diff --git a/20221122-Madison.ipynb b/20221122-Madison.ipynb
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+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "b2008c50-e0c5-4a52-9d11-10adce12220d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import nd2\n",
+ "from zarr_tools import convert\n",
+ "import dask.array as da\n",
+ "import tifffile as tf\n",
+ "import numpy as np\n",
+ "from napari import Viewer\n",
+ "\n",
+ "from napari_plugin_engine import napari_hook_implementation\n",
+ "from qtpy.QtWidgets import QWidget, QHBoxLayout, QVBoxLayout, QPushButton, QLabel, QLineEdit\n",
+ "from napari_tools_menu import register_dock_widget\n",
+ "from magicgui.widgets import create_widget\n",
+ "from magicgui import magicgui\n",
+ "from napari.layers import Shapes, Points, Image\n",
+ "from napari._qt.qthreading import thread_worker"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "id": "a2bbd55e-cb6c-48ac-849c-d08db2191dd3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "@magicgui\n",
+ "def make_matrix(\n",
+ " Manual_ref_line:Shapes,\n",
+ " n_cols : int = 5,\n",
+ " n_rows : int = 5,\n",
+ " row_multiplier : float = 1.,\n",
+ " check : bool = True,\n",
+ " size : int = 300\n",
+ " \n",
+ ") -> napari.types.LayerDataTuple:\n",
+ " manual_points = Manual_ref_line.data[0]\n",
+ " assert len(manual_points == 2), \"Select a line along your wells\"\n",
+ " manual_period = manual_points[1] - manual_points[0]\n",
+ " col_period = manual_period / (n_cols - 1)\n",
+ " \n",
+ " row_period = np.zeros_like(col_period)\n",
+ " row_period[-2:] = np.array([col_period[-1], -col_period[-2]])\n",
+ " extrapolated_wells = np.stack([\n",
+ " manual_points[0] + \\\n",
+ " col_period * i + \\\n",
+ " row_period * j * row_multiplier + \\\n",
+ " (col_period + row_period * row_multiplier) / 2 * k \\\n",
+ " for k in range(2 * check) \\\n",
+ " for i in range(n_cols) \\\n",
+ " for j in range(n_rows)\n",
+ " ])\n",
+ " \n",
+ " return (\n",
+ " extrapolated_wells[:,-2:],\n",
+ " {\"symbol\": \"square\",\n",
+ " \"size\" : size,\n",
+ " \"edge_color\": \"#ff0000\",\n",
+ " \"face_color\": \"#00000000\"\n",
+ " },\n",
+ " \"points\"\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 50,
+ "id": "187017c9-5906-4c92-be8c-eff6ae6a01c3",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'sizes': {'T': 46, 'P': 2, 'Z': 5, 'C': 2, 'Y': 5928, 'X': 9831},\n",
+ " 'path': 'E:\\\\Madison\\\\LPYgalvstet.nd2',\n",
+ " 'pixel_size_um': 0.325028674328292}"
+ ]
+ },
+ "execution_count": 50,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "v.layers[0].metadata"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "id": "273f8bbb-bd3a-45e1-8a51-392f695b3bda",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "@magicgui\n",
+ "def crop_regions(\n",
+ " dataset:Image,\n",
+ " ROIs: Points,\n",
+ " \n",
+ " \n",
+ ") -> napari.types.LayerDataTuple:\n",
+ " manual_points = Manual_ref_line.data[0]\n",
+ " assert len(manual_points == 2), \"Select a line along your wells\"\n",
+ " manual_period = manual_points[1] - manual_points[0]\n",
+ " col_period = manual_period / (n_cols - 1)\n",
+ " \n",
+ " row_period = np.zeros_like(col_period)\n",
+ " row_period[-2:] = np.array([col_period[-1], -col_period[-2]])\n",
+ " extrapolated_wells = np.stack([\n",
+ " manual_points[0] + \\\n",
+ " col_period * i + \\\n",
+ " row_period * j * row_multiplier + \\\n",
+ " (col_period + row_period * row_multiplier) / 2 * k \\\n",
+ " for k in range(2 * check) \\\n",
+ " for i in range(n_cols) \\\n",
+ " for j in range(n_rows)\n",
+ " ])\n",
+ " \n",
+ " return (\n",
+ " extrapolated_wells[:,-2:],\n",
+ " {\"symbol\": \"square\",\n",
+ " \"size\" : size,\n",
+ " \"edge_color\": \"#ff0000\",\n",
+ " \"face_color\": \"#00000000\"\n",
+ " },\n",
+ " \"points\"\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "73b70617-11a9-4f53-9ab4-5a590c976d02",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "6befd5f7-066f-4f0d-afd7-c689b374eb94",
+ "metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<FunctionGui make_matrix(Manual_ref_line: napari.layers.shapes.shapes.Shapes = None, n_cols: int = 5, n_rows: int = 5, check: bool = True, size: int = 300)>"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "make_matrix.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "ff6cc2f0-7568-4814-a687-13cb6be23c87",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "v = Viewer()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 48,
+ "id": "c6692e35-ebff-4624-9115-68fa6585ed44",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<napari._qt.widgets.qt_viewer_dock_widget.QtViewerDockWidget at 0x1dc227a8a60>"
+ ]
+ },
+ "execution_count": 48,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "v.window.add_dock_widget(make_matrix)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "id": "14307285-af98-4148-ae1c-1b99fecddef9",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[array([[2.30000000e+01, 1.00000000e+00, 2.00000000e+00, 7.69845734e+02,\n",
+ " 2.50737153e+02],\n",
+ " [2.30000000e+01, 1.00000000e+00, 2.00000000e+00, 3.56612270e+02,\n",
+ " 9.03414632e+03]])]"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "v.layers[\"Shapes\"].data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "a5b1d00c-aa57-40f3-9977-1de23e28e381",
+ "metadata": {
+ "jupyter": {
+ "source_hidden": true
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "\n",
+ "grey = np.array([np.arange(256)] * 3, dtype='uint8')\n",
+ "red = np.array([np.arange(256), np.zeros((256,)), np.zeros((256,))], dtype='uint8')\n",
+ "green = np.array([np.zeros((256,)), np.arange(256), np.zeros((256,))], dtype='uint8')\n",
+ "blue = np.array([np.zeros((256,)), np.zeros((256,)), np.arange(256)], dtype='uint8')\n",
+ "magenta = np.array([np.arange(256), np.zeros((256,)), np.arange(256)], dtype='uint8')\n",
+ "yellow = red + green\n",
+ "\n",
+ "METADATA = {'ImageJ': '1.53c',\n",
+ " 'images': 3,\n",
+ " 'channels': 3,\n",
+ " 'hyperstack': True,\n",
+ " 'mode': 'composite',\n",
+ " 'unit': '',\n",
+ " 'loop': False,\n",
+ " 'min': 254.0,\n",
+ " 'max': 3165.0,\n",
+ " 'Ranges': (254.0, 3165.0, 60.0, 300.0, 60.0, 300.0),\n",
+ " 'LUTs': [ grey, yellow, green]\n",
+ " }\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "5b18f021-1515-479b-ae5c-1425a570bfcd",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<ND2File at 0x11c5de03400: 'LPYgalvstet.nd2' uint16: {'T': 46, 'P': 2, 'Z': 5, 'C': 2, 'Y': 5928, 'X': 9831}>"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data = nd2.ND2File('E:/Madison/LPYgalvstet.nd2')\n",
+ "data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 65,
+ "id": "a5b17ec4-e729-4d15-9316-9325052623ba",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<Image layer 'slide' at 0x1358349f7c0>"
+ ]
+ },
+ "execution_count": 65,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "v.add_image(slide := data.to_dask()[:,0,1])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "775e16d3-ae36-48bc-9f65-bbb3f5af96d8",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "id": "b05b288b-c064-45bf-b422-7aa89452852e",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[ 45. , 0. , 706.2413435 , 8902.72711538],\n",
+ " [ 45. , 0. , 565.24692711, 4917.90037923]])"
+ ]
+ },
+ "execution_count": 43,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "manual_points = v.layers[\"Points\"].data\n",
+ "manual_points"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "id": "bf0831bf-5cfd-4536-ad1c-b1343fab7184",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "time, ch, y0, x0 = manual_points[0]\n",
+ "time, ch, y1, x1 = manual_points[1]\n",
+ "n_wells_within_01 = 5\n",
+ "n_rows = 6"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 49,
+ "id": "a2e2a49b-516e-4d78-b320-5b6f2c1f721d",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(array([ 0. , 0. , 140.99441639, 3984.82673615]),)"
+ ]
+ },
+ "execution_count": 49,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "manual_period = manual_points[0] - manual_points[1]\n",
+ "\n",
+ "manual_period, "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 50,
+ "id": "056c088e-c9ad-40ac-8368-32ea99f5c595",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(array([ 0. , 0. , 35.2486041 , 996.20668404]),\n",
+ " array([ 0. , 0. , 996.20668404, -35.2486041 ]))"
+ ]
+ },
+ "execution_count": 50,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "well_period = manual_period / (n_wells_within_01 - 1)\n",
+ "row_period = np.array([0,0,well_period[3], -well_period[2]])\n",
+ "\n",
+ "well_period, row_period"
+ ]
+ },
+ {
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+ "id": "d141d0f4-af1c-4a6c-8d00-dcaa060b80d7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "extrapolated_wells = np.stack([manual_points[1] + well_period * i + row_period * j + (well_period + row_period) / 2 * k for k in range(2) for i in range(n_wells_within_01) for j in range(n_rows)])"
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+ "id": "f6cc8a56-f6e7-48de-af74-a97eb2371eb3",
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+ "outputs": [
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+ "text/plain": [
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+ "execution_count": 68,
+ "metadata": {},
+ "output_type": "execute_result"
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+ "source": [
+ "slide.shape"
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+ },
+ {
+ "cell_type": "code",
+ "execution_count": 78,
+ "id": "6debf480-a364-4f05-a432-b49fe464beef",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "filter_outliers = np.logical_and(extrapolated_wells[:,2]< slide.shape[2], extrapolated_wells[:,3]< slide.shape[3])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 80,
+ "id": "d61aa3a7-580c-4165-a59f-120b41e6062b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "good_extrapolated_wells = extrapolated_wells[filter_outliers]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 81,
+ "id": "a9a3ccff-9156-485a-9700-e060fad638b4",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<Points layer 'good_extrapolated_wells' at 0x1357f3e1640>"
+ ]
+ },
+ "execution_count": 81,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "v.add_points(good_extrapolated_wells, size=500, edge_color=\"#ff0000\", face_color=\"#00000000\", symbol=\"square\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 58,
+ "id": "0c395757-2760-41ed-8821-555f6a5b3212",
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "TypeError",
+ "evalue": "world_to_data() missing 1 required positional argument: 'position'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
+ "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_4928/3178144984.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"Points\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mworld_to_data\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;31mTypeError\u001b[0m: world_to_data() missing 1 required positional argument: 'position'"
+ ]
+ }
+ ],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "5604fad4-5a71-4102-ad26-0b4393d26fe7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "crop = tf.imread('E:/Madison/crops/crops_2ch/crop1.tif')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "d63116cc-08b5-4ecc-bd6f-f6ed9cacf645",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(46, 2, 640, 640)"
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+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
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+ "source": [
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+ "execution_count": 19,
+ "id": "3b570ba7-5e50-44a7-bdb6-81064679f2ee",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "AICSImageIO: Unsupported file format.\n",
+ "AICSImageIO: Unsupported file format.\n",
+ "read_zarr D:\\Lena\\20221122-ecoli-chp\\day2\\BF_TRITC_maxZ.zarr\n",
+ "found channel axis 1\n",
+ "D:\\Lena\\20221122-ecoli-chp\\day2\\BF_TRITC_maxZ.zarr\\.detections.csv doesn't exists\n",
+ "D:\\Lena\\20221122-ecoli-chp\\day2\\BF_TRITC_maxZ.zarr\\.droplets.csv doesn't exists\n",
+ "AICSImageIO: Unsupported file format.\n",
+ "AICSImageIO: Unsupported file format.\n",
+ "read_zarr D:\\Lena\\20221122-ecoli-chp\\day1\\BF_TRITC_aligned.zarr\n",
+ "found channel axis 1\n",
+ "D:\\Lena\\20221122-ecoli-chp\\day1\\BF_TRITC_aligned.zarr\\.detections.csv doesn't exists\n",
+ "D:\\Lena\\20221122-ecoli-chp\\day1\\BF_TRITC_aligned.zarr\\.droplets.csv doesn't exists\n",
+ "AICSImageIO: Unsupported file format.\n",
+ "AICSImageIO: Unsupported file format.\n",
+ "read_zarr D:\\Lena\\20221122-ecoli-chp\\day2\\BF_TRITC_aligned.zarr\n",
+ "found channel axis 1\n",
+ "D:\\Lena\\20221122-ecoli-chp\\day2\\BF_TRITC_aligned.zarr\\.detections.csv doesn't exists\n",
+ "D:\\Lena\\20221122-ecoli-chp\\day2\\BF_TRITC_aligned.zarr\\.droplets.csv doesn't exists\n"
+ ]
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+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
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+ "\u001b[1;32m~\\miniconda3\\envs\\nd2\\lib\\site-packages\\dask\\array\\core.py\u001b[0m in \u001b[0;36m__array__\u001b[1;34m(self, dtype, **kwargs)\u001b[0m\n\u001b[0;32m 1579\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1580\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[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1581\u001b[1;33m \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\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[0;32m 1582\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mdtype\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mx\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[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1583\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
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+ "\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 569\u001b[0m \u001b[0mpostcomputes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__dask_postcompute__\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 570\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 571\u001b[1;33m \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[0m\u001b[0;32m 572\u001b[0m \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[0;32m 573\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;32m~\\miniconda3\\envs\\nd2\\lib\\site-packages\\dask\\threaded.py\u001b[0m in \u001b[0;36mget\u001b[1;34m(dsk, result, cache, num_workers, pool, **kwargs)\u001b[0m\n\u001b[0;32m 77\u001b[0m \u001b[0mpool\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mMultiprocessingPoolExecutor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpool\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 78\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 79\u001b[1;33m results = get_async(\n\u001b[0m\u001b[0;32m 80\u001b[0m \u001b[0mpool\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubmit\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 81\u001b[0m \u001b[0mpool\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_max_workers\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\\local.py\u001b[0m in \u001b[0;36mget_async\u001b[1;34m(submit, num_workers, dsk, result, cache, get_id, rerun_exceptions_locally, pack_exception, raise_exception, callbacks, dumps, loads, chunksize, **kwargs)\u001b[0m\n\u001b[0;32m 494\u001b[0m \u001b[1;32mwhile\u001b[0m \u001b[0mstate\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"waiting\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mstate\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"ready\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mstate\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"running\"\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 495\u001b[0m \u001b[0mfire_tasks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mchunksize\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 496\u001b[1;33m \u001b[1;32mfor\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mres_info\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfailed\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mqueue_get\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mqueue\u001b[0m\u001b[1;33m)\u001b[0m\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[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 497\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mfailed\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 498\u001b[0m \u001b[0mexc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mloads\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mres_info\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\\local.py\u001b[0m in \u001b[0;36mqueue_get\u001b[1;34m(q)\u001b[0m\n\u001b[0;32m 124\u001b[0m \u001b[1;32mwhile\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 125\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--> 126\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mq\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mblock\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.1\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 127\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mEmpty\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 128\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;32m~\\miniconda3\\envs\\nd2\\lib\\queue.py\u001b[0m in \u001b[0;36mget\u001b[1;34m(self, block, timeout)\u001b[0m\n\u001b[0;32m 177\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mremaining\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[1;36m0.0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 178\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mEmpty\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 179\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnot_empty\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mremaining\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 180\u001b[0m \u001b[0mitem\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get\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 181\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnot_full\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnotify\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;32m~\\miniconda3\\envs\\nd2\\lib\\threading.py\u001b[0m in \u001b[0;36mwait\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m 304\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 305\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mtimeout\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--> 306\u001b[1;33m \u001b[0mgotit\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mwaiter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtimeout\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 307\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 308\u001b[0m \u001b[0mgotit\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mwaiter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
+ ]
+ }
+ ],
+ "source": [
+ "[tf.imwrite(f'F:/Madison/2022-09-30_Cas9induction_positions/pos{i:02d}.tif', data=dmax[:,i].reshape((dmax.shape[0],1,3,2044,2048)), imagej=True, metadata=METADATA) for i in range(dmax.shape[1])]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b91bc70-eccb-4374-a447-70d4e194d45c",
+ "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
+}