diff --git a/CODE-OF-CONDUCT.md b/CODE-OF-CONDUCT.md
new file mode 100644
index 00000000..243525af
--- /dev/null
+++ b/CODE-OF-CONDUCT.md
@@ -0,0 +1,42 @@
+# Code of Conduct
+
+## Our Pledge
+
+In the interest of developing an open and welcoming environment, we as contributors and maintainers pledge to making participation in our project and our community a smooth experience for everyone, regardless of age, size, personal quirks, ethnicity, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, religion, sex inclinations, or sexual identity and orientation.
+
+## Our Standards
+
+Examples of behavior that contributes to creating a positive environment include:
+
+- Using welcoming and inclusive language
+- Being respectful of differing viewpoints and experiences
+- Gracefully accepting constructive criticism
+- Focusing on what is best for the community
+- Showing empathy towards other community members
+
+Examples of unacceptable behavior by participants include:
+
+- Trolling, insulting/derogatory comments, and personal or political attacks
+- Public or private harassment
+- Publishing others' private information, without explicit permission
+- Other conduct which could reasonably be considered inappropriate in a friendly setting
+
+## Our Responsibilities
+
+Project maintainers are responsible for suggesting best practices of acceptable behavior and are in a position to take appropriate and fair corrective action in response to any instances of unacceptable behavior.
+
+Project maintainers may claim the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, or to ban temporarily or permanently any contributor for other behaviors that they deem inappropriate, threatening, offensive, or harmful.
+
+## Scope
+
+This Code of Conduct applies both within project spaces and in public spaces when an individual is representing the project or its community. Examples of representing a project or community include using an official project e-mail address, public conferences and scientific gatherings, posting via an official social media account, or acting as an appointed representative at an online or offline event. Representation of a project may be further defined and clarified by project maintainers.
+
+## Reporting
+
+Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by contacting the project team or in the [Issues page](https://github.com/kumiori/irrevolutions/issues). All complaints will be reviewed and investigated and will result in a response that is deemed necessary and appropriate to the circumstances. The project team maintains confidentiality with regard to the details of an incident. 
+
+Project maintainers are bound to the Code of Conduct as well, failing to honour its best practices may lead to temporary or permanent repercussions as determined by other members of the project's leadership.
+
+## Attribution
+
+This Code of Conduct is adapted from the [Contributor Covenant](https://www.contributor-covenant.org), version 1.4, available at [https://www.contributor-covenant.org/version/1/4/code-of-conduct.html](https://www.contributor-covenant.org/version/1/4/code-of-conduct.html).
\ No newline at end of file
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
new file mode 100644
index 00000000..f9cfb6dc
--- /dev/null
+++ b/CONTRIBUTING.md
@@ -0,0 +1,34 @@
+# Contributing to Our Project
+We welcome contributions from everyone in the community
+
+### Reporting bugs
+
+If you find a bug in Irrevolutions, please report it on the [GitHub issue
+tracker](https://github.com/fenics/dolfinx/issues/new?labels=bug).
+
+
+### Suggesting enhancements
+
+If you want to suggest a new feature or an improvement of a current
+feature, you can submit this on the [issue
+tracker](https://github.com/fenics/dolfinx/issues).
+
+
+### Submitting a pull request
+
+To contribute code DOLFINx, create a pull request. If you want to
+contribute, but are unsure where to start, have a look at the [Discussions page](https://github.com/kumiori/irrevolutions/discussions).
+For substantial changes/contributions, please start with an issue or
+start a new Discussion thread.
+
+On opening a pull request, unit tests will run on GitHub CI. You can
+click on these in the pull request to see where (if anywhere) the tests
+are failing.
+
+
+### Code of conduct
+
+We suggest all our contributors to read the [code of
+conduct](CODE-OF-CONDUCT.md).
+
+
diff --git a/README.md b/README.md
index f4eccf82..8bb070b8 100644
--- a/README.md
+++ b/README.md
@@ -112,6 +112,9 @@ Each file should have at least the "copyright" line and a pointer to where the f
     along with `irrevolution`.  If not, see <https://www.gnu.org/licenses/>.
 
 
+### Further information
+
+
 
 ## Star History
 
diff --git a/contributed/DIC_CT_35/DIC_DATA_Build_Gmsh .ipynb b/contributed/DIC_CT_35/DIC_DATA_Build_Gmsh .ipynb
deleted file mode 100644
index 42654073..00000000
--- a/contributed/DIC_CT_35/DIC_DATA_Build_Gmsh .ipynb	
+++ /dev/null
@@ -1,512 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import math\n",
-    "import warnings\n",
-    "import matplotlib.pyplot as plt\n",
-    "warnings.filterwarnings(\"ignore\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Extraire DIC data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "filename = \"CT_R35_1\"\n",
-    "# load csv file XY positions\n",
-    "x_coord = pd.read_csv('/Users/xinyuanzhai/Desktop/Donnes_DIC/' +  filename +   '/x.csv',names=[\"X\"])\n",
-    "y_coord = pd.read_csv('/Users/xinyuanzhai/Desktop/Donnes_DIC/' +  filename +   '/y.csv',names=[\"Y\"])\n",
-    "ux = pd.read_csv('/Users/xinyuanzhai/Desktop/Donnes_DIC/' +  filename +   '/u1_cod.csv',header=None)\n",
-    "uy = pd.read_csv('/Users/xinyuanzhai/Desktop/Donnes_DIC/' +  filename +   '/u2_cod.csv',header=None)\n",
-    "\n",
-    "#create dataframe for DIC data\n",
-    "contour = pd.concat([x_coord, y_coord,ux,uy],axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x7f96f7ba05b0>"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1296x1080 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#plot of DIC data\n",
-    "x_ = contour['X']\n",
-    "y_ = contour['Y']\n",
-    "plt.figure(figsize=(18, 15))\n",
-    "plt.scatter(x_,y_)\n",
-    "\n",
-    "xtip = 261.5\n",
-    "ytip = 789.5\n",
-    "plt.scatter(xtip,ytip)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Xmin 5.5 Xmax 1493.5 Ymin 5.5 Ymax 1573.5\n"
-     ]
-    }
-   ],
-   "source": [
-    "#3rd contour to choose\n",
-    "ymax=contour['Y'].max()\n",
-    "ymin=contour['Y'].min()\n",
-    "xmax=contour['X'].max()\n",
-    "xmin=contour['X'].min()\n",
-    "print(\"Xmin {0} Xmax {1} Ymin {2} Ymax {3}\".format(xmin, xmax, ymin, ymax))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "y_mid  = ytip\n",
-    "pixel = 16\n",
-    "nc = 1\n",
-    "#given 2nd contour\n",
-    "x_n_min = xmin+nc*pixel\n",
-    "#x_n_max = xmax-nc*pixel\n",
-    "#y_n_min = ymin+(nc)*pixel\n",
-    "#y_n_max = ymax-(nc)*pixel"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "565.5\n",
-      "1013.5\n"
-     ]
-    }
-   ],
-   "source": [
-    "#contour left\n",
-    "left1 = contour[(contour.X==x_n_min) & (contour.Y < y_mid)]\n",
-    "left1_min = left1['Y'].min()\n",
-    "left1.sort_values(by=['Y'],ascending=False,inplace=True)\n",
-    "\n",
-    "\n",
-    "left2 = contour[(contour.X==x_n_min) & (contour.Y > y_mid)]\n",
-    "left2_max = left2['Y'].max()\n",
-    "left2.sort_values(by=['Y'],ascending=False,inplace=True)\n",
-    "\n",
-    "\n",
-    "\n",
-    "y_left_min = left1['Y'].min()\n",
-    "y_left_max = left2['Y'].max()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bias_top=pd.DataFrame()\n",
-    "for i in range(1,100):\n",
-    "    bias_top = bias_top.append(contour[(contour.X==x_n_min + i*pixel) & (contour.Y==y_left_max + i*pixel)])\n",
-    "\n",
-    "bias_top.sort_values(by=['X'],ascending=False,inplace=True)\n",
-    "\n",
-    "bias_bottom=pd.DataFrame()\n",
-    "for i in range(1,100):\n",
-    "    bias_bottom = bias_bottom.append(contour[(contour.X==x_n_min + i*pixel) & (contour.Y==y_left_min -i*pixel)])\n",
-    "    \n",
-    "bias_bottom.sort_values(by=['X'],ascending=True,inplace=True)\n",
-    "\n",
-    "#get the point max of bias\n",
-    "x_bias_max = bias_top['X'].max()\n",
-    "y_bias_max = bias_top['Y'].max()\n",
-    "y_bias_min = bias_bottom['Y'].min()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "top = contour[(contour.Y==y_bias_max) & (contour.X > x_bias_max)]\n",
-    "top.sort_values(by=['X'],ascending=False,inplace=True)\n",
-    "\n",
-    "bottom = contour[(contour.Y==y_bias_min) & (contour. X> x_bias_max)]\n",
-    "bottom.sort_values(by=['X'],ascending=True,inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x7f96fe5277c0>"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1296x1008 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#drop in to new dataframe\n",
-    "final = pd.concat([left1,bias_bottom,bottom,top,bias_top,left2],axis=0)\n",
-    "\n",
-    "#put crack tip to (0,0)\n",
-    "\n",
-    "final[\"X\"] = final[\"X\"] - xtip\n",
-    "final[\"Y\"] = final[\"Y\"] - ytip\n",
-    "\n",
-    "#\n",
-    "x_final = final['X']\n",
-    "y_final = final['Y']\n",
-    "plt.figure(figsize=(18, 14))\n",
-    "plt.scatter(x_final,y_final)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#save to csv\n",
-    "final.to_csv('xy_position_libre.csv',columns= ['X', 'Y'],index = False)\n",
-    "charge = final.drop(columns=['X', 'Y'])\n",
-    "charge.to_csv('charge_libre.csv',index = False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Build Gmsh file "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "L = 50.\n",
-    "scale=2e-5\n",
-    "\n",
-    "# load csv file XY positions\n",
-    "filename = \"xy_position_libre\"\n",
-    "DIC_0 = pd.read_csv('/Users/xinyuanzhai/DIC_gradam/CT_35_1/' +  filename +   '.csv')\n",
-    "\n",
-    "#convert to mm and then dimensionless with L = 50\n",
-    "DIC_0 = DIC_0*1000*scale/L"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "filename1 = \"charge_libre\"\n",
-    "U_xy = pd.read_csv('/Users/xinyuanzhai/DIC_gradam/CT_35_1/' +  filename1 +   '.csv')\n",
-    "\n",
-    "#convert to mm then dimensionless with L = 50\n",
-    "U_xy = U_xy*1000*scale/L\n",
-    "U_xy.columns = np.arange(len(U_xy.columns))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Get the value of \n",
-    "xval1 = DIC_0.loc[0,'X']\n",
-    "\n",
-    "\n",
-    "#Replace the value x and y in geo file\n",
-    "with open('mesh/DIC.geo', 'r') as file :\n",
-    "    filedata = file.read()\n",
-    "\n",
-    "filedata = filedata.replace('xval = ', 'xval = '+ str(xval1))\n",
-    "\n",
-    "# Write the file out again\n",
-    "with open('mesh/DIC_running.geo', 'w') as file:\n",
-    "    file.write(filedata)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#get mid point\n",
-    "#mid_p = DIC_0[(DIC_0.X==DIC_0['X'].max()) & (DIC_0.Y==DIC_0['Y'].min())]\n",
-    "#mid_value = mid_p.index.values[0]\n",
-    "\n",
-    "#Extract x y cood and the displacement\n",
-    "xcord = DIC_0.X\n",
-    "ycord = DIC_0.Y\n",
-    "\n",
-    "#Put the geo language in a list\n",
-    "data = []\n",
-    "\n",
-    "j = 5 #Nombre de point dans geofile\n",
-    "k = 4 #Nombre de line dans geofile \n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "for i in range(len(xcord)):\n",
-    "    data.append(\"//+\")\n",
-    "    data.append(\"Point(\" + str(i+j+1) + \") = {\" + str(xcord[i]) + \", \" + str(ycord[i]) + \", 0, f0};\")\n",
-    "\n",
-    "data.append(\"//+\")\n",
-    "data.append(\"Line(\" + str(k+1) + \") = {\" +str(2) + \", \" + str(j+1) + \"};\")  \n",
-    "    \n",
-    "for i in range(len(xcord)-1):\n",
-    "    data.append(\"//+\")\n",
-    "    data.append(\"Line(\" + str(i+k+2) + \") = {\" + str(i+j+1)+ \", \" + str(i+j+2) + \"};\")\n",
-    "\n",
-    "#two line\n",
-    "data.append(\"//+\")\n",
-    "data.append(\"Line(\" + str(len(xcord)+k+1) + \") = {\" + str(len(xcord)+j)+ \", \" + str(1) + \"};\")\n",
-    "\n",
-    "\n",
-    "    \n",
-    "# Write the point cood and line data in new geofile call DIC_1.geo\n",
-    "geofile = open(\"mesh/DIC_running.geo\",\"a\")\n",
-    "\n",
-    "for line in data:\n",
-    "    \n",
-    "    geofile.write(line+'\\n')\n",
-    "    \n",
-    "geofile.close()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "111"
-      ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# pls check this mid_line\n",
-    "mid_value = math.ceil(((len(xcord))/2)+k)+1\n",
-    "mid_value"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "last = str(len(xcord)+k+1)\n",
-    "\n",
-    "curve= \"\"\n",
-    "#Curve loop for lower side mesh\n",
-    "for i in range(len(xcord)+k):\n",
-    "    loop_value = i+1\n",
-    "    curve += str(loop_value) + ',' \n",
-    "\n",
-    "curve += last"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "contour0 = \"\"\n",
-    "#Curve loop for upper side mesh\n",
-    "for i in range(mid_value-k):\n",
-    "    loop_value = i+mid_value+1\n",
-    "    contour0 += str(loop_value) + ',' \n",
-    "\n",
-    "contour0 += last"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "first = str(mid_value-1)\n",
-    "contour1 = \"\"\n",
-    "#Curve loop for upper side mesh\n",
-    "for i in range(mid_value-k-2):\n",
-    "    loop_value = i+1+k\n",
-    "    contour1 += str(loop_value) + ',' \n",
-    "\n",
-    "contour1 += first"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "geofile = open(\"mesh/DIC_running.geo\",\"a\")\n",
-    "\n",
-    "#write in geofile for lower side:\n",
-    "geofile.write(\"//+\" +'\\n')\n",
-    "geofile.write(\"Curve loop(\" + str(1) + \") = {\" + curve + \"};\" +'\\n')\n",
-    "geofile.write(\"//+\" +'\\n')\n",
-    "geofile.write(\"Plane Surface(\" + str(1) + \")= {\" + str(1) + \"};\" +'\\n')\n",
-    "\n",
-    "\n",
-    "#Physique surface : usually we have 1 surfaces\n",
-    "geofile.write(\"//+\" +'\\n')\n",
-    "geofile.write('Physical Surface(\"1\") = {1};'+'\\n')\n",
-    "\n",
-    "#create physic curve for upper side :\n",
-    "\n",
-    "geofile.write(\"//+\" +'\\n')\n",
-    "geofile.write('Physical Curve(\"2\") = {' + contour0 + '};' +'\\n')\n",
-    "\n",
-    "geofile.write(\"//+\" +'\\n')\n",
-    "geofile.write('Physical Curve(\"3\") = {' + contour1 + '};' +'\\n')\n",
-    "\n",
-    "geofile.write(\"//+\" +'\\n')\n",
-    "geofile.write('Physical Curve(\"4\") = {' + str(mid_value) + '};' +'\\n')\n",
-    "\n",
-    "geofile.close()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Convert mesh"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 59,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "os.system(\"python3 export_msh.py\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# plot direction"
-   ]
-  }
- ],
- "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": "3.8.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/contributed/DIC_CT_35/DIC_FEniCS_Isotropy.ipynb b/contributed/DIC_CT_35/DIC_FEniCS_Isotropy.ipynb
deleted file mode 100644
index 92f2d72c..00000000
--- a/contributed/DIC_CT_35/DIC_FEniCS_Isotropy.ipynb
+++ /dev/null
@@ -1,1899 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "80102748",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from __future__ import division\n",
-    "from dolfin import *\n",
-    "\n",
-    "import pandas as pd\n",
-    "import argparse\n",
-    "import math\n",
-    "import os\n",
-    "import shutil\n",
-    "import sympy\n",
-    "import sys\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "44b089aa",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# ----------------------------------------------------------------------------\n",
-    "# Parameters for DOLFIN and SOLVER \n",
-    "# ----------------------------------------------------------------------------\n",
-    "set_log_level(LogLevel.INFO)  # log level\n",
-    "# set some dolfin specific parameters\n",
-    "info(parameters,True)\n",
-    "parameters[\"form_compiler\"][\"optimize\"] = True\n",
-    "parameters[\"form_compiler\"][\"cpp_optimize\"] = True\n",
-    "parameters[\"form_compiler\"][\"representation\"] = \"uflacs\"\n",
-    "\n",
-    "# -----------------------------------------------------------------------------\n",
-    "# parameters of the solvers\n",
-    "solver_u_parameters = {\"nonlinear_solver\": \"newton\",\n",
-    "                       \"newton_solver\": {\"linear_solver\": \"mumps\",\n",
-    "                                          \"maximum_iterations\": 100,\n",
-    "                                          \"absolute_tolerance\": 1e-8,\n",
-    "                                          \"relative_tolerance\": 1e-6,\n",
-    "                                          \"report\": True,\n",
-    "                                          \"error_on_nonconvergence\": True}}                              \n",
-    "# parameters of the PETSc/Tao solver used for the alpha-problem\n",
-    "tao_solver_parameters = {\"maximum_iterations\": 200,\n",
-    "                         \"report\": False,\n",
-    "                         \"line_search\": \"more-thuente\",\n",
-    "                         \"linear_solver\": \"mumps\",\n",
-    "                         \"method\": \"tron\",\n",
-    "                         \"gradient_absolute_tol\": 1e-8,\n",
-    "                         \"gradient_relative_tol\": 1e-8,\n",
-    "                         \"error_on_nonconvergence\": True}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "87ce251a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "filename = \"mesh/DIC_running\"\n",
-    "#Read mesh\n",
-    "mesh = Mesh()\n",
-    "hdf = HDF5File(mesh.mpi_comm(), filename + \".h5\", \"r\")\n",
-    "hdf.read(mesh, \"/mesh\", False)\n",
-    "ndim = mesh.topology().dim()\n",
-    "\n",
-    "boundaries = MeshFunction(\"size_t\", mesh,1)\n",
-    "hdf.read(boundaries, \"/boundaries\")\n",
-    "\n",
-    "subdomains = MeshFunction(\"size_t\", mesh,2)\n",
-    "hdf.read(subdomains, \"/subdomains\")\n",
-    "\n",
-    "rename_subdomains = MeshFunction(\"size_t\", mesh, 2)\n",
-    "rename_subdomains.set_all(0)\n",
-    "rename_subdomains.array()[subdomains.array()==1] = 1\n",
-    "\n",
-    "dx = Measure(\"dx\", domain=mesh, subdomain_data=rename_subdomains)\n",
-    "\n",
-    "rename_boundaries = MeshFunction(\"size_t\", mesh, 1)\n",
-    "rename_boundaries.set_all(0)\n",
-    "rename_boundaries.array()[boundaries.array()==2] = 2\n",
-    "rename_boundaries.array()[boundaries.array()==3] = 3\n",
-    "rename_boundaries.array()[boundaries.array()==4] = 4\n",
-    "\n",
-    "ds = Measure(\"ds\", domain=mesh, subdomain_data=rename_boundaries)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "a181c3a2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#material\n",
-    "E = Constant(1900.0)\n",
-    "nu = Constant(0.34)\n",
-    "Gc = Constant(0.12)\n",
-    "ell = Constant(0.012)\n",
-    "ut = Constant(1.0)\n",
-    "k_ell = Constant(1e-6)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "8f5785bd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Id = Identity(ndim)\n",
-    "\n",
-    "n = FacetNormal(mesh)\n",
-    "\n",
-    "ex  = Constant((1.0, 0.0))\n",
-    "\n",
-    "ey  = Constant((0.0, 1.0))\n",
-    "\n",
-    "def u_x(u):\n",
-    "    return u.dx(0)\n",
-    "\n",
-    "def utt(u):\n",
-    "    return grad(u).T\n",
-    "\n",
-    "# -----------------------------------------------------------------------------\n",
-    "# Strain and stress and Constitutive functions of the damage model\n",
-    "# -----------------------------------------------------------------------------\n",
-    "# Strain and stress\n",
-    "def eps(v):\n",
-    "    return sym(grad(v))\n",
-    "\n",
-    "def sigma_0(v):\n",
-    "    mu    = E/(2.0*(1.0 + nu))\n",
-    "    lmbda = E*nu/(1.0 - nu**2) # plane stress\n",
-    "    return 2.0*mu*eps(v) + lmbda*tr(eps(v))*Id\n",
-    "\n",
-    "# Constitutive functions of the damage model\n",
-    "def w(alpha):\n",
-    "    return alpha\n",
-    "\n",
-    "def a(alpha):\n",
-    "    return (1-alpha)**2\n",
-    "\n",
-    "def sigma(u, alpha):\n",
-    "    return (a(alpha)+k_ell)*sigma_0(u)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "44d047fe",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "modelname = \"DIC_AT1_l0.012_1900_I_integral\"\n",
-    "meshname  = modelname+\"-mesh.xdmf\"\n",
-    "\n",
-    "savedir   = \"DIC_isotropy/\"+modelname+\"/\"\n",
-    "\n",
-    "if MPI.rank(MPI.comm_world) == 0:\n",
-    "    if os.path.isdir(savedir):\n",
-    "        shutil.rmtree(savedir)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "099bd8a1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create function space for 2D elasticity + Damage\n",
-    "V_u     = VectorFunctionSpace(mesh, \"Lagrange\", 1, dim = 2)\n",
-    "V_alpha = FunctionSpace(mesh, \"Lagrange\", 1)\n",
-    "\n",
-    "# Define the function, test and trial fields\n",
-    "u       = Function(V_u, name=\"Displacement\")\n",
-    "du      = TrialFunction(V_u)\n",
-    "v       = TestFunction(V_u)\n",
-    "alpha   = Function(V_alpha, name=\"Damage\")\n",
-    "dalpha  = TrialFunction(V_alpha)\n",
-    "beta    = TestFunction(V_alpha)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "dd6af410",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "L = 50.\n",
-    "scale=2e-5\n",
-    "\n",
-    "# load csv file XY positions\n",
-    "filename = \"xy_position_libre\"\n",
-    "DIC_0 = pd.read_csv('/Users/xinyuanzhai/DIC_gradam/CT_35_1/' +  filename +   '.csv')\n",
-    "\n",
-    "#convert to mm and then dimensionless with L = 50\n",
-    "DIC_0 = DIC_0*1000*scale/L\n",
-    "\n",
-    "filename1 = \"charge_libre\"\n",
-    "U_xy = pd.read_csv('/Users/xinyuanzhai/DIC_gradam/CT_35_1/' +  filename1 +   '.csv')\n",
-    "\n",
-    "#convert to mm then dimensionless with L = 50\n",
-    "U_xy = U_xy*1000*scale/L\n",
-    "U_xy.columns = np.arange(len(U_xy.columns))\n",
-    "\n",
-    "#Number of point in this contour\n",
-    "xcord = DIC_0.X\n",
-    "ycord = DIC_0.Y"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "f3e95a77",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "#number of time step\n",
-    "N_image = int(len(U_xy.columns)/2)\n",
-    "#range(len(xcord)-1)\n",
-    "\n",
-    "#define the point around the contour\n",
-    "#eps = 1e-6\n",
-    "point = {}\n",
-    "for j in range(len(xcord)):\n",
-    "    point[j] = CompiledSubDomain(\"near(x[0], x1) && near(x[1], y1)\",x1 = DIC_0.X[j] ,y1 = DIC_0.Y[j])\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "244961de",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#define chargement for all points on the contour\n",
-    "charge = {}\n",
-    "for i in range(len(xcord)):\n",
-    "    charge[i] = Expression((\"ux\",\"uy\"), ux=0, uy=0, degree=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "5aa7f1d1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#define displacement BC for all points on the contour\n",
-    "bc = {} \n",
-    "bc_u = []\n",
-    "\n",
-    "for k in range(len(xcord)):\n",
-    "    \n",
-    "    bc[k] = DirichletBC(V_u,charge[k], point[k], method = \"pointwise\")\n",
-    "    \n",
-    "    bc_u.append(bc[k])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "37865dda",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#boundary condition for damage\n",
-    "bc_a1 = DirichletBC(V_alpha, Constant(0.), boundaries, 2)\n",
-    "bc_a2 = DirichletBC(V_alpha, Constant(0.), boundaries, 3)\n",
-    "bc_a3 = DirichletBC(V_alpha, Constant(0.), boundaries, 4)\n",
-    "bc_alpha = [bc_a1, bc_a2, bc_a3]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "f9de0db3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "elastic_energy = 0.5*inner(sigma(u, alpha), eps(u))*dx\n",
-    "\n",
-    "# Weak form of elasticity problem\n",
-    "E_u  = derivative(elastic_energy,u,v)\n",
-    "# Writing tangent problems in term of test and trial functions for matrix assembly\n",
-    "E_du = derivative(E_u, u, du)\n",
-    "\n",
-    "dissipated_energy  = 3*Gc/8*(w(alpha)/ell + ell*inner(grad(alpha), grad(alpha)))*dx\n",
-    "\n",
-    "damage_functional = elastic_energy + dissipated_energy\n",
-    "\n",
-    "# First and second directional derivative wrt alpha\n",
-    "E_alpha = derivative(damage_functional,alpha,beta)\n",
-    "E_alpha_alpha = derivative(E_alpha,alpha,dalpha)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "6295d296",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Variational problem for the displacement\n",
-    "problem_u = NonlinearVariationalProblem(E_u, u, bc_u, J=E_du)\n",
-    "# Set up the solvers                                        \n",
-    "solver_u  = NonlinearVariationalSolver(problem_u)\n",
-    "solver_u.parameters.update(solver_u_parameters)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "15223865",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# --------------------------------------------------------------------\n",
-    "# Implement the box constraints for damage field\n",
-    "# --------------------------------------------------------------------\n",
-    "# Variational problem for the damage (non-linear to use variational inequality solvers of petsc)\n",
-    "# Define the minimisation problem by using OptimisationProblem class\n",
-    "\n",
-    "class DamageProblem(OptimisationProblem):\n",
-    "\n",
-    "    def __init__(self,f,gradf,alpha,J,bcs):\n",
-    "        OptimisationProblem.__init__(self)\n",
-    "        self.total_energy = f\n",
-    "        self.Dalpha_total_energy = gradf\n",
-    "        self.J_alpha = J\n",
-    "        self.alpha = alpha\n",
-    "        self.bc_alpha = bcs\n",
-    "\n",
-    "    def f(self, x):\n",
-    "        self.alpha.vector()[:] = x\n",
-    "        return assemble(self.total_energy)\n",
-    "\n",
-    "    def F(self, b, x):\n",
-    "        self.alpha.vector()[:] = x\n",
-    "        assemble(self.Dalpha_total_energy, b)\n",
-    "        for bc in self.bc_alpha:\n",
-    "            bc.apply(b)\n",
-    "\n",
-    "    def J(self, A, x):\n",
-    "        self.alpha.vector()[:] = x\n",
-    "        assemble(self.J_alpha, A)\n",
-    "        for bc in self.bc_alpha:\n",
-    "            bc.apply(A)\n",
-    "\n",
-    "damage_problem = DamageProblem(damage_functional,E_alpha,alpha,E_alpha_alpha,bc_alpha)\n",
-    "\n",
-    "# Set up the solvers                                        \n",
-    "solver_alpha  = PETScTAOSolver()\n",
-    "solver_alpha.parameters.update(tao_solver_parameters)\n",
-    "\n",
-    "\n",
-    "\n",
-    "alpha_lb = interpolate(Expression(\"0.\", degree=0), V_alpha)  # lower bound, set to 0\n",
-    "alpha_ub = interpolate(Expression(\"1.\", degree=0), V_alpha)  # upper bound, set to 1\n",
-    "\n",
-    "alpha_0 = interpolate(Expression(\"0.\", degree=0), V_alpha)  # initial (known) alpha"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "6c304081",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "file_u = XDMFFile(MPI.comm_world, savedir+\"/u.xdmf\")\n",
-    "file_alpha = XDMFFile(MPI.comm_world, savedir+\"/alpha.xdmf\")\n",
-    "for file in [file_u,file_alpha]:\n",
-    "    file.parameters[\"flush_output\"]=True\n",
-    "    file.parameters[\"rewrite_function_mesh\"]=False"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "2c1c524e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "load_multipliers = np.linspace(0, N_image-1, N_image)\n",
-    "energies = np.zeros((len(load_multipliers),4))\n",
-    "iterations = np.zeros((len(load_multipliers),2))\n",
-    "force = np.zeros((len(load_multipliers),2))\n",
-    "SIF = np.zeros((len(load_multipliers),4))\n",
-    "\n",
-    "\n",
-    "# Numerical parameters of the alternate minimization\n",
-    "maxiteration = 2000\n",
-    "AM_tolerance = 1e-4"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "108bcbb0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#----------------------------------------------#\n",
-    "#-----------Create Auxiliary field ------------#\n",
-    "#----------------------------------------------#\n",
-    "\n",
-    "#Auxiliry field for T-stress\n",
-    "mu = float(E/(2.0*(1.0 + nu)))\n",
-    "kappav = float((3.0-nu)/(1.0+nu))\n",
-    "Force = 1\n",
-    "u_aux = Expression([\"(-F*(kappa+1)/(pi*8*mu))*2.3*std::log(sqrt((x[0])*(x[0])+x[1]*x[1]))- (F/(pi*4*mu))*sin(atan2(x[1], x[0]))*sin(atan2(x[1], x[0]))\",\n",
-    "                    \"(-F*(kappa-1)/(pi*8*mu))*atan2(x[1], x[0])+ (F/(pi*4*mu))*sin(atan2(x[1], x[0]))*cos(atan2(x[1], x[0]))\"],\n",
-    "                    degree=2, mu=mu, kappa=kappav, F=Force,domain=mesh)\n",
-    "\n",
-    "sigmax = Expression((('-F*pow(cos(atan2(x[1], x[0])),3)/(pi*((x[0])*(x[0])+x[1]*x[1]))','-F*pow(cos(atan2(x[1], x[0])),2)*sin(atan2(x[1], x[0]))/(pi*((x[0])*(x[0])+x[1]*x[1]))'),\n",
-    "                ('-F*pow(cos(atan2(x[1], x[0])),2)*sin(atan2(x[1], x[0]))/(pi*((x[0])*(x[0])+x[1]*x[1]))','-F*cos(atan2(x[1], x[0]))*pow(sin(atan2(x[1], x[0])),2)/(pi*((x[0])*(x[0])+x[1]*x[1]))')),degree=2,F=Force,domain=mesh)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "5ce4afa1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Auxiliry field for K1_K2\n",
-    "\n",
-    "#Define auxiliry field for Stress intensity factor for K1\n",
-    "k1_aux1 = 1\n",
-    "    #k2_aux1 = 0\n",
-    "u_kaux1 = Expression([\"sqrt(sqrt(x[0]*x[0]+x[1]*x[1])/(2*pi))*(k1_aux1*((kappa-cos(atan2(x[1], x[0])))*cos(atan2(x[1], x[0])/2)/(2*mu)))\",\n",
-    "                        \"sqrt(sqrt(x[0]*x[0]+x[1]*x[1])/(2*pi))*(k1_aux1*((kappa-cos(atan2(x[1], x[0])))*sin(atan2(x[1], x[0])/2)/(2*mu)))\"],\n",
-    "                         degree=2, mu=mu, kappa=kappav , k1_aux1=k1_aux1 , domain=mesh)\n",
-    "\n",
-    "\n",
-    "#Define auxiliry field for Stress intensity factor for K1\n",
-    "sigma_1 = Expression((('(1/sqrt(2*pi*sqrt(x[0]*x[0]+x[1]*x[1])))*(k1_aux1*(0.75*cos(atan2(x[1], x[0])/2)+0.25*cos(atan2(x[1], x[0])*5/2)))',\n",
-    "                       '(1/sqrt(2*pi*sqrt(x[0]*x[0]+x[1]*x[1])))*(k1_aux1*(-0.25*sin(atan2(x[1], x[0])/2)+0.25*sin(atan2(x[1], x[0])*5/2)))'),\n",
-    "                      ('(1/sqrt(2*pi*sqrt(x[0]*x[0]+x[1]*x[1])))*(k1_aux1*(-0.25*sin(atan2(x[1], x[0])/2)+0.25*sin(atan2(x[1], x[0])*5/2)))',\n",
-    "                        '(1/sqrt(2*pi*sqrt(x[0]*x[0]+x[1]*x[1])))*(k1_aux1*(1.25*cos(atan2(x[1], x[0])/2)-0.25*cos(atan2(x[1], x[0])*5/2)))')),\n",
-    "                        degree=2,mu=mu, kappa=kappav,k1_aux1=k1_aux1 ,  domain=mesh)\n",
-    "    \n",
-    "    \n",
-    "    \n",
-    "#Define auxiliry field for Stress intensity factor for K2\n",
-    "#k1_aux2 = 0\n",
-    "k2_aux2 = 1\n",
-    "u_kaux2 = Expression([\"sqrt(sqrt(x[0]*x[0]+x[1]*x[1])/(2*pi))*(k2_aux2*((2+kappa+cos(atan2(x[1], x[0])))*sin(atan2(x[1], x[0])/2)/(2*mu)))\",\n",
-    "                      \"sqrt(sqrt(x[0]*x[0]+x[1]*x[1])/(2*pi))*(k2_aux2*((2-kappa-cos(atan2(x[1], x[0])))*cos(atan2(x[1], x[0])/2)/(2*mu)))\"],\n",
-    "                        degree=2, mu=mu, kappa=kappav, k2_aux2=k2_aux2, domain=mesh)\n",
-    "\n",
-    "#Define auxiliry field for Stress intensity factor for K1\n",
-    "sigma_2 = Expression((('(1/sqrt(2*pi*sqrt(x[0]*x[0]+x[1]*x[1])))*(k2_aux2*(-1.75*sin(atan2(x[1], x[0])/2)-0.25*sin(atan2(x[1], x[0])*5/2)))',\n",
-    "                       '(1/sqrt(2*pi*sqrt(x[0]*x[0]+x[1]*x[1])))*(k2_aux2*(0.75*cos(atan2(x[1], x[0])/2)+0.25*cos(atan2(x[1], x[0])*5/2)))'),\n",
-    "                      ('(1/sqrt(2*pi*sqrt(x[0]*x[0]+x[1]*x[1])))*(k2_aux2*(0.75*cos(atan2(x[1], x[0])/2)+0.25*cos(atan2(x[1], x[0])*5/2)))',\n",
-    "                        '(1/sqrt(2*pi*sqrt(x[0]*x[0]+x[1]*x[1])))*(k2_aux2*(-0.25*sin(atan2(x[1], x[0])/2)+0.25*sin(atan2(x[1], x[0])*5/2)))')),\n",
-    "                        degree=2,mu=mu, kappa=kappav, k2_aux2=k2_aux2, domain=mesh)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "e4340103",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "#charge[1].ux = U_xy[1].values[1]*ut\n",
-    "        \n",
-    "#charge[1].uy = U_xy[1].values[1]*ut\n",
-    "\n",
-    "#solver_u.solve()\n",
-    "#plot(u,mode='displacement')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2a7a4a24",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[1;32m--- Starting of Time step  0: t = 0.000000 ---\u001b[1;m\n",
-      "AM Iteration:   1,  alpha_error:     0.00000000\n",
-      "\n",
-      "End of timestep   0 with load multiplier 0.000000\n",
-      "\n",
-      "Elastic and Surface Energies: [0.000010,0.000000]\n",
-      "\n",
-      "Elastic and Surface Energies: [1.0178007085231071e-05,0.0]\n",
-      "-----------------------------------------\n",
-      "\u001b[1;32m--- Starting of Time step  1: t = 1.000000 ---\u001b[1;m\n",
-      "AM Iteration:   1,  alpha_error:     0.00000000\n",
-      "\n",
-      "End of timestep   1 with load multiplier 1.000000\n",
-      "\n",
-      "Elastic and Surface Energies: [0.000062,0.000000]\n",
-      "\n",
-      "Elastic and Surface Energies: [6.222666553801626e-05,0.0]\n",
-      "-----------------------------------------\n",
-      "\u001b[1;32m--- Starting of Time step  2: t = 2.000000 ---\u001b[1;m\n",
-      "AM Iteration:   1,  alpha_error:     0.00000000\n",
-      "\n",
-      "End of timestep   2 with load multiplier 2.000000\n",
-      "\n",
-      "Elastic and Surface Energies: [0.000171,0.000000]\n",
-      "\n",
-      "Elastic and Surface Energies: [0.0001714363624599161,0.0]\n",
-      "-----------------------------------------\n",
-      "\u001b[1;32m--- Starting of Time step  3: t = 3.000000 ---\u001b[1;m\n",
-      "AM Iteration:   1,  alpha_error:     0.00000000\n",
-      "\n",
-      "End of timestep   3 with load multiplier 3.000000\n",
-      "\n",
-      "Elastic and Surface Energies: [0.000379,0.000000]\n",
-      "\n",
-      "Elastic and Surface Energies: [0.0003793468727054585,0.0]\n",
-      "-----------------------------------------\n",
-      "\u001b[1;32m--- Starting of Time step  4: t = 4.000000 ---\u001b[1;m\n",
-      "AM Iteration:   1,  alpha_error:     0.00000000\n",
-      "\n",
-      "End of timestep   4 with load multiplier 4.000000\n",
-      "\n",
-      "Elastic and Surface Energies: [0.000695,0.000000]\n",
-      "\n",
-      "Elastic and Surface Energies: [0.000695277035718305,0.0]\n",
-      "-----------------------------------------\n",
-      "\u001b[1;32m--- Starting of Time step  5: t = 5.000000 ---\u001b[1;m\n",
-      "AM Iteration:   1,  alpha_error:     0.00000000\n",
-      "\n",
-      "End of timestep   5 with load multiplier 5.000000\n",
-      "\n",
-      "Elastic and Surface Energies: [0.001052,0.000000]\n",
-      "\n",
-      "Elastic and Surface Energies: [0.0010515863286766711,0.0]\n",
-      "-----------------------------------------\n",
-      "\u001b[1;32m--- Starting of Time step  6: t = 6.000000 ---\u001b[1;m\n",
-      "AM Iteration:   1,  alpha_error:     0.00000000\n",
-      "\n",
-      "End of timestep   6 with load multiplier 6.000000\n",
-      "\n",
-      "Elastic and Surface Energies: [0.001557,0.000000]\n",
-      "\n",
-      "Elastic and Surface Energies: [0.0015567788498266491,0.0]\n",
-      "-----------------------------------------\n",
-      "\u001b[1;32m--- Starting of Time step  7: t = 7.000000 ---\u001b[1;m\n",
-      "AM Iteration:   1,  alpha_error:     0.00000000\n",
-      "\n",
-      "End of timestep   7 with load multiplier 7.000000\n",
-      "\n",
-      "Elastic and Surface Energies: [0.002164,0.000000]\n",
-      "\n",
-      "Elastic and Surface Energies: [0.002163765683854305,0.0]\n",
-      "-----------------------------------------\n",
-      "\u001b[1;32m--- Starting of Time step  8: t = 8.000000 ---\u001b[1;m\n",
-      "AM Iteration:   1,  alpha_error:     0.00000000\n",
-      "\n",
-      "End of timestep   8 with load multiplier 8.000000\n",
-      "\n",
-      "Elastic and Surface Energies: [0.002855,0.000000]\n",
-      "\n",
-      "Elastic and Surface Energies: [0.002854937323505551,0.0]\n",
-      "-----------------------------------------\n",
-      "\u001b[1;32m--- Starting of Time step  9: t = 9.000000 ---\u001b[1;m\n",
-      "AM Iteration:   1,  alpha_error:     0.00032142\n",
-      "AM Iteration:   2,  alpha_error:     0.00000199\n",
-      "\n",
-      "End of timestep   9 with load multiplier 9.000000\n",
-      "\n",
-      "Elastic and Surface Energies: [0.003152,0.000000]\n",
-      "\n",
-      "Elastic and Surface Energies: [0.0031523984318226135,9.794609423214239e-09]\n",
-      "-----------------------------------------\n",
-      "\u001b[1;32m--- Starting of Time step 10: t = 10.000000 ---\u001b[1;m\n",
-      "AM Iteration:   1,  alpha_error:     0.00098088\n",
-      "AM Iteration:   2,  alpha_error:     0.00000679\n",
-      "\n",
-      "End of timestep  10 with load multiplier 10.000000\n",
-      "\n",
-      "Elastic and Surface Energies: [0.003497,0.000000]\n",
-      "\n",
-      "Elastic and Surface Energies: [0.0034968510004373507,4.2098440613886935e-08]\n",
-      "-----------------------------------------\n",
-      "\u001b[1;32m--- Starting of Time step 11: t = 11.000000 ---\u001b[1;m\n",
-      "AM Iteration:   1,  alpha_error:     0.00104758\n",
-      "AM Iteration:   2,  alpha_error:     0.00000807\n",
-      "\n",
-      "End of timestep  11 with load multiplier 11.000000\n",
-      "\n",
-      "Elastic and Surface Energies: [0.003869,0.000000]\n",
-      "\n",
-      "Elastic and Surface Energies: [0.003869476633656877,8.061059459133385e-08]\n",
-      "-----------------------------------------\n",
-      "\u001b[1;32m--- Starting of Time step 12: t = 12.000000 ---\u001b[1;m\n",
-      "AM Iteration:   1,  alpha_error:     0.00106053\n",
-      "AM Iteration:   2,  alpha_error:     0.00000900\n",
-      "\n",
-      "End of timestep  12 with load multiplier 12.000000\n",
-      "\n",
-      "Elastic and Surface Energies: [0.004232,0.000000]\n",
-      "\n",
-      "Elastic and Surface Energies: [0.004231605227971303,1.2382813304958874e-07]\n",
-      "-----------------------------------------\n",
-      "\u001b[1;32m--- Starting of Time step 13: t = 13.000000 ---\u001b[1;m\n",
-      "AM Iteration:   1,  alpha_error:     0.00130558\n",
-      "AM Iteration:   2,  alpha_error:     0.00001761\n",
-      "\n",
-      "End of timestep  13 with load multiplier 13.000000\n",
-      "\n",
-      "Elastic and Surface Energies: [0.004652,0.000000]\n",
-      "\n",
-      "Elastic and Surface Energies: [0.004652273310305912,2.025278135398137e-07]\n",
-      "-----------------------------------------\n",
-      "\u001b[1;32m--- Starting of Time step 14: t = 14.000000 ---\u001b[1;m\n",
-      "AM Iteration:   1,  alpha_error:     0.00246026\n",
-      "AM Iteration:   2,  alpha_error:     0.00008739\n",
-      "\n",
-      "End of timestep  14 with load multiplier 14.000000\n",
-      "\n",
-      "Elastic and Surface Energies: [0.005135,0.000001]\n",
-      "\n",
-      "Elastic and Surface Energies: [0.005135452457291712,5.18482788442668e-07]\n",
-      "-----------------------------------------\n",
-      "\u001b[1;32m--- Starting of Time step 15: t = 15.000000 ---\u001b[1;m\n",
-      "AM Iteration:   1,  alpha_error:     0.00335110\n",
-      "AM Iteration:   2,  alpha_error:     0.00016657\n",
-      "AM Iteration:   3,  alpha_error:     0.00000825\n",
-      "\n",
-      "End of timestep  15 with load multiplier 15.000000\n",
-      "\n",
-      "Elastic and Surface Energies: [0.005657,0.000001]\n",
-      "\n",
-      "Elastic and Surface Energies: [0.005657357709105658,1.0485472792306804e-06]\n",
-      "-----------------------------------------\n",
-      "\u001b[1;32m--- Starting of Time step 16: t = 16.000000 ---\u001b[1;m\n",
-      "AM Iteration:   1,  alpha_error:     0.00450487\n",
-      "AM Iteration:   2,  alpha_error:     0.00026161\n",
-      "AM Iteration:   3,  alpha_error:     0.00001457\n",
-      "\n",
-      "End of timestep  16 with load multiplier 16.000000\n",
-      "\n",
-      "Elastic and Surface Energies: [0.006313,0.000002]\n",
-      "\n",
-      "Elastic and Surface Energies: [0.006312972386194336,1.8854410173470353e-06]\n",
-      "-----------------------------------------\n",
-      "\u001b[1;32m--- Starting of Time step 17: t = 17.000000 ---\u001b[1;m\n",
-      "AM Iteration:   1,  alpha_error:     0.00528191\n",
-      "AM Iteration:   2,  alpha_error:     0.00043220\n",
-      "AM Iteration:   3,  alpha_error:     0.00003591\n",
-      "\n",
-      "End of timestep  17 with load multiplier 17.000000\n",
-      "\n",
-      "Elastic and Surface Energies: [0.006983,0.000003]\n",
-      "\n",
-      "Elastic and Surface Energies: [0.006983047340356543,3.23376208120616e-06]\n",
-      "-----------------------------------------\n",
-      "\u001b[1;32m--- Starting of Time step 18: t = 18.000000 ---\u001b[1;m\n",
-      "AM Iteration:   1,  alpha_error:     0.00663591\n",
-      "AM Iteration:   2,  alpha_error:     0.00069920\n",
-      "AM Iteration:   3,  alpha_error:     0.00007245\n",
-      "\n",
-      "End of timestep  18 with load multiplier 18.000000\n",
-      "\n",
-      "Elastic and Surface Energies: [0.007686,0.000005]\n",
-      "\n",
-      "Elastic and Surface Energies: [0.007686017556057839,5.496839578738534e-06]\n",
-      "-----------------------------------------\n",
-      "\u001b[1;32m--- Starting of Time step 19: t = 19.000000 ---\u001b[1;m\n",
-      "AM Iteration:   1,  alpha_error:     0.00835327\n",
-      "AM Iteration:   2,  alpha_error:     0.00110473\n",
-      "AM Iteration:   3,  alpha_error:     0.00014527\n",
-      "AM Iteration:   4,  alpha_error:     0.00001908\n",
-      "\n",
-      "End of timestep  19 with load multiplier 19.000000\n",
-      "\n",
-      "Elastic and Surface Energies: [0.008513,0.000009]\n",
-      "\n",
-      "Elastic and Surface Energies: [0.008513416227707233,9.023824916294837e-06]\n",
-      "-----------------------------------------\n",
-      "\u001b[1;32m--- Starting of Time step 20: t = 20.000000 ---\u001b[1;m\n",
-      "AM Iteration:   1,  alpha_error:     0.01362788\n",
-      "AM Iteration:   2,  alpha_error:     0.00247134\n",
-      "AM Iteration:   3,  alpha_error:     0.00045342\n",
-      "AM Iteration:   4,  alpha_error:     0.00008353\n",
-      "\n",
-      "End of timestep  20 with load multiplier 20.000000\n",
-      "\n",
-      "Elastic and Surface Energies: [0.009594,0.000017]\n",
-      "\n",
-      "Elastic and Surface Energies: [0.009593636651832058,1.7382652753000578e-05]\n",
-      "-----------------------------------------\n",
-      "\u001b[1;32m--- Starting of Time step 21: t = 21.000000 ---\u001b[1;m\n",
-      "AM Iteration:   1,  alpha_error:     0.01429858\n",
-      "AM Iteration:   2,  alpha_error:     0.00328768\n",
-      "AM Iteration:   3,  alpha_error:     0.00075751\n",
-      "AM Iteration:   4,  alpha_error:     0.00017540\n",
-      "AM Iteration:   5,  alpha_error:     0.00004063\n",
-      "\n",
-      "End of timestep  21 with load multiplier 21.000000\n",
-      "\n",
-      "Elastic and Surface Energies: [0.010646,0.000029]\n",
-      "\n",
-      "Elastic and Surface Energies: [0.01064629510127055,2.9133425079243686e-05]\n",
-      "-----------------------------------------\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[1;32m--- Starting of Time step 22: t = 22.000000 ---\u001b[1;m\n",
-      "AM Iteration:   1,  alpha_error:     0.01497763\n",
-      "AM Iteration:   2,  alpha_error:     0.00424686\n",
-      "AM Iteration:   3,  alpha_error:     0.00122113\n",
-      "AM Iteration:   4,  alpha_error:     0.00035443\n",
-      "AM Iteration:   5,  alpha_error:     0.00010373\n",
-      "AM Iteration:   6,  alpha_error:     0.00003049\n",
-      "\n",
-      "End of timestep  22 with load multiplier 22.000000\n",
-      "\n",
-      "Elastic and Surface Energies: [0.011621,0.000046]\n",
-      "\n",
-      "Elastic and Surface Energies: [0.011621370449105118,4.587058753054835e-05]\n",
-      "-----------------------------------------\n",
-      "\u001b[1;32m--- Starting of Time step 23: t = 23.000000 ---\u001b[1;m\n",
-      "AM Iteration:   1,  alpha_error:     0.01803220\n",
-      "AM Iteration:   2,  alpha_error:     0.00626204\n",
-      "AM Iteration:   3,  alpha_error:     0.00218995\n",
-      "AM Iteration:   4,  alpha_error:     0.00076994\n",
-      "AM Iteration:   5,  alpha_error:     0.00027128\n",
-      "AM Iteration:   6,  alpha_error:     0.00009566\n",
-      "\n",
-      "End of timestep  23 with load multiplier 23.000000\n",
-      "\n",
-      "Elastic and Surface Energies: [0.012700,0.000073]\n",
-      "\n",
-      "Elastic and Surface Energies: [0.012699582915153941,7.308130282659415e-05]\n",
-      "-----------------------------------------\n",
-      "\u001b[1;32m--- Starting of Time step 24: t = 24.000000 ---\u001b[1;m\n",
-      "AM Iteration:   1,  alpha_error:     0.02753412\n",
-      "AM Iteration:   2,  alpha_error:     0.01199642\n",
-      "AM Iteration:   3,  alpha_error:     0.00537071\n",
-      "AM Iteration:   4,  alpha_error:     0.00243820\n",
-      "AM Iteration:   5,  alpha_error:     0.00111380\n",
-      "AM Iteration:   6,  alpha_error:     0.00051025\n",
-      "AM Iteration:   7,  alpha_error:     0.00023406\n",
-      "AM Iteration:   8,  alpha_error:     0.00010742\n",
-      "AM Iteration:   9,  alpha_error:     0.00004932\n",
-      "\n",
-      "End of timestep  24 with load multiplier 24.000000\n",
-      "\n",
-      "Elastic and Surface Energies: [0.014159,0.000135]\n",
-      "\n",
-      "Elastic and Surface Energies: [0.014159387183834074,0.0001352982835709058]\n",
-      "-----------------------------------------\n",
-      "\u001b[1;32m--- Starting of Time step 25: t = 25.000000 ---\u001b[1;m\n",
-      "AM Iteration:   1,  alpha_error:     0.02307902\n",
-      "AM Iteration:   2,  alpha_error:     0.01228844\n",
-      "AM Iteration:   3,  alpha_error:     0.00665480\n",
-      "AM Iteration:   4,  alpha_error:     0.00365337\n",
-      "AM Iteration:   5,  alpha_error:     0.00202392\n",
-      "AM Iteration:   6,  alpha_error:     0.00112861\n",
-      "AM Iteration:   7,  alpha_error:     0.00063366\n",
-      "AM Iteration:   8,  alpha_error:     0.00035818\n",
-      "AM Iteration:   9,  alpha_error:     0.00020328\n",
-      "AM Iteration:  10,  alpha_error:     0.00011553\n",
-      "AM Iteration:  11,  alpha_error:     0.00006570\n",
-      "\n",
-      "End of timestep  25 with load multiplier 25.000000\n",
-      "\n",
-      "Elastic and Surface Energies: [0.015276,0.000215]\n",
-      "\n",
-      "Elastic and Surface Energies: [0.015275907907559784,0.00021515136696027403]\n",
-      "-----------------------------------------\n",
-      "\u001b[1;32m--- Starting of Time step 26: t = 26.000000 ---\u001b[1;m\n",
-      "AM Iteration:   1,  alpha_error:     0.02477598\n",
-      "AM Iteration:   2,  alpha_error:     0.01562281\n",
-      "AM Iteration:   3,  alpha_error:     0.01010658\n",
-      "AM Iteration:   4,  alpha_error:     0.00665329\n",
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-      "End of timestep  26 with load multiplier 26.000000\n",
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-      "\u001b[1;32m--- Starting of Time step 27: t = 27.000000 ---\u001b[1;m\n",
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-      "End of timestep  27 with load multiplier 27.000000\n",
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-      "\u001b[1;32m--- Starting of Time step 28: t = 28.000000 ---\u001b[1;m\n",
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-      "End of timestep  28 with load multiplier 28.000000\n",
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-      "\n",
-      "Elastic and Surface Energies: [0.013042598677495295,0.005833312207193253]\n",
-      "-----------------------------------------\n",
-      "\u001b[1;32m--- Starting of Time step 29: t = 29.000000 ---\u001b[1;m\n",
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-      "End of timestep  29 with load multiplier 29.000000\n",
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-      "\n",
-      "Elastic and Surface Energies: [0.013307256129508132,0.006854182927234093]\n",
-      "-----------------------------------------\n",
-      "\u001b[1;32m--- Starting of Time step 30: t = 30.000000 ---\u001b[1;m\n",
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-     "text": [
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-      "\n",
-      "Elastic and Surface Energies: [0.013447053277944845,0.012511124074610736]\n",
-      "-----------------------------------------\n",
-      "\u001b[1;32m--- Starting of Time step 32: t = 32.000000 ---\u001b[1;m\n",
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-      "End of timestep  32 with load multiplier 32.000000\n",
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-      "-----------------------------------------\n",
-      "\u001b[1;32m--- Starting of Time step 33: t = 33.000000 ---\u001b[1;m\n",
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-     ]
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-    {
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-     "text": [
-      "AM Iteration:  46,  alpha_error:     0.00316986\n",
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-      "\n",
-      "End of timestep  33 with load multiplier 33.000000\n",
-      "\n",
-      "Elastic and Surface Energies: [0.013308,0.016751]\n",
-      "\n",
-      "Elastic and Surface Energies: [0.013307828495864427,0.01675109316777118]\n",
-      "-----------------------------------------\n",
-      "\u001b[1;32m--- Starting of Time step 34: t = 34.000000 ---\u001b[1;m\n",
-      "AM Iteration:   1,  alpha_error:     0.03733525\n",
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-      "\n",
-      "End of timestep  34 with load multiplier 34.000000\n",
-      "\n",
-      "Elastic and Surface Energies: [0.013245,0.018314]\n",
-      "\n",
-      "Elastic and Surface Energies: [0.013245393105169949,0.018314070429731644]\n",
-      "-----------------------------------------\n",
-      "\u001b[1;32m--- Starting of Time step 35: t = 35.000000 ---\u001b[1;m\n",
-      "AM Iteration:   1,  alpha_error:     0.04201192\n",
-      "AM Iteration:   2,  alpha_error:     0.03734371\n",
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-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "AM Iteration:  18,  alpha_error:     0.01300926\n",
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-      "AM Iteration: 114,  alpha_error:     0.00018610\n",
-      "AM Iteration: 115,  alpha_error:     0.00017992\n",
-      "AM Iteration: 116,  alpha_error:     0.00017393\n",
-      "AM Iteration: 117,  alpha_error:     0.00016813\n"
-     ]
-    }
-   ],
-   "source": [
-    "for (i_t, t) in enumerate(load_multipliers):\n",
-    "    \n",
-    "    for j in range(len(xcord)):\n",
-    "        \n",
-    "        charge[j].ux = U_xy[t].values[j]*ut\n",
-    "        \n",
-    "        charge[j].uy = U_xy[t+N_image].values[j]*ut\n",
-    "        \n",
-    "    if MPI.rank(MPI.comm_world) == 0:\n",
-    "        print(\"\\033[1;32m--- Starting of Time step {0:2d}: t = {1:4f} ---\\033[1;m\".format(i_t, t)) \n",
-    "    # Alternate Mininimization \n",
-    "    # Initialization\n",
-    "    iteration = 1\n",
-    "    err_alpha = 1.0\n",
-    "    # Iterations\n",
-    "    while err_alpha > AM_tolerance and iteration < maxiteration:\n",
-    "        # solve elastic problem\n",
-    "        solver_u.solve()\n",
-    "        # solve damage problem with box constraint \n",
-    "        solver_alpha.solve(damage_problem, alpha.vector(), alpha_lb.vector(), alpha_ub.vector())\n",
-    "        # test error\n",
-    "        alpha_error = alpha.vector() - alpha_0.vector()\n",
-    "        err_alpha = alpha_error.norm('linf')\n",
-    "        # monitor the results\n",
-    "        if MPI.rank(MPI.comm_world) == 0:\n",
-    "          print (\"AM Iteration: {0:3d},  alpha_error: {1:>14.8f}\".format(iteration, err_alpha))\n",
-    "\n",
-    "        # update iteration\n",
-    "        alpha_0.assign(alpha)\n",
-    "        \n",
-    "        \n",
-    "        #----------------------------------------------#\n",
-    "        #-------------I-integral T-stress--------------#\n",
-    "        #----------------------------------------------#\n",
-    "        I1  = inner((0.5*inner(sigma(u,alpha),eps(u_aux))+ 0.5*inner(sigmax,eps(u))),n[0])*ds(2) \\\n",
-    "        + inner((0.5*inner(sigma(u,alpha),eps(u_aux))+ 0.5*inner(sigmax,eps(u))),n[0])*ds(3) \\\n",
-    "        + inner((0.5*inner(sigma(u,alpha),eps(u_aux))+ 0.5*inner(sigmax,eps(u))),n[0])*ds(4)\n",
-    "\n",
-    "        I2  = dot(dot(sigma(u,alpha),u_x(u_aux)) + dot(sigmax,u_x(u)),n)*ds(2) \\\n",
-    "        + dot(dot(sigma(u,alpha),u_x(u_aux)) + dot(sigmax,u_x(u)),n)*ds(3) \\\n",
-    "        + dot(dot(sigma(u,alpha),u_x(u_aux)) + dot(sigmax,u_x(u)),n)*ds(4)\n",
-    "\n",
-    "        I_int  = assemble(I1-I2)\n",
-    "    \n",
-    "        #print('I-integral: T-stress = ' + str(I_int))\n",
-    "    \n",
-    "    \n",
-    "        #----------------------------------------------#\n",
-    "        #-------------I-integral KI and KII------------#\n",
-    "        #----------------------------------------------#\n",
-    "        #I_integral for seperating KI\n",
-    "\n",
-    "        Ik1_1  = inner((0.5*inner(sigma(u,alpha),eps(u_kaux1))+ 0.5*inner(sigma_1,eps(u))),n[0])*ds(2) \\\n",
-    "        + inner((0.5*inner(sigma(u,alpha),eps(u_kaux1))+ 0.5*inner(sigma_1,eps(u))),n[0])*ds(3) \\\n",
-    "        + inner((0.5*inner(sigma(u,alpha),eps(u_kaux1))+ 0.5*inner(sigma_1,eps(u))),n[0])*ds(4)\n",
-    "\n",
-    "\n",
-    "\n",
-    "        Ik1_2  = dot((dot(sigma(u,alpha),u_x(u_kaux1)) + dot(sigma_1,u_x(u))),n)*ds(2) \\\n",
-    "        + dot((dot(sigma(u,alpha),u_x(u_kaux1)) + dot(sigma_1,u_x(u))),n)*ds(3) \\\n",
-    "        + dot((dot(sigma(u,alpha),u_x(u_kaux1)) + dot(sigma_1,u_x(u))),n)*ds(4)\n",
-    "\n",
-    "\n",
-    "        I_k11  = assemble(Ik1_1-Ik1_2)\n",
-    "        I_k1 = I_k11\n",
-    "\n",
-    "        k1_I = I_k1*1/(2*k1_aux1)\n",
-    "\n",
-    "    \n",
-    "        #I_integral for seperating KII\n",
-    "        Ik2_1  = inner((0.5*inner(sigma(u,alpha),eps(u_kaux2))+ 0.5*inner(sigma_2,eps(u))),n[0])*ds(2) \\\n",
-    "        + inner((0.5*inner(sigma(u,alpha),eps(u_kaux2))+ 0.5*inner(sigma_2,eps(u))),n[0])*ds(3) \\\n",
-    "        + inner((0.5*inner(sigma(u,alpha),eps(u_kaux2))+ 0.5*inner(sigma_2,eps(u))),n[0])*ds(4)\n",
-    "\n",
-    "        Ik2_2  = dot(dot(sigma(u,alpha),u_x(u_kaux2)) + dot(sigma_2,u_x(u)),n)*ds(2) \\\n",
-    "        + dot(dot(sigma(u,alpha),u_x(u_kaux2)) + dot(sigma_2,u_x(u)),n)*ds(3) \\\n",
-    "        + dot(dot(sigma(u,alpha),u_x(u_kaux2)) + dot(sigma_2,u_x(u)),n)*ds(4)\n",
-    "\n",
-    "        I_k2  = assemble(Ik2_1-Ik2_2)\n",
-    "\n",
-    "        k2_I = I_k2*1/(2*k2_aux2)\n",
-    "        \n",
-    "        iteration = iteration + 1\n",
-    "    # updating the lower bound to account for the irreversibility\n",
-    "    alpha_lb.vector()[:] = alpha.vector()\n",
-    "    alpha.rename(\"Damage\", \"alpha\")\n",
-    "    u.rename(\"Displacement\", \"u\")\n",
-    "    \n",
-    "    # Dump solution to file \n",
-    "    file_alpha.write(alpha, t)\n",
-    "    file_u.write(u, t)\n",
-    "\n",
-    "    # ----------------------------------------\n",
-    "    # Some post-processing\n",
-    "    # ----------------------------------------\n",
-    "    # Save number of iterations for the time step    \n",
-    "    iterations[i_t] = np.array([t, iteration])\n",
-    "\n",
-    "\n",
-    "    # Calculate the energies\n",
-    "    elastic_energy_value = assemble(elastic_energy)\n",
-    "    surface_energy_value = assemble(dissipated_energy)\n",
-    "    energies[i_t] = np.array([t, elastic_energy_value, surface_energy_value, elastic_energy_value+surface_energy_value])\n",
-    "    force[i_t] = np.array([t, assemble(sigma(u,alpha)[1,1]*ds(2))])\n",
-    "    \n",
-    "    SIF[i_t] = np.array([t, k1_I, k2_I, I_int])\n",
-    "    \n",
-    "    if MPI.rank(MPI.comm_world) == 0:\n",
-    "        print(\"\\nEnd of timestep {0:3d} with load multiplier {1:4f}\".format(i_t, t))\n",
-    "        #print(\"\\nElastic and Surface Energies: [{0:6f},{1:6f}]\".format(elastic_energy_value, surface_energy_value))\n",
-    "        print(\"\\nElastic and Surface Energies: [{},{}]\".format(elastic_energy_value, surface_energy_value))\n",
-    "        print(\"-----------------------------------------\")\n",
-    "        # Save some global quantities as a function of the time\n",
-    "        np.savetxt(savedir + '/energies.txt', energies)\n",
-    "        np.savetxt(savedir + '/iterations.txt', iterations)\n",
-    "        np.savetxt(savedir + '/force.txt', force)\n",
-    "        np.savetxt(savedir + '/SIF.txt', SIF)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0f5787b8",
-   "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": "3.8.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/contributed/DIC_CT_35/charge_libre.csv b/contributed/DIC_CT_35/charge_libre.csv
deleted file mode 100644
index 4a3ecc58..00000000
--- a/contributed/DIC_CT_35/charge_libre.csv
+++ /dev/null
@@ -1,213 +0,0 @@
-0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55
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diff --git a/contributed/DIC_CT_35/direction_plot.ipynb b/contributed/DIC_CT_35/direction_plot.ipynb
deleted file mode 100644
index 9ea40d96..00000000
--- a/contributed/DIC_CT_35/direction_plot.ipynb
+++ /dev/null
@@ -1,194 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "9c055409",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import math\n",
-    "import warnings\n",
-    "import matplotlib.pyplot as plt\n",
-    "warnings.filterwarnings(\"ignore\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "106af010",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "L = 1.\n",
-    "scale=2e-5\n",
-    "\n",
-    "# load csv file XY positions\n",
-    "filename = \"xy_position_libre\"\n",
-    "DIC_0 = pd.read_csv('/Users/xinyuanzhai/DIC_gradam/CT_35_1/' +  filename +   '.csv')\n",
-    "\n",
-    "#convert to mm and then dimensionless with L = 50\n",
-    "DIC_0 = DIC_0*1000*scale/L\n",
-    "\n",
-    "filename1 = \"charge_libre\"\n",
-    "U_xy = pd.read_csv('/Users/xinyuanzhai/DIC_gradam/CT_35_1/' +  filename1 +   '.csv')\n",
-    "\n",
-    "#convert to mm then dimensionless with L = 50\n",
-    "U_xy = U_xy*1000*scale/L\n",
-    "U_xy.columns = np.arange(len(U_xy.columns))\n",
-    "\n",
-    "#Number of point in this contour\n",
-    "xcord = DIC_0.X\n",
-    "ycord = DIC_0.Y\n",
-    "\n",
-    "N_image = int(len(U_xy.columns)/2)\n",
-    "\n",
-    "widths = np.linspace(0, 2, xcord.size)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 120,
-   "id": "c747ae97",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-      "text/plain": [
-       "<Figure size 4320x4320 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(60, 60))\n",
-    "plt.plot(xcord,ycord,label='3rd Contour')\n",
-    "\n",
-    "t = 40\n",
-    "factor = 1\n",
-    "n = 0\n",
-    "\n",
-    "u0 = U_xy[40].values[0]*factor\n",
-    "v0 = U_xy[40+N_image].values[0]*factor\n",
-    "\n",
-    "\n",
-    "for j in range(len(xcord)):\n",
-    "    \n",
-    "    u = U_xy[t].values[j]*factor\n",
-    "    v = U_xy[t+N_image].values[j]*factor\n",
-    "\n",
-    "    \n",
-    "    a = plt.quiver(xcord[j], ycord[j], u, v,\n",
-    "               angles='uv', scale_units='xy', scale = 0.9)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 112,
-   "id": "f7b2ae2e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 504x504 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "x = np.arange(0,2.2,0.2)\n",
-    "y = np.arange(0,2.2,0.2)\n",
-    "\n",
-    "X, Y = np.meshgrid(x, y)\n",
-    "u = np.cos(x)*Y\n",
-    "v = np.sin(y)*Y\n",
-    "\n",
-    "n = 1\n",
-    "color_array = np.sqrt(((v-n)/2)**2 + ((u-n)/2)**2)\n",
-    "\n",
-    "fig, ax = plt.subplots(figsize=(7,7))\n",
-    "ax.quiver(X,Y,u,v, color_array, alpha=0.8)\n",
-    "\n",
-    "ax.xaxis.set_ticks([])\n",
-    "ax.yaxis.set_ticks([])\n",
-    "ax.axis([-0.2, 2.3, -0.2, 2.3])\n",
-    "ax.set_aspect('equal')\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 111,
-   "id": "b5b65eb7",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],\n",
-       "       [0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2],\n",
-       "       [0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4],\n",
-       "       [0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6],\n",
-       "       [0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8],\n",
-       "       [1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. ],\n",
-       "       [1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2],\n",
-       "       [1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4],\n",
-       "       [1.6, 1.6, 1.6, 1.6, 1.6, 1.6, 1.6, 1.6, 1.6, 1.6, 1.6],\n",
-       "       [1.8, 1.8, 1.8, 1.8, 1.8, 1.8, 1.8, 1.8, 1.8, 1.8, 1.8],\n",
-       "       [2. , 2. , 2. , 2. , 2. , 2. , 2. , 2. , 2. , 2. , 2. ]])"
-      ]
-     },
-     "execution_count": 111,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Y"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d902f461",
-   "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": "3.8.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/contributed/DIC_CT_35/export_msh.py b/contributed/DIC_CT_35/export_msh.py
deleted file mode 100644
index f388e3a9..00000000
--- a/contributed/DIC_CT_35/export_msh.py
+++ /dev/null
@@ -1,43 +0,0 @@
-#!/usr/bin/env python
-# coding: utf-8
-
-
-import os
-
-from dolfin import *
-
-filename = "mesh/DIC_running"
-
-
-os.system("gmsh -2 " + filename + ".geo -format msh2")
-os.system("dolfin-convert " + filename + ".msh " + filename + ".xml")
-os.remove(filename + ".msh")
-# xml to h5 (1-3)
-mesh = Mesh(filename + ".xml")
-# boundaries = MeshFunction("size_t", mesh, "mesh4_facet_region.xml")
-subdomains = MeshFunction("size_t", mesh, filename + "_physical_region.xml")
-boundaries = MeshFunction("size_t", mesh, filename + "_facet_region.xml")
-
-
-hdf = HDF5File(mesh.mpi_comm(), filename + ".h5", "w")
-hdf.write(mesh, "/mesh")
-hdf.write(boundaries, "/boundaries")
-hdf.write(subdomains, "/subdomains")
-hdf.close()
-
-
-os.remove(filename + "_physical_region.xml")
-os.remove(filename + "_facet_region.xml")
-os.remove(filename + ".xml")
-
-
-# mesh = Mesh()
-# hdf = HDF5File(mesh.mpi_comm(), filename + ".h5", "r")
-# hdf.read(mesh, "/mesh", False)
-# ndim = mesh.topology().dim()
-
-# boundaries = MeshFunction("size_t", mesh,1)
-# hdf.read(boundaries, "/boundaries")
-
-# subdomains = MeshFunction("size_t", mesh,2)
-# hdf.read(subdomains, "/subdomains")
diff --git a/contributed/DIC_CT_35/mesh/DIC.geo b/contributed/DIC_CT_35/mesh/DIC.geo
deleted file mode 100644
index 4d55a902..00000000
--- a/contributed/DIC_CT_35/mesh/DIC.geo
+++ /dev/null
@@ -1,34 +0,0 @@
-L = 50.;
-
-f0 = 0.2/L;
-
-eta = 0.01/L;
-
-xval = ;
-yval = 0;
-xtip = 0;
-tip = 0.3/L;
-
-
-
-//+
-Point(1) = {xval, yval+eta, 0, f0};
-//+
-Point(2) = {xval, yval-eta, 0, f0};
-//+
-Point(3) = {xtip-tip, yval+eta, 0, f0};
-//+
-Point(4) = {xtip-tip, yval-eta, 0, f0};
-//+
-Point(5) = {xtip,yval,0,f0};
-
-
-
-//+
-Line(1) = {1, 3};
-//+
-Line(2) = {3, 5};
-//+
-Line(3) = {5, 4};
-//+
-Line(4) = {4, 2};
diff --git a/contributed/DIC_CT_35/mesh/DIC_running.geo b/contributed/DIC_CT_35/mesh/DIC_running.geo
deleted file mode 100644
index adcbee96..00000000
--- a/contributed/DIC_CT_35/mesh/DIC_running.geo
+++ /dev/null
@@ -1,896 +0,0 @@
-L = 50.;
-
-f0 = 0.2/L;
-
-eta = 0.01/L;
-
-xval = -0.09600000000000002;
-yval = 0;
-xtip = 0;
-tip = 0.3/L;
-
-
-
-//+
-Point(1) = {xval, yval+eta, 0, f0};
-//+
-Point(2) = {xval, yval-eta, 0, f0};
-//+
-Point(3) = {xtip-tip, yval+eta, 0, f0};
-//+
-Point(4) = {xtip-tip, yval-eta, 0, f0};
-//+
-Point(5) = {xtip,yval,0,f0};
-
-
-
-//+
-Line(1) = {1, 3};
-//+
-Line(2) = {3, 5};
-//+
-Line(3) = {5, 4};
-//+
-Line(4) = {4, 2};
-//+
-Point(6) = {-0.09600000000000002, -0.0064, 0, f0};
-//+
-Point(7) = {-0.09600000000000002, -0.0128, 0, f0};
-//+
-Point(8) = {-0.09600000000000002, -0.019200000000000002, 0, f0};
-//+
-Point(9) = {-0.09600000000000002, -0.0256, 0, f0};
-//+
-Point(10) = {-0.09600000000000002, -0.032, 0, f0};
-//+
-Point(11) = {-0.09600000000000002, -0.038400000000000004, 0, f0};
-//+
-Point(12) = {-0.09600000000000002, -0.044800000000000006, 0, f0};
-//+
-Point(13) = {-0.09600000000000002, -0.0512, 0, f0};
-//+
-Point(14) = {-0.09600000000000002, -0.057600000000000005, 0, f0};
-//+
-Point(15) = {-0.09600000000000002, -0.064, 0, f0};
-//+
-Point(16) = {-0.09600000000000002, -0.0704, 0, f0};
-//+
-Point(17) = {-0.09600000000000002, -0.07680000000000001, 0, f0};
-//+
-Point(18) = {-0.09600000000000002, -0.0832, 0, f0};
-//+
-Point(19) = {-0.09600000000000002, -0.08960000000000001, 0, f0};
-//+
-Point(20) = {-0.08960000000000001, -0.09600000000000002, 0, f0};
-//+
-Point(21) = {-0.0832, -0.1024, 0, f0};
-//+
-Point(22) = {-0.07680000000000001, -0.10880000000000001, 0, f0};
-//+
-Point(23) = {-0.0704, -0.11520000000000001, 0, f0};
-//+
-Point(24) = {-0.064, -0.1216, 0, f0};
-//+
-Point(25) = {-0.057600000000000005, -0.128, 0, f0};
-//+
-Point(26) = {-0.0512, -0.13440000000000002, 0, f0};
-//+
-Point(27) = {-0.044800000000000006, -0.1408, 0, f0};
-//+
-Point(28) = {-0.038400000000000004, -0.1472, 0, f0};
-//+
-Point(29) = {-0.032, -0.15360000000000001, 0, f0};
-//+
-Point(30) = {-0.0256, -0.16, 0, f0};
-//+
-Point(31) = {-0.019200000000000002, -0.1664, 0, f0};
-//+
-Point(32) = {-0.0128, -0.1728, 0, f0};
-//+
-Point(33) = {-0.0064, -0.17920000000000003, 0, f0};
-//+
-Point(34) = {0.0, -0.18560000000000001, 0, f0};
-//+
-Point(35) = {0.0064, -0.19200000000000003, 0, f0};
-//+
-Point(36) = {0.0128, -0.1984, 0, f0};
-//+
-Point(37) = {0.019200000000000002, -0.2048, 0, f0};
-//+
-Point(38) = {0.0256, -0.2112, 0, f0};
-//+
-Point(39) = {0.032, -0.21760000000000002, 0, f0};
-//+
-Point(40) = {0.038400000000000004, -0.22400000000000003, 0, f0};
-//+
-Point(41) = {0.044800000000000006, -0.23040000000000002, 0, f0};
-//+
-Point(42) = {0.0512, -0.23680000000000004, 0, f0};
-//+
-Point(43) = {0.057600000000000005, -0.2432, 0, f0};
-//+
-Point(44) = {0.064, -0.24960000000000002, 0, f0};
-//+
-Point(45) = {0.0704, -0.256, 0, f0};
-//+
-Point(46) = {0.07680000000000001, -0.2624, 0, f0};
-//+
-Point(47) = {0.0832, -0.26880000000000004, 0, f0};
-//+
-Point(48) = {0.08960000000000001, -0.27520000000000006, 0, f0};
-//+
-Point(49) = {0.09600000000000002, -0.2816, 0, f0};
-//+
-Point(50) = {0.1024, -0.28800000000000003, 0, f0};
-//+
-Point(51) = {0.10880000000000001, -0.2944, 0, f0};
-//+
-Point(52) = {0.11520000000000001, -0.3008, 0, f0};
-//+
-Point(53) = {0.1216, -0.30720000000000003, 0, f0};
-//+
-Point(54) = {0.128, -0.31360000000000005, 0, f0};
-//+
-Point(55) = {0.13440000000000002, -0.31360000000000005, 0, f0};
-//+
-Point(56) = {0.1408, -0.31360000000000005, 0, f0};
-//+
-Point(57) = {0.1472, -0.31360000000000005, 0, f0};
-//+
-Point(58) = {0.15360000000000001, -0.31360000000000005, 0, f0};
-//+
-Point(59) = {0.16, -0.31360000000000005, 0, f0};
-//+
-Point(60) = {0.1664, -0.31360000000000005, 0, f0};
-//+
-Point(61) = {0.1728, -0.31360000000000005, 0, f0};
-//+
-Point(62) = {0.17920000000000003, -0.31360000000000005, 0, f0};
-//+
-Point(63) = {0.18560000000000001, -0.31360000000000005, 0, f0};
-//+
-Point(64) = {0.19200000000000003, -0.31360000000000005, 0, f0};
-//+
-Point(65) = {0.1984, -0.31360000000000005, 0, f0};
-//+
-Point(66) = {0.2048, -0.31360000000000005, 0, f0};
-//+
-Point(67) = {0.2112, -0.31360000000000005, 0, f0};
-//+
-Point(68) = {0.21760000000000002, -0.31360000000000005, 0, f0};
-//+
-Point(69) = {0.22400000000000003, -0.31360000000000005, 0, f0};
-//+
-Point(70) = {0.23040000000000002, -0.31360000000000005, 0, f0};
-//+
-Point(71) = {0.23680000000000004, -0.31360000000000005, 0, f0};
-//+
-Point(72) = {0.2432, -0.31360000000000005, 0, f0};
-//+
-Point(73) = {0.24960000000000002, -0.31360000000000005, 0, f0};
-//+
-Point(74) = {0.256, -0.31360000000000005, 0, f0};
-//+
-Point(75) = {0.2624, -0.31360000000000005, 0, f0};
-//+
-Point(76) = {0.26880000000000004, -0.31360000000000005, 0, f0};
-//+
-Point(77) = {0.27520000000000006, -0.31360000000000005, 0, f0};
-//+
-Point(78) = {0.2816, -0.31360000000000005, 0, f0};
-//+
-Point(79) = {0.28800000000000003, -0.31360000000000005, 0, f0};
-//+
-Point(80) = {0.2944, -0.31360000000000005, 0, f0};
-//+
-Point(81) = {0.3008, -0.31360000000000005, 0, f0};
-//+
-Point(82) = {0.30720000000000003, -0.31360000000000005, 0, f0};
-//+
-Point(83) = {0.31360000000000005, -0.31360000000000005, 0, f0};
-//+
-Point(84) = {0.32, -0.31360000000000005, 0, f0};
-//+
-Point(85) = {0.3264, -0.31360000000000005, 0, f0};
-//+
-Point(86) = {0.3328, -0.31360000000000005, 0, f0};
-//+
-Point(87) = {0.3392, -0.31360000000000005, 0, f0};
-//+
-Point(88) = {0.3456, -0.31360000000000005, 0, f0};
-//+
-Point(89) = {0.35200000000000004, -0.31360000000000005, 0, f0};
-//+
-Point(90) = {0.35840000000000005, -0.31360000000000005, 0, f0};
-//+
-Point(91) = {0.3648, -0.31360000000000005, 0, f0};
-//+
-Point(92) = {0.37120000000000003, -0.31360000000000005, 0, f0};
-//+
-Point(93) = {0.37760000000000005, -0.31360000000000005, 0, f0};
-//+
-Point(94) = {0.38400000000000006, -0.31360000000000005, 0, f0};
-//+
-Point(95) = {0.3904000000000001, -0.31360000000000005, 0, f0};
-//+
-Point(96) = {0.3968, -0.31360000000000005, 0, f0};
-//+
-Point(97) = {0.4032, -0.31360000000000005, 0, f0};
-//+
-Point(98) = {0.4096, -0.31360000000000005, 0, f0};
-//+
-Point(99) = {0.41600000000000004, -0.31360000000000005, 0, f0};
-//+
-Point(100) = {0.4224, -0.31360000000000005, 0, f0};
-//+
-Point(101) = {0.4288, -0.31360000000000005, 0, f0};
-//+
-Point(102) = {0.43520000000000003, -0.31360000000000005, 0, f0};
-//+
-Point(103) = {0.44160000000000005, -0.31360000000000005, 0, f0};
-//+
-Point(104) = {0.44800000000000006, -0.31360000000000005, 0, f0};
-//+
-Point(105) = {0.4544, -0.31360000000000005, 0, f0};
-//+
-Point(106) = {0.46080000000000004, -0.31360000000000005, 0, f0};
-//+
-Point(107) = {0.46720000000000006, -0.31360000000000005, 0, f0};
-//+
-Point(108) = {0.4736000000000001, -0.31360000000000005, 0, f0};
-//+
-Point(109) = {0.4800000000000001, -0.31360000000000005, 0, f0};
-//+
-Point(110) = {0.4864, -0.31360000000000005, 0, f0};
-//+
-Point(111) = {0.4928, -0.31360000000000005, 0, f0};
-//+
-Point(112) = {0.4928, 0.31360000000000005, 0, f0};
-//+
-Point(113) = {0.4864, 0.31360000000000005, 0, f0};
-//+
-Point(114) = {0.4800000000000001, 0.31360000000000005, 0, f0};
-//+
-Point(115) = {0.4736000000000001, 0.31360000000000005, 0, f0};
-//+
-Point(116) = {0.46720000000000006, 0.31360000000000005, 0, f0};
-//+
-Point(117) = {0.46080000000000004, 0.31360000000000005, 0, f0};
-//+
-Point(118) = {0.4544, 0.31360000000000005, 0, f0};
-//+
-Point(119) = {0.44800000000000006, 0.31360000000000005, 0, f0};
-//+
-Point(120) = {0.44160000000000005, 0.31360000000000005, 0, f0};
-//+
-Point(121) = {0.43520000000000003, 0.31360000000000005, 0, f0};
-//+
-Point(122) = {0.4288, 0.31360000000000005, 0, f0};
-//+
-Point(123) = {0.4224, 0.31360000000000005, 0, f0};
-//+
-Point(124) = {0.41600000000000004, 0.31360000000000005, 0, f0};
-//+
-Point(125) = {0.4096, 0.31360000000000005, 0, f0};
-//+
-Point(126) = {0.4032, 0.31360000000000005, 0, f0};
-//+
-Point(127) = {0.3968, 0.31360000000000005, 0, f0};
-//+
-Point(128) = {0.3904000000000001, 0.31360000000000005, 0, f0};
-//+
-Point(129) = {0.38400000000000006, 0.31360000000000005, 0, f0};
-//+
-Point(130) = {0.37760000000000005, 0.31360000000000005, 0, f0};
-//+
-Point(131) = {0.37120000000000003, 0.31360000000000005, 0, f0};
-//+
-Point(132) = {0.3648, 0.31360000000000005, 0, f0};
-//+
-Point(133) = {0.35840000000000005, 0.31360000000000005, 0, f0};
-//+
-Point(134) = {0.35200000000000004, 0.31360000000000005, 0, f0};
-//+
-Point(135) = {0.3456, 0.31360000000000005, 0, f0};
-//+
-Point(136) = {0.3392, 0.31360000000000005, 0, f0};
-//+
-Point(137) = {0.3328, 0.31360000000000005, 0, f0};
-//+
-Point(138) = {0.3264, 0.31360000000000005, 0, f0};
-//+
-Point(139) = {0.32, 0.31360000000000005, 0, f0};
-//+
-Point(140) = {0.31360000000000005, 0.31360000000000005, 0, f0};
-//+
-Point(141) = {0.30720000000000003, 0.31360000000000005, 0, f0};
-//+
-Point(142) = {0.3008, 0.31360000000000005, 0, f0};
-//+
-Point(143) = {0.2944, 0.31360000000000005, 0, f0};
-//+
-Point(144) = {0.28800000000000003, 0.31360000000000005, 0, f0};
-//+
-Point(145) = {0.2816, 0.31360000000000005, 0, f0};
-//+
-Point(146) = {0.27520000000000006, 0.31360000000000005, 0, f0};
-//+
-Point(147) = {0.26880000000000004, 0.31360000000000005, 0, f0};
-//+
-Point(148) = {0.2624, 0.31360000000000005, 0, f0};
-//+
-Point(149) = {0.256, 0.31360000000000005, 0, f0};
-//+
-Point(150) = {0.24960000000000002, 0.31360000000000005, 0, f0};
-//+
-Point(151) = {0.2432, 0.31360000000000005, 0, f0};
-//+
-Point(152) = {0.23680000000000004, 0.31360000000000005, 0, f0};
-//+
-Point(153) = {0.23040000000000002, 0.31360000000000005, 0, f0};
-//+
-Point(154) = {0.22400000000000003, 0.31360000000000005, 0, f0};
-//+
-Point(155) = {0.21760000000000002, 0.31360000000000005, 0, f0};
-//+
-Point(156) = {0.2112, 0.31360000000000005, 0, f0};
-//+
-Point(157) = {0.2048, 0.31360000000000005, 0, f0};
-//+
-Point(158) = {0.1984, 0.31360000000000005, 0, f0};
-//+
-Point(159) = {0.19200000000000003, 0.31360000000000005, 0, f0};
-//+
-Point(160) = {0.18560000000000001, 0.31360000000000005, 0, f0};
-//+
-Point(161) = {0.17920000000000003, 0.31360000000000005, 0, f0};
-//+
-Point(162) = {0.1728, 0.31360000000000005, 0, f0};
-//+
-Point(163) = {0.1664, 0.31360000000000005, 0, f0};
-//+
-Point(164) = {0.16, 0.31360000000000005, 0, f0};
-//+
-Point(165) = {0.15360000000000001, 0.31360000000000005, 0, f0};
-//+
-Point(166) = {0.1472, 0.31360000000000005, 0, f0};
-//+
-Point(167) = {0.1408, 0.31360000000000005, 0, f0};
-//+
-Point(168) = {0.13440000000000002, 0.31360000000000005, 0, f0};
-//+
-Point(169) = {0.128, 0.31360000000000005, 0, f0};
-//+
-Point(170) = {0.1216, 0.30720000000000003, 0, f0};
-//+
-Point(171) = {0.11520000000000001, 0.3008, 0, f0};
-//+
-Point(172) = {0.10880000000000001, 0.2944, 0, f0};
-//+
-Point(173) = {0.1024, 0.28800000000000003, 0, f0};
-//+
-Point(174) = {0.09600000000000002, 0.2816, 0, f0};
-//+
-Point(175) = {0.08960000000000001, 0.27520000000000006, 0, f0};
-//+
-Point(176) = {0.0832, 0.26880000000000004, 0, f0};
-//+
-Point(177) = {0.07680000000000001, 0.2624, 0, f0};
-//+
-Point(178) = {0.0704, 0.256, 0, f0};
-//+
-Point(179) = {0.064, 0.24960000000000002, 0, f0};
-//+
-Point(180) = {0.057600000000000005, 0.2432, 0, f0};
-//+
-Point(181) = {0.0512, 0.23680000000000004, 0, f0};
-//+
-Point(182) = {0.044800000000000006, 0.23040000000000002, 0, f0};
-//+
-Point(183) = {0.038400000000000004, 0.22400000000000003, 0, f0};
-//+
-Point(184) = {0.032, 0.21760000000000002, 0, f0};
-//+
-Point(185) = {0.0256, 0.2112, 0, f0};
-//+
-Point(186) = {0.019200000000000002, 0.2048, 0, f0};
-//+
-Point(187) = {0.0128, 0.1984, 0, f0};
-//+
-Point(188) = {0.0064, 0.19200000000000003, 0, f0};
-//+
-Point(189) = {0.0, 0.18560000000000001, 0, f0};
-//+
-Point(190) = {-0.0064, 0.17920000000000003, 0, f0};
-//+
-Point(191) = {-0.0128, 0.1728, 0, f0};
-//+
-Point(192) = {-0.019200000000000002, 0.1664, 0, f0};
-//+
-Point(193) = {-0.0256, 0.16, 0, f0};
-//+
-Point(194) = {-0.032, 0.15360000000000001, 0, f0};
-//+
-Point(195) = {-0.038400000000000004, 0.1472, 0, f0};
-//+
-Point(196) = {-0.044800000000000006, 0.1408, 0, f0};
-//+
-Point(197) = {-0.0512, 0.13440000000000002, 0, f0};
-//+
-Point(198) = {-0.057600000000000005, 0.128, 0, f0};
-//+
-Point(199) = {-0.064, 0.1216, 0, f0};
-//+
-Point(200) = {-0.0704, 0.11520000000000001, 0, f0};
-//+
-Point(201) = {-0.07680000000000001, 0.10880000000000001, 0, f0};
-//+
-Point(202) = {-0.0832, 0.1024, 0, f0};
-//+
-Point(203) = {-0.08960000000000001, 0.09600000000000002, 0, f0};
-//+
-Point(204) = {-0.09600000000000002, 0.08960000000000001, 0, f0};
-//+
-Point(205) = {-0.09600000000000002, 0.0832, 0, f0};
-//+
-Point(206) = {-0.09600000000000002, 0.07680000000000001, 0, f0};
-//+
-Point(207) = {-0.09600000000000002, 0.0704, 0, f0};
-//+
-Point(208) = {-0.09600000000000002, 0.064, 0, f0};
-//+
-Point(209) = {-0.09600000000000002, 0.057600000000000005, 0, f0};
-//+
-Point(210) = {-0.09600000000000002, 0.0512, 0, f0};
-//+
-Point(211) = {-0.09600000000000002, 0.044800000000000006, 0, f0};
-//+
-Point(212) = {-0.09600000000000002, 0.038400000000000004, 0, f0};
-//+
-Point(213) = {-0.09600000000000002, 0.032, 0, f0};
-//+
-Point(214) = {-0.09600000000000002, 0.0256, 0, f0};
-//+
-Point(215) = {-0.09600000000000002, 0.019200000000000002, 0, f0};
-//+
-Point(216) = {-0.09600000000000002, 0.0128, 0, f0};
-//+
-Point(217) = {-0.09600000000000002, 0.0064, 0, f0};
-//+
-Line(5) = {2, 6};
-//+
-Line(6) = {6, 7};
-//+
-Line(7) = {7, 8};
-//+
-Line(8) = {8, 9};
-//+
-Line(9) = {9, 10};
-//+
-Line(10) = {10, 11};
-//+
-Line(11) = {11, 12};
-//+
-Line(12) = {12, 13};
-//+
-Line(13) = {13, 14};
-//+
-Line(14) = {14, 15};
-//+
-Line(15) = {15, 16};
-//+
-Line(16) = {16, 17};
-//+
-Line(17) = {17, 18};
-//+
-Line(18) = {18, 19};
-//+
-Line(19) = {19, 20};
-//+
-Line(20) = {20, 21};
-//+
-Line(21) = {21, 22};
-//+
-Line(22) = {22, 23};
-//+
-Line(23) = {23, 24};
-//+
-Line(24) = {24, 25};
-//+
-Line(25) = {25, 26};
-//+
-Line(26) = {26, 27};
-//+
-Line(27) = {27, 28};
-//+
-Line(28) = {28, 29};
-//+
-Line(29) = {29, 30};
-//+
-Line(30) = {30, 31};
-//+
-Line(31) = {31, 32};
-//+
-Line(32) = {32, 33};
-//+
-Line(33) = {33, 34};
-//+
-Line(34) = {34, 35};
-//+
-Line(35) = {35, 36};
-//+
-Line(36) = {36, 37};
-//+
-Line(37) = {37, 38};
-//+
-Line(38) = {38, 39};
-//+
-Line(39) = {39, 40};
-//+
-Line(40) = {40, 41};
-//+
-Line(41) = {41, 42};
-//+
-Line(42) = {42, 43};
-//+
-Line(43) = {43, 44};
-//+
-Line(44) = {44, 45};
-//+
-Line(45) = {45, 46};
-//+
-Line(46) = {46, 47};
-//+
-Line(47) = {47, 48};
-//+
-Line(48) = {48, 49};
-//+
-Line(49) = {49, 50};
-//+
-Line(50) = {50, 51};
-//+
-Line(51) = {51, 52};
-//+
-Line(52) = {52, 53};
-//+
-Line(53) = {53, 54};
-//+
-Line(54) = {54, 55};
-//+
-Line(55) = {55, 56};
-//+
-Line(56) = {56, 57};
-//+
-Line(57) = {57, 58};
-//+
-Line(58) = {58, 59};
-//+
-Line(59) = {59, 60};
-//+
-Line(60) = {60, 61};
-//+
-Line(61) = {61, 62};
-//+
-Line(62) = {62, 63};
-//+
-Line(63) = {63, 64};
-//+
-Line(64) = {64, 65};
-//+
-Line(65) = {65, 66};
-//+
-Line(66) = {66, 67};
-//+
-Line(67) = {67, 68};
-//+
-Line(68) = {68, 69};
-//+
-Line(69) = {69, 70};
-//+
-Line(70) = {70, 71};
-//+
-Line(71) = {71, 72};
-//+
-Line(72) = {72, 73};
-//+
-Line(73) = {73, 74};
-//+
-Line(74) = {74, 75};
-//+
-Line(75) = {75, 76};
-//+
-Line(76) = {76, 77};
-//+
-Line(77) = {77, 78};
-//+
-Line(78) = {78, 79};
-//+
-Line(79) = {79, 80};
-//+
-Line(80) = {80, 81};
-//+
-Line(81) = {81, 82};
-//+
-Line(82) = {82, 83};
-//+
-Line(83) = {83, 84};
-//+
-Line(84) = {84, 85};
-//+
-Line(85) = {85, 86};
-//+
-Line(86) = {86, 87};
-//+
-Line(87) = {87, 88};
-//+
-Line(88) = {88, 89};
-//+
-Line(89) = {89, 90};
-//+
-Line(90) = {90, 91};
-//+
-Line(91) = {91, 92};
-//+
-Line(92) = {92, 93};
-//+
-Line(93) = {93, 94};
-//+
-Line(94) = {94, 95};
-//+
-Line(95) = {95, 96};
-//+
-Line(96) = {96, 97};
-//+
-Line(97) = {97, 98};
-//+
-Line(98) = {98, 99};
-//+
-Line(99) = {99, 100};
-//+
-Line(100) = {100, 101};
-//+
-Line(101) = {101, 102};
-//+
-Line(102) = {102, 103};
-//+
-Line(103) = {103, 104};
-//+
-Line(104) = {104, 105};
-//+
-Line(105) = {105, 106};
-//+
-Line(106) = {106, 107};
-//+
-Line(107) = {107, 108};
-//+
-Line(108) = {108, 109};
-//+
-Line(109) = {109, 110};
-//+
-Line(110) = {110, 111};
-//+
-Line(111) = {111, 112};
-//+
-Line(112) = {112, 113};
-//+
-Line(113) = {113, 114};
-//+
-Line(114) = {114, 115};
-//+
-Line(115) = {115, 116};
-//+
-Line(116) = {116, 117};
-//+
-Line(117) = {117, 118};
-//+
-Line(118) = {118, 119};
-//+
-Line(119) = {119, 120};
-//+
-Line(120) = {120, 121};
-//+
-Line(121) = {121, 122};
-//+
-Line(122) = {122, 123};
-//+
-Line(123) = {123, 124};
-//+
-Line(124) = {124, 125};
-//+
-Line(125) = {125, 126};
-//+
-Line(126) = {126, 127};
-//+
-Line(127) = {127, 128};
-//+
-Line(128) = {128, 129};
-//+
-Line(129) = {129, 130};
-//+
-Line(130) = {130, 131};
-//+
-Line(131) = {131, 132};
-//+
-Line(132) = {132, 133};
-//+
-Line(133) = {133, 134};
-//+
-Line(134) = {134, 135};
-//+
-Line(135) = {135, 136};
-//+
-Line(136) = {136, 137};
-//+
-Line(137) = {137, 138};
-//+
-Line(138) = {138, 139};
-//+
-Line(139) = {139, 140};
-//+
-Line(140) = {140, 141};
-//+
-Line(141) = {141, 142};
-//+
-Line(142) = {142, 143};
-//+
-Line(143) = {143, 144};
-//+
-Line(144) = {144, 145};
-//+
-Line(145) = {145, 146};
-//+
-Line(146) = {146, 147};
-//+
-Line(147) = {147, 148};
-//+
-Line(148) = {148, 149};
-//+
-Line(149) = {149, 150};
-//+
-Line(150) = {150, 151};
-//+
-Line(151) = {151, 152};
-//+
-Line(152) = {152, 153};
-//+
-Line(153) = {153, 154};
-//+
-Line(154) = {154, 155};
-//+
-Line(155) = {155, 156};
-//+
-Line(156) = {156, 157};
-//+
-Line(157) = {157, 158};
-//+
-Line(158) = {158, 159};
-//+
-Line(159) = {159, 160};
-//+
-Line(160) = {160, 161};
-//+
-Line(161) = {161, 162};
-//+
-Line(162) = {162, 163};
-//+
-Line(163) = {163, 164};
-//+
-Line(164) = {164, 165};
-//+
-Line(165) = {165, 166};
-//+
-Line(166) = {166, 167};
-//+
-Line(167) = {167, 168};
-//+
-Line(168) = {168, 169};
-//+
-Line(169) = {169, 170};
-//+
-Line(170) = {170, 171};
-//+
-Line(171) = {171, 172};
-//+
-Line(172) = {172, 173};
-//+
-Line(173) = {173, 174};
-//+
-Line(174) = {174, 175};
-//+
-Line(175) = {175, 176};
-//+
-Line(176) = {176, 177};
-//+
-Line(177) = {177, 178};
-//+
-Line(178) = {178, 179};
-//+
-Line(179) = {179, 180};
-//+
-Line(180) = {180, 181};
-//+
-Line(181) = {181, 182};
-//+
-Line(182) = {182, 183};
-//+
-Line(183) = {183, 184};
-//+
-Line(184) = {184, 185};
-//+
-Line(185) = {185, 186};
-//+
-Line(186) = {186, 187};
-//+
-Line(187) = {187, 188};
-//+
-Line(188) = {188, 189};
-//+
-Line(189) = {189, 190};
-//+
-Line(190) = {190, 191};
-//+
-Line(191) = {191, 192};
-//+
-Line(192) = {192, 193};
-//+
-Line(193) = {193, 194};
-//+
-Line(194) = {194, 195};
-//+
-Line(195) = {195, 196};
-//+
-Line(196) = {196, 197};
-//+
-Line(197) = {197, 198};
-//+
-Line(198) = {198, 199};
-//+
-Line(199) = {199, 200};
-//+
-Line(200) = {200, 201};
-//+
-Line(201) = {201, 202};
-//+
-Line(202) = {202, 203};
-//+
-Line(203) = {203, 204};
-//+
-Line(204) = {204, 205};
-//+
-Line(205) = {205, 206};
-//+
-Line(206) = {206, 207};
-//+
-Line(207) = {207, 208};
-//+
-Line(208) = {208, 209};
-//+
-Line(209) = {209, 210};
-//+
-Line(210) = {210, 211};
-//+
-Line(211) = {211, 212};
-//+
-Line(212) = {212, 213};
-//+
-Line(213) = {213, 214};
-//+
-Line(214) = {214, 215};
-//+
-Line(215) = {215, 216};
-//+
-Line(216) = {216, 217};
-//+
-Line(217) = {217, 1};
-//+
-Curve loop(1) = {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217};
-//+
-Plane Surface(1)= {1};
-//+
-Physical Surface("1") = {1};
-//+
-Physical Curve("2") = {112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,217};
-//+
-Physical Curve("3") = {5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110};
-//+
-Physical Curve("4") = {111};
diff --git a/contributed/DIC_CT_35/xy_position_libre.csv b/contributed/DIC_CT_35/xy_position_libre.csv
deleted file mode 100644
index c17fafdb..00000000
--- a/contributed/DIC_CT_35/xy_position_libre.csv
+++ /dev/null
@@ -1,213 +0,0 @@
-X,Y
--240.0,-16.0
--240.0,-32.0
--240.0,-48.0
--240.0,-64.0
--240.0,-80.0
--240.0,-96.0
--240.0,-112.0
--240.0,-128.0
--240.0,-144.0
--240.0,-160.0
--240.0,-176.0
--240.0,-192.0
--240.0,-208.0
--240.0,-224.0
--224.0,-240.0
--208.0,-256.0
--192.0,-272.0
--176.0,-288.0
--160.0,-304.0
--144.0,-320.0
--128.0,-336.0
--112.0,-352.0
--96.0,-368.0
--80.0,-384.0
--64.0,-400.0
--48.0,-416.0
--32.0,-432.0
--16.0,-448.0
-0.0,-464.0
-16.0,-480.0
-32.0,-496.0
-48.0,-512.0
-64.0,-528.0
-80.0,-544.0
-96.0,-560.0
-112.0,-576.0
-128.0,-592.0
-144.0,-608.0
-160.0,-624.0
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-1136.0,-784.0
-1152.0,-784.0
-1168.0,-784.0
-1184.0,-784.0
-1200.0,-784.0
-1216.0,-784.0
-1232.0,-784.0
-1232.0,784.0
-1216.0,784.0
-1200.0,784.0
-1184.0,784.0
-1168.0,784.0
-1152.0,784.0
-1136.0,784.0
-1120.0,784.0
-1104.0,784.0
-1088.0,784.0
-1072.0,784.0
-1056.0,784.0
-1040.0,784.0
-1024.0,784.0
-1008.0,784.0
-992.0,784.0
-976.0,784.0
-960.0,784.0
-944.0,784.0
-928.0,784.0
-912.0,784.0
-896.0,784.0
-880.0,784.0
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-48.0,512.0
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-16.0,480.0
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--16.0,448.0
--32.0,432.0
--48.0,416.0
--64.0,400.0
--80.0,384.0
--96.0,368.0
--112.0,352.0
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--144.0,320.0
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--192.0,272.0
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--224.0,240.0
--240.0,224.0
--240.0,208.0
--240.0,192.0
--240.0,176.0
--240.0,160.0
--240.0,144.0
--240.0,128.0
--240.0,112.0
--240.0,96.0
--240.0,80.0
--240.0,64.0
--240.0,48.0
--240.0,32.0
--240.0,16.0
diff --git a/contributed/NOTCH/Notch_problem.py b/contributed/NOTCH/Notch_problem.py
deleted file mode 100644
index 04d46779..00000000
--- a/contributed/NOTCH/Notch_problem.py
+++ /dev/null
@@ -1,576 +0,0 @@
-# Numpy -> numerical library for Python. We'll use it for all array operations.
-# It's written in C and it's faster (than traditional Python)
-import logging
-
-# Yaml (Yet another markup language) -> We'll use it to pass, read and structure
-# light text data in .yml files.
-# Json -> Another form to work with data. It comes from JavaScript. Similar functions
-# that Yaml. Used speacily with API request, when we need data "fetch".
-# Communication with the machine:
-# Sys -> allows to acess the system and launch commandes.
-# Os - > allows to acess the operation system.
-import sys
-
-import dolfinx
-import dolfinx.io
-import dolfinx.plot
-import gmsh
-import matplotlib.pyplot as plt
-import meshes
-import numpy as np
-import pyvista
-import ufl
-from algorithms import am
-from dolfinx.fem import (
-    assemble_scalar,
-    dirichletbc,
-    locate_dofs_geometrical,
-    set_bc,
-)
-from models import DamageElasticityModel as Brittle
-from petsc4py import PETSc
-from pyvista.utilities import xvfb
-from utils.viz import plot_mesh, plot_scalar, plot_vector
-
-# -> this serves to add a path to the code search for things
-sys.path.append("../")
-
-# pdb -> usefull for debugging, it can stop a code operation and allows to read
-# variables and do calculations
-# pdb.set_trace() #-> point of stop for debugging
-
-# Mpi4py -> Interface that allows parallel interoperability. MPI stands for' Message
-# Passager Interface' and will be used to communicate computer nodes when lauching code
-# in a parallel way
-# Petcs4py -> we use this library to handle with the data. Given acesses
-# to solvers
-
-# Dolfinx
-
-logging.basicConfig(level=logging.INFO)
-
-
-# UFL (Unified Format Language) -> we'll be used to represent abstract way to
-# represent the language in a quadratic form
-
-# XDMFF -> format used for the output binary data
-
-# Install 'gmsh' library -> we'll be used for the mesh.
-#!{sys.executable}: to use the current kernel to make the installation
-
-
-# meshes
-
-# visualisation
-
-# Parameters
-
-parameters = {
-    # In case of evolution (nonlinear) problems, it's necessary to define a max
-    # and a min. For the elastic solution, just one value in needed.
-    "loading": {
-        "type": "ID",  # ID -> Imposed Displacement | IF -> Imposed Force
-        "min": 0,
-        "max": 1.5,
-        "steps": 20,
-    },
-    "geometry": {"geom_type": "bar", "Lx": 1.0, "Ly": 0.01},
-    "model": {
-        "E": 1.0,
-        "nu": 0.3,
-        "mu": 0,  # don't change it -> calculated later
-        "lmbda": 0,  # don't change it -> calculated later
-        "w1": 1.0,
-        "ell": 0.01,
-        "k_res": 1.0e-8,
-    },
-    "solvers": {
-        "elasticity": {
-            "snes": {
-                "snes_type": "newtontr",
-                "snes_stol": 1e-8,
-                "snes_atol": 1e-8,
-                "snes_rtol": 1e-8,
-                "snes_max_it": 100,
-                "snes_monitor": "",
-                "ksp_type": "preonly",
-                "pc_type": "lu",
-                "pc_factor_mat_solver_type": "mumps",
-            }
-        },
-        "damage": {
-            "snes": {
-                "snes_type": "vinewtonrsls",
-                "snes_stol": 1e-5,
-                "snes_atol": 1e-5,
-                "snes_rtol": 1e-8,
-                "snes_max_it": 100,
-                "snes_monitor": "",
-                "ksp_type": "preonly",
-                "pc_type": "lu",
-                "pc_factor_mat_solver_type": "mumps",
-            },
-        },
-        "damage_elasticity": {
-            "max_it": 100,
-            "alpha_rtol": 1.0e-5,
-            "criterion": "alpha_H1",
-        },
-    },
-}
-E = parameters["model"]["E"]
-poisson = parameters["model"]["nu"]
-parameters["model"]["lmbda"] = E * poisson / ((1 + poisson) * (1 - 2 * poisson))
-parameters["model"]["mu"] = E / (2 * (1 + poisson))
-# parameters.get('loading') -> this parameters can be defined and obtained from
-# a external file. In the first exemple (mec647_VI_1), the parameters were
-# read from a .yml file.
-
-
-def mesh_V(
-    a,
-    h,
-    L,
-    n,
-    gamma,
-    de,
-    de2,
-    key=0,
-    show=False,
-    filename="mesh.unv",
-    order=1,
-):
-    """
-    Create a 2D mesh of a notched three-point flexure specimen using GMSH.
-    a = height of the notch
-    h = height of the specimen
-    L = width of the specimen
-    n = width of the load interface
-    gamma = notch angle
-    de = density of elements at specimen
-    de2 = density of elements at the notch and crack
-    key = 0 -> create model for Fenicxs (default)
-          1 -> create model for Cast3M
-    show = False -> doesn't open Gmsh to vizualise the mesh (default)
-           True -> open Gmsh to vizualise the mesh
-    filename = name and format of the output file for key = 1
-    order = order of the function of form
-    """
-    gmsh.initialize()
-    gmsh.option.setNumber("General.Terminal", 1)
-    gmsh.option.setNumber("Mesh.Algorithm", 5)
-    hopen = a * np.tan((gamma / 2.0) * np.pi / 180)
-    c0 = h / 40
-    load_len = n
-    tdim = 2
-
-    model = gmsh.model()
-    model.add("TPB")
-    model.setCurrent("TPB")
-    # Generating the points of the geometrie
-    p0 = model.geo.addPoint(0.0, a, 0.0, de2, tag=0)
-    p1 = model.geo.addPoint(hopen, 0.0, 0.0, de, tag=1)
-    p2 = model.geo.addPoint(L / 2, 0.0, 0.0, de, tag=2)
-    p3 = model.geo.addPoint(L / 2, h, 0.0, de, tag=3)
-    p4 = model.geo.addPoint(0.0, h, 0.0, de, tag=4)
-    if key == 0:
-        p5 = model.geo.addPoint(-L / 2, h, 0.0, de, tag=5)
-        p6 = model.geo.addPoint(-L / 2, 0.0, 0.0, de, tag=6)
-        p7 = model.geo.addPoint(-hopen, 0.0, 0.0, de, tag=7)
-        # Load facet
-        p21 = model.geo.addPoint(load_len, h, 0.0, de, tag=30)
-        p22 = model.geo.addPoint(-load_len, h, 0.0, de, tag=31)
-    elif key == 1:
-        p20 = model.geo.addPoint(0, a + c0, 0, de2, tag=20)
-    # Creating the lines by connecting the points
-    notch_right = model.geo.addLine(p0, p1, tag=8)
-    bot_right = model.geo.addLine(p1, p2, tag=9)
-    right = model.geo.addLine(p2, p3, tag=10)
-    # top_right = model.geo.addLine(p3, p4, tag=11)
-    if key == 0:
-        top_right = model.geo.addLine(p3, p21, tag=11)
-        top_left = model.geo.addLine(p22, p5, tag=12)
-        left = model.geo.addLine(p5, p6, tag=13)
-        bot_left = model.geo.addLine(p6, p7, tag=14)
-        notch_left = model.geo.addLine(p7, p0, tag=15)
-        # Load facet
-        load_right = model.geo.addLine(p21, p4, tag=32)
-        load_left = model.geo.addLine(p4, p22, tag=33)
-    elif key == 1:
-        top_right = model.geo.addLine(p3, p4, tag=11)
-        sym_plan = model.geo.addLine(p4, p20, tag=21)
-        fissure = model.geo.addLine(p20, p0, tag=22)
-    # Creating the surface using the lines created
-    if key == 0:
-        perimeter = model.geo.addCurveLoop(
-            [
-                notch_right,
-                bot_right,
-                right,
-                top_right,
-                load_right,
-                load_left,
-                top_left,
-                left,
-                bot_left,
-                notch_left,
-            ]
-        )
-    elif key == 1:
-        perimeter = model.geo.addCurveLoop(
-            [notch_right, bot_right, right, top_right, sym_plan, fissure]
-        )
-    surface = model.geo.addPlaneSurface([perimeter])
-    # model.geo.addSurfaceLoop([surface,16])
-    model.mesh.setOrder(order)
-
-    # Creating Physical Groups to extract data from the geometrie
-    if key == 0:
-        gmsh.model.addPhysicalGroup(tdim - 1, [left], tag=101)
-        gmsh.model.setPhysicalName(tdim - 1, 101, "Left")
-
-        gmsh.model.addPhysicalGroup(tdim - 1, [right], tag=102)
-        gmsh.model.setPhysicalName(tdim - 1, 102, "Right")
-
-        gmsh.model.addPhysicalGroup(tdim - 2, [p6], tag=103)
-        gmsh.model.setPhysicalName(tdim - 2, 103, "Left_point")
-
-        gmsh.model.addPhysicalGroup(tdim - 2, [p2], tag=104)
-        gmsh.model.setPhysicalName(tdim - 2, 104, "Right_point")
-
-        gmsh.model.addPhysicalGroup(tdim - 2, [p4], tag=105)
-        gmsh.model.setPhysicalName(tdim - 2, 105, "Load_point")
-
-        gmsh.model.addPhysicalGroup(tdim - 2, [p0], tag=106)
-        gmsh.model.setPhysicalName(tdim - 2, 106, "Notch_point")
-
-        gmsh.model.addPhysicalGroup(tdim - 1, [load_right], tag=107)
-        gmsh.model.setPhysicalName(tdim - 1, 107, "load_right")
-
-        gmsh.model.addPhysicalGroup(tdim - 1, [load_left], tag=108)
-        gmsh.model.setPhysicalName(tdim - 1, 108, "load_left")
-
-        gmsh.model.addPhysicalGroup(tdim, [surface], tag=110)
-        gmsh.model.setPhysicalName(tdim, 110, "mesh_surface")
-
-    # Cast3M can't read Physical Groups of points (dim = 0). Instead, we check the number in the mesh and input in manually in the code.
-    # The number of a node doesn't change if it's in a point of the geometry
-    if key == 1:
-        gmsh.model.addPhysicalGroup(tdim, [surface], tag=110)
-        gmsh.model.setPhysicalName(tdim, 110, "mesh_surface")
-
-        gmsh.model.addPhysicalGroup(tdim - 1, [fissure], tag=111)
-        gmsh.model.setPhysicalName(tdim - 1, 111, "fissure")
-
-        gmsh.model.addPhysicalGroup(tdim - 1, [sym_plan], tag=112)
-        gmsh.model.setPhysicalName(tdim - 1, 112, "sym_plan")
-
-        # gmsh.model.addPhysicalGroup(tdim-2, [p20], tag=113)
-        # gmsh.model.setPhysicalName(tdim-2, 113, 'Crack_tip')
-
-        # gmsh.model.addPhysicalGroup(tdim-2, [p4], tag=114)
-        # gmsh.model.setPhysicalName(tdim-2, 114, 'Load_point')
-
-        # gmsh.model.addPhysicalGroup(tdim-2, [p2], tag=115)
-        # gmsh.model.setPhysicalName(tdim-2, 115,'Right_point')
-    # Generating the mesh
-    model.geo.synchronize()
-    model.mesh.generate(tdim)
-    if show:
-        gmsh.fltk.run()
-    if key == 1:
-        gmsh.write(filename)
-    return gmsh.model
-
-
-a = 0.075
-h = 0.3
-n = 1 / 50
-L = 1
-gamma = 90
-de = a / 20
-de2 = a / 40
-gmsh_model = mesh_V(a, h, L, n, gamma, de, de2)
-# In this moment, it could be necessary to get the data of the cells and facets
-# In this case, we are taking info of the facets so that we can define subdomains
-# in order to apply Newman Bondary conditions, which means that is a condition
-# applied not in the displacement (variable of interest), but in the correspond
-# variable (in this case, force/pressure)
-mesh, facet_tags = meshes.gmsh_model_to_mesh(
-    gmsh_model, cell_data=False, facet_data=True, gdim=2
-)
-
-
-# Plot mesh
-plt.figure()
-ax = plot_mesh(mesh)
-fig = ax.get_figure()
-fig.savefig("mesh.png")
-
-# Functional setting
-# 'u' represents the displacement in this problem. In order to solve it, the
-# continuos field  'u' is replaced by a discrite form u = som[vec(function_forme)
-# *vec(nodal_displacement)]
-# In order to define the vec(function_forme), the ufl library is used.
-
-# A VectorElement represents a combination of basic elements such that each
-# component of a vector is represented by the basic element. The size is usually
-# omitted, the default size equals the geometry dimension.
-
-# ulf.VectorElement(<Type of the element>, <Geometry of the element>,
-# degree=<Degree of element: 1 - Linear, 2 - Quadratic, etc.>, dim= <Target
-# dimension of the element: 1 - Line, 2 - Area, 3 - Volume>)
-
-# Lagrange is a familly type of elements -> polynomial functions of forme;
-# The Lagrange elements are going to be defined in the mesh as such we take the
-# geometry of elements present in the mesh.
-
-element_u = ufl.VectorElement("Lagrange", mesh.ufl_cell(), degree=1, dim=2)
-element_alpha = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), degree=1)
-
-# After defining the Finite Element in ufl, a association with dolfinx is made.
-# To inputs are necessary, the mesh and the element type created. In some sense,
-# we obtain the "discretised model w/ elements definied".
-V_u = dolfinx.fem.FunctionSpace(mesh, element_u)
-V_alpha = dolfinx.fem.FunctionSpace(mesh, element_alpha)
-
-# In this model, we also defines functions necessaries to solve the problem.
-# This functions are definied in the entire space/model.
-# The discrete nodal valeus of
-u = dolfinx.fem.Function(V_u, name="Displacement")
-# the displacement
-u_ = dolfinx.fem.Function(V_u, name="BC_Displacement")
-u_imposed = dolfinx.fem.Function(V_u, name="Imposed_Displacement")
-alpha = dolfinx.fem.Function(V_alpha, name="Damage")
-# Bounds -> the values of alpha must be max([0,1],[alpha(t-1),1])
-alpha_ub = dolfinx.fem.Function(V_alpha, name="UpperBoundDamage")
-alpha_lb = dolfinx.fem.Function(V_alpha, name="LowerBoundDamage")
-alpha_lb.interpolate(lambda x: np.zeros_like(x[0]))
-alpha_ub.interpolate(lambda x: np.ones_like(x[0]))
-
-# In order to defined a function in a specific subspace of the model, it must be
-# specified in the model 'V_u.sub(i)', where i = 0 -> x, 1 -> y, 2-> z.
-# Don't forget to collapse, to choose only the DOF associated with the
-# subspace.
-
-# I don't think  this part works to definy the body force applied in a geometry.
-# It could be better to define it in the energy definition as constant. If not a
-# constant, we might need to define as a space function.
-# g = dolfinx.fem.Function(V_u, name="Body_pressure")
-# with g.vector.localForm() as loc:
-#   loc.set(-78500.0)
-
-# Integral measures -> in order to define the energy lately, it's necessary to
-# define the integral measures, as such one is a integral.
-dx = ufl.Measure("dx", domain=mesh)  # -> volume measure
-# We include here the subdomain data generated at the gmsh file.
-ds = ufl.Measure("ds", subdomain_data=facet_tags, domain=mesh)  # -> surface measure
-# ds(<number of the facet tags>)
-# dS = ufl.Measure("dS", domain = mesh) - inner boundaries of the mesh ->
-# not usefull
-
-
-model = Brittle(parameters.get("model"))
-state = {"u": u, "alpha": alpha}
-# The total energy density is calculated this time using a already written
-# function of the "model". This return the elasticity energy (with the a(alpha))
-# and the damage energy term. To count for externals forces, it need to substract it
-# from the total energy
-# - ufl.dot(force,u)*ds(107) - ufl.dot(force,u)*ds(108)
-total_energy = model.total_energy_density(state) * dx
-if parameters["loading"]["type"] == "ID":
-    total_energy = model.total_energy_density(state) * dx
-if parameters["loading"]["type"] == "IF":
-    # Getting load parameters
-    force = dolfinx.fem.Function(V_u, name="Contact_force")
-    loading_force = -1 * parameters["loading"]["max"]
-    force.interpolate(
-        lambda x: (np.zeros_like(x[0]), loading_force * np.ones_like(x[1]))
-    )
-    total_energy = (
-        model.total_energy_density(state) * dx
-        - ufl.dot(force, u) * ds(107)
-        - ufl.dot(force, u) * ds(108)
-    )
-
-# Boundary sets
-# Function that returns 'TRUE' if the point of the mesh is in the region you want
-# to apply the BC.
-
-
-def BC_points(x):
-    # x[0] is the vector of X-coordinate of all points ; x[1] is the vector of
-    # Y-coordinate
-    return np.logical_and(
-        np.logical_or(np.isclose(x[0], -L / 2), np.isclose(x[0], L / 2)),
-        np.isclose(x[1], 0),
-    )
-
-
-BC_entities = dolfinx.mesh.locate_entities_boundary(mesh, 0, BC_points)
-BC_dofs = dolfinx.fem.locate_dofs_topological(V_u, 0, BC_entities)
-u_.interpolate(lambda x: (np.zeros_like(x[0]), np.zeros_like(x[1])))
-
-# FOR IMPOSED FORCE :
-if parameters["loading"]["type"] == "IF":
-    bcs_u = [dirichletbc(u_, BC_dofs)]
-# FOR IMPOSED DISPLACEMENT :
-if parameters["loading"]["type"] == "ID":
-
-    def ID_points(x):
-        return np.logical_and(
-            np.equal(x[1], h),
-            np.logical_and(np.greater_equal(x[0], -1 * n), np.less_equal(x[0], n)),
-        )
-
-    ID_entities = dolfinx.mesh.locate_entities_boundary(mesh, 0, ID_points)
-    ID_dofs = dolfinx.fem.locate_dofs_topological(V_u, 0, ID_entities)
-    u_imposed.interpolate(
-        lambda x: (
-            np.zeros_like(x[0]),
-            -1 * parameters["loading"]["max"] * np.ones_like(x[1]),
-        )
-    )
-    bcs_u = [dirichletbc(u_, BC_dofs), dirichletbc(u_imposed, ID_dofs)]
-
-dofs_alpha_left = locate_dofs_geometrical(V_alpha, lambda x: np.isclose(x[0], -L / 2))
-dofs_alpha_right = locate_dofs_geometrical(V_alpha, lambda x: np.isclose(x[0], L / 2))
-BC_dofs_alpha = dolfinx.fem.locate_dofs_topological(V_alpha, 0, BC_entities)
-if parameters["loading"]["type"] == "IF":
-    bcs_alpha = [
-        dirichletbc(np.array(0.0, dtype=PETSc.ScalarType), BC_dofs_alpha, V_alpha)
-    ]
-if parameters["loading"]["type"] == "ID":
-    ID_dofs_alpha = dolfinx.fem.locate_dofs_topological(V_alpha, 0, ID_entities)
-    bcs_alpha = [
-        dirichletbc(
-            np.array(0.0, dtype=PETSc.ScalarType),
-            np.concatenate(
-                [dofs_alpha_left, dofs_alpha_right, BC_dofs_alpha, ID_dofs_alpha]
-            ),
-            V_alpha,
-        )
-    ]
-bcs_alpha = []
-# dofs_alpha_left, dofs_alpha_right
-bcs = {"bcs_u": bcs_u, "bcs_alpha": bcs_alpha}
-
-# Update the bounds
-set_bc(alpha_ub.vector, bcs_alpha)
-set_bc(alpha_lb.vector, bcs_alpha)
-
-solve_it = am.AlternateMinimisation(
-    total_energy, state, bcs, parameters.get("solvers"), bounds=(alpha_lb, alpha_ub)
-)
-
-# solve_it.elasticity
-# Loop for evolution
-Loads = np.linspace(
-    parameters.get("loading").get("min"),
-    parameters.get("loading").get("max"),
-    parameters.get("loading").get("steps"),
-)
-
-data = {"elastic": [], "surface": [], "total": [], "load": []}
-
-for i_t, t in enumerate(Loads):
-    # update bondary conditions
-    if parameters["loading"]["type"] == "ID":
-        u_imposed.interpolate(
-            lambda x: (np.zeros_like(x[0]), -1 * t * np.ones_like(x[1]))
-        )
-    if parameters["loading"]["type"] == "IF":
-        force.interpolate(
-            lambda x: (np.zeros_like(x[0]), loading_force * t * np.ones_like(x[1]))
-        )
-    # update lower bound for damage
-    alpha.vector.copy(alpha_lb.vector)
-    # solve for current load step
-    solve_it.solve()
-    # postprocessing
-    # global
-    surface_energy = assemble_scalar(
-        dolfinx.fem.form(model.damage_energy_density(state) * dx)
-    )
-    elastic_energy = assemble_scalar(
-        dolfinx.fem.form(model.elastic_energy_density(state) * dx)
-    )
-
-    data.get("elastic").append(elastic_energy)
-    data.get("surface").append(surface_energy)
-    data.get("total").append(surface_energy + elastic_energy)
-    data.get("load").append(t)
-
-    print(f"Solved timestep {i_t}, load {t}")
-    print(f"Elastic energy {elastic_energy:.3g}, Surface energy {surface_energy:.3g}")
-
-    # saving
-
-
-plt.plot(data.get("load"), data.get("surface"), label="surface")
-plt.plot(data.get("load"), data.get("elastic"), label="elastic")
-# plt.plot(data.get('load'), [1./2. * t**2*L for t in data.get('load')], label='anal elast', ls=':', c='k')
-
-plt.title("My specimen")
-plt.legend()
-# plt.yticks([0, 1/20], [0, '$1/2.\sigma_c^2/E_0$'])
-# plt.xticks([0, 1], [0, 1])
-
-try:
-    from dolfinx.plot import create_vtk_mesh as compute_topology
-except ImportError:
-    from dolfinx.plot import create_vtk_topology as compute_topology
-
-
-def plot_scalar(alpha, plotter, subplot=None, lineproperties={}):
-    if subplot:
-        plotter.subplot(subplot[0], subplot[1])
-    V = alpha.function_space
-    mesh = V.mesh
-
-    # topology, cell_types = dolfinx.plot.create_vtk_mesh(mesh, mesh.topology.dim)
-    # topology, cell_types = dolfinx.plot.create_vtk_topology(
-    # mesh, mesh.topology.dim)
-    topology, cell_types = compute_topology(mesh, mesh.topology.dim)
-    grid = pyvista.UnstructuredGrid(topology, cell_types, mesh.geometry.x)
-
-    plotter.subplot(0, 0)
-    grid.point_data["alpha"] = alpha.compute_point_values().real
-    grid.set_active_scalars("alpha")
-    plotter.add_mesh(grid, **lineproperties)
-    plotter.view_xy()
-    return plotter
-
-
-# postprocessing
-xvfb.start_xvfb(wait=0.05)
-pyvista.OFF_SCREEN = True
-plotter = pyvista.Plotter(
-    title="Displacement",
-    window_size=[1600, 600],
-    shape=(1, 2),
-)
-_plt = plot_scalar(alpha, plotter, subplot=(0, 0))
-_plt.screenshot("alpha.png")
-
-xvfb.start_xvfb(wait=0.05)
-pyvista.OFF_SCREEN = True
-plotter = pyvista.Plotter(
-    title="Displacement",
-    window_size=[1600, 600],
-    shape=(1, 1),
-)
-# plt = plot_scalar(u.sub(0), plotter, subplot=(0, 0))
-_plt = plot_vector(u, plotter, subplot=(0, 0))
-
-_plt.screenshot("displacement_MPI.png")
-
-
-plt.figure()
-plt.plot(data.get("load"), data.get("surface"), label="surface")
-plt.plot(data.get("load"), data.get("elastic"), label="elastic")
-# plt.plot(data.get('load'), [1./2. * t**2*L for t in data.get('load')], label='anal elast', ls=':', c='k')
-
-plt.title("My specimen")
-plt.legend()
-plt.savefig("energy.png")
diff --git a/contributed/dAlembert_stability.ipynb b/contributed/dAlembert_stability.ipynb
deleted file mode 100644
index da3becda..00000000
--- a/contributed/dAlembert_stability.ipynb
+++ /dev/null
@@ -1,60 +0,0 @@
-{
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "provenance": [],
-      "authorship_tag": "ABX9TyPbMDIEkUhHs22PArd5e2jj",
-      "include_colab_link": true
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "language_info": {
-      "name": "python"
-    }
-  },
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "view-in-github",
-        "colab_type": "text"
-      },
-      "source": [
-        "<a href=\"https://colab.research.google.com/github/kumiori/mec647/blob/main/dAlembert_stability.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "-g2hHa6LBoQW"
-      },
-      "outputs": [],
-      "source": [
-        "import os"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "## Stability, stabilities, _instabilities_, bifurcation, and problems\n",
-        "### From $\\partial$'Alembert"
-      ],
-      "metadata": {
-        "id": "EI7ryWlGDNWT"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [],
-      "metadata": {
-        "id": "T5BWCDctDcIV"
-      },
-      "execution_count": null,
-      "outputs": []
-    }
-  ]
-}
\ No newline at end of file
diff --git a/contributed/solveModel/parametersSolve.yml b/contributed/solveModel/parametersSolve.yml
deleted file mode 100755
index a5799598..00000000
--- a/contributed/solveModel/parametersSolve.yml
+++ /dev/null
@@ -1,25 +0,0 @@
-loading: 
-    min: 0
-    max: 1
-
-geometry: 
-    geom_type: bar
-    Lx: 5.
-    Ly: 15
-
-model: 
-    mu: 1.
-    lmbda: 0.
-
-solvers: 
-    snes: 
-        snes_type: newtontr
-        snes_stol: 1e-8
-        snes_atol: 1e-8
-        snes_rtol: 1e-8
-        snes_max_it: 100
-        snes_monitor: ""
-        ksp_type: preonly
-        pc_type: lu
-        pc_factor_mat_solver_type: mumps
-    
diff --git a/contributed/solveModel/solveEP2.py b/contributed/solveModel/solveEP2.py
deleted file mode 100644
index 9b7c2e5b..00000000
--- a/contributed/solveModel/solveEP2.py
+++ /dev/null
@@ -1,279 +0,0 @@
-# library include
-
-
-import logging
-import sys
-
-import dolfinx
-import dolfinx.io
-import dolfinx.plot
-import matplotlib.pyplot as plt
-import meshes
-import numpy as np
-import pyvista
-import ufl
-import yaml
-from dolfinx.fem import (
-    Function,
-    dirichletbc,
-)
-from meshes import primitives
-from petsc4py import PETSc
-from pyvista.utilities import xvfb
-from solvers import SNESSolver
-from utils.viz import plot_mesh, plot_scalar, plot_vector
-
-logging.basicConfig(level=logging.INFO)
-
-
-sys.path.append("./")
-
-# meshes
-
-# visualisation
-
-
-def plot_vector(u, plotter, subplot=None):
-    if subplot:
-        plotter.subplot(subplot[0], subplot[1])
-    V = u.function_space
-    mesh = V.mesh
-    topology, cell_types = dolfinx.plot.create_vtk_topology(mesh, mesh.topology.dim)
-    num_dofs_local = u.function_space.dofmap.index_map.size_local
-    geometry = u.function_space.tabulate_dof_coordinates()[:num_dofs_local]
-    values = np.zeros((V.dofmap.index_map.size_local, 3), dtype=np.float64)
-    values[:, : mesh.geometry.dim] = u.vector.array.real.reshape(
-        V.dofmap.index_map.size_local, V.dofmap.index_map_bs
-    )
-    grid = pyvista.UnstructuredGrid(topology, cell_types, geometry)
-    grid["vectors"] = values
-    grid.set_active_vectors("vectors")
-    # geom = pyvista.Arrow()
-    # glyphs = grid.glyph(orient="vectors", factor=1, geom=geom)
-    glyphs = grid.glyph(orient="vectors", factor=1.0)
-    plotter.add_mesh(glyphs)
-    plotter.add_mesh(
-        grid, show_edges=True, color="black", style="wireframe", opacity=0.3
-    )
-    plotter.view_xy()
-    return plotter
-
-
-def plot_scalar(alpha, plotter, subplot=None, lineproperties={}):
-    if subplot:
-        plotter.subplot(subplot[0], subplot[1])
-    V = alpha.function_space
-    mesh = V.mesh
-    topology, cell_types, _ = dolfinx.plot.create_vtk_mesh(mesh, mesh.topology.dim)
-    grid = pyvista.UnstructuredGrid(topology, cell_types, mesh.geometry.x)
-
-    plotter.subplot(0, 0)
-    grid.point_data["alpha"] = alpha.compute_point_values().real
-    grid.set_active_scalars("alpha")
-    plotter.add_mesh(grid, **lineproperties)
-    plotter.view_xy()
-    return plotter
-
-
-# Parameters
-
-
-parameters = {
-    "loading": {"min": 0, "max": 1},
-    "geometry": {"geom_type": "bar", "Lx": 5.0, "Ly": 15},
-    "model": {"mu": 1.0, "lmbda": 0.0},
-    "solvers": {
-        "snes": {
-            "snes_type": "newtontr",
-            "snes_stol": 1e-8,
-            "snes_atol": 1e-8,
-            "snes_rtol": 1e-8,
-            "snes_max_it": 100,
-            "snes_monitor": "",
-            "ksp_type": "preonly",
-            "pc_type": "lu",
-            "pc_factor_mat_solver_type": "mumps",
-        }
-    },
-}
-
-# parameters.get('loading')
-with open("./solveModel/parametersSolve.yml") as f:
-    parameters = yaml.load(f, Loader=yaml.FullLoader)
-
-# Mesh
-Lx = parameters["geometry"]["Lx"]
-Ly = parameters["geometry"]["Ly"]
-geom_type = parameters["geometry"]["geom_type"]
-
-
-gmsh_model, tdim = primitives.mesh_ep_gmshapi(geom_type, Lx, Ly, 1, 0.5, 0.3, tdim=2)
-
-mesh, mts = meshes.gmsh_model_to_mesh(
-    gmsh_model,
-    cell_data=False,
-    facet_data=True,
-    gdim=2,
-    exportMesh=True,
-    fileName="epTestMesh.msh",
-)
-
-# TODO: Plot mesh
-
-
-plt.figure()
-ax = plot_mesh(mesh)
-fig = ax.get_figure()
-fig.savefig("mesh.png")
-
-boundaries = [
-    (1, lambda x: np.isclose(x[0], 0)),
-    (2, lambda x: np.isclose(x[0], Lx)),
-    (3, lambda x: np.isclose(x[1], 0)),
-    (4, lambda x: np.isclose(x[1], Ly)),
-]
-
-facet_indices, facet_markers = [], []
-fdim = mesh.topology.dim - 1
-for marker, locator in boundaries:
-    facets = dolfinx.mesh.locate_entities(mesh, fdim, locator)
-    facet_indices.append(facets)
-    facet_markers.append(np.full(len(facets), marker))
-facet_indices = np.array(np.hstack(facet_indices), dtype=np.int32)
-facet_markers = np.array(np.hstack(facet_markers), dtype=np.int32)
-sorted_facets = np.argsort(facet_indices)
-facet_tag = dolfinx.mesh.MeshTags(
-    mesh, fdim, facet_indices[sorted_facets], facet_markers[sorted_facets]
-)
-
-# Functional setting
-
-element_u = ufl.VectorElement("Lagrange", mesh.ufl_cell(), degree=1, dim=2)
-V_u = dolfinx.fem.FunctionSpace(mesh, element_u)
-
-u = dolfinx.fem.Function(V_u, name="Displacement")
-g = dolfinx.fem.Function(V_u, name="Body pressure")
-
-u_ = dolfinx.fem.Function(V_u, name="Boundary Displacement")
-
-# Integral measure
-dx = ufl.Measure("dx", domain=mesh)
-ds = ufl.Measure("ds", domain=mesh)
-dS = ufl.Measure("ds", domain=mesh, subdomain_data=facet_tag)
-x = ufl.SpatialCoordinate(mesh)
-
-# Data
-zero = Function(V_u)
-# works in parallel!
-with zero.vector.localForm() as loc:
-    loc.set(0.0)
-
-one = Function(V_u)
-# works in parallel!
-with one.vector.localForm() as loc:
-    loc.set(1.0)
-
-g = Function(V_u)
-# works in parallel!
-"""
-with g.vector.localForm() as loc:
-    loc.set(1.0/100.)
-"""
-
-# x = ufl.SpatialCoordinate(mesh)
-# g = dolfinx.Expression ('4 *x[1]')
-
-# boundary conditions
-g.interpolate(lambda x: (np.zeros_like(x[0]), np.ones_like(x[1])))
-g.vector.ghostUpdate(addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD)
-
-
-def left(x):
-    return np.isclose(x[0], 0.0)
-
-
-def right(x):
-    return np.isclose(x[0], Lx)
-
-
-def bottom(x):
-    return np.isclose(x[1], 0.0)
-
-
-def top(x):
-    return np.isclose(x[1], Ly)
-
-
-# left side
-
-
-left_facets = dolfinx.mesh.locate_entities_boundary(mesh, 1, left)
-left_dofs = dolfinx.fem.locate_dofs_topological(V_u, mesh.topology.dim - 1, left_facets)
-
-
-# right side
-
-right_facets = dolfinx.mesh.locate_entities_boundary(mesh, 1, right)
-right_dofs = dolfinx.fem.locate_dofs_topological(
-    V_u, mesh.topology.dim - 1, right_facets
-)
-
-
-top_facets = dolfinx.mesh.locate_entities_boundary(mesh, 1, top)
-top_dofs = dolfinx.fem.locate_dofs_topological(V_u, mesh.topology.dim - 1, top_facets)
-
-bottom_facets = dolfinx.mesh.locate_entities_boundary(mesh, 1, bottom)
-bottom_dofs = dolfinx.fem.locate_dofs_topological(
-    V_u, mesh.topology.dim - 1, bottom_facets
-)
-
-# energy
-mu = parameters["model"]["mu"]
-lmbda = parameters["model"]["lmbda"]
-
-
-def _e(u):
-    return ufl.sym(ufl.grad(u))
-
-
-en_density = 1 / 2 * (2 * mu * ufl.inner(_e(u), _e(u))) + lmbda * ufl.tr(_e(u)) ** 2
-energy = en_density * dx - ufl.inner(u, g) * dS(4)
-
-# bcs = [dirichletbc(zero, bottom_dofs), dirichletbc(one, top_dofs)]
-bcs = [dirichletbc(zero, bottom_dofs)]
-
-# solving
-D_energy_u = ufl.derivative(energy, u, ufl.TestFunction(V_u))
-
-problem = SNESSolver(
-    D_energy_u,
-    u,
-    bcs,
-    bounds=None,
-    petsc_options=parameters.get("solvers").get("snes"),
-    prefix="elast",
-)
-
-
-uh = problem.solve()
-print(u)
-
-# plt.figure()
-# ax = plot_mesh(mesh)
-# fig = ax.get_figure()
-# fig.savefig(f"mesh.png")
-
-# postprocessing
-
-xvfb.start_xvfb(wait=0.05)
-pyvista.OFF_SCREEN = True
-
-plotter = pyvista.Plotter(
-    title="Displacement",
-    window_size=[1600, 600],
-    shape=(1, 2),
-)
-
-# _plt = plot_scalar(u_.sub(0), plotter, subplot=(0, 0))
-_plt = plot_vector(u, plotter, subplot=(0, 1))
-_plt.screenshot("displacement_MPI.png")
diff --git a/demo/demo_bifurcation.py b/demo/demo_bifurcation.py
index 605341d6..dbe39fa0 100644
--- a/demo/demo_bifurcation.py
+++ b/demo/demo_bifurcation.py
@@ -14,25 +14,18 @@
 import ufl
 import yaml
 from dolfinx.common import list_timings
-from dolfinx.fem import (
-    Constant,
-    Function,
-    FunctionSpace,
-    assemble_scalar,
-    dirichletbc,
-    form,
-    locate_dofs_geometrical,
-    set_bc,
-)
+from dolfinx.fem import (Constant, Function, FunctionSpace, assemble_scalar,
+                         dirichletbc, form, locate_dofs_geometrical, set_bc)
 from dolfinx.io import XDMFFile, gmshio
+from mpi4py import MPI
+from petsc4py import PETSc
+
 from irrevolutions.algorithms.am import AlternateMinimisation
 from irrevolutions.algorithms.so import BifurcationSolver
 from irrevolutions.meshes.primitives import mesh_bar_gmshapi
 from irrevolutions.models import DamageElasticityModel as Brittle
 from irrevolutions.utils import ColorPrint
 from irrevolutions.utils.plots import plot_energies
-from mpi4py import MPI
-from petsc4py import PETSc
 
 logging.basicConfig(level=logging.INFO)
 
diff --git a/demo/demo_elasticity.py b/demo/demo_elasticity.py
index a4daf9ca..728b2ec5 100644
--- a/demo/demo_elasticity.py
+++ b/demo/demo_elasticity.py
@@ -15,13 +15,14 @@
 import yaml
 from dolfinx import log
 from dolfinx.io import XDMFFile, gmshio
+from mpi4py import MPI
+from petsc4py import PETSc
+from pyvista.utilities import xvfb
+
 from irrevolutions.meshes.primitives import mesh_bar_gmshapi
 from irrevolutions.models import ElasticityModel
 from irrevolutions.solvers import SNESSolver as ElasticitySolver
 from irrevolutions.utils.viz import plot_vector
-from mpi4py import MPI
-from petsc4py import PETSc
-from pyvista.utilities import xvfb
 
 logging.basicConfig(level=logging.INFO)
 
diff --git a/demo/demo_traction.py b/demo/demo_traction.py
index e3d6072b..e75b3809 100644
--- a/demo/demo_traction.py
+++ b/demo/demo_traction.py
@@ -15,25 +15,18 @@
 import ufl
 import yaml
 from dolfinx.common import list_timings
-from dolfinx.fem import (
-    Constant,
-    Function,
-    FunctionSpace,
-    assemble_scalar,
-    dirichletbc,
-    form,
-    locate_dofs_geometrical,
-    set_bc,
-)
+from dolfinx.fem import (Constant, Function, FunctionSpace, assemble_scalar,
+                         dirichletbc, form, locate_dofs_geometrical, set_bc)
 from dolfinx.io import XDMFFile, gmshio
+from mpi4py import MPI
+from petsc4py import PETSc
+from pyvista.utilities import xvfb
+
 from irrevolutions.algorithms.am import AlternateMinimisation, HybridSolver
 from irrevolutions.meshes.primitives import mesh_bar_gmshapi
 from irrevolutions.models import DamageElasticityModel as Brittle
 from irrevolutions.utils.plots import plot_energies, plot_force_displacement
 from irrevolutions.utils.viz import plot_scalar, plot_vector
-from mpi4py import MPI
-from petsc4py import PETSc
-from pyvista.utilities import xvfb
 
 logging.basicConfig(level=logging.INFO)
 
diff --git a/demo/demo_vi.py b/demo/demo_vi.py
index 9851c0ac..5d42a3f0 100644
--- a/demo/demo_vi.py
+++ b/demo/demo_vi.py
@@ -12,18 +12,15 @@
 import pyvista
 import ufl
 import yaml
-from dolfinx.fem import (
-    Function,
-    FunctionSpace,
-    dirichletbc,
-)
+from dolfinx.fem import Function, FunctionSpace, dirichletbc
 from dolfinx.fem.assemble import assemble_scalar
 from dolfinx.mesh import CellType
-from irrevolutions.solvers import SNESSolver
-from irrevolutions.utils.viz import plot_profile, plot_scalar
 from mpi4py import MPI
 from pyvista.utilities import xvfb
 
+from irrevolutions.solvers import SNESSolver
+from irrevolutions.utils.viz import plot_profile, plot_scalar
+
 petsc4py.init(sys.argv)
 
 
diff --git a/playground/benchmark-umut-at2/vs_analytics_at2.py b/playground/benchmark-umut-at2/vs_analytics_at2.py
index e984ea7c..ab4fd3db 100644
--- a/playground/benchmark-umut-at2/vs_analytics_at2.py
+++ b/playground/benchmark-umut-at2/vs_analytics_at2.py
@@ -17,34 +17,21 @@
 import ufl
 import yaml
 from dolfinx.common import list_timings
-from dolfinx.fem import (
-    Constant,
-    Function,
-    assemble_scalar,
-    dirichletbc,
-    form,
-    locate_dofs_geometrical,
-)
+from dolfinx.fem import (Constant, Function, assemble_scalar, dirichletbc,
+                         form, locate_dofs_geometrical)
 from dolfinx.io import XDMFFile
-from irrevolutions.algorithms.am import HybridSolver
-from irrevolutions.algorithms.so import BifurcationSolver, StabilitySolver
-from irrevolutions.utils import (
-    ColorPrint,
-    Visualization,
-    _logger,
-    _write_history_data,
-    history_data,
-)
-from irrevolutions.utils.plots import (
-    plot_AMit_load,
-    plot_energies,
-    plot_force_displacement,
-)
-from irrevolutions.utils.viz import plot_profile, plot_scalar
 from mpi4py import MPI
 from petsc4py import PETSc
 from pyvista.utilities import xvfb
 
+from irrevolutions.algorithms.am import HybridSolver
+from irrevolutions.algorithms.so import BifurcationSolver, StabilitySolver
+from irrevolutions.utils import (ColorPrint, Visualization, _logger,
+                                 _write_history_data, history_data)
+from irrevolutions.utils.plots import (plot_AMit_load, plot_energies,
+                                       plot_force_displacement)
+from irrevolutions.utils.viz import plot_profile, plot_scalar
+
 petsc4py.init(sys.argv)
 comm = MPI.COMM_WORLD
 
@@ -165,13 +152,13 @@ def run_computation(parameters, storage=None):
     V_alpha = dolfinx.fem.FunctionSpace(mesh, element_alpha)
 
     u = dolfinx.fem.Function(V_u, name="Displacement")
-    u_ = dolfinx.fem.Function(V_u, name="BoundaryDisplacement")
+    dolfinx.fem.Function(V_u, name="BoundaryDisplacement")
 
     alpha = dolfinx.fem.Function(V_alpha, name="Damage")
 
     # Perturbations
-    β = Function(V_alpha, name="DamagePerturbation")
-    v = Function(V_u, name="DisplacementPerturbation")
+    Function(V_alpha, name="DamagePerturbation")
+    Function(V_u, name="DisplacementPerturbation")
 
     # Pack state
     state = {"u": u, "alpha": alpha}
@@ -181,7 +168,7 @@ def run_computation(parameters, storage=None):
     alpha_lb = dolfinx.fem.Function(V_alpha, name="LowerBoundDamage")
 
     dx = ufl.Measure("dx", domain=mesh)
-    ds = ufl.Measure("ds", domain=mesh)
+    ufl.Measure("ds", domain=mesh)
 
     # Useful references
     Lx = parameters.get("geometry").get("Lx")
@@ -193,7 +180,7 @@ def run_computation(parameters, storage=None):
 
     # Measures
     dx = ufl.Measure("dx", domain=mesh)
-    ds = ufl.Measure("ds", domain=mesh)
+    ufl.Measure("ds", domain=mesh)
 
     dofs_u_left = locate_dofs_geometrical(V_u, lambda x: np.isclose(x[0], 0.0))
     dofs_u_right = locate_dofs_geometrical(V_u, lambda x: np.isclose(x[0], Lx))
@@ -276,7 +263,7 @@ def run_computation(parameters, storage=None):
 
         stable = stability.solve(alpha_lb, eig0=z0, inertia=inertia)
 
-        with dolfinx.common.Timer("~Postprocessing and Vis") as timer:
+        with dolfinx.common.Timer("~Postprocessing and Vis"):
             if comm.rank == 0:
                 plot_energies(history_data, file=f"{prefix}/{_nameExp}_energies.pdf")
                 plot_AMit_load(history_data, file=f"{prefix}/{_nameExp}_it_load.pdf")
diff --git a/playground/benchmark-umut-at2/vs_analytics_at2_2d.py b/playground/benchmark-umut-at2/vs_analytics_at2_2d.py
index fcf8df1b..c1d07528 100644
--- a/playground/benchmark-umut-at2/vs_analytics_at2_2d.py
+++ b/playground/benchmark-umut-at2/vs_analytics_at2_2d.py
@@ -16,34 +16,23 @@
 import ufl
 import yaml
 from dolfinx.common import list_timings
-from dolfinx.fem import (
-    Constant,
-    Function,
-    assemble_scalar,
-    form,
-    locate_dofs_geometrical,
-)
+from dolfinx.fem import (Constant, Function, assemble_scalar, form,
+                         locate_dofs_geometrical)
 from dolfinx.io import XDMFFile, gmshio
+from mpi4py import MPI
+from petsc4py import PETSc
+from pyvista.utilities import xvfb
+
 from irrevolutions.algorithms.am import HybridSolver
 from irrevolutions.algorithms.so import BifurcationSolver, StabilitySolver
 from irrevolutions.meshes.primitives import mesh_bar_gmshapi
-from irrevolutions.models import BrittleMembraneOverElasticFoundation as ThinFilm
-from irrevolutions.utils import (
-    ColorPrint,
-    Visualization,
-    _logger,
-    _write_history_data,
-    history_data,
-)
-from irrevolutions.utils.plots import (
-    plot_AMit_load,
-    plot_energies,
-    plot_force_displacement,
-)
+from irrevolutions.models import \
+    BrittleMembraneOverElasticFoundation as ThinFilm
+from irrevolutions.utils import (ColorPrint, Visualization, _logger,
+                                 _write_history_data, history_data)
+from irrevolutions.utils.plots import (plot_AMit_load, plot_energies,
+                                       plot_force_displacement)
 from irrevolutions.utils.viz import plot_profile, plot_scalar, plot_vector
-from mpi4py import MPI
-from petsc4py import PETSc
-from pyvista.utilities import xvfb
 
 petsc4py.init(sys.argv)
 comm = MPI.COMM_WORLD
@@ -70,7 +59,7 @@ def stress(state):
     alpha = state["alpha"]
     dx = ufl.Measure("dx", domain=u.function_space.mesh)
 
-    return parameters["model"]["E"] * a(alpha) * u.dx() * dx
+    return parameters["model"]["E"] * ThinFilmAT2.a(alpha) * u.dx() * dx
 
 
 def run_computation(parameters, storage=None):
@@ -119,16 +108,16 @@ def run_computation(parameters, storage=None):
     V_alpha = dolfinx.fem.FunctionSpace(mesh, element_alpha)
 
     u = dolfinx.fem.Function(V_u, name="Displacement")
-    u_ = dolfinx.fem.Function(V_u, name="BoundaryDisplacement")
+    dolfinx.fem.Function(V_u, name="BoundaryDisplacement")
     u_zero = Function(V_u, name="InelasticDisplacement")
     zero_u = Function(V_u, name="BoundaryUnknown")
 
     alpha = dolfinx.fem.Function(V_alpha, name="Damage")
-    alphadot = dolfinx.fem.Function(V_alpha, name="Damage_rate")
+    dolfinx.fem.Function(V_alpha, name="Damage_rate")
 
     # Perturbations
-    β = Function(V_alpha, name="DamagePerturbation")
-    v = Function(V_u, name="DisplacementPerturbation")
+    Function(V_alpha, name="DamagePerturbation")
+    Function(V_u, name="DisplacementPerturbation")
 
     # Pack state
     state = {"u": u, "alpha": alpha}
@@ -138,17 +127,17 @@ def run_computation(parameters, storage=None):
     alpha_lb = dolfinx.fem.Function(V_alpha, name="LowerBoundDamage")
 
     dx = ufl.Measure("dx", domain=mesh)
-    ds = ufl.Measure("ds", domain=mesh)
+    ufl.Measure("ds", domain=mesh)
 
     # Useful references
     Lx = parameters.get("geometry").get("Lx")
 
     # Measures
     dx = ufl.Measure("dx", domain=mesh)
-    ds = ufl.Measure("ds", domain=mesh)
+    ufl.Measure("ds", domain=mesh)
 
-    dofs_u_left = locate_dofs_geometrical(V_u, lambda x: np.isclose(x[0], 0.0))
-    dofs_u_right = locate_dofs_geometrical(V_u, lambda x: np.isclose(x[0], Lx))
+    locate_dofs_geometrical(V_u, lambda x: np.isclose(x[0], 0.0))
+    locate_dofs_geometrical(V_u, lambda x: np.isclose(x[0], Lx))
 
     alpha_lb.interpolate(lambda x: np.zeros_like(x[0]))
     alpha_ub.interpolate(lambda x: np.ones_like(x[0]))
@@ -231,7 +220,7 @@ def run_computation(parameters, storage=None):
         _logger.critical(f"-- Solving Stability (Stability) for t = {t:3.2f} --")
         stable = stability.solve(alpha_lb, eig0=z0, inertia=inertia)
 
-        with dolfinx.common.Timer("~Postprocessing and Vis") as timer:
+        with dolfinx.common.Timer("~Postprocessing and Vis"):
             if comm.rank == 0:
                 plot_energies(history_data, file=f"{prefix}/{_nameExp}_energies.pdf")
                 plot_AMit_load(history_data, file=f"{prefix}/{_nameExp}_it_load.pdf")
diff --git a/playground/nb/JOSS_paper_figures_rayleigh_benchmark.ipynb b/playground/nb/JOSS_paper_figures_rayleigh_benchmark.ipynb
new file mode 100644
index 00000000..473f03c0
--- /dev/null
+++ b/playground/nb/JOSS_paper_figures_rayleigh_benchmark.ipynb
@@ -0,0 +1,5110 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "postproc\n"
+     ]
+    }
+   ],
+   "source": [
+    "import sympy as sp\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import postprocess as pp\n",
+    "import os\n",
+    "\n",
+    "from irrevolutions.utils import eigenspace as eig"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:py.warnings:<>:54: SyntaxWarning: invalid escape sequence '\\#'\n",
+      "\n",
+      "WARNING:py.warnings:<>:59: SyntaxWarning: invalid escape sequence '\\p'\n",
+      "\n",
+      "WARNING:py.warnings:<>:54: SyntaxWarning: invalid escape sequence '\\#'\n",
+      "\n",
+      "WARNING:py.warnings:<>:59: SyntaxWarning: invalid escape sequence '\\p'\n",
+      "\n",
+      "WARNING:py.warnings:/var/folders/ht/z8sb7wsd1bg0qpmyyfyq2rcm0000gr/T/ipykernel_53684/2705143184.py:54: SyntaxWarning: invalid escape sequence '\\#'\n",
+      "  plt.annotate(f'\\#{count}', datapoint, textcoords=\"offset points\", xytext=(0, -15), ha='center')\n",
+      "\n",
+      "WARNING:py.warnings:/var/folders/ht/z8sb7wsd1bg0qpmyyfyq2rcm0000gr/T/ipykernel_53684/2705143184.py:59: SyntaxWarning: invalid escape sequence '\\p'\n",
+      "  plt.xlabel('$\\pi^2 a$')\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import yaml\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "def load_parameters(directory):\n",
+    "    parameters_file = os.path.join(directory, 'parameters.yaml')\n",
+    "    if os.path.exists(parameters_file):\n",
+    "        with open(parameters_file, 'r') as f:\n",
+    "            parameters = yaml.safe_load(f)\n",
+    "        return parameters\n",
+    "    else:\n",
+    "        return None\n",
+    "    \n",
+    "def load_signature(directory):\n",
+    "    signature_file = os.path.join(directory, 'signature.md5')\n",
+    "    if os.path.exists(signature_file):\n",
+    "        with open(signature_file, 'r') as f:\n",
+    "            signature = yaml.safe_load(f)\n",
+    "        return signature\n",
+    "    else:\n",
+    "        return None\n",
+    "    \n",
+    "def compute_contour(a_values, bc_square_values):\n",
+    "    a, bcsq = np.meshgrid(a_values, bc_square_values)\n",
+    "    D_values = (np.pi**2 * a / bcsq)**(1/3)\n",
+    "    return a, bcsq, D_values\n",
+    "\n",
+    "def plot_phase_space(rootdir):\n",
+    "    successful_points = []\n",
+    "    unsuccessful_points = []\n",
+    "    points_count = {}\n",
+    "\n",
+    "    for subdir, _, _ in os.walk(rootdir):\n",
+    "        parameters = load_parameters(subdir)\n",
+    "        if parameters is not None:\n",
+    "            a = parameters.get('model', {}).get('a')\n",
+    "            b = parameters.get('model', {}).get('b')\n",
+    "            c = parameters.get('model', {}).get('c')\n",
+    "\n",
+    "            # Check if the computation is successful based on the existence of mode_shapes_data.npz\n",
+    "            success_file = os.path.join(subdir, 'mode_shapes_data.npz')\n",
+    "            datapoint = (np.pi**2 * a, b*c**2)\n",
+    "            \n",
+    "            if os.path.exists(success_file):\n",
+    "                successful_points.append(datapoint)\n",
+    "            else:\n",
+    "                unsuccessful_points.append(datapoint)\n",
+    "\n",
+    "            points_count[datapoint] = points_count.get(datapoint, 0) + 1\n",
+    "\n",
+    "    for datapoint, count in points_count.items():\n",
+    "        # plt.scatter(*datapoint, label=f'Multiplicity: {count}', marker='')\n",
+    "        # plt.scatter(*datapoint, marker='')\n",
+    "        plt.annotate(f'\\#{count}', datapoint, textcoords=\"offset points\", xytext=(0, -15), ha='center')\n",
+    "\n",
+    "    # Create the plot\n",
+    "    plt.scatter(*zip(*unsuccessful_points), label='Unsuccessful', marker='x')\n",
+    "    plt.scatter(*zip(*successful_points), label='Successful', marker='.', s=200)\n",
+    "    plt.xlabel('$\\pi^2 a$')\n",
+    "    plt.ylabel('$bc^2$')\n",
+    "    plt.legend()\n",
+    "    a_values = np.linspace(0, 100, 100)\n",
+    "    bc_sq_values = np.linspace(0, 100, 100)\n",
+    "    a, bc_sq, D_values = compute_contour(a_values, bc_sq_values)\n",
+    "    plt.contour(bc_sq, a , D_values, levels=5, colors='black', linestyles='dashed')\n",
+    "\n",
+    "    plt.show()\n",
+    "\n",
+    "# Example usage\n",
+    "# plot_phase_space(dirroot)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "File 'time_data.json' not found. Handle this case accordingly.\n"
+     ]
+    }
+   ],
+   "source": [
+    "experiment = '../../test/output/rayleigh-benchmark/MPI-1/bef047fb68f6bc3b5feb6b2f634b15fc'\n",
+    "experiment = '../../test/output/rayleigh-benchmark/MPI-1/ff9c4acbaf25a77cb9e99342154bed50'\n",
+    "\n",
+    "params, data, signature = pp.load_data(experiment)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "modes_data = np.load(os.path.join(experiment, 'mode_shapes_data.npz'), allow_pickle=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mode = pp.read_mode_data_from_npz(modes_data, time_step=0, num_modes=1, num_points=10)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Rayleigh benchmark"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "_fig, _axes = pp.plot_fields_for_time_step(mode)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from irrevolutions.utils.eigenspace import l2_norm\n",
+    "\n",
+    "def plot_profile_comparison(parameters, mode, idx=[1, 1], reverse = False):\n",
+    "\n",
+    "    _fig, _axes = pp.plot_fields_for_time_step(mode)\n",
+    "    _axes[0].set_title('Eigenvectors (bifurcation problem)')\n",
+    "    _axes[1].set_title('Eigenvectors (stability problem)')\n",
+    "    _parameters = f'a = {parameters.get(\"a\"):.2f}, b = {parameters.get(\"b\"):.2f}, c = {parameters.get(\"c\"):.2f}'\n",
+    "    _fig.suptitle(f\"Rayleigh eigenproblem with parameters {_parameters}\")\n",
+    "\n",
+    "    coeff_v = l2_norm([\n",
+    "        (mode['mesh'], mode['fields']['bifurcation_v']),\n",
+    "        (mode['mesh'], mode['fields']['bifurcation_β'])\n",
+    "        ])\n",
+    "\n",
+    "    coeff_k = l2_norm([\n",
+    "        (mode['mesh'], mode['fields']['stability_β'])\n",
+    "        ])\n",
+    "\n",
+    "    eigenspace_v, _normalisation_v = eig.solve_eigenspace_vector(parameters, idx=idx[0])\n",
+    "    eigenspace_k, _normalisation_k = eig.solve_eigenspace_cone(parameters, idx=idx[1])\n",
+    "\n",
+    "    v, β = eigenspace_v[\"v\"], eigenspace_v[\"β\"]\n",
+    "\n",
+    "    x_values = np.linspace(0, 1, 100)\n",
+    "    v_function = sp.lambdify('x', v)\n",
+    "    β_function = sp.lambdify('x', β)\n",
+    "\n",
+    "    v_values = [v_function(x) for x in x_values]\n",
+    "    β_values = [β_function(x) for x in x_values]\n",
+    "\n",
+    "    _axes[0].plot(x_values, np.array(β_values)*coeff_v, label=r'$\\beta(x)$', c='k')\n",
+    "    _axes[0].plot(x_values, np.array(v_values)*coeff_v, label=r'$v(x)$', c='k', linestyle='--')\n",
+    "\n",
+    "    _axes[0].legend()\n",
+    "    \n",
+    "    v, β = eigenspace_k[\"v\"], eigenspace_k[\"β\"]\n",
+    "    D = eigenspace_k[\"D\"]\n",
+    "    x_values = np.linspace(0, 1, 100)\n",
+    "    v_function = sp.lambdify('x', v)\n",
+    "    β_function = sp.lambdify('x', β)\n",
+    "    v_values = [v_function(x) for x in x_values]\n",
+    "    if reverse:\n",
+    "        β_values = [β_function(1-x) for x in x_values]\n",
+    "    else:\n",
+    "        β_values = [β_function(x) for x in x_values]\n",
+    "\n",
+    "    _axes[1].plot(x_values, np.array(β_values)*coeff_k, label=r'$\\beta(x)$', c='k')\n",
+    "    _axes[1].set_xticks([0, float(sp.N(D))], [0, \"$D$\"])\n",
+    "    _axes[1].legend()\n",
+    "\n",
+    "    return _fig, _axes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "case 2\n",
+      "[{x, A}, {x, A}]\n",
+      "sqrt(2*A**2/pi**2 + A**2/2) True False\n",
+      "depends_on_A\n",
+      "case 2\n",
+      "sqrt(0.184201574932019*sin(1.0*pi**1.0)*cos(1.0*pi**1.0)/pi**0.333333333333333 + 0.184201574932019*pi**0.666666666666667*sin(1.0*pi**1.0)**2 + 0.736806299728077*sin(1.0*pi**1.0)/pi**0.333333333333333 + 0.184201574932019*pi**0.666666666666667*cos(1.0*pi**1.0)**2 + 0.368403149864039*pi**0.666666666666667)*sqrt(C**2)\n",
+      "depends_on_C\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(<Figure size 1000x500 with 2 Axes>,\n",
+       " array([<Axes: title={'center': 'Eigenvectors (bifurcation problem)'}>,\n",
+       "        <Axes: title={'center': 'Eigenvectors (stability problem)'}>],\n",
+       "       dtype=object))"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "experiment = '../../test/output/rayleigh-benchmark/MPI-1/2f6fb02e066c6e61b2680447a53b61df'\n",
+    "modes_data = np.load(os.path.join(experiment, 'mode_shapes_data.npz'), allow_pickle=True)\n",
+    "mode = pp.read_mode_data_from_npz(modes_data, time_step=0, num_modes=1, num_points=10)\n",
+    "\n",
+    "params, data, signature = pp.load_data(experiment)\n",
+    "\n",
+    "a = params['model']['a']\n",
+    "b = params['model']['b']\n",
+    "c = params['model']['c']\n",
+    "\n",
+    "parameters = {\"a\": a, \"b\": b, \"c\": c}\n",
+    "\n",
+    "plot_profile_comparison(parameters, mode, idx=[1, 0], reverse=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Parametric Rayleigh benchmark"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m\u001b[36m0044f692a05422bb98c4bd3c6d04770c\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m00ffda65398984695ae5576fe383f02c\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m02bd5bbda8beff8f5cc0fbd88b599173\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m034189f38c18e6c04e59770762ddf137\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m043ebb377c5c5699e4d0c39a6efff1bd\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m046c79dade1d3ac0d860737fb091750e\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m04c979b82e2ec3add9a38959f42f52b1\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m06fd589cfddf7421b0f422bd92c4f077\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m0709aa4181151c6be88b7bcafc3e014c\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m08024b8d0f056a2e29555bbcff919b96\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m08c18d207e645c06bfbac1f997b0edad\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m0aa3df8dcb703508b12d1c306410ba06\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m0bd67cc936b53d779e505e7860e8d3a0\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m0bd72656f57f527e5863ef191c372831\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m0ce6d727b36b2329f0bbb7bbcdbedc32\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m0e5f3c92253e55d958c60f6c4b3c3c94\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m12e557a7aa59790b9b017adcc2d61778\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m133a813220736fd35ca5780e0704fc1f\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m14dffef10402801b89457241dc466ce3\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m154863f5eba04c9bd04c821204662c46\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m154b1d59bc43f05c208cd7e9315d48c9\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m182ec8780648c9a9ab4b05e10b28263a\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m186ab8e8139e59cdbddc72429ba378be\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m197773ce0c3bd5db1389a4e328fc1127\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m19c6e4fb5e77a4485581566f5ac85e7c\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m1a9321268f43fe71c8ad7f9ab0ffe154\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m1b32af79ec982846c16e8da2dece9111\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m1bde21d6d9e399af2546e2f9010b9350\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m1d99dc57effa20f609a0cf624bd5e04a\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m1da28d85e36e3d87bbd150884f1a6a4a\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m1dc98a04e21fe62758b71ea2affc59ef\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m1e684c7c8d3b74ab8946e9d16a169644\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m1ed7e041da9cefb263f8e5283ed9eabe\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m22c97e323c9093987f705e9112b3640a\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m23a4e99376dd709c7a7926c1b512d89c\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m245a0619f72f8fa174e14ad18ed79ef8\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m25739f21e5031ff02151d7be8da54f32\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m26df8fc60531553c4539463d1c28dfec\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m287b7dad39b1a76397aff28461a3ae8c\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m28a07bc7bb355d05782734af39a111e9\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m29a13ad515a4e0b380c0765b509f4bd8\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m2ab9598ff854eb657a12d777d1374f74\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m2afb851ba8413ce5291c1f7281b0b60a\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m2f170fb0e88ec9e9ada917e40dfe9b56\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m2f31290d5784e79695d28a07513df7cc\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m303619e93fae27433a982bc1565704fd\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m30b166d2e6f48e17020d5e5457e6e26a\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m3595514d555f90e66127a53c00ad6142\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m35992c02ce19e70d4b7646206dc625fb\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m35b902ddb2944770f8fcc585b48334e2\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m36ba82b093b807f3d4156df5aef56265\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m3768e99b08bf76083993540fa0e931b5\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m379c9f5ef533b7996ca5a6f6e2aebefb\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m37c112d0c344dfbd6591d46e6aa4bf2b\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m3834fb17dbfd82a9e6b6c78c159139b4\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m384d216e638296c3f2e43c1bc10e6aa2\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m38ec6d9ed3750e2a349aab9393a444c1\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m390c1586d26f4ecb8965bb7caafd1a01\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m3957404c3a99817081032e3304cc2eb2\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m39582b0fef781ef7dd59b969861740bb\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m3ced327f796a04949713bfbb43236c70\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m3e66ff5126900e8b3aacf615357995c8\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m3f02e8edb6dc1f797b9be04d540e0b77\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m3fa875212869f8bb87ecec023e3594ab\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m40e1eb6b7c27798bd70c26bb89188010\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m41a9fd1aaeefba3a8b4e181a3f57a26d\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m4332c6bbfc3a256c63470ab9c3d7e8a9\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m4363c09f4f41e45ec5a021d2b68e8276\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m43f6745ab4a8087d95d6dad67b61c455\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m446276eafc7b7cb1df263d2d43575334\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m45638e207aa96b4f92e27f59f0b544f5\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m459c554af4f32b088b68a93b3a02f60a\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m47724b1983ad0ec6fb3a085868e6406c\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m49dd09f415ed6893adec2902800ecfbb\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m4a42005d5d1db515c58f10fb2a977b4f\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m4dd050367ed3d746a44a8627a27c0d86\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m4fb5053042bc75c426a6bbcccc364670\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m5240559833b5075c63c9ced7cf58d9dc\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m53326ce0c7e2e31e9e711749f6c9872f\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m55aa665af4383cdfab486497a526b948\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m55decbb21da6eccb8c64662f82e90e07\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m55f85e04d355cf55f777c22037403b12\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m5869943e0762f690d3334659af3af68d\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m5971508ca172ee7705ba9867da5f2799\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m5bb43003ce8ab88ea3e2b5835b018f9b\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m5e00139ccb323f8a614dbc86e4adee17\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m5e1b92e1c2392c4742c4b8caac239078\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m5e62740f1e3d26cfadf3f31606f04c93\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m5f009deebc7c75e793bc5c2a3881177d\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m5f728c69b35c2f0b870a23f20c1cf1bd\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m63664dec0d71acbc99c1bc0610834138\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m63ef09428da038d6a13c1db516b12cba\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m6426c7b91e31a7164aa0ac3fe5ca7cc7\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m64765b11b3c0dfd58defa91fe80ef236\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m64beb86873a673b4b1d7d131a3b7cca1\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m64cb9ae1176525d58b69364473f71172\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m67450e668b5e0f33bd764955db697890\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m68c28c98150c0491f40b510fe53d5150\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m699b629be39a2827f23a164b8bb30c1a\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m6ca3ebf88d38ddd014eac49e0ef69663\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m6d28de9a1db1dc3ad984dc0fed840b85\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m6e3e4283c6d55967a64bf8d1b588732a\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m6e7f390a6ef123e2c2a6788f98edf934\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m6ea3722739a32d235c59aae7015d2132\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m6ebe9390869deaeaae9fe15f64d1d60e\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m7021e9afc054e604d2e15dbc07a9db83\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m704218ec1ab1084c07564e6f679b9f0b\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m70a947a5ab7ce7fea419f5942119a162\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m76162cca14bfb91a3e02d6e29627bc11\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m7762cc6ecd7e5663aefbc1ac3d1d183a\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m77b345b3ac23acd4b0c793607df72b86\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m787493f255c3e1fe0f7590724463af47\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m7a5fe95e68652560e04a4f04a6cc2ed0\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m7bf0064c79e577a339a3bdaa8b664053\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m7c63c15b4401024888916ca9b42b21c9\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m7d1d96b15a68e392abeffe502f84a042\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m7d21decc9d7b00098894bc65c19ccd48\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m7d44e0b425e269c22219e93ee416e825\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m7ed36b8dcee86d9a4d55b31fdd6f7199\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m7f1430bcb0a6e3812cc44d0015cc2e19\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m7fd56cac090b0910c9796189bb13f529\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m802c675e74c573280a1fb772daaedf9c\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m80bf33f54ca3cc39d55895987d22db6a\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m81211387b53a1545755003f8b2a1fc18\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m812f71e51faf4596904b7256abf7468f\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m818deb373f5d6257aa085493e2a12fb6\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m824d7bb2017e24e06fb6a4d7d87994f0\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m840d841d4f4ab073d4675980ae0c65e2\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m8498148bb21e28e65acd6e832dd392e5\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m8683c8a249116de1e4a1f2efb7540756\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m86ff1f0a410c7220fea17218daac0b51\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m873a5a261a51d2c352cec13cbf8fdf0c\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m879c22c6dd6ba9258ed2b3977c02dd3f\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m89829464cf9d8abc402d2b289511db80\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m89d72ebf25cb6d5a889723f29e937e64\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m8c2849493a108685ea6d16538cee0ff5\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m8c959694f7c0083563629bbd7f6d26b9\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m8d808fc699689924b60841e1e2164771\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m90167d1609a729e7134e4a923351f671\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m9283ffcb15cf65f0be191c4877fa3f8c\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m941d48ed62b7edc24a6b564a91b4d6c6\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m942438a56c8ffba16a34fefb4a9aca6a\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m9444e6515b8c6baac6410af233373084\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m95e026c0be6029963131d95f104d98f9\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m961ec9441a2fa5a82bbed869080b8b3a\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m96e85c7780b0f7c34955ed4e9e8f6075\u001b[m\u001b[m\n",
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+      "profile_comparison-f2ad2e96cf1f087e6a016e4dfe16cc9a.pdf\n",
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+      "profile_comparison-f582d6b1956c5d8756fcecd3ed31854c.pdf\n",
+      "profile_comparison-f5987d8fb47e09c39afd3e4aba484450.pdf\n",
+      "profile_comparison-f81b240be91d490535164f911aada9ed.pdf\n",
+      "profile_comparison-f8e44e6d6d9486ac535ec276bfb1b520.pdf\n",
+      "profile_comparison-fa49c46e19fa64edbd26b7a87ff6c06f.pdf\n",
+      "profile_comparison-fab2c241b23cc097ef2a6c33ca7d1cb4.pdf\n",
+      "profile_comparison-fcab5744d2c104f2879ab24a05eb5965.pdf\n",
+      "profile_comparison-fe1bd5e0aae1254c90a08b9fe2d4fcac.pdf\n",
+      "profile_comparison-fe65f734e95ed43aa28396952f76bc12.pdf\n",
+      "profile_comparison-ff220f0b4c41c9194a0d56982ad9d9df.pdf\n",
+      "profile_comparison-ff554b36ebfb02ba497f4de21e7bfe39.pdf\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "cwd = os.getcwd()\n",
+    "path_components = cwd.split(os.path.sep)\n",
+    "path_components = path_components[0:-2]\n",
+    "dirroot =  '/' + os.path.join(*path_components, 'test', 'output', 'rayleigh-benchmark-parametric', 'MPI-1')\n",
+    "\n",
+    "!ls $dirroot\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/be93e3a1e3cd2893f62f838768340107\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/55aa665af4383cdfab486497a526b948\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/ae1a96698d6d1339b5ae36a9fc93a239\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/9cfef1c0d73313a1414d49bc38ddeccf\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/446276eafc7b7cb1df263d2d43575334\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/ae03a35d8fe5b10029fb688ae9f08611\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/c2e711ccd4e5b1706d05d5bd98ef5a8e\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/d323b3bd3f8ced38f7e5ed1370e95681\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/0aa3df8dcb703508b12d1c306410ba06\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/818deb373f5d6257aa085493e2a12fb6\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/39582b0fef781ef7dd59b969861740bb\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/7021e9afc054e604d2e15dbc07a9db83\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/c3066ac9a8f4a735e144494a06586074\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/0bd67cc936b53d779e505e7860e8d3a0\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/5f009deebc7c75e793bc5c2a3881177d\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/f5987d8fb47e09c39afd3e4aba484450\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/7d21decc9d7b00098894bc65c19ccd48\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/9f6e9f4b03f77f59fd59a719ce310950\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/1da28d85e36e3d87bbd150884f1a6a4a\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/47724b1983ad0ec6fb3a085868e6406c\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/d42c5f930bc58a9b85355978626be7d8\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/046c79dade1d3ac0d860737fb091750e\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/9444e6515b8c6baac6410af233373084\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/3fa875212869f8bb87ecec023e3594ab\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/245a0619f72f8fa174e14ad18ed79ef8\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/81211387b53a1545755003f8b2a1fc18\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/00ffda65398984695ae5576fe383f02c\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/ce9436b3ab369c9ab0d19d5fd85d64fe\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/bfb5281dbaf30b871676f44cc1395433\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/6e3e4283c6d55967a64bf8d1b588732a\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/adc4418b5f4802de2918f2932e0ad1c6\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/961ec9441a2fa5a82bbed869080b8b3a\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/c18632626e637957d7850f27ba8ed27e\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/5bb43003ce8ab88ea3e2b5835b018f9b\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/5e1b92e1c2392c4742c4b8caac239078\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/08c18d207e645c06bfbac1f997b0edad\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/04c979b82e2ec3add9a38959f42f52b1\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/c20e9b135c36e94f7498a133cc45e442\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/e0d28a7fc82849a64a0fbc6f584ea8e9\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/873a5a261a51d2c352cec13cbf8fdf0c\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/bec3d3e00694ea0d182b04366cbf8cb0\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/7f1430bcb0a6e3812cc44d0015cc2e19\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/bc4a60b8d7abfbcdec8bb45d864e38a8\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/d5612d1467c7b6d3624c53bc688289df\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/ec046ac8dc9f3843ba2d44fc1f32bf0d\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/fe1bd5e0aae1254c90a08b9fe2d4fcac\n",
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+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/3e66ff5126900e8b3aacf615357995c8\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/a6830e9ed97c7590c32e359c547bae37\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/55f85e04d355cf55f777c22037403b12\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/8498148bb21e28e65acd6e832dd392e5\n",
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+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/e1690d753f1cfe4e6c8ed1999f242f70\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/ebfe4b19968441962ec69b81048f5a40\n",
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+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/787493f255c3e1fe0f7590724463af47\n",
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+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/49dd09f415ed6893adec2902800ecfbb\n",
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+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/dd6df4f37eb57a3022cd311c11ed40b8\n",
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+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/02bd5bbda8beff8f5cc0fbd88b599173\n",
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+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/1a9321268f43fe71c8ad7f9ab0ffe154\n",
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+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/95e026c0be6029963131d95f104d98f9\n",
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+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/bc600344f6d97e175bdff42d5a0d9660\n",
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+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/99b28ae6fc8d44b1389a5fe6da60a557\n",
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+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/68c28c98150c0491f40b510fe53d5150\n",
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+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/3595514d555f90e66127a53c00ad6142\n",
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+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/e5593a7c5757a917c4833284b849e8e4\n",
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+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/6d28de9a1db1dc3ad984dc0fed840b85\n",
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+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/76162cca14bfb91a3e02d6e29627bc11\n",
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+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/30b166d2e6f48e17020d5e5457e6e26a\n",
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+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/9b0cc6ef9c3464d738b9e6c4be7917cf\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/7ed36b8dcee86d9a4d55b31fdd6f7199\n",
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+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/ef35e224f60f9f4f67a652b6b2bfabc3\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/5f728c69b35c2f0b870a23f20c1cf1bd\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/aa1c7bcfbf8bd723b77f5710a6a9f594\n",
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+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/3834fb17dbfd82a9e6b6c78c159139b4\n",
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+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/379c9f5ef533b7996ca5a6f6e2aebefb\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/19c6e4fb5e77a4485581566f5ac85e7c\n",
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+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/29a13ad515a4e0b380c0765b509f4bd8\n",
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+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/70a947a5ab7ce7fea419f5942119a162\n",
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+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/0e5f3c92253e55d958c60f6c4b3c3c94\n",
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+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/fa49c46e19fa64edbd26b7a87ff6c06f\n",
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+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/ff220f0b4c41c9194a0d56982ad9d9df\n",
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+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/9b092eb12c6b86663f750cc82741a54d\n",
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+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/197773ce0c3bd5db1389a4e328fc1127\n",
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+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/384d216e638296c3f2e43c1bc10e6aa2\n",
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+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/cc67da412eb68626108ce80d9973105b\n",
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+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/6426c7b91e31a7164aa0ac3fe5ca7cc7\n",
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+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/0bd72656f57f527e5863ef191c372831\n",
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+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/043ebb377c5c5699e4d0c39a6efff1bd\n",
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+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/941d48ed62b7edc24a6b564a91b4d6c6\n",
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+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/2f170fb0e88ec9e9ada917e40dfe9b56\n",
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+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/4363c09f4f41e45ec5a021d2b68e8276\n",
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+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/a80319cc10e4c333d4b0bdd3b113526d\n",
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+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/d0b0b2ded7b4d07b0615afe8c5c3bd29\n",
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+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/a07cb68a370231f37c708389c12628ea\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/034189f38c18e6c04e59770762ddf137\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/41a9fd1aaeefba3a8b4e181a3f57a26d\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/1b32af79ec982846c16e8da2dece9111\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/5971508ca172ee7705ba9867da5f2799\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/89d72ebf25cb6d5a889723f29e937e64\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/812f71e51faf4596904b7256abf7468f\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/64cb9ae1176525d58b69364473f71172\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/b6b45a5ed94214a2ab70c5631f8bd38c\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/de17f9e07113ce99eff6166bf2d5151e\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/1ed7e041da9cefb263f8e5283ed9eabe\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/da90dc28fb90db28270d1efd575cbf90\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/840d841d4f4ab073d4675980ae0c65e2\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/64beb86873a673b4b1d7d131a3b7cca1\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/4a42005d5d1db515c58f10fb2a977b4f\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/77b345b3ac23acd4b0c793607df72b86\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/8c959694f7c0083563629bbd7f6d26b9\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/40e1eb6b7c27798bd70c26bb89188010\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/e5cebd7454caec5911970426e4ebfa02\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/5e00139ccb323f8a614dbc86e4adee17\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/53326ce0c7e2e31e9e711749f6c9872f\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/9283ffcb15cf65f0be191c4877fa3f8c\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/06fd589cfddf7421b0f422bd92c4f077\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/aea34470dff6023e11932e8f26ea8fae\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/b746fbbe0449671bc3fef0ba56043a91\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/4fb5053042bc75c426a6bbcccc364670\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/e3e24962746bbc142697dac71768e280\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/63ef09428da038d6a13c1db516b12cba\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/7bf0064c79e577a339a3bdaa8b664053\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/186ab8e8139e59cdbddc72429ba378be\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/23a4e99376dd709c7a7926c1b512d89c\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/a94574b4ab665c6845a57507127c7f19\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/ca10db54d8b535438ffcde1fd38529f8\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/0ce6d727b36b2329f0bbb7bbcdbedc32\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/d8eafafce323951597ee1a2ccd103a09\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/f8e44e6d6d9486ac535ec276bfb1b520\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/6ea3722739a32d235c59aae7015d2132\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/f2ad2e96cf1f087e6a016e4dfe16cc9a\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/22c97e323c9093987f705e9112b3640a\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/287b7dad39b1a76397aff28461a3ae8c\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/26df8fc60531553c4539463d1c28dfec\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/7d1d96b15a68e392abeffe502f84a042\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/55decbb21da6eccb8c64662f82e90e07\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/1e684c7c8d3b74ab8946e9d16a169644\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/eda0bed753c4b3d8ead98daa3fbaaa4f\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/cfacf6ef677c8603351e06e2bd728418\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/8683c8a249116de1e4a1f2efb7540756\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/35992c02ce19e70d4b7646206dc625fb\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/0044f692a05422bb98c4bd3c6d04770c\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/133a813220736fd35ca5780e0704fc1f\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/e91e3e141d58633d9f065fe841e1d704\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/d13a4206c4dc9798f6918905bd964ef1\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/38ec6d9ed3750e2a349aab9393a444c1\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/80bf33f54ca3cc39d55895987d22db6a\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/43f6745ab4a8087d95d6dad67b61c455\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/6ca3ebf88d38ddd014eac49e0ef69663\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/ba6bde1c11d365939c1e66712a8a818b\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/08024b8d0f056a2e29555bbcff919b96\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/86ff1f0a410c7220fea17218daac0b51\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/1dc98a04e21fe62758b71ea2affc59ef\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/182ec8780648c9a9ab4b05e10b28263a\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/fcab5744d2c104f2879ab24a05eb5965\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/8d808fc699689924b60841e1e2164771\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/b98b104f64f2bda9eb8854ff4d53ecd3\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/45638e207aa96b4f92e27f59f0b544f5\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/699b629be39a2827f23a164b8bb30c1a\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/154b1d59bc43f05c208cd7e9315d48c9\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/7d44e0b425e269c22219e93ee416e825\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/1d99dc57effa20f609a0cf624bd5e04a\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/459c554af4f32b088b68a93b3a02f60a\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/3768e99b08bf76083993540fa0e931b5\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/b091b8fc793c49134b42139b7933be99\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/f0d2d1392eba4f49035c1ce0d431ec6a\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/3f02e8edb6dc1f797b9be04d540e0b77\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/bb8fae8e118a003c63c47a3c93963786\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/5869943e0762f690d3334659af3af68d\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/802c675e74c573280a1fb772daaedf9c\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/303619e93fae27433a982bc1565704fd\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/a3c633c5b54b9f0cb5e4ed723d52b162\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/5240559833b5075c63c9ced7cf58d9dc\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/942438a56c8ffba16a34fefb4a9aca6a\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/b48b6c1cae8f9873ac2b5fdd6bfca86a\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/cb8f68bfb97cc6dac3d6e3db3377c222\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/6e7f390a6ef123e2c2a6788f98edf934\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/fe65f734e95ed43aa28396952f76bc12\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/35b902ddb2944770f8fcc585b48334e2\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/9a0ebd16dc2c6873f51f15b6614b73e8\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/1bde21d6d9e399af2546e2f9010b9350\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/824d7bb2017e24e06fb6a4d7d87994f0\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/f81b240be91d490535164f911aada9ed\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/7c63c15b4401024888916ca9b42b21c9\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/4332c6bbfc3a256c63470ab9c3d7e8a9\n",
+      "File 'time_data.json' not found. Handle this case accordingly.\n"
+     ]
+    }
+   ],
+   "source": [
+    "for subdir, dirs, files in os.walk(dirroot):\n",
+    "    if not os.path.isfile(subdir + \"/signature.md5\"):\n",
+    "        continue\n",
+    "    print(subdir)\n",
+    "    modes_data = np.load(os.path.join(subdir, 'mode_shapes_data.npz'), allow_pickle=True)\n",
+    "    mode = pp.read_mode_data_from_npz(modes_data, time_step=0, num_modes=1, num_points=-1)\n",
+    "    params, data, signature = pp.load_data(subdir)\n",
+    "\n",
+    "    # params, data, signature = pp.load_data(subdir)\n",
+    "    # cone_data = data[\"solver_KS_data\"][[isinstance(d[\"iterations\"], int) for d in data[\"solver_KS_data\"]]]\n",
+    "    # _offset = sum([not isinstance(d[\"iterations\"], int) for d in data[\"solver_KS_data\"]])\n",
+    "\n",
+    "    # _ell = params['model']['ell']\n",
+    "    # print(f\" scaling = {params['stability']['cone']['scaling']}\")\n",
+    "    # pd.DataFrame(cone_data)\n",
+    "    # for entry in cone_data:\n",
+    "        # plt.plot(entry[\"lambda_0\"])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.6229402944338904\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "# Example usage\n",
+    "mesh_coordinates = np.linspace(0, 1, 100)  # Adjust the number of points as needed\n",
+    "field_values = np.sin(np.pi * mesh_coordinates)  # Replace with your field values\n",
+    "field_values = np.sin(np.sin(np.pi * mesh_coordinates))  # Replace with your field values\n",
+    "# field_values = np.ones(len(mesh_coordinates))  # Replace with your field values\n",
+    "result = l2_norm([(mesh_coordinates, field_values)])\n",
+    "print(result)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mode = pp.read_mode_data_from_npz(modes_data, time_step=0, num_modes=1, num_points=10)\n",
+    "_fig, _axes = pp.plot_fields_for_time_step(mode)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## From the book of the numbers..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "case 2\n",
+      "[{x, A}, {x, A}]\n",
+      "sqrt(A**2/2 + 32*A**2/pi**2) True False\n",
+      "depends_on_A\n",
+      "case 2\n",
+      "sqrt(0.125*sin(1.0*pi**1.0)*cos(1.0*pi**1.0)/pi**0.333333333333333 + 0.125*pi**0.666666666666667*sin(1.0*pi**1.0)**2 + 0.5*sin(1.0*pi**1.0)/pi**0.333333333333333 + 0.125*pi**0.666666666666667*cos(1.0*pi**1.0)**2 + 0.25*pi**0.666666666666667)*sqrt(C**2)\n",
+      "depends_on_C\n"
+     ]
+    }
+   ],
+   "source": [
+    "eigenspace_v, _normalisation_v = eig.solve_eigenspace_vector(parameters)\n",
+    "eigenspace_k, _normalisation_k = eig.solve_eigenspace_cone(parameters)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "({'v': -8*sqrt(2)*sin(pi*x)/sqrt(pi**2 + 64),\n",
+       "  'β': -sqrt(2)*pi*cos(pi*x)/sqrt(pi**2 + 64),\n",
+       "  'D': 0},\n",
+       " {A: -sqrt(2)*pi/sqrt(pi**2 + 64)})"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "eigenspace_v, _normalisation_v"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "({'v': 0,\n",
+       "  'β': Piecewise((1.11498170631978*cos(4.0*pi**0.333333333333333*x) + 1.11498170631978, (x >= 0) & (x <= 0.25*pi**0.666666666666667)), (0, True)),\n",
+       "  'D': 0.25*pi**0.666666666666667},\n",
+       " {C: 1.11498170631978})"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "eigenspace_k, _normalisation_k"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "def solve_eigenspace_vector(parameters, idx = 0):\n",
+    "    \"\"\"\n",
+    "    Solve for the eigenspace in a vector space.\n",
+    "\n",
+    "    Parameters:\n",
+    "        parameters (dict): A dictionary containing the values for 'a', 'b', and 'c'.\n",
+    "        idx (int): Index to choose the appropriate solution in case of multiple solutions.\n",
+    "\n",
+    "    Returns:\n",
+    "        dict: A dictionary containing 'v', 'β', and 'D'.\n",
+    "    \"\"\"\n",
+    "    x = sp.symbols('x', real=True)\n",
+    "    v = sp.Function('v', real=True)(x)\n",
+    "    β = sp.Function('β', real=True)(x)\n",
+    "    C, A = sp.symbols('C A')\n",
+    "    \n",
+    "    a = parameters[\"a\"]\n",
+    "    b = parameters[\"b\"]\n",
+    "    c = parameters[\"c\"]    \n",
+    "    \n",
+    "    if b * c**2 < sp.pi**2 * a:\n",
+    "        print('case 1')\n",
+    "        _subs = {A: 0}\n",
+    "        A = 0\n",
+    "    elif b * c**2 > sp.pi**2 * a:\n",
+    "        print('case 2')\n",
+    "        _subs = {C: 0}\n",
+    "        C = 0\n",
+    "    \n",
+    "    \n",
+    "    β = C + A*sp.cos(sp.pi * x)\n",
+    "    v = c * A / sp.pi * sp.sin(sp.pi * x)\n",
+    "\n",
+    "    depends_on_A = np.any([sp.symbols('A') in expression.free_symbols for expression in [v, β]])\n",
+    "    depends_on_C = np.any([sp.symbols('C') in expression.free_symbols for expression in [v, β]])\n",
+    "    \n",
+    "    _norm = sp.sqrt(np.sum([sp.integrate(eigenfunction**2, (x, 0, 1)) for eigenfunction in (v, β)]))\n",
+    "\n",
+    "    print([expression.free_symbols for expression in [v, β]])\n",
+    "    print(_norm, depends_on_A, depends_on_C)\n",
+    "    \n",
+    "    if depends_on_A:\n",
+    "        print('depends_on_A')\n",
+    "        _normalise = [{sp.symbols('A'): ay} for ay in sp.solve(_norm - 1, A)]\n",
+    "    elif depends_on_C:\n",
+    "        print('depends_on_C')\n",
+    "        _normalise = [{sp.symbols('C'): cy} for cy in sp.solve(_norm - 1, C)]\n",
+    "        \n",
+    "    return {\"v\": v.subs(_normalise[idx]), \"β\": β.subs(_normalise[idx]), \"D\": 0}, _normalise[idx]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/ht/z8sb7wsd1bg0qpmyyfyq2rcm0000gr/T/ipykernel_50626/1247390959.py:17: UserWarning: The following kwargs were not used by contour: 'label'\n",
+      "  plt.contour(a, bc_squared, result, levels=[1], colors='white', linestyles='dashed', label=r'$\\mathsf{R}^*= 1$')\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "# Define the parameter ranges\n",
+    "bc_squared_values = np.linspace(0, 10, 100)\n",
+    "a_values = np.linspace(0, 10, 100)\n",
+    "\n",
+    "# Create a meshgrid for the parameters\n",
+    "bc_squared, a = np.meshgrid(bc_squared_values, a_values)\n",
+    "# a, bc_squared = np.meshgrid(a_values, bc_squared_values)\n",
+    "\n",
+    "# Compute the function value for each combination of parameters\n",
+    "result = np.minimum(bc_squared, np.pi**2 * a)\n",
+    "\n",
+    "# Create a contour plot\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "contour = plt.contourf(a, bc_squared, result, cmap='viridis', levels=10)\n",
+    "plt.colorbar(contour, label='Minimum Value')\n",
+    "plt.plot(a_values, np.pi**2 * a_values, color='red', label=r'$bc^2 = \\pi^2 a$')\n",
+    "plt.contour(a, bc_squared, result, levels=[1], colors='white', linestyles='dashed', label=r'$\\mathsf{R}^*= 1$')\n",
+    "\n",
+    "plt.ylim([0, 10])\n",
+    "plt.xlim([0, 4])\n",
+    "\n",
+    "plt.xlabel(r'$bc^2$')\n",
+    "plt.ylabel(r'$\\pi^2 a$')\n",
+    "\n",
+    "plt.title(r'$\\mathsf{R}^*= \\min(bc^2, \\pi^2 a)$ in vector space')\n",
+    "\n",
+    "plt.legend()\n",
+    "# Show the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# Define the parameter ranges\n",
+    "bc_squared_values = np.linspace(0, 10, 100)\n",
+    "a_values = np.linspace(0, 10, 100)\n",
+    "\n",
+    "# Create a meshgrid for the parameters\n",
+    "bc_squared, a = np.meshgrid(bc_squared_values, a_values)\n",
+    "# a, bc_squared = np.meshgrid(a_values, bc_squared_values)\n",
+    "\n",
+    "# Compute the function value for each combination of parameters\n",
+    "result = np.minimum(bc_squared, np.pi**2 * a)\n",
+    "\n",
+    "# Create a contour plot\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "contour = plt.contourf(a, bc_squared, result, cmap='viridis', levels=10)\n",
+    "plt.colorbar(contour, label='Minimum Value')\n",
+    "plt.plot(a_values, np.pi**2 * a_values, color='red', label=r'$bc^2 = \\pi^2 a$')\n",
+    "plt.contour(a, bc_squared, result, levels=[1], colors='white', linestyles='dashed', label=r'$\\mathsf{R}^*= 1$')\n",
+    "\n",
+    "plt.ylim([0, 10])\n",
+    "plt.xlim([0, 4])\n",
+    "\n",
+    "plt.xlabel(r'$bc^2$')\n",
+    "plt.ylabel(r'$\\pi^2 a$')\n",
+    "\n",
+    "plt.title(r'$\\mathsf{R}^*= \\min(bc^2, \\pi^2 a)$ in vector space')\n",
+    "\n",
+    "plt.legend()\n",
+    "# Show the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Phase space computations\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m\u001b[36m0d8834219758fd140ab651797b8d5b4a\u001b[m\u001b[m \u001b[1m\u001b[36m6372095e0b1eaed14e123e64b9316bb0\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m0ff2e717134c5abbc534bd2533ff9da5\u001b[m\u001b[m \u001b[1m\u001b[36m63c5c8c2f6acfa8286bc24001581c84b\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m13d6505ebc2b934521a5a502de43fce0\u001b[m\u001b[m \u001b[1m\u001b[36m65a8c8fecbf3a3245fe56b0f15146662\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m17c366f102af831827f7f3f31bfa20c6\u001b[m\u001b[m \u001b[1m\u001b[36m93666c80ed3523e3c2f7140d2788f85c\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m2f6fb02e066c6e61b2680447a53b61df\u001b[m\u001b[m \u001b[1m\u001b[36ma5890aa9faa3fdf524c243354051e794\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m322244626d33878ea33bbd8dc1e0b353\u001b[m\u001b[m \u001b[1m\u001b[36mbef047fb68f6bc3b5feb6b2f634b15fc\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m397ca1ad1d81ed49d979170b8b651df1\u001b[m\u001b[m \u001b[1m\u001b[36mca594bc3492a497c7daf8ad2979efea3\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m5000476aa59dc0b1b99ce2037442e8be\u001b[m\u001b[m \u001b[1m\u001b[36md7bd94f5b99ccb93acbdaaa53c030e83\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m5126c73c83a40d79ea28e777f96bbe9b\u001b[m\u001b[m \u001b[1m\u001b[36me09ed3e3cf1b53277982e2db255b40ca\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m56de9d66f3d1c4cfbe7a47384bf0129b\u001b[m\u001b[m \u001b[1m\u001b[36mff9c4acbaf25a77cb9e99342154bed50\u001b[m\u001b[m\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "cwd = os.getcwd()\n",
+    "path_components = cwd.split(os.path.sep)\n",
+    "path_components = path_components[0:-2]\n",
+    "dirroot =  '/' + os.path.join(*path_components, 'test', 'output', 'rayleigh-benchmark', 'MPI-1')\n",
+    "\n",
+    "!ls $dirroot\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "successful_points = []\n",
+    "unsuccessful_points = []\n",
+    "\n",
+    "for subdir, _, _ in os.walk(dirroot):\n",
+    "    parameters = load_parameters(subdir)\n",
+    "    if parameters is not None:\n",
+    "        a = parameters.get('model', {}).get('a')\n",
+    "        b = parameters.get('model', {}).get('b')\n",
+    "        c = parameters.get('model', {}).get('c')\n",
+    "        # Check if the computation is successful based on the existence of mode_shapes_data.npz\n",
+    "        success_file = os.path.join(subdir, 'mode_shapes_data.npz')\n",
+    "        if os.path.exists(success_file):\n",
+    "            successful_points.append((a, b*c**2))\n",
+    "        else:\n",
+    "            unsuccessful_points.append((a, b*c**2))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark/MPI-1'"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dirroot == '/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark/MPI-1'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/ht/z8sb7wsd1bg0qpmyyfyq2rcm0000gr/T/ipykernel_91885/1220707274.py:7: RuntimeWarning: divide by zero encountered in divide\n",
+      "  return (np.pi**2 * a / (b * c**2))**(1/3)\n",
+      "/var/folders/ht/z8sb7wsd1bg0qpmyyfyq2rcm0000gr/T/ipykernel_91885/1220707274.py:7: RuntimeWarning: invalid value encountered in divide\n",
+      "  return (np.pi**2 * a / (b * c**2))**(1/3)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_points = 100\n",
+    "points = [eig.book_of_the_numbers(scale_c=2, scale_b=3) for _ in range(num_points)]\n",
+    "x_values = [np.pi**2 * point[\"a\"] for point in points]\n",
+    "y_values = [point[\"b\"] * point[\"c\"]**2 for point in points]\n",
+    "\n",
+    "def compute_D(a, b, c):\n",
+    "    return (np.pi**2 * a / (b * c**2))**(1/3)\n",
+    "\n",
+    "# Create a scatter plot\n",
+    "plt.scatter(x_values, y_values, label='Random Points')\n",
+    "plt.plot(np.linspace(0, 10), np.linspace(0, 10), color='red', label=r'$bc^2 = \\pi^2 a$')\n",
+    "\n",
+    "\n",
+    "# Plot contour lines of the function D\n",
+    "a_values = np.linspace(0, 10, 100)\n",
+    "bc2_values = np.linspace(0, 10, 100)\n",
+    "A, BC2 = np.meshgrid(a_values, bc2_values)\n",
+    "D_values = compute_D(A, 1, BC2)  # Assuming b = 1 for simplicity\n",
+    "contour_lines = plt.contour(A, BC2, D_values, levels=np.linspace(.1, 1, 5), colors='green')\n",
+    "contourf = plt.contourf(A, BC2, D_values, levels=np.linspace(.1, 1, 5))\n",
+    "cbar = plt.colorbar(contourf)\n",
+    "\n",
+    "plt.xlabel(r'$\\pi^2 a$')\n",
+    "plt.ylabel(r'$b c^2$')\n",
+    "# plt.ylim([-3, 50])\n",
+    "plt.title('Phase Diagram')\n",
+    "plt.legend()\n",
+    "\n",
+    "\n",
+    "# Add contour labels\n",
+    "contour_labels = plt.clabel(contour_lines, inline=True, fontsize=12, colors='black')\n",
+    "\n",
+    "# Add annotations for iso-D values\n",
+    "# for i, txt in enumerate(contour_labels):\n",
+    "    # plt.annotate(f'D={np.around(D_values[0, i], decimals=2)}', (txt.get_position()[0], txt.get_position()[1]))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.1450293971110255"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "compute_D(1, 1, 1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Phase computation parametric"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m\u001b[36m0044f692a05422bb98c4bd3c6d04770c\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m00ffda65398984695ae5576fe383f02c\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m02bd5bbda8beff8f5cc0fbd88b599173\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m034189f38c18e6c04e59770762ddf137\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m043ebb377c5c5699e4d0c39a6efff1bd\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m046c79dade1d3ac0d860737fb091750e\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m04c979b82e2ec3add9a38959f42f52b1\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m06fd589cfddf7421b0f422bd92c4f077\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m0709aa4181151c6be88b7bcafc3e014c\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m08024b8d0f056a2e29555bbcff919b96\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m08c18d207e645c06bfbac1f997b0edad\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m0aa3df8dcb703508b12d1c306410ba06\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m0bd67cc936b53d779e505e7860e8d3a0\u001b[m\u001b[m\n",
+      "\u001b[1m\u001b[36m0bd72656f57f527e5863ef191c372831\u001b[m\u001b[m\n",
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+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "cwd = os.getcwd()\n",
+    "path_components = cwd.split(os.path.sep)\n",
+    "path_components = path_components[0:-2]\n",
+    "dirroot =  '/' + os.path.join(*path_components, 'test', 'output', 'rayleigh-benchmark-parametric', 'MPI-1')\n",
+    "\n",
+    "!ls $dirroot\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "successful_points = []\n",
+    "unsuccessful_points = []\n",
+    "data_for_plotting = []\n",
+    "\n",
+    "\n",
+    "for subdir, _, _ in os.walk(dirroot):\n",
+    "    parameters = load_parameters(subdir)\n",
+    "    signature = load_signature(subdir)\n",
+    "    if parameters is not None:\n",
+    "        a = parameters.get('model', {}).get('a')\n",
+    "        b = parameters.get('model', {}).get('b')\n",
+    "        c = parameters.get('model', {}).get('c')\n",
+    "    #     # Check if the computation is successful based on the existence of mode_shapes_data.npz\n",
+    "        success_file = os.path.join(subdir, 'mode_shapes_data.npz')\n",
+    "        if os.path.exists(success_file):\n",
+    "            successful_points.append((a, b*c**2))\n",
+    "            data = np.load(os.path.join(subdir, 'mode_shapes_data.npz'), allow_pickle=True)\n",
+    "            D_support = data['global_values'].item()['D_support']\n",
+    "            D_theory = data['global_values'].item()['D_theory']\n",
+    "            R_vector = data['global_values'].item()['R_vector']\n",
+    "            R_cone = data['global_values'].item()['R_cone']\n",
+    "            \n",
+    "            # Append data for plotting\n",
+    "            data_for_plotting.append({'pisq_a': np.pi**2*a, 'bc_squared': b*c**2, 'D_support': D_support, 'D_theory': D_theory, \n",
+    "                                      'R_vector': R_vector, 'R_cone': R_cone, 'signature': signature})\n",
+    "            \n",
+    "        else:\n",
+    "            unsuccessful_points.append((a, b*c**2))\n",
+    "            data = []"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "D_theory_values = [point['D_theory'] for point in data_for_plotting]\n",
+    "D_support_values = [point['D_support'] for point in data_for_plotting]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/ht/z8sb7wsd1bg0qpmyyfyq2rcm0000gr/T/ipykernel_26783/4181458989.py:3: RuntimeWarning: divide by zero encountered in divide\n",
+      "  return (x / y)**(1/3)\n",
+      "/var/folders/ht/z8sb7wsd1bg0qpmyyfyq2rcm0000gr/T/ipykernel_26783/4181458989.py:3: RuntimeWarning: invalid value encountered in divide\n",
+      "  return (x / y)**(1/3)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Define the function\n",
+    "def func(x, y):\n",
+    "    return (x / y)**(1/3)\n",
+    "\n",
+    "# Create a meshgrid\n",
+    "x = np.linspace(0, 10, 100)\n",
+    "y = np.linspace(0, 11, 100)\n",
+    "X, Y = np.meshgrid(x, y)\n",
+    "\n",
+    "# Calculate the function values on the meshgrid\n",
+    "Z = func(X, Y)\n",
+    "plt.contourf(X, Y, Z, cmap='viridis', levels=np.linspace(0, 1, 5), alpha=.9)\n",
+    "\n",
+    "D_theory_values = [point['D_theory'] for point in data_for_plotting]\n",
+    "D_support_values = [point['D_support'] for point in data_for_plotting]\n",
+    "\n",
+    "x_values, y_values = zip(*[(point['pisq_a'], point['bc_squared']) for point in data_for_plotting])\n",
+    "\n",
+    "# Create a scatter plot and color the points based on D_theory\n",
+    "scatter = plt.scatter(x_values, y_values, c=D_support_values, cmap='viridis', edgecolors='black', linewidths=.0, label='Data')\n",
+    "\n",
+    "# error_values = [np.abs(float((point['D_support']-point['D_theory']))) for point in data_for_plotting]\n",
+    "# plt.errorbar(x_values, y_values, xerr=np.abs(error_values), yerr=np.abs(error_values), fmt='none', ecolor='red', alpha=0.9)\n",
+    "# Add a colorbar to the plot\n",
+    "cbar = plt.colorbar(scatter)\n",
+    "cbar.set_label('$D^\\#$')\n",
+    "contour_lines = plt.contour(X, Y, Z, levels=np.linspace(0, 1, 5), colors='black', alpha=1, linestyles='solid', linewidths = .5)\n",
+    "\n",
+    "\n",
+    "# for i, (x, y, error) in enumerate(zip(x_values, y_values, error_values)):\n",
+    "#     if i % 5 == 0 and error !=0:  # Display error for every n-th point\n",
+    "#         plt.text(x, y, f'{error*100:.0f}\\%', fontsize=8, ha='left', va='bottom')\n",
+    "\n",
+    "plt.plot(np.linspace(0, 10), np.linspace(0, 10), color='red', label=r'$bc^2 = \\pi^2 a$')\n",
+    "\n",
+    "# manual_labels = {1.0: \"$\\pi^2 a = bc^2$\"}  # Add labels for specific contour levels\n",
+    "\n",
+    "contour_labels = plt.clabel(contour_lines, inline=True, fontsize=14, colors='black',\n",
+    "                            inline_spacing=10)\n",
+    "\n",
+    "# Customize the spines\n",
+    "plt.gca().spines['top'].set_color('none')\n",
+    "plt.gca().spines['right'].set_color('none')\n",
+    "plt.gca().spines['bottom'].set_color('none')\n",
+    "plt.gca().spines['left'].set_color('none')\n",
+    "plt.gca().xaxis.set_ticks_position('bottom')\n",
+    "plt.gca().yaxis.set_ticks_position('left')\n",
+    "\n",
+    "# plt.xlim(0, 10)  # Replace with your desired limits\n",
+    "# plt.ylim(0, 10)  # Replace with your desired limits\n",
+    "\n",
+    "# Set custom ticks\n",
+    "# plt.xticks(np.linspace(0, 10, 3))  # Replace with your desired tick positions\n",
+    "# plt.yticks(np.linspace(0, 10, 3))  # Replace with your desired tick positions\n",
+    "\n",
+    "# plt.legend()\n",
+    "# Customize the plot\n",
+    "plt.xlabel('$\\pi^2a$')\n",
+    "plt.ylabel('$bc^2$')\n",
+    "plt.title('Phase Diagram of the Cone, support size $D^\\#$, and target $|D^\\#-D^*|$')\n",
+    "# plt.show()\n",
+    "plt.tight_layout()\n",
+    "# plt.loglog()\n",
+    "plt.savefig('../../test/output/rayleigh-benchmark-parametric/phase_diagram.pdf', dpi=300)\n",
+    "plt.savefig('../../test/output/rayleigh-benchmark-parametric/phase_diagram.png', dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
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+       " ('6e7f390a6ef123e2c2a6788f98edf934', 0.0),\n",
+       " ('fe65f734e95ed43aa28396952f76bc12', 0.0),\n",
+       " ('35b902ddb2944770f8fcc585b48334e2', 0.10388829287288226),\n",
+       " ('9a0ebd16dc2c6873f51f15b6614b73e8', 0.0),\n",
+       " ('1bde21d6d9e399af2546e2f9010b9350', 0.0),\n",
+       " ('824d7bb2017e24e06fb6a4d7d87994f0', 0.0),\n",
+       " ('f81b240be91d490535164f911aada9ed', 0.050000000000000044),\n",
+       " ('7c63c15b4401024888916ca9b42b21c9', 0.028975344790746482),\n",
+       " ('4332c6bbfc3a256c63470ab9c3d7e8a9', 0.0)]"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# error_values = {'signature': np.abs(float((point['D_support']-point['D_theory']))) for point in data_for_plotting]\n",
+    "[(point.get('signature'), np.abs(float((point['D_support']-point['D_theory'])))) for point in data_for_plotting]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/ht/z8sb7wsd1bg0qpmyyfyq2rcm0000gr/T/ipykernel_26783/1541847198.py:3: RuntimeWarning: divide by zero encountered in divide\n",
+      "  return (x / y)**(1/3)\n",
+      "/var/folders/ht/z8sb7wsd1bg0qpmyyfyq2rcm0000gr/T/ipykernel_26783/1541847198.py:3: RuntimeWarning: invalid value encountered in divide\n",
+      "  return (x / y)**(1/3)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Define the function\n",
+    "def func(x, y):\n",
+    "    return (x / y)**(1/3)\n",
+    "\n",
+    "# Create a meshgrid\n",
+    "x = np.linspace(0, 10, 100)\n",
+    "y = np.linspace(0, 11, 100)\n",
+    "X, Y = np.meshgrid(x, y)\n",
+    "\n",
+    "# Calculate the function values on the meshgrid\n",
+    "Z = func(X, Y)\n",
+    "plt.contourf(X, Y, Z, cmap='viridis', levels=np.linspace(0, 1, 5), alpha=.9)\n",
+    "\n",
+    "D_theory_values = [point['D_theory'] for point in data_for_plotting]\n",
+    "D_support_values = [point['D_support'] for point in data_for_plotting]\n",
+    "\n",
+    "x_values, y_values = zip(*[(point['pisq_a'], point['bc_squared']) for point in data_for_plotting])\n",
+    "\n",
+    "# Create a scatter plot and color the points based on D_theory\n",
+    "scatter = plt.scatter(x_values, y_values, c=D_support_values, cmap='viridis', edgecolors='black', linewidths=.0, label='Data')\n",
+    "\n",
+    "error_values = [np.abs(float((point['D_support']-point['D_theory']))) for point in data_for_plotting]\n",
+    "plt.errorbar(x_values, y_values, xerr=np.abs(error_values), yerr=np.abs(error_values), fmt='none', ecolor='red', alpha=0.9)\n",
+    "# Add a colorbar to the plot\n",
+    "cbar = plt.colorbar(scatter)\n",
+    "cbar.set_label('$D^\\#$')\n",
+    "contour_lines = plt.contour(X, Y, Z, levels=np.linspace(0, 1, 5), colors='black', alpha=1, linestyles='solid', linewidths = .5)\n",
+    "\n",
+    "\n",
+    "for i, (x, y, error) in enumerate(zip(x_values, y_values, error_values)):\n",
+    "    if i % 5 == 0 and error !=0:  # Display error for every n-th point\n",
+    "        plt.text(x, y, f'{error*100:.0f}\\%', fontsize=8, ha='left', va='bottom')\n",
+    "\n",
+    "plt.plot(np.linspace(0, 10), np.linspace(0, 10), color='red', label=r'$bc^2 = \\pi^2 a$')\n",
+    "\n",
+    "# manual_labels = {1.0: \"$\\pi^2 a = bc^2$\"}  # Add labels for specific contour levels\n",
+    "\n",
+    "contour_labels = plt.clabel(contour_lines, inline=True, fontsize=14, colors='black',\n",
+    "                            inline_spacing=10)\n",
+    "                            # fmt=lambda x: manual_labels.get(x, str(x)))\n",
+    "\n",
+    "# Customize the spines\n",
+    "plt.gca().spines['top'].set_color('none')\n",
+    "plt.gca().spines['right'].set_color('none')\n",
+    "plt.gca().spines['bottom'].set_color('none')\n",
+    "plt.gca().spines['left'].set_color('none')\n",
+    "plt.gca().xaxis.set_ticks_position('bottom')\n",
+    "plt.gca().yaxis.set_ticks_position('left')\n",
+    "\n",
+    "plt.xlim(0, 10)  # Replace with your desired limits\n",
+    "# plt.ylim(0, 10)  # Replace with your desired limits\n",
+    "\n",
+    "# Set custom ticks\n",
+    "plt.xticks(np.linspace(0, 10, 3))  # Replace with your desired tick positions\n",
+    "plt.yticks(np.linspace(0, 10, 3))  # Replace with your desired tick positions\n",
+    "\n",
+    "# plt.legend()\n",
+    "# Customize the plot\n",
+    "plt.xlabel('$\\pi^2a$')\n",
+    "plt.ylabel('$bc^2$')\n",
+    "plt.title('Phase Diagram of the Cone, support size $D^\\#$, and target $|D^\\#-D^*|$')\n",
+    "# plt.show()\n",
+    "plt.tight_layout()\n",
+    "plt.savefig('../../test/output/rayleigh-benchmark-parametric/phase_diagram.pdf', dpi=300)\n",
+    "plt.savefig('../../test/output/rayleigh-benchmark-parametric/phase_diagram.png', dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[]"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Define the function\n",
+    "def func(x, y):\n",
+    "    # if y < x:\n",
+    "    #     return y\n",
+    "    # return (x)**(1/3)*(y)**(2/3)\n",
+    "    return np.where(y < x, y, x**(1/3) * y**(2/3))\n",
+    "    # return np.where(y < x, y, x)\n",
+    "x = np.linspace(0.01, 13, 1000)\n",
+    "y = np.linspace(0.01, 13, 1000)\n",
+    "X, Y = np.meshgrid(x, y)\n",
+    "Z = func(X, Y)\n",
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "plt.contourf(X, Y, Z, cmap='viridis', levels=np.linspace(0, 10, 11), alpha=.9)\n",
+    "plt.loglog()\n",
+    "# plt.xlim(0.011, 13)\n",
+    "# plt.ylim(0.011, 13)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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A01BTWkPmEKuMfwXAoqsrsSWCbJAHYjR85BVMzwUTC0XoEBYIBAoyTWY2+4xGo3k/LmSmG0pNVQu4TDAajZlNSZAKZ8tYBkcKL+Jag86UiCs3DP6mlIirJtz+JTTbuxiM+IWIS8NLoRUAnM1+QAi5YvBc9HWAEHKViP5gmMYPHUV/IiFqVItc1PwoSVKxmYM57SMQtqJpMpIkzao4xmg05nS8aqCqBVwkEgGYVSLiqlWryi4s2ha2cjLHH0W10B8KVJ0XToi4mXkptIKzLfvxxkaEiCsSz0VfxyrjXwkpHkAIuXLH9BcfDde9guxTAYi3GBh4YBHFrIG1mYN85LL/y2kfP374g/hDdmw2G9ddd12eLMucTHRDqan4IoZS0houkhtujmPwN5XahILh9i8ptQllT9Ib542NlNaQOcRz0delPHIhxZMSc4Lywva/AzR+8EhKvEVfa6Hv0TOJvba46TAaoGhqTn9lPRKqTKl4Aed0OqesFknGsJNVJYVgaDB/CZylpDXoZMBf3t6+/lB1vNcTcfuXMBjJfx5JNSFEXGmYKOQEZYKi4fpSN3X/rxtJSSwF19XQ98sOlHmGkpikouX0V0xKrRvyRcULuObm5imTDZMfUCEqSaRiJcEJgOr2wiURIm56xoo4IeSKS1LICW9c6ZECCg0feQXHjwZSayMfb2TwvkVoVl0JLascSqUb8k3FC7j29vYplfTw8DALFy4sskWCQlLNXjgQIm4mXgqtEN64EiLCqqVF1x2l6d1dWHYkvvuaHjx3tuH94nzQldapoOb4v2JSLbqh4gXc0qVLCYfDDA8PT3rs4MGDLF++vPhG5ZnBkeKFNss5jFrtXjgh4maPEHGlQ+THlQbDgSDNGw5hfCHhOVJrZPofPIPAB+pLbBmAhqLl9kcRw6jVohsqQsBFIpEp3Z1ms5m3vvWt7Nq1a9x6ctbZ0qVLC2KTVKSbnbZw8ZJRW4PlH/OH6vXCgRBxmSBEXGkRQq54WB7z0vSeLnTuOADxdiPuR84kcrGjxJaVL+WoG/JN2bYR2bt3L263m+HhYbxeL16vly1btmA2mznrrLPGvcFLly7FZDKxc+dOXC5X6kO74oorSmW+oEBUa1+4sYj2IrNH9IsrPWP7x4UUj2g7kk80Dcf3+3H+Zw/SaQdV5DwrAz9ejFpfPpdvDXIuRMiH/22u6Yby+QZMYPXq1Rlt397eTnt7e4GsmVsM+INlOZVhLNXYF24sSREnmB2iX1zpEY2A80xUpfbfu7H/bNSzGbisFs8328BUfsEzpQwagcw13VB+3wJBSamEMGq158IlEe1FMkOEVMsDUbGaO9JwnMYPHB0n3rz/1oLnewvKUrxBZbURqRbK1gNX9oguIiXF4G+in76q9sIlEaHU2SNCquWD8Mhlh/5ohIZNRzEcTkwE0EwSg99ZQOhdtSW2TFBulKeUF5Sccq5GHUs1FzSAKGrIFuGNKx9EocPsMT7jp+kdh1LiTanX0/dQR9mLt8QkhtyqUIUPLnOEgCsCHm9uIqMtbC1qK5FKCKPC3AqlghBxmSJEXPkgKlZnxrrVQ9N7j6AbToxWiC014X50CdFVlRFlUHP8E2SOEHBZIs2yj0hbuPwH4lY61e6FAyHisiXZ+FdMbygPhJBLg6pRc0cP9Z88gRRL+KHCl9hx//pMlIWmEhsnKGeEgBNMSSWEUeeKFw6EiMsF4Y0rL4SQSyCFVOqvP4bze6OtkfwfrKf//85Aq6mssVgKWk5/gswRAk6QlkoJoyaZC144ECIuF8Qs1fJjLgs5uT9G4xWHsT6SGOmkSTD05fkMfa0V9JVXJadouf0JMkcIOEHFM5e8cCBEXC6IWarlyVwTcoaXQjS/4xCmfYkoh2qVGfifRfivbSzemJ88kmjkm9uf0HCZIwRclpTiJ1bMQoYklRBGhdNtReaIFw6EiMsVIeLKk7kg5Mx/GqHpXV3oT8YAiM8z0PfLJYTfWllRD0HpEQKuQijmTNQklRZGhbkTSgUh4nJFFDiUL9Uq5OybB2j40FFkf6LuMrrMgvvRM4m91lJiy3JHQcrpT5A5QsAJqoa5FkoFIeLygfDGlS9VI+QUDdet3dR+oRvpdM+M4D856dvWgdpiKK1teULVcvsTZI4QcNlSgXkK2VIpYdQkc8kLB0LE5QNR4FDeVLKQk/wKDR8+iuPHA6m1kRsaGfxhO5q1sipNBeWFEHCCaam0MGrSC1fpIq7FMvl9t+mn7gklRFzuiAKH8qfShJyuO0rTu7uw/MEHgKYHz7fa8P77fJCrxwmgkXsIVTjhMkcIOEHVUUmh1CvbV3Nx01IublqaWvvk2W/FFwvzybPfyhJHMwDn1rXzmVetm3ZfQsTlB+GNK3+SQu656OvKVsgZ9wdpfschjC+GAVCdOvp/cgaB99WX2LLCIHLgio8QcBVEsUdqJWkNOisujArl74X7+rlX8tvuTnb1HeR9iy4AEkKtJzRMIB7hkM+NPx7m3Lp2VtYt4r6uv8y4TyHi8oPwxlUOE4VcOYg5y2+Haby8C11fHIDYIiPu3ywhcpGjxJYJqgkh4LJkDqXAVSTl7oVb4mgmEA+n/n3D3v+dtI0vFqI3lGjy+fJIT+rfMyFEXP4Q3rjKoSzCq5qG4y43DR87hhw+PRbrfBt9j5xJfEl1j1VUNSmnP0HmCAFXJHIdaF8OVJoXrhx7wwXdA/i7T9KumGjS2WgxJu7IP3n2WwHY5znGPIsrle9m05s409HMrr6DGR1HiLj8IbxxlUXJhFxUpfazJ3B9rTe1FNhYS//PzkCt0xfHhhIiQqjFRwi4IlANA+0rrZhhLOUg4sIDHkZOdKGrDWNeYKC2yY5fCrKv62n2v/hXlta0pPLdftT1F+ZZXOzqO8j7F13AQ8f2ssTRzMVNS1PbzAYh4vKL8MZVFsUUcvJQnMarjmD/xVBqbfjmFjzfXQCm6r/MakgoyDn9aULEZUz1f7MKhCS+bBVBOYRSwwMeVP0I1nYHOnOi59Op0CA9YQ/WNgeGJo1h3zDzTleeBuIRunxurmxfzaPd+wF4/6Lz2dV3MLXNbBEiLr8Ib1zlUWghpz8SoWnDIcxPJW4UVbPEwPfb8f1rs8i1KSHHjh1jx44d7Ny5M/X/1YYQcBVIKQoZklRaGBVKH0qNhT0Y68Z3Wn9+6CDzLIlqNJ3ZQKuznmeOvZh6PFnM0Bvyjmsf4o9HMj6+EHH5R0xxqDwKUblqespP04ZDGI5GAVAa9fRv6SD0TlfO+640yikHbu/evezdu5dLL72USy65hEsvvRSz2cwjjzyS1+OUGiHgKoxSjNRKUslhVChNKDXYO4Cxebx42/DIAd720yfp+c3Pecf8C3jfwjfyw6O/Y3CgG0jkva2sW5TKewvEI4zEwtj0JuZZXFnZIURcYRDeuMokH5Wr1l94aHz/EXTDCgDRs824Hz2T6Ots+Ta3IiiXHDi3282uXbu49NJLx62vXr2aY8eOcfBgZvnE5Uz1Z1YKBCS8cDF7X9GPqyphjKbRUTkbHjnA9d//M/q4SnzzHg594g38fMOyxIM6JbXdjya0DPmvl57g3Lp2ftfdmbUtbv8Smu1dDEb81JvsWe9HMJ6kiDub/QA4DTWlM0aQEcnQ6irjX1MizqKrm/5Jqobzjl5q7ho9n4Te5GDwnnY0h5isUGr27t1LU1MTLpdr0mNnnXUWnZ2dLF26dPITKxDhgcuWOZraUKk94aBEoVRtfH9xcyiKPq4iqRr6uIo5FJ20bWCKMOk+z7GczRGeuMIhihwql9nmyUkhlfqPHxsn3nwfrmfg/sVzWrxpgKLJOf3laxLDsWPH0oo3gObmZo4fP56nI5Ue4YETzDn6QwEaLcUJc0jo0TQN6XQyc9hiJK6XEx44vUzYYhyzdXEuAMITVzhS3jjLfryxEeGNqzCSIg4SXrkkFl0dcl+Mhg8fxbQ/BIAmw/B/zMf/z41Ft7McUcvEHxSJTJ0nbDYnOkK43W6am2df0V+uCAGXJVKJq4sGR4LU15QuH27AH6TBXrrjZ0uxQ6mWeU0ETh3F0pro9/bI6XCpORQlbDGm/lsJxtAbiiemhIgrLCKsWvmMDa8qf++l5cMDGE4l0hxUm8zg99sJv1l8rknylccWCAS49957Z9xu5cqVrFq1atK6yTT1zOgkXq9XCDhBaWgLWzlpLl0YszXopNs6u6kA5YjB30Q/fUXxwsk6HZJiRYnE0ZkSP7ekaBtLuCeMc3Fbwe0ZixBxheel0IqUNw6EkKtETj3RytpPPoshkBBvsfk6uu9vIPqqOJTB2K5qQ9M0/P6ZUzyi0Wja9ebmZsLhcNrHhoeHczGt7BACTjBnKVYo1d42H9+xE+icoUntRJRgjHBPGHtre8HtSIcQcYVHhFUrl6X/e4TX3f43ZDXx34PLXPzl3vMJN5ohvX4oG4qapq9JKFqOIdTTrUQkScJmm/m8bDQa066vXr2arVu3pn3M7XYDTCnwKg0h4ARZ0Rp00o23IsOoMBpKLZaIc7QvIDoSIHRsMFFtqmmgyeiNjqJ73iaSLGyALgAh5E6jaRr+7gEkLY6mSZgb6zBY0l80ZoMIq5Y3SlwlcLKPWt0wBiXGOXcNcuavRiMNx9fN46lvvg7FIi6bE9EANccQarKIwWazcd1112W9n/b2dpYtW8bOnTu55JJLUusHDx6kvb2dgwcPpnLhKh3xTcyWLL6rHm+AOufc7BFUjhQ7H85YY8NYU76fv/DGjeI/3o1Z8rKoXUanT3gWBk8N4HNbMbYsQG82zLCHqRFh1fIjMOijIdDF+Uuj6PwaLdcPY/vzaDL8vg8u4MUvngvyHG0/UGFceumlHDhwgAMHDqTWFi5cSF9f4nw/mzy5SkAIuCLRFjZz0pw/t21b2MpJSlvIAJVbzDCWYlalljtCxIHvyCssXBzCYBp/eqyfb6SeOCcPHSLefGbOIg5EWLUcCPvCtMUPseTMOPqTCvOvGcL0chwAzQDurzsZeYNKcMiPtd5RYmvLF6VMqlCTLFs2Odc42UKkGgoYQPSBE+RApU9mgNFZqeUw8L5cmMu94vynBmhbGMJgmrqlS9uZeqLuk3k5nhjJVXqkQTdLFsYx7YuyYMNgSrypZhi+xorvSivzm1Qs/t4SW1re5NoHrhi43W6ampqqJoQqBFyWCEf6KJXa2DdJOQy8LzfmqojTK8OYrOPF27kPHeP8zUc496HRRso2cwAlFs/bcUUT4NKgaRp1ukHsj4Rou2IQ/UCiWkE1JlqFWJ4arVRocw4T9JV55UKJSOTAyTn95auR73S8/PLLLF++vAhHKg4ihCrIiUpvKZKkmK1FKoW5GE416cJAolDh3IeO8botx6k/4kdWNFRd4rZt35XtNLQZOHzYQ83C/In/iWFVEPlxhSYWVjjnF0PM+8HojYpqlUDR0HlV5IDKgn/qx/sBK1xt48DLAayO7AtZBIXn4MGDPPHEE9x4442T1s1mc9rQaqUiBFyFU+qGvtWGyIcbz1yrUJUkNfVvY1DBNhBBVjQkVUM+vZbYTkKSCuMzEPlxxUGOKFx8Sydn/HqMeDODapfQeTRQQYqDvk9FDmioqoZW4gbu5Yyilcd7Ew6HJ+W4ud1udu7cycaNG0tkVWEQAi5LSj2JAUrf0DdJpbcUSVLs1iKVxFzxxinK6CkxatURaDBhHokhA6pOIno6vBoJxJFNhc2jEW1HCodxKMol1++l6dnB1Jp/nQl9d0KgyyMqUhw0PcSbZFSbxMFXjNS1VX7eb2GQ8lDEkJ9r6rJlywiHw+zcuZNIJEI4HMbpdHL11VdXTe5bEiHgcqQ1LNE9GKC2XlzwqwEh4qZmLoi4sGoHEtXi+65sZ9+V7Zz70DGMQYWoVce+KxMNlwfcOmxFupiLtiP5xXHExxuufQbHsUThUswk0/9fNQTePtpku+bBQMLrZpMYuTpxHuh7qQ6LrvQ37oKZWb16dalNKApCwAnyRjW0FIHi94erJKpdxOnrGhnoPkpD6+ipMSnakoT9ClFdPdk3EckckR+XH5qe6ueSG57FOBIDINRo4s/3rKbX6matNpKKrCRFW5J9L5vRzWstur2VhFqkSlLBKOIdzxZxIzaOamgpMhHRWiQ91VyharJbCMit9B+PpX3cPxzjVK8D27zSVC4n246AqFjNlDO2HONNH34qJd6Gzqph+7ZLGDq3Dt0ZZ7HrxQaOnxp/Yu/tg6dedBFoOhujWfg7pkIj0Qcul79iVKFWG+IbWeGUS0PfJNXmhROh1PRUc3GDpd5JNGTh6CE3Jl0AnRxH1SSiihXN2oJ9Yek9X8IjlwGqxopvvsCrf9iVWup+YzO7v7OSuD3hR9UbZPQdHRwLLKTr5X6Mcpy4pkdy1WI9wyIulIKyRHwvi0hb2MxJqnecVrW0FEkiRNzMVGtI1WAxYmhfkPpvHWCZevOSISpWp0cXinPhv/2Vhdt7UmsvXXMG+/7fa9HS5LOZbQbMtvkAVMewpeJRLlWocwkh4LJEEjHUKakWLxyIfLjZUK0irpIQFauTMfeFef11z1D/t2EAVBme/+I5HPrgGaU1rEpRRUZW0RECTpBXqs0LB6LJ72wYK+KgukKqlYQQcglcL3p5/bVPY+tNVBTHbHqe/N4qel4/+xmYoZEI+qFe6nVDmOUYCjKeuJMRQyOOedWX85sLGlLO47A04RTJGCHgqoRya+hbTV64JCKUOj3JvDjhjSs9c1nIzf9jL2s/9RyG002X/a0W/nLfBXiXzv49CPYNs1TXxeKzEvuwPRg83VZkhFPvHODpo61YFy+YYS8CQWERPs8sKYM+vinawuUllKqxIlUMvZ891VylWmkkq1bnRMWqpnHW5sNc8vFnUuJtYHktT2y7JDPx5g1xlr6Lxa1Kas3+YBDHDwPYHwzirJFYu7CbwAmRWjEWFSmnP0HmCAEnEMwSIeJmjxBx5UW1tx6R4iqrvnSAlbf/Hfn0NLRjb5vPH36ylnBDZt33DcN9LJqvTLuN3SbRiDtbc6sSRZNz+hNkjnjXBAWhNehkwF/6MV/5Roi42eP2L8HtX8JgxC+EXBlQrT3kDL4Yb/jo0yz96Suptb/fsJTd312FYtZlvL8GnWdW272qNcBIT3W8h4LKROTAZUs5xVBPU255cNWKqEzNDFGlWl5UUw8528kgr//o07i6fAAoBom9/7mCo+9ZmPU+z3hkmObt428+9V1xpDhIXpXm9QOp9d63HKP7xnOyPla1kGzkm+s+BJkhBFyVUC6D7cdSLUPu0yEqUzNDVKmWH0khB5VZ7NDwVw8Xf2IvlsEIAOFaI7vuWU3/efU57VcKga5fHb8WB9TE/ycf0zTQh9Q0e5ibqKIPXNERAq4EeLzV28x3riEqU2ePqFItXyqtarX90ZNccNM+dNGEgBpZbOPP912Af1Hu3ymfyYrSOP5mWPKqSHHQ9KA0JjxN4YiEbp4j5+NVB1LOHjgxnzJzhIDLkmwjqG1hMyfN4fwaU8ZUuxdOTGrIHBFSLV/KXshpGq+96yDL/uul1FLvBQ08edd5RF3GvBzir+85C9t1Gg776Em+ef0Aun4VpVHG/XgDAL9/sRnT4kV5OaZAkA1CwFUR5TYXdSzV2BcOhIjLlmqepVoNTBRyUHoxJ0cUzv/3/Sz+9cnUWtcVC3nuP5ajGvNXj+eY72LP4flcsvgUFmv6O/Un/25Ht1D0gRuLKipJi454xwUFpxr7wo1FVKZmj2g3Ut6US+WqaTDCmz60JyXeNAn23fxq9v7niryKtyTWjoX85eRi/nrQQiiUSK/XtETYdMcLTcQWno3emHmFa7WSKGKQcvoTRQyZIzxwWSJmoWZOtXrhQHjickEUOJQ/paxcrTns4/UffRrHiUReWtysY8+3X8fJt84v6HFtC5sJ0MwfTnl5zVuOowupyPMcmM9YXNDjCgSzRQg4QVGoxhmpExEiLntEgUNlUGwh17y7j4tvfBajLw5AsMnEznvPx3NObcGOORHnfCcnRauQ6dFyD6HKwgWXMULAZUuZOuDKOQ8OqtsLB0LE5YrwxlUGxRByHT9/hfO+dABZSVzZh15Vw59/eAGheZa8HkeQH5QcL4r5DoQfPHiQY8eOjVu7+OKLMZszm8xRzggBJygac8ELB0LE5YrwxlUOhRBykqKx/Bsv8OofdaXWut/YzO7vriJuE5cswczs3LmT9vZ2Lr300tTa8PAwW7Zs4YorrqgaESd+DVkileEkhkIRHh4meDpBX5LAXuPCYM1emFS7Fw7EtIZ8INqNVA7pmgJD5mJOF4yz5jPPs+D3vaP7vuYM9v2/16Lp5s45txIplypUtzsxo7a9vX3cusvl4q1vfSs7duxgw4YNpTAt7wgBlyW56rdCN/PNx1itaMDPkGcQxWFHrnWl1oM+HybPALXN89EZDBntc6544UBMa8gHot1I5ZGtV87SG+L1H3uGuhcS5wdVJ/Hcl86h6ypRNFDuaEg5D6TX8lQaeOzYMVpaWtI+1tzcnBJ41UB5SOYKJBcPXFu4sO7btnDu3q1YMMigdwitsQF5grtZ53AQb2ig/9QJNDW7UTLVOOg+HQZ/k2gvkgfGthsRLUcqg0xakNT+Y5h1l+9MibeoXc+ff3SBEG8VhIqU018+6ezsTLs+PDyM01k9ba2EgMuSag+heocHkerqpt1Ga2rC19c77TbpqPa+cBMRIi4/uP1LRN+4CmQmIdf6+x4ufd+TWN2JCTX+NitPbLmY3oubim2qoApYunQpx48f55FHHiEcHj/16Nlnn2X16tUlsiz/iBBqllSzfotHIkRkmZnaVEqSRCgeI5uU5WoesZUOEU7NH6JStTKZlCenaSzffIILv3EY6XQLif5za9n5g/OJ1JtKYqMge3INoSYJBALce++9M263cuVKVq1aNWnd5XJx8cUXs2vXLo4dO8aGDRtob2/nwIEDtLe3T8qNq2SEgMuScvfA5dJOJDw8hM7lGrf2rZ8/yPlHunjmjCV89n1Xp9ZjOdo5FwoakggRlz9EpWplc3DkHDbe8SfWPHw4tXbo7U386atno5giEIuU0DpBNqhafq6Jmqbh98/sYY9Go1M+tnr1alwuF4888ghbt27F6XSyceNGXBOua5WOEHBZIsvlLeDyzflHDtPo83H+kcMTHtHQNC0rQTuXChqSCBGXX4Q3rvKw+MJ8+KbfctYzx1Nrj33sAh6/7gJQJQiV0DhByZEkCZtt5vOj0Wic9nGTycR5552H2+3m+PHjbN26lQ0bNtDc3JwvU0uOEHCCSZjsdrx+Hzq7I7X2zBkdpz1wHeO21UlSzt7IueSFAyHi8o3wxlUO9SeH+di//pqWVzwAxA06fvqlS3n+ba8qsWWCXEjMQs21CjWBzWbjuuuuy2lfO3fupKWlhUsuuQSAAwcOsGPHDh588EE2btxYNWFUIeCypNxDqEmyaSdisNkxDntQxlwHx4ZNx2KRc/sKzUUvHAgRVwhEy5HyZvH+bj76mUewDydcbH6XhR99ewNHV7SW2DJBPshXCDVXDhw4ACSKGZIsW7aMhQsXsnXrVh555BE++tGPVkUzX1GFmiXShBDq0GDmVYYeb2ErE3NpJ2J31KD6fNNv5BnEXteQ9THGMlfaioxFVKcWBtFypPxY+bsXufG6bSnx1ruojm//7/uEeKsaJFTknP7yNZ9y7969Kc/bWFwuF1dfnXBEHD9+fNLjlYgQcFky9qvWGs78i1foXnC5Yqlx4dIZUQcH0bTxU4Y1RUHrc1PnrENvyr1abK61FRmLEHGFQbQcKRM0jfU/eIoP3fI4+pgCwMvnL+S7D7yXwTZXaW0TVB3hcBjTNNcks9nM6tWrGR4eLp5RBUSEULNkogeunMl2KoO1rg6z4sTX5yaqKahaIufNbDBgW7A4r2HkudZWZCzJcCogQqp5RoRVS4c+Eueq/3iClY+/nFrbfdk5bL35jaiGmZoUCSoJDVByDKFqM28yI2azmUhk5grmaqlGFQIuG771LeYf9wLl7zlqC1s5ac4+PCnrdDjnzc+jRdMz1woakiRnp/aHAkLEFQBRrVpc7J4gH/nsbzijswdIFJf++tOX8OcPvK66m2jOYcolB87pdHLs2LEpCxWOHTvGsmXLimxVYRAh1Gz41reY99uHSm1F1TGXQ6mQEHGACKkWCBFWLQ7NRwb59KafpcRbxKznx99+J3++eqUQb4KCs2HDBnbu3MmxY8fGrYfDYXbs2MHq1aurooABhAcue1RwHz+C1VWDoyY/ifyFJB/D7YvFXPXCgfDEFQMRVi0cZz19jA/f9CgWf6LJ6nCjjfu++y5Ovqp6em8J0qPmaRJDrpjNZj74wQ+yc+dODh48OO6xiy++uGrEGwgBlz2ShEmtITwQQVF6YV75nqByDaMWk2RbESHihIgrNCKsml/Wbu3k8jv+hE5JZDOdOLuJ+777LrxN4n2dCyh5HkifK+kqUauN8pDMFUjyq2qQTYSHwiix8LTbC2bPXA+lgginFouJYVURWp0d8WiceCQOgKSovPtbf+HK//xjSrwdeEMH3/vxlUK8CQQFRHjg8oBZthMcGICW+oye1xY2c5IAdc7ieFkqKYwKczuUCsITV0zENIeZiUdjxNy9WKRhnNYYSKCclLjqzpdY/mxvars/fHAlj/zrRWg64R+YK2ha7kUMWj7KUOcYQsBlQ38/VtnDf0v3p5YUSUXflYitbz/7IraffXGprEtLJYVRQYRSkwgRV1xEflx6YqEocv9Bli6VSARuTNh7w7zni3+l+aVEw29FJ7Hl/72Zpy47p6S2CkpDueTAzSWEgMsGVUXSoFYaDW9pqMihROKuOTZzHxrBzMzVMVsTESKu+AghN4G+Iyw6a9TDctF3D3HuT45jCiSa84Yden74b8s4vEGIt7mJhJpzDlx55dBVAkLAZYMso8k6hqTRi6kiq+gtCQ9c2JD7dIJCUWlhVBChVBiTEyca/hYVUegAwUEfrc0RkpeLJb93c/59R5FOh7yGF1h4+J5z8bda8PcMYJ9X/lX5AkE1IARcNjQ2ErI6+RfDuwFQNYWheTCvY3Fp7ZqBSgujggilTkR444rPxPw4mFtCTg4OYG/Vg6Zx3v+8wiXfPpQSb1GLjp/87HxCdUaMgCk+DAgBNxfJdRKDIHOEgMuWMQ0pIzo/Jmd5i7dKRoRSxyNEXGmYq0JOL6nIUZUrP/wcbfuGU+uaBJKqsfHa5wHovLKNY6vEeXAuopF7DpyoYcgckXWYA5qmEZaGqW+Zn9NcUI+3uK0iBkcqywuXZMBfmXYXAtFmpHTMtdYjZn+cjdc9P068qVJCwMmKhm0gim0gijGooIpLikBQNMSvLWs0cMZoWrAYo9GS9V7awsXtCt0WrswwZLI3nBBxowgRV1rmgpBrOD7Mv930JAufGQISos3bakYxymiyhKqTCDQYCTQYiZh1hNWaElssKBWqJuX0J8gcEULNElmWqa0r3pB3gQilpkMUN5Seag2tnvHXk3z0s49g8yaalAfrDPzyrnPpWeHig5c/hW0gSqDByP9tuxCA44cUbG1NpTRZUEJyr0IVZIoQcNnw2c8y0B+CzlIbkj2VWI0Kp0UcXlHQMAGRF1d6qknIrXr0Bd5/2w70cRWAU4tquf9Lr8a0IvHd6ryyDWNQIWrVAdDfrRC2tmMRw+ozJhqMcMFPnsMcVtBanDx1xfJSm5QVwotWfISAy4bPfpbBZ49A5y9LbUlWVGI16kREVepkhIgrDypZyEmqxj/94CnW/eiZ1NqLF7Sz+Y63MyKpaId6cJh8dF7ZBsBgj8LIITNqTQuWIk2UqRYiHi/m4CnanD7e9ud/YB2I4a0xsOsCOzQuRG82lNpEQZkjBFyWyOJOs2SI1iJTI0Rc+VBpQs4QjnPVl7fzuicOptae3LiMbTe9EVUvYwSwn0EwGuOFrhHQNCyNNRjqjCWzuVKJ9A2w0HKU+qUyoEt1NTCYJJadNcyhl0cINrwGg6Vy3lsxiaH4CAGXJZJc2QKuLWzlJJUZRgWRDzcdIi+uvKgEIecYDPDRT/+GRX9PzDRVJfjVZ17PX646d1zLJAC90UDNgszmPgtGiYdjNElHqW+eWvCceZbKP14+DAteVUTLskcj90IETeTQZYyQzFmSS9uQibSFzUVvJVItiKrUqRFVquVFuVatzusa4NMf+nlKvEUsBn707Xfylw+8bpJ4E+SO0n+K1vaZL73z6nxEfaEiWCSoVIQHLkuqJYRaqcUMIEKps0GEVMuPcvLInb37FT78+d9iDiTmOA832fnhf72L7rNENWkhWLP1AGt/sReTaXzbWteRIHJcw+iLc9llf02th8J/RzKa2HPZOezZuKzY5maEqEItPkLAZUua7+rQYIDa+sq5SFZDMYMQcTMzVsSBCKmWC6UWchc91Mnld/4JWU2IieOvauK+776LkcbyCu9WE6ZQjJqhCDrd+HU5riGpGnIcrP3R1LpBkZHkOKZQrMiWZo6oQi0+QsBlycQQamtYotsshoGUApEPNzPJcKrwxpUfxRZykqLy7m/v5A0/25da63zjEh68fT1Ri6h8LCQRi4GRWtMkD5zRF0eOg6qXCDaOFi6Ewnoko5FIuX8uWh4EnLh8ZowQcFmSzxy4UlLpxQxJRH+42SFCqjMTD4SIDvvQmUyYGpxFO24xhJwpEGXT//sdr3nyaGrt95tW8ei/XIRW4YVZlcCejcv4w+paXneWe9w15LLL/oq1P0qw0cjDD78OgP4ehW7DcoxWU6nMrTjcbjd79+7l4osvxuVyldqcgiMEXJZUehVqtSJCqTMjqlTTE+4fRIl60ds1LAstqKEwge4+UM1Y29qKdtNWKCHn6vXxsU/+itZDAwAoepmH/v3NPP3u1+a8b8HsMc6bz/Ejbto7pt+ud9iJsb1yxFs5hFC9Xi8HDx7k4MGD0273kY98pCoEnhBwWZLvIoa2sJmTBKgrUTPMSi5mSCLy4TJDeONGCZ7qweCKYJ43OtdYZzVgazegqRr+rkPYFy1BkotXuJ8Ucgm6Uv/KRswt/EcvH/30r3EOJHJegw4TP/7mBrrOW5CrmYIM0Rl0eIxnYjx5iHltk68jmqbxwj8MsGAGhVdGaOQu4PIRQe3t7WXZsmU4nek95+FwYixcNYg3EAIua6QqasBSDcUMSYSIywwh4iAy5EVfE8ZQY077uCRL2JfYCb5yEtvChUW2LkEuXrnlfzjE1V98HGM4DkB/m5Mffu/d9C2qK4yxghkx1Ttx+14NJxVi0ZdQYhqqqhGNwEuHF6G116HTVdFFpohceumlUz62c+dOLrnkkiJaU1iEgMsSSZRMly2iqCEz5npINR4cxtY+Xry9/TcHsIRjhMwGfvvOZYmUCX0YTVWL6oWbSEZCTtN4y+Zn2fDfu1NLXee28j/f3ECg1pL+OWOI+CMEBoaQZA2QMFhs2BsdOb+GasWiM3Nh/YUA/LHvT6n1axZ9iF+ceIiQEh63/dWv3sQjpx5hw7wP89fr7yd85EXqVqxh05qr2PzK/xbV9nxQDm1EWlpapnzswIEDLFtW3q1YMkUIuCyRZAlN0xgJeggrcTQ0wkEI6J3YnJXXpbxaihmSiKKGzJmr3jhJjkBiUBRv/80B3v7o31hwfAh9XCWuT4i1375zGdZWG6Hufqzzm0tobYKZhJwupnDlV//ABb/5R2pt7ztexc9veQuKcfrTvhpX8RztxuhUcbWbSfZMiviGGTjiwVbfiMUpflcTWepYik1vIxAfbZxdb6zj3NoVnOVYCoBVb+XX3Y9wMnSSgcgAISXMidBJDl7aTr3pddQ4zuKRU4+W6iXkRDnkwC1dujTt+vDwMFA9odMkQsBljcrJ4W4M5jpkvR4JcADxvigDA4epaZ2H0SxOcqVGhFIzY05646TR7BtLOEadJ4g+riKrGvq4iiWc6MEl6WTQyqvXQbo8OZs3zL/d8ifOfO5k6pFHr1/Djo+snnGygqZq9Hcdp2GpOVW0sXbbIUzBOBGrnt2Xn4n3RB+S1IJ5ipDzXKVz+ABWnRWLbtS7WW+q5/MH/j3lfbuw/gKeGnyapY4zU9sElSCDUQ/1pnqOB48zGPUU3fZq59lnn502tFqpCAGXJQoaJuvkbuU6nREd9YyccONsn4fBmNlJzuMtXSEDVEcxQxKRD5c9c8obN2YId8hswFNnxeaPpDxwIXOiB5eqqEhy+Q4Xd/uX0HpygC/fei/zTowAEDXquPeLr+eZt3RAdOaRaoETHlrPNI+ruF27rYuawTAj9WZ2X34mzgUWeg73YDY1Fuy1VBIRb4g3bnsBq6Kx4G0tHF/VkfKKPhUZ7bX3xqaL+aN7N0ElxFORffzz4tcQiiv4YmFCcYU6QxO/7dleqpeRM/nywAUCAe69994Zt1u5ciWrVq2acbtqDJ0mEQIuS3S66d86s66WwMAArvlts95nW9jMSXN45g0LRDUVMyQRIi575oo3TlNHWzX89p3L+O07l03KgQMInQxgnT+/VGbOyLLOI3zpP/6PGl/iNzzksnPrbZt46VULYZZjV61K56wKtMwWPaf6F6CzVE6bi7wz0E2d3ENrc5B37TqMvT9MrPkUL5xx5gTPKLRYnKjKAo56e1Jr33txH/Msr+PR424+uuQqfnToLyxxrGWexUlPyEuXz13sV5Q1+Rxmr2kafv/MX9hoNDrjNgAvv/yyEHCC8cymJ1TMH0XTtIpr+ltNXjgQIi5Xqt0bZ7DXEvUMYKwb9ZYnRVsSVVFBtZTtb/nSJ57n09/ZhiGuAHB0UQtf/Mom3C2ZVZpajSHWbjvJ2m2jbUuaXxlBF1ex+KLcdNVjqfVfXOhh+wfW5ecFVBi63oO8pqMfs00HGJFI5EXr9RqtjSMYDxwg2jr6HXpH6wqe97wybh+BeIQun5sr21fzaPd+AN6/6Hy+8rffcHHT0ooScJA/D5wkSdhsM59njMaZveEHDhygubn0OauFQgi4LBl7Iv9///gFy4ePokoSP21/A4+2ng+ATjUQj4UxGGeu+CoXqtELB0LE5Uo1e+OMTgchd4joUABj7eSUBzWqEHgljH1x+fXlklSVazY/wVU/G6163HveUr76hQ8QtGWRoyaBKRinZnA0EqCLq0iahi6ujlu3RmbnAak21IEelqXE22RkncRrX+Nn38tH0ZoXA3Bx09KUSBvLuXXt9ISG6Q15selHvZn+eKQgtlcCNpuN6667Li/76uzsrKq2IRMRAi5LxjbyXT58lLqoD02SsCjjT2qV2m6k2rxwINqL5INq9cZZmpuIeIYJHBtG0kUwOIwo4ThKWEbCiuOMJTPvpMiYwlFuuvMhLtn1t9Tar995IfdcvwF14rT0WRKJG4hY9YzUj4o/iy+KLq6i6OXUuqpohGrmZkuRWtmdVrzpLnkjhjVvZIl9MZ5GN8dfeYYBEgLOH4/gi41Pj7HpTaysW8SPuv4CJDxyI7EwNr2JeRYX+zhW+BeTR7QyqEIdy/DwMH19fVM29a0GhIDLkrGjtDpdi1MeuJBu1K2ryDF0hsrLEalWLxyI9iL5oFq9caY6FyZcqIqCEghjcBgx15fnEPFaj4/bbt3M2S8nKk0VWeL7n9jAr9+9Nqf9joRr2X15oto0yU1XPZYqYrjzp/8EwLGDMqGGc3I6VqXx9kef5u2/fQaj6sdgHC9WGo760HU9jvLodqyLHbwBuETVCEafRNLr+O3bnybwjgsm7TMp3pL810tPcG5dO7/r7izgKykM5dAHbizHjx8Hqq91yFiEgMuSsR64r73mvWm3MdiNGefMlHqk1liq0QsHQsTli7HeOKgeISfrdMg15ftaFh/p4Stf3Exz3zAAQYuRr37hA+w9/+yc963WtnLq8N+Z3zH1pcE3GMevX0R2Pr7KxRKKUuvxYSCKpBt/XtcpGpKqoQPsg6PhT3NMQZJlLKHJ4ebAFGHSfZ7K8ryVK253ZeUQZoMQcNkiqdM+HFaGcDaWb8XaTFSzFy6JyIfLnaQ3rhrDqgCxYJhA/yBxFJBA0iQMOgOO1uaSFDSsfuYlvvDVn2A9LQj6Gp3ccvuHOXrGvLzsX2c2MRQ7i/hLL9O2REbWy+y+fEmqD1z/8Sh94YXoGiqvWXmuhCxGhuocaT1w5pEoOkDRSfjrE1EXVdUIRi1Ieh0hS/m2n8kX5dDIdyzDw8OYTJUXAcsEIeCyRC/riAb70Jlq0elGwyzxeIS4LkDNghYMFRg+nSuIoob8Uo3eON+pPkJaGOP8mnEnSkVR6T9yFFdTC0ZH8b477/rVbj7x/UfQqYlmwi+d1caX/mMTnvqavB5H57ATsL+Ofxw8hV03zMnlZ6JqMv6oHc3Yis5e/WIkHb99xwX89h0X4Bjo5FWvDo177Ib3/hH7YAR/vYm7f/EmAA6/rGOw9rxSmFoSyi0Hzuut/nxnIeCyRKeTaattYyToIRTxAxp+PdQ0u3A5zyi1eXmh2sZrTWSuibh4MEjQ60E5PXlAp0pYnLUYZlGyPxuqyRsX6PMQNSkYayaLI0knY1xYh7e7n1rjPPSmwgoaWVG4/p5HeNdvnkqt7bz4HO686Uoi5sIcW5IkDC2tRGglGegTo9UTeOX5BLwHsTmnDiJHggqDsdYiWlVaNC13D1y+h5w0NzcTDpeur2oxEAIuS5LhkxprHclTfDPQHdeo3MvW3GOuiDjvyeNE7Tp080crB+MkwgzGk4PUtC7IW0iwGoocQkEf+tbpq9cMrU4CpwZwthcuVcIaCPOFr/6E1c8eTK397H1v5P4PvxVNFpKqFMh1jbxwIsCrtW5srskiLhxUeOFQHcxbUALrBEk2bNhQahMKjhBwWTK2CjXflFMhQ7V74aD6RZy3+wTxFhs6w+Sfu85lJ25XGDl1Emdrfi84lRpWDQ16wTW79IeoFiuYHU3uIW6/ZTOLX+kFIKbX8V+ffA/b18+dsFy5ojUt4m89Npynuqm3eVEUDVXRCIclOo8uRppXufnP2VJuIdS5gBBwWSKXaUd2QXZUq4iLh8NEzaQVb0kkvY6oVSIeCqG35LfpdCWGVWOhMPrm8U1wb7vz16z823FUSeJ/3reWX77tXAA0CjPc/uwXj3PbrQ9QO5wYKTTisPAfX/oQB5ZXR3pGNSDXN+KjEa+iEFL+igUNRW9FquDitVwotyKGuYAQcFlSriN1CsFc8MJBdYq4oGcQXev4PK73/34v1nCUoNnIz96yGgBdnYPgqUFqLLOf3ZsJlRRWTdd8e+XfjlPvCaDKEtZwdOzGeeeSv3Ry050PYYrGATjZ2sAtt19Dd5sYHl+OyDodv9twIZZQdE5UmwrKByHgsqSQIVRB6ag2EafK4z1E7//9Xr7wf79Fr6jEdYkcqqSIU4vwla6EsKq5tobhgQEMDfbU2vPnLEx54IJjCgd0Wh7z0DSNq376Jz68eXtqqXPZGfzHlz6Ir8pvniqd36Zp0jvXyHcRgmBmhIDLkmKEUD3e8siDg7njhYMqE3ETvqbWcBS9oiKrGnrU8d6kIjExrArlJeQMNjNy//g+j7fe9K5J26lxBZMhi3mj6Y4ZjfOp727jrTv+mlrb/taVfPdTlxGfJvwtEJQHUh4mMQinSKaIMqYskQr8zrWF83NhyDeDI9Xd3DdJazBRgTjgr+zXKyvjT4pBs5G4TkaVJeI6eZw3SS6GC24MBn/TaGg1FEiJuXLAUd9ArHdkysc1VUM9MYJtfu5hTcdIgK9//kfjxNuPPrKeb/7bFUK8CQSCKRFnhyyZSzlwSebCdIaxVIMnztbQiKevF11TQpAmw6UTc+CUfi/OhuaS2FiOhQ5GhxWnVs/IiQFwGdE7Ros7ou4R9FGJ+iXtOZ8H2k70c/st99N6ahCAsMnAHTe/lycvnltzRgWVj6hCLT5CwGXJXM6Bq9YZqemodBGnMxqxxA2EQxFkS6I1RlK0JVFDUcwxHTpjaSeHlFuhg7HGRkONjbDHS6Q3iIaGpEnUNbWgz0MD3eX7D/Ol//g/HP5EV//BOge33raJg2eJ/mGCykIjD41882PKnEIIuCyZq21E5poXDipfxNnnzYOeHkLeIeRmV8prpGkaqtuLRTVgn1c+XePLrdDBXOfEXDd9U99MWff4s3zquw+jVxK5dkcWt3DL7dfQ31Sb1+MIBMVCFDEUHyHgskQuggeunBr6TmQueeFgNCeuG2/FijhrXCHY40aRNTQN9JqEpXE+Or1h5h0UmXIvdMgWSVX5yI8f570P/SW19vT5Z/Of/34VIauYnSwQCGaPEHBZMpdDqHPRC5ekNeikm8SQ5EoTcrJeh73COsRXk5AzhaPcfMcvuPjJv6fWHn7PWu697h2oOlFPJqhsRA5c8RECLkvSNfuca8w1L1ySSg+pViKVLuTqBke47dYHOOvgSQAUWeKe69/Jb961psSWCQT5QQi44iNu+7JkLnvgIOGFm8tUS5uRSqOcW49MxRmHT/Hf/3JXSrwFrCZuuf3DQrwJBIKcEAIuS3TThDyGBvN3UWkLm/F4y/Mi1Ra2zpm+cOkQIq50VIqQO//pF/nOp79PU38i7N7bXMunvns9z513VoktEwjyiJaoQs3lT5ShZo4QcHmmNTz3PHNCxAkRVyrKVshpGu95+En+40sPYA0lpl28ePYC/uW/b+CVxS0lNk4gyD+altufIHNEDlwOSLKEps7tb95cLmhIInLiSk855cjJisINdz/COx95KrX259cv4xufu5KoqfwqfgWCfCBy4IqPEHA5IMsSyhwXcEnmakFDkrFtRqDyKlSrhXwLuZg/RHQkiMFmwui0z7i9NRDiltt/ynnPHUytPfiBN/G/H7oUTRYBD4FAkD+EgMuBYo3TKud+cDDqhZvrIg6EN65cyFXI+Xo9BAIhoiYDuhorajCCbsCL1WjAtaAp7XOaez3cfsv9LDqWmCQR0+v49mcu5/eXrszx1QgE5Y1G7h444QrJHHFLmANTNfPVNBVNU4tsTWmZ61WpYxF5ceVDNjlyQ6+48cqgttSir7Uj6WR0Dgu01BJwWHAfPIE2IWnnVS8c47//5e6UePPWWLnpzmuFeBPMGbQc/wSZIzxwOTDWAxdXYgyFBgmrEJMk+j2DSHrQW/U46pvQG3KfnVgJCC9cAuGJKy9m65ELuIcIWA3INnPa/UgGHfFmF94TfbgWNgPwhj/t53Pf2IIxFgfgRFsDt9z+YU61NhTipQgEglkyPDzMrl27ADCbzZhMJi655JISW5U/hIDLgaQHLhoL4w56MNibMADj0pSjMHT8FK4FzRiMlpyO5/GWbxgVREHDRISIyz+xQIDwyDCqBLIGFlcdesvsf1dJIQfQT1/q30kx5w8EkZunn0cq6XUEYjFcmsYHfvIHrnlgR+qxfSs6uO3Wq/E7yv/z1jSNwKk+VGJomoaMjLmuHoM1vXgVCKaj3IoYDh48yDPPPMOGDRtwuVwAhMNhdu7cWTUiTgi4HEgKuL7QIAZ785TbmQx1DHf30Lj4jKyP1RY2c9Iczvr5xaItbOUkwguXRBQ35AclEmGkv4eY3YBu3mgxQWhoEMNgnJrm+egy9HKn88pFVG3cSfGK7c/xsS27kFUNVZb44RUXs2XdKvQOK5/9j/9j/e5/pLZ9bP15fO9f303cUP6n1WDfAJGIF9N8B5c99hKWUIyQxcBDFywBt4Rz0cKi5fgKqoQyioO63W6eeOIJPvrRj2I2j96Q7NixA7fbLQScIBFC9Ye9YHLOuK1OcxAY8WCrqSuCZaVHhFLHI7xx2aPEogz1dyO11aOb8Jiu1oFaC0PHT1DfughJN3GLmUkKuailF3VCVrA1FKVxyJ8ScNZQFNdIkO997ResfPE4AKok8aOP/hNbrrgEKkD0hAY8xAxBzE2J89Y7f9tJvSfAYJ2NX79jBVqdxvDRV6g9Y3GJLRUIsmPnzp2sXr16nHgDaG9vp7l5amdLpSGKGHJAkiUCsTB6/cwhB73eRMRXJk1GC4woaEiPKG7IjkB/H1Jb/fQbLajH19uT03GMoRaIjV8LWozEdTKqLBHXyVjDUX52049S4i1sMnDbrVez5crXV4R4AwiHvBhrp/6NSrKEvsVEeGC4eEYJKhwJTcvtjzzNF3e73Rw/fpxly5ZNemzZsmWsXr06L8cpB4QHLgdkSSJd2P/f//ZzVgwdYX/tGfznOe8bfSAPhanlngeXRIRS0yNCqpmhqSoRKTbjnaYkSUSleM7Hk+Omcf+9Zd0qIOGJax4Y4YOPPIMzkEhlGKxzcOtt13DwrLacj1ssgn2DGBpMM26ntxqJDI5gxlV4owRVQblMUzhw4ABOp3OS960aEQIuB2Q5/fw2gxrHGo9gUCdcUDK8wdA0De9IHyElSvz0DqKygj/uwF7fmKXVxUWEUtMjQqqzIzo0DPWOcWvfvvshzn/hCKoscc+738jP3py4o1ZtBpRgCJ01+2Iho6GGmC+AzjF68t+ybhUbn3ieqx57FoOSuAt7sa2BT952NX0NNVAu47tmwXsee4b3Pf3iuLX2Ex70cRV7IMKPP/FAal2NKmx9xwVsWy9aoQhmJl9FDIFAgHvvvXfG7VauXMmqVasmrR87dgynM3GjvHfvXiBRgTo8PJw2rFrJCAGXA5IsYZH1BNQ4sjz6Vv61/kzmhzz8tf7M1JqqxjHYZ59kHY9FcQ93I9U0Isk1qQ9KD8T8cQaGDlN/xmKkMu7uLhr8To8QcTOjKgqSfvxp6oIXjtI47E/kpIWjow/oZFRFmZQnlwnm2jrUvhAKYXQOM5Kq8Zn//T3//Ks9qW3++Koz+PLnbiBkNmHw53CwEmD3S9R7xgtOfVxFUjX0cXXcY5qi4fAax1XuCgSFRtM0/P6Zf1jRaDTtutfrpbm5mb17944Llw4PD/Pggw9y9dVXV42IEwIuB2RZosZax8hID7J99CT3SNv5PNJ2/rhtI8owTbWzTwru8/Ygu9IPvZZ1eiS5iaHjJ6hb1J6d8UVCtBaZHhFSnR6jqwafx42uYbRQ6OlXL0554ILmMTdF/jD6ptwTlK1NrYSHBtGdGuTOn/2Wt+4/nHrsgdev5p5rr0Yt4xun6QiaLAzW2sZFA+yBCPq4SlwvM1g3mp6hRjSC5pnDrQJBohtvjh6409EsSZKw2WZOEzIap3aIuN1uLr744nFrLpeL9vZ2duzYwYYNG3IytVwQAi4HJElCkiTqzQ4GQ0MYLOn7R0VjXmqa62ddlh8IDKNYHNN+OJIkoSlGoqEAxhIN7c4E4YWbHuGNS4/OaMIQk8alj37mhivTbmtU9XnzSC/QdHzr+7/kVa+cACAuy3zjmit4+C0Xz/DM8ubX73wbD79pEbYzRlux/PgTD6SqUD/y/U0AxMMxgsecmOoqI1VDUHrylQNns9m47rrrct5PsvfbWJqbm9mxYwfhcLgqvHCVeRtZJkin+8BZTQ4ajVYIDhAND6cej8S8xGUvjvm1WOwztxpJEogE0M+i6a/e5CDoGcrY7umIBv14+roZ6OtmsP8UgaH+nPeZrEodHBGeuOkYW6UqKlVHsdpqUId8026jDvqw5qlFz9JXTnD/rd9MiTe/xcynbvpExYs3SNz4qbE6Yr7IlNtomkbwSEyIN0HFksyBm2rd7XYX05yCITxwOSCPuds3G220GG3EYhGCoSH6jRqNC1owGDNX+WoaR91/7PoJK3u7eL5lCV+6+AOpdU3JyvTJx1QUBt0niBrN6B2u1PpIPIb31CvU1mQmQiciQqmzIxVSFd64FKYaJ3ZPHL97aNKUBE3TUHs91FjqMNpz90Rf8vwBbr9rM5ZIIr+mu7Gez3zu4xxpm5fzvssFY908Qt0acccQllb7uMdivgih4yqGujOneLZAMAVlUoU6mwpUr9dbJGsKixBwOSCnCYkaDCachkb8Zi0r8ZZg8n6NSgxrLIJRGd+oKh+tpzRNo6/3GNQ2oZ+wQ1lvgNpGhvzDSJKE2VaT9XFEa5HZMzakCiI3zlJXjzHqIHCqn7gUT43SMmp6bE0LkfU5nso0jQ/87o/8609/hXw6FnTgzMX822c+xpDTMcOTKw9T/XzUaAMjL/by0OrXYo1ECRrMRNyNmJqmHyUmEKSjXEZpNTc3Mzw8POM21YAQcDmQHKWVb4xAWNPG5cztnX8WrX4Pe+eflVpTVQWD2ZBmD5kxMtgLroZpc/R0dhcj3sGcBFwSkQ83O4Q3bjw6o5Ga+a35329c4abND3HZH3en1rZfuJLbrruaqDH331e5IhuNmBoW8pt3LEytVe+rFRScMvHAnXXWWTzyyCNpH0t63qYKsVYaQsDlQKEEnKumiW5vL3rHaPf5Xy69kF8uvXDcdvHIEK4FuVehhuMxJHnm5gtxg5Fo0I/Rap9x26kQrUUyR3jjCoc9EOTr//Vjzv/7y6m1+y77J354+dsqZrKCQCAYZenSpZhMJg4ePMjSpUvHPfbyyy+zbNmyqihgACHgckIqkICTdXqcBjPecAC9OX1eTzw0gmIx5GXgdFwbP8B744E9XLv398iayrOtS/j3t10NgM7qIBwcyUnAgciHywbhjcs/re4Bvv3NH3BGdy8AUb2e2z92FY9dVD2jdgSCYlEuIVSADRs2sGPHDhYuXJgSawcOHMDr9VZNCxEQAi4ndLrCFfHWOBqQ/IOMjAygWRzoDIl+TPFoCF04QJ3ZwZCjMHcR1liUhsAIsqZxXvfhmZ+QBSIfLjuENy4/LHv5MN/89n3U+hINQ4ccdj736WvpPLujxJYJBBVKmYRQITG0/tJLL2XHjh2YzWbC4TBOp5OPfvSjpTYtrwgBlwMzeeCGBgPU1mdfGeew1+Ow1xPwDxEJnI7dG8xY6xck9k84632PRTfhZQQNRgZsNac9cKMXNCUcxGjKr2AQodTMEd643Fi3+1luvfcnGOOJAXVH5zfz6c99gu7mhhJbVno0VSXU34uqD3Pl7v1YwzFCBgu//qe3oq+SsJNgbtDe3k57e3k3us8VIeByYDoPXGtYotucn1sSm72WdDKwLWzmJLkPtzfLhnFFE1uXrWHrsjWTtpMjIcy1+esNJfLhckNMccgQTePabb/jYw8/llra+5qzuPlTH8FvE+9dzO8jFDqFcbETnWzjfd95gfphP4MuOz9/66vRDziwNFRH9Z6gEJRPCHWuIARcDhQyhFpMahqaCfUeR6qdeuahEvTjsua/nYIQcbkjwqozY4zG+OIPH2T9nudTaw+/aS13XnMlin7qAp6YP0DI50XVNGQkzHYHxprqayuiRCKE472YFrvSPm5sdhD3hggPDWCuFZ5KQRrKKIQ6V6gOBVIi5DIRcB5vYOaNpkGWdTTWz0Mb6keNje/QrmkayvAANTod1prC9IcSkxpypzXoHDfJQTCKa8THPf/53ynxpkoS3/nAe/jaR943pXhTIhE8x19hKOol2mQn3uwg2mxnWPUzePwV4qH8pC+UC+FhN8a26VsE6Z0WYspwcQwSVB5ajn+CjBEeuBzQFagKNRPawmZOmnO/mOhNZlrmLyLgHSQ0MoSKhgQYZB01TW3IupnbjOSCqEzND8IbN57FJ3v4zjd+QGv/IAAhk5FbbriGnauWTfkcJRZjqLcb2hom3eHKDhuaw8ZQdw91TfPRVcmwd0UfRs/Mr0VXbyDqHcbodBXeKIFAMC1CwOVAtYRQx2Jz1mMrUY9DUZmaHyYWOcDcFHKr//Yid3z3x9hPe8v6ap185t8+zsuLF0z7vEBfH7RNHyaUWuvx9/ThbJt+X5XAe/7wJO964g/IhvE3aYtPDqBTVGr8YX7xqftS61pU41fr3sgv33xRsU0VlCsakGsbEeGFyxgh4HJg7CzUUuPx5l7MUC6IfLj8MJerVd/zhye56f6H0KsqAC8tauMz//Zx+utc0z5P0zQiWmxSOvb7//gMtnCUgNnIz950PgAR4miqilRG54FssIYjNI4EJ5Wj6xQVWdUAlfph/+gD8cRzBIKxaEKAFR0h4HJAnth/o0TkK4xaDoiihvwzl6pVZVXlX3/ySz7w2J9Sa39euYxbb9hEaBbhTjUURrUaSPqi3v/HZ7jqj8/S0dOPXlGIn04l+NmbzkdqcBL1DGNqqCvESykaQbOJfocN2TheiNb4w4CKopMZdCWad6txBRkjwSoJHQsElYwQcDlQjSHUckCIuMJQ7flxlnCE2++6n0v++vfU2oNvfxP//f53o87SS6YqCowZK2cLR2n0+tArCrKqoUfBFo4mHpQlVFVJux9NVfH39BDVYsRlAA2dKmOU9NibW5CnqXwtNr9880X85JwzkNslpDHntF986r5UG5H3fvdaAGJHAtibziiVqYJyRnjgio4QcDlQLlWokL+ecOWCEHGFoVrz45oGh/j2N+/lrGMnAYjLMnd++MqM87T0Niv0DIAj8Z4EzEb6nQ5qgmH0JDxwAbMRAHXIh9HVMmkfSiTKUO9JtNZ6JElirFSLaBqRnuPUNsxDbymfxriWxnn4Dh3CuLRmygbl0ZM+LPb5RbZMUDGU0SituYIQcDkwkweuNSzRneM0hrmMEHGFo5ry484+cpxvf/MHNA6PAOCzWvj8Jz/C3nPOznhfkixj0vTET//3z950Pj970/lpc+CMMQmdyThpH8OnK1jTXc4kSYL59XhP9lDfvjhj+wqFJEk45i8hcPgVNIeCsWW0pYimqESPJMSb3lq53xOBoNoQAi4HguER+vq7QUvkw9ntTsym0oq1aipmgNKJuIjfR2jEh0aiv7jFYcfkmL5PViVS6flxb3i2k9vueQBLJBHWPNnUwKc/93FeaZ3sGZst9to6PAMe5IbRzzsp2pKonhFq0vRFjAyPEHdZxnnd0ok/pd5O2DOEua4wvRWzQZJl7PPOQIlGiBzuR4uqEAdJ1eFoWlJq8wRljiRCqEVHCLgc0EtW9NQkrvAqeD0+RgyDNNYvSI2lKia5FDMosSg+Tz+KCmgaOlnCXteA3lj6ZOViirh4JMJwby9xkxnZMdpPJRwMoPN4qG1uRm+2FNSGUlBxQk7TuPrRP/AvP/818unyt/1Lz+Bzn7mW4RwnJehtVpyxGN5Tg0gtteOqTDVNQ+v1UGN1YayxT3puKOhD1zz++Ff98VkavT76nY6UgJMtJsIjI5gpHwGXRGc0YW1u41fr3oQ1HBEFC4LZMYcFXDAYxHraOz3234VGCLgcmBhCNehtaKqF/sETNDUsLJFVmePt6yEYUdDbXCnhGQcG+gYw6aG2pbW0BlIcEafEogz29iDVNU5u4Gq1oVltDPb2UT+vBb2pOi9qlVDooIsrfP5/fs67//xUau2xtedx+7VXETUa8nIMk8tJY42DgNtNVIuhkhhbY9B02OctRJqisbV2+iqWrF4FUhWsNcEwj9xyFwA/fdN5/OI1r8qLrYVC9HkTZMQczoG76667uOmmmyb9u9AIAZcD6XLgJElGixkJRwIlCadmWszg7eshjAmDfbIg0dtcxOIxhnpOUjuvLd+mZkyhRZyvrx+prnHabaT6BnwD/dS2lv79KBTlXOjg8Ae547s/4rwXDqbW7t34dn70nvWQZ693IqQ4L6vnJqtXgXEVrMk1WziKNJddFiUm1O8hGkqE3fU6Ceu8xorv5ycoPJ2dnZx55pmTPGzamCZ4WhEb4k0r4NxuN48//jiBQIAlS5bw1re+ddzj27dvR5IkmpubWb58eUENLUd0+vQ/eIPeht/vLXk+3EwosSjBSByDfercLllvIBKRiUVCGEylDx0WSsRpmkZYic9qOHA4rlRFA9eZKDch19bbz3e+8QMW9bgBiBj03Hbd1TyxZlXJbJqIUWckGI+nqleBcRWsybWAyYBRyo+3UDB7woPD+Dw+VHsNst0FQERV8Xedwmo14GhrLq2BlcwcuR/ZvHkzfr8fu93OihUrWLFixbiUqWKmT00p4Hp7e7nuuuuw2WzY7Xa2b9/Otm3b+MpXvkJTUxMA69atY8+ePXzpS1/iV7/6VbFsLht0Opmbjmxjme8YBxzt3HnG5anHVKW03+bZFDP4PAPobTPn4OhtTvxDnrIIpUJhRFw8GECbIFDf++wePvHn3yNrKs8sXsLnrrgaAMlRQ8Q7jLm2shu4zpZyEHIrXuriG9++D5c/AICnxs7nPvMxDiwtr55k1qZGgieOpqpXAR655a5UDtyG228EQO3x0NjcVEpT5xzhwWG8vihy/fgUCUmWkRoaCEYiaMdOUdMuWqVkxRwQcMuXL085qwKBAJ2dnWzevJnOzk4eeOABVqxYUVR7phRw3//+97n55ptZs2ZNau3xxx/n05/+NLfffjuLFydK4Jub5+4di14vs9x/jLq4n+X+Y6U2J8VsixkUVZt0t/DVHQ+y8lQXz89fwhcuvXrctuVEvkWcpqogjfeo2aJRGv0jyJrG+a8cTq1LkoymxSfuouoplZD7p117+eIPf4JBSTTNPdLawqc/93FONU0/r7QUSJJETU0dXo8PuS7hbfvpm85LVaECqMM+aqzOqvfglhs+jw+5fuoUCdlkIhiJYPEHMNjLO3oiKD02m401a9awZs0afD4f69ato7Ozk66uLu68806am5u5+OKLOeOMwt1kTingmpubx4k3gPXr13PRRRdx9913c8UVVxTUsEpA1sl02tsTHjh7+/jH9AlhVMpecDN64dLE6lf2HKYh5GNlz+EJm5aXgIP8ijiDzQ5DJ8Ay6oULGI3022sSHrhFHal1xTeCaX52uVHFQNM0oiNeNEXBWONE1uc31bVYQk5SVa7b+js+8qvHU2tPn3M2n//kRwhYSx/OnwqTy4VrRMbf6yVu0aU8ceqIH73bi8NWg8npnGEvgnwScg+iOlwzpkjoamoIDg7hFAIuMzRy98CV3yVmEnv27KGjo2OS40qSJFpaWmhpaWH//v3cdNNN9Pb20tnZyZYtW5AkiRUrVkxKQ8uVKc/s86ZI3rXb7dx888088MAD+Hw+7PbJpfSl4JFHHsFsNrNs2TKam5sJh8O43W4OHDjA6tWrC+Ip1OnkcWHTJNGYnzpXadsDzMYLp9NJTPQjPT+vI+GBm9cxftsy9RbkS8RJsoxJ1o17P35x3hp+cd6aSduaZPIuivKBEosyMtBHWIuj2e1g0qEN9GAm0aPQZM+tvcZECinkTNEoX/rBg1z69F9Ta1vffBHf3HQFShmNoZoKY00NdTU1xPwBon0+QMNor8EwvzzOl3ONaCSKPEtRFlPUAltTjUhlI8AKqQWam5t5/PHH6e3txeFwpHLgxpKMaiUF3bp164BEyDXfTHkV0jSNYDCIz+ejt7d3UpHCpk2b2LNnD/v378+7UdkQDoc5ePAgBw4cSK2ZTCY2bNhQsDCvPs2FRFHjGMwqRkN5eAim88LZaxsY6BtAb3Ol1saGTZPEgyPUl1HD0YnkS8Q56moZ9AwhO6d+rerIMM4Si/N0xCMR+vu6kZqbkGB0CkBdLTFg0OulNh7HUgDbk0IO8tNHrs47wje/9UPO6XoFAFWS+O7Vl/Gz9W/Ie6VpoTHYbSIcVwaUYQCh6iiXRr6F1AIdHR10dCScG4FAgK6uLrZs2cL+/fu55557WLFixZRCzWbL/3lgSgF32WWX8cADD9DZ2Ynb7eYnP/nJpG3WrFmD1Wpl+/bteTcsU5qbm1m9ejVud6JCzeVysXTp0oIeU9aNv5iEY8OYLHrqa8sl2X96L5zeaMKkh1g8hqxPXxGnKQpGKY7BUh5tJKYiHyLOYLVRG1cYGuxDqm0Y38BVVdGGBnHWujCVidd5LEP9PUjTJMXLTidDAwOYHDXIU/QwywcTGwJDZmKu48Qpvv2NHzB/wANA0GTklhs/zK6V5+TXUMGcQidLRLXJOb/pKM9Yg2C2FEsL2Gy2VFFDb28vmzZtorOzk56eHu68807sdjtr165N23YkX0wbB9q0aRMwvetvxYoVfPWrX6WzszNtKxG32120Qof29nba29tn3jBP6E0qipzo6yTLEk21zej1k2cjljO1La0M9ZwkEpHR28bn5cSDXoySUhY94GZDPkScqaaGZrsdf5+bSDyOqoEsgVGvw7FwYVkmnscCfqIWEzPJMqm+Hl9/L84iVBNnE169sPMF/vN7/4M9lLjpcNe5+My/fZyDiyrj+ycoX2zzGgm+4kaqm75yXI1GsdvLI3pScZSJBw6KrwUkSUoVNezatYubbrppXJWq3+/nzDPP5D3veU9ejzurRJ7pXH+dnZ3s37+fhx9+mHnz5vGDH/xg3OO7d++mo6OjKvvE2SwOGuvLu+R8No19a+e1EYuE8A95UFQNTdPQ6WTq62sxmMvb8zaRtnDC3pMkREM2Qk6SZRwt88hvxljhCPi86OrGi+/3Pf0U73/6KX52wYX8/IILgcRJJqIpRbVttkJu446d/NvmLehOx7peXLSAz3zuOgZqXUWzVVC9yHodFgOEY3Ekw9SXPZ13CMtZxbvwC6qPpJd3bJUqFDkHbrbs37+fTZs20dzcnHJZjiUZiq1GATcxhFrJGEyWsunzlg+KOT+11GhpwkJrD77MwsEB1h58OSXgoHQ3yVMJOVlV+dSDD/P+x/+c2vZP5y3n1k98iLCYwSnIIzXt81GPnCRssKKzTeikH4shD3uoXVS+1eWC8mVsMedUhZ1FzYHLlPXr10/52MaNG3niiSfyXkI7keHhYY4fPz7uv1evXo3ZbJ7xuc899xzPP//8rI5z3XXXAemLGNJRylYikPl4rWphroi4dLcRUb2BgMlEdEJuY6lvOcYWPAyH3fzXfT/lzX97KbX2wIa3cPd734lWhqFqQeXjOqONyPAIwaFB4iqggSyDxWrEKjxvOZGvIoZAIMC9994743YrV65k1ar0U1hy0QLZcP3116f9d6HJWcCl87pNxGaz4ff7cz3UtHi9Xvr6+li2bFlqze128+CDD3L11VfP+MFFo9GMbUw3C1VQXswFEWdzOBNhVMfoSLTPXvWBSdtpmoZJKo8WHM1DHjbf90OWnDoJQEyWueXqy3jootUQnLkJtUCQNXo9NNambmY0IAgET3uGBVmS8zD7hALUNG1W1+JoNJp2PVctUAymqhnIlJwF3JIlS/jlL385Y3JeofvFbdiwYdJac3Mzzc3N7NixI+3jYzEajRnbWEkCbq564aD6RZzBasPo6UeZIWlPGxzE0bygOEZNw9nHX+HrP7qbhpERAHwWK//+zx9n35ln0SquoQLBnCZZEDATRmP6gsFctUCmuN1u7rjjDo4cOYLNZmPTpk0zRhvtdjv33HMP11xzTU4VqjkLuMsuu4zPfOYzdHV18bnPfW7K7Xp6enI9VFa0t7ezY8eOGbdbtWrVlO7YqZhqmL2g/MhHcUM5U9c0n/6+bmhKPypIHfZSa3MWtIXIbHh951/54oP/gzkWA+BkQyOfu/ZGTjS3lNQugUCQI3kKodpstlSaUj6ZrRbIlE9/+tOpAgW/388999xDIBCY1qnV0dHB9ddfPyvn13TkJQfutttu49prr+UDH/gA69atY926deNahwSDQXp7e/NxqIxJuksL0c6kkjxwUDgvXDTgw+/zomoSoGHQ6ahpbEaSyu/9qVZvnM5opLG5Dd+Am5AaR7VZkWQZLRDAjIzdUVva/nWaxtV/2M7HH/1lamn/GUv4wj9/Am8Z9tUTCAQZUkZtRNJRCC3w8MMP09LSwg033EBLSwu9vb1s2bKFBx54gLVr19LUNHVvToCR01GIbMmLgLPb7dx3333ccccdbNu2jYcffhibzcaSJUuARLz329/+dj4OlZYdO3ZgMpm45JJLJj1mMiUq2cLh/OfVVJqASzLjjNRZoqoKg90nUAwWdJbRDv8RTaX35Akcdjv22vqcj5NvqlbEGQy45rXh1DRiAX+iCXNTG1KJvW76eJzPPfQT3r53T2rtsfMu4M73Xk1sigbSAoGgsiiHSQzF1gJPPvkkN998c0oQdnR08PnPf57HH3+czZs3c9NNN/HEE0/wwAMPEAgEUrrIZrNht9vp6urK6fh5UyB2u52vfOUr3HTTTZxzzjlomsb+/fvx+Xx861vfKujg+5dffhmv15v2seR6QWahZhhCHRrMfx+YTGkL5yeBU9M0+k8eQ3M0oLOM96BIkoy+pgFfJEbQ68nL8fJNMqQ6OJIQctWEJEkY7Q5MTlfJxZsjEODbP/ivceLth297F1+96hoh3uYYmphnJSgwxdYCdrs97f7Wr1+Ppml0dnZy9913Y7PZUsUZ+/fvZ/fu3TzxxBPTdu+YDXmfyL127VrWrl2b791Oy7Jly9IqboBjx47hdDoLUnmSiQeuNSzRbS6fE1iuXriApx/s9dOOptFbHPh8A1id03c/LxWpvLgq9MaVA239br7xw7tY0N8HQMRg4KtXXcMfz80s11RQuUR8I/j9I0SUOAogSxJGZKxmC7b6hlKbJ8gnZXB5K7YWmK5adt26ddx5553cdtttqYrTw4cP09XVRW9vLytWrMi5EjUvAm7Pnj1s3bqVI0eOAAk34kUXXZT3sRFT0dLSkjaunRxqm++qkyS6WfaBKzdmmpE6G0LhMPIshnRrJjsh7xCWaQbEl5pqDamWkhVdB/nq//wAZzDhdfbYHXz+ozfwwqLFJbZMUCz8g/2MqCpybS0yo+EeBfBGIkROnaRuvhiTVhXkS7zl2Imk2Fpgus4VZ5555qQpVB0dHXR0dOTt+DmHULdv385dd91Fc3MzF154IU1NTXR1dbF582auu+46Dhw4kA87p2Xp0qUcOHBgUk+6LVu2sGzZsoINtddXaA5cEo83+5BuPM0P9qtPPMj2+7/MV594MLWmM5qJRkJZH6dYVHNItdis3/sU3/n+d1Pi7fC8+Xzs058X4m0OEfGNJMSbI/0FTjaZCNttjPTP3EdUIJgtxdYC00WgbDZbXsVaOnL2wB0+fJif/vSnk9Z3797N448/zq233srNN9/MhRdemObZ+ePSSy9l7969vPzyy0QiEcLhMOeff35BxFs8Fkdv0Fd0G5F8eOEmsurUYRpCPladOpzX/RYLEVLNDUlVufax3/ChHY+l1p4++zXces21BM1iQPhcwu8fQa6d3usuG40ER0aomXarykbTNAL9fUQVBdCQAburDr2l+n4PORcx5GlMTDG1QFdXF0ePHmXx4vQ3pw5HYSdq5yzgpmq4l8yF279/P/fccw8tLS1Tvsh8sXr16oLuP4kSV9EbKrcKNUkubUX0aX5sz83vYFV3F8/NH73rUKIRjKbSd77OBBFSzRxjNMotP93Mm/aPjqPbdtEb+N57rkQpcSGFoPhEFGVW4R21xkFoyIOltjzzZHMh6PHg9Y8g1daNKyYKeYcxDvRR17oAqZpGxuU6iSGPZazF0gJ+v59Pf/rT2Gy2VE7b8uXLUyHc6Tx0+WDWAu5LX/oSHR0drFixYtyIiplYsWIF3/72t7nnnnu46aabsjKy3IhH45gsRvSGzC9MQyWciToV2RQ0mM0mgkocWTf6FfrCW6+etJ0U8WFprLwZg9Xe+Def1I14+fqP7uHVx18BQJEkvveeK9l2yZtKa5igJGiahnLa2zQTstGE4vVNu008EiHgGSCmamhoyCQ69Ztd5ZtXGxry4I1FkRsmN9aWa2qIqSqDJ16hob1w3RmKThkUMZSCZcuW0dXVxZNPPsnu3buBhGMrmfvW19eXth9cb28vR44cYc2aNVkfe9YC7tChQ+zfv5+HH34YgOXLl7NixQpWrFjBPffcM+0A16lKbSuVeEwBwJChgCu3SlTIPpRqr2sieOIoWk3TlHcZStiPw15YF3KhmeiNi0ciBPsGiEYUVBUkCfRGGYvTjtnlKpmdajyOpNMV/I5vImec6ubO++6iZSjRLiZoMvGlD13LU685p6h2CMoHSZKQZ/k91FQVSZ56W1+fG388js7pSq0pwFA4jP7YK9TNn4/OkH6kUikZCfiQ66auspVkmZjTRXBwAKuoxq1YOjo6uO2224BEOllnZyf79u3jwIEDKTG3Z88empubx2kmq9VKMBic1Sz56Zi1gEv2eevt7WX//v10dnayf//+1AXjwIEDXHbZZePch2NJjpqoBmKxOAD6Cq1CTUemXjhJkmiYv5DBnpMoRis68+hzNU1FGfHgcNiwuSo/NJL0xr3i60UNRDE565GNoxVAGuAbDhLxdeNc0Fo0uyIjXvwjI0QVFU2nA1XBIElYTGbsM3QAzwcXvPA3bnvgPqyRCABuVy03fexGDovKwjmPERllNht6PFhbF6Z9yN/fR0DWoXNOLoTQmc1oZjOD3d00ti8q+o3LdISGh1Bt9hk9kLLRRNDjoVp8++XQyLfYXHTRRal/JytML7vsMmCyoOvt7eWJJ54ARnvRFa0P3Nq1a1MGJvu8jRVzyYZ1yUG0Y4VcZ2dnzoaWE0kPXDYh1HIk6YXLVMTp9HqaFiwiHBghODKEejoL1aiTqGmrrvyOWDiELhTH4Eo/WUJnsRKL6vD3urG3FN7bHBgYwKcoyK46xn4LNcAfixE9cYy6BYULXV++84/86y8fQne6OesLCxfx/z5yPYNOZ8GOKagcrGYL3kgE+XT3+3RomoZJTu811jQNfzCYNgQ5FrW2jsBAP/bGwt+wzJZoJIzsmF1phlJNcccqeimzJSnW0jFR0CWdXvv37+fIkSMprZQLsxZwmzZtmrTW0tLC+vXrU+JsrKDr6uqiq6uLlpYWNm3alHPDunIiHi2NB07TNJR4BFlnQJbze+xcqlLNthrMtvEnrFg4iH94CEVN/LdOBkddA3rj1Cf0cmbE70Gun6mqzkRoxIe9wHPZIz4fvlhsyouEbDAQdbgY6TlFzbz5eT22TlH4118+xOVP/jm19qflr+P2D3yYiLH8QlmC0mCrbyBy6iRhKfG7mIimacjuXmoXps8BCw4OwCxy3GS9npDPS1lN081g4sQc1DxzlmSBw6ZNm/D7/dx999057zOvkxgmCrpqJXZawGWaA5ck00KGcDAxLD4aVZB0RjQ1jk7WMJkMOOta8ho+yHVCg6ZpDPWcJKrp0FtdqXUFGHAPYDaCq7l4YcZ8ESI+qyp3yVJDaGAAS0Ph8loCXi/yDBc3Wa8nGI3i0LS8fT+s4RC3bb6PC176R2rtf9+ynvve9i60KvK2CvJD3fw2fP1uAiM+VIcD2WRCUxS0IQ9mWU/twjOm9NLHYzFky+yCi0qZqSCDwUgwFkM2zDwqTl9God9cmYsh1Gyx2+1s3Lgx5z65eR+lNReIR7MPoWZayBDwefD5IxhMtZgmnA/imkrfqaM0zV+EJOV+Ac02lDoWz6kTqCYX+jStI/R2F9FYlGF3d8WJOEWa/GO549cPct7xLp5duISb35WowJUNRpTYSEFtiagKE9/d9+7dgy0aJWA08ovVp6uanLUEBwewzRCGmg3NnkHuvO8uOnpOARDT6fjGlR/gd+cXd2yeoLJwNDbjIFGVqXh9SLKEtbV95puKDHRNOeW/AVjrGxg5cRzq06dbJNHiccwVGpFIixBwGWGz2XIu7hQCLgvip4sYIOGFi8Vmla6bMbFoiJGRIEZLem+LJMnozI0M9nfT0LQgL8fMJZQaDQWISYa04i2JbDAS8vtxxKJlWT02FekuEUYlji0awajE0249v8bBqZHxLRLsRiP+aDQnW8aeJ9+7dw/ve/YpzujvQ68qxE+H1n+xeg2yXo8ayX2qxKtfOcrXf3Q3df7EaxmxWvn3f/4E+5cUZsKJoPrItM+b0WgmFAkjz6KHZDlexOxWK75QcFovojzkwbZwUfGMEpQVLS0ttLTklm8j4h5ZkAyhQmELGXwjQ1OKtySSJBMJx+nuOcYp9wl63CfxDHQTi+Y2viqbMVuB4SH0lpmTd/W2Wnye/mzMKhlGbfJP5anFSxn+4Cai11/Pl9/9Zi7sWEjE6yFkMPMvF13ISDjC5950MUsbE3fiqxa0cvNb0g9azoSxYtIWjdLg96FXFWRNQ68q2E4LRE1VkXNsb/6mfc/x33d/KyXeTjQ0cd2nPi/Em6CgWOrqkKcZFJ5EjUSwWsurryaAvb4Bh6KiDg9NekwNh5EH+qmf31p23sOc0HL8E2RMOd68lD3xMR43g0FHoSZ9RqMKhlkMMTDZGgmGB7DYE0JBAfqHPDitFmyOzNt4ZBtKjavaJLnw1R0PsvJUF8/PX8IXLk2EGSVJShU3VAp2vYWhaGRcQvbf3v5uvlXn5Im/H8Lx+JM88bl/5pJn9rNu8Ws4MTTMsYFhXu7rxx+JsmpBK+ctbOX7Tz6Tsy1GebRFQ8BoZMDuoCYUSnngAqeLCdRhD9a2LD2zmsaHdjzGx37369TSvo6lfOGfr2PEVlYp44IqxVFTg9fvQ56il6Smquh9Xizt5Tlj197UjCUaxe/pP92EOOExsdnsmKvN86blIQdOI2/jtOYKQsBlwXgPXHZv4awKGdL8IG599icYVIXnms7k14sT82UlSZq0rcFaizc8gk7vw2zJvJluNiIu3d3kyp7EfNSVPZU5HzWJ3dVAqO844VoZWZ9IRnRZzKxZspAn/n4IXziC1x/g/Fe/BgBn3Ehb2Er30Ah/O+lmvrOGF939k0Kq2WC12RgOhdBZLPxi9Rp+sXrNpBw4TdMwy7qsWrkY4jFu+sWD/NOzT6fWfrv6Qr5x5dXE9eKUISgOFlctDHnwDvQhueqQxnz3lBEvpnic2jIWQmo8TsjjQafJGE1GLDPkxAkEmSLOxlkQi4zPgcuUWRcypLkbMagKBnVizlV6DOYafD5PVgIOMs+H08nSpOadz8/rSHjg5o3OR9U0Df003dfLlcamhQwN9uDXhpFq63jq8HGeOnwcZWQYc1Sh1mbjaP8I9I9wyWvPwGY2Uh814TCZaKup4cdPPZ+XkVyW2jqiPd0EwxI6c8JFmypcIPH+SgNuXFl4JmoCfv7zx99nxZGu1NoP3vEeHnzzusTYiTQEBwcIhcIomgYS6GUJm8OJqcCDnAXVj6W2DrOrlsBAP7FgIq1DRsNWW4/eXJ4zlpVYFO+pXmIKyM46JElCjUXxHTmGxWrGUYQ+kYK5gRBwWTDWA5dtK5HZYEgzMf65pjMBUt43gHBwCKPNlXYfEUU7Pa4m+3TH2Xrh7K5aBge96K2jeXDJsOlYFL+H+tb89icrBgnhqcMQlQkdOwayhKxBja2WL197Fd/51a7Utj/a/gzz6hzs+sdRPrvuQn60fS+vXtRIW62Tk0Ne3MHcJpM457WiHxggOOQhbrags1gSLRq8Q5j1epztizP+zBe4e7nzvrtZMNAHQMRg4Csf+DB/XrEy7fZKLIan+yRKjXNcW5M4MBTwY/IOU5ttCFcgOI0kSWXVqHc6lGiUwRMnkWubx1WKywYjuBoIRSMoJ0/iaqvCaSUij63oCAGXBWOrUAtZxGCvqcXjGcFoHhVEY4Vbyh41yu3PbeF1fYdRJZkHXv1mft2R2E5ntBCNBDDl6IWbjYgzmK2YZA+xCUPux6LEIljMhikfL1dURaHPfQzFVY9ss2GuHQ2HXPSaM/jjs3+l86Qv1WA5EI7SdWqQKy9ezqN7XwTgkxdcyFd+/ntefe58XuhNFHHk4pGzNTRgIzFSKx7wIel0WBYszCox+txDL/Of//MDHKFE1epATQ2f/+gNvDRNiMrT3Y3W0JS2Ekq22YnE43hPdeOcX1ktYwSCbPH2upFrp/awyUYTEU0jPOTBnGFlbrkj+sAVH1GFmgW5hlCTDA1O74UxmW3YrDri0fTbaZqGf6QHc009r+s/TH3YR314BGs8ktpGVeLo9DM3lJyOtnAiVDGbytTaeW0YYj7iAe8kW2P+IcxEcDbNy8meUtDffwK1vgl5Qg7YBWcuxB+Ns/1oHw1mhZbaUaF8bkcrPZ4Reod82MyjLVNMI1JqvurgSO5tPkw1TmzNLVgbGrMSb29/ejff+f53U+Kta14r1336/00r3kIeD8oM44JkvZ6goqAqhWmzIxCUE0osSlSZ+fenM5kJ+nL/3QsEleUGKRPGhVCN2b2Fs82Dq3E1ofN5CPg9qBjQG+1omkrI50bT6zC7mpB1ev7a2JHywAX1o5WSshZDP5tS1hnIJB+udl4b8UgY39Bgqku6QYa6lqaK6v2WJBwYIWaxopsgjtrqnHzr6ren/ttpNXPJp76FyebAZjayckkrP9q+F0h45EZCEWxmI/PqHOw7TErEnSRxMs9HflwmSKrKxx/9JR/44xOptd2vPocvf+ijhGbILwqGgsjO8S1u0jUTll11+PvceR/pJRCUG6FBD7oa16y2jcYrrAx/NggPXNERAi4LxnngjIWfh2pz1GFz1BENB4iE/UiSjGS3oJpqUx6X2y74wKTnaZqGKY/zWtvCZk4yu3w4vclMbUt1hM4CwRF0zsn9+E56vKz90vfHrRmDXky2hBcuKd6S/Nevd3FuRyu/e/alceulEHKmaJQvPvg/vOHAvtTalkvexF3v2ogyTSPmJMqYeY/pmgm/79mn+Pl5F/KL1WuIZzAbshgE+vuJ+MPE4yqaBrIMRpMeW2MderOl1OYJKhSNDMbWldlvIi9U4Usqd4SAy4LYuEkMxXsLjWYbRnNCPFlVBbf7OHp7+nwLTdNQ/G4a5y3Kux25zkutNJQ05cBf/+2DrDrRxXMLlvD5t189adtAOP20hX2Hu6c8TrGEXL3Xy9d/dDevOnEMAEWS+K/L3svDF79x1vuQkFLn63TNhBv8vlRD4XKqNx46+gqKyYlsrxt38lOAoVOD1DTUYKqZuRm1QDARnV6PGo9PSrNIRwUW4U+LRO45cFX2lhQFkQOXBfFo/jxwM+XBTYUs62hqbIPwILHQ8LjHosEhpLCHlpb8zEgdSyb5cNWClOZu2Rg/PUYrPr6li5SH29C2sJW2sJXBkWBecuTGsqT7BD/8ztdS4i1gMnPTx27MSLwBjC2QTjYTjss6VEkiLusYsDsIGI2o8TiGWQz1Lgbek92oltpERWAadI5avP3DKDmOOhPMTSz1DeAfntW2xixTbwTZs3fvXo4dO1ZqM/KK+BZlwfgihuzfwkwH209EpzfQ2LiAWCxCwOdBPR0Oqq1vRK8vXK5ZPobeVxIGSSamqePE8FOLltLmHeSpRaMjpTRNxSjlM2Q93iMHuXnl1vzjAF9+4EdYo4kil57aem762A0cnZd5qNteW8uAdwSdo2bKZsIAkmcA24KFWducLzRVJRKOo3dO/3vV19Tj7+vH2VYd4X9B8ZAkCYvFSCgWnfImAUD1DWNrmn5EYkVSxiHUcDjMrl272LBhQ6lNyStCwGVBNBJL/dtYhBy4mTAYTLjqilvZOZdEnLO+BX//CXS1Dam1LcvXsGX5mnHbad4hahrz398pJeTMox65jIScpnHFzj9y46+2oDvtTfxH+2I+/9HrGZqhknQqDFYbluEhwtEo8unRXWObCQMoAR81dntZzHsM9vWhm+VYuVhEVM0KssMxbx7x4yeIabZxY/eSqL5hHDVWDNMMua9UyrmNyIEDB0ptQkEQIdQsyEcVajUwV8KpkizjNNlQglMP11aDfpwma15C1pqq4h3swTN4iqHBU0ROHzcZWgVmHV7VKQqf2fYzPvnLh1Li7Q8rVvIvN3wma/GWxDW/DWs0jOoZRFNHq+rUWBTNM4DTYMRWVx7jgxRl9s2s1TK+EAnKn9qFC7DrVSSfh/jwAPGhAZThAfTBIWoba7HUVqH3rYw5duwY7e3tpTajIMxd9ZEDsfCoBy4fRQyzmos6gXgsQijgBUnC5qhLNZAtNnPFE+dwNSCPeBgZGkCxOdCdvrtWomF0wQAusx1bTe6NOT0D3QQ1Bam2NiUG/UE/hn4PtfZ6TBbbrD1ytlCI2x74Iee/9EJqbfNb38aP129Ay2Eyx1hqWubhUFX8fe5UtanJYMC6sLxOmJl4AcvAYSiocKwNDVgbZt6uqijTGx+3283q1atLbUZBEAIuC8aFUM25vYWZ5sGFQ378w8PE42A0JTwofSPdGIwSNbUNGIzFb4MwV0ScraYOW00dwREPEV+iUbHJYMbalJ8cr373MaK1rklTKnRWO6rVTv+Qh0bAZEm8x9MJuZbBAb5x310s7u0BIKbTccd7P8jjqydP8sgVSZZxtJR3c2ZLrYtQ7xB6u3PGbQ16EZgQCDJCI3cBVwABeODAgaoVbyAEXFZEQmNz4Ir3Fob8w4wMBzCaXIy9xpvMCc+Px91HbWNDqtVIMZkrIg7AWlNHvjNYAt4BIjWOaUeMybV1DHsGabaMf3+TQg4SBQ/LT7zCXT/9MQ2BROh12Gbj3//5ExzoODPPVlcOeosFPf0zbqeE/Nhc2Y2dEwjmMvnKgQsEAtx7770zbrdy5UpWrVo15ePDw8OYZ2hIXukIAZcF0TE9vkym4rRI0DQVr2cYk2Vqv7zRXM/QQD/NbaURUHNJxOUbfzSEbJ85BBs1GoiFglMmQV/9/D/43KObMSqJPM2j9Y18/OprOV7fCHluSVJpqHY7sUE35vr0vRPjkTBKLIRfcuCf4++VQFAqNE3D75863zhJdIZ2PwcPHqxq7xsIAZcVkTE5cEZT7m9ha1iie4Y8uBGPG6N55gu8LNsJ+oex2l0525UNQsRlR0zSJlUUff2RBznveBfPLlzC5zckmgXr7DUEh704Jwo4TeMDu3/HNTt/k1ra134Wt112HarFRtvspqBVOVbidiue/j7CBhmdK5FMrkYiyD4ftToLNa5FIN4rgSBz8uSBkyQJm23ma4fROHWrloMHD7J06dIpH68WhIDLgmio+B64WFRBNyE3552vPM07jz3Nb9ov4DeLLgBAbzATDnhLJuBAiLhsSHfuW338MA0BH6uPH552W0M8xmd+93+85e/PpNYeW76W762/ivg0Idm5iN5opqlhIfFYhMDgEKBhMJiw1pdX0YVAUHHkScDZbDauu+66rJ8fDocJh8O4XK78GFTGiGzdLBjrgTPlWMQwW7Q00wBW9R+k1T/Iqv6DRbEhE+ZKi5F8ka6GeO/CDvptDvYu7EitqfEYBt3oTUNN0M8dP/1uSrypSNz3xsv49ts+KMTbNOgNJpx1LTjr5mGdZX84gUBQ/hw4cIBly5aV2oyiIM7wWVCIHLiZwqjpWhtEZT1Bg4moPOFjLJM2CMITN3vM6Ahp44dhJ8OmY5FGRrA1JqpeFwz0cvtD/8384QEAwnoDX3/XR9h91rnFMVogEAhOUw6NfN1uN83N6XNcqxEh4LIgGh5t5JtrG5HZYjKbiEaVcf3ebl951aTtYtEgNbXlU0UnRNzscLqaCHq6keobp9xGjUao0Sc8m+cefZEv/vKHOMKJZPtBu5Nbr7ieg/MWFcNcQRGIBHz4/V7Cmop2WtybJRm73YnJVj6/cYEAKIs+cF6vl5dffnnS5IVwOJHY+swzz/Dyyy9jNpu59NJLS2FiXhECLgs0TSMSimKyGIuWA2d3NdJ38hWM5qkv8AmCmK1NRbFptlSTiNMmeMnyhU5voNHewMBAH9Q3TjqGEvRjj8SpaWjln/bt4l+3/xT96ekHh5va+OKVN9Cfh0bCgvLA5+ljRFXR1dSOC6/HgAH/CDXREI7a8vqdCwSlZunSpWmLF8LhMHfffTfnn39+VRU3CAGXJSkBZ86vgJtqKoMkSbgaGhkeHMJoSj+KJRrup65lfl7tyReVLOJioSAjPg9hRUFFAyQMsoRVb8RR15w3QWey2plvtuL19BJCQU1ONpBkHBYnljobH/nDVq58ZkfqOU8tOYf/fPdHCRuru9/RXCLoG2IEDd0UXjadvQZf0I/eP4ylhMVKAsFYyiGEOtcQAi5LkoUM5jwKuJmmMpgsduqadPiGPUTDcSTJgKapSJKCwaSnfl4rev3UpdWlphJFXMg/zFDIj3zaEzLWGxJQFUI9R2matzhvIk6SZVwN83FNWDdHI3z+4R+w9mBnam3beW/mh2/eiJqnsViC8sAf9KObwZsqW+34RoaEgBOUD2Us4LzexOScZCi1WhACLksip1uJ5NsDNxMGo4W6plY0TUOJR5AkGV0Zi7aJJKtTT5KoTs1UyMUjYXyeQZKz03Uy2Osb0J+eTZpP4rEoHv8IOtfoQPYrDuzBEo0SMhrZsmwNam0jnr6T1DcvyPvxk9T7hrhtyz0s7T0OgCLJ3LXufTz6utcX7JiC0qDEokQkidmcVaJaoipZ1hf3HFSN+Dz9hCJh4lrixCJLEiadDkdtI3pD5ZxfS0oZCji3283evXsZHh4GYOfOnanh9tVQqSoEXJYkBZwxzzlws2nqC4mQqt5QuWGzbLxxQ6dOEonLGKyu1JoCDPYOYDKAa15rXm30DfePE28AGw88RUPAx4DNwZZla5AkmTAaqqIg69I1A8mNjt7j3P7Q3TT4hwEImMx85T3X8fwZr877sQSlJxLwobNODp1ecWAPAFuWrUmtyVYr0VAAs8NVLPOqDk3T6O8+iuqoR66xjrsgxoD+/l7qXHWYrPZSmSjIgebmZjZs2FBqMwqGiL1kSTgYAUCWpbxMY5iLZNIrbujUSeI6Bwbr5GHkequLmGRluKc7b7bFwiF8Q0PE+geI9fUT7e8jHkpvp1xTh2+oL2/HTnLhwU6+83/fTIm3Hmc9n/zQzUK8VTE6gxEtNnlEkCUWxTJhXY3FKsr7Xo54ek+gORuR9enP4TpnPZ6hAbTTnjnB1Eg5/gkyRyiPLIkEx/SCMxuIRuLTbJ05UxUzVBuz8cTFIqGE5804zaB3vZFIIEg8Gsk5nDrU200kLmFtXAzA5f/Yw+UvPIWmqiz29qPXVByREL948FsAbF12Ib9Z/JqcjjkOTWPj3t9z7R+2IZ+OS/yj9Qy+vPETDNtq8nccQdlhsjmQRzxgHj8qLZQmjKeLRjBMMddVMDNKLEpYA8MMOaSys56RATfOxnlFsqwC0cg9hFqGIdhyRwi4LBkr4CwWIz5vKG/7nqmYodqYKS/O7/GMC5sCXP7CHizxKCG9kW2vToSV9DYX/sGBnEKpw+5uYjoretPoBdMai9IQ9CWOoarImoYehYZAYs0SjSLl6R5SpyjcuP1nvGP/rtTan159Ht94xyZiItdpTmDW6QhrKpI0KizGhk4BNFXFPIXXSDA7Rob60c+i9Y4kyUTi+b1BFwjygTgDZEkoMFrNYraIMEY+mMobp6jjXeyXv7CHzz71a3SqgnK6sXFSxEUjk8NPs0WJRQlFVQz28Z9n0GBk4HRekiMSAlUhLskMnG7zEFBVbHnIQ7KFg3zx4R+y8pUXU2v/d9Hb+d+LN6QfxSGoSlyN83F3H0WbokWNpqlIQ/24WheXwLrqIRz0o7O5ZrWtKtxDMyLaiBQfIeCyJBSIpP5tsRZGwM2VMOpYZhNStcSj6FQlEV5UFSzxUdEWDYazbrbr8wygt03usbftNWvY9pqEQPzplm/REPTRZ7bwvqs/C4A0NECz2ZLx8cbSMtTP7Q/dTftgDwBRnZ5vv/2D/OG1F+S0X0HlIUkyza2L8bhPEtZAdtYhSRKapqJ6hzDLMnWtiwrSUHouEY/F0s4gToeSJi9RMAEh4IqOEHBZEvIXVsDNtTDqWCaGVHUyjE0hDumNCc/baQ9caEwityQb8Pe7cTS1ZHxcRZ0s/L6640FWnuri+flL+MKlV7Pt1RdijUXxKokTujrUT1N9brkxrznRxZe3fh9XyA+A12LjSxuv5x8LluS0X0HlIkky9S0LUVUFv6c/cVMiSzia25BE37/8IOuJBwPorbO4SY7FCm9PpTM3L1clRQi4LBkbQi2UBw7mphcuSdIbF9NbUPxDGO0J71gyXHr+yYO8pv94avuoz4PJXk8s4s/qeJIkTToHrew5TEPIx8qew4ljn/bEBTzd2LweXPXzciqaeOM/9vJvjz6AUUnk2Byvb+GWK2+kp3amkWmCuYAs66hpyPxmRDAzBrOVaMg/o4CL+YYxmfLfZ1IgyBVxK5clIX/hBVxrWIRI2sJm9EYT0REP6pgwxrZXr0GTwBENsbr7IGosihaNodMbUk1+M0UnJ/pCjeX5eR0MWBw8P69j3LrdbKGheUH24k3TuHrXI/z7r3+cEm/72s/ikx+6Ka140zSVkYFehvtP4e3rIRYKZndcgUAAgF6WMVhriAwNTLlNPBQkHg5hFb32ZkTScvsTZI7wwGXJ+BCquDsrJG1hM/2mWrwjHiSjEaMjUTkWk/UEDCYiqkJsZAiLMzHcO9sIk6OhGffJbgyO0ea9X7j06knbxYMj1NfXT1qfLYZ4jM/+9n958z/2ptZ+u+Ii/nvdVShpmgEPubsJxRR0NbWpysTAiBfD0ACuukYMOebfCQRzEavVijeuoTfbCA+60RnNGByJPpNKJEwsMIKsM2AyGLA6Z65WnfMIEVZ0hIDLkuAYD5y1gCHU2U5mqHbsRhsh2YQm64h4+kCS+Nzr1oF6KUabC8vpPDhNVTBmWRUsyzpsVhOhaBjdFMPhVSWOSVYwTOjTNVucAR//se37vOZkIiSrInHfmy9n6+q3pK00Heg+RtzmQm8d/1PVW2vQgIHBPhoamjCYhIgTzA6/p594NIamgU4n4WhsHteyZK5gddYR6H4F1V6PubYZJRom4ukHEn0lza4mlKCPGsvcPvcKyhch4LJkbAjVahMeuEJjsTrReY5BbRN6w9T5YbGQh9p5i7I+Tk1DM/T3EvQF0NnrxhU1xPxDmA0StfPastr3wv5T3L7lbuYNJ0I2IYORr7/zI+w5a0Xa7QNDA8QtDmTd1D9TXU0D3sF+GuYvzMomwdwhMDSIf8SPbHEhGxI3IIqq0nf8BBaLiZosCn8qnYb57Xh6TxBBh97uSt24qUocxduP01GDtWZyZTpANOgn7E/0gjSYTFjmuJdOhEGLjxBwWRIsQg5cknL2wqmqwoi3H1UDWdKw2+sLNvy5yd5Ez3A/Old6ARcPDeFqaci5vUJNYwt2VcHX7yauJs5KOhlcLU1Zv7Zzj77IrQ/fiz2SaPg8YHfxxStvoKtlauEVDIeR7eMvHhv/vgdrNErQaGTra0/3vpP0xCNh9KbKnY0rKCyBoQH8QQW9vWHcuiTL6O0NhGMR1N5TuFrml8jC0iBJEvXzFhKPRvAND6BqEqBhMRiwt7anPZdEAn58Hg8KRvSWxGSUcDCCb/gYVocNe13DpOdkSywUJOwfAcDsqMna818UhIArOkLAZUnQN7c9cJqmMjBwiqgGemtt6kTnHurHIKk01M9HlvM73N1gtDBfambIO0AIFc1kBkkmFgmATsPgqMFkmzwIPBtkWYezOT8Xs7f/dSf/sv1n6E7PUzzUvIAvXnkDg470d/ZJYqqa+oFu/PseNv79KRYP9Y1rYLz1tWvQ250EfUPUmMSoH8FkNE3DPxKYJN7GojOYCAfCxMKhOZlTqTeaqG2aeYJL2O/DO+hFb6sfd/HUGUxgMBEMB1H73dQ05jbiLOz3ERjyomi6lEgM9XnRSR7sdc68necElY0QcFkS9I2OziqGgCsnL5ymafS6X0G2N2OYcIdqsNUmHu87TkvTwryLOL3BRKOrFU3TiEWCaJqK8XRy/0nT9A2Ai42sqlz7x21s3Pv71NqeM5fztXf9M+EpcuzGM3pLa41GqQ/6Eg2MtUQDY2tUNBcVzIx/oA+ddfqbBQCDzYl/yENtDqPoqp2RQQ962zRC2GglFBjGEgllnZca8g7h84bQW2rHXaCTQs476KVGVTCXUWWsRO4hVNFzIXOEgMuSaDhGLBrHYNRjs8+t0NXwUC+yvWnKUKUkSejsTQwN9VJfX5iLgSRJGM3jhdpMM1WLiTka5v/9+n9Yc6gztbZ19Vu4702Xo86yTFYec0oLGo0MWh3UnB7lpcg6gsZEOFeNRdHpxTg3QXpicQXJOLsbKUURcbCpCI0Mg8E+43Z6mwu/JzshrGkaI4PDGBxNU+/f4mSkvx+T3Vk+0zjEMPuSIARcDgRGQrgaHNgdxRFw5eKFC8fj6EzjRci7u57CEo8Q0pv41ZILkSSJcFzJeqxVLowdxwXFF3INI0N8ZcvdLHGfAECRZL63/v387txLMtqPSa8j2f9962vXsPW1a9LmwBH2YRNFDIKp0GZ/ZdSyvIpqmoamqXn3uJcTkUAQndE5q23j8eyaUfoG3OhtM7cokq21BAb7sDfkFqrNK0KAFR0h4HLA7w0WVcAlKeV0hng8iiLpUzME3931FO8+/DTtI33oVYX46RP4r5ZciM7sJBQYxmqfOXyTb1LeuCILuTN7jnHblnto8A8D4DdZuO2y69i3+FUZ78tR20B/fx+6mtHqtpRoO40SDWM3zy0PsCAzZBmUWW6rkzO72fL3uwkHwsRjGkgSEhoGsw6ry4XZXl15WhnpkyzFTDwSRzLNLIJlnZ5oRIz3musIAZcDAW+iG77ZYkSnl1GyvOvKhFLPSFViEWSdIfXflniEulAiN0vSNHSqgiWeaHIs6XQoSmnztIop5Na8vJ/P/+bHWE5PjOhxNXDLlTdyvCG74gK90USty8XQ0AByTf0kT6YS8mNBwdE0tyoHBZlhr2tgsHcAvc017XbxSIga+8whwiSDx18B2YnObEM34R5iZMCHEotiq82+4XW5kUkgIdu2ehqzzwXLwLFaFEQbkeIjBFwO+L2j44zsDjPeoeKNNyqVF85osqGODIMpUc4e0pvwWBw4YiGk1HD5RFFHLOTD7HQV3cZ0FFTIaRobn9nBtX98GPn0rfff2zr48uWfwJtjtZjZ5qDJZMY32E9EiaNoiRO8USdT46jBbKvJwwsQVDN6owmjXkNRFKQ0kz4gEQKVYz4sNYtntU9v7ynQuabsUWgwOwgOezGY/BitsxeF5Yy9rp6Bnn4MtukjCpqqYDJll5OaiUjMduJMwSgjARcOhzlw4ADhcKJbRCQSIRwOs3r1apqbyyjsnCNCwOWA3ztaiVpMAVdKL5wky5h0cuq3+qslF/KrJRdOyoEDMBDHYCyvlgT5FnI6ReFft/+Ut+1/MrX2h9es5ltv/xAxvWGaZ2ZwDL0BV55amgjmJq55rXi6jxOXLOgnFP8osQhSxEt92+zyKDVNIxKIYpjh5kFvcRLwDFeNgNMbTRh0KpqmTju5QgkMYF80OyE8EYvdht8/9SSYJPFICEdNdbyv+SYcDrN3715Wr16NeUx6ycGDB3nwwQfZuHEj7e3tJbQwf5Sbhq8o/MOB1L/tjuILlaHBwMwbFQCHrYZ42Ddu7VdLLuRnZ78hJd7iYT92S/meYNrC5pSY83gDKTGXCfZQgP/8xffGibcHLt7A19/5z3kTbwJBPpAkifq2dv4/e/8dJtl513nDn/vkU7mq0yTNKFiS4zhIlsE4EWSwjcjeXYOBd8HgZ8EsaU1YWNbA8i4sC8/Cw5oVy/rhJbNag40tGyyMjRyRJRuNA9bIsjSjmelYuc6pk+/3j+qu7p6u7qqeTtU953Ndc6l16tSpu+qE+3v/YrFgglcncWskbhW6NXKWZOrMTSibWOeuxqktotmjxbVGwajRd4eDyskbwF0kiaMNr0kpidqLVI4fu+bELbtYhrA1dD8Rt7ELpWv6jL1CSLmjf7vF+fPn11nfVrjtttswTZMHH3xw1z7roEktcDug01i1uOUL+yvgDtIKZ9l58lFAy22gZ0obXg+7LfK6RvYAkhe2y7Va5I7XF/lPf/H/cLo2D0CgavzXb/xePvScu/ZusCkpO8TKF7Hyo2VSbkYSxYgRs02TZIz8aruAEILJMzfTqS3iuS2iSIKUKIrAtDQqp28YWQhvRvn4capX5tCyG7vKSCmJnUUqJ8fQIj8mp7pY7F3fvu8f8Ej2nlTA7YD2Ggvcfgs4ONiyIvn8BKbh0u7UCeKEhJ45V1cUJnMlTGs8iumOynaE3HMvPs7b3vm7FLu9/eqZPG/7jn/DF07dsj+DTUk5QIQQyGS08kBjU6dsl8lVpsjtUetTzbSYvOEknaUFfD8mWa7Np6gC09Qonz69Y5G4F4xLEsOZM2d4y1veMvA13/c5duzo9PxNBdwOaNVWBVyhdHCxXmsTGqSUJHGIompbxmnsBoaZYcIc495818AwIfe1n/skP3H/H2Esu1AuTBzn5//lW5gr7V7/w5SU/SLyPZylKkm0LBI0QXZyYsu+utnJKRafuoyRGa5gNCON0rkWVE2neCztiLGbPPTQQwC8+MUvPuCR7B6pgNsBrVqn/3extD0hE4QerU6dIEpAghASXVPJZ4uYxujHWnGldjtNuo0mkRsh0JAyRrVUjJxJfuLoZN3sF1cLOSETfuzRD/HGj93f3+eRm57FL3/rD+KMc4PplJRNaF66RNgVGFYRdUVnJdC4uISeERRPDhYQiqJiGGJoke4ocMmVjlYtuJQtGBML3CBWslLvueceSqXSQQ9n10gF3A5oVdcKuNFdhh2nTsvpYholzKti3avNFlnLoZifGvl42VqdlhCYehH9qrascTOk2r3AxKnxybrx3BaB30XTdDL5PfJDLCOlpN1aJEh6wdQagmJpemTr5CnPwogC/u2Df8LLn3ykv/3/POcr+fWXfxuRL8E/mGSSlJRrxV+cw9IqGNbGZBvDKhGHIbNf+jLmJk3ZZbZId36WTPH4QBEXBV3CyKErs3SvIUEo5fCxWy5Ux3G49957h+53xx13cOedd276+opoazabeJ7Hd3zHdxwp8QapgNsRzepqJuaoLtSu36HdDTCN0sDXTb1A13dQlTq5IfWGAFy/TUeCaQzO+FRVHRHmaC7MUpy+toKyu0W7uYTb7SJVG83I0w1DmrMXsQydUuXaM7c2o9VaohV0UXLlfoufUCa0a5fJqzql0vBYiGK3xb9/4F6eufgkAAmC//cl38ZfP+drOBYKSIuhp6whjgJ8r4OqmWMbBxoGXeZjC/Xq1eMaVFXH8Axm2gL96lXhMknxRqrNWVyRoOYnEIpC5HfRPIeyalPInwZv4FtTUjZFSkmn0xm6XxBsXSTesizuuquXVHbhwgUeeOABnv/853PbbbftyjjHgVTA7YCu4xN4IYalj2yBazstDK205T66lsXpjibg2oGDbm9d7VxRNLx2Czbvj7zn1JYuE2KjrhmrquqomUkimbAwd4HpY2d2TcS1Wku0hUArrI9NE0JBz0/ihB5JfZZKeXNRe7p2hZ9/4O3MdGpAr2jxb77qX/PQmbO7MsbrjSgKaLeqyOVTnMsUMY5IDKXTqdHxXQKpoFlZ4q6D0qphaxql8u4vTnZCy2kM7coAoGdLtDo1Jja5RxRFZap8CikTOq0qCQmGbmOXbtjlEaeMPbvYzF4IQTY7fD41jNGLJZ85c4aZmRn++3//79x9992cPXs0nuGpgNshjaU206cqlMrDLzgpJWEYow5e0K4jSXT8wB0aD+dLyQiHQxd5Os0lcsX9D7bvtKsE2Gj64MBoIRQUe5J6dZbK5M7T46VMaIUu2hZuaFW3cAKPfOgPtDC88NIX+Km//30yYc+EsJQp8Z9e/W94cmJ/Jqco8IjiAMPMHvoG4VImLC5dJhACLVPui5kFp4XeWqJSmtnUynMYaDTmcVFRMxVWphRF1cHK4ScxcwtPcWz6xrERcdE2am6Nsq8QCvni6CEfKUeT3XKhZrNZ3vzmN+/OwdZgWRa33XYbDzzwALfddtu6Ir+HlVTA7ZBmtSfg8kUbVVWI4837ocZxAGKj2+KnH78PXcZ8pngL98/0MmR0PYsXOEMF3KB75mc/++cYScQjE7fy3lMvAXrWriTsDth77+l2u2jm1rFuQij4QTQ0MHoUmo0F1Nx6q+S3nl/tFPFXty13itjEwvAN//wgP/iJ/40qe+fySxM38Ct3/xtqI1gtdoKUklprDldGfMvFz5JNYlzgPTe+kKKZJ2PvrH7XQSClZG7hKUR+Bv2q86rbvUr+i/V5psszaIdQxHXdFo5U0DZJZBGKCrkpqrXLTE6c2ufR7QZjHJmekrJNZmZmOH/+PBcvXjwSrtRUwO2QxuJqHFyxnKW21N50X0XRkMnGyuR6EqPL9dslCWKEtsaD9jDkgOPJBKEcjAUgCBPMNXPzN3/5E3zPP38QRSZ8ZuoWfukl3wWAapdwWtUdWwlD1re6+dbzn+DHHn436nKv1m95/BO869av5K9u+0pCuSq4lSThXz/0Tr7p8x/qb/vkmefzm6/8/+DvsbiQUnKl9hRJZQahKPyLD3+GiW6bqp3nXS+8m3mniXJ5jqJdoFjq7XMYaDTmELnpLUW5mp+k3lpk6hAKnLbbQssOWZwoCl6c7MriZDfQthG6qY3BeFMOCWOi9X/nd36HM2fOcM8992x4bcXq1mg09nlUe0Mq4HZIfaHZ/7symRsi4FRUdeNV/plSrwDsivUNIAybTJaGuxONARLukcozAPrWNwA/bDBZ3v9MVCmTfszTCnbkM+m1EFLywsUn+tsVRSNJRusnGwYenXYdKQEhyWTyWHavZIG86kliRz5qEqNICUnMRLeNHa2v0m0HHj/54Xfw4qc/19/2zufdzR+9+JuRe1xPD2CpeYVkYmbT7Fg9WySU0PEUnMVLFDJZ8vmtYx/HAS+KUKzhv5+fSJIkPnTu4iBJGKVpmpYt02ktki8eYCDqMoVsmTmngT7Eohw6DSZy43+NpYwH41DI1/O8LTswrAi3o5KNmgq4HVKbXyPgJob3/jQNjSRZbyFaK9ygZ43RdWWkUhc53aIRdtH11SzYtcKtd7wELavueWHfQQihbGi429VMIkVFS+J14iiOAlRz68BUmSQsLV4ikhq6Xepvb7QdRLNOuTyJimCtI7urmcSKCssWuKqdp6v1LGoqMNmp8fMP/C431S4DEAmF//FVb+CB279qB998dGSS4AqJOuT86LkigTNHLjdN22ujdOpj3a5MSkk4Yoymlq3QaS9RKB6umoWDAia+5fFPkIl8XM3kXbf23PVCUYmTzcMr9hPdsMl0avhx2IvVG0AShWSQhzo2MWWf2cV+pteKZVmcPXuWu+++e+DrFy5cwDTNI+E+hVTA7Zh1Am5yuIArF2aYW7qArk1uKqj8cIljE6NV4c7ZJbz2PL4QaNrGoEwpE3y5xOTxg2vzpGvrrSrvvrk3qa3EpK0ggzbZLZIEpJTMzz2FmtkYT6WZWSBLtValmM/hrrEwrMS8XR0DF/kOdzpNfvlv/h8q3V4D6Y5h82tf+wOcO/HMHX3n7dDsLKIUR6uHJ/XeNaNZedpudcwFXAIjuuCEEHAI+2aOuiSSSYw6gtu7l7wSYprZPXWTT1ROUq1dxhXKBktc6DTIIJkoj2G/zZSUIbz4xS/mgQce4OUvf/m6RIVz586xsLDAG9/4xgMc3e6SCrgdsl7ADa86LoTg2ORpFhuzRAEYeqkfF+MHTVQ1ZmbiBKo6+qmZzM/QcGs4bgepZ9F1mySJiL0GphCok8cOxPq2QjaTpdV10IzVTN0VEbdCHIdYW9SlAmjW51AzW8dT6ZkJHLeGpUqiZLXp9opoW0FKyd1Pfppf/PT9mHEvImg2P8Uvv/rfcHmE+nC7xdd/8SN83ec/RHJVb8MbGwuoSUze7/Inf/Ub/e1JHKEKjXfd8hX8n9MvoOs2sTPjmdygKGrPbT0Ckedg2sMXQOOGoSgbQn9WrG5riZw6uanBixOZJNRrc/hhhFQMFEUjbjQwVEE+X+yHBuw2E5WTFAKPllNjuZMWmoCJ3ERqeUvZNuPgQoWee/Tuu+/mwQcfBHr9Tz3Pw7IsfviHf/hIZJ+ukAq4HVKbWxVwE1OjPWiFUJgunyRJYpqdpeU4LpiqTKBro9e2WUspU6EEdL02gddAUVRy+eX6Uz4H1vQeIJMrEYbzdIP1Im6FOApQ4zal6a1LdPhBhGoPF6JhLJgqTVFvzROYmWXr3CqR7/KvP/t3vOWf/6G/7fMzt/Cfv+7NtK39FRFW6FPxOsirrC1qEqOwGrO3gkwSBAp25KMZNp4/vgIOwFTVgW7Gq1EjD9M6fKUo8pkCNc9B26Jor0wSbFUduPBIkpj5uYtomWl0ffV1bTn7vN5qUYhDsnvUOV03LCaM1NKWsguMiYBb4RWveMVBD2HPSQXcDqnON/p/T04VtvVeRVEpF3Y35se28tgMFpL1AxRxxfIMeqdBx60ShhKhGiRJgK4KbNOkMEJ9tSjpxayt5Zu//AlgvUXPyJTpOg2mJ0/TdVu0OzVWcnKNJOJt5/6O16xpi/WhZ9zF77zsu4g2iQfaSzzdpJkpESQhYo3VNe9318XsrZCEIaqqr3E9j3eWYDFbYslpoGZKm+4TeQ4lezy7FgzDzhTIBvO4vos6oCixTGLoLFGZvnHg+6uLV9C2sCrrVoFmu4ppZg9lmZWUlJS9IxVwOyT0I+qLLcpTBaZmtifg9pOVpvcHKeIyuRKZXAkpJXHko6rG9uJ8Brjjrs4m7e+68nqmgJ3pnZec7/DTH/yfnJ0939/vj190D/e94BtGjtXabf72mS/nb5/5cubrlwjKE/2J/E/+6jf6ZUS+61t/EoDQaaF7AmPZohj5LvlN6o/tJTJJaNbnSZbPh6ooFMozA0WIYWUpJSG1dhUtV9mwT+g2Kej6yBYmmSR0WktImWBY2T1zL26HUmkGvVOn49Z6nRjMLHHko0R+rxPDJkV8w9AnkuqGeM6rMTITtJrVXSlynZKyV4jxyNG5rkgF3C6wdLlOeapAeSKHrquE4cZab+PAiS58OaoyuzSHriqAQNEVjEKGXHn/OjQIITbtyrAVg7Te2iSIFSLfIZdfL2yONxf4Dw+8nZPNBQACVeO3XvE9fPTmzZsh7yfTxRNcrl5AblJKJOq2UZwAI7MqdJSoi53Z384ajdocXS9Et8r9uoJhEjN35WlyWZt8aaMbNJMpYRgZWq0l/CQhQaJIga4IyrnySO204iikUZsnCBN0u4wQCm7LRWk0yGazZAsbBaDbaeA6HaJ4RWgKLMsgV5za9Xps2VyZbK7c74Wq2VkMa+uSIZ12bV0m9VYEWxQITzmahIGH322jajpWpjgWNQS3ZMxcqNcDqYDbBRYu17j1Bb0aa1MzRa5cqh3wiDYSxxFzrTky9gSKsea0JxBVQxYbTzB55qaxLhBr6tqGeKqrkyEAFOlj2atC4tmzj/OzH/w9Cr4DQMPK8yt3/1+cn75pL4e7LYSicLJyhmpjFpekl8EpezFSweI8ujAx14i3yGtTyu6v9alevUKYWBhXWb2EomJkJnB9F1mfp1DeGBagaQaVyrVZkKIoYHH+EkZmBmONl1s3MkAGp+sSxfMUlz9XSsnSwkUSmUUzyuhr3uOFEc6Vp5icPommX1u86VaomkFmZGsiG2MCNuMQZuimXBtOp4HTaRPHCrqZJ/ZCmo2nMQyV8sTxsXxGC7nzJIZxSYI4TKQCbhdYXCPYpo4VxlLAzbfnN83gVFUdRU5Qu3SRidM37v/gRiRfqLBUq6Lbm5fOiIIO+eyqReerH/8kP/zRP0Ff7oBxoXSc//TqH2JhDIvgCqEwWeqVj/mbm15M0lyka5fIrRGjMomJnBqlXJ7MHrf2WksYePg+6PbmcViakcFxq+SLya5OMvXqPEZm81hR1cjQ9dtY3Tamnae2dAXUMtqAosCKqqHY01QXLjNz8mAF/LYSww+oi0rK/tJuLOJ6CZpR6Wt7TVFBt0ikZP7KU0yfOHPoCl6n7A2pgNsF5i8u9f8+dqLMozx1cIMZgOu1SfQ86hYmeCEEeDpB18EY04By3bAol4rUaovomYkNrsbArZPPmGRzFYRM+M5H3su/ePRv+q9/+uSz+fWv+X5cw7760GPHh1/wDQB43TaK2yCREgGYmkZh5vS+u1PazepI7j7dLtOsz1OaOD5031GIQp8oURhmK9PNPJ12HVU3CYIEw956ghN6iU6rSq5wcEI+lyuxVG+iW8NjZw01FXBHncBzcNwY3RpsWRdCoNnT1BavMDkzPOlrf5G75EJNr/PtkAq4XWDuYrX/9/GT41dYtRM46NbwicrQc7iN+tgKOADLynH8eIZmfYEgjEhkL/9A1xSmJibQDQsjCvjRB/+Qlz356f773vesV/A/v+L1JIds5WrZ+bEI1I8TOVLRWiEU4l2M1+q0axhWaaR9gyih06yiW8PvQVUz6Hbr5A4w70g3bFSWhu4XdOtMVcbPYpyyu7TbDfQh17oQgiDqxYSq2v5nzW9F6gLdf1IBtwvMXljs/31sDAVcgtiwrvnZz/85RhLxSOVW3ntytfVWEo3/XSiEQqkyuNhuyW3yc393L7ctPgVALATveMl38N5nv+rAMk2PAtu5Knazo85KjcTREMRxgtBGe0MyBnFllcoMi4tX0LODEx4iv00+Y6EbR6f4aMpgwiBGH8E5oFtlOq0lipXdsXKnHF5SAbcLtKodnFaXbMHmxKnxE3CDMJK4Hxe2Ft+LDmA0u8OZ2mV+/gO/y7TTi0Hs6ia//qrv45HTzzvgkR1+tuPBGyVcKwp92o0qURCTJL2yMnHkY1hZVE3HMDXy5RkURRDLZKROIkKAUMTIYnPjsmb/0XSD6emTNOoL+GGMahVRhELot9GUmGK2iJ0d3/JEKbuHHPF6FEKQyIO/djdw8Ouh645UwO0Ss08t8oyzp5mYKmCaGr4/PkJIFxBKuS5u6pHKMwDWWd+kTJCaONBacdfKi57+PG/90P8iE3oALGVL/PLdP8RTE6cOeGRHg2y+SLPpbOhqcTVht8nE1NalTZxmjXazg2lV6HuBzF72qOcsgVSIhM38009RrFRwWw0Me3hmp6aCrul44eZN2tfvPx6ToKrpTEydREpJ12mQJAHliYm0cO91xqgOAiklyhj6K8dwSEee8ctHPqRcfmIeAEURnLhhb9reXCvl7CSBtz4z9r0nX7JOvAF47gK36JOc9Hoirl519nOY18xrv/Bhfv6Bt/fF2+OTp/l33/TTqXjbRSw7j5Bd5Bb+0SSJ0fVkyz6aXbdFp+1jWhvvESEEdm4KKROiyMe0pmjVW2gi6pVV2QKvUyVwHHLFSaKgueW+AIHXJFccL2u5EIJMrkyuMJWKt+sQQx9tOg69GvnS1jUGDwQpd/YvZdukAm6XuPSl+f7fp87sb3HVYSiKSkEziMLNBVngt5GJwlzjClJKTnq95eA4izglifmBT/wFb/7E/0ZdfgB8/MYX8u9f9xPUx7g/6GFl6tgNJH6VKOhueC3yHUTUoDJ5cstjOI0mhrF1UoZplwiDDgCGWUYRGp2lL5MMcPkD+G4DVWqY9nHa9XkKhTyR1x64L0Ac+VgmIxUQTknZL3KFMqG39eJDSomuibSMSAqQulB3jae/NNf/+4Ybx0vAAZSyFYRbY6l1CTszhbrcwSAKPQK/haroZDOTJDJmsTnLdOnEuvZbwFi5Ve2gy7/70Du489Ln+9v+z9lX88d3fhNyWwW2UkZFCIXp42foOk0cp06c9PILVAVKxSKmvbVVIAp9okigjvDUEUJBLse+ua0OmcxxglaNRPRqzAmh9PqMJhJdz2EstxTzuyFTlWNAnU67itBy/WtdJjGh3yBjmxSvsahwSspeYZgZsrkujtNBN3MbXpcyIeouMnPyxv0f3AikLtT9JxVwu8TFx2b7f5+5eWM7obEgScja0/h+B7/bBCFQFY2svSo4FaESSIUoDtFUvW+JO+g+qmuZ7NT4Dx94OzfWrwAQCYW3v+y7+OBtG7sypOw+draInd2+hdN3W0OtbytoeoYo6KKbWRIp0Y3McueFnhVCymSgFSJebpuVzZfJ5ss47Rq+3wQEmqIwceKG8W9JlHLdki9MoKktOp0aYQiqbpPEIYoMMUyNiZM3je/1mwq4fScVcLtEY7FFs9qhOJHjxlvGMD4B6MYhipHHHlJrSDdLNJ0aE4XV6vfjYo27dfEpfu6B/0G52wKgbWT41a/7QT53/LYDGU/K6AhVJZEJ6gj9o6SMURR95X/WH0cIhBjNhZTNV9jnjmMpKTvCzhaws4VeGz3PQdNyaGkZmZQBpL6mXeTCFy8DUChmqExuNIEfNMmAOPBvvPIQr3/6o3zjlYf624QQG3qOQk/EHWRs3Euf/DS/cv//3RdvVwpT/NQ3vTUVb4cEO1smClsj7RuFXTS9N2kpmiAKN8bdDSINDUo5KiiKipUpHBrxttIP9Vr/pWyf1AK3izz5hcuc/arbAbj51mPUlr605f5xEuH7HVTVwDT2IaB6G5b3rXbdd2uclHz7ub/lex7+6/6mzx17Br/6tT9I2xo/oZwyGCEEhjmK9W19W55sobicOLF1lVOZxJj27jeoP2jiKKRdWyDsRsgEBBLVVMmWy5h2ev2njAES2Glh7HGsbTfmpAJuF3nis0/3/37G7cd4+BODBZzrNum0m0SeRFdtksRBqgsYGYNy6fiexTjoYmOR0/eeuGvDflHkUxhSxmC/YuO0OOKHPvanfO3jn+xv++CtX8Hbv+oNRCPU+koZLwoT0yxduYxpbR4n6rbnyOR6YQi+t8jUiVN43TZO00E3Nr/OgnCJmZmbd33MB4nntGjOVzH1Sq8f6or+TaA918LLtSlOpRX5U1KuR1IBt4t86dzF/t/PuH3wQ7XZWsRvhehKAX1Ffyg9q4F0JfPek8wcu3GkyvPbpZgpsui00M2tK7vLqE0uv3U5iBX6Qm4PrHF5r8PPfPD3eO7cqhD+wzu/iXee/fq0LdYhRdMMJo+fpL44RxLrGOZqgJrfbRJFXezcFFHkIYTH5PETqJpONl9BJlU6jSqGVV53f4SBCzhMHj9aCQph0KU9X8cyBvdB1fUckdOloy2SK49p4lTK9cMYuUHPnTtHo9Fgfn4ez/M4c+YMr3jFKw56WLtOKuB2kcZii6XZOpPHy9zyzOOo6vrG3p7v0G36mOpgt4cQAiMps7R0mampG3Z9fIZuY6ttvMhD0wbHVYR+k3Jm+617dtuteqI5z3/4wNs50er1mfVVnf/2yu/l4ze9aEfHTTl4NN1g6sRpAt/FbTVIgMh3UTUV3bIRwqFYKWBmZta9L1ecIJsv06zPEwcxUoJQIF/OY2/SS/Qw065VMYytiw1rmk23VSe3w5rEnfoSQdvt90JWdIFVypHJj1dR8pTxZVzi2B588EHOnj3L2bNnAfA8j/e85z38zu/8Dm9605uwrMMRUzgKaRLDLvPYp58CwLL0DdmonVZ9U/G2ghCCqBuRDKk8f61U8tNklIjAqxInYX97FLpEXpWybZMxry1t7+okh2tNdHju7Hn+y1//el+81e0CP/e6H0/F2xHDMDOUpk5QmTrB9KlnMHXyJiZmTlKZPomZGXwNCkWhNHGcieOnmDxxioljp66ppMlhIO6O1o5PkRncTuPaPiMKWXzyCaI66LKIqZYw1RJ6UsRfiKhefHLL7hspKX3GoBPD+fPnuf322ymVSv1tlmVxzz334Ps+73nPe3blc8aFVMDtMo898mT/72c+b30rp2hAf9TXVh/h2xY/yWurj/S3mWqJ9rJ42QtK2QlOlU+QJUANG2hRk5KpcaJ84prF21p2IuS+9vwn+MX3/zb5wAXgqfIJ3nrPW3l86sYdjysl5TCxUtNuGJpmEnneNX1G/dLTWMok6oB4Uk0z0ZMStcsXB7wzJWX8mJubY2ZmZsN2y7I4e/YsFy9exLvGe2UcSV2ou8wXPvVE/+/nPv8097/z4f7/JwnrJPNrq4/wg1ceQJMJNT3H+ybuAJbLeOw0o2cECtm9dY9cnegAm7tWhUz47of/mm8/94H+todPPYf/+tXfR9fYOvswJeUosq14vmvwX7ntOmqc3XIWEEIBTyEMuujpfZiyBePgQj137hzz8/O8/vWv3/DairCbn5/nzJkz+z20PSEVcLvMlS8vUF9oUp4u8sznnULVFOKo5w5VrrJ3WkmIJhMUJMoaE7KUEkU5OsHYw4ScEQX8+D/8AS996p/629777Ffxv17y7SRpYa+U6xTVGM1B4gcNKgOsDkPf1+qgacPjXQ29gFOrUTo2WmJTynXKGAi4Qda3FVYsb0cpBi4VcHvA5z7xJV7+zXdgWTrPfM5JPv9or7yIZmqwGnaGp+jUtBwKknO50/3tftxkJr/7SQwHzSAhd7Md8XMP/C63LvXcNLEQ/P5XvJ73PftVBzXMlJSxwMyaRJ0IRdn6Ma2ZAm1I2Z9ByNFC7AD6yQ0pKePMIMvbCs1mE9ha5B02UgG3Bzz6sS/y8m/uuUNf8OKb+wIuly/RWGz0ExneN3FH3226gpQSzVYH9nk8KqwIudOtK/zEZ97BTLcBgKOZvO3Fb+QfZ54JB9Dp4aAIfYeo00ZZzluRCih2BuMasoFTjhI5HP8yOXMKscnzwOkuYuTL15QwFAQh+oh1j30vPJDuKymHB7FLiQiO43DvvfcO3e+OO+7gzjvvHPm4586d62emHhVSAbcHPPrRx0jiBEVVeNFLbuZPfv8fALCsHHbBxW930ZWN8SRSSnxR49jkTfs95H3nhQtf4Ece/RPs2Adg0Srx63d8P5fyxzm5hzGmUkrabo1uFPZM/gJMVaWYnTyQGmKLzSuAScaorIuPDJ0uSuMSx8onR6oJGCcRjc4SfpwgpUARoCuCQqaEoR8dl8H1hrRPstiZoysEemaif42Gfgst9DhtVTBiC+LtH3sxZmDLvKtJkpiJRKHkHZ2wjpRdRjLaxTTsGPSe0Z1OZ+juQRCMfOgHH3yQYrHIy1/+8msd3ViSCrg9wGl2+eIjT/Lsu27hxKkKp05PcOliFYBicRpHb+K0m8Q+GEqGKAmRio9u6xyr3LQnRXzHBin5+gsf5Xu++Ncoy3fsl4o38F9f9K9pDikwvFO6vkPVqaMbZRR9tZyLn0RcqV2hkitim/vXmmixNUusFdEHuMh0zUaqFvONKxwrnxrw7lUcr0Xd7WCYFXRtdZKVwEKnTsHQKWQHF4NNGW+EEEznj5MkMQ23SkKvlVbFyGPusEZbya4w261j2lsXkYu8OsX8sR19VsrRZ7cscEIIstnhdUQNYzTz8fz8POfOneONb3zjkYp/g1TA7RkPf/BzPPuuWwC462W3culPq/3Xspki2UyRKA6Xe6FaWObRb4ejJDHf+8V38+qLH+9v++Sxs7z9eW8g3OO2WEHYpea2B7ZwUhQNw56k5tTQnBpC7T0YNCEo5adQ9kBQR1GAFwtMffNbUAhBrGZx/fam5V38wKXR7WJagwWaaZbpBB3UbpOsfTTrpV0PKIpKJbe7xYp1zSCnCLpbFPaOgg4l3T5SHS5SxptsNsub3/zmXTvee97zHl7/+tevqw13VDjCpp6D5aG/+2z/76985TMH7qOpOtlMGWsfrT77ieM1WajPcqU2S3PhKf7tP/7uOvH2Vzd/Lb/9/DfuuXgDaLpNDHNzS4PrLhElCcKYQmglhFYiUvJcqc1Sb+9+Tb6GW8O0hpfP1/QMHW/z2KOm28IwS1seQzdytD13u0NMuQ6oZKfIJgGhu0SSrGY1xHFvW0EI8nZpT8cgZUK9Nc9c7QpXqrPMVq+wWJ/F89OYu0OF3OG/PeC+++7j7rvvPlKJC2tJLXB7RG2uyRcf+TLPvONmTp2e4KZnTPPklxYOelj7gpSS+folpJpHM8qc9Or8yhf/Nze5ve8fCoXfe/Z38NEb7tqn8ST4UYK5iU50nUVMs4CqrjfJC6FgWhN4kUe9vUA5v3sWkO2U+Yu3cE0EccIo+YexMPADF9PIjP7BKdcFpcwERSlpuTVC2Qumy6g6+cLeewX8wGWptYRhTKLp6+0JtU4H02szUUzdt4eCMevY8cADD3DXXXcdmZpvg0gF3B7y0fd8mmfecTMAr7z7uTz5pb8/4BFtD8/v4HQ7JIlECIFt2SO54RYbV1CMCYRQeFbraX7pC39MOeytpluazS886zv5JzvHfjmN/cBF0QYLlzjyUVRtg3hbi6ZZOJ5LMYl3Lzt4OzVaN9kexyFyRPeuYeTwgtZYC7i2W8cL/OU6iJC3C5jGznrqXs/4gYvjtZESFAHF3OSm168QguI+x0kmScxis4plDV4YGUaOMPJpdJYo5Sb3dWwph5uHHnqI2267bYN4azQaNJvNIyPqUhfqHvKJ9z+K3+1lynzVVz8LwzwcejmMfOaWnqbe8oACilJEiAJtJ2J26WmCsLvpe4PQI0RDCIVXLX6W3zj3v/ri7Wl7kh95wZv5XOkmpJLD9Vr79I0Em9noPb+JNcQFCWCYZeqd3XOlakIgR+x363otnG5zw/Y4Gb1n7qifdRB0/Q5Xli7h+AKhFFHUEogStZbLXO0SURwOPUbKKkHoMVe9TLXdIaGAFAUimedKdY7FxpWx6W3aaC9hmlsLM00zcTxvbMacMhhBrxPDjv7t0ljOnz/PzMzMQJG2sLBAsXh0YoFTAbeHdDsen/zbcwDk8hYv3SQWbpyI4pDF2gK6NoGhr7fW6JqNoU2wVK8ShINrfbTcBoZe4Lsufoif/+JfYCxXC/1M8Sb+7fN/kMt274Gt6zadfYpxMY0MSTxYdI4anC2EIN5FDVTOTRF49aH7+V4T25ih5QS0OtV1rzU7DWQ8Wv2IwG+QH8NMVC9wqLfbGMYEmrreGawbOTRtgoXalZGF6vVOGPosNpbQ9AqGvpr4IoTAMitIUWC+dmnbx+24DRZqV5hb6v1brM/iBzu7f/0oHun+0/USbbc6dL+UA2YMmtnPz8/z6KOP0mw2OXfuXP/fQw89xEMPPcQ//uM/HqlkhsNhEjrE/N2ff5xXfkuv2OA3fPML+fAHPnfAI9qaRmsR09h6ojeNCq1OjcnyCQDCKKDp1AjDGBn4/MzT9/Oa+j/3979slrmhu8QPP/FefvWZ/6K/PdmnOVkIganuxlpl96wAQijkTRM3dNH0zd27ceBhW3lQTTrdNqbRi2OTMiGIYpASKZOhpWcMRe5JNu1OaXaaGMbWyRy6MUWjvUilcDQDkXeTWnsJ09z8/lWECmqJRnuRUn5jRvbVxHHEQu0yilJAU0usvY1qzQ6a1mRq+TmwXUaNA1UUjWjEhUrK9c19992H7/tcvHhx4OtHyfoGqYDbc5747NM8/ugFbn3+Gc7cPM3zXnSGz376wkEPayBSSoIwxhyhvE4QSpIkxvNd6u0WpllhAof/+ORfcra9+v3+2+QLeEPjPOWoy9nGl9cdYz8LE5SyZRZaVYyry21sQ5Mpu1xKoZSdAKdKu7uIblV6kyu989B1q4hYkltT9sQw8rScBlNGBs93UNUshmHQbs+Ryx9HCMHrrjwEwP0nVhNEOu05Jkaoq7TfBKFHnKgMiyoUQuAH2+j7dJ0SRQFRrKAOeaorioYXjuaWXqxdQdcGF7k29BxxElJtzl1bosE4dD9P2TXEGBjJ3/KWtxz0EPaV8VuSH0Hu/4N/6P/9Lf/yJQc4kq2JkxDJxlTN181/itfNf2rdNkMv0mjP0eh0MM0Kp7qL/PZnf68v3jxF5xdv+1e868bX8mj+NDU9y6ezJ6jXVkWcquyfhNM1k6l8mdBbJIpW3b+6kcX320PfH/gNipnhZT+2Syk7QUbV8Ns1uu1Fuu1FvPYiGa1IdkBtt3DZjytJQAiEUMjZUzideTyvjp0E2Ekv7tL3WzideUw1izZsVj8AXK+NYYxWvHk33ddHlbZbxxwhnhMgioeLJ8dtIJT8lm5OVdHx/Zgk2b6FTFdGm37C0CFzREstHSnGwIV6vTF+T/UjyKf+7nNceXKBEzdN8+yzN/DsszfwhXNPH/SwNiBlghhgF3th4wn05fIC98+8uL+97bbJZs7wguaX+YXH/ox83BNGVT3HLzzzjZzPnUQH3nbm1WTtniUpH3ap154iky0yU9jfmCxDtzlROYXTbeAGjV7fWQF+7CBldlM3ZJLE6EqCfg0Nw0dBIAaKtUGsPOcsM0+9PYummihCJW9PE8UBzbDXmsztLGJoWSxrCt+vYW1RA+/gGP2hLfeqUNQRQggx+k86wn6u56KppaH7GXrPJVvZphUuY9p0fB9N2/q+EtLD2sItvJ+0nRqe5yORCAS2bZLL7KwjxiASmdBsLyITCUKQyxTHuyXebtRyS2/xbZMKuH1AJpK/+h8f5Id/7Q0A/Kt//TJ+4cf/7IBHtRFNNZFsdK2Eirrh5grCNkKx+Yb5R/jRJ/8abTnI/InMMf7DM9/Iorkaa2DqefygjWnk0XWbrKwgEgddG7GT9i6TtUtk17SilVKy0LhMJGx0fb2rMQw6aPhMla4tzmckRO8aGSWge2UXRSjo2nrBqakGf3fDVwOw9lvomrJ75U92EcvIUm930I3h1pURjTVjS5LENFqLhEGE7PXDQjdU8rnyholZSkmzvYDfDfqWMqGAaeoUcpVNFxK6buK5m3dVWMsov2ecjLafEIJkO4UNl8llSjjeJZJERRnQTg4gCBqUc6VtH3u38QOXan0JTeRR1VWrsdsJaLWfZqI8sSsleqSU1Oqz+H6MqZX6z4RqtY6qxZRL0wf23EwZP1IBt098/H2f4Z7vfxWnbzvOrc88wVe+8nY+8Q+PHfSw1iGEwNA3TvS/duvrN2zT1YQ3X/g437nwSH/bl+1pCqHLb33uXs4VbuJXb309r5v/FHYc0IwDHlgWF6aRx/OGuy33CyEEM+VTeH6Htlfvu+sUARPZAqaxtzWoirkJ5qoLmEOC+QGMNVHkxWyRaqu+ZRJAEDSYKBxs4K5ctiYkcc/tm7HzWGYWy8winMZIx7C0w/uo6nod6vUqplZBV8Rq4EoM1aUqmZxJMd+7xqRMmF+8iCbK6GqWdbdjDEvVRUqlEra1UfRmrSKtzmVguIAz9FEU8XZEmSCKQ5qtJcIg6r1VgG5oFPITm4qOmcoplhqzeL7ENMp9wRKGLlJ2KedK2ObBxm8GoUe1XsfUNloBVdVApUK1VmNqQt2xlX5+8SK6KGNddX5MrZdRvLg0y/TkcbSxE3FyF3qhLl80KSNzeJ+KhwyZSP7sN+7np+99EwDf9f2v5NP/+GV8b7xqXBVzJaqNOoZeIoq8XnyYBOgV6hGKShbJL1/+AF+59MX++/7y2FeyZOT5vot/h4LkbOspXjf/Kf6vp96HJhMiFL61/s/cP/Ni7p95MYkcQ4uQmTuQtmaqoqFrEim3tsL5QZOpNSnwppGhnE+otpYw9PI6K1uSxARhnYl8+ZosA1Ec0movsVK9w7IsstcQA1itz+J7Iaa6ak1odNugVimXJsnbeVpuG10f3OsVIPBrTJeHZ0yOI0HYpdFoYumb9KrVi3iOg6o2yGVKvaQBUdnUnW9qpZ4YPJYZmFWctW0cv4uu2QPe3cMP6kwVh59LdcTM7SSJiJMuC/OzmFoJY21sawSLC/MUijly2cGfOVk63rNQdlavt2ImQ8bafdfktdBq1zC10pb7mHqZRrvKVOXaLfWN1gIaxS0zyi1tgnpjkanJk9f8OXtGGse27xxyx8Th4tGPPsZn/qFXXmNiKs/rv/ulBzyijRi6TSlfoNF6ksh3yRkVcmaFnDlBzpjgVAy//dif9sVbjMJv3/SN/O5Nr8XVLKpGjqqR41zhRuw4QEsShJRoMqESdLDjYPmT0pXWWiZLxwnDxU0L7oZhh3zGwNDXT8y2mePk5CkMtUscNfr/DLXLyclTAy01WyGlZLF6mYX5OaSfQURZRJTFaUbMzV3YVvHlxeolZGBhaeV1wtTQshiiTLVaRVM18raO51c3FGuNk5DAX2SiWB5Di8NotNp1TG1rC6iuZXE6baIoIA7EwAlcygSnu4TbrRKHkrmFLw84EhSyFSwtIgg3WrillPh+lXIuv+E6GnysAkHUGbqfH9aRoY6llwcuQCy9RKflbXntKIpKpTDDRLH3L2NtLuj3k0Qm+MFoGTSBn+yoYLbv+SiKRhKHON1F3G4Vp7uE010i7j83IVyuAJCSklrg9pn/339+N895yTMwLJ3XfPOL+MePnOfxL84e9LDWEUU+OWMKXV3/kL/ZneOXvvSnTC1PDo5q8IvP+A4+U3kWQN+6tsLr5j9FpChoSUKkKNSMHN3lllWjru6vFxShcHzyBmqtBfwgRqKjKBpx7GFoUMzlN53UhBAj1fQahYWlp1GTAtaG+DoTMGnW2lCBjLV19mjHqSNDC3WL7FdLK9Fo1JiZPkXGKtBoLxJESc/YK8A2DQql07vxtQ6MwI+wNunBuxYpTZZqlzH1jefR7VaRMiFjVlZdjLHP7NwFCoUS2cx6gVguTGP7Dm23QZgkSClQ6LlNZyozaOoIAwJMI4umNomTEFUZ/J4wckhij4y+teXJ0HJ02s2h18240fVa6OpoiyBVsfAD95qt+FEkCeIlFBQyRmWdGPaCJl7QJGtPYWpFWp0lSuNWFzHNFN93UgG3zyw8XeWdb/8Ab/iJ16GoCj/01tfws2/5I7zu+LhSHcfBUNe7O17SeIyfffKd2ElvnHNGkV+4/Vt43CqhbNIj9P6ZF/Oa2U8wEXWpG3l+6OwP9V8z9dEmkesJIZR+Pa0w8ojiEFMv7lsCQsepI6IMyhbi2tTytBoNMse2noi7roumDp+sZaziB73ixNvNYhx3pJTIZDRLs6FmcPwqxlUhVE53EUsvbBBQ+rKgbjd6FrKrRdxKjOFOmSqfoNacx/M7GPqqGzxJIsKoSda2SILh1jyAKOz171VHFJDjwXbissQ1Z0tLKfH8NnlrBm1AX2bLKBInEU53kaw9NbBawEGz8xi4lO2SmkEOgPv/4EEe/6devbRjJ8p8/1u+bsM+08c2ul0y2b0pY7EWx62jyDUxU1LyrfOf4G1P/HlfvH0he4offeabeFwrMVM5hSI6+EF1nfvA81s47gL3z7yYd5542TrLnBfUKObGsazF+KBrFraZvybxJqWk1V6kXpulXp/F84a7wQBc1xk4eWz8AIPukCSUMBrNxWNoOdzufvXE3V+EEMgRi9UOin8MQxdNMTe1fkHv92u1Gpu+7jh16vVZ6vW5ka+Dq6kUZzg2eQxFaSNlEylbmEbA8alTGIaFKkYTcIaWG3rdjBu2WSCKR/vd4qSLZVybaI6TCEPNbHn/qYqGKnS6fnM8S4qkdeD2nVTAHQBJnPDff+bPiJYnuZd9zbP597/y7f3Xv/9Hvo5O2+P7f+TruPGWaQCe+4LT/MCP3r3nYwvCoN+TUpUxP3Lxfv6vSx/oXygfLj+Hn77te2joOZK4N+lMlo5xYvIEuuogaFJrfBGEQjYzzfuPfyX/58RX9QWc7zco5/LoQ2o/pVwbzeYCc5cvEDYFeDZ0bZqLTeZnLwydPONoNB+IoWbwvF3sY3uEn92aOpqlxI+alErHCKPV3zWIHEx9uDtOlVnaTm3dtna7ytzlC7i1ALo2dC2aiy3mZi9ck2BWFJVK8RiT5eNMlo9RyPW6MyhCJZGjifUkiVEOWT0YRVHRR8rYBcNUhra024xWa4mMOTxpwzIKBFET2z5cruiUvSF1oR4QC09Xqc42mLmhl5327Oef5nkvPNOrSTbbxHV8nvzSPK7j89wXnOZ5LzrDn73jI3s+LkFvPs1GHj/35fu4o70aLP1I/mZ+9aZvRw4IVBZCoZTvic2J4jFanSotd54kEYBAUQSWoTFVLo0UQJ2yfWq1WWJHwbqq+KqhZkFCc7EOU/R6qw5iG0Lq6oSDq1FG7LKRJBGGdphcatvDtHSSYHivWt0Q5DIlOp2nWaniJ+Rov6GmGgRep1/8r9laxG9GmGqRtX3KDDUDElpLLZiQZDIbrfzbxdBtpFhifeXBwUSJg23dsOPP3G+KhTLVWg1ji0xUP6wzOXHtxYbjWKKO2KpvLK1vkFrRDoBUwB0gf/ab93Pzc2/gm77/q1FVhR/7+Xv4099/sP96p+2xMNdk+liRJ87PsTDX3PJ4fuDSaTVIkgQhwLBM8rnBfQw3w7bzmPWL/OpT7+KMtwRAIFQ+VHkuX8yeWife1C1WpoXcBIXc/ldPj5MIz2+jKBqWkdvWd7+aIOzSbtZIli1TqqFSKE6NHAS+n/iBQ+gkGOrm4thUCzRrNewTgwXcqN7aOImGxjAapoYMhhcnDpM2k7nDnaiwFaXCDHMLT2Eom9+HXlRnaqK3+KmUJ6kuLWHq2wwxWD52FAW4DRdri8xXU83Rqtd2RcAJITAtHRkOP9eGqW66j+PU8bouUkoURSFfmNizzifbxdBtSqUS9UYVTVkfjxgnIVHSZKIyuW8L07HMyJbsPIkh1X/bJhVwB4AXuQSxz5lnH+PJz1/iqX++zI3POkkmY/L6734pXzh3qR/vlsma3PSMGd7zfz616fGkTFhceJrEVzDVXN/dGXoxc42nKFTKZLOlkcb2XGeBH//Sn1COugA0VZtffMa/4vNXTbJJEmFnxmcl6HZbdJoNYi9BVzIksktDWcTImJQrxwfWzNoMKSVLi08Td1n3e8pQsti+hFWwKJeP780XuUbarTrGCNlySmzidJtk7Y2Tt2EZJN7wiTiSbaZyZ7bcp5CfZGFhFkvbXIhEcYCdPdrWWCEEM9NnWKpeIQ4Fpr76u4eRA0rA5MRkX6wYus3E5CSNZpUo8Uf6DCklYll8t1pLW4q3FTSZo9WuUsjvfJFVLk4zt/A0lrr5sbyoyvT0xtplXa9Ns7qEGlloaxYf1c4cigVT0zdcs1tyN7HNLPZMllZnCc93+5nSlm1SGHIvjIKiMrIAUrWD/z1SxoNUwO0iURzS8hqEkezf4LquUrJ7KeGNbh0/igELXcnzO7/8V4DHh//mE/zFZ34Lw9QplrM86+xJnvuC0zz00cd5w/e9nD97x0e48ZZppo8XWZht8tQTC+s+d37uKYy4hLgq3kZRVCxKdGodEGJDptrVvPTKZ3jz5/4CI4kAeNqc4D/c+p3MXhWbIaUkosFE7sad/mS7Qrtdxa25GGoOffmKVtHQsZCuZL77JDMnbhw5IWBp8SKKn0W7KhtTCIGlFolaPg0xT6k0Pmn8UZAwyrfTVQvfdQYKuFJhmlnnAtaAivP9z4l9MtnhhYE1VWeiMkG1toSpbqwPFkQOuiUpFcZLCO8FilCYnjxFGAW0O1Vk0ruWivn8wExRQ7eZnjxFXZ8j6gaoytYWFz9qcCx/CoDIjxnFPqwqOqHvwC6UW1MUlempk1RrsySR1u8aABCEHYQWMT11YoPl2vM7tBZrmGK9qxfAVPPIQDI/+yQzx2/ekSV9NynkJinsQa3vQn6Spfn5dQJ/EEHUoVIezwSwNAt1/0kF3C7R9pq0XR9L5DFWHjYSpJ8w68+RiBBLm8JYzjJ62WvOctvZ07zj196L44Q8/cQslmVy8uYZSuUcb/qRu3nW827g/e/qtar65n95F7/1/30vd73s1nUCrtVeRI/yiDUxR6+tfhorCfAUg/dNvAhDydJpNDYXcFLy7U88wHd86QP9TedKN/Fzp+7G0TLrJgQ/bKIakpmJM2PxUA3CLk6tg6luXiPNlCWWli4zPT3cVed5HZKuukG8rUVTTbqtJoVisi3L3p4i5cjVDjZ7zAqhMDl1nKWlWQxR2iB4/bCDmVUoFkYr92GaWY7NWDTaCwRe1BuiBNVQKJYLm8fiHVF0zaBSGk2wBmEXGSU4/gIF+9Sm+8VJhJnR++dqO1PosDjG7aCpOjNTpwlCj47T6Pf2rZQ2j3lt1qoYYvNrQAiBHhdotRcpFqZ3bazjiK4ZGLZCHGxecy+RMaoZj28McSrg9p1UwO0CbuDguhG2sjEzSAgFmzJO1ESqqxf47MUqnVbPTakqOnbW5se/9T/xn//w33Hzc28gX7T52tc8j88/epFO21v9rM56t4rndNGV9Q/B19Y+TTlyqGtZ3jfxot44Ih232yRzleVFj0N+8HP38bLZT/e3/f2pl/COZ38bOUVFdJt43U4vNkUVTJbHq5lyu1nbVLytIIQg6SZEUTA0fqTTbvSCvYdgigLN5gLl0njULlNUMZILZuU8boahWxw/diOt9iK+7/QyjemJrkpp+225etmLx+Fg27EeGpIkZmnhaWRXYCo5ckmFdvsKdmZj7GUQu6hGxER5VeApqoBotM8Se5ARaugWlRHuCT9wIVA2WN6uRhEqXqdF8TpIupyonKRau4zveeusmNCzvKlmzGRlczGfcv2RCrhdoOM5GAPE21qyWhEnbPVLdDzx+cu87DVn+5a4n/vue1laavEr33cv/+53v49n3XEzuqHxk7/wzdz3Rx/DaXtksibTx4vwT6vHjcMEXelZ3V5b64mwU34VTSZkY4/fefz3AXhf5UW8r/CcdQIuH3T4yU//Abc3ngIgQfCnt7+O+298ZT8oOmsXB7rbxoXQCzEZviI11QLtdnVo7FoSxiO5IoUQxOH4tLMxbZOoM9wi6CctZgqbTwKO28R3OshEomkKuVJlfLPejhhSJsxffhIrLvUt6pqiU1SmcJ0GviKRSoJmGhi6QalQwLqqVZqdzeDVN7firODHHSqF/U8yWqHrtnrZ0SOQRNePZWeicpIg9Gi3a8TL31vRBJVyeXwtbyukFrh9JxVwO8SPPGSkjVZRL0l4Tfsc/6rxEAqS5PcELdXmffnnMZc/SyJ1Wq02v/Vjf8gbf+qbeNk9PevZ67/7q/jMp77Mc19wmr9//2fXH3P5nrGSgPJyDSlNJij0+o+ubLOSAJLVG+xEZ56feuR/MdPt1Y/yVJ3/fva7eHjmuTv7QfYZmbDht39t7dNYSYin6Lyv8qI1+x7dB0yhMM1s+0lssXktqUTGGBl1YCyg53doLiygBAa6uirYas051AxMzJweH3fxEaVRn+uJtwGhCRm91P87VBwmJwe3rsplK7SbT6KyeZyUlBLFSA5WEOxiyZqjhqFbTFS2bk02llxn52kcSAXcDvEiF0PZ6FZ6jfM5AN6fXRVEAgUrCajETk/AIVBJ+h0OdMUiiD1aNYe3/8yfcelLc/yLH/0GFEXhhS++mZtumSEMIj7zqSf7x1Q0ARI8xaCu9Va02dhDkwmRUPrbHKGgm72J+blL5/mxf/pDslHPNVszC/z6i76Pp4qHzzy/HU0hRqhNpugqjJD8J6VE1fanxdUoCCGYnDlOdX4OU2wUAVHsI02PqcrGOEDfd2nOLmJS2ODSspQcsiuZv/xljp28ZSziHo8qgeNjiuGlM2I3JorDTcvZTE6dYGnhMiYbk0eSJCZUm8xM37gbQ75mdNPGaTnrFguboaRZl4eDtBfqvpMKuF1gUBscWw7qbSrxhE4klFWBpWbpLrs7gqSLuWYR89e//yG+/LlL/PCvvYHiZJ5SJctP/dK38eDffZ4//p//QLvVpTJTpD0b876JF/Xj3X7n8d/vx8C95dY3AeDLJseyZb7m6U/yfV/4S9TltldPFk7y6y/6PurW+LpJt0K3NPDWb1trdVvBj1tM5oevanP5Ek13eBycnzSZKd24naHuOYZuM3PyDM3mPIEbksS9eoCKrpIp5shlB2fNNquLmFukIwohMMMCreY8xTGJ+TtqSCmJ/ZhRUkhNkcdxahQ3aWau6yYzJ25ccx30HiqqLrDyNpXcTQcuxLOZIh2tDnJrASelxMiMT8xtyuHA8zw+8pGPYJomr3jFKw56OHtGKuB2SNYosOjWsMT6GLiuWP8kfo3zOc56T5BoGX6v8krsJKSr6Lw/f7a/T5KECLPCK7/rTmzd5G/+4GN87pOP8zPf/n/zy3/2I0ye6LlFXvF1z+GFL76ZBz/4eSam8vzMD/821hYukzD2yJeyfNcX38M3PvUP/e0PTz+H3zn7nfiHuK1VvjhBzVnYMpFBSoliKyMVwLSsHG2rityien4U+9jF7Fi6FBWhUC4dh9Jo+wdhF+nJoSEAilDwO+7Ix025BkYUVUKIod6q7V4HB0G2UMCtddG3KD4d0GKqtNq9QUpJq7lA2O2ZyYWmkC9NjH982JFH7kIZkZ27YB988EGazSYzMzNcuHCBM2d2XqNvnEkF3A7RFA1NY4P5eK3rFAAp0ZBIGa8TbStEsY8iVF7yirNUKpPUqlX8qIup2dzwjGN84M8+Tqfh8L3//lswbYN80eZ133Ynly4s8dXf8DI+/LcfQ41sNNXkfZUXYSUBXUWnGzeYyOn8+y/9DXcufL7/efff+Ar+5PZvRA4QIZ7fwfcdDN0e+557hm6TreRw6w6GsjEoWkqJLxrMTN448jEnp0+vK+S77lhxC7NgjVUNuJ3QdZqYymiFrWJ/Y9JGEHbpNGsQS1AFuWIlnUyvASFEvxjvMILYpWgP75s57uTyFeJkAbfRwlLXP2eSJMYXLSaPnUBVetNUp1OjM7+EkeTQ1ria641ZlJzC5LHTY1H097plDGLg1lrbHnvssQMcyf6QCrhdoJgtUm01N1jh1vInxgSxIlEHxK1EsY8Xd8jpFT76/kfJlzJkCzZNr8N0bnUy/PBffgohBM9+yTP4qte9EIBTZyb5j//lDXz7d76UP/y9v+Uz//jPvGvqVgQCzVR5pmnws//0h9zYvgJALBTe8exv4+9v+IoN42jW5/GaHYSvoAuLTlKnZS1gFrKUKuNbcDWfn0DVdDqtBpEXowubJIlI1BAjozNTuWlb1jIhBFPTp/EDl3arThIl/VIaU8VTY9lK61rZ3jN3decoDqnNPY10JKZYFc712iwiA5Vjp8au5U/XbxP47tguTIysCe4IO5oJpjFaBue4UyxOk8n6tJpLRH6vVqCiCMyCRSW/WsDX6dRxZ1tYorih3qEpssiOZOHyk8ycuuUAvkVKysGQCrhdwFBNJvIFap0GIrEwlNW4Di92CKVPyTjGu7UCsUhQwga6YhElPrGMUIVGTt+4ok5iAz/y+NwnH+clX3+WTN7CaXf5f3/5L+m2u9zyvNPc9Jxe4sFzX3CG//L2H+Tz/3SRd9/3EJ/99AVubF7irQ+9nYrfAsDRLH7rBd/DZydv2/BZS3MXEE0FU8n23Wm6akJokixFLHpPMnXipj349XaHjF0gYxeIkwjf76Ao+sAq99vBNDKYk9ure3bY8H0HERkY2nCrmVhO2ojjiMWLT2JHhQGTaQa6sPj0U0yfvhlVPfhHTLM+j1dvo/gqumLhJDVa5gJmMUtpYnwWJvnSJHVnDnOL4rZB4pItHc541c3QNZOJiY1tttbSXqpiDSn6q7oGnU6V3AH0YL7ukayrcnDNx0jZFgf/dD0iGJrFsdIxuqGD63dIpKTjN8kq01h678GzUui3HdXQhIGpZlBE7xS8pn0OYJ171dSyuGELU7P4s9+8n+lTEzz0gc/yr37itbzjl/+Km559ipd90wt58dc9j8njvRi457zgNM95wWkWf+8PKf/Iz6MFvQj/9sQxHnjzf+LY8TNcHYbuddtE7dMoW/hwpEwQOYVMZvwsF4cBCXhem8j1kXHP3y40Bd02sQ6oI0GUhLhLJ3vlRfThblRpRmSzRZx2HdG9dWjnh1BzUdGRSQJCoOgqZjaPpuzfY6fTqiHc04jl0ilR3CVZTuCRSBI9pjJ1CjFqG4s9Jgx93HYbfUBtw0h6GFkLyzoa1rdR8QOXsHG6/6zcilgPyZcOv3v5UDIGLtTrjVTA7TK2nsXWew/YidwENbeGF3roZFGERpC4CCEwrgrcfaF3EV2uxBh9ZX/7yrTitj2e+ufLfOP3vYq//9+fBOCb3vQqfusn/pjH/+kipqXzTT/wNRw/Mwm/+ZtMvfWtqzfUS19K/l3v4tumpvbyq6ekpKSkXK/skoBzHId777136H533HEHd95556585mElFXB7iCIUJrOTSClxwzaxDMipGWruxmDwcED9/zAOCKMOpeWA5ed+xa0sXKqycKlGJr/qpu00XD75ycf52Lsf4q3iEZ73udVMU97wBnjHO8BKq+mnLHPvvdDpQC4Hb37z7h0Tdu94KSkp1yVSSjqdztD9giDYh9GMN6mA2weEEGSNVdejobX7zZ5X+PWp1wDwgq+6jde+7DayBZunvvwUH33fI0RJRKGY47kvvZU//833AT2LXKfZJZO3mL6hQvZjHj966a95rnOxf8yHXvptPHzDN8Lb/mrTsYVxgBuDpowQcC4lQrhktKPjwpFS0vRbG/rJriVKPGxdxVC3X24liH26Ya8l0lZESUDGUNBHOQ875PV/8KtknAZtu8AfPb56XSZJTJh4sFLweLn5vKHaSOmSM7IEkYefaChXuUHvfPgjADx81fHiOOjFUl5FGDuU7L1zHScyoeX76IqFHzuYSmaoyzdKXIrWaBm544SUCV7cRUowVbOftXlU6EZdImluGeKxQpR0KO5CgkcQ+/hhSCzFcsarJJIeqgIZPYM6oJvJuPFDv/qG/f3AXbLACSHIZoefQ8MYrySpg+Bo3emHhIJV4kpzgbwxveG1f/rYef7pY+cJY48o8rDUAi2vQaGY64u3Ff7XL76T537FrXzuT/6Wt138S04GvbZYoVC598Q38PHGzfDez2w5lljGzDsdbH14YHQQuxQNgTVCwPs4IKWk5TeJkxghIGfk0dX1N329W0MmhaGFTcOkwfQ1BEcvOVVUURpp35gGk5m9D8B+TdNFi0LagcPfv+vTI71HyhoVq0IiY+adNtaa1k4AZnMBgL+/tHo8N2hgKZmBE2+cRBi6R8Hcm4B8L3Rp+QJdtegESwOThK4miLsULYGlH44FShD7NLttwkhiKQVA4CdtNFWSszPY2tFJwJl1alj61vdGIiM02hTN0o4+q+O3cfwYQx0s5r24xmS2tOFZMm7su4DbpVaF2WyWN6eW/JFIBdwBoCkaipB0giWy+kRfPLx18f3oMuZT5gzvtm4ho5UAiOOexW0Q4d9/iF9++t3k4y4ALdXmN2/4Fs5nts7qWkEVKpoyYlN26WFphyNAuOZW8YMYQ+T7AmLJayOUEEUIEqmABF+GI4kIKY1+Xb7tkPTKo42E3OdWNIKepWpYiRUvbDK1XHZDESqasnGg7y8+f8M2KeNNrSaqohEnI15314CumUTdNrpqIUZqVNyzNHpxc1MB5wRtvMBHIhAC8lbumqyyu4EfedSdDqZSZG1Ht5V6as1OG5lJyBiHz6I4iKym4cVDiv5GNSqZyR19TpREtH0PS928MLqlVqi5VWbyR6MWZMrhJRVwB4SuGOiygBvUexuEQIk9FGKEVPribeW1QXxV4wv84Ozf9pMfLuhF/uPU3SyKDIUB7b02w9Z0upE/0NW1QpQEWOr4uw0AFtvziCSPdZUryVru1tAJa9hqBlXRiZLWSMc01AzdqLltAbet3MZ9ToRUhYofVbH1zZNbpEzQlBBtTTmQnG7TCtoY2uYuUC9soY/Q13OvUIWKOurCZAhe5NJwOqjSRlNWXcS1VgdFbTCZn9r3rhx1t4mpbL6YMtU8DbeOrWcPvG3WblAwC0ivgRv6G6y/YeyRJC2mrI29X7dLy29gKqWh+yFtvMjFOkJWzh2z3yvQlFTAHRSqAiIRZNcItXP2zQB8IPe8/jYpEzboJin5jsWP8W1Ln+xvesQ8xX+uvBZHmCR+zJy3QMY2KVolhpE38oRxnTBmoIiLkgAh2xTt8a+v1PGbEGe3jAPK6RU6YY3cFhPgbqGpbIh3HISUCdqoprrdQsCEVaDqV7HUyoYxxklEnNSYzqwXeLZuE8sO7aCOqZU2vM8JaqhS3bK2XBh75Ky9dUFldI1u5CNH7LIdxC4lY/2Y/ahLo+P2JvWra96pOaSUzLfmOFY4vm9CyQk6KDI7VPBbSomGV8NQDMI4AAHZAWEEh4WiVSIbR7SCKpEUSEARkqxqkLV3J8M+jMBQhp9HQ83gBM1UwPWRuxADl5Yh2S6pgDsg8laeesfpFc5d5v1rhNsKnmxx3Fp9OOlJxJuv/A0vbX2xv+3+zHP53dIriJfdVYpQsUQJv+vRSGqUMsOFSsUu0w7auJFDnCioikGchGgixtI0Cua1i7dYxjTdGlEklx+6Ass0yO9B/JPr++gjrKBVoRPLaORVYxB7FPXtW5SKVpn5dg1L29wlA+DFTY7vQ/zb1ZiayTFVo+HV8WOQsjd5KYrEVlUK9sY4TYCckcNSI1pBnSDuTaYCiBMXneLQwsASj8yQmKadkjeLhEmVrkyQI1ikhQgwr7IqNt32lhYZIQSmrND0apT2aYHjBT66Mrweoxe1iAIPW9HRlp8z1W4TVZOUDkEM1yA0VaOyp23EtiHCU72xSlrI90BIBdwBYWoWmt4mjnqdGAYRJB5ZS+9PPIXI4Seffhe3dmeBXvvV3y+8jL/KvWCgm1VXLFy/Sd6OUUfI4MobefJGLy4qSgI0JTdS5tdWtL0mbdfDEgX0lTFK8J2QTneWiXxlV+OI4hj0EYZsa3mcsIFEjjS5I7yhQdSDUIRC1tTxgs3jd4LYJW8aB+bqUoR6TZPi4Mm0zHxnHinNTftSBnGHvLU/7tWKPYEmmlS7VyiZm8eF+nGb4lWldoLIJ0k0hoXQCaHgBTEDau/uCaPMc05QxxQWtrY+Bs5U8pDAUqvORKF0YDF848s2VMTh90ynHHJSAXeATGanqDlVvDDBFKuZkFImeLJF1tIpWj3LzUlviZ96+i+ZCnsxW12h8Wvlr+cfl92um2GJAs1ufVvBvYpQMNSd143r+C1cN8FWNlraVEVHpUy1VWOmNLNrMUTbWsQJyKhFOv4ieWuwpQkgSBxyxrVPdAWziKCFE9RRRa5fUiRKAmLZO3bOHGxRkVLS8ur4fohMJAiBpisUM+V97WiwHaaz0yy5S8SxhrmmSXmcRESyRcG0yYzQ+WG3KFhFMkaWJWcJgY2hrlq9Exnhx01KdqZfgHsFN+z0BM8IJNcY/uNHXTrdDnLZ/WQYBoVhYQ+CLS/0IHLR0LYsDWSJIo1Og+liGoi/Fl0TyHj4gs6PHMojlLq4rhizTgy+7+N5g5P/jgrjOQNcR1SyEyQyptFtEMfLE7QqOG5N9R8iZztP8m8vvYdM0itcWNVy/NzEV3NBu3Ho8YUQhHuX7LcljudhDHH1mJRodOtUdsl9OKoRK5YRCiqKULCVAm1vAVsvoq2xSEgp8eIGedMgu8NsvrxZIG/2RG2QuAggY+hkjc2FtR91qbVqGEl+NSFAgvQkC94iuZw9fLI/AIQQTGWnCOOAdtBguZMWpq4xaR5MNxBN0TiWn8ELXZywQSJ7OshQVSqZqZ1bP7c5dyUyYak1jww0DGU1jioIImadKxTzeTLGYPGYMyyaTndDN5cVwri7LrZ20zHEGkHkY2ipFW6FklVmrrWEPSTbXig+pna0etLumDEQcA899BDz8/M0Gg2azSbNZpP77rsPy7K4/fbbue22jX3ADzOpgBsDFKFuKmC+rvZPfO/cB1GXZ4gnrRn+6w3fwhOejzbqqv8A7is3cBCxMYL7SeAHEayJBQ4ijzAJem7mbRa2NXQFRhCsXtQmq/esm5qik1cm8KIOTrCEbWYR9FyxxzKVXc0w3MzSdjVhHFBrNrAobXDVCCGwKdBtd1FFm6x5ML1Uh6GrBpUxS3yx9AyWPlrgualZtH0PXRlujd5OXdc4iblSu0iWKcRVAfOq0FAp0mo5UFDIDChKa+lZGso8O/XZmkqWjt+mkgq4PopQKNkZWl4bU914X0kp8ZMaU9nDUU7peuOuu+466CHsK6mAG1OETHjj/Id5TW21MOqn8s/g7Sdfi68YKL4/8rGU/a1wAPREmK6M5mJYcT81nCq+5yNDFU0YtGQNxZBk7Ax5e7TVbt7KU2u3MLew/IWxjxf1WrXowsRYziSz1ByqHmCqPc1r65l9Lw+xQqvbxGJrsWcoNh13fAXcbuIGHZyuQxImvYQJBSzLpGDvvHTEZth6lpZYAIYLOEMbfp0EkUfTadJyWpSMY1uO2xRZ2m57oIADmMiWWerUsHaYSS3SQK4NZIwcqtKl5TcJI4kqTCQJkgBDE8xkJ3ccG3wkGQML3PVGKuDGECsOeMvl9/Kizpf7294z8WL+fPoVyOWHfs7K0Wg7GENEUhB3KWbGv3PCQnMWxbMwRL5vtVOFBhF4zYAwWqSSH+5+M1STQiai6TSx1PWiL05COv4SSgIVZQoRC4KkixM6oKiEiYMlbGLZSxxpyDZCb5DL5sjss0gK/GgE2QDEGl7ojmxVOowsNGehq6OvcTWSQNSOmXUvM1We3rOMyqxl43TddW7Oq/GSBlPZIVnGoUuj3sQkhyHC0RYGkbZprTFdNZjKVWh6LYIohkQHIZCEJETDj03PmnRA65Oxx9RspjS7Z3GLu6jCRFOGd2y5rrnWQNCUayYVcGNGJWzx7y7+FTf6iwBEKLzj+Nfx4fLZdfuZmoVQm1vWGJNSguZtKHy5H9hGhoa39cS3QhA7GGF502xcTTEIHZ+23iRvDbfEZYwsuqrT8lqEUYJMBLEMiQOfojIJaxbPhmJjYONHLgK7V+x3+ec0hA0RtJtd4nxM3i6N8tV3hSRhqPsZemP0wu6RFXBLrXlUz0YZ4KNUhIqdFKk2Fjg2cWpPPj9n5ollA9drYl2VjJPIGF82mMiV0dSte93Wm/W+RXVUEWAIG8/vblprTFN1JrI9F3WUhEgkmtCpu0sQDj++L1sc2yJ5Zye4gYPTbZPEPauMqikUMqVDF28nhEhrvaWMLamAGyNucmf5yaf/kspyWyxHMflvp76Jz+fODNx/Kj/NQnsOJc5tiBWLkpBYaTN9QO1eTM0GZXiXAykTVKluKt5W0BUTt9sZScBBz0IxkV1NEJivz5JVNk8YMNUMXuwQJj66sn6SMbDptNpYRmZfamcFkYcfdkhksLxFgAK6amOqAyaTI2IUcIMObtcB2XORZuwsQTfCFltPoFqU7fUL3qOEjqJVIqMHtL0WYSxBCoSQmIZGxRrsCl1bmqbl1dHjzJ6ep5XMZuj1Wl70axsE51oSGWMYyq5blBKZsNCYBV/DEPbqWimEqlvDyKpM5PdGNKYcMKkLdd9JBdwYIKXk2QsP89bqR7GWI/CvKHl+tvAy5qXNZlEuQghmCsdx/DaO30QmAolEUSCTsciZx/fvSwygkMnR7HQwlcEZnFJKWvEs2aQ0krUp8kcryHo1QeQjAzG8cr2axYkb6Gy0ElgiT9ttUNnjyafdbdJpOJTE1Ibx+mEXJ6n3ky8APNlh0tjafbeWbugQJ3G/bMU44Ece9cYSBBqGWHUczzZnKRnDFyCq0PC7ziihakOJkoiWUyOJE0CgmzoFq9xLyMhuXYqn1a3TdbpEYQSJAEVi2DpB7JMRa5I5Rvztw8Qndw3FozVVp5jL0XJamGJjHGUsI2K1w3R29xd3C41ZdH9w+y5TZIickLpYopzbWc/SlDFkjJ4p1wupgDtgZJLwNZc+yA90Hu1v+5w2wy9l76YlLJJ2zHx0mZny5kVIs2Z+bALZO14Tx+kFm4MkUgJCxcVWK+usbEHiIrSQkllEdtZbtV7T/QK2DOkKnffbz+5vV1CJZYgmtmcF63gtTDFqzabNVV4Y7G09Fi90cRouJoMtTqZiE8Y+XdHGXu4YoOpsahXseE2C0O+VkokC4kCiBBoykiRSEkUh7W5jX13DVxPEPrXqEmaS2/DT64o5sliPr6EKfCITmp0l4qgn1tyojRaamDKLsvy5oRMzq14iX8xv+TstNmbB1dFFBh1WFyRdEEmCK9pk+l0eRiseLbWAjHFtSQoZI4umarS7bYIw7s2tEjRdYBk6BevYSMcJ44BWp04UxiBBUQV2JkNugCXcCdoIT9+QWbsWTeh0nTbFbHJgCUIpe4CUu9CJIRWA2yUVcAeIKmPe+PR7+HrnS/1tHzSewX/LvJxwTVssuhYNY4nSEAvAQSKlZKFxGeEaaFh0kzZS9gK5pIyoKU9SLE6hChUhBCUri6lVcIM2jgzRxNYxRAAJMeoI++0Ze/x8aTstjCGlIXTFJIiboOXxZZtyduNE2nRquG0HNdTRhI4EwiQmliGmqiGEQEiBkApu3SeKFynnDqY+W7Nd64m3HbJdR2C9s0i35WLGWRSh0onr2Gqx10N3zcEUoWIlOZx6FxADs6HrnSVw9U3DACwli5+4+HEXU7XJKAU6YY28sXmJlUB2yWZ2FntlqCYTuWuPOas7VbxmF5Ms/bsuBLfr0zaeZrIys27x4LgOujI8YcqUOZbac0wXTlzz2FJSUlIBt6f4kUe7UyfyouWMLwXDNijlJ8knAT/29Lt5jvt0f/8/tO7gT60XbKhGqwgVz+3AGBf+XmrOo7o2YeLhJW0ySn5d4LmRmDjNOjeceAb6moDvjJGnpc6iJavb1lrd1qFGCCGQUuIGbWIZYeu5oXFpK+8ZzZqzuUrbyrKwGwTdAHsEP6AuTdpygeniTC/WcA319iJBM+pZ8dYM117OVnaS1rKwXj4WBkHTx9FbZEesUbdbJDIm7MZsVpBBQSFKwnXxXZuhjtI/bZlae4GokWCJntUvSDwMJdMTb5tgYNFptQYKOM/1MMXWItRUMjhJE1O1EULBVnK0gyq2ll8XvyqlxKNNLmuPXDNwL2i5NYJGONByrQkdLdRZrM1xfPKG1Q4y8WgrHCEEbtPBNVtkDvA7puwucsS+0im7Ryrg9ohmZwm33nOHGavrV6QvUZtf4G3eJzgZNgEIUPmv2VfwoHELAK/1/xmA95nPWn1foBDGwVg2oI7ikNCNUJIEKWNyamnDPoZiYWBxZeEpTh97xjoxZdgacTvacgINYx8vdFlqzBK4AXpkoqDQEbOotko+XyJjDp5EC5kyC50FLIZbejaz4ksp0c29u10SGS/HTQ3f11AsrIy+IfPUC138pr+lFS+rFEhY/6DVhYnjdPZdwLW9BqbMbmo+s5UcnaRFXtk6xi+QXUqZ0cYexSFe08NaI0xC6ZPVhscRapFFq1unYK/u2/YaaJE1mglwzbWlCZ28WsaLOvg4IAQxPvl8nmOZ6QOvM+a03aFhB0aQpenWKGW3X6xZQ6dRbaDPWGP5TEu5BnbqQk3ZNmkQwh7Qdut49WBgLNPz4nl+t/2hvnirC4ufyr+uL94ALBliyfV1AFShE8ajF+/dT1puHVNmCBMPa0hdulxUpuEsrd+YSDpBjSgZXPsgjH26QQM10VDaOlacRRUaQihYZNG7Fq3FBm23PvD9qlDRLDE0cN+Jm9jqYJHniRbFzN5VXxcoSDHaA1DKpB+jtZa20xzqgu1/1lWWxtAfrXbYbjPMKqpLjW7c2fT1SIboGYGpjZbB0HRqWFebskeMxVKFRhgE67ZFcTQ0g3orLDVHVi2iqQrHJo9TyU4duHhrdRto0XDXqyIU/O7qM0kZoZgx9GIPpZRYSZZWZ/A9mzKYKImoteapNuaoNefxQuegh5RygKQWuD3AaXcGTqRfHZznJ/xPoi9bQJ7Uivys/Qoa2vrMRm9AnFckA4wR+hseBEksiaU/UnKBEALf9VhrDIuCmIJSwg1aeApoqtWL9Uo84jhASUBIyKubW0kMadGptbHM7MAV/WRhhrn4MmaYRwyYsN24iYo2cDL26VAulfc06FoIgWaoo9XvUlwq1saklsiLUBl+DpQB3dBlMlpg/W5i6zmW5NI6a9jVmEoGP3FpxPNktVK/xEsiE5ykTr6Qo5wdPX4vDmK0XXzsCSFIRvzdFFUQm11CP0RJeucpUQJ0q1fPbVwsUWEUjBxrmkSriT35bJ5mt4Uutl5EdKMWGdFL6Ai8ANKWokORUrLUuELQCTHjnhteAo1GDewq5fI0pr4Ladg7G+TBfv51SCrgdhk3aCOC9RN9N2rxfeHn+N7ofH/bPyrT/Jz1AqSdQ79q0l7rOl1Bs5SRYoEOAiEgSHyyymiZsHF4VTbncuxEZvn9YRTgyxa6MLFEjohgpAnSlBlanRoTxY0ZdkIIjpVP0nCrBF5AFEpAIBSJYWpMZCq4fgffaxNHsucRU8EwdCrZMsaIFp6dYNkmUbB1dp6UEsPW+r/HSiZlEsWE0WCr71Z04zZxFJAQMT93EdXUKOQrmPred+8wNJNY9SHZ2mprKhmiOCCKffyo526USUK+nKO8C4k9kQyG70Tvt1fU9eemYJeZa14ZyT2vmRqTxel+dX8AU5085NX9V8duaRnadpO4u7lVMoi7vQXZcjypTN1uQ5FSMrd0Ed0xsYS+zl2vY0IXquEckzPH9uU5tSlpJ4Z9JxVwu4wfeOvqiPnhIr/nf5zb5aqr4K+12/hd/cUkcYTr1RGxQ0Yrbiggu0IofTI7zEjbS0zTos3wor2bIVQFL3aJZE+o2UoefU1gty+7ZLfobbqWsBtuuqIXQvQm/GzvoSiR68SSpWcgN/i1/aCYmWDev4zmbd6D1dfbHCv2rG/V5mwvSzC0eyJASBhB4y9XlCBOIvRA6QXzA3R7/2rNOYyKxURx7+sIGoZB12ljD2gcvoIbtzGVzDoLb1dvXVPslaKtF0tu3IY4IVSDddfcIHzV4Vh2veVTEQq6rSHdra1wkQzIZnpCddyr+9umTbvpbPo8Wouir79Op4rHeTp4AiUwsLXVe1ZKiRs1URKJvWahl/ZiHU7TraI5xkDPwQpmlKFeX2Jmam86koxEaoHbd1IBt8v0TNu9C9mIFvhN/x/74k0Cv6vfybu1Z4IQ6BhkgyJh4hGGbTy1iWWU1k0kkfQxCsrIzdxX6NVvqiHjpCeKMvlNg/x3Ss4qUjUWCQIPQxm+AlTXxMq0nBqB46CEKlnR6z3YpU6igq0Wtm11HLVArRBi08ljq9f2EiEEM+WTVFvzdLshVpLriwIPB81SmC6eQBEqi7VLUBdYYk226YhuUEkCsidQB1lKDGkTLUXUxTzlwuBir1Ec0nKqJFECEsxM5prqyZXyE1xqXiCSITl1fWN6KROcpIkh7L54k1Lia22mKtPXZLnK50rU2zUMYeEvW4MKSpl21EQ3NnfFxjLCzlkDhfVEcZq58NKye37jmCIZouYYWDttt3G8Jm6nTRL17ns9Y1LKTW3rt8oYeVpmE8KtBVwsI7LZjdbTmcopFq7M4vi11Yz6JCGjFDZkcmtWOgUNw3e6aGK4mI678dgmuqXsDends8tkrTxLYo7bI5+3eR/imOy5SiIEn1OmeLe+3j2qKQZ+4pIVeUig5VVRDRNV09FNjWw2u63sQCkli9VLRO0QM7GW53ZJq7ZEM7NEZXIacw/6Zk6Vj7EwdwVjTRmMRMb4iYcqNIzl1byUEiPT+7veWsBfdLHWlLwQQpAhBwl0kgb2Nvu4CuVw5+UIIZgsHkMWJC23RiITEDBtTff7bTp+k7iRoF8Vc2jT6ySR2yKjMpEJiUxQhuQvaULDbTiU8vIqUbUci9PsYkZ2/zW33qJj1yhOTJKxRr9eTc0ml8vT7bi4SX15wu/F6AVJF11YhIpPmPjEMsQuWhwrnLpm66ip2ShZkI7Ejx1EInouehIayRxZo7LBEufFHYyCQTk3WMwqQuHY5CmqrQWCboiVZBFCIZIBiR5gZ22KmY3WwpZbw3Pc3iJLVbCzWfKZ8vLiq4qMez9HLlca6tIO44DFhUuojoom9P75jdshV2pPUJie3JbALhSKtKrtTZNipJREtkfO2ih6DdXEyGho3TXvHZCXEeBRyo4+pp2yblGLwM7u3aJ2N4m8CG1Ad5irMaWN021ROoguF1Iid+pCTS142yYVcLuMrhp8hTLPz7sfI0svs29RZPgF86v5sjI4i1ERGrGMUYVKgSKRGTEzfcO2P1tKydzCk+htE1Ost4TpGOBCdXaWieMnMDSLprOE13KI/F4QnqJrWHmbUmH7mXAZM0e+XMRr9HpZhlEXJRYYmMR0aYs2QlNRbY3judN4oYO31Fkn+K4mR4FO1MRQs7hxi4w6XBjo9njGCW4XIQTFTVyEnVa7dz6vQhEKRqzToUFuQMJLJEPaSW1k26IZ9q6R0poivwvVp1EaynrLHz3Bh6fRnK3CcUHGGr0ziJ3J0OpUCSNBRiv0M4HXus0D6ZMt27vSymyidIwLzhdRI5XssvtYSomXOLTjBXTNRFkuaSNlgp5omMbW15UiFKaWhXfbq5PImKxmkzU2jtcLXarzs2juStJM715z6y0WtcsosUpuufG9BKrVKyg5lYmpEwOtK4lMWJy7hOltLGcihMCKMnTm6qjHlZHFdcbMIyeg1eiVSVlrqfWlg5ZROFba3F1XLk2zGM5iRYMFUoiPWTKwjL0vbrmyqA2bAWbSe95IJM3qIs3sIpWpmT1Z1O4WV2eMD9v7wEgF2L6TCrhd5u7aZ/iexoOoyzfSY8oEbzNfRU1keG14nhfGs4Qo/Bfr5f336IpBFAeoy6vdpBMSTmzfFN50ltDaxpbuEjO0qdXmkUmC1u5V6tdWAqciiN2I2daTTB2/AUPbXhX3icIMs+EFkmrUmxiXh6GiYQAyTPAtH4GgVlvAlMPdrUoiCBIXVIa6B33FYSp7dKq7+6GL222DBN2wyC270WMv2jSTUhcmSqzRiWugCoSqoKoqqqFiZ7NUzFsR5z820nNeCIU4XC0v4nhN4lqII7uEshfrqQkVKQAhyIg8RmLSqi2ROTFcwAWhT702R9QJKMc9q2EUhTSUWQw9S0YrECYBXdGmWC7vSreIRCbMzT1Fvltcdy0JIbDJYUlJJ2qS1QqrrysQx6uJN37o0mrVScJ4g5tSCEHB3rzcTBD6VK/MYgYbxZYqNApxGVe2iVjtTmJIC9qwEFxk+uTpDc+FRmsBo2tuWYvOSEza9TqZ46NbR7NmnuxMnla3TuAHICVCFUxmp4c+m3TVYGrqOI3WEqEbYSaZXks36SPNhGyuZ23ca6SUzM0/idYyBi9qHVgKZ5k6cRJjQBan4zVx261eqMDyuS7nR3ffS9mzpMdRCEKQy5S2/VxVtNGy00MZYOlpSu/1RCrgroHLF76EoigYeZtysVd0U5EJb5z/EN9Q+0x/v39QTvIb5ivw16xedZINz9koCdDXRJ8b0qbjNihv09rgtZwNbrVBOM0mRSYGPoSEEJiezeL8JU6evGXAuzen6zvIRrzhQbl6bAWzbfGE9yhKqGKqwx9kGZHDoYNNnlZUpaANHnegdMlPlPpuxsNMN3BoVhdJOhGG7P1GvnRoZZbIlUv9rN3NUIVKlp5LXpQFleLVrr/tx455gcvC7NOosUpeFIACnnSJZYQhTHRh4tJEVyyUrorrt8lc1Z/X9Tt0WnUSPyJKQqIgIpfkUddYYTWhU5BFQj+gKi4xMXGMmdxt2x7vZtQacxidzXusCiHIySJO3OxbMaVMEIpGIhMWFi+SNHvnRV3+HaNGl8vm4xSmJilkV8Wb47fodto94aOrFHOTNJtLPfG2BRmRx5Gt1YXVMqZvU6/NMz213jrvO12MEWKkZDfBCx0sfXtWr4JdZoTyghvQVYOp8gmSUkyr2wCZUNDz2Mbe9G1OZELHq5MkEtvMYmo2TWcJtaVvvagNLOq1BWZmTve3hXHA4vzTKB2Bho664pJu+lyuPU5xemqoAK015+g2Ouie3nf5z/MkgeZj5/LkciUKA2pLSilpdWvIJEZVdfSMAc3h319a8cH2xE4zivedIyfgLly4wIULFyiVSnieB8Bdd921q59hdHsiKe4EXK59iZPHJnnr0od5YefJ/j5/PXEX/y0+jR6s/sTv0wdPRLGMsK6qnTRqMP5aoiAc6FpbS5D42GSHriA1R6Pl1tY9YKIkotlaIA5iBKDbJsU1AdLtdh0j2XoiEUKgBirKNoqfCgSKUMhTxInqoCho6CiKSqzEGLZOsVDBHmFiSmRCo72A33J6FhRFoGVMCsWJkdw5URz2CgYnCaquk7cru1oGohs41C7NYkYmawOHNKFDF9xuE9/q9suFSCnpJm2SpHdOpJDoio2p2CQyHmhVEIqAEcJVIhmStfK4fpv6pTlyYWGd9rNEbwyedJF4ZJUCbtKrgVitz6JNGRiquezav0BSjzAwUFHxcMgpm1sLdGGQDbLYxu7FKEkp8dsu5pA6ZUKIde4gX/OYsI/1LDlNbUNAuRAKVmDTma0jTgg0zaC+MIfo0BdhiYyZ1b5MIhKyjGIFEz3heFWsX9jxSSbXl5qJ/QhGiJHSpUnX276A2ymKUCkNiAHcLaI4pF6bw293MfxetqYjGiTZmNAPKYie0IqTCI9eiAcCLLL97i9RJyCc7Hk9EpmwMHsB0x1w7wiBFdg0Ly2SnIwpblLGZrF6iWQpxBTrLaM2WewoS6fWorkU0cwsUpqeIZ8pIaWkWr+C13T7os+XDoEVEBBSUjZf0EcEZPMH3JZszFpp7YcWOGiOlIA7f/48jz32GPfcc09/24ULF7jvvvt4/etfv+ufJ4TgBj/hly+8k1tkr4xGhMLbp17Gu+NpOm6DvKKty6S8WsRFSbghoDyR8ZZByz0htUgSRCAERsaikJkYyS0W4JEVw290VWi06lUCzwMpcf02igtGsGq98GWHy1adwvQkuWyZbqvTs/wMISdKdEZZUrIsZPsJDgpZ0Zv0XatNZWIKQxucGTgIP3RZvPw0hmugCZW+QHITatVLGDN5JsuDXbBhHFBbukLY8jCj3oM9WLaKZUolKqXBAe7bZW72yyi+IMJDAoaw+gkg0BMEuh8S4pOIhCBysBMbZY3lNYh8WmoHoWuU1mRW6vkFXvzKC1T+uAuzw8eS2BE5u8SlC49jbpGRaIkMjmxhSIuMyNFKmqh1WGw9hZLX6UYuuU6+n0kaymCkos+GNKkuXUEXBjKKQRFoGYNSYXqgpdUPXZrNKkHLXd5fQc+YZEsl8pkyfuSieOpAA2SYBPiyi1i+iSISfKXbawGXN2l36yhNsaVYNxKD5uIiIhIYy7+Xl7gk9NyvYRiQU8sb+t+8Lv4SAPerz+hvM7EI8DGvMn0ZoUW7W1+fFLHF+iFIfALZRcje48GKcrS7DbrNJkkYI5Xe26GFwAAAE/xJREFUQqxUHPybbpcoDmk0F4i9sJfpbKjkChPY5kbRmMiYenOe0PUhkSi6SqZQJLdFsoWUcjl2t0MSREigG3awwyymsuqWjpIAta2iKiqJSGhEC71FJwYCgSShxSJqYpBTy5iJjdNtUspN0WgvYDhbu6QtbKpXrmDdmN3wrHa8JlHV39IbklMKdGSTrFegdWkRTkrarTpaQ1kn+hShYvk2hjSpm7OUlGMbrkEfD7uSoZDde5f0YWG/tcBBcWQEnOd5fOADH+BNb3rTuu1nzpzhwQcf5Ny5c5w9e3ZXP/N2WeWX5Meo0FP3HcXibcWv5OGlAjpQpERHaZDRSwPLYURJiJ90Ngiq0AyZsgffjEvVS3j1zjoh5ckm7UyVUETYQzvej24tito+sqXjyBYmmV6g+pq3K0LB9E3aTy+xqF5Ek9ZIzdmEUDZtm3U1XTrYyvp6Ur7V5djUxligrUhkwuLlS5ibxArp0iScc2koC5SK61e6YRwwf+kpTNfAXOPuU4SK2VUJ3A4Lkc/05OmrDzsyrtdmafZprK7d+52XCRKPtnTJKsW+ULVkhvnkMgVZJEtuw/cxhImRmNS9Jeae/jJTJ09z9uXn+dGfeYpiQSDeJ3FbMWzRhScQHvlKhZZTRXO1oZeNTQ5XdnrZ1DJBSJUkinHrPff32vIRIf7Idf2ipo+JzsqFJVshc9UnyM9MUMyvitOOW6d5eREjMjBXrFEx0IROs0p3qkMmX0C5Kh1SSkknqaMlGlmxGsieyAQnaOIXXG4sPouF+QsjtcxSPbV3X0uXiGD5vrGWj2njxm0UtHXf3xolwGkZITa2hFMNrVfDbw1REuLGLfREJ7Pme/mzDovUyJDrfx/ZCpmtPkFupkK5cO1JIvXmPM5CHTOyls+WABLq1cs0SzrTM2f613CrU6U5t4gZmKhrMo/b1UVahUWmjp/ZcH+HccDClafQ2hqqUFCXrZsGZQJ8nKRJViniSRcdvdfjV3aoxVfIUdwQ2pHIBEe2aEaLlLTVa8lvuegjdKIwY5vZK09w45nnrtveaTaGekKgt0iOZIiRmFRnr2AHmU2z6BWhkPcLdCstdExkLHsxeZbORH565FZye8m4FGU+CC1wUBzumgtrOH/+PMViEcvaeCHffvvtPProo7v2Wd8hH+NHk4f5Dfnhvni7JLL8+NQ38EijuC6eLZfk8PwmnbCGn3RJZIKfdHGiBmHibhBvkYzIFAe7jRaWLhLPB5ihtW4V1hMSJrqn0k12rzeeQBDKAB1jnai4GgMTNdJGzkKSUqJJDTdpb7lfIhM86eIkbVzRIrQ8lEmF48c2PtyHUW/OY7hbP5Q1NDr1+oYJsrZ0BdPd/PNUoRIv+pv2Yh1G1+/QuDSL7WU2/M6GsMjKPJ2ksW5cZmJgDQlMKjFB0g2ZvulT/Owv9sTbCpmsZOJ4l+INi+uOG8sI3+ySP14mZxXxOi7qCBnJvYm5dxwVjRCf3hRr9Cwj18ggi5cZWjiXG7ScGrBsebu8gBENPkcaGsmCT7frEKrr+wm3kxrZJLchhGHFZW86Go7XWnZTrud1yZd4XfKlddt0YeBJF4CsKKw7n4pQyIk8RqzhxKsWaA8d76p4Nx8PY4BbNBD+BmuWmcusP4dJhBu3yMn8BtFiCpu8KOHhEsvVxAwrsvCuNGl1qhs+cxQarQW8K+2+dXotujRQajA31wsx6bh1OpdrWFc9x6AXKqC3deavXFj3naSUzF9+CqNjDLS4G8LEwsZN2sRE6MtubidpUKQyMC5XEQp5UUJBoRXXMJYtaXEwmqA2FYvESTbc91F3tM4etpIlWJ4/rNDGF90t91eEihqoHJs5zfETZzh+/DST5eNjId6Angt1J/92if3UAgfNkbHAPfbYYwNPGECpVGJhYQHP8zbdZzu8ST66bh1/jil+VryAdjvBHLDyypCFqOdCaVJHmBolsTEmJCRAm+y5iK7G9TuEix7GFmZ5U1q0kxb2Fg3lpUiIk+ENuKWUSJkQ4PesKkOwydKSzaH2P4BOUsdKMiQiWbawbRSscRJRUxaZqByjWJjcdubW1fgtB30EC4ruarTdGoXlEh5RHPbcpluUO4Gea9NtNq4ps65ZXUQPNz+vvcD6Qt/CFSQbXWubvS+Rkn/xPQ10faMQKlckf/S+J/jb9zzN7NMm7/vL48TBcQprvoOUyehFjeXqHwKBIUwiuVH49I47Wv/QzWJBdanTqdYoZCs0m1WMIc3XVaERNB30nMlK05Bu0iGznB25GXps0FpcHPjaC+U8OjEkcL/yjP54VcSmiTzQEylREhKKXveHta7TVeTAyvtKVsHU1p/7Un6Kuc6TmMt117qyQ05ufc9mRQFHtteFPGhSp12tUshtL15NSklnqb5q+RyAEAJRT+iUGrSWqujJ1ospo61Rby9QWS4k3ewsone2tgSrQiORDobslbiRUmKT6WfzbkaOAg2W1tSEG91LoQqNbqez7r6/Fi2iCm2kuOfEDXvPb2XMpm65Cxa4XTLg7acWOGiOjAVufn6eUqk08LVisRc31WyOFnc1jLXi7QPcyE+LV9AQBqq79Y2vC4OiKGNIFSYEgeXjaV183SMuxORPVZgsDY7BajdrGCOY5TNxhoa6hBzwFPGVLuVj08T24El1La5sY8vMyI8yRSgIKfGTrVeRUspewWLqmMJCTVQ6cYNO0sRJWnSSFp24gZM0uOHUrUxVTu5YvAEk4fDv3PseKlGwuoJuu/WBVoVBBI4/fKer8EOXpDV8xd9znfXOaYi/wWK0GZqR8Mqv3fzJqOuCb/y2kB/40Q7f8t2NdeINQFG3UQ9w+WLxEhd7i1hImyyu3Nr6CsvXyhaTmuhIHK9J0HJHGp7qKCiWQqj0zlOchCO5RYUjiZSN5yhEIbzKJduJm2RGiAO1RAZfDh63K9sDBXqgBORKGxcIilCoTB2nHs3jx11kPJqCEGwUyKqjbNuS3OwsonvDXY6a0GlU5xHO8JlaCIHf7PT/32t1Rop1zVIgonf/tmWN3GZ99dagCAVdGnhhz3uhmqPFAvpJFx19QwFbRRttWo2TCLHNKVhJFMJ4NAvf9cp+aoGDZsxk/LXj+z6mufVE32w2mZkZHGz+8MMP88gjj4z0WW9e/u9HOcGvixf3yqUbEm2EhxgACUyUjkNptN0BYi9AGeF0qUKjkKugWSZ+p0sS9yr567bBVOEGdNVA10waVxYx4sG/l594iDjpFfPdxqpIRyeOA3zEQLeZlAmtpEZ+zUNWF0YvXkSut8qE2XBbHSiGsp3K/Vd1HhgZKfma+qN8bX10E30cRyNXME9IUCKl998RH/wyjFFefdUD/0sBRBKaCeLui/3N39q8xKsW17sEkyRenmiGtefqdVgViUJMhIoGEhJilGSjCExkgpJs/R1631Nwv7yF+8XNG17XhI7vjS5YVNHrUmAey9Ceq49sWdTQiXWI3XidO/nX1K/csG9IQG4EizVsvLaiJKRDTwBeHTPbFQ7FmYl+LcCrSWRMNs4T4KEyOFHjaiwy+HR7nVCWUYVG6Huwjbq2kR+OnEgUexHmSHZ6iIPVRVccxv1SHluxPkZwNCsvgIJKlPQ+zyxkiDv+0PeGBGRFAXnVsMy8jfTiwW9aQxenL/Z7/ZeHE2vJwTas3wSzoPOqf//8HR8DwHEc7r333qH733HHHdx5550btu9UCxwmjoyA24qVk7mSSjyIIAjodDqbvr6Wv/jzP+eWW27hZXfeyQO7MsKUlIOnxLbWFLvGww8/TBAEGIYx8IH8Y8v/UlJSRuPhhx/m4x//+Kb31G4jFIFV2rmnBJZd8iPMxUGwfUvkKFrgMHFdCLhRMAyDXG54zSnHcbh06RKNRmNfboyUlKPOI488QqfTIZfLpfdUSsousF/3VDa7uzUFgyAgiiIymeEmYMPYXiLbUeS6EHC+34t52Spo8c477xzpQr/33ntHttSlpKSkpKQcVd74xjce9BC2xSha4DBxZJIYUlJSUlJSUlKuF46MgCsWi5tmlqz4u1cyUFJSUlJSUlKOHteTFjgyAm5mZmbTwMSVk3kUsk5SUlJSUlJSBnM9aYEjI+DOnDmzqepuNBqcPn3tbY5SUlJSUlJSxp/rSQscGQF322234XkejUZjw2vnz5/n+c/fWY2alJSUlJSUlPHmetICR0bAWZbFq1/9aj7ykY+s277SF+222247oJGlpKSkpKSk7AfXkxY4UmVEbrvtNkzT5MEHH6RUKvX94K9//esPeGQpKSkpKSkp+8H1ogWOlICDnv/7zJkzBz2MlJSUlJSUlAPietACR07A7TV33HFHv+1PSkrKzknvqZSU3SW9p64PhNxWt+6UlJSUlJSUlJSD5sgkMaSkpKSkpKSkXC+kAi4lJSUlJSUl5ZCRCriUlJSUlJSUlENGKuBSUlJSUlJSUg4ZaRbqiFy4cIELFy6sqylz1113HfCoUlIOH+95z3uwLIuzZ8/2+xbOz89z7tw57rrrriPTpzAlZS/wPI+PfOQjmKbJK17xik33S+eso08q4Ebg/PnzPPbYY9xzzz39bRcuXOC+++47coUBU1L2Gs/zOH/+POfOnetvM02Te+65JxVvKSmb8OCDD9JsNpmZmeHChQtb1jhL56zrg1TADcHzPD7wgQ/wpje9ad32M2fO8OCDD3Lu3DnOnj17QKNLSTl8zMzMcNdddzE/Pw9AqVQ6Uu1tUlL2grXWtscee2zT/dI56/ohFXBDWOmfZlnWhtduv/12Hn300fRmSEnZJtdDlfSUlIMgnbOuH9IkhiE89thjA28E6FkOFhYW+vEFKSkpKSkpB0k6Z10/pAJuCPPz85RKpYGvFYtFAJrN5j6OKCUlJSUlZTDpnHX9kLpQh+D7PqZpbrnPSmBpSkrKaDQaDS5evLju/++6665NLQcpKSmjkc5Z1w+pgNsBKzdJao5OSRmdZrPJwsLCujic+fl5/viP/5g3vvGNqYhLSdkj0jnraJG6UFNSUvaVe+65Z0PW6czMDDMzMzzwwAMHNKqUlJSUw0Uq4HaA7/sAqcUgJWUXOHPmDOfPnz/oYaSkHFnSOetokQq4lJSUsWBlUlmpD5eSkpKSsjmpgBtCsVjcNGNnJY5gJbMnJSVlax544AEefPDBga+l8TkpKTsnnbOuH1IBN4SVXo2DWLlJ0myelJTReOyxxzadXNL7KSVl56Rz1vVDKuCGcObMmU0nnEajwenTp/d5RCkph5ezZ8+u68+4lgsXLmxaQT4lJWU00jnr+iEVcEO47bbb8DyPRqOx4bXz58/z/Oc/f/8HlZJySDl27NjAGLeVBvdr+z2mpKRsn3TOun5IBdwQLMvi1a9+NR/5yEfWbV/pN5c24U5JGZ3bbruNc+fObRBx9913H2fPnk3vp5SUEfB9f1M3aTpnXT8IKaU86EEcBi5cuMCFCxcolUr9G+euu+464FGlpBxOHnroITzP609Et99+ezqxpKRswUMPPcT8/DyNRoOFhQUATp8+jWVZA++fdM46+qQCLiUlJSUlJSXlkJG6UFNSUlJSUlJSDhmpgEtJSUlJSUlJOWSkAi4lJSUlJSUl5ZCRCriUlJSUlJSUlENGKuBSUlJSUlJSUg4ZqYBLSUlJSUlJSTlkaAc9gJSUlOuTd77znQDMzc0xNzfHD//wD3Ps2LEDHlVKSkrK4eD/397dcrWuRVEYnkcn0VSTohtNqCYaikaB4T8gwLSGqiJQqUJA0aknPtEhPugmmqPIobT3Hi7pIHePvo9roNm7bo619gcBDsCPC8NQg8FAtm1LkuI41vn5ue7u7ghxAPAFtFAB/Lg0TVWWZf3Z931ZlqUoilqcFQCYgwAH4EeVZVm3TT9yHEdVVbU0KwAwCy1UAD/Ktm3d39+vPC+KQkdHRy3MCADMQwUOQOuiKFKn01EQBG1PBQCMQAUOQKvKstR0OtV4PG57KgBgjF9vb29vbU8CwPYaDoc6OTmR67ptTwUAjEGAA9CaMAzV7/cJbwDwH7EGDkAr4jiW53lL4Y1jRADga1gDB6CRoig0Go2U5/lf//fw8FAXFxdKkkRZlqnf79ff+3ysyGd5nuvh4UGdTkdVVSkIAlmWpaIo5HneJn4KABiDFiqAbyuKQpeXl9rf39fe3p6yLFNVVXWgiqJoaWdpr9eTJJ2dna098+3q6mptGIvjWNPpVDc3N/XtDWEYaj6fq9vt6vr6evM/DgD+x6jAAfi2yWSyFKqKolAQBPV1WEmSyPf9le+tOwfunyRJotFopPF4XI8jSZ7n6enpieobgK3EGjgA3/J+Af3HUJVl2VJ428S9pre3t+r1eisbHd5brgQ4ANuIChyAb/kczj6vYcvzvHGAm81mKopCp6enK39LkkSWZbGDFcBWogIHYCOiKFqqhv1tU8JXPD8/S/qzdu6jNE3V7XYbjwEAJiLAAdiI+Xyug4OD+vMmAtx7Fe9jm/b93VVVrV1fBwDbgAAHoLE4juU4zlLQKstSWZY1fvfu7u7KsyRJJP1Z/zabzRqPAwAmIcABaOzx8XGlzVlV1ZfOhvs3ruuuPW5kPp/Lsqx6jd0mqn0AYBICHIBG8jxXnucr7cydnR2ladro3YPBQC8vL0vPwjBUt9uV4zj1+OxEBbBt2IUKoJEsy+S67kqICoJAr6+vjd7t+74Wi4Umk4k6nY4Wi4UGg4Fs29ZwOFQYhnIcR8fHx43GAQDTcBMDAACAYWihAgAAGIYABwAAYBgCHAAAgGEIcAAAAIYhwAEAABiGAAcAAGAYAhwAAIBhCHAAAACGIcABAAAYhgAHAABgGAIcAACAYQhwAAAAhiHAAQAAGOY3hCGA0oxuQuQAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Define the function\n",
+    "def func(x, y):\n",
+    "    # if y < x:\n",
+    "    #     return y\n",
+    "    # return (x)**(1/3)*(y)**(2/3)\n",
+    "    return np.where(y < x, y, x**(1/3) * y**(2/3))\n",
+    "    # return np.where(y < x, y, x)\n",
+    "\n",
+    "# Create a meshgrid\n",
+    "x = np.linspace(0, 15, 1000)\n",
+    "y = np.linspace(0, 15, 1000)\n",
+    "X, Y = np.meshgrid(x, y)\n",
+    "\n",
+    "# Calculate the function values on the meshgrid\n",
+    "Z = func(X, Y)\n",
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "plt.contourf(X, Y, Z, cmap='viridis', levels=np.linspace(0, 10, 11), alpha=.9)\n",
+    "\n",
+    "R_cone_values = [point['R_cone'] for point in data_for_plotting]\n",
+    "\n",
+    "x_values, y_values = zip(*[(point['pisq_a'], point['bc_squared']) for point in data_for_plotting])\n",
+    "\n",
+    "# Create a scatter plot and color the points based on D_theory\n",
+    "scatter = plt.scatter(x_values, y_values, c=R_cone_values, cmap='viridis', edgecolors='black', linewidths=.1, label='Data')\n",
+    "\n",
+    "error_values = np.clip(np.abs([(point['R_cone'] - func(point['pisq_a'], point['bc_squared']).item())/func(point['pisq_a'], point['bc_squared']).item() \n",
+    "                               for point in data_for_plotting]), \n",
+    "                       0, 1)\n",
+    "# Add a colorbar to the plot\n",
+    "cbar = plt.colorbar(scatter, ticks=np.linspace(0, 10, 11))\n",
+    "scatter.colorbar.mappable.set_clim(0, 10)\n",
+    "\n",
+    "cbar.set_label('$R^\\#$')\n",
+    "contour_lines = plt.contour(X, Y, Z, levels=[1], colors='white', alpha=1, linestyles='solid', linewidths = 2)\n",
+    "contour_labels = plt.clabel(contour_lines, inline=True, fontsize=14, colors='white',\n",
+    "                            inline_spacing=10, fmt=lambda x: f\"$R=$ {x:.0f}\")\n",
+    "\n",
+    "plt.plot(np.linspace(0, 10), np.linspace(0, 10), color='red', label=r'$bc^2 = \\pi^2 a$')\n",
+    "\n",
+    "plt.errorbar(x_values, y_values, yerr=error_values, xerr=error_values, fmt='none', ecolor='red', alpha=0.9)\n",
+    "\n",
+    "\n",
+    "for i, (x, y, error) in enumerate(zip(x_values, y_values, error_values)):\n",
+    "    if i % 10 == 0 and error > 1e-2:  # Display error for every n-th point\n",
+    "        plt.text(x, y, f'{error*100:.0f}\\%', fontsize=8, ha='left', va='bottom', color='white')\n",
+    "\n",
+    "# Customize the plot\n",
+    "plt.gca().spines['top'].set_color('none')\n",
+    "plt.gca().spines['right'].set_color('none')\n",
+    "plt.gca().spines['bottom'].set_color('none')\n",
+    "plt.gca().spines['left'].set_color('none')\n",
+    "plt.gca().xaxis.set_ticks_position('bottom')\n",
+    "plt.gca().yaxis.set_ticks_position('left')\n",
+    "\n",
+    "plt.xlim(0.0, 10)  # Replace with your desired limits\n",
+    "plt.ylim(0.0, 10)  # Replace with your desired limits\n",
+    "\n",
+    "\n",
+    "plt.xticks(np.linspace(0, 10, 3))  # Replace with your desired tick positions\n",
+    "plt.yticks(np.linspace(0, 10, 3))  # Replace with your desired tick positions\n",
+    "\n",
+    "# plt.gca().dist = 105\n",
+    "# plt.subplots_adjust(bottom=1)\n",
+    "# ax.margins(0.05)\n",
+    "# plt.legend()\n",
+    "plt.xlabel('$\\pi^2a$')\n",
+    "plt.ylabel('$bc^2$')\n",
+    "plt.title('Minimal Eigenvalue in the Cone and target $|R^\\#-R^*|/R^*$')\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "# plt.loglog()\n",
+    "\n",
+    "\n",
+    "# plt.savefig('../../test/output/rayleigh-benchmark-parametric/phase_diagram_R_cone.pdf', dpi=300)\n",
+    "# plt.savefig('../../test/output/rayleigh-benchmark-parametric/phase_diagram_R_cone.png', dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 413,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Define the function\n",
+    "def func(x, y):\n",
+    "    return np.where(y < x, y, x)\n",
+    "\n",
+    "# Create a meshgrid\n",
+    "x = np.linspace(0, 13, 100)\n",
+    "y = np.linspace(0, 13, 100)\n",
+    "X, Y = np.meshgrid(x, y)\n",
+    "\n",
+    "# Calculate the function values on the meshgrid\n",
+    "Z = func(X, Y)\n",
+    "plt.contourf(X, Y, Z, cmap='viridis', levels=np.linspace(0, 10, 11), alpha=.9)\n",
+    "\n",
+    "R_vector_values = [point['R_vector'] for point in data_for_plotting]\n",
+    "\n",
+    "x_values, y_values = zip(*[(point['pisq_a'], point['bc_squared']) for point in data_for_plotting])\n",
+    "\n",
+    "# Create a scatter plot and color the points based on D_theory\n",
+    "scatter = plt.scatter(x_values, y_values, c=R_vector_values, cmap='viridis', edgecolors='black', linewidths=.1, label='Data')\n",
+    "\n",
+    "error_values = np.clip(np.abs([(point['R_vector'] - func(point['pisq_a'], point['bc_squared']).item())/func(point['pisq_a'], point['bc_squared']).item() for point in data_for_plotting]), 0, 3)\n",
+    "plt.errorbar(x_values, y_values, xerr=np.abs(error_values), yerr=np.abs(error_values), fmt='none', ecolor='red', alpha=0.9)\n",
+    "# Add a colorbar to the plot\n",
+    "cbar = plt.colorbar(scatter, ticks=np.linspace(0, 10, 11))\n",
+    "scatter.colorbar.mappable.set_clim(0, 10)\n",
+    "\n",
+    "cbar.set_label('$R^\\#$')\n",
+    "contour_lines = plt.contour(X, Y, Z, levels=[1], colors='white', alpha=1, linestyles='solid', linewidths = 2)\n",
+    "\n",
+    "for i, (x, y, error) in enumerate(zip(x_values, y_values, error_values)):\n",
+    "    if i % 10 == 0 and error > 1e-3:  # Display error for every n-th point\n",
+    "        plt.text(x, y, f'{error*100:.0f}\\%', fontsize=8, ha='left', va='bottom', color='white')\n",
+    "\n",
+    "plt.plot(np.linspace(0, 10), np.linspace(0, 10), color='red', label=r'$bc^2 = \\pi^2 a$')\n",
+    "\n",
+    "# # manual_labels = {1.0: \"$\\pi^2 a = bc^2$\"}  # Add labels for specific contour levels\n",
+    "\n",
+    "contour_labels = plt.clabel(contour_lines, inline=True, fontsize=14, colors='white',\n",
+    "                            inline_spacing=10, fmt=lambda x: f\"$R =$ {x:.0f}\")\n",
+    "#                             # fmt=lambda x: manual_labels.get(x, str(x)))\n",
+    "\n",
+    "# Customize the spines\n",
+    "plt.gca().spines['top'].set_color('none')\n",
+    "plt.gca().spines['right'].set_color('none')\n",
+    "plt.gca().spines['bottom'].set_color('none')\n",
+    "plt.gca().spines['left'].set_color('none')\n",
+    "plt.gca().xaxis.set_ticks_position('bottom')\n",
+    "plt.gca().yaxis.set_ticks_position('left')\n",
+    "\n",
+    "plt.xlim(0, 10)  # Replace with your desired limits\n",
+    "# plt.ylim(0, 10)  # Replace with your desired limits\n",
+    "\n",
+    "# Set custom ticks\n",
+    "plt.xticks(np.linspace(0, 10, 3))  # Replace with your desired tick positions\n",
+    "plt.yticks(np.linspace(0, 10, 3))  # Replace with your desired tick positions\n",
+    "\n",
+    "# plt.legend()\n",
+    "# Customize the plot\n",
+    "plt.xlabel('$\\pi^2a$')\n",
+    "plt.ylabel('$bc^2$')\n",
+    "plt.title('Minimal Eigenvalue in the Ball and target $|R^\\#-R^*|/R^*$')\n",
+    "# plt.show()\n",
+    "plt.tight_layout()\n",
+    "plt.savefig('../../test/output/rayleigh-benchmark-parametric/phase_diagram_R_ball.pdf', dpi=300)\n",
+    "plt.savefig('../../test/output/rayleigh-benchmark-parametric/phase_diagram_R_ball.png', dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 161,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/ht/z8sb7wsd1bg0qpmyyfyq2rcm0000gr/T/ipykernel_91885/930303367.py:10: RuntimeWarning: divide by zero encountered in divide\n",
+      "  return (np.pi**2 * a / (bcsq))**(1/3)\n",
+      "/var/folders/ht/z8sb7wsd1bg0qpmyyfyq2rcm0000gr/T/ipykernel_91885/930303367.py:10: RuntimeWarning: invalid value encountered in divide\n",
+      "  return (np.pi**2 * a / (bcsq))**(1/3)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_points = 100\n",
+    "points = [eig.book_of_the_numbers(scale_c=2, scale_b=3) for _ in range(num_points)]\n",
+    "x_values = [np.pi**2 * point[\"a\"] for point in points]\n",
+    "y_values = [point[\"b\"] * point[\"c\"]**2 for point in points]\n",
+    "\n",
+    "# def compute_D(a, b, c):\n",
+    "#     return (np.pi**2 * a / (b * c**2))**(1/3)\n",
+    "\n",
+    "def compute_D(a, bcsq):\n",
+    "    return (np.pi**2 * a / (bcsq))**(1/3)\n",
+    "\n",
+    "# Create a scatter plot\n",
+    "\n",
+    "plt.contourf(X, Y, Z, cmap='viridis', levels=np.linspace(0, 1, 5), alpha=0.9)\n",
+    "\n",
+    "# Plot contour lines of the function D\n",
+    "a_values = np.linspace(0, 10, 100)\n",
+    "bc2_values = np.linspace(0, 10, 100)\n",
+    "A, BC2 = np.meshgrid(a_values, bc2_values)\n",
+    "# D_values = compute_D(A, BC2)  # Assuming b = 1 for simplicity\n",
+    "\n",
+    "\n",
+    "contour_lines = plt.contour(X, Y, Z, levels=np.linspace(0, 1, 5), colors='green')\n",
+    "# contourf = plt.contourf(A, BC2, D_values, levels=np.linspace(0, 1, 5))\n",
+    "\n",
+    "\n",
+    "# cbar = plt.colorbar(contourf)\n",
+    "\n",
+    "plt.plot(np.linspace(0, 10), np.linspace(0, 10), color='red', label=r'$bc^2 = \\pi^2 a$')\n",
+    "\n",
+    "\n",
+    "plt.xlabel(r'$\\pi^2 a$')\n",
+    "plt.ylabel(r'$b c^2$')\n",
+    "# plt.ylim([-3, 50])\n",
+    "plt.title('Phase Diagram')\n",
+    "plt.legend()\n",
+    "\n",
+    "\n",
+    "# plt.scatter(x_values, y_values, label='Random Points',edgecolors='black')\n",
+    "D_theory_values = [point['D_theory'] for point in data_for_plotting]\n",
+    "x_values, y_values = zip(*[(point['pisq_a'], point['bc_squared']) for point in data_for_plotting])\n",
+    "\n",
+    "error_values = [float(point['D_support']-point['D_theory']) for point in data_for_plotting]\n",
+    "\n",
+    "\n",
+    "# Create a scatter plot and color the points based on D_theory\n",
+    "scatter = plt.scatter(x_values, y_values, c=D_theory_values, cmap='viridis', edgecolors='red')\n",
+    "\n",
+    "plt.errorbar(x_values, y_values, xerr=error_values, yerr=error_values, fmt='none', ecolor='gray', alpha=0.5)\n",
+    "\n",
+    "# Add a colorbar to the plot\n",
+    "cbar = plt.colorbar(scatter)\n",
+    "cbar.set_label('D_theory')\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Add contour labels\n",
+    "contour_labels = plt.clabel(contour_lines, inline=True, fontsize=12, colors='black')\n",
+    "\n",
+    "# Add annotations for iso-D values\n",
+    "# for i, txt in enumerate(contour_labels):\n",
+    "    # plt.annotate(f'D={np.around(D_values[0, i], decimals=2)}', (txt.get_position()[0], txt.get_position()[1]))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])"
+      ]
+     },
+     "execution_count": 165,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "D_support_values = [point['D_support'] for point in data_for_plotting]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/ht/z8sb7wsd1bg0qpmyyfyq2rcm0000gr/T/ipykernel_91885/2951459962.py:3: RuntimeWarning: divide by zero encountered in divide\n",
+      "  return (x / y)**(1/3)\n",
+      "/var/folders/ht/z8sb7wsd1bg0qpmyyfyq2rcm0000gr/T/ipykernel_91885/2951459962.py:3: RuntimeWarning: invalid value encountered in divide\n",
+      "  return (x / y)**(1/3)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Define the function\n",
+    "def func(x, y):\n",
+    "    return (x / y)**(1/3)\n",
+    "\n",
+    "# Create a meshgrid\n",
+    "x = np.linspace(0, 10, 100)\n",
+    "y = np.linspace(0, 10, 100)\n",
+    "X, Y = np.meshgrid(x, y)\n",
+    "\n",
+    "# Calculate the function values on the meshgrid\n",
+    "Z = func(X, Y)\n",
+    "\n",
+    "# Create a filled contour plot\n",
+    "plt.contourf(X, Y, Z, cmap='viridis', levels=np.linspace(0, 1, 5))\n",
+    "plt.plot(np.linspace(0, 10), np.linspace(0, 10), color='red', label=r'$bc^2 = \\pi^2 a$')\n",
+    "\n",
+    "# Add a colorbar to the plot\n",
+    "cbar = plt.colorbar()\n",
+    "cbar.set_label('$(x/y)^(1/3)$')\n",
+    "\n",
+    "# Customize the plot\n",
+    "plt.xlabel('x')\n",
+    "plt.ylabel('y')\n",
+    "plt.title('Contourf Plot of $(x/y)^(1/3)$')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Cherry pick the profile"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1'"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dirroot == '/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1'\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'/Users/kumiori3/Documents/WIP/Nature/mec647/test/output/rayleigh-benchmark-parametric/MPI-1/profile_comparison-2f6fb02e066c6e61b2680447a53b61df.pdf'"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "os.path.join(dirroot, f'profile_comparison-{signature}.pdf')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "be93e3a1e3cd2893f62f838768340107\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "55aa665af4383cdfab486497a526b948\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "ae1a96698d6d1339b5ae36a9fc93a239\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "9cfef1c0d73313a1414d49bc38ddeccf\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "446276eafc7b7cb1df263d2d43575334\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "ae03a35d8fe5b10029fb688ae9f08611\n",
+      "case 2\n",
+      "[{x, A}, {x, A}]\n",
+      "1.0249981966475*sqrt(A**2/pi**2 + 0.475908872692863*A**2) True False\n",
+      "depends_on_A\n",
+      "case 2\n",
+      "sqrt(0.139045048633087*sin(1.0*pi**1.0)*cos(1.0*pi**1.0)/pi**0.333333333333333 + 0.139045048633087*pi**0.666666666666667*sin(1.0*pi**1.0)**2 + 0.556180194532348*sin(1.0*pi**1.0)/pi**0.333333333333333 + 0.139045048633087*pi**0.666666666666667*cos(1.0*pi**1.0)**2 + 0.278090097266174*pi**0.666666666666667)*sqrt(C**2)\n",
+      "depends_on_C\n",
+      "c2e711ccd4e5b1706d05d5bd98ef5a8e\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "d323b3bd3f8ced38f7e5ed1370e95681\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "0aa3df8dcb703508b12d1c306410ba06\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "818deb373f5d6257aa085493e2a12fb6\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "39582b0fef781ef7dd59b969861740bb\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "7021e9afc054e604d2e15dbc07a9db83\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "c3066ac9a8f4a735e144494a06586074\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "0bd67cc936b53d779e505e7860e8d3a0\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "5f009deebc7c75e793bc5c2a3881177d\n",
+      "case 2\n",
+      "[{x, A}, {x, A}]\n",
+      "1.16347268130367*sqrt(A**2/pi**2 + 0.369366601544115*A**2) True False\n",
+      "depends_on_A\n",
+      "case 2\n",
+      "sqrt(0.231065424287268*sin(1.0*pi**1.0)*cos(1.0*pi**1.0)/pi**0.333333333333333 + 0.231065424287268*pi**0.666666666666667*sin(1.0*pi**1.0)**2 + 0.924261697149072*sin(1.0*pi**1.0)/pi**0.333333333333333 + 0.231065424287268*pi**0.666666666666667*cos(1.0*pi**1.0)**2 + 0.462130848574536*pi**0.666666666666667)*sqrt(C**2)\n",
+      "depends_on_C\n",
+      "f5987d8fb47e09c39afd3e4aba484450\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "7d21decc9d7b00098894bc65c19ccd48\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "9f6e9f4b03f77f59fd59a719ce310950\n",
+      "case 2\n",
+      "[{x, A}, {x, A}]\n",
+      "sqrt(2)*sqrt(0.389876358479114*A**2/pi**2 + A**2)/2 True False\n",
+      "depends_on_A\n",
+      "case 2\n",
+      "sqrt(0.156269979037593*sin(1.0*pi**1.0)*cos(1.0*pi**1.0)/pi**0.333333333333333 + 0.156269979037593*pi**0.666666666666667*sin(1.0*pi**1.0)**2 + 0.625079916150371*sin(1.0*pi**1.0)/pi**0.333333333333333 + 0.156269979037593*pi**0.666666666666667*cos(1.0*pi**1.0)**2 + 0.312539958075186*pi**0.666666666666667)*sqrt(C**2)\n",
+      "depends_on_C\n",
+      "1da28d85e36e3d87bbd150884f1a6a4a\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "47724b1983ad0ec6fb3a085868e6406c\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "d42c5f930bc58a9b85355978626be7d8\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "046c79dade1d3ac0d860737fb091750e\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "9444e6515b8c6baac6410af233373084\n",
+      "case 2\n",
+      "[{x, A}, {x, A}]\n",
+      "1.14741297213846*sqrt(A**2/pi**2 + 0.379778603596825*A**2) True False\n",
+      "depends_on_A\n",
+      "case 2\n",
+      "sqrt(0.172505400873148*sin(1.0*pi**1.0)*cos(1.0*pi**1.0)/pi**0.333333333333333 + 0.172505400873148*pi**0.666666666666667*sin(1.0*pi**1.0)**2 + 0.690021603492591*sin(1.0*pi**1.0)/pi**0.333333333333333 + 0.172505400873148*pi**0.666666666666667*cos(1.0*pi**1.0)**2 + 0.345010801746296*pi**0.666666666666667)*sqrt(C**2)\n",
+      "depends_on_C\n",
+      "3fa875212869f8bb87ecec023e3594ab\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "245a0619f72f8fa174e14ad18ed79ef8\n",
+      "case 2\n",
+      "[{x, A}, {x, A}]\n",
+      "1.28639219157913*sqrt(A**2/pi**2 + 0.302150428063509*A**2) True False\n",
+      "depends_on_A\n",
+      "case 2\n",
+      "sqrt(0.186072847637724*sin(1.0*pi**1.0)*cos(1.0*pi**1.0)/pi**0.333333333333333 + 0.186072847637724*pi**0.666666666666667*sin(1.0*pi**1.0)**2 + 0.744291390550896*sin(1.0*pi**1.0)/pi**0.333333333333333 + 0.186072847637724*pi**0.666666666666667*cos(1.0*pi**1.0)**2 + 0.372145695275448*pi**0.666666666666667)*sqrt(C**2)\n",
+      "depends_on_C\n",
+      "81211387b53a1545755003f8b2a1fc18\n",
+      "case 2\n",
+      "[{x, A}, {x, A}]\n",
+      "1.12114641497632*sqrt(A**2/pi**2 + 0.397782194392812*A**2) True False\n",
+      "depends_on_A\n",
+      "case 2\n",
+      "sqrt(0.115517635045689*sin(1.0*pi**1.0)*cos(1.0*pi**1.0)/pi**0.333333333333333 + 0.115517635045689*pi**0.666666666666667*sin(1.0*pi**1.0)**2 + 0.462070540182758*sin(1.0*pi**1.0)/pi**0.333333333333333 + 0.115517635045689*pi**0.666666666666667*cos(1.0*pi**1.0)**2 + 0.231035270091379*pi**0.666666666666667)*sqrt(C**2)\n",
+      "depends_on_C\n",
+      "00ffda65398984695ae5576fe383f02c\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "ce9436b3ab369c9ab0d19d5fd85d64fe\n",
+      "case 2\n",
+      "[{x, A}, {x, A}]\n",
+      "1.37455388563356*sqrt(A**2/pi**2 + 0.264634501701232*A**2) True False\n",
+      "depends_on_A\n",
+      "case 2\n",
+      "sqrt(0.214213885264008*sin(1.0*pi**1.0)*cos(1.0*pi**1.0)/pi**0.333333333333333 + 0.214213885264008*pi**0.666666666666667*sin(1.0*pi**1.0)**2 + 0.856855541056033*sin(1.0*pi**1.0)/pi**0.333333333333333 + 0.214213885264008*pi**0.666666666666667*cos(1.0*pi**1.0)**2 + 0.428427770528016*pi**0.666666666666667)*sqrt(C**2)\n",
+      "depends_on_C\n",
+      "bfb5281dbaf30b871676f44cc1395433\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "6e3e4283c6d55967a64bf8d1b588732a\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "adc4418b5f4802de2918f2932e0ad1c6\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "961ec9441a2fa5a82bbed869080b8b3a\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "c18632626e637957d7850f27ba8ed27e\n",
+      "case 2\n",
+      "[{x, A}, {x, A}]\n",
+      "1.16671987856582*sqrt(A**2/pi**2 + 0.367313431533773*A**2) True False\n",
+      "depends_on_A\n",
+      "case 2\n",
+      "sqrt(0.191462954526186*sin(1.0*pi**1.0)*cos(1.0*pi**1.0)/pi**0.333333333333333 + 0.191462954526186*pi**0.666666666666667*sin(1.0*pi**1.0)**2 + 0.765851818104746*sin(1.0*pi**1.0)/pi**0.333333333333333 + 0.191462954526186*pi**0.666666666666667*cos(1.0*pi**1.0)**2 + 0.382925909052373*pi**0.666666666666667)*sqrt(C**2)\n",
+      "depends_on_C\n",
+      "5bb43003ce8ab88ea3e2b5835b018f9b\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "5e1b92e1c2392c4742c4b8caac239078\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "08c18d207e645c06bfbac1f997b0edad\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "04c979b82e2ec3add9a38959f42f52b1\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "c20e9b135c36e94f7498a133cc45e442\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "e0d28a7fc82849a64a0fbc6f584ea8e9\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "873a5a261a51d2c352cec13cbf8fdf0c\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "bec3d3e00694ea0d182b04366cbf8cb0\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "7f1430bcb0a6e3812cc44d0015cc2e19\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "bc4a60b8d7abfbcdec8bb45d864e38a8\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "d5612d1467c7b6d3624c53bc688289df\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "ec046ac8dc9f3843ba2d44fc1f32bf0d\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "fe1bd5e0aae1254c90a08b9fe2d4fcac\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "3e66ff5126900e8b3aacf615357995c8\n",
+      "case 2\n",
+      "[{x, A}, {x, A}]\n",
+      "1.22265093597691*sqrt(A**2/pi**2 + 0.334476057125797*A**2) True False\n",
+      "depends_on_A\n",
+      "case 2\n",
+      "sqrt(0.0634705390029896*sin(1.0*pi**1.0)*cos(1.0*pi**1.0)/pi**0.333333333333333 + 0.0634705390029896*pi**0.666666666666667*sin(1.0*pi**1.0)**2 + 0.253882156011958*sin(1.0*pi**1.0)/pi**0.333333333333333 + 0.0634705390029896*pi**0.666666666666667*cos(1.0*pi**1.0)**2 + 0.126941078005979*pi**0.666666666666667)*sqrt(C**2)\n",
+      "depends_on_C\n",
+      "a6830e9ed97c7590c32e359c547bae37\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "55f85e04d355cf55f777c22037403b12\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "8498148bb21e28e65acd6e832dd392e5\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "e1690d753f1cfe4e6c8ed1999f242f70\n",
+      "case 2\n",
+      "[{x, A}, {x, A}]\n",
+      "sqrt(2)*sqrt(0.976972311860614*A**2/pi**2 + A**2)/2 True False\n",
+      "depends_on_A\n",
+      "case 2\n",
+      "sqrt(0.198315094885309*sin(1.0*pi**1.0)*cos(1.0*pi**1.0)/pi**0.333333333333333 + 0.198315094885309*pi**0.666666666666667*sin(1.0*pi**1.0)**2 + 0.793260379541235*sin(1.0*pi**1.0)/pi**0.333333333333333 + 0.198315094885309*pi**0.666666666666667*cos(1.0*pi**1.0)**2 + 0.396630189770617*pi**0.666666666666667)*sqrt(C**2)\n",
+      "depends_on_C\n",
+      "ebfe4b19968441962ec69b81048f5a40\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "787493f255c3e1fe0f7590724463af47\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "49dd09f415ed6893adec2902800ecfbb\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "dd6df4f37eb57a3022cd311c11ed40b8\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "14dffef10402801b89457241dc466ce3\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "02bd5bbda8beff8f5cc0fbd88b599173\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "1a9321268f43fe71c8ad7f9ab0ffe154\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "95e026c0be6029963131d95f104d98f9\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "bc600344f6d97e175bdff42d5a0d9660\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "99b28ae6fc8d44b1389a5fe6da60a557\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "68c28c98150c0491f40b510fe53d5150\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "3595514d555f90e66127a53c00ad6142\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "e5593a7c5757a917c4833284b849e8e4\n",
+      "case 2\n",
+      "[{x, A}, {x, A}]\n",
+      "1.3894603456363*sqrt(A**2/pi**2 + 0.258986836479791*A**2) True False\n",
+      "depends_on_A\n",
+      "case 2\n",
+      "sqrt(0.218341110101021*sin(1.0*pi**1.0)*cos(1.0*pi**1.0)/pi**0.333333333333333 + 0.218341110101021*pi**0.666666666666667*sin(1.0*pi**1.0)**2 + 0.873364440404083*sin(1.0*pi**1.0)/pi**0.333333333333333 + 0.218341110101021*pi**0.666666666666667*cos(1.0*pi**1.0)**2 + 0.436682220202041*pi**0.666666666666667)*sqrt(C**2)\n",
+      "depends_on_C\n",
+      "154863f5eba04c9bd04c821204662c46\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "7762cc6ecd7e5663aefbc1ac3d1d183a\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "6d28de9a1db1dc3ad984dc0fed840b85\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "76162cca14bfb91a3e02d6e29627bc11\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "6ebe9390869deaeaae9fe15f64d1d60e\n",
+      "case 2\n",
+      "[{x, A}, {x, A}]\n",
+      "1.03013557424391*sqrt(A**2/pi**2 + 0.471173909420131*A**2) True False\n",
+      "depends_on_A\n",
+      "case 2\n",
+      "sqrt(0.223748411074631*sin(1.0*pi**1.0)*cos(1.0*pi**1.0)/pi**0.333333333333333 + 0.223748411074631*pi**0.666666666666667*sin(1.0*pi**1.0)**2 + 0.894993644298525*sin(1.0*pi**1.0)/pi**0.333333333333333 + 0.223748411074631*pi**0.666666666666667*cos(1.0*pi**1.0)**2 + 0.447496822149262*pi**0.666666666666667)*sqrt(C**2)\n",
+      "depends_on_C\n",
+      "ba40fab16ef2ffaa80483f7a471cf2bb\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "a190267e13d71e0a2cefd795da6d16a2\n",
+      "case 2\n",
+      "[{x, A}, {x, A}]\n",
+      "0.864827147650205*sqrt(A**2/pi**2 + 0.668515338594289*A**2) True False\n",
+      "depends_on_A\n",
+      "case 2\n",
+      "sqrt(0.209611900693968*sin(1.0*pi**1.0)*cos(1.0*pi**1.0)/pi**0.333333333333333 + 0.209611900693968*pi**0.666666666666667*sin(1.0*pi**1.0)**2 + 0.838447602775871*sin(1.0*pi**1.0)/pi**0.333333333333333 + 0.209611900693968*pi**0.666666666666667*cos(1.0*pi**1.0)**2 + 0.419223801387935*pi**0.666666666666667)*sqrt(C**2)\n",
+      "depends_on_C\n",
+      "390c1586d26f4ecb8965bb7caafd1a01\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "30b166d2e6f48e17020d5e5457e6e26a\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "0709aa4181151c6be88b7bcafc3e014c\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "9b0cc6ef9c3464d738b9e6c4be7917cf\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "7ed36b8dcee86d9a4d55b31fdd6f7199\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "ef35e224f60f9f4f67a652b6b2bfabc3\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "5f728c69b35c2f0b870a23f20c1cf1bd\n",
+      "case 2\n",
+      "[{x, A}, {x, A}]\n",
+      "sqrt(2)*sqrt(0.0275813980055675*A**2/pi**2 + A**2)/2 True False\n",
+      "depends_on_A\n",
+      "case 2\n",
+      "sqrt(0.15933232897846*sin(1.0*pi**1.0)*cos(1.0*pi**1.0)/pi**0.333333333333333 + 0.15933232897846*pi**0.666666666666667*sin(1.0*pi**1.0)**2 + 0.63732931591384*sin(1.0*pi**1.0)/pi**0.333333333333333 + 0.15933232897846*pi**0.666666666666667*cos(1.0*pi**1.0)**2 + 0.31866465795692*pi**0.666666666666667)*sqrt(C**2)\n",
+      "depends_on_C\n",
+      "aa1c7bcfbf8bd723b77f5710a6a9f594\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "3834fb17dbfd82a9e6b6c78c159139b4\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "379c9f5ef533b7996ca5a6f6e2aebefb\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "19c6e4fb5e77a4485581566f5ac85e7c\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "96e85c7780b0f7c34955ed4e9e8f6075\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "29a13ad515a4e0b380c0765b509f4bd8\n",
+      "case 2\n",
+      "[{x, A}, {x, A}]\n",
+      "0.928087772844877*sqrt(A**2/pi**2 + 0.580486203424807*A**2) True False\n",
+      "depends_on_A\n",
+      "case 2\n",
+      "sqrt(0.215788680626446*sin(1.0*pi**1.0)*cos(1.0*pi**1.0)/pi**0.333333333333333 + 0.215788680626446*pi**0.666666666666667*sin(1.0*pi**1.0)**2 + 0.863154722505782*sin(1.0*pi**1.0)/pi**0.333333333333333 + 0.215788680626446*pi**0.666666666666667*cos(1.0*pi**1.0)**2 + 0.431577361252891*pi**0.666666666666667)*sqrt(C**2)\n",
+      "depends_on_C\n",
+      "70a947a5ab7ce7fea419f5942119a162\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "0e5f3c92253e55d958c60f6c4b3c3c94\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "fa49c46e19fa64edbd26b7a87ff6c06f\n",
+      "case 2\n",
+      "[{x, A}, {x, A}]\n",
+      "1.26762729219637*sqrt(A**2/pi**2 + 0.31116220633933*A**2) True False\n",
+      "depends_on_A\n",
+      "case 2\n",
+      "sqrt(0.204618354232031*sin(1.0*pi**1.0)*cos(1.0*pi**1.0)/pi**0.333333333333333 + 0.204618354232031*pi**0.666666666666667*sin(1.0*pi**1.0)**2 + 0.818473416928122*sin(1.0*pi**1.0)/pi**0.333333333333333 + 0.204618354232031*pi**0.666666666666667*cos(1.0*pi**1.0)**2 + 0.409236708464061*pi**0.666666666666667)*sqrt(C**2)\n",
+      "depends_on_C\n",
+      "ff220f0b4c41c9194a0d56982ad9d9df\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "25739f21e5031ff02151d7be8da54f32\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "9b092eb12c6b86663f750cc82741a54d\n",
+      "case 2\n",
+      "[{x, A}, {x, A}]\n",
+      "1.32107616259455*sqrt(A**2/pi**2 + 0.286493182526239*A**2) True False\n",
+      "depends_on_A\n",
+      "case 2\n",
+      "sqrt(0.142125512922437*sin(1.0*pi**1.0)*cos(1.0*pi**1.0)/pi**0.333333333333333 + 0.142125512922437*pi**0.666666666666667*sin(1.0*pi**1.0)**2 + 0.568502051689748*sin(1.0*pi**1.0)/pi**0.333333333333333 + 0.142125512922437*pi**0.666666666666667*cos(1.0*pi**1.0)**2 + 0.284251025844874*pi**0.666666666666667)*sqrt(C**2)\n",
+      "depends_on_C\n",
+      "197773ce0c3bd5db1389a4e328fc1127\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "384d216e638296c3f2e43c1bc10e6aa2\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "cc67da412eb68626108ce80d9973105b\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "2f31290d5784e79695d28a07513df7cc\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "90167d1609a729e7134e4a923351f671\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "36ba82b093b807f3d4156df5aef56265\n",
+      "case 2\n",
+      "[{x, A}, {x, A}]\n",
+      "1.29169200878477*sqrt(A**2/pi**2 + 0.299676065955124*A**2) True False\n",
+      "depends_on_A\n",
+      "case 2\n",
+      "sqrt(0.144342162084983*sin(1.0*pi**1.0)*cos(1.0*pi**1.0)/pi**0.333333333333333 + 0.144342162084984*pi**0.666666666666667*sin(1.0*pi**1.0)**2 + 0.577368648339934*sin(1.0*pi**1.0)/pi**0.333333333333333 + 0.144342162084984*pi**0.666666666666667*cos(1.0*pi**1.0)**2 + 0.288684324169967*pi**0.666666666666667)*sqrt(C**2)\n",
+      "depends_on_C\n",
+      "6426c7b91e31a7164aa0ac3fe5ca7cc7\n",
+      "case 2\n",
+      "[{x, A}, {x, A}]\n",
+      "1.240726821646*sqrt(A**2/pi**2 + 0.324801228187032*A**2) True False\n",
+      "depends_on_A\n",
+      "case 2\n",
+      "sqrt(0.19984995081053*sin(1.0*pi**1.0)*cos(1.0*pi**1.0)/pi**0.333333333333333 + 0.19984995081053*pi**0.666666666666667*sin(1.0*pi**1.0)**2 + 0.799399803242118*sin(1.0*pi**1.0)/pi**0.333333333333333 + 0.19984995081053*pi**0.666666666666667*cos(1.0*pi**1.0)**2 + 0.399699901621059*pi**0.666666666666667)*sqrt(C**2)\n",
+      "depends_on_C\n",
+      "b33a98e1400345111df1f4a5186d8830\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "0bd72656f57f527e5863ef191c372831\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "a3c39f11e5abdfdb1f68559d8d04e975\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "043ebb377c5c5699e4d0c39a6efff1bd\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "64765b11b3c0dfd58defa91fe80ef236\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "941d48ed62b7edc24a6b564a91b4d6c6\n",
+      "case 2\n",
+      "[{x, A}, {x, A}]\n",
+      "1.11437762606521*sqrt(A**2/pi**2 + 0.402629170585736*A**2) True False\n",
+      "depends_on_A\n",
+      "case 2\n",
+      "sqrt(0.164505708441684*sin(1.0*pi**1.0)*cos(1.0*pi**1.0)/pi**0.333333333333333 + 0.164505708441684*pi**0.666666666666667*sin(1.0*pi**1.0)**2 + 0.658022833766736*sin(1.0*pi**1.0)/pi**0.333333333333333 + 0.164505708441684*pi**0.666666666666667*cos(1.0*pi**1.0)**2 + 0.329011416883368*pi**0.666666666666667)*sqrt(C**2)\n",
+      "depends_on_C\n",
+      "2f170fb0e88ec9e9ada917e40dfe9b56\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "4363c09f4f41e45ec5a021d2b68e8276\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "e8a1433809df201ba869c62c16c0870c\n",
+      "case 2\n",
+      "[{x, A}, {x, A}]\n",
+      "sqrt(2)*sqrt(0.813076119802649*A**2/pi**2 + A**2)/2 True False\n",
+      "depends_on_A\n",
+      "case 2\n",
+      "sqrt(0.189747008527704*sin(1.0*pi**1.0)*cos(1.0*pi**1.0)/pi**0.333333333333333 + 0.189747008527704*pi**0.666666666666667*sin(1.0*pi**1.0)**2 + 0.758988034110815*sin(1.0*pi**1.0)/pi**0.333333333333333 + 0.189747008527704*pi**0.666666666666667*cos(1.0*pi**1.0)**2 + 0.379494017055408*pi**0.666666666666667)*sqrt(C**2)\n",
+      "depends_on_C\n",
+      "12e557a7aa59790b9b017adcc2d61778\n",
+      "case 2\n",
+      "[{x, A}, {x, A}]\n",
+      "0.845563057157335*sqrt(A**2/pi**2 + 0.69932330883479*A**2) True False\n",
+      "depends_on_A\n",
+      "case 2\n",
+      "sqrt(0.203702624336535*sin(1.0*pi**1.0)*cos(1.0*pi**1.0)/pi**0.333333333333333 + 0.203702624336535*pi**0.666666666666667*sin(1.0*pi**1.0)**2 + 0.814810497346139*sin(1.0*pi**1.0)/pi**0.333333333333333 + 0.203702624336535*pi**0.666666666666667*cos(1.0*pi**1.0)**2 + 0.407405248673069*pi**0.666666666666667)*sqrt(C**2)\n",
+      "depends_on_C\n",
+      "2ab9598ff854eb657a12d777d1374f74\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "a80319cc10e4c333d4b0bdd3b113526d\n",
+      "case 2\n",
+      "[{x, A}, {x, A}]\n",
+      "1.23373288255977*sqrt(A**2/pi**2 + 0.3284942137848*A**2) True False\n",
+      "depends_on_A\n",
+      "case 2\n",
+      "sqrt(0.2011399233956*sin(1.0*pi**1.0)*cos(1.0*pi**1.0)/pi**0.333333333333333 + 0.2011399233956*pi**0.666666666666667*sin(1.0*pi**1.0)**2 + 0.804559693582401*sin(1.0*pi**1.0)/pi**0.333333333333333 + 0.2011399233956*pi**0.666666666666667*cos(1.0*pi**1.0)**2 + 0.4022798467912*pi**0.666666666666667)*sqrt(C**2)\n",
+      "depends_on_C\n",
+      "67450e668b5e0f33bd764955db697890\n",
+      "case 2\n",
+      "[{x, A}, {x, A}]\n",
+      "1.22833837228112*sqrt(A**2/pi**2 + 0.331385854483588*A**2) True False\n",
+      "depends_on_A\n",
+      "case 2\n",
+      "sqrt(0.219429767069151*sin(1.0*pi**1.0)*cos(1.0*pi**1.0)/pi**0.333333333333333 + 0.219429767069151*pi**0.666666666666667*sin(1.0*pi**1.0)**2 + 0.877719068276604*sin(1.0*pi**1.0)/pi**0.333333333333333 + 0.219429767069151*pi**0.666666666666667*cos(1.0*pi**1.0)**2 + 0.438859534138302*pi**0.666666666666667)*sqrt(C**2)\n",
+      "depends_on_C\n",
+      "fab2c241b23cc097ef2a6c33ca7d1cb4\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "f44e21cb4fe6fab825843b049649c479\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "63664dec0d71acbc99c1bc0610834138\n",
+      "case 2\n",
+      "[{x, A}, {x, A}]\n",
+      "0.974685896582915*sqrt(A**2/pi**2 + 0.526308810620162*A**2) True False\n",
+      "depends_on_A\n",
+      "case 2\n",
+      "sqrt(0.129437641546106*sin(1.0*pi**1.0)*cos(1.0*pi**1.0)/pi**0.333333333333333 + 0.129437641546106*pi**0.666666666666667*sin(1.0*pi**1.0)**2 + 0.517750566184423*sin(1.0*pi**1.0)/pi**0.333333333333333 + 0.129437641546106*pi**0.666666666666667*cos(1.0*pi**1.0)**2 + 0.258875283092211*pi**0.666666666666667)*sqrt(C**2)\n",
+      "depends_on_C\n",
+      "f582d6b1956c5d8756fcecd3ed31854c\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "37c112d0c344dfbd6591d46e6aa4bf2b\n",
+      "case 2\n",
+      "[{x, A}, {x, A}]\n",
+      "0.937725208262756*sqrt(A**2/pi**2 + 0.568615667922903*A**2) True False\n",
+      "depends_on_A\n",
+      "case 2\n",
+      "sqrt(0.218624644557606*sin(1.0*pi**1.0)*cos(1.0*pi**1.0)/pi**0.333333333333333 + 0.218624644557606*pi**0.666666666666667*sin(1.0*pi**1.0)**2 + 0.874498578230423*sin(1.0*pi**1.0)/pi**0.333333333333333 + 0.218624644557606*pi**0.666666666666667*cos(1.0*pi**1.0)**2 + 0.437249289115212*pi**0.666666666666667)*sqrt(C**2)\n",
+      "depends_on_C\n",
+      "d0b0b2ded7b4d07b0615afe8c5c3bd29\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "af46e6dbd26ef9975009e0f908a0703d\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "4dd050367ed3d746a44a8627a27c0d86\n",
+      "case 2\n",
+      "[{x, A}, {x, A}]\n",
+      "0.824053111462949*sqrt(A**2/pi**2 + 0.736308132500034*A**2) True False\n",
+      "depends_on_A\n",
+      "case 2\n",
+      "sqrt(0.186151378196862*sin(1.0*pi**1.0)*cos(1.0*pi**1.0)/pi**0.333333333333333 + 0.186151378196862*pi**0.666666666666667*sin(1.0*pi**1.0)**2 + 0.744605512787448*sin(1.0*pi**1.0)/pi**0.333333333333333 + 0.186151378196862*pi**0.666666666666667*cos(1.0*pi**1.0)**2 + 0.372302756393724*pi**0.666666666666667)*sqrt(C**2)\n",
+      "depends_on_C\n",
+      "a26e29a64522fde13f1a0939a6436b09\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "b16a7008d41ce565818e053e6ef9451f\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "5e62740f1e3d26cfadf3f31606f04c93\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "c9874acaa4e7cea139377a1cc4aa96da\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "a07a340d8cd563655bef328ad2631adc\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "879c22c6dd6ba9258ed2b3977c02dd3f\n",
+      "case 2\n",
+      "[{x, A}, {x, A}]\n",
+      "1.20393130305273*sqrt(A**2/pi**2 + 0.344958293885304*A**2) True False\n",
+      "depends_on_A\n",
+      "case 2\n",
+      "sqrt(0.0636117850615129*sin(1.0*pi**1.0)*cos(1.0*pi**1.0)/pi**0.333333333333333 + 0.0636117850615129*pi**0.666666666666667*sin(1.0*pi**1.0)**2 + 0.254447140246052*sin(1.0*pi**1.0)/pi**0.333333333333333 + 0.0636117850615129*pi**0.666666666666667*cos(1.0*pi**1.0)**2 + 0.127223570123026*pi**0.666666666666667)*sqrt(C**2)\n",
+      "depends_on_C\n",
+      "c4e8f9a815f3c85c2b3bc35ded8d4c55\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "7fd56cac090b0910c9796189bb13f529\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "8c2849493a108685ea6d16538cee0ff5\n",
+      "case 2\n",
+      "[{x, A}, {x, A}]\n",
+      "1.28083068527927*sqrt(A**2/pi**2 + 0.304780064897491*A**2) True False\n",
+      "depends_on_A\n",
+      "case 2\n",
+      "sqrt(0.13889432923305*sin(1.0*pi**1.0)*cos(1.0*pi**1.0)/pi**0.333333333333333 + 0.13889432923305*pi**0.666666666666667*sin(1.0*pi**1.0)**2 + 0.555577316932201*sin(1.0*pi**1.0)/pi**0.333333333333333 + 0.13889432923305*pi**0.666666666666667*cos(1.0*pi**1.0)**2 + 0.2777886584661*pi**0.666666666666667)*sqrt(C**2)\n",
+      "depends_on_C\n",
+      "704218ec1ab1084c07564e6f679b9f0b\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "e2f0baf33acd97c5fa0650b3ac188b33\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "7a5fe95e68652560e04a4f04a6cc2ed0\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "28a07bc7bb355d05782734af39a111e9\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "3ced327f796a04949713bfbb43236c70\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "a68e8bf1ea79b67d5e12e0814b0e9b02\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "2afb851ba8413ce5291c1f7281b0b60a\n",
+      "case 2\n",
+      "[{x, A}, {x, A}]\n",
+      "0.807183621709452*sqrt(A**2/pi**2 + 0.76740623239409*A**2) True False\n",
+      "depends_on_A\n",
+      "case 2\n",
+      "sqrt(0.161717167560375*sin(1.0*pi**1.0)*cos(1.0*pi**1.0)/pi**0.333333333333333 + 0.161717167560375*pi**0.666666666666667*sin(1.0*pi**1.0)**2 + 0.646868670241501*sin(1.0*pi**1.0)/pi**0.333333333333333 + 0.161717167560375*pi**0.666666666666667*cos(1.0*pi**1.0)**2 + 0.323434335120751*pi**0.666666666666667)*sqrt(C**2)\n",
+      "depends_on_C\n",
+      "ff554b36ebfb02ba497f4de21e7bfe39\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "89829464cf9d8abc402d2b289511db80\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "3957404c3a99817081032e3304cc2eb2\n",
+      "case 2\n",
+      "[{x, A}, {x, A}]\n",
+      "1.06051532680018*sqrt(A**2/pi**2 + 0.444565857008846*A**2) True False\n",
+      "depends_on_A\n",
+      "case 2\n",
+      "sqrt(0.224795135026764*sin(1.0*pi**1.0)*cos(1.0*pi**1.0)/pi**0.333333333333333 + 0.224795135026764*pi**0.666666666666667*sin(1.0*pi**1.0)**2 + 0.899180540107055*sin(1.0*pi**1.0)/pi**0.333333333333333 + 0.224795135026764*pi**0.666666666666667*cos(1.0*pi**1.0)**2 + 0.449590270053528*pi**0.666666666666667)*sqrt(C**2)\n",
+      "depends_on_C\n",
+      "bdd49d4f44c93368e0813b6e75c2cff9\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "a07cb68a370231f37c708389c12628ea\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "034189f38c18e6c04e59770762ddf137\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "41a9fd1aaeefba3a8b4e181a3f57a26d\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "1b32af79ec982846c16e8da2dece9111\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "5971508ca172ee7705ba9867da5f2799\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "89d72ebf25cb6d5a889723f29e937e64\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "812f71e51faf4596904b7256abf7468f\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "64cb9ae1176525d58b69364473f71172\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "b6b45a5ed94214a2ab70c5631f8bd38c\n",
+      "case 2\n",
+      "[{x, A}, {x, A}]\n",
+      "1.40552929192986*sqrt(A**2/pi**2 + 0.253098867813504*A**2) True False\n",
+      "depends_on_A\n",
+      "case 2\n",
+      "sqrt(0.177172376996749*sin(1.0*pi**1.0)*cos(1.0*pi**1.0)/pi**0.333333333333333 + 0.177172376996749*pi**0.666666666666667*sin(1.0*pi**1.0)**2 + 0.708689507986997*sin(1.0*pi**1.0)/pi**0.333333333333333 + 0.177172376996749*pi**0.666666666666667*cos(1.0*pi**1.0)**2 + 0.354344753993498*pi**0.666666666666667)*sqrt(C**2)\n",
+      "depends_on_C\n",
+      "de17f9e07113ce99eff6166bf2d5151e\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "1ed7e041da9cefb263f8e5283ed9eabe\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "da90dc28fb90db28270d1efd575cbf90\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "840d841d4f4ab073d4675980ae0c65e2\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "64beb86873a673b4b1d7d131a3b7cca1\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "4a42005d5d1db515c58f10fb2a977b4f\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "77b345b3ac23acd4b0c793607df72b86\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "8c959694f7c0083563629bbd7f6d26b9\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "40e1eb6b7c27798bd70c26bb89188010\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "e5cebd7454caec5911970426e4ebfa02\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "5e00139ccb323f8a614dbc86e4adee17\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "53326ce0c7e2e31e9e711749f6c9872f\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "9283ffcb15cf65f0be191c4877fa3f8c\n",
+      "case 2\n",
+      "[{x, A}, {x, A}]\n",
+      "sqrt(2)*sqrt(0.837574937414457*A**2/pi**2 + A**2)/2 True False\n",
+      "depends_on_A\n",
+      "case 2\n",
+      "sqrt(0.229091469664474*sin(1.0*pi**1.0)*cos(1.0*pi**1.0)/pi**0.333333333333333 + 0.229091469664474*pi**0.666666666666667*sin(1.0*pi**1.0)**2 + 0.916365878657895*sin(1.0*pi**1.0)/pi**0.333333333333333 + 0.229091469664474*pi**0.666666666666667*cos(1.0*pi**1.0)**2 + 0.458182939328948*pi**0.666666666666667)*sqrt(C**2)\n",
+      "depends_on_C\n",
+      "06fd589cfddf7421b0f422bd92c4f077\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "aea34470dff6023e11932e8f26ea8fae\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "b746fbbe0449671bc3fef0ba56043a91\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "4fb5053042bc75c426a6bbcccc364670\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "e3e24962746bbc142697dac71768e280\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "63ef09428da038d6a13c1db516b12cba\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "7bf0064c79e577a339a3bdaa8b664053\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "186ab8e8139e59cdbddc72429ba378be\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "23a4e99376dd709c7a7926c1b512d89c\n",
+      "case 2\n",
+      "[{x, A}, {x, A}]\n",
+      "1.22116262449171*sqrt(A**2/pi**2 + 0.335291850044759*A**2) True False\n",
+      "depends_on_A\n",
+      "case 2\n",
+      "sqrt(0.0776021926907775*sin(1.0*pi**1.0)*cos(1.0*pi**1.0)/pi**0.333333333333333 + 0.0776021926907775*pi**0.666666666666667*sin(1.0*pi**1.0)**2 + 0.31040877076311*sin(1.0*pi**1.0)/pi**0.333333333333333 + 0.0776021926907775*pi**0.666666666666667*cos(1.0*pi**1.0)**2 + 0.155204385381555*pi**0.666666666666667)*sqrt(C**2)\n",
+      "depends_on_C\n",
+      "a94574b4ab665c6845a57507127c7f19\n",
+      "case 2\n",
+      "[{x, A}, {x, A}]\n",
+      "1.37189872338322*sqrt(A**2/pi**2 + 0.265659836117528*A**2) True False\n",
+      "depends_on_A\n",
+      "case 2\n",
+      "sqrt(0.177053970982077*sin(1.0*pi**1.0)*cos(1.0*pi**1.0)/pi**0.333333333333333 + 0.177053970982077*pi**0.666666666666667*sin(1.0*pi**1.0)**2 + 0.708215883928309*sin(1.0*pi**1.0)/pi**0.333333333333333 + 0.177053970982077*pi**0.666666666666667*cos(1.0*pi**1.0)**2 + 0.354107941964155*pi**0.666666666666667)*sqrt(C**2)\n",
+      "depends_on_C\n",
+      "ca10db54d8b535438ffcde1fd38529f8\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "0ce6d727b36b2329f0bbb7bbcdbedc32\n",
+      "case 2\n",
+      "[{x, A}, {x, A}]\n",
+      "1.20941481404067*sqrt(A**2/pi**2 + 0.34183728968343*A**2) True False\n",
+      "depends_on_A\n",
+      "case 2\n",
+      "sqrt(0.225981389098093*sin(1.0*pi**1.0)*cos(1.0*pi**1.0)/pi**0.333333333333333 + 0.225981389098093*pi**0.666666666666667*sin(1.0*pi**1.0)**2 + 0.903925556392373*sin(1.0*pi**1.0)/pi**0.333333333333333 + 0.225981389098093*pi**0.666666666666667*cos(1.0*pi**1.0)**2 + 0.451962778196186*pi**0.666666666666667)*sqrt(C**2)\n",
+      "depends_on_C\n",
+      "d8eafafce323951597ee1a2ccd103a09\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "f8e44e6d6d9486ac535ec276bfb1b520\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "6ea3722739a32d235c59aae7015d2132\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "f2ad2e96cf1f087e6a016e4dfe16cc9a\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "22c97e323c9093987f705e9112b3640a\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "287b7dad39b1a76397aff28461a3ae8c\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "26df8fc60531553c4539463d1c28dfec\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "7d1d96b15a68e392abeffe502f84a042\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "55decbb21da6eccb8c64662f82e90e07\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "1e684c7c8d3b74ab8946e9d16a169644\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "eda0bed753c4b3d8ead98daa3fbaaa4f\n",
+      "case 2\n",
+      "[{x, A}, {x, A}]\n",
+      "1.17609497632587*sqrt(A**2/pi**2 + 0.361480783181255*A**2) True False\n",
+      "depends_on_A\n",
+      "case 2\n",
+      "sqrt(0.0289185946618053*sin(1.0*pi**1.0)*cos(1.0*pi**1.0)/pi**0.333333333333333 + 0.0289185946618053*pi**0.666666666666667*sin(1.0*pi**1.0)**2 + 0.115674378647221*sin(1.0*pi**1.0)/pi**0.333333333333333 + 0.0289185946618053*pi**0.666666666666667*cos(1.0*pi**1.0)**2 + 0.0578371893236105*pi**0.666666666666667)*sqrt(C**2)\n",
+      "depends_on_C\n",
+      "cfacf6ef677c8603351e06e2bd728418\n",
+      "case 2\n",
+      "[{x, A}, {x, A}]\n",
+      "0.944677668237276*sqrt(A**2/pi**2 + 0.560276886321485*A**2) True False\n",
+      "depends_on_A\n",
+      "case 2\n",
+      "sqrt(0.20513744547868*sin(1.0*pi**1.0)*cos(1.0*pi**1.0)/pi**0.333333333333333 + 0.20513744547868*pi**0.666666666666667*sin(1.0*pi**1.0)**2 + 0.820549781914721*sin(1.0*pi**1.0)/pi**0.333333333333333 + 0.20513744547868*pi**0.666666666666667*cos(1.0*pi**1.0)**2 + 0.410274890957361*pi**0.666666666666667)*sqrt(C**2)\n",
+      "depends_on_C\n",
+      "8683c8a249116de1e4a1f2efb7540756\n",
+      "case 2\n",
+      "[{x, A}, {x, A}]\n",
+      "1.09295581996723*sqrt(A**2/pi**2 + 0.418566811959751*A**2) True False\n",
+      "depends_on_A\n",
+      "case 2\n",
+      "sqrt(0.204843639070687*sin(1.0*pi**1.0)*cos(1.0*pi**1.0)/pi**0.333333333333333 + 0.204843639070687*pi**0.666666666666667*sin(1.0*pi**1.0)**2 + 0.81937455628275*sin(1.0*pi**1.0)/pi**0.333333333333333 + 0.204843639070687*pi**0.666666666666667*cos(1.0*pi**1.0)**2 + 0.409687278141375*pi**0.666666666666667)*sqrt(C**2)\n",
+      "depends_on_C\n",
+      "35992c02ce19e70d4b7646206dc625fb\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "0044f692a05422bb98c4bd3c6d04770c\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "133a813220736fd35ca5780e0704fc1f\n",
+      "case 2\n",
+      "[{x, A}, {x, A}]\n",
+      "1.32121192962224*sqrt(A**2/pi**2 + 0.286434305780594*A**2) True False\n",
+      "depends_on_A\n",
+      "case 2\n",
+      "sqrt(0.204202523242716*sin(1.0*pi**1.0)*cos(1.0*pi**1.0)/pi**0.333333333333333 + 0.204202523242716*pi**0.666666666666667*sin(1.0*pi**1.0)**2 + 0.816810092970865*sin(1.0*pi**1.0)/pi**0.333333333333333 + 0.204202523242716*pi**0.666666666666667*cos(1.0*pi**1.0)**2 + 0.408405046485432*pi**0.666666666666667)*sqrt(C**2)\n",
+      "depends_on_C\n",
+      "e91e3e141d58633d9f065fe841e1d704\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "d13a4206c4dc9798f6918905bd964ef1\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "38ec6d9ed3750e2a349aab9393a444c1\n",
+      "case 2\n",
+      "[{x, A}, {x, A}]\n",
+      "1.29030321260027*sqrt(A**2/pi**2 + 0.300321515725262*A**2) True False\n",
+      "depends_on_A\n",
+      "case 2\n",
+      "sqrt(0.164186875823625*sin(1.0*pi**1.0)*cos(1.0*pi**1.0)/pi**0.333333333333333 + 0.164186875823625*pi**0.666666666666667*sin(1.0*pi**1.0)**2 + 0.6567475032945*sin(1.0*pi**1.0)/pi**0.333333333333333 + 0.164186875823625*pi**0.666666666666667*cos(1.0*pi**1.0)**2 + 0.32837375164725*pi**0.666666666666667)*sqrt(C**2)\n",
+      "depends_on_C\n",
+      "80bf33f54ca3cc39d55895987d22db6a\n",
+      "case 2\n",
+      "[{x, A}, {x, A}]\n",
+      "1.01433926423351*sqrt(A**2/pi**2 + 0.485963364697932*A**2) True False\n",
+      "depends_on_A\n",
+      "case 2\n",
+      "sqrt(0.226884503772196*sin(1.0*pi**1.0)*cos(1.0*pi**1.0)/pi**0.333333333333333 + 0.226884503772196*pi**0.666666666666667*sin(1.0*pi**1.0)**2 + 0.907538015088783*sin(1.0*pi**1.0)/pi**0.333333333333333 + 0.226884503772196*pi**0.666666666666667*cos(1.0*pi**1.0)**2 + 0.453769007544392*pi**0.666666666666667)*sqrt(C**2)\n",
+      "depends_on_C\n",
+      "43f6745ab4a8087d95d6dad67b61c455\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "6ca3ebf88d38ddd014eac49e0ef69663\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "ba6bde1c11d365939c1e66712a8a818b\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "08024b8d0f056a2e29555bbcff919b96\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "86ff1f0a410c7220fea17218daac0b51\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "1dc98a04e21fe62758b71ea2affc59ef\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "182ec8780648c9a9ab4b05e10b28263a\n",
+      "case 2\n",
+      "[{x, A}, {x, A}]\n",
+      "1.18489183324469*sqrt(A**2/pi**2 + 0.356133306601046*A**2) True False\n",
+      "depends_on_A\n",
+      "case 2\n",
+      "sqrt(0.132757059176979*sin(1.0*pi**1.0)*cos(1.0*pi**1.0)/pi**0.333333333333333 + 0.132757059176979*pi**0.666666666666667*sin(1.0*pi**1.0)**2 + 0.531028236707918*sin(1.0*pi**1.0)/pi**0.333333333333333 + 0.132757059176979*pi**0.666666666666667*cos(1.0*pi**1.0)**2 + 0.265514118353959*pi**0.666666666666667)*sqrt(C**2)\n",
+      "depends_on_C\n",
+      "fcab5744d2c104f2879ab24a05eb5965\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "8d808fc699689924b60841e1e2164771\n",
+      "case 2\n",
+      "[{x, A}, {x, A}]\n",
+      "1.36371161930608*sqrt(A**2/pi**2 + 0.268859212739202*A**2) True False\n",
+      "depends_on_A\n",
+      "case 2\n",
+      "sqrt(0.168232512161732*sin(1.0*pi**1.0)*cos(1.0*pi**1.0)/pi**0.333333333333333 + 0.168232512161732*pi**0.666666666666667*sin(1.0*pi**1.0)**2 + 0.672930048646928*sin(1.0*pi**1.0)/pi**0.333333333333333 + 0.168232512161732*pi**0.666666666666667*cos(1.0*pi**1.0)**2 + 0.336465024323464*pi**0.666666666666667)*sqrt(C**2)\n",
+      "depends_on_C\n",
+      "b98b104f64f2bda9eb8854ff4d53ecd3\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "45638e207aa96b4f92e27f59f0b544f5\n",
+      "case 2\n",
+      "[{x, A}, {x, A}]\n",
+      "0.895899920186144*sqrt(A**2/pi**2 + 0.622946870687278*A**2) True False\n",
+      "depends_on_A\n",
+      "case 2\n",
+      "sqrt(0.129522555447145*sin(1.0*pi**1.0)*cos(1.0*pi**1.0)/pi**0.333333333333333 + 0.129522555447145*pi**0.666666666666667*sin(1.0*pi**1.0)**2 + 0.518090221788579*sin(1.0*pi**1.0)/pi**0.333333333333333 + 0.129522555447145*pi**0.666666666666667*cos(1.0*pi**1.0)**2 + 0.25904511089429*pi**0.666666666666667)*sqrt(C**2)\n",
+      "depends_on_C\n",
+      "699b629be39a2827f23a164b8bb30c1a\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "154b1d59bc43f05c208cd7e9315d48c9\n",
+      "case 2\n",
+      "[{x, A}, {x, A}]\n",
+      "0.794406736799016*sqrt(A**2/pi**2 + 0.792289987215668*A**2) True False\n",
+      "depends_on_A\n",
+      "case 2\n",
+      "sqrt(0.220678930883439*sin(1.0*pi**1.0)*cos(1.0*pi**1.0)/pi**0.333333333333333 + 0.220678930883439*pi**0.666666666666667*sin(1.0*pi**1.0)**2 + 0.882715723533758*sin(1.0*pi**1.0)/pi**0.333333333333333 + 0.220678930883439*pi**0.666666666666667*cos(1.0*pi**1.0)**2 + 0.441357861766879*pi**0.666666666666667)*sqrt(C**2)\n",
+      "depends_on_C\n",
+      "7d44e0b425e269c22219e93ee416e825\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "1d99dc57effa20f609a0cf624bd5e04a\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "459c554af4f32b088b68a93b3a02f60a\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "3768e99b08bf76083993540fa0e931b5\n",
+      "case 2\n",
+      "[{x, A}, {x, A}]\n",
+      "sqrt(2)*sqrt(0.988988953513421*A**2/pi**2 + A**2)/2 True False\n",
+      "depends_on_A\n",
+      "case 2\n",
+      "sqrt(0.2230723991807*sin(1.0*pi**1.0)*cos(1.0*pi**1.0)/pi**0.333333333333333 + 0.2230723991807*pi**0.666666666666667*sin(1.0*pi**1.0)**2 + 0.892289596722799*sin(1.0*pi**1.0)/pi**0.333333333333333 + 0.2230723991807*pi**0.666666666666667*cos(1.0*pi**1.0)**2 + 0.446144798361399*pi**0.666666666666667)*sqrt(C**2)\n",
+      "depends_on_C\n",
+      "b091b8fc793c49134b42139b7933be99\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "f0d2d1392eba4f49035c1ce0d431ec6a\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "3f02e8edb6dc1f797b9be04d540e0b77\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "bb8fae8e118a003c63c47a3c93963786\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "5869943e0762f690d3334659af3af68d\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "802c675e74c573280a1fb772daaedf9c\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "303619e93fae27433a982bc1565704fd\n",
+      "case 2\n",
+      "[{x, A}, {x, A}]\n",
+      "0.802434872898332*sqrt(A**2/pi**2 + 0.776516012301451*A**2) True False\n",
+      "depends_on_A\n",
+      "case 2\n",
+      "sqrt(0.181757025964449*sin(1.0*pi**1.0)*cos(1.0*pi**1.0)/pi**0.333333333333333 + 0.181757025964449*pi**0.666666666666667*sin(1.0*pi**1.0)**2 + 0.727028103857795*sin(1.0*pi**1.0)/pi**0.333333333333333 + 0.181757025964449*pi**0.666666666666667*cos(1.0*pi**1.0)**2 + 0.363514051928897*pi**0.666666666666667)*sqrt(C**2)\n",
+      "depends_on_C\n",
+      "a3c633c5b54b9f0cb5e4ed723d52b162\n",
+      "case 2\n",
+      "[{x, A}, {x, A}]\n",
+      "1.01847570094373*sqrt(A**2/pi**2 + 0.482023997906991*A**2) True False\n",
+      "depends_on_A\n",
+      "case 2\n",
+      "sqrt(0.147583002292172*sin(1.0*pi**1.0)*cos(1.0*pi**1.0)/pi**0.333333333333333 + 0.147583002292172*pi**0.666666666666667*sin(1.0*pi**1.0)**2 + 0.590332009168689*sin(1.0*pi**1.0)/pi**0.333333333333333 + 0.147583002292172*pi**0.666666666666667*cos(1.0*pi**1.0)**2 + 0.295166004584345*pi**0.666666666666667)*sqrt(C**2)\n",
+      "depends_on_C\n",
+      "5240559833b5075c63c9ced7cf58d9dc\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "942438a56c8ffba16a34fefb4a9aca6a\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "b48b6c1cae8f9873ac2b5fdd6bfca86a\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "cb8f68bfb97cc6dac3d6e3db3377c222\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "6e7f390a6ef123e2c2a6788f98edf934\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "fe65f734e95ed43aa28396952f76bc12\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "35b902ddb2944770f8fcc585b48334e2\n",
+      "case 2\n",
+      "[{x, A}, {x, A}]\n",
+      "1.21118861598054*sqrt(A**2/pi**2 + 0.340836772283391*A**2) True False\n",
+      "depends_on_A\n",
+      "case 2\n",
+      "sqrt(0.222348536145379*sin(1.0*pi**1.0)*cos(1.0*pi**1.0)/pi**0.333333333333333 + 0.222348536145379*pi**0.666666666666667*sin(1.0*pi**1.0)**2 + 0.889394144581516*sin(1.0*pi**1.0)/pi**0.333333333333333 + 0.222348536145379*pi**0.666666666666667*cos(1.0*pi**1.0)**2 + 0.444697072290758*pi**0.666666666666667)*sqrt(C**2)\n",
+      "depends_on_C\n",
+      "9a0ebd16dc2c6873f51f15b6614b73e8\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "1bde21d6d9e399af2546e2f9010b9350\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "824d7bb2017e24e06fb6a4d7d87994f0\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "f81b240be91d490535164f911aada9ed\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n",
+      "7c63c15b4401024888916ca9b42b21c9\n",
+      "case 2\n",
+      "[{x, A}, {x, A}]\n",
+      "sqrt(2)*sqrt(0.484148729912874*A**2/pi**2 + A**2)/2 True False\n",
+      "depends_on_A\n",
+      "case 2\n",
+      "sqrt(0.134957438245491*sin(1.0*pi**1.0)*cos(1.0*pi**1.0)/pi**0.333333333333333 + 0.134957438245491*pi**0.666666666666667*sin(1.0*pi**1.0)**2 + 0.539829752981963*sin(1.0*pi**1.0)/pi**0.333333333333333 + 0.134957438245491*pi**0.666666666666667*cos(1.0*pi**1.0)**2 + 0.269914876490981*pi**0.666666666666667)*sqrt(C**2)\n",
+      "depends_on_C\n",
+      "4332c6bbfc3a256c63470ab9c3d7e8a9\n",
+      "case 1\n",
+      "[set(), {C}]\n",
+      "sqrt(C**2) False True\n",
+      "depends_on_C\n",
+      "case 1\n",
+      "sqrt(C**2)\n",
+      "depends_on_C\n"
+     ]
+    }
+   ],
+   "source": [
+    "successful_points = []\n",
+    "unsuccessful_points = []\n",
+    "data_for_plotting = []\n",
+    "\n",
+    "\n",
+    "for subdir, _, _ in os.walk(dirroot):\n",
+    "    parameters = load_parameters(subdir)\n",
+    "    signature = load_signature(subdir)\n",
+    "    \n",
+    "    if signature is None:\n",
+    "        continue\n",
+    "    print(signature)\n",
+    "\n",
+    "    if parameters is not None:\n",
+    "        a = parameters.get('model', {}).get('a')\n",
+    "        b = parameters.get('model', {}).get('b')\n",
+    "        c = parameters.get('model', {}).get('c')\n",
+    "\n",
+    "        success_file = os.path.join(subdir, 'mode_shapes_data.npz')\n",
+    "        if os.path.exists(success_file):\n",
+    "            successful_points.append((a, b*c**2))\n",
+    "            modes_data = np.load(os.path.join(subdir, 'mode_shapes_data.npz'), allow_pickle=True)\n",
+    "            mode = pp.read_mode_data_from_npz(modes_data, time_step=0, num_modes=1, num_points=10)\n",
+    "            \n",
+    "            parameters = {\"a\": a, \"b\": b, \"c\": c}\n",
+    "            fig, ax = plot_profile_comparison(parameters, mode, idx=[1, 0], reverse=False)\n",
+    "            fig.savefig(os.path.join(subdir, 'profile_comparison.pdf'), dpi=300)\n",
+    "            fig.savefig(os.path.join(dirroot, f'profile_comparison-{signature}.pdf'), dpi=300)\n",
+    "            plt.close(fig)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "File 'time_data.json' not found. Handle this case accordingly.\n",
+      "case 2\n",
+      "[{x, A}, {x, A}]\n",
+      "sqrt(A**2/2 + 32*A**2/pi**2) True False\n",
+      "depends_on_A\n",
+      "case 2\n",
+      "sqrt(0.125*sin(1.0*pi**1.0)*cos(1.0*pi**1.0)/pi**0.333333333333333 + 0.125*pi**0.666666666666667*sin(1.0*pi**1.0)**2 + 0.5*sin(1.0*pi**1.0)/pi**0.333333333333333 + 0.125*pi**0.666666666666667*cos(1.0*pi**1.0)**2 + 0.25*pi**0.666666666666667)*sqrt(C**2)\n",
+      "depends_on_C\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "experiment = '../../test/output/rayleigh-benchmark/MPI-1/bef047fb68f6bc3b5feb6b2f634b15fc'\n",
+    "# experiment = '../../test/output/rayleigh-benchmark/MPI-1/93666c80ed3523e3c2f7140d2788f85c'\n",
+    "\n",
+    "modes_data = np.load(os.path.join(experiment, 'mode_shapes_data.npz'), allow_pickle=True)\n",
+    "mode = pp.read_mode_data_from_npz(modes_data, time_step=0, num_modes=1, num_points=10)\n",
+    "\n",
+    "params, data, signature = pp.load_data(experiment)\n",
+    "\n",
+    "a = params['model']['a']\n",
+    "b = params['model']['b']\n",
+    "c = params['model']['c']\n",
+    "\n",
+    "parameters = {\"a\": a, \"b\": b, \"c\": c}\n",
+    "\n",
+    "plot_profile_comparison(parameters, mode, idx=[1, 0], reverse=False)\n",
+    "\n",
+    "plt.savefig(os.path.join('../../test/output/rayleigh-benchmark/MPI-1', 'profile_comparison.pdf'), dpi=300)\n",
+    "plt.savefig(os.path.join('../../test/output/rayleigh-benchmark/MPI-1', 'profile_comparison.png'), dpi=300)\n",
+    "\n",
+    "\n"
+   ]
+  }
+ ],
+ "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": "3.12.5"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/playground/nb/postprocess.py b/playground/nb/postprocess.py
index a520b889..5a7966cf 100644
--- a/playground/nb/postprocess.py
+++ b/playground/nb/postprocess.py
@@ -1,5 +1,4 @@
 import hashlib
-
 # import xmltodict
 # import pickle
 import json
@@ -9,7 +8,6 @@
 import matplotlib.patches as patches
 import matplotlib.pyplot as plt
 import numpy as np
-
 # import pandas
 import pandas as pd
 import yaml
@@ -111,7 +109,7 @@ def plot_fills(ax, ell, tc):
 
 def plot_spectrum(params, data, tc, ax=None, tol=1e-12):
     E0 = params["model"]["E"]
-    w1 = params["model"]["sigma_D0"] ** 2 / E0
+    params["model"]["sigma_D0"] ** 2 / E0
     ell = params["model"]["ell"]
     fig = plt.figure()
     for i, d in enumerate(data["eigs"]):
@@ -137,8 +135,8 @@ def plot_spectrum(params, data, tc, ax=None, tol=1e-12):
     ax2 = plt.twinx()
     ax2.plot(data["load"].values, data["alpha_max"].values, label="$$max(\\alpha)$$")
     ax2.legend()
-    tbif = t_bif(ell)
-    tstab = t_stab(ell)
+    t_bif(ell)
+    t_stab(ell)
     ax2.set_ylabel("max $\\alpha$")
     ax2.set_ylim(0, 1.03)
 
@@ -168,14 +166,14 @@ def plot_spectrum(params, data, tc, ax=None, tol=1e-12):
 
 def plot_sigmaeps(params, dataf, tc):
     E0 = params["material"]["E"]
-    w1 = params["material"]["sigma_D0"] ** 2 / E0
+    params["material"]["sigma_D0"] ** 2 / E0
     ell = params["material"]["ell"]
-    Lx = params["geometry"]["Lx"]
-    Ly = params["geometry"]["Ly"]
+    params["geometry"]["Lx"]
+    params["geometry"]["Ly"]
 
     fig = plt.figure()
 
-    t = np.linspace(0.0, params["loading"]["load_max"], 100)
+    np.linspace(0.0, params["loading"]["load_max"], 100)
     fig = plt.figure()
     plt.ylabel("$$\\sigma$$")
     plt.xlabel("$$t$$")
@@ -204,7 +202,7 @@ def plot_sigmaeps(params, dataf, tc):
     ax.set_xticklabels(["0", "$t_c$", "$t_b$", "$t_s$"])
     plt.ylim([0, sigmaC * 1.1])
 
-    stable = dataf["stable"].values
+    dataf["stable"].values
 
     # ax.axvline(tc, c='k', lw=.5, label='$t^{cr}$')
     # ax.axvline(t_stab(ell), c='k', ls='-', lw=2, label='$t^{cr}_s$')
@@ -227,7 +225,7 @@ def plot_energy(params, dataf, tc):
     Lx = params["geometry"]["Lx"]
     Ly = params["geometry"]["Ly"]
     En0 = w1 * Lx * Ly
-    t = np.linspace(0.0, 3.0, 100)
+    np.linspace(0.0, 3.0, 100)
     fig = plt.figure()
     plt.xlabel("$$t$$")
 
@@ -290,8 +288,6 @@ def plot_energy(params, dataf, tc):
 def plot_stability(prefix, tol=1e-5):
     # dirtree = os.path.join(dirroot, signature)
     fig = plt.figure()
-    stab_diag = []
-    global_dfs = []
 
     debug = False
     for subdir, dirs, files in os.walk(prefix):
@@ -425,7 +421,7 @@ def format_params(params):
 
 def _plot_spectrum(data):
     def _stab_cnd(data):
-        return [0 if data["cone-stable"][i] == True else 1 for i in range(len(data))]
+        return [0 if data["cone-stable"][i] is True else 1 for i in range(len(data))]
 
     # _uniq_cnd = [.3 if d[0]>0 else .7 for d in data['eigs']]
     """docstring for plotSpaceVsCone"""
@@ -505,7 +501,7 @@ def read_mode_data_from_npz(npz_file, time_step, num_points=-1, num_modes=1):
         if num_points == -1:
             num_points = len(npz_file["mesh"])
         # Replace with actual x_values
-        x_values = np.linspace(0, 1, num_points)
+        np.linspace(0, 1, num_points)
 
         mode_data["fields"] = {
             "bifurcation_β": field_β_bif_values,
diff --git a/playground/pizza-notch/pizza-notch.py b/playground/pizza-notch/pizza-notch.py
index 2e1da815..66f8f7bc 100644
--- a/playground/pizza-notch/pizza-notch.py
+++ b/playground/pizza-notch/pizza-notch.py
@@ -15,33 +15,22 @@
 import pyvista
 import ufl
 import yaml
-from dolfinx.fem import (
-    Function,
-    FunctionSpace,
-    assemble_scalar,
-    dirichletbc,
-    form,
-    locate_dofs_topological,
-    set_bc,
-)
+from dolfinx.fem import (Function, FunctionSpace, assemble_scalar, dirichletbc,
+                         form, locate_dofs_topological, set_bc)
 from dolfinx.io import XDMFFile, gmshio
 from dolfinx.mesh import locate_entities_boundary
+from mpi4py import MPI
+from petsc4py import PETSc
+from pyvista.utilities import xvfb
+
 from irrevolutions.algorithms.am import HybridSolver
 from irrevolutions.algorithms.so import BifurcationSolver, StabilitySolver
 from irrevolutions.meshes.pacman import mesh_pacman
 from irrevolutions.models import DamageElasticityModel as Brittle
-from irrevolutions.utils import (
-    ColorPrint,
-    _logger,
-    _write_history_data,
-    history_data,
-    set_vector_to_constant,
-)
+from irrevolutions.utils import (ColorPrint, _logger, _write_history_data,
+                                 history_data, set_vector_to_constant)
 from irrevolutions.utils.lib import _local_notch_asymptotic
 from irrevolutions.utils.viz import plot_mesh, plot_scalar, plot_vector
-from mpi4py import MPI
-from petsc4py import PETSc
-from pyvista.utilities import xvfb
 
 description = """We solve here a basic 2d of a notched specimen.
 Imagine a dinner a pizza which is missing a slice, and lots of hungry friends
@@ -105,7 +94,7 @@ def run_computation(parameters, storage):
     # Define the state
     u = Function(V_u, name="Displacement")
     alpha = Function(V_alpha, name="Damage")
-    alphadot = Function(V_alpha, name="Damage rate")
+    Function(V_alpha, name="Damage rate")
 
     # upper/lower bound for the damage field
     alpha_lb = Function(V_alpha, name="Lower bound")
@@ -119,7 +108,7 @@ def run_computation(parameters, storage):
 
     # Measures
     dx = ufl.Measure("dx", domain=mesh)
-    ds = ufl.Measure("ds", domain=mesh)
+    ufl.Measure("ds", domain=mesh)
 
     # Set Bcs Function
 
@@ -205,12 +194,6 @@ def run_computation(parameters, storage):
         total_energy, state, bcs, cone_parameters=parameters.get("stability")
     )
 
-    mode_shapes_data = {
-        "time_steps": [],
-        "point_values": {
-            "x_values": [],
-        },
-    }
 
     _logger.setLevel(level=logging.CRITICAL)
 
@@ -237,7 +220,7 @@ def run_computation(parameters, storage):
 
         stable = stability.solve(alpha_lb, eig0=bifurcation._spectrum, inertia=inertia)
 
-        with dolfinx.common.Timer("~Postprocessing and Vis") as timer:
+        with dolfinx.common.Timer("~Postprocessing and Vis"):
             fracture_energy = comm.allreduce(
                 assemble_scalar(form(model.damage_energy_density(state) * dx)),
                 op=MPI.SUM,
@@ -362,7 +345,7 @@ def test_2d():
     )
     ColorPrint.print_bold(f"===================-{_storage}-=================")
 
-    with dolfinx.common.Timer("~Computation Experiment") as timer:
+    with dolfinx.common.Timer("~Computation Experiment"):
         history_data, stability_data, state = run_computation(parameters, _storage)
 
     ColorPrint.print_bold(history_data["eigs-cone"])
diff --git a/pyproject.toml b/pyproject.toml
index 2ada7543..bf8f42f7 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,36 +1,28 @@
-[project]
+[build-system]
+requires = ["setuptools>=42", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[tool.poetry]
 name = "irrevolutions"
-authors = [
-    {name = "Andrés A León Baldelli", email = "leon.baldelli@cnrs.fr"},
-    ]
 version = "0.1.0"
-requires-python = ">=3.8"
-
-# Short description
-description = "Three solvers for irreversible evolutionary processes with a general energetic notion of stability."
-
-# Path to README.md
-readme = "README.md"
-
-# Path to license file
-license = {file = "LICENSE"}
+description = "A Python package for solving nonlinear and nonconvex evolutionary problems using a general energetic notion of stability and dolfinx."
+authors = ["Your Name <leon.baldelli@cnrs.fr>"]
 
-# Direct dependencies
-dependencies = [
-    "fenics-dolfinx==0.7.*",
-    "pandas>=2.2",
-    "sympy",
-    "pytest",
-    "matplotlib",
-    "gmsh>=4.11.0",
-    "PyYAML>=6.0.1",
-    "scipy>=1.12.0",
-    "pyvista>=0.43",
-    "numba>=0.60.0",
-]
+# Optional dependencies are specified in this section
+[tool.poetry.dependencies]
+python = "^3.8"
+pandas = "^1.2"
+sympy = "^1.8"
+pytest = "^6.0"
+matplotlib = "^3.4"
+gmsh = "^4.11.0"
+PyYAML = "^6.0.1"
+scipy = "^1.12.0"
+pyvista = "^0.43"
+numba = "^0.60.0"
 
-[project.optional-dependencies]
-test = ["pytest"]
+[tool.poetry.extras]
+dolfinx_support = []
 
-[tool.ruff.lint.isort.sections]
-"mpi" = ["mpi4py", "petsc4py"]
\ No newline at end of file
+[tool.poetry.scripts]
+irrevolutions = 'irrevolutions:main'
diff --git a/src/irrevolutions/algorithms/am.py b/src/irrevolutions/algorithms/am.py
index 44de69ca..f3357053 100644
--- a/src/irrevolutions/algorithms/am.py
+++ b/src/irrevolutions/algorithms/am.py
@@ -1,13 +1,10 @@
+from dolfinx.fem.petsc import assemble_vector, set_bc
 import logging
 
 import dolfinx
 import numpy as np
 import ufl
-from dolfinx.fem import (
-    Function,
-    assemble_scalar,
-    form,
-)
+from dolfinx.fem import Function, assemble_scalar, form
 from dolfinx.io import XDMFFile
 from mpi4py import MPI
 from petsc4py import PETSc
@@ -15,14 +12,11 @@
 from irrevolutions.solvers import SNESSolver
 from irrevolutions.solvers.function import functions_to_vec
 from irrevolutions.solvers.snesblockproblem import SNESBlockProblem
-from irrevolutions.utils import ColorPrint, norm_H1, norm_L2, set_vector_to_constant
+from irrevolutions.utils import (ColorPrint, norm_H1, norm_L2,
+                                 set_vector_to_constant)
 
 comm = MPI.COMM_WORLD
 
-from dolfinx.fem.petsc import (
-    assemble_vector,
-    set_bc,
-)
 
 logging.basicConfig()
 
diff --git a/src/irrevolutions/algorithms/ls.py b/src/irrevolutions/algorithms/ls.py
index a291d30d..d5a25c70 100644
--- a/src/irrevolutions/algorithms/ls.py
+++ b/src/irrevolutions/algorithms/ls.py
@@ -4,11 +4,7 @@
 import mpi4py
 import numpy as np
 from dolfinx.cpp.log import LogLevel, log
-from dolfinx.fem import (
-    Function,
-    assemble_scalar,
-    form,
-)
+from dolfinx.fem import Function, assemble_scalar, form
 from petsc4py import PETSc
 
 from irrevolutions.utils import norm_H1
@@ -147,8 +143,6 @@ def admissible_interval(self, state, perturbation, alpha_lb, bifurcation):
         beta = bifurcation[1]
 
         one = max(1.0, max(alpha.vector[:]))
-        upperbound = one
-        lowerbound = alpha_lb
 
         # positive
         mask = np.int32(np.where(beta.vector[:] > 0)[0])
diff --git a/src/irrevolutions/algorithms/so.py b/src/irrevolutions/algorithms/so.py
index 11e5c997..45b14d02 100644
--- a/src/irrevolutions/algorithms/so.py
+++ b/src/irrevolutions/algorithms/so.py
@@ -324,7 +324,7 @@ def normalise_eigen(self, u, mode="max-beta"):
             float: Coefficient used for normalization.
         """
         if mode == "max-beta":
-            v, beta = u[0], u[1]
+            _v, beta = u[0], u[1]
             coeff_glob = beta.vector.norm(3)
 
             logging.debug(f"{rank}, |β|_infty {beta.vector.norm(3):.3f}")
@@ -345,7 +345,7 @@ def normalise_eigen(self, u, mode="max-beta"):
                 addv=PETSc.InsertMode.INSERT_VALUES, mode=PETSc.ScatterMode.FORWARD
             )
 
-        _norm = np.sqrt(sum(n**2 for n in [v_i.vector.norm(2) for v_i in u]))
+        np.sqrt(sum(n**2 for n in [v_i.vector.norm(2) for v_i in u]))
 
         return coeff_glob
 
@@ -381,7 +381,7 @@ def solve(self, alpha_old: dolfinx.fem.function.Function):
         # Check if the system is damage-critical and log it
         self.log_critical_state()
 
-        with dolfinx.common.Timer("~Second Order: Bifurcation") as timer:
+        with dolfinx.common.Timer("~Second Order: Bifurcation"):
             # Set up constraints
             constraints = self.setup_constraints(alpha_old)
             self.inertia_setup(constraints)
@@ -677,7 +677,6 @@ def __init__(
                     "1": "converged atol",
                     "2": "converged residual",
                 }
-                _reason = None
 
     def solve(self, alpha_old: dolfinx.fem.function.Function, eig0=None, inertia=None):
         """
@@ -836,7 +835,7 @@ def update_xk(self, xk, y, s):
         _cone_restricted = self._cone_project_restricted(xk)
 
         _logger.debug(f"xk view after cone-project at iteration {self.iterations}")
-        n2 = _cone_restricted.normalize()
+        _cone_restricted.normalize()
 
         # _logger.info(f"Cone project update: normalisation {n2}")
 
@@ -995,7 +994,7 @@ def _convergenceTest(self, x, errors):
         # self.data["error_x_L2"].append(error_x_L2)
 
         _acrit = self._aerror < self.parameters.get("cone").get("cone_atol")
-        _rnorm = self._residual_norm < self.parameters.get("cone").get("cone_rtol")
+        self._residual_norm < self.parameters.get("cone").get("cone_rtol")
 
         _crits = (_acrit, False)
 
diff --git a/src/irrevolutions/meshes/__init__.py b/src/irrevolutions/meshes/__init__.py
index 00eebe2c..68b9a82b 100644
--- a/src/irrevolutions/meshes/__init__.py
+++ b/src/irrevolutions/meshes/__init__.py
@@ -19,7 +19,7 @@ def mesh_bounding_box(mesh, i):
 def get_tag(kwargs):
     return (
         ""
-        if (kwargs.get("tag") == None or kwargs.get("tag") == -1)
+        if (kwargs.get("tag") is None or kwargs.get("tag") == -1)
         else f"({kwargs.get('tag')})"
     )
 
diff --git a/src/irrevolutions/meshes/boolean.py b/src/irrevolutions/meshes/boolean.py
index c0b1a227..056c44af 100644
--- a/src/irrevolutions/meshes/boolean.py
+++ b/src/irrevolutions/meshes/boolean.py
@@ -27,7 +27,7 @@ def mesh_bar_gmshapi(name, msh_file=None, comm=MPI.COMM_WORLD):
         L, H, r = 1.0, 1.0, 0.1
         hole = gmsh.model.occ.addCircle(L / 2, L / 2, 0.0, r, tag=1)
         domain = gmsh.model.occ.addRectangle(0, 0, 0.0, L, H, tag=2, roundedRadius=0.1)
-        boolean = gmsh.model.occ.cut([(2, hole)], [(2, domain)], tag=3)
+        gmsh.model.occ.cut([(2, hole)], [(2, domain)], tag=3)
 
 
 def mesh_moonslice_gmshapi(
diff --git a/src/irrevolutions/meshes/extended_pacman.py b/src/irrevolutions/meshes/extended_pacman.py
index 02340b0a..a3eea205 100644
--- a/src/irrevolutions/meshes/extended_pacman.py
+++ b/src/irrevolutions/meshes/extended_pacman.py
@@ -1,3 +1,10 @@
+from meshes import _addPoint as addPoint
+from meshes import _addPlaneSurface as _addPlaneSurface
+from meshes import _addPhysicalSurface as _addPhysicalSurface
+from meshes import _addLine as addLine
+from meshes import _addCurveLoop as addCurveLoop
+from meshes import _addCircleArc as addCircleArc
+from pathlib import Path
 import os
 import sys
 
@@ -5,26 +12,7 @@
 from mpi4py import MPI
 
 sys.path.append("../")
-from pathlib import Path
 
-from meshes import (
-    _addCircleArc as addCircleArc,
-)
-from meshes import (
-    _addCurveLoop as addCurveLoop,
-)
-from meshes import (
-    _addLine as addLine,
-)
-from meshes import (
-    _addPhysicalSurface as _addPhysicalSurface,
-)
-from meshes import (
-    _addPlaneSurface as _addPlaneSurface,
-)
-from meshes import (
-    _addPoint as addPoint,
-)
 
 
 def mesh_extended_pacman(
@@ -65,7 +53,7 @@ def mesh_extended_pacman(
         p2 = addPoint(
             -radius * np.cos(omega / 2), -radius * np.sin(omega / 2), 0.0, lc, tag=2
         )
-        p3 = addPoint(radius, 0, 0.0, lc / refinement, tag=12)
+        addPoint(radius, 0, 0.0, lc / refinement, tag=12)
 
         p10 = addPoint(
             -rho * radius * np.cos(omega / 2),
@@ -81,7 +69,7 @@ def mesh_extended_pacman(
             lc,
             tag=20,
         )
-        p30 = addPoint(rho * radius, 0, 0.0, lc, tag=120)
+        addPoint(rho * radius, 0, 0.0, lc, tag=120)
 
         top = addLine(p1, p0, tag=3)
         bot = addLine(p0, p2, tag=4)
@@ -128,7 +116,6 @@ def mesh_extended_pacman(
     import sys
 
     import yaml
-
     # , merge_meshtags, locate_dofs_topological
     from mpi4py import MPI
 
diff --git a/src/irrevolutions/meshes/pacman.py b/src/irrevolutions/meshes/pacman.py
index 53c3f4ba..c0065a55 100644
--- a/src/irrevolutions/meshes/pacman.py
+++ b/src/irrevolutions/meshes/pacman.py
@@ -33,7 +33,7 @@ def mesh_pacman(
         omega = np.deg2rad(geom_parameters.get("omega"))
         radius = geom_parameters.get("r")
         lc = geom_parameters.get("meshsize")
-        elltomesh = geom_parameters.get("elltomesh")
+        geom_parameters.get("elltomesh")
 
         refinement = geom_parameters.get("refinement")
 
@@ -45,7 +45,7 @@ def mesh_pacman(
         # print("Model name: " + gmsh.model.getCurrent())
 
         # get all elementary entities in the model
-        entities = gmsh.model.occ.getEntities()
+        gmsh.model.occ.getEntities()
 
         # for e in entities:
         #     print("Entity " + str(e) + " of type " + gmsh.model.getType(e[0], e[1]))
@@ -69,7 +69,7 @@ def mesh_pacman(
         p2 = model.geo.addPoint(
             -radius * np.cos(omega / 2), -radius * np.sin(omega / 2), 0.0, lc, tag=2
         )
-        p3 = model.geo.addPoint(radius, 0, 0.0, lc / refinement, tag=12)
+        model.geo.addPoint(radius, 0, 0.0, lc / refinement, tag=12)
 
         top = model.geo.addLine(p1, p0, tag=3)
         bot = model.geo.addLine(p0, p2, tag=4)
diff --git a/src/irrevolutions/meshes/primitives.py b/src/irrevolutions/meshes/primitives.py
index 8a4519fa..56d63cb6 100644
--- a/src/irrevolutions/meshes/primitives.py
+++ b/src/irrevolutions/meshes/primitives.py
@@ -143,12 +143,12 @@ def mesh_rightCrack_gmshapi(
         p2 = model.geo.addPoint(Lx, Ly, 0.0, lc, tag=2)
         p3 = model.geo.addPoint(0, Ly, 0, lc, tag=3)
         # pLa= model.geo.addPoint(0, Ly/2-s/2, 0, lc, tag=4)
-        pRa = model.geo.addPoint(Lx, Ly / 2 + s / 2 - sep, 0, lc, tag=6)
-        pRb = model.geo.addPoint(Lx, Ly / 2 + s / 2 + sep, 0, lc, tag=7)
+        model.geo.addPoint(Lx, Ly / 2 + s / 2 - sep, 0, lc, tag=6)
+        model.geo.addPoint(Lx, Ly / 2 + s / 2 + sep, 0, lc, tag=7)
         pLa = model.geo.addPoint(0, Ly / 2 - s / 2 - sep, 0, lc, tag=8)
         pLb = model.geo.addPoint(0, Ly / 2 - s / 2 + sep, 0, lc, tag=5)
         plM = model.geo.addPoint(L0, Ly / 2 - s / 2, 0, lc, tag=9)
-        prM = model.geo.addPoint(Lx - L0, Ly / 2 + s / 2, 0, lc, tag=10)
+        model.geo.addPoint(Lx - L0, Ly / 2 + s / 2, 0, lc, tag=10)
         # points = [p0, p1, p2, p3]
         bottom = model.geo.addLine(p0, p1, tag=0)
         right = model.geo.addLine(p1, p2, tag=1)
@@ -366,7 +366,6 @@ def mesh_circle_gmshapi(name, R, lc, tdim, order=1, msh_file=None, comm=MPI.COMM
 
     import dolfinx.plot
     from mesh import gmsh_to_dolfin
-
     # , merge_meshtags, locate_dofs_topological
     from mpi4py import MPI
     from xdmf import XDMFFile
diff --git a/src/irrevolutions/meshes/tdcb_2D.py b/src/irrevolutions/meshes/tdcb_2D.py
index 90e0969e..0cf5e06d 100644
--- a/src/irrevolutions/meshes/tdcb_2D.py
+++ b/src/irrevolutions/meshes/tdcb_2D.py
@@ -46,16 +46,16 @@ def mesh_tdcb(
         p4 = model.geo.addPoint(a0, eta, 0, lc, tag=4)
         p400 = model.geo.addPoint(a0, -eta, 0, lc, tag=400)
 
-        p5 = model.geo.addPoint(cx, cy, 0.0, lc, tag=5)
-        p500 = model.geo.addPoint(cx, cy - rad, 0.0, lc, tag=500)
-        p501 = model.geo.addPoint(cx, cy + rad, 0.0, lc, tag=501)
-        p502 = model.geo.addPoint(cx - rad, cy, 0.0, lc, tag=502)
-        p503 = model.geo.addPoint(cx + rad, cy, 0.0, lc, tag=503)
-        p6 = model.geo.addPoint(cx, -cy, 0.0, lc, tag=6)
-        p600 = model.geo.addPoint(cx, -cy + rad, 0.0, lc, tag=600)
-        p601 = model.geo.addPoint(cx, -cy - rad, 0.0, lc, tag=601)
-        p602 = model.geo.addPoint(cx - rad, -cy, 0.0, lc, tag=602)
-        p603 = model.geo.addPoint(cx + rad, -cy, 0.0, lc, tag=603)
+        model.geo.addPoint(cx, cy, 0.0, lc, tag=5)
+        model.geo.addPoint(cx, cy - rad, 0.0, lc, tag=500)
+        model.geo.addPoint(cx, cy + rad, 0.0, lc, tag=501)
+        model.geo.addPoint(cx - rad, cy, 0.0, lc, tag=502)
+        model.geo.addPoint(cx + rad, cy, 0.0, lc, tag=503)
+        model.geo.addPoint(cx, -cy, 0.0, lc, tag=6)
+        model.geo.addPoint(cx, -cy + rad, 0.0, lc, tag=600)
+        model.geo.addPoint(cx, -cy - rad, 0.0, lc, tag=601)
+        model.geo.addPoint(cx - rad, -cy, 0.0, lc, tag=602)
+        model.geo.addPoint(cx + rad, -cy, 0.0, lc, tag=603)
 
         # left = model.geo.addLine(p200, p2, tag=5)
         left_top = model.geo.addLine(p1, p2, tag=5)
diff --git a/src/irrevolutions/models/__init__.py b/src/irrevolutions/models/__init__.py
index 97e062e7..091e3605 100644
--- a/src/irrevolutions/models/__init__.py
+++ b/src/irrevolutions/models/__init__.py
@@ -1,3 +1,4 @@
+from dolfinx.fem.function import Function
 import os
 
 import ufl
@@ -251,7 +252,6 @@ def stress(self, strain, alpha):
         return ufl.as_tensor(sigma)
 
 
-from dolfinx.fem.function import Function
 
 
 class VariableThickness:
diff --git a/src/irrevolutions/practice/default.py b/src/irrevolutions/practice/default.py
index b3aa1daa..1759edd3 100644
--- a/src/irrevolutions/practice/default.py
+++ b/src/irrevolutions/practice/default.py
@@ -21,6 +21,8 @@
 
 
 #!/usr/bin/env python3
+from irrevolutions.utils import ColorPrint
+from meshes.primitives import mesh_bar_gmshapi
 import json
 import logging
 import pdb
@@ -33,25 +35,16 @@
 import ufl
 import yaml
 from dolfinx.common import list_timings
-from dolfinx.fem import (
-    Constant,
-    Function,
-    FunctionSpace,
-    assemble_scalar,
-    dirichletbc,
-    form,
-    locate_dofs_geometrical,
-    set_bc,
-)
+from dolfinx.fem import (Constant, Function, FunctionSpace, assemble_scalar,
+                         dirichletbc, form, locate_dofs_geometrical, set_bc)
 from dolfinx.io import XDMFFile, gmshio
 from mpi4py import MPI
 from petsc4py import PETSc
 
 sys.path.append("../")
 
+
 # from algorithms.am import AlternateMinimisation, HybridSolver
-from irrevolutions.utils import ColorPrint
-from meshes.primitives import mesh_bar_gmshapi
 
 # Configuration handling (load parameters from YAML)
 
@@ -88,12 +81,12 @@ def load_parameters(file_path):
     parameters["geometry"]["geom_type"] = "traction-bar"
     parameters["geometry"]["ell_lc"] = 5
     # Get mesh parameters
-    Lx = parameters["geometry"]["Lx"]
-    Ly = parameters["geometry"]["Ly"]
-    tdim = parameters["geometry"]["geometric_dimension"]
+    parameters["geometry"]["Lx"]
+    parameters["geometry"]["Ly"]
+    parameters["geometry"]["geometric_dimension"]
 
-    _nameExp = parameters["geometry"]["geom_type"]
-    ell_ = parameters["model"]["ell"]
+    parameters["geometry"]["geom_type"]
+    parameters["model"]["ell"]
 
     signature = hashlib.md5(str(parameters).encode("utf-8")).hexdigest()
 
@@ -190,8 +183,8 @@ def setup_boundary_conditions(V_u, V_alpha, Lx):
     Returns:
         list of dolfinx.DirichletBC: List of boundary conditions.
     """
-    dofs_alpha_left = locate_dofs_geometrical(V_alpha, lambda x: np.isclose(x[0], 0.0))
-    dofs_alpha_right = locate_dofs_geometrical(V_alpha, lambda x: np.isclose(x[0], Lx))
+    locate_dofs_geometrical(V_alpha, lambda x: np.isclose(x[0], 0.0))
+    locate_dofs_geometrical(V_alpha, lambda x: np.isclose(x[0], Lx))
 
     dofs_u_left = locate_dofs_geometrical(V_u, lambda x: np.isclose(x[0], 0.0))
     dofs_u_right = locate_dofs_geometrical(V_u, lambda x: np.isclose(x[0], Lx))
@@ -199,7 +192,7 @@ def setup_boundary_conditions(V_u, V_alpha, Lx):
     zero_u = Function(V_u)
     u_ = Function(V_u, name="Boundary Displacement")
 
-    zero_alpha = Function(V_alpha)
+    Function(V_alpha)
 
     bc_u_left = dirichletbc(zero_u, dofs_u_left)
     bc_u_right = dirichletbc(u_, dofs_u_right)
@@ -266,7 +259,7 @@ def define_energy_functional(state, model):
     """
     u = state["u"]
     dx = ufl.Measure("dx", domain=u.function_space.mesh)
-    ds = ufl.Measure("ds", domain=u.function_space.mesh)
+    ufl.Measure("ds", domain=u.function_space.mesh)
 
     # state = {"u": u, "alpha": alpha}
     # Define the external load
@@ -467,12 +460,14 @@ def run_time_loop(parameters, solver, model, bcs):
     _x = _cpp.fem.interpolation_coords(V_u.element, mesh, cells)
 
     alpha = state["alpha"]
-    u = state["u"]
+    state["u"]
 
     # Main time loop
     for i_t, t in enumerate(loads):
         # Update boundary conditions or external loads if necessary
-        datum = lambda x: (t * np.ones_like(x[0]), np.zeros_like(x[1]))
+        def datum(x):
+            return t * np.ones_like(x[0]), np.zeros_like(x[1])
+
         bcs["bcs_u"][1].g.interpolate(datum(_x), cells)
         bcs["bcs_u"][1].g.x.scatter_forward()
 
diff --git a/src/irrevolutions/practice/discrete_atk.py b/src/irrevolutions/practice/discrete_atk.py
index 6ab99bcf..66e56479 100644
--- a/src/irrevolutions/practice/discrete_atk.py
+++ b/src/irrevolutions/practice/discrete_atk.py
@@ -1,4 +1,7 @@
 #!/usr/bin/env python3
+from irrevolutions.utils import ColorPrint, norm_H1, norm_L2
+from solvers import SNESSolver
+from algorithms.so import BifurcationSolver, StabilitySolver
 import json
 import logging
 import os
@@ -14,26 +17,13 @@
 import ufl
 import yaml
 from dolfinx.common import list_timings
-from dolfinx.fem import (
-    Constant,
-    Function,
-    assemble_scalar,
-    dirichletbc,
-    form,
-    locate_dofs_geometrical,
-    set_bc,
-)
+from dolfinx.fem import (Constant, Function, assemble_scalar, dirichletbc,
+                         form, locate_dofs_geometrical, set_bc)
 from dolfinx.fem.petsc import assemble_vector
 from dolfinx.io import XDMFFile
 from mpi4py import MPI
 from petsc4py import PETSc
-
-sys.path.append("../")
-from algorithms.so import BifurcationSolver, StabilitySolver
-from irrevolutions.utils import ColorPrint, norm_H1, norm_L2
-from solvers import SNESSolver
-
-sys.path.append("../")
+from utils.plots import plot_energies
 
 
 """Discrete endommageable springs in series
@@ -210,7 +200,7 @@ def solve(self, outdir=None):
 
 def discrete_atk(arg_N=2):
     # Mesh on node model_rank and then distribute
-    model_rank = 0
+    pass
 
     with open("./parameters.yml") as f:
         parameters = yaml.load(f, Loader=yaml.FullLoader)
@@ -232,15 +222,15 @@ def discrete_atk(arg_N=2):
     parameters["geometry"]["geom_type"] = "discrete-damageable"
     # Get mesh parameters
     Lx = parameters["geometry"]["Lx"]
-    Ly = parameters["geometry"]["Ly"]
-    tdim = parameters["geometry"]["geometric_dimension"]
+    parameters["geometry"]["Ly"]
+    parameters["geometry"]["geometric_dimension"]
 
     _nameExp = parameters["geometry"]["geom_type"]
-    ell_ = parameters["model"]["ell"]
+    parameters["model"]["ell"]
     # lc = ell_ / 5.0
 
     # Get geometry model
-    geom_type = parameters["geometry"]["geom_type"]
+    parameters["geometry"]["geom_type"]
     _N = parameters["model"]["N"]
 
     # Create the mesh of the specimen with given dimensions
@@ -303,7 +293,7 @@ def discrete_atk(arg_N=2):
     alpha_lb = dolfinx.fem.Function(V_alpha, name="LowerBoundDamage")
 
     dx = ufl.Measure("dx", domain=mesh)
-    ds = ufl.Measure("ds", domain=mesh)
+    ufl.Measure("ds", domain=mesh)
 
     # Useful references
     Lx = parameters.get("geometry").get("Lx")
@@ -315,12 +305,12 @@ def discrete_atk(arg_N=2):
 
     # Measures
     dx = ufl.Measure("dx", domain=mesh)
-    ds = ufl.Measure("ds", domain=mesh)
+    ufl.Measure("ds", domain=mesh)
 
     # Boundary sets
 
-    dofs_alpha_left = locate_dofs_geometrical(V_alpha, lambda x: np.isclose(x[0], 0.0))
-    dofs_alpha_right = locate_dofs_geometrical(V_alpha, lambda x: np.isclose(x[0], Lx))
+    locate_dofs_geometrical(V_alpha, lambda x: np.isclose(x[0], 0.0))
+    locate_dofs_geometrical(V_alpha, lambda x: np.isclose(x[0], Lx))
 
     dofs_u_left = locate_dofs_geometrical(V_u, lambda x: np.isclose(x[0], 0.0))
     dofs_u_right = locate_dofs_geometrical(V_u, lambda x: np.isclose(x[0], Lx))
@@ -362,7 +352,7 @@ def a(alpha):
         return (1 - alpha) ** 2 + k_res
 
     def a_atk(alpha):
-        k_res = parameters["model"]["k_res"]
+        parameters["model"]["k_res"]
         _k = parameters["model"]["k"]
         return (1 - alpha) / ((_k - 1) * alpha + 1)
 
@@ -381,7 +371,7 @@ def elastic_energy_density_atk(state):
         """
         # Parameters
         _mu = parameters["model"]["mu"]
-        _N = parameters["model"]["N"]
+        parameters["model"]["N"]
 
         alpha = state["alpha"]
         u = state["u"]
@@ -395,7 +385,7 @@ def damage_energy_density(state):
         Return the damage dissipation density from the state.
         """
         # Get the material parameters
-        _mu = parameters["model"]["mu"]
+        parameters["model"]["mu"]
         _w1 = parameters["model"]["w1"]
         _ell = parameters["model"]["ell"]
         # Get the damage
@@ -423,7 +413,7 @@ def stress(state):
     # f = Constant(mesh, 0)
     f = Constant(mesh, np.array(0, dtype=PETSc.ScalarType))
 
-    external_work = f * state["u"] * dx
+    f * state["u"] * dx
 
     load_par = parameters["loading"]
     loads = np.linspace(load_par["min"], load_par["max"], load_par["steps"])
diff --git a/src/irrevolutions/practice/discrete_atk_homogeneous.py b/src/irrevolutions/practice/discrete_atk_homogeneous.py
index 94b519d0..f277673b 100644
--- a/src/irrevolutions/practice/discrete_atk_homogeneous.py
+++ b/src/irrevolutions/practice/discrete_atk_homogeneous.py
@@ -1,4 +1,9 @@
 #!/usr/bin/env python3
+from irrevolutions.utils import ColorPrint, norm_H1, norm_L2
+from utils.viz import plot_matrix
+from utils.plots import plot_energies
+from solvers import SNESSolver
+from algorithms.so import BifurcationSolver, StabilitySolver
 import json
 import logging
 import os
@@ -14,27 +19,13 @@
 import ufl
 import yaml
 from dolfinx.common import list_timings
-from dolfinx.fem import (
-    Constant,
-    Function,
-    assemble_scalar,
-    dirichletbc,
-    form,
-    locate_dofs_geometrical,
-    set_bc,
-)
+from dolfinx.fem import (Constant, Function, assemble_scalar, dirichletbc,
+                         form, locate_dofs_geometrical, set_bc)
 from dolfinx.fem.petsc import assemble_vector
 from dolfinx.io import XDMFFile
 from mpi4py import MPI
 from petsc4py import PETSc
 
-sys.path.append("../")
-from algorithms.so import BifurcationSolver, StabilitySolver
-from irrevolutions.utils import ColorPrint, norm_H1, norm_L2
-from solvers import SNESSolver
-from utils.viz import plot_matrix
-
-sys.path.append("../")
 
 
 """Discrete endommageable springs in series
@@ -211,7 +202,7 @@ def solve(self, outdir=None):
 
 def discrete_atk(arg_N=2):
     # Mesh on node model_rank and then distribute
-    model_rank = 0
+    pass
 
     with open("./parameters.yml") as f:
         parameters = yaml.load(f, Loader=yaml.FullLoader)
@@ -233,15 +224,15 @@ def discrete_atk(arg_N=2):
     parameters["geometry"]["geom_type"] = "discrete-damageable"
     # Get mesh parameters
     Lx = parameters["geometry"]["Lx"]
-    Ly = parameters["geometry"]["Ly"]
-    tdim = parameters["geometry"]["geometric_dimension"]
+    parameters["geometry"]["Ly"]
+    parameters["geometry"]["geometric_dimension"]
 
     _nameExp = parameters["geometry"]["geom_type"]
-    ell_ = parameters["model"]["ell"]
+    parameters["model"]["ell"]
     # lc = ell_ / 5.0
 
     # Get geometry model
-    geom_type = parameters["geometry"]["geom_type"]
+    parameters["geometry"]["geom_type"]
     _N = parameters["model"]["N"]
 
     # Create the mesh of the specimen with given dimensions
@@ -299,7 +290,7 @@ def discrete_atk(arg_N=2):
     alpha_lb = dolfinx.fem.Function(V_alpha, name="LowerBoundDamage")
 
     dx = ufl.Measure("dx", domain=mesh)
-    ds = ufl.Measure("ds", domain=mesh)
+    ufl.Measure("ds", domain=mesh)
 
     # Useful references
     Lx = parameters.get("geometry").get("Lx")
@@ -311,12 +302,12 @@ def discrete_atk(arg_N=2):
 
     # Measures
     dx = ufl.Measure("dx", domain=mesh)
-    ds = ufl.Measure("ds", domain=mesh)
+    ufl.Measure("ds", domain=mesh)
 
     # Boundary sets
 
-    dofs_alpha_left = locate_dofs_geometrical(V_alpha, lambda x: np.isclose(x[0], 0.0))
-    dofs_alpha_right = locate_dofs_geometrical(V_alpha, lambda x: np.isclose(x[0], Lx))
+    locate_dofs_geometrical(V_alpha, lambda x: np.isclose(x[0], 0.0))
+    locate_dofs_geometrical(V_alpha, lambda x: np.isclose(x[0], Lx))
 
     dofs_u_left = locate_dofs_geometrical(V_u, lambda x: np.isclose(x[0], 0.0))
     dofs_u_right = locate_dofs_geometrical(V_u, lambda x: np.isclose(x[0], Lx))
@@ -358,7 +349,7 @@ def a(alpha):
         return (1 - alpha) ** 2 + k_res
 
     def a_atk(alpha):
-        k_res = parameters["model"]["k_res"]
+        parameters["model"]["k_res"]
         _k = parameters["model"]["k"]
         return (1 - alpha) / ((_k - 1) * alpha + 1)
 
@@ -377,7 +368,7 @@ def elastic_energy_density_atk(state):
         """
         # Parameters
         _mu = parameters["model"]["mu"]
-        _N = parameters["model"]["N"]
+        parameters["model"]["N"]
 
         alpha = state["alpha"]
         u = state["u"]
@@ -391,7 +382,7 @@ def damage_energy_density(state):
         Return the damage dissipation density from the state.
         """
         # Get the material parameters
-        _mu = parameters["model"]["mu"]
+        parameters["model"]["mu"]
         _w1 = parameters["model"]["w1"]
         _ell = parameters["model"]["ell"]
         # Get the damage
@@ -419,12 +410,12 @@ def stress(state):
     # f = Constant(mesh, 0)
     f = Constant(mesh, np.array(0, dtype=PETSc.ScalarType))
 
-    external_work = f * state["u"] * dx
+    f * state["u"] * dx
 
     load_par = parameters["loading"]
     loads = np.linspace(load_par["min"], load_par["max"], load_par["steps"])
 
-    solver = _AlternateMinimisation(
+    _AlternateMinimisation(
         total_energy, state, bcs, parameters.get("solvers"), bounds=(alpha_lb, alpha_ub)
     )
 
diff --git a/src/irrevolutions/practice/enpassant.py b/src/irrevolutions/practice/enpassant.py
index 579ff496..788f4fb3 100644
--- a/src/irrevolutions/practice/enpassant.py
+++ b/src/irrevolutions/practice/enpassant.py
@@ -10,29 +10,25 @@
 To change the data, change the geometry files according to presentation
 """
 
+from utils.viz import plot_mesh, plot_scalar, plot_vector
+from pyvista.utilities import xvfb
+from petsc4py import PETSc
+from models import DamageElasticityModel as Brittle
+from meshes import primitives
+from dolfinx.fem import assemble_scalar, dirichletbc, locate_dofs_geometrical
+from algorithms import am
+import ufl
+import pyvista
+import numpy as np
+import meshes
+import matplotlib.pyplot as plt
+import dolfinx.plot
+import dolfinx.io
+import dolfinx
 import logging
 import sys
 
 sys.path.append("../")
-import dolfinx
-import dolfinx.io
-import dolfinx.plot
-import matplotlib.pyplot as plt
-import meshes
-import numpy as np
-import pyvista
-import ufl
-from algorithms import am
-from dolfinx.fem import (
-    assemble_scalar,
-    dirichletbc,
-    locate_dofs_geometrical,
-)
-from meshes import primitives
-from models import DamageElasticityModel as Brittle
-from petsc4py import PETSc
-from pyvista.utilities import xvfb
-from utils.viz import plot_mesh, plot_scalar, plot_vector
 
 logging.basicConfig()
 # logging.getLogger().setLevel(logging.DEBUG)
diff --git a/src/irrevolutions/practice/multiaxial-disc.py b/src/irrevolutions/practice/multiaxial-disc.py
index cada246c..bf650202 100644
--- a/src/irrevolutions/practice/multiaxial-disc.py
+++ b/src/irrevolutions/practice/multiaxial-disc.py
@@ -10,7 +10,6 @@
 import pandas as pd
 import yaml
 from dolfinx.common import list_timings
-
 #
 from mpi4py import MPI
 from petsc4py import PETSc
@@ -81,10 +80,9 @@ def multiaxial_disc(nest):
     thanks to: Camilla Zolesi"""
 
     # parameters: INPUT
-    model_rank = 0
 
     with open("../test/parameters.yml") as f:
-        parameters = yaml.load(f, Loader=yaml.FullLoader)
+        yaml.load(f, Loader=yaml.FullLoader)
 
     # history_data: OUTPUT
 
diff --git a/src/irrevolutions/practice/pacman-cone.py b/src/irrevolutions/practice/pacman-cone.py
index 0cb6131b..31a25642 100644
--- a/src/irrevolutions/practice/pacman-cone.py
+++ b/src/irrevolutions/practice/pacman-cone.py
@@ -1,4 +1,11 @@
 #!/usr/bin/env python3
+from irrevolutions.utils import ColorPrint
+from utils.viz import plot_mesh, plot_scalar, plot_vector
+from utils.lib import _local_notch_asymptotic
+from models import DamageElasticityModel as Brittle
+from meshes.pacman import mesh_pacman
+from algorithms.so import BifurcationSolver, StabilitySolver
+from algorithms.am import AlternateMinimisation, HybridSolver
 import hashlib
 import json
 import logging
@@ -16,32 +23,17 @@
 import ufl
 import yaml
 from dolfinx.common import list_timings, timing
-from dolfinx.fem import (
-    Constant,
-    Function,
-    FunctionSpace,
-    assemble_scalar,
-    dirichletbc,
-    form,
-    locate_dofs_topological,
-    set_bc,
-)
+from dolfinx.fem import (Constant, Function, FunctionSpace, assemble_scalar,
+                         dirichletbc, form, locate_dofs_topological, set_bc)
 from dolfinx.io import XDMFFile, gmshio
 from dolfinx.mesh import locate_entities_boundary
-
 #
 from mpi4py import MPI
 from petsc4py import PETSc
 from pyvista.utilities import xvfb
 
 sys.path.append("../")
-from algorithms.am import AlternateMinimisation, HybridSolver
-from algorithms.so import BifurcationSolver, StabilitySolver
-from irrevolutions.utils import ColorPrint
-from meshes.pacman import mesh_pacman
-from models import DamageElasticityModel as Brittle
-from utils.lib import _local_notch_asymptotic
-from utils.viz import plot_mesh, plot_scalar, plot_vector
+
 
 logging.basicConfig(level=logging.DEBUG)
 
@@ -95,9 +87,7 @@ def check_snes_convergence(snes):
 
 
 def pacman_cone(resolution=2, slug="pacman"):
-    Lx = 1.0
-    Ly = 0.1
-    _nel = 30
+    pass
 
     outdir = os.path.join(os.path.dirname(__file__), "output")
     prefix = os.path.join(outdir, "pacman-cone")
@@ -192,7 +182,7 @@ def pacman_cone(resolution=2, slug="pacman"):
 
     # Measures
     dx = ufl.Measure("dx", domain=mesh)
-    ds = ufl.Measure("ds", domain=mesh)
+    ufl.Measure("ds", domain=mesh)
 
     # Set Bcs Function
 
@@ -239,7 +229,7 @@ def pacman_cone(resolution=2, slug="pacman"):
     )
 
     bcs = {"bcs_u": bcs_u, "bcs_alpha": bcs_alpha}
-    bcs_z = bcs_u + bcs_alpha
+    bcs_u + bcs_alpha
 
     # Mechanical model
 
@@ -256,7 +246,7 @@ def pacman_cone(resolution=2, slug="pacman"):
 
     # Solvers
 
-    solver = AlternateMinimisation(
+    AlternateMinimisation(
         total_energy, state, bcs, parameters.get("solvers"), bounds=(alpha_lb, alpha_ub)
     )
 
@@ -328,7 +318,7 @@ def pacman_cone(resolution=2, slug="pacman"):
         ColorPrint.print_bold("   Solving second order: Rate Pb.    ")
         ColorPrint.print_bold("===================-=================")
 
-        is_stable = bifurcation.solve(alpha_lb)
+        bifurcation.solve(alpha_lb)
         is_elastic = bifurcation.is_elastic()
         inertia = bifurcation.get_inertia()
 
@@ -385,10 +375,7 @@ def pacman_cone(resolution=2, slug="pacman"):
 
         # Viz
         if "SINGULARITY_CONTAINER" not in os.environ:
-            from utils.plots import (
-                plot_AMit_load,
-                plot_energies,
-            )
+            from utils.plots import plot_AMit_load, plot_energies
 
             if comm.rank == 0:
                 plot_energies(history_data, file=f"{prefix}/{_nameExp}_energies.pdf")
@@ -421,7 +408,7 @@ def pacman_cone(resolution=2, slug="pacman"):
         #     shape=(1, 2),
         # )
 
-    _timings = list_timings(MPI.COMM_WORLD, [dolfinx.common.TimingType.wall])
+    list_timings(MPI.COMM_WORLD, [dolfinx.common.TimingType.wall])
 
     performance = {
         "N": [],
diff --git a/src/irrevolutions/practice/pacman_hybrid.py b/src/irrevolutions/practice/pacman_hybrid.py
index c07c0e9a..63db732c 100644
--- a/src/irrevolutions/practice/pacman_hybrid.py
+++ b/src/irrevolutions/practice/pacman_hybrid.py
@@ -1,3 +1,19 @@
+from irrevolutions.utils import ColorPrint, set_vector_to_constant
+from utils.viz import plot_mesh, plot_scalar, plot_vector
+from utils.lib import _local_notch_asymptotic
+from solvers.function import functions_to_vec
+from petsc4py import PETSc
+from mpi4py import MPI
+from models import DamageElasticityModel as Brittle
+from meshes.pacman import mesh_pacman
+from algorithms.am import HybridSolver
+import ufl
+import petsc4py
+import numpy as np
+import dolfinx
+from datetime import date
+import logging
+import json
 import os
 import sys
 from pathlib import Path
@@ -6,35 +22,15 @@
 import matplotlib.pyplot as plt
 import pyvista
 import yaml
-from dolfinx.fem import (
-    Function,
-    FunctionSpace,
-    dirichletbc,
-    locate_dofs_topological,
-    set_bc,
-)
+from dolfinx.fem import (Function, FunctionSpace, dirichletbc,
+                         locate_dofs_topological, set_bc)
 from dolfinx.io import XDMFFile, gmshio
 from dolfinx.mesh import locate_entities_boundary
 from pyvista.utilities import xvfb
 
 sys.path.append("../")
-import json
-import logging
-from datetime import date
 
-import dolfinx
-import numpy as np
-import petsc4py
-import ufl
-from algorithms.am import HybridSolver
-from irrevolutions.utils import ColorPrint, set_vector_to_constant
-from meshes.pacman import mesh_pacman
-from models import DamageElasticityModel as Brittle
-from mpi4py import MPI
-from petsc4py import PETSc
-from solvers.function import functions_to_vec
-from utils.lib import _local_notch_asymptotic
-from utils.viz import plot_mesh, plot_scalar, plot_vector
+
 
 logging.basicConfig(level=logging.INFO)
 
@@ -104,11 +100,9 @@ def check_snes_convergence(snes):
 
 def pacman_hybrid(nest):
     # Parameters
-    Lx = 1.0
-    Ly = 0.1
+    pass
     # tdim = 2
     # _ell = 0.3
-    _nel = 30
 
     with open(f"{prefix}/parameters.yaml") as f:
         parameters = yaml.load(f, Loader=yaml.FullLoader)
@@ -174,7 +168,7 @@ def pacman_hybrid(nest):
 
     # Measures
     dx = ufl.Measure("dx", domain=mesh)
-    ds = ufl.Measure("ds", domain=mesh)
+    ufl.Measure("ds", domain=mesh)
 
     # Set Bcs Function
 
@@ -221,7 +215,7 @@ def pacman_hybrid(nest):
     )
 
     bcs = {"bcs_u": bcs_u, "bcs_alpha": bcs_alpha}
-    bcs_z = bcs_u + bcs_alpha
+    bcs_u + bcs_alpha
 
     # Bounds for Newton solver
 
@@ -250,8 +244,8 @@ def pacman_hybrid(nest):
     Eu = ufl.derivative(total_energy, u, ufl.TestFunction(V_u))
     Ealpha = ufl.derivative(total_energy, alpha, ufl.TestFunction(V_alpha))
 
-    F = [Eu, Ealpha]
-    z = [u, alpha]
+    [Eu, Ealpha]
+    [u, alpha]
 
     hybrid = HybridSolver(
         total_energy,
@@ -271,7 +265,7 @@ def pacman_hybrid(nest):
         with open(f"{prefix}/parameters.yaml", "w") as file:
             yaml.dump(parameters, file)
 
-    snes = hybrid.newton.snes
+    hybrid.newton.snes
 
     lb = dolfinx.fem.petsc.create_vector_nest(hybrid.newton.F_form)
     ub = dolfinx.fem.petsc.create_vector_nest(hybrid.newton.F_form)
diff --git a/src/irrevolutions/practice/thinfilm-bar.py b/src/irrevolutions/practice/thinfilm-bar.py
index fbea3503..01d179b6 100644
--- a/src/irrevolutions/practice/thinfilm-bar.py
+++ b/src/irrevolutions/practice/thinfilm-bar.py
@@ -1,4 +1,14 @@
 #!/usr/bin/env python3
+from irrevolutions.utils import ColorPrint, setup_logger_mpi, table_timing_data
+from utils.viz import plot_profile, plot_scalar, plot_vector
+from utils.plots import plot_AMit_load, plot_energies, plot_force_displacement
+from utils.parametric import parameters_vs_elle
+from solvers.function import vec_to_functions
+from models import BrittleMembraneOverElasticFoundation as ThinFilm
+from meshes.primitives import mesh_bar_gmshapi
+from default import ResultsStorage, Visualization
+from algorithms.so import BifurcationSolver, StabilitySolver
+from algorithms.am import HybridSolver
 import json
 import logging
 import os
@@ -14,38 +24,18 @@
 import ufl
 import yaml
 from dolfinx.common import list_timings
-from dolfinx.fem import (
-    Constant,
-    Function,
-    FunctionSpace,
-    assemble_scalar,
-    dirichletbc,
-    form,
-    locate_dofs_geometrical,
-    set_bc,
-)
+from dolfinx.fem import (Constant, Function, FunctionSpace, assemble_scalar,
+                         dirichletbc, form, locate_dofs_geometrical, set_bc)
 from dolfinx.io import XDMFFile, gmshio
-
 #
 from mpi4py import MPI
 from petsc4py import PETSc
 from pyvista.utilities import xvfb
 
 sys.path.append("../")
-from algorithms.am import HybridSolver
-from algorithms.so import BifurcationSolver, StabilitySolver
-from default import ResultsStorage, Visualization
+# from meshes.pacman import mesh_pacman
 
 # logging.basicConfig(level=logging.DEBUG)
-from irrevolutions.utils import ColorPrint, setup_logger_mpi, table_timing_data
-from meshes.primitives import mesh_bar_gmshapi
-from models import BrittleMembraneOverElasticFoundation as ThinFilm
-from solvers.function import vec_to_functions
-from utils.parametric import parameters_vs_elle
-from utils.plots import plot_AMit_load, plot_energies, plot_force_displacement
-
-# from meshes.pacman import mesh_pacman
-from utils.viz import plot_profile, plot_scalar, plot_vector
 
 
 # ------------------------------------------------------------------
@@ -150,7 +140,7 @@ def main(parameters, storage=None):
     u = Function(V_u, name="Displacement")
     zero_u = Function(V_u, name="Boundary Displacement")
     alpha = Function(V_alpha, name="Damage")
-    zero_alpha = Function(V_alpha, name="Damage Boundary Field")
+    Function(V_alpha, name="Damage Boundary Field")
     alphadot = dolfinx.fem.Function(V_alpha, name="Damage_rate")
 
     state = {"u": u, "alpha": alpha}
@@ -158,16 +148,15 @@ def main(parameters, storage=None):
     # Perturbation
     β = Function(V_alpha, name="DamagePerturbation")
     v = Function(V_u, name="DisplacementPerturbation")
-    perturbation = {"v": v, "beta": β}
 
-    z = [u, alpha]
+    [u, alpha]
     # need upper/lower bound for the damage field
     alpha_lb = Function(V_alpha, name="Lower bound")
     alpha_ub = Function(V_alpha, name="Upper bound")
 
     # Measures
     dx = ufl.Measure("dx", domain=mesh)
-    ds = ufl.Measure("ds", domain=mesh)
+    ufl.Measure("ds", domain=mesh)
 
     dofs_u_left = locate_dofs_geometrical(V_u, lambda x: np.isclose(x[0], 0.0))
     dofs_u_right = locate_dofs_geometrical(V_u, lambda x: np.isclose(x[0], Lx))
@@ -175,7 +164,7 @@ def main(parameters, storage=None):
     zero_u = Function(V_u)
     u_ = Function(V_u, name="Boundary Displacement")
 
-    zero_alpha = Function(V_alpha)
+    Function(V_alpha)
 
     bc_u_left = dirichletbc(zero_u, dofs_u_left)
     bc_u_right = dirichletbc(u_, dofs_u_right)
@@ -291,8 +280,8 @@ def main(parameters, storage=None):
         ColorPrint.print_bold("   Solving second order: Rate Pb.    ")
         ColorPrint.print_bold("===================-=================")
 
-        is_stable = bifurcation.solve(alpha_lb)
-        is_elastic = bifurcation.is_elastic()
+        bifurcation.solve(alpha_lb)
+        bifurcation.is_elastic()
         inertia = bifurcation.get_inertia()
 
         ColorPrint.print_bold("   Solving second order: Stability Pb.    ")
@@ -322,7 +311,7 @@ def main(parameters, storage=None):
                 subplot=(0, 0),
                 lineproperties={"c": "k", "label": "$\\beta$"},
             )
-            ax = _plt.gca()
+            _plt.gca()
             _plt.legend()
             _plt.fill_between(data[0], data[1].reshape(len(data[1])))
             _plt.title("Perurbation")
@@ -342,7 +331,7 @@ def main(parameters, storage=None):
                 subplot=(0, 0),
                 lineproperties={"c": "k", "label": "$\\beta$"},
             )
-            ax = _plt.gca()
+            _plt.gca()
             _plt.legend()
             _plt.fill_between(data[0], data[1].reshape(len(data[1])))
             _plt.title("Perurbation from the Cone")
@@ -385,7 +374,7 @@ def main(parameters, storage=None):
         history_data["inertia"].append(inertia)
 
         # postprocessing
-        with dolfinx.common.Timer("~Postprocessing and Vis") as timer:
+        with dolfinx.common.Timer("~Postprocessing and Vis"):
             if comm.rank == 0:
                 plot_energies(history_data, file=f"{prefix}/{_nameExp}_energies.pdf")
                 plot_AMit_load(history_data, file=f"{prefix}/{_nameExp}_it_load.pdf")
@@ -400,7 +389,7 @@ def main(parameters, storage=None):
             file.write_function(alpha, t)
 
     # postprocessing
-    with dolfinx.common.Timer("~Postprocessing and Vis") as timer:
+    with dolfinx.common.Timer("~Postprocessing and Vis"):
         if comm.Get_rank == 1:
             xvfb.start_xvfb(wait=0.05)
             pyvista.OFF_SCREEN = True
diff --git a/src/irrevolutions/practice/traction-AT1_cone.py b/src/irrevolutions/practice/traction-AT1_cone.py
index e7076ecd..d4f3d98d 100644
--- a/src/irrevolutions/practice/traction-AT1_cone.py
+++ b/src/irrevolutions/practice/traction-AT1_cone.py
@@ -1,4 +1,15 @@
 #!/usr/bin/env python3
+from utils.viz import plot_scalar, plot_vector
+from pyvista.utilities import xvfb
+import pyvista
+from utils.plots import plot_AMit_load, plot_force_displacement
+import hashlib
+from irrevolutions.utils import ColorPrint
+from utils.plots import plot_energies
+from models import DamageElasticityModel as Brittle
+from meshes.primitives import mesh_bar_gmshapi
+from algorithms.so import BifurcationSolver, StabilitySolver
+from algorithms.am import AlternateMinimisation, HybridSolver
 import json
 import logging
 import os
@@ -14,27 +25,14 @@
 import ufl
 import yaml
 from dolfinx.common import list_timings
-from dolfinx.fem import (
-    Constant,
-    Function,
-    FunctionSpace,
-    assemble_scalar,
-    dirichletbc,
-    form,
-    locate_dofs_geometrical,
-    set_bc,
-)
+from dolfinx.fem import (Constant, Function, FunctionSpace, assemble_scalar,
+                         dirichletbc, form, locate_dofs_geometrical, set_bc)
 from dolfinx.io import XDMFFile, gmshio
 from mpi4py import MPI
 from petsc4py import PETSc
 
 sys.path.append("../")
-from algorithms.am import AlternateMinimisation, HybridSolver
-from algorithms.so import BifurcationSolver, StabilitySolver
-from irrevolutions.utils import ColorPrint
-from meshes.primitives import mesh_bar_gmshapi
-from models import DamageElasticityModel as Brittle
-from utils.plots import plot_energies
+
 
 """Traction endommageable bar
 
@@ -102,7 +100,6 @@
 mesh, mts, fts = gmshio.model_to_mesh(gmsh_model, comm, model_rank, tdim)
 
 
-import hashlib
 
 signature = hashlib.md5(str(parameters).encode("utf-8")).hexdigest()
 
@@ -363,7 +360,6 @@
 
 # Viz
 
-from utils.plots import plot_AMit_load, plot_force_displacement
 
 if comm.rank == 0:
     plot_energies(history_data, file=f"{prefix}/{_nameExp}_energies.pdf")
@@ -371,11 +367,7 @@
     plot_force_displacement(history_data, file=f"{prefix}/{_nameExp}_stress-load.pdf")
 
 
-import sys
 
-import pyvista
-from pyvista.utilities import xvfb
-from utils.viz import plot_scalar, plot_vector
 
 #
 xvfb.start_xvfb(wait=0.05)
diff --git a/src/irrevolutions/practice/traction-AT1_first_order.py b/src/irrevolutions/practice/traction-AT1_first_order.py
index 02d5efe0..b5e193f9 100644
--- a/src/irrevolutions/practice/traction-AT1_first_order.py
+++ b/src/irrevolutions/practice/traction-AT1_first_order.py
@@ -1,4 +1,15 @@
 #!/usr/bin/env python3
+from utils.viz import plot_scalar, plot_vector
+from pyvista.utilities import xvfb
+import pyvista
+from utils.plots import plot_AMit_load, plot_force_displacement
+import hashlib
+from irrevolutions.utils import ColorPrint
+from utils.plots import plot_energies
+from models import DamageElasticityModel as Brittle
+from meshes.primitives import mesh_bar_gmshapi
+from algorithms.so import BifurcationSolver, StabilitySolver
+from algorithms.am import AlternateMinimisation, HybridSolver
 import json
 import logging
 import os
@@ -14,27 +25,14 @@
 import ufl
 import yaml
 from dolfinx.common import list_timings
-from dolfinx.fem import (
-    Constant,
-    Function,
-    FunctionSpace,
-    assemble_scalar,
-    dirichletbc,
-    form,
-    locate_dofs_geometrical,
-    set_bc,
-)
+from dolfinx.fem import (Constant, Function, FunctionSpace, assemble_scalar,
+                         dirichletbc, form, locate_dofs_geometrical, set_bc)
 from dolfinx.io import XDMFFile, gmshio
 from mpi4py import MPI
 from petsc4py import PETSc
 
 sys.path.append("../")
-from algorithms.am import AlternateMinimisation, HybridSolver
-from algorithms.so import BifurcationSolver, StabilitySolver
-from irrevolutions.utils import ColorPrint
-from meshes.primitives import mesh_bar_gmshapi
-from models import DamageElasticityModel as Brittle
-from utils.plots import plot_energies
+
 
 sys.path.append("../")
 
@@ -105,7 +103,6 @@
 mesh, mts, fts = gmshio.model_to_mesh(gmsh_model, comm, model_rank, tdim)
 
 
-import hashlib
 
 signature = hashlib.md5(str(parameters).encode("utf-8")).hexdigest()
 
@@ -358,7 +355,6 @@
 
 # Viz
 
-from utils.plots import plot_AMit_load, plot_force_displacement
 
 if comm.rank == 0:
     plot_energies(history_data, file=f"{prefix}/{_nameExp}_energies.pdf")
@@ -366,11 +362,7 @@
     plot_force_displacement(history_data, file=f"{prefix}/{_nameExp}_stress-load.pdf")
 
 
-import sys
 
-import pyvista
-from pyvista.utilities import xvfb
-from utils.viz import plot_scalar, plot_vector
 
 #
 xvfb.start_xvfb(wait=0.05)
diff --git a/src/irrevolutions/practice/traction-AT2_cone.py b/src/irrevolutions/practice/traction-AT2_cone.py
index 53867297..133ebf27 100644
--- a/src/irrevolutions/practice/traction-AT2_cone.py
+++ b/src/irrevolutions/practice/traction-AT2_cone.py
@@ -1,4 +1,15 @@
 #!/usr/bin/env python3
+from utils.viz import plot_scalar, plot_vector
+from pyvista.utilities import xvfb
+import pyvista
+from utils.plots import plot_AMit_load, plot_force_displacement
+import hashlib
+from irrevolutions.utils import ColorPrint
+from utils.plots import plot_energies
+from models import DamageElasticityModel as Brittle
+from meshes.primitives import mesh_bar_gmshapi
+from algorithms.so_merged import BifurcationSolver, StabilitySolver
+from algorithms.am import AlternateMinimisation, HybridSolver
 import json
 import logging
 import os
@@ -12,27 +23,14 @@
 import petsc4py
 import ufl
 import yaml
-from dolfinx.fem import (
-    Constant,
-    Function,
-    FunctionSpace,
-    assemble_scalar,
-    dirichletbc,
-    form,
-    locate_dofs_geometrical,
-    set_bc,
-)
+from dolfinx.fem import (Constant, Function, FunctionSpace, assemble_scalar,
+                         dirichletbc, form, locate_dofs_geometrical, set_bc)
 from dolfinx.io import XDMFFile, gmshio
 from mpi4py import MPI
 from petsc4py import PETSc
 
 sys.path.append("../")
-from algorithms.am import AlternateMinimisation, HybridSolver
-from algorithms.so_merged import BifurcationSolver, StabilitySolver
-from irrevolutions.utils import ColorPrint
-from meshes.primitives import mesh_bar_gmshapi
-from models import DamageElasticityModel as Brittle
-from utils.plots import plot_energies
+
 
 sys.path.append("../")
 
@@ -85,7 +83,6 @@ def w(self, alpha):
 gmsh_model, tdim = mesh_bar_gmshapi(geom_type, Lx, Ly, _lc, tdim)
 mesh, mts, fts = gmshio.model_to_mesh(gmsh_model, comm, model_rank, tdim)
 
-import hashlib
 
 signature = hashlib.md5(str(parameters).encode("utf-8")).hexdigest()
 outdir = os.path.join(os.path.dirname(__file__), "output")
@@ -309,18 +306,13 @@ def w(self, alpha):
 df = pd.DataFrame(history_data)
 print(df.drop(["solver_data", "cone_data"], axis=1))
 
-from utils.plots import plot_AMit_load, plot_force_displacement
 
 if comm.rank == 0:
     plot_energies(history_data, file=f"{prefix}/{_nameExp}_energies.pdf")
     plot_AMit_load(history_data, file=f"{prefix}/{_nameExp}_it_load.pdf")
     plot_force_displacement(history_data, file=f"{prefix}/{_nameExp}_stress-load.pdf")
 
-import sys
 
-import pyvista
-from pyvista.utilities import xvfb
-from utils.viz import plot_scalar, plot_vector
 
 xvfb.start_xvfb(wait=0.05)
 pyvista.OFF_SCREEN = True
diff --git a/src/irrevolutions/practice/traction-ATJJ.py b/src/irrevolutions/practice/traction-ATJJ.py
index 81a61afb..d4d86af3 100644
--- a/src/irrevolutions/practice/traction-ATJJ.py
+++ b/src/irrevolutions/practice/traction-ATJJ.py
@@ -1,10 +1,16 @@
 #!/usr/bin/env python3
+from irrevolutions.utils import ColorPrint
+from models import DamageElasticityModel
+from meshes.primitives import mesh_bar_gmshapi
+from algorithms.so import BifurcationSolver, StabilitySolver
+from algorithms.am import HybridSolver
+from utils.plots import plot_energies
 import json
 import logging
 import os
 import sys
 from pathlib import Path
-
+import hashlib
 import dolfinx
 import dolfinx.mesh
 import dolfinx.plot
@@ -14,27 +20,15 @@
 import ufl
 import yaml
 from dolfinx.common import list_timings
-from dolfinx.fem import (
-    Constant,
-    Function,
-    FunctionSpace,
-    assemble_scalar,
-    dirichletbc,
-    form,
-    locate_dofs_geometrical,
-    set_bc,
-)
+from dolfinx.fem import (Constant, Function, FunctionSpace, assemble_scalar,
+                         dirichletbc, form, locate_dofs_geometrical, set_bc)
 from dolfinx.io import XDMFFile, gmshio
 from mpi4py import MPI
 from petsc4py import PETSc
 
 sys.path.append("../")
 
-from algorithms.am import HybridSolver
-from algorithms.so import BifurcationSolver, StabilitySolver
-from irrevolutions.utils import ColorPrint
-from meshes.primitives import mesh_bar_gmshapi
-from models import DamageElasticityModel
+
 
 logging.getLogger().setLevel(logging.ERROR)
 
@@ -205,10 +199,10 @@ def traction_with_parameters(parameters, slug=""):
 
     # Measures
     dx = ufl.Measure("dx", domain=mesh)
-    ds = ufl.Measure("ds", domain=mesh)
+    ufl.Measure("ds", domain=mesh)
 
-    dofs_alpha_left = locate_dofs_geometrical(V_alpha, lambda x: np.isclose(x[0], 0.0))
-    dofs_alpha_right = locate_dofs_geometrical(V_alpha, lambda x: np.isclose(x[0], Lx))
+    locate_dofs_geometrical(V_alpha, lambda x: np.isclose(x[0], 0.0))
+    locate_dofs_geometrical(V_alpha, lambda x: np.isclose(x[0], Lx))
 
     dofs_u_left = locate_dofs_geometrical(V_u, lambda x: np.isclose(x[0], 0.0))
     dofs_u_right = locate_dofs_geometrical(V_u, lambda x: np.isclose(x[0], Lx))
@@ -507,7 +501,7 @@ def traction_with_parameters(parameters, slug=""):
             "label": f"$\\alpha$ with $\ell$ = {parameters['model']['ell']:.2f}",
         },
     )
-    ax = _plt.gca()
+    _plt.gca()
     _plt.legend()
     _plt.fill_between(data[0], data[1].reshape(len(data[1])))
     _plt.title("Damage profile")
@@ -523,8 +517,8 @@ def _plot_bif_spectrum_profile(
 
     import matplotlib.pyplot as plt
     from utils.viz import plot_profile
-    # __import__('pdb').set_trace()
 
+    # __import__('pdb').set_trace()
     # fields = spectrum["perturbations_beta"]
     # fields = spectrum["perturbations_beta"]
     fields = [item.get("beta") for item in spectrum]
@@ -532,8 +526,9 @@ def _plot_bif_spectrum_profile(
     num_cols = 1
     num_rows = (n + num_cols - 1) // num_cols
 
-    if plotter == None:
+    if plotter is None:
         import pyvista
+
         # from pyvista.utilities import xvfb
 
         plotter = pyvista.Plotter(
@@ -604,8 +599,9 @@ def _plot_bif_spectrum_profile_fullvec(
     num_cols = 1
     num_rows = (n + num_cols - 1) // num_cols
 
-    if plotter == None:
+    if plotter is None:
         import pyvista
+
         # from pyvista.utilities import xvfb
 
         plotter = pyvista.Plotter(
@@ -681,8 +677,9 @@ def _plot_perturbations_profile(
 
     figure = plt.figure()
 
-    if plotter == None:
+    if plotter is None:
         import pyvista
+
         # from pyvista.utilities import xvfb
 
         plotter = pyvista.Plotter(
@@ -742,8 +739,6 @@ def _plot_perturbations_profile(
 
     return plotter, _plt
 
-    pass
-
 
 def param_ell():
     # for ell in [0.1, 0.2, 0.3]:
diff --git a/src/irrevolutions/practice/traction-bar-clean.py b/src/irrevolutions/practice/traction-bar-clean.py
index 52300ce0..410fe56d 100644
--- a/src/irrevolutions/practice/traction-bar-clean.py
+++ b/src/irrevolutions/practice/traction-bar-clean.py
@@ -1,4 +1,15 @@
 #!/usr/bin/env python3
+from irrevolutions.utils import ColorPrint, _logger, simulation_info
+from utils.viz import plot_profile, plot_scalar, plot_vector
+from utils.plots import plot_AMit_load, plot_energies, plot_force_displacement
+from solvers.function import vec_to_functions
+from pyvista.utilities import xvfb
+from models import DamageElasticityModel as Brittle
+from meshes.primitives import mesh_bar_gmshapi
+from algorithms.so import BifurcationSolver, StabilitySolver
+from algorithms.am import AlternateMinimisation, HybridSolver
+import pyvista
+import hashlib
 import json
 import logging
 import os
@@ -12,33 +23,15 @@
 import petsc4py
 import ufl
 import yaml
-from dolfinx.fem import (
-    Constant,
-    Function,
-    FunctionSpace,
-    assemble_scalar,
-    dirichletbc,
-    form,
-    locate_dofs_geometrical,
-    set_bc,
-)
+from dolfinx.fem import (Constant, Function, FunctionSpace, assemble_scalar,
+                         dirichletbc, form, locate_dofs_geometrical, set_bc)
 from dolfinx.io import XDMFFile, gmshio
 from mpi4py import MPI
 from petsc4py import PETSc
 
 sys.path.append("../")
-import hashlib
 
-import pyvista
-from algorithms.am import AlternateMinimisation, HybridSolver
-from algorithms.so import BifurcationSolver, StabilitySolver
-from irrevolutions.utils import ColorPrint, _logger, simulation_info
-from meshes.primitives import mesh_bar_gmshapi
-from models import DamageElasticityModel as Brittle
-from pyvista.utilities import xvfb
-from solvers.function import vec_to_functions
-from utils.plots import plot_AMit_load, plot_energies, plot_force_displacement
-from utils.viz import plot_profile, plot_scalar, plot_vector
+
 
 
 class BrittleAT2(Brittle):
@@ -140,7 +133,7 @@ def main(parameters, model="at2", storage=None):
     Ly = parameters["geometry"]["Ly"]
     tdim = parameters["geometry"]["geometric_dimension"]
     _nameExp = parameters["geometry"]["geom_type"]
-    ell_ = parameters["model"]["ell"]
+    parameters["model"]["ell"]
     lc = parameters["model"]["ell"] / parameters["geometry"]["mesh_size_factor"]
     geom_type = parameters["geometry"]["geom_type"]
 
@@ -190,7 +183,7 @@ def main(parameters, model="at2", storage=None):
     alpha_ub = Function(V_alpha, name="Upper bound")
 
     dx = ufl.Measure("dx", domain=mesh)
-    ds = ufl.Measure("ds", domain=mesh)
+    ufl.Measure("ds", domain=mesh)
 
     dofs_alpha_left = locate_dofs_geometrical(V_alpha, lambda x: np.isclose(x[0], 0.0))
     dofs_alpha_right = locate_dofs_geometrical(V_alpha, lambda x: np.isclose(x[0], Lx))
@@ -207,7 +200,6 @@ def main(parameters, model="at2", storage=None):
     # Perturbation
     β = Function(V_alpha, name="DamagePerturbation")
     v = Function(V_u, name="DisplacementPerturbation")
-    perturbation = {"v": v, "beta": β}
 
     for f in [zero_u, zero_alpha, u_, alpha_lb, alpha_ub]:
         f.vector.ghostUpdate(
@@ -397,9 +389,9 @@ def main(parameters, model="at2", storage=None):
 
         ColorPrint.print_bold("   Written timely data.    ")
 
-    df = pd.DataFrame(history_data)
+    pd.DataFrame(history_data)
 
-    with dolfinx.common.Timer("~Postprocessing and Vis") as timer:
+    with dolfinx.common.Timer("~Postprocessing and Vis"):
         if comm.Get_size() == 1:
             # if comm.rank == 0 and comm.Get_size() == 1:
             plot_energies(history_data, file=f"{prefix}/{_nameExp}_energies.pdf")
@@ -446,7 +438,7 @@ def plot_perturbations(comm, Lx, prefix, β, v, bifurcation, stability, i_t):
             subplot=(0, 0),
             lineproperties={"c": "k", "label": "$\\beta$"},
         )
-        ax = _plt.gca()
+        _plt.gca()
         _plt.legend()
         _plt.fill_between(data[0], data[1].reshape(len(data[1])))
         _plt.title("Perurbation")
@@ -466,7 +458,7 @@ def plot_perturbations(comm, Lx, prefix, β, v, bifurcation, stability, i_t):
             subplot=(0, 0),
             lineproperties={"c": "k", "label": "$\\beta$"},
         )
-        ax = _plt.gca()
+        _plt.gca()
         _plt.legend()
         _plt.fill_between(data[0], data[1].reshape(len(data[1])))
         _plt.title("Perurbation from the Cone")
diff --git a/src/irrevolutions/practice/traction-cone.py b/src/irrevolutions/practice/traction-cone.py
index e135b55b..c3c1c02b 100644
--- a/src/irrevolutions/practice/traction-cone.py
+++ b/src/irrevolutions/practice/traction-cone.py
@@ -1,10 +1,15 @@
 #!/usr/bin/env python3
+from irrevolutions.utils import ColorPrint
+from models import DamageElasticityModel as Brittle
+from meshes.primitives import mesh_bar_gmshapi
+from algorithms.so import BifurcationSolver, StabilitySolver
+from algorithms.am import HybridSolver
 import json
 import logging
 import os
 import sys
 from pathlib import Path
-
+import hashlib
 import dolfinx
 import dolfinx.mesh
 import dolfinx.plot
@@ -14,26 +19,14 @@
 import ufl
 import yaml
 from dolfinx.common import list_timings
-from dolfinx.fem import (
-    Constant,
-    Function,
-    FunctionSpace,
-    assemble_scalar,
-    dirichletbc,
-    form,
-    locate_dofs_geometrical,
-    set_bc,
-)
+from dolfinx.fem import (Constant, Function, FunctionSpace, assemble_scalar,
+                         dirichletbc, form, locate_dofs_geometrical, set_bc)
 from dolfinx.io import XDMFFile, gmshio
 from mpi4py import MPI
 from petsc4py import PETSc
-
+from irrevolutions.utils.parametric import (parameters_vs_ell, parameters_vs_SPA_scaling)
 sys.path.append("../")
-from algorithms.am import HybridSolver
-from algorithms.so import BifurcationSolver, StabilitySolver
-from irrevolutions.utils import ColorPrint
-from meshes.primitives import mesh_bar_gmshapi
-from models import DamageElasticityModel as Brittle
+
 
 logging.getLogger().setLevel(logging.ERROR)
 
@@ -137,10 +130,10 @@ def traction_with_parameters(parameters, slug=""):
 
     # Measures
     dx = ufl.Measure("dx", domain=mesh)
-    ds = ufl.Measure("ds", domain=mesh)
+    ufl.Measure("ds", domain=mesh)
 
-    dofs_alpha_left = locate_dofs_geometrical(V_alpha, lambda x: np.isclose(x[0], 0.0))
-    dofs_alpha_right = locate_dofs_geometrical(V_alpha, lambda x: np.isclose(x[0], Lx))
+    locate_dofs_geometrical(V_alpha, lambda x: np.isclose(x[0], 0.0))
+    locate_dofs_geometrical(V_alpha, lambda x: np.isclose(x[0], Lx))
 
     dofs_u_left = locate_dofs_geometrical(V_u, lambda x: np.isclose(x[0], 0.0))
     dofs_u_right = locate_dofs_geometrical(V_u, lambda x: np.isclose(x[0], Lx))
@@ -359,7 +352,8 @@ def traction_with_parameters(parameters, slug=""):
 
     # Viz
 
-    from utils.plots import plot_AMit_load, plot_energies, plot_force_displacement
+    from utils.plots import (plot_AMit_load, plot_energies,
+                             plot_force_displacement)
 
     if comm.rank == 0:
         plot_energies(history_data, file=f"{prefix}/{_nameExp}_energies.pdf")
diff --git a/src/irrevolutions/practice/traction-parametric.py b/src/irrevolutions/practice/traction-parametric.py
index 3bafd81a..f0971287 100644
--- a/src/irrevolutions/practice/traction-parametric.py
+++ b/src/irrevolutions/practice/traction-parametric.py
@@ -1,4 +1,14 @@
 #!/usr/bin/env python3
+from irrevolutions.utils import ColorPrint
+from utils.viz import plot_scalar, plot_vector
+from utils.plots import plot_energies, plot_force_displacement
+from pyvista.utilities import xvfb
+from models import DamageElasticityModel as Brittle
+from meshes.primitives import mesh_bar_gmshapi
+from algorithms.so import BifurcationSolver, StabilitySolver
+from algorithms.am import AlternateMinimisation, HybridSolver
+import pyvista
+import hashlib
 import json
 import logging
 import os
@@ -13,32 +23,15 @@
 import ufl
 import yaml
 from dolfinx.common import list_timings
-from dolfinx.fem import (
-    Constant,
-    Function,
-    FunctionSpace,
-    assemble_scalar,
-    dirichletbc,
-    form,
-    locate_dofs_geometrical,
-    set_bc,
-)
+from dolfinx.fem import (Constant, Function, FunctionSpace, assemble_scalar,
+                         dirichletbc, form, locate_dofs_geometrical, set_bc)
 from dolfinx.io import gmshio
 from mpi4py import MPI
 from petsc4py import PETSc
 
 sys.path.append("../")
-import hashlib
 
-import pyvista
-from algorithms.am import AlternateMinimisation, HybridSolver
-from algorithms.so import BifurcationSolver, StabilitySolver
-from irrevolutions.utils import ColorPrint
-from meshes.primitives import mesh_bar_gmshapi
-from models import DamageElasticityModel as Brittle
-from pyvista.utilities import xvfb
-from utils.plots import plot_energies, plot_force_displacement
-from utils.viz import plot_scalar, plot_vector
+
 
 
 class BrittleAT2(Brittle):
@@ -69,10 +62,10 @@ def store_results(self, parameters, history_data, state):
         Args:
             history_data (dict): Dictionary containing simulation data.
         """
-        t = history_data["load"][-1]
+        history_data["load"][-1]
 
-        u = state["u"]
-        alpha = state["alpha"]
+        state["u"]
+        state["alpha"]
 
         if self.comm.rank == 0:
             with open(f"{self.prefix}/parameters.yaml", "w") as file:
@@ -197,10 +190,10 @@ def main(parameters, model="at2", storage=None):
     alpha_ub = Function(V_alpha, name="Upper bound")
 
     dx = ufl.Measure("dx", domain=mesh)
-    ds = ufl.Measure("ds", domain=mesh)
+    ufl.Measure("ds", domain=mesh)
 
-    dofs_alpha_left = locate_dofs_geometrical(V_alpha, lambda x: np.isclose(x[0], 0.0))
-    dofs_alpha_right = locate_dofs_geometrical(V_alpha, lambda x: np.isclose(x[0], Lx))
+    locate_dofs_geometrical(V_alpha, lambda x: np.isclose(x[0], 0.0))
+    locate_dofs_geometrical(V_alpha, lambda x: np.isclose(x[0], Lx))
 
     dofs_u_left = locate_dofs_geometrical(V_u, lambda x: np.isclose(x[0], 0.0))
     dofs_u_right = locate_dofs_geometrical(V_u, lambda x: np.isclose(x[0], Lx))
@@ -243,7 +236,7 @@ def main(parameters, model="at2", storage=None):
     load_par = parameters["loading"]
     loads = np.linspace(load_par["min"], load_par["max"], load_par["steps"])
 
-    solver = AlternateMinimisation(
+    AlternateMinimisation(
         total_energy, state, bcs, parameters.get("solvers"), bounds=(alpha_lb, alpha_ub)
     )
 
@@ -389,7 +382,7 @@ def main(parameters, model="at2", storage=None):
     # print(df.drop(['solver_data', 'cone_data'], axis=1))
     print(df.drop(["cone_data"], axis=1))
 
-    with dolfinx.common.Timer("~Postprocessing and Vis") as timer:
+    with dolfinx.common.Timer("~Postprocessing and Vis"):
         if comm.rank == 0:
             plot_energies(history_data, file=f"{prefix}/{_nameExp}_energies.pdf")
             # plot_AMit_load(history_data, file=f"{prefix}/{_nameExp}_it_load.pdf")
@@ -495,7 +488,7 @@ def param_vs_s(base_parameters, base_signature):
         )
         ColorPrint.print_bold(f"===================-{signature}-=================")
 
-        with dolfinx.common.Timer("~Computation Experiment") as timer:
+        with dolfinx.common.Timer("~Computation Experiment"):
             history_data, performance, state = main(parameters, _storage)
 
         _timings = table_timing_data()
@@ -546,7 +539,7 @@ def param_vs_dry(base_parameters, base_signature):
         )
         ColorPrint.print_bold(f"===================-{signature}-=================")
 
-        with dolfinx.common.Timer("~Computation Experiment") as timer:
+        with dolfinx.common.Timer("~Computation Experiment"):
             history_data, performance, state = main(parameters, _storage)
 
         _timings = table_timing_data()
@@ -574,11 +567,9 @@ def param_vs_dry(base_parameters, base_signature):
 if __name__ == "__main__":
     import argparse
 
-    from utils.parametric import (
-        parameters_vs_ell,
-        parameters_vs_n_refinement,
-        parameters_vs_SPA_scaling,
-    )
+    from utils.parametric import (parameters_vs_ell,
+                                  parameters_vs_n_refinement,
+                                  parameters_vs_SPA_scaling)
 
     admissible_models = {"at1", "at2", "thinfilm"}
 
diff --git a/src/irrevolutions/practice/unstabinst.py b/src/irrevolutions/practice/unstabinst.py
index eef2bea1..778cf3e2 100644
--- a/src/irrevolutions/practice/unstabinst.py
+++ b/src/irrevolutions/practice/unstabinst.py
@@ -1,30 +1,25 @@
 # library include
+from utils.viz import plot_mesh, plot_scalar, plot_vector
+from pyvista.utilities import xvfb
+from petsc4py import PETSc
+from models import DamageElasticityModel as Brittle
+from dolfinx.fem import (assemble_scalar, dirichletbc, locate_dofs_geometrical,
+                         set_bc)
+from algorithms import am
+import ufl
+import pyvista
+import numpy as np
+import meshes
+import matplotlib.pyplot as plt
+import gmsh
+import dolfinx.plot
+import dolfinx.io
+import dolfinx
+import logging
 import sys
 
 sys.path.append("../")
-import logging
-import sys
 
-import dolfinx
-import dolfinx.io
-import dolfinx.plot
-import gmsh
-import matplotlib.pyplot as plt
-import meshes
-import numpy as np
-import pyvista
-import ufl
-from algorithms import am
-from dolfinx.fem import (
-    assemble_scalar,
-    dirichletbc,
-    locate_dofs_geometrical,
-    set_bc,
-)
-from models import DamageElasticityModel as Brittle
-from petsc4py import PETSc
-from pyvista.utilities import xvfb
-from utils.viz import plot_mesh, plot_scalar, plot_vector
 
 logging.basicConfig(level=logging.INFO)
 
@@ -104,7 +99,7 @@ def mesh_V(
     gmsh.option.setNumber("General.Terminal", 1)
     gmsh.option.setNumber("Mesh.Algorithm", 5)
     hopen = a * np.tan((gamma / 2.0) * np.pi / 180)
-    c0 = h / 40
+    h / 40
     load_len = min(h / 40, L / 80)
     tdim = 2
 
diff --git a/src/irrevolutions/solvers/__init__.py b/src/irrevolutions/solvers/__init__.py
index c9180050..969ad05c 100644
--- a/src/irrevolutions/solvers/__init__.py
+++ b/src/irrevolutions/solvers/__init__.py
@@ -1,3 +1,6 @@
+from dolfinx.fem.petsc import (apply_lifting, assemble_matrix, assemble_vector,
+                               create_matrix, create_vector, set_bc)
+from dolfinx.cpp.log import LogLevel, log
 import sys
 
 import dolfinx
@@ -8,17 +11,7 @@
 
 petsc4py.init(sys.argv)
 
-from dolfinx.cpp.log import LogLevel, log
-
 # from damage.utils import ColorPrint
-from dolfinx.fem.petsc import (
-    apply_lifting,
-    assemble_matrix,
-    assemble_vector,
-    create_matrix,
-    create_vector,
-    set_bc,
-)
 
 # import pdb;
 # pdb.set_trace()
diff --git a/src/irrevolutions/solvers/slepcblockproblem.py b/src/irrevolutions/solvers/slepcblockproblem.py
index 1b197626..6a1240ad 100644
--- a/src/irrevolutions/solvers/slepcblockproblem.py
+++ b/src/irrevolutions/solvers/slepcblockproblem.py
@@ -4,7 +4,6 @@
 # from yaml.tokens import BlockSequenceStartToken
 import dolfinx
 import ufl
-
 # plot_matrix
 from petsc4py import PETSc
 from slepc4py import SLEPc
diff --git a/src/irrevolutions/solvers/snesblockproblem.py b/src/irrevolutions/solvers/snesblockproblem.py
index 39aaa165..18ebacb0 100644
--- a/src/irrevolutions/solvers/snesblockproblem.py
+++ b/src/irrevolutions/solvers/snesblockproblem.py
@@ -284,7 +284,6 @@ def _plot_solution(self, it):
             f"./output/test_hybrid/test_newtonblock_MPI{self.comm.size}-{it}-.png"
         )
         _plt.close()
-        pass
 
     def _monitor_nest(self, snes, it, norm):
         self.compute_norms_nest(snes)
diff --git a/src/irrevolutions/utils/__init__.py b/src/irrevolutions/utils/__init__.py
index 10a87658..4bfb03dd 100644
--- a/src/irrevolutions/utils/__init__.py
+++ b/src/irrevolutions/utils/__init__.py
@@ -1,3 +1,7 @@
+from dolfinx.io import XDMFFile
+from slepc4py import __version__ as slepc_version
+from petsc4py import __version__ as petsc_version
+from dolfinx import __version__ as dolfinx_version
 import json
 import logging
 import os
@@ -16,6 +20,64 @@
 
 comm = MPI.COMM_WORLD
 
+error_codes = {
+    "PETSC_SUCCESS": 0,
+    "PETSC_ERR_BOOLEAN_MACRO_FAILURE": 1,
+    "PETSC_ERR_MIN_VALUE": 54,
+    "PETSC_ERR_MEM": 55,
+    "PETSC_ERR_SUP": 56,
+    "PETSC_ERR_SUP_SYS": 57,
+    "PETSC_ERR_ORDER": 58,
+    "PETSC_ERR_SIG": 59,
+    "PETSC_ERR_FP": 72,
+    "PETSC_ERR_COR": 74,
+    "PETSC_ERR_LIB": 76,
+    "PETSC_ERR_PLIB": 77,
+    "PETSC_ERR_MEMC": 78,
+    "PETSC_ERR_CONV_FAILED": 82,
+    "PETSC_ERR_USER": 83,
+    "PETSC_ERR_SYS": 88,
+    "PETSC_ERR_POINTER": 70,
+    "PETSC_ERR_MPI_LIB_INCOMP": 87,
+    "PETSC_ERR_ARG_SIZ": 60,
+    "PETSC_ERR_ARG_IDN": 61,
+    "PETSC_ERR_ARG_WRONG": 62,
+    "PETSC_ERR_ARG_CORRUPT": 64,
+    "PETSC_ERR_ARG_OUTOFRANGE": 63,
+    "PETSC_ERR_ARG_BADPTR": 68,
+    "PETSC_ERR_ARG_NOTSAMETYPE": 69,
+    "PETSC_ERR_ARG_NOTSAMECOMM": 80,
+    "PETSC_ERR_ARG_WRONGSTATE": 73,
+    "PETSC_ERR_ARG_TYPENOTSET": 89,
+    "PETSC_ERR_ARG_INCOMP": 75,
+    "PETSC_ERR_ARG_NULL": 85,
+    "PETSC_ERR_ARG_UNKNOWN_TYPE": 86,
+    "PETSC_ERR_FILE_OPEN": 65,
+    "PETSC_ERR_FILE_READ": 66,
+    "PETSC_ERR_FILE_WRITE": 67,
+    "PETSC_ERR_FILE_UNEXPECTED": 79,
+    "PETSC_ERR_MAT_LU_ZRPVT": 71,
+    "PETSC_ERR_MAT_CH_ZRPVT": 81,
+    "PETSC_ERR_INT_OVERFLOW": 84,
+    "PETSC_ERR_FLOP_COUNT": 90,
+    "PETSC_ERR_NOT_CONVERGED": 91,
+    "PETSC_ERR_MISSING_FACTOR": 92,
+    "PETSC_ERR_OPT_OVERWRITE": 93,
+    "PETSC_ERR_WRONG_MPI_SIZE": 94,
+    "PETSC_ERR_USER_INPUT": 95,
+    "PETSC_ERR_GPU_RESOURCE": 96,
+    "PETSC_ERR_GPU": 97,
+    "PETSC_ERR_MPI": 98,
+    "PETSC_ERR_RETURN": 99,
+    "PETSC_ERR_MEM_LEAK": 100,
+    "PETSC_ERR_MAX_VALUE": 101,
+    "PETSC_ERR_MIN_SIGNED_BOUND_DO_NOT_USE": "INT_MIN",
+    "PETSC_ERR_MAX_SIGNED_BOUND_DO_NOT_USE": "INT_MAX",
+}
+
+
+# Reverse the dictionary to create an inverse mapping
+translatePETScERROR = {v: k for k, v in error_codes.items()}
 
 class ColorPrint:
     """
@@ -84,7 +146,7 @@ def format(self, record):
 
     comm = MPI.COMM_WORLD
     rank = comm.Get_rank()
-    size = comm.Get_size()
+    comm.Get_size()
 
     # Desired log level for the root process (rank 0)
     root_process_log_level = logging.INFO  # Adjust as needed
@@ -193,9 +255,6 @@ def get_branch_details():
     "commit_hash": commit_hash,
 }
 
-from dolfinx import __version__ as dolfinx_version
-from petsc4py import __version__ as petsc_version
-from slepc4py import __version__ as slepc_version
 
 library_info = {
     "dolfinx_version": dolfinx_version,
@@ -285,7 +344,6 @@ def find_offending_columns_lengths(data):
     return lengths
 
 
-from dolfinx.io import XDMFFile
 
 
 class ResultsStorage:
@@ -432,7 +490,7 @@ def save_binary_data(filename, data):
             item.view(viewer)
     elif isinstance(data, PETSc.Mat):
         data.view(viewer)
-    elif isinstance(data, PEtest_binarydataioTSc.Vec):
+    elif isinstance(data, PETSc.Vec):
         data.view(viewer)
     else:
         raise ValueError("Unsupported data type for saving")
diff --git a/src/irrevolutions/utils/eigenspace.py b/src/irrevolutions/utils/eigenspace.py
index bccb282a..d606e186 100644
--- a/src/irrevolutions/utils/eigenspace.py
+++ b/src/irrevolutions/utils/eigenspace.py
@@ -103,7 +103,7 @@ def solve_eigenspace_cone(parameters, idx=0):
         dict: A dictionary containing 'v', 'β', and 'D'.
     """
     x = sp.symbols("x", real=True)
-    v = sp.Function("v", real=True)(x)
+    sp.Function("v", real=True)(x)
     β = sp.Function("β", real=True)(x)
     C, A = sp.symbols("C A")
 
@@ -124,11 +124,11 @@ def solve_eigenspace_cone(parameters, idx=0):
             (C * (1 + sp.cos(sp.pi * x / D)), (0 <= x) & (x <= D)), (0, True)
         )
 
-        _min = (np.pi**2 * a) ** (1 / 3) * (b * c**2) ** (2 / 3)
+        (np.pi**2 * a) ** (1 / 3) * (b * c**2) ** (2 / 3)
 
     elif b * c**2 == sp.pi**2 * a:
         print("case eq")
-        _min = b * c**2
+        b * c**2
         _subs = {C: 0}
         C = 0
         β = C + A * sp.cos(sp.pi * x)
@@ -187,3 +187,32 @@ def book_of_the_numbers(scale_b=3, scale_c=3):
             break
 
     return {"a": a, "b": b, "c": c}
+
+
+def l2_norm(components):
+    """
+    Compute the L2 norm of a vector field defined by components on their respective domains.
+
+    Parameters:
+        components (list): List of tuples (x, f) where x is the mesh coordinates and f is the field values.
+
+    Returns:
+        float: L2 norm of the vector field.
+    """
+    norms = []
+
+    for x, f in components:
+        # Compute the square of the field values
+        squared_values = f**2
+
+        # Compute the integral using the trapezoidal rule
+        integral = np.trapz(squared_values, x)
+
+        # Take the square root to get the L2 norm
+        component_norm = np.sqrt(integral)
+        norms.append(component_norm)
+
+    # Compute the vector norm by summing the squared norms and taking the square root
+    vector_norm = np.sqrt(np.sum(np.array(norms)**2))
+
+    return vector_norm
\ No newline at end of file
diff --git a/src/irrevolutions/utils/mesh_bar_gmshapi.py b/src/irrevolutions/utils/mesh_bar_gmshapi.py
index 8ad21142..953b09c8 100644
--- a/src/irrevolutions/utils/mesh_bar_gmshapi.py
+++ b/src/irrevolutions/utils/mesh_bar_gmshapi.py
@@ -72,7 +72,6 @@ def mesh_bar_gmshapi(
     import dolfinx.plot
     from dolfinx.io import XDMFFile
     from gmsh_mesh import gmsh_model_to_mesh
-
     # from mesh import gmsh_to_dolfin
     # , merge_meshtags, locate_dofs_topological
     from mpi4py import MPI
diff --git a/src/irrevolutions/utils/viz.py b/src/irrevolutions/utils/viz.py
index 64ede825..4fcb1391 100644
--- a/src/irrevolutions/utils/viz.py
+++ b/src/irrevolutions/utils/viz.py
@@ -1,3 +1,11 @@
+import scipy
+from dolfinx.plot import vtk_mesh as compute_topology
+import matplotlib.tri as tri
+import matplotlib.pyplot as plt
+from pyvista.utilities import xvfb
+import pyvista
+from mpi4py import MPI
+import logging
 import sys
 from datetime import date
 
@@ -7,22 +15,15 @@
 
 sys.path.append("../")
 
-import logging
 
 logging.basicConfig(level=logging.INFO)
 
-from mpi4py import MPI
 
 comm = MPI.COMM_WORLD
 # import pdb
-import pyvista
-from pyvista.utilities import xvfb
 
 xvfb.start_xvfb(wait=0.05)
 
-import matplotlib.pyplot as plt
-import matplotlib.tri as tri
-from dolfinx.plot import vtk_mesh as compute_topology
 
 # try:
 #     from dolfinx.plot import create_vtk_mesh as compute_topology
@@ -206,7 +207,7 @@ def plot_perturbations(comm, Lx, prefix, β, v, bifurcation, stability, i_t):
             subplot=(0, 0),
             lineproperties={"c": "k", "label": "$\\beta$"},
         )
-        ax = _plt.gca()
+        _plt.gca()
         _plt.legend()
         _plt.fill_between(data[0], data[1].reshape(len(data[1])))
         _plt.title("Perurbation")
@@ -226,7 +227,7 @@ def plot_perturbations(comm, Lx, prefix, β, v, bifurcation, stability, i_t):
             subplot=(0, 0),
             lineproperties={"c": "k", "label": "$\\beta$"},
         )
-        ax = _plt.gca()
+        _plt.gca()
         _plt.legend()
         _plt.fill_between(data[0], data[1].reshape(len(data[1])))
         _plt.title("Perurbation from the Cone")
@@ -236,7 +237,6 @@ def plot_perturbations(comm, Lx, prefix, β, v, bifurcation, stability, i_t):
     return plotter
 
 
-import scipy
 
 
 def plot_matrix(M):
diff --git a/test/test_1d.py b/test/test_1d.py
index 9385115c..f97fff5c 100644
--- a/test/test_1d.py
+++ b/test/test_1d.py
@@ -15,38 +15,22 @@
 import ufl
 import yaml
 from dolfinx.common import list_timings
-from dolfinx.fem import (
-    Constant,
-    Function,
-    assemble_scalar,
-    dirichletbc,
-    form,
-    locate_dofs_geometrical,
-    set_bc,
-)
+from dolfinx.fem import (Constant, Function, assemble_scalar, dirichletbc,
+                         form, locate_dofs_geometrical, set_bc)
 from dolfinx.fem.petsc import assemble_vector
 from dolfinx.io import XDMFFile
+from mpi4py import MPI
+from petsc4py import PETSc
+
 from irrevolutions.algorithms.am import HybridSolver
 from irrevolutions.algorithms.so import BifurcationSolver, StabilitySolver
 from irrevolutions.solvers import SNESSolver
 from irrevolutions.solvers.function import vec_to_functions
-from irrevolutions.utils import (
-    ColorPrint,
-    _logger,
-    _write_history_data,
-    history_data,
-    norm_H1,
-    norm_L2,
-)
-from irrevolutions.utils.plots import (
-    plot_AMit_load,
-    plot_energies,
-)
-
+from irrevolutions.utils import (ColorPrint, _logger, _write_history_data,
+                                 history_data, norm_H1, norm_L2)
+from irrevolutions.utils.plots import plot_AMit_load, plot_energies
 #
 from irrevolutions.utils.viz import plot_profile
-from mpi4py import MPI
-from petsc4py import PETSc
 
 """The fundamental problem of a 1d bar in traction.
 0|(WWWWWWWWWWWWWWWWWWWWWW)|========> t
@@ -285,7 +269,7 @@ def run_computation(parameters, storage=None):
     alpha_lb = dolfinx.fem.Function(V_alpha, name="LowerBoundDamage")
 
     dx = ufl.Measure("dx", domain=mesh)
-    ds = ufl.Measure("ds", domain=mesh)
+    ufl.Measure("ds", domain=mesh)
 
     # Useful references
     Lx = parameters.get("geometry").get("Lx")
@@ -482,7 +466,7 @@ def stress(state):
 
         stable = stability.solve(alpha_lb, eig0=z0, inertia=inertia)
 
-        with dolfinx.common.Timer("~Postprocessing and Vis") as timer:
+        with dolfinx.common.Timer("~Postprocessing and Vis"):
             if comm.Get_size() == 1:
                 if bifurcation._spectrum:
                     vec_to_functions(bifurcation._spectrum[0]["xk"], [v, β])
@@ -695,7 +679,7 @@ def test_1d():
     parameters, signature = load_parameters(
         os.path.join(os.path.dirname(__file__), "parameters.yml"), ndofs=_N
     )
-    pretty_parameters = json.dumps(parameters, indent=2)
+    json.dumps(parameters, indent=2)
     # print(pretty_parameters)
     # _storage = f"output/one-dimensional-bar/MPI-{MPI.COMM_WORLD.Get_size()}/{args.N}/{signature}"
     _storage = (
@@ -703,7 +687,7 @@ def test_1d():
     )
     ColorPrint.print_bold(f"===================-{_storage}-=================")
 
-    with dolfinx.common.Timer("~Computation Experiment") as timer:
+    with dolfinx.common.Timer("~Computation Experiment"):
         history_data, stability_data, state = run_computation(parameters, _storage)
 
     from irrevolutions.utils import ResultsStorage, Visualization
diff --git a/test/test_binarydataio.py b/test/test_binarydataio.py
index 598f9541..39d5abaf 100644
--- a/test/test_binarydataio.py
+++ b/test/test_binarydataio.py
@@ -3,6 +3,7 @@
 from petsc4py import PETSc
 
 from .test_errorcodes import translatePETScERROR
+import irrevolutions.solvers.restriction as restriction
 
 
 def save_binary_data(filename, data):
@@ -13,7 +14,7 @@ def save_binary_data(filename, data):
             item.view(viewer)
     elif isinstance(data, PETSc.Mat):
         data.view(viewer)
-    elif isinstance(data, PEtest_binarydataioTSc.Vec):
+    elif isinstance(data, PETSc.Vec):
         data.view(viewer)
     else:
         raise ValueError("Unsupported data type for saving")
@@ -117,7 +118,7 @@ def load_minimal_constraints(filename):
 
     # Assuming you have a constructor for your class
     # Modify this accordingly based on your actual class structure
-    reconstructed_obj = Restriction()
+    reconstructed_obj = restriction.Restriction()
     for key, value in minimal_constraints.items():
         setattr(reconstructed_obj, key, value)
 
@@ -138,22 +139,22 @@ def load_minimal_constraints(filename):
     matrix.setUp()
 
     Istart, Iend = matrix.getOwnershipRange()
-    for I in range(Istart, Iend):
-        matrix[I, I] = 4
-        i = I // n
+    for i in range(Istart, Iend):
+        matrix[i, i] = 4
+        i = i // n
         if i > 0:
-            J = I - n
-            matrix[I, J] = -1
+            j = i - n
+            matrix[i, j] = -1
         if i < m - 1:
-            J = I + n
-            matrix[I, J] = -1
-        j = I - i * n
+            j = i + n
+            matrix[i, j] = -1
+        j = i - i * n
         if j > 0:
-            J = I - 1
-            matrix[I, J] = -1
+            j = i - 1
+            matrix[i, j] = -1
         if j < n - 1:
-            J = I + 1
-            matrix[I, J] = -1
+            j = i + 1
+            matrix[i, j] = -1
 
     matrix.assemblyBegin()
     matrix.assemblyEnd()
diff --git a/test/test_cone_convergence.py b/test/test_cone_convergence.py
index 1bfa338a..9a25013e 100644
--- a/test/test_cone_convergence.py
+++ b/test/test_cone_convergence.py
@@ -3,14 +3,15 @@
 import pickle
 
 import dolfinx
-import irrevolutions.solvers.restriction as restriction
 import numpy as np
 import ufl
 from dolfinx.io import XDMFFile
+from mpi4py import MPI
+
+import irrevolutions.solvers.restriction as restriction
 from irrevolutions import utils
 from irrevolutions.algorithms.so import StabilitySolver
 from irrevolutions.utils import _logger
-from mpi4py import MPI
 
 _logger.setLevel(logging.CRITICAL)
 
@@ -125,5 +126,5 @@ def load_minimal_constraints(filename, spaces):
 
 atol = tester.parameters["cone"]["cone_atol"]
 
-assert tester._isin_cone(_xk) == True
-assert np.isclose(_lmbda_k, -0.044659195907104675, atol=1e-4) == True
+assert tester._isin_cone(_xk)
+assert np.isclose(_lmbda_k, -0.044659195907104675, atol=1e-4)
diff --git a/test/test_cone_project.py b/test/test_cone_project.py
index cdd07bfe..d9d55557 100644
--- a/test/test_cone_project.py
+++ b/test/test_cone_project.py
@@ -1,19 +1,14 @@
 import sys
 
 import dolfinx
-import irrevolutions.solvers.restriction as restriction
 import numpy as np
-from irrevolutions.utils import (
-    _logger,
-    load_binary_matrix,
-    load_binary_vector,
-    sample_data,
-)
 from mpi4py import MPI
 from petsc4py import PETSc
-from test_restriction import (
-    get_inactive_dofset,
-)
+from test_restriction import get_inactive_dofset
+
+import irrevolutions.solvers.restriction as restriction
+from irrevolutions.utils import (_logger, load_binary_matrix,
+                                 load_binary_vector, sample_data)
 
 sys.path.append("../")
 
@@ -88,9 +83,9 @@ def _cone_project_restricted(v, _x, constraints):
 
 
 def test_cone_project():
-    full_matrix = load_binary_matrix("data/solver/A.mat")
-    matrix = load_binary_matrix("data/solver/Ar.mat")
-    guess = load_binary_vector("data/solver/x0r.vec")
+    load_binary_matrix("data/solver/A.mat")
+    load_binary_matrix("data/solver/Ar.mat")
+    load_binary_vector("data/solver/x0r.vec")
 
     F, v = sample_data(10, positive=False)
     V_u, V_alpha = F[0].function_spaces[0], F[1].function_spaces[0]
diff --git a/test/test_extend.py b/test/test_extend.py
index 29f9f4d6..9b0207cb 100644
--- a/test/test_extend.py
+++ b/test/test_extend.py
@@ -1,14 +1,12 @@
 from logging import getLevelName
 
 import dolfinx
-import irrevolutions.solvers.restriction as restriction
 from dolfinx.cpp.la.petsc import get_local_vectors
-from irrevolutions.utils import _logger, sample_data
 from mpi4py import MPI
-from test_restriction import (
-    __log_incipit,
-    get_inactive_dofset,
-)
+from test_restriction import __log_incipit, get_inactive_dofset
+
+import irrevolutions.solvers.restriction as restriction
+from irrevolutions.utils import _logger, sample_data
 
 comm = MPI.COMM_WORLD
 rank = comm.Get_rank()
diff --git a/test/test_linsearch.py b/test/test_linsearch.py
index 3285bb1e..446d5a92 100644
--- a/test/test_linsearch.py
+++ b/test/test_linsearch.py
@@ -17,40 +17,23 @@
 import ufl
 import yaml
 from dolfinx.common import list_timings
-from dolfinx.fem import (
-    Constant,
-    Function,
-    FunctionSpace,
-    assemble_scalar,
-    dirichletbc,
-    form,
-    locate_dofs_geometrical,
-    set_bc,
-)
+from dolfinx.fem import (Constant, Function, FunctionSpace, assemble_scalar,
+                         dirichletbc, form, locate_dofs_geometrical, set_bc)
 from dolfinx.io import XDMFFile, gmshio
+from mpi4py import MPI
+from petsc4py import PETSc
+from pyvista.utilities import xvfb
+
 from irrevolutions.algorithms.am import AlternateMinimisation, HybridSolver
 from irrevolutions.algorithms.ls import LineSearch
 from irrevolutions.algorithms.so import BifurcationSolver, StabilitySolver
 from irrevolutions.meshes.primitives import mesh_bar_gmshapi
 from irrevolutions.models import DamageElasticityModel as Brittle
 from irrevolutions.solvers.function import vec_to_functions
-from irrevolutions.utils import (
-    ColorPrint,
-    _write_history_data,
-    history_data,
-    norm_H1,
-)
-from irrevolutions.utils.plots import (
-    plot_energies,
-)
-from irrevolutions.utils.viz import (
-    plot_profile,
-    plot_scalar,
-    plot_vector,
-)
-from mpi4py import MPI
-from petsc4py import PETSc
-from pyvista.utilities import xvfb
+from irrevolutions.utils import (ColorPrint, _write_history_data, history_data,
+                                 norm_H1)
+from irrevolutions.utils.plots import plot_energies
+from irrevolutions.utils.viz import plot_profile, plot_scalar, plot_vector
 
 logging.basicConfig(level=logging.INFO)
 
@@ -128,7 +111,7 @@ def test_linsearch():
 
     # Measures
     dx = ufl.Measure("dx", domain=mesh)
-    ds = ufl.Measure("ds", domain=mesh)
+    ufl.Measure("ds", domain=mesh)
 
     dofs_alpha_left = locate_dofs_geometrical(V_alpha, lambda x: np.isclose(x[0], 0.0))
     dofs_alpha_right = locate_dofs_geometrical(V_alpha, lambda x: np.isclose(x[0], Lx))
@@ -385,7 +368,7 @@ def test_linsearch():
         )
         _stress = model.stress(model.eps(u), alpha)
 
-        stress = comm.allreduce(
+        comm.allreduce(
             assemble_scalar(form(_stress[0, 0] * dx)),
             op=MPI.SUM,
         )
diff --git a/test/test_rayleigh.py b/test/test_rayleigh.py
index 3b763e75..9d040bcf 100644
--- a/test/test_rayleigh.py
+++ b/test/test_rayleigh.py
@@ -12,12 +12,13 @@
 import ufl
 import yaml
 from dolfinx.fem import dirichletbc, locate_dofs_geometrical
+from mpi4py import MPI
+from petsc4py import PETSc
+
 from irrevolutions.algorithms.so import BifurcationSolver, StabilitySolver
 from irrevolutions.solvers.function import vec_to_functions
 from irrevolutions.utils import ColorPrint, _logger, indicator_function
 from irrevolutions.utils.viz import get_datapoints, plot_profile
-from mpi4py import MPI
-from petsc4py import PETSc
 
 test_dir = os.path.dirname(__file__)
 
@@ -57,7 +58,7 @@ def test_rayleigh(parameters=None, storage=None):
         parameters, signature = load_parameters(
             os.path.join(test_dir, "parameters.yml"), ndofs=50
         )
-        pretty_parameters = json.dumps(parameters, indent=2)
+        json.dumps(parameters, indent=2)
         storage = (
             f"output/rayleigh-benchmark/MPI-{MPI.COMM_WORLD.Get_size()}/{signature}"
         )
@@ -142,8 +143,8 @@ def test_rayleigh(parameters=None, storage=None):
     ]
     dolfinx.fem.form(F_)
 
-    dofs_alpha_left = locate_dofs_geometrical(V_alpha, lambda x: np.isclose(x[0], 0.0))
-    dofs_alpha_right = locate_dofs_geometrical(V_alpha, lambda x: np.isclose(x[0], 1))
+    locate_dofs_geometrical(V_alpha, lambda x: np.isclose(x[0], 0.0))
+    locate_dofs_geometrical(V_alpha, lambda x: np.isclose(x[0], 1))
 
     dofs_u_left = locate_dofs_geometrical(V_u, lambda x: np.isclose(x[0], 0.0))
     dofs_u_right = locate_dofs_geometrical(V_u, lambda x: np.isclose(x[0], 1))
@@ -159,7 +160,6 @@ def test_rayleigh(parameters=None, storage=None):
     # Perturbations
     β = dolfinx.fem.Function(V_alpha, name="DamagePerturbation")
     v = dolfinx.fem.Function(V_u, name="DisplacementPerturbation")
-    perturbation = {"v": v, "β": β}
 
     # Pack state
     state = {"u": u, "alpha": alpha}
@@ -189,9 +189,7 @@ def test_rayleigh(parameters=None, storage=None):
     )
     bifurcation.solve(zero_alpha)
     bifurcation.get_inertia()
-    stable = stability.solve(
-        zero_alpha, eig0=bifurcation.spectrum[0]["xk"], inertia=(1, 0, 10)
-    )
+    stability.solve(zero_alpha, eig0=bifurcation.spectrum[0]["xk"], inertia=(1, 0, 10))
 
     _logger.setLevel(level=logging.INFO)
 
diff --git a/test/test_rayleigh_parametric.py b/test/test_rayleigh_parametric.py
index 28857889..6b5f302d 100644
--- a/test/test_rayleigh_parametric.py
+++ b/test/test_rayleigh_parametric.py
@@ -8,13 +8,16 @@
 import numpy as np
 import ufl
 import yaml
-from dolfinx.fem import assemble_scalar, dirichletbc, form, locate_dofs_geometrical
+from dolfinx.fem import (assemble_scalar, dirichletbc, form,
+                         locate_dofs_geometrical)
+from mpi4py import MPI
+from petsc4py import PETSc
+
 from irrevolutions.algorithms.so import BifurcationSolver, StabilitySolver
-from irrevolutions.utils import ColorPrint, _logger, indicator_function
+from irrevolutions.utils import ColorPrint, _logger
 from irrevolutions.utils import eigenspace as eig
+from irrevolutions.utils import indicator_function
 from irrevolutions.utils.viz import get_datapoints
-from mpi4py import MPI
-from petsc4py import PETSc
 
 sys.path.append("../")
 sys.path.append("../playground/nb")
@@ -140,8 +143,8 @@ def rayleigh(parameters, storage=None):
 
     G = 1 / 2 * (a * alpha.dx(0) ** 2 + b * (u.dx(0) - c * alpha) ** 2) * dx
 
-    dofs_alpha_left = locate_dofs_geometrical(V_alpha, lambda x: np.isclose(x[0], 0.0))
-    dofs_alpha_right = locate_dofs_geometrical(V_alpha, lambda x: np.isclose(x[0], 1))
+    locate_dofs_geometrical(V_alpha, lambda x: np.isclose(x[0], 0.0))
+    locate_dofs_geometrical(V_alpha, lambda x: np.isclose(x[0], 1))
 
     dofs_u_left = locate_dofs_geometrical(V_u, lambda x: np.isclose(x[0], 0.0))
     dofs_u_right = locate_dofs_geometrical(V_u, lambda x: np.isclose(x[0], 1))
@@ -155,8 +158,8 @@ def rayleigh(parameters, storage=None):
     bcs = {"bcs_u": bcs_u, "bcs_alpha": []}
 
     # Perturbations
-    β = dolfinx.fem.Function(V_alpha, name="DamagePerturbation")
-    v = dolfinx.fem.Function(V_u, name="DisplacementPerturbation")
+    dolfinx.fem.Function(V_alpha, name="DamagePerturbation")
+    dolfinx.fem.Function(V_u, name="DisplacementPerturbation")
 
     # Pack state
     state = {"u": u, "alpha": alpha}
@@ -187,7 +190,7 @@ def rayleigh(parameters, storage=None):
 
     bifurcation.solve(zero_alpha)
     bifurcation.get_inertia()
-    stable = stability.solve(zero_alpha, eig0=bifurcation.spectrum, inertia=(1, 0, 10))
+    stability.solve(zero_alpha, eig0=bifurcation.spectrum, inertia=(1, 0, 10))
     # (size of the) support of the cone-eigenfunction - if any.
     #
 
diff --git a/test/test_restriction.py b/test/test_restriction.py
index b45b7fc6..7018a1db 100644
--- a/test/test_restriction.py
+++ b/test/test_restriction.py
@@ -1,12 +1,13 @@
 import sys
 
 import dolfinx
-import irrevolutions.solvers.restriction as restriction
 import numpy as np
 from dolfinx.cpp.la.petsc import get_local_vectors
-from irrevolutions.utils import _logger, sample_data
 from mpi4py import MPI
 
+import irrevolutions.solvers.restriction as restriction
+from irrevolutions.utils import _logger, sample_data
+
 sys.path.append("../")
 
 
diff --git a/test/test_sample_data.py b/test/test_sample_data.py
index 162d0cc8..1c3a7239 100644
--- a/test/test_sample_data.py
+++ b/test/test_sample_data.py
@@ -5,10 +5,11 @@
 import numpy as np
 import ufl
 from dolfinx.cpp.la.petsc import get_local_vectors, scatter_local_vectors
-from irrevolutions.utils import _logger
 from mpi4py import MPI
 from petsc4py import PETSc
 
+from irrevolutions.utils import _logger
+
 sys.path.append("../")
 
 
diff --git a/test/test_scatter.py b/test/test_scatter.py
index 2d2a7c1a..333769cb 100644
--- a/test/test_scatter.py
+++ b/test/test_scatter.py
@@ -3,7 +3,6 @@
 import sys
 
 import dolfinx
-import irrevolutions.solvers.restriction as restriction
 import numpy as np
 import petsc4py
 import ufl
@@ -12,6 +11,8 @@
 from mpi4py import MPI
 from petsc4py import PETSc
 
+import irrevolutions.solvers.restriction as restriction
+
 petsc4py.init(sys.argv)
 
 sys.path.append("../")
diff --git a/test/test_spa.py b/test/test_spa.py
index 68bf9728..b8f16b15 100644
--- a/test/test_spa.py
+++ b/test/test_spa.py
@@ -4,14 +4,15 @@
 import sys
 
 import dolfinx
-import irrevolutions.solvers.restriction as restriction
 import numpy as np
 import ufl
 from dolfinx.io import XDMFFile
-from irrevolutions.utils import _logger
 from mpi4py import MPI
 from test_cone_project import _cone_project_restricted
 
+import irrevolutions.solvers.restriction as restriction
+from irrevolutions.utils import _logger
+
 from . import test_binarydataio as bio
 
 sys.path.append("../")
@@ -166,7 +167,7 @@ def _convergenceTest(x, xold, y=None):
 
     V_u = dolfinx.fem.FunctionSpace(mesh, element_u)
     V_alpha = dolfinx.fem.FunctionSpace(mesh, element_alpha)
-    u = dolfinx.fem.Function(V_u, name="Displacement")
+    # u = dolfinx.fem.Function(V_u, name="Displacement")
     alpha = dolfinx.fem.Function(V_alpha, name="Damage")
     ufl.Measure("dx", alpha.function_space.mesh)
 
@@ -212,7 +213,7 @@ def _convergenceTest(x, xold, y=None):
         f"lambda_0 = {lmbda_t:.4e}, residual norm = {y.norm(): .4e}, error = {errors[-1]: .4e}"
     )
 
-    assert np.isclose(lmbda_t, -0.044659195907104675, atol=1e-4) == True
+    assert np.isclose(lmbda_t, -0.044659195907104675, atol=1e-4)
 
 
 if __name__ == "__main__":