{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import jsoncfg\n", "import matplotlib.pyplot as plt\n", "import decimal" ] }, { "cell_type": "code", "execution_count": 149, "metadata": {}, "outputs": [], "source": [ "json_c = jsoncfg.load(\"/home/johughes/serialize_c.json\")['data']\n", "dd = {}\n", "for key in json_c[0].keys():\n", " if type(json_c[0][key]) == str:\n", " dd[key] = np.array([float.fromhex(step[key]) for step in json_c])\n", " else:\n", " dd[key] = np.array([step[key] for step in json_c])\n", "\n", "#data_c = type('data', (object,), dd)\n", "data_c = dd" ] }, { "cell_type": "code", "execution_count": 156, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "377" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data_rs = jsoncfg.load(\"roberts_rs.json\")['data']\n", "data = data_rs\n", "data_rs = {\n", " 'ida_phi': np.array([step['ida_phi']['data'] for step in data_rs]).reshape((len(data_rs), 6, 3)),\n", " 'ida_psi': np.array([step['ida_psi']['data'] for step in data_rs]),\n", " 'ida_alpha': np.array([step['ida_alpha']['data'] for step in data_rs]),\n", " 'ida_beta': np.array([step['ida_beta']['data'] for step in data_rs]),\n", " 'ida_sigma': np.array([step['ida_sigma']['data'] for step in data_rs]),\n", " 'ida_gamma': np.array([step['ida_gamma']['data'] for step in data_rs]),\n", " 'ida_yy': np.array([step['nlp']['ida_yy']['data'] for step in data_rs]),\n", " 'ida_yp': np.array([step['nlp']['ida_yp']['data'] for step in data_rs]),\n", " 'ida_yypredict': np.array([step['nlp']['ida_yypredict']['data'] for step in data_rs]),\n", " 'ida_yppredict': np.array([step['nlp']['ida_yppredict']['data'] for step in data_rs]),\n", " 'ida_delta': np.array([step['ida_delta']['data'] for step in data_rs]),\n", " 'ida_savres': np.array([step['nlp']['ida_savres']['data'] for step in data_rs]),\n", " 'ida_ee': np.array([step['ida_ee']['data'] for step in data_rs]),\n", " 'ida_ewt': np.array([step['nlp']['ida_ewt']['data'] for step in data_rs]),\n", " 'ida_kk': np.array([step['ida_kk'] for step in data_rs]),\n", " 'ida_kused': np.array([step['ida_kused'] for step in data_rs]),\n", " 'ida_knew': np.array([step['ida_knew'] for step in data_rs]),\n", " 'ida_phase': np.array([step['ida_phase'] for step in data_rs]),\n", " 'ida_ns': np.array([step['ida_ns'] for step in data_rs]),\n", " 'ida_hin': np.array([step['ida_hin'] for step in data_rs]),\n", " 'ida_h0u':np.array([step['ida_h0u'] for step in data_rs]),\n", " 'ida_hh': np.array([step['ida_hh'] for step in data_rs]),\n", " 'ida_hused': np.array([step['ida_hused'] for step in data_rs]),\n", " 'ida_rr': np.array([step['ida_rr'] for step in data_rs]),\n", " 'ida_tn': np.array([step['nlp']['ida_tn'] for step in data_rs]),\n", " 'ida_tretlast': np.array([step['ida_tretlast'] for step in data_rs]),\n", " 'ida_cj': np.array([step['nlp']['lp']['ida_cj'] for step in data_rs]),\n", " 'ida_cjlast':np.array([step['ida_cjlast'] for step in data_rs]),\n", " 'ida_cjold': np.array([step['nlp']['lp']['ida_cjold'] for step in data_rs]),\n", " 'ida_cjratio': np.array([step['nlp']['lp']['ida_cjratio'] for step in data_rs]),\n", " 'ida_ss': np.array([step['nlp']['ida_ss'] for step in data_rs]),\n", " 'ida_oldnrm': np.array([step['nlp']['ida_oldnrm'] for step in data_rs]),\n", " 'ida_epsNewt': np.array([step['ida_eps_newt'] for step in data_rs]),\n", " 'ida_epcon': np.array([step['ida_epcon'] for step in data_rs]),\n", " 'ida_toldel': np.array([step['nlp']['ida_toldel'] for step in data_rs]),\n", " 'ida_hmax_inv': np.array([step['ida_hmax_inv'] for step in data_rs]),\n", " 'ida_nst': np.array([step['counters']['ida_nst'] for step in data_rs]),\n", " 'ida_nre': np.array([step['nlp']['ida_nre'] for step in data_rs]),\n", " 'ida_ncfn': np.array([step['counters']['ida_ncfn'] for step in data_rs]),\n", " 'ida_netf': np.array([step['counters']['ida_netf'] for step in data_rs]),\n", " 'ida_nni': np.array([step['counters']['ida_nni'] for step in data_rs]),\n", " 'ida_nsetups': np.array([step['nlp']['ida_nsetups'] for step in data_rs]),\n", " 'ida_maxord': np.array([step['ida_maxord'] for step in data_rs]),\n", " 'mat_J': np.array([step['nlp']['lp']['mat_j']['data'] for step in data_rs]).reshape((len(data), 3, 3)),\n", " 'nje': np.array([step['nlp']['lp']['nje'] for step in data_rs]),\n", " 'nls_delta': np.array([step['nls']['delta']['data'] for step in data_rs]),\n", " 'nls_curiter': np.array([step['nls']['curiter'] for step in data_rs]),\n", " 'nls_niters': np.array([step['nls']['niters'] for step in data_rs]),\n", " 'nls_nconvfails': np.array([step['nls']['nconvfails'] for step in data_rs]),\n", "}\n", "#data_rs = type('data', (object,), dd)\n", "display(len(data_rs['ida_nst']))" ] }, { "cell_type": "code", "execution_count": 157, "metadata": { "scrolled": true }, "outputs": [], "source": [ "for frame in range(0, min(len(data_rs['ida_ns']), len(data_c['ida_ns']))):\n", " for key in data_c.keys():\n", " if not np.allclose(data_c[key][frame], data_rs[key][frame], atol=1e-23, rtol=1e-20):\n", " print(\"Fame {} Step {}: {}\".format(frame, data_c['ida_nst'][frame], key))" ] }, { "cell_type": "code", "execution_count": 155, "metadata": {}, "outputs": [ { "ename": "KeyError", "evalue": "'ida_cvals'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;31m#plt.plot(data_rs['ida_ee'][0:32] - data_c['ida_ee'][0:32], '-o')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;31m#plt.plot(data_rs['ida_ewt'][0:10] - data_c['ida_ewt'][0:10], '-o')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_rs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'ida_cvals'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mdata_c\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'ida_cvals'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'o-'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mKeyError\u001b[0m: 'ida_cvals'" ] } ], "source": [ "#plt.plot(data_rs['ida_yy'][0:5,2] - data_c['ida_yy'][0:5, 2], '-o')\n", "#plt.plot(data_rs['ida_yp'][0:32] - data_c['ida_yp'][0:32], '-o')\n", "#plt.plot(data_rs['ida_delta'][0:15] - data_c['ida_delta'][0:15], '-o')\n", "#plt.plot(data_rs['ida_ee'][0:32] - data_c['ida_ee'][0:32], '-o')\n", "#plt.plot(data_rs['ida_ewt'][0:10] - data_c['ida_ewt'][0:10], '-o')\n", "plt.plot(data_rs['ida_cvals'][0:] - data_c['ida_cvals'][0:], 'o-')" ] }, { "cell_type": "code", "execution_count": 131, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[[0., 0., 0.],\n", " [0., 0., 0.],\n", " [0., 0., 0.],\n", " [0., 0., 0.],\n", " [0., 0., 0.],\n", " [0., 0., 0.]],\n", "\n", " [[0., 0., 0.],\n", " [0., 0., 0.],\n", " [0., 0., 0.],\n", " [0., 0., 0.],\n", " [0., 0., 0.],\n", " [0., 0., 0.]],\n", "\n", " [[0., 0., 0.],\n", " [0., 0., 0.],\n", " [0., 0., 0.],\n", " [0., 0., 0.],\n", " [0., 0., 0.],\n", " [0., 0., 0.]],\n", "\n", " [[0., 0., 0.],\n", " [0., 0., 0.],\n", " [0., 0., 0.],\n", " [0., 0., 0.],\n", " [0., 0., 0.],\n", " [0., 0., 0.]],\n", "\n", " [[0., 0., 0.],\n", " [0., 0., 0.],\n", " [0., 0., 0.],\n", " [0., 0., 0.],\n", " [0., 0., 0.],\n", " [0., 0., 0.]],\n", "\n", " [[0., 0., 0.],\n", " [0., 0., 0.],\n", " [0., 0., 0.],\n", " [0., 0., 0.],\n", " [0., 0., 0.],\n", " [0., 0., 0.]],\n", "\n", " [[0., 0., 0.],\n", " [0., 0., 0.],\n", " [0., 0., 0.],\n", " [0., 0., 0.],\n", " [0., 0., 0.],\n", " [0., 0., 0.]],\n", "\n", " [[0., 0., 0.],\n", " [0., 0., 0.],\n", " [0., 0., 0.],\n", " [0., 0., 0.],\n", " [0., 0., 0.],\n", " [0., 0., 0.]],\n", "\n", " [[0., 0., 0.],\n", " [0., 0., 0.],\n", " [0., 0., 0.],\n", " [0., 0., 0.],\n", " [0., 0., 0.],\n", " [0., 0., 0.]]])" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "'o-'" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#display(data_c['ida_delta'] - data_rs['ida_delta'][:30])\n", "#plt.plot(data_rs['ida_yy'][25:150,:] - data_c['ida_yy'][25:150, :], '-')\n", "#plt.plot(data_rs['ida_savres'][0:35,1] - data_c['ida_savres'][0:35,1], 'x-')\n", "#plt.plot(data_rs['ida_ee'][60:70] - data_c['ida_ee'][60:70], 'x-')\n", "#plt.plot(data_rs['ida_yypredict'][0:7] - data_c['ida_yypredict'][0:7] - 0.0, 'o-')\n", "display(data_rs['ida_phi'][0:9] - data_c['ida_phi'][0:9], 'o-')\n", "#plt.plot(data_c['ida_ss'][10:50] - 0*data_c['ida_ss'][10:50], 'o-')\n", "#plt.plot(data_c['ida_phase'][0:10])\n", "#plt.plot(range(60,120), data_rs['ida_ee'][60:120] - data_c['ida_ee'][60:120], 'x-')" ] }, { "cell_type": "code", "execution_count": 159, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "[,\n", " ,\n", " ]" ] }, "execution_count": 159, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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G3p3kjeMOdJgmZW0/AfwqcA7wGPDRbnxs+ZIcD/wL8N6q+umhpg4YG1fGiVnHqvp5VZ0DrKL/6eTXD5FhLGs4O2OS3wCuBF4F/A5wMvD+cWZcjGOlGPYCZ87YXwU8OqYsjap6tHt+Avg8/W+Axw9+xOyenxhfwufNlWki1raqHu++SZ8D/oFfnOYYS74kL6L/A/efq+pfu+GJWsNBGSdtHbtMPwHuoX9e/sQkywdkeD5fd/wEFn66cZQZL+pO01VVPQN8mglYw8N1rBTDfcC67o6G4+hfnNo+5kwkeWmSlx3cBi4AHqCfbVM3bRNw53gSNubKtB14V3fHxeuApw6eLllKs87V/gH9dTyYb2N318paYB3wtSOcJcBNwINV9bEZhyZmDefKOCnrmGRlkhO77ZcAb6V/HeQrwKXdtNlreHBtLwXuru6K7xJn/M6M8g/9ayAz13Ds3ysLMu6r30v1oH9HwHfpn6e8atx5ukxn0b/T45vA7oO56J8bvQvY0z2fvMS5Pkv/NML/0f8t57K5MtH/eHxjt67fBnpjyndL9/7fov8NePqM+Vd1+R4CLl6CfG+gf4rgW8D93eNtE7aGc2WciHUEfgv4RpfjAeBvuvGz6BfSNHA7sKIbf3G3P90dP2sJ1nCujHd3a/gA8E/84s6lJf86L/bhXz5LkhrHyqkkSdICWQySpIbFIElqWAySpIbFIElqWAySpIbFIElqWAySpMb/A0KJuRiY2dzCAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(data_c['ida_yp'][:] - data_rs['ida_yp'][:],'-o')" ] } ], "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": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 }