auswertung_pso-parameter.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Third-party imports\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import tikzplotlib\n",
    "import locale\n",
    "import pyansys\n",
    "import ipywidgets as widgets\n",
    "from ipywidgets import interactive, interact_manual, interact\n",
    "from subprocess import call\n",
    "import pyvista as pv\n",
    "import itkwidgets\n",
    "\n",
    "# Local imports\n",
    "from femodel import Threepartmodel\n",
    "from util_mapdl import Material\n",
    "\n",
    "\n",
    "locale.setlocale(locale.LC_ALL, 'de_DE')\n",
    "plt.rcParams['axes.formatter.use_locale'] = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Abhängigkeit des Konvergenzverhaltens von PSO-Parametern:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fb0042f8c40>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "solution_2010 = pd.read_csv('./auswertung/solution_20-10_99-50_nx.csv')\n",
    "solution_3010 = pd.read_csv('./auswertung/solution_30-10_99-55_nx.csv')\n",
    "solution_3505 = pd.read_csv('./auswertung/solution_35-05_99-55_nx.csv')\n",
    "solution_3503 = pd.read_csv('./auswertung/solution_35-03_99-70_catia_2.csv')\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15, 15))\n",
    "\n",
    "data =[[solution_2010, 'dashdot', 'black', '$c_{1}=1\\,\\,c_{2}=2;\\,w_{1}=0.99\\,\\,w_{2}=0.55;\\,unstetig$'],\n",
    "       [solution_3010, 'dotted', 'black', '$c_{1}=3\\,\\,c_{2}=1;\\,w_{1}=0.99\\,\\,w_{2}=0.55;\\,unstetig$'],\n",
    "       [solution_3505, 'dashed', 'black', '$c_{1}=3.5\\,\\,c_{2}=0.5;\\,w_{1}=0.99\\,\\,w_{2}=0.55;\\,unstetig$'],\n",
    "       [solution_3503, 'solid', 'black', '$c_{1}=3.5\\,\\,c_{2}=0.3;\\,w_{1}=0.99\\,\\,w_{2}=0.70;\\,stetig$'],]\n",
    "\n",
    "for dataset in data:\n",
    "    dataset[0].plot(ax=ax,\n",
    "                    y='F',\n",
    "                    linestyle=dataset[1],\n",
    "                    color=dataset[2],\n",
    "                    linewidth=1,\n",
    "                    label=dataset[3],\n",
    "                    # marker='x',\n",
    "                    )\n",
    "    \n",
    "ax.set_xlabel('Number of iterations / $1$')\n",
    "ax.set_ylabel('Objective function value / $1$')\n",
    "ax.set_xlim(0,200)\n",
    "ax.set_ylim(0,0.7)\n",
    "ax.set_box_aspect(1)\n",
    "ax.grid(True, which='major',linestyle='dashed')\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "solution_3503 = pd.read_csv('./auswertung/solution_rho.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Auswertung der gefundenen Optima:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run ANSYS and instantiate FE-Model\n"
     ]
    }
   ],
   "source": [
    "# %% Run ANSYS and instantiate FE-Model\n",
    "\n",
    "print('Run ANSYS and instantiate FE-Model')\n",
    "\n",
    "ansys_path = '/home/y0065120/Dokumente/Leichtwerk/Projects/ansys-0/'\n",
    "jobname ='job-0'\n",
    "\n",
    "mapdl = pyansys.launch_mapdl(run_location=ansys_path,\n",
    "                             nproc=2,\n",
    "                             override=True,\n",
    "                             loglevel='error',\n",
    "                             additional_switches='-smp -d X11C',\n",
    "                             jobname=jobname,\n",
    "                             allow_ignore=True,\n",
    "                             mode='console')\n",
    "\n",
    "n_sec = 20\n",
    "\n",
    "femodel = Threepartmodel(mapdl,\n",
    "                         mesh_density_factor=1,\n",
    "                         propeller = [],\n",
    "                         n_sec=n_sec)\n",
    "\n",
    "femodel.materials = {'flaxpreg': Material(mapdl, 'FLAXPREG-T-UD', 1),\n",
    "                     'balsa': Material(mapdl, 'balsaholz', 2)}\n",
    "\n",
    "femodel.element_data = pd.read_csv('./mf3218/element_data.csv', index_col=(0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f057fe53704b402292ecdb1ed60ce7d4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=199, description='Iteration Number', max=199), Output()), _dom_classes=(…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "@interact(number=widgets.IntSlider(min=0, max=len(solution_3503['F'])-1, value=len(solution_3503['F'])-1, description='Iteration Number'))\n",
    "def get_iteration_dataset(number):\n",
    "    global_vars = list(solution_3503.iloc[number, [82, 83]])\n",
    "    rho = list(solution_3503.iloc[number, [i*2 + 84 for i in range(14)]])\n",
    "    div = list(solution_3503.iloc[number, [i*2 + 85 for i in range(14)]])\n",
    "        \n",
    "    items = {}\n",
    "    items['phi0'] =widgets.FloatSlider(value=global_vars[0],min=0.,max=180., description='phi0')\n",
    "    items['phi1'] =widgets.FloatSlider(value=global_vars[1],min=0.,max=180., description='phi1')\n",
    "\n",
    "    for i in range(14):\n",
    "        items['rho' + str(i)] = widgets.FloatSlider(value=rho[i],min=0.,max=1.,step=0.01, description='rho' + str(i))\n",
    "        items['div' + str(i)] = widgets.FloatSlider(value=div[i],min=0.,max=1.,step=0.01, description='div' + str(i))\n",
    "    \n",
    "    @interact_manual(gui=widgets.Checkbox(value=False, description='Open APDL Gui', disabled=False),**items)\n",
    "    def solve_femodel(gui,**kwargs):\n",
    "            global_vars = []\n",
    "            global_vars.append(kwargs['phi0'])\n",
    "            global_vars.append(kwargs['phi1'])\n",
    "            \n",
    "            args = []\n",
    "            for i in range(14):\n",
    "                args.append((kwargs['rho' + str(i)], kwargs['div' + str(i)]))\n",
    "            for i in range(6):\n",
    "                args.append((0, 0.5))\n",
    "    \n",
    "            femodel.clear()\n",
    "            femodel.cdread()\n",
    "            femodel.change_design_variables(global_vars, *args)\n",
    "            femodel.__solve__()\n",
    "    \n",
    "            mass , i_f, i_m = femodel.post_processing()\n",
    "        \n",
    "            if gui:\n",
    "                call('cp mf3218/plasma.cmap /tmp/ansys_tmp/', shell=True)\n",
    "                mapdl.open_gui()\n",
    "            \n",
    "            fig3, ax3 = plt.subplots(figsize=(25.5, 12))\n",
    "            \n",
    "            plt.subplot(2,1,1)\n",
    "            \n",
    "            plt.plot(i_f, marker='x', label='i_f')\n",
    "            plt.plot(i_m, marker='x', label='i_m')\n",
    "            plt.xlim(0,19)\n",
    "            plt.xticks(np.linspace(0,19,20))\n",
    "            plt.ylim(0,1.2)\n",
    "            plt.grid(True, which='major', linestyle='--')\n",
    "            plt.xlabel('Section Number')\n",
    "            plt.ylabel('Versagensindex nach Puck / $1$')\n",
    "            plt.legend()\n",
    "            \n",
    "            plt.subplot(2,1,2)\n",
    "            \n",
    "            plt.plot([kwargs['rho' + str(i)] for i in range(14)], label='rho', marker='x')\n",
    "            plt.plot([kwargs['div' + str(i)] for i in range(14)], label='div', marker='x')\n",
    "            plt.xlim(0,19)\n",
    "            plt.xticks(np.linspace(0,19,20))\n",
    "            plt.ylim(0,1)\n",
    "            plt.grid(True, which='major', linestyle='--')\n",
    "            plt.ylabel('Wert der Designvariablen')\n",
    "            plt.xlabel('Section Number')\n",
    "            plt.legend()\n",
    "            \n",
    "            print('Masse: ' + str(np.round(mass*1e6,2)) + 'g')\n",
    "            tikzplotlib.save(\"designvars.tex\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ac9b2a26f1c74307807e05f1cec64ac0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=1, description='Layer:', max=5, min=1), Checkbox(value=True, description…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "@interact_manual(switch=widgets.Checkbox(value=True, description='True = Fib', disabled=False),\n",
    "                 layer=widgets.IntSlider(min=1, max=5, value=1, description='Layer:'))\n",
    "def plot_puck(layer, switch):\n",
    "    mapdl.post1()\n",
    "    mapdl.layer(layer)\n",
    "    \n",
    "    if switch:\n",
    "        mapdl.etable('fib','fail','pfib')\n",
    "    else:\n",
    "        mapdl.etable('fib','fail','pmat')\n",
    "\n",
    "    with open('pretab.txt', 'w') as f:\n",
    "        f.write(mapdl.pretab('fib'))\n",
    "\n",
    "    etable = []\n",
    "    with open('pretab.txt', 'r') as f:\n",
    "        for line_number, line in enumerate(f):\n",
    "            split_line = line.split()\n",
    "            try:\n",
    "                etable.append([int(split_line[0]), float(split_line[1])])\n",
    "            except:\n",
    "                pass\n",
    "    etable=np.array(etable)          \n",
    "\n",
    "    etb_scalar = []\n",
    "    for elem in mapdl.result.grid['ansys_elem_num']:\n",
    "        etb_scalar.append(etable[elem-1,1])\n",
    "    etb_scalar = np.array(etb_scalar)\n",
    "\n",
    "    #plotter = pv.Plotter(notebook=True)\n",
    "    #plotter = pv.Plotter()\n",
    "    plotter = pv.PlotterITK()\n",
    "    plotter.add_mesh(mapdl.result.grid,\n",
    "                     scalars=etb_scalar, #etable[:,1],\n",
    "                     #cmap='plasma',\n",
    "                     #clim=[0,1],\n",
    "                     )\n",
    "    #plotter.show_axes()\n",
    "    #plotter.set_background(color='w')\n",
    "    #plotter.add_scalar_bar(position_x=0.2)\n",
    "    cpos = plotter.show(cpos=[0, 0, 1],\n",
    "                        cmap='coolwarm'\n",
    "                       # use_ipyvtk=True,\n",
    "                       ) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5f04f0cc11f346708bbfbeb72847dda6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Text(value='Pressure by Lift', description='column'), Button(description='Run Interact',…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "@interact_manual(column='Pressure by Lift')\n",
    "def plot_column(column):\n",
    "\n",
    "    etable=np.array(femodel.element_data[column])          \n",
    "\n",
    "    etb_scalar = []\n",
    "    for elem in mapdl.result.grid['ansys_elem_num']:\n",
    "        etb_scalar.append(etable[elem-1])\n",
    "    etb_scalar = np.array(etb_scalar)\n",
    "\n",
    "    #plotter = pv.Plotter(notebook=True)\n",
    "    #plotter = pv.Plotter()\n",
    "    plotter = pv.PlotterITK()\n",
    "    plotter.add_mesh(mapdl.result.grid,\n",
    "                     scalars=etb_scalar, #etable[:,1],\n",
    "                     #cmap='plasma',\n",
    "                     #clim=[0,1],\n",
    "                     )\n",
    "    #plotter.show_axes()\n",
    "    #plotter.set_background(color='w')\n",
    "    #plotter.add_scalar_bar(position_x=0.2)\n",
    "    cpos = plotter.show(cpos=[0, 0, 1],\n",
    "                        cmap='coolwarm'\n",
    "                       # use_ipyvtk=True,\n",
    "                       ) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid = mapdl.result.grid\n",
    "points = list(np.array(grid.points))\n",
    "\n",
    "listpoints = []\n",
    "for x in points:\n",
    "    listpoints.append(list(x))\n",
    "    \n",
    "listpoints = sorted(listpoints,key=lambda l:l[1])\n",
    "listpoints = np.array(listpoints)\n",
    "listpoints[:,1] = np.round(listpoints[:,1],0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-6.39999871e+00,  5.00000000e+01, -3.80015414e-07],\n",
       "       [-5.64578795e+00,  5.00000000e+01, -4.53247598e-07],\n",
       "       [-4.89157718e+00,  5.00000000e+01, -5.26479779e-07],\n",
       "       ...,\n",
       "       [ 2.55500896e+01,  4.12000000e+02, -1.12433351e+00],\n",
       "       [ 2.61615010e+01,  4.12000000e+02, -1.31813334e+00],\n",
       "       [ 2.67690983e+01,  4.12000000e+02, -1.52354616e+00]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "listpoints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid = mapdl.result.grid\n",
    "points = list(np.array(grid.points))\n",
    "\n",
    "df = pd.DataFrame(points, columns=['x', 'y', 'z'])\n",
    "df['y'] = df['y'].round(0)\n",
    "df=df.sort_values(by=['y', 'x'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "pointsarray = np.array(df)\n",
    "flag = 0\n",
    "with open('surface.txt', 'w') as f:\n",
    "    for idline, line in enumerate(pointsarray):\n",
    "        if line[1] != flag:\n",
    "            flag = line[1]\n",
    "            f.write('\\n')\n",
    "        \n",
    "        f.write('(%s, %s, %s)\\n' % (line[1], line[0], line[2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "ename": "AssertionError",
     "evalue": "Number of manager items must equal union of block items\n# manager items: 3, # tot_items: 0",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-11-883d9b2e07a9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mmapdl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnsle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m's'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'corner'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmapdl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmesh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnodes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'x'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'y'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'z'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'y'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'y'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mround\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[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mby\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'y'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'x'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, index, columns, dtype, copy)\u001b[0m\n\u001b[1;32m    495\u001b[0m                 \u001b[0mmgr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minit_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    496\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 497\u001b[0;31m                 \u001b[0mmgr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minit_ndarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    498\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    499\u001b[0m         \u001b[0;31m# For data is list-like, or Iterable (will consume into list)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/pandas/core/internals/construction.py\u001b[0m in \u001b[0;36minit_ndarray\u001b[0;34m(values, index, columns, dtype, copy)\u001b[0m\n\u001b[1;32m    232\u001b[0m         \u001b[0mblock_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    233\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 234\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mcreate_block_manager_from_blocks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mblock_values\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    235\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    236\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mcreate_block_manager_from_blocks\u001b[0;34m(blocks, axes)\u001b[0m\n\u001b[1;32m   1663\u001b[0m                 ]\n\u001b[1;32m   1664\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1665\u001b[0;31m         \u001b[0mmgr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBlockManager\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mblocks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1666\u001b[0m         \u001b[0mmgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_consolidate_inplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1667\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mmgr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, blocks, axes, do_integrity_check)\u001b[0m\n\u001b[1;32m    147\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    148\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdo_integrity_check\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 149\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_verify_integrity\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    150\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    151\u001b[0m         \u001b[0;31m# Populate known_consolidate, blknos, and blklocs lazily\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36m_verify_integrity\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    326\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mconstruction_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtot_items\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mblock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    327\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mtot_items\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 328\u001b[0;31m             raise AssertionError(\n\u001b[0m\u001b[1;32m    329\u001b[0m                 \u001b[0;34m\"Number of manager items must equal union of \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    330\u001b[0m                 \u001b[0;34mf\"block items\\n# manager items: {len(self.items)}, # \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAssertionError\u001b[0m: Number of manager items must equal union of block items\n# manager items: 3, # tot_items: 0"
     ]
    }
   ],
   "source": [
    "mapdl.nsle('s','corner')\n",
    "\n",
    "df = pd.DataFrame(mapdl.mesh.nodes, columns=['x', 'y', 'z'])\n",
    "df['y'] = df['y'].round(0)\n",
    "df=df.sort_values(by=['y', 'x'])\n",
    "\n",
    "pointsarray = np.array(df)\n",
    "flag = 0\n",
    "with open('surface.txt', 'w') as f:\n",
    "    for idline, line in enumerate(pointsarray):\n",
    "        if line[1] != flag:\n",
    "            flag = line[1]\n",
    "            f.write('\\n')\n",
    "        \n",
    "        f.write('(%s, %s, %s)\\n' % (line[1], line[0], line[2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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