{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Spline modelling electron insert factors using routine measurements\n", "\n", "## Description\n", "The code here provided is for the modelling of the portion of the electron output factor that is dependent on the shape of the shielding insert mounted within the applicator. This allows modelling insert factors using only the measured factors already available at a centre. Should all outliers be removed from the data set the user might expect as low as 0.5% standard uncertainty for factor prediction with as little as 8 data points.\n", "\n", "The paper outlining this method is currently in press:\n", " \n", " > S. Biggs, M. Sobolewski, R. Murry, J. Kenny, Spline modelling electron insert factors using routine measurements. Physica Medica (2015), [doi:10.1016/j.ejmp.2015.11.002](http://dx.doi.org/10.1016/j.ejmp.2015.11.002). \n", "\n", "If you have any issues please don't hesitate to contact me (mail@simonbiggs.net), I likely will be more than happy to help. \n", "\n", "Any use of the code accepts the AGPL3+ license which includes no warranty that this code is fit for a particular purpose. Attempts have been made to make the code transparent and it is recommended that an experienced python programmer and physicist who understands the procedure outlined in the paper and the requirements of your centre identifies whether or not this method and code is fit for your use.\n", "\n", "\n", "## Installation for Windows\n", " * Download and install the relevant Anaconda Python 3.5 install from [continuum.io/downloads](https://www.continuum.io/downloads).\n", " * Install this package using pip:\n", " * `pip install electronfactors`\n", " * Installing shapely\n", " * Download the relevant shapely package from [http://www.lfd.uci.edu/~gohlke/pythonlibs/#shapely](http://www.lfd.uci.edu/~gohlke/pythonlibs/#shapely).\n", " * If you have 64-bit windows you will be looking for `Shapely-*.*.*-cp35-none-win_amd64.whl` (with * replaced by most recent shapely version).\n", " * Install shapely by:\n", " * Opening a command prompt and \"cd\" into the directory with the downloaded file\n", " * Then running `pip install Shapely-*.*.*-cp35-none-win_*.whl` (with * replaced with relevant file name).\n", " * Installing descartes\n", " * Run the following in a command prompt, `pip install descartes`\n", "\n", "\n", "## Installation for Ubuntu \n", "From a fresh install of Ubuntu, using pip to install all dependencies the following method can be used. However, of course if you prefer, system packages may be used from apt-get, or conda from anaconda.\n", "\n", " sudo apt-get build-dep python-numpy matplotlib python-scipy pandas ipython pyzmq python-shapely pyyaml\n", " sudo apt-get install python3-pip\n", " sudo pip3 install --upgrade numpy matplotlib scipy pandas shapely descartes bokeh pyyaml jupyter electronfactors" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Running this notebook locally\n", "\n", "Once the install instructions above have been followed this notebook can be run locally by opening a jupyter notebook server.\n", "\n", "The jupyter notebook server can be started by changing directory within a command prompt into the folder which contains this file and then running the following:\n", "\n", " jupyter notebook\n", "\n", "Details about what the jupyter notebook is can be found [here](http://jupyter-notebook-beginner-guide.readthedocs.org/en/latest/what_is_jupyter.html)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Explanation of use\n", "\n", "### Importing all the packages to be used in this notebook" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "from bokeh.io import output_notebook, output_file\n", "\n", "from electronfactors import generic_shape_convert, parameterise, create_report" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Some measured data\n", "\n", "For this example we are using a set of seven clinical shapes accompanied by five generic shapes chosen to fill out the data set resulting in minimal measurement required after this point. These measurments were taken for shapes on a 10 cm applicator, for 12 MeV, at 100 SSD.\n", "\n", "The generic shapes used were:\n", "\n", " * 3, 5, 7, and 9 cm circles\n", " * and a 3 x 13 cm ellipse\n", "\n", "Here this data will be inputted as a python dictionary. A (simpler?) method to import via csv is explained in detail on the [github repository](https://github.com/SimonBiggs/electronfactors/blob/master/README.md#importing-your-insert-shapes).\n", "\n", "The following series of cells is used to input this data." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "input_dict = dict()\n", "\n", "applicator = 10\n", "energy = 12\n", "ssd = 100" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(-6.0, 6.0)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "key = '3cm circle'\n", "input_dict[key] = dict()\n", "\n", "input_dict[key]['factor'] = 0.9296\n", "\n", "XCoords, YCoords = generic_shape_convert(3/0.95, 3/0.95, 'circle')\n", "input_dict[key]['XCoords'] = XCoords\n", "input_dict[key]['YCoords'] = YCoords\n", "input_dict[key]['applicator'] = applicator\n", "input_dict[key]['energy'] = energy\n", "input_dict[key]['ssd'] = ssd\n", "\n", "plt.figure(figsize=(5,5))\n", "plt.plot(XCoords, YCoords)\n", "plt.xlim([-applicator/2 - 1, applicator/2 + 1])\n", "plt.ylim([-applicator/2 - 1, applicator/2 + 1])" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(-6.0, 6.0)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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pYZK4gq721LLLod//HiZPhkcf1YTUPNm4MSyzf9NNcNBBsaspDrXsCuy442DtWrj33tiV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\ngg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "key = '5cm circle'\n", "input_dict[key] = dict()\n", "\n", "input_dict[key]['factor'] = 0.9705\n", "\n", "XCoords, YCoords = generic_shape_convert(5/0.95, 5/0.95, 'circle')\n", "input_dict[key]['XCoords'] = XCoords\n", "input_dict[key]['YCoords'] = YCoords\n", "input_dict[key]['applicator'] = applicator\n", "input_dict[key]['energy'] = energy\n", "input_dict[key]['ssd'] = ssd\n", "\n", "plt.figure(figsize=(5,5))\n", "plt.plot(XCoords, YCoords)\n", "plt.xlim([-applicator/2 - 1, applicator/2 + 1])\n", "plt.ylim([-applicator/2 - 1, applicator/2 + 1])" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(-6.0, 6.0)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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ohkuKQpNpeoKiIurGSlFomZTCriLqxkpRqGWnsKuIWnZSFGrZKewqUlcHixbFrkKkaR9+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\n6H4fQrgvdj3N1B8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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "key = '7cm circle'\n", "input_dict[key] = dict()\n", "\n", "input_dict[key]['factor'] = 1.0032\n", "\n", "XCoords, YCoords = generic_shape_convert(7/0.95, 7/0.95, 'circle')\n", "input_dict[key]['XCoords'] = XCoords\n", "input_dict[key]['YCoords'] = YCoords\n", "input_dict[key]['applicator'] = applicator\n", "input_dict[key]['energy'] = energy\n", "input_dict[key]['ssd'] = ssd\n", "\n", "plt.figure(figsize=(5,5))\n", "plt.plot(XCoords, YCoords)\n", "plt.xlim([-applicator/2 - 1, applicator/2 + 1])\n", "plt.ylim([-applicator/2 - 1, applicator/2 + 1])" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(-6.0, 6.0)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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U23ffbI85hQAvveS/l7FjfSvXoEG+CLidDjFLHIWdNMrs2TB6tN/Kyrz7W14O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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "key = '9cm circle'\n", "input_dict[key] = dict()\n", "\n", "input_dict[key]['factor'] = 1.0084\n", "\n", "XCoords, YCoords = generic_shape_convert(9/0.95, 9/0.95, 'circle')\n", "input_dict[key]['XCoords'] = XCoords\n", "input_dict[key]['YCoords'] = YCoords\n", "input_dict[key]['applicator'] = applicator\n", "input_dict[key]['energy'] = energy\n", "input_dict[key]['ssd'] = ssd\n", "\n", "\n", "plt.figure(figsize=(5,5))\n", "plt.plot(XCoords, YCoords)\n", "plt.xlim([-applicator/2 - 1, applicator/2 + 1])\n", "plt.ylim([-applicator/2 - 1, applicator/2 + 1])" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(-6.0, 6.0)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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3lbNkCVxzje0vHDlSzQ9Ecm5kt3o1DBqU7Kv8RoyABg2gTh0726qgE4ko7Jxz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\nCP8PPHeH3iS20J0AAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "key = '3x13cm ellipse'\n", "input_dict[key] = dict()\n", "\n", "input_dict[key]['factor'] = 0.9530\n", "\n", "XCoords, YCoords = generic_shape_convert(3/0.95, 13/0.95, 'ellipse')\n", "input_dict[key]['XCoords'] = XCoords\n", "input_dict[key]['YCoords'] = YCoords\n", "input_dict[key]['applicator'] = applicator\n", "input_dict[key]['energy'] = energy\n", "input_dict[key]['ssd'] = ssd\n", "\n", "\n", "plt.figure(figsize=(5,5))\n", "plt.plot(XCoords, YCoords)\n", "plt.xlim([-applicator/2 - 1, applicator/2 + 1])\n", "plt.ylim([-applicator/2 - 1, applicator/2 + 1])" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(-6.0, 6.0)" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "key = '1st clinical insert'\n", "input_dict[key] = dict()\n", "\n", "input_dict[key]['factor'] = 0.9931\n", "\n", "XCoords = [-3.04, -3.04, -3.04, -2.95, -2.48, -1.69, -1.31, -1.28, -1.25, -1.23,\n", " -1.23, -1.21, -1.18, -1.16, -1.16, -1.14, -1.12, -1.08, -1.08, -1.04, -1.01, -0.99,\n", " -0.99, -0.97, -0.95, -0.92, -0.92, -0.89, -0.87, -0.84, -0.82, -0.8, -0.77, -0.75,\n", " -0.73, -0.71, -0.67, -0.63, -0.6, -0.56, -0.53, -0.51, -0.48, -0.46, -0.43, -0.41,\n", " -0.39, -0.36, -0.32, -0.29, -0.26, -0.22, -0.19, -0.17, -0.15, -0.12, -0.12, -0.09,\n", " -0.07, -0.05, -0.02, 0.02, 0.05, 0.09, 0.09, 0.12, 0.12, 0.15, 0.15, 0.17, 0.17,\n", " 0.19, 0.19, 0.22, 0.22, 0.24, 0.24, 0.26, 0.29, 0.29, 0.32, 0.34, 0.36, 0.39,\n", " 0.39, 0.41, 0.43, 0.43, 0.51, 0.99, 2.05, 2.68, 3.24, 3.6, 3.75, 3.77, 3.65, 3.21,\n", " 2.71, 2.68, 2.65, 2.63, 2.61, 2.59, 2.56, 2.54, 2.52, 2.48, 2.46, 2.46, 2.44,\n", " 2.41, 2.39, 2.37, 2.35, 2.32, 2.29, 2.27, 2.24, 2.22, 2.2, 2.2, 2.18, 2.15, 2.13,\n", " 2.11, 1.86, 1.83, 1.81, 1.79, 1.77, 1.74, 1.72, 1.69, 1.66, 1.64, 1.55, 1.55,\n", " 1.55, 1.53, 1.47, 1.45, 1.42, 1.4, 1.38, 1.36, 1.33, 1.31, 1.28, 1.25, 1.23, 1.23,\n", " 1.21, 1.18, 1.18, 1.16, 1.14, 1.12, 1.08, 1.06, 1.04, 1.01, 0.99, 0.97, 0.95,\n", " 0.95, 0.92, 0.89, 0.87, 0.84, 0.82, 0.8, 0.77, 0.75, 0.73, 0.71, 0.67, 0.65, 0.63,\n", " 0.6, 0.58, 0.56, 0.53, 0.53, 0.51, 0.48, 0.48, 0.46, 0.43, 0.41, 0.17, -0.51,\n", " -1.74, -2.13, -2.78, -3.04, -3.09, -3.04]\n", "YCoords = [-0.58, 0.63, 1.62, 2.32, 3.12, 3.38, 3.23, 3.23, 3.23, 3.23, 3.21, 3.21,\n", " 3.21, 3.21, 3.19, 3.19, 3.19, 3.19, 3.16, 3.16, 3.16, 3.14, 3.12, 3.12, 3.12,\n", " 3.12, 3.09, 3.09, 3.04, 3.04, 3.04, 3.04, 3.04, 3.04, 3.04, 3.04, 3.04, 3.06,\n", " 3.06, 3.09, 3.09, 3.12, 3.14, 3.14, 3.14, 3.14, 3.16, 3.16, 3.16, 3.16, 3.19,\n", " 3.21, 3.21, 3.21, 3.21, 3.21, 3.23, 3.23, 3.23, 3.26, 3.26, 3.28, 3.28, 3.31,\n", " 3.33, 3.33, 3.36, 3.36, 3.38, 3.38, 3.4, 3.43, 3.45, 3.45, 3.47, 3.47, 3.51, 3.53,\n", " 3.55, 3.57, 3.57, 3.6, 3.62, 3.62, 3.64, 3.64, 3.64, 3.67, 3.67, 3.72, 3.64, 3.19,\n", " 1.81, 0.92, -0.67, -2.05, -3.04, -3.69, -3.84, -3.84, -3.84, -3.84, -3.84, -3.84,\n", " -3.84, -3.84, -3.84, -3.84, -3.84, -3.81, -3.81, -3.81, -3.81, -3.81, -3.81, -3.81,\n", " -3.81, -3.79, -3.79, -3.79, -3.79, -3.77, -3.77, -3.77, -3.77, -3.77, -3.62, -3.62,\n", " -3.62, -3.6, -3.6, -3.6, -3.57, -3.57, -3.57, -3.57, -3.57, -3.55, -3.53, -3.53,\n", " -3.53, -3.53, -3.53, -3.53, -3.53, -3.51, -3.51, -3.51, -3.51, -3.51, -3.51, -3.47,\n", " -3.47, -3.47, -3.45, -3.45, -3.45, -3.45, -3.45, -3.45, -3.45, -3.45, -3.45, -3.45,\n", " -3.45, -3.47, -3.47, -3.47, -3.47, -3.47, -3.47, -3.47, -3.51, -3.51, -3.51, -3.51,\n", " -3.51, -3.53, -3.53, -3.53, -3.53, -3.53, -3.53, -3.55, -3.55, -3.55, -3.57, -3.57,\n", " -3.6, -3.6, -3.77, -3.74, -3.09, -2.11, -1.74, -1.35, -0.58, -0.58]\n", "input_dict[key]['XCoords'] = XCoords\n", "input_dict[key]['YCoords'] = YCoords\n", "input_dict[key]['applicator'] = applicator\n", "input_dict[key]['energy'] = energy\n", "input_dict[key]['ssd'] = ssd\n", "\n", "\n", "plt.figure(figsize=(5,5))\n", "plt.plot(XCoords, YCoords)\n", "plt.xlim([-applicator/2 - 1, applicator/2 + 1])\n", "plt.ylim([-applicator/2 - 1, applicator/2 + 1])" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(-6.0, 6.0)" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "key = '2nd clinical insert'\n", "input_dict[key] = dict()\n", "\n", "input_dict[key]['factor'] = 0.9931\n", "\n", "XCoords = [0.99, -0.14, -1.0, -1.73, -2.56, -3.17, -3.49, -3.57, -3.17, -2.52, -1.76,\n", " -1.04, -0.17, 0.77, 1.63, 2.36, 2.79, 2.91, 3.04, 3.22, 3.34, 3.37, 3.08, 2.54,\n", " 1.88, 1.02, 0.99]\n", "YCoords = [5.05, 4.98, 4.42, 3.24, 1.68, 0.6, -0.64, -1.48, -2.38, -3.77, -4.81,\n", " -5.26, -5.51, -5.58, -5.23, -4.64, -3.77, -2.77, -1.68, -0.29, 1.23, 2.68, 3.8,\n", " 4.6, 5.01, 5.08, 5.05]\n", "input_dict[key]['XCoords'] = XCoords\n", "input_dict[key]['YCoords'] = YCoords\n", "input_dict[key]['applicator'] = applicator\n", "input_dict[key]['energy'] = energy\n", "input_dict[key]['ssd'] = ssd\n", "\n", "\n", "plt.figure(figsize=(5,5))\n", "plt.plot(XCoords, YCoords)\n", "plt.xlim([-applicator/2 - 1, applicator/2 + 1])\n", "plt.ylim([-applicator/2 - 1, applicator/2 + 1])" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(-6.0, 6.0)" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "key = '3rd clinical insert'\n", "input_dict[key] = dict()\n", "\n", "input_dict[key]['factor'] = 0.9989\n", "\n", "XCoords = [-3.26, -3.21, 3.11, 3.21, 3.32, 3.74, 4.05, 4.16, 4.26, 4.53, 4.53, 4.63,\n", " 4.63, 4.84, 4.84, 4.95, 4.95, 5.05, 5.05, 5.16, 5.16, 5.26, 5.26, 5.37, 5.37,\n", " 5.26, 5.26, 5.16, 5.16, 5.05, 5.05, 4.95, 4.95, 4.68, 4.58, 4.47, 4.19, -1.85,\n", " -2.05, -2.16, -2.26, -2.53, -2.53, -2.58, -2.79, -2.89, -3.0, -3.26, -3.26, -3.32,\n", " -3.74, -3.84, -3.95, -4.21, -4.21, -4.63, -4.63, -4.74, -4.74, -4.84, -4.84, -4.74,\n", " -4.74, -4.63, -4.63, -4.37, -4.26, -4.16, -3.74, -3.68, -3.68, -3.42, -3.32, -3.26]\n", "YCoords = [3.11, 3.16, 3.16, 3.05, 3.05, 2.63, 2.63, 2.53, 2.53, 2.26, 2.16, 2.05,\n", " 1.95, 1.74, 1.63, 1.53, 1.11, 1.0, 0.79, 0.68, 0.58, 0.47, 0.05, -0.05, -1.0,\n", " -1.11, -1.53, -1.63, -1.74, -1.84, -2.05, -2.16, -2.26, -2.53, -2.53, -2.63, -2.56,\n", " -2.56, -2.63, -2.53, -2.53, -2.26, -2.16, -2.11, -2.11, -2.0, -2.0, -1.74, -1.63,\n", " -1.58, -1.58, -1.47, -1.47, -1.21, -1.11, -0.68, -0.58, -0.47, -0.05, 0.05, 1.53,\n", " 1.63, 2.05, 2.16, 2.26, 2.53, 2.53, 2.63, 2.63, 2.68, 2.79, 3.05, 3.05, 3.11]\n", "input_dict[key]['XCoords'] = XCoords\n", "input_dict[key]['YCoords'] = YCoords\n", "input_dict[key]['applicator'] = applicator\n", "input_dict[key]['energy'] = energy\n", "input_dict[key]['ssd'] = ssd\n", "\n", "\n", "plt.figure(figsize=(5,5))\n", "plt.plot(XCoords, YCoords)\n", "plt.xlim([-applicator/2 - 1, applicator/2 + 1])\n", "plt.ylim([-applicator/2 - 1, applicator/2 + 1])" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(-6.0, 6.0)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "key = '4th clinical insert'\n", "input_dict[key] = dict()\n", "\n", "input_dict[key]['factor'] = 0.9634\n", "\n", "XCoords = [-1.05, -1.03, -1.01, -0.98, -0.96, -0.94, -0.88, -0.86, -0.83, -0.79,\n", " -0.76, -0.74, -0.72, -0.68, -0.66, -0.64, -0.61, -0.59, -0.57, -0.54, -0.52, -0.49,\n", " -0.46, -0.44, -0.42, -0.39, -0.37, -0.35, -0.32, -0.29, -0.29, -0.27, -0.27, -0.24,\n", " -0.24, -0.22, -0.2, -0.15, -0.15, -0.13, -0.09, -0.05, -0.02, 0.0, 0.05, 0.07,\n", " 0.07, 0.09, 0.13, 0.15, 0.2, 0.2, 0.22, 0.24, 0.27, 0.29, 0.32, 0.37, 0.39, 0.42,\n", " 0.44, 0.46, 0.49, 0.52, 0.57, 0.57, 0.59, 0.61, 0.64, 0.8, 1.29, 1.75, 2.02, 2.11,\n", " 2.24, 2.28, 2.21, 2.11, 1.99, 1.82, 1.42, 1.08, 0.78, 0.34, -0.07, -0.72, -1.11,\n", " -1.63, -2.03, -2.08, -2.08, -2.08, -2.11, -2.14, -2.14, -2.14, -2.14, -2.16, -2.18,\n", " -2.21, -2.21, -2.23, -2.23, -2.25, -2.25, -2.25, -2.28, -2.31, -2.31, -2.31, -2.33,\n", " -2.33, -2.33, -2.36, -2.36, -2.38, -2.4, -2.4, -2.4, -2.43, -2.43, -2.45, -2.45,\n", " -2.45, -2.47, -2.47, -2.47, -2.51, -2.51, -2.51, -2.53, -2.53, -2.53, -2.55, -2.58,\n", " -2.58, -2.6, -2.6, -2.6, -2.62, -2.62, -2.62, -2.65, -2.67, -2.67, -2.67, -2.67,\n", " -2.67, -2.67, -2.67, -2.73, -2.73, -2.73, -2.73, -2.73, -2.73, -2.73, -2.73, -2.73,\n", " -2.73, -2.73, -2.75, -2.75, -2.75, -2.75, -2.75, -2.75, -2.75, -2.73, -2.73, -2.73,\n", " -2.73, -2.73, -2.69, -2.69, -2.69, -2.69, -2.69, -2.69, -2.67, -2.67, -2.67, -2.67,\n", " -2.67, -2.67, -2.65, -2.65, -2.65, -2.65, -2.62, -2.62, -2.6, -2.6, -2.6, -2.58,\n", " -2.58, -2.58, -2.55, -2.53, -2.51, -2.51, -2.47, -2.47, -2.45, -2.43, -2.43, -2.43,\n", " -2.4, -2.4, -2.38, -2.36, -2.36, -2.33, -2.31, -2.31, -2.28, -2.25, -2.25, -2.23,\n", " -2.23, -2.21, -2.21, -2.18, -2.18, -2.16, -2.16, -2.14, -2.14, -2.11, -2.11, -2.08,\n", " -2.06, -2.06, -2.03, -2.03, -2.01, -1.99, -1.99, -1.96, -1.96, -1.96, -1.94, -1.92,\n", " -1.88, -1.86, -1.86, -1.84, -1.81, -1.79, -1.79, -1.77, -1.77, -1.74, -1.74, -1.72,\n", " -1.72, -1.72, -1.69, -1.66, -1.64, -1.64, -1.62, -1.62, -1.59, -1.57, -1.55, -1.55,\n", " -1.52, -1.49, -1.49, -1.47, -1.47, -1.44, -1.42, -1.4, -1.37, -1.35, -1.33, -1.29,\n", " -1.27, -1.27, -1.25, -1.22, -1.2, -1.2, -1.18, -1.15, -1.13, -1.11, -1.07, -1.05]\n", "YCoords = [2.18, 2.18, 2.18, 2.2, 2.2, 2.23, 2.23, 2.23, 2.25, 2.25, 2.25, 2.25,\n", " 2.25, 2.25, 2.25, 2.25, 2.25, 2.25, 2.25, 2.25, 2.25, 2.25, 2.25, 2.25, 2.25,\n", " 2.25, 2.25, 2.23, 2.23, 2.23, 2.2, 2.2, 2.18, 2.18, 2.16, 2.16, 2.14, 2.11, 2.08,\n", " 2.08, 2.06, 2.06, 2.03, 2.03, 2.01, 1.99, 1.96, 1.96, 1.96, 1.94, 1.94, 1.92,\n", " 1.88, 1.88, 1.88, 1.86, 1.86, 1.84, 1.84, 1.81, 1.81, 1.79, 1.79, 1.74, 1.74,\n", " 1.72, 1.72, 1.69, 1.69, 1.58, 1.27, 0.93, 0.55, 0.26, -0.08, -0.48, -0.75, -1.14,\n", " -1.48, -1.84, -2.37, -2.61, -2.87, -3.0, -3.0, -2.82, -2.61, -2.03, -1.38, -1.29,\n", " -1.27, -1.25, -1.25, -1.25, -1.22, -1.2, -1.18, -1.18, -1.15, -1.15, -1.13, -1.13,\n", " -1.11, -1.11, -1.07, -1.05, -1.05, -1.03, -1.0, -0.98, -0.98, -0.96, -0.93, -0.91,\n", " -0.88, -0.88, -0.85, -0.83, -0.81, -0.78, -0.76, -0.76, -0.74, -0.72, -0.72, -0.68,\n", " -0.66, -0.64, -0.61, -0.59, -0.59, -0.57, -0.54, -0.52, -0.49, -0.46, -0.46, -0.44,\n", " -0.42, -0.42, -0.39, -0.37, -0.35, -0.32, -0.29, -0.27, -0.24, -0.22, -0.2, -0.17,\n", " -0.15, -0.13, -0.09, -0.07, -0.05, -0.02, 0.0, 0.02, 0.05, 0.07, 0.09, 0.09, 0.13,\n", " 0.15, 0.17, 0.2, 0.22, 0.27, 0.27, 0.29, 0.32, 0.35, 0.37, 0.37, 0.39, 0.42, 0.44,\n", " 0.46, 0.49, 0.49, 0.52, 0.54, 0.57, 0.59, 0.61, 0.64, 0.66, 0.68, 0.72, 0.72,\n", " 0.74, 0.74, 0.76, 0.78, 0.81, 0.83, 0.85, 0.85, 0.88, 0.91, 0.93, 0.96, 0.98,\n", " 1.0, 1.0, 1.03, 1.05, 1.05, 1.07, 1.13, 1.13, 1.15, 1.15, 1.2, 1.22, 1.22, 1.22,\n", " 1.25, 1.27, 1.29, 1.29, 1.33, 1.33, 1.35, 1.35, 1.37, 1.37, 1.4, 1.4, 1.42, 1.42,\n", " 1.44, 1.47, 1.47, 1.49, 1.49, 1.49, 1.52, 1.52, 1.55, 1.57, 1.57, 1.57, 1.59,\n", " 1.62, 1.64, 1.64, 1.66, 1.66, 1.69, 1.72, 1.74, 1.74, 1.77, 1.77, 1.79, 1.81,\n", " 1.81, 1.84, 1.84, 1.86, 1.86, 1.88, 1.88, 1.92, 1.92, 1.94, 1.94, 1.94, 1.96,\n", " 1.96, 1.99, 1.99, 2.01, 2.01, 2.01, 2.03, 2.03, 2.03, 2.06, 2.08, 2.08, 2.08,\n", " 2.08, 2.11, 2.11, 2.14, 2.16, 2.16, 2.16, 2.18]\n", "input_dict[key]['XCoords'] = XCoords\n", "input_dict[key]['YCoords'] = YCoords\n", "input_dict[key]['applicator'] = applicator\n", "input_dict[key]['energy'] = energy\n", "input_dict[key]['ssd'] = ssd\n", "\n", "\n", "plt.figure(figsize=(5,5))\n", "plt.plot(XCoords, YCoords)\n", "plt.xlim([-applicator/2 - 1, applicator/2 + 1])\n", "plt.ylim([-applicator/2 - 1, applicator/2 + 1])" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(-6.0, 6.0)" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "key = '5th clinical insert'\n", "input_dict[key] = dict()\n", "\n", "input_dict[key]['factor'] = 0.9683\n", "\n", "XCoords = [-0.32, -0.26, 0.79, 1.11, 1.21, 1.42, 1.53, 1.95, 2.05, 2.32, 2.32, 2.42,\n", " 2.42, 2.53, 2.53, 2.42, 2.42, 2.32, 2.32, 2.21, 2.21, 2.11, 2.11, 2.0, 2.0, 1.68,\n", " 1.68, 1.42, 1.11, 0.89, 0.05, -0.05, -1.0, -1.21, -1.42, -1.89, -1.89, -2.0, -2.0,\n", " -2.11, -2.11, -2.21, -2.21, -2.11, -2.11, -2.0, -2.0, -1.79, -1.79, -1.68, -1.68,\n", " -1.47, -1.47, -1.37, -1.37, -1.26, -1.26, -0.58, -0.47, -0.32]\n", "YCoords = [3.0, 3.05, 3.05, 2.74, 2.74, 2.53, 2.53, 2.11, 2.11, 1.84, 1.53, 1.42,\n", " 1.11, 1.0, -0.89, -1.0, -1.32, -1.42, -1.63, -1.74, -1.84, -1.95, -2.05, -2.16,\n", " -2.26, -2.58, -2.68, -2.95, -2.95, -3.16, -3.16, -3.05, -3.05, -2.84, -2.84, -2.37,\n", " -2.05, -1.95, -1.63, -1.53, -1.42, -1.32, -0.16, -0.05, 0.16, 0.26, 0.58, 0.79,\n", " 1.0, 1.11, 1.42, 1.63, 1.74, 1.84, 1.95, 2.05, 2.16, 2.84, 2.84, 3.0]\n", "input_dict[key]['XCoords'] = XCoords\n", "input_dict[key]['YCoords'] = YCoords\n", "input_dict[key]['applicator'] = applicator\n", "input_dict[key]['energy'] = energy\n", "input_dict[key]['ssd'] = ssd\n", "\n", "\n", "plt.figure(figsize=(5,5))\n", "plt.plot(XCoords, YCoords)\n", "plt.xlim([-applicator/2 - 1, applicator/2 + 1])\n", "plt.ylim([-applicator/2 - 1, applicator/2 + 1])" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(-6.0, 6.0)" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "key = '6th clinical insert'\n", "input_dict[key] = dict()\n", "\n", "input_dict[key]['factor'] = 1.0067\n", "\n", "XCoords = [-2.32, -2.26, -1.74, -1.63, -1.53, -1.42, -1.32, -1.11, -0.68, -0.58,\n", " 0.37, 0.47, 0.58, 0.95, 1.05, 1.11, 1.74, 2.11, 2.11, 2.16, 2.47, 2.58, 2.68,\n", " 2.89, 3.32, 3.42, 3.84, 4.42, 4.42, 4.53, 4.53, 4.74, 4.74, 4.84, 4.84, 4.95,\n", " 4.95, 5.05, 5.05, 5.16, 5.16, 5.26, 5.26, 5.37, 5.37, 5.21, 5.21, 5.26, 5.26,\n", " 5.16, 5.16, 4.95, 4.95, 4.74, 4.74, 4.53, 4.53, 4.21, 4.21, 3.84, 3.74, 3.63,\n", " 3.53, 3.42, 3.32, 3.21, 3.11, 3.0, 2.89, 2.79, 2.37, 2.26, 2.05, 1.95, 1.84, 1.74,\n", " 1.42, 1.32, 0.58, 0.47, 0.26, 0.05, -1.0, -1.11, -1.21, -1.58, -1.58, -1.79, -1.79,\n", " -1.89, -1.89, -2.0, -2.0, -2.16, -2.47, -2.58, -2.68, -3.05, -3.05, -3.16, -3.16,\n", " -3.26, -3.26, -3.37, -3.37, -3.42, -3.95, -4.32, -4.32, -4.42, -4.42, -4.53, -4.53,\n", " -4.63, -4.63, -4.74, -4.74, -4.84, -4.84, -4.95, -4.95, -5.05, -5.05, -5.16, -5.16,\n", " -5.26, -5.26, -5.37, -5.37, -5.47, -5.47, -5.58, -5.58, -5.68, -5.68, -5.21, -5.11,\n", " -4.74, -4.74, -4.37, -4.26, -4.16, -4.05, -3.95, -3.53, -3.32, -3.21, -3.11, -2.37,\n", " -2.32]\n", "YCoords = [5.21, 5.26, 5.26, 5.16, 5.16, 5.05, 5.05, 4.84, 4.84, 4.95, 4.95, 4.84,\n", " 4.84, 4.47, 4.47, 4.53, 4.53, 4.16, 4.05, 4.0, 4.0, 3.89, 3.89, 3.68, 3.68, 3.58,\n", " 3.58, 3.0, 2.89, 2.79, 2.68, 2.47, 2.37, 2.26, 2.16, 2.05, 1.95, 1.84, 1.63, 1.53,\n", " 1.42, 1.32, 1.11, 1.0, 0.47, 0.32, 0.21, 0.16, -0.37, -0.47, -0.58, -0.79, -1.11,\n", " -1.32, -2.05, -2.26, -2.47, -2.79, -2.89, -3.26, -3.26, -3.37, -3.37, -3.47, -3.47,\n", " -3.58, -3.58, -3.68, -3.68, -3.79, -3.79, -3.89, -3.89, -4.0, -4.0, -4.11, -4.11,\n", " -4.21, -4.21, -4.11, -4.11, -3.89, -3.89, -3.79, -3.79, -3.42, -3.32, -3.11, -3.0,\n", " -2.89, -2.79, -2.68, -2.58, -2.42, -2.42, -2.32, -2.32, -1.95, -1.84, -1.74, -1.63,\n", " -1.53, -1.42, -1.32, -1.21, -1.16, -1.16, -0.79, -0.68, -0.58, -0.47, -0.37, -0.26,\n", " -0.16, -0.05, 0.05, 0.16, 0.26, 0.47, 0.58, 0.68, 0.79, 0.89, 1.0, 1.21, 1.32,\n", " 1.53, 1.63, 1.74, 1.84, 2.05, 2.16, 2.47, 2.58, 3.21, 3.68, 3.68, 4.05, 4.26,\n", " 4.63, 4.63, 4.74, 4.74, 4.84, 4.84, 5.05, 5.05, 5.16, 5.16, 5.21]\n", "input_dict[key]['XCoords'] = XCoords\n", "input_dict[key]['YCoords'] = YCoords\n", "input_dict[key]['applicator'] = applicator\n", "input_dict[key]['energy'] = energy\n", "input_dict[key]['ssd'] = ssd\n", "\n", "\n", "plt.figure(figsize=(5,5))\n", "plt.plot(XCoords, YCoords)\n", "plt.xlim([-applicator/2 - 1, applicator/2 + 1])\n", "plt.ylim([-applicator/2 - 1, applicator/2 + 1])" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(-6.0, 6.0)" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "key = '7th clinical insert'\n", "input_dict[key] = dict()\n", "\n", "input_dict[key]['factor'] = 1.0052\n", "\n", "XCoords = [1.63, 0.34, -1.05, -1.16, -1.16, -1.29, -1.29, -1.39, -1.44, -1.44, -1.48,\n", " -1.59, -1.59, -1.68, -1.78, -1.82, -1.93, -1.97, -2.06, -2.12, -2.16, -2.25, -2.25,\n", " -2.31, -2.36, -2.4, -2.45, -2.49, -2.55, -2.59, -2.59, -2.64, -2.74, -2.79, -2.83,\n", " -2.83, -2.88, -2.93, -2.98, -2.98, -2.98, -3.07, -3.17, -3.22, -3.36, -3.36, -3.41,\n", " -3.46, -3.51, -3.56, -3.56, -3.6, -3.65, -3.65, -3.69, -3.69, -3.75, -3.8, -3.84,\n", " -3.84, -3.84, -3.89, -3.89, -3.89, -3.89, -3.89, -3.94, -3.99, -3.99, -3.99, -3.99,\n", " -4.03, -4.03, -4.03, -4.03, -4.03, -4.03, -4.03, -4.03, -4.03, -4.03, -4.03, -4.03,\n", " -4.03, -4.03, -4.03, -4.08, -4.08, -4.08, -4.08, -4.08, -4.08, -4.08, -4.08, -4.08,\n", " -4.08, -4.08, -4.03, -4.03, -4.03, -4.03, -4.03, -4.03, -4.03, -4.03, -4.03, -4.03,\n", " -4.03, -4.03, -4.03, -4.03, -4.03, -3.99, -3.17, -2.12, -0.62, -0.58, -0.43, -0.34,\n", " -0.28, -0.15, 0.0, 0.05, 0.09, 0.19, 0.24, 0.28, 0.39, 0.39, 0.43, 0.53, 0.58,\n", " 0.67, 0.72, 0.77, 3.51, 3.51, 3.6, 3.65, 3.65, 3.69, 3.69, 3.75, 3.75, 3.8, 3.8,\n", " 4.08, 4.23, 3.51, 1.59, 1.63]\n", "YCoords = [4.46, 4.71, 4.08, 3.94, 3.88, 3.79, 3.75, 3.69, 3.69, 3.65, 3.6, 3.51,\n", " 3.45, 3.41, 3.32, 3.26, 3.22, 3.12, 3.07, 2.98, 2.93, 2.78, 2.74, 2.64, 2.59,\n", " 2.55, 2.49, 2.45, 2.4, 2.4, 2.31, 2.25, 2.16, 2.06, 1.97, 1.92, 1.92, 1.87, 1.82,\n", " 1.78, 1.73, 1.63, 1.54, 1.48, 1.35, 1.29, 1.25, 1.2, 1.15, 1.15, 1.05, 1.05, 1.01,\n", " 0.96, 0.92, 0.86, 0.82, 0.72, 0.67, 0.58, 0.53, 0.43, 0.34, 0.28, 0.24, 0.19,\n", " 0.09, 0.05, 0.0, -0.09, -0.15, -0.19, -0.34, -0.38, -0.43, -0.48, -0.53, -0.62,\n", " -0.67, -0.72, -0.77, -0.82, -0.86, -0.92, -1.01, -1.15, -1.2, -1.29, -1.39, -1.48,\n", " -1.54, -1.63, -1.73, -1.78, -1.82, -1.97, -2.02, -2.02, -2.06, -2.16, -2.25, -2.31,\n", " -2.35, -2.45, -2.49, -2.55, -2.68, -2.74, -2.78, -2.83, -2.88, -2.98, -3.36, -4.52,\n", " -4.95, -4.99, -4.99, -5.04, -4.99, -4.99, -4.89, -4.89, -4.89, -4.85, -4.75, -4.75,\n", " -4.71, -4.71, -4.65, -4.61, -4.56, -4.52, -4.52, -4.52, -4.52, -1.48, -1.39, -1.29,\n", " -1.15, -1.05, -0.96, -0.86, -0.86, -0.82, -0.72, -0.62, 0.34, 2.45, 3.84, 4.52,\n", " 4.46]\n", "input_dict[key]['XCoords'] = XCoords\n", "input_dict[key]['YCoords'] = YCoords\n", "input_dict[key]['applicator'] = applicator\n", "input_dict[key]['energy'] = energy\n", "input_dict[key]['ssd'] = ssd\n", "\n", "\n", "plt.figure(figsize=(5,5))\n", "plt.plot(XCoords, YCoords)\n", "plt.xlim([-applicator/2 - 1, applicator/2 + 1])\n", "plt.ylim([-applicator/2 - 1, applicator/2 + 1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Parameterising the shapes via equivalent ellipse\n", "\n", "Before modelling the shapes need to be able to be parameterised. This is done as so:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1st clinical insert:\n" ] }, { "data": { "image/png": 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GBvL1119bOiS7xCjiLaXcBxQZoy59UJ5Y47RGvHNychBCGDwpx9I+qzGwhxzA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\nhDhz6wbqny0djyEIIX4rhNAKIfwsHUtrEUK8c+t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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "2nd clinical insert:\n" ] }, { "data": { "image/png": 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DNZJ3N+DCNdsXf9in3AIta4HHjx9vNsoEnD95O3PNG75P3jk5Oea6d3V1dYvT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\nhu/HfxcWFmIwGFi8eDETJ06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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "3cm circle:\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "3rd clinical insert:\n" ] }, { "data": { "image/png": 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v6elp9Zwl1nJk/rFjx5Kfn8+TTz7ZrrsneXt78+qrr/Kf//mflternrkO6KmY\n60V9fb3mt9BT9MvDw8PmxdyRXF1defjhh9m7d6/V87XcauHChdTV1bU4VLGz0n0xP3bsmNYxAP30\nbRsaGtpVzPWSvz2MnB0cm9/Dw8Pm55kcvf89PDxITExk586d7Xq9i4sLr7zyCm+88Qageua6MHLk\nSPbv309dXZ3WUXSjvcVccQ72aLNooS1TVdzOtGnTKCgocKpzbrou5qGhodx99918+umnWkfRTd9W\n9cyNx5H5m9ss7ek3t8SI+9/NzY25c+fy4Ycfqp65XixcuJBVq1ZpHUM31JG50prm6w+MNq+5PXz/\n+99n1apVnfbeqLfSfTH/3ve+x1dffUVpaammOVTfVjtGzg4qv1aGDBniVBfY6b6Y+/r68uCDD/Lx\nxx9rHUUX7HGCS+k8mo/InaWAtUYI4VSf7HVfzEEfrRa99A3bO/RML/nbw8jZwbH5m4eudvQE4o2M\nvP/nz5/P2rVrnWIQhU2KuRBimhAiWwiRK4T4hS3WeaP77ruP4uJi3QxT1JLRxxEr9qXOqdwsNDSU\n0NDQds/3YiQdLuZCCBfg/wFTgUHAXCFEbEfXeyNXV1eWLVvGb37zG1uutk300jds79AzveRvDyNn\nB8fmb+9op9YYff+PHj3aKQ4EbXFkPhLIk1IWSClNwBrgIRus9ybPP/88aWlp7N+/36rlbT1Bv16o\nnrnSms50ZF5XV2eTIZbOciW5LYp5b6Dohudnr3/Npry9vfntb39705wLLVm0aBHbtm2jpqbGZtvX\nS9/Q09OzXcVcL/nbw8jZwfE9c1sfmWux/0NDQykuLua5557r8NBCs9nsFMXczZEbW7x4seUjW0BA\nAAkJCZZLbZsH9rf2PCwsjPr6etatW2e5B+btlv/FL37BiRMnWLlyJYsXL8bPz8/yhmzeflufX7hw\noUOvt9Vzf39/cnNzDZtfPbfv88rKSvz9/XWTp73PS0pKGDNmDFu3bqWkpIQnn3wSd3f3NtWL5ud9\n+/blyJEj7NmzhwkTJrT59Y5+npKSwsqVK2/aH9YQHf0YI4S4B/i1lHLa9ef/F5BSytduWU7a4iPT\nnj17eOKJJzhx4sQdj0CWLl1KZmYmkyZN6vB29aK8vJwPP/yQ5557Tusoig5t374dX19fEhMTtY5i\nEyaTidWrV/OnP/2JRx55pF3rkFISFBREVlYWvXr1snFC+xNCIKW84/AkW7RZDgH9hBARQggPYA6w\n2Qbrva2xtKVGAAAep0lEQVQJEyYwePBg/v73v99x2UGDBnW6q7/sddNepXOoqKjoVPeHdXd3p0eP\nHh06TySE0NWkffbS4WIupWwC/g+wA/gWWCOlPNHR9bbm9ddf57XXXqO4uNiem7mJXvq2rq6u+Pv7\nt/nGtXrJ3x5Gzg6OzW+Pm30bff+npKTg5+fHlStXtI5iVzYZZy6l/FxKGSOl7C+l/IMt1tma2NhY\nli1bxvz5851yDgoj3+dRsZ+mpiYuXbrU5pt9OwOTyYS7u7vWMezKEFeA3s4vf/lLhBAOG3uup7G2\n7SnmesrfVkbODo7Lf+nSJfz8/HBzs+24BqPv//Hjx6tirmeurq6sXr2a9957r92T2BtVUFAQJSUl\nWsdQdKakpISgoCCtY+hSZxp/3xLDFnO4djfuVatWsWjRIs6fP2/XbempbxgREUFBQUGbLqjQU/62\nMnJ2cFz+goICIiIibL5eo+//lJQUdWRuBPfddx9PP/008+bNc5oRHt26dUNK2eaToErnlp+fb/iW\niL2oYm4QL774Iu7u7vz617+22zb09EMihCAyMrJNR0x6yt9WRs4Ojsl/+fJlqqqq7DKO2uj7f/z4\n8Vy+fBkvLy+to9hVpyjmrq6ufPjhh6xcuZK1a9dqHcchmlstigLXWizh4eG4uHSKH2mbqqmpobCw\nkJiYGK2j2FWn+Z8PCQlh69atPPvss5Z7hoaEhFBQUGCT8aV66xs2H5lb2zfXW/62MHJ2cEz+/Px8\nu/TLm9dtZMuXLychIUGdADWS+Ph4PvvsM5544gl27tzJ7NmzmT9/Ph9//HGnu2CguW9eUVGhdRRF\nB1S/vGXHjx/n3nvv1TqG3XWqYg4wYsQIkpOTmTdvHqmpqbz22mvMnTuXjz/+uEM3ddDbD4oQgpiY\nGI4fP27V8nrL3xZGzg72z19WVsbVq1ftNu+I0fd/SUmJKuZGNWbMGD766CNmz57NoUOHeO211/Dx\n8aGsrEzraDYVFxdHZmamTeZ8VowrMzOTwYMHq375bUgpOXDggCrmRjZ58mRWrFjBzJkzycrK6nC/\nTI99wz59+tDY2GiZ3rY1esxvLSNnB/vml1KSlZVFfHy83bZh5P2fl5eHq6sroaGhWkexu05bzAFm\nzpzJ3//+d6ZNm4aPjw8nT57UOpJNNc8Gl5mZqXUURSNFRUV4eHgQEhKidRRdWrduHSNGjNA6hkN0\n6mIO8Nhjj/HHP/6RkydPkpOTY/Vt526l175hfHw8x44du+NUv3rNbw0jZwf75s/MzCQuLg4h7jjd\ndbsZdf83NDTw9ttv8/vf/17rKA7R6Ys5wPz58/nXv/6FyWTiyJEjHDhwQOtINtOjRw/8/Pw4c+aM\n1lEUB2tqauL48ePExcVpHUWX1q9fT2xsrNPsH6co5nDtphapqan4+vqyf/9+Dh482KbX67lvOHTo\nUA4dOtTqMnrOfydGzg72y5+VlUXPnj0JCAiwy/qbGXH/Syl58803ef755y23ZOvsnKaYA8TExHD4\n8GFiYmLYs2dPmwu6XiUkJHD27FlKS0u1jqI4iJSSffv2kZSUpHUUXUpLS6OyspIZM2ZoHcVhnKqY\nw7W2RGpqKlOnTmXXrl2kpaVZ9To99w3d3d0ZNWoU+/bta3EZPee/EyNnB/vkz87OxsPDg759+9p8\n3bcy4v7/61//yrPPPouLi4vlpsmdndMVcwBPT082bNjA008/za5du/jqq6+0jtRhI0aMIC8vj0uX\nLmkdRbEzKSWpqakkJSXZ9cSnUWVmZrJ7924WL16sdRSHcspiDteG9f3tb3/jD3/4A3v27OHTTz9t\n9eIbvfcNvby8GDZsWIujdfSevzVGzg62z3/mzBkaGhqIjY216XpbovX+b8tN2U0mE4sWLeL111+n\na9euAKpn7ix+9rOfkZycTEZGBitWrDD0HC733HMPWVlZ1NbWah1FsaPU1FQSExOd4qg8Ozub/Px8\nq8eK/+53vyM0NNTpjsoBhKMuBRdCSD1fdv71118zbtw4XFxceOSRR4iKitI6Urts27YNs9nsVCd+\nnMnJkyfZunUry5Ytw9XVVes4dpWdnc2OHTvYsWMHw4cPv+Py6enpTJkyhaNHj9K7d28HJHQMIQRS\nyjv+5nb6I/Nmo0aNIjU1FSEEn3zyCTt27DDknYvGjx/PiRMn7H4bPcXxGhsb2bZtG9OmTev0hbys\nrIzt27dbXcgbGhpYvHgxr7/+eqcq5G2hivkNhg8fzpAhQ5gxYwaVlZW89957lsm5tO4bWqtLly5M\nnDiRrVu33nQOwCj5b8fI2cF2+dPS0ujRowfR0dE2WZ+1tNj/1dXVxMXFWVXI4Vp7JSwsjEWLFn3n\ne6pn7sQ8PT157LHHGDlyJCtXruTQoUOGmpkwISEBIQRHjx7VOopiI1VVVaSlpTFt2jSto+jOF198\nwdtvv827777rFOcRWqKK+S2mTJnC3r17uXr1KsOGDeMHP/gB6enp7N271zAX5QghmD59Ort376au\nrg4w5ljhZkbODrbJv337dkaOHElgYGDHA7WRnvf/oUOHmDt3LuvXr2+xvaLGmTupl156ie9973us\nWbOGuro6unfvzhNPPMHAgQP55z//yfbt26mvr9c65h316tWLAQMG8MUXX2gdRemgkydPUlxcTGJi\notZRdOXEiRPMnDmTFStWMHbsWK3jaE4V81sIIfjzn/98U0F3cXEhODiYpUuXcvXqVd566y1D3BTi\nvvvus8wWaeS+s5GzQ8fyX758mc2bN/PAAw/g7u5uu1BtoMf9X1BQwNSpU/nTn/7EzJkzW11W9cyd\n2O0KOoCPjw8PPfQQjz76KGlpafzzn/+kpKRE47Qt69KlC7Nnz+bTTz9VY88NSErJhg0bGDJkCHfd\ndZfWcXSjtLSUyZMn89Of/pQFCxZoHUc31DjzVkgp+clPfsInn3zCnDlz6NKli+V7ZrOZb775hpSU\nFOLi4hg/fjxeXl4apm1Zamoqubm5LF68WN1azECc+f/t1KlTFBQU8OWXX9709aqqKiZMmMADDzzA\nb37zG43SOZYaZ24Dtx6hnzt3jgsXLnDx4kVcXFwYMWIES5cupaGhgbfeeov09PQ2XXrsKImJiXh4\neLBnzx6toyhWKioq4sCBA8yePdvpCnlLTp8+TWJiImPHjuXll1/WOo7uqHfJHTQX9DFjxpCamsre\nvXv56KOPLLMt+vj48OCDD/L4449z5MgR3nnnHY4dO6arfroQgrvvvpuMjAxOnTqldZw202PPti3a\nmr+uro7k5GRmzpyJv7+/fUK1gR72f0pKCqNHj+ZHP/oRb775ZpuGIKqeuWIhhODJJ5/kxIkTnDhx\ngoyMDLKyssjIyLAsExYWxpIlS5g2bRoHDhzgnXfe4fjx47op6l26dOHhhx9mw4YNlguhFP1pbGzk\nX//6FwMGDCAmJkbrOJqprq7Gzc0NgHfeeYc5c+bw0UcfsWzZMqceS94a1TNvpxMnTpCUlET//v0t\nb7pmUkoqKyspKirCbDYTHh7OkCFDHDbLXWvS09NJSUnhBz/4gWVWOUUfzGYzycnJAE7dXjl16hSf\nffYZmzZt4uOPP+bLL79k8+bNTnsS2NqeuSrmHXDs2DF27tzZ4vellBw/fpxt27ZRWlrKoEGDeOih\nhzQ/sti3bx8ZGRksWbLkppO6inaklGzbto2ysjLmz5//nQMEZ3HmzBk2b97Mhx9+yB//+Ee6du3K\nhx9+6NQHHqqY21hKSkq7ryQzm82sWLGCp556itDQUCZMmMBdd93l0KKen59vuZJPSsmOHTs4d+4c\nCxcu1Gz8srVuzG5E1uT/8ssvOXHiBIsWLdLdqChH7v9NmzYxbtw4Nm7cyPz583nllVc6/AmlIz+7\neqBGs+iIi4sLixYtwtXVldGjR7Njxw7+8Y9/cOTIEUwmk8PzCCGYMmUKAQEBJCcn63IEjjM5cuQI\n6enpzJ8/X3eF3JGuXLlCQUEBH330Ee+++y6//e1vnbbV1B7qyNxBTCYTXbp04cc//jHe3t6cPn2a\nr7/+mnPnzjF06FBGjBjh8JELTU1NrFmzBjc3N2bPnu20H+21dPjwYb788ksWL15Mt27dtI6jCSkl\nx44dY/v27bi5ufHWW28xf/58rWPphmqz6NCvfvUrVqxYwbx58/Dx8QGgvLycQ4cOkZGRQVRUFCNH\njiQ8PNxhLZimpiY2btxITU0Nc+bMceojQ0eSUrJ3716ysrJYsGCBJhNo6cGlS5fYsmUL1dXV+Pr6\nEh4ezmeffWb5+VBUMbc5W/XdfvnLX/L+++/fVNAB6uvrycjI4Ouvv8bDw4NRo0YxePBgmx0tt9b3\nlFLy+eefk5+fz4IFC/Dz87PJNm2ls/XMzWYzW7Zsobi4mHnz5uHr66tdOCvYY/+bzWYOHjzIl19+\nyciRIykqKmLYsGGsXr0aDw8Pm27LWXrmHaoUQojXgZlAPXAKWCKlrO7IOju7V155BeA7Bd3T05OR\nI0cyYsQITp48ycGDB9m1axfDhg1j2LBhBAQE2C2TEIJp06axb98+3n//fRYsWED37t3ttj1n1tjY\nSHJyMvX19SxatAhPT0+tIzmUlJLs7Gz27t2Ll5cXTzzxBOXl5dTU1LB27VrVI++ADh2ZCyEmAbul\nlGYhxB8AKaV8oYVlDX1kbmstHaHf6OLFixw8eJBjx44RHBxMfHw8AwcOtGsr5OjRo+zevZtHHnmE\niIgIu23HGdXW1rJu3Tr8/PyYNWuWU52jkFKSk5PD3r17gWtzjEdHRyOEIDc3l9LSUnbt2qVxSn1y\neJtFCDELmC2lXNjC91Uxv4U1BR2uHc3l5eWRmZnJmTNn6NevH3FxcfTr188u94I8efIkmzZtYsSI\nESQlJamjJRs4ffo0GzZsYOjQoUyYMEHzaw0cRUpJXl4eKSkpmM1mxo8fT0xMzE3/flXMW6dFMd8M\nrJFSftTC9w1dzO3Vd2su6P369QPA39+foUOHtvjDXldXx7fffktmZibl5eUMGjSIIUOGEBoa2mqB\naGvfs6amhuTkZFxcXHj44Yc17aMbuWduNpvZuHEj+fn5zJo1i6ioKK0jtVl79r+UkpMnT5KSkkJj\nYyPjx48nNjb2tu9Rexdz1TP/94p2AiE3fgmQwItSyk+vL/MiYGqpkDdbvHix5U0REBBAQkKCZSc3\nT4aj1+fp6el2Wf8rr7xCbGwsu3fvBq5Ne3r16lVCQ0OBf9+yq3myo8jISIYPH06PHj2orq6msrKS\nTz75hMbGRqKiohg3bhwBAQE3LQ9w4cKFFtd3u+fl5eWMHTuWwsJCli9fzj333EPv3r2tfr16ns/l\ny5c5ePAgJpOJ+++//6ZPOHrIZ4/nffr0ITs7m5SUFEwmE1OmTGHAgAEUFBRQUFBw29c3T39xY9HV\n+uddy+cpKSmsXLkSaNst+zp8ZC6EWAz8B3CflLLF+6kZ/cjcUc6dO8eoUaMYPnw4CQkJVr1GSsm5\nc+fIyMjg+PHjBAQEEB0dTUxMDCEhIR3+SH/mzBk2bNjA4MGDGT9+vM1HG3Q2Ukq+/fZbPv/8c0aN\nGkVSUlKnb6uUl5dz5MgRMjIyCA4OZvjw4QwYMOCO/+7GxkbWrVvHww8/zB/+8AcHpTUWh7RZhBDT\ngDeAsVLK8jssq4q5lXJyckhMTPzOD4IQgrFjxzJ48OAWX9vU1ERhYSE5OTnk5ubS1NRkKeyRkZHt\nPul2+fJlduzYQX5+PlOmTGHgwIGdvkC1R1lZGVu3bqWuro4ZM2bQp08frSPZTWNjI9nZ2XzzzTeU\nlZUxZMgQhg0bZvVIKLPZzKZNm4iMjOSTTz6xy/mfzsBRxTwP8ACaC/kBKeXSFpY1dDF3dN+tpqaG\nysrKm75WVFTEQw89xAMPPGDVxy8pJRcvXiQnJ4fMzEyqq6uJiooiOjqa/v37t+vCjIKCArZu3Yqv\nry/3338/PXr0aPM62soIPfP6+nr27t1LRkYGY8eOZcSIEZa2ihHyt+bW/BcvXrQchffs2ZNhw4YR\nGxvb5mL81VdfUV9fzxdffGHXIZqqZ24FKWX/jrxeaZmfn993TjqGh4fz2GOPcfr0aauKgxCCoKAg\ngoKCCAsLIygoiLy8PHJzc/n8888JDg62FPbg4GCrjrQjIiJ46qmnOHToEB988AEJCQmMHTvW6cZL\nN2u+FH3nzp1ERUXxox/9SPcXAbXHpUuXyM7O5sSJE5SXl5OQkMATTzzRoSkIysvL+fWvf+207x1b\nU1eAGsyyZcs4deoU99xzT4fW09jYSH5+Pjk5OZw6dYqrV68SERFBREQEkZGRVvXaa2pq+OKLL8jL\ny2P48OGMGjUKb2/vDuUyCrPZzLFjx0hNTcXNzY1p06YRHh6udSybkVJy4cIFsrOzycnJoaamhujo\naGJjY202JHbDhg28/PLLzJo1ywaJOy91OX8n9d577/GrX/2KuXPn2nS4YHV1Nfn5+ZZRB5cvX/5O\ncW9pvHl5eTn79u3jxIkTDBkyhHvvvVcXtzuzB5PJRHp6Ovv378ff35+kpCSHT2dsL01NTRQUFJCT\nk0NOTg4uLi7ExsYSGxtLWFiYTa83KCgoYOPGjezbt4+BAwfabL2dkSrmNqanvtvLL7/Mu+++26aC\n3p5x5gUFBZYCX1tbS58+fSzFvVevXt/54a6uriYtLY309HRiY2O55557CAkJaWEL1tNDz/ny5csc\nPXqUr7/+mtDQUJKSkqw+uamH/LcjpaSqqorCwkJOnjxJXl4e3bp1IzY2lpiYGIKCghBC2Dx/cyFf\nt24dkyZNstl6W6Knn932cEjPXNHGSy+9BNDmgt4Wfn5+DB482DJy5vLly5bivnnzZiorKwkODqZX\nr16WR1BQEFOnTmXMmDEcOnSIjz/+GE9PT+Lj4xk8eLDhjtYbGhrIyckhKyuLwsJCYmNjWbBggU1+\nQWnBbDZTVlZGQUEBRUVFFBQUYDabLb+gJ02aZPc7+ji6kDsTdWRuYO05QreV+vp6Lly4QHFxseXP\niooKevToQc+ePenVqxc9e/akoaHBciPskJAQ4uLi7D6/TEeYzWZOnz5NVlYWOTk59OnTh7i4OGJj\nYw03vr6xsZFz585RWFhIYWEhRUVFlmlmmx+BgYEOaxGpQt4+qs3iJLQs6LcymUyUlpZSXFxseZSV\nlREYGGjpuVdUVFBSUkJQUBCRkZFERkYSHh6u2YgGs9nMhQsXOHPmDAUFBRQWFtKjRw/i4uIYNGiQ\nIUamSCmpra2lrKyM0tJSSktLKSsrs+znG4u3VvOEq0LefqqY25ie+27WFHSt+rZNTU2UlZVRXFxM\nSUkJ5eXllJeXU1VVZTnSbWhowM/Pj9DQUPr06UOfPn0IDg62fN9W2Zuamrh06RLl5eWWdkNhYSH+\n/v6WVkNERITNC54t9/2VK1duKtjNfwIEBwcTHBxMUFCQpQVmi08THc2vdSHX88+uNVTP3Ik099Df\neOMNS89zxIgRDBky5DvLms1mqqsdO+W8l5cXffv2pW/fvpavNTU1UV1dzaVLl6ioqOD8+fOcP3+e\nvLw8GhsbAXB1dcXLywsPDw969OiBj48P3t7e+Pr64uPjg4eHBx4eHri4uNDQ0IDJZPrOn80XX126\ndImamhp8fX0JCAggMDCQ/v37M378+JuGU5pMJi5dumTTf39tba1V65RSUldXR21tLbW1tdTU1Nz0\n94qKCpqamujevbvlMXz4cLp164a3t/d32iVXrlzhypUrNs/v4uKCn59fi+0Zs9nM7t27LfOvNE/a\npo7I7UsdmXcip0+fpr6+nosXL/L4448TFxfHvffea5m7pbl37ebmputLp6WUNDY2YjKZMJlMNDU1\n0dTUhJTS8riVEMLycHFxsfzp6uqKq6sr7u7uuLq6OnQ63xvzms3mm/7e/Gj+tzX/XQhhyXxjfhcX\nF9zc3Cz/Ni01/79ERUXRq1cvIiMjLSNfGhsb2bRpE4GBgbz99tu4urrSrVs3w5401gPVZnFyRUVF\nTJw4EQ8PDy5evEiXLl1YuHAh8+fPJyYmRut4HdZ8Arb50XwitvlRUlJiOaqtra3l8uXLXL16FR8f\nH3x9fW963Pg1V1fXm4pw83v21q/d+GhoaKC6uvqmR1VVFVevXsXPzw9/f3+6du1qefj7+xMYGEif\nPn0ICwsjLCyMPn360Lt3b8NcdFVUVMTevXvZuXMnKSkpVFZWEhUVRXV1NUOHDuWjjz5SV3baiCrm\nNmbEvltFRQXvvfceEydOpLq6mgkTJmgdqV1ste+bmpq4fPnyTQX+xoJfW1trOTq+9QHc9utCCNzd\n3W8q2M1/9/HxQQhhyPfOjazJf/bsWfbu3UtlZSVLly7V1Q1NjL7/Vc9coVu3bvz85z8H/j1fsjNz\ndXW1FFzFtsLCwpg/f77WMZyaOjJXFEXRMWuPzPXzWUhRFEVpN1XMrWT0NoWR8xs5O6j8WjN6fmup\nYq4oitIJqJ65oiiKjqmeuaIoihNRxdx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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "3x13cm ellipse:\n" ] }, { "data": { "image/png": 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CtLXnXVBQwOzZs4mNjWXSpEnMnz/fpv15AqPPc3cgj+sQxZYvX868efMYMmSI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\ncuXKERERwd69e5k5cyY7dux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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "4th clinical insert:\n" ] }, { "data": { "image/png": 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2EbV15H3+/Hk6depUZ7MUSoava9eupKSkkJycXGm9v78/R44cAe7eRl8+B4qk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\ntmxJ/fr1mThxIgkJCfz222+cOXOGESN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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "5cm circle:\n" ] }, { "data": { "image/png": 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ggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "5th clinical insert:\n" ] }, { "data": { "image/png": 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WT4dTp7mzZr4DaKOUilZKWYFJwCoDjis8IDMzk2HDhhEbG4uvry9hYWGeDkl4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\nvXv3ArBs2TK3xSqM5Zaauaj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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "6th clinical insert:\n" ] }, { "data": { "image/png": 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IWwCNwT106FAMGTIEGzduxFNPPQUvLy+hSzNq5eXlOHHiBAoKCjB16lT6QrcDrly5gsOH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\nlJWVwcnJCVKptNlFIpHA1tYWYrEYNjY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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "7cm circle:\n" ] }, { "data": { "image/png": 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BpbUmNzeXs846Swa5hWSYWyTQ3E4pxZQpUzhw4AClpaXWFGWhcM1dreDk3iD8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\nEEJEEEuGuVJqilLKq5Ryxic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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "7th clinical insert:\n" ] }, { "data": { "image/png": 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Pj2nTpl31GOoN+QFgPkuMvNOAfE3TzmqapgfWAOZ/rhJmaW1t5Z577iE+Pp5B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\nUjuUUicuemR//+t84E0gVtO0FKASkPh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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "9cm circle:\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "parameterised = parameterise(\n", " display=True, working_directory=None, optimise_position=True,\n", " input_dict=input_dict)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Creating an interactive report\n", "\n", "Once the data has been parameterised the model can be created. This is done so in the following cell. Of interest is that these interactive reports can be saved to a html file and placed on a shared drive. No python is required to use these interactive reports, only a modern web browser.\n", "\n", "The table in the report can be sorted and items can be selected. These slections are mirrored in the plots. Portions of the plots / colourbar can be selected which is then mirrored in the remaining plots." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "text/html": [ " \n", "\n", "\n", " \n", "\n", "
\n", " \n", " BokehJS successfully loaded.\n", "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "
\n", "\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "output_notebook() # Delete this line to output to file\n", "# output_file('example.html') # Uncomment this line to output to file\n", "\n", "create_report(\n", " input_dict=parameterised, standard_output=False) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Copyright information\n", "Copyright © 2015 Simon Biggs\n", "\n", "This program is free software: you can redistribute it and/or modify\n", "it under the terms of the GNU Affero General Public License as published\n", "by the Free Software Foundation, either version 3 of the License, or\n", "(at your option) any later version.\n", "\n", "This program is distributed in the hope that it will be useful,\n", "but WITHOUT ANY WARRANTY; without even the implied warranty of\n", "MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n", "GNU Affero General Public License for more details.\n", "\n", "You should have received a copy of the GNU Affero General Public License\n", "along with this program. If not, see ." ] } ], "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": "3.5.0" } }, "nbformat": 4, "nbformat_minor": 0 }