{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2018-11-04T07:56:17.196018Z", "start_time": "2018-11-04T07:56:17.125812Z" } }, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "ExecuteTime": { "end_time": "2018-11-04T09:24:19.712710Z", "start_time": "2018-11-04T09:24:19.707733Z" } }, "outputs": [], "source": [ "b300 = np.loadtxt('b.txt')" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "ExecuteTime": { "end_time": "2018-11-04T09:26:12.475539Z", "start_time": "2018-11-04T09:26:12.243915Z" } }, "outputs": [ { "data": { "text/plain": [ "Text(0,0.5,'Reflectance (%)')" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import qxplots as qxp\n", "import makeqx as mkq\n", "import matplotlib.pyplot as plt\n", "#import jtplot submodule from jupyterthemes\n", "from jupyterthemes import jtplot\n", "# currently installed theme will be used to\n", "# set plot style if no arguments provided\n", "jtplot.style()\n", "tfi300 = mkq.digitize_qx(b300[1:], dlevels=2)\n", "qxp.plot_qx(tfi300)\n", "Rs = mkq.tmm_eval_wsweep(qx=tfi300, cthick=b300[0], inc_ang=0, lam_low=400, lam_high=800, lam_pts=100, n_subs=1.52)\n", "lams = np.linspace(400, 800, 100, endpoint=True)\n", "plt.figure(figsize=(5,2))\n", "#/plt.plot(lams, 100*Rs0)\n", "plt.plot(lams, Rs)\n", "plt.ylim(0,1)\n", "plt.axhspan(1, 4)\n", "plt.xlabel('Wavelength (nm)')\n", "plt.ylabel('Reflectance (%)')\n" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "ExecuteTime": { "end_time": "2018-11-04T09:27:29.254227Z", "start_time": "2018-11-04T09:27:29.248110Z" } }, "outputs": [ { "data": { "text/plain": [ "(300.0, 0.3205847444028737)" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b300[0], np.mean(Rs)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "ExecuteTime": { "end_time": "2018-11-04T09:36:30.268889Z", "start_time": "2018-11-04T09:36:30.264991Z" } }, "outputs": [ { "data": { "text/plain": [ "array([3.00000000e+02, 1.99104411e-01, 3.97175479e-01, 0.00000000e+00,\n", " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.33625577e-01,\n", " 1.88200352e-01, 2.51221581e-01, 2.13149354e-01, 2.55723817e-01,\n", " 3.98156565e-01, 0.00000000e+00, 1.00000000e+00, 5.93796224e-01,\n", " 7.93208853e-01, 5.81334551e-01, 1.07981287e-01, 3.33500477e-01,\n", " 1.00000000e+00, 9.10710674e-01, 1.00000000e+00, 1.00000000e+00,\n", " 5.90433598e-01, 1.00000000e+00, 1.10454822e-01, 1.00000000e+00,\n", " 4.66701959e-01, 1.00000000e+00, 4.52278506e-02, 0.00000000e+00,\n", " 1.00000000e+00, 3.48498922e-01, 0.00000000e+00, 0.00000000e+00,\n", " 1.00000000e+00, 8.72983745e-02, 2.68344294e-01, 0.00000000e+00,\n", " 1.15323797e-01])" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b300" ] }, { "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": "3.7.0" }, "latex_envs": { "LaTeX_envs_menu_present": true, "autoclose": false, "autocomplete": true, "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, "eqNumInitial": 1, "hotkeys": { "equation": "Ctrl-E", "itemize": "Ctrl-I" }, "labels_anchors": false, "latex_user_defs": false, "report_style_numbering": false, "user_envs_cfg": false } }, "nbformat": 4, "nbformat_minor": 2 }