diff --git a/notebooks/04/dinner-seat-allocation.ipynb b/notebooks/04/dinner-seat-allocation.ipynb
index dd01365f..ec7e5980 100644
--- a/notebooks/04/dinner-seat-allocation.ipynb
+++ b/notebooks/04/dinner-seat-allocation.ipynb
@@ -120,6 +120,7 @@
     "import networkx as nx\n",
     "import matplotlib.pyplot as plt\n",
     "import pandas as pd\n",
+    "import numpy as np\n",
     "\n",
     "\n",
     "def seat_allocation(members, capacity, kmax, domain=pyo.NonNegativeReals):\n",
@@ -1087,11 +1088,18 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "By adding two more nodes to the graph above, we can formulate the problem as a slightly different flow problem where all the data is formulated as the arc capacity, see figure below. In a network like this, we can imagine a problem of sending resources from the _root node_ \"door\" to the _sink node_ \"seat\", subject to the restriction that for any node apart from $s$ and $t$, the sum of incoming and outgoing flows are equal (_balance constraint_). If there exists a flow in this new graph that respects the arc capacities and the sum of outgoing flows at $s$ is equal to $\\sum_{f \\in F} m_f = 39$, it means that there exists a family-to-table assignment that meets our requirements.\n",
+    "By adding two more nodes to the graph above, we can formulate the problem as a slightly different flow problem where all the data is formulated as the arc capacity, see figure below. In a network like this, we can imagine a problem of sending resources from the _root node_ \"door\" to the _sink node_ \"seat\", subject to the restriction that for any node that is not the start nor the target, the sum of incoming and outgoing flows are equal (_balance constraint_). If there exists a flow in this new graph that respects the arc capacities and the sum of outgoing flows at the source is equal to the total number of individuals, that is $\\sum_{f \\in F} m_f = 39$, it means that there exists a family-to-table assignment that meets our requirements.\n",
     "\n",
     "<img src=\"seating_flow_model.png\" width=\"800\" height=\"600\">"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Thus, if we maximize the total flow going out of `door` and reaching `seat` and it matches the total number of individuals, the problem is solved. As unimpressive as this sounds, this means that it can be treated as a special case of a famous **maximum flow problem**, for which there exist algorithms that are way more efficient than a generic LO solver. One such algorithm is the Bellman-Ford algorithm, implicitly invoked in the following code using the Python package `networkx`."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 10,
@@ -1131,10 +1139,10 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>t0</th>\n",
-       "      <td>1</td>\n",
+       "      <td>3</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>3</td>\n",
+       "      <td>1</td>\n",
        "      <td>3</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
@@ -1149,17 +1157,17 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>t2</th>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
        "      <td>0</td>\n",
        "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
        "      <td>3</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>t3</th>\n",
-       "      <td>1</td>\n",
        "      <td>0</td>\n",
+       "      <td>1</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>3</td>\n",
@@ -1168,9 +1176,9 @@
        "    <tr>\n",
        "      <th>t4</th>\n",
        "      <td>3</td>\n",
-       "      <td>3</td>\n",
        "      <td>2</td>\n",
-       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
        "      <td>1</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
@@ -1180,11 +1188,11 @@
       ],
       "text/plain": [
        "    f0  f1  f2  f3  f4  f5\n",
-       "t0   1   0   0   3   3   1\n",
+       "t0   3   0   0   1   3   1\n",
        "t1   0   2   0   3   3   0\n",
-       "t2   1   3   0   3   3   0\n",
-       "t3   1   0   0   0   3   0\n",
-       "t4   3   3   2   0   1   0"
+       "t2   0   3   1   3   3   0\n",
+       "t3   0   1   0   0   3   0\n",
+       "t4   3   2   1   2   1   0"
       ]
      },
      "execution_count": 10,
@@ -1193,7 +1201,7 @@
     }
    ],
    "source": [
-    "def model_as_network(members, capacity, k):\n",
+    "def model_as_network(members, capacity, kmax):\n",
     "    # create lists of families and tables\n",
     "    families = [f\"f{i}\" for i in range(len(members))]\n",
     "    tables = [f\"t{j}\" for j in range(len(capacity))]\n",
@@ -1203,7 +1211,7 @@
     "\n",
     "    # add edges\n",
     "    G.add_edges_from([\"door\", f, {\"capacity\": n}] for f, n in zip(families, members))\n",
-    "    G.add_edges_from([(f, t) for f in families for t in tables], capacity=k)\n",
+    "    G.add_edges_from([(f, t) for f in families for t in tables], capacity=kmax)\n",
     "    G.add_edges_from([t, \"seat\", {\"capacity\": n}] for t, n in zip(tables, capacity))\n",
     "\n",
     "    return G\n",
@@ -1211,7 +1219,7 @@
     "\n",
     "members = [6, 8, 2, 9, 13, 1]\n",
     "capacity = [8, 8, 10, 4, 9]\n",
-    "G = model_as_network(members, capacity, k=3)\n",
+    "G = model_as_network(members, capacity, kmax=3)\n",
     "\n",
     "# we solve the maximum flow problem using the networkx function\n",
     "flow_value, flow_dict = nx.maximum_flow(G, \"door\", \"seat\")\n",
@@ -1238,10 +1246,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 401 µs, sys: 4 µs, total: 405 µs\n",
-      "Wall time: 408 µs\n",
-      "CPU times: user 3.66 ms, sys: 4.57 ms, total: 8.23 ms\n",
-      "Wall time: 15.1 ms\n"
+      "CPU times: user 405 µs, sys: 8 µs, total: 413 µs\n",
+      "Wall time: 414 µs\n",
+      "CPU times: user 3.9 ms, sys: 5.22 ms, total: 9.13 ms\n",
+      "Wall time: 16.3 ms\n"
      ]
     }
    ],
@@ -1250,6 +1258,132 @@
     "\n",
     "%time results, seatplan = seating_allocation_maximize_flow_to_tables(members=[6, 8, 2, 9, 13, 1], capacity=[8, 8, 10, 4, 9], kmax=3, domain=pyo.NonNegativeIntegers)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## A more systematic comparison between LO solvers and network algorithms\n",
+    "\n",
+    "We now create increasingly larger instances of the dinner seating allocation problem and compare the performance of the MILO solvers `highs`, `cbc`, `gurobi`, and the algorithm `maximum_flow` from the `networkx` library. We will use the following code to generate the instances."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tqdm.notebook import tqdm\n",
+    "from time import perf_counter as pc\n",
+    "\n",
+    "\n",
+    "def max_flow(members, capacity, kmax, domain=pyo.NonNegativeReals):\n",
+    "    m = pyo.ConcreteModel(\"Seating arrangement as network problem\")\n",
+    "    m.F = pyo.Set(initialize=range(len(members)))\n",
+    "    m.T = pyo.Set(initialize=range(len(capacity)))\n",
+    "    m.M = pyo.Param(m.F, initialize=members)\n",
+    "    m.C = pyo.Param(m.T, initialize=capacity)\n",
+    "    m.x = pyo.Var(m.F, m.T, bounds=(0, kmax), domain=domain)\n",
+    "\n",
+    "    @m.Objective(sense=pyo.maximize)\n",
+    "    def goal(m):\n",
+    "        return pyo.quicksum(m.x[f, t] for f in m.F for t in m.T)\n",
+    "\n",
+    "    @m.Constraint(m.T)\n",
+    "    def capacity(m, t):\n",
+    "        return pyo.quicksum(m.x[f, t] for f in m.F) <= m.C[t]\n",
+    "\n",
+    "    @m.Constraint(m.F)\n",
+    "    def seat(m, f):\n",
+    "        return pyo.quicksum(m.x[f, t] for t in m.T) == m.M[f]\n",
+    "\n",
+    "    return m\n",
+    "\n",
+    "\n",
+    "cbc = pyo.SolverFactory(\"cbc\")\n",
+    "gurobi = pyo.SolverFactory(\"gurobi_direct\")\n",
+    "highs = pyo.SolverFactory(\"appsi_highs\")\n",
+    "\n",
+    "\n",
+    "def Reset(model) -> None:\n",
+    "    for v in model.component_data_objects(ctype=pyo.Var, descend_into=True):\n",
+    "        v.set_value(None)\n",
+    "\n",
+    "\n",
+    "np.random.seed(2023)\n",
+    "kmax = 3\n",
+    "nmax = 500\n",
+    "mmax = 2 * nmax\n",
+    "sizes = list(zip(range(10, nmax, 10), range(20, mmax, 20)))\n",
+    "\n",
+    "df = pd.DataFrame(index=[\"cbc\", \"gurobi\", \"nx\", \"highs\"], columns=sizes)\n",
+    "for n, m in tqdm(sizes):\n",
+    "    members, capacity = np.random.randint(1, 10, n), np.random.randint(3, 8, m)\n",
+    "    model = max_flow(members, capacity, kmax)\n",
+    "    t = pc()\n",
+    "    cbc.solve(model)\n",
+    "    df.loc[\"cbc\"][(n, m)] = pc() - t\n",
+    "    Reset(model)\n",
+    "    t = pc()\n",
+    "    gurobi.solve(model)\n",
+    "    df.loc[\"gurobi\"][(n, m)] = pc() - t\n",
+    "    t = pc()\n",
+    "    highs.solve(model)\n",
+    "    df.loc[\"highs\"][(n, m)] = pc() - t\n",
+    "    Reset(model)\n",
+    "    G = model_as_network(members, capacity, kmax)\n",
+    "    t = pc()\n",
+    "    nx.maximum_flow(G, \"door\", \"seat\")\n",
+    "    df.loc[\"nx\"][(n, m)] = pc() - t"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We stored all the runtimes in a dataframe, which we can now plot. We see that the network algorithm `maximum_flow` is the fastest, followed by `highs`, `cbc`, and `gurobi`. The network algorithm is the fastest because it is able to exploit the special structure of the problem, which the MILO solvers do not."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.rcParams[\"figure.figsize\"] = [12, 6]\n",
+    "df.transpose().plot()\n",
+    "plt.ylabel(\"runtime (s)\")\n",
+    "plt.xticks(\n",
+    "    range(len(df.columns)),\n",
+    "    df.columns,\n",
+    "    rotation=45,\n",
+    "    ha=\"right\",\n",
+    "    fontsize=8,\n",
+    "    rotation_mode=\"anchor\",\n",
+    ")\n",
+    "plt.xlabel(\"Size of the instances expressed in (family members, tables)\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/notebooks/04/dinner-seat-allocation_runtimes.xlsx b/notebooks/04/dinner-seat-allocation_runtimes.xlsx
deleted file mode 100644
index 2cefddde..00000000
Binary files a/notebooks/04/dinner-seat-allocation_runtimes.xlsx and /dev/null differ
diff --git a/notebooks/04/in_development/dinner-seat-allocation_withplots.ipynb b/notebooks/04/in_development/dinner-seat-allocation_withplots.ipynb
deleted file mode 100644
index 9a34db24..00000000
--- a/notebooks/04/in_development/dinner-seat-allocation_withplots.ipynb
+++ /dev/null
@@ -1,1788 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "uRtp1E6mSnM2"
-   },
-   "source": [
-    "# Dinner seating arrangement"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "executionInfo": {
-     "elapsed": 11922,
-     "status": "ok",
-     "timestamp": 1623766790525,
-     "user": {
-      "displayName": "Joaquim Gromicho",
-      "photoUrl": "",
-      "userId": "14375950305363805729"
-     },
-     "user_tz": -120
-    },
-    "id": "ZqMzwFwqxHRQ",
-    "outputId": "2fa9999b-2efc-46f2-af99-cae5af57ba7d"
-   },
-   "outputs": [],
-   "source": [
-    "# install pyomo and select solver\n",
-    "import sys\n",
-    "\n",
-    "SOLVER = \"cbc\"\n",
-    "\n",
-    "if \"google.colab\" in sys.modules:\n",
-    "    !pip install highspy >/dev/null\n",
-    "    SOLVER = \"appsi_highs\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "uRtp1E6mSnM2"
-   },
-   "source": [
-    "## Problem description\n",
-    "\n",
-    "Consider organizing a wedding dinner at which your objective is that the guests from different families mingle with each other. One of the ways to do so is to seat people at the tables in such a way that no more people than a given threshold $k$ from the same family take a seat at the same table. How could we solve a problem like this? First, we need the problem data -- for each family $f$ we need to know the number of its members $m_f$, and for each table $t$ we need to know its capacity $c_t$. Using this data and the tools we learned so far, we can formulate this problem as a linear optimization (LO) problem.\n",
-    "\n",
-    "If we do not care about the specific people, but only about the number of people from a given family, then we can use variable $x_{ft}$  for the number of persons from family $f$ to be seated at table $t$. Since we were not provided with any objective function, we can focus on finding a feasible solution by setting the objective function to be constant, say $0$, which means that we do not differentiate between the various feasible solutions. \n",
-    "\n",
-    "The mathematical formulation of this seating problem is:\n",
-    "\n",
-    "$$\n",
-    "\\begin{align*}\n",
-    "    \\min_{x_{ft}} \\quad & 0 \\label{ch4eq.Dina.problem.1}\\\\\n",
-    "    \\text{s.t.} \\quad & \\sum\\limits_{f} x_{ft} \\leq c_t & \\forall \\, t \\in T \\\\\n",
-    "    & \\sum\\limits_{t} x_{ft} = m_f & \\forall \\, f \\in F \\\\\n",
-    "    & 0 \\leq x_{ft} \\leq k.\n",
-    "\\end{align*}\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "AqUu7i2kxV98"
-   },
-   "source": [
-    "## Implementation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "id": "g3Jo-pwb4ltx"
-   },
-   "outputs": [],
-   "source": [
-    "import pyomo.environ as pyo\n",
-    "from IPython.display import display\n",
-    "import networkx as nx\n",
-    "import matplotlib.pyplot as plt\n",
-    "import pandas as pd\n",
-    "\n",
-    "def TableSeat( members, capacity, k, domain=pyo.NonNegativeReals ):\n",
-    "    m   = pyo.ConcreteModel(\"Dina's seat plan\")\n",
-    "    m.F = pyo.Set( initialize=range( len(members) ) )\n",
-    "    m.T = pyo.Set( initialize=range( len(capacity) ) )\n",
-    "    m.M = pyo.Param( m.F, initialize=members )\n",
-    "    m.C = pyo.Param( m.T, initialize=capacity )\n",
-    "    m.x = pyo.Var( m.F, m.T, bounds=(0,k), domain=domain )\n",
-    "    \n",
-    "    @m.Objective( sense=pyo.maximize )\n",
-    "    def goal(m):\n",
-    "        return 0\n",
-    "\n",
-    "    @m.Constraint( m.T )    \n",
-    "    def capacity( m, t ):\n",
-    "        return pyo.quicksum( m.x[f,t] for f in m.F  ) <= m.C[t]\n",
-    "    \n",
-    "    @m.Constraint( m.F )\n",
-    "    def seat( m, f ):\n",
-    "        return pyo.quicksum( m.x[f,t] for t in m.T ) == m.M[f]\n",
-    "        \n",
-    "    return m\n",
-    "\n",
-    "def TableSeatAsMaxFlow( members, capacity, k, domain=pyo.NonNegativeReals ):\n",
-    "    m   = pyo.ConcreteModel(\"Dina's seat plan\")\n",
-    "    m.F = pyo.Set( initialize=range( len(members) ) )\n",
-    "    m.T = pyo.Set( initialize=range( len(capacity) ) )\n",
-    "    m.M = pyo.Param( m.F, initialize=members )\n",
-    "    m.C = pyo.Param( m.T, initialize=capacity )\n",
-    "    m.x = pyo.Var( m.F, m.T, bounds=(0,k), domain=domain )\n",
-    "    \n",
-    "    @m.Objective( sense=pyo.maximize )\n",
-    "    def goal(m):\n",
-    "        return pyo.quicksum( m.x[f,t] for t in m.T for f in m.F )\n",
-    "\n",
-    "    @m.Constraint( m.T )    \n",
-    "    def capacity( m, t ):\n",
-    "        return pyo.quicksum( m.x[f,t] for f in m.F  ) <= m.C[t]\n",
-    "    \n",
-    "    @m.Constraint( m.F )\n",
-    "    def seat( m, f ):\n",
-    "        return pyo.quicksum( m.x[f,t] for t in m.T ) <= m.M[f]\n",
-    "        \n",
-    "    return m\n",
-    "\n",
-    "def Reset( model ) -> None:\n",
-    "    for v in model.component_data_objects(ctype=pyo.Var, descend_into=True):\n",
-    "        v.set_value(None)\n",
-    "        \n",
-    "def GetSolution( model ):\n",
-    "    import pandas as pd\n",
-    "    sol = pd.DataFrame()\n",
-    "    for idx,x in model.x.items():\n",
-    "        sol.loc[idx]=x()\n",
-    "    return sol\n",
-    "    \n",
-    "def Report( model, results, type=float ):\n",
-    "    solver = pyo.SolverFactory('cbc')\n",
-    "    print(results.solver.status, results.solver.termination_condition )\n",
-    "    if results.solver.termination_condition == 'optimal':\n",
-    "        sol = GetSolution(model).astype(type)\n",
-    "        display( sol )\n",
-    "        print('objective       ', pyo.value( seatplan.goal ) )\n",
-    "        print('places at table ', list(sol.sum(axis=0)))\n",
-    "        print('members seated  ', list(sol.sum(axis=1)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 337
-    },
-    "executionInfo": {
-     "elapsed": 293,
-     "status": "ok",
-     "timestamp": 1623769075200,
-     "user": {
-      "displayName": "Joaquim Gromicho",
-      "photoUrl": "",
-      "userId": "14375950305363805729"
-     },
-     "user_tz": -120
-    },
-    "id": "glauQ3YqSnM6",
-    "outputId": "5d75d95c-4176-4d59-f4fa-4d1d27adbdf4"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING: Constant objective detected, replacing with a placeholder to prevent\n",
-      "    solver failure.\n",
-      "CPU times: user 11.2 ms, sys: 13.4 ms, total: 24.6 ms\n",
-      "Wall time: 121 ms\n",
-      "ok optimal\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>0</th>\n",
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-       "      <td>0.0</td>\n",
-       "      <td>3.000000e+00</td>\n",
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-      "text/plain": [
-       "     0    1    2    3             4\n",
-       "0  2.0  3.0  1.0  0.0  1.000089e-12\n",
-       "1  2.0  0.0  3.0  0.0  3.000000e+00\n",
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-       "4  1.0  3.0  3.0  3.0  3.000000e+00\n",
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-     "metadata": {},
-     "output_type": "display_data"
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-     "text": [
-      "objective        0.0\n",
-      "places at table  [8.0, 8.0, 10.0, 4.0, 9.000000000001]\n",
-      "members seated   [6.000000000001, 8.0, 2.0, 9.0, 13.0, 1.0]\n"
-     ]
-    }
-   ],
-   "source": [
-    "seatplan = TableSeat( [6,8,2,9,13,1], [8,8,10,4,9], 3 )\n",
-    "\n",
-    "%time results = pyo.SolverFactory('cbc').solve(seatplan)\n",
-    "\n",
-    "Report( seatplan, results )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "ModuleNotFoundError",
-     "evalue": "No module named 'pyperclip'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
-      "\u001b[0;32m/var/folders/cm/z3t28j296f98jdp1vqyplkz00000gn/T/ipykernel_56216/1237050218.py\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mpyperclip\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mpyperclip\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mGetSolution\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseatplan\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstyle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_latex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'pyperclip'"
-     ]
-    }
-   ],
-   "source": [
-    "import pyperclip \n",
-    "pyperclip.copy( GetSolution(seatplan).astype(int).style.to_latex() )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "id": "GJxtvN9gS2xW"
-   },
-   "outputs": [],
-   "source": [
-    "def TableSeatMinimizeMaxGroupAtTable( members, capacity, nature=pyo.NonNegativeReals ):\n",
-    "    m   = pyo.ConcreteModel(\"Dina's seat plan\")\n",
-    "    m.F = pyo.Set( initialize=range( len(members) ) )\n",
-    "    m.T = pyo.Set( initialize=range( len(capacity) ) )\n",
-    "    m.M = pyo.Param( m.F, initialize=members )\n",
-    "    m.C = pyo.Param( m.T, initialize=capacity )\n",
-    "    m.x = pyo.Var( m.F, m.T, domain=nature )\n",
-    "    m.k = pyo.Var( domain=nature )\n",
-    "    \n",
-    "    @m.Objective( sense=pyo.minimize )\n",
-    "    def goal(m):\n",
-    "        return m.k\n",
-    "    \n",
-    "    @m.Constraint( m.T )    \n",
-    "    def capacity( m, t ):\n",
-    "        return pyo.quicksum( m.x[f,t] for f in m.F  ) <= m.C[t]\n",
-    "    \n",
-    "    @m.Constraint( m.F )\n",
-    "    def seat( m, f ):\n",
-    "        return pyo.quicksum( m.x[f,t] for t in m.T ) == m.M[f]\n",
-    "\n",
-    "    @m.Constraint( m.F, m.T )\n",
-    "    def bound( m, f, t ):\n",
-    "        return m.x[f,t] <= m.k\n",
-    "\n",
-    "    return m"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 337
-    },
-    "executionInfo": {
-     "elapsed": 269,
-     "status": "ok",
-     "timestamp": 1623768701481,
-     "user": {
-      "displayName": "Joaquim Gromicho",
-      "photoUrl": "",
-      "userId": "14375950305363805729"
-     },
-     "user_tz": -120
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-    "id": "amuwHXmGt_va",
-    "outputId": "9056e7b0-1a7e-4017-e31b-ecbffa4a312b"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 8.62 ms, sys: 10.8 ms, total: 19.5 ms\n",
-      "Wall time: 106 ms\n",
-      "ok optimal\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
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-      "text/plain": [
-       "     0    1    2    3    4\n",
-       "0  2.6  2.2  0.0  0.0  1.2\n",
-       "1  1.0  2.6  1.8  0.0  2.6\n",
-       "2  0.0  0.0  2.0  0.0  0.0\n",
-       "3  1.8  0.6  2.6  1.4  2.6\n",
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-     "metadata": {},
-     "output_type": "display_data"
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "objective        2.6\n",
-      "places at table  [8.0, 8.0, 10.0, 4.0, 9.0]\n",
-      "members seated   [6.000000000000001, 8.0, 2.0, 9.0, 13.0, 1.0]\n"
-     ]
-    }
-   ],
-   "source": [
-    "seatplan = TableSeatMinimizeMaxGroupAtTable( [6,8,2,9,13,1], [8,8,10,4,9], nature=pyo.NonNegativeReals )\n",
-    "\n",
-    "%time results = pyo.SolverFactory('cbc').solve(seatplan)\n",
-    "\n",
-    "Report( seatplan, results )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'pyperclip' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m/var/folders/cm/z3t28j296f98jdp1vqyplkz00000gn/T/ipykernel_56216/747071037.py\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpyperclip\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mGetSolution\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseatplan\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstyle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprecision\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_latex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m: name 'pyperclip' is not defined"
-     ]
-    }
-   ],
-   "source": [
-    "pyperclip.copy( GetSolution(seatplan).astype(float).style.format(precision=2).to_latex() )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "id": "9mHD-wjuuLg2"
-   },
-   "outputs": [],
-   "source": [
-    "def TableSeatMinimizeNumberOfTables( members, capacity, k, nature=pyo.NonNegativeReals ):\n",
-    "    m   = pyo.ConcreteModel(\"Dina's seat plan\")\n",
-    "    m.F = pyo.Set( initialize=range( len(members) ) )\n",
-    "    m.T = pyo.Set( initialize=range( len(capacity) ) )\n",
-    "    m.M = pyo.Param( m.F, initialize=members )\n",
-    "    m.C = pyo.Param( m.T, initialize=capacity )\n",
-    "    m.x = pyo.Var( m.F, m.T, bounds=(0,k), domain=nature )\n",
-    "    m.y = pyo.Var( m.T, within=pyo.Binary )\n",
-    "    \n",
-    "    @m.Objective( sense=pyo.minimize )\n",
-    "    def goal(m):\n",
-    "        return pyo.quicksum(m.y[t] for t in m.T)\n",
-    "    \n",
-    "    @m.Constraint( m.T )    \n",
-    "    def capacity( m, t ):\n",
-    "        return pyo.quicksum( m.x[f,t] for f in m.F  ) <= m.C[t]*m.y[t]\n",
-    "    \n",
-    "    @m.Constraint( m.F )\n",
-    "    def seat( m, f ):\n",
-    "        return pyo.quicksum( m.x[f,t] for t in m.T ) == m.M[f]\n",
-    "\n",
-    "    return m"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 337
-    },
-    "executionInfo": {
-     "elapsed": 264,
-     "status": "ok",
-     "timestamp": 1623769068597,
-     "user": {
-      "displayName": "Joaquim Gromicho",
-      "photoUrl": "",
-      "userId": "14375950305363805729"
-     },
-     "user_tz": -120
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-    "id": "ouJSU34n38KF",
-    "outputId": "7e40163d-ddd3-4a07-9009-fd40d68b52c0"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 8.71 ms, sys: 11.9 ms, total: 20.7 ms\n",
-      "Wall time: 81.5 ms\n",
-      "ok optimal\n"
-     ]
-    },
-    {
-     "data": {
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-       "   0  1  2  3  4\n",
-       "0  3  0  3  0  0\n",
-       "1  3  2  0  0  3\n",
-       "2  0  0  1  1  0\n",
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-     "text": [
-      "objective        5.0\n",
-      "places at table  [8, 8, 10, 4, 9]\n",
-      "members seated   [6, 8, 2, 9, 13, 1]\n"
-     ]
-    }
-   ],
-   "source": [
-    "seatplan = TableSeatMinimizeNumberOfTables( [6,8,2,9,13,1], [8,8,10,4,9], 3, pyo.NonNegativeIntegers )\n",
-    "\n",
-    "%time results = pyo.SolverFactory('cbc').solve(seatplan)\n",
-    "\n",
-    "Report( seatplan, results, int )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "5klBA6ebVDf3"
-   },
-   "source": [
-    "# Reformulation as max flow problem"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "\n",
-    "# https://stackoverflow.com/questions/17687213/how-to-obtain-the-same-font-style-size-etc-in-matplotlib-output-as-in-latex\n",
-    "params = {'text.usetex' : True,\n",
-    "          'font.size'   : 10, # the book seems to be in 10pt, change if needed\n",
-    "          'font.family' : 'lmodern',\n",
-    "          }\n",
-    "\n",
-    "plt.rcParams.update(params)\n",
-    "default_size_inches = (3.54,3.54) \n",
-    "plt.rcParams['figure.figsize'] = default_size_inches"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "id": "aSJ7UOokVfnu"
-   },
-   "outputs": [],
-   "source": [
-    "def ModelAsNetwork( members, capacity, k ):\n",
-    "    families = [f'f{i}' for i in range(len(members))]\n",
-    "    tables = [f't{j}' for j in range(len(capacity))]\n",
-    "    G = nx.DiGraph()\n",
-    "    G.add_node('door',layer=0)\n",
-    "    for f in families:\n",
-    "        G.add_node(f,layer=1)\n",
-    "    for t in tables:\n",
-    "        G.add_node(t,layer=2)\n",
-    "    G.add_node('seat',layer=3)\n",
-    "    for f,n in zip(families,members):\n",
-    "        G.add_edge('door', f, capacity=n)\n",
-    "    for f in families:\n",
-    "        for t in tables:\n",
-    "            G.add_edge(f,t, capacity=k)\n",
-    "    for t,n in zip(tables,capacity):\n",
-    "        G.add_edge(t, 'seat', capacity=n)\n",
-    "    return G"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "G = ModelAsNetwork( [6,8,2,9,13,1], [8,8,10,4,9], 3 )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pos = nx.multipartite_layout(G, subset_key='layer')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "labels = { (e[0],e[1]) : e[2] for e in G.edges(data='capacity') }"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1300x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.rcParams['text.usetex'] =False\n",
-    "with plt.xkcd():\n",
-    "    fig = plt.figure(figsize=(13,8))\n",
-    "    ax = fig.add_subplot(111)\n",
-    "    nx.draw_networkx(G,pos=pos,ax=ax,node_size=800,with_labels=True,alpha=.6)\n",
-    "    _=nx.draw_networkx_edge_labels(G,pos=pos,ax=ax,edge_labels=labels,font_color='black',rotate=False,alpha=1)\n",
-    "    fig.savefig( 'net_flow.pdf', bbox_inches='tight', pad_inches=0 )\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "id": "vRHT74fEV6KX"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 996 µs, sys: 48 µs, total: 1.04 ms\n",
-      "Wall time: 1.05 ms\n"
-     ]
-    }
-   ],
-   "source": [
-    "%time flow_value, flow_dict = nx.maximum_flow(G, 'door', 'seat')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 206
-    },
-    "executionInfo": {
-     "elapsed": 214,
-     "status": "ok",
-     "timestamp": 1643283766270,
-     "user": {
-      "displayName": "Joaquim Gromicho",
-      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
-      "userId": "14375950305363805729"
-     },
-     "user_tz": -60
-    },
-    "id": "JL9vgcULV-TG",
-    "outputId": "15de4861-1a77-485e-e91f-c03f8d55a917"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>f0</th>\n",
-       "      <th>f1</th>\n",
-       "      <th>f2</th>\n",
-       "      <th>f3</th>\n",
-       "      <th>f4</th>\n",
-       "      <th>f5</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>t0</th>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t1</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t2</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t3</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t4</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     f0   f1   f2   f3   f4   f5\n",
-       "t0  2.0  0.0  0.0  2.0  3.0  1.0\n",
-       "t1  0.0  1.0  1.0  3.0  3.0  0.0\n",
-       "t2  1.0  3.0  0.0  3.0  3.0  0.0\n",
-       "t3  0.0  1.0  0.0  0.0  3.0  0.0\n",
-       "t4  3.0  3.0  1.0  1.0  1.0  0.0"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "members, capacity = [6,8,2,9,13,1], [8,8,10,4,9]\n",
-    "families = [f'f{i}' for i in range(len(members))]\n",
-    "tables = [f't{j}' for j in range(len(capacity))]\n",
-    "pd.DataFrame(flow_dict).loc[tables,families]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "flow_edges = [(a,b) for a,B in flow_dict.items() for b,v in B.items() if v>0 and a != 'door' and b != 'seat']\n",
-    "flow_nodes = [n for n in G.nodes if n.startswith('f') or n.startswith('t')]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "id": "tngwI9ctWAqe"
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 800x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "with plt.xkcd():\n",
-    "    fig = plt.figure(figsize=(8,5))\n",
-    "    nx.draw_networkx(G,ax=fig.add_subplot(111),pos=pos,node_size=300,edge_color='blue',edgelist=flow_edges,nodelist=flow_nodes)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig.savefig( 'flow.pdf', bbox_inches='tight', pad_inches=0 )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cbc    = pyo.SolverFactory('cbc')\n",
-    "gurobi = pyo.SolverFactory('gurobi_direct')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pathlib import Path\n",
-    "\n",
-    "if Path('dina_times.xlsx').is_file():\n",
-    "    df = pd.read_excel('dina_times.xlsx').set_index('Unnamed: 0')\n",
-    "else:\n",
-    "    from tqdm.notebook import tqdm\n",
-    "    from time import perf_counter as pc\n",
-    "    import numpy as np\n",
-    "    np.random.seed(2022)\n",
-    "    k = 3\n",
-    "    nmax = 500\n",
-    "    mmax = 2*nmax\n",
-    "    sizes = list(zip(range(10,nmax,10),range(20,mmax,20)))\n",
-    "    df = pd.DataFrame(index=['cbc','gurobi','nx'],columns=sizes)\n",
-    "    for n,m in tqdm(sizes):\n",
-    "        members, capacity = np.random.randint(1,10,n), np.random.randint(3,8,m)\n",
-    "        model = TableSeatAsMaxFlow(members,capacity,k)\n",
-    "        t=pc() \n",
-    "        cbc.solve(model)\n",
-    "        df.loc['cbc'][(n,m)] = pc()-t\n",
-    "        Reset(model)\n",
-    "        t=pc() \n",
-    "        gurobi.solve(model)\n",
-    "        df.loc['gurobi'][(n,m)] = pc()-t\n",
-    "        G = ModelAsNetwork(members,capacity,k)\n",
-    "        t = pc()\n",
-    "        nx.maximum_flow(G, 'door', 'seat')\n",
-    "        df.loc['nx'][(n,m)] = pc()-t\n",
-    "        \n",
-    "    df.to_excel('dina_times.xlsx')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "aux = df.T"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>(10, 20)</th>\n",
-       "      <th>(20, 40)</th>\n",
-       "      <th>(30, 60)</th>\n",
-       "      <th>(40, 80)</th>\n",
-       "      <th>(50, 100)</th>\n",
-       "      <th>(60, 120)</th>\n",
-       "      <th>(70, 140)</th>\n",
-       "      <th>(80, 160)</th>\n",
-       "      <th>(90, 180)</th>\n",
-       "      <th>(100, 200)</th>\n",
-       "      <th>...</th>\n",
-       "      <th>(400, 800)</th>\n",
-       "      <th>(410, 820)</th>\n",
-       "      <th>(420, 840)</th>\n",
-       "      <th>(430, 860)</th>\n",
-       "      <th>(440, 880)</th>\n",
-       "      <th>(450, 900)</th>\n",
-       "      <th>(460, 920)</th>\n",
-       "      <th>(470, 940)</th>\n",
-       "      <th>(480, 960)</th>\n",
-       "      <th>(490, 980)</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Unnamed: 0</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>cbc</th>\n",
-       "      <td>0.094727</td>\n",
-       "      <td>0.084132</td>\n",
-       "      <td>0.120244</td>\n",
-       "      <td>0.172141</td>\n",
-       "      <td>0.223353</td>\n",
-       "      <td>0.320543</td>\n",
-       "      <td>0.374295</td>\n",
-       "      <td>0.452540</td>\n",
-       "      <td>0.667402</td>\n",
-       "      <td>0.710549</td>\n",
-       "      <td>...</td>\n",
-       "      <td>13.350693</td>\n",
-       "      <td>14.753052</td>\n",
-       "      <td>18.444643</td>\n",
-       "      <td>17.659208</td>\n",
-       "      <td>16.619090</td>\n",
-       "      <td>17.909117</td>\n",
-       "      <td>22.313041</td>\n",
-       "      <td>22.550129</td>\n",
-       "      <td>21.942068</td>\n",
-       "      <td>21.800375</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>gurobi</th>\n",
-       "      <td>0.043392</td>\n",
-       "      <td>0.034781</td>\n",
-       "      <td>0.134984</td>\n",
-       "      <td>0.112127</td>\n",
-       "      <td>0.304981</td>\n",
-       "      <td>0.329835</td>\n",
-       "      <td>0.420639</td>\n",
-       "      <td>0.484730</td>\n",
-       "      <td>0.665472</td>\n",
-       "      <td>0.897768</td>\n",
-       "      <td>...</td>\n",
-       "      <td>14.986658</td>\n",
-       "      <td>17.618462</td>\n",
-       "      <td>21.198270</td>\n",
-       "      <td>20.555059</td>\n",
-       "      <td>18.400325</td>\n",
-       "      <td>22.321826</td>\n",
-       "      <td>24.893338</td>\n",
-       "      <td>25.507576</td>\n",
-       "      <td>21.337107</td>\n",
-       "      <td>25.497099</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>nx</th>\n",
-       "      <td>0.002665</td>\n",
-       "      <td>0.007776</td>\n",
-       "      <td>0.020841</td>\n",
-       "      <td>0.031215</td>\n",
-       "      <td>0.051511</td>\n",
-       "      <td>0.067398</td>\n",
-       "      <td>0.088514</td>\n",
-       "      <td>0.114161</td>\n",
-       "      <td>0.173439</td>\n",
-       "      <td>0.194617</td>\n",
-       "      <td>...</td>\n",
-       "      <td>4.319810</td>\n",
-       "      <td>5.824088</td>\n",
-       "      <td>5.281828</td>\n",
-       "      <td>3.809972</td>\n",
-       "      <td>4.966340</td>\n",
-       "      <td>6.697136</td>\n",
-       "      <td>6.940013</td>\n",
-       "      <td>7.345105</td>\n",
-       "      <td>6.026856</td>\n",
-       "      <td>6.781781</td>\n",
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-       "<p>3 rows × 49 columns</p>\n",
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-       "            (10, 20)  (20, 40)  (30, 60)  (40, 80)  (50, 100)  (60, 120)  \\\n",
-       "Unnamed: 0                                                                 \n",
-       "cbc         0.094727  0.084132  0.120244  0.172141   0.223353   0.320543   \n",
-       "gurobi      0.043392  0.034781  0.134984  0.112127   0.304981   0.329835   \n",
-       "nx          0.002665  0.007776  0.020841  0.031215   0.051511   0.067398   \n",
-       "\n",
-       "            (70, 140)  (80, 160)  (90, 180)  (100, 200)  ...  (400, 800)  \\\n",
-       "Unnamed: 0                                               ...               \n",
-       "cbc          0.374295   0.452540   0.667402    0.710549  ...   13.350693   \n",
-       "gurobi       0.420639   0.484730   0.665472    0.897768  ...   14.986658   \n",
-       "nx           0.088514   0.114161   0.173439    0.194617  ...    4.319810   \n",
-       "\n",
-       "            (410, 820)  (420, 840)  (430, 860)  (440, 880)  (450, 900)  \\\n",
-       "Unnamed: 0                                                               \n",
-       "cbc          14.753052   18.444643   17.659208   16.619090   17.909117   \n",
-       "gurobi       17.618462   21.198270   20.555059   18.400325   22.321826   \n",
-       "nx            5.824088    5.281828    3.809972    4.966340    6.697136   \n",
-       "\n",
-       "            (460, 920)  (470, 940)  (480, 960)  (490, 980)  \n",
-       "Unnamed: 0                                                  \n",
-       "cbc          22.313041   22.550129   21.942068   21.800375  \n",
-       "gurobi       24.893338   25.507576   21.337107   25.497099  \n",
-       "nx            6.940013    7.345105    6.026856    6.781781  \n",
-       "\n",
-       "[3 rows x 49 columns]"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "      <th>(80, 160)</th>\n",
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-       "      <th>(90, 180)</th>\n",
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-       "      <th>(100, 200)</th>\n",
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-       "      <th>(120, 240)</th>\n",
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-       "      <th>(130, 260)</th>\n",
-       "      <td>1.398418</td>\n",
-       "      <td>1.499225</td>\n",
-       "      <td>0.380307</td>\n",
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-       "    <tr>\n",
-       "      <th>(140, 280)</th>\n",
-       "      <td>1.792202</td>\n",
-       "      <td>2.119854</td>\n",
-       "      <td>0.743684</td>\n",
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-       "    <tr>\n",
-       "      <th>(150, 300)</th>\n",
-       "      <td>2.093646</td>\n",
-       "      <td>3.018001</td>\n",
-       "      <td>0.779502</td>\n",
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-       "    <tr>\n",
-       "      <th>(160, 320)</th>\n",
-       "      <td>2.556776</td>\n",
-       "      <td>2.847643</td>\n",
-       "      <td>0.880181</td>\n",
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-       "    <tr>\n",
-       "      <th>(170, 340)</th>\n",
-       "      <td>3.167584</td>\n",
-       "      <td>3.389886</td>\n",
-       "      <td>0.821782</td>\n",
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-       "    <tr>\n",
-       "      <th>(180, 360)</th>\n",
-       "      <td>3.204695</td>\n",
-       "      <td>3.516493</td>\n",
-       "      <td>1.099206</td>\n",
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-       "    <tr>\n",
-       "      <th>(190, 380)</th>\n",
-       "      <td>3.318382</td>\n",
-       "      <td>3.798687</td>\n",
-       "      <td>0.953098</td>\n",
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-       "    <tr>\n",
-       "      <th>(200, 400)</th>\n",
-       "      <td>4.007743</td>\n",
-       "      <td>4.747369</td>\n",
-       "      <td>1.488640</td>\n",
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-       "    <tr>\n",
-       "      <th>(210, 420)</th>\n",
-       "      <td>4.617070</td>\n",
-       "      <td>5.444104</td>\n",
-       "      <td>1.397566</td>\n",
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-       "    <tr>\n",
-       "      <th>(220, 440)</th>\n",
-       "      <td>4.536492</td>\n",
-       "      <td>5.973851</td>\n",
-       "      <td>1.335216</td>\n",
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-       "    <tr>\n",
-       "      <th>(230, 460)</th>\n",
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-       "      <th>(240, 480)</th>\n",
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-       "    <tr>\n",
-       "      <th>(250, 500)</th>\n",
-       "      <td>5.963372</td>\n",
-       "      <td>6.540107</td>\n",
-       "      <td>2.483940</td>\n",
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-       "    <tr>\n",
-       "      <th>(260, 520)</th>\n",
-       "      <td>7.100784</td>\n",
-       "      <td>8.144801</td>\n",
-       "      <td>2.478391</td>\n",
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-       "    <tr>\n",
-       "      <th>(270, 540)</th>\n",
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-       "    <tr>\n",
-       "      <th>(280, 560)</th>\n",
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-       "    <tr>\n",
-       "      <th>(290, 580)</th>\n",
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-       "      <td>1.566175</td>\n",
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-       "    <tr>\n",
-       "      <th>(300, 600)</th>\n",
-       "      <td>7.224040</td>\n",
-       "      <td>11.965881</td>\n",
-       "      <td>2.732364</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>(310, 620)</th>\n",
-       "      <td>9.012477</td>\n",
-       "      <td>10.761895</td>\n",
-       "      <td>2.571371</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>(320, 640)</th>\n",
-       "      <td>9.899090</td>\n",
-       "      <td>11.485113</td>\n",
-       "      <td>3.268258</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>(330, 660)</th>\n",
-       "      <td>9.273829</td>\n",
-       "      <td>10.026702</td>\n",
-       "      <td>3.513739</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>(340, 680)</th>\n",
-       "      <td>11.059414</td>\n",
-       "      <td>10.695830</td>\n",
-       "      <td>2.378719</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>(350, 700)</th>\n",
-       "      <td>10.015416</td>\n",
-       "      <td>12.357908</td>\n",
-       "      <td>3.213909</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>(360, 720)</th>\n",
-       "      <td>10.911340</td>\n",
-       "      <td>12.428512</td>\n",
-       "      <td>2.645393</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>(370, 740)</th>\n",
-       "      <td>11.468855</td>\n",
-       "      <td>13.175973</td>\n",
-       "      <td>3.614456</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>(380, 760)</th>\n",
-       "      <td>11.775707</td>\n",
-       "      <td>14.321704</td>\n",
-       "      <td>3.125310</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>(390, 780)</th>\n",
-       "      <td>12.814383</td>\n",
-       "      <td>14.046117</td>\n",
-       "      <td>3.316323</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>(400, 800)</th>\n",
-       "      <td>13.350693</td>\n",
-       "      <td>14.986658</td>\n",
-       "      <td>4.319810</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>(410, 820)</th>\n",
-       "      <td>14.753052</td>\n",
-       "      <td>17.618462</td>\n",
-       "      <td>5.824088</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>(420, 840)</th>\n",
-       "      <td>18.444643</td>\n",
-       "      <td>21.198270</td>\n",
-       "      <td>5.281828</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>(430, 860)</th>\n",
-       "      <td>17.659208</td>\n",
-       "      <td>20.555059</td>\n",
-       "      <td>3.809972</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>(440, 880)</th>\n",
-       "      <td>16.619090</td>\n",
-       "      <td>18.400325</td>\n",
-       "      <td>4.966340</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>(450, 900)</th>\n",
-       "      <td>17.909117</td>\n",
-       "      <td>22.321826</td>\n",
-       "      <td>6.697136</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>(460, 920)</th>\n",
-       "      <td>22.313041</td>\n",
-       "      <td>24.893338</td>\n",
-       "      <td>6.940013</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>(470, 940)</th>\n",
-       "      <td>22.550129</td>\n",
-       "      <td>25.507576</td>\n",
-       "      <td>7.345105</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>(480, 960)</th>\n",
-       "      <td>21.942068</td>\n",
-       "      <td>21.337107</td>\n",
-       "      <td>6.026856</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>(490, 980)</th>\n",
-       "      <td>21.800375</td>\n",
-       "      <td>25.497099</td>\n",
-       "      <td>6.781781</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "Unnamed: 0        cbc     gurobi        nx\n",
-       "(10, 20)     0.094727   0.043392  0.002665\n",
-       "(20, 40)     0.084132   0.034781  0.007776\n",
-       "(30, 60)     0.120244   0.134984  0.020841\n",
-       "(40, 80)     0.172141   0.112127  0.031215\n",
-       "(50, 100)    0.223353   0.304981  0.051511\n",
-       "(60, 120)    0.320543   0.329835  0.067398\n",
-       "(70, 140)    0.374295   0.420639  0.088514\n",
-       "(80, 160)    0.452540   0.484730  0.114161\n",
-       "(90, 180)    0.667402   0.665472  0.173439\n",
-       "(100, 200)   0.710549   0.897768  0.194617\n",
-       "(110, 220)   0.853879   1.041919  0.224248\n",
-       "(120, 240)   1.145336   1.099542  0.257275\n",
-       "(130, 260)   1.398418   1.499225  0.380307\n",
-       "(140, 280)   1.792202   2.119854  0.743684\n",
-       "(150, 300)   2.093646   3.018001  0.779502\n",
-       "(160, 320)   2.556776   2.847643  0.880181\n",
-       "(170, 340)   3.167584   3.389886  0.821782\n",
-       "(180, 360)   3.204695   3.516493  1.099206\n",
-       "(190, 380)   3.318382   3.798687  0.953098\n",
-       "(200, 400)   4.007743   4.747369  1.488640\n",
-       "(210, 420)   4.617070   5.444104  1.397566\n",
-       "(220, 440)   4.536492   5.973851  1.335216\n",
-       "(230, 460)   4.707198   6.090197  1.404608\n",
-       "(240, 480)   5.154174   6.671727  1.521730\n",
-       "(250, 500)   5.963372   6.540107  2.483940\n",
-       "(260, 520)   7.100784   8.144801  2.478391\n",
-       "(270, 540)   7.140916   8.861260  2.542129\n",
-       "(280, 560)   8.229529  10.485070  2.379134\n",
-       "(290, 580)   8.741797  11.048967  1.566175\n",
-       "(300, 600)   7.224040  11.965881  2.732364\n",
-       "(310, 620)   9.012477  10.761895  2.571371\n",
-       "(320, 640)   9.899090  11.485113  3.268258\n",
-       "(330, 660)   9.273829  10.026702  3.513739\n",
-       "(340, 680)  11.059414  10.695830  2.378719\n",
-       "(350, 700)  10.015416  12.357908  3.213909\n",
-       "(360, 720)  10.911340  12.428512  2.645393\n",
-       "(370, 740)  11.468855  13.175973  3.614456\n",
-       "(380, 760)  11.775707  14.321704  3.125310\n",
-       "(390, 780)  12.814383  14.046117  3.316323\n",
-       "(400, 800)  13.350693  14.986658  4.319810\n",
-       "(410, 820)  14.753052  17.618462  5.824088\n",
-       "(420, 840)  18.444643  21.198270  5.281828\n",
-       "(430, 860)  17.659208  20.555059  3.809972\n",
-       "(440, 880)  16.619090  18.400325  4.966340\n",
-       "(450, 900)  17.909117  22.321826  6.697136\n",
-       "(460, 920)  22.313041  24.893338  6.940013\n",
-       "(470, 940)  22.550129  25.507576  7.345105\n",
-       "(480, 960)  21.942068  21.337107  6.026856\n",
-       "(490, 980)  21.800375  25.497099  6.781781"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "aux"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "RuntimeError",
-     "evalue": "Failed to process string with tex because latex could not be found",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/texmanager.py\u001b[0m in \u001b[0;36m_run_checked_subprocess\u001b[0;34m(cls, command, tex, cwd)\u001b[0m\n\u001b[1;32m    254\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 255\u001b[0;31m             report = subprocess.check_output(\n\u001b[0m\u001b[1;32m    256\u001b[0m                 \u001b[0mcommand\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcwd\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcwd\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcwd\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtexcache\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/subprocess.py\u001b[0m in \u001b[0;36mcheck_output\u001b[0;34m(timeout, *popenargs, **kwargs)\u001b[0m\n\u001b[1;32m    423\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 424\u001b[0;31m     return run(*popenargs, stdout=PIPE, timeout=timeout, check=True,\n\u001b[0m\u001b[1;32m    425\u001b[0m                **kwargs).stdout\n",
-      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/subprocess.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(input, capture_output, timeout, check, *popenargs, **kwargs)\u001b[0m\n\u001b[1;32m    504\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 505\u001b[0;31m     \u001b[0;32mwith\u001b[0m \u001b[0mPopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mpopenargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mprocess\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    506\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/subprocess.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, args, bufsize, executable, stdin, stdout, stderr, preexec_fn, close_fds, shell, cwd, env, universal_newlines, startupinfo, creationflags, restore_signals, start_new_session, pass_fds, user, group, extra_groups, encoding, errors, text, umask)\u001b[0m\n\u001b[1;32m    950\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 951\u001b[0;31m             self._execute_child(args, executable, preexec_fn, close_fds,\n\u001b[0m\u001b[1;32m    952\u001b[0m                                 \u001b[0mpass_fds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcwd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0menv\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/subprocess.py\u001b[0m in \u001b[0;36m_execute_child\u001b[0;34m(self, args, executable, preexec_fn, close_fds, pass_fds, cwd, env, startupinfo, creationflags, shell, p2cread, p2cwrite, c2pread, c2pwrite, errread, errwrite, restore_signals, gid, gids, uid, umask, start_new_session)\u001b[0m\n\u001b[1;32m   1820\u001b[0m                         \u001b[0merr_msg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrerror\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merrno_num\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1821\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0mchild_exception_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merrno_num\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merr_msg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merr_filename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1822\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mchild_exception_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr_msg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'latex'",
-      "\nThe above exception was the direct cause of the following exception:\n",
-      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
-      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    339\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    340\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 341\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    342\u001b[0m             \u001b[0;31m# Finally look for special method names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    343\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, base64, **kwargs)\u001b[0m\n\u001b[1;32m    149\u001b[0m         \u001b[0mFigureCanvasBase\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    150\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 151\u001b[0;31m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    152\u001b[0m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    153\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'svg'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[1;32m   2336\u001b[0m                 )\n\u001b[1;32m   2337\u001b[0m                 \u001b[0;32mwith\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"_draw_disabled\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnullcontext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2338\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2339\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2340\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mbbox_inches\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     93\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mwraps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 95\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     96\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_rasterizing\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     97\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     70\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     73\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   3123\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3124\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3125\u001b[0;31m             mimage._draw_list_compositing_images(\n\u001b[0m\u001b[1;32m   3126\u001b[0m                 renderer, self, artists, self.suppressComposite)\n\u001b[1;32m   3127\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    129\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    132\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     70\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     73\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   3064\u001b[0m             \u001b[0m_draw_rasterized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0martists_rasterized\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3065\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3066\u001b[0;31m         mimage._draw_list_compositing_images(\n\u001b[0m\u001b[1;32m   3067\u001b[0m             renderer, self, artists, self.figure.suppressComposite)\n\u001b[1;32m   3068\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    129\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    132\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     70\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     73\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1370\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1371\u001b[0m         \u001b[0mticks_to_draw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1372\u001b[0;31m         \u001b[0mtlb1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtlb2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_ticklabel_bboxes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mticks_to_draw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1373\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1374\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mtick\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mticks_to_draw\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36m_get_ticklabel_bboxes\u001b[0;34m(self, ticks, renderer)\u001b[0m\n\u001b[1;32m   1297\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrenderer\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1298\u001b[0m             \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_renderer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1299\u001b[0;31m         return ([tick.label1.get_window_extent(renderer)\n\u001b[0m\u001b[1;32m   1300\u001b[0m                  for tick in ticks if tick.label1.get_visible()],\n\u001b[1;32m   1301\u001b[0m                 [tick.label2.get_window_extent(renderer)\n",
-      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m   1297\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrenderer\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1298\u001b[0m             \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_renderer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1299\u001b[0;31m         return ([tick.label1.get_window_extent(renderer)\n\u001b[0m\u001b[1;32m   1300\u001b[0m                  for tick in ticks if tick.label1.get_visible()],\n\u001b[1;32m   1301\u001b[0m                 [tick.label2.get_window_extent(renderer)\n",
-      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/text.py\u001b[0m in \u001b[0;36mget_window_extent\u001b[0;34m(self, renderer, dpi)\u001b[0m\n\u001b[1;32m    957\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    958\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setattr_cm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 959\u001b[0;31m             \u001b[0mbbox\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minfo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdescent\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_layout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_renderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    960\u001b[0m             \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_unitless_position\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    961\u001b[0m             \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/text.py\u001b[0m in \u001b[0;36m_get_layout\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    376\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    377\u001b[0m         \u001b[0;31m# Full vertical extent of font, including ascenders and descenders:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 378\u001b[0;31m         _, lp_h, lp_d = _get_text_metrics_with_cache(\n\u001b[0m\u001b[1;32m    379\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"lp\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fontproperties\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    380\u001b[0m             ismath=\"TeX\" if self.get_usetex() else False, dpi=self.figure.dpi)\n",
-      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/text.py\u001b[0m in \u001b[0;36m_get_text_metrics_with_cache\u001b[0;34m(renderer, text, fontprop, ismath, dpi)\u001b[0m\n\u001b[1;32m     95\u001b[0m     \u001b[0;31m# Cached based on a copy of fontprop so that later in-place mutations of\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     96\u001b[0m     \u001b[0;31m# the passed-in argument do not mess up the cache.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 97\u001b[0;31m     return _get_text_metrics_with_cache_impl(\n\u001b[0m\u001b[1;32m     98\u001b[0m         weakref.ref(renderer), text, fontprop.copy(), ismath, dpi)\n\u001b[1;32m     99\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/text.py\u001b[0m in \u001b[0;36m_get_text_metrics_with_cache_impl\u001b[0;34m(renderer_ref, text, fontprop, ismath, dpi)\u001b[0m\n\u001b[1;32m    103\u001b[0m         renderer_ref, text, fontprop, ismath, dpi):\n\u001b[1;32m    104\u001b[0m     \u001b[0;31m# dpi is unused, but participates in cache invalidation (via the renderer).\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 105\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mrenderer_ref\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_text_width_height_descent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfontprop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mismath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    106\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    107\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mget_text_width_height_descent\u001b[0;34m(self, s, prop, ismath)\u001b[0m\n\u001b[1;32m    224\u001b[0m         \u001b[0m_api\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcheck_in_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"TeX\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mismath\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mismath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    225\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mismath\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"TeX\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 226\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_text_width_height_descent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mismath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    227\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    228\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mismath\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mget_text_width_height_descent\u001b[0;34m(self, s, prop, ismath)\u001b[0m\n\u001b[1;32m    639\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mismath\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'TeX'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    640\u001b[0m             \u001b[0;31m# todo: handle properties\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 641\u001b[0;31m             return self.get_texmanager().get_text_width_height_descent(\n\u001b[0m\u001b[1;32m    642\u001b[0m                 s, fontsize, renderer=self)\n\u001b[1;32m    643\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/texmanager.py\u001b[0m in \u001b[0;36mget_text_width_height_descent\u001b[0;34m(cls, tex, fontsize, renderer)\u001b[0m\n\u001b[1;32m    366\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m''\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    367\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 368\u001b[0;31m         \u001b[0mdvifile\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake_dvi\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfontsize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    369\u001b[0m         \u001b[0mdpi_fraction\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpoints_to_pixels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1.\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mrenderer\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    370\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mdviread\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDvi\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdvifile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m72\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mdpi_fraction\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mdvi\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/texmanager.py\u001b[0m in \u001b[0;36mmake_dvi\u001b[0;34m(cls, tex, fontsize)\u001b[0m\n\u001b[1;32m    298\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mTemporaryDirectory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdir\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcwd\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtmpdir\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    299\u001b[0m                 \u001b[0mtmppath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtmpdir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 300\u001b[0;31m                 cls._run_checked_subprocess(\n\u001b[0m\u001b[1;32m    301\u001b[0m                     [\"latex\", \"-interaction=nonstopmode\", \"--halt-on-error\",\n\u001b[1;32m    302\u001b[0m                      \u001b[0;34mf\"--output-directory={tmppath.name}\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/texmanager.py\u001b[0m in \u001b[0;36m_run_checked_subprocess\u001b[0;34m(cls, command, tex, cwd)\u001b[0m\n\u001b[1;32m    257\u001b[0m                 stderr=subprocess.STDOUT)\n\u001b[1;32m    258\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mFileNotFoundError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 259\u001b[0;31m             raise RuntimeError(\n\u001b[0m\u001b[1;32m    260\u001b[0m                 \u001b[0;34m'Failed to process string with tex because {} could not be '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    261\u001b[0m                 'found'.format(command[0])) from exc\n",
-      "\u001b[0;31mRuntimeError\u001b[0m: Failed to process string with tex because latex could not be found"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 1300x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "plt.rcParams['text.usetex'] =True\n",
-    "fig = plt.figure(figsize=(13,5))\n",
-    "ax=fig.add_subplot(111)\n",
-    "aux.plot(ax=ax)\n",
-    "plt.xticks(np.arange(len(df.columns)),df.columns,rotation = 45)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "RuntimeError",
-     "evalue": "Failed to process string with tex because latex could not be found",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/texmanager.py\u001b[0m in \u001b[0;36m_run_checked_subprocess\u001b[0;34m(cls, command, tex, cwd)\u001b[0m\n\u001b[1;32m    254\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 255\u001b[0;31m             report = subprocess.check_output(\n\u001b[0m\u001b[1;32m    256\u001b[0m                 \u001b[0mcommand\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcwd\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcwd\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcwd\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtexcache\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/subprocess.py\u001b[0m in \u001b[0;36mcheck_output\u001b[0;34m(timeout, *popenargs, **kwargs)\u001b[0m\n\u001b[1;32m    423\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 424\u001b[0;31m     return run(*popenargs, stdout=PIPE, timeout=timeout, check=True,\n\u001b[0m\u001b[1;32m    425\u001b[0m                **kwargs).stdout\n",
-      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/subprocess.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(input, capture_output, timeout, check, *popenargs, **kwargs)\u001b[0m\n\u001b[1;32m    504\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 505\u001b[0;31m     \u001b[0;32mwith\u001b[0m \u001b[0mPopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mpopenargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mprocess\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    506\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/subprocess.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, args, bufsize, executable, stdin, stdout, stderr, preexec_fn, close_fds, shell, cwd, env, universal_newlines, startupinfo, creationflags, restore_signals, start_new_session, pass_fds, user, group, extra_groups, encoding, errors, text, umask)\u001b[0m\n\u001b[1;32m    950\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 951\u001b[0;31m             self._execute_child(args, executable, preexec_fn, close_fds,\n\u001b[0m\u001b[1;32m    952\u001b[0m                                 \u001b[0mpass_fds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcwd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0menv\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/subprocess.py\u001b[0m in \u001b[0;36m_execute_child\u001b[0;34m(self, args, executable, preexec_fn, close_fds, pass_fds, cwd, env, startupinfo, creationflags, shell, p2cread, p2cwrite, c2pread, c2pwrite, errread, errwrite, restore_signals, gid, gids, uid, umask, start_new_session)\u001b[0m\n\u001b[1;32m   1820\u001b[0m                         \u001b[0merr_msg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrerror\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merrno_num\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1821\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0mchild_exception_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merrno_num\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merr_msg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merr_filename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1822\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mchild_exception_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr_msg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'latex'",
-      "\nThe above exception was the direct cause of the following exception:\n",
-      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
-      "\u001b[0;32m/var/folders/cm/z3t28j296f98jdp1vqyplkz00000gn/T/ipykernel_56216/2412267707.py\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0;34m'dina_times.pdf'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbbox_inches\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'tight'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpad_inches\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36msavefig\u001b[0;34m(self, fname, transparent, **kwargs)\u001b[0m\n\u001b[1;32m   3326\u001b[0m                         ax.patch._cm_set(facecolor='none', edgecolor='none'))\n\u001b[1;32m   3327\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3328\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3329\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3330\u001b[0m     def ginput(self, n=1, timeout=30, show_clicks=True,\n",
-      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[1;32m   2336\u001b[0m                 )\n\u001b[1;32m   2337\u001b[0m                 \u001b[0;32mwith\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"_draw_disabled\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnullcontext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2338\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2339\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2340\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mbbox_inches\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     93\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mwraps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 95\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     96\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_rasterizing\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     97\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     70\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     73\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   3123\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3124\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3125\u001b[0;31m             mimage._draw_list_compositing_images(\n\u001b[0m\u001b[1;32m   3126\u001b[0m                 renderer, self, artists, self.suppressComposite)\n\u001b[1;32m   3127\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    129\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    132\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     70\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     73\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   3064\u001b[0m             \u001b[0m_draw_rasterized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0martists_rasterized\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3065\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3066\u001b[0;31m         mimage._draw_list_compositing_images(\n\u001b[0m\u001b[1;32m   3067\u001b[0m             renderer, self, artists, self.figure.suppressComposite)\n\u001b[1;32m   3068\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    129\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    132\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     70\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     73\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1370\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1371\u001b[0m         \u001b[0mticks_to_draw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1372\u001b[0;31m         \u001b[0mtlb1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtlb2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_ticklabel_bboxes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mticks_to_draw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1373\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1374\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mtick\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mticks_to_draw\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36m_get_ticklabel_bboxes\u001b[0;34m(self, ticks, renderer)\u001b[0m\n\u001b[1;32m   1297\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrenderer\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1298\u001b[0m             \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_renderer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1299\u001b[0;31m         return ([tick.label1.get_window_extent(renderer)\n\u001b[0m\u001b[1;32m   1300\u001b[0m                  for tick in ticks if tick.label1.get_visible()],\n\u001b[1;32m   1301\u001b[0m                 [tick.label2.get_window_extent(renderer)\n",
-      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m   1297\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrenderer\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1298\u001b[0m             \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_renderer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1299\u001b[0;31m         return ([tick.label1.get_window_extent(renderer)\n\u001b[0m\u001b[1;32m   1300\u001b[0m                  for tick in ticks if tick.label1.get_visible()],\n\u001b[1;32m   1301\u001b[0m                 [tick.label2.get_window_extent(renderer)\n",
-      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/text.py\u001b[0m in \u001b[0;36mget_window_extent\u001b[0;34m(self, renderer, dpi)\u001b[0m\n\u001b[1;32m    957\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    958\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setattr_cm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 959\u001b[0;31m             \u001b[0mbbox\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minfo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdescent\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_layout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_renderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    960\u001b[0m             \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_unitless_position\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    961\u001b[0m             \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/text.py\u001b[0m in \u001b[0;36m_get_layout\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    376\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    377\u001b[0m         \u001b[0;31m# Full vertical extent of font, including ascenders and descenders:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 378\u001b[0;31m         _, lp_h, lp_d = _get_text_metrics_with_cache(\n\u001b[0m\u001b[1;32m    379\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"lp\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fontproperties\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    380\u001b[0m             ismath=\"TeX\" if self.get_usetex() else False, dpi=self.figure.dpi)\n",
-      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/text.py\u001b[0m in \u001b[0;36m_get_text_metrics_with_cache\u001b[0;34m(renderer, text, fontprop, ismath, dpi)\u001b[0m\n\u001b[1;32m     95\u001b[0m     \u001b[0;31m# Cached based on a copy of fontprop so that later in-place mutations of\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     96\u001b[0m     \u001b[0;31m# the passed-in argument do not mess up the cache.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 97\u001b[0;31m     return _get_text_metrics_with_cache_impl(\n\u001b[0m\u001b[1;32m     98\u001b[0m         weakref.ref(renderer), text, fontprop.copy(), ismath, dpi)\n\u001b[1;32m     99\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/text.py\u001b[0m in \u001b[0;36m_get_text_metrics_with_cache_impl\u001b[0;34m(renderer_ref, text, fontprop, ismath, dpi)\u001b[0m\n\u001b[1;32m    103\u001b[0m         renderer_ref, text, fontprop, ismath, dpi):\n\u001b[1;32m    104\u001b[0m     \u001b[0;31m# dpi is unused, but participates in cache invalidation (via the renderer).\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 105\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mrenderer_ref\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_text_width_height_descent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfontprop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mismath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    106\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    107\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/backends/_backend_pdf_ps.py\u001b[0m in \u001b[0;36mget_text_width_height_descent\u001b[0;34m(self, s, prop, ismath)\u001b[0m\n\u001b[1;32m    105\u001b[0m         \u001b[0;31m# docstring inherited\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    106\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mismath\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"TeX\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 107\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_text_width_height_descent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mismath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    108\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mismath\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    109\u001b[0m             \u001b[0mparse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_text2path\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmathtext_parser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m72\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mget_text_width_height_descent\u001b[0;34m(self, s, prop, ismath)\u001b[0m\n\u001b[1;32m    639\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mismath\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'TeX'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    640\u001b[0m             \u001b[0;31m# todo: handle properties\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 641\u001b[0;31m             return self.get_texmanager().get_text_width_height_descent(\n\u001b[0m\u001b[1;32m    642\u001b[0m                 s, fontsize, renderer=self)\n\u001b[1;32m    643\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/texmanager.py\u001b[0m in \u001b[0;36mget_text_width_height_descent\u001b[0;34m(cls, tex, fontsize, renderer)\u001b[0m\n\u001b[1;32m    366\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m''\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    367\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 368\u001b[0;31m         \u001b[0mdvifile\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake_dvi\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfontsize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    369\u001b[0m         \u001b[0mdpi_fraction\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpoints_to_pixels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1.\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mrenderer\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    370\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mdviread\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDvi\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdvifile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m72\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mdpi_fraction\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mdvi\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/texmanager.py\u001b[0m in \u001b[0;36mmake_dvi\u001b[0;34m(cls, tex, fontsize)\u001b[0m\n\u001b[1;32m    298\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mTemporaryDirectory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdir\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcwd\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtmpdir\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    299\u001b[0m                 \u001b[0mtmppath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtmpdir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 300\u001b[0;31m                 cls._run_checked_subprocess(\n\u001b[0m\u001b[1;32m    301\u001b[0m                     [\"latex\", \"-interaction=nonstopmode\", \"--halt-on-error\",\n\u001b[1;32m    302\u001b[0m                      \u001b[0;34mf\"--output-directory={tmppath.name}\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/texmanager.py\u001b[0m in \u001b[0;36m_run_checked_subprocess\u001b[0;34m(cls, command, tex, cwd)\u001b[0m\n\u001b[1;32m    257\u001b[0m                 stderr=subprocess.STDOUT)\n\u001b[1;32m    258\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mFileNotFoundError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 259\u001b[0;31m             raise RuntimeError(\n\u001b[0m\u001b[1;32m    260\u001b[0m                 \u001b[0;34m'Failed to process string with tex because {} could not be '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    261\u001b[0m                 'found'.format(command[0])) from exc\n",
-      "\u001b[0;31mRuntimeError\u001b[0m: Failed to process string with tex because latex could not be found"
-     ]
-    }
-   ],
-   "source": [
-    "fig.savefig( 'dina_times.pdf', bbox_inches='tight', pad_inches=0 )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "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": "3.9.16"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}