diff --git a/fftma_module/gen/analysis.ipynb b/fftma_module/gen/analysis.ipynb
index 643fc8b..34aba8c 100644
--- a/fftma_module/gen/analysis.ipynb
+++ b/fftma_module/gen/analysis.ipynb
@@ -9,13 +9,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 159,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
     "import pandas as pd\n",
     "import matplotlib.pyplot as plt\n",
-    "import numpy as np  "
+    "import numpy as np  \n",
+    "import plotly.express as px"
    ]
   },
   {
@@ -34,7 +35,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 160,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -44,7 +45,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 161,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -58,7 +59,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 177,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -100,7 +101,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 180,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -112,7 +113,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 181,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -123,7 +124,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 188,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -160,7 +161,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 190,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -179,12 +180,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 151,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def plot_pie(df, function):\n",
-    "    plt.figure(dpi=125, figsize=(6, 6))\n",
+    "def plot_pie(df, function, plt):\n",
+    "    \n",
     "    labels = relations[function].copy()\n",
     "    total = df.loc[function][('time', 'sum')]\n",
     "    sizes = []\n",
@@ -205,7 +206,7 @@
     "    porcent = 100.*sizes/sizes.sum()\n",
     "    explode.append(0 if rest/total > 0.01 else 0.1)\n",
     "\n",
-    "    plt.title(function)\n",
+    "    plt.set_title(function)\n",
     "\n",
     "    patches, texts = plt.pie(sizes, startangle=90, radius=1.2)\n",
     "    labels_formated = ['{0} - {1:1.2f} %'.format(i,j) for i,j in zip(labels, porcent)]\n",
@@ -219,15 +220,45 @@
     "    plt.legend(patches, labels_formated, loc='upper left', bbox_to_anchor=(-0.1, 1.),\n",
     "               fontsize=8)\n",
     "    \n",
-    "    plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
-    "    plt.show()\n",
+    "    plt.axis('equal')\n",
     "\n",
     "def plot_analysis(df):\n",
-    "    for func in relations.keys():\n",
-    "        plot_pie(df, func)\n",
+    "    fig, axs = plt.subplots(2, 2, dpi=100, figsize=(6, 6))\n",
+    "    fig.suptitle('Time comparisons')\n",
+    "    functions = list(relations.keys())\n",
+    "    for i in range(2):\n",
+    "        for j in range(2):\n",
+    "            plot_pie(df,functions[2*i + j], axs[i, j])\n",
     "    "
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "parents = {\n",
+    "    \"Py_kgeneration\": \"\",\n",
+    "    \"generate\": \"Py_kgeneration\",\n",
+    "    \"gasdev\": \"generate\",\n",
+    "    \"fftma2\":  \"Py_kgeneration\",\n",
+    "    \"covariance\": \"fftma2\",\n",
+    "    \"fourt\": \"fftma2\",\n",
+    "    \"prebuild_gwn\": \"fftma2\",\n",
+    "    \"ran2\": \"gasdev\",\n",
+    "    \"cov_value\": \"covariance\",\n",
+    "}\n",
+    "\n",
+    "def plot_treemap(df):\n",
+    "    df[\"parent\"] = [parents.get(item, \"\") for item in df.index]\n",
+    "    df2 = df.reset_index()\n",
+    "    df2[\"time_sum\"] = df2[[(\"time\", \"sum\")]]\n",
+    "    df2 = df2[[\"function\", \"parent\", \"time_sum\"]]\n",
+    "    fig3 = px.treemap(df2, names='function', parents='parent',values='time_sum', color=\"parent\")\n",
+    "    fig3.show()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -237,7 +268,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 191,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -265,23 +296,23 @@
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
+       "      <th colspan=\"5\" halign=\"left\">time</th>\n",
        "      <th colspan=\"3\" halign=\"left\">memory</th>\n",
        "      <th colspan=\"3\" halign=\"left\">cpu</th>\n",
-       "      <th colspan=\"5\" halign=\"left\">time</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th>min</th>\n",
        "      <th>max</th>\n",
-       "      <th>median</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>sum</th>\n",
+       "      <th>count</th>\n",
        "      <th>min</th>\n",
        "      <th>max</th>\n",
-       "      <th>mean</th>\n",
+       "      <th>median</th>\n",
        "      <th>min</th>\n",
        "      <th>max</th>\n",
        "      <th>mean</th>\n",
-       "      <th>sum</th>\n",
-       "      <th>count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>function</th>\n",
@@ -301,243 +332,243 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Py_kgeneration</th>\n",
-       "      <td>1.2</td>\n",
-       "      <td>1.2</td>\n",
-       "      <td>1.2</td>\n",
-       "      <td>75.100000</td>\n",
-       "      <td>75.100000</td>\n",
-       "      <td>75.100000</td>\n",
-       "      <td>0.633546</td>\n",
-       "      <td>0.633546</td>\n",
-       "      <td>0.633546</td>\n",
-       "      <td>0.633546</td>\n",
+       "      <td>0.573509</td>\n",
+       "      <td>0.573509</td>\n",
+       "      <td>0.573509</td>\n",
+       "      <td>0.573509</td>\n",
        "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>generate</th>\n",
        "      <td>1.3</td>\n",
        "      <td>1.3</td>\n",
        "      <td>1.3</td>\n",
-       "      <td>100.100000</td>\n",
-       "      <td>100.100000</td>\n",
-       "      <td>100.100000</td>\n",
-       "      <td>0.397899</td>\n",
-       "      <td>0.397899</td>\n",
-       "      <td>0.397899</td>\n",
-       "      <td>0.397899</td>\n",
+       "      <td>19.612195</td>\n",
+       "      <td>19.612195</td>\n",
+       "      <td>19.612195</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>generate</th>\n",
+       "      <td>0.432266</td>\n",
+       "      <td>0.432266</td>\n",
+       "      <td>0.432266</td>\n",
+       "      <td>0.432266</td>\n",
        "      <td>1</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>20.100000</td>\n",
+       "      <td>20.100000</td>\n",
+       "      <td>20.100000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>gasdev</th>\n",
-       "      <td>-0.2</td>\n",
+       "      <td>0.000080</td>\n",
+       "      <td>0.023294</td>\n",
+       "      <td>0.000620</td>\n",
+       "      <td>0.317450</td>\n",
+       "      <td>512</td>\n",
+       "      <td>-0.5</td>\n",
        "      <td>0.2</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>100.100000</td>\n",
-       "      <td>5.669727</td>\n",
-       "      <td>0.000106</td>\n",
-       "      <td>0.007636</td>\n",
-       "      <td>0.000579</td>\n",
-       "      <td>0.296354</td>\n",
-       "      <td>512</td>\n",
+       "      <td>0.589648</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>fftma2</th>\n",
-       "      <td>-0.1</td>\n",
-       "      <td>-0.1</td>\n",
-       "      <td>-0.1</td>\n",
-       "      <td>30.534783</td>\n",
-       "      <td>30.534783</td>\n",
-       "      <td>30.534783</td>\n",
-       "      <td>0.234361</td>\n",
-       "      <td>0.234361</td>\n",
-       "      <td>0.234361</td>\n",
-       "      <td>0.234361</td>\n",
+       "      <td>0.138753</td>\n",
+       "      <td>0.138753</td>\n",
+       "      <td>0.138753</td>\n",
+       "      <td>0.138753</td>\n",
        "      <td>1</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>18.850000</td>\n",
+       "      <td>18.850000</td>\n",
+       "      <td>18.850000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>covariance</th>\n",
-       "      <td>-0.1</td>\n",
-       "      <td>-0.1</td>\n",
-       "      <td>-0.1</td>\n",
-       "      <td>31.918182</td>\n",
-       "      <td>31.918182</td>\n",
-       "      <td>31.918182</td>\n",
-       "      <td>0.224350</td>\n",
-       "      <td>0.224350</td>\n",
-       "      <td>0.224350</td>\n",
-       "      <td>0.224350</td>\n",
+       "      <td>0.135896</td>\n",
+       "      <td>0.135896</td>\n",
+       "      <td>0.135896</td>\n",
+       "      <td>0.135896</td>\n",
        "      <td>1</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>13.433333</td>\n",
+       "      <td>13.433333</td>\n",
+       "      <td>13.433333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>cov_value</th>\n",
-       "      <td>-0.2</td>\n",
+       "      <th>ran2</th>\n",
+       "      <td>0.000077</td>\n",
+       "      <td>0.001476</td>\n",
+       "      <td>0.000132</td>\n",
+       "      <td>0.092725</td>\n",
+       "      <td>702</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>0.2</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>100.100000</td>\n",
-       "      <td>0.859143</td>\n",
-       "      <td>0.000103</td>\n",
-       "      <td>0.000966</td>\n",
-       "      <td>0.000134</td>\n",
-       "      <td>0.093502</td>\n",
-       "      <td>700</td>\n",
+       "      <td>0.142593</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>ran2</th>\n",
+       "      <th>cov_value</th>\n",
+       "      <td>0.000080</td>\n",
+       "      <td>0.000154</td>\n",
+       "      <td>0.000084</td>\n",
+       "      <td>0.058473</td>\n",
+       "      <td>700</td>\n",
        "      <td>-0.2</td>\n",
        "      <td>0.2</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>100.100000</td>\n",
-       "      <td>0.998148</td>\n",
-       "      <td>0.000101</td>\n",
-       "      <td>0.000583</td>\n",
-       "      <td>0.000133</td>\n",
-       "      <td>0.093435</td>\n",
-       "      <td>702</td>\n",
+       "      <td>0.100000</td>\n",
+       "      <td>0.000714</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>fourt</th>\n",
+       "      <th>cgrid</th>\n",
+       "      <td>0.001218</td>\n",
+       "      <td>0.001218</td>\n",
+       "      <td>0.001218</td>\n",
+       "      <td>0.001218</td>\n",
+       "      <td>1</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000553</td>\n",
-       "      <td>0.000795</td>\n",
-       "      <td>0.000664</td>\n",
-       "      <td>0.001993</td>\n",
-       "      <td>3</td>\n",
+       "      <td>100.100000</td>\n",
+       "      <td>100.100000</td>\n",
+       "      <td>100.100000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>cgrid</th>\n",
+       "      <th>length</th>\n",
+       "      <td>0.000266</td>\n",
+       "      <td>0.000268</td>\n",
+       "      <td>0.000267</td>\n",
+       "      <td>0.000802</td>\n",
+       "      <td>3</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.001623</td>\n",
-       "      <td>0.001623</td>\n",
-       "      <td>0.001623</td>\n",
-       "      <td>0.001623</td>\n",
-       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>length</th>\n",
+       "      <th>fourt</th>\n",
+       "      <td>0.000122</td>\n",
+       "      <td>0.000138</td>\n",
+       "      <td>0.000130</td>\n",
+       "      <td>0.000391</td>\n",
+       "      <td>3</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.000355</td>\n",
-       "      <td>0.000359</td>\n",
-       "      <td>0.000358</td>\n",
-       "      <td>0.001073</td>\n",
-       "      <td>3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>clean_real</th>\n",
+       "      <th>maxfactor</th>\n",
+       "      <td>0.000079</td>\n",
+       "      <td>0.000080</td>\n",
+       "      <td>0.000079</td>\n",
+       "      <td>0.000238</td>\n",
+       "      <td>3</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.000463</td>\n",
-       "      <td>0.000463</td>\n",
-       "      <td>0.000463</td>\n",
-       "      <td>0.000463</td>\n",
-       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>prebuild_gwn</th>\n",
+       "      <th>build_real</th>\n",
+       "      <td>0.000104</td>\n",
+       "      <td>0.000104</td>\n",
+       "      <td>0.000104</td>\n",
+       "      <td>0.000104</td>\n",
+       "      <td>1</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.000454</td>\n",
-       "      <td>0.000454</td>\n",
-       "      <td>0.000454</td>\n",
-       "      <td>0.000454</td>\n",
-       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>build_real</th>\n",
+       "      <th>prebuild_gwn</th>\n",
+       "      <td>0.000084</td>\n",
+       "      <td>0.000084</td>\n",
+       "      <td>0.000084</td>\n",
+       "      <td>0.000084</td>\n",
+       "      <td>1</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.000410</td>\n",
-       "      <td>0.000410</td>\n",
-       "      <td>0.000410</td>\n",
-       "      <td>0.000410</td>\n",
-       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>maxfactor</th>\n",
+       "      <th>clean_real</th>\n",
+       "      <td>0.000082</td>\n",
+       "      <td>0.000082</td>\n",
+       "      <td>0.000082</td>\n",
+       "      <td>0.000082</td>\n",
+       "      <td>1</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.000105</td>\n",
-       "      <td>0.000106</td>\n",
-       "      <td>0.000105</td>\n",
-       "      <td>0.000316</td>\n",
-       "      <td>3</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "               memory                     cpu                          \\\n",
-       "                  min  max median         min         max        mean   \n",
-       "function                                                                \n",
-       "Py_kgeneration    1.2  1.2    1.2   75.100000   75.100000   75.100000   \n",
-       "generate          1.3  1.3    1.3  100.100000  100.100000  100.100000   \n",
-       "gasdev           -0.2  0.2    0.0    0.000000  100.100000    5.669727   \n",
-       "fftma2           -0.1 -0.1   -0.1   30.534783   30.534783   30.534783   \n",
-       "covariance       -0.1 -0.1   -0.1   31.918182   31.918182   31.918182   \n",
-       "cov_value        -0.2  0.2    0.0    0.000000  100.100000    0.859143   \n",
-       "ran2             -0.2  0.2    0.0    0.000000  100.100000    0.998148   \n",
-       "fourt             0.0  0.0    0.0    0.000000    0.000000    0.000000   \n",
-       "cgrid             0.0  0.0    0.0    0.000000    0.000000    0.000000   \n",
-       "length            0.0  0.0    0.0    0.000000    0.000000    0.000000   \n",
-       "clean_real        0.0  0.0    0.0    0.000000    0.000000    0.000000   \n",
-       "prebuild_gwn      0.0  0.0    0.0    0.000000    0.000000    0.000000   \n",
-       "build_real        0.0  0.0    0.0    0.000000    0.000000    0.000000   \n",
-       "maxfactor         0.0  0.0    0.0    0.000000    0.000000    0.000000   \n",
+       "                    time                                     memory       \\\n",
+       "                     min       max      mean       sum count    min  max   \n",
+       "function                                                                   \n",
+       "Py_kgeneration  0.573509  0.573509  0.573509  0.573509     1    1.3  1.3   \n",
+       "generate        0.432266  0.432266  0.432266  0.432266     1    0.8  0.8   \n",
+       "gasdev          0.000080  0.023294  0.000620  0.317450   512   -0.5  0.2   \n",
+       "fftma2          0.138753  0.138753  0.138753  0.138753     1    0.5  0.5   \n",
+       "covariance      0.135896  0.135896  0.135896  0.135896     1    0.5  0.5   \n",
+       "ran2            0.000077  0.001476  0.000132  0.092725   702    0.0  0.2   \n",
+       "cov_value       0.000080  0.000154  0.000084  0.058473   700   -0.2  0.2   \n",
+       "cgrid           0.001218  0.001218  0.001218  0.001218     1    0.0  0.0   \n",
+       "length          0.000266  0.000268  0.000267  0.000802     3    0.0  0.0   \n",
+       "fourt           0.000122  0.000138  0.000130  0.000391     3    0.0  0.0   \n",
+       "maxfactor       0.000079  0.000080  0.000079  0.000238     3    0.0  0.0   \n",
+       "build_real      0.000104  0.000104  0.000104  0.000104     1    0.0  0.0   \n",
+       "prebuild_gwn    0.000084  0.000084  0.000084  0.000084     1    0.0  0.0   \n",
+       "clean_real      0.000082  0.000082  0.000082  0.000082     1    0.0  0.0   \n",
        "\n",
-       "                    time                                      \n",
-       "                     min       max      mean       sum count  \n",
-       "function                                                      \n",
-       "Py_kgeneration  0.633546  0.633546  0.633546  0.633546     1  \n",
-       "generate        0.397899  0.397899  0.397899  0.397899     1  \n",
-       "gasdev          0.000106  0.007636  0.000579  0.296354   512  \n",
-       "fftma2          0.234361  0.234361  0.234361  0.234361     1  \n",
-       "covariance      0.224350  0.224350  0.224350  0.224350     1  \n",
-       "cov_value       0.000103  0.000966  0.000134  0.093502   700  \n",
-       "ran2            0.000101  0.000583  0.000133  0.093435   702  \n",
-       "fourt           0.000553  0.000795  0.000664  0.001993     3  \n",
-       "cgrid           0.001623  0.001623  0.001623  0.001623     1  \n",
-       "length          0.000355  0.000359  0.000358  0.001073     3  \n",
-       "clean_real      0.000463  0.000463  0.000463  0.000463     1  \n",
-       "prebuild_gwn    0.000454  0.000454  0.000454  0.000454     1  \n",
-       "build_real      0.000410  0.000410  0.000410  0.000410     1  \n",
-       "maxfactor       0.000105  0.000106  0.000105  0.000316     3  "
+       "                              cpu                          \n",
+       "               median         min         max        mean  \n",
+       "function                                                   \n",
+       "Py_kgeneration    1.3   19.612195   19.612195   19.612195  \n",
+       "generate          0.8   20.100000   20.100000   20.100000  \n",
+       "gasdev            0.0    0.000000  100.100000    0.589648  \n",
+       "fftma2            0.5   18.850000   18.850000   18.850000  \n",
+       "covariance        0.5   13.433333   13.433333   13.433333  \n",
+       "ran2              0.0    0.000000  100.100000    0.142593  \n",
+       "cov_value         0.0    0.000000    0.100000    0.000714  \n",
+       "cgrid             0.0  100.100000  100.100000  100.100000  \n",
+       "length            0.0    0.000000    0.000000    0.000000  \n",
+       "fourt             0.0    0.000000    0.000000    0.000000  \n",
+       "maxfactor         0.0    0.000000    0.000000    0.000000  \n",
+       "build_real        0.0    0.000000    0.000000    0.000000  \n",
+       "prebuild_gwn      0.0    0.000000    0.000000    0.000000  \n",
+       "clean_real        0.0    0.000000    0.000000    0.000000  "
       ]
      },
-     "execution_count": 191,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -549,41 +580,972 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 68,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/oli/.local/lib/python3.6/site-packages/ipykernel_launcher.py:25: MatplotlibDeprecationWarning: normalize=None does not normalize if the sum is less than 1 but this behavior is deprecated since 3.3 until two minor releases later. After the deprecation period the default value will be normalize=True. To prevent normalization pass normalize=False \n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 750x750 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<Figure size 750x750 with 1 Axes>"
+       "<Figure size 600x600 with 4 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
-    },
+    }
+   ],
+   "source": [
+    "plot_analysis(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
     {
      "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 750x750 with 1 Axes>"
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
+       "data": [
+        {
+         "domain": {
+          "x": [
+           0,
+           1
+          ],
+          "y": [
+           0,
+           1
+          ]
+         },
+         "hovertemplate": "function=%{label}<br>time_sum=%{value}<br>parent=%{parent}<extra></extra>",
+         "labels": [
+          "Py_kgeneration",
+          "generate",
+          "fftma2",
+          "covariance",
+          "gasdev",
+          "fourt",
+          "cov_value",
+          "ran2",
+          "build_real",
+          "prebuild_gwn",
+          "clean_real",
+          "cgrid",
+          "length",
+          "maxfactor"
+         ],
+         "marker": {
+          "colors": [
+           "#636efa",
+           "#EF553B",
+           "#EF553B",
+           "#00cc96",
+           "#ab63fa",
+           "#00cc96",
+           "#FFA15A",
+           "#19d3f3",
+           "#636efa",
+           "#00cc96",
+           "#636efa",
+           "#636efa",
+           "#636efa",
+           "#636efa"
+          ]
+         },
+         "name": "",
+         "parents": [
+          "",
+          "Py_kgeneration",
+          "Py_kgeneration",
+          "fftma2",
+          "generate",
+          "fftma2",
+          "covariance",
+          "gasdev",
+          "",
+          "fftma2",
+          "",
+          "",
+          "",
+          ""
+         ],
+         "type": "treemap",
+         "values": [
+          173.93129,
+          137.192987,
+          36.737828,
+          36.638978,
+          101.71883800005251,
+          0.09082599999999999,
+          15.471843000005594,
+          31.86060899999753,
+          0.00317,
+          0.001027,
+          0.000707,
+          0.001641,
+          0.001153,
+          0.000372
+         ]
+        }
+       ],
+       "layout": {
+        "autosize": true,
+        "legend": {
+         "tracegroupgap": 0
+        },
+        "margin": {
+         "t": 60
+        },
+        "template": {
+         "data": {
+          "bar": [
+           {
+            "error_x": {
+             "color": "#2a3f5f"
+            },
+            "error_y": {
+             "color": "#2a3f5f"
+            },
+            "marker": {
+             "line": {
+              "color": "#E5ECF6",
+              "width": 0.5
+             },
+             "pattern": {
+              "fillmode": "overlay",
+              "size": 10,
+              "solidity": 0.2
+             }
+            },
+            "type": "bar"
+           }
+          ],
+          "barpolar": [
+           {
+            "marker": {
+             "line": {
+              "color": "#E5ECF6",
+              "width": 0.5
+             },
+             "pattern": {
+              "fillmode": "overlay",
+              "size": 10,
+              "solidity": 0.2
+             }
+            },
+            "type": "barpolar"
+           }
+          ],
+          "carpet": [
+           {
+            "aaxis": {
+             "endlinecolor": "#2a3f5f",
+             "gridcolor": "white",
+             "linecolor": "white",
+             "minorgridcolor": "white",
+             "startlinecolor": "#2a3f5f"
+            },
+            "baxis": {
+             "endlinecolor": "#2a3f5f",
+             "gridcolor": "white",
+             "linecolor": "white",
+             "minorgridcolor": "white",
+             "startlinecolor": "#2a3f5f"
+            },
+            "type": "carpet"
+           }
+          ],
+          "choropleth": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "type": "choropleth"
+           }
+          ],
+          "contour": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "colorscale": [
+             [
+              0,
+              "#0d0887"
+             ],
+             [
+              0.1111111111111111,
+              "#46039f"
+             ],
+             [
+              0.2222222222222222,
+              "#7201a8"
+             ],
+             [
+              0.3333333333333333,
+              "#9c179e"
+             ],
+             [
+              0.4444444444444444,
+              "#bd3786"
+             ],
+             [
+              0.5555555555555556,
+              "#d8576b"
+             ],
+             [
+              0.6666666666666666,
+              "#ed7953"
+             ],
+             [
+              0.7777777777777778,
+              "#fb9f3a"
+             ],
+             [
+              0.8888888888888888,
+              "#fdca26"
+             ],
+             [
+              1,
+              "#f0f921"
+             ]
+            ],
+            "type": "contour"
+           }
+          ],
+          "contourcarpet": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "type": "contourcarpet"
+           }
+          ],
+          "heatmap": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "colorscale": [
+             [
+              0,
+              "#0d0887"
+             ],
+             [
+              0.1111111111111111,
+              "#46039f"
+             ],
+             [
+              0.2222222222222222,
+              "#7201a8"
+             ],
+             [
+              0.3333333333333333,
+              "#9c179e"
+             ],
+             [
+              0.4444444444444444,
+              "#bd3786"
+             ],
+             [
+              0.5555555555555556,
+              "#d8576b"
+             ],
+             [
+              0.6666666666666666,
+              "#ed7953"
+             ],
+             [
+              0.7777777777777778,
+              "#fb9f3a"
+             ],
+             [
+              0.8888888888888888,
+              "#fdca26"
+             ],
+             [
+              1,
+              "#f0f921"
+             ]
+            ],
+            "type": "heatmap"
+           }
+          ],
+          "heatmapgl": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "colorscale": [
+             [
+              0,
+              "#0d0887"
+             ],
+             [
+              0.1111111111111111,
+              "#46039f"
+             ],
+             [
+              0.2222222222222222,
+              "#7201a8"
+             ],
+             [
+              0.3333333333333333,
+              "#9c179e"
+             ],
+             [
+              0.4444444444444444,
+              "#bd3786"
+             ],
+             [
+              0.5555555555555556,
+              "#d8576b"
+             ],
+             [
+              0.6666666666666666,
+              "#ed7953"
+             ],
+             [
+              0.7777777777777778,
+              "#fb9f3a"
+             ],
+             [
+              0.8888888888888888,
+              "#fdca26"
+             ],
+             [
+              1,
+              "#f0f921"
+             ]
+            ],
+            "type": "heatmapgl"
+           }
+          ],
+          "histogram": [
+           {
+            "marker": {
+             "pattern": {
+              "fillmode": "overlay",
+              "size": 10,
+              "solidity": 0.2
+             }
+            },
+            "type": "histogram"
+           }
+          ],
+          "histogram2d": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "colorscale": [
+             [
+              0,
+              "#0d0887"
+             ],
+             [
+              0.1111111111111111,
+              "#46039f"
+             ],
+             [
+              0.2222222222222222,
+              "#7201a8"
+             ],
+             [
+              0.3333333333333333,
+              "#9c179e"
+             ],
+             [
+              0.4444444444444444,
+              "#bd3786"
+             ],
+             [
+              0.5555555555555556,
+              "#d8576b"
+             ],
+             [
+              0.6666666666666666,
+              "#ed7953"
+             ],
+             [
+              0.7777777777777778,
+              "#fb9f3a"
+             ],
+             [
+              0.8888888888888888,
+              "#fdca26"
+             ],
+             [
+              1,
+              "#f0f921"
+             ]
+            ],
+            "type": "histogram2d"
+           }
+          ],
+          "histogram2dcontour": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "colorscale": [
+             [
+              0,
+              "#0d0887"
+             ],
+             [
+              0.1111111111111111,
+              "#46039f"
+             ],
+             [
+              0.2222222222222222,
+              "#7201a8"
+             ],
+             [
+              0.3333333333333333,
+              "#9c179e"
+             ],
+             [
+              0.4444444444444444,
+              "#bd3786"
+             ],
+             [
+              0.5555555555555556,
+              "#d8576b"
+             ],
+             [
+              0.6666666666666666,
+              "#ed7953"
+             ],
+             [
+              0.7777777777777778,
+              "#fb9f3a"
+             ],
+             [
+              0.8888888888888888,
+              "#fdca26"
+             ],
+             [
+              1,
+              "#f0f921"
+             ]
+            ],
+            "type": "histogram2dcontour"
+           }
+          ],
+          "mesh3d": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "type": "mesh3d"
+           }
+          ],
+          "parcoords": [
+           {
+            "line": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "parcoords"
+           }
+          ],
+          "pie": [
+           {
+            "automargin": true,
+            "type": "pie"
+           }
+          ],
+          "scatter": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scatter"
+           }
+          ],
+          "scatter3d": [
+           {
+            "line": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scatter3d"
+           }
+          ],
+          "scattercarpet": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scattercarpet"
+           }
+          ],
+          "scattergeo": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scattergeo"
+           }
+          ],
+          "scattergl": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scattergl"
+           }
+          ],
+          "scattermapbox": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scattermapbox"
+           }
+          ],
+          "scatterpolar": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scatterpolar"
+           }
+          ],
+          "scatterpolargl": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scatterpolargl"
+           }
+          ],
+          "scatterternary": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scatterternary"
+           }
+          ],
+          "surface": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "colorscale": [
+             [
+              0,
+              "#0d0887"
+             ],
+             [
+              0.1111111111111111,
+              "#46039f"
+             ],
+             [
+              0.2222222222222222,
+              "#7201a8"
+             ],
+             [
+              0.3333333333333333,
+              "#9c179e"
+             ],
+             [
+              0.4444444444444444,
+              "#bd3786"
+             ],
+             [
+              0.5555555555555556,
+              "#d8576b"
+             ],
+             [
+              0.6666666666666666,
+              "#ed7953"
+             ],
+             [
+              0.7777777777777778,
+              "#fb9f3a"
+             ],
+             [
+              0.8888888888888888,
+              "#fdca26"
+             ],
+             [
+              1,
+              "#f0f921"
+             ]
+            ],
+            "type": "surface"
+           }
+          ],
+          "table": [
+           {
+            "cells": {
+             "fill": {
+              "color": "#EBF0F8"
+             },
+             "line": {
+              "color": "white"
+             }
+            },
+            "header": {
+             "fill": {
+              "color": "#C8D4E3"
+             },
+             "line": {
+              "color": "white"
+             }
+            },
+            "type": "table"
+           }
+          ]
+         },
+         "layout": {
+          "annotationdefaults": {
+           "arrowcolor": "#2a3f5f",
+           "arrowhead": 0,
+           "arrowwidth": 1
+          },
+          "autotypenumbers": "strict",
+          "coloraxis": {
+           "colorbar": {
+            "outlinewidth": 0,
+            "ticks": ""
+           }
+          },
+          "colorscale": {
+           "diverging": [
+            [
+             0,
+             "#8e0152"
+            ],
+            [
+             0.1,
+             "#c51b7d"
+            ],
+            [
+             0.2,
+             "#de77ae"
+            ],
+            [
+             0.3,
+             "#f1b6da"
+            ],
+            [
+             0.4,
+             "#fde0ef"
+            ],
+            [
+             0.5,
+             "#f7f7f7"
+            ],
+            [
+             0.6,
+             "#e6f5d0"
+            ],
+            [
+             0.7,
+             "#b8e186"
+            ],
+            [
+             0.8,
+             "#7fbc41"
+            ],
+            [
+             0.9,
+             "#4d9221"
+            ],
+            [
+             1,
+             "#276419"
+            ]
+           ],
+           "sequential": [
+            [
+             0,
+             "#0d0887"
+            ],
+            [
+             0.1111111111111111,
+             "#46039f"
+            ],
+            [
+             0.2222222222222222,
+             "#7201a8"
+            ],
+            [
+             0.3333333333333333,
+             "#9c179e"
+            ],
+            [
+             0.4444444444444444,
+             "#bd3786"
+            ],
+            [
+             0.5555555555555556,
+             "#d8576b"
+            ],
+            [
+             0.6666666666666666,
+             "#ed7953"
+            ],
+            [
+             0.7777777777777778,
+             "#fb9f3a"
+            ],
+            [
+             0.8888888888888888,
+             "#fdca26"
+            ],
+            [
+             1,
+             "#f0f921"
+            ]
+           ],
+           "sequentialminus": [
+            [
+             0,
+             "#0d0887"
+            ],
+            [
+             0.1111111111111111,
+             "#46039f"
+            ],
+            [
+             0.2222222222222222,
+             "#7201a8"
+            ],
+            [
+             0.3333333333333333,
+             "#9c179e"
+            ],
+            [
+             0.4444444444444444,
+             "#bd3786"
+            ],
+            [
+             0.5555555555555556,
+             "#d8576b"
+            ],
+            [
+             0.6666666666666666,
+             "#ed7953"
+            ],
+            [
+             0.7777777777777778,
+             "#fb9f3a"
+            ],
+            [
+             0.8888888888888888,
+             "#fdca26"
+            ],
+            [
+             1,
+             "#f0f921"
+            ]
+           ]
+          },
+          "colorway": [
+           "#636efa",
+           "#EF553B",
+           "#00cc96",
+           "#ab63fa",
+           "#FFA15A",
+           "#19d3f3",
+           "#FF6692",
+           "#B6E880",
+           "#FF97FF",
+           "#FECB52"
+          ],
+          "font": {
+           "color": "#2a3f5f"
+          },
+          "geo": {
+           "bgcolor": "white",
+           "lakecolor": "white",
+           "landcolor": "#E5ECF6",
+           "showlakes": true,
+           "showland": true,
+           "subunitcolor": "white"
+          },
+          "hoverlabel": {
+           "align": "left"
+          },
+          "hovermode": "closest",
+          "mapbox": {
+           "style": "light"
+          },
+          "paper_bgcolor": "white",
+          "plot_bgcolor": "#E5ECF6",
+          "polar": {
+           "angularaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "radialaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "scene": {
+           "xaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           },
+           "yaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           },
+           "zaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           }
+          },
+          "shapedefaults": {
+           "line": {
+            "color": "#2a3f5f"
+           }
+          },
+          "ternary": {
+           "aaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "baxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "caxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "title": {
+           "x": 0.05
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          },
+          "yaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          }
+         }
+        }
+       }
+      },
+      "image/png": "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",
+      "text/html": [
+       "<div>                            <div id=\"b5827d57-2604-463f-bb2c-029ba251b466\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"b5827d57-2604-463f-bb2c-029ba251b466\")) {                    Plotly.newPlot(                        \"b5827d57-2604-463f-bb2c-029ba251b466\",                        [{\"domain\":{\"x\":[0.0,1.0],\"y\":[0.0,1.0]},\"hovertemplate\":\"function=%{label}<br>time_sum=%{value}<br>parent=%{parent}<extra></extra>\",\"labels\":[\"Py_kgeneration\",\"generate\",\"fftma2\",\"covariance\",\"gasdev\",\"fourt\",\"cov_value\",\"ran2\",\"build_real\",\"prebuild_gwn\",\"clean_real\",\"cgrid\",\"length\",\"maxfactor\"],\"marker\":{\"colors\":[\"#636efa\",\"#EF553B\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#00cc96\",\"#FFA15A\",\"#19d3f3\",\"#636efa\",\"#00cc96\",\"#636efa\",\"#636efa\",\"#636efa\",\"#636efa\"]},\"name\":\"\",\"parents\":[\"\",\"Py_kgeneration\",\"Py_kgeneration\",\"fftma2\",\"generate\",\"fftma2\",\"covariance\",\"gasdev\",\"\",\"fftma2\",\"\",\"\",\"\",\"\"],\"values\":[173.93129,137.192987,36.737828,36.638978,101.71883800005251,0.09082599999999999,15.471843000005594,31.86060899999753,0.00317,0.001027,0.000707,0.001641,0.001153,0.000372],\"type\":\"treemap\"}],                        {\"template\":{\"data\":{\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"white\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2}}},\"legend\":{\"tracegroupgap\":0},\"margin\":{\"t\":60}},                        {\"responsive\": true}                    ).then(function(){\n",
+       "                            \n",
+       "var gd = document.getElementById('b5827d57-2604-463f-bb2c-029ba251b466');\n",
+       "var x = new MutationObserver(function (mutations, observer) {{\n",
+       "        var display = window.getComputedStyle(gd).display;\n",
+       "        if (!display || display === 'none') {{\n",
+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
+       "    x.observe(outputEl, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "                        })                };                });            </script>        </div>"
       ]
      },
      "metadata": {},
@@ -591,7 +1553,7 @@
     }
    ],
    "source": [
-    "plot_analysis(df)"
+    "plot_treemap(df)"
    ]
   },
   {
@@ -603,7 +1565,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 192,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [
     {
@@ -631,23 +1593,23 @@
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
+       "      <th colspan=\"5\" halign=\"left\">time</th>\n",
        "      <th colspan=\"3\" halign=\"left\">memory</th>\n",
        "      <th colspan=\"3\" halign=\"left\">cpu</th>\n",
-       "      <th colspan=\"5\" halign=\"left\">time</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th>min</th>\n",
        "      <th>max</th>\n",
-       "      <th>median</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>sum</th>\n",
+       "      <th>count</th>\n",
        "      <th>min</th>\n",
        "      <th>max</th>\n",
-       "      <th>mean</th>\n",
+       "      <th>median</th>\n",
        "      <th>min</th>\n",
        "      <th>max</th>\n",
        "      <th>mean</th>\n",
-       "      <th>sum</th>\n",
-       "      <th>count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>function</th>\n",
@@ -667,243 +1629,243 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Py_kgeneration</th>\n",
-       "      <td>-21.2</td>\n",
-       "      <td>-21.2</td>\n",
-       "      <td>-21.2</td>\n",
-       "      <td>37.944037</td>\n",
-       "      <td>37.944037</td>\n",
-       "      <td>37.944037</td>\n",
-       "      <td>4.518677</td>\n",
-       "      <td>4.518677</td>\n",
-       "      <td>4.518677</td>\n",
-       "      <td>4.518677</td>\n",
+       "      <td>2.673944</td>\n",
+       "      <td>2.673944</td>\n",
+       "      <td>2.673944</td>\n",
+       "      <td>2.673944</td>\n",
        "      <td>1</td>\n",
+       "      <td>-3.0</td>\n",
+       "      <td>-3.0</td>\n",
+       "      <td>-3.0</td>\n",
+       "      <td>7.477049</td>\n",
+       "      <td>7.477049</td>\n",
+       "      <td>7.477049</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>generate</th>\n",
-       "      <td>-20.0</td>\n",
-       "      <td>-20.0</td>\n",
-       "      <td>-20.0</td>\n",
-       "      <td>41.633546</td>\n",
-       "      <td>41.633546</td>\n",
-       "      <td>41.633546</td>\n",
-       "      <td>3.283041</td>\n",
-       "      <td>3.283041</td>\n",
-       "      <td>3.283041</td>\n",
-       "      <td>3.283041</td>\n",
+       "      <td>1.986983</td>\n",
+       "      <td>1.986983</td>\n",
+       "      <td>1.986983</td>\n",
+       "      <td>1.986983</td>\n",
        "      <td>1</td>\n",
+       "      <td>-4.1</td>\n",
+       "      <td>-4.1</td>\n",
+       "      <td>-4.1</td>\n",
+       "      <td>10.269492</td>\n",
+       "      <td>10.269492</td>\n",
+       "      <td>10.269492</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>gasdev</th>\n",
-       "      <td>-23.9</td>\n",
-       "      <td>3.0</td>\n",
+       "      <td>0.000080</td>\n",
+       "      <td>0.023351</td>\n",
+       "      <td>0.000361</td>\n",
+       "      <td>1.479755</td>\n",
+       "      <td>4096</td>\n",
+       "      <td>-3.1</td>\n",
+       "      <td>0.5</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>100.100000</td>\n",
-       "      <td>2.239697</td>\n",
-       "      <td>0.000099</td>\n",
-       "      <td>0.121965</td>\n",
-       "      <td>0.000595</td>\n",
-       "      <td>2.437584</td>\n",
-       "      <td>4096</td>\n",
+       "      <td>0.271851</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>fftma2</th>\n",
-       "      <td>-1.2</td>\n",
-       "      <td>-1.2</td>\n",
-       "      <td>-1.2</td>\n",
-       "      <td>28.555285</td>\n",
-       "      <td>28.555285</td>\n",
-       "      <td>28.555285</td>\n",
-       "      <td>1.231253</td>\n",
-       "      <td>1.231253</td>\n",
-       "      <td>1.231253</td>\n",
-       "      <td>1.231253</td>\n",
+       "      <td>0.684455</td>\n",
+       "      <td>0.684455</td>\n",
+       "      <td>0.684455</td>\n",
+       "      <td>0.684455</td>\n",
        "      <td>1</td>\n",
+       "      <td>1.1</td>\n",
+       "      <td>1.1</td>\n",
+       "      <td>1.1</td>\n",
+       "      <td>0.100000</td>\n",
+       "      <td>0.100000</td>\n",
+       "      <td>0.100000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>covariance</th>\n",
-       "      <td>-1.2</td>\n",
-       "      <td>-1.2</td>\n",
-       "      <td>-1.2</td>\n",
-       "      <td>28.555285</td>\n",
-       "      <td>28.555285</td>\n",
-       "      <td>28.555285</td>\n",
-       "      <td>1.223604</td>\n",
-       "      <td>1.223604</td>\n",
-       "      <td>1.223604</td>\n",
-       "      <td>1.223604</td>\n",
+       "      <td>0.680862</td>\n",
+       "      <td>0.680862</td>\n",
+       "      <td>0.680862</td>\n",
+       "      <td>0.680862</td>\n",
        "      <td>1</td>\n",
+       "      <td>1.1</td>\n",
+       "      <td>1.1</td>\n",
+       "      <td>1.1</td>\n",
+       "      <td>0.100000</td>\n",
+       "      <td>0.100000</td>\n",
+       "      <td>0.100000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ran2</th>\n",
-       "      <td>-9.1</td>\n",
-       "      <td>1.3</td>\n",
+       "      <td>0.000077</td>\n",
+       "      <td>0.001325</td>\n",
+       "      <td>0.000088</td>\n",
+       "      <td>0.464902</td>\n",
+       "      <td>5268</td>\n",
+       "      <td>-1.6</td>\n",
+       "      <td>0.5</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>100.100000</td>\n",
-       "      <td>0.456891</td>\n",
-       "      <td>0.000095</td>\n",
-       "      <td>0.001958</td>\n",
-       "      <td>0.000135</td>\n",
-       "      <td>0.710129</td>\n",
-       "      <td>5268</td>\n",
+       "      <td>0.100000</td>\n",
+       "      <td>0.000816</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>cov_value</th>\n",
-       "      <td>-3.8</td>\n",
-       "      <td>0.9</td>\n",
+       "      <td>0.000080</td>\n",
+       "      <td>0.000145</td>\n",
+       "      <td>0.000082</td>\n",
+       "      <td>0.293202</td>\n",
+       "      <td>3564</td>\n",
+       "      <td>-1.3</td>\n",
+       "      <td>0.2</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>100.100000</td>\n",
-       "      <td>0.365993</td>\n",
-       "      <td>0.000101</td>\n",
-       "      <td>0.001366</td>\n",
-       "      <td>0.000137</td>\n",
-       "      <td>0.488994</td>\n",
-       "      <td>3564</td>\n",
+       "      <td>0.100000</td>\n",
+       "      <td>0.000898</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>fourt</th>\n",
+       "      <th>cgrid</th>\n",
+       "      <td>0.001203</td>\n",
+       "      <td>0.001203</td>\n",
+       "      <td>0.001203</td>\n",
+       "      <td>0.001203</td>\n",
+       "      <td>1</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.000824</td>\n",
-       "      <td>0.001123</td>\n",
-       "      <td>0.000944</td>\n",
-       "      <td>0.002831</td>\n",
-       "      <td>3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>cgrid</th>\n",
+       "      <th>fourt</th>\n",
+       "      <td>0.000328</td>\n",
+       "      <td>0.000441</td>\n",
+       "      <td>0.000371</td>\n",
+       "      <td>0.001112</td>\n",
+       "      <td>3</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.001635</td>\n",
-       "      <td>0.001635</td>\n",
-       "      <td>0.001635</td>\n",
-       "      <td>0.001635</td>\n",
-       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>length</th>\n",
+       "      <td>0.000266</td>\n",
+       "      <td>0.000268</td>\n",
+       "      <td>0.000267</td>\n",
+       "      <td>0.000802</td>\n",
+       "      <td>3</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.000352</td>\n",
-       "      <td>0.000360</td>\n",
-       "      <td>0.000356</td>\n",
-       "      <td>0.001069</td>\n",
-       "      <td>3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>maxfactor</th>\n",
+       "      <td>0.000080</td>\n",
+       "      <td>0.000080</td>\n",
+       "      <td>0.000080</td>\n",
+       "      <td>0.000240</td>\n",
+       "      <td>3</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.000103</td>\n",
-       "      <td>0.000112</td>\n",
-       "      <td>0.000106</td>\n",
-       "      <td>0.000318</td>\n",
-       "      <td>3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>build_real</th>\n",
+       "      <td>0.000135</td>\n",
+       "      <td>0.000135</td>\n",
+       "      <td>0.000135</td>\n",
+       "      <td>0.000135</td>\n",
+       "      <td>1</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.000274</td>\n",
-       "      <td>0.000274</td>\n",
-       "      <td>0.000274</td>\n",
-       "      <td>0.000274</td>\n",
-       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prebuild_gwn</th>\n",
+       "      <td>0.000100</td>\n",
+       "      <td>0.000100</td>\n",
+       "      <td>0.000100</td>\n",
+       "      <td>0.000100</td>\n",
+       "      <td>1</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.000267</td>\n",
-       "      <td>0.000267</td>\n",
-       "      <td>0.000267</td>\n",
-       "      <td>0.000267</td>\n",
-       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>clean_real</th>\n",
+       "      <td>0.000083</td>\n",
+       "      <td>0.000083</td>\n",
+       "      <td>0.000083</td>\n",
+       "      <td>0.000083</td>\n",
+       "      <td>1</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.000217</td>\n",
-       "      <td>0.000217</td>\n",
-       "      <td>0.000217</td>\n",
-       "      <td>0.000217</td>\n",
-       "      <td>1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "               memory                     cpu                         \\\n",
-       "                  min   max median        min         max       mean   \n",
-       "function                                                               \n",
-       "Py_kgeneration  -21.2 -21.2  -21.2  37.944037   37.944037  37.944037   \n",
-       "generate        -20.0 -20.0  -20.0  41.633546   41.633546  41.633546   \n",
-       "gasdev          -23.9   3.0    0.0   0.000000  100.100000   2.239697   \n",
-       "fftma2           -1.2  -1.2   -1.2  28.555285   28.555285  28.555285   \n",
-       "covariance       -1.2  -1.2   -1.2  28.555285   28.555285  28.555285   \n",
-       "ran2             -9.1   1.3    0.0   0.000000  100.100000   0.456891   \n",
-       "cov_value        -3.8   0.9    0.0   0.000000  100.100000   0.365993   \n",
-       "fourt             0.0   0.0    0.0   0.000000    0.000000   0.000000   \n",
-       "cgrid             0.0   0.0    0.0   0.000000    0.000000   0.000000   \n",
-       "length            0.0   0.0    0.0   0.000000    0.000000   0.000000   \n",
-       "maxfactor         0.0   0.0    0.0   0.000000    0.000000   0.000000   \n",
-       "build_real        0.0   0.0    0.0   0.000000    0.000000   0.000000   \n",
-       "prebuild_gwn      0.0   0.0    0.0   0.000000    0.000000   0.000000   \n",
-       "clean_real        0.0   0.0    0.0   0.000000    0.000000   0.000000   \n",
+       "                    time                                     memory       \\\n",
+       "                     min       max      mean       sum count    min  max   \n",
+       "function                                                                   \n",
+       "Py_kgeneration  2.673944  2.673944  2.673944  2.673944     1   -3.0 -3.0   \n",
+       "generate        1.986983  1.986983  1.986983  1.986983     1   -4.1 -4.1   \n",
+       "gasdev          0.000080  0.023351  0.000361  1.479755  4096   -3.1  0.5   \n",
+       "fftma2          0.684455  0.684455  0.684455  0.684455     1    1.1  1.1   \n",
+       "covariance      0.680862  0.680862  0.680862  0.680862     1    1.1  1.1   \n",
+       "ran2            0.000077  0.001325  0.000088  0.464902  5268   -1.6  0.5   \n",
+       "cov_value       0.000080  0.000145  0.000082  0.293202  3564   -1.3  0.2   \n",
+       "cgrid           0.001203  0.001203  0.001203  0.001203     1    0.0  0.0   \n",
+       "fourt           0.000328  0.000441  0.000371  0.001112     3    0.0  0.0   \n",
+       "length          0.000266  0.000268  0.000267  0.000802     3    0.0  0.0   \n",
+       "maxfactor       0.000080  0.000080  0.000080  0.000240     3    0.0  0.0   \n",
+       "build_real      0.000135  0.000135  0.000135  0.000135     1    0.0  0.0   \n",
+       "prebuild_gwn    0.000100  0.000100  0.000100  0.000100     1    0.0  0.0   \n",
+       "clean_real      0.000083  0.000083  0.000083  0.000083     1    0.0  0.0   \n",
        "\n",
-       "                    time                                      \n",
-       "                     min       max      mean       sum count  \n",
-       "function                                                      \n",
-       "Py_kgeneration  4.518677  4.518677  4.518677  4.518677     1  \n",
-       "generate        3.283041  3.283041  3.283041  3.283041     1  \n",
-       "gasdev          0.000099  0.121965  0.000595  2.437584  4096  \n",
-       "fftma2          1.231253  1.231253  1.231253  1.231253     1  \n",
-       "covariance      1.223604  1.223604  1.223604  1.223604     1  \n",
-       "ran2            0.000095  0.001958  0.000135  0.710129  5268  \n",
-       "cov_value       0.000101  0.001366  0.000137  0.488994  3564  \n",
-       "fourt           0.000824  0.001123  0.000944  0.002831     3  \n",
-       "cgrid           0.001635  0.001635  0.001635  0.001635     1  \n",
-       "length          0.000352  0.000360  0.000356  0.001069     3  \n",
-       "maxfactor       0.000103  0.000112  0.000106  0.000318     3  \n",
-       "build_real      0.000274  0.000274  0.000274  0.000274     1  \n",
-       "prebuild_gwn    0.000267  0.000267  0.000267  0.000267     1  \n",
-       "clean_real      0.000217  0.000217  0.000217  0.000217     1  "
+       "                             cpu                         \n",
+       "               median        min         max       mean  \n",
+       "function                                                 \n",
+       "Py_kgeneration   -3.0   7.477049    7.477049   7.477049  \n",
+       "generate         -4.1  10.269492   10.269492  10.269492  \n",
+       "gasdev            0.0   0.000000  100.100000   0.271851  \n",
+       "fftma2            1.1   0.100000    0.100000   0.100000  \n",
+       "covariance        1.1   0.100000    0.100000   0.100000  \n",
+       "ran2              0.0   0.000000    0.100000   0.000816  \n",
+       "cov_value         0.0   0.000000    0.100000   0.000898  \n",
+       "cgrid             0.0   0.000000    0.000000   0.000000  \n",
+       "fourt             0.0   0.000000    0.000000   0.000000  \n",
+       "length            0.0   0.000000    0.000000   0.000000  \n",
+       "maxfactor         0.0   0.000000    0.000000   0.000000  \n",
+       "build_real        0.0   0.000000    0.000000   0.000000  \n",
+       "prebuild_gwn      0.0   0.000000    0.000000   0.000000  \n",
+       "clean_real        0.0   0.000000    0.000000   0.000000  "
       ]
      },
-     "execution_count": 192,
+     "execution_count": 59,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -915,51 +1877,972 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfQAAAIUCAYAAADset6VAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAA9hAAAPYQGoP6dpAACg30lEQVR4nOzdd3gUxRvA8e9cGkmAo/cSWggBBKRJFUFAiSiCgoAgiA3EBgqx4dkj+lOsICpiFywoEgEFFCkiHUGqwNF7SG9X9vfHXuIRkpCEJHt3eT/Pc0+4vS3vhcy+O7OzM0rTNIQQQgjh3UxGByCEEEKIyycJXQghhPABktCFEEIIHyAJXQghhPABktCFEEIIHyAJXQghhPABktCFEEIIHyAJXQghhPABktCFEEIIHyAJXZQJSqm5Simr0XEIUEr1UkppSqleRscihC/xNzoAIYpKKVXQcYuvKdFAhBDCAygZy114K6XU7TkWjQb6AqNyLP8ViANMmqZllEZsIm9KKRMQCGRqmuY0Oh4hfIUkdOEzlFLvAPdrmqaMjkVcTClVDkniQpQYuYcuyoSc99CVUmGu+7iPKqXuV0odUEqlKqV+UUrVV7qnlVJHlVJpSqkflVJVctnv9UqpVUqpFKVUklIqVinVsoAxVVJKvaGUsiqlMlzH+lQpVc1tnRpKqY+UUqeUUulKqW1KqTty7Oeyv4srhkVKqX5Kqa2uY+1USg3OsV4VpdRrSqntSqlkpVSiUmqxUqpNjvWy7pPfppR6QSl1DEgFKuZ2D10p1Uwp9Z1S6qTr2EeVUl8rpcxu6/i7vsd+1+/LqpR6SSkVlMd36a6UWu/a3wGl1Ogc6wUopZ5RSu1zrXNOKbVaKdW3IP9/QngauYcuyrqR6M2/bwNVgCnAfGAF0At4BWgKPAC8BtyZtaFSahTwCbAUmAqEAOOB1UqpdpqmWfM6qFKqPLAKaAHMATYD1YAbgXrAWaVUMPC76/jvAAeBW4G5SqlKmqa9WVzfxaUZMA+Y5fpeY4FvlFLXaZr2q2udxsAg4BtXPDWBe4GVSqlITdOO59jn00Cm63hBrn/n/F0Eov8Og1yxnwTqAjcAlYAE16ofAncA3wL/AzoDj7t+hzfn2G1T13ofub7Lna7f2yZN0/5xrWNxbf8hsB6oCHQArkS/TSOEd9E0TV7y8okXetLT8vhsLmB1ex8GaMBpwOy2/CXX8q2Av9vyL4EMIMj1vjxwHpid4zg1gficy3OJ51nXcW7O5bOsW2EPudYZ6fZZALAWSAIqFMd3cS2zutYd7LasInAc2Oy2LAi9L4J7vGFAOvC027Jerv3tB4JzrJ/1WS/X+7au97fk8/tq41rngxzLX3UtvyaX79LDbVl1V4yvuS3bCiwy+u9WXvIqrpc0uYuy7htN0xLc3v/l+vm5pmn2HMsD0WuOoHe+qwR8pZSqlvUCHK51L9WzfgiwTdO0BTk/0DQtq2PLAPTa6ldun9mAt9AvKK4upu+S5TiQHY+maYnAp0A7pVQt17IMzXUPXCnlp5SqCiQDe9Brtjl9omlaWi7L3WXF3F8pFZLHOgNcP1/Psfx/rp9ROZbv1DRtldt3OeOKsbHbOvFAS6VUs0vEJ4RXkIQuyrrDOd5nJZcjeSyv7PqZlQRWAGdyvPoBNS5x3CbAjkus0xDYp13ciWyX2+fuivpdsvzrdjGRZa/rZxjoPdSVUo8opfah1/LPon/nKwAzFzuYy7ILaJp2ED1R34V+q2Gpqy+A+/4aAk7g3xzbnkRPzJf6XYDeouL+naehX5TtdfUJeFUpdcWl4hXCU0lCF2Wdo5DLs3rQZ5WdUei19Zyvm4orwEIo6ncpjCfQk+8fwO1Af/Tv+w+5n08uVTsHQNO0yegXBS8BweitEP8operlXLWAcV7yO2ua9gf6hdWd6BdXdwGblVJ3FfAYQngU6RQnRNHsd/08rWnasiJu3+oS6xwCrlBKmXLU0iPcPi9OTZVSKkctPdz10+r6eQvwm6Zp49w3VEpVQq+tF5mmaduB7cALSqmuwBrgPuAp9O9qQm8ZyWqhQClVE72WXaTfhaZpccDHwMeujop/oHeW+7Co30MIo0gNXYiiWQokAk8opQJyfqiUqn6J7b8D2iilcvbORimVVYv8GagFDHP7zB+9l3oysLJooeepDm69xZVSFdEH69nqatoGveZ7Qc1eKXUrF9+PLzClVEXX93K3Hb2JPeuRtJ9dPx/Osd4k18/YIhy3qvt7TdOS0Zv0g3LfQgjPJjV0IYpA07REpdR44DP0Ztqv0e8lN0DvoLUGmJjPLl5Fr+1+o5SaA2xCf9TsRvRa6TZgNvojYXOVUu3Ra8m3AN2AhzVNSyrmr7UX+Egp1RE4hd4UXRP98bUsi4BpSqmP0Xvbt0Z/XO7AZRy3N/COUuobVwz+6LcyHOgXPmiatk0p9Qlwj6s1YCXQCf0xth80TfutCMfdqZT6Hf13H4f+yNot6E9LCOF1JKELUUSapn2plDoORAOPodfsjqE/X/7xJbZNVkr1QH987Wb0xHQaWA4cda2T5hp8Jcb1eUX0ntpjNU2bW/zfiH3otf9XgeboHdqGaZq21G2dl4BQYAR6y8Fm9AuYmMs47jb0Fo+B6DX9VNey6zVNW+e23l3oFw5j0H9nJ4GX0X+HRfEW+gVUP/T/u0PozfuvFnF/QhhKhn4VQqD0UfR2aJp2g9GxCCGKRu6hCyGEED5AEroQQgjhAyShCyGEED5A7qELIYQQPkBq6EIIIYQPkIQuhBBC+ABJ6EIIIYQPkIQuhBBC+ABJ6EIIIYQPkIQuhBBC+ABJ6EIIIYQPkIQuhBBC+ABJ6EIIIYQPkIQuhBBC+ABJ6EIIIYQPkIQuhBBC+ABJ6EIIIYQPkIQuhBBC+ABJ6EIIIYQPkIQuhBBC+ABJ6EIIIYQPkIQuhBBC+ABJ6EIIIYQPkIRuMKVUmFJKU0o9anQsnk4p1cv1u+pldCxCCOFpJKHnQik1xpU4sl7pSqm9Sql3lFI1jY7P1ymlJiilxhgdhxCi6JRSTyilBhkdR1nib3QAHm4acBAoB3QHxgMDlFKtNE1LNTQy3zYBOAvMzbH8DyAYyCztgIQQhfYE8C3wg8FxlBmS0PO3WNO0ja5/f6iUOgdMAm4CvjIuLO+hlFJAOU3T0i53X5qmOYH0y49KCFEYSikTEKhpmpQ/DyZN7oWzwvWzkasp/pGcKyiluro+G17UgyjdbKVUplJqsNvyK5RSK5VSaUqpo0qpp5RSY13HC8uxj+uVUquUUilKqSSlVKxSqmWOdeYqpZKVUnWVUj+4/n1GKfWaUsovx7ompdTDSql/XLcgTiml3ldKVc6xnlUptUgp1V8ptRFIA+51fTZWKbVCKXVaKZWhlNqplBqfc3ugJXC12y2P312f5XoPXSl1q1Jqk+v3clYp9blSqm5Rv6sQJc31t7zRVZb2K6XuVUpZlFJajvVud/vbjlNKfa2Uqp9jnd+VUjuUUpFKqd+UUqlKqWNKqSm5HDdIKfWsUupfVxk8opSarpQKyrGe5rrFOFIp9Q+QAVzn+uxRpdRapdQ5V1yblFK35NweCAXucCvHc90+r6uUmuM6j2S4zit3XuavtcyTGnrhNHH9PAesAUYCb+RYZySQBPxYlAO4ksscYBhws6Zpsa7ldYHfAA14GUgB7kIvaDn3MQr4BFgKTAVC0G8XrFZKtdM0zeq2up9rvb+AR4FrgcnAfmCm23rvA2OAj4G3gEbARKCdUqqbpmk2t3Wbo7dgvA98AOxxLR8P/AMsBOzAQOA9pZRJ07R3Xes8DLwNJAMvupadyuf3lRXTBuBxoCbwENDN9V3ji/BdhSgxSql2wBLgBPAM+t/lNOBMjvWeBJ4H5gMfAtWBB4A/cvnbruza5/eu9W8BXlFKbdc0bbFrfyb0stcdmA3sAloDjwDhwKAcofYGhgLvoN8Cs7qWP+TazxdAIHAb8I1S6oas8xUwyhXzetexQC9nKL0f0jr0c9k7ru99PfCRUqqipmkzLvU7FHnQNE1eOV7oiUsD+gDVgHroCfYskArUBe5xrRPhtl0A+h/n3EIcK8y1n0fRL7C+dh2jX4713gKcQFu3ZVXQLy40IMy1rDxwHpidY/uaQLz7cvR71BrwdI51NwMb3d53d603Isd6/XMuRy/0GtA/l+8anMuyJcD+HMt2AL/nsm4v1757uf2+TwHb0Zv1s9aLcq33bGG/q7zkVdIv9GSYAtRxW9YUsOmnZA2gIfpF7xM5tm3lWu8Jt2W/u/62R7ktC0S/YPjWbdntgAPonmOf97q27+q2THOtG5lL/ME53ge4yuDyHMuTczsXoif640DVHMu/cp2jLjpPyKtgL2lyz98y9AR9BD3RJqPXmo+hXwWno9fIs/RHvwD4vAjHCgS+AW4ABmia9kuOz68D/tQ0bWvWAk3T4tCvkt31BSoBXymlqmW90AvnX8A1uRx7Vo73q4DGbu9vBRKAX3PscxP67yTnPg9qmrY050E0t/voSimzax8rgcZKKXMucV1KB6AG8J7mdm9P02sJu9ETe06X+q5ClBhXC9y1wA+aph3PWq5p2r/AYrdVB6PfEp2fo8ydBPZxcZlLxu28o2laJnrtOGc53gXszrHPrFuJOfe5UtO0nTm/Q45yXBkwo5ejKwvw/RUwBPjJ9dY9jqWufV1yPyJ30uSev/uBvehXyqeAPZreMQtN0+KVUj8BI4CnXeuPBI7xXwEpjMfRa9fXa5r2ey6fNwT+zGX5vzneN3P9zCuGxBzv0zVNO5Nj2Xn0Jjz3fZqB03nss0aO9wdzW0kp1Q14FuiCfhvAnRn9oqEwGrp+7snls93oLQvuCvJdhShJNdCf1MhZbsmxrBmg0JN3bmw53h/VXNVcN+eBK3LsswU5mvZzxOYur3J8A/AU0BZwv/ee8/i5qY5e4bjH9SpIHKKAJKHnb732Xy/33HwK3KqU6ore5HQjem3RWYRjLUWvhU9RSv2uFb03aVaryyj0q/mc7DneOwq4z9Nc2BrhLucJ4qIe7UqpJsBy9EQ7Cb3VIxMYgH4PrzRaiwryXYXwBCb0BHk9uf/dJud4n9fftsqxz+3o5S83R3K8z60c90C/ZfAH+uOlJ9AvLsaiV24uJaucf47ezyc3fxdgPyIXktAvzxL0ZDYSvTk7BPisiPtah94cvAi9g8nNmqa5J99D6PfZcsq5bL/r52lN05YVMZac9qM3E67Riv742UD0q/kbNU07nLVQKZXbLYCCXOmD/jsBvRNezhaJ5m6fC+EpTqPfqrtUWd6PnowPapq2t5iOvR9og36vu6BlLKch6PH31zQtu0OuUmpsLuvmdowz6J2G/Yrx/CRc5B76ZXAl3K/Qe4KOAbZrmlbkq0vXH/ht6DX1z1y9UrMsBboopdpmLVBKVeHiWvNS9Gb1J5RSATmPoZSqXoTQ5qP3xH065wdKKX+lVKUC7COrBpFdY3DdN8/tRJCC3ix3KRvRT5D3uT92o5S6Hr1pMTavDYUwgqZpDvS+OYOUUnWyliulmqLXxrN8j15mnnHdd8ZtXaWUqlqEw89H79B7d84PlFLBSqnQAuzDgZ6osx/1VPojs4NyWfeicuz6/t8BQ5RSrXKJoyjnJ+EiNfTL9ynwIHqHkqmXuzNN035wXe1+ip6Y73V9NB29l+qvSqm3+e+xtcPovd011/aJSn+2+zNgs1Lqa/Sr4gboncTWoD9uVpiYViql3gced11Q/ILezNYMvaPNQ+gjQuXnF/Qm9p9c+yqPfmI5DdTOse4mYLxS6in0+4qnNU27qE+Apmk2pdRU9MfWViqlvuK/x9asXPxIoRCewAL0A9YopWaiJ8eJ6E93tAXQNG2/6+//ZSBMKfUDes22EXAz+qNgrxXyuJ+hVz5muVrG1riOHeFa3h/9Ijk/sehN9kuUUl+i3+++H72cXpFj3U3AtUqpSei92g9qmvYXEI1+vvxLKfUBsBP9HHYlektglUJ+L5HF6G72nvjiv8fWOhRw/R3oV651i3CsMNexHs2xfLxr+atuy9qi37tKR7/fFY3+XKoG1MyxfS/0WwLx6PfC/kVPfO3d1pkLJOcSkwXX4zM5lt+NXuBT0S82/gZeAWq7rWMFFuXxXQcC21zxHASmoNfQsx+7c61XE/3WQ6Lrs9/dvlP2Y2tu6w9Ff/wsHf0xvs9z/l8U9rvKS14l+UJ/xnsz+jgS/wLj0BN0Wo71BqP3IE92vXahP7sd7rbO78COXI4xF7DmWBbgKnc7XOUlzlWmpwEV3dbTgHfyiP1O9M7C6a54xuRWjtBve610nS803B5hQ78QeAe9QpKJfi9+GXC30f833vxSrl+uuAxKqS1AnKZpfQw49gz0Wnx5TW/OEkJ4IVctvKWmac0uta4QuZF76JdJKdUBveb8aSkcKzjH+6rovdlXSzIXwnvkUpaboT/x8bshAQmfIDX0InJ16GiPPnRoNaCx5vaomWsAiUt18EjWNC3n4yf5HXMreoHfhd4sPQ6oA/TRNO2PwsQvhDCOUuoEepP4AfTxFMajPwXSTtO0vJ49FyJf0imu6G5Bv++0BxiuXfzceH3yGJjBzbPo954K6mfXcbOGnd0MjJNkLoTXWQIMB2qh30f/E304V0nmosikhl5ClFJZc6jn54CmaQdKIx4hhBC+TRK6EEII4QOkU5wQQgjhAyShCyGEED5AEroQQgjhAyShCyGEED5AEroQQgjhAyShCyGEED6gUAPLbNq0qTz6yGRyISCKygkcb9++fYFHyBPGknIv8iBl2cMU+Dn0TZs2XWUymWaZTCYzbnNaC1FImtPpTHA6nfe1b99+ndHBiPxJuRf5kLLsYQqU0Ddt2lTeZDKtrlSpUs0aNWrEKaVkNBov4HRqZDqcgZkOLcjh1PydTs3PoWl+Dqfm59R/+msaJg2UpqFMJs1pDkzIqJyZFgzKiVIaKA2lnJj87Zj8bZj8bZopwI6fvw2/ABvK34Eq+Hle0zR1+vTpKvHx8aecTmd3ubr3XFLuvY+madgcWkCmwxloc2iBTqfTz6Hh53Bq/k5NM+k/8XNqemuLhqaq+6elm+3ny/Hf/6+GMjkx+Tmyyjwmf7uWfQ7Qy7+GkrLsYQra5F7HZDKZa9SoERcaGppWohGJQrM7naaUDEdous0RnGl3lsuwO4NsDmc5m8MZePHaigsqWuq/n5pJc/j7Ywu22y/ezul6XUzD5Eru/uVSCQhJITA0hYCQ9LwSfY0aNeISExPNTqezDvq8ysIzSbn3UJqmkWZzlEvNdIRm2JzlMuyOcpkOZzmbQwvSNC33Au6+yO1tgL+NYJw5coEDsOVX7kGZHDUD/TNS7EkhdTe/ei8//bEI2IslQS78DFLQhG4ClFyhG0/TNNJtznIpmfbQ1AxH+TSbPTTD7gy+9JYFVthmVYXTHojTHogtLZS08/oMc8rk1BN8sJ7gA0NT8C+XCeD6O1LIPVlPJ+XeQ9gdTr+UDHtoSqajfGqmIzTd5gh1apqfoUFpTj+TIyPEz55GcNLB14DXgEQs5s3AxuyXJWG/oXGWIUWebS0sOrZ9cQbizhoTtamk9u2NbA6nf0KazZyUbquUmuGo4CjBgqz0Wdwun+Y0YUstjy21PKnnXDv3sxMYmgTBycV1GFG6SqrcS5m/kKZpJGfYQxPT7JWSM+zmDLujOC/aS1JFoJfrpbOYjwELgR+B37AkZBoRWFkgNaQSsmjRogrffvttxaJun5ppDz6RkFZr76mkiF0nEtscj08LS0q3V/rjt2V+Q/v3IOt1bfsWDLv+6ou2f+9/L9OmfmV2/7M91/1v27Q+ex839+nCc9EPk5mRAaCsR47T65a7MUf0pG3f2/KN894pL9C6z1B633oPCYlJgH4yuv72iey3HrlwZc3hT0ZiZVPKyfqBqafrRawaPxOLeRwW86XmjReixG3YsKFc3bp1W5fW8UaNGtUgIiIiMusVFBR05TPPv9jw0LmUsJ0nEtscPJsScexsfK0BPTsGD+3f45L7O7BvD52b1WG65fHsZS89+Sju54uOTWvxxZz3L9rWZrMx6M5JtLl2GIPvmozdbgcgPT2DnoPHcT4+8XK+al30+d6XAGewmL/GYh6OxWy+nJ2Ki8l86EVks9kICAjI8/MVK1ZUiI+P97vlllsKXBLSbY7AuJTMaglptqq53/+Gbr360K1Xn+z3E8cMo2OXCwv79i2b+GfbZurUq5/nscIjW/HFohUEBATgdDqZdM9ovv7kI/XYg8OoWD6UF6ZMICExmSdfeTfPfezY/S/7Dh5m+/L5PPfGbD77LpaJY2/jwy8XcE3XDjQJy/v4oCk/e2ov9Ct5BxbzamABsABLwuF8NhTCJ3z22WeHNU0jPs1m3r3/UPVrOl4RfHXULdUS0mzZ68x42ULbDp35Z9vmfPdls9l4burD9L4u6oLlT7z4Wva/z54+xYCubel/w6CLtl/6+59UqVSRH+a8zp2TLCz5bS039O3J8zM+YOKYYVSuVOS6SU4VgWGulw2L+Xf0mvv3WBJOFNdByiqvrqF//vnnlRo3btyyefPmkePHj69buXLlNnv27AkE2L59e1CvXr2atmrVqkV4eHjkSy+9lF0LVEq1j46OrtW6desWdevWbf3mm29WzfrsUts98sgjdVq1atVi4sSJ9davXx/cvn375pGRkS2aNGnScsqUKbUB1q5dG/zpp59WX7BgQZWIiIjIRx99tDbAd999V7F9+/bNW7Zs2aJ169YtfvrppwoOp2Y6m5xR9d/TSc33nkpqfTY5o3ZeyTyn0ydPsH71H9wwZFj2srS0VF5+egpPx7yR77bBwSHZFyS2zEwy0tOzP6tS2Uz3Tu0IDcm/lS/A35+MTBtOp5OU1DQCAwI4ceoMX/2whEn33F6Qr5DFD7gamAEcwmJejcU8Eou5QL8HUXbkV+bvueeeeq1atWoRERER2aFDh+bbtm0LAkhOTlZRUVGNmzRp0rJ58+aR3bp1a5a1v0mTJtVp2LBhq5YtW7b47LPPqrgfK7fyCtC1a9dmH3/8ceWs9RYtWlShRYsWkYX5HmmZjnJHz6fW23kisc2RuNSm8776wtz16t5Uq1Eze511q37n9MkTDLj51kvu7/0Z0+l7wyAaNGqS5zoLv/2KnMfIEhDgT2qafg5ITUsnMDCAv3fuZfd+K0Nv7FeYr1YYAUBf4B3gMBbzAizm/ljM8nhkEXltDf3YsWP+EydODPvtt992t2vXLv3NN9+sGh8f7w9gt9sZPnx4488+++xgu3bt0pOSkkzt27eP6NatW8rVV1+dChAUFKRt375915YtW8p17969xYQJE84ppS65nZ+fn7Zjx45dAOfPnzetXr16b3BwsJacnKw6duzYon///ol9+vRJGT169Jn4+Hi/OXPmHAHYuXNn4PPPP19nxYoVe6tUqeLcuPVv8/X9+jZdum675h8YWKR74gu/+ZLuvftStdp/LdZvvPgMQ0fdSa069S79OzxymIfHjeDIISs9evfjtjF3aYqkAh+/edMwrunagSv7j6BZowY8M+ke7pz0LK8+/TD+/pf1p9XN9XoDi3kOMAtLgvVydii8X35lHsBisZysU6fOUYDZs2dXnjhxYoNVq1bt+/77780JCQl++/fv/wfg1KlTfgBff/21eeHChZW3bNmys1KlSs6bb765Uda+cpbXHTt2BF1zzTXNDx8+vH306NHnPv3006pjx449DzBnzpyqt99++9lLxe/UNBWXnFklLjWzerrNEer+2Q/zvmDSU89lv09MSOCNl57hvc++5cC+Pfnu9+8tG/l78wbe/3IBs954Jc/1ch7DXd+eV/Ft7HLaXDuMq65sTe9uHblu5ETmvvHspb5WcfEHBrleB7CYZwMfYEmIK60AfIHX1tB///330ObNm6e2a9cuHWDixInnAgICNIBt27aV+/fff4OHDx/eOCIiIrJjx44RKSkpfn///Xd2lXPcuHHnANq1a5fu5+enHT58OKAg240fPz674KamppqGDx8eFh4eHtm+ffsWx48fD9y0aVNIbvEuXLjQfOjQoaCu3bpFRkS2anf7yJFNlcnPdOzYkSIlc03T+GHeF9x823814T//+I0Tx44waNjIAu2jbv0GfPPLalZs3o0tM4NlP/9U6D5xL0y9n62/fs03s6fz6x9/Ub9OTcLq1WHsI88w5O5Hmffj0kLtL4fqwFRgPxbzt1jMnS9nZ8K75VfmARYuXFixbdu2Ec2aNWv5yiuv1Nm1a1cIQIcOHVL3799f7vbbb2/wwQcfVA4MDNQAli1bVuGmm26Kq1KlitNkMjF+/PgzbvvSy2vXrhERERGRt9xySxOlFP/++2/g7bfffn7r1q2hhw4dCkhISDCtWLGiUtb5JDcOp2Y6mZBec/eJpNbHE9LCcibzzX+tJTU5mR69/6sJv/z0Y9w1cdIFF+u5SUtL5aUnH2XaK2+i8hkPIrdjuDOZTHzw6tNsWzaP96c/xTsfz2PQdb2wOxyMuP8Jhtz9KCtWr883lmLUGIgBjmAxz8RiDi+tA3s7r62h50fTNCpWrGjfvXv3zrzWCQkJyT4R+Pn5aXa7XRVkO7PZnP1U5qRJk+pWrVrV/s8//+wMCAigX79+TdLT0y8qVZqmkekgpMvVvYl556Og/GLfv3c3jz9wNwBtO3S+4B6Yu43r1pCRkUHXq/+7n75+7R/s2v4313e5AoBTJ44zcfRQnop5nV59r8/zmCGh5el/42BiF8xXd97WK7/w8pSYlMxrsz5l6Zfv8vLbH3P1Ve25fcgA2vS9jRv7XU1wcLki7dfFBAwBhmAxrwFeBRbK864iy759+wKnTp3aYO3atbtatmyZ8ddffwVfe+21zQEiIyMzd+/e/c+iRYsq/PrrrxWfeeaZetu2bbuojLsnRE3T6N69e+JPP/10MLfjDRgw4PwHH3xQpXr16vYuXbok1qpVy5FznejHn6jzw48/VkeZ/B963HJB3xd3C77+nIG33oaf33/X9ls3rGPrhnW8/sLTZGRkkBB/nhuv7sjClRsu2Pao9SAnjx3l7mEDAUhKTMDp1EhMiOeFN2bme4y8HDp6nJ9XrGHJF+9wx0PTuGfkYNpf0YKrBt7BP799e8nti1EIcB9wLxZzLPA8loRSu6rwRl5bQ+/Vq1fKnj17QrLuk82cObOqzWZTAG3atEkvX768w/3e+I4dO4KymtryUtjt4uPj/evVq5cZEBDAtm3bgtasWZPdc6RixYqOxMREv7PJGVX3nExqeWXPftXWrf7Df++uHdnbb99y8ZM6TcIjmL90FfOXrsozmQMs+Pozbrp1+AUF9KHoZ1i2cSeL//ybxX/+Tc3adXjn0/m5JvPDBw9gs+mdb2yZmaxYEkt4i5YU9XGy6JfeZtoj9xASHExKahpKKZRS2Gx2Mm22S++g4LoBPwDrsZh7FueOhWfLr8yfP3/ez9/fX2vQoIHN6XQyY8aMGlnb7d+/P0ApxciRIxNmzZp1VNM0Dhw4ENi3b9+khQsXVjl//rzJ6XQya9asalnbDBw4MHHNmjUV//rrr+zWud9++y279e2uu+46++WXX1b7/PPPq40dO/aC5vZMuzPgSFxqg5ETp9aa/8sa//lLV+WZzJOTEvn154UMGnZhn5OsMrz4z7955d2PaNKs+UXJHKBZi5b8vu3f7HVHjhvPoGEjL0jmeR0jLw9Ne403LJMxmUykpOll2WQykZJq2NhCCrgB+AuLeR4Wc2OjAvF0Ra6hG/3caN26de1vvfWW9eabb24aGBioXX311YkhISHOqlWrOgICAvjpp5/+feCBB+q/++67NZ1Op6pcubJ9/vz5B9CHQMpVYbebNm3a8TFjxjT+6quvqjZs2DDjqquuyu7RfuPgW23zvvmubvfOHar2vn4g9z08hZi3P+D56Emkp6Vis9mIaNmamHc+LPR3T0pMYPniRXz365pCbWd57EF69b2OXv0GsH7tH3w55338/PywOxx07taT+x5+zKlIJjUtjfDuN5ORmUlCUjL12l/HqFuiePnxB3Ld75oNW0lLT6dvz6sAuH/MUIbf/wSvvDeXUUOiMFesUOjvWAAdgJVYzD8CU7AkyIhzpcDIcp9fmW/evHnmjTfeGBcREdGycuXK9gEDBsRnbbdp06bgadOm1dM0DYfDoW655Za4zp07p3Xu3Dntr7/+Cm3btm1k+fLlHX369ElYv16vALZq1Spjzpw5B+69996GaWlpJpvNplq2bJl6zTXXHAS45pprUv38/Dh06FDQ4MGDE0FvWj+dmF7zXEpmLaemFaiytGTh90S2bkPDfDqz5XT65Akm3jGU+UtXFWj9whzjywWLaRPZjJbN9XWj7x/L3Y89T6bNxtMP313gGEvQUGAQFvMs4DksCXne6iiLCjqWe4S/v/+SZs2aJYeEhKRfcoNScv78eVPlypWdAJ999lmlZ555pu6BAwf+MTKmtExH0ImEtPrJGXave8bSZNIctcudTauanly+JI+Tbtc4eOwMjdZMplzykUtvcGk24H3gWSwJl+ycJArGE8u9J5Z5TdM4l5JZ9XRiRl2705n3s6weqm5ganJV+6kilfkSKMuFkYB+r30GlgSP+Ps0mlffQ3/llVdqLFiwoIrT6VTly5d3fPrpp7ne7yoNNofT/2RCep34VFs1DU0euyhdAcBEYBQW84vAW1gSMgyOSZQATyrzAIlptvInE9Prp9scuXaGFSXKDLwMTMBingZ8iiUhr5HnywSvrqF7irPJGVVPJabXdzgNHlv5MnlxDT0nK/AwloQfS2LnZYWU+7zZHE6/Y/Fp9RPTbFUvvbZn8+Iaek4bgNFYEnYbHYhRvLZTnCfIsDkC959ObnY8Pi3M25N5Fh9pWggDfsBinoPFXCI38EXZFZeSWXnvqaRWvpDMfUxHYAsW88NldXAaSehFdCYpo9q+08ktUzLtxTYmokfwrQfBxgLbsJi7GR2I8H42h9P/4NmUxkfPpzZ2ODWvvl3pw8oBbwArsJgbGh1MaZOEXkiZdmfA/tPJzU4kpDUsaE9WYahGwB9YzC9hMXtdhyXhGc6nZFbaeyqpZVK6rfKl1xYeoBfwNxbznUYHUpokIRVCQpqtwr7TSZE+Vyv3fSbgcfTnWAs15rYo25yapo7EpdY/cj61idTKvU5F4CMs5h+xmC8ewN4HFf0P1GIusfnQsSR41NzImqZxMjG99pmkjDpGx1LSfPzGUztgExZzNHpPeN+6wVAaSqrce1iZB7017tC5lMZpNkeJdhIVJe5GoCsW82gsCYuNDqYkeXUNPWvmpYiIiMj169cH53w/ffr0y55n2+5w+h04m9I0K5mfPnmC+0YO4carO3JL325Mumc0cecK/+hzRno6D48bycCeHbi1X3fuHXEzhw8eyP583K03cH3XNtnzGH/2wXt57uvEsSM8MPY2bry6Izf3voovP56d/dnKZUu4qVcnBvZozyN3jyI5KffZXN+f8So39eri16fvkJBDR49nLx/z8DOs2bC10N/Pg5VDn9XtayzmyxqPVniOPXv2BOYs73Xr1m29du3a/KcMzEOiqzWusMl8365/GDtkADf16sTgPl2YNnki6Wn/jbDWpn5lhlzbNbtcb/5rba77+XH+l3SLbJC93rihA7M/W754Ebf07cbQ/j24ufdVvP3K8+T2tJLNZuPhcSO5tV93Hrl7VPYc5xnp6YwdMoDE+PjCfDVvVw34CYv5YaMDKUlendBnz55dferUqSd27969s1OnTmnu70+fPu0/Z86cy0roKRn2kH2nkyNT3AaJ8fPz456HHmXhyg18++sa6jVoyBsvTCvS/oeMvIOFKzfwzS+r6dXvep6d8uAFnz827aXsYWBH3T0h131omsYjd49i4JDbWLhyAwtWrMue7zg1JRnLYw8y48PP+WnVJqrXrMXsN1+9aB/JSYnELpjP98tXO+4cOzzz7TnzAPj1j3WEBJejW8e2Rfp+Hm4oeseZy77oE8bbt29f0OWWd3C1xiWk1Tp0LiXc4dT8s5JgQQWWK8fjL0znx9/X880vq0lLTeXjmW9esM7H3y3OLtdXdu6a5746dumRvd5H83/KXn5Vj6uzl89f8gd/rvqdFUtiL9p+7crlVKxUmW9+WU2FimbW/L4MgNlvvsptY+6mYqVKhfpuPsAPfQbHmVjMPnn7xGsT+pgxY+pv3Lix/HPPPVe3Xbt2ETnfP/DAAw2sVmu5iIiIyN69ezcF/Yr9wQcfrNOuXbuIWrVqXTF9+vTqb775ZtW2bdtG1K1bt/Xs2bOzO7wMvOnm5p06dWxxU+8ugRPvGMrZ06cAqFq9Bld26pIdR+t2HTh+9HCh4w8qV44evftlTwhxRbuORdrPX6tXEhgYSD9XEs+KEWD1b8uIaHkFjZrqkxUNGz2OxT9+f9E+TH5+OB0O7DYbqalpKjDAn9S0NJ6f8QExT+Q+3KuP6AKsw2KOMDoQUTDfffddxcjIyBbh4eGRHTt2bL5p06ZyALmVd4D58+dXzirfU6ZMqZ21/PDhw/4DBgxo3Lp16xbh4eGRDz74YB2npqlD51IbtW/VvO7rLz3DiBv68PQj4wsVX8NGTQhv0QrQL/5btWnH8SOFL9f5CS1fAZNJP3VnZKRjy8zIdaY1f/8A0tNSAUhPSyUgIJC9u3ZwcP8++g+8uVhj8jL3AT9jMXvdaJ6X4rUJfe7cuUdatWqVGhMTc2TLli27c75/++23D4eFhaXv3r1754oVK/7N2i4lJcVvy5Ytu5ctW7Zn2rRp9Y8dOxa4devW3V9++eX+qVOnNgA4nZReffJzr5b/KvY3vv11De06dWHmGzEXxeBwOPh67gf06jfgsr/PF3NmXbSfN2OeZci1XXls/J0cPWTNdbv9+/ZQuUo1pky4k6HX9eThu27PXvfEsaPUrvffvOh16jfg7OmT5Kx1hISEMuruCYwc2M9v8ZLl/g/dNYJpr85i8r2jqFjB528fNgb+xGK+xuhARP6OHTvmP27cuMZz58617t27d+edd955ZujQoU2cTid5lff4+Hi/rVu37t64ceOumTNn1jx48GAAwIgRIxrdf//9p7dv377rn3/+2blly5bQt2Z/0iIx3VYFIOH8eb74aRkvv/1BkeNNTU3h+68/u6hc3zP8Jm7t151Xn32S1NSUPLffsmEdQ/v3YPSgfvyy6IcLPtu68S+GXNuVa9qF06lrT67pf/E5qEvPawgtX4Fb+3WnfMWKdOrWk9eee4qpz75c5O/kQ/qil3ufmujFJ5sd8jNixIg40CdfCAwMdA4fPvw8QI8ePVITExP9/zl4vJ4jIKTm4h++ZdF388jISCczI4NKVS4cQ0LTNF58cjIVzJUYOe6+y4rpw7f/xxHrQZ7++o3sZS++OYtadeqhaRpff/IBD4y9jQUr1l20rcNuZ/3aVXz24y80bd6C+Z/N4bHxY/nq598KFcOwO+5i+NhxjnpBZ9L3b9gQevTEKa7r1ZX7n3iZ02fP06NzOx4cN/yyvqcHqwQsxWK+F0vCx0YHI3K3cuXK0PDw8LROnTqlAYwfPz5uypQpDbKSdG5GjRoVB1C7dm17vXr1Mvfu3RtUtWpVx7p16ypOnjw5YPLkyQAqNT0j6N9//1W9XdvdeOvwfOcXvxRbZiZTJtxJl57X0Of6G7KXL1n3N7Xr1ic1NYUXHp/EGy9M48mX/nfR9j2v7U+/gYMIDg7hwL493DdyMLXq1OWKKzsC+tTK3y1bS9y5s0y+ZzSb/1pL+6suHG7BZDLxzPT/mvs//3Am1/QfgN3uIHriXWRmZjLsjrsYfE2HIn9PL9cC/cmXm7EkrDY6mOLgtTX0ogoODs4e69fPz08LCQlxAtnTfZ5NSqu5ef2ffDnnfd75dD7fL/+TR6e9QGbGhSNfxkybyqnjx3j1vTnZzV85jR7Uj6H9ezBy4LV5xvPJrLdZvngR7376DcHB/w0HXauOXrNWSjF8zD0cPWwl/nzcRdvXrluPiJatadq8BQA3DBnGrh3bsNls1K5bjxNHj2ave/zIYarVqIW/f97XcXa7nUefm8GMZx/l8+9/pnrVynwzezoLFv/GgUNH89zOBwQAc7CYXyyro0z5opzl3WazZQ93vXnz5l1btv+zf8Fv602LVm9R9zz0aPZ2IaG5t0zt37s7u6PaS08+mus6NpuNxybcSfUatZj67IUte7Xr1tf3HxLKsNHj2Lz+z1z3UblK1ezzQeNmzeneuy9bNvx10XpVqlaje+++/BKb/yjHx48eZtWKXxk2+i7efe1Fhowcw/Ovv0vMtKn5blcGVAOWYzGPNDqQ4uCzCb1SpUqOpKSkAg3H6nRq6sDZlOz7bokJ8YSWL0+lylWwZWby7RdzL1g/ZtpUjlgP8MYHnxMQGJjnfj/94RfmL13FFz8ty/3z2e+yeOF3vP/lAiq63c6x2+2cO3M6+/2ynxdStVp1KlWuctE+ul1zLadOHufUCb1n+uoVv9KoWXMCAgLo1qsPu3Zs4+C/+syi8z79iOtuHJzv7+Kttz8MGHHzddSqUS17XnMApTByPuTS9AT6s6uS1D1Mr169Uvbu3Ru8YcOGcgCzZ8+uXLNmTVujRo1shSnvZrPZ2alTp8THn3y6wYEzyRE2hzPw9MkTnDpx7JLbNgmPyO6Q9sSLr130ud1uZ+r94zBXqsy0V2ZcUMtPjI8nzXVP2+l0svSnBUS0uiLX42SVZ4BzZ06zYc2q7HUP/rsXp1O/TklJTmLV8l8Ib9Ey37inP/M4jz3zIiaTibTUVBQKZTKRnk+TfxkSCHyKxTzG6EAu12U8h+55z42669y5c2p4eHhas2bNWtavXz/D/b6aO6emqYNnU5q6DxbTrde1xH7/DTdd3RFz5Spc1f1qTp88Aej3tb76eDaNmoZz+416zbtO/YbM+PDzQsV36sQx/vf8U9RrEMZdw/RHUgICg/jip2VkZmYw8Y5hZGZmYDKZqFSlKm/O+TJ723dfe4nqNWsxdNSdhISE8tRLr/PAmGFomkb5ChWZ7ppjPbR8BSzT3+Thu27HYbfTtHkLnn8j78ffDlsPsmbNer9ln78FwO2DB3DzXZP5ZtEyunVoQ+sWzQr1Hb3YWMCJxXy3PKueg4Hlvk6dOvYPP/zwwB133NHIbrcrs9nsmDdv3n6TyVTg8p7l0y++Pvnw5Eeb3XTNVUopRXBIKE/HvE7N2nUvK8alP33P8sU/Ed6iJcOu6wnozeNPvPgaB/fv5fnoR1BKYXc4aNHqCqZa/qvB3z/6ViZMfoKWbdox75MP+e3XxQT4++N0Orn9rvF07tbTdYwFLP1pAf7+/jgcDvpG3cTg4aPzjOnnBd8QHtkquxXvzgkP89zUh7DZbNz90GOX9X19iAn9Qh4sCXONDqaoyvRsa05NU9azKY2TM+yVjI7FE5hMmqN+4Ol0c2ZqaEkex8NmaMrPbOC+sprUfbXcp2bayx08m9JcRn7T+dBsa8XBCYzz1qTus03ul6JpGofOpTaSZC7ycQ/wttFBiOKTZnMESTIX+ciqqd9mdCBFUSYTuqZpHIpLDZOJFi4mN44vcj8W8/NGByEuX7rNEXjwjCRzcUkm9Hvql/88cikrkwn9yPm0BjKXsSiEp7CYHzI6CAM4AU3TNK+/zsu0OwMOnk1pbnc6Zca9YpR9x7YAt269TADwLRZzT6MDKYwyd6V6MiG9Znxqpgz3mTevP3mXkDewmM9hSShc70fvdtzpdCacPn26Zo0aNeKUUl551nY6NXXkfFqjTIcz70dSyrBMZTOlOwr/X6tpcCbFgcpIJCC98PNZeIFg9PHfe2BJ+NvoYAqiTCX0+NRM8+mk9HqXXrPskmyeJwV8jMV8CEvCKqODKQ3t27dP3rRp033x8fGzEhMTzXjpn0dCurN6hkMr0iQtZUGmX7pfvDOpSNuqjETqbf0ffg6f6TOZU0VgARZzBywJ540O5lKKnNBbf9K6xKZP3X7H9mJ/NCY10x589HyaTw3zJ0qdPzAfi7kdloSTRgdTGtq3b79u06ZN3Z1OZx288BbdsyvjJp5Lc9xvdByebEzgih2j1M+FP59rGgHpZ305mWdpDHyJxRyFJcF5ybUN5JM19D179gT++OOP5ilTppwBsDmc/s2aNI5848MviGjZulRjWblsCa+/MA2nw0HTiEief/1dyleoeNF6TqeTV56JZvWKX1FKMfKu+xg+5h5AH1jihScmc8R6ALvdzi0jx3D7XRdPGmGz2XjsvjEcO3KIeg0b8erMj/H39ycjPZ37Rg7mzY++LIszLBW3WuhJvTeWhMJNxeWl2rdvnwzsNTqOwgqLjh0K5D5NociW4n8mrZy/TzxyVpKuA54FnjY6kPx43RV3QbhPpeh61rxJcey3sFMpFnT6UoDY7+dzYN8eFv6xkS9+Ws4ns97m3z27AHjtuSdp0qw53/66hi9jV7Dwmy/ZsXXzRfuQ6RJLTQ/gFaODEHkLi47tAMzFS28TCI/0JBbzTUYHkR+vTugFmUqxT9/+LdNsjvIAyxf/xKib+nF91zbMfvO/YRvPnj7FY+PHMuKGPgy5tivvTH8h+7Pru1zBG0WcSrGg05eCPsLU4OGj8fPzw1y5Mv0H3sySH78DYM/OHXTv3RfQx4C+snNXFn0/76J9FMd0icr3equWlElYzLcYHYS4WFh0bCXgO/ROTUIUF4X+OFu40YHkxWsTekGmUly3+e9jb86dH5S1TVJiAp/9+AtfLlrBJ++/lT1e8lOPTGDY6Lv4ctFy5i35g3/+3nrBdIVFnUqxoNOXZq1bp179/9at14ATx/XJUCKvaMvPP3yL0+kk7txZ1q5ckescyzJdYqmbI3Ope6RZQAOjgxA+KauTnEfOK+2199ALMJWiOhafFua+zYBBeoWqcpWq1G0QxrEjh6hgNrN+zUrizv43GUpqSgrW/f8NBX25UylerslPv8Drzz/NsOt6UqVadTp06cb5c+cuWq+g0yVmjQmdB2miLLgKwHdYzJ2wJMgsFx4gLDr2DmCY0XEInxYJfAzcanQgOXltQr8kk3+gw6ld0AIRGFQu+99+Jj8cDkf2gAif/fgrQeXKkZv8plJ8/IG7gf8mYHBXu2491q36Pft9ftOX1q5bj+NHj9CmfSd93aOHqe2aQrVylaoXTKry/OOP0CQ8/4ph1nSJMz//lqceGc+QkWOIbN2G22/qx4LluU/ZCJLNiyAS+AAYYXQgZV1YdGwT4B2j4xBlwi1YzGOxJHxsdCDuvLbJPb+pFE0hFSsmJScV6LuFhJanY9cezHlvRvay4ppKsTDTl/aNGsT3X32Kw+Eg4fx5lv60IPu+d/z5OGw2GwC7dvzNb0t/ZtjocfnGVvTpEuUeehEM9/TOMr4uLDrWH/gS8MimUOGTXsNirml0EO6KXEMviWfFCyOvqRTT7c5ytZu0rNkkPILBfbpQr0EYb338Vb77eumt2bz23JMM7tOF4pxK8VLTlw7t34N3PplPjVq1uWHIMP7ZtpmBPdujUIy6+36aueY43rF1E69Mi8bP35+Q0PK8+t4cqtesledxZbpEQ7yNxbwcS0Ky0YGUUdOATkYHIcqUKsBbeNAtHp+aPlXTNP49ndw8q1e7KByTSXM0CjiZGWpLL9HewT445WKWN7EkPGx0EGVNWHRsJLAVffxtUUgP+3+7+mH/77sbHYcXG4glYZHRQYAXN7nn5lxyZlVJ5sJAE7GYS2wERZGn95BkLozzHhZzBaODAB9K6DaH0++UjNN+2ZT0i7scfsBsLGY/owMpK8KiY0cBVxsdhyjT6gMvGR0EFDyhO0Fv0vZUx+PT6sk8x97Bh6dcBLgSeNDoIMoC1wAyF/dGFaL0TcBi7mJ0EAVN6PGaptkzMzM9cvrBpHRbaEKarZrRcYiCyXQCTjt+tqLN8OQFnsNirn/p1cRlegmoYXQQQqDn0g+wmA3NkQWt0Z51Op0rT506FRUQEGAzmUweU7XSNI2jcWlhmsOjJ8HxCk6TRqbmUKYizI1c4GNocCYhnZDTm/HPTCyx4xisPPA2MMjgOHxWWHTslcC9RschhJuWwD0YOBZCgRJ6+/btnZs2bZqWlpbWev/+/dXxoPus6XYtNDHDmfuIMKJQTEpTmineFOgsyUnENExpcTTYMxfl28+834TFfBWWhHVGB+KjXsSH+gAJn/E4FvOHWBIMeRqswPec27dvf8w1L3KDwmxXklIynaanf4+LtTs1aW4vBsovLfmd0JfPNcpIbVRiB3E6CEw7jUkrEzOPPgNcb3QQviYsOrYb+nSWQniaOugtR29easWSUKjE3L59+0zg30uuWErComPHAmFGx+Ez/GxOk3bQVi4j0+hIfMV1WMwdsSRsMDoQH/O80QEIkY9oLObZWBLSSvvAXttkFRYdG4g+OpQoRibNc26n+IhnjA7Al4RFx/YGrjE6DiHyUQu4z4gDe21CB+5CaufFTp5DL3ZRMthMsZLaufAGU7GYQ0r7oF6Z0MOiY4OAJ42OwxeZ0CShFz9pSSoGYdGx1wFdjY5DiAKoCUwo7YN6ZUJHn6qyjtFB+CKpoZeIG7GY2xodhA+YanQAQhTCFCzm0NI8oLcm9IeNDsBXyT30EiO19MsQFh3bBuhldBxCFEJ1Svleutcl9LDo2D7AFUbH4atMUkMvKYOwmJsbHYQXe8joAIQogvFYzKV2TvW6hA5MNDoAXyb30EuMAu42OghvFBYdWxX9NpsQ3qYJ0Le0DuZVCT0sOrYeMNDoOHyZ3EMvUXcYPdazlxoLBBkdhBBFVGrN7l6V0NHHyZWpKUuQ3EMvUdWQ8d0LJSw6ViFjtgvvNhCLuVQ6cXtNQncV7DuMjsPXKS/6m/BS0uxeOFcDTY0OQojL4A+MKo0DedPJuyvQwOggfJ3JtydM8QS9sZjrGh2EFxlmdABCFANJ6DncZnQAZYHU0EucCRhudBDeICw61g8YYnQcQhSDlqUxYqRXnLzDomNNwC1Gx1EWeM5M9z5tpNEBeIne6M/yCuELSryW7hUJHX1AiVpGB1EWSA29VLTFYm5hdBBeQJrbhS+5raSfSfeWk7c0t5cSGVim1NxqdACeLCw6NgAYbHQcQhSjmkC7kjyAxyd01300KdilRKF5/N+Ej7jW6AA83LVAZaODEKKY9SvJnXvDybs9UNXoIMoKuYdeaq4q7YkbvMwAowMQogSU+YQuNZlSJPfQS00A+jPWInd9jA5AiBLQrSTnSfeGk7cU7FIk99BLlfxt5yIsOrY2IJ0GhS8KpARnDfTohB4WHVsOfUAZUUqkhl6qpPUpd72NDkCIElRize6efvLuDpQzOoiyRMlIcaWpNRazPGd9MWm5EL6szCZ0KdilTGke/zfhSxTyN54bqaELX9YCi7l+SezY00/ePY0OoKyRe+ilThK6m7Do2DCgodFxCFHCSqTce2xCdw33eoXRcZQ1cg+91PUyOgAPc6XRAQhRCtqUxE49+eTdFChvdBBljdTQS11jLOZgo4PwICVyohPCw7QqiZ16ckKXgm0AGSmu1JmA5kYH4UGk3IuyoGVJ7NSTT95tjQ6gLFKa1NANEGF0AB5EErooC2pjMRf70MaS0MUF5B66IWQQFSAsOtYMhBkdhxClpNhr6Z588m5rdABlkdxDN4QkdJ10ghVlSbHfR/fIhB4WHRsK1DE6jrJIauiGkCZ3XaTRAQhRispMDb1EHroXlyY1dEOEYzH7GR2EB2hgdABClCJJ6KIEyaivRgkCGhkdhAeoZ3QAQpQiSeii5EjV3FDS7C4JXZQtNbCYA4tzh5LQRTaT1NGNVNvoADyAJHRR1hTro2uemtClYBtAauiGMhsdgAeoa3QAQpSyMpHQpYZuAJMmFXQDlemEHhYdWxkINToOIUpZleLcmacm9GL9kqJglNTRjVTR6AAMJvPCi7KoTNTQQ4wOoCwyyS10I5XpGjpS5kXZJAldlAypnxuqrCd0mXFOlEWS0EXJkHvohirrTe6S0EVZVCbuoUtCN4DcQzeU1NCFKHvKRA1dCrchnJLRjSMJXYiyp1hb5jwuoYdFxwYA/kbHURaZPO/PoSwp649sSUIXZZGzOHfmiYmzWL+gKLgEQs2d0985XV6lpVcgLa2CSs2sSGpmRZVir0iqvaJKcVYk1VlRpVCBNK28SlOhpPsFk+EXrDL9g7AFBGILDMAR5IejnAktWKGFKEU5o7+bF7AZHYDB5GrSIO/aB3X6wt7nbLDKTA8hwxZMRmaoSreFkG4LJd0RqtLtoaQ7Q1S6I5R0LZR0LUSlE0ImwWQQrDJUOTJNQWSagrD5BSq7fyB2P38cAf44AvxwBppwBprQghRaEBCsFDIZkc5enDvzuIRujYlyhEXH2vHA2HyfUqeoUuNUVt+4YuojZ8LpCCYjtTxpaaEqPa0iqRkVVGpG7hcLqVp50qigUk2hpJtyXiz44wjyxxFkQgvxsYuFDKMDMFia0QGUVTb8A89QudoF5b2E+8f64bCXIzO9HJnpISojM5iMjBAybCH6hYQ9lAxbqEpzhJDhCCXdGarSnSGka6EqnRAytGAyVLDKUMH6hYQKwuYfqOx+gdj9/bH7++MI9MPpupjQAhVaOaCcUgSU7DcrNN9O6C5pQAWjgxDFw4nJL4XgCikEV6D4LxacwWSkZF0sVCA1o4JKyzCTYquoUmy5XSyUV2mqPGkXtSy4XSwEK7TQUr5YkIQuygwHfv4pBJdPIbj8uVK8kDDhdLguJDKCycgspzIzQknPDCbDFqrS7SGuC4lQMuyhpGmhrlaJEJWhhZCuQsjQglWGqRyZqhyZfkHYTIHK5heI3T9Ab5Hw98MRaEILNOEMMqEFordI5DUJS7G2zElCF17NiclUUhcLCqczJJeLhYqk2iqqFJuZFEdFleLIebEQSrophAy/YJURkMfFQohSF90zLusJPdXoAITvc2LyS6VcaCrl9D4rpXYxoWn/XUhkpgfrrRKZATjO/VCMR/HUhJ5udABCaCV/sZBanrTUUJWeFkq69afi2bW3khq68GFKpRMUnE5QcDy4n0eK9ckiT03oUriFT3NdLJRPIbi8q3D/Y3BIRpMauiiLkotzZ57as1QKtyhrEo0OwGByES/KojKR0E8bHYAQpSzB6AAMds7oAIQwwMni3JmnJvRjRgcgRCk7bnQARrLGRJ1FOgaKsqdYc50kdCE8w2GjA/AAR40OQIhSViYSuhRsUdZIQpdyL8qWTIr59rKnJnSpoYuyRhK6JHRRtpywxkQV69PvnprQpWCLskQDjhgdhAeQ34EoS4q94ioJXQjjnbbGREmHMEnoomwp9r93j0zo1pioc8BZo+MQopRYjQ7AQ+w3OgAhSlGxDyblkQndpayPnCXKjm1GB+AhthsdgBCl6O/i3qEnJ/QdRgcgRCnZYnQAnsAaE3UciDM6DiFKSbFfyHtyQpdaiygrJKH/R2rpoixIsMZEWYt7p56c0OUkJ8oCByXQ9ObFNhsdgBCloEQuXD05oW+nmCd/F8ID7bHGRMnEJP/ZaHQAQpSCEmmB9tiE7nqMR+6jC18nLVEX2mR0AEKUghIp9x6b0F1+NzoAIUrYBqMD8DB7kdkWhe/7vSR26ukJfYXRAQhRwpYZHYAncQ2FKeVe+LJD1pioEhlzwdMT+krAbnQQQpSQ49aYKBlv4WJykSN82W8ltWOPTujWmKgkpJOM8F2SuHK33OgAhChBJdYC5dEJ3UWa34SvkoSeC9fzuQeMjkOIEiIJXQgfJAk9b1JLF75orzUmqsSmB/eGhL4aSDI6CCGK2Q5rTNQJo4PwYL8YHYAQJeDnkty5xyd01/PoC42OQ4hiJn/T+VsMpBodhBDFbH5J7tzjE7rLPKMDEKKYfWl0AJ7MGhOVAiwyOg4hitFhYF1JHsBbEvpSIMHoIIQoJtvlcbUC+droAIQoRt+4xlkoMV6R0K0xUZnAD0bHIUQx+croALzEz0Ci0UEIUUxKvKXZKxK6S4neexCiFEnNswBc/Wd+MDoOIYrBQWtMVIkP8+xNCf1XIM7oIIS4TOusMVEHjQ7Ci8jFj/AFpVIh9ZqEbo2JsgFzjY5DiMv0hdEBeJlfgRJ7bleIUuAEPiiNA3lNQnd5D/2XI4Q3SgI+NToIb2KNibIDs4yOQ4jLsKSkJmPJyasSuuuXssToOIQoornWmCjp5FV4s4EMo4MQoojeLa0DeVVCd3nH6ACEKAINeNvoILyRNSbqNDIWhfBO+9EHSSoV3pjQlwD/Gh2EEIX0szUmap/RQXgxuRgS3ui9kn723J3XJXTXL+c9o+MQopDeMjoAb2aNidpICY+yJUQxSwXmlOYBvS6hu3wEnDc6CCEKaJc1JkomG7l8M4wOQIhC+NAaExVfmgf0yoTu6lj0mtFxCFFALxgdgI/4BpAhc4U3SANeLu2DemVCd3kLOGt0EEJcwg5kcJRiYY2JcgLTjI5DiAJ4zxoTdbK0D+q1Cd0aE5UMvGJ0HEJcwtOuRCSKgTUm6ntgs9FxCJGPFAzKTV6b0F3eBUr9KkiIAlpvjYn6weggfNDTRgcgRD7etsZEnTHiwF6d0K0xUWlAjNFxCJGHp4wOwBdZY6J+BtYaHYcQuUgCXjXq4F6d0F1mATLZhfA0v1tjon41Oggf9qTRAQiRi+nWmCjDJhHz+oTummLxQaPjEMKNA5hkdBC+zBoT9TsypbLwLP9iYO0cfCChA1hjohYBC42OQwiXd6wxUVuMDqIMeAS9iVMITzDRVcE0jE8kdJeH0J/9E8JIx5BOW6XCGhN1HHmMTXiGb60xUUuNDsJnEro1JsoKvGh0HKLMe8gaEyW1xtLzNrDV6CBEmZaM3lpkOJ9J6C6vAnuNDkKUWbHWmKjvjA6iLLHGRDmA8eiz2QlhhOesMVFHjQ4CfCyhW2OiMoG7ARnIQ5S2VGCi0UGURdaYqHXA+0bHIcqkLXjQHAM+ldABrDFRfwDTjY5DlDlTXLd9hDEeRVrnROnKAEZZY6JsRgeSxecSuss0YJPRQYgyY7E1Jupdo4Moy6wxUSnAcCDT6FhEmfGENSbKoyYL8smE7rpiGoHeDCpESToDjDU6CAHWmKjNyOh8onQsA94wOoicfDKhA1hjovbiIT0Phc/S0JvcThkdiMj2GrDc6CCETzuNXu49riOmzyZ0AGtM1GzgB6PjED5ruic8eyr+4zrJjgbOGR2L8EkacIcRU6MWhE8ndJcxwG6jgxA+ZzXSvOuRXAPO3I4+BK8QxclijYlaYnQQeVGa5nGtBsUuLDq2KbAeqGx0LMInHAQ6GzVFoiiYsOjYR4DXjY5D+Iz51pioYUYHkZ+yUEPHGhP1LzAUsBsdi/B6icANksw9nzUm6g3gA6PjED5hE3prr0crEwkdwBoTtQyZAUtcHgcw1BoTtdPoQESB3Q/8bnQQwqudAG6yxkR5/FwhZSahA1hjot4GZhsdh/BaD0snOO/ieoR1CLDf6FiEV0oHBlljoo4ZHUhBlKmE7jIR8NhODcJjvWuNiXrH6CBE4VljouKAgUCc0bEIr+IARltjotYbHUhBlbmE7nbFvtroWITX+AJ40OggRNFZY6J2Adejz4wlxKVowDhrTNQ3RgdSGGUuoQNYY6JSgRvQB9YXIj/foD93KhP+eDlXTWsgejOqEPmZYI2J+sToIAqrTCZ0AGtMVALQD9hudCzCY/0AjHBN0Sl8gDUm6nfgZvSJNYTIzSRrTNQso4MoijKb0AGsMVFngT6A9FoWOcUCw6wxUfKoo49xDQwyBJnIRVzsadfjjl6pTCd0ANfzxL2R5nfxn1+AIdaYKDnh+yhrTFQselKX5neRZZo1JuoFo4O4HGU+oQO4Jte4GpnUQcBXwEBrTJQ0yfo4a0zUIqAvcN7oWIShHMC91pio540O5HJJQnexxkQlAQOAeUbHIgzzKjBSauZlhzUmajXQHThsdCzCEGnorXE+MT5JmRjLvTDComMVMAN5TKkscaIPGvO20YEIY4RFx9YBFgNXGB2LKDXn0Vvj1hgdSHGRhJ6HsOjYaOAlQBkdiyhR6ei18u+NDkQYKyw6tiL6kw3XGByKKHlHgf6+NoyzNLnnwRoTFQMMAhIMDkWUnBNAH0nmAsAaE5UIXIcMD+3rfgc6+FoyB6mhX5Jr6tXvkKY4X7MC/RnzU0YHIjxPWHTsaGAmEGJ0LKJYvQo87qtjS0hCL4Cw6NgQYBYwyuhYxGXT0G+lPOOrhVoUj7Do2NbAt0C40bGIy5YIjPX11jhJ6IUQFh07Hr3DXKDBoYiiOQeMssZELTY6EOEdXPfV56A/sy680z/AYGtM1F6jAylpktALKSw69kpgLtDa4FBE4axBb2KXx5NEoYVFxz4AxCBN8N5EA94DplpjolKMDqY0SEIvgrDo2EDgaSAa8Dc4HJG/FOAJ4B2ZYEVcDld/mg/RB6ESnu0gcKdr7P4yQxL6ZQiLjm0PfIzU1j3VcuBua0zUQaMDEb7BNU7FBPTaenmDwxEXK3O1cneS0C+T1NY9UgLwqDUm6kOjAxG+KSw6tiHwAfrQscIzHECfw/x3owMxiiT0YhIWHdsCeA19+FhhDA196N5HrTFRx4wORvi+sOjYUcDLQF2jYynDUoBXgNesMVFpRgdjJEnoxSwsOrYf8DrQ0uhYyphV6Il8vdGBiLLF9VjrZGAK0gxfmpzAJ8CT1pioE0YH4wkkoZeAsOhYP+Bu4DmgusHh+Lp96PfLFhgdiCjbwqJjawHPA3cio3CWtN+BSdaYKJn22o0k9BIUFh1rRr9ynwhUNjgcX3MKeBGYZY2JshkdjBBZXAPSvAxEGR2LD9oMPGeNifrR6EA8kST0UhAWHVsBuA+YBNQyOBxv9y96X4VPrDFR6UYHI0RewqJjr0Bvhh+GdJi9XL8DL1tjon4xOhBPJgm9FIVFxwYBY9ELeSODw/E2G4DpwPfyPLnwJmHRsWHoLXXjgGBjo/EqGvATeiJfZ3Qw3kASugFc99hvQb/P3huZojUvDmAJ8D9rTNRvRgcjxOUIi46thn77bRxQz+BwPFky8CXwtjUmaofRwXgTSegGC4uObYReax+LFPIsVvTxsz+2xkQdNTgWIYpVWHSsCf359bHoUzQHGRqQ59iAPhLfl9aYqGSjg/FGktA9hKuQ90O/er8BKGdsRKUuAfgG+AxYZY2Jkj9M4fPComMrAyPQe8ZfaXA4RjgGfI7eJ2aX0cF4O0noHsj1XGs/4Eb05O6rj74dBmKBRcAK6eQmyrKw6Nhw9PI+EOiO73ak+xtY6HptlIv34iMJ3cO5au5d0JP7QKCFsRFdFgewDj2Bx1pjorYbHI8QHiksOrYScB16mb8e737sNRNYiSuJy4yHJUcSupcJi46tgX713gPoCrTFc+dnPw/85XqtA/6yxkSdNzYkIbyLqxNtG6AbetnvAtQ3NKj8nQD+dL3WApusMVEZxoZUNkhC93KuR+HaAu2B5q5XONCQ0hutyo7eke1fYC+wCT2J75XmNCGKX1h0bE2gE9ABvcw3c70qlGIYGcB+YA96ud8G/GmNibKWYgzCjSR0HxUWHVsOaIpe2MOAajleVV0/Q9Efm8t6mdz+nQbEode0z7v9Ow44gj7s6r+A1RoTZS+dbyaEyIsr0Wcl9yboZbxKLq/8Er8NOOd6nc3x7xPoyXsvcEjGhPAsktCFEKKMcc3rrgBNWtF8hyR0IYQQwgfIjEBCCCGED5CELoQQQvgASehCCCGED5CELoQQQvgASehCCCGED5CELoQQQvgASehCCCGED5CELoQQQvgASehCCCGED5CELoQQQvgASehCCCGED5CELoQQQvgASehCCCGED5CELoQQQvgASehCCCGED5CELoQQQvgASehCCCGED5CELoQQQvgASehCCCGED5CELoQQQvgASehCCCGED5CELoQQQvgASehCCCGED5CELoQQQvgASehCCCGED5CELoQQQvgASehCCCGED5CELi5JKRWmlNKUUmOMjkWIskQp1VEptVYpleIqg22VUtcppbYqpdJdyyoZHafwDP5GByCEEOJiSqkA4BsgHXgESAWOAH8A/wD3AxlAilLqCWCnpmk/GBRrVeBOYCDQAggAdgNvaJo2z4iYyiJJ6EII4ZmaAA2BuzVN+xBAKXUdUAF4WtO0ZVkruhL6t8APBsQJ0AV4EfgZeAGwA0OAr5VSkZqmPWNQXGWKJHQhhPBMNVw/4y+xzBP8AzTTNO1Q1gKl1HvAMmCqUmq6pmkphkVXRsg9dA+nlOqllNroul+2Xyl1r1LKopTS3NYZq5RaoZQ6rZTKUErtVEqNz2VfHZRSS5VSZ5VSaUqpg0qpOTnWqaSUmquUSlBKxSulPgEq5RFbhFLqW6VUnCu+jUqpG3McT1NK3ZHLtv1dn91wOb8fIXyRUmousNL19htXWfkd+MS1bINr2VzXuSAUuMO1THNtT9a5QikVrpT63FWuzyilnle6+kqpH5VSiUqpk0qpyTniCFRKPaeU2uTaNkUptUopdY37epqmHXRP5q5lGnqLQRDQuFh/QSJXUkP3YEqpdsAS4ATwDOAHTAPO5Fh1PPoV8kL0pq6BwHtKKZOmae+69lUD+MW1bQz6FX4YMNjteAr4EegOzAJ2ATfz30nEPbaWwBrgmGt/KcBQ4Ael1BBN0xZomrZRKXXAtTznPoYB54Glhfy1CFEWvI9etp4A3gI2AKeAPcA96OeBg8B+9Frwh8B6YLZr+/059jcPvTxHA1HAU0AccC+wApgKjAReU0pt0DTtD9d2FYG7gK+AD9Cb+8cBS5VSnTRN23qJ71HL9fNswb+6KDJN0+TloS/0BJ0C1HFb1hSw4boAdi0LzmXbJcB+t/eDAA3okM/xbnKt85jbMj/0TjgaMMZt+TLgbyDIbZlCT/J73Za9BGQCld2WBaIn84+M/h3LS16e+gJ6ucrdLW7LxuRWjoFkYG4u+7C41n/fbZkfeuc6JzDVbXkl9I53c3OsG5hjn5WAk5cqv0AV9IuQP4z+XZaVlzS5eyillB9wLfCDpmnHs5ZrmvYvsNh9XU3T0ty2MyulqqE31zVWSpldH8W7ft7g6j2bmwHoNfyZbvt2AG/niK0K0BuYD1RQSlVzHbMqeo27mVKqrmv1eeg9Xge77aIf+klBer8KUTo+zPqHq0xvRL8A/8hteTx6C0Bj93U1TcsEUEqZXGXf37X9lXkdTCllAr5AL+cPFOP3EPmQhO65agDBwL+5fHbBMqVUN6XUMqVUCnriPoNeMwbISugrge/Qm+7Puu6bjVVKBbntqiFwQtO05BzH25PjfVP0k8HzrmO5v551ix9N07ahP74yzG37YehNcCty/eZCiOJ2OMf7BCBd07ScTeEJQGX3BUqpO5RSf6M/PncOvZxH8d+5JTdvA9cBd7nOAaIUyD10L6eUagIsR0+ak9Cb0jLRa9uP4Lpo0/Q2sFuUUleh32PvD8wBJiulrsoliecn60LwNfK+B+5+0TEPeNJVi08CbgS+0jTNXohjCiGKzlHAZaBfrOv/UOp2YC5657ZXgdOu7R5Hf6zu4o2VegaYAERrmvZZkSMWhSYJ3XOdRr8ibprLZ+7LBqL3Ir1R07Tsq/CcvVCzaJq2DliHnmBHoDeL3YbeJHcI6KOUKp8jwTfPsZsDrp82ze1Z2HzMQ28ZGIJ+T60i8HUBthNCFIx26VWK5Bb08j7YVSkAQCn1bG4rK6XuR79vP0PTtFdKKCaRB2ly91Cu+1zLgEFKqTpZy5VSTYHr3VbNusp2v6o2A2Pd96eUquzqxe5uq+tnVrP7z+gXeePdtvMjxz0wTdNOA78D9yqlaueMXSlVPcf6u4Dt6E3tw9B77f+RczshRJGlkMfjpZcpt/NLZ/SBZC6glBqG3iP/C/TWQlHKpIbu2SzoHcjWKKVmovc4nQjsANq61vkFvYn9J6XU+0B54G70Gr57sr0DmKCUWoD+SEsF13qJ6Ikc4Cf0XuoxSqkwYCd6Z7bc7pXdD6wGtiulPkC/iq+JXtDrAW1yrD8PeA691eEjTdOchfpNCCHyswm4Vik1CTgOHNQ07a9i2O8i9HPAAqVULNAIuA/93FA+ayWlVCfgU/R77MuBkTnqD2s1TTuAKFGS0D2YpmmblFLXo9+rfh79/vg09LGSI1zr7FFK3YI+3OJr6I+TzETvuOI+aMxKoBN683pN9M4v64GRmqYddO3L6RoYZgZwO3oz3kJgMrAlR2w7lVId0JvSx6D3cD/tWu+5XL7OPFeMIUjvdiGK2yT0Z9BfQO9M+wlQHAl9Lvqz5Pei97vZiX5uuBX9sboskeiPo1bnwvNOlrH8d6tOlBDldltEeAml1A9AS03TmhkdixBCCM8g99A9nFIqOMf7Zug92H83JCAhhBAeSWroHk4pdQK92esA+nPi49E7sbXTNG2fgaEJIYTwIHIP3fMtAYaj38fKAP4EnpBkLoQQwp3U0IUQQggfIPfQhRBCCB8gCV0IIYTwAZLQhRBCCB8gCV0IIYTwAZLQhRBCCB8gCV0IIYTwAZLQhRBCCB9QqIFlNm3aVB6og1wIlBVO4Hj79u2TL7mm8FlS7oUHkHNRARR4YJlNmzZdZTKZZplMJjNuc+MKn6Y5nc4Ep9N5X/v27dcZHYwofVLuhYeQc1EBFCihb9q0qbzJZFpdqVKlmjVq1IhTSsnwcl5C0zRsDi3A5nAG2pz6T7tDC3D9tysNzaSB0jRUDb/kzHLO1ED0aVPRlJ/zTGKGf9K5E8nNV977dkBG3EngPHAC2I8l4ZRhX0yUOCn33sWpadidmr/dqfnbHVqA3en0dzi1AIcDf4fm9Lc7NH+nhp/mujDT0FSI5rBVSzobiJ+fPaC8TR3U6mBSOJXCaTIphwnl9PdTNn8/lRloUpkB/n62AD+VaVKle22naZo6ffp0lfj4+FNOp7O71NRzV9Am9zomk8lco0aNuNDQ0LQSjUgUms3h9E/NdARn2p1BNoczMNPhDLTZnUE2pxbocDgDLzwLK/KqaAWZnFqwMz00e4EGNcpppCotxOEf8nxARtyFG1jMycBBYL/rtRfYBPyNJcFWfN9QGETKvYfKtDv9UzPtoWmZjpA0myMk3e4MsTucgRevmVXW/XIt+iZM9kDwU0o5gvw1P4fD38+R9aHT7acN9Ot8OwD+fqbMAD+VGehnygj0N2UEB/illg/yT/b3MzkoITVq1IhLTEw0O53OOujnGpFDQRO6CVByhe4ZbA6nf1K6vUJKhr1CSqa9QqbdWa6kjpV9IZ77FXl5oLXr5S4Di/lvYCOwAVgP7MSSIH8/3kXKvQewOZz+KRn20NRMR2i6zRGSbnOG2J3OgGI9iAIwOQG/gqxudzgD7Q4C03CUd18e6G9KDwnwSw4J8k8uH+SfXC7AL6PYQtT/DhXSlyNPRZ9tzWJuX4xx5Nh3wqYS27cXciXw8ikZ9oolncCLSRDQ0fUa71p2Cov5V2Ap8AuWhNNGBSeKLiw6tkTKvTUmSsq8m5QMe3Bimq1SUoa9UrrNEVLyR1SaVgzN6Jl2Z7lMu7NcfJqtGoCfSdlDAv2SQwL9kyuWC0gIDvRLv+yDiDz55JXOnj17AqdPn17dfVndunVbr127Nri0Y9m3b19g7969m4aFhbVq0qRJyxdffLFG1mdPPvlkrSZNmrSMiIiIbNOmTcRvv/0WAvp97/jUTPORuNT6e04mRca8OatNw9rVmvTt0bn6oD7dyo0bOjB7/+lpaTz58H0M7tOFwX268ODY4cSdO5trLM9FP8yQa7ty17AbSUpMIOtYE0bdwhHrwRL9PQA1gduBz4CTWMxbsJhjsJg7lfSBhSgOERERkefPny+Rc6ZT01R8ambFw3GpDXaeSGy9/0xy5JnkjDqFTeY7tm7mjpv7c2u/7gzt34O/1vyR/dmhg/u5d8TN3NqvOzf36cKShd//t6FCg4tbYn6Y9wVt6ldmxZLY7GXbt2zi9hv7MvS6ngy6pjMfz3wz11iOHj7EbVF9/K/t1rHSiy++WG/f6aSWe04mRq5Yu6F+7z59wgvzvUTB+OR86Pv27QuaM2dO9SlTppwpzv3abDYCAgre0uV0Ohk0aFCTyZMnn7zzzjvPAxw5csQfYO3atcEff/xx9V27dv1jNpud7733XpUHH3wobPEf6xIT0mxVHU7tgv+bjl16MOOjLy46xrdfzCU9LY3vlq1FKcWzUx7ik1lv8ciTz12w3r7dOzl88ADfLVvLrBnTWfT9PIaPuYfvv/qUjl17UD+sEXp/t1KhgLau11Qs5n+Br4AvsSTsLq0ghCiIrHK/e/funcW5X6dTU+dTMysnpNkqp2Y6Kjo17bIuFjRN45G7R/H86+9yVY9eWA/8y73DB/Hj7xsoFxzMtEkTuGnoSAYPH03cubOMiLqG7h07UitYgVJoynRBQj925DDff/UJV1zZ8YLjPB/9MBMmP06vfgNIOH+em67pRM8+/WkSHnHBevM++ZDb7riLATffys29r2L42HvQQssHP/V4dPBTL7/OnpNJkRWD/eMqhwSeL86m+bLMq2vo3333XcXIyMgW4eHhkR07dmy+adOmcgAPPPBAA6vVWi4iIiKyd+/eTbPWnz9/fuW2bdtG1K1bt/WUKVNqZy0/fPiw/4ABAxq3bt26RXh4eOSDDz5YJ+uzunXrth4/fnzd1q1btxgyZEijwsS3cOHCCoGBgVpWMgeoX7++HUAphd1uV/EJif5nkzOqHo9LqlOjXsPguJTMmjmTeX6UUqSnpWK32bDb7aSmJFOjdt2L1gsICMCWmYHT6SQtNYWAgEDOnDrJ4h+/Y9Td9xfma5WEpsDTwC5Xzf0xLOYal9pIlD3Lli0Lbd++ffPmzZtHhoeHR37++eeVAP7444+Qdu3aRYSHh0e2bt26xS+//BIKcNtttzWcNm1azaztd+/eHVitWrU2GRkZ6scff6zQtm3biBYtWkQ2bdq05RtvvFEta70hQ4aE3XrrrWEdOnRoHh4e3hJAKdX+7NmzfgD33HNPvVatWrWIiIiI7NChQ/Nt27YFZW2rlGofHR1dq3Xr1i3q1q3b+s0336ya9dnmzZvLde/RIyI8okXbiJatrnzj7XcbJWfYK50+ddL02PixjLihD0Ou7co7018o9O8m/nwc5+POclWPXgCENW5KhYpmVv++DIA9O/+h+zV9AahStRrhLVqx6Mfvs841F9TQnU4nz055kOjnphMYmKOvnVIkJSYCkJamn0vMlSpfFI9/gD9prnOT5nSiTCa++WwOXXpeQ70GDcmwO4LPJGXU3XsqqdXek0ktTiak18y0O32ykllavDahHzt2zH/cuHGN586da927d+/OO++888zQoUObOJ1O3n777cNhYWHpu3fv3rlixYp/s7aJj4/327p16+6NGzfumjlzZs2DBw8GAIwYMaLR/ffff3r79u27/vnnn51btmwJnTNnTvZfaFxcnP+2bdt2LVy4sFDt0jt27AiuUqWK7YYbbmjcokWLyL59+zbZuXNnIEDrKzuqMfdMyIxo3rx1y2aNwz75YFZQ9HOv5LmvLRvWMbR/D0YP6scvi37IXn7LyDGEhFbgmnbN6N0unOSkRIaPufui7cOaNKNjlx7cdv3VHDt8iKjBQ3n12SeY9ORz+Pt7VBlqC0wHDmMxf4rF3MHgeISHOHXqlN+wYcOavvjii8f27Nmzc9euXTv79u2blJ6eroYNG9bkqaeeOr53796dr7322pERI0Y0TUhIMI0bN+7sV199lZ2o33///Wo333zzuaCgIK1r166pGzdu3L1r166da9as2f3qq6/W3r9/f3YT3N9//x3y66+/7jt48OA/OWOxWCwnd+zYsWv37t0777nnntMTJ05s4P55UFCQtn379l2LFi3a98QTTzTIyMxUJ84nVx18y62RA4fdEfrtsj/9vv11DX2jbgLgqUcmMGz0XXy5aDnzlvzBP39vvaCcF0TlKlWpXqMmS39aAOjN79YD/3L8yGEAIlu3IXbBfACOHrKybdN6jh49ot84V0rT3Do/fjb7Xdp26EzkFW0vOs5z/3uXd197kf6dW3Fjz448OPVpqtWoedF6I8bey4olsYwa1I/R904kOTGRX39eyO13jb9o3XS7I+R0Unq9PSeTrrCeS2mUnGEvhX4DvsejzuSFsXLlytDw8PC0Tp06pQGMHz8+bsqUKQ2yknRuRo0aFQdQu3Zte7169TL37t0bVLVqVce6desqTp48OWDy5MkApKammnbv3p3d8ezOO+88ZzIV/trHbrerdevWVVy5cuWuDh06pMe8Mr3GrcNua/79r2sc+w8cCF66+Gd+WrWJGrVq89Xc2Uy5/04++X7JRfvpeW1/+g0cRHBwCAf27eG+kYOpVacuV1zZkT//WIGmOVm+aQ8mk4mnJ03gvddeYuKUpy7az8QpT2Uv/23pz9SqU5c69Rvw9KT7SUlOYuSNvf3H3nh1ob9nCQkCRgGjsJjXAW8D38jjcGXXb7/9Vr5Ro0bp1113XTKAn58fNWvWdKxfvz7YZDIxZMiQRID+/fsnV61a1bZu3bqQ/v37J9vtdlauXBnSo0eP1Pnz51f94Ycf9gGcOnXKf+TIkWEHDx4s5+fnp8XHx/tv2bIluEmTJjaAm2666XzlypWducWycOHCirNmzaqRkpLi53Q6SUhIuOBcOm7cuHMALVu3cfj5+6vVf/97RUJCgn9GRgbXD7ole73KVaqSmprC+jUriTv7Xz/R1JQUrPv/pbBmfPQFM156lo/efYMm4RG063gVfq4L9uffmMn/nn+Kof17ULtufTp1uxo/f3/XiBSguWro+3bvZNnin5jzbWyux5jz7hs8OHUaA26+laOHrNx5axSRV7S9qMm9es1azPriu+z3j943hslPv8CGtauY/9kcAgMDeTB6GnXq/XctpKGpxDRblcQ0W5XgAL+UquWDTlUOCTivSvm5d2/ltQm9KIKDg7MLp5+fn2az2bIH1tm8efOukJCQXB/PqVChQq7PVm7atKncyJEjGwN07Ngx+bPPPjvs/nnDhg0zW7Rokdq23ZW2Y+fT6vYefHuNJx6PNiWnpbP854U0i4ikRi295f+moSOJeXoqtsxMAnI0cVWukt1iR+Nmzeneuy9bNvzFFVd25NsvP2HATbcQVE6//hhw86189M7r+f4ekpMS+eT9t5n5xXd89M4bdLiqG1GDhzKi31WBt/XtTHCwx3Wiv8r1eg2LeTowC0uC9JYVeXJPACNGjDj3wQcfVEtMTDxfuXJle8eOHdMB7rnnnob9+vVLWLJkyX6TyURkZGSLtLS07Cv38uXL51ru9+3bFzh16tQGa9eu3dWyZcuMv/76K/jaa69t7r5OQFA5v8NxqXUSUm3V/Pz8VabNnve51nUO+uzHX7PLcV4m33sHR6wHAJj99Y9Uqlzlgs+bR7Zm5uffZr8fdE1nmroSbd36DXh99qfZn42//RbCe/TIOudpmqvBdsv6Pzl+9DA39tQfaDh75jT7ox/m7OmT9L1hECuWxjL9vTkA1GsYxhVXdmTrxr8uSujulv28kHoNw4ho2ZpB13Tmi5+W8c/fW3nvfy/zwhszc90mzeYIPXo+tfHpRFNG1fJBJ4NNmgwmcwle2+Teq1evlL179wZv2LChHMDs2bMr16xZ09aoUSNbpUqVHElJSQV6ntJsNjs7deqU+NRTT2XfU7darQHuTW95ad++ffru3bt37t69e2fOZA5w06Cbk0+eOh28csue1udSMmr9sfwXU6NmzQkICKBuwzC2bPyL1BT9b/SPZUtp2LjpRckc4NSJ49n/PnfmNBvWrCKi1RUA1GsQxp9//IamaWiaxqrlv9CkeYt8437z5We59+EpBAeHkJaailIKpRQ2u11l2jy6AlwbeAPYj8U8EYs5l4E0hK/q06dP8qFDh4KWLFlSHsDhcHDq1Cm/K664It3pdLJgwYKKAL/++mvo2bNnA6666qpUgLvvvvtcbGxs5VmzZlUfNWpU9iMgCQkJfmFhYRkmk4nFixeX37NnT4Gaec+fP+/n7++vNWjQwOZ0OpkxY0Z2fw+7w+kHcOBcWmR8amZ1DS37yiKsSTPKBYew+If/Eu75uHOEhJanY9cezHlvRvby0ydPcOrEsYuO/b/3P2H+0lXMX7rqomQOcObUyex/f/flJwQHh9CpW09AP3c4nXqdZs3vyzmwbzc33XyrftGiVHYNfejocSzftJvFf/7N4j//5op2HZgWM4Oho8dR0VyJ4OCQ7N7z5+POsX3LRprmc85JTEjgi49mcd/DUwFIT0tFmUyYlInUlJQ8t8uS6XAGnUhIa2g9lxaeatMqnk9zlKmKaGFcxnPoxj4rXqdOHfuHH3544I477mhkt9uV2Wx2zJs3b7/JZKJz586p4eHhac2aNWtZv379DPf76Ln55ptvDk6YMKF+s2bNWiqltODgYOesWbMOZTW9FZbTqalTSek141JVradiZvjdf8dQNE2jfIWKTH/nQwD6XHcD/2zbzPCo3gQGBhIcEkLM2x9k7+P+0bcyYfITtGzTjnmffMhvvy4mwN8fp9PJ7XeNp7OrkI5/JJrnoh9m8LVdAWjUpBlPx7yRZ2xbNqwjPT2dLj2vAeC2O+5i6sS7+Hjmm9w25CabuWIFb0iSddCb4KdgMb8IzJGm+NJh5PPi1atXd3z99df7H3vssfoPPvigyWQyMW3atGMjRoxImDdv3v6HHnqowdSpU+sFBQVpX3zxxX6z2ewECAsLs7Vp0yZl+fLllT755JNDWft74YUXjj388MMNYmJi6rRs2TL1iiuuuHR2ATp16pR24403xkVERLSsXLmyfcCAAfEAJxLSap1LzqwFbtVdN/7+/rz50Re8/PRUPnzndUwmE0NHj+PW28fy0luzee25JxncpwtKKYJDQnk65nVq5tLBNT/fffkJsQu+AU2jUdNw3vjws+zWipXLljDn3RmY/PyoXrMW73wyn+DgYMiIZ+Evv5gWrVzh/+irH+a7fz8/P16d+TFvvDgNh92O3W5n5LjxtGmf99OnM15+hvsmRVMuWH9q+O4HH2VEVG8CAgKwvPp2gb+bw+kMSM50Vn5h1fmFdy2KfcAaE5X7PYEyrKBjuUf4+/svadasWXJISIg0deZB0zTiUjKrnErMqFfsIzmVgqaB55ND7PEXjPyUbtc4eOwMjdZMplzyEaNCu5QDwGQsCT8YHYgvkXJ/aZqmcSY5o/rZpMza3ljmK+BIrxV/opwptFyyI9SPfzMrl7/0VsbQ7JmcPn4Uy2+nOZbkAH2QqkesMVG7DA7NY3htk7unSUq3he47ndziWHxaI28s2F6uMbAAi/lXLOb87zcIUUzOp2ZW2n0yqdXJhPQGXl/mFRc9h+4F+gN/h0XHvhUWHXvxc3NlkCT0y2RzOP0Pnk1pfPBsSkTpDNEo8nEtsA2L+WUsZvm/ECXC5nD6Wc+mNDoSl9rE5nAGXXoLb6A0Z3GM/Vr6/IEHgH1h0bH3h0XHFqjvlK+ShH4Z4lMzzXtPJbVMSrfJ1aHnCACigX+wmK81OhjhW+JSMivtPZXUKjHddnGPNG/m9tial6oKvANsDouOzTlZVJkhCb0InE5NHYlLbXA4LrVpYUZ1E6UqDPgFi/ktLOZSH8Nf+Jaslrij51Ob+GqZd+YxpaKXuQJYHxYd+1BYdKwvfJ9CkYReSCkZ9uC9p5Miz6dmVr/02sJgCr05bnOJzg4ofFpcSmZln2+JUxrOnJOle69ywAzg57Do2FoGx1KqJKEXkKZpnExIr3ngTEoLL5i+VFwoAvgTi/lpLOYyfY9NFJzN4fRz1cob+2qtPItCaT6U0LNch95pbuAl1/QRRf4jLal5kcHz5kbOtDsDDselNkrNtFcwOhZRZAHAc8C1WMy3ynzsRbMrokWJlPsWu3d5VJlPzbSXO3QutZnN4fSGcRmKgeYrTe45VQcWhkXHzgImWWOi0owOqCRJDR19VrTWrVu3aNKkScumTZu2vO++++o5HPoASnEpmZX2nU5qWZBkfvTwIW4b0Iuh/XswuE8XHr1vDInx8QCkpiRz38ghXH1FE7q3bFiguM6dPcM17cJ5eNzI7GWLf/wue/+D+3Thk/ffyXN7g+c/91Q9gU1YzB0vuabwGnv27AmcPn36BbfB6tat23rt2rWF7j9xPiWz0oEzKS2KmsxXLlvCTb06MbBHex65exTJSYm5rvf13A8Ycm3X7PL8xZz3sz+b+XoMvdo0ZWj/Hgzt34PHH7h4wiWAxPh4xg0dyJBru/LiE5Ozl8edO8u4W2/AVtCRHxV4aS/3groP2BQWHdvS6EBKks8k9AL/4eaiatWq9nnz5u3fv3//P3///ffODRs2hL7zzjtVj8Sl1nd1gilQM22NmrWY+91i5i9dxffL/6R6zVrMfCMGAH//AMZOeIj3v/qhwHE9H/0IPfv0v2BZrTp1ee+zb/l++Z98smAJ33w2hw1/rr5oW/f5zzt06c6i7+cB5Jj/vMyqB6zCYh5rdCCieOzbty9ozpw5l9WvRdM0jsen1TlyPrVJ1tzkdru9UPtITUnG8tiDzPjwc35atYnqNWsx+81Xc103avBQvlu2lvlLV/HpD0v59P232bXj7+zPB9x8a/Ywry+7jSLpLvaH+XTs2oPvlq3l4P597HNN2f7ac0/y0OPPEBBQ8MfjfbDJPacWwNqw6FifffrFqxO6Uqr9I488UqdVq1YtJk6cWG/9+vXB7du3bx4ZGdmiSZMmLd3nPJ80aVKdqKioxr17927apEmTlldddVX4qVOn/AC6deuWFhkZmQkQEhKitWzZMm3voWO1zqdmFmpO7sCgoOzhDR0OR/Y46Vmfde7WkwoVzQXa1/dff0bd+g25slOXC5a363hV9lSFFSqaCWvaLHt6RHcePv+5JwgC5mAxv4vF7N2DgpQh3333XcXIyMgW4eHhkR07dmy+adOmcgAPPPBAA6vVWi4iIiKyd+/eTbPWnz9/fuW2bdtG1K1bt7X7+eDw4cP+AwYMaNy6desW4eHhkQ888GBd67mUJmeTM2pf3+UK3njpGUbc0IenH7l4qs/8rP5tGREtr6BR03AAho0ex+Ifv891XfdzQVpqaqEvHkCvKKSnpeJ0OrFlZhAQGMia35ZR0VyJK64sRCOUQvl4DT1LRfTOcmOMDqQkeHVCB33WtB07dux6//33jzZr1ixj9erVe3fu3Llr27ZtO3/66afKy5cvD81ad+vWraFffvnlwf379/9TrVo1+4wZMy66orceOhz48+Il1br3ub5IHd9smZkM7d+Dq9s04fDB/YyfFF3ofRw9fIhvP/uYB3KZAtXd/r27+XvTBq7qcfGUp140/7nRJgCLsZg9dshLoTt27Jj/uHHjGs+dO9e6d+/enXfeeeeZoUOHNnE6nbz99tuHw8LC0nfv3r3Tfe6G+Ph4v61bt+7euHHjrpkzZ9bMml55xIgRje6///7T27dv37Vp69//btn+T43vvv22UtZ2CefP88VPy/KsGeflxLGj1K5XL/t9nfoNOHv6ZJ7J+tfYH7m5Txeu79qG0fdMpIVr0iWAXxb9yK39unPXsBtZv3ZVrttHDR7KEetBhl3Xk87de1GjVm0+ePt/lzx35Mbhm/fQcxMAfBwWHfus0YEUN69P6OPHj8+ePSk1NdU0fPjwsPDw8Mj27du3OH78eOCmTZuyRwy7+uqrE2rVquUA6Ny5c/KBAwcuGOXp9NlzgTfceFPLMeMfUi3btCtSPAGBgcxfuorfNu8lrGkzvv1ibqG21zSNZx6dSPQL07Nr+7k5deIYD48byVMv5z2Bw8QpTzF/6SpemzWXdX/8fsH855PuGc2ShbnXHMqgPsBvWMzyKKIHW7lyZWh4eHhap06d0gDGjx8fd/r06YCsJJ2bUaNGxQHUrl3bXq9evcy9e/cGJSYmmtatW1dx8uTJDSJatGjVsWPH1oetB03u84/feOtwSmMO7r5RN7Fg+Z/8+PsGYhfMx7p/HwC33j6WxX9u45tfVnP/o08wZcKdHD96cUtcSEgo/3v/E775ZTUTJj/Ou9NfZOyEhzhsPcij943h0fvGsGfn9gLFUkZq6O6mhUXHzg2LjvWZFjqvr6plzagEMGnSpLpVq1a1//PPPzsDAgLo169fk/T09Ow/0nLlymWPhOTn56fZ7fbsz06dOVeu/3XXR/bqF6VG35N7k/T+vbuzO6e07dCZJ158Lc+4AgIDGTR0JM9OeYix4x8q8PdJTkpk365/mDrhTgBSU1JIT0vj7ttu4oOvfwT0qRXvGX4zdz/4KP1uGFSgfeY2//mt/XvQq+/1+V44lCEdgNVYzP2wJBy65NrCKwQHB2efH/z8/DSbzZY9IdXva/86diaNxrlNUBUSmnuDzaXOAbXr1mPdqt+z3x8/cphqNWpdslWsbv0GtG7XnpXLlhLWpFn2bTXQb7NFtLyCnX9vpU69BnnuY/uWTcSdO8PV117HmMHX8+Kb76NpGtMmTWDOt/lPTKaUVpZq6O7uAOqFRccOscZEJRgdzOXy+hq6u/j4eP969eplBgQEsG3btqA1a9ZULMh2J8+eC7nu+gGRXa+5Vt3z0KN5rtckPCK7k0puyfz40cOkpaUC4HQ6+WXRj4S3KFynygoVzfyx/UD2XMSTnnqeLj2vyU7mZ06d5J7hgxg7/iFuvHV4gfaZ1/zndpsNmy2zUPH5uHBgDRazT/eE9Va9evVK2bt3b/CGDRvKAcyePbtyzZo1bY0aNbJVqlTJkZSUVKDOq2az2dmp81Vpz77wclPN1fktr/nHc7rUOaBbrz7s2rGNg//uBWDepx9x3Y2Dc93X/r27s/8dd+4s69esyj5fuMdy6OB+9uzcTtOIyDzjstlszHjZwqPTXgQgLS0VpcBkUqSmFmhWWOXUlE/lg0LoA6wJi46tb3Qgl6vINXRPe1YcYNq0acfHjBnT+KuvvqrasGHDjKuuuir350XcJKXbQl+a/kb4jm2bVVpaKisW/wToTWF3P5h3cs/N3l3/8M70FwA9obdo3Yapz72S/fktfbtxPu4cyUlJ9O3Yko5du/PSm/qjKkP79+CdT+ZTo1btXPed5b3/vcyJY0f5cs4svpwzC4ARd97HoGEjc10/v/nPbxg8rMCd9MqQuug94PtiSfC4v3GjGfm8eJ06dewffvjhgTvuuKOR3W5XZrPZMW/evP0mk4nOnTunhoeHpzVr1qxl/fr1M9zvo+cUl5JZ+aV355Z79bknLnv+8ZxCy1fAMv1NHr7rdhx2O02bt+D5N97L/ty9nH8xZxZb1q8jICAATdO4fdx92eX07ekvsPPvrfj7+2Py8+OJF14lrHHTvA7LJ7PeYuCQ26haXe/HO2Hy49x/xzAAJj1ZkFvFGo6y1+TuriXwW1h07NXWmKhLX9l5qDI9H3p8aqb5yPm0xllX6WWdF8+HXhLOAb2wJOwwOhCj+GK5j0vJrHzsfGpjb56FpDhlzYfuXzkweS81Qh2a5z67lst86CVhL3C1NSbqZEkdoCSV2UQWn5ppPhKX1kSSuchDVeBXLOZmRgciisf5lMxKx86nNZJknguF8uRkXorCgeVh0bFe2UG2TCazlAx78NHzaY01NPkDFvmpBSzHYi7Y0H7CY8WnZpqlzOdLrnP+Ewn8GhYd63X3Iwua0J2ApmneXxgy7c6AQ+dSmzmlZl4g2XdkCnBrxkfVB1ZgMdcxOhBRNEnptvJHzqc1kWSeD/nN5NQG+DEsOtarJuIqaKe4406nM+H06dM1a9SoEaeU8sqzu9OpqSPn0xrZHE6fee6wOGUoh8nk+O+/VtPgTIoDlZFIQPrZfLb0eY3RB5/phiUh2ehgRMFl2ByBh+NSm/hCZaQkSWt7rq4GvgqLjr3FGhNVYjfti1OBEnr79u2TN23adF98fPysxMREM156PZeQ7qyR4dDkoes8aH4pfkHO1AuWqYxE6m39H34On+gTdTmuAD7HYh6MJcF5ybWF4RxOzWQ9l9rU16c+LRZKmtzzMAiYCdxjcBwFUuA/9Pbt26/btGlTd6fTWQcvvPc+7fe4qfHpjjFGx+HJ/hc05+8Idv43ALSmEZB+VpL5f24CXgamGh2IYSzmkpk2uZgfEdQ0jUPnUhpl2B1yAV8gXllHKy13h0XHbrTGRM02OpBLKdSVa/v27ZPRu/V7lbDo2PHAGKPj8HRa+pG0cn5l6tG0opiCxbwTS8InRgdSFn3++eeVpk2bVjcwMFD79NNPD2YNA5vT8fj0uskZ9koF3e+GP1eTmZ5Ot2uKNhHX31s28vzUh0lPT6dm7Tq8OGMWNWtf3O1ix9bNTLdEs+efHXTpeQ0zPvrigs83/rmG1194mrT0NNA0LK++RZv2nS5Yx2az8dh9Yzh25BD1Gjbi1Zkf4+/vT0Z6OveNHMybH31JxUoF/uoA0rvg0t4Ki47dbI2J2mh0IPnxupp2YYVFx14HvG10HMKnzMZi7mZ0EGXR7Nmzq0+dOvXE7t27d+aVzM8lZ1Q5l5JRq6D7tNvtbPxzNWtWLi9STE6nkyceuIfHLC/z0x8b6XFNX1599olc161esyaPPfMyjz7z4kWfnT55gqcnjeeFGTNZsPxP5i1eSaOmzS9ab+3K5VSsVJlvfllNhYpm1vy+DIDZb77KbWPuLnQy10lGv4Qg4Nuw6NgqRgeSH59O6GHRsa2B+UCBhoQUooACgflYzNWMDqQsGTNmTP2NGzeWf+655+q2a9cuAi6eTnXNXxsrH09ID9vw52qG9u+Rve2+3Tu5vos+k9mxI4fp3rIhb7z0DMOuv5qv537AN59/zM8LvmFo/x7MmjG9UHHt/Hsrfv7+dOqqH++W28ewctkSMtIvvlVVs3ZdWrdrT2Bg0EWfzf/0I66/6RYaN9OTeGBQEBXNFz85lTVlKkB6WioBAYHs3bWDg/v30X/gzYWKPYvU0AukIfBFWHSsx+ZNn+0sEhYdWwH4AahgcCjCN9UBPgYGGh1IWTF37twjO3fuDHnggQdOjRo1Kj5rOtVffvllT6dOndLenTmz+ujbRzb+fsW6S+4rKTGRpuEteOSJZ13vE0hKTGCK5eVCx3Xy+FFq1/1vGPDQ8hUILV+BM6dOUq9hWIH3s3/fHurUrc89wwcRH3eOdp268NDjzxASEnrBel16XsOynxdya7/utL6yA5269WTCqFt4/vV3Cx17FunlXmDXAU8DHjn1qsdeaRSDN9AfNxKipNyAxfyg0UGUVTmnU73+llEVz5w+yemTxy+5rX9AAFGDh5Z4jIXhsNvZtH4tr82cyxeLVpCYEM/M/8VctJ7JZOKZ6W/yzS+rmRYzg6/nfsA1/QdgtzuInngXk+4ZzV9r/jDgG5QZ08KiY/sbHURufDKhh0XHDgTGGR2HKBOmYzG3MTqIsu5cckYV905wfn5+OBz/PTqcmZFxwfrBwcGYTAU7/SUmJDC0fw+G9u/Bw3fdftHnterU48Sx/zqTpiQnkZyUSPWaBb6Nr++nbj169O5HxUqVCAgI4PqbhvD3lg35bnP86GFWrfiVYaPv4t3XXmTIyDE8//q7xEwr3IMYZXtelkIzoTe9e9wIkj6X0MOiY6sBHxgdhygzgoCvsZhDjA6krMmaTvXPv9ZXOJmQ3mDxj99RvVYdatSqQ70GYZw4dpS4c/qASIu+n5fvvkLLVyApMffJGSuazdlTps748POLPo+8oi12m431a1cB8O3nc7n62usIKle4QcYGDLqFDWtXZV98rP5tGc1btMp3m+nPPM5jz7yIyWTSp0ZGoUwm0gs2ZWo2eQi90Kqi33LzKL54D302UNPoIESZEgHEAL7f/O5BU8pmTad657i7mjgcDr+K5kq8NvNjlFLUqFWbMfc9yMiBfaharQbdL/E4Wu/rbmDR9/MY2r8Hva8fyH0PTylwHCaTiZfeep/nox8hIyODGjVr8eKbs7I/v3/0rUyY/AQt27TDun8fd982iPS0VDLS0+nbsSV3TXyEYXfcRdsOnenV73qGXX81JpOJJuERPPXyG3ke9+cF3xAe2YqmzVsAcOeEh3lu6kPYbDbufuixAscPSpNe7kVyTVh07B3WmCiPeYS1QNOneouw6NhhwNdGx+Gtvgp44Y8ufjt7Gh2Hl3ICXbAkrDc6kOLiDdOnnk3KqHo8IS3M6Di8VQUc6bUSTgY6a5RP2Z9h9ugOxKU0fWphnQMirDFRHjE2ts80uYdFx1YC3jQ6DlFmmdCfT/fFVi+PlGF3BJ5MTK9/6TVFvpTSpJd7kVUF/md0EFl8JqED05GmdmGsNsAjRgdRFmiaxpG4tIZOTZMxJi6TUkpzSq+4yzE6LDq2j9FBgI8k9LDo2O7AXUbHIQRgwWIOMzoIXxeXklklNdNe0eg4fITmlBr65ZrlCVOten1CD4uODUTvCCd/kcIThADvGB2EL3NqmjqdlFHX6Dh8hjS5F4emwFNGB+H1CR24G2hhdBBCuInCYu5rdBC+6nRiRg2bwxlodBw+QympoRePKWHRsS2NDMCrE7qrdh5tdBxC5OJVLGavLl+eyO5w+p1LyahtdBw+RYETuYdeDALQH181jLf3yB0H1DM6CCFy0Qa4HfjU6ECKU+tPWpfIfOjb79heoOfbTyWm13I4pSNc8VKaQ2ZnKS43hEXHtrfGRBkyXoPX1iBctfPHjY5DiHw8i8UsTcPFJMPuCIhLtcmTLMVNoUkNvVhZjDqw1yZ0YCwgz6AKTxaGPH1RbE4mpNfVtOKpSo679QZWLIkt9HbvvvYSsQvmAzDz9RimW3KvU6xctoRxt95wWTGWpiVLlvrd1KsTA3u055G7R5GclPswuE6nk5efnkJUt3bc0P1Kvpo7O/uzP5Yv5bYBvejQpGaevxeAxPh4xg0dyJBru/LiE5Ozl8edO8u4W2/AZrMV3xczxg1h0bEl0pJ1KV6Z0MOiYwOQ2rnwDk9iMQcYHYS3S820l0tIs1Ut6Pp2u71E4rj/0SeIutmzZmm7XMmpqdqURycHzfjwc35atYnqNWsx+81Xc1039vv5HNi3h4V/bOSLn5bzyay3+XfPLgAaNGrCs6+9zZh7H8j3eLE/zKdj1x58t2wtB/fvY9/unQC89tyTPPT4MwQE+ERxsRhxUK9M6MAY9MnmhfB0dYARRgfh7U4mpNcFaFO/Mu9Mf4Gh1/VkYM8O2bXlrM/e+9/LjIjqzVsxz5KSnMSzUx5ixA19uKVvN56b+jC2zMzs9f9as5IRUb25ofuVvPbcU2QNg52z9j753jv4cf6XADz9yAQ+/3DmRfHZbDZefGIyA3u0Z8QNfdiwdvUlv9OZUye5d8Rgbu59FfeOGMyUCXcy83W9T9W1HSI5ffIEAI+NH8voQf0Afda4nq0bk5mRwY/zv+Se4YOYev84hlzbleEDruHoIWuhfq8AS37/3S+yZStno6bhAAwbPY7FP36f67pLf/qewcNH4+fnh7lyZfoPvJklP34HQFjjpjSPbI2ff/5ds/z9A0hPS8XpdGLLzCAgMJA1vy2jorkSV1zZsdDxeyhDaulel9BdtfMnjI5DiEKYfOlVRF7SbY4g96lRUYr5S/5g5mffEvP0VI4dOZz9kcnPjy9jVzDpqef53/NPc2WnLny5aDnf/LIap9PJF3P+mzTlwL49fPLDUr75dTWb1q1h8Q/fFjnG776Yi/XAv3y//E8++X4xu3Zsu+Q2rzwTTZv2HVmwYh0vzpjJxnVrsj/r3L0nf61eidPpZM/OHSQl6VOybtmwjhat2xIYFATAP9s288DUaXy3bC2de1zNnJkzCh374ePHVZ16/929rFO/AWdPn8y1lePEsaNcsG69Bpw4frRQx4saPJQj1oMMu64nnbv3okat2nzw9v94YIrhj3EXN0tpH9DrEjowGv3epBDeojUW83VGB+GtziRl1HB/P3j4KADqNQyjfeeubP5rbfZnNw8bmf3vFUtjmTvrLYb278Gw63qyef2fHLYeyP584JDbCAgIIDg4hKjBQ1m3emWRY/xrzR/6/gIDCQgMZJBbHHlus3pl9nrVatSkZ5/+2Z9d1b0X61b/zu4df9M8shWduvZg45+rWbf6dzp3/2/+pCuu7ES9BnpjZZsrOxWpho5SFFPXhAIJCQnlf+9/wje/rGbC5Md5d/qLjJ3wEIetB3n0vjE8et8Y9uzcXmrxlKBSr6V71WNrYdGx/sCTRschRBE8CiwxOghvY3c4/RLSbNXyXcktFwWHlP/vjabxv9mfEta4aYGOpVwdvf38/XE6/5vNKyOj8BPNqSJ0GnffpnP3q3kz5jkaN2tO5+5XU7VaDdatXsnfmzbw5Ev/zQUS5KqpA5j8TDhyqVXv37ubxx+4G4C2HTrzxIuvXfB5/Tq1nT+v25h98ONHDlOtRi38c2k6r123HsePHqFN+076ukcPU7tO0Z8c3r5lE3HnznD1tdcxZvD1vPjm+2iaxrRJE5jzbeE7LXqgx4FbSutgXpXQgduARkYHIUQR9MFiboslYavRgVyOgj4vXlzOJmdWc2raBS2JP87/kvGTojl25DCb1//JY5aXc932mv5RfPzemzwd8wb+/v4kxscTfz6OBo0aAxC7YD7XD7oFh93Ozz98y6i7xgNQP6wR27ds5NoBN3L08CG2blhH3wE35RvnVd2vzt4fmpZ9zz0/nbr1ZOE3X3HfI1M5d+Y0fyxfyi0jxwBQo1ZtKlSsyLeff8wHXy+kUpUqvPrcE6QmJ9OidZtL7ttdk/AI5i9dlefn113Ty/7wcy+WO/jvXho1DWfepx9x3Y2Dc123b9Qgvv/qU/rdMIjkxESW/rSAtz8u2ozVNpuNGS9bmP7uRwCkpaWilH5hk5qaUqR9eqCbwqJja1tjok6UxsG8LaGPNjoAIS7D/ehDFYsC0DSN86mZ1XMudzgcDL2uJ2mpqUx9Loa69Rvkuv1jz7zIjJefZWj/HphMJvz8/XnkiWezE3qjpuHccfN1JMafp1e/AVx30xAAxt73EFMm3MmQa7vSJDyCVm07XDLWwSPu4N89uxjc+yoqmCtxZacu7Nq+Nd9tplhe5ulJE7i591VUr1mL1m3bU6GiOfvzzt2v5o/lS6nXMAyAatVrUK3lFZhMxXun1FyhPM9Mf5OH77odh91O0+YteP6N97I/H9q/B+98Mp8atWpzw5Bh/LNtMwN7tkehGHX3/TRroY92+tfqlTz1yARSkpPQNI1fYxfy5Iuv0qvfgFyP+8mstxg45DaqVtfvqEyY/Dj33zEMgElPPlus39FA/sCdwIulcTCV1bPT04VFx9YEjgEySlQJ+SrghT+6+O3seek1RRElArWwJKQZHUhBbNq0KcLf339Js2bNkkNCQgrf7nyZEtJsFQ+dS2nmvqxN/cqs2mGlotmc12ZeIz0tDf+AAPz9/Yk/H8eoG/vy4lvvc0W7S19AFJcKONLr2M/b9vhXq1BqBy0izZ7J6eNHsfx2mmNJjktv4DkOAY2tMVHOkj6QN9XQhyHJXHi3isBg4AujA/EGcSmZ+d8793KHrft56uHxaJqGzWZj6OhxpZrMs8kgcSWtIdCPUuhD400JfbjRAQhRDMYgCf2SMu1O/+R0t0fVXLYdOW9ANEW3asUvvP3K8xctv/P+R7juxsH53tsuPco7mmm92x1IQteFRcc2Aq4yOg4hikFvLOb6WBKOGB1IATgBrbiGWy2M86mZVTS8f8aQHr370aN3P6PDyJfX/Ja1/7d371FylHUax7/V3TMTINKCEJRrKyAXBcGEoCAQdRHcwkVhkT3HVRSRiyIriNLILqcwXkpE1HXFywpyM6CAiNALooEEApFAIAmEQBZCJQQCCZep3CYzmeneP6o5BDaQmaSrfnV5Puf0gT8mVU9gup9+q9563xbQopnNrx9H1+qNLQPfXf+auh2SiUJHK21JfpSIJncmMklmEz3bbDbDpUuXbjdmzJiXHCe5kVzvir6tW4OZuk+aSUMMOWtaTafVHNjwD1tqtRhcvZwVa4Z4uS/2W9Fx2Izo8bXL4jxJVgpdl9slT44nA4U+duzYlTNnzjy1t7f3l8uXL6/ymie+49NsUXpx9dAW2RyIZcsohkorW32VZU6sA8cOaLFizRC/eKCXNUOZ/c34HDEXeupnudfqjX2BDa+jKJtMs9wTVcMLF1qHGI6ZM2eOJlqTPpGVJS+a3nv0onCtn8S5im735UueOnXZzQu/vuvJE6yzvJlmC17ua2a5zAFawJjAd1+I6wRZGKHrcrvk0VHAz61DDMfYsWNXAvOTOt/06xrfS+pcRbflS6sHBpYsXvXMGN3eSIADHA5cE9cJUr2We63ecIhWhxPJm09YB0ijWr0xiugRH0nI2lIl08PejDliwz+y8VJd6MB4tE2q5NMEvOroDf9Y4RwObGEdokgGyir0BB0e58HTXuiHWAcQiUkPGomuz5svmi4dt7aUhTuvubF9rd54b1wHT3uh69lzybNYL79lTa3eKKFbEYnrL3dl5Un0vIjtfa9CF7FzsHWAlHkPMGaDPyUdNVDqso5QNLFdmUttodfqjR2AHaxziMRob7wc7DLSOQdYByii/nJXansgpw5pT/7suDT/j9ToXPLOAT5oHSJFVOgG+svd1hGKZjMglvU+0lzoB1oHEEnAQdYBUkSFbqC/1K176MmL5XZbmgtdI3QpAt1HB2r1Rg+wr3WOIuordWlb6uTFMtM9lYVeqzcqwFjrHCIJGI9X1QgJ3gdodpaB/nK3Cj15xSl0YB9gc+sQIgkYDexkHSIFdLndSJ8K3cKucUyMS2uh63K7FMme1gFSQIVupL/Uo0JPXhnYq9MHTWuha0KcFEnH39gZNM46QFH1lTRCN9Lxy+5pLfS9rQOIJKjQhV6rN8roKoWZvrJG6EYKU+i6pyhFUvQyewfRJUgxMFDq1mREG/kv9Fq90QVsZ51DJEFFL3StCGmor6xCN7JPpw+YukInenPrMR4pkjF41SJ/qKrQDa0pdWmpOBs7dXqmexoLXZfbpWgcir0pyY7WAYqs3+lWodt5WycPlsZC15tbiqjIt5k0Qje0RvfQLeW+0Iv8wSbFVeTfexW6kabjtNaWemLZ+UuGZetOHiyNhb6VdQARA2+3DmBIV+WslJqsobvHOkaB5X6E3tFvLCIZoRG6JK/s0E/XZtYxCkyFLpJDRb4yVeSrE6aGnFJpkErFOkeBqdBFcqjIE5M0QjTSX9YMd2O5v4f+VusAIgYK+cFaqzdKaJU4M3oG3VzuR+giRVTUD9ai/r1Tob/UpRnutnI/Qu+zDiBioKjFVtS/dyoMlHo0w91WRzs4jYW+xjpAEe3qPLNwz9KiqnWOAivqPXRdbjfUX6pohG5rqJMHS+PsRo3QE9NqfbJ0z8x61zVsx8tjHYddrBMVWBrfi0loWQcospZT1hcqW81OHiyNHyIaocdsNKuXf71y3UOfKU/eudsZHGedRwAYsA5gpKMfaCIZk/tC1wg9Jns6ixZM7Lps8Thn/v6Ow2HWeeQ1ivp7r0KXIhvs5MHSWOgaoXeQQ7N5bPnuB75Zuba8LeH7HYd3WWeS9SpqoXf0HqJIxqzu5MHSWOhF/WDrqLewKjyncu2s48tTal3O0HjrPLJBK6wDWAh8t69Wbwyg2e5STLkvdI3QN8HeTvDkd7oue2Z/54mxuqyeKcutAxhaijZokWJa1cmDpbHQNUIfIYdm8/jylPvPrvyhextn+f7ArtaZZMRC6wCGnkeFLsWkEbpEqqzsPbcyadax5bt363KGDrTOI5tkqXUAQ89bBxAxsqyTB0tjoRfyXuJI7Os8+b/f6brsuX2cp8Y5DhOs80hHPG0dwJAKXYoq6OTB0ljoT1gHSKMSzaHPlP92/1mV60dt5azcD9jdOpN0lApdpHgWdvJgaSz0edYB0mRrwhfP65r08NGle95dcZofsM4jsRhAl9xFiijo5MFSt5Z74LvPAb3WOay935n/WKP73Gkze07b4tjy3RMqTnN760wSm8V4YZGXQFWhSxG9EPhu7ifFATwOFG6SV5mhwRPKt9//b5Ubtqg6q/e1ziOJKfLldlChSzEFnT5gWgt9HgUq9G3oXfYfXVfPPar09z3KTvOD1nkkcYF1AGOBdQARAx29fw7pLfTHrAMkYbwz79GJXb99+d3OYs1WL7Y51gGMPUW0sM6W1kFEEhR0+oAq9IRVGFx7Yvm2GadXbqxu6fS91zqPpMJs6wCWAt9t1eqN2cAh1llEElSYEXruZrpvx0tLz++6at6RpRl7lZ3WwdZ5JFUKXehtD6FCl2KZ3+kDprXQFxA9ypP5DRsOLj3yiFe5PNzNeXa81laX9ViCF75gHSIFZlkHEElQC7iv0wdNZaEHvjtYqzeeBPayzrIxuhgcOLl8y/2nVf681WhnjS6ry5vR6DzykHUAkQTNC3y3t9MHTWWht80jY4X+Dl58zuu64rHDSzPfU9JldRkeFXrkUWAt0GUdRCQB0+M4aOoWlllHxy9HxOXQ0uw5d3SfNf3enq9uc0T5gQklp7WtdSbJjLutA6RB4LsDwFzrHCIJuTeOg6Z5hH4r8APrEG+kh4E1p5ZvfuCUyi3bbO70axEY2RhDqNDXNQvYzziDSBKKNUIPfPdhYLF1jtfb0Vn27KVdP5wyr+cLq87suuFDmzv9e1pnksx6EC9cbh0iRWZYBxBJwMvE9Gh2mkfoEI3Sv2QdAuCjpZmzzq9ctWZnZ+kBjoPWVZdOuNM6QMrcah1AJAH3Bb4by94NqR2ht/2P5clH0d93duX3d8/r+fz8S7t/tN8upaUfcBzKlpkkV6ZYB0iTwHcDoslxInkWy/1zSP8IfTIGz6Pv4jy3+NuVy584pDTnfSVHi11ILAbR/fP1aQB7W4cQiVFs7/tUj9AD310BTEvqfEeWZjw0reeM+6Z0n7X9YeU5E0oOWyV1bimcKXjhSusQKdSwDiASo2XEWOhpH6FDdF/tI3EdfHPWrDqj8seZny//ZftRztr94zqPyOvcYB0gpe4BQqBqHUQkBn8KfHcoroOneoTeFst99Hc5zy78Xdd3p87tOXHw1Moth45y1u4Wx3lE1qMJ3GgdIo0C3x0EbrfOIRKT6+M8eOoLPfDdR+nYrjSt1lGl6TPv7Tl9xuTus3c6uDz3MMfRSEASdw9e+Lx1iBTTZXfJo5eAO+I8QRYuuUN02f3Ujf3DW9C38szK9Q9+tvzXnXqcwbEdzCWyMWL9lp4DtxJtXuFYBxHpoJvaV6Bik/oRetufNuYP7e4sDq7tnjj1kZ4vNk+q3HpojzP4zg7nEhmpFvBH6xBpFvjuUmJ8tEfEyHVxnyArI/S/El1232XDP9pqfbJ0z8xzuyYxht6xjkMt5mwiI3EnXpi6FRBT6HJAGxxJXvQCf4v7JJkYoQe+2wT++81+ZjSrl19QuXzq/J4TFv6k+5Jx2zm94xxHl+wkdX5lHSAjfg+ssg4h0iF/Dnx3bdwnyUSht11GtBjHa+zpLFpwfbd318M9J5VOqNx+WLczWEs+msiwLEWz24elvQZF7JcoRRIyKYmTZKbQA99dAtwM4NBsHleeMuP+ntNm3tpdf+e40vxDHYfRxhFFNuRyvDD2b+k5cql1AJEOeIKEHsXMyj10AN7Kiku+Ufn91p8uT31nlzM03jqPyAi0gF9bh8iSwHen1eqNh4F9rLOIbIJL4tqM5fUyM0IHmDXqlMmfqdzx9i5naGfrLCIjNBkvfNI6RAb93DqAyCZYBfw2qZNlqtDxwhbwU+sYIhvhR9YBMupqoqVgRbLo6sB3e5M6WbYKPXIF0Yo7IlnxAF54m3WILAp8dxXRI2wiWdMELk7yhNkrdC9cTcL/kUQ20fesA2TcxUTbKItkyU2B785P8oTZK/TIT4EXrUOIDMNcNnKlQ4kEvrsITSiU7PlB0ifMZqFH+0hfaB1DZBi+3577IZvmu8Bq6xAiw3R34Lv3JX3SbBZ65L+IFuoQSasngGutQ+RB4LvPAT+zziEyTN+yOGl2Cz26l574JQ2RETgHLxyyDpEjF6IZ75J+NwS+O83ixNkt9MgvAG10IWk0FS/UrmodFPjuS2hCrKTbAPBNq5Nnu9C9sA/4unUMkddpAmdah8ipHwMvWIcQeQM/C3x3gdXJs13oAF74B2CydQyRdVyBFz5kHSKP2pu2+NY5RNbjBWCiZYDsF3rkq4A2vZA0WAmcZx0i534OPGUdQuR1vMB3Ted45KPQvXAeWhJW0mEiXrjEOkSeBb67BviSdQ6RdcwDfmUdIh+FHrkAeNY6hBTag2jN9kQEvjsZ+I11DpG2bwS+O2gdIj+FHi02c6p1DCmsQeCLekwtUWcDz1iHkMK7PvDdhnUIyFOhA3jhzcCl1jGkkHy8cJZ1iCJp3688zTqHFNqzwCnWIV6Rr0KPnIkmzEiyZgPftg5RRIHv3oxW4xMbLeAL7fURUiF/he6FK4DPET0LLBK3fuBzeKGesrBzBno2XZL3s8B3b7cOsa78FTqAF04DLrKOIYXwNbxwjnWIIgt8dxlRqYsk5VHgHOsQr5fPQo/8BzDDOoTk2iS88JfWIQQC370GmGSdQwphLfCv7ccnUyW/he6FA8CxaEc2icc84GTrEPIaJwGzrENI7p0f+G4qV4LMb6EDeOFi4DiiR4pEOmUV8M944SrrIPKqwHf7gE8BL1pnkdyaSrTrXyrlu9ABvPAutIGLdNYpeOGj1iHk/wt8NwCOB7QegHTaAuC4wHdTO+E6/4UO4IX/CVxlHUNyYSJe+DvrEPLG2qvImW1hKbnUC7jtCZipVYxCj5wM3GsdQjLtSrzwfOsQsmGB714M6IuXdMJa4JjAdx+zDrIhxSl0L1wDHAXMtY4imTSZaNKVZMeXgFROXpJMOTnw3TutQwxHcQodwAtfBo4AFlpHkUx5GDhGi8dkyzqT5LT7nWys7wW+e7l1iOEqVqEDeOEzRKWulaVkOBYB/4gXLrcOIiMX+O5C4HA0811G7g/Av1uHGAmn1WpZZ7DhVQ8A7gBGW0eR1FoETMALtTdAxtXqjXFE7/e3WGeRTJgOfCSNi8e8meKN0F/hhfcDRwIaecn6LERlnhuB7z5ANIemzzqLpN4M4ONZK3MocqEDeOE9wEeB1OyWI6mwEPiwyjxfAt+9C/gEKnV5Y38HPtbemjdzil3oAF74ADABLRErEZV5jrWfUT8KWG2dRVJnOnBEVsscVOgRL3wYOBR4xjqKmHoEOERlnm+B794BuKjU5VVTiMo807dgVeiv8MLHgUOAx62jiIk7gA/hhU9bB5H4Bb47hWj2e6pX/pJE3ER0z3yFdZBNpUJfVzQy+wDwN+sokqgrgSPxwsxeapORC3z3XuBAor2tpZiuBI7N4gS49SnuY2tvxqtWgJ8CX7aOIrGbqOVci61Wb2xJ9MzxEdZZJFHfB84LfDc3JahCfzNe9StExV62jiIdt5po17SrrYOIvVq9UQZ+ApxuHEXitwI4IfDdG62DdJoKfUO86uHAJGAb6yjSMY8R7Weudf3lNWr1xpeJvsRXrLNILOaRkY1WNobuoW+IF/4V2A+YZpxEOuMa4ACVuaxP4LuXEM2A13yK/LkBGJ/XMgeN0IfPq5aBC4Bz0RehLOoHvoYX/tI6iKRfrd7YA7gaGGedRTbZEPCtwHcvtA4SNxX6SHnVQ4CrgF2so8iwPQR8AS+cbR1EsqNWb1SINuc4D12Cz6oXgH9pLyiUeyr0jeFVq8CPgBMBxziNvLEBYCLg44WD1mEkm9obu1wF7GmdRUbkz8CXA98tzIJhKvRN4VUPBX6F3uhpdD9wIl74iHUQyb5avbEZ0WNOZ6Av8Wn3PPDVwHevsw6SNBX6pvKq3UT31c8FeozTCKwiGpVfhBcOWYeRfKnVGx8GLgd2No4i63cZcHbguy9bB7GgQu8Ur7oH8Avgw9ZRCqpFtOrTt/DCZ63DSH61F6L5IXASmiCbFk8Ap7TX6S8sFXqneVWX6NLcPtZRCmQacGZ75zyRRNTqjfcSFfuR1lkKbJBoPtMFge8WfltcFXocvGoJ+CzwbXRpLk4LgHPwwuutg0hx1eqNfwAuAt5nnaVAWsCfgPMD39U8mTYVepy8ag/wFaAObGucJk8WAN8FrtTsdUmDWr3xypf47wA7GsfJu1uIivwh6yBpo0JPglfdDDgBOAvY3ThNls0FLgQmqcgljdqz4c8k+hL/FuM4eXM7UZHfZx0krVToSYouxR8NfAP4oHGaLJkKXAzcjBfqF1ZSr1ZvvI1ot8bTgTHGcbLuTqIi1/LbG6BCt+JVDwJOA44BNjdOk0YvAlcAv8YLH7cOI7IxavVGD9Gl+LOAvYzjZEkTaAA/Dnz3TuswWaFCt+ZVtwQ+DXweONg2TCrcRbRYzw14Yb91GJFOqNUbDvBRolH7P6Etmd/I88BvgF8HvrvIOkzWqNDTxKvuTlTsx1Gce+0t4O9EOyH9ES98yjiPSKxq9cYOwMlE73U9BRM9evYXogV7bgp8d61tnOxSoadVtFDNJ9qvg8nXN/oh4G6iEr8RLyzMWssi62qvE39M+7WHcZykzSZaI/93ge8+Zx0mD1ToWeBVtwY+DhxOVO672QYasSYwh2hyyxTgLryw1zKQSNrU6o29iYr9U8D7jePEYTkwGbgVuC3w3aeN8+SOCj2LvOoY4CCicj8IGEu61pF/kejb9yyie+J34YWFXFtZZGPU6o1diMr9Y8B4YGvbRBttNnAbUYnfq8vp8VKh54FXLQPvIppFuxfR7m+v/LMa01mHgCXAYqKFXua0X7O1lrpIZ9Xqjd2BA9d57Qd0WWZaj5d49XPgQeD2wHeX2EYqFhV63kWz6Ldvv8YQrVi3DbAl0E30odC9zqsM9AEriXYue+Wfq4ClwNPt1xIt7iJio/043P68Wu47t187AqNiPv0gMJ9o9P1Kgc8JfHdxzOeVDVChi4jkSK3e2Jao3Hdqv3YGdgC2ADZrv0YRfYFfd2/3FtEX+GXreb2wzr8/F/juQBJ/FxkZFbqIiEgOaC9fERGRHFChi4iI5IAKXUREJAdU6CIiIjmgQhcREckBFbqIiEgOqNBFRERyQIUuIiKSAyp0ERGRHFChi4iI5IAKXUREJAdU6CIiIjmgQhcREckBFbqIiEgOqNBFRERyQIUuIiKSA/8H5uVgSo/J7D4AAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<Figure size 750x750 with 1 Axes>"
+       "<Figure size 600x600 with 4 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 750x750 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 750x750 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/oli/.local/lib/python3.6/site-packages/ipykernel_launcher.py:25: MatplotlibDeprecationWarning: normalize=None does not normalize if the sum is less than 1 but this behavior is deprecated since 3.3 until two minor releases later. After the deprecation period the default value will be normalize=True. To prevent normalization pass normalize=False \n"
-     ]
-    },
+    }
+   ],
+   "source": [
+    "plot_analysis(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
     {
      "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 750x750 with 1 Axes>"
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
+       "data": [
+        {
+         "domain": {
+          "x": [
+           0,
+           1
+          ],
+          "y": [
+           0,
+           1
+          ]
+         },
+         "hovertemplate": "function=%{label}<br>time_sum=%{value}<br>parent=%{parent}<extra></extra>",
+         "labels": [
+          "Py_kgeneration",
+          "generate",
+          "gasdev",
+          "fftma2",
+          "covariance",
+          "ran2",
+          "cov_value",
+          "cgrid",
+          "fourt",
+          "length",
+          "maxfactor",
+          "build_real",
+          "prebuild_gwn",
+          "clean_real"
+         ],
+         "marker": {
+          "colors": [
+           "#636efa",
+           "#EF553B",
+           "#00cc96",
+           "#EF553B",
+           "#ab63fa",
+           "#FFA15A",
+           "#636efa",
+           "#636efa",
+           "#ab63fa",
+           "#636efa",
+           "#636efa",
+           "#636efa",
+           "#ab63fa",
+           "#636efa"
+          ]
+         },
+         "name": "",
+         "parents": [
+          "",
+          "Py_kgeneration",
+          "generate",
+          "Py_kgeneration",
+          "fftma2",
+          "gasdev",
+          "",
+          "",
+          "fftma2",
+          "",
+          "",
+          "",
+          "fftma2",
+          ""
+         ],
+         "type": "treemap",
+         "values": [
+          2.673944,
+          1.986983,
+          1.4797549999999982,
+          0.684455,
+          0.680862,
+          0.46490200000001797,
+          0.29320199999999785,
+          0.001203,
+          0.001112,
+          0.000802,
+          0.00024000000000000003,
+          0.000135,
+          0.0001,
+          8.3e-05
+         ]
+        }
+       ],
+       "layout": {
+        "autosize": true,
+        "legend": {
+         "tracegroupgap": 0
+        },
+        "margin": {
+         "t": 60
+        },
+        "template": {
+         "data": {
+          "bar": [
+           {
+            "error_x": {
+             "color": "#2a3f5f"
+            },
+            "error_y": {
+             "color": "#2a3f5f"
+            },
+            "marker": {
+             "line": {
+              "color": "#E5ECF6",
+              "width": 0.5
+             },
+             "pattern": {
+              "fillmode": "overlay",
+              "size": 10,
+              "solidity": 0.2
+             }
+            },
+            "type": "bar"
+           }
+          ],
+          "barpolar": [
+           {
+            "marker": {
+             "line": {
+              "color": "#E5ECF6",
+              "width": 0.5
+             },
+             "pattern": {
+              "fillmode": "overlay",
+              "size": 10,
+              "solidity": 0.2
+             }
+            },
+            "type": "barpolar"
+           }
+          ],
+          "carpet": [
+           {
+            "aaxis": {
+             "endlinecolor": "#2a3f5f",
+             "gridcolor": "white",
+             "linecolor": "white",
+             "minorgridcolor": "white",
+             "startlinecolor": "#2a3f5f"
+            },
+            "baxis": {
+             "endlinecolor": "#2a3f5f",
+             "gridcolor": "white",
+             "linecolor": "white",
+             "minorgridcolor": "white",
+             "startlinecolor": "#2a3f5f"
+            },
+            "type": "carpet"
+           }
+          ],
+          "choropleth": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "type": "choropleth"
+           }
+          ],
+          "contour": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "colorscale": [
+             [
+              0,
+              "#0d0887"
+             ],
+             [
+              0.1111111111111111,
+              "#46039f"
+             ],
+             [
+              0.2222222222222222,
+              "#7201a8"
+             ],
+             [
+              0.3333333333333333,
+              "#9c179e"
+             ],
+             [
+              0.4444444444444444,
+              "#bd3786"
+             ],
+             [
+              0.5555555555555556,
+              "#d8576b"
+             ],
+             [
+              0.6666666666666666,
+              "#ed7953"
+             ],
+             [
+              0.7777777777777778,
+              "#fb9f3a"
+             ],
+             [
+              0.8888888888888888,
+              "#fdca26"
+             ],
+             [
+              1,
+              "#f0f921"
+             ]
+            ],
+            "type": "contour"
+           }
+          ],
+          "contourcarpet": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "type": "contourcarpet"
+           }
+          ],
+          "heatmap": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "colorscale": [
+             [
+              0,
+              "#0d0887"
+             ],
+             [
+              0.1111111111111111,
+              "#46039f"
+             ],
+             [
+              0.2222222222222222,
+              "#7201a8"
+             ],
+             [
+              0.3333333333333333,
+              "#9c179e"
+             ],
+             [
+              0.4444444444444444,
+              "#bd3786"
+             ],
+             [
+              0.5555555555555556,
+              "#d8576b"
+             ],
+             [
+              0.6666666666666666,
+              "#ed7953"
+             ],
+             [
+              0.7777777777777778,
+              "#fb9f3a"
+             ],
+             [
+              0.8888888888888888,
+              "#fdca26"
+             ],
+             [
+              1,
+              "#f0f921"
+             ]
+            ],
+            "type": "heatmap"
+           }
+          ],
+          "heatmapgl": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "colorscale": [
+             [
+              0,
+              "#0d0887"
+             ],
+             [
+              0.1111111111111111,
+              "#46039f"
+             ],
+             [
+              0.2222222222222222,
+              "#7201a8"
+             ],
+             [
+              0.3333333333333333,
+              "#9c179e"
+             ],
+             [
+              0.4444444444444444,
+              "#bd3786"
+             ],
+             [
+              0.5555555555555556,
+              "#d8576b"
+             ],
+             [
+              0.6666666666666666,
+              "#ed7953"
+             ],
+             [
+              0.7777777777777778,
+              "#fb9f3a"
+             ],
+             [
+              0.8888888888888888,
+              "#fdca26"
+             ],
+             [
+              1,
+              "#f0f921"
+             ]
+            ],
+            "type": "heatmapgl"
+           }
+          ],
+          "histogram": [
+           {
+            "marker": {
+             "pattern": {
+              "fillmode": "overlay",
+              "size": 10,
+              "solidity": 0.2
+             }
+            },
+            "type": "histogram"
+           }
+          ],
+          "histogram2d": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "colorscale": [
+             [
+              0,
+              "#0d0887"
+             ],
+             [
+              0.1111111111111111,
+              "#46039f"
+             ],
+             [
+              0.2222222222222222,
+              "#7201a8"
+             ],
+             [
+              0.3333333333333333,
+              "#9c179e"
+             ],
+             [
+              0.4444444444444444,
+              "#bd3786"
+             ],
+             [
+              0.5555555555555556,
+              "#d8576b"
+             ],
+             [
+              0.6666666666666666,
+              "#ed7953"
+             ],
+             [
+              0.7777777777777778,
+              "#fb9f3a"
+             ],
+             [
+              0.8888888888888888,
+              "#fdca26"
+             ],
+             [
+              1,
+              "#f0f921"
+             ]
+            ],
+            "type": "histogram2d"
+           }
+          ],
+          "histogram2dcontour": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "colorscale": [
+             [
+              0,
+              "#0d0887"
+             ],
+             [
+              0.1111111111111111,
+              "#46039f"
+             ],
+             [
+              0.2222222222222222,
+              "#7201a8"
+             ],
+             [
+              0.3333333333333333,
+              "#9c179e"
+             ],
+             [
+              0.4444444444444444,
+              "#bd3786"
+             ],
+             [
+              0.5555555555555556,
+              "#d8576b"
+             ],
+             [
+              0.6666666666666666,
+              "#ed7953"
+             ],
+             [
+              0.7777777777777778,
+              "#fb9f3a"
+             ],
+             [
+              0.8888888888888888,
+              "#fdca26"
+             ],
+             [
+              1,
+              "#f0f921"
+             ]
+            ],
+            "type": "histogram2dcontour"
+           }
+          ],
+          "mesh3d": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "type": "mesh3d"
+           }
+          ],
+          "parcoords": [
+           {
+            "line": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "parcoords"
+           }
+          ],
+          "pie": [
+           {
+            "automargin": true,
+            "type": "pie"
+           }
+          ],
+          "scatter": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scatter"
+           }
+          ],
+          "scatter3d": [
+           {
+            "line": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scatter3d"
+           }
+          ],
+          "scattercarpet": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scattercarpet"
+           }
+          ],
+          "scattergeo": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scattergeo"
+           }
+          ],
+          "scattergl": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scattergl"
+           }
+          ],
+          "scattermapbox": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scattermapbox"
+           }
+          ],
+          "scatterpolar": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scatterpolar"
+           }
+          ],
+          "scatterpolargl": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scatterpolargl"
+           }
+          ],
+          "scatterternary": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scatterternary"
+           }
+          ],
+          "surface": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "colorscale": [
+             [
+              0,
+              "#0d0887"
+             ],
+             [
+              0.1111111111111111,
+              "#46039f"
+             ],
+             [
+              0.2222222222222222,
+              "#7201a8"
+             ],
+             [
+              0.3333333333333333,
+              "#9c179e"
+             ],
+             [
+              0.4444444444444444,
+              "#bd3786"
+             ],
+             [
+              0.5555555555555556,
+              "#d8576b"
+             ],
+             [
+              0.6666666666666666,
+              "#ed7953"
+             ],
+             [
+              0.7777777777777778,
+              "#fb9f3a"
+             ],
+             [
+              0.8888888888888888,
+              "#fdca26"
+             ],
+             [
+              1,
+              "#f0f921"
+             ]
+            ],
+            "type": "surface"
+           }
+          ],
+          "table": [
+           {
+            "cells": {
+             "fill": {
+              "color": "#EBF0F8"
+             },
+             "line": {
+              "color": "white"
+             }
+            },
+            "header": {
+             "fill": {
+              "color": "#C8D4E3"
+             },
+             "line": {
+              "color": "white"
+             }
+            },
+            "type": "table"
+           }
+          ]
+         },
+         "layout": {
+          "annotationdefaults": {
+           "arrowcolor": "#2a3f5f",
+           "arrowhead": 0,
+           "arrowwidth": 1
+          },
+          "autotypenumbers": "strict",
+          "coloraxis": {
+           "colorbar": {
+            "outlinewidth": 0,
+            "ticks": ""
+           }
+          },
+          "colorscale": {
+           "diverging": [
+            [
+             0,
+             "#8e0152"
+            ],
+            [
+             0.1,
+             "#c51b7d"
+            ],
+            [
+             0.2,
+             "#de77ae"
+            ],
+            [
+             0.3,
+             "#f1b6da"
+            ],
+            [
+             0.4,
+             "#fde0ef"
+            ],
+            [
+             0.5,
+             "#f7f7f7"
+            ],
+            [
+             0.6,
+             "#e6f5d0"
+            ],
+            [
+             0.7,
+             "#b8e186"
+            ],
+            [
+             0.8,
+             "#7fbc41"
+            ],
+            [
+             0.9,
+             "#4d9221"
+            ],
+            [
+             1,
+             "#276419"
+            ]
+           ],
+           "sequential": [
+            [
+             0,
+             "#0d0887"
+            ],
+            [
+             0.1111111111111111,
+             "#46039f"
+            ],
+            [
+             0.2222222222222222,
+             "#7201a8"
+            ],
+            [
+             0.3333333333333333,
+             "#9c179e"
+            ],
+            [
+             0.4444444444444444,
+             "#bd3786"
+            ],
+            [
+             0.5555555555555556,
+             "#d8576b"
+            ],
+            [
+             0.6666666666666666,
+             "#ed7953"
+            ],
+            [
+             0.7777777777777778,
+             "#fb9f3a"
+            ],
+            [
+             0.8888888888888888,
+             "#fdca26"
+            ],
+            [
+             1,
+             "#f0f921"
+            ]
+           ],
+           "sequentialminus": [
+            [
+             0,
+             "#0d0887"
+            ],
+            [
+             0.1111111111111111,
+             "#46039f"
+            ],
+            [
+             0.2222222222222222,
+             "#7201a8"
+            ],
+            [
+             0.3333333333333333,
+             "#9c179e"
+            ],
+            [
+             0.4444444444444444,
+             "#bd3786"
+            ],
+            [
+             0.5555555555555556,
+             "#d8576b"
+            ],
+            [
+             0.6666666666666666,
+             "#ed7953"
+            ],
+            [
+             0.7777777777777778,
+             "#fb9f3a"
+            ],
+            [
+             0.8888888888888888,
+             "#fdca26"
+            ],
+            [
+             1,
+             "#f0f921"
+            ]
+           ]
+          },
+          "colorway": [
+           "#636efa",
+           "#EF553B",
+           "#00cc96",
+           "#ab63fa",
+           "#FFA15A",
+           "#19d3f3",
+           "#FF6692",
+           "#B6E880",
+           "#FF97FF",
+           "#FECB52"
+          ],
+          "font": {
+           "color": "#2a3f5f"
+          },
+          "geo": {
+           "bgcolor": "white",
+           "lakecolor": "white",
+           "landcolor": "#E5ECF6",
+           "showlakes": true,
+           "showland": true,
+           "subunitcolor": "white"
+          },
+          "hoverlabel": {
+           "align": "left"
+          },
+          "hovermode": "closest",
+          "mapbox": {
+           "style": "light"
+          },
+          "paper_bgcolor": "white",
+          "plot_bgcolor": "#E5ECF6",
+          "polar": {
+           "angularaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "radialaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "scene": {
+           "xaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           },
+           "yaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           },
+           "zaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           }
+          },
+          "shapedefaults": {
+           "line": {
+            "color": "#2a3f5f"
+           }
+          },
+          "ternary": {
+           "aaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "baxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "caxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "title": {
+           "x": 0.05
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          },
+          "yaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          }
+         }
+        }
+       }
+      },
+      "image/png": "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",
+      "text/html": [
+       "<div>                            <div id=\"fce0079b-3a70-47fa-9c60-1150e7f0921b\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"fce0079b-3a70-47fa-9c60-1150e7f0921b\")) {                    Plotly.newPlot(                        \"fce0079b-3a70-47fa-9c60-1150e7f0921b\",                        [{\"domain\":{\"x\":[0.0,1.0],\"y\":[0.0,1.0]},\"hovertemplate\":\"function=%{label}<br>time_sum=%{value}<br>parent=%{parent}<extra></extra>\",\"labels\":[\"Py_kgeneration\",\"generate\",\"gasdev\",\"fftma2\",\"covariance\",\"ran2\",\"cov_value\",\"cgrid\",\"fourt\",\"length\",\"maxfactor\",\"build_real\",\"prebuild_gwn\",\"clean_real\"],\"marker\":{\"colors\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#EF553B\",\"#ab63fa\",\"#FFA15A\",\"#636efa\",\"#636efa\",\"#ab63fa\",\"#636efa\",\"#636efa\",\"#636efa\",\"#ab63fa\",\"#636efa\"]},\"name\":\"\",\"parents\":[\"\",\"Py_kgeneration\",\"generate\",\"Py_kgeneration\",\"fftma2\",\"gasdev\",\"\",\"\",\"fftma2\",\"\",\"\",\"\",\"fftma2\",\"\"],\"values\":[2.673944,1.986983,1.4797549999999982,0.684455,0.680862,0.46490200000001797,0.29320199999999785,0.001203,0.001112,0.000802,0.00024000000000000003,0.000135,0.0001,8.3e-05],\"type\":\"treemap\"}],                        {\"template\":{\"data\":{\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"white\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2}}},\"legend\":{\"tracegroupgap\":0},\"margin\":{\"t\":60}},                        {\"responsive\": true}                    ).then(function(){\n",
+       "                            \n",
+       "var gd = document.getElementById('fce0079b-3a70-47fa-9c60-1150e7f0921b');\n",
+       "var x = new MutationObserver(function (mutations, observer) {{\n",
+       "        var display = window.getComputedStyle(gd).display;\n",
+       "        if (!display || display === 'none') {{\n",
+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
+       "    x.observe(outputEl, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "                        })                };                });            </script>        </div>"
       ]
      },
      "metadata": {},
@@ -967,7 +2850,7 @@
     }
    ],
    "source": [
-    "plot_analysis(df)"
+    "plot_treemap(df)"
    ]
   },
   {
@@ -979,7 +2862,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 193,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [
     {
@@ -1007,23 +2890,23 @@
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
+       "      <th colspan=\"5\" halign=\"left\">time</th>\n",
        "      <th colspan=\"3\" halign=\"left\">memory</th>\n",
        "      <th colspan=\"3\" halign=\"left\">cpu</th>\n",
-       "      <th colspan=\"5\" halign=\"left\">time</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th>min</th>\n",
        "      <th>max</th>\n",
-       "      <th>median</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>sum</th>\n",
+       "      <th>count</th>\n",
        "      <th>min</th>\n",
        "      <th>max</th>\n",
-       "      <th>mean</th>\n",
+       "      <th>median</th>\n",
        "      <th>min</th>\n",
        "      <th>max</th>\n",
        "      <th>mean</th>\n",
-       "      <th>sum</th>\n",
-       "      <th>count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>function</th>\n",
@@ -1043,243 +2926,243 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Py_kgeneration</th>\n",
-       "      <td>240.1</td>\n",
-       "      <td>240.1</td>\n",
-       "      <td>240.1</td>\n",
-       "      <td>24.536011</td>\n",
-       "      <td>24.536011</td>\n",
-       "      <td>24.536011</td>\n",
-       "      <td>47.638534</td>\n",
-       "      <td>47.638534</td>\n",
-       "      <td>47.638534</td>\n",
-       "      <td>47.638534</td>\n",
+       "      <td>19.763131</td>\n",
+       "      <td>19.763131</td>\n",
+       "      <td>19.763131</td>\n",
+       "      <td>19.763131</td>\n",
        "      <td>1</td>\n",
+       "      <td>73.1</td>\n",
+       "      <td>73.1</td>\n",
+       "      <td>73.1</td>\n",
+       "      <td>46.861856</td>\n",
+       "      <td>46.861856</td>\n",
+       "      <td>46.861856</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>generate</th>\n",
-       "      <td>197.0</td>\n",
-       "      <td>197.0</td>\n",
-       "      <td>197.0</td>\n",
-       "      <td>23.163878</td>\n",
-       "      <td>23.163878</td>\n",
-       "      <td>23.163878</td>\n",
-       "      <td>35.573628</td>\n",
-       "      <td>35.573628</td>\n",
-       "      <td>35.573628</td>\n",
-       "      <td>35.573628</td>\n",
+       "      <td>14.644248</td>\n",
+       "      <td>14.644248</td>\n",
+       "      <td>14.644248</td>\n",
+       "      <td>14.644248</td>\n",
        "      <td>1</td>\n",
+       "      <td>31.7</td>\n",
+       "      <td>31.7</td>\n",
+       "      <td>31.7</td>\n",
+       "      <td>60.993471</td>\n",
+       "      <td>60.993471</td>\n",
+       "      <td>60.993471</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>gasdev</th>\n",
-       "      <td>-14.9</td>\n",
-       "      <td>6.5</td>\n",
+       "      <td>0.000080</td>\n",
+       "      <td>0.023625</td>\n",
+       "      <td>0.000332</td>\n",
+       "      <td>10.893141</td>\n",
+       "      <td>32768</td>\n",
+       "      <td>-3.1</td>\n",
+       "      <td>2.5</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>100.100000</td>\n",
-       "      <td>1.806851</td>\n",
-       "      <td>0.000098</td>\n",
-       "      <td>0.017469</td>\n",
-       "      <td>0.000797</td>\n",
-       "      <td>26.105193</td>\n",
-       "      <td>32768</td>\n",
+       "      <td>1.942624</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>fftma2</th>\n",
-       "      <td>43.1</td>\n",
-       "      <td>43.1</td>\n",
-       "      <td>43.1</td>\n",
-       "      <td>28.564730</td>\n",
-       "      <td>28.564730</td>\n",
-       "      <td>28.564730</td>\n",
-       "      <td>12.060570</td>\n",
-       "      <td>12.060570</td>\n",
-       "      <td>12.060570</td>\n",
-       "      <td>12.060570</td>\n",
+       "      <td>5.116014</td>\n",
+       "      <td>5.116014</td>\n",
+       "      <td>5.116014</td>\n",
+       "      <td>5.116014</td>\n",
        "      <td>1</td>\n",
+       "      <td>41.4</td>\n",
+       "      <td>41.4</td>\n",
+       "      <td>41.4</td>\n",
+       "      <td>6.226482</td>\n",
+       "      <td>6.226482</td>\n",
+       "      <td>6.226482</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>covariance</th>\n",
-       "      <td>42.6</td>\n",
-       "      <td>42.6</td>\n",
-       "      <td>42.6</td>\n",
-       "      <td>28.493006</td>\n",
-       "      <td>28.493006</td>\n",
-       "      <td>28.493006</td>\n",
-       "      <td>12.036319</td>\n",
-       "      <td>12.036319</td>\n",
-       "      <td>12.036319</td>\n",
-       "      <td>12.036319</td>\n",
+       "      <td>5.103886</td>\n",
+       "      <td>5.103886</td>\n",
+       "      <td>5.103886</td>\n",
+       "      <td>5.103886</td>\n",
        "      <td>1</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>6.238614</td>\n",
+       "      <td>6.238614</td>\n",
+       "      <td>6.238614</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ran2</th>\n",
-       "      <td>-7.1</td>\n",
-       "      <td>2.6</td>\n",
+       "      <td>0.000077</td>\n",
+       "      <td>0.001241</td>\n",
+       "      <td>0.000083</td>\n",
+       "      <td>3.453295</td>\n",
+       "      <td>41552</td>\n",
+       "      <td>-2.4</td>\n",
+       "      <td>1.2</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>100.100000</td>\n",
-       "      <td>0.449610</td>\n",
-       "      <td>0.000094</td>\n",
-       "      <td>0.001763</td>\n",
-       "      <td>0.000191</td>\n",
-       "      <td>7.933390</td>\n",
-       "      <td>41552</td>\n",
+       "      <td>0.405054</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>cov_value</th>\n",
-       "      <td>-4.1</td>\n",
-       "      <td>2.5</td>\n",
+       "      <td>0.000080</td>\n",
+       "      <td>0.000254</td>\n",
+       "      <td>0.000089</td>\n",
+       "      <td>2.195379</td>\n",
+       "      <td>24624</td>\n",
+       "      <td>-2.4</td>\n",
+       "      <td>1.7</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>100.100000</td>\n",
-       "      <td>0.460892</td>\n",
-       "      <td>0.000181</td>\n",
-       "      <td>0.000461</td>\n",
-       "      <td>0.000199</td>\n",
-       "      <td>4.906137</td>\n",
-       "      <td>24624</td>\n",
+       "      <td>0.017077</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>fourt</th>\n",
+       "      <td>0.002385</td>\n",
+       "      <td>0.003391</td>\n",
+       "      <td>0.002721</td>\n",
+       "      <td>0.008164</td>\n",
+       "      <td>3</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>100.100000</td>\n",
-       "      <td>33.400000</td>\n",
-       "      <td>0.004642</td>\n",
-       "      <td>0.006769</td>\n",
-       "      <td>0.005419</td>\n",
-       "      <td>0.016257</td>\n",
-       "      <td>3</td>\n",
+       "      <td>0.100000</td>\n",
+       "      <td>0.033333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>cgrid</th>\n",
+       "      <td>0.001590</td>\n",
+       "      <td>0.001590</td>\n",
+       "      <td>0.001590</td>\n",
+       "      <td>0.001590</td>\n",
+       "      <td>1</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>100.100000</td>\n",
-       "      <td>100.100000</td>\n",
-       "      <td>100.100000</td>\n",
-       "      <td>0.003691</td>\n",
-       "      <td>0.003691</td>\n",
-       "      <td>0.003691</td>\n",
-       "      <td>0.003691</td>\n",
-       "      <td>1</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>length</th>\n",
+       "      <td>0.000270</td>\n",
+       "      <td>0.000458</td>\n",
+       "      <td>0.000395</td>\n",
+       "      <td>0.001185</td>\n",
+       "      <td>3</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>100.100000</td>\n",
-       "      <td>33.366667</td>\n",
-       "      <td>0.000633</td>\n",
-       "      <td>0.001058</td>\n",
-       "      <td>0.000914</td>\n",
-       "      <td>0.002742</td>\n",
-       "      <td>3</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>maxfactor</th>\n",
+       "      <th>build_real</th>\n",
+       "      <td>0.000536</td>\n",
+       "      <td>0.000536</td>\n",
+       "      <td>0.000536</td>\n",
+       "      <td>0.000536</td>\n",
+       "      <td>1</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>100.100000</td>\n",
-       "      <td>20.020000</td>\n",
-       "      <td>0.000179</td>\n",
-       "      <td>0.000185</td>\n",
-       "      <td>0.000182</td>\n",
-       "      <td>0.000910</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>prebuild_gwn</th>\n",
-       "      <td>0.5</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.000599</td>\n",
-       "      <td>0.000599</td>\n",
-       "      <td>0.000599</td>\n",
-       "      <td>0.000599</td>\n",
-       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>build_real</th>\n",
+       "      <th>maxfactor</th>\n",
+       "      <td>0.000080</td>\n",
+       "      <td>0.000082</td>\n",
+       "      <td>0.000081</td>\n",
+       "      <td>0.000405</td>\n",
+       "      <td>5</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.000510</td>\n",
-       "      <td>0.000510</td>\n",
-       "      <td>0.000510</td>\n",
-       "      <td>0.000510</td>\n",
-       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>clean_real</th>\n",
+       "      <td>0.000258</td>\n",
+       "      <td>0.000258</td>\n",
+       "      <td>0.000258</td>\n",
+       "      <td>0.000258</td>\n",
+       "      <td>1</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.000403</td>\n",
-       "      <td>0.000403</td>\n",
-       "      <td>0.000403</td>\n",
-       "      <td>0.000403</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>prebuild_gwn</th>\n",
+       "      <td>0.000236</td>\n",
+       "      <td>0.000236</td>\n",
+       "      <td>0.000236</td>\n",
+       "      <td>0.000236</td>\n",
        "      <td>1</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "               memory                       cpu                          \\\n",
-       "                  min    max median         min         max        mean   \n",
-       "function                                                                  \n",
-       "Py_kgeneration  240.1  240.1  240.1   24.536011   24.536011   24.536011   \n",
-       "generate        197.0  197.0  197.0   23.163878   23.163878   23.163878   \n",
-       "gasdev          -14.9    6.5    0.0    0.000000  100.100000    1.806851   \n",
-       "fftma2           43.1   43.1   43.1   28.564730   28.564730   28.564730   \n",
-       "covariance       42.6   42.6   42.6   28.493006   28.493006   28.493006   \n",
-       "ran2             -7.1    2.6    0.0    0.000000  100.100000    0.449610   \n",
-       "cov_value        -4.1    2.5    0.0    0.000000  100.100000    0.460892   \n",
-       "fourt             0.0    0.0    0.0    0.000000  100.100000   33.400000   \n",
-       "cgrid             0.0    0.0    0.0  100.100000  100.100000  100.100000   \n",
-       "length            0.0    0.0    0.0    0.000000  100.100000   33.366667   \n",
-       "maxfactor         0.0    0.0    0.0    0.000000  100.100000   20.020000   \n",
-       "prebuild_gwn      0.5    0.5    0.5    0.000000    0.000000    0.000000   \n",
-       "build_real        0.0    0.0    0.0    0.000000    0.000000    0.000000   \n",
-       "clean_real        0.0    0.0    0.0    0.000000    0.000000    0.000000   \n",
+       "                     time                                         memory  \\\n",
+       "                      min        max       mean        sum  count    min   \n",
+       "function                                                                   \n",
+       "Py_kgeneration  19.763131  19.763131  19.763131  19.763131      1   73.1   \n",
+       "generate        14.644248  14.644248  14.644248  14.644248      1   31.7   \n",
+       "gasdev           0.000080   0.023625   0.000332  10.893141  32768   -3.1   \n",
+       "fftma2           5.116014   5.116014   5.116014   5.116014      1   41.4   \n",
+       "covariance       5.103886   5.103886   5.103886   5.103886      1   41.0   \n",
+       "ran2             0.000077   0.001241   0.000083   3.453295  41552   -2.4   \n",
+       "cov_value        0.000080   0.000254   0.000089   2.195379  24624   -2.4   \n",
+       "fourt            0.002385   0.003391   0.002721   0.008164      3    0.0   \n",
+       "cgrid            0.001590   0.001590   0.001590   0.001590      1    0.0   \n",
+       "length           0.000270   0.000458   0.000395   0.001185      3    0.0   \n",
+       "build_real       0.000536   0.000536   0.000536   0.000536      1    0.0   \n",
+       "maxfactor        0.000080   0.000082   0.000081   0.000405      5    0.0   \n",
+       "clean_real       0.000258   0.000258   0.000258   0.000258      1    0.0   \n",
+       "prebuild_gwn     0.000236   0.000236   0.000236   0.000236      1    0.4   \n",
        "\n",
-       "                     time                                          \n",
-       "                      min        max       mean        sum  count  \n",
-       "function                                                           \n",
-       "Py_kgeneration  47.638534  47.638534  47.638534  47.638534      1  \n",
-       "generate        35.573628  35.573628  35.573628  35.573628      1  \n",
-       "gasdev           0.000098   0.017469   0.000797  26.105193  32768  \n",
-       "fftma2          12.060570  12.060570  12.060570  12.060570      1  \n",
-       "covariance      12.036319  12.036319  12.036319  12.036319      1  \n",
-       "ran2             0.000094   0.001763   0.000191   7.933390  41552  \n",
-       "cov_value        0.000181   0.000461   0.000199   4.906137  24624  \n",
-       "fourt            0.004642   0.006769   0.005419   0.016257      3  \n",
-       "cgrid            0.003691   0.003691   0.003691   0.003691      1  \n",
-       "length           0.000633   0.001058   0.000914   0.002742      3  \n",
-       "maxfactor        0.000179   0.000185   0.000182   0.000910      5  \n",
-       "prebuild_gwn     0.000599   0.000599   0.000599   0.000599      1  \n",
-       "build_real       0.000510   0.000510   0.000510   0.000510      1  \n",
-       "clean_real       0.000403   0.000403   0.000403   0.000403      1  "
+       "                                   cpu                         \n",
+       "                 max median        min         max       mean  \n",
+       "function                                                       \n",
+       "Py_kgeneration  73.1   73.1  46.861856   46.861856  46.861856  \n",
+       "generate        31.7   31.7  60.993471   60.993471  60.993471  \n",
+       "gasdev           2.5    0.0   0.000000  100.100000   1.942624  \n",
+       "fftma2          41.4   41.4   6.226482    6.226482   6.226482  \n",
+       "covariance      41.0   41.0   6.238614    6.238614   6.238614  \n",
+       "ran2             1.2    0.0   0.000000  100.100000   0.405054  \n",
+       "cov_value        1.7    0.0   0.000000  100.100000   0.017077  \n",
+       "fourt            0.0    0.0   0.000000    0.100000   0.033333  \n",
+       "cgrid            0.0    0.0   0.000000    0.000000   0.000000  \n",
+       "length           0.0    0.0   0.000000    0.000000   0.000000  \n",
+       "build_real       0.0    0.0   0.000000    0.000000   0.000000  \n",
+       "maxfactor        0.0    0.0   0.000000    0.000000   0.000000  \n",
+       "clean_real       0.0    0.0   0.000000    0.000000   0.000000  \n",
+       "prebuild_gwn     0.4    0.4   0.000000    0.000000   0.000000  "
       ]
      },
-     "execution_count": 193,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1291,51 +3174,972 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/oli/.local/lib/python3.6/site-packages/ipykernel_launcher.py:25: MatplotlibDeprecationWarning: normalize=None does not normalize if the sum is less than 1 but this behavior is deprecated since 3.3 until two minor releases later. After the deprecation period the default value will be normalize=True. To prevent normalization pass normalize=False \n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 750x750 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 750x750 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAApEAAAJpCAYAAAD16mm/AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAABM5AAATOQGPwlYBAABRIElEQVR4nO3deXhU5d3G8fvJTAiLcERAEGQTBZVNxbpWXGpr61p3RW1dqlIB97a+demjbd1ttbXVUlptXWlLQRHXihuK1qIoVBGlLAKCCHLYIcu8f5xEIySQCUl+55z5fq5rrjHJzOQOhnDnOc/icrmcAAAAgHwUWQcAAABA8lAiAQAAkDdKJAAAAPJGiQQAAEDeKJEAAADIGyUSAAAAeaNEAgAAIG+USAAAAOSNEgkAAIC8USIBAACQN0okAAAA8kaJBICYc87d75zLOecOsc4CAFUokQAAAMgbJRIAAAB5o0QCAAAgb5RIAAXLOXeCc+5159wa59xnzrm/O+d2ds75yjmIZ1d77B7OuVudc1Occ0ucc+udc/9zzv3eOde5ltfv55x7sPJx6yqfN9U5d6dzbocaHn9u5cfXOucWVc6F7LSFr2E759xNzrn3Kp8XOucmOueO3uhxe1V+TW9s5rVGVD7mV1v8wwNQ8CiRAAqSc+4SSWMkfU3SG5KekzRI0r8l9azhKVdJuqzyvydJelKSk/RDSf/ZuEg65wZJelPSGZJWSnpM0uuSiiVdIqnPRo+/WdKfJO0u6eXK23cqs21Xy9fQW9LUymwtJD0j6T+S9pU03jl3ZdVjc7ncW5JmSNrHOderlj+WMyrvH6zl4wDwhax1AABoas65nSTdKmmDpG/ncrkXKt+flTRS0jk1PO0Pki7J5XKLq71OkaRrJF0v6ReSzq32+IslNZd0ZS6Xu2Ojz7+rpLDa2/tJ+nHl+w7N5XJvV75/G0Xl85gavoaMpH9I6lr53DtyuVxF5cd2lvSspJudc0/ncrnplU97SNLPJQ2pvK/+er0Ulc8ZlYUTADaLkUgAhehcSc0kPVBVICUpl8uVSbpc0qqNn5DL5V6oXiAr31eRy+VukLRA0rEbPaVD5f2/anitGblc7pNq7/qholHNu6oKZOXjVkkaISlXw9dwjKT+ksbkcrnbqgpk5fM+knSFpIyk86s956HK+yE1vN4ZGz0GADaLEgmgEB1Yef/3jT+Qy+WWKxrF24Rzrp1z7hzn3B3OuT9Vzlm8X9El6nbOueqXnadU3v/OOXdI5ShnbQ6qvH+0hjzvSXqnhud8q/L+n7W85iuV9/tUe63Zkl6TtKtzbq+NHk+JBJAXLmcDKERVi1o+ruXj8zZ+h3PudEWXurfZzOu2lrSs8r9vk/R1SYdIekHSKufcZEkTJN2fy+XCas+rmk85t5bXnSNpj43e16Py/iHn3OaKX/uN3n5I0gGKSuNbkuSc21tSb0mvVRZNANgiSiQAbIFzrruk+yvfvFRREVyQy+XWVn78NUn7K7okLUnK5XIrnHOHKRr1PEZRmTxM0jcl/Z9z7qBcLvfhVsSqupL0tKTFm3ncZxu9PVrSnZJOc879qPIyOKOQAPJGiQRQiD5RtDq6q6T3avh4143ePlLRHMrbc7ncXTU8fqeaPkkul8spWsk9SZKcc9srKnCnS/qlpFOq5ekhqbuk92t4qe41vG9+5f2oXC43pqbPX0umpc65ZyQdLekQ59xLkk6TVKqoYAJAnTAnEkAherXy/sSNP+CcC/TlfMMqbSvv52/0fjnnBkvqWJdPmsvlPpXkK9/sV+1DVfMXT9FGKldy71HDyz1XeX98XT73RqovsDlMUidJz+RyuaX1eC0ABYoSCaAQ3adoe5/vVZZASV9sm3OHormN1c2svD/TOdeq2uO7SLq3pk/gnBvqnKtpv8kjK++rz8eseo1LnXMDq71GK0m/VbXL5NWMUTSKeoZz7lrnXMlGn9855w50zh1Yw3MfU7R35Yn6clsiLmUDyIuLrrYAQGGp3Gz8TkkVkl5SNK9wH0Ubez8h6UxJZ+RyuYedc80ULULpK2mRopHM5pIOVbTZtxQtVumZy+XmVL7+VEkDFRW99yWVSdq18n3rJB2ey+WqRkTlnLtN0pWKLitPVLRn5MGS1itanX2Moj0kX6z2nF0UbTDeU9Knkt6tvG+vaPRye0mX5XK5O2v4+v8i6XuVb66U1LFqjicA1AUjkQAKUuXcxpMUnfCyn6QjFBXCfRWVPElaWvnYDYq24bmn8mNHS9pN0SjhNxUVv41dK+nPivZ4/IaiEthC0ihJe1QvkJWf40eK9nR8X9EinEMUXbLeX1+u+N74a/hQ0p6KNjyfX/l1nKBopfXbkoap9tNnqo88jqVAAsgXI5EAUE3lJe13FZXEzrlcbpFxJACIJUYiARQk51wv59y2G72vRNFxiLtLep4CCQC1Y4sfAIXqZEnXO+emKFrk0kbRfMUdFO2tONwwGwDEHpezARQk59zXFJ2TvZ+ic66zis7AfkbSTblcrrbTbAAAokQCAACgHpgTCQAAgLxRIgEAAJA3SiQAAADyRokEAABA3iiRAAAAyBslEgAAAHmjRAIAACBvlEgAAADkjRIJAACAvFEiAQAAkDdKJAAAAPKWrc+TpkyZcrSkkyRt17BxgK9YJekjSbcPGjRohXUYAADwJZfL5fJ6wpQpU3Zyzv2tWbNmOzdr1qyRYgFSWVmZysrKVpWWlk6UNJwiCQBAfNRnJHJYcXFxt65duy4KgmBVgycCKuVyOS1YsKDTsmXLDistLb1S0nXWmQAAQKQ+cyJ3LC4uLqZAorE559SlS5dF2Wx2G0k7W+cBAABfqk+JbFFUVOQaPAlQA+ecstmsJG1jnQUAAHyJ1dkAAADIGyUSAAAAeaNEbsE+++zT59xzz+1qnQMAACBO6rVPZI18MKjBXqtOny+c0pAv98QTT7Q+5phjei9ZsmRq+/btyxvytRvapEmTWv74xz/uMm3atFaZTEbf+c53Pr/33ns/DoKgYuPHLlq0KDNw4MC+n376afHmvraqr7+mj7344ovvH3zwwWveeeedkgsuuKD7Rx991GLVqlWZDh06lJ5wwglLb7vttk9KSkpq3Ctq8eLFmdNPP73n66+/3rp79+7rR40aNfvAAw9cW/Xxs846q9tOO+20/vrrr19c3z8PAADQ9BiJNLBu3bp6L0yaM2dO8VFHHdW7Z8+e6ydNmvT+Y489NvODDz5oceqpp/ao6fFnnnlmj1133XXNll738MMPXzV37tx3qt9OPfXUz7p06bLhoIMOWiNJxcXFuSFDhix94oknZk6bNm36LbfcMu+hhx7qcMUVV3Su7XWvueaaHVatWpV5/fXX3zvggANWXnjhhV/kfP7551u9/fbbra699loKJAAACVMwJXLt2rXu7LPP7rrddtsNLCkp2WvQoEF9XnrppZaS9MEHHzSrGoXr0KHDHs65QSeeeGKPqudWVFRo6NChOwZBsEf79u0HXn755V8pTZ999lnm1FNP7d62bduB22yzzZ777bdf78mTJ7eo+vjll1/eedddd939V7/6VfsuXbr0b9my5V71/Tr+8Y9/BNlsNvfXv/513sCBA9cffPDBa+699965zzzzTNvp06eXVH/sLbfc0mHFihXZK664YoslrXnz5rlu3bqVVd06duxY/uyzz257+umnf1ZUFH2b7L777hsuueSSpfvvv//a3r17bzjjjDPC448/funkyZNrXTk9c+bM5ieddNKyAQMGrB82bNiSWbNmNZek9evXu2HDhnX//e9/P7dy9TUAAEiQgimRF1100Y5PPvlk23vvvXf2a6+99l6PHj3WH3vssb0XL16c6dWr14b7779/liS988470+fOnfvOyJEjP6567pgxY9q1atWqfNKkSe977+ffeeedO4wdO7ZN1cePPfbYnZYsWVL82GOPfTh58uT3BgwYsObII4/svXjx4kzVY+bNm1cybty4tn/7298+mjx58nv1/TrWr19fVFxcnMtkvnhptWzZskKSJk6c+EWZmzJlSvPbb799h4ceemh2VQnMxyOPPBKEYZgdOnToZ7U9Zvr06SUvvPBCcOCBB66s7TH9+vVb++KLL7YuLS3VE0880aZPnz5rJem6667reMABB6wcPHjwFkdJAQBA/BREiVyxYkXRgw8+2OGGG26Yf8opp6wYNGjQuocffnhuSUlJxd13390+m82qXbt25ZLUuXPnsm7dupVVvS1JvXv3XnvHHXd80r9///XDhw9f2rdv3zXPPfdca0l65plntpk2bVqrCRMmzBo8ePCa/v37rx85cuT81q1blz/wwANtq16jtLTUPfroo7MPPPDAtfvuu+/aTVPWzRFHHLHis88+y1577bUd161b55YsWZK58soru0jSJ598UixFo65DhgzZ6frrr5+/yy67bKjP57nvvvvaf/3rXw979epVuvHH9txzz11LSkr26t+/f79999131a9//euFtb3ODTfcsCiTyeS6d+/ef/z48W3/9Kc/zZk2bVrJ6NGj2994440LhwwZ0m3HHXfsf+SRR+60dOnSTG2vAwAA4qUgSuT7779fUlZW5g477LAvTtkpKSnJDRw4cPWMGTNabO65krT77rt/pfRtv/32G5YsWVIsSW+99VaLNWvWZNq1a7dHy5Yt96y6LViwoGTWrFlfXF7u3Lnzhs6dO5dt7vNUf/6QIUO61fSYvffee93dd98955577um0zTbb7NWlS5eB3bt339CuXbuyqhHHESNGdNlll13WXXTRRcu29LXVZNasWcWTJk0KzjnnnBpHIf/+97/Peu2119679957Zz///PPBz372s461vVa7du3Kx48fP3vhwoXT3nzzzQ8GDRq07vzzz+/+i1/8Yv6oUaPazZ07t2TWrFnTW7RoUXHVVVftUJ+8AACg6TEZrQ6Ki4u/svLYOaeKimgh9KpVqzLt27cvff755z/Y+HnVRzNbtGixycrpjf373//+4jJ327Zta10hPnTo0GVDhw5d9vHHH2fbtGlT4ZzTqFGjOvbq1Wu9JE2aNKnNhx9+2CKbzQ6SojOoJalTp057jBgx4pPNjRxK0r333ts+CIKy008/Pazp4zvvvHOppNJBgwatKy8v1xVXXNHde7+4LnMb77rrrnZt2rQpP/PMM5d/61vf6nXMMccsLykpyZ188smf//znP691gQ4QJz2umlAiqUPlbXtJbSW1ktRyo/ua3tdM0S/wrvK+6K/FN703ODNtN0kV1W45SWWS1khaVcNtdQ3vWynpM0mfyIfrGvPPAAAKokTutttu64uLi3MTJ07cpnfv3sukaGHHu+++2+rCCy9cLEklJSUVklRWttnBwk3svffea2655Zbi4uLiXJ8+fep16bhKv3791ufz+K5du5ZJ0p133tmuWbNmFccee+wKSRozZsysNWvWfLEC/LXXXmt16aWX9nj66adn7Lbbbpv9HBUVFXrkkUfan3TSSUtr27Zno8e70tJSV15e7rLZ7GYfv3Dhwuytt97aedKkSTMkqby8XKWlpU6KLveXl5dznCZM9bhqQntJPSpvXRUVxO31ZWGsKo2tG/LztnTrV0jasyFfUz5YIWnRFm6fSFosH27x7zoAbKwgSmSbNm0qzjzzzCXXXXfdju3bty/r2bPnhhtvvLHTunXrikaMGPGZJO28884bnHP6+9//vu2JJ54YtmrVqqKmfRc3dtxxx60YOHDgquOOO27nG2+8cX7fvn3XzZs3r/jxxx/f9uSTT/68MRaO3HjjjR0GDx68uk2bNuUTJkxoc8MNN+x49dVXL6jaA7Jv375fKYqLFy8ulqQ99thjXdVjXnjhhZbnnntuz4kTJ87s2bPnF/Mex48f33rBggXNfvjDH25yKfuee+7Zrri4OLfXXnutbd68ecVrr73W6oYbbuhy1FFHfV6Xwjl06NCuw4YNW1T1+fbff/9Vo0ePbnfUUUetGDVqVPuvfe1rq7b0GsDW6HHVhO0k7aQvi2L1W3el64z2NpW3Gvd/rWadfDBb0kc13ObKh7HeNxeAnYIokZJ09913z6+oqND555/fc82aNZl+/fqtfvzxx2d26NChXJJ69uxZesUVVyy84YYbulxyySU9jj/++KVjxoyZs6XXLSoq0r/+9a8PL7vssi5Dhw7t8fnnn2fbt29fus8++6zq3LnzJotSGsKbb77Z6tZbb+2yZs2aop49e667/fbb5w4bNiyv+Y+rV6/OzJkzp/mGDRu+Mvo3atSo9nvuuefqPffcc5NLYdlsNvfrX/+605w5c5rncjl17tx5w3nnnfdpXfZ5HDNmTJs5c+aUjB07dnbV+37yk58seeutt1oNHjx4twEDBqy++eabN3uZHairHldN2FFSX0m7bXRrb5krpprryz+fjZXKB3MkzVJUKj+U9J6kt+XDpU2WEEAsuar5cnU1ZcqUx1u3bj24d+/eHzVSJuArZs6cufPKlStfHjRo0LHWWRAvPa6a0FzRZeA9JPWvvPVVNEcxMf7RzL+8d9HMwdY58jRf0tRqt7clzebSOFA4CmYkEkCy9bhqQpGigrhP5e1rikojP8ds7Fh5O7ra+0L54F19tVhOkw/zm2wOIBH44QsglnpcNaG7pH0VlcV9JO2ldM1ZTKNA0kGVtypr5IPXJb0iaZKkyfLhaotwABoWJRJALFSWxkOr3braJkIDaSnpsMqbJJXJB1P1ZamcJB9+apQNwFagRAIwUbn45VBJh1Te9zQNhKaSlbR35e0ySZIPZioqla9Iek4+ZJEdkACUSABNosdVEwJJR0g6XFFp3Nk2EWKkd+XtPEmSD96R9JSkJxVd/mZOJRBDlEgAjabHVRN2kXRM5e3r4mcO6mZg5e0qScvlg+cUlcqn5cNPTJMB+AI/0AE0mB5XTcgqWlRxjKJVu7vYJkIKbCvp5MpbrnI+5VOVt8lshg7YoUQC2CqVl6mrRhuPULRCF2gMTtG+oHtK+qmkpfLBOEmjJU2kUAJNixIJIG89rprQRtJxkk6R9C1JzWwToUC1UzSP8jxJS+SDf0r6m6SXKJRA46NEAqgbH7SUdOy319/0Lan7EEkl1pGAajpIurDytlg+GKOoUL4iH1aYJgNSqsFKZI+rJgxqqNeqizk3HzWlKT/f5txxxx3tH3744XYffvhhC0nq16/fml/+8pfzDz300DVb+9rOuU3+XP/whz/874ILLvi8psd/8MEHza699todXn311TZLly4t7tChw4aTTjpp2U033fRJ8+bNc5J0+eWXd/71r3+9w8bPbd68ecXatWvfrul1Fy9enDn99NN7vv766627d+++ftSoUbMPPPDAtVUfP+uss7rttNNO66+//votnqONBPFBVtI3JQ2R9F1J21yYfeKly0qHUSARZx0lXVR5+0Q++IeiQvkqxzICDadgRyLXrVvnqkrV1nrppZdan3zyycsGDx68qkWLFrlf/vKXnY499tje77777n979uxZurWvf9ddd8357ne/G1a93b59+1ov00ybNq15RUWF++1vfzt31113Xff222+3uPjii3usXr26aOTIkfMl6Wc/+9miSy+99Cub+x522GF9Bg4cWOspEtdcc80Oq1atyrz++uvv3XXXXdtfeOGFPaZPn/6+JD3//POt3n777Vb33XffvK39WhETPhio6BLhqZK2r/6hbxf9u/NlGmYSC6iHHSSNqLzNkQ/ul3S/fDjXNBWQAgVTIvfZZ58+u+6669psNpsbO3bsdr179177xhtvzPTed3z44YfbffzxxyVBEJQffvjhy3/3u9/ND4KgQpJ+85vftLv66qu73n///f/70Y9+1HXRokXNBg0atOrBBx+c071791JJevzxx2dX/1yPPvronG233XbPCRMmtBk+fPjSrc3etm3b8m7dutVpn7STTjppxUknnbSi6u3dd999w4wZMxbdd99920uaL0lBEFRUfX2SNHny5BazZs1qfvfdd9f6Q3XmzJnNTzrppGUDBgxYP2zYsCUPP/xwe0lav369GzZsWPeRI0fOyWYL5tspnXywjaTTJZ2v6KjBGrVwpbv0dbM/+m+uJ/s8Iml6SPKSfiYfTJR0n6Qx8uE6y1BAUhVZB2hKY8aMadesWbPciy++OGPkyJFzJamoqCh3xx13fDx16tT/jhw5cvarr77aetiwYTtWf966deuK7rjjjo7333//7GefffaDhQsXNhsxYsSONX8WaeXKlUVlZWWuXbt2DbJB7hVXXNGtbdu2A/v377/bnXfe2a6iIr/pPWEYZrbddttas9x7773tu3fvvv7b3/72qtoe069fv7Uvvvhi69LSUj3xxBNt+vTps1aSrrvuuo4HHHDAysGDB2/1pXsY8cHe8sFISQsljdRmCmSVEdmxCxo9F9B4nKRvSHpQ0eXuu+WD/saZgMQpqKGj7t27r7v33nvnV3/fdddd98Vl3T59+mxYunTpwssvv7ybpC8uzZaVlbk//vGP8/r27bteks4///xPb7/99s61fZ4RI0bs2KFDhw3HHXfcitoeU1dXXnnlwm9+85srWrVqVfHkk08GP/nJT7qvWrUqc80119TprNnp06eX3Hfffdt77+fX9PE1a9a4cePGtRsxYsRmN/C94YYbFp199tndunfv3r9Lly4b/vSnP82ZNm1ayejRo9u//vrr7w8ZMqTbyy+/HAwYMGD1Aw88MLddu3asjIwzH7SRdIaiUcc98336YUVTuzd4JsDGtpKGSRomH7yh6BepR+VDfjEGtqCgSuSAAQM2+aEwbty41rfccssOs2bNar569epMeXm5W79+vVu5cmVR69atK6RowUlVgZSkzp07ly5btqzGP7uf/vSnncaPH7/ds88++0HLli1rnHP54YcfNhs4cGDfqrcvvvjiT26++eZFNT32tttu+6LcHXjggWtXr15ddPfdd3eqS4mcPXt28ZFHHrnLkUce+fkVV1zxWU2PeeCBB9quXr266MILL9zsZfd27dqVjx8//iuX7ffbb7/ev/jFL+aPGjWq3dy5c0tmzZo1fciQId2vuuqqHf74xz/WWFphzAf7SrpA0VzHVvV9mWaurMfX3IwZb+Z23bXBsgH29q28/Uo+eEDSXfLhR8aZgNgqqMvZLVu2/Mp14A8++KDZqaeeusvuu+++9pFHHpk1efLk926++ea5UjTXr+px2Wz2K2XQOadcbtN+eN1113X87W9/22n8+PEz991337WbPKBSjx49Nvz73/9+r+p22WWXLanr17DffvutXrx4cfHatWvd5h43Z86c4kMPPbTPoEGDVj388MO1znW8//772x966KFh165d87r0ftddd7Vr06ZN+Zlnnrn85Zdfbn3MMccsLykpyZ188smfv/baa63zeS00Mh9k5IPT5IM3Jb0u6VxtRYGsMjw7jpX4SKtA0nBJH8gHj8kHB1sHAuKooEYiNzZ58uSWFRUVGjly5MeZTEaS9OCDD25Xn9e65pprOt511107jBs37sMtzQ8sLi5Wv3791m/uMbV5++23W7Rp06a8RYsWta4snz17dvGhhx7ap3///qv//ve/z6n62jY2Y8aMZm+88Ubrhx56KK/ftBcuXJi99dZbO0+aNGmGJJWXl6u0tNRJUmlpqSsvL99swUUTiRbK/EDSJYoWFDSoA4um7yzlcpLj/zfSqkjSsZKOlQ+mSPq1pNHyYYPMdweSrqBLZJ8+fdaXlZW5G2+8cfsTTzxx+QsvvLDNX/7ylw75vs7VV1/d6bbbbus8cuTI/+2yyy7r582bl5U2XQWdr4cffjhYtGhR8UEHHbSqRYsWuQkTJrT5zW9+s8PQoUO/GAF64YUXWp577rk9J06cOLNnz56ls2fPLj7kkEP6dOnSZcNvfvOb+QsXLvzi//HGK7zvueee9u3bty89+eSTQ+Vh6NChXYcNG7aoavui/ffff9Xo0aPbHXXUUStGjRrV/mtf+1qtC3TQBHzQRdLFii5bb9tYnybrKroMLnp32ssVA1mQgEIwSNFCnJvlg7sl/UE+XG4bCbBV0CVy//33X+u9//i3v/1tpxtvvLHL3nvvveq6666bP3z48J75vM7999/fobS01J1zzjm9qr//sssu++RXv/rVwvrma9asWe6Pf/zj9tdee23XXC6nbt26rb/hhhs+vvzyy7+Y37h69erMnDlzmm/YsMFJ0vjx49vMmzevZN68eSU77bTTgOqvl8vlvtigvby8XKNHj25/6qmnLs1na54xY8a0mTNnTsnYsWO/mB/5k5/8ZMlbb73VavDgwbsNGDBg9c0331zvrxlbwQcDJF0p6TRJxU3xKYdnx33+8oaBTfGpgLjYUdLNkq6VD+6TdKd8OMs4E2DC1TS3b3OmTJnyeOvWrQf37t2bycZoEjNnztx55cqVLw8aNOhY6yyx5IMjFJXHw5v6U5fn3JJd1j+wXYWKap4zgTr5RzP/8t5FMwdb50C9VEgaJ+nn8uFU2yhA0yqohTVAqvjgKPngP5KelkGBlKSMy3X4VtF/3rX43EBMFEk6QdJb8sHYytOegIJAiQSSxgdHyAevS3pC0TwtUxdlH2MOLBBtYP5dSW/LB/+kTKIQUCKBpPDBYfLBK4pGHve1jlOln5vdL6uyrT4jHkgJJ+l4RWVyTOVcZSCVKJFA3PngIPngBUnPS/q6dZyNFTm1PbbotanWOYCYcYouc0+VD/7BsYpIo/qUyLUVFRX5rcYB6imXy6msrEySCu+SqQ/2kw+ek/SypEOM02zWhdknNlhnAGLKSTpR0juVZbKfdSCgodSnRM4vLS0tDcNwmwZPA1STy+W0YMGCTmVlZaskFc5uAD4YIB9MkDRZRgtm8tXbzR9Qog3rrHMAMVa9TP5RPuhkHQjYWvXZJ/Lu0tLSwR9//PEuixdz6hkaT1lZmcrKylaVlpZOlHS7dZ5G54PtJf1C0nlK2FQT59T6lMxLrz9Q/s39rLMAMVek6CSp0+SDWyTdIR/WekwuEGd57xMpSVOmTDla0kmS6nVEIFBHVSOQtw8aNGiFdZhG44MSSZdK+qmkNrZh6m9ORcfXD9nwa0pkPbBPZEH7WNHf/YfkQ6aKIVHqVSIBNBAfnCTpVkl5nZIUR7mc1vZb/6fy1WrBVJc8USIh6T+SLpcPX7EOAtRVoi6ZAanhg73kg5ck/V0pKJCS5JxanJn5FxuPA/Wzt6SXK7cF6rXFRwMxQIkEmpIPdqg8b/dNSakbefp+9pn6zLMG8KUTJL0nH/xKPtjWOgywOZRIoCn4oFg++KmkmZLOVkr/7u2gZXsGWrXcOgeQcM0kXSbpffngNOswQG1S+Q8ZECs+OEDS25J+KSnV8wWdU/F52SenW+cAUqKTpEfkg6flg52swwAbo0QCjcUHgXxwj6RJkvpax2kqQzITW1lnAFLmCEnT5YP/kw+KrcMAVSiRQGPwwYmS3pc0VNEmwwWjnVYM2F6fL7HOAaRMC0k3Snqr8uoGYI4SCTQkH+woHzwm6R+SdrCOY8E5ZS7IPvG+dQ4gpfpJmiQf/IGFN7BGiQQagg+K5IOLJb0n6VjrONZOzrzc1joDkGJO0gWSZsgHQ6zDoHBRIoGt5YMBis65vktSa+M0sdBGq/vt6JYstM4BpFxHSQ/JB0/JB52tw6DwUCKB+vJBRj64RtFJE/tYx4kT5+Quyjz2oXUOoEB8W9I0+eBU6yAoLJRIoD6iEyVekfRzSayWrMGxmde2t84AFJDtJD0qHzwsHzCdBE2CEgnkywc/kDRV0v7GSWJtG7dut15uwVzrHECBOV3RqOS3rIMg/SiRQF35oIN8ME7SH5XyTcMbyojs2DnWGYAC1EXS0/LB3fJBS+swSC9KJFAXPjhK0jRJx1lHSZJvF73ZxToDUKCcpGGS3pYPmLONRkGJBDbHB63kg3slPaFoJSTy0NyV7tzP/e8j6xxAAest6VX54Hr5IGsdBulCiQRqE/32/rakC62jJNmI7NgF1hmAApeVdJ2iMtndOgzSgxIJ1MQHV0h6VdIu1lGS7tCiqT2sMwCQFG1F9lbl9Bxgq1Eigep80EY+GCPpdkW/vWMrNXPl3fdx73MMIhAP20kaLx/cJB9krMMg2SiRQBUf9Fe0cfgJ1lHSZnh23KfWGQB8wUm6StLz8kEn6zBILkokIEk++J6k18Xl60ZxQNF/d5FyOescAL7iYElT5YNDrYMgmSiRKGw+KJEP/iDpL5LYT62RZF1F54OL3plunQPAJjpKek4+uFo+cNZhkCyUSBQuH/RQtHjmAuMkBWF49rHPrTMAqFFG0i8kTZAP2lmHQXJQIlGYfHCkpCmSBllHKRR7uZm7Fami3DoHgFp9R9Hm5HtbB0EyUCJRWHzg5IPrFW0evp11nEKScbkORxS9+a51DgCb1VXSy/LBKdZBEH+USBSO6AzZfyjadJe5PwYuyj6+2joDgC1qIelR+eBn1kEQb5RIFAYf7Chpkti+x1RfN7tfVmWl1jkAbJGT5OWDR+SD5tZhEE+USKRfdHzhvyXtaR2l0BU5bfvdzKtTrXMAqLPTJL3EfpKoCSUS6RbN63lJ0g7WURC5IPPEBusMAPIS/SLugz2sgyBeKJFILx/8n6RHJXEpJkZ2cQsGNtf6tdY5AOSlq6RJ8sFx1kEQH5RIpI8PsvLBHyXdKBbQxI5z2ubUzIvvWOcAkLdWksbKBz+xDoJ4oEQiXXwQSHpK0g+so6B252aeso4AoH6cpJvlgz/LB1nrMLBFiUR6+KCLohNoDreOgs3r5j7do5XWrrTOAaDezpE0rnLrNBQoSiTSwQc7KyqQfa2jYMucU/PvZZ5l43Eg2Y6S9Lx8wMENBYoSieTzwUBFe0B2t46Cuvte9tlm1hkAbLX9FC246WodBE2PEolk88GBkl6U1NE4CfLUSZ/vsa1Wfm6dA8BW203Sa/LBbtZB0LQokUguH3xb0rOStjVOgnpwTsU/yD453ToHgAaxo6Izt/e2DoKmQ4lEMkWbiD8uiUndCXZa5oVtrDMAaDDtJU2UDw62DoKmQYlE8vjgAkmPSCq2joKt004rBnbUsk+tcwBoMK0lPS0fHG0dBI2PEolkiTa5/YP43k0F51R0YfaJGdY5ADSo5oo2JT/dOggaF/8QIzl8cJOkm61joGGdmHm5rXUGAA0uK+kBimS6USKRDD64RdJV1jHQ8NpoTb+u7tMF1jkANLiMoiJ5qnUQNA5KJOLPBz+X9GPrGGgczskNyzz2kXUOAI0iI+kh+eBk6yBoeJRIxJsPrpN0jXUMNK5jMq+xzyeQXhlJD8sHJ1oHQcOiRCK+fHCVpOutY6DxtXLrd93FzZ9jnQNAo8lKelQ+ON46CBoOJRLx5IPLJd1kHQNNZ3h27FzrDAAaVVbSaPngOOsgaBiUSMSPD0ZIusM6BprWEUX/2dE6A4BGVyzpb/LBMdZBsPUokYgXHwyV9BvrGGh6zV1prwFu1ofWOQA0umaS/iEfHGUdBFuHEon48MF5kn5vHQN2RmTHLbTOAKBJNJM0Rj443DoI6o8SiXjwwRBJIyU56yiwc0jR1B7WGQA0mRJJ/5QP9rIOgvqhRMJe9Jvo/eL7seAVu/Lu+7r33rPOAaDJtJb0lHzQyzoI8sc/2rDlgz0k/VPRZGtAw7PjllhnANCktpf0jHywvXUQ5IcSCTs+6CHpSUW/iQKSpP2L3ust5XLWOQA0qV6SnpQPtrEOgrqjRMKGD9pJelrSDtZREC9ZV7HDoUVTp1nnANDkBimaI8mVqYSgRKLp+aCFpPGS+lhHQTwNyz623DoDABPflHS/fMAiywSgRKJp+SAj6RFJ+1tHQXzt5T7cvUgV5dY5AJgYIg6cSARKJJra7yRx5BU2q8jl2h9Z9MY71jkAmLlMPviRdQhsHiUSTccH10i60DoGkuGH2cfXWGcAYOoW+eB06xCoHSUSTcMH35P0c+sYSI7d3dz+xSrbYJ0DgBkn6c/ywd7WQVAzSiQanw/2U3QaDVBnzik4PvPKVOscAEw1lzROPmAnjxiiRKJx+aCzos3ES6yjIHkuyEwos84AwFwXSWPlA/4diRlKJBpP9Bf+n2IvSNRTL7dwYAutZ24kgH3FFa3YoUSiMd2r6C8+UC/OqdWpmRdYpQ1Akr4nH1xpHQJfokSicfjgUklnG6dACpybeYqfUwCq3CIffMc6BCL8cEbD88E3JN1uHQPp0NUtGbiN1qywzgEgFookPSIfcOJZDFAi0bB8sJOkv0nKWEdBOjin5t/PPMtZ2gCqBJIelw+2tQ5S6CiRaDg+2EbSY5K2s46CdDkr+xyrMgFU11vSo5VH6cIIJRINwwdO0l8k9bOOgvTpqM8HttWKZdY5AMTKEZKusw5RyCiRaChXSjrBOgTSyTkV/yD71H+tcwCInWvkg8OsQxQqSiS2ng/2lfRL6xhIt9MyE1tbZwAQO0WSHpIPOloHKUSUSGwdHwSSHpFUbB0F6badVg7oqGWfWucAEDudJD0oH9Bpmhh/4NhaoyT1tA6B9HNORUOz42dY5wAQS4dL+ql1iEJDiUT9+eBCSSdZx0DhODHzCiv/AdTGyweDrUMUEkok6scH/SXdaR0DhaW11vTt5hbPt84BIJYyijYi72AdpFBQIpE/H7SUNFpSc+soKCzOyQ3LPDbLOgeA2Oos6YHKbefQyCiRqI/fStrNOgQK09GZyazCBLA5R0j6iXWIQkCJRH58METSudYxULhaufW79nYfz7bOASDWfi4f7G8dIu0okag7H/SSdK91DGB4dtw86wwAYi0r6a+V06/QSCiRqJto/62/SGLDZ5g7oug/O1pnABB7O0u62TpEmlEiUVeXSDrQOgQgSSWutNdAN+tD6xwAYm+4fHCodYi0okRiy3zQWxxriJgZkf3nAusMAGLPSfqzfMBVtEZAicTmRZex75PUwjoKUN3BRe/2ss4AIBF6SLrDOkQaUSKxJZdKOsA6BLCxYlfedf+i//7XOgeARDhfPvi2dYi0oUSidtFl7F9YxwBqMyIz9jPrDAASY5R8sK11iDShRKJmXMZGAuxb9H4fp4oK6xwAEqGLpLusQ6QJJRK1uVRcxkbMZVyu02FFb0+zzgEgMb4nHxxrHSItKJHYFJexkSDDso+tsM4AIFFGygfbWYdIA0okvorL2EiYPdys3TIqL7POASAxOkq6xTpEGlAisbGh4jI2EqTI5dofWfTGO9Y5ACTKefLBftYhko4SiS/5oIO4jI0EGpodv9Y6A4BEcZLukQ8y1kGSjBKJ6m6V1NY6BJCv3d3c/sUq22CdA0Ci7CFpuHWIJKNEIuKDAyR93zoGUB/OKTgh8/JU6xwAEufn8kFn6xBJRYmEKofzf6doeB9IpPMzE1hcAyBfrSX9yjpEUlEiIUkXKRrWBxKrl/tkYAutX2OdA0DinCofHG4dIokokYXOBx0l/dw6BrC1nFOr0zMTWaUNoD5+Jx80sw6RNJRI3CYpsA4BNIRzMk/zMw1AffSW9GPrEEnDD9xC5oODJJ1lHQNoKDu6JXtsozWcYAOgPn4qH/S0DpEklMhC5YOsosU0QGo4p5KzM89wljaA+mgh6Q7rEElCiSxcF0nqbx0CaGhnZZ8rsc4AILGOlw8OtA6RFJTIQuSDQNJ11jGAxrC9lu/RViuWWecAkFi3WQdICkpkYfqppHbWIYDG4Jyy52ef/K91DgCJtb98cKJ1iCSgRBYaH3STdLF1DKAxnZZ5obV1BgCJdpN8UGwdIu4okYXnl5KaW4cAGlNbrRzQScsWW+cAkFi7SLrQOkTcUSILiQ/2lHSGdQygsTmnoqHZxz+wzgEg0a6TD7iqsRmUyMJyszgfGwXihMwr21lnAJBoHST9xDpEnFEiC4UPDpX0LesYQFNp49b26+4WzbfOASDRLpcPuliHiCtKZOG4yToA0NSGZcZ9ZJ0BQKK1kHSDdYi4okQWAh98V9K+1jGApnZ05vXO1hkAJN7Z8kE/6xBxRIlMOx8UKVqRDRSclm5D713dvP9Z5wCQaEWSvHWIOKJEpt9pkna3DgFYGZEd+7F1BgCJd4J8wFHBG6FEppkPnKSrrWMAlg4vmtLVOgOAxHOSfmYdIm4okel2vBiFRIErcWU77ek+nGmdA0DiMRq5EUpkujEKCUgakR37iXUGAInnJF1nHSJOKJFp5YPvSNrLOgYQBwcVTetlnQFAKpwoH/S1DhEXlMj0YhQSqFTsync8sGj6f61zAEg8J+n/rEPEBSUyjXxwiKQDrWMAcTI8M26pdQYAqXCafLCTdYg4oESmE6OQwEb2KXq/t1NFhXUOAImXEWdqS6JEpo8P9pF0uHUMIG4yLtfpG0VvTbPOASAVzuZMbUpkGl1jHQCIq2HZx1dYZwCQCs0kXWkdwholMk18MEDS0dYxgLga6GbtnlF5mXUOAKlwnnzQxjqEJUpkuvxI0coxADUocrl2RxW9/o51DgCp0FrSudYhLFEi08IHHSWdYh0DiLuh2fHrrDMASI3h8kHBdqmC/cJT6AJFczQAbMZubl6/Zipdb50DQCr0knSUdQgrlMg08EGxpKHWMYAkcE7BiZmXuaQNoKFcYh3ACiUyHU6Q1Nk6BJAUP8g8WW6dAUBqfKNQj0KkRKbDCOsAQJLs5D4Z2FLr1ljnAJAaF1sHsECJTDof7CmOOATy4pxanp6ZyCVtAA3lLPlgO+sQTY0SmXyMQgL1cE726Yx1BgCp0ULS+dYhmholMsl80F7S6dYxgCTqos8Gttbq0DoHgNQYJh8U1C+nlMhk+4Gk5tYhgCRyTiVnZ56Zbp0DQGp0lXS8dYimRIlMqui3nR9axwCS7Mzsv/glDEBDKqjt9iiRyXW0pG7WIYAk217LB26ncKl1DgCpcZh8UDD/NlMik+sc6wBA0jmn7AXZCf+1zgEgNZyk71uHaCqUyCSKFtQcaR0DSINTMy8G1hkApMrZ8oGzDtEUKJHJNERSsXUIIA221ar+nfXZJ9Y5AKTGTpIGW4doCpTIZDrbOgCQFs6p6IfZx2da5wCQKgUx5YwSmTQ+6C9pT+sYQJp8N/Nqe+sMAFLlJPlgG+sQjY0SmTwFM2EXaCqt3dq+PdwnH1vnAJAarSSdYh2isVEikyTaG/IM6xhAGo3IjvufdQYAqXK2dYDGRolMliMkdbIOAaTRkUVv7GCdAUCqHCQf7GwdojFRIpPlbOsAQFq1cBt67+bmzrLOASBVzrYO0JgokUnhg7aSjrWOAaTZiOzY+dYZAKTKWWneM5ISmRynSCqxDgGk2TeK3iqY48oANIlukvaxDtFYKJHJcap1ACDtSlxZz73czA+scwBIlROtAzQWSmQS+KCdCmT3e8DaiOzYRdYZAKQKJRKmjpWUsQ4BFIKDiqb1ss4AIFV2kg9SeUgIJTIZTrAOABSKrKvY8etF06Zb5wCQKidZB2gMlMi4i45N+qZ1DKCQDM+OW2adAUCqpPKSNiUy/o4Sq7KBJvU1N6OPU0WFdQ4AqdFHPuhnHaKhUSLjj0vZQBPLuFzHbxa99a51DgCpkrrRSEpknPmgRNJ3rGMAheii7GMrrTMASJXUzYukRMbbNyW1tg4BFKIB7n99Myovs84BIDX6yQe9rUM0JEpkvHEpGzBS5HLbHVM0eap1DgCpkqrRSEpkXPkgI87KBkxdmB2/3joDgFQ52jpAQ6JExtfXJbWzDgEUsl3dx/2bqZQiCaCh7CMfbGsdoqFQIuPrW9YBgELnnNqclHlpqnUOAKmRkfQN6xANhRIZX2wwDsTADzJPsl8kgIZ0hHWAhkKJjCMfbCdpkHUMAFJPt2hgS61bbZ0DQGqk5kojJTKeDhP/b4BYcE4tz8z86x3rHABSo7t80Mc6REOgqMQTl7KBGPl+9pmsdQYAqZKK0UhKZDxRIoEY6ayle7TRqtA6B4DUSMW8SEpk3Pigl6Se1jEAfMk5NTs3+/Q06xwAUuMQ+aCZdYitRYmMH0YhgRg6I/N8S+sMAFKjlaQDrUNsLUpk/FAigRhqr3BgO4WfWecAkBqJnxdJiYyT6KjDw6xjANiUc8pcmH3iPescAFKDEokGtbekba1DAKjZyZkXt7XOACA19pAPWluH2BqUyHg5xDoAgNptq9X9O+uzT6xzAEiFIkn7WofYGpTIeNnfOgCA2jknd1H2sZnWOQCkxgHWAbYGJTJe9rMOAGDzvpt5tYN1BgCpQYlEA/BBT0kdrWMA2Lxt3Lrde7qF86xzAEiF/eQDZx2iviiR8cGlbCAhRmTHzbbOACAVAkl9rUPUFyUyPriUDSTEd4r+3dk6A4DUSOwlbUpkfFAigYRo4TbssrubM8s6B4BUoERiK/iguaQ9rGMAqLsR2bHzrTMASAVKJLbKIEnF1iEA1N03it7uZp0BQCrsIh+0tw5RH5TIeOBSNpAwzVxZz0HugxnWOQCkQiJHIymR8cDKbCCBRmTHLrbOACAVEjmYRImMh0R+8wCF7utF03tJuZx1DgCJt4d1gPqgRFrzwfaSuljHAJC/rKvY8aCiaf+1zgEg8fpbB6gPSqS9RH7jAIgMz45bZp0BQOLtKB+0tQ6RL0qkvX7WAQDU397ug12dKiqscwBIvMQNKlEi7VEigQTLuNz23yqa8q51DgCJN8A6QL4okfYokUDCXZR9bKV1BgCJx0gk8pbYg9cBRPq7//XLqqzUOgeARGMkEnnwQXdJra1jANg6RU5tjy16bap1DgCJ1k8+cNYh8kGJtMWlbCAlLsw+scE6A4BE20ZST+sQ+aBE2qJEAinR280fUKIN66xzAEi0RM2LpETaokQCKeGcWp+SeWmqdQ4AiZaoeZGUSFuUSCBFzss8aR0BQLIlqhdQIq34ICNpV+sYABpOd7d4YCutXWWdA0Bi9bIOkA9KpJ1ukppbhwDQcJxTizMz/2LjcQD1tZN1gHxQIu0k6rcNAHXz/eyzxdYZACRWW/kgsA5RV5RIO4n6bQNA3eygpQMDrVpunQNAYiVmmx9KpB1KJJBCzqnZudmnplvnAJBYiekHlEg7ifkmAZCfIZmJLa0zAEgsRiKxRYn5JgGQn/YKB7bX8iXWOQAkUmL6ASXSTg/rAAAah3PKXJh94n3rHAASiRKJzfBBS0ntrWMAaDwnZ17a1joDgERKzHQ3SqSNrtYBADSuQKv7d9GST6xzAEicHvKBsw5RF5RIG92sAwBoXM7JXZR9bKZ1DgCJ01xSJ+sQdUGJtEGJBArAcZnXOlhnAJBIiZgXSYm0QYkECsA2bt3uO7mFc61zAEicztYB6oISaSMR3xwAtt6I7Ng51hkAJE5H6wB1QYm0wSUuoEB8u+jf/NIIIF/MiUSt2lkHANA0WrjSXfq62R9Z5wCQKIxEolbsEQkUkBHZsQusMwBIFEokakWJBArIYUVTu1tnAJAolEjUINpAtK11DABNp5kr6/E1N2OGdQ4AiZGItROUyKbXVlLGOgSApjU8O26xdQYAiZGItROUyKbHpWygAB1YNH0XKZezzgEgEQL5IGsdYksokU2PEgkUoKyr6Hxw0TvTrXMASIztrANsCSWy6SViiBpAwxuefexz6wwAEiP2g06UyKYX+28KAI1jLzdztyJVlFvnAJAIsR90okQ2PUokUKAyLtfhiKI337XOASARWlsH2BJKZNNjex+ggF2UfXy1dQYAidDCOsCWUCKbXuy/KQA0nr5udr+sykqtcwCIvdj3BUpk02tuHQCAnSKnbb+beXWqdQ4AsdfSOsCWUCKbHiUSKHAXZJ5gJBLAljASiU2UWAcAYGsXt6B/c61fa50DQKxRIrEJRiKBAuecWp+Seekd6xwAYo0SiU1QIgHovMyT1hEAxBtzIrEJSiQAdXOfDmyltSutcwCILUYisQlKJAA5pxZnZZ6bZp0DQGxRIrEJFtYAkCR9P/tssXUGALFFicQmGIkEIEnqpGV7BFq13DoHgFhiTiQ2QYkEIElyTsXnZZ+cbp0DQCxlrQNsCSWy6TWzDgAgPoZkJrayzgAglsqtA2wJJbLp5awDAIiPdloxoIOW88slgI1VWAfYktgPlaZQmXUAAPHhnDLd9OkA6xwAYoeRSGyCM3MBfIVz8Z9AD6DJxX4kkhLZ9BiJBAAAW8JIJDZBiQQAAFtCicQmKJEAAGBLuJyNTVAiAQDAljASiU1QIgEAwJZQIrEJSiQAANgSLmdjE5RIAACwJYxEYhOUSAAAsCWx31eaEtn0Yv9NAQAAzIXWAbaEEtn0VlkHAAAAsbfCOsCWUCKb3nLrAAAAIPYYicQmllsHAAAAscdIJDax3DoAAACIPUYisYnl1gEAAEDsMRKJTSy3DgAAAGKPkUhsYrl1AAAAEHuMRGITy60DAACA2GMkEptYbh0AAADEWk7SSusQW0KJbHrLrQMAAIBYWyUfVliH2BJKZNNbbh0AAADE2nLrAHVBiWxqPlwnaZ11DAAAEFsLrQPUBSXSxqfWAQAAQGxRIlGr+dYBAABAbC2wDlAXlEgblEgAAFAbRiJRK0okAACoDSORqBUlEgAA1IaRSNSKEgkAAGrDSCRqRYkEAAC1YSQStaJEAgCAmqyWD2N/brZEibTyiaRy6xAAACB2EjEKKVEibfiwTNJi6xgAACB2EjEfUqJEWuKSNgAA2Fhi+gEl0k5ivkkAAECT+cg6QF1RIu38zzoAAACInQ+sA9QVJdLODOsAAAAgdiiR2CJKJAAA2NhM6wB1RYm08751AAAAECsL5MPV1iHqihJpxYfLJH1mHQMAAMRGYkYhJUqkNS5pAwCAKomZDylRIq1RIgEAQBVKJOqMEgkAAKpwORt1RokEAABVGIlEnVEiAQCAJG2QNMc6RD4okbZmS1pvHQIAAJj7SD4stw6RD0qkJR9WKGHzHwAAQKN42zpAviiR9t61DgAAAMxNsQ6QL0qkvcR90wAAgAb3lnWAfFEi7b1pHQAAAJjKicvZqIe3JSVqIi0AAGhQH8qHK6xD5IsSaS06aJ2tfgAAKFyJnNpGiYwHLmkDAFC4KJGot/9YBwAAAGYokag3SiQAAIUpkYtqJEpkXEyVVGodAgAANLlZ8mFoHaI+KJFx4MP1kqZbxwAAAE0ukZeyJUpknHBJGwCAwpPYf/8pkfHBCm0AAArPK9YB6osSGR+TrQMAAIAmtUpczkYD+K+kpdYhAABAk3lVPiyzDlFflMi48GFO0svWMQAAQJN50TrA1qBExstL1gEAAECTSfS/+5TIeGEkEgCAwrBaCV6ZLVEi4+YdScutQwAAgEb3mnyY6INGKJFx4sMKMRoJAEAhSPSlbIkSGUfPWwcAAACN7kXrAFuLEhk//7IOAAAAGtVapeCQEUpk3PjwPUmfWMcAAACNZrJ8uME6xNaiRMbTROsAAACg0bxgHaAhUCLjiXmRAACk11PWARoCJTKenpaUsw4BAAAa3CJJb1mHaAiUyDjy4SdK+AakAACgRk9XHnWceJTI+HrcOgAAAGhwT1oHaCiUyPiiRAIAkC5lkp61DtFQKJFx5cN3Jc21jgEAABrMq/JhaB2ioVAi4228dQAAANBgUnWVkRIZb6n6ZgMAoMCl6t91SmS8vShphXUIAACw1d6TDz+yDtGQKJFx5sNSRXtGAgCAZEvVKKREiUyC1H3TAQBQgFL37zklMv6eVLQlAAAASKYFkt6wDtHQKJFx58PPJb1iHQMAANTbaPmwwjpEQ6NEJsMj1gEAAEC9PWwdoDFQIpPhH5I2WIcAAAB5mykfTrEO0RgokUkQXdJOzVmbAAAUkNReTaREJsdD1gEAAEDeKJEw94TYeBwAgCR5Wz78wDpEY6FEJoUP10kaYx0DAADUWSoX1FShRCYLl7QBAEiGnKTR1iEaEyUyWV6QtNA6BAAA2KJJ8uHH1iEaEyUySaKNSh+1jgEAALYo1ZeyJUpkEnFJGwCAeCtVtMdzqlEik8aHb0maYR0DAADUapx8+Jl1iMZGiUym+6wDAACAWv3BOkBToEQm033iGEQAAOLoI0kTrUM0BUpkEvlwiQpgrgUAAAk0Uj7MWYdoCpTI5LrXOgAAAPiKDZLutw7RVCiRSeXDVyRNt44BAAC+8M/Kq4UFgRKZbIxGAgAQHwWxoKYKJTLZHpC02joEAADQTPnwResQTYkSmWQ+XKEC2BEfAIAEGGkdoKlRIpOPS9oAANharwJaUFOFEpl00Qk2/7aOAQBAARsjHy61DtHUKJHpcI91AAAACthd1gEsUCLTYbSk1J/RCQBADL0sHxbkFUFKZBr4cK2k31rHAACgAN1mHcAKJTI97hbb/QAA0JTelzTBOoQVSmRa+HCZpD9ZxwAAoIDcXijnZNeEEpkuv5JUZh0CAIAC8ImkB61DWKJEpokP50p61DoGAAAF4Dfy4QbrEJYokelzq3UAAABSbpU47IMSmTo+nCbpKesYAACk2B/lw+XWIaxRItPpFusAAACkVJmkO61DxAElMo18+JKkN6xjAACQQqPlw3nWIeKAEplezI0EAKBhVUi6yTpEXFAi02ucok1QAQBAw3hUPvyvdYi4oESmlQ8rJF1nHQMAgJQol+StQ8QJJTLdxkiaYh0CAIAU+Kt8+KF1iDihRKZZdBTTNdYxAABIuA2SrrcOETeUyLTz4dOSXrGOAQBAgv2p8lQ4VEOJLAxXWwcAACCh1kn6pXWIOKJEFgIfviLpGesYAAAk0L3y4QLrEHFEiSwcP5WUsw4BAECCrBb7QtaKElkofPiWpH9axwAAIEF+Kx9+ah0iriiRheVaRbvtAwCAzVsh6TbrEHFGiSwkPnxf0oPWMQAASIBfyofLrEPEGSWy8Fwnaa11CAAAYmyWpDutQ8QdJbLQRPtcMTwPAEDtrpQPN1iHiDtKZGG6RdLH1iEAAIih5+XDcdYhkoASWYh8uEbSj6xjAAAQM+WSLrUOkRSUyELlw9GSXraOAQBAjIyUD6dbh0gKSmRhu0Rs+QMAgCQtV7T4FHVEiSxkPpwq6Y/WMQAAiIHr5cPPrEMkCSUS1yj67QsAgEI1Q9Ld1iGShhJZ6KLfurx1DAAADF0uH5ZZh0gaSiQk6XeS3rMOAQCAgQny4VPWIZKIEglV/vZ1iXUMAACa2CpJF1mHSCpKJCI+/Jc4VxsAUFiulg/nWYdIKkokqrtU0hLrEAAANIE3xGKarUKJxJd8uFTSxdYxAABoZKWSfiAfslfyVqBE4qt8+Kik8dYxAABoRLdyMs3Wo0SiJj+UtMI6BAAAjeADST+3DpEGlEhsyocLJP3YOgYAAA0sJ+kC+XC9dZA0oESiNiMlvWQdAgCABjRKPnzZOkRaUCJRMx/mJJ0vaa11FAAAGsAn4ipbg6JEonY+/FAciQgASIfh8uFy6xBpQonEltwhaYp1CAAAtsID8uE/rUOkDSUSm+fDcklnS1pnnAQAgPqYI2m4dYg0okRiy6K9tP7POgYAAHkql3SWfMi2dY2AEom6ukvSc9YhAADIwy3y4STrEGlFiUTdRKu1z5a0zDgJAAB18R+xOLRRUSJRdz5cKOkC6xgAAGzBaklnyIel1kHSjBKJ/PhwjKQ/W8cAAGAzrpAPZ1qHSDtKJOrjYkkzrEMAAFCDx+XDP1iHKASUSOTPh6slnS6Js0cBAHGyWNIPrEMUCkok6seHUyX9xDoGAACVcpLOkQ+XWAcpFJRI1J8P75L0hHUMAAAk3SofPmUdopBQIrG1vq/oNAAAAKy8JOlq6xCFxuVyOesMSDof7CnpVUktrKMAAArOIkl7yoeLrIMUGkYisfV8+LakH1rHAAAUnHJJp1EgbVAi0TB8+BdJ91jHAAAUlKvlw5esQxQqSiQa0iWSJluHAAAUhH/Kh7dYhyhklEg0nOh4qZMU7dMFAEBjeV/S2dYhCh0lEg0rOl/7FEll1lEAAKm0QtLx8uFK6yCFjhKJhufDlyX9yDoGACB1cpK+Jx9+YB0ElEg0Fh/eKekR6xgAgFS5QT58zDoEIpRINKYfSJpqHQIAkAoPy4feOgS+RIlE4/HhGklHS1pgHQUAkGiTJJ1rHQJfxYk1aHw+GCjpFUmtraMAABJnlqT95MPPrIPgqxiJROPz4TtixTYAIH+fSzqKAhlPlEg0DR8+LWmYdQwAQGKUSjqBldjxRYlE0/HhSEm3WscAACTC+fLhi9YhUDtKJJraVZL+Zh0CABBrv5QP/2IdAptHiUTT8mFO0vclvWYdBQAQS49KutY6BLaM1dmw4YP2kiZL2tk6CgAgNl6VdLh8uM46CLaMkUjYiFbaHSmJFXcAACk6nOIoCmRyUCJhx4cfSjpCUmgdBQBg6gNJR8iH/HuQIJRI2PLhW5KOkrTGOgoAwMQ8Sd+UDz+1DoL8UCJhz4evSjpO0nrrKACAJrVY0RzIj62DIH+USMSDD/8lTrUBgEKyXNK3Kqc2IYEokYgPHz4u6SxJFdZRAACNarWkI+XDd62DoP4okYgXHz4q6QJJ7D0FAOm0XtJx8uFk6yDYOpRIxI8P/yTpMusYAIAGVybpNPnweesg2HqUSMSTD+8SJxYAQJqUSzpbPhxnHQQNgxNrEG8+uFHS/1nHAABslTJJZ1VOWUJKMBKJePPhTyV56xgAgHrbIOkUCmT6MBKJZPDBlZJus44BAMjLekknyocTrIOg4VEikRw+uEjS3ZKcdRQAwBatVbQK+znrIGgclEgkiw/OkTRKTMUAgDhbJelo+fAl6yBoPJRIJI8PTpP0gKSsdRQAwCZWSPqOfPiadRA0LkokkskHx0kaLanEOgoA4AufSzpCPnzTOggaHyUSyeWDb0kaJ6mFcRIAgPSZpG/Kh1Otg6BpMK8MyeXDZyV9R9HcGwCAnTmSvk6BLCyUSCRbNGn7EEmLjJMAQKGaImk/+fAD6yBoWpRIJJ8Pox9g0nvWUQCgwDwl6RD5cLF1EDQ9SiTSwYdzJR0o6UXjJABQKP4s6Vj5kClFBYqFNUgXHzRT9IPtDOsoAJBiXj683joEbDESiXTx4QZJZ0m60ToKAKRQmaTzKJCQGIlEmvngfEm/F5uSA0BDWCXpZPnwaesgiAdKJNLNB9+W9HdJ21hHAYAEWyTpKPnwLesgiA9KJNLPB3tKmiBpB+soAJBAb0k6Xj6cZx0E8cKcSKSfD9+W9DVJb1hHAYCEeVDRJuIUSGyCkUgUjmjl9t2SzreOAgAxVy7pR/Lhr62DIL4okSg80YKbuyU1s44CADG0VNKp8uHz1kEQb5RIFCYf7CfpH5K6WEcBgBh5R9J35cM51kEQf8yJRGHy4euSBkl6xToKAMTEaEkHUCBRV4xEorD5oFjSryQNt44CAEYqJP1UPrzFOgiShRIJSJIPvi/pXknNraMAQBNaJmmIfPiMdRAkDyUSqOKDvRTNk+xpHQUAmsDLks6QD+dbB0EyMScSqBKdxLCHpIeNkwBAYyqX5CUdRoHE1mAkEqiJD76naBug1tZRAKABzVd0+ZpFhdhqlEigNj7oJekRRafdAEDSjZN0nny4zDoI0oESCWxOtHr7Bkk/FtM/ACTTOklXyIe/tw6CdKFEAnXhg8MkPSCps3UUAMjD+5JOkw/ftQ6C9GFkBagLH06UNFDS49ZRAKCORknamwKJxsJIJJAvHwyTdJukFtZRAKAGCyRdKB9OsA6CdKNEAvXhg10k/VnS162jAEA1f5Z0uXwYWgdB+lEigfryQZGkEZJulNTSOA2AwvaxpPM5eQZNiRIJbK1oK6A/SxpsHQVAQRop6Ufy4QrrICgslEigIfjASfqhpJvFBuUAmsYcST+QD5+3DoLCRIkEGpIPdpR0j6SjraMASK2cop8zP5EPV1mHQeGiRAKNwQenSvqNpO2towBIlQ8lXSAfvmgdBGCfSKAx+HC0pN0U7dNWYZwGQPKtlXStpH4USMQFI5FAY/PB3pJ+K2k/6ygAEukJSRfLh7OtgwDVUSKBphAtvPmeooU3nYzTAEiGOZIukQ85KQuxRIkEmpIP2ki6TtLFkoqN0wCIp7WKfuG8VT5cZx0GqA0lErDgg10l3SXpW9ZRAMTK3yVdKR/Osw4CbAklErDkg+9K+pWknsZJANiapmje44vWQYC6YnU2YMmH4yTtrmjV5UrbMAAMzJN0tqQ9KJBIGkYigbjwQXtJP5V0kaQS4zQAGtdSSb+U9Hv5cL11GKA+KJFA3Pigq6SfKRqdyNiGAdDAVkv6taTbOOsaSUeJBOLKB30k/VzSSZKccRoAW6dU0khJP5cPF1uHARoCJRKIOx/sJelGSUdYRwGQt5ykRyVdKx/Osg4DNCRKJJAUPjhY0k2S9reOAqBOnpR0jXz4tnUQoDFQIoGk8cFRihbgHGAdBcAmKiT9Q9JN8uFU4yxAo6JEAknlg4MkXSXpSOsoAFQq6QFJt8iHM63DAE2BEgkknQ8GKCqTp4jV3EBTWyNplKTb5cOPrcMATYkSCaSFD3pK+pGkcyQ1N04DpF0o6XeS7pQPl1iHASxQIoG08UFHSZdK+qGkwDYMkDqfSrpT0u/Y5xGFjhIJpJUP2ki6UFGZ5GxuYOu8rmjk8W/y4QbrMEAcUCKBtPNBkaTvKDpO8duSimwDAYmxTtEej3fLh1OswwBxQ4kECokPekgaKuk8Se1twwCxNUfSPZL+JB8uNc4CxBYlEihEPiiRdLKi0Uk2Lweik2Wek3S3pAnyYYVxHiD2KJFAofPBHorK5BBJrWzDAE1uiaSHJN3D/o5AfiiRACI+CBSNTp4pabAkZxsIaDTrJY2X9FdJT8mHZcZ5gESiRALYlA+6KRqZPEvS7sZpgIbyqqJTZUbLh8uNswCJR4kEsHk+2FPR6OQQSZ2M0wD5+p+i4viAfDjLOgyQJpRIAHXjg4ykbygqlCeI+ZOIr2WSxkj6q3w4yToMkFaUSAD580ErSUdLOk7SkeJkHNj7WNK4ytvLzHMEGh8lEsDW8UGxpIMVFcrjJHW1DYQC8p6ksZLGshk40PQokQAaVjSH8ruKCuVA2zBImZykNxQVx3FsyQPYokQCaDzRCTnHKiqUX5fUzDQPkuhzSS9IelbS4/LhJ8Z5AFSiRAJoGj5oqahIHqZogc5e4hxvbGqdoq14npf0L0lTOD0GiCdKJAAbPthW0VzKwypvfcUG54WoQtJb+rI0TpIP19lGAlAXlEgA8eCD7fVloTxU0s62gdBISiW9K+l1RZepJ8qHn9tGAlAflEgA8RSVyn0l7Vd5+5qk1qaZUB9zFS2Gqbq9JR+utY0EoCFQIgEkgw+KFB3BuLekQZX3AyW1sIyFr1gp6U1Fo4xRafThYttIABoLJRJAcvkgq6hYDlI0p3K3yre7i/mVjSmn6DjB6ZKmVbt9wCIYoHBQIgGkT7QSvI+iQrlbtdvOkooNkyVNmaRZkmZIer/a7T35cLVlMAD2KJEACkd0us7OigplD0Wn61TduknqqMIawSyTtEDSPEVzF6vfz5H0P/lwg1k6ALFGiQSAKj5oJqmLvlouq27bS2pb7ZY1SlkXayQtlbSs2v1nisph9aK4QD4stwoJINkokQBQHz7YRl8tlW0lbVftv1spunRe/dashvdVvd9JWi9pQ+X9lv57rb4siV8tjOyzCKAJUCIBAACQN44cAwAAQN4okQAAAMgbJRIAAAB5o0QCAAAgb5RIAAAA5I0SCQAAgLxRIgEAAJA3SiQAAADyRokEAABA3iiRAAAAyBslEgAAAHmjRAIAACBvlEgAAADkjRIJAACAvFEiAQAAkDdKJAAAAPJGiQQAAEDeKJEAAADIGyUSAAAAeaNEAgAAIG+USAAAAOSNEgkAAIC8USIBAACQN0okAAAA8kaJBAAAQN4okQAAAMgbJRIAAAB5o0QCAAAgb5RIAAAA5I0SCQAAgLxRIgEAAJA3SiQAAADyRokEAABA3v4fAfNxlIHegvwAAAAASUVORK5CYII=\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<Figure size 750x750 with 1 Axes>"
+       "<Figure size 600x600 with 4 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
-    },
+    }
+   ],
+   "source": [
+    "plot_analysis(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
     {
      "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 750x750 with 1 Axes>"
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
+       "data": [
+        {
+         "domain": {
+          "x": [
+           0,
+           1
+          ],
+          "y": [
+           0,
+           1
+          ]
+         },
+         "hovertemplate": "function=%{label}<br>time_sum=%{value}<br>parent=%{parent}<extra></extra>",
+         "labels": [
+          "Py_kgeneration",
+          "generate",
+          "gasdev",
+          "fftma2",
+          "covariance",
+          "ran2",
+          "cov_value",
+          "fourt",
+          "cgrid",
+          "length",
+          "build_real",
+          "maxfactor",
+          "clean_real",
+          "prebuild_gwn"
+         ],
+         "marker": {
+          "colors": [
+           "#636efa",
+           "#EF553B",
+           "#00cc96",
+           "#EF553B",
+           "#ab63fa",
+           "#FFA15A",
+           "#636efa",
+           "#ab63fa",
+           "#636efa",
+           "#636efa",
+           "#636efa",
+           "#636efa",
+           "#636efa",
+           "#ab63fa"
+          ]
+         },
+         "name": "",
+         "parents": [
+          "",
+          "Py_kgeneration",
+          "generate",
+          "Py_kgeneration",
+          "fftma2",
+          "gasdev",
+          "",
+          "fftma2",
+          "",
+          "",
+          "",
+          "",
+          "",
+          "fftma2"
+         ],
+         "type": "treemap",
+         "values": [
+          19.763131,
+          14.644248,
+          10.89314099999912,
+          5.116014,
+          5.103886,
+          3.453295000001251,
+          2.1953789999997255,
+          0.008164,
+          0.00159,
+          0.001185,
+          0.000536,
+          0.00040500000000000003,
+          0.000258,
+          0.000236
+         ]
+        }
+       ],
+       "layout": {
+        "autosize": true,
+        "legend": {
+         "tracegroupgap": 0
+        },
+        "margin": {
+         "t": 60
+        },
+        "template": {
+         "data": {
+          "bar": [
+           {
+            "error_x": {
+             "color": "#2a3f5f"
+            },
+            "error_y": {
+             "color": "#2a3f5f"
+            },
+            "marker": {
+             "line": {
+              "color": "#E5ECF6",
+              "width": 0.5
+             },
+             "pattern": {
+              "fillmode": "overlay",
+              "size": 10,
+              "solidity": 0.2
+             }
+            },
+            "type": "bar"
+           }
+          ],
+          "barpolar": [
+           {
+            "marker": {
+             "line": {
+              "color": "#E5ECF6",
+              "width": 0.5
+             },
+             "pattern": {
+              "fillmode": "overlay",
+              "size": 10,
+              "solidity": 0.2
+             }
+            },
+            "type": "barpolar"
+           }
+          ],
+          "carpet": [
+           {
+            "aaxis": {
+             "endlinecolor": "#2a3f5f",
+             "gridcolor": "white",
+             "linecolor": "white",
+             "minorgridcolor": "white",
+             "startlinecolor": "#2a3f5f"
+            },
+            "baxis": {
+             "endlinecolor": "#2a3f5f",
+             "gridcolor": "white",
+             "linecolor": "white",
+             "minorgridcolor": "white",
+             "startlinecolor": "#2a3f5f"
+            },
+            "type": "carpet"
+           }
+          ],
+          "choropleth": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "type": "choropleth"
+           }
+          ],
+          "contour": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "colorscale": [
+             [
+              0,
+              "#0d0887"
+             ],
+             [
+              0.1111111111111111,
+              "#46039f"
+             ],
+             [
+              0.2222222222222222,
+              "#7201a8"
+             ],
+             [
+              0.3333333333333333,
+              "#9c179e"
+             ],
+             [
+              0.4444444444444444,
+              "#bd3786"
+             ],
+             [
+              0.5555555555555556,
+              "#d8576b"
+             ],
+             [
+              0.6666666666666666,
+              "#ed7953"
+             ],
+             [
+              0.7777777777777778,
+              "#fb9f3a"
+             ],
+             [
+              0.8888888888888888,
+              "#fdca26"
+             ],
+             [
+              1,
+              "#f0f921"
+             ]
+            ],
+            "type": "contour"
+           }
+          ],
+          "contourcarpet": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "type": "contourcarpet"
+           }
+          ],
+          "heatmap": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "colorscale": [
+             [
+              0,
+              "#0d0887"
+             ],
+             [
+              0.1111111111111111,
+              "#46039f"
+             ],
+             [
+              0.2222222222222222,
+              "#7201a8"
+             ],
+             [
+              0.3333333333333333,
+              "#9c179e"
+             ],
+             [
+              0.4444444444444444,
+              "#bd3786"
+             ],
+             [
+              0.5555555555555556,
+              "#d8576b"
+             ],
+             [
+              0.6666666666666666,
+              "#ed7953"
+             ],
+             [
+              0.7777777777777778,
+              "#fb9f3a"
+             ],
+             [
+              0.8888888888888888,
+              "#fdca26"
+             ],
+             [
+              1,
+              "#f0f921"
+             ]
+            ],
+            "type": "heatmap"
+           }
+          ],
+          "heatmapgl": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "colorscale": [
+             [
+              0,
+              "#0d0887"
+             ],
+             [
+              0.1111111111111111,
+              "#46039f"
+             ],
+             [
+              0.2222222222222222,
+              "#7201a8"
+             ],
+             [
+              0.3333333333333333,
+              "#9c179e"
+             ],
+             [
+              0.4444444444444444,
+              "#bd3786"
+             ],
+             [
+              0.5555555555555556,
+              "#d8576b"
+             ],
+             [
+              0.6666666666666666,
+              "#ed7953"
+             ],
+             [
+              0.7777777777777778,
+              "#fb9f3a"
+             ],
+             [
+              0.8888888888888888,
+              "#fdca26"
+             ],
+             [
+              1,
+              "#f0f921"
+             ]
+            ],
+            "type": "heatmapgl"
+           }
+          ],
+          "histogram": [
+           {
+            "marker": {
+             "pattern": {
+              "fillmode": "overlay",
+              "size": 10,
+              "solidity": 0.2
+             }
+            },
+            "type": "histogram"
+           }
+          ],
+          "histogram2d": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "colorscale": [
+             [
+              0,
+              "#0d0887"
+             ],
+             [
+              0.1111111111111111,
+              "#46039f"
+             ],
+             [
+              0.2222222222222222,
+              "#7201a8"
+             ],
+             [
+              0.3333333333333333,
+              "#9c179e"
+             ],
+             [
+              0.4444444444444444,
+              "#bd3786"
+             ],
+             [
+              0.5555555555555556,
+              "#d8576b"
+             ],
+             [
+              0.6666666666666666,
+              "#ed7953"
+             ],
+             [
+              0.7777777777777778,
+              "#fb9f3a"
+             ],
+             [
+              0.8888888888888888,
+              "#fdca26"
+             ],
+             [
+              1,
+              "#f0f921"
+             ]
+            ],
+            "type": "histogram2d"
+           }
+          ],
+          "histogram2dcontour": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "colorscale": [
+             [
+              0,
+              "#0d0887"
+             ],
+             [
+              0.1111111111111111,
+              "#46039f"
+             ],
+             [
+              0.2222222222222222,
+              "#7201a8"
+             ],
+             [
+              0.3333333333333333,
+              "#9c179e"
+             ],
+             [
+              0.4444444444444444,
+              "#bd3786"
+             ],
+             [
+              0.5555555555555556,
+              "#d8576b"
+             ],
+             [
+              0.6666666666666666,
+              "#ed7953"
+             ],
+             [
+              0.7777777777777778,
+              "#fb9f3a"
+             ],
+             [
+              0.8888888888888888,
+              "#fdca26"
+             ],
+             [
+              1,
+              "#f0f921"
+             ]
+            ],
+            "type": "histogram2dcontour"
+           }
+          ],
+          "mesh3d": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "type": "mesh3d"
+           }
+          ],
+          "parcoords": [
+           {
+            "line": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "parcoords"
+           }
+          ],
+          "pie": [
+           {
+            "automargin": true,
+            "type": "pie"
+           }
+          ],
+          "scatter": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scatter"
+           }
+          ],
+          "scatter3d": [
+           {
+            "line": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scatter3d"
+           }
+          ],
+          "scattercarpet": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scattercarpet"
+           }
+          ],
+          "scattergeo": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scattergeo"
+           }
+          ],
+          "scattergl": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scattergl"
+           }
+          ],
+          "scattermapbox": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scattermapbox"
+           }
+          ],
+          "scatterpolar": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scatterpolar"
+           }
+          ],
+          "scatterpolargl": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scatterpolargl"
+           }
+          ],
+          "scatterternary": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scatterternary"
+           }
+          ],
+          "surface": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "colorscale": [
+             [
+              0,
+              "#0d0887"
+             ],
+             [
+              0.1111111111111111,
+              "#46039f"
+             ],
+             [
+              0.2222222222222222,
+              "#7201a8"
+             ],
+             [
+              0.3333333333333333,
+              "#9c179e"
+             ],
+             [
+              0.4444444444444444,
+              "#bd3786"
+             ],
+             [
+              0.5555555555555556,
+              "#d8576b"
+             ],
+             [
+              0.6666666666666666,
+              "#ed7953"
+             ],
+             [
+              0.7777777777777778,
+              "#fb9f3a"
+             ],
+             [
+              0.8888888888888888,
+              "#fdca26"
+             ],
+             [
+              1,
+              "#f0f921"
+             ]
+            ],
+            "type": "surface"
+           }
+          ],
+          "table": [
+           {
+            "cells": {
+             "fill": {
+              "color": "#EBF0F8"
+             },
+             "line": {
+              "color": "white"
+             }
+            },
+            "header": {
+             "fill": {
+              "color": "#C8D4E3"
+             },
+             "line": {
+              "color": "white"
+             }
+            },
+            "type": "table"
+           }
+          ]
+         },
+         "layout": {
+          "annotationdefaults": {
+           "arrowcolor": "#2a3f5f",
+           "arrowhead": 0,
+           "arrowwidth": 1
+          },
+          "autotypenumbers": "strict",
+          "coloraxis": {
+           "colorbar": {
+            "outlinewidth": 0,
+            "ticks": ""
+           }
+          },
+          "colorscale": {
+           "diverging": [
+            [
+             0,
+             "#8e0152"
+            ],
+            [
+             0.1,
+             "#c51b7d"
+            ],
+            [
+             0.2,
+             "#de77ae"
+            ],
+            [
+             0.3,
+             "#f1b6da"
+            ],
+            [
+             0.4,
+             "#fde0ef"
+            ],
+            [
+             0.5,
+             "#f7f7f7"
+            ],
+            [
+             0.6,
+             "#e6f5d0"
+            ],
+            [
+             0.7,
+             "#b8e186"
+            ],
+            [
+             0.8,
+             "#7fbc41"
+            ],
+            [
+             0.9,
+             "#4d9221"
+            ],
+            [
+             1,
+             "#276419"
+            ]
+           ],
+           "sequential": [
+            [
+             0,
+             "#0d0887"
+            ],
+            [
+             0.1111111111111111,
+             "#46039f"
+            ],
+            [
+             0.2222222222222222,
+             "#7201a8"
+            ],
+            [
+             0.3333333333333333,
+             "#9c179e"
+            ],
+            [
+             0.4444444444444444,
+             "#bd3786"
+            ],
+            [
+             0.5555555555555556,
+             "#d8576b"
+            ],
+            [
+             0.6666666666666666,
+             "#ed7953"
+            ],
+            [
+             0.7777777777777778,
+             "#fb9f3a"
+            ],
+            [
+             0.8888888888888888,
+             "#fdca26"
+            ],
+            [
+             1,
+             "#f0f921"
+            ]
+           ],
+           "sequentialminus": [
+            [
+             0,
+             "#0d0887"
+            ],
+            [
+             0.1111111111111111,
+             "#46039f"
+            ],
+            [
+             0.2222222222222222,
+             "#7201a8"
+            ],
+            [
+             0.3333333333333333,
+             "#9c179e"
+            ],
+            [
+             0.4444444444444444,
+             "#bd3786"
+            ],
+            [
+             0.5555555555555556,
+             "#d8576b"
+            ],
+            [
+             0.6666666666666666,
+             "#ed7953"
+            ],
+            [
+             0.7777777777777778,
+             "#fb9f3a"
+            ],
+            [
+             0.8888888888888888,
+             "#fdca26"
+            ],
+            [
+             1,
+             "#f0f921"
+            ]
+           ]
+          },
+          "colorway": [
+           "#636efa",
+           "#EF553B",
+           "#00cc96",
+           "#ab63fa",
+           "#FFA15A",
+           "#19d3f3",
+           "#FF6692",
+           "#B6E880",
+           "#FF97FF",
+           "#FECB52"
+          ],
+          "font": {
+           "color": "#2a3f5f"
+          },
+          "geo": {
+           "bgcolor": "white",
+           "lakecolor": "white",
+           "landcolor": "#E5ECF6",
+           "showlakes": true,
+           "showland": true,
+           "subunitcolor": "white"
+          },
+          "hoverlabel": {
+           "align": "left"
+          },
+          "hovermode": "closest",
+          "mapbox": {
+           "style": "light"
+          },
+          "paper_bgcolor": "white",
+          "plot_bgcolor": "#E5ECF6",
+          "polar": {
+           "angularaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "radialaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "scene": {
+           "xaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           },
+           "yaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           },
+           "zaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           }
+          },
+          "shapedefaults": {
+           "line": {
+            "color": "#2a3f5f"
+           }
+          },
+          "ternary": {
+           "aaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "baxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "caxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "title": {
+           "x": 0.05
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          },
+          "yaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          }
+         }
+        }
+       }
+      },
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAskAAAFoCAYAAAClnWY7AAAAAXNSR0IArs4c6QAAIABJREFUeF7t3XecXWWdx/HvLVPSJiEJKYQuCAKGIgiCFFEQUEABCyq6sDaKSlVUxCht6baNq+uqiI2iLrhLERWQXiUIUkMNCSEhPZly277OuSkTdjIzZ+7zu+d5cj7zzy7Jmec85/27Yz4czr3J1Wq1mvhCAAEEEEAAAQQQQACB1QI5IplXAwIIIIAAAggggAACawsQybwiEEAAAQQQQAABBBB4gwCRzEsCAQQQQAABBBBAAAEimdcAAggggAACCCCAAAL9C3AnmVcIAggggAACCCCAAALcSeY1gAACCCCAAAIIIIAAd5J5DSCAAAIIIIAAAgggkEiAxy0ScXEwAggggAACCCCAQBYEiOQsTJlrRAABBBBAAAEEEEgkQCQn4uJgBBBAAAEEEEAAgSwIEMlZmDLXiAACCCCAAAIIIJBIgEhOxMXBCCCAAAIIIIAAAlkQIJKzMGWuEQEEEEAAAQQQQCCRAJGciIuDEUAAAQQQQAABBLIgQCRnYcpcIwIIIIAAAggggEAiASI5ERcHI4AAAggggAACCGRBgEjOwpS5RgQQQAABBBBAAIFEAkRyIi4ORgABBBBAAAEEEMiCAJGchSlzjQgggAACCCCAAAKJBIjkRFwcjAACCCCAAAIIIJAFASI5C1PmGhFAAAEEEEAAAQQSCRDJibg4GAEEEEAAAQQQQCALAkRyFqbMNSKAAAIIIIAAAggkEiCSE3FxMAIIIIAAAggggEAWBIjkLEyZa0QAAQQQQAABBBBIJEAkJ+LiYAQQQAABBBBAAIEsCBDJWZgy14gAAggggAACCCCQSIBITsTFwQgggAACCCCAAAJZECCSszBlrhEBBBBAAAEEEEAgkQCRnIiLgxFAAAEEEEAAAQSyIEAkZ2HKXCMCCCCAAAIIIIBAIgEiOREXByOAAAIIIIAAAghkQYBIzsKUuUYEEEAAAQQQQACBRAJEciIuDkYAAQQQQAABBBDIggCRnIUpc40IIIAAAggggAACiQSI5ERcHIwAAggggAACCCCQBQEiOQtT5hoRQAABBBBAAAEEEgkQyYm4OBgBBBBAAAEEEEAgCwJEchamzDUigAACCCCAAAIIJBIgkhNxcTACCCCAAAIIIIBAFgSI5CxMmWtEAAEEEEAAAQQQSCRAJCfi4mAEEEAAAQQQQACBLAgQyVmYMteIAAIIIIAAAgggkEiASE7ExcEIIIAAAggggAACWRAgkrMwZa4RAQQQQAABBBBAIJEAkZyIi4MRQAABBBBAAAEEsiBAJGdhylwjAggggAACCCCAQCIBIjkRFwcjgAACCCCAAAIIZEGASM7ClLlGBBBAAAEEEEAAgUQCRHIiLg5GAAEEEEAAAQQQyIIAkZyFKXONCCCAAAIIIIAAAokEiOREXByMAAIIIIAAAgggkAUBIjkLU+YaEUAAAQQQQAABBBIJEMmJuDgYAQQQQAABBBBAIAsCRHIWpsw1IoAAAggggAACCCQSIJITcXEwAggggAACCCCAQBYEiOQsTJlrRAABBBBAAAEEEEgkQCQn4uJgBBBAAAEEEEAAgSwIEMlZmDLXiAACCCCAAAIIIJBIgEhOxMXBCCCAAAIIIIAAAlkQIJKzMGWuEQEEEEAAAQQQQCCRAJGciIuDEUAAAQQQQAABBLIgQCRnYcpcIwIIIIAAAggggEAiASI5ERcHI4AAAggggAACCGRBgEjOwpS5RgQQQAABBBBAAIFEAkRyIi4ORgABBBBAAAEEEMiCAJGchSlzjQgggAACCCCAAAKJBIjkRFwcjAACCCCAAAIIIJAFASI5C1PmGhFAAAEEEEAAAQQSCRDJibg4GAEEEEAAAQQQQCALAkRyFqbMNSKAAAIIIIAAAggkEiCSE3FxMAIIIIAAAggggEAWBIjkLEyZa0QAAQQQQAABBBBIJEAkJ+LiYAQQQAABBBBAAIEsCBDJWZgy14gAAggggAACCCCQSIBITsTFwQgggAACCCCAAAJZECCSszBlrhEBBBBAAAEEEEAgkQCRnIiLgxFAAAEEEEAAAQSyIEAkZ2HKXCMCCCCAAAIIIIBAIgEiOREXByOAAAIIIIAAAghkQYBIzsKUuUYEEEAAAQQQQACBRAJEciIuDkYAAQQQQAABBBDIggCRnIUpc40IIIAAAggggAACiQSI5ERcgz/4+puqgz+YIxFAAAEEEEAgMwKHvCevYrF+uaWydOOfG2uGww7KZ8aumRdKJBtpR5F8/Y0Vo9VZFgEEEEAAAQRCFBjWLl1+XstakXz8aaUhX8phBxdEJA+Zr99vJJJtXEUkG8GyLAIIIIAAAgELEMnhDI9INpoVkWwEy7IIIIAAAggELEAkhzM8ItloVkSyESzLIoAAAgggELBAf5G88UY5TRif04qumsaOzun+h6t60xY5tbTkNKZDWrZCKldqGj82p9vurD/HzOMWdi8GItnIdl2RnM9LP768ReVy/cTVmjT3tZquvb6ix5+sDWk3B+yX10aTc7riN2E8A73X7nnddV/9h/uLny3G/yNw74ONvWlhSHB8EwIIIIAAAk0WGOyd5FxOGrdBTkuW1dTTs+5NEsl2AySSjWwHiuQzvlnWwkU1RdG8y9S8/uVjBX3tnJKWLE2+IZ8iOfqhrvXT+tHvX3pOi049q/4mheHD6u/sLQ39PQvJwfgOBBBAAAEEUhIYbCQPdntE8mClkh9HJCc3G9R3DDaSVy32zS8Xdd0NVR16UF43/Lmqhx6p31ndcYe8PnBIXt+6aOWt5z7O3juSNxiT01dPLug/r6zohZdq+pejC9pqi5zmzZeefi76TzTST39V0cQJOX3yIwWN7pC6u6Vf/66imc/XtMmUnI77eEGPPl7T1m/Kxb//62vX3OV+34F5vWO3vFSTnni6pqv+UFG5Iv3gohbdcEtFB727oDPOLmnypJyO+XBBw4fXAzha48lnajrx0wXttENec+bW9J0fVvSJDxdW30nedae8Djs4r0JeWrxU+sVVFb06t6b37JtX9J+gWlqkDcfn4t+f/tOKXl8wtDvvgxogByGAAAIIIGAgQCQboBotSSQbwSaN5G+dWYwfuZg8Maettsxp+n/VH5049mMFvfpard/PUFwVyb+5tqKvfKmoW26rP76w31557f62vC7+QVkdo3L62ikFPf5ULX4s4+wzivHzTH+7p6rNN83ppE8XdOa3ynE8R8H+vR+X9dgTNb19l7zevU9eF3ynrJ3emtcR76///1FYH39cIQ7fv9xe1XcvaNGd91bja4juJH/jjKL+cltVdz9Q1dvflo8/nuas88oaOUK65Nst+vzKj7tZ9bjF0zNrmvaVos69pKzX5te0z5557b1HXuddVtb+e0fxXNA3/62kxUsUx3f0XNYf/ieMx0uMXmIsiwACCCAQoACRHM7QiGSjWQ02kgsFKbqD+rGjCvrqOSW1tuR0/llFnXZ2KQ7Ry85t0XmXljXv9XXfNV0VyW2t0quvafXnM3/2UwXNfKEesdFXFJf5gvTHm6o652tFnfTl0upHI846rahrrqto2XLpzJOL+sJX6s8/RHdwv/jZgr48rfz/gn3q9jm9d/+CLv5+Wd85vyUO6+deqO+ztSV6c4FUrSq+G33RtBZ97tTSOiO5pSjtPDUfrxF9RXeNf3hJi75wZknv2DWv7bbN6Qf/WY/id++b12ab5PTTXxLJRi9flkUAAQQQMBIgko1gDZYlkg1QoyUHiuTVb9yrSrNfremq/67omZn1wDz9pKLueaAaP05w1GEFnXvpuh+1iI6PIvnQgwrxB5NHd5PvuLcexaeeUNTd9695U9z7Dshr/Picbr+rqq+dUoyfiV711daW05VX1x9vOPn46JGJ+jmnTM6t/uforu+Wm+fU3V3/vuh56ugZ6nMuqUfyBZeXNXde/feiO9Dv2juv6F8Coscjosc4PnvKuiN57AbSpIlrh+/0i1s07cKydnhLLj7vT66sR3F0Z7n3PxuNkGURQAABBBBwLkAkOyc1W5BINqIdKJJXvXGvr9Pv/Y58fHc5eswiCuU/3dr/Jz9Ekbzj9nn99g8VnXZiMY7q6PuOP7agJ5+t6dY76t8fPf8bRev1N1b17TOL8V3aN371juI3RvKnji5o9pxa/DjHG7+iSD7/svqjEhuMzun8bxT17YvL8bPH0XPSF36z2G8kR3eSd9kxr+/+qB7nra1SFMnR3e493752FBPJRi9alkUAAQQQMBcgks2JnZ2ASHZGufZCjURy9Ga36PGE6HGL6JncBQv7f4Na7zfuHXJAXtu9Oa9Lp5f13v3z2m6bvC7/YVljOnL6+mkF/eOJ+jPJ0TPDN/+1qvsfqmrUSOnoIwq64qpK/NmL67qTHL3hLnpjYfR4RVe34ueGKxXFH+fWO5Kj0D7jC0WdfnYpftziiPcX4r2ceEZJhaL03fNb4sc5unvWfATcU8/W4nCP7kpHoR09Bx39i8KF36s/k8ydZKMXKssigAACCDRVgEhuKndDJyOSG+Jb9zc3EsnRqid9pqBRI3Lxm+QG+uodydEjENGjFNHjGtGb5j77yaKmbCS9MluaNbsWPxN8xW/XfLrF2DH1z2q+5daqbrurutbjFW+8kxz9c/TIRnRnN7ojPXee9LNfV7RocW2tSI6OO+4TBW3zppyWr5Cuvq6iww8qxI9nRNcTPQay6ca5+K7xoe9d8+kWb9spr8OjT7coSAsWSr/4bSV+FptIHugVwO8jgAACCIQiQCSHMimJSDaaVaN/41708Wwvv1LTrSv/Rp2hbrP35xZHnxARvaEu+gQKvhBAAAEEEECg+QJEcvPNh3pGInmocgN8XyORvNGknE45vqCzLyirs2voG4wej/jQB+qfsRzF8ldPLsbPIz/8KH+73dBV+U4EEEAAAQSGLkAkD92u2d9JJBuJDzWSjzy0oOivbY7+Io1H/lGP2UkTcjrhXwt97jR6Y9wPf9r3neHo8YaPHVnQjjvk4meDH3yk/tdf9/c34hlxsCwCCCCAAAIISCKSw3kZEMlGsxpqJBtth2URQAABBBBAwAMBItmDIQxyC0TyIKGSHkYkJxXjeAQQQAABBNZ/ASI5nBkTyUazIpKNYFkWAQQQQACBgAWI5HCGRyQbzYpINoJlWQQQQAABBAIWIJLDGR6RbDQrItkIlmURQAABBBAIWIBIDmd4RLLRrIhkI1iWRQABBBBAIGABIjmc4RHJRrPqK5Kjv8yDLwQQWL8F5r9e0933V8XPu92cn3i6qmdm1uxOwMoIGAoQyYa4jpcmkh2DrlpuXZF84NKfG52RZRFAIG2B0oQtdM2sfeJI/vzRPdrh5avS3tJ6d/7S1H01/ebNiOT1brLZuSAiOZxZE8lGs+ovkruu+anRWVkWAQTSFCiceM5akbzdw99Rz203prml9e7chXOuIJLXu6lm64KI5HDmTSQbzYpINoJlWQQ8FiCS7YdDJNsbcwZbASLZ1tfl6kSyS81eaxHJRrAsi4DHAkSy/XCIZHtjzmArQCTb+rpcnUh2qUkkG2myLAJhCBDJ9nMiku2NOYOtAJFs6+tydSLZpeZ6GMmPlvKaUqhpXJ53khu9VFh2PRIYbCSXatLZy9o0t5LTZR3duqarqDt6ivrCiB61qeb0Z25WJacfr2jVC5W8xuRr+uSwknZtqQSrTiQHOzo2vlKASA7npUAkG80qlMctqpLy/RhcuKxVH2wv683F6Ei+EECgP4HBRvJLlby+vbRVPx7TFf/8HbuoXd8a1aNNC1W5/pk7bUmb3t1W0cFtZc0oFXTp8lb9ZHSn2nJhzpJIDnNu7HqNAJEczquBSDaa1VAi+b+7irqpu6jRuZoOaKvo911FTR/dFe8w+v9v7ykq+nPtrcWKPjW8pKKkf1nUro8PK+veUiG+K7V/W1lHtJfj73m4VNCvOosqK6eJ+aqOH17SBvma/qerqBcreb1QyWmXlqo+OqykK1a06IFSQdH94u2LVX1+RI/+2FXU1Z0tGpuv6ZjhJe3eUt9TX/swYmRZBIISGEwkL6/l9JUlbXq9mtPkQlXj8zU9UipocqGmNxeququnsPpnLjrm+XJe0U/0S5VcfOyBbRXd1F3QnEpeHx5W0j6tlfjntq+f4Qjv1u6i3tVW1qpPaf/UonZd1NGtiYH+1yEiOagfCTbbhwCRHM7Lgkg2mlXSSH6lktNZS9t0eUe3RuZrunhZq16p5PWD0V1xvP66s0XnjepWe66mS5e1avuWqg5pK+u4Re1xUB89rKQF1ZxOXNyun4/pVGctp5OXtOnbK+9ORcH7ZDmvM0b26Mbuoq7tLOr8lX9QPhjHdIsuGtWlqMK/uqQtvnu8V2tFpy5p0+eHl+I7yf3tw4iRZREISmAwkRxdUPTowyXLWuOf7+grCtfosYvosabeP3PRvzRH/2Ia/e/CsFxNJyxu144t0b/w9uif5bx+tKJV3+3oUn8/w70Bny3ndcnyVv1wdFf8L9whfhHJIU6NPfcWIJLDeT0QyUazShrJt3QX9UipHrHRV3Q36TedLfEfotOXt2qjQlUf6HWH+PquoqaN6o4j+RujerRFof44RHRnObpL9EQ5rzt7Cvr6yvW6atHvDdOvN+jUn7qLeqiUX/170V2o7prUvvJPzR+taNGG+Vp8R7r3H9j97cOIkWURCErAIpIfL+d12oj6/y58Y2lb/NjEnq2V+F+KT17Srl+M6YzvJK/rZ3gV4GvVnM5d1qZPD+vR1JZwH58ikoP6kWCzfQgQyeG8LIhko1kljeTobtHcaj6+QxR9PVXO6/vL63eaLljWqmfK+dURG/3xFj2ScWFHPZKjO8KTVv6n01X/fH9PQVd3tagjt+YNdytq0nc6uuNHM6L1v7TyD94ltZyuXNGiWdVcfHcp+sM0+oP4yDdEcn/7MGJkWQSCErCI5JnlvE5c+bP6zaVtOqy9rLe1VLSomtNJS9r1yzGd6u9nOAKMHq+K/uvUscNL8feG/EUkhzw99h4JEMnhvA6IZKNZJY3k6D+r9r5jdHdP/RGLKJJ/uKI1fkPP+9rqzxr3/lpXJD9dzuvenoK+vPJOcu/vic4V/f4XV/7BG905LtdyOn5ET/wmouh8E/LV/xfJ/e3DiJFlEQhKIK1I7u9neG41p3OWtumkET3adj14Ay6RHNSPBJvlTnLQrwEi2Wh8SSM5elYwulP7ndHdGp6r6fyVHw8VRXL0vOE1nfXHK4blpD93F1XM1bRfa2Wdd5KHSfGjEud2dGtyvqZo/b/1FHTc8FL85sDekRy92z16w9Ch7eX4WcnojlP0n3M/PqykM5a0xW8M3Kml0u8+jBhZFoGgBFxEcu+fuehndTB3kvv7GZ62tE0HrnxEIyjMdWyWSF4fppjta+BOcjjzJ5KNZpU0kqNt/KKzJX6OOHoeOHrHevQpFN/v9ekWt/UUVakpfj75hJWfVLGuO8nR4xerPt2iu5aL3/Tzr8NL8Z2kN0byqkc7ijlp60JVu7VWNH15i04aUYo/AeP6rpb4jYHRIxjRYyF97cOIkWURCErARSRf21Vc/TMXPf40mEhe18/wMcPK+o8VLWp5g+LJI3v09kAfuyCSg/qRYLPcSQ76NUAkG41vKJEcPT286h3n0aMXv+xs0QWjuo12yLIIIOBaYLCR7Pq8WVqPSM7StNfPa+VOcjhzJZKNZpU0kqM33py0uD3+mLdNCtX4Ey2iu7/RG234QgCBMASIZPs5Ecn2xpzBVoBItvV1uTqR7FKz11pJIzn61uhZ4+hxhujTK7YsVHX8iJJG9fp0CqOtsiwCCDgSIJIdQfazDJFsb8wZbAWIZFtfl6sTyS41G4xko62wLAIINEmASLaHJpLtjTmDrQCRbOvrcnUi2aUmkWykybIIhCFAJNvPiUi2N+YMtgJEsq2vy9WJZJeaRLKRJssiEIYAkWw/JyLZ3pgz2AoQyba+Llcnkl1qEslGmiyLQBgCRLL9nIhke2POYCtAJNv6ulydSHapSSQbabIsAmEIEMn2cyKS7Y05g60AkWzr63J1ItmlJpFspMmyCIQhQCTbz4lItjfmDLYCRLKtr8vViWSXmkOM5PYPHWe0C5ZNS6D08D2qzHwirdNz3pQEBhPJ/LwPfjjlxx5S+YkZa30DkTx4P470U4BI9nMufe2KSDaaVZLPSY7+0LzwnVONdsKyzRY4um2yJl88jUhuNrwH5xtMJLedeI6eX7xvr932/rs2rS6iv3M04/zJr2ujbZer+KszieTkdHyH5wJEsucD6rU9ItloVkOJ5GmzHzDaDcs2U+CpLQ4jkpsJ7tG5BhvJj83cRy/eHf21QXytS+A9X+4iknl5rJcCRHI4YyWSjWZFJBvBBrAskRzAkIy2SCS7gyWS3Vmykl8CRLJf8+hvN0Sy0ayIZCPYAJYlkgMYktEWiWR3sESyO0tW8kuASPZrHkRyCvMgklNA9+SURLIng0hhG0SyO3Qi2Z0lK/klQCT7NQ8iOYV5hBLJ+037o+455T3qHj0sBaX185RE8vo518FcVUiR/F8vHaGPTvmxRhTGD+bSmn4Mkdx0ck7YJAEiuUnQDk7D4xYOEPtagkg2gg1gWSI5gCEZbTGESK6pppxy6qwsUnthdPz/+/hFJPs4FfbkQoBIdqHYnDWIZCNnl5G8+W1PaeN7nlPPyDa9svsW2uLWp3TnVw6SatKb//dRbfjPOcrValq45Yb655G7qJbPabO/PaMp9z0f/3rPqHY99pFd1Tl2hMY9PVfbXDdDyuc0Z6dNtNmdz+iek+t3ksc/+aq2uukx5Ss1dY4drn8e9TZVigXtc94NuvOrB6lnRFustc0fZ8S//uzBOxjphb0skRz2/BrZ/dAiuaYHFv1Szyz/q2qq6q2jDtfUjiMUxeyDi67UzBV3xFua0PpmvXPsiXq1+zHdt/Dn+tBG01dv9do5J2r3Mcdpw7atdev8S7Wo9HL8/VM7PqAdRh0eH/fTl4/Uzh0f0SNLrtExG1+pK17+2Oo7yU8v/4seXvxbVWsVjSxuqP3HnR7/35vnnaMNW7fS3O4ntaw8Tx0tk3Xghl9XTnktLL2o21//bv3Xi5P1rvGnalRxkhaXXtHtC76nzspCteTatdfY4zWx7S2JWYnkxGR8QyACRHIgg5KihqpFH5LJl2MBV5E84rWl2vU/bo8fiSgPb9XUX9yrEfOX6q4z3qsNn5ijrW58XPd98V3RKPX2f79Vz++3jRZsNUF7XnKz7jzzYFXaitrooReV76nEgf3Of7sxjt/X3zxRG9/7XBzMd555UBzWe152ix78/L5aNrFDm93xjEa/8LoePWYP7fzTuzR36saavetmsdI7L7xJMz6xh5ZOGeNYbf1YjkheP+Y4lKsYSiQ/t+IuzVhyrQ6deKEqtW5dM/sEHbDh17W0PDf+9cMnXaxirk1/mX+xRhbGa7cxn9QVs47WUZO/H0fp0vKr+t2cL+mTG/9K9y36mXqqy7XvuJPj779q9ud09JT/0ojCOP385Y9o25EHao8Nor+8KKdVj1tEa1856xh9eKP/UEdxkv72+veVy+W099iTdMu889RZXaL3TzhPuVw+3tueYz+njdt30jWzT9SuYz6hLYbvqUeWXKvZXTN0yIRz9Ls5X9B2o96nt4w8SPN6ntbNr52jj035mfK5YiJSIjkRFwcHJEAkhzMsItloVq4iObobHN39jWI1+po4Y5a2+tPjcSRHd5ILpbIqrfU/fN7y+7+ra4PhevGdW2mf82/QswftoLlvnaLS8Nb490fMW6rd/v023Tbt0Pif8+WK9j/rOt3x1YM1duY8TXrkZf39uL3i3yv0lBU9r/zXcz+gyQ+9qA2ffFUzjtlDo15ZpKm/vq9+fr76FCCSs/vCGEok3/b65RrbsrmmdnwwhuuprlBLvl23zr9M41q30I4dR8a//nLnQ3EEHzX5B/rr/Eviu8bRXedHl/xBC0svad9xX1JVlfhucDFX/5n/7ezP6F3jTo3v5F7x8kd10IRpmti2bfx7vZ9JLlU71ZKvvy/h2eW36allt+h9E8+LI3li23ar9/aneedqs2G7a6P2HRXdvT52k2vi76nWyqqopO7KsjjMj9v0d6sf4/j9nC/pHRt8WpPb35rohUEkJ+Li4IAEiORwhkUkG83KVSRHj1YMW7A8fowi+hr94uva4eoH40htXd6trW94TCNeWxI/ZThs4XK9vOeb9Pz+22rU7EXa/NanNO7Z17RkygZ64oid1bq0S2/97QP1RzVWfr3rG9fp7tMP1KQZs7Tln59QaUT9D9foq9hV0t2nHqBaIa+9LrpZfzvrkPhRD1Vrmvne7Y3kwl+WSA5/hkO9gqFE8o2vfVNbDn+nthl5wFqn/d/XztJWw/dd/eur7sp+YuMr9cKKe/XY0uv0/okX6PpXv6xdxhytjdt31ryeZ+NHNDori+O7wQt6no9jd1Lb9nEkf2DSZRrdstFakTy8ME4PLvqlZnU9HP96d2WpRhYn6P0Tz48jeZNhu2rbkfV/KV71z1G8/2neefr4lCvW2vO8nmf0hzmnaGRxzZsBS9Uu7T3upPgak3wRyUm0ODYkASI5nGkRyUazchXJm9zznDZ4bp4e/fju8U4nPjpLW91cv5Mc3TnOV6qrn0Pe7ncPq3OD4XEkr/rKVara8i9PauScxfEzxLtNX3MnObpb/K6zr6/fSX5unib84xXN+OQ7+hTZ5Sd36qV3bqWtbnxMj390Ny2dPNpILvxlieTwZzjUKxhKJEd3kse0bKKdOo6KT7u8Mj9+vOKuBT/S2NbNV//6y50P6v5Fv9CRk7+nSq2kK2d9Qh+cdLmum3u6onDOqxDfOY7WWRW1v3nlX+NnhVdH8uTLNLq4diTP6XpMf198lQ6fdIla8yPiZ6OfWvbnfiN5SvvOumb28Tp202vjO8bRneQl5Tnx3eiro19feYd5qI7R9xHJjejxvT7Sez30AAAc5UlEQVQLEMk+T2ftvRHJRrNyFckdsxZqp5/drXtOO0DltqJ2/tnd8R3jKJKn/vI+Ld5srF7ce2uNmrNYU6+8N368Yt72G2mz25/WY0fvpmqxoMkPvxTHdfS4xN7n36jHP7Jr/Exy9Oa+6I160Z3l6G7xHpf/WQ8ev69WjB+p6LzR9z112I6xUPTYx/in52rE3CXxnWe+1i1AJGf31TGUSH5uxR3xm+YOm3hx/Ma938/5ovYff4aWVebH8fqBSZeqkGvVn+ddEMf0bmOOiYH/Mv9ClWs9GpYfo33GfSH+tehu8SETz43fbBfF7p0Lpus947+qTYa9rX4nuY9Ifn7FPZrV+VD8KEZ3dZlumXe+SrXOOMDXdSc5erb56tknaMeOI+I73f9Yep2iiK8/k/zF+Ne3GrGfuiqLddfCH8X7a8kl+5hJIjm7P0fr+5UTyeFMmEg2mpWrSI62t/X//kOTZrysrjHDNWfnTbXpnc/q7jMOXP3oRbWQ15JNxuq17SZr+2sf0uMf2jW++zzhsdnxG/KiT7f455E7a/mEjviTMN78xxnxhz69stvm8Zv3Hjx+v/gO9KpPtyiUKnGQP3XYTlq0+bhYKHq0Y+/zbtAL+22jmQduZ6S2fixLJK8fcxzKVQwlkqNPoXhg0RV6etlfFb3R4K0dh2vHjqPW/nSLWi1+pnevsZ9TMdceb+35FXfFjzxEj0VMad8p/rXHl/6P/r746viOcBSy3dWl8fPF0WMW0Rvq+orkglp047xp8WMWI4rjtfuYY3XzvG9rm5EHanFpVp+PW0R3qheUXog/3SJ6g+CY4sZrfbrF3xZ8P/7Ui+guc/RJHduNOiQxJ5GcmIxvCESASA5kUHy6hd2gXEZy9DFutVz9s0yj+I2eQ77/pOgTLfjyUYBI9nEqzdnTUCK5OTsL7yxEcngzY8eDEyCSB+fkw1HcSTaagqtIju7gRm+au/+E/bR8Yoe2u/ah+GPdnjq0/hgEX/4JEMn+zaRZOyKS3UkTye4sWckvASLZr3n0txsi2WhWriI52t6U+5+PP6kiV63Fn00cfc7xqo91M9o+yzYgQCQ3gBf4txLJ7gZIJLuzZCW/BIhkv+ZBJKcwD5eRnML2OWUDAkRyA3iBfyuR7G6ARLI7S1byS4BI9mseRHIK8yCSU0D35JREsieDSGEbRLI7dCLZnSUr+SVAJPs1DyI5hXkQySmge3JKItmTQaSwDSLZHTqR7M6SlfwSIJL9mgeRnMI8iOQU0D05JZHsySBS2AaR7A6dSHZnyUp+CRDJfs2DSE5hHkRyCuienJJI9mQQKWyDSHaHTiS7s2QlvwSIZL/mQSSnMA8iOQV0T05JJHsyiBS2QSS7QyeS3Vmykl8CRLJf8yCSU5iHi0iettFupjv/w8LnNKPzddNzZHFxIjmLU69fM5HsbvZEsjtLVvJLgEj2ax5EcgrzcBXJ33ztRZPdL958D+37yl1EsoEukWyAGsiSRLK7QRHJ7ixZyS8BItmveRDJKczDaSTP+L3zK1h8+EVEsnPV+oJEshFsAMsSye6GRCS7s2QlvwSIZL/mQSSnMA8iOQV0T05JJHsyiBS2QSS7QyeS3Vmykl8CRLJf8yCSU5gHkZwCuienJJI9GUQK2yCS3aETye4sWckvASLZr3kQySnMg0hOAd2TUxLJngwihW0Qye7QiWR3lqzklwCR7Nc8iOQU5mEZyTVJuUFc0/wVVV1+7xIV8zmNac/rlD1Grf4unkkeBOAQDyGShwi3HnwbkexuiESyO0tW8kuASPZrHkRyCvOwiOTfPbFCzy4o6y3jWzSlo6Br/7lC+Zx0yNbDtNcmbTr1Twu186RWPTW/pKO2G65SVRpezGn7CS06+9bFOn3PUepoy8caRLLdi4JItrP1fWUi2d2EiGR3lqzklwCR7Nc8iOQU5mERydc91alqraYPbjtc987q1g4TWuN/vuiuJTp3/zH6zB8X6MfvH6vnF5V107NdOmG3kfGVL+murj5mFQWRbPeiIJLtbH1fmUh2NyEi2Z0lK/klQCT7NQ8iOYV5WEXy+GF57bVpm+57pUd3vtSl9mJOLy6q6KIDxujLtyyK/+/spRVd888V+tLuo/Tqsop+8vAyfW7XUdpweP0uMneSbV8QRLKtr8+rE8nupkMku7NkJb8EiGS/5kEkpzAPs0geno8frfjijQt12Xs3iCP4u/ct1cV9RPKndhwR/97pe3ZoRMvaTzFzJ9nuRUEk29n6vjKR7G5CRLI7S1byS4BI9mseRHIK87CO5OkPLNPyUlWbjS7qoTk9+vTOI/WfDy9b607yqNac/v5qSWOH1e8gf2aXkdq4o8CdZOPXA5FsDOzx8kSyu+EQye4sWckvASLZr3kQySnMwyKSXV4Gd5Jdaq69FpFsZ+v7ykSyuwkRye4sWckvASLZr3kQySnMg0hOAd2TUxLJngwihW0Qye7QiWR3lqzklwCR7Nc8iOQU5kEkp4DuySmJZE8GkcI2iGR36ESyO0tW8kuASPZrHkRyCvMgklNA9+SURLIng0hhG0SyO3Qi2Z0lK/klQCT7NQ8iOYV5EMkpoHtySiLZk0GksA0i2R06kezOkpX8EiCS/ZoHkZzCPIjkFNA9OSWR7MkgUtgGkewOnUh2Z8lKfgkQyX7Ng0hOYR5EcgronpySSPZkEClsg0h2h04ku7NkJb8EiGS/5kEkpzAPIjkFdE9OSSR7MogUtkEku0Mnkt1ZspJfAkSyX/MgklOYB5GcAronpySSPRlECtsgkt2hE8nuLFnJLwEi2a95EMkpzINITgHdk1MSyZ4MIoVtEMnu0Ilkd5as5JcAkezXPIjkFOZBJKeA7skpiWRPBpHCNohkd+hEsjtLVvJLgEj2ax5EcgrzIJJTQPfklESyJ4NIYRtEsjt0ItmdJSv5JUAk+zUPIjmFeRDJKaB7ckoi2ZNBpLANItkdOpHszpKV/BJwHskH5XXYwQW/LnI92U2uVqvV1pNr8eoyiGSvxtHUzRDJTeX26mREsrtxEMnuLFnJLwHnkXxwQYcdlPfrIteT3RDJRoMkko1gA1iWSA5gSEZbJJLdwRLJ7ixZyS8BItmvefS3GyLZaFZEshFsAMsSyQEMyWiLRLI7WCLZnSUr+SXgPJJ53MJswESyES2RbAQbwLJEcgBDMtoikewOlkh2Z8lKfgk4j2QetzAbMJFsREskG8EGsCyRHMCQjLZIJLuDJZLdWbKSXwJEsl/z4HGLFOZBJKeA7skpiWRPBpHCNohkd+hEsjtLVvJLgEj2ax5EcgrzIJJTQPfklESyJ4NIYRtEsjt0ItmdJSv5JUAk+zUPIjmFeRDJKaB7ckoi2ZNBpLANItkdOpHszpKV/BIgkv2aB5GcwjyI5BTQPTklkezJIFLYBpHsDp1IdmfJSn4JEMl+zYNITmEeRHIK6J6ckkj2ZBApbINIdodOJLuzZCW/BIhkv+ZBJKcwDyI5BXRPTkkkezKIFLZBJLtDJ5LdWbKSXwJEsl/zIJJTmAeRnAK6J6ckkj0ZRArbIJLdoRPJ7ixZyS8BItmveRDJKcyDSE4B3ZNTEsmeDCKFbRDJ7tCJZHeWrOSXAJHs1zyI5BTmQSSngO7JKYlkTwaRwjaIZHfoRLI7S1byS4BI9mseRHIK8yCSU0D35JREsieDSGEbSSJ5/jO1FHYYzinfcWynir86U+UnZqy16cI5V2j6zZvpmZn4hTNNdtpbgEgO5/XAX0ttNCsi2Qg2gGWJ5ACGZLTFwUZy8S07Ge1g/Vq2+9/PIpLXr5FyNZKI5HBeBkSy0ayIZCPYAJYlkgMYktEWBxPJRqfOzLLcSc7MqNfbCyWSwxktkWw0KyLZCDaAZYnkAIZktEUi2Qi217JEsr0xZ7AVIJJtfV2uTiS71Oy1FpFsBBvAskRyAEMy2iKRbARLJNvDcoamCRDJTaNu+EREcsOEfS9AJBvBBrAskRzAkIy2SCQbwRLJ9rCcoWkCRHLTqBs+EZHcMCGRbEQY7LJEcrCja3jjRHLDhAMuwOMWAxJxgOcCRLLnA+q1PSLZaFbcSTaCDWBZIjmAIRltkUg2guVOsj0sZ2iaAJHcNOqGT0QkN0zInWQjwmCXJZKDHV3DGyeSGyYccAHuJA9IxAGeCxDJng+IO8n2A+JOsr2xr2cgkn2djP2+iOQmGPOXidgjcwZTASLZlNfp4txJdsq5ZjEi2Qg2gGWJ5ACGZLRFItkIttey3Em2N+YMtgJEsq2vy9WJZJeavdYiko1gA1iWSA5gSEZbJJKNYIlke1jO0DQBIrlp1A2fiEhumLDvBYhkI9gAliWSAxiS0RaJZCNYItkeljM0TYBIbhp1wycikhsmJJKNCINdlkgOdnQNb5xIbphwwAV43GJAIg7wXIBI9nxAvbZHJBvNijvJRrABLEskBzAkoy0SyUaw3Em2h+UMTRMgkptG3fCJiOSGCbmTbEQY7LJEcrCja3jjRHLDhAMuwJ3kAYk4wHMBItnzAXEn2X5A3Em2N/b1DESyr5Ox3xeR3ARjPgLOHpkzmAoQyaa8ThfnTrJTzjWLEclGsAEsSyQHMCSjLRLJRrC9luVOsr0xZ7AVIJJtfV2uTiS71Oy1FpFsBBvAskRyAEMy2iKRbARLJNvDcoamCRDJTaNu+EREcsOEfS9AJBvBBrAskRzAkIy2SCQbwRLJ9rCcoWkCRHLTqBs+EZHcMCGRbEQY7LJEcrCja3jjRHLDhAMuwOMWAxJxgOcCRLLnA+q1PSLZaFbcSTaCDWBZIjmAIRltkUg2guVOsj0sZ2iaAJHcNOqGT0QkN0zInWQjwmCXJZKDHV3DGyeSGyYccAHuJA9IxAGeCxDJng+IO8n2A+JOsr2xr2cgkn2djP2+iOQmGPMRcPbInMFUgEg25XW6OHeSnXKuWYxINoINYFkiOYAhGW2RSDaC7bUsd5LtjTmDrQCRbOvrcnUi2aVmr7WIZCPYAJYlkgMYktEWiWQjWCLZHpYzNE2ASG4adcMnIpIbJux7ASLZCDaAZYnkAIZktEUi2QiWSLaH5QxNEyCSm0bd8ImI5IYJiWQjwmCXJZKDHV3DGyeSGyYccAEetxiQiAM8FyCSPR9Qr+0RyUaz4k6yEWwAyxLJAQzJaItEshEsd5LtYTlD0wSI5KZRN3wiIrlhQu4kGxEGuyyRHOzoGt44kdww4YALcCd5QCIO8FyASPZ8QNxJth8Qd5LtjX09A5Hs62Ts90UkN8GYj4CzR+YMpgJEsimv08W5k+yUc81iRLIRbADLEskBDMloi0SyEWyvZbmTbG/MGWwFiGRbX5erE8kuNXutRSQbwQawLJEcwJCMtkgkG8ESyfawnKFpAkRy06gbPhGR3DBh3wsQyUawASxLJAcwJKMtEslGsESyPSxnaJoAkdw06oZPRCQ3TEgkGxEGuyyRHOzoGt44kdww4YAL8LjFgEQc4LkAkez5gHptj0g2mhV3ko1gA1iWSA5gSEZbJJKNYLmTbA/LGZomQCQ3jbrhExHJDRNyJ9mIMNhlieRgR9fwxonkhgkHXIA7yQMScYDnAkSy5wPiTrL9gLiTbG/s6xmIZF8nY78vIrkJxnwEnD0yZzAVIJJNeZ0uzp1kp5xrFiOSjWADWJZIDmBIRlskko1gey3LnWR7Y85gK0Ak2/q6XJ1IdqnZay0i2Qg2gGWJ5ACGZLRFItkIlki2h+UMTRMgkptG3fCJiOSGCftegEg2gg1gWSI5gCEZbZFINoIlku1hOUPTBIjkplE3fCIiuWFCItmIMNhlieRgR9fwxonkhgkHXIDHLQYk4gDPBYhkzwfUa3tEstGsuJNsBBvAskRyAEMy2iKRbATLnWR7WM7QNAEiuWnUDZ+ISG6YkDvJRoTBLkskBzu6hjdOJDdMOOAC3EkekIgDPBcgkj0fEHeS7QfEnWR7Y1/PQCT7Ohn7fRHJTTDmI+DskTmDqQCRbMrrdHHuJDvlXLMYkWwEG8CyRHIAQzLaIpFsBNtrWe4k2xtzBlsBItnW1+XqRLJLzV5rEclGsAEsSyQHMCSjLRLJRrBEsj0sZ2iaAJHcNOqGT0QkN0zY9wJEshFsAMsSyQEMyWiLRLIRLJFsD8sZmiZAJDeNuuETEckNExLJRoTBLkskBzu6hjdOJDdMOOACPG4xIBEHeC5AJHs+oF7bI5KNZsWdZCPYAJYlkgMYktEWiWQjWO4k28NyhqYJEMlNo274RERyw4TcSTYiDHZZIjnY0TW8cSK5YcIBF+BO8oBEHOC5AJHs+YC4k2w/IO4k2xv7egYi2dfJ2O+LSG6CMR8BZ4/MGUwFiGRTXqeLcyfZKeeaxYhkI9gAliWSAxiS0RaJZCPYXstyJ9nemDPYChDJtr4uVyeSXWr2WotINoINYFkiOYAhGW2RSDaCJZLtYTlD0wSI5KZRN3wiIrlhwr4XIJKNYANYlkgOYEhGWySSjWCJZHtYztA0ASK5adQNn4hIbpiQSDYiDHZZIjnY0TW8cSK5YcIBF+BxiwGJOMBzASLZ8wH12h6RbDQr7iQbwQawLJEcwJCMtkgkG8FyJ9keljM0TYBIbhp1wycikhsm5E6yEWGwyxLJwY6u4Y0TyQ0TDrgAd5IHJOIAzwWIZM8HxJ1k+wFxJ9ne2NczEMm+TsZ+X0RyE4z5CDh7ZM5gKkAkm/I6XZw7yU451yxGJBvBBrAskRzAkIy2SCQbwfZaljvJ9sacwVaASLb1dbk6kexSs9daRLIRbADLEskBDMloi0SyESyRbA/LGZomQCQ3jbrhExHJDRP2vYDTSDbY4+LN99C+r9ylGZ2vG6ye7SWJ5OzOn0i2nz13ku2NOYOtAJFs6+tydSLZpabBnWSj7cXL/mHhc0SyATCRbIAayJJEsv2giGR7Y85gK0Ak2/q6XJ1IdqnpOJKNtsayxgJEsjGwx8sTyfbDIZLtjTmDrQCRbOvrcnUi2aUmkWykGdayRHJY83K5WyLZpWbfaxHJ9sacwVaASLb1dbk6kexSk0g20gxrWSI5rHm53C2R7FKTSLbX5AxpCBDJaagP7ZxE8tDcBvwuF2/cG/AkHOClAJHs5Viasiki2Z6ZO8n2xpzBVoBItvV1uTqR7FKzwTvJRltZ57I11ZRTbvXvW/9zs68vrfMd3TZZky+epsrMJ9LaAudNSYBItocnku2NOYOtAJFs6+tydSLZpWYDkWy0DZZNSaD08D1Eckr2aZ6WSLbXJ5LtjTmDrQCRbOvrcnUi2aXmECPZaAssiwACTRYgku3BiWR7Y85gK0Ak2/q6XJ1IdqlJJBtpsiwCYQgQyfZzIpLtjTmDrQCRbOvrcnUi2aUmkWykybIIhCFAJNvPiUi2N+YMtgJEsq2vy9WJZJeaRLKRJssiEIYAkWw/JyLZ3pgz2AoQyba+Llcnkl1qDjKSjU7JsgggkLJAacIWumbWPrr7/qo+f3SPdnj5qpR3tP6dvjR1X02/eTM9M7O2/l0cV5QJASI5nDETyUazWtfnJKtWk3I5rf6/fZ1/1TH97W0wxxhdW9DLNurW3/cPZrZB47H5wQjMf70WR/JhBxf6Pjx6nbzxK/rfhKF+vXG9waw1lO8Z6v4Mvu+Jp6tEsoErSzZHgEhujrOLsxDJLhT7WKOvSDY6FcsigAACCCCAQCACRHIgg5JEJBvNikg2gmVZBBBAAAEEAhYgksMZHpFsNCsi2QiWZRFAAAEEEAhYgEgOZ3hEstGsiGQjWJZFAAEEEEAgYAEiOZzhEclGs4oimS8EEEAAAQQQQOCNAoe8J69isf6rpbK0fEVjRmM6Gvt+vrtvASKZVwYCCCCAAAIIIIAAAm8QIJJ5SSCAAAIIIIAAAgggQCTzGkAAAQQQQAABBBBAoH8B7iTzCkEAAQQQQAABBBBAgDvJvAYQQAABBBBAAAEEEOBOMq8BBBBAAAEEEEAAAQQSCfC4RSIuDkYAAQQQQAABBBDIggCRnIUpc40IIIAAAggggAACiQSI5ERcHIwAAggggAACCCCQBQEiOQtT5hoRQAABBBBAAAEEEgkQyYm4OBgBBBBAAAEEEEAgCwJEchamzDUigAACCCCAAAIIJBIgkhNxcTACCCCAAAIIIIBAFgSI5CxMmWtEAAEEEEAAAQQQSCRAJCfi4mAEEEAAAQQQQACBLAgQyVmYMteIAAIIIIAAAgggkEiASE7ExcEIIIAAAggggAACWRAgkrMwZa4RAQQQQAABBBBAIJEAkZyIi4MRQAABBBBAAAEEsiBAJGdhylwjAggggAACCCCAQCIBIjkRFwcjgAACCCCAAAIIZEGASM7ClLlGBBBAAAEEEEAAgUQCRHIiLg5GAAEEEEAAAQQQyIIAkZyFKXONCCCAAAIIIIAAAokEiOREXByMAAIIIIAAAgggkAUBIjkLU+YaEUAAAQQQQAABBBIJEMmJuDgYAQQQQAABBBBAIAsCRHIWpsw1IoAAAggggAACCCQSIJITcXEwAggggAACCCCAQBYEiOQsTJlrRAABBBBAAAEEEEgkQCQn4uJgBBBAAAEEEEAAgSwIEMlZmDLXiAACCCCAAAIIIJBIgEhOxMXBCCCAAAIIIIAAAlkQIJKzMGWuEQEEEEAAAQQQQCCRAJGciIuDEUAAAQQQQAABBLIgQCRnYcpcIwIIIIAAAggggEAiASI5ERcHI4AAAggggAACCGRBgEjOwpS5RgQQQAABBBBAAIFEAkRyIi4ORgABBBBAAAEEEMiCAJGchSlzjQgggAACCCCAAAKJBIjkRFwcjAACCCCAAAIIIJAFASI5C1PmGhFAAAEEEEAAAQQSCRDJibg4GAEEEEAAAQQQQCALAkRyFqbMNSKAAAIIIIAAAggkEiCSE3FxMAIIIIAAAggggEAWBIjkLEyZa0QAAQQQQAABBBBIJEAkJ+LiYAQQQAABBBBAAIEsCBDJWZgy14gAAggggAACCCCQSIBITsTFwQgggAACCCCAAAJZECCSszBlrhEBBBBAAAEEEEAgkQCRnIiLgxFAAAEEEEAAAQSyIEAkZ2HKXCMCCCCAAAIIIIBAIgEiOREXByOAAAIIIIAAAghkQYBIzsKUuUYEEEAAAQQQQACBRAJEciIuDkYAAQQQQAABBBDIggCRnIUpc40IIIAAAggggAACiQSI5ERcHIwAAggggAACCCCQBQEiOQtT5hoRQAABBBBAAAEEEgkQyYm4OBgBBBBAAAEEEEAgCwJEchamzDUigAACCCCAAAIIJBIgkhNxcTACCCCAAAIIIIBAFgSI5CxMmWtEAAEEEEAAAQQQSCRAJCfi4mAEEEAAAQQQQACBLAgQyVmYMteIAAIIIIAAAgggkEiASE7ExcEIIIAAAggggAACWRAgkrMwZa4RAQQQQAABBBBAIJEAkZyIi4MRQAABBBBAAAEEsiBAJGdhylwjAggggAACCCCAQCIBIjkRFwcjgAACCCCAAAIIZEGASM7ClLlGBBBAAAEEEEAAgUQCRHIiLg5GAAEEEEAAAQQQyIIAkZyFKXONCCCAAAIIIIAAAokEiOREXByMAAIIIIAAAgggkAUBIjkLU+YaEUAAAQQQQAABBBIJEMmJuDgYAQQQQAABBBBAIAsCRHIWpsw1IoAAAggggAACCCQSIJITcXEwAggggAACCCCAQBYEiOQsTJlrRAABBBBAAAEEEEgkQCQn4uJgBBBAAAEEEEAAgSwIEMlZmDLXiAACCCCAAAIIIJBIgEhOxMXBCCCAAAIIIIAAAlkQIJKzMGWuEQEEEEAAAQQQQCCRAJGciIuDEUAAAQQQQAABBLIgQCRnYcpcIwIIIIAAAggggEAiASI5ERcHI4AAAggggAACCGRBgEjOwpS5RgQQQAABBBBAAIFEAv8HD44BqFKAtLwAAAAASUVORK5CYII=",
+      "text/html": [
+       "<div>                            <div id=\"3214b210-2bfa-4e28-942a-10af55c9539e\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"3214b210-2bfa-4e28-942a-10af55c9539e\")) {                    Plotly.newPlot(                        \"3214b210-2bfa-4e28-942a-10af55c9539e\",                        [{\"domain\":{\"x\":[0.0,1.0],\"y\":[0.0,1.0]},\"hovertemplate\":\"function=%{label}<br>time_sum=%{value}<br>parent=%{parent}<extra></extra>\",\"labels\":[\"Py_kgeneration\",\"generate\",\"gasdev\",\"fftma2\",\"covariance\",\"ran2\",\"cov_value\",\"fourt\",\"cgrid\",\"length\",\"build_real\",\"maxfactor\",\"clean_real\",\"prebuild_gwn\"],\"marker\":{\"colors\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#EF553B\",\"#ab63fa\",\"#FFA15A\",\"#636efa\",\"#ab63fa\",\"#636efa\",\"#636efa\",\"#636efa\",\"#636efa\",\"#636efa\",\"#ab63fa\"]},\"name\":\"\",\"parents\":[\"\",\"Py_kgeneration\",\"generate\",\"Py_kgeneration\",\"fftma2\",\"gasdev\",\"\",\"fftma2\",\"\",\"\",\"\",\"\",\"\",\"fftma2\"],\"values\":[19.763131,14.644248,10.89314099999912,5.116014,5.103886,3.453295000001251,2.1953789999997255,0.008164,0.00159,0.001185,0.000536,0.00040500000000000003,0.000258,0.000236],\"type\":\"treemap\"}],                        {\"template\":{\"data\":{\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"white\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2}}},\"legend\":{\"tracegroupgap\":0},\"margin\":{\"t\":60}},                        {\"responsive\": true}                    ).then(function(){\n",
+       "                            \n",
+       "var gd = document.getElementById('3214b210-2bfa-4e28-942a-10af55c9539e');\n",
+       "var x = new MutationObserver(function (mutations, observer) {{\n",
+       "        var display = window.getComputedStyle(gd).display;\n",
+       "        if (!display || display === 'none') {{\n",
+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
+       "    x.observe(outputEl, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "                        })                };                });            </script>        </div>"
       ]
      },
      "metadata": {},
@@ -1343,7 +4147,7 @@
     }
    ],
    "source": [
-    "plot_analysis(df)"
+    "plot_treemap(df)"
    ]
   },
   {
@@ -1355,7 +4159,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 196,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [
     {
@@ -1391,23 +4195,23 @@
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
-       "      <th colspan=\"3\" halign=\"left\">cpu</th>\n",
        "      <th colspan=\"3\" halign=\"left\">memory</th>\n",
        "      <th colspan=\"5\" halign=\"left\">time</th>\n",
+       "      <th colspan=\"3\" halign=\"left\">cpu</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
-       "      <th>max</th>\n",
-       "      <th>mean</th>\n",
        "      <th>min</th>\n",
        "      <th>max</th>\n",
        "      <th>median</th>\n",
        "      <th>min</th>\n",
-       "      <th>count</th>\n",
        "      <th>max</th>\n",
        "      <th>mean</th>\n",
-       "      <th>min</th>\n",
        "      <th>sum</th>\n",
+       "      <th>count</th>\n",
+       "      <th>min</th>\n",
+       "      <th>max</th>\n",
+       "      <th>mean</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>function</th>\n",
@@ -1427,243 +4231,243 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Py_kgeneration</th>\n",
-       "      <td>32.278779</td>\n",
-       "      <td>415.639768</td>\n",
-       "      <td>32.278779</td>\n",
-       "      <td>-199.3</td>\n",
-       "      <td>-199.3</td>\n",
-       "      <td>-199.3</td>\n",
+       "      <td>2781.1</td>\n",
+       "      <td>2781.1</td>\n",
+       "      <td>2781.1</td>\n",
+       "      <td>173.931290</td>\n",
+       "      <td>173.931290</td>\n",
+       "      <td>173.931290</td>\n",
+       "      <td>173.931290</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>415.639768</td>\n",
-       "      <td>415.639768</td>\n",
-       "      <td>415.639768</td>\n",
-       "      <td>415.639768</td>\n",
+       "      <td>19.901069</td>\n",
+       "      <td>19.901069</td>\n",
+       "      <td>173.931290</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>generate</th>\n",
-       "      <td>32.749276</td>\n",
-       "      <td>329.579564</td>\n",
-       "      <td>32.749276</td>\n",
-       "      <td>-281.6</td>\n",
-       "      <td>-281.6</td>\n",
-       "      <td>-281.6</td>\n",
+       "      <td>2781.2</td>\n",
+       "      <td>2781.2</td>\n",
+       "      <td>2781.2</td>\n",
+       "      <td>137.192987</td>\n",
+       "      <td>137.192987</td>\n",
+       "      <td>137.192987</td>\n",
+       "      <td>137.192987</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>329.579564</td>\n",
-       "      <td>329.579564</td>\n",
-       "      <td>329.579564</td>\n",
-       "      <td>329.579564</td>\n",
+       "      <td>23.489213</td>\n",
+       "      <td>23.489213</td>\n",
+       "      <td>137.192987</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>fftma2</th>\n",
-       "      <td>30.472359</td>\n",
-       "      <td>86.059132</td>\n",
-       "      <td>30.472359</td>\n",
-       "      <td>85.3</td>\n",
-       "      <td>85.3</td>\n",
-       "      <td>85.3</td>\n",
+       "      <td>2.6</td>\n",
+       "      <td>2.6</td>\n",
+       "      <td>2.6</td>\n",
+       "      <td>36.737828</td>\n",
+       "      <td>36.737828</td>\n",
+       "      <td>36.737828</td>\n",
+       "      <td>36.737828</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>86.059132</td>\n",
-       "      <td>86.059132</td>\n",
-       "      <td>86.059132</td>\n",
-       "      <td>86.059132</td>\n",
+       "      <td>6.498040</td>\n",
+       "      <td>6.498040</td>\n",
+       "      <td>36.737828</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>covariance</th>\n",
-       "      <td>30.505800</td>\n",
-       "      <td>85.894170</td>\n",
-       "      <td>30.505800</td>\n",
-       "      <td>80.9</td>\n",
-       "      <td>80.9</td>\n",
-       "      <td>80.9</td>\n",
+       "      <td>-1.8</td>\n",
+       "      <td>-1.8</td>\n",
+       "      <td>-1.8</td>\n",
+       "      <td>36.638978</td>\n",
+       "      <td>36.638978</td>\n",
+       "      <td>36.638978</td>\n",
+       "      <td>36.638978</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>85.894170</td>\n",
-       "      <td>85.894170</td>\n",
-       "      <td>85.894170</td>\n",
-       "      <td>85.894170</td>\n",
+       "      <td>6.515506</td>\n",
+       "      <td>6.515506</td>\n",
+       "      <td>36.638978</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>gasdev</th>\n",
-       "      <td>100.100000</td>\n",
-       "      <td>0.000909</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>8.0</td>\n",
+       "      <td>-54.9</td>\n",
+       "      <td>11.1</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>-61.2</td>\n",
+       "      <td>0.000076</td>\n",
+       "      <td>0.007025</td>\n",
+       "      <td>0.000455</td>\n",
+       "      <td>101.718838</td>\n",
        "      <td>262144.0</td>\n",
-       "      <td>0.011277</td>\n",
-       "      <td>0.000909</td>\n",
-       "      <td>0.000193</td>\n",
-       "      <td>242.610944</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>100.100000</td>\n",
+       "      <td>0.000455</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>fourt</th>\n",
-       "      <td>25.100000</td>\n",
-       "      <td>0.050588</td>\n",
-       "      <td>0.100000</td>\n",
-       "      <td>2.3</td>\n",
-       "      <td>0.4</td>\n",
-       "      <td>-0.2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.024883</td>\n",
+       "      <td>0.040913</td>\n",
+       "      <td>0.030275</td>\n",
+       "      <td>0.090826</td>\n",
        "      <td>3.0</td>\n",
-       "      <td>0.066393</td>\n",
-       "      <td>0.050588</td>\n",
-       "      <td>0.042309</td>\n",
-       "      <td>0.151763</td>\n",
+       "      <td>0.100000</td>\n",
+       "      <td>0.100000</td>\n",
+       "      <td>0.030275</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>cov_value</th>\n",
-       "      <td>100.100000</td>\n",
-       "      <td>0.000221</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>3.0</td>\n",
+       "      <td>-7.4</td>\n",
+       "      <td>2.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>-7.1</td>\n",
+       "      <td>0.000086</td>\n",
+       "      <td>0.000268</td>\n",
+       "      <td>0.000099</td>\n",
+       "      <td>15.471843</td>\n",
        "      <td>156816.0</td>\n",
-       "      <td>0.000575</td>\n",
-       "      <td>0.000221</td>\n",
-       "      <td>0.000193</td>\n",
-       "      <td>34.674709</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>100.100000</td>\n",
+       "      <td>0.000099</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ran2</th>\n",
-       "      <td>100.100000</td>\n",
-       "      <td>0.000218</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>2.7</td>\n",
+       "      <td>-17.0</td>\n",
+       "      <td>3.4</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>-17.8</td>\n",
+       "      <td>0.000075</td>\n",
+       "      <td>0.002592</td>\n",
+       "      <td>0.000112</td>\n",
+       "      <td>31.860609</td>\n",
        "      <td>333450.0</td>\n",
-       "      <td>0.000691</td>\n",
-       "      <td>0.000218</td>\n",
-       "      <td>0.000190</td>\n",
-       "      <td>74.079918</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>100.100000</td>\n",
+       "      <td>0.000112</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>build_real</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.001935</td>\n",
-       "      <td>0.000000</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>0.003170</td>\n",
+       "      <td>0.003170</td>\n",
+       "      <td>0.003170</td>\n",
+       "      <td>0.003170</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>0.001935</td>\n",
-       "      <td>0.001935</td>\n",
-       "      <td>0.001935</td>\n",
-       "      <td>0.001935</td>\n",
+       "      <td>0.100000</td>\n",
+       "      <td>0.100000</td>\n",
+       "      <td>0.003170</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prebuild_gwn</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.002618</td>\n",
-       "      <td>0.000000</td>\n",
        "      <td>2.2</td>\n",
        "      <td>2.2</td>\n",
        "      <td>2.2</td>\n",
+       "      <td>0.001027</td>\n",
+       "      <td>0.001027</td>\n",
+       "      <td>0.001027</td>\n",
+       "      <td>0.001027</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>0.002618</td>\n",
-       "      <td>0.002618</td>\n",
-       "      <td>0.002618</td>\n",
-       "      <td>0.002618</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.001027</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>clean_real</th>\n",
+       "      <td>0.7</td>\n",
+       "      <td>0.7</td>\n",
+       "      <td>0.7</td>\n",
+       "      <td>0.000707</td>\n",
+       "      <td>0.000707</td>\n",
+       "      <td>0.000707</td>\n",
+       "      <td>0.000707</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.001551</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.4</td>\n",
-       "      <td>0.4</td>\n",
-       "      <td>0.4</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.001551</td>\n",
-       "      <td>0.001551</td>\n",
-       "      <td>0.001551</td>\n",
-       "      <td>0.001551</td>\n",
+       "      <td>0.000707</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>cgrid</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.003504</td>\n",
-       "      <td>0.000000</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>0.001641</td>\n",
+       "      <td>0.001641</td>\n",
+       "      <td>0.001641</td>\n",
+       "      <td>0.001641</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>0.003504</td>\n",
-       "      <td>0.003504</td>\n",
-       "      <td>0.003504</td>\n",
-       "      <td>0.003504</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.001641</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>length</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000827</td>\n",
-       "      <td>0.000000</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>0.000310</td>\n",
+       "      <td>0.000531</td>\n",
+       "      <td>0.000384</td>\n",
+       "      <td>0.001153</td>\n",
        "      <td>3.0</td>\n",
-       "      <td>0.001141</td>\n",
-       "      <td>0.000827</td>\n",
-       "      <td>0.000668</td>\n",
-       "      <td>0.002481</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000384</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>maxfactor</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000195</td>\n",
-       "      <td>0.000000</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>0.000092</td>\n",
+       "      <td>0.000094</td>\n",
+       "      <td>0.000093</td>\n",
+       "      <td>0.000372</td>\n",
        "      <td>4.0</td>\n",
-       "      <td>0.000198</td>\n",
-       "      <td>0.000195</td>\n",
-       "      <td>0.000193</td>\n",
-       "      <td>0.000780</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000093</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                       cpu                        memory                \\\n",
-       "                       max        mean        min    max median    min   \n",
-       "function                                                                 \n",
-       "Py_kgeneration   32.278779  415.639768  32.278779 -199.3 -199.3 -199.3   \n",
-       "generate         32.749276  329.579564  32.749276 -281.6 -281.6 -281.6   \n",
-       "fftma2           30.472359   86.059132  30.472359   85.3   85.3   85.3   \n",
-       "covariance       30.505800   85.894170  30.505800   80.9   80.9   80.9   \n",
-       "gasdev          100.100000    0.000909   0.000000    8.0    0.0  -61.2   \n",
-       "fourt            25.100000    0.050588   0.100000    2.3    0.4   -0.2   \n",
-       "cov_value       100.100000    0.000221   0.000000    3.0    0.0   -7.1   \n",
-       "ran2            100.100000    0.000218   0.000000    2.7    0.0  -17.8   \n",
-       "build_real        0.000000    0.001935   0.000000    0.0    0.0    0.0   \n",
-       "prebuild_gwn      0.000000    0.002618   0.000000    2.2    2.2    2.2   \n",
-       "clean_real        0.000000    0.001551   0.000000    0.4    0.4    0.4   \n",
-       "cgrid             0.000000    0.003504   0.000000    0.0    0.0    0.0   \n",
-       "length            0.000000    0.000827   0.000000    0.0    0.0    0.0   \n",
-       "maxfactor         0.000000    0.000195   0.000000    0.0    0.0    0.0   \n",
+       "                memory                        time                          \\\n",
+       "                   min     max  median         min         max        mean   \n",
+       "function                                                                     \n",
+       "Py_kgeneration  2781.1  2781.1  2781.1  173.931290  173.931290  173.931290   \n",
+       "generate        2781.2  2781.2  2781.2  137.192987  137.192987  137.192987   \n",
+       "fftma2             2.6     2.6     2.6   36.737828   36.737828   36.737828   \n",
+       "covariance        -1.8    -1.8    -1.8   36.638978   36.638978   36.638978   \n",
+       "gasdev           -54.9    11.1     0.0    0.000076    0.007025    0.000455   \n",
+       "fourt              0.0     2.2     0.0    0.024883    0.040913    0.030275   \n",
+       "cov_value         -7.4     2.0     0.0    0.000086    0.000268    0.000099   \n",
+       "ran2             -17.0     3.4     0.0    0.000075    0.002592    0.000112   \n",
+       "build_real         0.0     0.0     0.0    0.003170    0.003170    0.003170   \n",
+       "prebuild_gwn       2.2     2.2     2.2    0.001027    0.001027    0.001027   \n",
+       "clean_real         0.7     0.7     0.7    0.000707    0.000707    0.000707   \n",
+       "cgrid              0.0     0.0     0.0    0.001641    0.001641    0.001641   \n",
+       "length             0.0     0.0     0.0    0.000310    0.000531    0.000384   \n",
+       "maxfactor          0.0     0.0     0.0    0.000092    0.000094    0.000093   \n",
        "\n",
-       "                    time                                                  \n",
-       "                   count         max        mean         min         sum  \n",
-       "function                                                                  \n",
-       "Py_kgeneration       1.0  415.639768  415.639768  415.639768  415.639768  \n",
-       "generate             1.0  329.579564  329.579564  329.579564  329.579564  \n",
-       "fftma2               1.0   86.059132   86.059132   86.059132   86.059132  \n",
-       "covariance           1.0   85.894170   85.894170   85.894170   85.894170  \n",
-       "gasdev          262144.0    0.011277    0.000909    0.000193  242.610944  \n",
-       "fourt                3.0    0.066393    0.050588    0.042309    0.151763  \n",
-       "cov_value       156816.0    0.000575    0.000221    0.000193   34.674709  \n",
-       "ran2            333450.0    0.000691    0.000218    0.000190   74.079918  \n",
-       "build_real           1.0    0.001935    0.001935    0.001935    0.001935  \n",
-       "prebuild_gwn         1.0    0.002618    0.002618    0.002618    0.002618  \n",
-       "clean_real           1.0    0.001551    0.001551    0.001551    0.001551  \n",
-       "cgrid                1.0    0.003504    0.003504    0.003504    0.003504  \n",
-       "length               3.0    0.001141    0.000827    0.000668    0.002481  \n",
-       "maxfactor            4.0    0.000198    0.000195    0.000193    0.000780  "
+       "                                            cpu                          \n",
+       "                       sum     count        min         max        mean  \n",
+       "function                                                                 \n",
+       "Py_kgeneration  173.931290       1.0  19.901069   19.901069  173.931290  \n",
+       "generate        137.192987       1.0  23.489213   23.489213  137.192987  \n",
+       "fftma2           36.737828       1.0   6.498040    6.498040   36.737828  \n",
+       "covariance       36.638978       1.0   6.515506    6.515506   36.638978  \n",
+       "gasdev          101.718838  262144.0   0.000000  100.100000    0.000455  \n",
+       "fourt             0.090826       3.0   0.100000    0.100000    0.030275  \n",
+       "cov_value        15.471843  156816.0   0.000000  100.100000    0.000099  \n",
+       "ran2             31.860609  333450.0   0.000000  100.100000    0.000112  \n",
+       "build_real        0.003170       1.0   0.100000    0.100000    0.003170  \n",
+       "prebuild_gwn      0.001027       1.0   0.000000    0.000000    0.001027  \n",
+       "clean_real        0.000707       1.0   0.000000    0.000000    0.000707  \n",
+       "cgrid             0.001641       1.0   0.000000    0.000000    0.001641  \n",
+       "length            0.001153       3.0   0.000000    0.000000    0.000384  \n",
+       "maxfactor         0.000372       4.0   0.000000    0.000000    0.000093  "
       ]
      },
-     "execution_count": 196,
+     "execution_count": 65,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1675,44 +4479,972 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 750x750 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 750x750 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<Figure size 750x750 with 1 Axes>"
+       "<Figure size 600x600 with 4 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
-    },
+    }
+   ],
+   "source": [
+    "plot_analysis(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
     {
      "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 750x750 with 1 Axes>"
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
+       "data": [
+        {
+         "domain": {
+          "x": [
+           0,
+           1
+          ],
+          "y": [
+           0,
+           1
+          ]
+         },
+         "hovertemplate": "function=%{label}<br>time_sum=%{value}<br>parent=%{parent}<extra></extra>",
+         "labels": [
+          "Py_kgeneration",
+          "generate",
+          "fftma2",
+          "covariance",
+          "gasdev",
+          "fourt",
+          "cov_value",
+          "ran2",
+          "build_real",
+          "prebuild_gwn",
+          "clean_real",
+          "cgrid",
+          "length",
+          "maxfactor"
+         ],
+         "marker": {
+          "colors": [
+           "#636efa",
+           "#EF553B",
+           "#EF553B",
+           "#00cc96",
+           "#ab63fa",
+           "#00cc96",
+           "#636efa",
+           "#FFA15A",
+           "#636efa",
+           "#00cc96",
+           "#636efa",
+           "#636efa",
+           "#636efa",
+           "#636efa"
+          ]
+         },
+         "name": "",
+         "parents": [
+          "",
+          "Py_kgeneration",
+          "Py_kgeneration",
+          "fftma2",
+          "generate",
+          "fftma2",
+          "",
+          "gasdev",
+          "",
+          "fftma2",
+          "",
+          "",
+          "",
+          ""
+         ],
+         "type": "treemap",
+         "values": [
+          173.93129,
+          137.192987,
+          36.737828,
+          36.638978,
+          101.71883800005251,
+          0.09082599999999999,
+          15.471843000005594,
+          31.86060899999753,
+          0.00317,
+          0.001027,
+          0.000707,
+          0.001641,
+          0.001153,
+          0.000372
+         ]
+        }
+       ],
+       "layout": {
+        "autosize": true,
+        "legend": {
+         "tracegroupgap": 0
+        },
+        "margin": {
+         "t": 60
+        },
+        "template": {
+         "data": {
+          "bar": [
+           {
+            "error_x": {
+             "color": "#2a3f5f"
+            },
+            "error_y": {
+             "color": "#2a3f5f"
+            },
+            "marker": {
+             "line": {
+              "color": "#E5ECF6",
+              "width": 0.5
+             },
+             "pattern": {
+              "fillmode": "overlay",
+              "size": 10,
+              "solidity": 0.2
+             }
+            },
+            "type": "bar"
+           }
+          ],
+          "barpolar": [
+           {
+            "marker": {
+             "line": {
+              "color": "#E5ECF6",
+              "width": 0.5
+             },
+             "pattern": {
+              "fillmode": "overlay",
+              "size": 10,
+              "solidity": 0.2
+             }
+            },
+            "type": "barpolar"
+           }
+          ],
+          "carpet": [
+           {
+            "aaxis": {
+             "endlinecolor": "#2a3f5f",
+             "gridcolor": "white",
+             "linecolor": "white",
+             "minorgridcolor": "white",
+             "startlinecolor": "#2a3f5f"
+            },
+            "baxis": {
+             "endlinecolor": "#2a3f5f",
+             "gridcolor": "white",
+             "linecolor": "white",
+             "minorgridcolor": "white",
+             "startlinecolor": "#2a3f5f"
+            },
+            "type": "carpet"
+           }
+          ],
+          "choropleth": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "type": "choropleth"
+           }
+          ],
+          "contour": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "colorscale": [
+             [
+              0,
+              "#0d0887"
+             ],
+             [
+              0.1111111111111111,
+              "#46039f"
+             ],
+             [
+              0.2222222222222222,
+              "#7201a8"
+             ],
+             [
+              0.3333333333333333,
+              "#9c179e"
+             ],
+             [
+              0.4444444444444444,
+              "#bd3786"
+             ],
+             [
+              0.5555555555555556,
+              "#d8576b"
+             ],
+             [
+              0.6666666666666666,
+              "#ed7953"
+             ],
+             [
+              0.7777777777777778,
+              "#fb9f3a"
+             ],
+             [
+              0.8888888888888888,
+              "#fdca26"
+             ],
+             [
+              1,
+              "#f0f921"
+             ]
+            ],
+            "type": "contour"
+           }
+          ],
+          "contourcarpet": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "type": "contourcarpet"
+           }
+          ],
+          "heatmap": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "colorscale": [
+             [
+              0,
+              "#0d0887"
+             ],
+             [
+              0.1111111111111111,
+              "#46039f"
+             ],
+             [
+              0.2222222222222222,
+              "#7201a8"
+             ],
+             [
+              0.3333333333333333,
+              "#9c179e"
+             ],
+             [
+              0.4444444444444444,
+              "#bd3786"
+             ],
+             [
+              0.5555555555555556,
+              "#d8576b"
+             ],
+             [
+              0.6666666666666666,
+              "#ed7953"
+             ],
+             [
+              0.7777777777777778,
+              "#fb9f3a"
+             ],
+             [
+              0.8888888888888888,
+              "#fdca26"
+             ],
+             [
+              1,
+              "#f0f921"
+             ]
+            ],
+            "type": "heatmap"
+           }
+          ],
+          "heatmapgl": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "colorscale": [
+             [
+              0,
+              "#0d0887"
+             ],
+             [
+              0.1111111111111111,
+              "#46039f"
+             ],
+             [
+              0.2222222222222222,
+              "#7201a8"
+             ],
+             [
+              0.3333333333333333,
+              "#9c179e"
+             ],
+             [
+              0.4444444444444444,
+              "#bd3786"
+             ],
+             [
+              0.5555555555555556,
+              "#d8576b"
+             ],
+             [
+              0.6666666666666666,
+              "#ed7953"
+             ],
+             [
+              0.7777777777777778,
+              "#fb9f3a"
+             ],
+             [
+              0.8888888888888888,
+              "#fdca26"
+             ],
+             [
+              1,
+              "#f0f921"
+             ]
+            ],
+            "type": "heatmapgl"
+           }
+          ],
+          "histogram": [
+           {
+            "marker": {
+             "pattern": {
+              "fillmode": "overlay",
+              "size": 10,
+              "solidity": 0.2
+             }
+            },
+            "type": "histogram"
+           }
+          ],
+          "histogram2d": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "colorscale": [
+             [
+              0,
+              "#0d0887"
+             ],
+             [
+              0.1111111111111111,
+              "#46039f"
+             ],
+             [
+              0.2222222222222222,
+              "#7201a8"
+             ],
+             [
+              0.3333333333333333,
+              "#9c179e"
+             ],
+             [
+              0.4444444444444444,
+              "#bd3786"
+             ],
+             [
+              0.5555555555555556,
+              "#d8576b"
+             ],
+             [
+              0.6666666666666666,
+              "#ed7953"
+             ],
+             [
+              0.7777777777777778,
+              "#fb9f3a"
+             ],
+             [
+              0.8888888888888888,
+              "#fdca26"
+             ],
+             [
+              1,
+              "#f0f921"
+             ]
+            ],
+            "type": "histogram2d"
+           }
+          ],
+          "histogram2dcontour": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "colorscale": [
+             [
+              0,
+              "#0d0887"
+             ],
+             [
+              0.1111111111111111,
+              "#46039f"
+             ],
+             [
+              0.2222222222222222,
+              "#7201a8"
+             ],
+             [
+              0.3333333333333333,
+              "#9c179e"
+             ],
+             [
+              0.4444444444444444,
+              "#bd3786"
+             ],
+             [
+              0.5555555555555556,
+              "#d8576b"
+             ],
+             [
+              0.6666666666666666,
+              "#ed7953"
+             ],
+             [
+              0.7777777777777778,
+              "#fb9f3a"
+             ],
+             [
+              0.8888888888888888,
+              "#fdca26"
+             ],
+             [
+              1,
+              "#f0f921"
+             ]
+            ],
+            "type": "histogram2dcontour"
+           }
+          ],
+          "mesh3d": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "type": "mesh3d"
+           }
+          ],
+          "parcoords": [
+           {
+            "line": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "parcoords"
+           }
+          ],
+          "pie": [
+           {
+            "automargin": true,
+            "type": "pie"
+           }
+          ],
+          "scatter": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scatter"
+           }
+          ],
+          "scatter3d": [
+           {
+            "line": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scatter3d"
+           }
+          ],
+          "scattercarpet": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scattercarpet"
+           }
+          ],
+          "scattergeo": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scattergeo"
+           }
+          ],
+          "scattergl": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scattergl"
+           }
+          ],
+          "scattermapbox": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scattermapbox"
+           }
+          ],
+          "scatterpolar": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scatterpolar"
+           }
+          ],
+          "scatterpolargl": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scatterpolargl"
+           }
+          ],
+          "scatterternary": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scatterternary"
+           }
+          ],
+          "surface": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "colorscale": [
+             [
+              0,
+              "#0d0887"
+             ],
+             [
+              0.1111111111111111,
+              "#46039f"
+             ],
+             [
+              0.2222222222222222,
+              "#7201a8"
+             ],
+             [
+              0.3333333333333333,
+              "#9c179e"
+             ],
+             [
+              0.4444444444444444,
+              "#bd3786"
+             ],
+             [
+              0.5555555555555556,
+              "#d8576b"
+             ],
+             [
+              0.6666666666666666,
+              "#ed7953"
+             ],
+             [
+              0.7777777777777778,
+              "#fb9f3a"
+             ],
+             [
+              0.8888888888888888,
+              "#fdca26"
+             ],
+             [
+              1,
+              "#f0f921"
+             ]
+            ],
+            "type": "surface"
+           }
+          ],
+          "table": [
+           {
+            "cells": {
+             "fill": {
+              "color": "#EBF0F8"
+             },
+             "line": {
+              "color": "white"
+             }
+            },
+            "header": {
+             "fill": {
+              "color": "#C8D4E3"
+             },
+             "line": {
+              "color": "white"
+             }
+            },
+            "type": "table"
+           }
+          ]
+         },
+         "layout": {
+          "annotationdefaults": {
+           "arrowcolor": "#2a3f5f",
+           "arrowhead": 0,
+           "arrowwidth": 1
+          },
+          "autotypenumbers": "strict",
+          "coloraxis": {
+           "colorbar": {
+            "outlinewidth": 0,
+            "ticks": ""
+           }
+          },
+          "colorscale": {
+           "diverging": [
+            [
+             0,
+             "#8e0152"
+            ],
+            [
+             0.1,
+             "#c51b7d"
+            ],
+            [
+             0.2,
+             "#de77ae"
+            ],
+            [
+             0.3,
+             "#f1b6da"
+            ],
+            [
+             0.4,
+             "#fde0ef"
+            ],
+            [
+             0.5,
+             "#f7f7f7"
+            ],
+            [
+             0.6,
+             "#e6f5d0"
+            ],
+            [
+             0.7,
+             "#b8e186"
+            ],
+            [
+             0.8,
+             "#7fbc41"
+            ],
+            [
+             0.9,
+             "#4d9221"
+            ],
+            [
+             1,
+             "#276419"
+            ]
+           ],
+           "sequential": [
+            [
+             0,
+             "#0d0887"
+            ],
+            [
+             0.1111111111111111,
+             "#46039f"
+            ],
+            [
+             0.2222222222222222,
+             "#7201a8"
+            ],
+            [
+             0.3333333333333333,
+             "#9c179e"
+            ],
+            [
+             0.4444444444444444,
+             "#bd3786"
+            ],
+            [
+             0.5555555555555556,
+             "#d8576b"
+            ],
+            [
+             0.6666666666666666,
+             "#ed7953"
+            ],
+            [
+             0.7777777777777778,
+             "#fb9f3a"
+            ],
+            [
+             0.8888888888888888,
+             "#fdca26"
+            ],
+            [
+             1,
+             "#f0f921"
+            ]
+           ],
+           "sequentialminus": [
+            [
+             0,
+             "#0d0887"
+            ],
+            [
+             0.1111111111111111,
+             "#46039f"
+            ],
+            [
+             0.2222222222222222,
+             "#7201a8"
+            ],
+            [
+             0.3333333333333333,
+             "#9c179e"
+            ],
+            [
+             0.4444444444444444,
+             "#bd3786"
+            ],
+            [
+             0.5555555555555556,
+             "#d8576b"
+            ],
+            [
+             0.6666666666666666,
+             "#ed7953"
+            ],
+            [
+             0.7777777777777778,
+             "#fb9f3a"
+            ],
+            [
+             0.8888888888888888,
+             "#fdca26"
+            ],
+            [
+             1,
+             "#f0f921"
+            ]
+           ]
+          },
+          "colorway": [
+           "#636efa",
+           "#EF553B",
+           "#00cc96",
+           "#ab63fa",
+           "#FFA15A",
+           "#19d3f3",
+           "#FF6692",
+           "#B6E880",
+           "#FF97FF",
+           "#FECB52"
+          ],
+          "font": {
+           "color": "#2a3f5f"
+          },
+          "geo": {
+           "bgcolor": "white",
+           "lakecolor": "white",
+           "landcolor": "#E5ECF6",
+           "showlakes": true,
+           "showland": true,
+           "subunitcolor": "white"
+          },
+          "hoverlabel": {
+           "align": "left"
+          },
+          "hovermode": "closest",
+          "mapbox": {
+           "style": "light"
+          },
+          "paper_bgcolor": "white",
+          "plot_bgcolor": "#E5ECF6",
+          "polar": {
+           "angularaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "radialaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "scene": {
+           "xaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           },
+           "yaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           },
+           "zaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           }
+          },
+          "shapedefaults": {
+           "line": {
+            "color": "#2a3f5f"
+           }
+          },
+          "ternary": {
+           "aaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "baxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "caxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "title": {
+           "x": 0.05
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          },
+          "yaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          }
+         }
+        }
+       }
+      },
+      "image/png": "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",
+      "text/html": [
+       "<div>                            <div id=\"590f59df-384b-46f4-b607-48b138e7c8f3\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"590f59df-384b-46f4-b607-48b138e7c8f3\")) {                    Plotly.newPlot(                        \"590f59df-384b-46f4-b607-48b138e7c8f3\",                        [{\"domain\":{\"x\":[0.0,1.0],\"y\":[0.0,1.0]},\"hovertemplate\":\"function=%{label}<br>time_sum=%{value}<br>parent=%{parent}<extra></extra>\",\"labels\":[\"Py_kgeneration\",\"generate\",\"fftma2\",\"covariance\",\"gasdev\",\"fourt\",\"cov_value\",\"ran2\",\"build_real\",\"prebuild_gwn\",\"clean_real\",\"cgrid\",\"length\",\"maxfactor\"],\"marker\":{\"colors\":[\"#636efa\",\"#EF553B\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#00cc96\",\"#636efa\",\"#FFA15A\",\"#636efa\",\"#00cc96\",\"#636efa\",\"#636efa\",\"#636efa\",\"#636efa\"]},\"name\":\"\",\"parents\":[\"\",\"Py_kgeneration\",\"Py_kgeneration\",\"fftma2\",\"generate\",\"fftma2\",\"\",\"gasdev\",\"\",\"fftma2\",\"\",\"\",\"\",\"\"],\"values\":[173.93129,137.192987,36.737828,36.638978,101.71883800005251,0.09082599999999999,15.471843000005594,31.86060899999753,0.00317,0.001027,0.000707,0.001641,0.001153,0.000372],\"type\":\"treemap\"}],                        {\"template\":{\"data\":{\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"white\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2}}},\"legend\":{\"tracegroupgap\":0},\"margin\":{\"t\":60}},                        {\"responsive\": true}                    ).then(function(){\n",
+       "                            \n",
+       "var gd = document.getElementById('590f59df-384b-46f4-b607-48b138e7c8f3');\n",
+       "var x = new MutationObserver(function (mutations, observer) {{\n",
+       "        var display = window.getComputedStyle(gd).display;\n",
+       "        if (!display || display === 'none') {{\n",
+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
+       "    x.observe(outputEl, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "                        })                };                });            </script>        </div>"
       ]
      },
      "metadata": {},
@@ -1720,7 +5452,7 @@
     }
    ],
    "source": [
-    "plot_analysis(df)"
+    "plot_treemap(df)"
    ]
   },
   {
@@ -1757,6 +5489,15 @@
     "plot_analysis(df)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_treemap(df)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -2058,26 +5799,37 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "plot_analysis(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_treemap(df)"
+   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.18"
+   "pygments_lexer": "ipython3",
+   "version": "3.6.9"
   }
  },
  "nbformat": 4,
diff --git a/fftma_module/gen/requirements-analysis.txt b/fftma_module/gen/requirements-analysis.txt
index e2acba4..eb9f12e 100644
--- a/fftma_module/gen/requirements-analysis.txt
+++ b/fftma_module/gen/requirements-analysis.txt
@@ -1,4 +1,4 @@
-matplotlib-venn
 pandas
 matplotlib
-numpy
\ No newline at end of file
+squarify
+plotly
\ No newline at end of file