.nd2')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "4af272eb-f8ed-4b9c-8d87-95a805d9537a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dask.array<transpose, shape=(39, 1, 25, 7383, 22392), dtype=uint16, chunksize=(1, 1, 1, 7383, 22392), chunktype=numpy.ndarray>\n",
+      "rechunk dask.array<rechunk-merge, shape=(39, 1, 25, 7383, 22392), dtype=uint16, chunksize=(1, 1, 25, 512, 512), chunktype=numpy.ndarray>\n",
+      "Y:/Lena/Data/20220111-MIC-resistant/timelapse-30min/0ng-TRITC-19h.zarr\n",
+      "start writing base datset\n",
+      "Start writing 1 dataset (bin 2)\n"
+     ]
+    },
+    {
+     "ename": "TypeError",
+     "evalue": "join() argument must be str, bytes, or os.PathLike object, not 'int'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_7052/2903376027.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m convert2zarr(\n\u001b[0m\u001b[0;32m      2\u001b[0m     \u001b[1;34m'Y:/Lena/Data/20220111-MIC-resistant/timelapse-30min/0ng-TRITC-19h.nd2'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m     \u001b[0mchunksize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m25\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m512\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m512\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m )\n",
+      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_7052/2953746347.py\u001b[0m in \u001b[0;36mconvert2zarr\u001b[1;34m(path, chunksize, nzooms)\u001b[0m\n\u001b[0;32m     17\u001b[0m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"Start writing {j} dataset (bin {2**j})\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     18\u001b[0m         \u001b[0mimg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mimg\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[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 19\u001b[1;33m         \u001b[0mimg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_zarr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbaseurl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mj\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     20\u001b[0m         \u001b[0mdatasets\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;34m'path'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;34mf'{j}'\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     21\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\miniconda3\\envs\\nd2\\lib\\ntpath.py\u001b[0m in \u001b[0;36mjoin\u001b[1;34m(path, *paths)\u001b[0m\n\u001b[0;32m    115\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mresult_drive\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mresult_path\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    116\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mTypeError\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mBytesWarning\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--> 117\u001b[1;33m         \u001b[0mgenericpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_check_arg_types\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'join'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0mpaths\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    118\u001b[0m         \u001b[1;32mraise\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    119\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\miniconda3\\envs\\nd2\\lib\\genericpath.py\u001b[0m in \u001b[0;36m_check_arg_types\u001b[1;34m(funcname, *args)\u001b[0m\n\u001b[0;32m    150\u001b[0m             \u001b[0mhasbytes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    151\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--> 152\u001b[1;33m             raise TypeError(f'{funcname}() argument must be str, bytes, or '\n\u001b[0m\u001b[0;32m    153\u001b[0m                             f'os.PathLike object, not {s.__class__.__name__!r}') from None\n\u001b[0;32m    154\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mhasstr\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mhasbytes\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mTypeError\u001b[0m: join() argument must be str, bytes, or os.PathLike object, not 'int'"
+     ]
+    }
+   ],
+   "source": [
+    "convert2zarr(\n",
+    "    'Y:/Lena/Data/20220111-MIC-resistant/timelapse-30min/0ng-TRITC-19h.nd2',\n",
+    "    chunksize=(1,1,25,512,512)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "bdb27aea-e704-4e21-a731-c8584ce8ff50",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'dask.array<transpose, shape=(39, 1, 25, 7383, 22392), dtype=uint16, chunksize=(1, 1, 1, 7383, 22392), chunktype=numpy.ndarray>'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "img"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "a9abcdd3-3288-4b84-bf6e-fa9847b68f49",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "img = aicsimageio.AICSImage(\"Y:/Lena/Data/20211207-control-timelapse/00ng-TRITC_30min.nd2\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "129d9c43-54d4-463d-a7d3-746e4b3b6e2d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<tifffile.TiffFile 'TRITC-30min-Z-1…axIP_XY1.ome.tif'> OME series expected 1 frames, got 29\n"
+     ]
+    },
+    {
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+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "aicsimageio.imread_dask(\"Y:/Lena/Data/20210628-timelapse-00ng/TRITC-30min-Z-110-2.5-T+28C-MaxIP_XY1.ome.tif\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "1919e59c-7ad8-4938-a663-7cff8b1b9162",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'.'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "dask.array<from-zarr, shape=(1, 3, 156, 3900, 2948), dtype=uint16, chunksize=(1, 1, 156, 128, 128), chunktype=numpy.ndarray>"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "img"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "7f25498a-f536-4734-b180-650d2da1d183",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(3900, 2948)\n",
+      "0 4095\n"
+     ]
+    },
+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def prev(img):\n",
+    "    a = img[0,0,1,].compute()\n",
+    "\n",
+    "    print(a.shape)\n",
+    "\n",
+    "    print(a.min(), a.max())\n",
+    "\n",
+    "    plt.imshow(a)\n",
+    "\n",
+    "prev(img)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "88473b18-4f00-4e00-8d41-e34cf8663ef5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1950, 1474)\n",
+      "0 4095\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": [
+    "img2 = img[:,:,:,::2,::2]\n",
+    "prev(img2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "0d05f247-aee0-47f1-9983-f182cdaa62d5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(975, 737)\n",
+      "0 4095\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": [
+    "img4 = img2[:,:,:,::2,::2]\n",
+    "prev(img4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "4d35dfb0-ab7c-4baf-b1a6-694bb9414644",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(488, 369)\n",
+      "0 4095\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": [
+    "img8 = img4[:,:,:,::2,::2]\n",
+    "prev(img8)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "f19daf78-415a-4b3f-82bd-17923a8d7b20",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "baseurl = '/home/aaristov/Downloads/Gustave_Peixoto_image_1_210309_5_Cut_2.zarr'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "7920ae04-f76f-4eb5-9216-e1f4d73de0c7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "store = zarr.DirectoryStore('/home/aaristov/Downloads/Gustave_Peixoto_image_1_210309_5_Cut_2.zarr')\n",
+    "\n",
+    "grp = zarr.group(store)\n",
+    "\n",
+    "grp.attrs['multiscales'] = {\n",
+    "    \"multiscales\": [\n",
+    "        {\n",
+    "            \"datasets\": [\n",
+    "                {\n",
+    "                    \"path\": \"0\"\n",
+    "                },\n",
+    "                {\n",
+    "                    \"path\": \"1\"\n",
+    "                },\n",
+    "                {\n",
+    "                    \"path\": \"2\"\n",
+    "                },\n",
+    "                {\n",
+    "                    \"path\": \"3\"\n",
+    "                },\n",
+    "                {\n",
+    "                    \"path\": \"4\"\n",
+    "                }\n",
+    "            ],\n",
+    "            \"name\": \"gaussian\",\n",
+    "            \"type\": \"gaussian\",\n",
+    "            \"version\": \"0.1\"\n",
+    "        },\n",
+    "        \n",
+    "    ]\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a8c777e4-5a12-4a7d-b526-1d73debdb4ed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grp."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "beddfcdd-58ba-4a76-a2af-b2eb14445231",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "img.to_zarr(path.join(baseurl, '0'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "2ef138a5-c1b9-44b7-be64-fbada5a38648",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "img2.to_zarr(path.join(baseurl, '1'))\n",
+    "img4.to_zarr(path.join(baseurl, '2'))\n",
+    "img8.to_zarr(path.join(baseurl, '3'))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d64caf2c-b422-4f87-8c12-acdfdd8c4417",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import napari"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b9be9001-9516-48fc-94cf-b42fe58ceb0f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "v = napari.Viewer()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0a530f71-001c-4eeb-9470-23c432cb2754",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}