Created
August 7, 2017 21:21
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "code", | |
| "execution_count": 1, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "import numpy as np\n", | |
| "import scipy as sp\n", | |
| "\n", | |
| "from scipy.ndimage.filters import gaussian_filter" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "def shellify(cell):\n", | |
| " return cell^sp.ndimage.morphology.binary_erosion(cell)\n", | |
| "\n", | |
| "def fake_dist(cell, blur_sigmas=[1,2,4,8,16], final_blur_sigma=2):\n", | |
| " \n", | |
| " # create shell\n", | |
| " shell = shellify(cell).astype(float)\n", | |
| " \n", | |
| " # blur at different sigmas and sum\n", | |
| " blur_sum = np.zeros_like(shell)\n", | |
| " for s in blur_sigmas:\n", | |
| " blur = gaussian_filter(shell, sigma=s)\n", | |
| " blur /= np.max(blur)\n", | |
| " blur_sum += blur\n", | |
| "\n", | |
| " # log the result and scale/shift to (0,1)\n", | |
| " fake_dist = np.log(blur_sum)\n", | |
| " fake_dist -= np.min(fake_dist)\n", | |
| " fake_dist /= np.max(fake_dist)\n", | |
| " \n", | |
| " # shift so that max has value zero\n", | |
| " fake_dist = fake_dist-np.max(fake_dist)\n", | |
| " \n", | |
| " # want inside negative outside positive, so make a mask to do that\n", | |
| " mask = 2*cell.astype(float)-1 \n", | |
| " \n", | |
| " # invert interior\n", | |
| " fake_dist = mask*fake_dist\n", | |
| " \n", | |
| " # slight blur to remove sharp transition at shell\n", | |
| " fake_dist = gaussian_filter(fake_dist, sigma=final_blur_sigma)\n", | |
| "\n", | |
| " return fake_dist" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 3, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "d = np.load('20160711_C01_048.czi_1.npz')\n", | |
| "my_cell = d['cell']" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 4, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "my_fake_dist = fake_dist(my_cell)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 5, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
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EGh/luTeMMUnGmKTw8PATDusJfHyEhwYnsPtQIW8t2Gw7jlIeY8PuXD5dlsmonq2IblzX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\nlVLKA/0fwGHV7vl+FXgAAAAASUVORK5CYII=\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x114abea20>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "import matplotlib\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "%matplotlib inline\n", | |
| "\n", | |
| "plt.plot(my_fake_dist[:,32,32]);" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "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": "3.6.0" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 2 | |
| } |
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