diff --git a/fftma_module/gen/analysis.ipynb b/fftma_module/gen/analysis.ipynb
index 8e346d9..e1b2f00 100644
--- a/fftma_module/gen/analysis.ipynb
+++ b/fftma_module/gen/analysis.ipynb
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -35,7 +35,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -45,7 +45,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -59,7 +59,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 132,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -102,7 +102,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -114,7 +114,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -125,7 +125,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -145,7 +145,7 @@
     "        time_max.append(df_final.loc[function, ('time', 'max')].max())\n",
     "        cpu_max.append(df_final.loc[function, ('cpu', 'max')].max())\n",
     "        time_mean.append(df_final.loc[function, ('time', 'mean')].mean())\n",
-    "        cpu_mean.append(df_final.loc[function, ('time', 'mean')].mean())\n",
+    "        cpu_mean.append(df_final.loc[function, ('cpu', 'mean')].mean())\n",
     "        time_sum.append(df_final.loc[function, ('time', 'sum')].sum())\n",
     "        time_count.append(df_final.loc[function, ('time', 'count')].sum())\n",
     "        try:\n",
@@ -162,7 +162,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -181,13 +181,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [],
    "source": [
     "def plot_pie(df, function, plt):\n",
     "    \n",
-    "    labels = relations[function].copy()\n",
+    "    labels = relations[function][:]\n",
     "    total = df.loc[function][('time', 'sum')]\n",
     "    sizes = []\n",
     "    explode = []\n",
@@ -235,7 +235,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 117,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -269,7 +269,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [
     {
@@ -297,23 +297,23 @@
        "  <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>mean</th>\n",
-       "      <th>sum</th>\n",
-       "      <th>count</th>\n",
+       "      <th>median</th>\n",
        "      <th>min</th>\n",
        "      <th>max</th>\n",
-       "      <th>median</th>\n",
+       "      <th>mean</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",
@@ -333,243 +333,243 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Py_kgeneration</th>\n",
-       "      <td>0.573509</td>\n",
-       "      <td>0.573509</td>\n",
-       "      <td>0.573509</td>\n",
-       "      <td>0.573509</td>\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>1</td>\n",
-       "      <td>1.3</td>\n",
-       "      <td>1.3</td>\n",
-       "      <td>1.3</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>1.3</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>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>0.000080</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>0.2</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>100.100000</td>\n",
-       "      <td>0.589648</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",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>fftma2</th>\n",
-       "      <td>0.138753</td>\n",
-       "      <td>0.138753</td>\n",
-       "      <td>0.138753</td>\n",
-       "      <td>0.138753</td>\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>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>0.135896</td>\n",
-       "      <td>0.135896</td>\n",
-       "      <td>0.135896</td>\n",
-       "      <td>0.135896</td>\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>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>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",
+       "      <th>cov_value</th>\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>0.142593</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",
        "    </tr>\n",
        "    <tr>\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",
+       "      <th>ran2</th>\n",
        "      <td>-0.2</td>\n",
        "      <td>0.2</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.100000</td>\n",
-       "      <td>0.000714</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",
        "    </tr>\n",
        "    <tr>\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",
+       "      <th>fourt</th>\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>0.000000</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",
        "    </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",
+       "      <th>cgrid</th>\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>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",
+       "      <th>length</th>\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>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",
+       "      <th>clean_real</th>\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>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",
+       "      <th>prebuild_gwn</th>\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>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",
+       "      <th>build_real</th>\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>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",
+       "      <th>maxfactor</th>\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": [
-       "                    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",
+       "               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",
        "\n",
-       "                              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  "
+       "                    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  "
       ]
      },
-     "execution_count": 118,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -581,12 +581,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 119,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 600x600 with 4 Axes>"
       ]
@@ -601,7 +601,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 120,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
     {
@@ -629,15 +629,15 @@
           "gasdev",
           "fftma2",
           "covariance",
-          "ran2",
           "cov_value",
+          "ran2",
+          "fourt",
           "cgrid",
           "length",
-          "fourt",
-          "maxfactor",
-          "build_real",
+          "clean_real",
           "prebuild_gwn",
-          "clean_real"
+          "build_real",
+          "maxfactor"
          ],
          "marker": {
           "colors": [
@@ -648,12 +648,12 @@
            "#ab63fa",
            "#FFA15A",
            "#19d3f3",
-           "#636efa",
-           "#636efa",
            "#ab63fa",
            "#636efa",
            "#636efa",
+           "#636efa",
            "#ab63fa",
+           "#636efa",
            "#636efa"
           ]
          },
@@ -664,37 +664,36 @@
           "generate",
           "Py_kgeneration",
           "fftma2",
-          "gasdev",
           "covariance",
-          "",
-          "",
+          "gasdev",
           "fftma2",
           "",
           "",
+          "",
           "fftma2",
+          "",
           ""
          ],
          "type": "treemap",
          "values": [
-          0.573509,
-          0.432266,
-          0.3174500000000009,
-          0.138753,
-          0.135896,
-          0.0927249999999985,
-          0.05847299999999959,
-          0.001218,
-          0.000802,
-          0.000391,
-          0.000238,
-          0.000104,
-          8.4e-05,
-          8.2e-05
+          0.633546,
+          0.397899,
+          0.2963540000000001,
+          0.234361,
+          0.22435,
+          0.09350199999999986,
+          0.09343499999999917,
+          0.001993,
+          0.001623,
+          0.001073,
+          0.000463,
+          0.000454,
+          0.00041,
+          0.000316
          ]
         }
        ],
        "layout": {
-        "autosize": true,
         "legend": {
          "tracegroupgap": 0
         },
@@ -712,11 +711,6 @@
              "line": {
               "color": "#E5ECF6",
               "width": 0.5
-             },
-             "pattern": {
-              "fillmode": "overlay",
-              "size": 10,
-              "solidity": 0.2
              }
             },
             "type": "bar"
@@ -728,11 +722,6 @@
              "line": {
               "color": "#E5ECF6",
               "width": 0.5
-             },
-             "pattern": {
-              "fillmode": "overlay",
-              "size": 10,
-              "solidity": 0.2
              }
             },
             "type": "barpolar"
@@ -931,10 +920,9 @@
           "histogram": [
            {
             "marker": {
-             "pattern": {
-              "fillmode": "overlay",
-              "size": 10,
-              "solidity": 0.2
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
              }
             },
             "type": "histogram"
@@ -1520,11 +1508,10 @@
         }
        }
       },
-      "image/png": "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",
       "text/html": [
-       "<div>                            <div id=\"c8217d31-9e64-4079-bd7b-0dac6bb223db\" 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(\"c8217d31-9e64-4079-bd7b-0dac6bb223db\")) {                    Plotly.newPlot(                        \"c8217d31-9e64-4079-bd7b-0dac6bb223db\",                        [{\"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\",\"length\",\"fourt\",\"maxfactor\",\"build_real\",\"prebuild_gwn\",\"clean_real\"],\"marker\":{\"colors\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#EF553B\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#636efa\",\"#636efa\",\"#ab63fa\",\"#636efa\",\"#636efa\",\"#ab63fa\",\"#636efa\"]},\"name\":\"\",\"parents\":[\"\",\"Py_kgeneration\",\"generate\",\"Py_kgeneration\",\"fftma2\",\"gasdev\",\"covariance\",\"\",\"\",\"fftma2\",\"\",\"\",\"fftma2\",\"\"],\"values\":[0.573509,0.432266,0.3174500000000009,0.138753,0.135896,0.0927249999999985,0.05847299999999959,0.001218,0.000802,0.000391,0.000238,0.000104,8.4e-05,8.2e-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},\"title\":{\"text\":\"Time treemap\"}},                        {\"responsive\": true}                    ).then(function(){\n",
+       "<div>                            <div id=\"39726ffb-b775-4f55-aac1-aab94725d325\" 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(\"39726ffb-b775-4f55-aac1-aab94725d325\")) {                    Plotly.newPlot(                        \"39726ffb-b775-4f55-aac1-aab94725d325\",                        [{\"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\", \"cov_value\", \"ran2\", \"fourt\", \"cgrid\", \"length\", \"clean_real\", \"prebuild_gwn\", \"build_real\", \"maxfactor\"], \"marker\": {\"colors\": [\"#636efa\", \"#EF553B\", \"#00cc96\", \"#EF553B\", \"#ab63fa\", \"#FFA15A\", \"#19d3f3\", \"#ab63fa\", \"#636efa\", \"#636efa\", \"#636efa\", \"#ab63fa\", \"#636efa\", \"#636efa\"]}, \"name\": \"\", \"parents\": [\"\", \"Py_kgeneration\", \"generate\", \"Py_kgeneration\", \"fftma2\", \"covariance\", \"gasdev\", \"fftma2\", \"\", \"\", \"\", \"fftma2\", \"\", \"\"], \"type\": \"treemap\", \"values\": [0.633546, 0.397899, 0.2963540000000001, 0.234361, 0.22435, 0.09350199999999986, 0.09343499999999917, 0.001993, 0.001623, 0.001073, 0.000463, 0.000454, 0.00041, 0.000316]}],                        {\"legend\": {\"tracegroupgap\": 0}, \"template\": {\"data\": {\"bar\": [{\"error_x\": {\"color\": \"#2a3f5f\"}, \"error_y\": {\"color\": \"#2a3f5f\"}, \"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"bar\"}], \"barpolar\": [{\"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"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\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"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}}}, \"title\": {\"text\": \"Time treemap\"}},                        {\"responsive\": true}                    ).then(function(){\n",
        "                            \n",
-       "var gd = document.getElementById('c8217d31-9e64-4079-bd7b-0dac6bb223db');\n",
+       "var gd = document.getElementById('39726ffb-b775-4f55-aac1-aab94725d325');\n",
        "var x = new MutationObserver(function (mutations, observer) {{\n",
        "        var display = window.getComputedStyle(gd).display;\n",
        "        if (!display || display === 'none') {{\n",
@@ -1566,7 +1553,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [
     {
@@ -1594,23 +1581,23 @@
        "  <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>mean</th>\n",
-       "      <th>sum</th>\n",
-       "      <th>count</th>\n",
+       "      <th>median</th>\n",
        "      <th>min</th>\n",
        "      <th>max</th>\n",
-       "      <th>median</th>\n",
+       "      <th>mean</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",
@@ -1630,243 +1617,243 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Py_kgeneration</th>\n",
-       "      <td>2.673944</td>\n",
-       "      <td>2.673944</td>\n",
-       "      <td>2.673944</td>\n",
-       "      <td>2.673944</td>\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>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>1.986983</td>\n",
-       "      <td>1.986983</td>\n",
-       "      <td>1.986983</td>\n",
-       "      <td>1.986983</td>\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</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>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>-23.9</td>\n",
+       "      <td>3.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>100.100000</td>\n",
-       "      <td>0.271851</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",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>fftma2</th>\n",
-       "      <td>0.684455</td>\n",
-       "      <td>0.684455</td>\n",
-       "      <td>0.684455</td>\n",
-       "      <td>0.684455</td>\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>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>0.680862</td>\n",
-       "      <td>0.680862</td>\n",
-       "      <td>0.680862</td>\n",
-       "      <td>0.680862</td>\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>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>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>-9.1</td>\n",
+       "      <td>1.3</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.100000</td>\n",
-       "      <td>0.000816</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",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>cov_value</th>\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>-3.8</td>\n",
+       "      <td>0.9</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.100000</td>\n",
-       "      <td>0.000898</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",
        "    </tr>\n",
        "    <tr>\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",
+       "      <th>fourt</th>\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>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",
+       "      <th>cgrid</th>\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": [
-       "                    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",
+       "               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",
        "\n",
-       "                             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  "
+       "                    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  "
       ]
      },
-     "execution_count": 121,
+     "execution_count": 59,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1878,12 +1865,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 122,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 600x600 with 4 Axes>"
       ]
@@ -1898,7 +1885,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
@@ -1928,8 +1915,8 @@
           "covariance",
           "ran2",
           "cov_value",
-          "cgrid",
           "fourt",
+          "cgrid",
           "length",
           "maxfactor",
           "build_real",
@@ -1945,11 +1932,11 @@
            "#ab63fa",
            "#FFA15A",
            "#19d3f3",
-           "#636efa",
            "#ab63fa",
            "#636efa",
            "#636efa",
            "#636efa",
+           "#636efa",
            "#ab63fa",
            "#636efa"
           ]
@@ -1963,35 +1950,34 @@
           "fftma2",
           "gasdev",
           "covariance",
-          "",
           "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
+          4.518677,
+          3.283041,
+          2.4375840000000117,
+          1.231253,
+          1.223604,
+          0.7101290000000031,
+          0.48899400000001014,
+          0.002831,
+          0.001635,
+          0.001069,
+          0.000318,
+          0.000274,
+          0.000267,
+          0.000217
          ]
         }
        ],
        "layout": {
-        "autosize": true,
         "legend": {
          "tracegroupgap": 0
         },
@@ -2009,11 +1995,6 @@
              "line": {
               "color": "#E5ECF6",
               "width": 0.5
-             },
-             "pattern": {
-              "fillmode": "overlay",
-              "size": 10,
-              "solidity": 0.2
              }
             },
             "type": "bar"
@@ -2025,11 +2006,6 @@
              "line": {
               "color": "#E5ECF6",
               "width": 0.5
-             },
-             "pattern": {
-              "fillmode": "overlay",
-              "size": 10,
-              "solidity": 0.2
              }
             },
             "type": "barpolar"
@@ -2228,10 +2204,9 @@
           "histogram": [
            {
             "marker": {
-             "pattern": {
-              "fillmode": "overlay",
-              "size": 10,
-              "solidity": 0.2
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
              }
             },
             "type": "histogram"
@@ -2817,11 +2792,10 @@
         }
        }
       },
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAt8AAAFoCAYAAACPLZeUAAAAAXNSR0IArs4c6QAAIABJREFUeF7t3Qd4VFX+xvF3Wiqh9yIo2Bu69t4rdld37e2PolhQ2UVFxQYiFlwVe+9lVWzYu4IFV1QUULEgICBSAmnT/s+9ISFIQmYycyb33HznefZZMHfO/Z3P7wAvhzM3gWQymRQvBBBAAAEEEEAAAQQQMC4QIHwbN+YGCCCAAAIIIIAAAgi4AoRvFgICCCCAAAIIIIAAAjkSIHznCJrbIIAAAggggAACCCBA+GYNIIAAAggggAACCCCQIwHCd46guQ0CCCCAAAIIIIAAAoRv1gACCCCAAAIIIIAAAjkSIHznCJrbIIAAAggggAACCCBA+GYNIIAAAggggAACCCCQIwHCd46guQ0CCCCAAAIIIIAAAoRv1gACCCCAAAIIIIAAAjkSIHznCJrbIIAAAggggAACCCBA+GYNIIAAAggggAACCCCQIwHCd46guQ0CCCCAAAIIIIAAAoRv1gACCCCAAAIIIIAAAjkSIHznCJrbIIAAAggggAACCCBA+GYNIIAAAggggAACCCCQIwHCd46guQ0CCCCAAAIIIIAAAoRv1gACCCCAAAIIIIAAAjkSIHznCJrbIIAAAggggAACCCBA+GYNIIAAAggggAACCCCQIwHCd46guQ0CCCCAAAIIIIAAAoRv1gACCCCAAAIIIIAAAjkSIHznCJrbIIAAAggggAACCCBA+GYNIIAAAggggAACCCCQIwHCd46guQ0CCCCAAAIIIIAAAoRv1gACCCCAAAIIIIAAAjkSIHznCJrbIIAAAggggAACCCBA+GYNIIAAAggggAACCCCQIwHCd46guQ0CCCCAAAIIIIAAAoRv1gACCCCAAAIIIIAAAjkSIHznCJrbIIAAAggggAACCCBA+GYNIIAAAggggAACCCCQIwHCd46guQ0CCCCAAAIIIIAAAoRv1gACCCCAAAIIIIAAAjkSIHznCJrbIIAAAggggAACCCBA+GYNIIAAAggggAACCCCQIwHCd46guQ0CCCCAAAIIIIAAAp4M33sffYHmzFu4xu5svfkGOmLALhp2zV16/Ynr1aNrR7qJAAIIIIAAAggggICnBTwZvj+bMk2VldFauMvG3K92bVppyMC/1/63NiXFal1SrElffKsBe22v4qKCZoE+7NRLdd5pR2jX7fs3y/25KQIIIIAAAggggIA9Ap4M33/lG3D8MHXp1F733vgvT8mWV1Rq2wMG6ZZrzmkwfEejMUUiYU/VTTEIIIAAAggggAACzSNgdfh+8Y2PVzl2MuKGBzTl2x914RlH6brbntCvs+epW5cOGjb4WHVoV6Krxz6iGTNnqWP7Njr3tCN0wJ7b1ar/NmeBbrjzKX3yv29VXl6pPr266uR/HKCD99mh3s589uU0nXTetbVfKyzI1+ev3qnho+/VtzN+1mnHHKiR/3lUu26/ua4Zdpp73ePPv+X+79fZ81VcWKCdtt1UF55xtDp1aJtWHQceP0w7bbOpunZur4effl2Lly7Thuv21uhLTtcX33yvOx4ar3kLFqlvnx66cujJ2qDfWu74zl8Wxt79jN756H9asHCx2rRupW222EAXnHG0unRs517z7Csf6NLr7tWjtw7X2Hue0dfTZioSCmn3HbfQ8PNOaLZ/YWieXx7cFQEEEEAAAQQQyK6Ar8L31Tc/rJfemKgdt95Ew887XsFAUIOG3ajf5i7Qumv31IgLT1Lnju10xQ0P6LX3PtM7T9/kHl1ZtKRUh548XO3alOjSISeoc8e2evmtSbrl3mc16uKB9QbwWCyuz7+arlPPv07XDT9DO26zidq2bqUrbnxQ702corW6d9bA4waoR7dO6t2zi+58+EXdct+zGnzKYTpwz+3ccHzVTQ8pkUzqv/dcqbxIOOU6DjnpEpUuK9ORA3bVKf88QHN+/0PHnHmV+68DTgi/5Nzj5Oy4n3rBdXL+UvDYuEvdVXPxtffo7Q+/0OhLBqpv7x6av3CRrrzxIbUqLtQjt17iXvPC6x/ropF3ab2+vXTFhSdpw3693Xmed9mt2n2HLXTtJQOzuwIZDQEEEEAAAQQQaEECvgvfjz/31iofwHzo6dc0+rbH3aMhe+y4pdvaT//3nU4eMtrd3e2/ST83GP/n3v9q/APXqF+fHrXtP+PfN+q3OfP10sMrd7jrro2p03/SUadfoXGjzqs9duL8BcCp4fHbL9VmG/Z1L6+simrnQ892d6tvHHFW7RBffzdT/xh0pcZcNkgH7LFtynU44buiskqvPnadAoGAO97gS27W+xOn6IPnb1Gb1sXuf7v1/ud0z2Mv63+v3+1eN++PRW4o79mtU20Nj/z3DY265VF98vLtbgiv+dcEJ8Afc9hetdc5hs6u/aSXxqkgP68F/RJhqggggAACCCCAQPYEfBe+n5/woXv8o+b1/Ksf6pJr79GER0drrR5d3P887YdfdcRpl2nctUO063abu7vjzn9755mxq8jWBHcn0LZvW7KaekPh+6nx7+jLN+9VMFgdjL+Z9pOOPuMKXXHhye5udd3XdgcO0oC9d3B36lOtwwnfztNdnPprXs6u9qTPp+rtZ26q/W8PPfO6Rt/6mOvh7ID/ubjUPZLy2ZTpWrykVPF4wv2LwbLl5bV/YakJ30/ecbk22WDt2rGem/CBe6Tmufuu1nrr9MzeCmQkBBBAAAEEEECgBQn4LnxPeOsTffTCrauF77efvtE9lrFK+F6xY33c4Gv05dQfFAmHVmm9cyTEOV7SUOBsKHz/tYaJk6fqtAvGKBwOKbhip7rmRtFYXHvtvKXGXnm2Uq3DCd9rr9VNY68cvEr4/uKrGe5ueM2rJnx/NuEOFeTn67jBV2vWnPm67PwTtfH6a7tHXV5+c6KuG/fEauH7pYdGufeoeU14+xNdeOXt7hGWzTeq3tHnhQACCCCAAAIIIJCeAOF7+/4686Kx+uGn33TndRfUq9e9a0fl50VW+1qq4XvqjJ911MARGjroH+4HMP/6KirMd/9ikGodTQnfc+f9qYNPulhX//tUHbb/zrUl3PXIi7r5nv+uFr6dM+BbbLJu7XXPvPyeLh9zv154YKT69ume3irjagQQQAABBBBAAAFXgPC9fX/d/ehLuu2B5/Xmkze4T0Kpec3/Y7EK8iPuhzLre9WE79tGnqfddqh+zrdz5vuvO99V0Zh2OmSwDt1vJ118znGrDDXzlznuDrNzJjvVOpoSvmf8+JuOHXy1br3mXPfJJc7L2dV3nlPu1PDa42Pcs+A1x07OH/h3nXrMgbW1Xnnjgxr/2kea+NI4d8ecFwIIIIAAAggggED6AoTv7fvXPmWkV/fOOv/0v6tr5w6a/sOv7gcRnXPPdT8kWZfYeTzhvscM1XFH7O3uJvft3V2jxz2+Wvh23uN8qPP2h8br/NOPcs+ZV0WjevrFd/XE+Hf09F0jtH7fXinX0ZTwHU8ktddR52vzjfq5T31ZWrpc/7n3WXXv2sH9gOiYS89wQ/mbH0x2H9/o7G6feeKh2nTDdTT5qxkacf39OmjvHXTF0JPTX2W8AwEEEEAAAQQQQMAVIHyv+M6UTpC+8a6n3O+YWVZe6T73et/dt9FZJx1a75GTmvXjfJjz1Xc+lfMdN5+97yr3CSN/3fmuubbuc74L8iLuuetBJx6irTZfv3Y5plJHU8J3UWGB3p/0lcbc/oT7BBfnEYgDjztI++2+jU4ecq2cnfGLzznWPZfuhO+7rx+qB558VZO/nu4+53uvXbbS8HOPV0EBTzrh9w4EEEAAAQQQQKCpAlaE76ZOjvelL1Bz7KTu02HSH4V3IIAAAggggAACCNQnQPhmXawiQPhmQSCAAAIIIIAAAuYECN/mbK0cmfBtZdsoGgEEEEAAAQQsESB8W9IoykQAAQQQQAABBBCwX4DwbX8PmQECCCCAAAIIIICAJQKEb0saRZkIIIAAAggggAAC9gsQvu3vITNAAAEEEEAAAQQQsESA8G1JoygTAQQQQAABBBBAwH4Bwrf9PWQGCCCAAAIIIIAAApYIEL4taRRlIoAAAggggAACCNgvQPi2v4fMAAEEEEAAAQQQQMASAcK3JY2iTAQQQAABBBBAAAH7BQjf9veQGSCAAAIIIIAAAghYIkD4tqRRlIkAAggggAACCCBgvwDh2/4eMgMEEEAAAQQQQAABSwQI35Y0ijIRQAABBBBAAAEE7BcgfNvfQ2aAAAIIIIAAAgggYIkA4duSRlEmAggggAACCCCAgP0ChG/7e8gMEEAAAQQQQAABBCwRIHxb0ijKRAABBBBAAAEEELBfgPBtfw+ZAQIIIIAAAggggIAlAoRvSxpFmQgggAACCCCAAAL2CxC+7e8hM0AAAQQQQAABBBCwRIDwbUmjKBMBBBBAAAEEEEDAfgHCt/09ZAYIIIAAAggggAAClggQvi1pFGUigAACCCCAAAII2C9A+La/h8wAAQQQQAABBBBAwBIBwrcljaJMBBBAAAEEEEAAAfsFCN/295AZIIAAAggggAACCFgiQPi2pFGUiQACCCCAAAIIIGC/AOHb/h4yAwQQQAABBBBAAAFLBAjfljSKMhFAAAEEEEAAAQTsFyB8299DZoAAAggggAACCCBgiQDh25JGUSYCCCCAAAIIIICA/QKEb/t7yAwQQAABBBBAAAEELBEgfFvSKMpEAAEEEEAAAQQQsF+A8G1/D5kBAggggAACCCCAgCUChG9LGkWZCCCAAAIIIIAAAvYLEL7t7yEzQAABBBBAAAEEELBEgPBtSaMoEwEEEEAAAQQQQMB+AcK3/T1kBggggAACCCCAAAKWCBC+LWkUZSKAAAIIIIAAAgjYL0D4tr+HzAABBBBAAAEEEEDAEgHCtyWNokwEEEAAAQQQQAAB+wUI3/b3kBkggAACCCCAAAIIWCJA+LakUZSJAAIIIIAAAgggYL8A4dv+HjIDBBBAAAEEEEAAAUsECN+WNIoyEUAAAQQQQAABBOwXIHzb30NmgAACCCCAAAIIIGCJAOHbkkZRJgIIIIAAAggggID9AoRv+3vIDBBAAAEEEEAAAQQsESB8W9IoykQAAQQQQAABBBCwX4DwbX8PmQECCCCAAAIIIICAJQKEb0saRZkIIIAAAggggAAC9gsQvu3vITNAAAEEEEAAAQQQsESA8G1JoygTAQQQQAABBBBAwH4Bwrf9PWQGCCCAAAIIIIAAApYIEL4taRRlIoAAAggggAACCNgvQPi2v4fMAAEEEEAAAQQQQMASAcK3JY2iTAQQQAABBBBAAAH7BQjf9veQGSCAAAIIIIAAAghYIkD4tqRRlIkAAggggAACCCBgvwDh2/4eMgMEEEAAAQQQQAABSwQI35Y0ijIRQAABBBBAAAEE7BcgfNvfQ2aAAAIIIIAAAgggYIkA4duSRlEmAggggAACCCCAgP0ChG/7e8gMEEAAAQQQQAABBCwRIHxb0ijKRAABBBBAAAEEELBfgPBtfw+ZAQIIIIAAAggggIAlAoRvSxpFmQgggAACCCCAAAL2CxC+7e8hM0AAAQQQQAABBBCwRIDwbUmjKBMBBBBAAAEEEEDAfgHCt/09ZAYIIIAAAggggAAClggQvi1pFGUigAACCCCAAAII2C9A+DbUwxdeTUjJ5MrRA4HV77Smr6f63nTHdaqoGXtN763va6m+17ku3bHXVFPNfZtSk1ff21hdNavFcTEx78buv6b7mn5vJuvHS+9N9ddwfTWn+t6G1oah39cYFgEEWo5A3z4BbbxBPdml5RAYmynh2xCtE75fmBA3NDrDIoAAAggggAAC5gSGDAoTvg3xEr4NwRK+DcEyLAIIIIAAAggYFyB8myMmfBuyJXwbgmVYBBBAAAEEEDAuQPg2R0z4NmRL+DYEy7AIIIAAAgggYFyA8G2OmPBtyLah8B0MSnfdFFEsVn3jRFKaNz+pZ16Ia+q0Oh/QTKOuvXcLqnu3gB583I4z5jtuG9RHnyTcGZ4zMKxPv0ho0ufVP+eFAAIIIIAAAs0vQPg21wPCtyHbxsL30MtjWrQ4KSeMb7lZUCcdE9LFV0W1tDT9grwUvp2HL9R9UMNfZ+N8/YarIjp/eNT9UlGhFI1J0eqf8kIAAQQQQAABDwgQvs01gfBtyDbV8F1z+8v/Fdb4VxI6aL+gXnkzoclfVu8Eb75JUIceENQV163YKq+n3rrhu13bgC46L6S7H47r51+TOumfIfVbO6AFf0gzZibVsb1036Nxdekc0AlHh9SmtVRZKT3237h+/CmpXj0COuXYkL6amtS6fQPu1x97ZuWu/IH7BLX91kEpKX03I6knn4srFpduvS6iV96Ia789Qxp6WVTdugZ0/FEhFRVVB2tnjGnfJ3XWaSH13ySoufOSGnt7XMcdFard+d6qf1AH7x9UKCgtKZUeejKu3+cltdeuQfXsHlAkInXqGHC/Pu6+uBb+2bR/KTDUcoZFAAEEEEDANwKEb3OtJHwbsk03fF8xLOwePenWJaB+6wQ07t7qIyQnHxPS7/OTmvBmw8cyasL348/E9e9zw3rj3epjHLvtGNS2fwtqzK0xtS4J6OIhIU2dnnSPp1w2NKx3P0zo/YkJ9VkroMGnhTTsipgbyp2/CPznrpi++S6pbbYMas9dgho1Nqb+mwZ1+IDqHzuBfdApITdQv/VeQjePiujDSQl3Ds7O96VDw3rr3YQ+/iyhbf4W1MH7BTX8mphaFUvXXxnRGRdUb3XXHDuZ8WNSI/4d1tXXxzT/j6R22SGonbcL6pobY9pjZyeUh3T5tVEtWSo31C8rk557yY5jNoaWGMMigAACCCBgTIDwbYxWhG9DtqmG71BIcnZ8jzkypIuuiiovEtDI4WFdcFnUDbg3Xh3RNTfEtGBhw7u8NeE7P0/6fb5qny8+8MSQfvy5Ohw7Lye0BkPSi68mdNXFYQ3+V7T2iMjwC8J6enxcy5ZLw84L6+x/V4djZ8f5nIEh/WtEbLW/CGy2cUD77hHSmFtiGjsy4gb2mT9X15kXkbsjnkjI3T2/bkREp58fbTB8R8LSFpsF3TGcl7PLffv1EZ09LKrttwpqow0CuvXu6rC9565B9e4V0H2PEL4NLV+GRQABBBBo4QKEb3MLgPBtyLax8F37gcuENOf3pJ58Pq7vf6wOrhcODmviZwn3WMWRB4d09Q0NHzlxrnfC90H7hRQOS87u9weTqsP2+WeG9fGnKz/MeODeQXXsGNB7HyV08ZCwe+a85pWfH9DDT1Uf8zhvkHN0pPqePboFan/u7FKv0yegysrq9znn1Z0z6lddXx2+R90U07wF1V9zdsx33zko5y8XzjER5zjLwCENh+/27aSuXVYN1OPGRDRidEybbBhw73vPw9Vh29kJr/tzQy1kWAQQQAABBFqsAOHbXOsJ34ZsGwvfNR+4rO/2O28fdHfDneMmTgB//Z01PwnECd+bbxzUE8/FdcFZYTesO+8bdHJI035I6p0Pqt/vnK92wvALExK6cljY3VX+66tu2P5r+D7xnyHNmZt0j7X89eWE75E3Vh8ZadcmoJGXhnXlmJh7tts5hz768vAaw7ez873l5kHdfGd16M/Lk5zw7ezO77DNqmGb8G1o0TIsAggggAACKwQI3+aWAuHbkG0m4dv5kKJzTMM5duKcef5z0Zo/WFj3A5cH7B3URusFdcO4mPbdI6iN1g/qpttjats6oEsuCOnr76rPfDtnsl97O6FPJydU0kr65+EhPfhkXB3br9zp/mv4dj4o6Xwg1DlmUlEp91x2PC73sYF1w7cT4IeeHdaFl0XdYyeHDwi5tZw1NKpQWLp5ZMQ91lJZtfLM9/Qfku5fCJxddCfAO+fMnb+AjP5P9Zlvdr4NLVSGRQABBBBAoB4Bwre5ZUH4NmSbSfh2Shr8fyGVFAfcDzc29qobvp2jIM6REufYivNhx4EnhNWjuzR7jvTbnKR75vrBJ1Y+7aR92+pnjb/xTkLvfpRY5ZjJX8O383Pn6IqzE+3soM9bIN3/WFyLlyRXCd/OdaccF9L6fQNaXiY9NT6uQ/YLucdUnPk4x2HW6hlwd7kP2nfl007+1j+oQ5ynnYSkPxdJDz0Rd8+6E74bWwF8HQEEEEAAgewKEL6z61l3NMK3IdtMv8Ol8xjAWbOTeufDzL75TN3nbjtPDHE+COk8kYQXAggggAACCCDQkADh29zaIHwbss0kfHfvGtCQQSFdNiqm8oqmF+gcE/n7odXPCHdC+EXnhd3z3l98lVmgb3pFvBMBBBBAAAEEbBAgfJvrEuHbkG1Tw/cRB4XkfPt15xvMfPl1dUju2jmgM08N1Vup84HG2++rfyfbOeZxzBEhbb5JwD17/fmX1d/Gfk3fgdIQB8MigAACCCCAgEUChG9zzSJ8G7Jtavg2VA7DIoAAAggggAACKQsQvlOmSvtCwnfaZKm9gfCdmhNXIYAAAggggID3BAjf5npC+DZkS/g2BMuwCCCAAAIIIGBcgPBtjpjwbciW8G0IlmERQAABBBBAwLgA4dscMeHbkC3h2xAswyKAAAIIIICAcQHCtzliwrchW8K3IViGRQABBBBAAAHjAoRvc8SEb0O29YVv55vcpP1K5bmAzkO8M3k1dI9Mxs10zEzf35BHY55NmXNjY9atpSnjZ9Jb3ut5gRcmxFVYIO29exN+f/D87Pxd4GdfJOQ87pUXAn4UIHyb6yrh25BtQ+F7n9IHDN2RYRFAwDaB0IDjNWiY3PB904ikYi89YtsUWmy9iR0P0qgH2hG+W+wK8P/ECd/mekz4NmS7pvBd8fR9hu7KsAggYItAoLBYRXe8tEr4XnbiHraU3+LrzBv7HOG7xa8CfwMQvs31l/BtyJbwbQiWYRHwiQDh2+5GEr7t7h/VNy5A+G7cqKlXEL6bKtfI+wjfhmAZFgGfCBC+7W4k4dvu/lF94wKE78aNmnoF4bupcoRvQ3IMi0DLECB8291nwrfd/aP6xgUI340bNfUKwndT5VpI+P4qGlSPUFIdgnyi39BSYdgWKpBO+P41HtQ1y/LUMZjU6UXR2h9fU1Kpd6tC2i0vnjXFz6IhPVIe0aJEQL1DCZ1RVOX+HsBrVQHCNyvC7wKEb3MdJnwbsrXl2ElCUnANBqOX5emwgpjWCztX8kIAgWwJpBO+J1SGNTUa1IWtqlT3x04k/r8lBbqnTUVWyvozEdCQpfm6pFWV1g0n9ER5RDNiQV1eUpmV8f00COHbT91kLvUJEL7NrQvCtyHbpoTv5yvCerUyrDaBpPbOj+vZirDGrfhD1fnxe1VhOU/03jQc14lFUYUlnbS4QMcWxjQpGtK8eEB75Md0eEHMndUX0ZAeLQ8rpoC6BBMaVBRVu2BSL1WE9Us8qJ/jAW0ZSegfhVE9WBaRs+Pl/GG+cTihM4qr9GJFWE+VR9Q+mNTxRVFtG6muqb46DDEyLAK+FUg1fDv/+nRLWZ6iSalbKKk/EgH3x85fiCOS++u2ZyjhBuYLlubrH4UxfVoV0u+JgE4qimpyNKRZ8YCcp4hf0qpSBQHph1hQd5dFtDwZUCQgnVpUpU3CCTnh+/t40P217rx+igfl/AX8jiyFez81k/Dtp24yF8J3btcA4duQd7rhe3Y8oOGl+bqpdaVaBZMasyxPs+NB3dqmwv3D9bHyiJx/Yi4IJHXDsjxtHEnogPyYTllc4Ab1fxZG3T84z1pSoAfalqs8GdB5S/N1ZUmV1gol3CA9LRbU0BU7Z8+UhzWydaW6BJP63A3pEV1XUiEn3V+0NN/d7d4xL67zl+brjKKo+wf9muowxMiwCPhWINXw7QC8XBmWc/RkUFHVKj8uTQZ0+uICPdau3HVyfj84pCDm/u/NyrDuK4/oxtYV6hpM6orSfO2VX/3r+t9L83VAQUy75sX1YVVIT1dEdHPr1XfPx1eE9XM8qHOLq3zbh6ZOjPDdVDneZ4sAO9/mOkX4NmSbbvh+ozKsL6PV4dh5fVQV0uPlETd8j1uep+6hhA6ts6P9QkVYI0oq3T9sLy2p0tqh6mMhzk74da0r9V0s6P6h6uyGOa+KpPO1QvcP6dcrw5ocDdZ+zdntrkzK3RFzXneWRdQpmHR30OuG7zXVYYiRYRHwrYCp8H1FSZV6hRL6OhbUvWV5GrsiVN9RFlHPYFIDCmKqSkrhQPWRM+ds96AlBXpiRYCvAXd+P7qnLE9XlVS6/2LGa1UBwjcrwu8ChG9zHSZ8G7JNN3w7xznmJap3tpzX9FhQtyzPc8P3qGV5+j4WrA3HTsx2jqaMbl0dvp0dbGdnq2bny/m588/OT1VE1Dqw8g/NsqQ0tnWle0TFGb9mN2tpMqCHyyL6LRFwj7XMTwS0f35MR/wlfK+pDkOMDIuAbwVMhW/n9wXnL89TY0E9VBZxf59wXneVRdzfJw4uiLl/uXeOuDmHS+JJubvbT9YJ3zW74Re1Wvl7i28b0cSJEb6bCMfbrBEgfJtrFeHbkG264dv5g9D5w/KCFf+8+3FV9VETJ3zfXpbnHh05ML/6LHfdV0Ph2/mQ1KSqkP61Yue77nucezlfP2fFvZyd7lgyoEHFVe5OmHO/zsHEauF7TXUYYmRYBHwr0Fzhe6e8uAYvLdCYkgr3KSYLEwGduaSgNnw7x8ueKA/r0lZVasuOd4Prj/Dt21+aTGyFAOHb3FIgfBuyTTd8Ox+AcnaWx7apVFEgqZHL8t0PUDrh2zmT/XR59TGTwoDcs5zhQNJ9vFhD4btQco+MXN26Ut2CSfcDVu9XhXRKUdTd8aobvm9Ynqf1QgkdVBBzd8Cc8+Y75MV1bGFUQ5fmux/o7B+Jr7EOQ4wMi4BvBbIRvsuSAZ28uEAPtS1XfqD6zHdjO9/9IwmNKM3TnW0r3A9hOp/3cD4T8kjbckUVcD+0eWVJpToTvNe49gjfvv2lycQI38bXAOHbEHG64dsp46HyiHtO2/kn413y4u5TSW6p87STd6vC7j8RO+e/z1yX6nnPAAAgAElEQVTx5JKGwrfzz8s1TzupTAZUGEjq1KKoNggnVgvfNUdcnDOg64YS2jovrnHLIxpcHHWfiPJCRcT9QKdzFMU5HlNfHYYYGRYB3wpkI3w7OFcty9dPsYAublWlkcvyGg3fzrGTW5fn6dtYUK0CSZ1QFHWfauQcZ9szP6bbl+e5T1Kq+3KCekmdI2y+bUoaEyN8p4HFpVYKsPNtrm2Eb0O2TQnfzunsFZ95dI+gON/oYhTP1zXUIYZFoHkF0gnfzVspd69PgPDNuvC7AOHbXIcJ34Zs0w3fzoceBy8pcB8n6DypwHmyiLNbfXJR1FCFDIsAAs0pQPhuTv3M7034ztyQEbwtQPg21x/CtyHbdMO3U4Zzlts51uH88+86oYQGFUf5p15D/WFYBJpbgPDd3B3I7P6E78z8eLf3BQjf5npE+DZk25TwbagUhkUAAQ8KEL492JQ0SiJ8p4HFpVYKEL7NtY3wbciW8G0IlmER8IkA4dvuRhK+7e4f1TcuQPhu3KipVxC+myrXyPsI34ZgGRYBnwgQvu1uJOHb7v5RfeMChO/GjZp6BeG7qXKEb0NyDItAyxAgfNvdZ8K33f2j+sYFCN+NGzX1CsJ3U+UI34bkGBaBliFA+La7z4Rvu/tH9Y0LEL4bN2rqFYTvpsoRvg3JMSwCLUOA8G13nwnfdveP6hsXIHw3btTUKwjfTZXLYvgu+PsphqpgWK8JVDx9n9dKop5mEkglfDvX5A84upkqzM1to5PeVXzWzNzcLIt3IXxnEZOhPClA+DbXFsK3Idt0PnDphO/RO22mpJIKKNDg/2dSas3YmYyxpvearN1Uzbked0hhbyUGHpbr23I/jwqkGr5b3fGSpr8R8ugsMiur95ZlStx0JuE7M0bejYARAcK3EVZ3UMK3IdumhO8Rcz4zVA3DNrfA2vmt9WXPfQjfzd0ID90/nfD93NkeKjyLpew/opLwnUVPhkIgmwKE72xqrjoW4duQLeHbEKylwxK+LW2cwbIJ3xLh2+ACY2gEMhQgfGcIuIa3E74N2RK+DcFaOizh29LGGSyb8E34Nri8GBqBjAUI3xkTNjgA4duQLeHbEKylwxK+LW2cwbK9Gr5LY/M1fdkb2qrtsQZnXz00O9/GibkBAk0WIHw3ma7RNxK+GyVq2gW2hO/dRryoiUP2UmWbwqZNlHelJED4TompRV3k1fCdyyYQvnOpzb0QSE+A8J2eVzpXE77T0UrjWsJ3Glgt4FLCdwtocppTzEb4nrbsNf1a/pmqEmXapu2Jah3ppvcX3qL8YLEqE8u0a4fzNGH+5Tqg81XKCxbp40V3qXfhtvpx+XuKJatUHl+kndqf6Xz2Xh/8eYsKQ+20ecnh+qr0Oe3RcajeX/ifVa4LBEJ6b+FYdcvfVPOrpmnTkkPVq/Bv+vDPcapILFU0UaHdO56vZbEF+nzxI8oPlahVqKO2bntCvTqE7zQXDZcjkEMBwrc5bMK3Idtshu8+705Xz4kzVdUqX7O3XVtrvzNdH/57PykprffyV+r07VznsTVatE4nfXvElkoGA+r9/vfq8clP7n+vKinQN0dvpfL2xeowY57WHz9FCgY0t38v9f7we008r3rnu+O039Xv1W8UjCdV3r5I3x75N8XDIe1yzSv68KL9VFWc72qt/+IU97//sP8mhvT8Nyzh2389zXRGmYbvhOJ6fu75OrzbzVoe/0MLq2ZqYdXPahXupHWLd9e0Za+rKrFcScVVHOqofsW76vnfL9CBXUbql7JJ6le8m2aWfaTF0Vnu9W8sGKXDu411f/7Fkie0S4dz9HPZxNWue/uPMTqk6/VaUPWDvi19WRu02lczlr+pndsP1u+V3yovWKxJi+7RLu3PcWt564/rtFWb49Qm0n01MsJ3pquI9yNgToDwbc6W8G3INlvhu3h+qba64z33aEisKE+bPTRJxX+U6qOh+6rTd3PVb8JUfXLO7u7O1Ta3vaOfdltff/brrB2uf00fDttf8fywuk/+RcGquBvcd7p2ghuqF67XRT0nzXSD+IfD9nMD+w43vqHPz9hVy7q0Vu8Pvlebnxfqq+O30xb3faR5m/XUnK16u1o7jX5VU47bTqU92hrS89+whG//9TTTGWUavsvii/TOwht0YOera0v54M9btV7xnuqSv6FmV0xxQ/ZmrQ93w/CmrQ/TzLIPtE3bk/Tp4geUSCbc0N4u0ssN0J8suk97dbqoNnw7u+b1Xrf4fu3VcVjtdWsVbqPS2Dxt0eao2jqemnOGOuWv5/7c2V3fss0/1DV/Y8J3pouG9yOQQwHCtzlswrch22yFb2f32tmtdkKw8+oy5Tf1e32qG76dne9QNKZ4Xtj92obP/k8V7Yr0y079tMvIV/TDfpto3qY9FC3Kc79evKBUW9/2rt4dcZD782Asrj2Gj9cHF+2v9j8uUNcvZ+l/p+zofi1UFZNzHvztqw9Vt8m/qNO03zXl+O1UMnuxNnvsk+r780pZgPCdMlWLuTDT8O3sfD8791wd2e0WLYv9oTmVX6ssvtDd5a7e+X5N0WSFNi05RK/Mv1Rtwj21Xqs9tSw2T/Mqp2m7dqfq29JXtCw+Xxu22l+f/CVUr120Q0rXbVwywL2XE9bnVHylUCCiyUse1+4dzldhqK2WxOaoJNRZwUD171N1X+x8t5jlzkQtFCB8m2sa4duQbbbCt3PEpPDP5e5xEufV5peF2uSpz93wm7e8Uuu+8o2K5y91vzdm4aLlmrVDX/20xwYqmbNYfd6Zrg4/zNfSHu303eFbKK+0Qps+8Vn1kZUVr90vHa+PL9xHXaf8pnXe/E7R4uqg7rzCFVF9fP7eSoaC2vG61/T+8APcIy9KJPXjvqvvYhmi9MWwhG9ftDGrk8g0fDvFfLdsgn4p+1SxZKV75rtNpId7Tts53x1NlGuXDue6P/6m9EU3IB/Z7VaVxn7X239cr05566p1pLu+X/62dmh3ur4ufX6VHe2t2x6f0nXVZ8Nvcc+Yx5LOme8LtCw23w3g4UC+Agpqt45DFNTq36WT8J3VJcVgCGRVgPCdVc5VBiN8G7LNVvjuNXGm2s1coK+O3dattMtXv6nfa9U7385OdzCeqD3nvdF/v1B5uyI3fNe8AvGE1nlrmlrNXeKe0d563Mqdb2d3e/fLXqje+Z65QJ2/nq0pJ2xfr8iW93yoX3fqp34TvtHUf2yt0m5tDMn5c1jCtz/7msmsshG+M7m/F95L+PZCF6gBgfoFCN/mVgbh25BttsJ3698Wqf/9H2viBXsrlh/WFvd/7O5wO+F7s0c+0ZLe7fXLzuuqZO4SbfbwJPeYyYKNu6v3ezP0zT+3ViIcUrcvfnVDu3NsZOeREzT16K3cM9/OhzKdD1g6O+HO7vZ2N72pzwftqrKOreTc13nf9IM3d4Wc4y8dZ8xT8byl7k45r/QECN/pebWEqwnfPOe7Jaxz5mivAOHbXO8I34ZssxW+nfLWfflrdZ0ySxVtizR3i7W01oc/6OOh+9QeQUmEglraq73mb9RNGz8zWVP/vpW7W975mznuBymdp518e8QWWt65tftklPVenKKApNlb93E/dPn5oN3cHfOap52EonE36E8/uL8W9+ngCjlHXHa+5hX9vNv6+nGfjQyp+XdYwrd/e9vUmRG+Cd9NXTu8D4FcCBC+zSkTvg3ZZjN8O48LTAacuCw3VDvnvD8d7DzhhJctAoRvWzqVuzoJ34Tv3K027oRA+gKE7/TNUn0H4TtVqTSvy1b4dnacnQ87fnrmblrepbU2emay+/jA6QdVHwfhZYcA4duOPuWySsI34TuX6417IZCuAOE7XbHUryd8p26V1pXZCt/OTXt8+pP75JJAIuk+W9t5TnfN4wPTKoqLm02A8N1s9J69MeGb8O3ZxUlhCEgifJtbBoRvQ7bZDN+GSmTYHAoQvnOIbcmtCN+Eb0uWKmW2UAHCt7nGE74N2RK+DcFaOizh29LGGSyb8E34Nri8GBqBjAUI3xkTNjgA4duQLeHbEKylwxK+LW2cwbIJ34Rvg8uLoRHIWIDwnTEh4dscYf0jE75zLe7t+xG+vd2f5qiO8E34bo51xz0RSFWA8J2qVPrXsfOdvllK7yB8p8TUYi4ifLeYVqc8UcI34TvlxcKFCDSDAOHbHDrh25At4dsQrKXDEr4tbZzBsgnfhG+Dy4uhEchYgPCdMWGDAxC+DdlmI3yP6L61oepWDvtj5RI9vHCG8fu09BsQvlv6Clh9/pmE70ihtO7eIUlJyf1+tc6roR//9Wt1a1nTe7Lbs98mJ7R0tnO/la/9R1QqcdOZis+amd2b5WC0vLHPadQD7TR33qpzysGtuQUCOREgfJtjJnwbss1W+L58/i+GKpRK23TXWSojfBsTXjkw4TsHyJbdItPwvc+NEY1bVGXFrI8sCGv6DYRvK5pFkQisECB8m1sKhG9DtlkN31OeNVJl6S6DCd9GZFcflPCdI2iLbpON8N13+jIrZjxxrSLCtxWdokgEVgoQvs2tBsK3IVvCtyFYS4clfFvaOINlE745821weTE0AhkLEL4zJmxwAMK3IVvCtyFYS4clfFvaOINlE74J3waXF0MjkLEA4TtjQsK3OcL6RyZ851rc2/cjfHu7P81Rna3he617R2nWiRcqGY6kzMaxk5SpuBABzwgQvs21gp1vQ7Ymw3fd5xM0Vv63C6J6aMpy5YUCalcY1JDtSmrfwpnvxvSy93XCd/Ys/TIS4Zudb7+sZebhTwHCt7m+Er4N2ZoI3698X64vf4+qa6ugjtyoSGM+LlVJXkBlsaSG79xGb86s0Nfzq9SpKKSpC6K6aKfW+uDXSm3bI09dW4V0yduLdf72rdWhMOjOmvBtqPn1DEv4zp21LXfKdvju8O54Ff0yQ4FoVLOPO0/tJr6ugt9+UrCqQvMOPM79+eJtdldFj3XU7dm7tWjbPd0f1331ue0y/TJohJLBoPrcOlyzjz1X3Z+6XYmCQlV0XUsL9j1aNTvfve++Wj8PukKFs35Q6ykTtXDXg9Tt6Tvda6s6dtOCff5eOzQ737asSupEYKUA4dvcaiB8G7I1Eb5f/7FCvy6J6bQtW2nW0riWVCS0SeeIbv6kVPv2LdCvS+KauyyuEzcv1jPflqljUVC79SlwZ/hneULXf7xU1+zZtvapwIRvQ80nfOcO1uI7ZTN8B6sq1fPBMfr1/4ar6KdpiucXqusL9+uXM0Yob8EcdXzrWS3ZencV/TBVC/Y9Sn3GXaafz7xyNb1Orz2p5f02UbRdJ3V642n9ucsAKZlURfc+WvuWS/TT2dc0GL6TobCW991Iy9fbXD0fukFzj/g/xYtbu/cgfFu8UCm9xQoQvs21nvBtyNZU+K6MJ3XQeoWatyyux74pUyQoffdHVAP/1krzliVUFU9qwHqFemlGuXvUZJ++Bfr+z5ie+Ga5ztq6RO1X7Hqz822o8Q0My853br1tuFs2w3fewnlygvPsY85xp17358FolXrdd60bzNe6d6TmDThebT5/T/MOOmE1poI5P6vN5PcUbdvR3b2OduiiDu+MVyIvTyXffqHvLxnXYPgOL13k7rIn8grk/Pj3Q09RZddehG8bFiM1IlCPAOHb3LIgfBuyNRW+a8L1bZ+Vauvu+dqmR56ufG+JDl6/UPOXrx6++7QNu7vgF+5Q4obxui92vg01n53v3MFafKdshm9n57vXfaPcne7iH75RIhxR51cfd3+eP3+2Orz7guYcNUjdnxynRH6hlmy5s8rX6levnnOsxHn/7OOGqNszd+rPnfZXZde11G/02fr+4jrh+66r3N3zkm8+VdHP0+XsfC9bv7/K1tlQeX/8rqoOXaRA9e857HxbvFApvcUKEL7NtZ7wbcjWdPj+eFalnptWrnXahd1z3z8uimn7nvmr7Xy/MbPCnWFRpPoPwZP7F8sJ5M6L8G2o+YTv3MFafKdshm+HoePbz6vwl+kKxGP67bghajfpTRX+9qMCVVWad/AJ7k52ydTP1XX8/fr+4tsalOvywoMKVSzXnKPOVJsv3lebye+rsksvhZeXavm6m6r1lI/dp510ff5+JcNhJUPOt7kPVJ/5fvZu9ykozu63E/ZrXoRvixcqpbdYAcK3udYTvg3Zmgjf2S6V8J1t0YbH49hJ7qxtuVO2w7eX50349nJ3qA2B+gUI3+ZWBuHbkC3h2xCspcMSvi1tnMGymzt8d375UYXLSmtnmIjk6/dDTzYyY8K3EVYGRcCoAOHbHC/h25At4dsQrKXDEr4tbZzBsps7fBuc2mpDE75zqc29EMiOAOE7O471jUL4NmRL+DYEa+mwhG9LG2ewbMI332TH4PJiaAQyFiB8Z0zY4ACEb0O2hG9DsJYOS/i2tHEGy85G+H5gUZXBCrM39AEFYU2/IaGls53vz7vytf+ISiVuOlPxWTOzd7McjZQ39jmNeqCd5s5bdU45uj23QcC4AOHbHDHh25At4dsQrKXDEr4tbZzBsjMN3+vsVv2dam15zf0qSfi2pVnUiYAkwre5ZUD4NmRL+DYEa+mwhG9LG2ew7EzCt8Gycjo0O9855eZmCKQlQPhOiyutiwnfaXGlfjHhO3WrlnAl4bsldDm9ORK+OfOd3orhagRyK0D4NudN+DZkS/g2BGvpsIRvSxtnsGzCN+Hb4PJiaAQyFiB8Z0zY4ACEb0O2hG9DsJYOS/i2tHEGyyZ8E74NLi+GRiBjAcJ3xoSEb3OE9Y9M+M61uLfvR/j2dn+aozrCN+G7OdYd90QgVQHCd6pS6V/Hznf6Zim9g/CdElOLuYjw3WJanfJECd+E75QXCxci0AwChG9z6IRvQ7aEb0Owlg5L+La0cQbLJnwTvg0uL4ZGIGMBwnfGhA0OQPg2ZEv4NgRr6bCEb0sbZ7Bswjfh2+DyYmgEMhYgfGdMSPg2R1j/yITvXIt7+36Eb2/3pzmqI3wTvptj3XFPBFIVIHynKpX+dex8p2+W0jsI3ykxtZiLCN8tptUpT5TwTfhOebFwIQLNIED4NodO+DZkS/g2BGvpsIRvSxtnsGzCN+Hb4PJiaAQyFiB8Z0zY4ACEb0O2hG9DsJYOS/i2tHEGyyZ8E74NLi+GRiBjAcJ3xoSEb3OE9Y9M+M61uLfvR/j2dn+aozrCN+G7OdYd90QgVQHCd6pS6V/Hznf6Zim9g/CdElOLuYjw3WJanfJECd+E75QXCxci0AwChG9z6IRvQ7aEb0Owlg5L+La0cQbLJnwTvg0uL4ZGIGMBwnfGhA0OQPg2ZEv4NgRr6bCEb0sbZ7Bswjfh2+DyYmgEMhYgfGdMSPg2R1j/yITvXIt7+36Eb2/3pzmqI3wTvptj3XFPBFIVIHynKpX+dex8p2+W0jsI3ykxtZiLCN8tptUpT5TwTfhOebFwIQLNIED4NodO+DZkS/g2BGvpsIRvSxtnsGzCN+Hb4PJiaAQyFiB8Z0zY4ACEb0O2hG9DsJYOS/i2tHEGyyZ8E74NLi+GRiBjAcJ3xoSEb3OE9Y9M+M61uLfvR/j2dn+aozrCN+G7OdYd90QgVQHCd6pS6V/Hznf6Zim9g/CdElOLuYjw3WJanfJECd+E75QXCxci0AwChG9z6IRvQ7aEb0Owlg5L+La0cQbLJnwTvg0uL4ZGIGMBwnfGhA0OQPg2ZEv4NgRr6bCEb0sbZ7Bswjfh2+DyYmgEMhYgfGdMSPg2R1j/yITvXIt7+36Eb2/3pzmqI3wTvptj3XFPBFIVIHynKpX+dex8p2+W0jsI3ykxtZiLCN8tptUpT5TwTfhOebFwIQLNIED4NodO+DZkS/g2BGvpsIRvSxtnsGzCN+Hb4PJiaAQyFiB8Z0zY4ACEb0O2hG9DsJYOS/i2tHEGyyZ8E74NLi+GRiBjAcJ3xoSEb3OE9Y9M+M61uLfvR/j2dn+aozrCN+G7OdYd90QgVQHCd6pS6V/Hznf6Zim9g/CdElOLuYjw3WJanfJECd+E75QXCxci0AwChG9z6IRvQ7aEb0Owlg5L+La0cQbLJnwTvg0uL4ZGIGMBwnfGhA0OQPg2ZEv4NgRr6bCEb0sbZ7Bswjfh2+DyYmgEMhYgfGdMSPg2R1j/yITvXIt7+36Eb2/3pzmqI3wTvptj3XFPBFIVIHynKpX+dex8p2+W0jsI3ykxtZiLCN8tptUpT5TwTfhOebFwIQLNIED4NodO+DZkS/g2BGvpsIRvSxtnsGzCN+Hb4PJiaAQyFiB8Z0zY4ACEb0O2hG9DsJYOS/i2tHEGyyZ8E74NLi+GRiBjAcJ3xoSEb3OE9Y9M+M61uLfvR/j2dn+aozrCN+G7OdYd90QgVQHCd6pS6V/Hznf6Zim9g/CdElOLuYjw3WJanfJECd+E75QXCxci0AwChG9z6IRvQ7aEb0Owlg5L+La0cQbLJnwTvg0uL4ZGIGMBwnfGhA0OQPg2ZEv4NgRr6bCEb0sbZ7Bswjfh2+DyYmgEMhYgfGdMSPg2R1j/yITvXIt7+36Eb2/3pzmqI3wTvptj3XFPBFIVIHynKpX+dex8p2+W0jsI3ykxtZiLCN8tptUpT5TwTfhOebFwIQLNIED4NodO+DZkm9XwbajG0jbddZbK9PDCGYbuwLA1AoRv1sJfBQjfhG9+VSDgZQHCt7nuEL4N2WYrfBsqr3bYHyuXEL5NI0sifOcA2bJbEL4J35YtWcptYQKEb3MNJ3wbss1G+DZUGsM2gwDhuxnQPX5Lwjfh2+NLlPJauADh29wCIHwbsiV8G4K1dFjCt6WNM1g24ZvwbXB5MTQCGQsQvjMmbHAAwrchW8K3IVhLhyV8W9o4g2UTvgnfBpcXQyOQsQDhO2NCwrc5wvpHJnznWtzb9yN8e7s/zVEd4Zvw3RzrjnsikKoA4TtVqfSvY+c7fbOU3tGU8J3SwFxkrcCQwt5KDDzM2vopPLsC6YTv6W+EsnvzVUZLSgoYHL/hoXtvWabETWcqPmtms9w/k5vmjX1Oox5op7nzHD9eCPhPgPBtrqeEb0O26YZvQ2UwrMcEKp6+z2MVUU5zCaQavvMHHN1cJebkvtFJ7xK+cyLNTRBIT4DwnZ5XOlcTvtPRSuPadMJ3GsNyKQII+EQglfDtk6n6chrsfPuyrUyqjgDh29xyIHwbsiV8G4JlWAR8IkD4truRhG+7+0f1jQsQvhs3auoVhO+myjXyPsK3IViGRcAnAoRvuxtJ+La7f1TfuADhu3Gjpl5B+G6qHOHbkBzDItAyBAjfdveZ8G13/6i+cQHCd+NGTb2C8N1UuQzCt6FbMiwCCFgmEBpwvAYNkwoLpJtGJBV76RHLZtByy03seBBPO2m57W8RMyd8m2sz4duQbUPHTgzdjmERQMBSgRcmxN3wvffudR4nmExKgeZ5/F9WGG2vP0WEz75I8KjBFK24zD4Bwre5nhG+DdnWF74N3YphEUAAAQQQQACBrAoQvrPKucpghG9DtoRvQ7AMiwACCCCAAALGBQjf5ogJ34ZsCd+GYBkWAQQQQAABBIwLEL7NERO+DdkSvg3BMiwCCCCAAAIIGBcgfJsjJnwbsnXCNy8EEEAAAQQQQMBGgb59Atp4A4s/+O1hdMK3h5tDaQgggAACCCCAAAL+EiB8+6ufzAYBBBBAAAEEEEDAwwKEbw83h9IQQAABBBBAAAEE/CVA+PZXP5kNAggggAACCCCAgIcFCN8ebg6lIYAAAggggAACCPhLgPDtr34yGwQQQAABBBBAAAEPCxC+PdwcSkMAAQQQQAABBBDwlwDh21/9ZDYIIIAAAggggAACHhYgfHu4OZSGAAIIIIAAAggg4C8Bwre/+slsEEAAAQQQQAABBDwsQPj2cHMoDQEEEEAAAQQQQMBfAoRvf/WT2SCAAAIIIIAAAgh4WIDw7eHmUBoCCCCAAAIIIICAvwQI3/7qJ7NBAAEEEEAAAQQQ8LAA4dvDzaE0BBBAAAEEEEAAAX8JEL791U9mgwACCCCAAAIIIOBhAcK3h5tDaQgggAACCCCAAAL+EiB8+6ufzAYBBBBAAAEEEEDAwwKEbw83h9IQQAABBBBAAAEE/CVA+PZXP5kNAggggAACCCCAgIcFCN8ebg6lIYAAAggggAACCPhLgPDtr34yGwQQQAABBBBAAAEPCxC+PdwcSkMAAQQQQAABBBDwlwDh21/9ZDYIIIAAAggggAACHhYgfHu4OZSGAAIIIIAAAggg4C8Bwre/+slsEEAAAQQQQAABBDwsQPj2cHMoDQEEEEAAAQQQQMBfAoRvf/WT2SCAAAIIIIAAAgh4WIDw7eHmUBoCCCCAAAIIIICAvwQI3/7qJ7NBAAEEEEAAAQQQ8LAA4dvDzaE0BBBAAAEEEEAAAX8JEL791U9mgwACCCCAAAIIIOBhAcK3h5tDaQgggAACCCCAAAL+EiB8+6ufzAYBBBBAAAEEEEDAwwKEbw83h9IQQAABBBBAAAEE/CVA+PZXP5kNAggggAACCCCAgIcFCN8ebg6lIYAAAggggAACCPhLgPDtr34yGwQQQAABBBBAAAEPCxC+PdwcSkMAAQQQQAABBBDwlwDh21/9ZDYIIIAAAggggAACHhYgfHu4OZSGAAIIIIAAAggg4C8Bwre/+slsEEAAAQQQQAABBDwsQPj2cHMoDQEEEEAAAQQQQMBfAoRvf/WT2SCAAAIIIIAAAgh4WIDw7eHmUBoCCCCAAAIIIICAvwQI3/7qJ7NBAAEEEEAAAQQQ8LAA4dvDzaE0BBBAAAEEEEAAAX8JEL791U9mgwACCCCAAAIIIOBhAcK3h5tDaQgggAACCCCAAAL+EiB8+6ufzAYBBBBAAAEEEEDAwwKEbw83h9IQQAABBBBAAAEE/CVA+PZXP5kNAggggAACCCCAgIcFCN8ebg6lIYAAAggggAACCPhLgPDtr34yGwQQQAABBBBAAAEPCxC+PdwcSkMAAQQQQFGEQbsAAAJMSURBVAABBBDwlwDh21/9ZDYIIIAAAggggAACHhYgfHu4OZSGAAIIIIAAAggg4C8Bwre/+slsEEAAAQQQQAABBDwsQPj2cHMoDQEEEEAAAQQQQMBfAoRvf/WT2SCAAAIIIIAAAgh4WIDw7eHmUBoCCCCAAAIIIICAvwQI3/7qJ7NBAAEEEEAAAQQQ8LAA4dvDzaE0BBBAAAEEEEAAAX8JEL791U9mgwACCCCAAAIIIOBhAcK3h5tDaQgggAACCCCAAAL+EiB8+6ufzAYBBBBAAAEEEEDAwwKEbw83h9IQQAABBBBAAAEE/CVA+PZXP5kNAggggAACCCCAgIcFCN8ebg6lIYAAAggggAACCPhLgPDtr34yGwQQQAABBBBAAAEPCxC+PdwcSkMAAQQQQAABBBDwlwDh21/9ZDYIIIAAAggggAACHhYgfHu4OZSGAAIIIIAAAggg4C8Bwre/+slsEEAAAQQQQAABBDwsQPj2cHMoDQEEEEAAAQQQQMBfAoRvf/WT2SCAAAIIIIAAAgh4WIDw7eHmUBoCCCCAAAIIIICAvwQI3/7qJ7NBAAEEEEAAAQQQ8LAA4dvDzaE0BBBAAAEEEEAAAX8JEL791U9mgwACCCCAAAIIIOBhAcK3h5tDaQgggAACCCCAAAL+EiB8+6ufzAYBBBBAAAEEEEDAwwKEbw83h9IQQAABBBBAAAEE/CVA+PZXP5kNAggggAACCCCAgIcFCN8ebg6lIYAAAggggAACCPhLgPDtr34yGwQQQAABBBBAAAEPC/w/MY2WIfAxXdYAAAAASUVORK5CYII=",
       "text/html": [
-       "<div>                            <div id=\"6cbc27dd-12e5-4228-87a6-b448bca59389\" 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(\"6cbc27dd-12e5-4228-87a6-b448bca59389\")) {                    Plotly.newPlot(                        \"6cbc27dd-12e5-4228-87a6-b448bca59389\",                        [{\"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\",\"#19d3f3\",\"#636efa\",\"#ab63fa\",\"#636efa\",\"#636efa\",\"#636efa\",\"#ab63fa\",\"#636efa\"]},\"name\":\"\",\"parents\":[\"\",\"Py_kgeneration\",\"generate\",\"Py_kgeneration\",\"fftma2\",\"gasdev\",\"covariance\",\"\",\"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},\"title\":{\"text\":\"Time treemap\"}},                        {\"responsive\": true}                    ).then(function(){\n",
+       "<div>                            <div id=\"1109b423-6acd-47fa-8105-1f774368a62d\" 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(\"1109b423-6acd-47fa-8105-1f774368a62d\")) {                    Plotly.newPlot(                        \"1109b423-6acd-47fa-8105-1f774368a62d\",                        [{\"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\", \"maxfactor\", \"build_real\", \"prebuild_gwn\", \"clean_real\"], \"marker\": {\"colors\": [\"#636efa\", \"#EF553B\", \"#00cc96\", \"#EF553B\", \"#ab63fa\", \"#FFA15A\", \"#19d3f3\", \"#ab63fa\", \"#636efa\", \"#636efa\", \"#636efa\", \"#636efa\", \"#ab63fa\", \"#636efa\"]}, \"name\": \"\", \"parents\": [\"\", \"Py_kgeneration\", \"generate\", \"Py_kgeneration\", \"fftma2\", \"gasdev\", \"covariance\", \"fftma2\", \"\", \"\", \"\", \"\", \"fftma2\", \"\"], \"type\": \"treemap\", \"values\": [4.518677, 3.283041, 2.4375840000000117, 1.231253, 1.223604, 0.7101290000000031, 0.48899400000001014, 0.002831, 0.001635, 0.001069, 0.000318, 0.000274, 0.000267, 0.000217]}],                        {\"legend\": {\"tracegroupgap\": 0}, \"template\": {\"data\": {\"bar\": [{\"error_x\": {\"color\": \"#2a3f5f\"}, \"error_y\": {\"color\": \"#2a3f5f\"}, \"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"bar\"}], \"barpolar\": [{\"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"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\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"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}}}, \"title\": {\"text\": \"Time treemap\"}},                        {\"responsive\": true}                    ).then(function(){\n",
        "                            \n",
-       "var gd = document.getElementById('6cbc27dd-12e5-4228-87a6-b448bca59389');\n",
+       "var gd = document.getElementById('1109b423-6acd-47fa-8105-1f774368a62d');\n",
        "var x = new MutationObserver(function (mutations, observer) {{\n",
        "        var display = window.getComputedStyle(gd).display;\n",
        "        if (!display || display === 'none') {{\n",
@@ -2863,7 +2837,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 124,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
@@ -2891,23 +2865,23 @@
        "  <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>mean</th>\n",
-       "      <th>sum</th>\n",
-       "      <th>count</th>\n",
+       "      <th>median</th>\n",
        "      <th>min</th>\n",
        "      <th>max</th>\n",
-       "      <th>median</th>\n",
+       "      <th>mean</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",
@@ -2927,243 +2901,243 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Py_kgeneration</th>\n",
-       "      <td>19.763131</td>\n",
-       "      <td>19.763131</td>\n",
-       "      <td>19.763131</td>\n",
-       "      <td>19.763131</td>\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>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>14.644248</td>\n",
-       "      <td>14.644248</td>\n",
-       "      <td>14.644248</td>\n",
-       "      <td>14.644248</td>\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>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>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>-14.9</td>\n",
+       "      <td>6.5</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>100.100000</td>\n",
-       "      <td>1.942624</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",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>fftma2</th>\n",
-       "      <td>5.116014</td>\n",
-       "      <td>5.116014</td>\n",
-       "      <td>5.116014</td>\n",
-       "      <td>5.116014</td>\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>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>5.103886</td>\n",
-       "      <td>5.103886</td>\n",
-       "      <td>5.103886</td>\n",
-       "      <td>5.103886</td>\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>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>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>-7.1</td>\n",
+       "      <td>2.6</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>100.100000</td>\n",
-       "      <td>0.405054</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",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>cov_value</th>\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>-4.1</td>\n",
+       "      <td>2.5</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>100.100000</td>\n",
-       "      <td>0.017077</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",
        "    </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>0.100000</td>\n",
-       "      <td>0.033333</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",
        "    </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>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</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",
        "    </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>0.000000</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",
        "    </tr>\n",
        "    <tr>\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",
+       "      <th>maxfactor</th>\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>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",
+       "      <th>build_real</th>\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",
-       "    </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>0.000403</td>\n",
+       "      <td>0.000403</td>\n",
+       "      <td>0.000403</td>\n",
+       "      <td>0.000403</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": [
-       "                     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",
+       "               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",
        "\n",
-       "                                   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  "
+       "                     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  "
       ]
      },
-     "execution_count": 124,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3175,12 +3149,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 125,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 600x600 with 4 Axes>"
       ]
@@ -3195,7 +3169,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 126,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [
     {
@@ -3228,10 +3202,10 @@
           "fourt",
           "cgrid",
           "length",
-          "build_real",
           "maxfactor",
-          "clean_real",
-          "prebuild_gwn"
+          "prebuild_gwn",
+          "build_real",
+          "clean_real"
          ],
          "marker": {
           "colors": [
@@ -3246,9 +3220,9 @@
            "#636efa",
            "#636efa",
            "#636efa",
+           "#ab63fa",
            "#636efa",
-           "#636efa",
-           "#ab63fa"
+           "#636efa"
           ]
          },
          "name": "",
@@ -3264,31 +3238,30 @@
           "",
           "",
           "",
+          "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
+          47.638534,
+          35.573628,
+          26.105192999998817,
+          12.06057,
+          12.036319,
+          7.93339000000048,
+          4.906137000000049,
+          0.016257,
+          0.003691,
+          0.0027419999999999996,
+          0.00091,
+          0.000599,
+          0.00051,
+          0.000403
          ]
         }
        ],
        "layout": {
-        "autosize": true,
         "legend": {
          "tracegroupgap": 0
         },
@@ -3306,11 +3279,6 @@
              "line": {
               "color": "#E5ECF6",
               "width": 0.5
-             },
-             "pattern": {
-              "fillmode": "overlay",
-              "size": 10,
-              "solidity": 0.2
              }
             },
             "type": "bar"
@@ -3322,11 +3290,6 @@
              "line": {
               "color": "#E5ECF6",
               "width": 0.5
-             },
-             "pattern": {
-              "fillmode": "overlay",
-              "size": 10,
-              "solidity": 0.2
              }
             },
             "type": "barpolar"
@@ -3525,10 +3488,9 @@
           "histogram": [
            {
             "marker": {
-             "pattern": {
-              "fillmode": "overlay",
-              "size": 10,
-              "solidity": 0.2
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
              }
             },
             "type": "histogram"
@@ -4114,11 +4076,10 @@
         }
        }
       },
-      "image/png": "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",
       "text/html": [
-       "<div>                            <div id=\"052597a7-fd5e-4223-b4cd-f76061de1ab1\" 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(\"052597a7-fd5e-4223-b4cd-f76061de1ab1\")) {                    Plotly.newPlot(                        \"052597a7-fd5e-4223-b4cd-f76061de1ab1\",                        [{\"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\",\"#19d3f3\",\"#ab63fa\",\"#636efa\",\"#636efa\",\"#636efa\",\"#636efa\",\"#636efa\",\"#ab63fa\"]},\"name\":\"\",\"parents\":[\"\",\"Py_kgeneration\",\"generate\",\"Py_kgeneration\",\"fftma2\",\"gasdev\",\"covariance\",\"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},\"title\":{\"text\":\"Time treemap\"}},                        {\"responsive\": true}                    ).then(function(){\n",
+       "<div>                            <div id=\"d5169320-a173-4c90-abbf-ab16fdb31b03\" 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(\"d5169320-a173-4c90-abbf-ab16fdb31b03\")) {                    Plotly.newPlot(                        \"d5169320-a173-4c90-abbf-ab16fdb31b03\",                        [{\"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\", \"maxfactor\", \"prebuild_gwn\", \"build_real\", \"clean_real\"], \"marker\": {\"colors\": [\"#636efa\", \"#EF553B\", \"#00cc96\", \"#EF553B\", \"#ab63fa\", \"#FFA15A\", \"#19d3f3\", \"#ab63fa\", \"#636efa\", \"#636efa\", \"#636efa\", \"#ab63fa\", \"#636efa\", \"#636efa\"]}, \"name\": \"\", \"parents\": [\"\", \"Py_kgeneration\", \"generate\", \"Py_kgeneration\", \"fftma2\", \"gasdev\", \"covariance\", \"fftma2\", \"\", \"\", \"\", \"fftma2\", \"\", \"\"], \"type\": \"treemap\", \"values\": [47.638534, 35.573628, 26.105192999998817, 12.06057, 12.036319, 7.93339000000048, 4.906137000000049, 0.016257, 0.003691, 0.0027419999999999996, 0.00091, 0.000599, 0.00051, 0.000403]}],                        {\"legend\": {\"tracegroupgap\": 0}, \"template\": {\"data\": {\"bar\": [{\"error_x\": {\"color\": \"#2a3f5f\"}, \"error_y\": {\"color\": \"#2a3f5f\"}, \"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"bar\"}], \"barpolar\": [{\"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"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\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"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}}}, \"title\": {\"text\": \"Time treemap\"}},                        {\"responsive\": true}                    ).then(function(){\n",
        "                            \n",
-       "var gd = document.getElementById('052597a7-fd5e-4223-b4cd-f76061de1ab1');\n",
+       "var gd = document.getElementById('d5169320-a173-4c90-abbf-ab16fdb31b03');\n",
        "var x = new MutationObserver(function (mutations, observer) {{\n",
        "        var display = window.getComputedStyle(gd).display;\n",
        "        if (!display || display === 'none') {{\n",
@@ -4160,7 +4121,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 127,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [
     {
@@ -4196,23 +4157,23 @@
        "  <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>max</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>min</th>\n",
+       "      <th>sum</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>function</th>\n",
@@ -4232,243 +4193,243 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Py_kgeneration</th>\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>32.278779</td>\n",
+       "      <td>32.278779</td>\n",
+       "      <td>32.278779</td>\n",
+       "      <td>-199.3</td>\n",
+       "      <td>-199.3</td>\n",
+       "      <td>-199.3</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>19.901069</td>\n",
-       "      <td>19.901069</td>\n",
-       "      <td>173.931290</td>\n",
+       "      <td>415.639768</td>\n",
+       "      <td>415.639768</td>\n",
+       "      <td>415.639768</td>\n",
+       "      <td>415.639768</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>generate</th>\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>32.749276</td>\n",
+       "      <td>32.749276</td>\n",
+       "      <td>32.749276</td>\n",
+       "      <td>-281.6</td>\n",
+       "      <td>-281.6</td>\n",
+       "      <td>-281.6</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>23.489213</td>\n",
-       "      <td>23.489213</td>\n",
-       "      <td>137.192987</td>\n",
+       "      <td>329.579564</td>\n",
+       "      <td>329.579564</td>\n",
+       "      <td>329.579564</td>\n",
+       "      <td>329.579564</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>fftma2</th>\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>30.472359</td>\n",
+       "      <td>30.472359</td>\n",
+       "      <td>30.472359</td>\n",
+       "      <td>85.3</td>\n",
+       "      <td>85.3</td>\n",
+       "      <td>85.3</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>6.498040</td>\n",
-       "      <td>6.498040</td>\n",
-       "      <td>36.737828</td>\n",
+       "      <td>86.059132</td>\n",
+       "      <td>86.059132</td>\n",
+       "      <td>86.059132</td>\n",
+       "      <td>86.059132</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>covariance</th>\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>30.505800</td>\n",
+       "      <td>30.505800</td>\n",
+       "      <td>30.505800</td>\n",
+       "      <td>80.9</td>\n",
+       "      <td>80.9</td>\n",
+       "      <td>80.9</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>6.515506</td>\n",
-       "      <td>6.515506</td>\n",
-       "      <td>36.638978</td>\n",
+       "      <td>85.894170</td>\n",
+       "      <td>85.894170</td>\n",
+       "      <td>85.894170</td>\n",
+       "      <td>85.894170</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>gasdev</th>\n",
-       "      <td>-54.9</td>\n",
-       "      <td>11.1</td>\n",
+       "      <td>100.100000</td>\n",
+       "      <td>2.395994</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>8.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.000076</td>\n",
-       "      <td>0.007025</td>\n",
-       "      <td>0.000455</td>\n",
-       "      <td>101.718838</td>\n",
+       "      <td>-61.2</td>\n",
        "      <td>262144.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>100.100000</td>\n",
-       "      <td>0.000455</td>\n",
+       "      <td>0.011277</td>\n",
+       "      <td>0.000909</td>\n",
+       "      <td>0.000193</td>\n",
+       "      <td>242.610944</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>fourt</th>\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>25.100000</td>\n",
+       "      <td>8.433333</td>\n",
        "      <td>0.100000</td>\n",
-       "      <td>0.100000</td>\n",
-       "      <td>0.030275</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>-0.2</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",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>cov_value</th>\n",
-       "      <td>-7.4</td>\n",
-       "      <td>2.0</td>\n",
+       "      <td>100.100000</td>\n",
+       "      <td>0.572549</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.000086</td>\n",
-       "      <td>0.000268</td>\n",
-       "      <td>0.000099</td>\n",
-       "      <td>15.471843</td>\n",
+       "      <td>-7.1</td>\n",
        "      <td>156816.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>100.100000</td>\n",
-       "      <td>0.000099</td>\n",
+       "      <td>0.000575</td>\n",
+       "      <td>0.000221</td>\n",
+       "      <td>0.000193</td>\n",
+       "      <td>34.674709</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ran2</th>\n",
-       "      <td>-17.0</td>\n",
-       "      <td>3.4</td>\n",
+       "      <td>100.100000</td>\n",
+       "      <td>0.527507</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.7</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.000075</td>\n",
-       "      <td>0.002592</td>\n",
-       "      <td>0.000112</td>\n",
-       "      <td>31.860609</td>\n",
+       "      <td>-17.8</td>\n",
        "      <td>333450.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>100.100000</td>\n",
-       "      <td>0.000112</td>\n",
+       "      <td>0.000691</td>\n",
+       "      <td>0.000218</td>\n",
+       "      <td>0.000190</td>\n",
+       "      <td>74.079918</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>build_real</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</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>0.100000</td>\n",
-       "      <td>0.100000</td>\n",
-       "      <td>0.003170</td>\n",
+       "      <td>0.001935</td>\n",
+       "      <td>0.001935</td>\n",
+       "      <td>0.001935</td>\n",
+       "      <td>0.001935</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>prebuild_gwn</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</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>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.001027</td>\n",
+       "      <td>0.002618</td>\n",
+       "      <td>0.002618</td>\n",
+       "      <td>0.002618</td>\n",
+       "      <td>0.002618</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>0.000000</td>\n",
-       "      <td>0.000707</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",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>cgrid</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</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>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.001641</td>\n",
+       "      <td>0.003504</td>\n",
+       "      <td>0.003504</td>\n",
+       "      <td>0.003504</td>\n",
+       "      <td>0.003504</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>length</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</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>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000384</td>\n",
+       "      <td>0.001141</td>\n",
+       "      <td>0.000827</td>\n",
+       "      <td>0.000668</td>\n",
+       "      <td>0.002481</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>maxfactor</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</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>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000093</td>\n",
+       "      <td>0.000198</td>\n",
+       "      <td>0.000195</td>\n",
+       "      <td>0.000193</td>\n",
+       "      <td>0.000780</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                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",
+       "                       cpu                       memory                \\\n",
+       "                       max       mean        min    max median    min   \n",
+       "function                                                                \n",
+       "Py_kgeneration   32.278779  32.278779  32.278779 -199.3 -199.3 -199.3   \n",
+       "generate         32.749276  32.749276  32.749276 -281.6 -281.6 -281.6   \n",
+       "fftma2           30.472359  30.472359  30.472359   85.3   85.3   85.3   \n",
+       "covariance       30.505800  30.505800  30.505800   80.9   80.9   80.9   \n",
+       "gasdev          100.100000   2.395994   0.000000    8.0    0.0  -61.2   \n",
+       "fourt            25.100000   8.433333   0.100000    2.3    0.4   -0.2   \n",
+       "cov_value       100.100000   0.572549   0.000000    3.0    0.0   -7.1   \n",
+       "ran2            100.100000   0.527507   0.000000    2.7    0.0  -17.8   \n",
+       "build_real        0.000000   0.000000   0.000000    0.0    0.0    0.0   \n",
+       "prebuild_gwn      0.000000   0.000000   0.000000    2.2    2.2    2.2   \n",
+       "clean_real        0.000000   0.000000   0.000000    0.4    0.4    0.4   \n",
+       "cgrid             0.000000   0.000000   0.000000    0.0    0.0    0.0   \n",
+       "length            0.000000   0.000000   0.000000    0.0    0.0    0.0   \n",
+       "maxfactor         0.000000   0.000000   0.000000    0.0    0.0    0.0   \n",
        "\n",
-       "                                            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  "
+       "                    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  "
       ]
      },
-     "execution_count": 127,
+     "execution_count": 60,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7412,21 +7373,21 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 3
+    "version": 2
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.9"
+   "pygments_lexer": "ipython2",
+   "version": "2.7.18"
   }
  },
  "nbformat": 4,