small_tests.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 36., -32.],\n",
       "       [-32.,  84.]])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q = np.array([[1/np.sqrt(5), 2/np.sqrt(5)],\n",
    "              [-2/np.sqrt(5),1/np.sqrt(5)]])\n",
    "Q_inv = np.linalg.inv(Q)\n",
    "lambda_mat = np.array([[100.0, 0],\n",
    "                       [0.0,  20.0]])\n",
    "D = Q@lambda_mat@Q_inv\n",
    "D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 3 [2, 3]\n"
     ]
    }
   ],
   "source": [
    "xy = [2,3]\n",
    "x,y = xy\n",
    "print(x, y, xy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "qolo = plt.imread(\"Qolo_T_CB_top_bumper.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# #qolo = mpimg.imread(\"Qolo_T_CB_top_bumper.png\")\n",
    "\n",
    "# height = 6\n",
    "# width = 10\n",
    "# x = 10\n",
    "# fig, ax = plt.subplots(figsize = (width, height))\n",
    "# newax = fig.add_axes([x/width,0.6,0.1,0.1])\n",
    "# newax.imshow(qolo)\n",
    "# plt.axis('off')\n",
    "# # newax.tick_params(\n",
    "# #     axis='both',          # changes apply to the x-axis\n",
    "# #     which='both',      # both major and minor ticks are affected\n",
    "# #     bottom=False,      # ticks along the bottom edge are off\n",
    "# #     top=False,         # ticks along the top edge are off\n",
    "# #     left = False,\n",
    "# #     right = False,\n",
    "# #     labelbottom=False) # labels along the bottom edge are off\n",
    "\n",
    "\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from matplotlib.transforms import Affine2D\n",
    "# import mpl_toolkits.axisartist.floating_axes as floating_axes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# tr = Affine2D().rotate_deg(30)\n",
    "\n",
    "# grid_helper = floating_axes.GridHelperCurveLinear(\n",
    "#     tr, extremes=(-0.5, 3.5, 0, 4),\n",
    "#     # grid_locator1=MaxNLocator(nbins=4),\n",
    "#     # grid_locator2=MaxNLocator(nbins=4),\n",
    "# )\n",
    "\n",
    "# ax1 = fig.add_subplot(\n",
    "#     131, axes_class=floating_axes.FloatingAxes, grid_helper=grid_helper\n",
    "# )\n",
    "# aux_ax = ax1.get_aux_axes(tr)\n",
    "# aux_ax.bar([0, 1, 2, 3], [3, 2, 1, 3])\n",
    "# ax1 = plt.imshow(qolo)\n",
    "\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.offsetbox.AnnotationBbox at 0x13f2baaf9a0>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9Pb3p7NweMD091fz931+bk046Je9617u2X1a3ZEnq6+v7oujFs0kvxk6tVkutVku1Wu0LpBfDqLu7Oz09PalUKjn88MNz0EEHZfny5Xv4kwMAAHvaXnO77v/58Q+lri5pamrK1q1bs6VjW+orlWx4/pepG7Z/mpubM23atGzevDkf/vCHkySfnHd5Tp02PYceeliOOOKIvPDC83n22Wezdu3a/OIXv8iqnz+em776lZ0eb9KkSVm9enWOP/74PPzww0P5UQEAgFdhMG7XvdeE0SUf+0CaGhtTX1+f4cOHpyiKdHZ25plnW3P9Df+YFzZtyn/+53+mKIo8/NMf5/N/9fep1Wo56KCD0tDQkJaWlmzatClbtmxJZ1dnenuqqVZ7s2XLlp1+R+nnP/95jj766CTJ5MmT89Of/nSoPzIAADAA+3wYveMd79jlPzvy8Al55NHHkuwYMQ0NwzNs2LCMHDkyq9euy6c+9ak88sgjKXpfyNNPP5P/ffO/paGhIUceeWQeeuihjBw5Mt3d3Tt8z2jbtm0vOV5RFKlUKpk6dWp+8pOf5PTTT88999yzuz8yAACwmw1GGA35d4yKosiiRYt+Y1s17/6TP8xhLS059tgj0tDQkLa2zVn7i2fT3t7+q31qmdh8YMaOHZtRo0bl+uv/KU1NTTn22GOSVFKtVnPcccdl7dq12bhx48vOYcqUKbnvvvtSFEX+5E/+JJVKJffcc09fLAEAAOUy5Ldl21l4/Nn7L8wxRx+Ro46alPq6+nR0dKS+vpLDWg7OhOaD0tnZnZ6eamq13owYMSINDQ0piiJLlizJkiW351//9V/T3d2du+66K7/4xS9+6xz+9m//Nkly6qmnZv/998+LJ83e9KY37d4PCwAA7BP2+F3pvvzlv0/DsOGpr69PU0NDil91U2dnd3p7e9PWviX51R3n6uuHp1arpVKp7HBHunPPPTeHHXZYTjjhhDz22GMve7yiKPpu3vDQQw+lKIqceOKJ2bJlS5566qlB/awAAMDeabeG0Tve8Y7U1dWlVqvl+9///isa8+DK5Rk5ckSGDatLpb6Sukpdhg8fnq1bO/PEk0+nvr6SsQcekM2bO5IkV33mipzzx+9OY2Njku2h09bWlvb29mzZsiWnnnpq3/aRI0fmRz/6Ud/PL+rt7c2jjz6aE044oS+0Vq9enWT7ZXZu4Q0AAOXyqsNo2rRp2W+//XLwwQcnST5/+cW5csHf5X/8j/+xwxehJk48NBMmTExRFDn33PPyrW/9S5KkWq2mKIp0d/emVisyrH772aCmpobsv//IHDTuwGzY+HzfM4oqlUp6enqy3377Jdl+ad7dd9+du+66Kw8//HBfCL3nPe/J9ddf3zfHe++9N11dXTn99NMzfPjwnHLKKXnggQdSqVT6noFUqVRy3333vdpfCQAAsI951WF07LHHZvjw4alUKmlra8u8q76Y1atX57jjjsu//uu/vmT/hx56KMcff/yvz+BUtp/BqVar6erqSqWxkuHDh6enpyejD9g/I0Y0ZELzQXm+bVPqi/pUKpV0d3enKIqcddZZ2X///bNkyZK84x3vyNatW/PJT36y772/8IUvJEn+67/+K294wxtSqVQyatSoviBK0hdbSXLCCSekp6fnt16OBwAAvLa86jD6p3/6p1e8b1EUeeMb35gDDzwwSXLooYfm1JOPT1PDiNTV1aWnpyd1dcNSVy3y/PObcuCYA1KpJJVKLYc2T8hz6zakVquls7MrXV1deeqpp3LVVVcl2X7m6Nhjj+2LnEqlkhUrVuSKK67IokWLMnLkyCxfvjxFUWTZsmWZM2dORo4cmSTZtGlTfvd3f7cvmI455pg8/vjjr/ZXAwAA7CMG9eYL/3DDrfnzP/vjJMn3l/w0qaukbdPGjB07NuvXr0+SPPLzNTnpxGPS1taWceMOTE9PT2q13owfPy51dXWpVqsZNmxY6uvrk7pk06bN6ezamhdeeCFf/X9uz3NP39t3vLe//e35yj8uSpG6FNVa3vn2k7NkyZJs2rQpRVFk6tSpSZLNmzfn3e9+dz7zmc/0hdTJJ5/c9/e7617oAADAvmFQw+iQQw/L7/zu72TupdemUqmkVhQZNXpsiqKWzZs358QTT0yyPVRGjRqVtvYtqSQ56KDXpbGxsS9UmpqaUl9fn87Ozmzd2pkf3/1/0tnZmdWP35UXNnX2He9/fW17FCVJ6ir5t8X3J0kaGhry2GOPvewzioYPH97397VabTf/JgAAgL3Z4N6uu1LJ88+/kKKopii2360uleRP3v3hHHXUUWlqakqSrH1mY44/br8MH1aXarXouxlCb29vKpVKent7++52V6vVUqTIGadNyWGHHZ5TThmXI444Ij//+c9TK5JKipx26hG5+7+eSJFa/u7v/i5Jcs899/y3aVV2+Osf//Efp66uLi0tLTvcBhwAACiHwQ2jopIDDxiboprUimpSFEldJbfe8pU8+eQTOf7445MkJx5/VEaNGpnGxsY89+y6NDU1pSgqKYpqarWkVutNR8e2PP9CW9o2tadj89Y8t+75vKV1XR55/OmsWrVq+/FqtdQqRX70458ntSKpSy6++OJUq9V873vf2z6lX92YYcKECWloaEiSPPvsszn55JNz//3bzzB50CsAAJTLoIbRT378o/zwzh+ms3Nr/r+lDydF0tPTk6TIkUcemYaGhnR1daWxcVgqlUrq6+tyxJGHZfXTz2Tr1q19Z4u6OnuyuWNLNm1qT8eWrXnr234/P162LBl2cA4c81zf8T72kXfl2oXfSiqVFJVa/vDtp+ZTn9yW3t7ezJw5MyNHjkxRFKmrq8uKFSt2Oe8XgwkAACiHQQ2jBZ+/JEnS1DQyf3jW1L5nBX3oT19Ikhx00EGZdvIbUxTFr275vf37PT09PVm/fkP222/7XeM6Ozvz/PObsmXr1ow78MAMHzYso0aNSl31uR2Ot3r1qh0ug3vxkrxhw4Zl+PDhfTF0+OGH5/DDD8/rXve6vsv0XjxblCTt7e2D+WsBAAD2Mq86jPqeR5Tk7LPPTrL99tc/+tGPdhopDz74YJ5//vm+7R/5swv6Ht5aVzc8RVHk4Anj0zC8Ppu3bMlB4w5KrVbNhg2/zNgxY5JsP6NTX1+fj33sYznqqKPyp3/6p0mSb33rWzn33HP73vvUU6fkoYceSqVSyRve8IZMmjQpBx54YJ5++umXfI+oKIpMnjw53d3dbtUNAAAl86rD6NRTT83++++fiRMnJkmu+fSl+YvPXp1PfOITeeCBB3bY94/+6I/yne98J7fffntfmHR0bMvIESNSrVa331ihKLJ169bUqsmzz65PXd2wrFnzi7671NVqtQwbVp9arZbTTz89STJr1qy+Yyxb9l955tlnU9SKHHHEMUmS448/Pg8//HDmzZuX73//+2lpacnYsWN3mF+lUslPf/rTHUIPAAAoh1cdRj/5yU/6Iuess87KpZ+7Jq3PtWbxosUvOStz1lln/eqSuf+2vSjSsbUzw4YNS29vLb29vemt1rKtqzP19fXZtq0ro0aNSnd3d2op0tnZnVqtSHt7e373zKnZurUzq556Kv/0zdsyZcqU3H33Xb962yJNTU2pVCp5+OGHkyTXXHNNfvrTn6a+vj4rV67M61//+owcOTL19fX52c9+liSZNm1afvKTn7zaXwsAALAPedVh9N8jZ9GiRa943xeNOuCAbG5ry/Dh9amrq9seRj3VX13+dnTa2jZnW2dnqtVqUk3+4pOX5+GHH866deuydWtnnnn2ubzQ3pMk+b/O/ZNccullLzuHxYsX5yMf+UhOPvnkPPXUUymKIscff3xOOOGEPPDAA6IIAABKqG5PT+C6hV9NtVrNtm1dad+8OUlSqdSy337bL6+rVqupVavZ1tWV1NWlu7s7XV1d6e7uzvoNv0zrhs3ZtGlTku136P5tKpVKRo4cmfvvvz+nnHJKfvazn+WRRx7Jgw8+2PfAWQAAoFyGNIx29f2df/qX72T107/I8vt/lvHjx+WYY47KhOaD0t1TzROrns7mjo5UKpV0d3enra0tHR0d6e7uzv0/eywbNmxIb29v3vzmN+dbt92WQw899LfO44477kiS/PjHP8673vWuvu0PPvjg7vmgAADAPmVwH/D6G168lO4d73jHS/5Z8/iDcvzrjsozzzyX0aNHZ8uWLdn0wgs5deqbUkkl9963MgdPPDy//OUv09HRkUcffTRnnnlm6urqcsABB+S5557Lk08+mVGjRmXSpEk59NBDU61Ws2zZspcc62c/+1mKokhDQ0P222+/JL++QQMAAFA+lWIvuQ3be9/9hymKSrq7O1MURerrh6ezszMvtLXlmGNPyBNPPJE5c+bk8ccfz6RJk/LBD34wSfK6170uDQ0NOfzww/PCCy9k8+bN6erqSk9PT6rVajo6OnZ6vJ///Oc5+uijUxRFpkyZ8rIPfAUAAPYe7e3tGTNmTNra2jJ69Ojd8p5Desbo5fT09PzqWUZ12bJlazo729Ld25sURZ5++un09vbmoYceyubNm3P11VcnST5x2bzc8NV/zGGHHZajjz4q69ZvyDO/+EWeeeaZ/OIXa/Lk4z/f5fFmzpyZp59+OqeccsoOD3cFAADKZ685Y7Ty/uW5/PJPpGtbd7p6elKrFanVqjn6yNdn29aujJtwaKq1atavW5/6+vrU12+/i11d3Y5fkyqKItVqNb29venp6en7a09PT99NG1asWJFJkyalubk599577x76xAAAwEC8ps8YTT7plOy336h0dz6f+koltRQ57pijUqvVUqkrsu7Zp1NfX5/hdUlnZ2eOP/bIpFJk67aurHlmY4qiSFEUqdVqqdVqfXe06+3tTW9vb7q7u9Pd3Z1qdfutwMeOHSuKAACAJHtRGFUqlUw9ZVqWLbs7mzdvTv3wSrq6ulKtFunq2n7mp7GxMcOGDctB4w7IU08/ncMOOzQdHR1pqK9l3LgDsm1bV77xz9/e5TGKosi0adOSJCtXrhyiTwYAAOzt9ppL6V70R38wK9s6e1LUaqmvr6S7uzcd27alrq4uTU1NGdnUmGOOPjL3LV+Z7t5qkmTEiKaM2m9kvvv/3r6HZw8AAAy2wbiUbq8Lo13Z1YcviqLvNuAAAMBr32CE0ZA+4HUwiCIAAODV2ufDCAAA4NUSRgAAQOkJIwAAoPSEEQAAUHrCCAAAKD1hBAAAlJ4wAgAASk8YAQAApSeMAACA0hNGAABA6QkjAACg9IQRAABQesIIAAAoPWEEAACUnjACAABKTxgBAAClJ4wAAIDSE0YAAEDpCSMAAKD0hBEAAFB6wggAACg9YQQAAJTegMNo4cKFmTRpUpqamjJ9+vTce++9r2jczTffnEqlknPOOWeghwYAANitBhRGt9xyS+bOnZv58+dnxYoVmTx5cmbNmpX169e/7LjVq1fnE5/4RM4444wBTRYAAGAwDCiMvvSlL+UDH/hA5syZk+OPPz433HBDRo4cma997Wu7HFOtVvPe9743n/3sZ3PEEUcMeMIAAAC7W7/DqLu7O8uXL8/MmTN//SZ1dZk5c2aWLVu2y3Gf+9znMn78+Lzvfe97Rcfp6upKe3v7Di8AAIDB0O8w2rhxY6rVapqbm3fY3tzcnNbW1p2Oufvuu3PTTTflxhtvfMXHWbBgQcaMGdP3amlp6e9UAQAAXpFBvyvd5s2bc/755+fGG2/MuHHjXvG4efPmpa2tre+1du3aQZwlAABQZsP6O2DcuHGpr6/PunXrdti+bt26TJgw4SX7P/nkk1m9enXOPvvsvm21Wm37wYcNy2OPPZYjjzzyJeMaGxvT2NjY3+kBAAD0W7/PGDU0NGTKlClZunRp37ZarZalS5dmxowZL9n/uOOOywMPPJCVK1f2vd75znfmrW99a1auXOkSOQAAYI/r9xmjJJk7d24uvPDCTJ06NdOmTcu1116bjo6OzJkzJ0lywQUX5JBDDsmCBQvS1NSUE044YYfxBxxwQJK8ZDsAAMCeMKAwmj17djZs2JArr7wyra2tOemkk7JkyZK+GzKsWbMmdXWD/vUlAACA3aJSFEWxpyfxSrS3t2fMmDFpa2vL6NGj9/R0AACAPWQw2sBpHQAAoPSEEQAAUHrCCAAAKD1hBAAAlJ4wAgAASk8YAQAApSeMAACA0hNGAABA6QkjAACg9IQRAABQesIIAAAoPWEEAACUnjACAABKTxgBAAClJ4wAAIDSE0YAAEDpCSMAAKD0hBEAAFB6wggAACg9YQQAAJSeMAIAAEpPGAEAAKUnjAAAgNITRgAAQOkJIwAAoPSEEQAAUHrCCAAAKD1hBAAAlJ4wAgAASk8YAQAApSeMAACA0hNGAABA6QkjAACg9IQRAABQesIIAAAoPWEEAACUnjACAABKTxgBAAClJ4wAAIDSE0YAAEDpCSMAAKD0hBEAAFB6wggAACg9YQQAAJSeMAIAAEpPGAEAAKUnjAAAgNITRgAAQOkJIwAAoPSEEQAAUHrCCAAAKD1hBAAAlJ4wAgAASk8YAQAApSeMAACA0hNGAABA6QkjAACg9IQRAABQesIIAAAoPWEEAACUnjACAABKTxgBAAClJ4wAAIDSE0YAAEDpCSMAAKD0hBEAAFB6wggAACg9YQQAAJSeMAIAAEpPGAEAAKUnjAAAgNITRgAAQOkJIwAAoPSEEQAAUHrCCAAAKD1hBAAAlJ4wAgAASk8YAQAApSeMAACA0hNGAABA6QkjAACg9IQRAABQegMOo4ULF2bSpElpamrK9OnTc++99+5y3xtvvDFnnHFGxo4dm7Fjx2bmzJkvuz8AAMBQGlAY3XLLLZk7d27mz5+fFStWZPLkyZk1a1bWr1+/0/3vvPPOnHfeefnhD3+YZcuWpaWlJW9729vyzDPPvKrJAwAA7A6VoiiK/g6aPn16Tj311Fx33XVJklqtlpaWlnz0ox/NZZdd9lvHV6vVjB07Ntddd10uuOCCV3TM9vb2jBkzJm1tbRk9enR/pwwAALxGDEYb9PuMUXd3d5YvX56ZM2f++k3q6jJz5swsW7bsFb3H1q1b09PTkwMPPHCX+3R1daW9vX2HFwAAwGDodxht3Lgx1Wo1zc3NO2xvbm5Oa2vrK3qPSy+9NBMnTtwhrn7TggULMmbMmL5XS0tLf6cKAADwigz5Xemuueaa3HzzzbntttvS1NS0y/3mzZuXtra2vtfatWuHcJYAAECZDOvvgHHjxqW+vj7r1q3bYfu6desyYcKElx37hS98Iddcc01+8IMf5E1vetPL7tvY2JjGxsb+Tg8AAKDf+n3GqKGhIVOmTMnSpUv7ttVqtSxdujQzZszY5bi//uu/zlVXXZUlS5Zk6tSpA5stAADAIOj3GaMkmTt3bi688MJMnTo106ZNy7XXXpuOjo7MmTMnSXLBBRfkkEMOyYIFC5Ikf/VXf5Urr7wy3/zmNzNp0qS+7yLtv//+2X///XfTRwEAABiYAYXR7Nmzs2HDhlx55ZVpbW3NSSedlCVLlvTdkGHNmjWpq/v1yagvf/nL6e7uzrve9a4d3mf+/Pn5zGc+M/DZAwAA7AYDeo7RnuA5RgAAQLKXPMcIAADgtUYYAQAApSeMAACA0hNGAABA6QkjAACg9IQRAABQesIIAAAoPWEEAACUnjACAABKTxgBAAClJ4wAAIDSE0YAAEDpCSMAAKD0hBEAAFB6wggAACg9YQQAAJSeMAIAAEpPGAEAAKUnjAAAgNITRgAAQOkJIwAAoPSEEQAAUHrCCAAAKD1hBAAAlJ4wAgAASk8YAQAApSeMAACA0hNGAABA6QkjAACg9IQRAABQesIIAAAoPWEEAACUnjACAABKTxgBAAClJ4wAAIDSE0YAAEDpCSMAAKD0hBEAAFB6wggAACg9YQQAAJSeMAIAAEpPGAEAAKUnjAAAgNITRgAAQOkJIwAAoPSEEQAAUHrCCAAAKD1hBAAAlJ4wAgAASk8YAQAApSeMAACA0hNGAABA6QkjAACg9IQRAABQesIIAAAoPWEEAACUnjACAABKTxgBAAClJ4wAAIDSE0YAAEDpCSMAAKD0hBEAAFB6wggAACg9YQQAAJSeMAIAAEpPGAEAAKUnjAAAgNITRgAAQOkJIwAAoPSEEQAAUHrCCAAAKD1hBAAAlJ4wAgAASk8YAQAApSeMAACA0hNGAABA6QkjAACg9IQRAABQesIIAAAoPWEEAACUnjACAABKb8BhtHDhwkyaNClNTU2ZPn167r333pfd/9vf/naOO+64NDU15cQTT8zixYsHemgAAIDdakBhdMstt2Tu3LmZP39+VqxYkcmTJ2fWrFlZv379Tve/5557ct555+V973tf7r///pxzzjk555xz8uCDD76qyQMAAOwOlaIoiv4Omj59ek499dRcd911SZJarZaWlpZ89KMfzWWXXfaS/WfPnp2Ojo58//vf79v25je/OSeddFJuuOGGV3TM9vb2jBkzJm1tbRk9enR/pwwAALxGDEYbDOvvgO7u7ixfvjzz5s3r21ZXV5eZM2dm2bJlOx2zbNmyzJ07d4dts2bNyne/+91dHqerqytdXV19P7e1tSXZ/ksAAADK68UmGMA5nl3qdxht3Lgx1Wo1zc3NO2xvbm7Oo48+utMxra2tO92/tbV1l8dZsGBBPvvZz75ke0tLS3+nDAAAvAb98pe/zJgxY3bLe/U7jIbKvHnzdjjLtGnTphx++OFZs2bNbvvwsDPt7e1paWnJ2rVrXbbJoLLWGCrWGkPFWmOotLW15bDDDsuBBx64296z32E0bty41NfXZ926dTtsX7duXSZMmLDTMRMmTOjX/knS2NiYxsbGl2wfM2aMf9EYEqNHj7bWGBLWGkPFWmOoWGsMlbq63ff0oX6/U0NDQ6ZMmZKlS5f2bavValm6dGlmzJix0zEzZszYYf8kueOOO3a5PwAAwFAa0KV0c+fOzYUXXpipU6dm2rRpufbaa9PR0ZE5c+YkSS644IIccsghWbBgQZLk4x//eM4888x88YtfzFlnnZWbb7459913X77yla/svk8CAAAwQAMKo9mzZ2fDhg258sor09rampNOOilLlizpu8HCmjVrdjitddppp+Wb3/xmPvWpT+Xyyy/P0Ucfne9+97s54YQTXvExGxsbM3/+/J1eXge7k7XGULHWGCrWGkPFWmOoDMZaG9BzjAAAAF5Ldt+3lQAAAPZRwggAACg9YQQAAJSeMAIAAEpvrwmjhQsXZtKkSWlqasr06dNz7733vuz+3/72t3PcccelqakpJ554YhYvXjxEM2Vf15+1duONN+aMM87I2LFjM3bs2MycOfO3rk14UX//XHvRzTffnEqlknPOOWdwJ8hrRn/X2qZNm3LRRRfl4IMPTmNjY4455hj/HeUV6e9au/baa3PsscdmxIgRaWlpySWXXJLOzs4hmi37qrvuuitnn312Jk6cmEqlku9+97u/dcydd96ZU045JY2NjTnqqKPy9a9/vd/H3SvC6JZbbsncuXMzf/78rFixIpMnT86sWbOyfv36ne5/zz335Lzzzsv73ve+3H///TnnnHNyzjnn5MEHHxzimbOv6e9au/POO3Peeeflhz/8YZYtW5aWlpa87W1vyzPPPDPEM2df09+19qLVq1fnE5/4RM4444whmin7uv6ute7u7vze7/1eVq9enVtvvTWPPfZYbrzxxhxyyCFDPHP2Nf1da9/85jdz2WWXZf78+XnkkUdy00035ZZbbsnll18+xDNnX9PR0ZHJkydn4cKFr2j/p556KmeddVbe+ta3ZuXKlbn44ovz/ve/P7fffnv/DlzsBaZNm1ZcdNFFfT9Xq9Vi4sSJxYIFC3a6/7nnnlucddZZO2ybPn168Wd/9meDOk/2ff1da7+pt7e3GDVqVPGNb3xjsKbIa8RA1lpvb29x2mmnFV/96leLCy+8sPiDP/iDIZgp+7r+rrUvf/nLxRFHHFF0d3cP1RR5jejvWrvooouK3/md39lh29y5c4vTTz99UOfJa0uS4rbbbnvZfT75yU8Wb3zjG3fYNnv27GLWrFn9OtYeP2PU3d2d5cuXZ+bMmX3b6urqMnPmzCxbtmynY5YtW7bD/kkya9asXe4PycDW2m/aunVrenp6cuCBBw7WNHkNGOha+9znPpfx48fnfe9731BMk9eAgay1733ve5kxY0YuuuiiNDc354QTTsjVV1+darU6VNNmHzSQtXbaaadl+fLlfZfbrVq1KosXL87b3/72IZkz5bG72mDY7pzUQGzcuDHVajXNzc07bG9ubs6jjz660zGtra073b+1tXXQ5sm+byBr7TddeumlmThx4kv+5YP/biBr7e67785NN92UlStXDsEMea0YyFpbtWpV/uM//iPvfe97s3jx4jzxxBP5yEc+kp6ensyfP38ops0+aCBr7T3veU82btyYt7zlLSmKIr29vfnQhz7kUjp2u121QXt7e7Zt25YRI0a8ovfZ42eMYF9xzTXX5Oabb85tt92WpqamPT0dXkM2b96c888/PzfeeGPGjRu3p6fDa1ytVsv48ePzla98JVOmTMns2bNzxRVX5IYbbtjTU+M15s4778zVV1+d66+/PitWrMh3vvOdLFq0KFddddWenhrs1B4/YzRu3LjU19dn3bp1O2xft25dJkyYsNMxEyZM6Nf+kAxsrb3oC1/4Qq655pr84Ac/yJve9KbBnCavAf1da08++WRWr16ds88+u29brVZLkgwbNiyPPfZYjjzyyMGdNPukgfy5dvDBB2f48OGpr6/v2/aGN7whra2t6e7uTkNDw6DOmX3TQNbapz/96Zx//vl5//vfnyQ58cQT09HRkQ9+8IO54oorUlfn/59n99hVG4wePfoVny1K9oIzRg0NDZkyZUqWLl3at61Wq2Xp0qWZMWPGTsfMmDFjh/2T5I477tjl/pAMbK0lyV//9V/nqquuypIlSzJ16tShmCr7uP6uteOOOy4PPPBAVq5c2fd65zvf2Xd3nZaWlqGcPvuQgfy5dvrpp+eJJ57oi+8kefzxx3PwwQeLInZpIGtt69atL4mfF4N8+3fqYffYbW3Qv/tCDI6bb765aGxsLL7+9a8XDz/8cPHBD36wOOCAA4rW1taiKIri/PPPLy677LK+/f/zP/+zGDZsWPGFL3yheOSRR4r58+cXw4cPLx544IE99RHYR/R3rV1zzTVFQ0NDceuttxbPPfdc32vz5s176iOwj+jvWvtN7krHK9XftbZmzZpi1KhRxZ//+Z8Xjz32WPH973+/GD9+fPH5z39+T30E9hH9XWvz588vRo0aVfzLv/xLsWrVquLf//3fiyOPPLI499xz99RHYB+xefPm4v777y/uv//+IknxpS99qbj//vuLp59+uiiKorjsssuK888/v2//VatWFSNHjiz+4i/+onjkkUeKhQsXFvX19cWSJUv6ddy9IoyKoij+4R/+oTjssMOKhoaGYtq0acWPf/zjvn925plnFhdeeOEO+3/rW98qjjnmmKKhoaF44xvfWCxatGiIZ8y+qj9r7fDDDy+SvOQ1f/78oZ84+5z+/rn23wkj+qO/a+2ee+4ppk+fXjQ2NhZHHHFE8Zd/+ZdFb2/vEM+afVF/1lpPT0/xmc98pjjyyCOLpqamoqWlpfjIRz5SvPDCC0M/cfYpP/zhD3f6v79eXF8XXnhhceaZZ75kzEknnVQ0NDQURxxxRPGP//iP/T5upSicywQAAMptj3/HCAAAYE8TRgAAQOkJIwAAoPSEEQAAUHrCCAAAKD1hBAAAlJ4wAgAASk8YAQAApSeMAACA0hNGAABA6QkjAACg9IQRAABQev8/u5snPMJ0jIQAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.offsetbox import (OffsetImage, AnnotationBbox)\n",
    "#The OffsetBox is a simple container artist.\n",
    "#The child artists are meant to be drawn at a relative position to its #parent\n",
    "\n",
    "fig, ax = plt.subplots(figsize =(10,6))\n",
    "imagebox = OffsetImage(qolo, zoom = 0.05)\n",
    "#Annotation box for solar pv logo\n",
    "#Container for the imagebox referring to a specific position *xy*.\n",
    "ab = AnnotationBbox(imagebox, (0, 0.5), frameon = False)\n",
    "ax.add_artist(ab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 6\n"
     ]
    }
   ],
   "source": [
    "X = np.array([[1,2,3,4],[5,6,7,8]])\n",
    "r, theta = X[:,1]\n",
    "print(r,theta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "for i in range(3): \n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 3]\n"
     ]
    }
   ],
   "source": [
    "x = np.array([1,2,3])\n",
    "y = x[[0,2]]\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#blackbox io\n",
    "\n",
    "import networkx as nx\n",
    "import pylab as py\n",
    "blue_edges = [('B', 'C'), ('B', 'D'), ('B', 'E'), ('E', 'F')]\n",
    "red_edges = [('A', 'B'), ('A', 'C'), ('C', 'E'), ('D', 'E'), ('D', 'F')]\n",
    "\n",
    "G=nx.Graph() #define G\n",
    "G.add_edges_from(blue_edges)\n",
    "G.add_edges_from(red_edges)\n",
    "\n",
    "pos = {'A':(0,0), 'B':(1,1), 'C':(1,-1), 'D':(2,1), 'E':(2,-1), 'F':(3,0)}\n",
    "\n",
    "# nx.draw_networkx(G, pos=pos, edgelist = [], node_color = 'k')\n",
    "# nx.draw_networkx(G, pos=pos, nodelist = [], edgelist = blue_edges, edge_color = 'b')\n",
    "# nx.draw_networkx(G, pos=pos, nodelist = [], edgelist = red_edges, edge_color = 'r')\n",
    "\n",
    "\n",
    "nx.draw(G, pos=pos, edgelist = [], node_color = 'k')\n",
    "nx.draw(G, pos=pos, nodelist = [], edgelist = blue_edges, edge_color = 'b')\n",
    "nx.draw(G, pos=pos, nodelist = [], edgelist = red_edges, edge_color = 'r')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.7 64-bit",
   "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": "3.10.7"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "7a30b7485557a43924c0104e177ec6bab6e0008128359b6d23b2129353110e8f"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}