diff --git a/multiwell MIC 20220113.ipynb b/multiwell MIC 20220113.ipynb
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index 0000000000000000000000000000000000000000..523a5ab5b3d9d38823903266520140cd31da3093
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+++ b/multiwell MIC 20220113.ipynb
@@ -0,0 +1,959 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The autoreload extension is already loaded. To reload it, use:\n",
+ " %reload_ext autoreload\n"
+ ]
+ }
+ ],
+ "source": [
+ "from droplet_growth import mic, poisson, register\n",
+ "from functools import partial\n",
+ "import tifffile as tf\n",
+ "import pandas as pd\n",
+ "from glob import glob\n",
+ "import re\n",
+ "import numpy as np\n",
+ "from multiprocessing import Pool\n",
+ "from aicsimageio import imread, imread_dask\n",
+ "import os\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "\n",
+ "%load_ext autoreload\n",
+ "%autoreload 2"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "See http://localhost:8888/lab/tree/multiwell%20align%20count%20dec-jan%202022.ipynb for alignment and counting"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
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+ "metadata": {},
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+ "counts_day1 = pd.concat(map(pd.read_csv, glob('E:Andrey/20220113-MIC-W8110_RFPplus-amp/day1/composites/*-counts.csv')), ignore_index=True)\n",
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+ "metadata": {},
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+ "counts_day2 = pd.concat(map(pd.read_csv, glob('E:Andrey/20220113-MIC-W8110_RFPplus-amp/day2/composites/*-counts.csv')), ignore_index=True)\n",
+ "counts_day2"
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+ "text/plain": [
+ " ng label\n",
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+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "counts_day1[['ng','label']]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "np.testing.assert_array_equal(counts_day1[['ng','label']], counts_day2[['ng','label']], )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "table = counts_day1.copy()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "table.loc[:,'n24'] = counts_day2.n_cells\n",
+ "table.loc[:,'final_state'] = table.n24 > 10"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "table.to_csv('E:Andrey/20220113-MIC-W8110_RFPplus-amp/processing/table.csv', index=None)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
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\n",
+ "text/plain": [
+ "<Figure size 900x600 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "stats = mic.get_stats(table)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\nikon\\miniconda3\\envs\\nd2\\lib\\site-packages\\seaborn\\categorical.py:1296: UserWarning: 75.8% 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: 44.8% 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: 25.8% 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: 5.7% 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: 93.2% 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: 72.4% 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: 61.0% 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: 28.6% 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: 88.2% 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: 88.3% 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: 89.5% 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: 79.6% 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: 9.1% 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: 95.6% 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: 93.7% 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: 90.0% 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: 75.9% 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: 65.4% 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: 95.0% 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: 94.8% 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: 92.0% 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: 85.7% 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: 62.5% 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: 94.9% 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: 93.9% 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: 91.2% 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: 80.0% 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: 50.0% 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: 94.2% 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: 94.7% 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: 92.5% 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: 84.9% 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: 63.6% 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: 95.5% 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: 95.4% 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: 93.3% 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: 89.7% 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: 77.3% 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: 96.8% 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: 96.4% 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: 93.6% 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: 82.4% 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: 33.3% 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: 20.0% 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": {
+ "text/plain": [
+ "<AxesSubplot:xlabel='ng', ylabel='n24'>"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
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\n",
+ "text/plain": [
+ "<Figure size 1800x1200 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, ax = plt.subplots(dpi=300)\n",
+ "sns.swarmplot(ax=ax, data=table.query('n_cells < 6'), x='ng', y='n24', hue='n_cells', dodge=True, size=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<AxesSubplot:xlabel='ng', ylabel='final_state'>"
+ ]
+ },
+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1800x1200 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, ax = plt.subplots(dpi=300)\n",
+ "sns.lineplot(ax=ax, data=table.query('0 < n_cells < 5 and ng<10'), x='ng', y='final_state', hue='n_cells')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "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"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "q1 = mic.fit.single_prob(stats, show_individual_fits=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "q1.to_csv('E:Andrey/20220113-MIC-W8110_RFPplus-amp/processing/q1.csv', index=None)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "stats.to_csv('E:Andrey/20220113-MIC-W8110_RFPplus-amp/processing/stats.csv', index=None)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "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": 4
+}
diff --git a/multiwell align count dec-jan 2022.ipynb b/multiwell align count dec-jan 2022.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..bdeb89179e6d64654b7233e37fc8e00dd09a162d
--- /dev/null
+++ b/multiwell align count dec-jan 2022.ipynb
@@ -0,0 +1,2344 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "53a4f1b5-8cc1-4f9a-82db-be5b7d3a19cd",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import asyncio\n",
+ "from aicsimageio import imread, imread_dask\n",
+ "from droplet_growth import mic, register, poisson\n",
+ "from glob import glob\n",
+ "import tifffile as tf\n",
+ "import numpy as np\n",
+ "import os\n",
+ "from threading import Thread\n",
+ "import re\n",
+ "%load_ext autoreload\n",
+ "%autoreload 2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "id": "5e952cf6-c393-487d-a843-558a28689c6d",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(818, 2612)"
+ ]
+ },
+ "execution_count": 34,
+ "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": 41,
+ "id": "20d30f56-539b-4491-8e01-23a4a4506c1d",
+ "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": 36,
+ "id": "e17feda0-0cf8-4f4c-8f48-383048572f4b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def align2D(path_nd2_bf_tritc, regex='(\\d+)ug'):\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}ug')\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):\n",
+ " \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",
+ " return aligned"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "77d0b6bb-82ae-4d3e-a64a-d61e90473191",
+ "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": "7284f239-f87a-4d66-b035-ddda9b63241a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "proj = img[0,0].max(axis=0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "b9ae6101-5f9a-435e-b2ce-0e5eddc9ce54",
+ "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": "9c586445-841c-4683-8db5-41e0cc1c33a2",
+ "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": "dd1cbfb0-765a-4325-92ed-9666379bb9e0",
+ "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": "1578e0a7-eec5-46b9-9b78-d6e207db2cdf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "_ = await asyncio.gather(t)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "id": "b038cf24-9db2-4912-82c6-fa650ff67036",
+ "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": "473108fb-8450-47cd-a8cb-49749c1ba222",
+ "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": "b22f2838-ac59-4804-bb17-fee5ef347998",
+ "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": "1198c820-0cd8-4af9-9fc4-b07b732e6e35",
+ "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": "46260bb7-0a62-4f20-a63b-61e9d946a851",
+ "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": "a7b2727f-fba2-4823-a547-af1660168fc6",
+ "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": "9c788d5f-e902-40ae-9562-88cf03565523",
+ "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": "dcf07d40-ecfa-47f8-b4fe-476c1c80c26f",
+ "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": "3d882863-bd4b-4e37-b2e0-f3b32e4c3bd6",
+ "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": "106ffa84-0ebc-4d34-a880-7f124fee0514",
+ "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": "f5ac3c9d-3016-4950-9ec5-ea1410f8d634",
+ "metadata": {},
+ "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": "9fa11667-e200-4f2d-91c2-1d84dc182496",
+ "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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\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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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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5+f42xYIVS4ulwZKSkkJkZCRdunSxE84bALYZbrE0UPLz82ndurUVygaCFctqkr1iBfueeJLc1av9bYqlCWOFsuFgxbIa5K5axa6J/8fBl15i54RrrWBamiwBAQEkJiaSkJDAH/7whwpjZ37wwQdMnz69Hq2DtLQ0Bg0aRP/+/VmxYgUXXnghGRkZZGRk8MILLxzzvmHDhnHhhReWiXV6PKxYVoPc5FUl+1pQQO5331eQ2mJpvISGhrJmzRrWrVtHcHAwM2fOPGba0aNHM2XKlHq0Dr744gv69OnD6tWrGTJkCB999BEtW7Y8rlguXbqUkJAQFi9eXOmymp1YFneW12Q7/7bJ5Hk8eFVRr5cJd91VYXq7FNNSX+SuXs2BF2fVSWtnyJAhbN26lYMHDzJ27Fj69u3L4MGDWbt2LQBz5szhlltuAeDtt98mISGBfv36cfbZZwOwfv16Bg4cSGJiIn379mXLli0APPnkkyQkJJCQkMDTTz8NwPbt2+nVqxc33HADvXv3ZsSIEeTl5ZWxZ82aNdx99928//77JCYmkpeXR5cuXThw4ABTpkxh27ZtJCYmctdddx21PqNGjWLevHmVrn+zGw0vDvVfU3JXryZ7+XIOvTmfZ3r14v2Fb+EKDj5qWtvvZKkpex95hIKNmypM48nOpmDTJlAFEUJ69iQgIuKY6UN69aT9vfdWqny3283HH3/MyJEjeeCBB+jfvz+LFi1i6dKljB8/njVr1pRJP23aND799FPi4uLIyMgAYObMmUyePJk//vGPFBYW4vF4WLVqFbNnz2blypWoKoMGDeKcc84hJiaGLVu2MH/+fF566SXGjRvHu+++y5VXXllSRmJiItOmTSM5OZnnnnuuTPnTp09n3bp1R9jly4IFC1i1ahVZWVlERUVx+PBhhgwZctS0b775ZvMTy9oirH9/wvr3J7RvP1JuuokD/3qOtnf82d9mWZox3qwsI5QAqnizsioUy8qQl5dHYmIiYDzL6667jkGDBvHuu+8Cpu8vPT2drKysMvedeeaZXHPNNYwbN46LLzZvQj799NN5+OGHSUlJ4eKLL6Z79+7897//5Xe/+x3h4eEAXHzxxaxYsYLRo0fTtWvXkrIHDBjA9u3ba1QXX3766ScyMzO54oorePfdd5kwYQKRkZEViqsVy2py6K23OPzZEiJHDCf6kt+T/sorRJw7lLByL7ayWGqDyniAuatXs3PCtWhRERIURMfH/0lY//41Kre4z7KqzJw5k5UrV7J48WIGDBjAqlWruOKKKxg0aBCLFy/mwgsv5MUXX6wwD98XqwUEBBzRDK8JTz/9NLfffjtdu3blgQceYMKECdazrAsOLXiLvVOnApDz9de0vfcegjp2ZM9fpnDiovdwOb+SFkt9Eta/PyfMfpXc774nbOBpNRbKYzFkyBDmzZvH/fffz7Jly2jTpg1RUVFl0mzbto1BgwYxaNAgPv74Y3bt2kVmZiYnnngit956Kzt37mTt2rWcffbZXHPNNUyZMgVV5b333uP111+vsY2RkZEcPnz4qNfS0tJYvnw5M2fOJCgoiB07drBnzx46duxY4Q9DsxvgqQ2yPv20zHHO8q/oOP1RilJS2PfYP/xklcViBLPN/02sM6EEmDp1KqtWraJv375MmTLlqC9Ku+uuu+jTpw8JCQmcccYZ9OvXj4ULF5KQkEBiYiLr1q1j/PjxnHrqqVxzzTUMHDiQQYMGcf3119O/Fmxv3bo1Z555JgkJCUcM8Lz44otcf/31BAUFAXD55ZezYMGC4+bZJF4rkZSUVOl4liJS4wGeQ2+9xd4HppYcx0yYQPu/3M3+xx8n/eVXiJ85g8ihQ2u1TEvzY+PGjfTq1cvfZlgcmqVnWdOpQ60uu4yXDhxgX1ERqsq+V16hQ1AQ8Xffzab8fNZcfwMxgYEl6S0WS+OnWYqlqtZoy0lO5oa2bWkXFIQEBRHscrF8QBJ5OTmM+vQT2oSG8tNNN+P1eq1HabE0EZqlWNaU3ORk8HrNgcdDcM+eeDIy2P6HPxDSvTuxt07i8GefkfXBB/411GKx1BpWLKtB2MCBSIsWIAKqhPTsQYvevSncuo2USbfS+tprCR0wgL0PPkTRnj3+NtdisdQCzXLqUG30I/Zr0YKBYWH0bRHKeYve5/kDaYxrGQNffMHkdu15LyuT97p04Y1Bg7G9lhZL46dZepY17bNUVdbk5TErPZ2bd+0keuxYbm4TS8+bb0ZCQrixTRvWvbeIkx5+mMHh4VwZE+PvKlsslhrSLMWyNhGXiw4PP0TUb35D+osv0vLSceByseeuuwjp2ZOIc8/lz21iKdi61d+mWixVxoZoK8WKZS0gAQF0fGw6kSNGcOi114m66CLwetl51Xja3DaZHK+XPXf/BS0s9LepFkuVsCHaSjmuWIpIJxH5UkQ2iMh6EZnsnG8lIktEZIvzN8Y5LyLyrIhsFZG1ItIsFktLYCBxTzxOxHnnkfXBB4SdcTqan0/KDRN5cP8+8jdsIG3GDH+baWni2BBt/g3R5gbuUNUfRCQSWCUiS4BrgC9UdbqITAGmAH8BRgHdnW0QMMP52+SRoCDinnqSlEmTyFn+FcHdu1O4ZQvXxMQQNXYs6S/OIvKccwh1IqlYLJXFhmjzf4i243qWqpqqqj84+4eBjUAcMAYoXhQ6Fxjr7I8BXlPDt0BLEengm2d5b/V4NjQmXMHBxD/7LOFnnEHhli0EtG5Nv9AwKCwksH079vxlCt4K+n0slupytBBtNaU4RFtSUhInnHAC1113Hf/973+56qqrgOOHaHvppZfweDyACdH2yCOP8Nhjj7Fjxw5CQ0PLhGiLiIgoCdEG1GuINqAkRNvRtlNOOaVqU4dEpAvQH1gJtFPVVOfSXqCdsx8H7PK5LcU5l+pzzg18DYykCfabukJCiH/+OXb96UZyv/uOXK8XPvqI6HHjyHz7bfY//jjt//Y3f5tpaUTYEG2NKESbiEQA7wK3qWqW71xFVVURqfS6Pkdk/+psUMNAGg0RV2gonWa8wM4bJuJOTgaXi8yFC4k47zwOvTmfiHOHETHkLH+baWlC2BBtpfgtRJuIBGGEcp6q/ts5va+4ee383e+c3w108rk93jl3rLy7VMaGhkbuqlXH7Uh3hYXR6cUXWZefb1b7ANnLlhF0QidS770Xj9OXY7HUFjZEm8EvIdrEuJBzgYOqepvP+X8C6T4DPK1U9W4R+Q1wC3AhZmBnJnAI00xXYJaqPiMirYB3gDPOP//8kIULFxITE4OqMnnyZD766CPCwsKYM2cOpx4n+nh9h2jLXb2aHX+8ErxeJCSEE+bMrvDDGRUQwI+/u5j8jRvB40FCQ9HCQqIuGEHck0/WyBZL08WGaGtYVEYszwJWAD8BTvQI7sX0Wy4ETgB2AONU9aAjrs8BvwHaAOmYPspngVeB1RgvtCcQAjzy6KOPPnTw4EHy8/N5++23yc7OZtmyZRQVFTF58mRWrlxZoY1VFcuackOrVkxuE4vLEd5vcnK4ec9uCit4lu5Dh9gx4VoKfv4ZvF5c0dF4MzPp+PjjRF/0mxrbZGl6WLFsWNRZ8F+nad7Bd8oRZsT8fWA58AdgE7Bsz549d5922mn06dOHTp06ER8fz+LFi1m5ciU9evRg2bJldOjQ4Zhl+cOz3Dnh2tJJ5l4vQXFxtL3rLiIvGHGEIBeX6T50iB1Xjadw2zZQxRURAS4XJ37wPkHt29fIJkvTw4plw6LORqKPMeXoVKAL8DkQBUQCk9u3b09aWhrjx49nz549DBs2jIyMDFJTU4mPj2f37iO7PGfNmkVSUhJJSUmkpaXVVTWOSnFHeuzkyXSe9wYnzJmNKzyc3bfdxs7xV5vm9lEIjImh89w5BDnvEfdmZ+PNySH13vvQ4pBvFoulQVIv03acQZxTgTuAQlVdAGQCvYF8EcHr9dKpU+m40PFEctasWSQnJ5OcnExsbGx9VKMMvh3p4YMH0/Xf79J+6gMUbN3Krxf/ntT778d94MAR9wW2bk3nuXMIjIszJzwecr75hkNvzq/nGlgslqpQ52LpM+XoAPA6ULxyfR/QHtDU1NSSOVVxcXHs2lU6TTMlJYW4YmEBJk6cWCKSDQkJDCTmsss46dNPaDV+PBnvLWLbBSNJf+UVgso1y4PatqXLvDcI8BH5/f/4BwW//FrfZlsslkpSp2LpM+XICyxX1ScpnXL0AWbUfP/cuXM56aST2LVrF6NHj+a1115j165d7N27l+jo6Ar7KxsaAVFRtLtnCid+8AFhSUns/+fj/KdLVw5/8UWZvtKg9u3pumA+ruhoALSwkN233YZWIQqKxWKpP+pMLJ1R8Vcwze0kYJiIrAFigEeA6cClQOznn3/OPffcw2uvvcaoUaMICwtj586d3HPPPRVGDmnIhJzYlU4vzqTTSy9RpErKzbew89pryf95c0maoLg4ur7zNhIaCkDB5s2kPfusv0y2WI7AhmjzoTYC4R5tA87CzKtcC6xxtguB1sAXwBbMQE8rVVWv16s33XSTnnjiiZqQkKDff/+9VpYBAwZUOq2pcv0SCJr++hu6aeAg3dDrFN0zdaoWHTxYcj3/l190Y0If3dCjp27o0VNzfvih3m20NDw2bNjgbxM0PDy8ZP+KK67QJ554wo/WHMn8+fP1uuuuO+L8r7/+qr17967w3rFjx+p7771X6bLqTCyruNWIhi6WxWW6Dx3S1Acf0g2n9NZNpw3U9Dlz1FtYqKqquevX64aevXRDj566sV+iunNz691OS8OiOmKZ88MPmjbzxVr7wfUVyxkzZuiNN96o6enpOmbMGO3Tp48OGjRIf/zxR1VVnT17tt58882qqrpw4ULt3bu39u3bV4cMGaKqquvWrdPTTjtN+/Xrp3369NHNmzerquoTTzyhvXv31t69e+tTTz2lqkbsevbsqddff72ecsopOnz4cM0t951YvXq1durUSdu0aaP9+vXT3Nxc7dy5s6alpemll16qLVq00H79+umdd9551Lq9+OKLeskll1T6WdTZPMsqUiMj6nueZVUpX2bB1q3se3Q6OV9/TXDXrrSb8hcizjmH7G+/Zdc1EwAI7t6dk/5j3w7ZnPGdZ+mvEG0RERFkZ2fjdrv5/e9/z8iRI9mwYQNt2rThgQceYOnSpfz5z39mzZo1zJkzpyRcWp8+ffjkk09KQrS1bNmSSZMmMXjw4DIh2jZs2MA111zDt99+WxKi7Y033iAmJoZu3bqRnJxMYmIi48aNY/To0WVCtAFlygTo0qULycnJZGdnc9FFF7Fu3bpj1m3YsGGsWrWKXbt21U6INkvtE9KtG51efon4GS+A18uu//sTO2+YSFBsLB2eeByAwi1bSH1gqn8NtTQqbIi2ylPnIdostYeIEHnuuUSceSYH573JgRde4JfRY4i54gpaTZzIwVmzyHjrLVr070/M2DH+NtfiZ2yItkYUos1SN0hwMK0nXEP0mNGkPfMsh+bNIyAykuCTu1O4eQt7p0whpEtnwmx0dctxsCHaSvFbiDZL3RPYqhUd/j6Vru/9m5BevSjcvAUCzW/Zjiv+SEEtNkEsTRcbos3glxBt9USTH+CpKudFRPCX2LbEBwejqhR4vYz4ZRsHqrCGvHPnzrXaz2OpX2wgjYaF9SzrCa3idKrPDx/mvE0baXnF5YgILQIC+OqU3uSnpFQ6jx07dvi72hZLk8H2WTZgXMHBdPjb35DAQA699joUFfHL+cOJuepKAlu1ImzQoDptblksllKsWDYC2k2ZQm7yKgo2bABVI5wAgYF0fv01K5gWSz1gm+GNAHG56DTjBaRFSNkLbjfpL7/iH6MslmaGFctGQlC7dnR46OEjzhf+asO6WSz1gRXLRkT0Rb8hsG3bMueCu3b1kzUWS/PCimUjI+baCWWOwwZU/OZLi6Um2BBtpVixbGwUFJY5zFn5nZ8MsTQHipc7rlu3juDgYGbOnHnMtKNHj2bKlCn1aB188cUX9OnTh9WrVzNkyBA++ugjWrZseVyxXLp0KSEhISxevLjSZVmxbGSEDTwNCQ4uOS7YVHEkGkvzInf1ag68OIvc1atrPe8hQ4awdetWDh48yNixY+nbty+DBw9m7dq1gIkAdMsttwDw9ttvk5CQQL9+/Tj77LMBWL9+PQMHDiQxMZG+ffuyZcsWAJ588kkSEhJISEjg6aefBmD79u306tWLG264gd69ezNixIgj1oavWbOGu+++m/fff5/ExETy8vLo0qULBw4cYMqUKWzbto3ExMQjVvAUM2rUKObNm1fp+tupQ42MsP79OWHOHHZecw1aWIh73z6Kdu8myOc9RZamh79CtBXjdrv5+OOPGTlyJA888AD9+/dn0aJFLF26lPHjxx+xpnratGl8+umnJSHawATXmDx5cpkQbatWrWL27NmsXLmyJETbOeecQ0xMDFu2bGH+/Pm89NJLjBs3jnfffbdMiLbExESmTZtWJkRbMdOnT2fdunUVrvVesGABq1atIisry4Zoa6qEndqf6N+NLTnOeN/GvbTYEG1VwYZoa8BUZ314RfQPDWXeCZ0B+OmJJzjv5ptqNX9Lw8KGaLMh2poNtR28Q71efk46Dc3NpUNQEPmbNxPSvXuZNLUt0JaGjQ3RVooN0WYpQVwuIkeMKDnOeP99P1pjaSjYEG0GG6LtGDSGEG11UWbO//7HzgnXAhDQqhXdv/5vGW/SH3W11B42RFvDwnqWjZiw005DnH4dz8GD5DtTOCwWS+1jxbIRI4GBRAw7t+Q4Y5FtilssdYUVy0ZO9JjSl5ll/ec/qDNNw2Kx1C5WLBs54WecAUFmUoM3O5vclSv9bJHF0jSxYtnIcQUHE35W6dwwO0HdYqkbrFg2AVqOHVuyf/jTT/EWFPjPGIuliWLFsgkQMeQsCAgAQPPzyf7qKz9bZGkq2BBtpVixrCdEpM62gPBwvs7KBMxKoTnXTLCrdyy1gg3RVspxxVJEXhWR/SKyzufcVBHZLSJrnO1Cn2v3iMhWEflZRC6otCVNnKq+Creq2x+cD4aIMDwmBvcxlnpZmjY2RJt/Q7TNAZ4DXit3/ilVfdz3hIicAlwG9AY6Ap+LyMmqauez1DER55wDIibqjNvN4c8/97dJllrEhmjzf4i244qlqn4lIl2Ol85hDLBAVQuAX0VkKzAQ+J9vIhF5FbgI2K+qCZXMu1FTH83iuZ06cVpYOKrKO5NurfPyLA2Lo4Voq0gsK0NxiDYwnuV1113HoEGDSsKaHS9E27hx47j44osBE6Lt4YcfJiUlhYsvvpju3buXCdEGlIRoGz16dL2GaJswYUJJiLZjUZOoQ7eIyHggGbhDVQ8BccC3PmlSnHPlmQPsAe4QkeTmsH65PuqY8c47pP71fkSEMyMjaeUM+lgaPzZEW+MN0TYDeBATAONB4Ang2sre7HirO4GxqppEDQNpWAwRw4YB95sDr5cLIiP9ao+lfrEh2kppMCHaVHWfqnpU1Qu8hGlqA+wGOvkkjQd2H22QCLgN6CEiaxITE/noo49KLjz66KN069aNHj168Omnn1bHxGZJYKtWtOjTp+T4t1HRfrTG4g9siDaD30K0OX2WHxb3L4pIB1VNdfZvBwap6mUi0ht4EyOerwO/BzYCNwHZwCdAEZCGaZ4HqGorQB999FFeeeUV3G43LpeLjRs3smfPHs4//3w2b95MQAVNyuYaou1oHHxjHvseeqjk+KTPPyc43r6fpzFiQ7Q1LCozdWg+ZoCmh4ikiMh1wD9E5CcRWQucC9wOoKrrgYXABmAwMNk5/xVw0MnyKVVNBOYBBQAbNmxgwYIFrF+/nj/84Q9kZGQQGBhI165d6datG999Z1/3Wlkih59f5jjLx2O3WCzV57hiqaqXq2oHVQ1S1XhVfUVVr1LVPqraV1VHF3uZTvqHVfUkVT0B+PA42bcWkbXjx49nzJgxhISEkJeXR1xcXIlAxsfHs3v37iNunDVrFklJSSQlJZGWlla1Wjdhgtq1I6THyYAZVMr8z3/8bJHF0jTwxwqeW0QkA5gICNAKYPny5SUJWrVqdVSBhFKRnDVrFsnJySQnJxMbG1v3VjciokaPBkzzv3DLFvI3b/azRRZL46e+xTIdOAkjkE8Br6tq/CmnnMK2bdsAiIuLIycnp+SGlJQU4nzeiT1x4sQSkbQcnagLyi6cylpsm+KNleYwra6xUN9i6fEZRV+EM4qenp5Oq1atALO+9OeffyY2NpZff/2VLVu2MHDgwGPnaDmC4Ph4grt2KTnO+vBD+6VrhLRo0YL09HT7v2sg1MurcJ1BoqFAGxFJAR4AbgTiRWTt0KFDcbvdFBQUEBYWRlBQENdeey2BgYE8//zzFY6EW45O1G9Hc+DZZwEo2r2b/B9/JNRZDWFpHMTHx5OSkmL75BsIdSaWvgIJtAT+6hwnYkbJtwO/dQaH9OGHH+aUU04hMDCQefPmMWrUqLoyrVkQdcGIErFEhMwPF1uxbGQEBQXRtWtXf5thcbCvwq0H/PVK2s9POom4oGDAeVXuV8uRwHppTFgsTQ4bz7IJ8x+f4AaegwfJse/nsViqjRXLJswS37WxAQFkfVj5QKcWi6UsViybMBsLCgho08YceL0cXrLEvp/HYqkmViybONG/vcjsqOLNzibbZ/K/xWKpPFYsmziRw0eUHgQF2aa4xVJNrFg2cUIT+xHQsqU5cLvJXrYMj30/j8VSZaxYNnHE5SLyQmfOqipaWMjhz7/wr1EWSyPEimUzIGpEaVNcQkLI+vB4waAsFkt5rFg2A8KSknA5L4TSwkJy/vc/3AcO+Nkqi6VxYcWyGSCBgURe4HiXquD1kvWJfV2HxVIVrFg2E3zDtklYmG2KWyxVxIplMyHs9NORFi0A0Lw88tasoTAlxc9WWSyNByuWzQRXcDCR5w0zB05QDxsU2GKpPFYsmxGRPk1xV1SUbYpbLFXAimUzIuKssxDnXcne7GwKtmwh/2f7fh6LpTJYsWxGuMLCCD/nbHPg9YLLRdZiu/zRYqkMViybGVEXjCzZD4iOtu/nsVgqiRXLZkbE0HPAeaeRJzOToj17yFu9xr9GWSyNACuWzYyAyEjCzzjDHHi9JhKRbYpbLMfFimUzJGpk6ah4QMuWZH38Mep2+9Eii6XhY8WyGRIxbBi4zL/ek55u3s/zv2/9bJXF0rCxYtkMCYyJISwpyRx4vUhoqG2KWyzHwYplM6UksAamKX54yRK8+fl+tMhiadhYsWymRJ4/vGTfvX8/3pwcspfZ9/NYLMfCimUzJahdW1ok9jMHHg+u6CjbFLdYKsCKZTMmaoTPqHhUNNnLl+PJyvKjRRZLw8WKZTMmckRpU7xozx7zfp4ln/vRIoul4WLFshkTHB9PSM+e5sDjITA21jbFLZZjYMWymeM7QV0iI8n59lvcaWl+tMhiaZhYsWzmRPq8+bFo507zfp6PP/GjRRZLw+S4Yikir4rIfhFZ53OulYgsEZEtzt8Y57yIyLMislVE1orIqXVpvKXmhJx4IsFdu5oDt5ug+HjbFLdYjkJlPMs5wMhy56YAX6hqd+AL5xhgFNDd2SYCM2rHTEtdEjmy7MvM8n78kcJdu/xokcXS8DiuWKrqV8DBcqfHAHOd/bnAWJ/zr6nhW6CliHQon+fRvFWL/4gaXjoqXvjrrwDWu7RYylHdPst2qprq7O8F2jn7cYCvS5LinCvPHGAWcJKIJFfTBkstEdKrF0Fxzr+pqIjg7t3JtEGBLZYy1HiAR8036ohvlYi8CgwBFvh4khtFZAnwCnAe4AGihw8fzsSJE+nWrRt9+vThsssuo1u3bvTt25cffvihpiZajoOIlHmZmQQHU7h1GwWb7ft5LJZiqiuW+4qb187f/c753UAnZ38OsAdwU9rv2YbSvk4XgKp2j4uL4/PPP2fLli1MmDCBTz75hC1btjBr1ixuvPHGappoqQqRw88v2S/YvBkCA+3bHy0WH6orlh8AVzv7VwPv+5wfLyICFAIZgNun3zOK0r7O9kAQgMfjIT8/HxHh559/JjQ0lL179zJ48GAyMjJITS1u8VvqitB+/QiIjTUHRUW06NWTzMWLUa/Xv4ZZLA2Eykwdmg/8D+ghIikich0wHRguIluA851jgI+AX4CtwEvA/eWyC/Tp64wBAgEOHjzI4cOHAdi9ezcdO3Zk9+7dAMTHx5fsA8yaNYukpCSSiuMxWmoFcbmIGjECRMyJgADce1LJW7PGr3ZZLA2FwOMlUNXLj3HpvKOkVeDm4mMR6VIZI6T4C1oJJk6cyMSJE0tEEyDNrjipFSKHD+fQvHkA5G/YiLRoQdaHHxJ2qp0ua7HU9woet4h0cLzVMMAlIikxMTFERkYCEBcXx549e4hzRmdTUlJK9n2ZOHEiycnJJCcnE1vcfLTUiLCkAQS0bGkOCgtp0SeBrI8/QYuK/GqXxdIQqG+xzAKudrzVr4GvVDU+ICCAFi1aoKqcfPLJ5OXl0b59e7799luio6Pp0OGIqZqWOkACA4k4/7ySprgCnkOHyPnWvp/HYjluM7y6ON7jUKCNiORiBnzCgakicjuwyUm35bzzzuPcc8+lW7duhIaGMnz4cLp160ZYWBizZ8+uKxMtRyFqxAgy33kXgPy1P+GKiiTrww+JGDLEz5ZZLP6lzsSygr7OoyavKzssVSN88GBc4eF4c3KgoIDQgQM5vORzvFPzcIWG+ts8i8Vv2KhDljJIcLB5Va7TFPe63Xhzc8lebt/PY2neWLG0HEHkiOHgLHXM++EHAmNjybQT1C3NHCuWliOIOOsspEULc1BQQGj/RHKWf2Xfz2Np1lixtByBKzSUiLPPBpf5eHgLCtGiIg4vWeJnyywW/2HF0nJUIkeMAGepY87KlQR16mSb4pZmjRVLy1GJGHoOBAWZg/x8QvsnkvvtSor276/4RouliWLF0nJUAiIiiDjjjNKmeG4eqHL4E/t+HkvzxIql5ZhEjhhe2hT/+mtCevYk80MbQd3SPLFiaTkmEcOGlXiWmpdHaN++5K9dy77HHiN39Wo/W2ex1C9WLJs4IlLtLahVK/53+DBuVVSVxa++CsCBV2ez6dLLSAwNPeKeLl26+LfCFksdYcWyiaOO0FV3G/vYdAIdITwzJgYAlwihgYF8/vQzR6TfsWOHn2tssdQNViwtFRJ5/vmlAYGLiiDQCScgQtjA0/xnmMVSz1ixtFRIUNu2hPbvDwEBAEScfTahp54KHk+VgjZbLI0dK5aW4xI5fDh4PADkrPyW+JkzCGzbltS/T0Pdbj9bZ7HUD1YsLcclcvjwkn3NyWX/P/5Jy8svo2DjRg69Od+Pllks9YcVS8txCY6Po8Upp5RMI8p85x0OzHyRFn37kvbMMxTts6t6LE0fK5aWSuG7VhyAggLCBgxAi4rY/9hj/jPMYqknrFhaKkXkiBFHnFOg9cSJZH30Edlff13/Rlks9YgVyyZOTSal+24tTjqRHI8H1dI3gKyeOZP4Wyexo7CQFVeNJ9hlP06Wpov9dFsqzfe5uUDpRPf44GA6BAby4L69dAkO5rpWrfxsocVSd1ixtFSaZ9MPICIUeL2ICKrKtPYd+F9uLp9kZfF/rVrTqTism8XSxLBiaak0ISJ4VQlxuVBVglwuTgsL45LoaKan7ceNcl/bdmWa6hZLU8GKZROnpmvDfbfPn34Gl8tV0o8JgMvFgyd1IyUlha733svZERH29ROWJokVS0ulCRt4GhISUnIc3KsXeL148/LYO+1BYv74Rzbl57PvkUfNe8ctliaEFUtLpQnr358TZr9Km5tuJCAmBvLzkbAw8HrJ/uILsr9YyrR9+3Dv3Uva8y/421yLpVaxYmmpEmH9+xN76610/Oc/Kfz1V8JPP91ccLnY++CD/FpYQPQlv+fg3Lnkb97sX2MtllrEiqWlWkScdSYxV1xO9tKltOjXF7xePOnp3NW2LW3vuIOAyEj2/n0a6rvqx2JpxFixtFSbtnfeSdAJnXCnpUGLFgBcHN2Sgo0baXvnHeStWkXmovf9bKXFUjtYsbRUG1dYGB0fnY577z7C+vcHwKNK6t8eIHLUKEL792f/P/+J+9AhP1tqsdQcK5aWGhF2an9aX3ctuf/7HyE9exIgQlFKCgeee572Ux/Ak5VF2lNP+9tMi6XGWLG01Jg2kyYRcvLJuNPSyHf6KA/OmYMWFtHqyivJePtt8tas8a+RFksNsWJpqTGu4GA6PjYdT1YWmwryS86n3ncfrW/8E4GxsTaquqXRUyOxFJHtIvKTiKwRkWTnXCsRWSIiW5y/MbVjqqUh06JXL2JvvpnE0DCCTjwRVCnYvJmMBW/R7t57bFR1S6OnNjzLc1U1UVWTnOMpwBeq2h34wjm2NANaX38da/Ly8Bw4AE5AjbTnniO4+8mEn3WWjapuadTURTN8DDDX2Z8LjK2DMiwNEAkM5J7UPWhhISFdu5qTHg97//Y32t13r42qbmnU1FQsFfhMRFaJyETnXDtVTXX29wLtyt9Urvle0KdPH/r06UNUVBTdu3dn6NChDB06lO7duzN8+HAO2aknjYYdRUW0veMOCjZvJjA+HlTJW7WK3JUrbVR1S6OmpmJ5lqqeCowCbhaRs30vqonVdax4XbMBN8CXX37JqFGjuPfee9myZUtJlJstW7Zw3nnnMX369BqaaalPYv54BWGnD8aTng6BgQDsfewfRP32twR1PoF90x7EW1joZystlqpRI7FU1d3O3/3Ae8BAYJ+IdABw/h6rk+o1p58zFeD999/n6quvBiAlJYWUlBQArr76ahYtWlQTMy31jLhcdHzkESQggKC4OHMyP5/90x+l/f33U7hjBwdfecW/RlosVaTaYiki4SISWbwPjADWAR8AVzvJrgaOtt6tpPkORIwYMYKtW7fyn//8B4D09HTS09MBaN++Pfv27Su5cdasWSQlJZGUlHSUbC0NhaAOHWh3330U7dhBYLt2oEr2l8vwHj5M5MiRHJj5IoW7dvnbTIul0kh1o1qLyIkYbxIgEHhTVR8WkdbAQuAEYAcwTlUPlrs3TlV3i0hb4Mvly5efctFFF9G1a1f+9a9/MXr0aESkpK8yJibmiH7LWbNmMWvWLADS0tLYsWNHZe2u90je/ijTX+X6lqmqpNwyiZwVK1CPBzweXC2j6Tz3NXZcfjmhpyXRaebM0kDCFksDptqepar+oqr9nK23qj7snE9X1fNUtbuqnl9eKJ00vs33t7/77js6dOjA+eefz3fffUfr1q1p5bz8KjU1lbZt2x5R/sSJE0lOTiY5OZnY2NjqVsNSh4gIHab9HVd4OIHO/8ibkcnBObNpc+skcpZ/ZaOqWxoN9b6Cp1zzPRYYmZCQwMiRI3nnnXdISEggLi6O+Ph4AObOncuYMWPq20xLLRHYujXtp/0d9969BDg/gJnvLSL4xBMJ6dHDRlW3NBqq3QyvdoFlm++hQGTfvn3bFxQUUFRUhMvlomPHjqgqqampdO7cmYULF5Z4mkcjKSmJ5OTkypZvm+F+KHPPX/5C5ocfmt5qr5eAdu2Im/4oOydcS6trr6Xd3XfVq50WS1Wpd7E8BjUywoplwyn3WGV6srL4ZfQYtLAQz0HTMxNz9dV4c7LJfG8RXd/7Ny1OPrlebbVYqoINpGGpFwKioujw8EN4Dh7EFRUFwKG5c4kaNao0qnrD+OG2WI6KFUtLvRFxpnkVhffwYXBGwPc+/Aixt002UdXfW+RfAy2WCrBiaalXil9F4YqIAKDol19wHzxYElXdk5HhXwMtlmNgxdJSr7jCwug4fTrenBwkPByAA8+/QKvrr8OTlcX+J5/ys4UWy9GxYmmpd8L696f1ddehxVOGPB7SX5xFzJV/tFHVLQ0WK5YWv9Bm0i2E9OiBhIYCkL92LYHt29uo6pYGixVLi18ofhWFFhUhzmt0055+hjY3/slGVbc0SKxYWvxGi549iZ00Cc133ttTUEDm55+XRlXfb6OqWxoOViwtfqX1ddcS2q8fEhwMQN7X3xA+5CwTVX26japuaThYsbT4FQkMpONj08HlKhHMtGf/Rcz4q8j66CNyvvnGzxZaLAYrlpZaR0SqtIV07cqDO7ajhYWoKt7sbF5/4kl2FBay/MorCXa5jptHly5d/F1tSxPHiqWl1il+LUhVtjfS0wk7fTASFISI8JvoaJLuvpuuwSHsef75495f2XimFkt1sWJpaRAUv4rCFRpa8t6e9NdeI/L8821UdUuDwIqlpdapajO8eAvu2JG7N/8Mbrdpjh88yJx33iY7L5dXTz+jwnstlrrGiqWlQfFBVhafHz6M1zm+OLol72Zkck5EBOc768ktFn8Q6G8DLE2PmoZac6en88tvR+PJzEQ8HiYkJBAYEcHzrVoRc/nlhJ91JmH9+5e5x3qXlrrGBv+tB5pb8N/a4LyICP4VF4+qIiIsPZzFsMgovKoUqjJh105+LJ7M7tBAPsuWJopthlsaJF9kZ7MoM7PkV3RoRCReVVwihIhwa5tYwsR+fC31h/20WWqd6kwdOtp298+bCG7fHlwuXCK4XC4QQQICOD08nNWnn87BN9/EW1jo7ypbmgFWLC0NloCoKDo++gh4neEeVbOJ0P7BaYR07crev0/jl9+OZnhEpG2GW+oUK5aWBk34GWcQc8UVZU96PGQvW84Jr79G/AsvQGAAz8TFseOyy8mtZN+1xVJVrFhaGjxt77yjZN14MTkrV1K4fTuRw87lxEWL+OveVIpSU9lx5VXsuvEmCrZu9ZO1lqaKFUtLg8cVFkbEBSPKnNPsbH4ZdSHbRo8h57vv+HdmJid9+gmxt91G7vff88voMaTefz9F+2yYN0vtYMXS0iho0a17mWMJCcEVEUHh5s3suvY6vjzxJDI/+IDWE2/gpCWfmVdULHqfbRdcwP6nn8aTne0nyy1NBSuWlkZB2MDTTET1gAAkKIiQk0/Gm50NAQG4oqJoGxjI3gem8nPSaaS//Apt//xnTvpoMZHDhpE+80W2DR/BwddeR+3IuaWa2Enp9UBzm5ReV2Xmrl5N7nffEzbwNML696dw+3YOvbWQjH//G29mJhIWhublmRHzgAAihw+n3X334t67j/1PPEHut98S1KkTsbdNJmrUKMRlfQVL5bFiWQ9YsaxbvPn5XNyuHU9dMJL8tWshIMBc8HgACO3fn7b33Yv30CH2P/4EBT//TIuEBNreeSfhgwfVq62WxosVy3rAimX9lZu3bj2HFswn68PF5t0+gYHgvCkyqPMJxP75z2huLmnP/gt3airhZw+h7R130qLHyfVus6VxYcWyHvC3gDT1Mo9Wriczk8z33+fQ/AUU/voruFwlk9sDWrYk5uqrkcAA0l96Ge/hw0SPGUPs5FsJ6tCh3m23NA6sWNYDDUVAmmqZFZWrquSuXMmh+Qs4vGRJ6WoggOBgokaNIiAygoy3FoIIrcZfReuJEwmIiqpH6y2NASuW9UBDE5CmVmZxuccjNiCQS1pGc2nLGNoGBpZENPKokpybS5bXw3kRkRz2enkxPZ03Mw5RWEFdOnfuzPbt22uxFpaGjBXLesCKZf2UW1kCgKEREVzRMobTw8NLRFNV+bWwkGyvl76hoewuKuLZA2l8mJV1zA9oA/n+WOoBG/zX0mSojnAV/Porhxa8RcbChZCXx4khIQC4Wrakc2gojwUF8fSw84geOxbNzy+ZtgQ24HBzw4qlpclQE/FqIcKoyCgmtGpFt5AQPIcO4c3IIN/rJWfdOgo2bjSh44Avs7NZm5/HqMhI8n78kaBOnQiIiakX8ezSpYtf3mRpuxzqsBkuIiOBZzCtnpdVdXoFyW0zvImU2xia4ccjoUULromJ4YLIKAKcfH2b6m5VgspNaJewMILj4giKjycoPp7g+DiCOnUiKM7su8LDa8W25vRZamjUiWcpIgHA88BwIAX4XkQ+UNUNFd33ySefMHnyZDweD9dffz1TpkypC/MsTZTa/jJ7MjI49PY7pD35JMVSLCIElRNmt9dLUXY22Zs2IT//TLAIgeXSHPK42VVYxO6iIlKKikgpKiSlyBynFhVRVKuWW+qCOvEsReR0YKqqXuAc3wOgqo8e4xb1eDycfPLJLFmyhPj4eHr27ImqEhAQcFzhtJ5lwym3KdZ11003k710aclxQGwsQR07sPn7ZKICAghzuQiuhGdb3IwXynrCXlUOe72kFhWxvbCQXwoL2VFUWCKsf4xuyfDISJYcPsxT6Qea3PNtLNSVWF4CjFTV653jq4BBqnqLT5qJwESA8PDwAfHx8aSmptK9e3dUlR9//JHWrVsTFxfHpk2bcLvd9OjRg+DgYDZu3EheXl7JP8+KZcMptynWNXf1anZcNd6sBAoMpPPrrxHWv3+ZMr0FBXgOHcKdno774EHce1IpTNmFOzUVd1oa7vSDeLOy8GRnl65frwaphYWkuIvwKLhV8aBmH8XtnDP7PsfOuSLnXJEqReqlyLmvyKsUOvcUepUiJ22hKmeHhTMoPJxvcnJ4/JuvHZEXzB85ckPwccNN+nLX5Kj3OHkW3y/CHUmnkVhYyNLsw7x/+HC1nldVn3KnTp1Yu3Ytme+9R/aXy4gaNZKYSy811fGXWFZ0j+OZvgh8paq3iMgM4DxVPdkR2b85t0UDPwO9gI2++Q0YMGBAZe3duHEjvXr1KjletWoVR7s9LS2N2NjYymZbY45lR33gW1d/2uFLXdhR2f9pN4+HXh4P723ZQmDv3tUur2jfPjxpBwgWIcrlolWb1sS3bElbr9JGvcSoEu31EqkQCoTgaEe1S7RUl2JlfDU4mGXBQQ2mGV5eLC8BbgHWOWL5LEYseztprwKuBSKd23tgRLMNUPzJbwGUfVfqsals2qrkGQw05nhgFdU1EHBXMb+G+jyq8j+tTr2PRqWfRbvAwKA2AQFBxccHPJ6ifW53dbs4WwD5QRASYMYVAPCoeoqgoHzizkFBIREuV4CRaiXb6/XsKCoSKv+8KiRYJMRlBoAB8IKnULWggrKPsLECKv1/LW9H+8BADXe5pHzZdTV16Hugu4h0BXYDlwFXVHwLu4FOPsfhzrljsb4iT7UqiEiyqibVVjonbY6q1s4QqB+oqK5VeQ4+9zTI51HF/2mV632MfPzyLKprv+99tfUMasOuus6zfB51Ipaq6haRW4BPMYr9qqquP85tvgK7D+MtfuBcC3K2YuKpWEiryqxazMtiaco02+9KnU1KV9WPgI+qkL68wLqBXBEJBs4AQqvoqVbF1sp+AJrTB6W51LW51BNqoa5V+K74m1q3s0Gt4PEVWBG5EB/PFFhN1TzVurCvKv+Af9eZIfXAcepanQ9ig3weVfyf1tYX0C/PogZCV9cCWev515Kol8mjoQTSsFgslgZNs38JiYiMFJGfRWSriNglQxbLMRCRliLyjohsEpGNzqyXZkOzFkufZZmjgFOAy0XklKOkaxaCKiKdRORLEdkgIutFZLK/baprRCRARFaLyIf+tqUuqSWhewb4RFV7Av0oN7e5ISAitzuf3XUiMl9EWtRW3s1aLIGBwFZV/UVVC4EFwBjfBJUV1HL33Ccihc72cR3ZXhe4gTtU9RRgMHCzb12rKqYiMlBEDolIgYjki8i7dWx/dZhMJb70tSmqIhIkIrkisq+meVWBagmdj8huBi4H1gOoaqGqZtTUqNr0VkUkDrgVSFLVBMz4xmXVyOeogtvcxTIO2OVznOKc8+W4guqLiAQBUzFBRGKAoSLy29o0uq5Q1VRV/cHZP4z5Qvk+jwrF9CgUADeragjQFfhtQ3oWIhIP/AZ4uRLJKyWqleQdYH8t5XVcRCQaOBt4BaosdM8AnwDjgB+BSc6PxssiUhtzRWvbWw3EzJwJBMKAPVW5uSLBbe5iWRkqI6i+XANkqupyVc0BlgM31515dYOIdAH6AyuLz1VCTMugqj+q6pvF9wLpGO+8ofA0cDfgrShRFUW1QkQkCRgCPFvTvKpAVyANmF0VoSsnsoGYz8MzqtofyAFq1CVVQxE/AlXdDTwO7ARSMd/Dz6qR1VEFt7mLZflVQ7Ux2b0HcMDneDvQsYZ51isiEgG8C9ymqlnHSNOFcmJ6nDzPwixFfaOWzKwRInIRsF9VV1Ui+dNUQlQrySJgUi3lVVkCgVOBGVUUuhKRdbY8YJ1z7R0nz5pQLRE/FiISg2n1dcV858JF5Mqq5FGR4DZ3sSxZNeRMfr+M0lVDxdSFoDZYnG6Ed4F5qnrU+YCVEdNy6dth5sg+4XwYGwJnAqNFZDuma2WYiBwh5FUU1QoRkb8DGao6r6Z5VZEUIEVVi3/YKit0viLbB8gC/ulcOw+oMD5tFfOvDW/1fOBXVU1T1SLMfNYzqpJBhYKrqs16Ay4ENgPbgPuOcj0Q+MV5eMGYfpveFeR3A3DA5/gTTJ+M3+taiWchwGvA0xWkCcII358rmWcoxtN+39/1q8DGocCHx7j2KEZstgN7gVzgjWqW8w2m39cNeDCBbX6ppzquAHo4+1OBf1binvbAdp/jCUAGsBbjIcfU0Kby+Q8BFtcgv0GYAagw57M8F5hUxTz+ALziczweeEFVrVhW8gFWKKjl0oYARc4/PhzTdBnt7zpUsp5nOV/gtcAaZ7vQ5/pxxbRcfuI8s9X+rttx7DymWFYnXSXLvA3YV491TASSqyp01RHZKtpVq/kDfwc2YboLXgdCqnj/MQXXbx/Qprxh4m0WOqL5mb/tqcV6VSimR0l/o5M+z2f7m7/rUYP6N1qxrIGd1RLZhpJ/NW06quDa5Y4Wi8VSCZr7AI/FYrFUCiuWFovFUgmsWFosFkslsGJpqTVEJLsSaV4uXiIpIveWu/ZNbZRRm4jIMmfVTV2Xc6uzNrpGczBFZI6Yd1jVm+3NBSuWlkpTG2KoqteravFk5jLXVfWMpiSGznK54v3jieFNwHBV/eNx8rRi6CesWFpqFTWvP24rIh5gmhNNZp6IiCOGy0QkSUSmA2HFAiIi9xYLpYhEiEiGiPwgIh4ROWrgEp/0C0Rkm4i8JCL7ROQzEQl1rmUWC4qItHFW7CAi14jIIqAv8J2I3CIif3aW3X0rIq18irpKRNaISI6IXO3cHy4is0XkO+eeMT75fiAiS4EvfPK4CRNcZZWTz1YRuc25ZyZwIvCxmIg3JcIqJtrR42Ii4KwFejr3DMBMu3ldRD4VkQ5HeUSnAotF5E0R2euU+ZOI3F7pfyhWoEvw95wmuzWeDch2/g4FlmGWzW0C5mEm8GY75/8PM7/Sg5mLmYaZo1l8fQhGSDzAT5jlZdk+WyAQVVwmZvXMRuAl557PMCuDivP7LWZVzFDMmuuFmPfOLwIOYgIh3ALcj4mE9C0muMlWzKToQiff3ZgoU09h1oK/ilnid8CxcTXmZXofFNvk2NgSs2ghHJiPmV+7EbMcFGCmU8YWx5avnGewHhiAWYtc6BxPcp7pKEwQlh2YlUPxTl7zgUsxq4G+BpIwy3S3YOYFHgJmO2ndwMNOGQcxkXQAWlbx/z4HuMTZX1acT3Pb/G6A3RrPRlmxzMSsk3cB/ysnhv/nfFGznbS7ge8cIduHWSUR5QjUec4X3ONsRZgllYWYicrF5xWz1NDjCNjXPuX9hAl88IyTrsi5VuAcX+jYm+vke8C5/7Bzv9u59qNz/Q3H5kJnS8GsW/4vZmK918l7m1N2oVPePufaQUfkMjCCfNg5/yAwzSnzGaceHufeg8AJGNH1OuffwKzB/8Cp31LHjlTnegHws/P8iif/Z2IEd6ZzLhXT3ZEHvOU8i7nOM8jHiPck5/86ACPQqzBLWjs45+dQTiwxocvmYAT6J+B2f38+63qzzXBLdflOVVNU1YvxHrtUkLYNxiPKBVpg4hY+ghHaF5xz+ZgvdAHwR4ynOsA5t9fZ3M75RzDBTXw/v4UYMVLn/PeYpZmK+XJnYoTlAEawEp1rYMQpCBiNiTP5eyDaOSeYJawAHZzj/ZQK1rlOvf4EfO7suzChx8IxwRg6YwSzv4+9Jzh13u7YFOHUa5qTdy5wCSY4xOlAW2Axxkt/AvPj871zTwhGnOdi4hdcpap/cuo1S1UfwaySWQXchfGSlzllngLMcwKo/AsjigMwXvXDHJtEIE5VE9QE2ZhdQdomQYN6u6OlUVHgs++h4s/SPoz3A8aLGonx/tyYQAVvYcK3FRMNqKoWiYlU39FJX4ARq1Anz7ZOHsUUv0LgIEYciz3LJIxwnIhZ83unkzbX5958jNfmxQi0YAQoHyPE8zABgIu9waWYoAtRjj1TgVYYsVKnnCJgk6oeFJEiJ6+xGI+zOEr6r5h+0zyMF57o1KsQI8qvYmJprsdE0PkF4+nGOrYMxXh5BcAFTvm9feqFiLRxdpdivOMVmP/X+ZjluF4RSQASgCUigpNnKsfmF+BEEfkXRsSrEzeyUWE9S0tdUoT50nnKnQ+l1DtbhPG8fJkHBIjITxjvrriJuxwjRIGUeoXbMR4cGE8MygqoG9Psj3fuS6N07b44aVxOfoMwIuvFNE8jnete4A6Mh/yxc1zsSX3s2LgL07QucOzfiRGuHT62ZGCargMwQrsa0+8b4VOfazFdAPswfZMPYcT/d5guiw5OnS7BiP9ox/7nVLUrptmfX+55xmFEeB7Gk1+J6XL4E6UBjQVYr6qJztZHVUdwDFT1EKaFsKxcPk0WK5aWWkNVI5zdVZgv7CyMZxSkqnOca+9hPJEkjCf2PWZAw415DQeqegDIc5p3Hky/nRtoTanAFfM4pR5mG4zopGNEuJgZGBHchWmq98R4lhk+aQqAx4DrMOJ8rVNWOKbJfwAjoCdgPLoxGKGe4RyvxwjjjxiPbaXzDNLLPaMnnTp/iRG+XzAebQvMj0hxvMhrgeecZ5ng/J3ipHGp6hpMf+HtTp1vcUbLT3BsAuOtblfVH537/+jYthDjfT4AnOqM/P8MxIrzDhwx7wkq46H64nirLlV9F/grNQ8E3OCxYmmpNMViqKrLVPUin/O3FIuhqg5V1WRn/y/A1Zg+TV8xzVbV0zH9aNNUtRdmQGIt8H65Yosw0ecV4x1uwoyKf+fkucnJ50pV/StGoLth5ize4pNPD4x4PImJOZpebC9GaN9y+t++c9IXYsTxfkx/66vO+R8xInoLxnNsgxHiIIwQ98I0vRMo6+HdWe44BfNj8QFGbKf41K+j8zxSVbUfcB8m0O7LmIC2U0XkR2CBqr6GeUVFPNAd8wPg682WJw7T9XE2RtCjgCvUvF/qEuAxJ+81VBw4Nw5YJiJrMANR91SQtklgow5ZLBZLJbCepcVisVQCK5YWi8VSCaxYWiwWSyWwYmmxWCyVwIqlxWKxVIL/BymtdE9ft3P6AAAAAElFTkSuQmCC\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": "b3dd9489-7d47-472d-afd1-97242e19a9ae",
+ "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": "5e859532-2f2b-459c-b77d-64d8c3c63ed2",
+ "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",
+ "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": "22945792-4814-4c2c-875e-4627b612832d",
+ "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": "fd6c3580-aeee-4d8b-b041-56c3e3b38bf5",
+ "metadata": {},
+ "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": 9,
+ "id": "727cab2e-b2df-49be-a2e7-7ed958e7eee9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "timelapse = imread_dask('E:Andrey/20220111-MIC-resistant/timelapse-30min/0ng-TRITC-19h.nd2')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "9ada8adb-0e39-4d55-8b74-16c888080c58",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "timelapse = timelapse.rechunk((1,1,1,1000,1000))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "cf3e5d5d-5e3e-4840-ac39-ad30cb0f2517",
+ "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",
+ " <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" />\n",
+ " <line x1=\"0\" y1=\"1\" x2=\"25\" y2=\"1\" />\n",
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+ "outputs": [
+ {
+ "ename": "MemoryError",
+ "evalue": "Unable to allocate 48.0 GiB for an array with shape (39, 1, 7383, 22392) and data type float64",
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+ "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_11096/827520790.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmean_proj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtimelapse\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompute\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\\aicsimageio\\utils\\dask_proxy.py\u001b[0m in \u001b[0;36mcompute\u001b[1;34m(self, **kwargs)\u001b[0m\n\u001b[0;32m 63\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mcompute\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mAny\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 64\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_file_ctx\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_file_ctx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclosed\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mnullcontext\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;31m# type: ignore\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 65\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__wrapped__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompute\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 66\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 67\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__array__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\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[0mAny\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndarray\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\\dask\\base.py\u001b[0m in \u001b[0;36mcompute\u001b[1;34m(self, **kwargs)\u001b[0m\n\u001b[0;32m 286\u001b[0m \u001b[0mdask\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbase\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompute\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 287\u001b[0m \"\"\"\n\u001b[1;32m--> 288\u001b[1;33m \u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcompute\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtraverse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\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 289\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 290\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;32m~\\miniconda3\\envs\\nd2\\lib\\site-packages\\dask\\base.py\u001b[0m in \u001b[0;36mcompute\u001b[1;34m(traverse, optimize_graph, scheduler, get, *args, **kwargs)\u001b[0m\n\u001b[0;32m 570\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 571\u001b[0m \u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mschedule\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdsk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkeys\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[1;32m--> 572\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mrepack\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpostcomputes\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 573\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 574\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;32m~\\miniconda3\\envs\\nd2\\lib\\site-packages\\dask\\base.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m 570\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 571\u001b[0m \u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mschedule\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdsk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkeys\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[1;32m--> 572\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mrepack\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpostcomputes\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 573\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 574\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;32m~\\miniconda3\\envs\\nd2\\lib\\site-packages\\dask\\array\\core.py\u001b[0m in \u001b[0;36mfinalize\u001b[1;34m(results)\u001b[0m\n\u001b[0;32m 1162\u001b[0m \u001b[1;32mwhile\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresults2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mtuple\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlist\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 1163\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresults2\u001b[0m\u001b[1;33m)\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[1;32m-> 1164\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mconcatenate3\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresults\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 1165\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 1166\u001b[0m \u001b[0mresults2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mresults2\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[0m\n",
+ "\u001b[1;32m~\\miniconda3\\envs\\nd2\\lib\\site-packages\\dask\\array\\core.py\u001b[0m in \u001b[0;36mconcatenate3\u001b[1;34m(arrays)\u001b[0m\n\u001b[0;32m 5001\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5002\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5003\u001b[1;33m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mempty\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mshape\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[0mdtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdeepfirst\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marrays\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 5004\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5005\u001b[0m for (idx, arr) in zip(\n",
+ "\u001b[1;31mMemoryError\u001b[0m: Unable to allocate 48.0 GiB for an array with shape (39, 1, 7383, 22392) and data type float64"
+ ]
+ }
+ ],
+ "source": [
+ "mean_proj = timelapse.mean(axis=2).compute()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "id": "004aaaa5-220c-4eb8-94be-002581381441",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "tf.imwrite('E:Andrey/20220111-MIC-resistant/timelapse-30min/0ng-TRITC-19h_maxIP.tif', max_proj)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 90,
+ "id": "6837db08-bb45-47ee-9864-7f3459f07752",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "tf.imwrite('E:Andrey/20220111-MIC-resistant/timelapse-30min/0ng-TRITC-19h_meanIP.tif', mean_proj)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 91,
+ "id": "3028c8e0-a242-44f8-9cf4-f46509b4cc55",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(39, 22392)"
+ ]
+ },
+ "execution_count": 91,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "max_proj.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "df9f1fe8-9da8-40f0-953e-36c3992c043d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import napari"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "65a7b489-8bb3-4e0a-9c84-87f9901c2abf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "v = napari.Viewer()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "c04d25a6-7eaf-4d68-bb33-ec65001d3470",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<Image layer 'fluo' at 0x1a3d83ff4c0>"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "v.add_image(fluo, contrast_limits=(400,600))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "625f2ef1-23b4-4258-8997-2d458f43916d",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(1, 1, 1, 7383, 22392)"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "fluo = tf.imread('E:Andrey/20220111-MIC-resistant/timelapse-30min/0ng-TRITC-19h_maxIP.tif')\n",
+ "bf = imread('E:Andrey/20220111-MIC-resistant/timelapse-30min/0ng-BF.nd2')\n",
+ "bf.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "ca44c0dd-d47b-43bd-bd24-09ebe6d3bb5c",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(39, 7383, 22392)"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "fluo[:,0].shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "fa8e6661-463f-454c-870b-88ac57797b75",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{'tvec': array([-131.95049998, 367.1073057 ]), 'success': 0.0007202691664457523, 'angle': -0.0005945250071590635, 'scale': 1.047794112867297, 'Dscale': 8.279302929971457e-05, 'Dangle': 0.0016747301823595087, '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": "804294e8-f8ea-4075-8b6f-2d48bd151b6a",
+ "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": 16,
+ "id": "78f90d3a-93d7-4aaf-974c-01c2a0425895",
+ "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"
+ ]
+ },
+ {
+ "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/0ng-TRITC-19h_maxIP.aligned.tif', np.array(aligned_maxIP[1]).reshape((39, 1, 1, 6544, 20896)))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "beb692c5-185e-4391-8929-1bb8578c47cd",
+ "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": "60792f0f-b8de-42cf-ae89-a623a1a3c76a",
+ "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": "8246f045-55db-4ea3-92e3-153804da0d81",
+ "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": "81d25f67-ff6b-4277-8ed5-78f11c9a9f7f",
+ "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": "8cf4c469-d553-426a-860a-3f34da7714d9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "tvec = register.get_transform(bf[0,0,0,::8,::8], template16, plot=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "a32ce49e-174a-4f19-8d94-2e3563701851",
+ "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": "21f48fed-7f6d-4abf-a782-1c3b11787d16",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "tvec_scaled = register.scale_tvec(tvec, 8)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "id": "5618e588-69bf-43e3-947c-be8c8a363e71",
+ "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": "72757255-c220-4e77-873f-63d81aa0bba9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from scipy.ndimage.interpolation import zoom, rotate, shift"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "id": "419b7ef4-b31f-49f6-a23e-e4286d79c6d7",
+ "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": "1db8c60f-6779-439f-bac7-936d5dbf883a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "rfluo = rotate(input=fluo, angle=tvec_scaled['angle'], axes=(4,3))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "id": "977a66bc-4c61-4d72-90cf-42fbfeaeb32d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "del fluo"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "id": "37812a43-d279-4053-9e5a-bc99fce74b2a",
+ "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": "e7db816c-a33f-40df-829e-e271eabf0e35",
+ "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": "e9c6dbb9-13dc-4396-83d9-8d5e61d5663f",
+ "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": "d92537a8-bf3b-45ed-856f-87b775788034",
+ "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
+}