plot_mode_shape.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "import mph\n",
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% Imports\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "# load client\n",
    "client = mph.start(cores=1)\n",
    "client.clear()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "# filepath to COMSOL model to load\n",
    "directory = \"D:\\COMSOL\\Leo\\Results\"\n",
    "fp = os.path.join(directory, \"SiN_Membrane_PureSIPad_HeightScan.mph\")\n",
    "\n",
    "# load model\n",
    "model = client.load(fp)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [],
   "source": [
    "# Access Java API directly\n",
    "java_model = model.java\n",
    "\n",
    "mesh_tag = \"mesh5\"  # obtain via COMSOL\n",
    "mesh = java_model.component(\"comp1\").mesh(mesh_tag)  # load the mesh\n",
    "\n",
    "# load data set\n",
    "dataset_tag = \"dset9\"\n",
    "data_set_spec = java_model.result().dataset(dataset_tag)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "<java object 'com.comsol.model.impl.NumericalFeatureImpl'>"
     },
     "metadata": {},
     "output_type": "execute_result",
     "execution_count": 5
    }
   ],
   "source": [
    "# We want to set up an evaluation function, following MPh syntax.\n",
    "# MPH mapping 'evaluations':  'self.model.java.result().numerical()'\n",
    "\n",
    "# To create an evaluation object, use create(<tag>, \"Eval\") (or EvalGlobal for global evaluation)\n",
    "extract_w = java_model.result().numerical().create(\"w_derivs_arrs\", \"Eval\")\n",
    "\n",
    "# set properties of evaluation object - page 578 COMSOL docs\n",
    "extract_w.set(\"data\", \"dset9\")  # data set used for evaluation\n",
    "extract_w.set(\"unit\", \"um\")  # set units for evaluation\n",
    "# extract_w.set(\"expr\", \"w\")  # expression to evaluate"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "<java object 'com.comsol.model.impl.NumericalFeatureImpl'>"
     },
     "metadata": {},
     "output_type": "execute_result",
     "execution_count": 6
    }
   ],
   "source": [
    "# Next, we want to calculate derivatives\n",
    "# extract_w.set(\"expr\", [\"w\", \"dtang(dtang(w, x), x)\", \"dtang(dtang(w, y), y)\", \"dtang(w, x)\"])\n",
    "extract_w.set(\"expr\", [\"w\", \"dtang(w, x)\", \"dtang(dtang(w, x), x)\"])\n",
    "\n",
    "# calling getData() returns a an array where array[i, j, k] is the expression with index i evaluated\n",
    "# using solution number j and coordinates data point k, i.e. [expression][solnum][coordinates]\n",
    "# We can also use getData(<expressionIndex>) if we eg. only care about expression 0."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "outputs": [
    {
     "name": "stdout",
     "text": [
      "12\n"
     ],
     "output_type": "stream"
    }
   ],
   "source": [
    "extract_w.set(\"outersolnum\", \"3\")  # osn to set\n",
    "osn = extract_w.getString(\"outersolnum\")\n",
    "\n",
    "print(osn)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% Outer soution number for parametric sweep models\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "name": "stdout",
     "text": [
      "1\n",
      "0.00021200000000000003\n",
      "2\n",
      "0.00021200000000000003\n",
      "3\n",
      "0.00021199999999999998\n",
      "4\n",
      "0.000212\n",
      "5\n",
      "0.000212\n",
      "6\n",
      "0.000212\n",
      "7\n",
      "0.000212\n",
      "8\n",
      "0.000212\n",
      "9\n",
      "0.000212\n",
      "10\n",
      "0.00021200000000000003\n"
     ],
     "output_type": "stream"
    }
   ],
   "source": [
    "w_full = []\n",
    "deriv_x_full = []\n",
    "deriv_xx_full = []\n",
    "\n",
    "for i in range(1, 11):\n",
    "    \n",
    "    extract_w.set(\"outersolnum\", str(i))  # osn to set\n",
    "    print(extract_w.getString(\"outersolnum\"))\n",
    "    \n",
    "    # get results data\n",
    "    displacement_field_z = np.array(extract_w.getData(0))[0] # has same number of elements as the mesh stuff\n",
    "    print(np.amax(displacement_field_z))\n",
    "    \n",
    "    deriv_x = np.array(extract_w.getData(1))[0]\n",
    "    deriv_xx = np.array(extract_w.getData(2))[0]\n",
    "    \n",
    "    w_full.append(displacement_field_z)\n",
    "    deriv_x_full.append(deriv_x)\n",
    "    deriv_xx_full.append(deriv_xx)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [
    {
     "name": "stdout",
     "text": [
      "0.00021200000000000003\n",
      "0.0 0.0\n"
     ],
     "output_type": "stream"
    },
    {
     "data": {
      "text/plain": "<Figure size 600x600 with 2 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "osn = 0\n",
    "extract_w.set(\"outersolnum\", str(osn+1))\n",
    "\n",
    "plot_data = w_full[osn]\n",
    "\n",
    "XX, YY, ZZ = np.array(extract_w.getCoordinates())\n",
    "print(np.amax(plot_data))\n",
    "\n",
    "i0 = np.argmax(plot_data)\n",
    "\n",
    "x0, y0, z0 = XX[i0], YY[i0], ZZ[i0]\n",
    "print(x0, y0)\n",
    "\n",
    "# mask_x = np.logical_and(XX < 45.5, XX > 44.5)\n",
    "# mask_y = np.logical_and(YY > -0.05, YY < 0.05)\n",
    "\n",
    "mf = np.logical_and(XX > -20, YY < 20)\n",
    "\n",
    "# mask_z1 = np.logical_and(ZZ < 0.05, ZZ > 0)\n",
    "# mask_z2 = np.logical_and(ZZ < 0.1, ZZ > 0.05)\n",
    "\n",
    "# mf = np.logical_and(mask_x, mask_y)\n",
    "# mf1 = np.logical_and(mf, mask_z1)\n",
    "# mf2 = np.logical_and(mf, mask_z2)\n",
    "\n",
    "fig_heatmap, ax = plt.subplots(nrows=1, ncols=1, figsize=(6, 6))\n",
    "\n",
    "triplot1 = ax.tricontourf(XX[mf], YY[mf], plot_data[mf], levels=100)\n",
    "fig_heatmap.colorbar(triplot1, ax=ax)\n",
    "\n",
    "ax.scatter(x0, y0, s=10, c=\"k\")\n",
    "\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% Plot \n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.collections.PathCollection at 0x8a960e08>"
     },
     "metadata": {},
     "output_type": "execute_result",
     "execution_count": 87
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(XX[mf], ZZ[mf], s=0.1)\n",
    "# plt.ylim(-0.25, 0.25)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% Check displacement field at Z points\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "outputs": [
    {
     "name": "stdout",
     "text": [
      "solid.rho*2*(pi*solid.freq)^2*w^2, solid.rho * 2 * (pi * solid.freq)^2 * w^2 / (4.2398E-10 / 2)^2, solid.Wk, w^2 / (4.2398E-10 / 2)^2\n",
      "[2.28956451e-17 6.34589067e-17 6.34410642e-17 1.19648802e-16\n",
      " 1.10797957e-16 1.26643516e-16 9.71587511e-17 9.69266395e-17\n",
      " 2.05795467e-16 1.51064078e-16 2.27092738e-16 2.26813885e-16]\n"
     ],
     "output_type": "stream"
    }
   ],
   "source": [
    "# We calculate the total energy for the fundamental mode. \n",
    "# For that, we already have an evaluation node in the COMSOL file. Its tag is int1\n",
    "\n",
    "int_volume = java_model.result().numerical(\"int1\")  # load evaluation node with tag\n",
    "sol_number = 0\n",
    "\n",
    "# check if the correct eval expression is loaded\n",
    "print(int_volume.getString(\"expr\"))\n",
    "\n",
    "# Just retrieve data, without re-computing it (even though it *should recompute it*)\n",
    "int_volume_real = np.squeeze(np.array(int_volume.getReal()))[sol_number]\n",
    "print(int_volume_real)\n",
    "\n",
    "energy_total = int_volume_real[sol_number]\n",
    "# print(energy_total)\n",
    "\n",
    "# Compare with method computeResult()\n",
    "# int_volume_compute = np.squeeze(np.array(int_volume.computeResult()))\n",
    "# print(int_volume_compute)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "name": "stdout",
     "text": [
      "(3, 101)\n"
     ],
     "output_type": "stream"
    }
   ],
   "source": [
    "# get regular grid - distance 1 um\n",
    "x_np = np.linspace(44.5, 45.5, 101, endpoint=True)\n",
    "y_np = np.array([0.] * 101)\n",
    "# x_np = np.hstack([seq_geom_neg, seq_geom_pos])\n",
    "# x_np = np.hstack([seq_outer_neg, seq_outer_pos])\n",
    "\n",
    "# x_np, y_np = np.meshgrid(x_np, y_np) # extend to grid\n",
    "\n",
    "# x_np, y_np = x_np.flatten(), y_np.flatten()  # flatten grid - 301^2 entries\n",
    "\n",
    "z_np = np.array([0.] * len(x_np))\n",
    "\n",
    "grid_interp = np.array([x_np, y_np, z_np])\n",
    "print(grid_interp.shape)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "outputs": [
    {
     "name": "stdout",
     "text": [
      "108\n",
      "36\n",
      "(36,)\n"
     ],
     "output_type": "stream"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xtm, ytm, ztm = np.array(mesh.getVertex())\n",
    "\n",
    "# plt.scatter(xtm, ytm, s=0.1)\n",
    "\n",
    "xm = np.logical_and(xtm > -150, xtm < -148)\n",
    "ym = np.logical_and(ytm < 0.1, ytm > -0.1)\n",
    "\n",
    "# print(np.sum(xm), np.sum(ym))\n",
    "\n",
    "zm = np.logical_and(ztm < 0.026, ztm > 0.024)\n",
    "# zm = ztm == 0\n",
    "\n",
    "tm = np.logical_and(xm, ym)\n",
    "print(np.sum(tm))\n",
    "\n",
    "tm = np.logical_and(tm, zm)\n",
    "print(np.sum(tm))\n",
    "\n",
    "xtm, ytm = xtm[tm], ytm[tm]\n",
    "ztm = ztm[tm]\n",
    "\n",
    "plt.scatter(xtm, ytm, s=0.1)\n",
    "plt.ylim(-1, 1)\n",
    "\n",
    "print(ytm.shape)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% Mesh analysis\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "outputs": [],
   "source": [
    "grid_interp = np.array([xtm, ytm, ztm])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% Interpolation grid\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "outputs": [
    {
     "data": {
      "text/plain": "<java object 'com.comsol.model.impl.NumericalFeatureImpl'>"
     },
     "metadata": {},
     "output_type": "execute_result",
     "execution_count": 44
    }
   ],
   "source": [
    "# Interpolate data set to evaluate displacement field using mesh coordinates\n",
    "\n",
    "mesh_vertices_java = mesh.getVertex()\n",
    "\n",
    "# Choose selection for solution data set - see COMSOL Docs on Interp method\n",
    "dset_tag = \"dset9\"  # dset7 has a surface selection - prev. dset5\n",
    "tag_itp = \"mesh_interp\"\n",
    "tag_grid = \"grid_interpe\"\n",
    "interpol =  java_model.result().numerical().create(tag_grid, \"Interp\")\n",
    "interpol.set(\"data\", dset_tag)\n",
    "interpol.set(\"expr\", [\"w\", \"dtang(dtang(w, x), x)\", \"dtang(w, x)\", \"dtang(dtang(w, x), y)\"])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "outputs": [],
   "source": [
    "# interpol.setInterpolationCoordinates(mesh_vertices_java) # expects Java type\n",
    "interpol.setInterpolationCoordinates(grid_interp) # expects Java type"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [],
   "source": [
    "displacement_field_z = np.array(interpol.getData(0))[0] # has same number of elements as the mesh stuff\n",
    "deriv_xx_itp = np.array(interpol.getData(1))[0]\n",
    "deriv_x_itp = np.array(interpol.getData(2))[0]\n",
    "deriv_xy_itp = np.array(interpol.getData(3))[0]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% Data for interpolation evaulation object\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "outputs": [],
   "source": [
    "plot_range_ts = np.copy(xtm)\n",
    "# derivs_z05 = np.copy(derivs_full_xx)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% Copy\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "outputs": [
    {
     "name": "stdout",
     "text": [
      "(36,)\n"
     ],
     "output_type": "stream"
    }
   ],
   "source": [
    "w_full_itp = []\n",
    "derivs_full_xx = []\n",
    "derivs_full_x = []\n",
    "\n",
    "for ii in range(1, 8):\n",
    "    idx = str(ii)\n",
    "    interpol.set(\"outersolnum\", idx)\n",
    "    \n",
    "    # get results data\n",
    "    displacement_field_z = np.array(interpol.getData(0))[2] # has same number of elements as the mesh stuff\n",
    "    deriv_xx_itp = np.array(interpol.getData(1))[2]\n",
    "    # deriv_x_itp = np.array(interpol.getData(2))[0]\n",
    "    # deriv_xy_itp = np.array(interpol.getData(3))[0]\n",
    "    \n",
    "    derivs_full_xx.append(deriv_xx_itp)\n",
    "    # derivs_full_x.append(deriv_x_itp)\n",
    "    w_full_itp.append(displacement_field_z)\n",
    "\n",
    "print(displacement_field_z.shape)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% Data using for loop\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "outputs": [
    {
     "data": {
      "text/plain": "Text(0.5, 1.0, 'wxx [1/m] for (1, 2) mode. \\n Z = 0 nm. -1 < Y < 1nm. -150 < X < -148. \\n Normalized by surface integral kinetic energy. ')"
     },
     "metadata": {},
     "output_type": "execute_result",
     "execution_count": 49
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_0804 = plt.figure()\n",
    "\n",
    "vals_normalize = [1.345, 1.307, 1.265, 1.178, 0.977, 0.885, 0.834]\n",
    "\n",
    "for dxx, norm, ii in zip(derivs_full_xx, vals_normalize, range(1, 8)):\n",
    "    plt.plot(xtm, dxx / norm, \".\", label=f\"Ratio H SI pad to H SIN: {ii}\")\n",
    "\n",
    "plt.legend(fontsize=5)\n",
    "plt.title(\"wxx [1/m] for (1, 2) mode. \\n Z = 0 nm. -1 < Y < 1nm. -150 < X < -148. \\n Normalized by surface integral kinetic energy. \")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "outputs": [
    {
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_25132\\287928623.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mval\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnorm\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mii\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtotal_curvs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvals_normalize\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m11\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m     \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m11\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mval\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mnorm\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\".\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34mf\"{ii}\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      9\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[1;31m# plt.legend()\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\.conda\\envs\\PythonCOMSOL\\lib\\site-packages\\matplotlib\\pyplot.py\u001b[0m in \u001b[0;36mplot\u001b[1;34m(scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   2767\u001b[0m     return gca().plot(\n\u001b[0;32m   2768\u001b[0m         \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscalex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mscalex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscaley\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mscaley\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2769\u001b[1;33m         **({\"data\": data} if data is not None else {}), **kwargs)\n\u001b[0m\u001b[0;32m   2770\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2771\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\.conda\\envs\\PythonCOMSOL\\lib\\site-packages\\matplotlib\\axes\\_axes.py\u001b[0m in \u001b[0;36mplot\u001b[1;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1633\u001b[0m         \"\"\"\n\u001b[0;32m   1634\u001b[0m         \u001b[0mkwargs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcbook\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnormalize_kwargs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmlines\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mLine2D\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1635\u001b[1;33m         \u001b[0mlines\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_lines\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1636\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mlines\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1637\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\.conda\\envs\\PythonCOMSOL\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m    310\u001b[0m                 \u001b[0mthis\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    311\u001b[0m                 \u001b[0margs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 312\u001b[1;33m             \u001b[1;32myield\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_plot_args\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mthis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    313\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    314\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget_next_color\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\.conda\\envs\\PythonCOMSOL\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36m_plot_args\u001b[1;34m(self, tup, kwargs, return_kwargs)\u001b[0m\n\u001b[0;32m    496\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    497\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 498\u001b[1;33m             raise ValueError(f\"x and y must have same first dimension, but \"\n\u001b[0m\u001b[0;32m    499\u001b[0m                              f\"have shapes {x.shape} and {y.shape}\")\n\u001b[0;32m    500\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m2\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: x and y must have same first dimension, but have shapes (10,) and (1,)"
     ],
     "ename": "ValueError",
     "evalue": "x and y must have same first dimension, but have shapes (10,) and (1,)",
     "output_type": "error"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "total_curvs = \n",
    "\n",
    "fig_2003 = plt.figure()\n",
    "\n",
    "vals_normalize = [579.968, 552.308, 535.476, 522.411, 517.584, 512.848, 509.644, 507.472, 506.012, 504.240]\n",
    "\n",
    "for val, norm, ii in zip(total_curvs, vals_normalize, range(1, 11)):\n",
    "    plt.plot(range(1, 11), val / norm, \".\", label=f\"{ii}\")\n",
    "\n",
    "# plt.legend()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "outputs": [
    {
     "name": "stdout",
     "text": [
      "[0.02563499170976743, 0.02526076795976664, 0.02480464528407073, 0.02445734952779781, 0.023927048544444503, 0.02353943872524213, 0.02319118169372345, 0.022879497853284296, 0.022600726139675297, 0.022387153955933957]\n",
      "1.1283599518495742\n"
     ],
     "output_type": "stream"
    }
   ],
   "source": [
    "max_vals = [np.amax(ww) / nn for ww, nn in zip(derivs_full_xx, vals_normalize)]\n",
    "print(max_vals)\n",
    "\n",
    "print(max_vals[1] / max_vals[-1])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [
    {
     "data": {
      "text/plain": "Text(0.5, 1.0, 'Curvature (wxx) at -150 < X < 0. \\n Z = 0 [um]. H-SI = x1 H-SiN')"
     },
     "metadata": {},
     "output_type": "execute_result",
     "execution_count": 18
    },
    {
     "data": {
      "text/plain": "<Figure size 600x600 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_range = np.copy(xtm)\n",
    "# plot_range2 = np.copy(xtm)\n",
    "\n",
    "fig_linear, ax2 =  plt.subplots(ncols=1, figsize = (6, 6))\n",
    "\n",
    "# ax1.grid()\n",
    "ax2.grid()\n",
    "# ax1.plot(plot_range, derivs_z05[0], \".\", ms=1.8)\n",
    "ax2.plot(plot_range, deriv_xx_itp, \".\", ms=1.8)\n",
    "# ax1.set_title(f\"Curvature transition site. Z = 0.05 [um]. \\n H-AL = x1 H-SiN\", fontsize=7)\n",
    "ax2.set_title(f\"Curvature (wxx) at -150 < X < 0. \\n Z = 0 [um]. H-SI = x1 H-SiN\", fontsize=7)\n",
    "# ax1.set_xlim(44.5, 45.5)\n",
    "# ax1.set_ylim(-3, 14)\n",
    "# ax2.set_ylim(-3, 14)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% Plot linear cuts\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "outputs": [
    {
     "name": "stdout",
     "text": [
      "[47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47]\n",
      "[-0.37602337 -0.40975143 -0.32864509 -0.41050642 -0.2782645  -0.34611475\n",
      " -0.23030906 -0.17080696 -0.18723764  0.15167128 -0.05168137  0.52830395\n",
      "  0.00258122  0.39588907  0.01501727         nan         nan         nan\n",
      "         nan         nan         nan         nan         nan         nan\n",
      "         nan         nan         nan         nan         nan         nan\n",
      "         nan         nan         nan         nan         nan         nan\n",
      "         nan         nan         nan         nan         nan         nan\n",
      "         nan         nan         nan         nan         nan]\n"
     ],
     "output_type": "stream"
    }
   ],
   "source": [
    "print([len(derivs_z05[i]) for i in range(12)])\n",
    "\n",
    "print(derivs_z05[-1])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1200x900 with 24 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.axes_grid1 import ImageGrid\n",
    "\n",
    "# fig, axes = plt.subplots(nrows=3, ncols=4, figsize=(12, 9))\n",
    "fig = plt.figure(figsize=(12, 9))\n",
    "grid = ImageGrid(fig, 111,  # similar to subplot(111)\n",
    "                 nrows_ncols=(3, 4),  # creates 2x2 grid of axes\n",
    "                 axes_pad=0.5,  # pad between axes in inch.\n",
    "                )\n",
    "\n",
    "#axes_flat = []\n",
    "#for ax in axes:\n",
    "#    for a in ax:\n",
    "#        axes_flat.append(a)\n",
    "\n",
    "for ax, im, idxx in zip(grid, derivs_ts, range(1, 13)):\n",
    "    # print(im)\n",
    "    # Iterating over the grid returns the Axes.\n",
    "    # imm = np.arange(100).reshape((10, 10))\n",
    "    contrast = np.around((np.amax(im) - np.amin(im)), 2)\n",
    "    ax.plot(plot_range_ts, im, \".\", ms=1.5)\n",
    "    ax.grid()\n",
    "    ax.set_ylim(-3, 1)\n",
    "    ax.set_xlim(44, 46)\n",
    "    ax.set_title(f\"H AL: {idxx} x hSiN. \\n Contrast: {contrast}\", fontsize=7)\n",
    "\n",
    "# fig.tight_layout()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% Plot grid\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 400x1200 with 6 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_heatmap, (ax1, ax2, ax3) = plt.subplots(nrows=3, ncols=1, figsize=(4, 12))\n",
    "\n",
    "triplot1 = ax1.tricontourf(x_mask, y_mask, displacement_field_z, levels=30)\n",
    "fig_heatmap.colorbar(triplot1, ax=ax1)\n",
    "\n",
    "triplot2 = ax2.tricontourf(x_mask, y_mask, deriv_y, levels=30)\n",
    "fig_heatmap.colorbar(triplot2, ax=ax2)\n",
    "\n",
    "triplot3 = ax3.tricontourf(x_mask, y_mask, deriv_xy, levels=30)\n",
    "fig_heatmap.colorbar(triplot3, ax=ax3)\n",
    "\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% Plot Heatmap\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# test interpolation coordinates\n",
    "test_cords = np.array(interpol.getCoordinates())\n",
    "print(test_cords.shape)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# Plotting - use triangular plots as simple 3D surface plots require a 2D Z array\n",
    "fig = plt.figure(figsize=(8, 8))\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "\n",
    "# Plot the surface\n",
    "ax.view_init(elev=20., azim=20.)\n",
    "ax.plot_trisurf(x_np, y_np, deriv_yy, cmap='viridis', edgecolor='none')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "name": "stdout",
     "text": [
      "1e-06\n"
     ],
     "output_type": "stream"
    }
   ],
   "source": [
    "# get Q factor\n",
    "sumCurvature = (deriv_xx + deriv_yy)\n",
    "\n",
    "# Parameters\n",
    "h = 50.0e-9\n",
    "E1 = 200.0e9\n",
    "E2 = E1/(5.7e10 * h) # See Figure 2 in Villanueva et al.\n",
    "nu = 0.27\n",
    "\n",
    "# Integration element \n",
    "dx, dy = 1e-6, 1e-6"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "name": "stdout",
     "text": [
      "2.4617683998705698e-23 5.949438407156385e-23\n"
     ],
     "output_type": "stream"
    }
   ],
   "source": [
    "# Formulas from Yeghishe\n",
    "\n",
    "dW = np.nansum(sumCurvature**2) * dx * dy * h**3 * np.pi * E2 / (12 * (1 - nu**2))  # SI units\n",
    "\n",
    "intermed = np.nansum(sumCurvature**2 - 2 * (1 - nu) * (deriv_xx * deriv_yy - deriv_xy ** 2))  # SI units\n",
    "dW_full = dx * dy * h**3 * np.pi * E2 / (12 * (1 - nu**2)) * intermed\n",
    "\n",
    "print(dW, dW_full)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "name": "stdout",
     "text": [
      "7.190471760693249 2.9752852200880087\n"
     ],
     "output_type": "stream"
    }
   ],
   "source": [
    "W =  2.8172456e-17  # calculated in COMSOL\n",
    "\n",
    "Q = (2*np.pi*W/dW) / 1e6 # no units, number -> divide by 1e6 to get millions\n",
    "Q_full = (2*np.pi*W/dW_full) / 1e6\n",
    "\n",
    "print(Q, Q_full)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  },
  "pycharm": {
   "stem_cell": {
    "cell_type": "raw",
    "source": [],
    "metadata": {
     "collapsed": false
    }
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}