{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# tmat cycle 4\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import matplotlib.patches as patches\n", "from pathlib import Path\n", "import numpy as np\n", "import os\n", "import sys\n", "import glob\n", "import pandas as pd\n", "import xml.etree.ElementTree as et\n", "import datetime\n", "from skimage.io import imread, imsave\n", "from imageio import volread as imread\n", "\n", "import tifffile\n", "import pystackreg\n", "from pystackreg import StackReg\n", "from skimage.filters import threshold_otsu\n", "\n", "from pystackreg.util import to_uint16 # make sure version 0.2.5 (not anything below)\n", "from ims_to_tiff import convert_to_tif " ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "tmats = glob.glob('tmat_Cyc_4/*')" ] }, { "cell_type": "code", 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[ 0. 0. 1. ]]\n", "tmat_Cyc_4/Cycle_4_F169_tmat.npy [[ 1. 0. -2.29512269]\n", " [ 0. 1. 0.15694242]\n", " [ 0. 0. 1. ]]\n", "tmat_Cyc_4/Cycle_4_F170_tmat.npy [[ 1. 0. -2.24491267]\n", " [ 0. 1. 0.1469056 ]\n", " [ 0. 0. 1. ]]\n", "tmat_Cyc_4/Cycle_4_F171_tmat.npy [[ 1. 0. -2.2147398 ]\n", " [ 0. 1. -0.06205582]\n", " [ 0. 0. 1. ]]\n", "tmat_Cyc_4/Cycle_4_F172_tmat.npy [[ 1. 0. -2.24022533]\n", " [ 0. 1. -0.07363828]\n", " [ 0. 0. 1. ]]\n", "tmat_Cyc_4/Cycle_4_F173_tmat.npy [[ 1. 0. -2.19628525]\n", " [ 0. 1. -0.09471189]\n", " [ 0. 0. 1. ]]\n", "tmat_Cyc_4/Cycle_4_F174_tmat.npy [[ 1. 0. -2.18365181]\n", " [ 0. 1. -0.07930643]\n", " [ 0. 0. 1. ]]\n", "tmat_Cyc_4/Cycle_4_F175_tmat.npy [[ 1. 0. -2.55686819]\n", " [ 0. 1. -1.36060623]\n", " [ 0. 0. 1. ]]\n", "tmat_Cyc_4/Cycle_4_F176_tmat.npy [[ 1. 0. -2.60363451]\n", " [ 0. 1. -1.00754287]\n", " [ 0. 0. 1. ]]\n", "tmat_Cyc_4/Cycle_4_F177_tmat.npy [[ 1. 0. -2.60717087]\n", " [ 0. 1. -0.01167187]\n", " [ 0. 0. 1. ]]\n", 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166.55290812]\n", " [ 0. 0. 1. ]]\n", "tmat_Cyc_4/Cycle_4_F197_tmat.npy [[ 1. 0. -2.04661587]\n", " [ 0. 1. 0.0350847 ]\n", " [ 0. 0. 1. ]]\n", "tmat_Cyc_4/Cycle_4_F198_tmat.npy [[ 1. 0. -2.03614507]\n", " [ 0. 1. -0.07966256]\n", " [ 0. 0. 1. ]]\n", "tmat_Cyc_4/Cycle_4_F199_tmat.npy [[ 1. 0. -2.02474494]\n", " [ 0. 1. -1.99885497]\n", " [ 0. 0. 1. ]]\n", "tmat_Cyc_4/Cycle_4_F200_tmat.npy [[ 1. 0. -1.59723439]\n", " [ 0. 1. 1.47087705]\n", " [ 0. 0. 1. ]]\n", "tmat_Cyc_4/Cycle_4_F201_tmat.npy [[ 1. 0. -1.56450866]\n", " [ 0. 1. -0.08870236]\n", " [ 0. 0. 1. ]]\n", "tmat_Cyc_4/Cycle_4_F202_tmat.npy [[ 1. 0. -1.54181429]\n", " [ 0. 1. 0.03623003]\n", " [ 0. 0. 1. ]]\n", "tmat_Cyc_4/Cycle_4_F203_tmat.npy [[ 1. 0. -1.54170886]\n", " [ 0. 1. -0.20318978]\n", " [ 0. 0. 1. ]]\n", "tmat_Cyc_4/Cycle_4_F204_tmat.npy [[ 1. 0. -1.55400653]\n", " [ 0. 1. -0.12961161]\n", " [ 0. 0. 1. ]]\n", "tmat_Cyc_4/Cycle_4_F205_tmat.npy [[ 1. 0. -2.16003367]\n", " [ 0. 1. -0.12504599]\n", " [ 0. 0. 1. ]]\n", "tmat_Cyc_4/Cycle_4_F206_tmat.npy [[ 1. 0. -2.11531366]\n", " [ 0. 1. -0.18271148]\n", " [ 0. 0. 1. ]]\n", "tmat_Cyc_4/Cycle_4_F207_tmat.npy [[ 1. 0. -2.13240147]\n", " [ 0. 1. -0.04899578]\n", " [ 0. 0. 1. ]]\n", "tmat_Cyc_4/Cycle_4_F208_tmat.npy [[ 1. 0. -2.07926401]\n", " [ 0. 1. 0.03667246]\n", " [ 0. 0. 1. ]]\n", "tmat_Cyc_4/Cycle_4_F209_tmat.npy [[ 1. 0. -2.0648847 ]\n", " [ 0. 1. 0.20023424]\n", " [ 0. 0. 1. ]]\n", "tmat_Cyc_4/Cycle_4_F210_tmat.npy [[ 1. 0. -1.92034233]\n", " [ 0. 1. 0.21244238]\n", " [ 0. 0. 1. ]]\n", "tmat_Cyc_4/Cycle_4_F211_tmat.npy [[ 1. 0. -1.90247758]\n", " [ 0. 1. -1.11949293]\n", " [ 0. 0. 1. ]]\n", "tmat_Cyc_4/Cycle_4_F212_tmat.npy [[ 1. 0. -1.90489459]\n", " [ 0. 1. 0.16350636]\n", " [ 0. 0. 1. ]]\n", "tmat_Cyc_4/Cycle_4_F213_tmat.npy [[ 1. 0. -1.87514022]\n", " [ 0. 1. 0.05261399]\n", " [ 0. 0. 1. ]]\n", "tmat_Cyc_4/Cycle_4_F214_tmat.npy [[ 1. 0. -1.86880563]\n", " [ 0. 1. -0.03471226]\n", " [ 0. 0. 1. ]]\n", "tmat_Cyc_4/Cycle_4_F216_tmat.npy [[ 1. 0. -1.9653039 ]\n", " [ 0. 1. 0.24787049]\n", " [ 0. 0. 1. ]]\n", "tmat_Cyc_4/Cycle_4_F217_tmat.npy [[ 1. 0. -1.91928937]\n", " [ 0. 1. -0.04866028]\n", " [ 0. 0. 1. ]]\n", "tmat_Cyc_4/Cycle_4_F218_tmat.npy [[ 1. 0. -2.01792329]\n", " [ 0. 1. 0.17343293]\n", " [ 0. 0. 1. ]]\n", "tmat_Cyc_4/Cycle_4_F219_tmat.npy [[ 1. 0. -1.95313315]\n", " [ 0. 1. 0.12166406]\n", " [ 0. 0. 1. ]]\n", "tmat_Cyc_4/Cycle_4_F220_tmat.npy [[ 1. 0. -1.75310799]\n", " [ 0. 1. 0.1629827 ]\n", " [ 0. 0. 1. ]]\n", "tmat_Cyc_4/Cycle_4_F221_tmat.npy [[ 1. 0. -1.81242748]\n", " [ 0. 1. -0.22132348]\n", " [ 0. 0. 1. ]]\n", "tmat_Cyc_4/Cycle_4_F222_tmat.npy [[ 1. 0. -1.77937509]\n", " [ 0. 1. 0.0563247 ]\n", " [ 0. 0. 1. ]]\n", "tmat_Cyc_4/Cycle_4_F223_tmat.npy [[ 1. 0. -1.75041499]\n", " [ 0. 1. -0.36312293]\n", " [ 0. 0. 1. ]]\n", "tmat_Cyc_4/Cycle_4_F224_tmat.npy [[ 1.00000000e+00 0.00000000e+00 -1.82997483e+00]\n", " [ 0.00000000e+00 1.00000000e+00 -1.49099220e-03]\n", " [ 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n" ] } ], "source": [ "for i in tmats:\n", " x = np.load(i)\n", " print(i, x)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 1. , 0. , -98.97177198],\n", " [ 0. , 1. , 166.55290812],\n", " [ 0. , 0. , 1. ]])" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.load('tmat_Cyc_4/Cycle_4_F196_tmat.npy')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.7.4" } }, "nbformat": 4, "nbformat_minor": 2 }