From aaf99c456ab6466a00f38f6c21cc6ac7cb8cb3af Mon Sep 17 00:00:00 2001
From: mdtanker <matt.d.tankersley@gmail.com>
Date: Tue, 19 Nov 2024 15:59:11 -0500
Subject: [PATCH] docs: minor updates

---
 docs/examples/bishop_basement_model.ipynb   |    3 +-
 docs/tutorial/09_uncertainty_analysis.ipynb | 1853 ++++++++-----------
 2 files changed, 783 insertions(+), 1073 deletions(-)

diff --git a/docs/examples/bishop_basement_model.ipynb b/docs/examples/bishop_basement_model.ipynb
index 2f04029..0acbb74 100644
--- a/docs/examples/bishop_basement_model.ipynb
+++ b/docs/examples/bishop_basement_model.ipynb
@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2024-07-22T22:22:21.519589Z",
@@ -45,6 +45,7 @@
     "from polartoolkit import utils as polar_utils\n",
     "\n",
     "from invert4geom import (\n",
+    "    cross_validation,\n",
     "    inversion,\n",
     "    optimization,\n",
     "    plotting,\n",
diff --git a/docs/tutorial/09_uncertainty_analysis.ipynb b/docs/tutorial/09_uncertainty_analysis.ipynb
index 5b3bd81..038b52b 100644
--- a/docs/tutorial/09_uncertainty_analysis.ipynb
+++ b/docs/tutorial/09_uncertainty_analysis.ipynb
@@ -13,7 +13,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Import packages"
+    "## Import packages"
    ]
   },
   {
@@ -154,7 +154,7 @@
        "      <td>1271.000000</td>\n",
        "      <td>1271.000000</td>\n",
        "      <td>1271.000000</td>\n",
-       "      <td>1271.000000</td>\n",
+       "      <td>1.271000e+03</td>\n",
        "      <td>1271.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -162,112 +162,112 @@
        "      <td>15000.000000</td>\n",
        "      <td>20000.00000</td>\n",
        "      <td>1001.0</td>\n",
-       "      <td>-0.129619</td>\n",
-       "      <td>0.027052</td>\n",
-       "      <td>-0.102566</td>\n",
-       "      <td>-0.102566</td>\n",
+       "      <td>-0.128924</td>\n",
+       "      <td>0.004007</td>\n",
+       "      <td>-0.124917</td>\n",
+       "      <td>-0.124917</td>\n",
        "      <td>0.1</td>\n",
-       "      <td>1.079930</td>\n",
-       "      <td>-1.209548</td>\n",
-       "      <td>-1.182496</td>\n",
-       "      <td>-1.312453</td>\n",
-       "      <td>0.129957</td>\n",
+       "      <td>1.130048</td>\n",
+       "      <td>-1.258972</td>\n",
+       "      <td>-1.254965</td>\n",
+       "      <td>-7.021979e-01</td>\n",
+       "      <td>-0.552767</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td>8947.792584</td>\n",
        "      <td>11836.81698</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>7.147140</td>\n",
-       "      <td>2.155198</td>\n",
-       "      <td>7.753708</td>\n",
-       "      <td>7.752822</td>\n",
+       "      <td>7.128555</td>\n",
+       "      <td>0.714063</td>\n",
+       "      <td>7.271000</td>\n",
+       "      <td>7.269461</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>6.599125</td>\n",
-       "      <td>4.456456</td>\n",
-       "      <td>5.206855</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>5.206855</td>\n",
+       "      <td>6.906876</td>\n",
+       "      <td>4.602393</td>\n",
+       "      <td>4.828938</td>\n",
+       "      <td>1.110660e-16</td>\n",
+       "      <td>4.828938</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.00000</td>\n",
        "      <td>1001.0</td>\n",
-       "      <td>-17.180401</td>\n",
-       "      <td>-3.056951</td>\n",
-       "      <td>-16.600946</td>\n",
-       "      <td>-16.638689</td>\n",
+       "      <td>-17.179165</td>\n",
+       "      <td>-1.033526</td>\n",
+       "      <td>-16.887669</td>\n",
+       "      <td>-17.040892</td>\n",
        "      <td>0.1</td>\n",
-       "      <td>-7.883841</td>\n",
-       "      <td>-19.211428</td>\n",
-       "      <td>-20.378081</td>\n",
-       "      <td>-1.312453</td>\n",
-       "      <td>-19.065628</td>\n",
+       "      <td>-8.029374</td>\n",
+       "      <td>-19.592049</td>\n",
+       "      <td>-19.972714</td>\n",
+       "      <td>-7.021979e-01</td>\n",
+       "      <td>-19.270516</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td>7000.000000</td>\n",
        "      <td>10000.00000</td>\n",
        "      <td>1001.0</td>\n",
-       "      <td>-5.537833</td>\n",
-       "      <td>-1.791999</td>\n",
-       "      <td>-6.495998</td>\n",
-       "      <td>-6.438877</td>\n",
+       "      <td>-5.448853</td>\n",
+       "      <td>-0.601914</td>\n",
+       "      <td>-5.750137</td>\n",
+       "      <td>-5.739415</td>\n",
        "      <td>0.1</td>\n",
-       "      <td>-3.944309</td>\n",
-       "      <td>-2.605854</td>\n",
-       "      <td>-3.679587</td>\n",
-       "      <td>-1.312453</td>\n",
-       "      <td>-2.367134</td>\n",
+       "      <td>-4.384273</td>\n",
+       "      <td>-3.001264</td>\n",
+       "      <td>-3.103592</td>\n",
+       "      <td>-7.021979e-01</td>\n",
+       "      <td>-2.401394</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td>15000.000000</td>\n",
        "      <td>20000.00000</td>\n",
        "      <td>1001.0</td>\n",
-       "      <td>-1.136301</td>\n",
-       "      <td>-0.467464</td>\n",
-       "      <td>-0.043199</td>\n",
-       "      <td>-0.069880</td>\n",
+       "      <td>-1.160222</td>\n",
+       "      <td>-0.153040</td>\n",
+       "      <td>-0.758131</td>\n",
+       "      <td>-0.713654</td>\n",
        "      <td>0.1</td>\n",
-       "      <td>-0.765707</td>\n",
-       "      <td>-1.096958</td>\n",
-       "      <td>-1.716714</td>\n",
-       "      <td>-1.312453</td>\n",
-       "      <td>-0.404261</td>\n",
+       "      <td>-0.620986</td>\n",
+       "      <td>-1.444045</td>\n",
+       "      <td>-1.845960</td>\n",
+       "      <td>-7.021979e-01</td>\n",
+       "      <td>-1.143762</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td>23000.000000</td>\n",
        "      <td>30000.00000</td>\n",
        "      <td>1001.0</td>\n",
-       "      <td>3.743994</td>\n",
-       "      <td>1.619375</td>\n",
-       "      <td>5.174720</td>\n",
-       "      <td>5.179023</td>\n",
+       "      <td>3.675021</td>\n",
+       "      <td>0.541706</td>\n",
+       "      <td>3.825804</td>\n",
+       "      <td>3.815224</td>\n",
        "      <td>0.1</td>\n",
-       "      <td>4.823461</td>\n",
-       "      <td>0.340265</td>\n",
-       "      <td>1.410374</td>\n",
-       "      <td>-1.312453</td>\n",
-       "      <td>2.722826</td>\n",
+       "      <td>5.463603</td>\n",
+       "      <td>0.461464</td>\n",
+       "      <td>0.793943</td>\n",
+       "      <td>-7.021979e-01</td>\n",
+       "      <td>1.496141</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td>30000.000000</td>\n",
        "      <td>40000.00000</td>\n",
        "      <td>1001.0</td>\n",
-       "      <td>19.581120</td>\n",
-       "      <td>4.927227</td>\n",
-       "      <td>20.083561</td>\n",
-       "      <td>20.253775</td>\n",
+       "      <td>19.599674</td>\n",
+       "      <td>1.599823</td>\n",
+       "      <td>19.781652</td>\n",
+       "      <td>19.936108</td>\n",
        "      <td>0.1</td>\n",
-       "      <td>22.159800</td>\n",
-       "      <td>12.728748</td>\n",
-       "      <td>13.698727</td>\n",
-       "      <td>-1.312453</td>\n",
-       "      <td>15.011180</td>\n",
+       "      <td>23.315668</td>\n",
+       "      <td>13.969991</td>\n",
+       "      <td>14.301612</td>\n",
+       "      <td>-7.021979e-01</td>\n",
+       "      <td>15.003810</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -276,33 +276,33 @@
       "text/plain": [
        "           northing      easting  upward  upper_surface_grav  \\\n",
        "count   1271.000000   1271.00000  1271.0         1271.000000   \n",
-       "mean   15000.000000  20000.00000  1001.0           -0.129619   \n",
-       "std     8947.792584  11836.81698     0.0            7.147140   \n",
-       "min        0.000000      0.00000  1001.0          -17.180401   \n",
-       "25%     7000.000000  10000.00000  1001.0           -5.537833   \n",
-       "50%    15000.000000  20000.00000  1001.0           -1.136301   \n",
-       "75%    23000.000000  30000.00000  1001.0            3.743994   \n",
-       "max    30000.000000  40000.00000  1001.0           19.581120   \n",
+       "mean   15000.000000  20000.00000  1001.0           -0.128924   \n",
+       "std     8947.792584  11836.81698     0.0            7.128555   \n",
+       "min        0.000000      0.00000  1001.0          -17.179165   \n",
+       "25%     7000.000000  10000.00000  1001.0           -5.448853   \n",
+       "50%    15000.000000  20000.00000  1001.0           -1.160222   \n",
+       "75%    23000.000000  30000.00000  1001.0            3.675021   \n",
+       "max    30000.000000  40000.00000  1001.0           19.599674   \n",
        "\n",
        "       lower_surface_grav  observed_grav  gravity_anomaly  uncert  \\\n",
        "count         1271.000000    1271.000000      1271.000000  1271.0   \n",
-       "mean             0.027052      -0.102566        -0.102566     0.1   \n",
-       "std              2.155198       7.753708         7.752822     0.0   \n",
-       "min             -3.056951     -16.600946       -16.638689     0.1   \n",
-       "25%             -1.791999      -6.495998        -6.438877     0.1   \n",
-       "50%             -0.467464      -0.043199        -0.069880     0.1   \n",
-       "75%              1.619375       5.174720         5.179023     0.1   \n",
-       "max              4.927227      20.083561        20.253775     0.1   \n",
-       "\n",
-       "       starting_gravity     true_res       misfit          reg          res  \n",
-       "count       1271.000000  1271.000000  1271.000000  1271.000000  1271.000000  \n",
-       "mean           1.079930    -1.209548    -1.182496    -1.312453     0.129957  \n",
-       "std            6.599125     4.456456     5.206855     0.000000     5.206855  \n",
-       "min           -7.883841   -19.211428   -20.378081    -1.312453   -19.065628  \n",
-       "25%           -3.944309    -2.605854    -3.679587    -1.312453    -2.367134  \n",
-       "50%           -0.765707    -1.096958    -1.716714    -1.312453    -0.404261  \n",
-       "75%            4.823461     0.340265     1.410374    -1.312453     2.722826  \n",
-       "max           22.159800    12.728748    13.698727    -1.312453    15.011180  "
+       "mean             0.004007      -0.124917        -0.124917     0.1   \n",
+       "std              0.714063       7.271000         7.269461     0.0   \n",
+       "min             -1.033526     -16.887669       -17.040892     0.1   \n",
+       "25%             -0.601914      -5.750137        -5.739415     0.1   \n",
+       "50%             -0.153040      -0.758131        -0.713654     0.1   \n",
+       "75%              0.541706       3.825804         3.815224     0.1   \n",
+       "max              1.599823      19.781652        19.936108     0.1   \n",
+       "\n",
+       "       starting_gravity     true_res       misfit           reg          res  \n",
+       "count       1271.000000  1271.000000  1271.000000  1.271000e+03  1271.000000  \n",
+       "mean           1.130048    -1.258972    -1.254965 -7.021979e-01    -0.552767  \n",
+       "std            6.906876     4.602393     4.828938  1.110660e-16     4.828938  \n",
+       "min           -8.029374   -19.592049   -19.972714 -7.021979e-01   -19.270516  \n",
+       "25%           -4.384273    -3.001264    -3.103592 -7.021979e-01    -2.401394  \n",
+       "50%           -0.620986    -1.444045    -1.845960 -7.021979e-01    -1.143762  \n",
+       "75%            5.463603     0.461464     0.793943 -7.021979e-01     1.496141  \n",
+       "max           23.315668    13.969991    14.301612 -7.021979e-01    15.003810  "
       ]
      },
      "execution_count": 3,
@@ -365,71 +365,71 @@
        "      <th>mean</th>\n",
        "      <td>18971.649226</td>\n",
        "      <td>16518.655913</td>\n",
-       "      <td>486.549468</td>\n",
-       "      <td>484.562022</td>\n",
-       "      <td>9.730989</td>\n",
-       "      <td>0.011213</td>\n",
-       "      <td>487.090278</td>\n",
+       "      <td>489.096872</td>\n",
+       "      <td>487.025168</td>\n",
+       "      <td>9.781937</td>\n",
+       "      <td>0.011132</td>\n",
+       "      <td>489.043995</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td>10839.669022</td>\n",
        "      <td>8540.046257</td>\n",
-       "      <td>76.458294</td>\n",
-       "      <td>78.839833</td>\n",
-       "      <td>1.529166</td>\n",
-       "      <td>0.002999</td>\n",
-       "      <td>81.264380</td>\n",
+       "      <td>79.152932</td>\n",
+       "      <td>81.118110</td>\n",
+       "      <td>1.583059</td>\n",
+       "      <td>0.003039</td>\n",
+       "      <td>81.974472</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td>3505.581182</td>\n",
        "      <td>1697.178371</td>\n",
-       "      <td>407.011208</td>\n",
-       "      <td>395.058844</td>\n",
-       "      <td>8.140224</td>\n",
-       "      <td>0.005825</td>\n",
-       "      <td>390.350078</td>\n",
+       "      <td>406.379413</td>\n",
+       "      <td>405.305719</td>\n",
+       "      <td>8.127588</td>\n",
+       "      <td>0.005624</td>\n",
+       "      <td>399.717642</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td>11575.107009</td>\n",
        "      <td>10553.975772</td>\n",
-       "      <td>420.851161</td>\n",
-       "      <td>422.696570</td>\n",
-       "      <td>8.417023</td>\n",
-       "      <td>0.009493</td>\n",
-       "      <td>421.465206</td>\n",
+       "      <td>429.926990</td>\n",
+       "      <td>424.663596</td>\n",
+       "      <td>8.598540</td>\n",
+       "      <td>0.009240</td>\n",
+       "      <td>431.849241</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td>19995.535031</td>\n",
        "      <td>16357.717739</td>\n",
-       "      <td>470.155613</td>\n",
-       "      <td>475.672915</td>\n",
-       "      <td>9.403112</td>\n",
-       "      <td>0.011310</td>\n",
-       "      <td>469.886386</td>\n",
+       "      <td>454.924262</td>\n",
+       "      <td>458.214230</td>\n",
+       "      <td>9.098485</td>\n",
+       "      <td>0.012080</td>\n",
+       "      <td>455.590917</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td>27651.208305</td>\n",
        "      <td>24233.535276</td>\n",
-       "      <td>513.202664</td>\n",
-       "      <td>507.374482</td>\n",
-       "      <td>10.264053</td>\n",
-       "      <td>0.014116</td>\n",
-       "      <td>512.821218</td>\n",
+       "      <td>520.183444</td>\n",
+       "      <td>514.207570</td>\n",
+       "      <td>10.403669</td>\n",
+       "      <td>0.013525</td>\n",
+       "      <td>524.127350</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td>38163.601456</td>\n",
        "      <td>27603.626012</td>\n",
-       "      <td>655.102400</td>\n",
-       "      <td>656.749723</td>\n",
-       "      <td>13.102048</td>\n",
-       "      <td>0.015091</td>\n",
-       "      <td>659.704598</td>\n",
+       "      <td>666.725645</td>\n",
+       "      <td>668.402196</td>\n",
+       "      <td>13.334513</td>\n",
+       "      <td>0.015138</td>\n",
+       "      <td>665.539254</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -438,23 +438,23 @@
       "text/plain": [
        "            easting      northing  true_upward      upward     uncert  \\\n",
        "count     15.000000     15.000000    15.000000   15.000000  15.000000   \n",
-       "mean   18971.649226  16518.655913   486.549468  484.562022   9.730989   \n",
-       "std    10839.669022   8540.046257    76.458294   78.839833   1.529166   \n",
-       "min     3505.581182   1697.178371   407.011208  395.058844   8.140224   \n",
-       "25%    11575.107009  10553.975772   420.851161  422.696570   8.417023   \n",
-       "50%    19995.535031  16357.717739   470.155613  475.672915   9.403112   \n",
-       "75%    27651.208305  24233.535276   513.202664  507.374482  10.264053   \n",
-       "max    38163.601456  27603.626012   655.102400  656.749723  13.102048   \n",
+       "mean   18971.649226  16518.655913   489.096872  487.025168   9.781937   \n",
+       "std    10839.669022   8540.046257    79.152932   81.118110   1.583059   \n",
+       "min     3505.581182   1697.178371   406.379413  405.305719   8.127588   \n",
+       "25%    11575.107009  10553.975772   429.926990  424.663596   8.598540   \n",
+       "50%    19995.535031  16357.717739   454.924262  458.214230   9.098485   \n",
+       "75%    27651.208305  24233.535276   520.183444  514.207570  10.403669   \n",
+       "max    38163.601456  27603.626012   666.725645  668.402196  13.334513   \n",
        "\n",
        "          weight  inverted_topography  \n",
        "count  15.000000            15.000000  \n",
-       "mean    0.011213           487.090278  \n",
-       "std     0.002999            81.264380  \n",
-       "min     0.005825           390.350078  \n",
-       "25%     0.009493           421.465206  \n",
-       "50%     0.011310           469.886386  \n",
-       "75%     0.014116           512.821218  \n",
-       "max     0.015091           659.704598  "
+       "mean    0.011132           489.043995  \n",
+       "std     0.003039            81.974472  \n",
+       "min     0.005624           399.717642  \n",
+       "25%     0.009240           431.849241  \n",
+       "50%     0.012080           455.590917  \n",
+       "75%     0.013525           524.127350  \n",
+       "max     0.015138           665.539254  "
       ]
      },
      "execution_count": 4,
@@ -521,45 +521,45 @@
        " 'grid_method': 'eq_sources',\n",
        " 'constraints_weights_column': 'weight',\n",
        " 'cv': True,\n",
-       " 'cv_kwargs': {'n_trials': 100,\n",
-       "  'damping_limits': (1e-20, 10),\n",
-       "  'depth_limits': (100, 100000.0),\n",
+       " 'cv_kwargs': {'n_trials': 200,\n",
+       "  'damping_limits': (1e-10, 1),\n",
        "  'progressbar': False,\n",
        "  'fname': 'tmp/regional_sep'},\n",
+       " 'depth': 'default',\n",
        " 'block_size': None,\n",
        " 'constraints_df':          easting      northing  true_upward      upward     uncert    weight  \\\n",
-       " 0    3899.714996  26468.618776   655.102400  656.749723  13.102048  0.005825   \n",
-       " 1   30636.914105   6974.789900   407.011208  405.935844   8.140224  0.015091   \n",
-       " 2   17659.550795  13659.470931   470.155613  476.177579   9.403112  0.011310   \n",
-       " 3   28491.676758  27073.768551   515.239653  516.320627  10.304793  0.009417   \n",
-       " 4   38163.601456   1697.178371   419.197817  414.706788   8.383956  0.014227   \n",
-       " 5   21462.843076  17815.369689   472.257595  475.672915   9.445152  0.011209   \n",
-       " 6   20042.577619  27603.626012   417.272882  428.155359   8.345458  0.014358   \n",
-       " 7    3737.943068   7448.480613   578.101458  589.051636  11.562029  0.007481   \n",
-       " 8   11200.681244  16357.717739   497.531876  490.529262   9.950638  0.010099   \n",
-       " 9   19995.535031  26455.594497   460.766046  449.104781   9.215321  0.011775   \n",
-       " 10  26810.739853   4728.744481   422.504506  417.237780   8.450090  0.014005   \n",
-       " 11  31542.083372  15655.552259   445.703156  446.071538   8.914063  0.012585   \n",
-       " 12  15475.763060  22011.476055   414.325222  395.058844   8.286504  0.014563   \n",
-       " 13   3505.581182  19732.370745   611.906912  609.229309  12.238138  0.006677   \n",
-       " 14  11949.532774  14097.080073   511.165674  498.428336  10.223313  0.009568   \n",
+       " 0    3899.714996  26468.618776   666.725645  668.402196  13.334513  0.005624   \n",
+       " 1   30636.914105   6974.789900   406.379413  405.305719   8.127588  0.015138   \n",
+       " 2   17659.550795  13659.470931   480.393921  486.547024   9.607878  0.010833   \n",
+       " 3   28491.676758  27073.768551   517.183455  518.268507  10.343669  0.009347   \n",
+       " 4   38163.601456   1697.178371   435.459979  430.794727   8.709200  0.013184   \n",
+       " 5   21462.843076  17815.369689   454.924262  458.214230   9.098485  0.012080   \n",
+       " 6   20042.577619  27603.626012   411.155008  421.877931   8.223100  0.014789   \n",
+       " 7    3737.943068   7448.480613   582.902996  593.944123  11.658060  0.007358   \n",
+       " 8   11200.681244  16357.717739   511.624141  504.423182  10.232483  0.009551   \n",
+       " 9   19995.535031  26455.594497   450.505648  439.104058   9.010113  0.012318   \n",
+       " 10  26810.739853   4728.744481   430.317216  424.953101   8.606344  0.013501   \n",
+       " 11  31542.083372  15655.552259   424.023626  424.374090   8.480473  0.013905   \n",
+       " 12  15475.763060  22011.476055   429.536764  409.563040   8.590735  0.013550   \n",
+       " 13   3505.581182  19732.370745   612.137575  609.458963  12.242752  0.006672   \n",
+       " 14  11949.532774  14097.080073   523.183432  510.146633  10.463669  0.009133   \n",
        " \n",
        "     inverted_topography  \n",
-       " 0            659.704598  \n",
-       " 1            390.350078  \n",
-       " 2            469.886386  \n",
-       " 3            511.487068  \n",
-       " 4            419.051467  \n",
-       " 5            479.203723  \n",
-       " 6            407.377421  \n",
-       " 7            595.654726  \n",
-       " 8            505.083045  \n",
-       " 9            453.873191  \n",
-       " 10           423.878946  \n",
-       " 11           446.912631  \n",
-       " 12           414.208783  \n",
-       " 13           615.526747  \n",
-       " 14           514.155368  }"
+       " 0            665.539254  \n",
+       " 1            399.717642  \n",
+       " 2            482.225863  \n",
+       " 3            501.636547  \n",
+       " 4            435.562397  \n",
+       " 5            455.590917  \n",
+       " 6            405.403138  \n",
+       " 7            596.269994  \n",
+       " 8            519.832060  \n",
+       " 9            443.764228  \n",
+       " 10           431.656105  \n",
+       " 11           420.550320  \n",
+       " 12           432.042377  \n",
+       " 13           617.446443  \n",
+       " 14           528.422640  }"
       ]
      },
      "execution_count": 8,
@@ -683,26 +683,24 @@
     {
      "data": {
       "text/plain": [
-       "{'Density contrast(s)': '[2644.2598596] kg/m3',\n",
-       " 'Reference level': '484.56202153982827 m',\n",
+       "{'Density contrast(s)': '[2857.9296283] kg/m3',\n",
+       " 'Reference level': '487.0251682643297 m',\n",
        " 'Max iterations': 200,\n",
        " 'L2 norm tolerance': '0.3',\n",
        " 'Delta L2 norm tolerance': '1.008',\n",
        " 'Deriv type': 'annulus',\n",
        " 'Solver type': 'scipy least squares',\n",
-       " 'Solver damping': 0.010453883574576146,\n",
+       " 'Solver damping': 0.021017217578137074,\n",
        " 'Upper confining layer': 'Not enabled',\n",
        " 'Lower confining layer': 'Not enabled',\n",
        " 'Regularization weighting grid': 'Not enabled',\n",
-       " 'Time elapsed': '2 seconds',\n",
-       " 'Avg. iteration time': '0.48 seconds',\n",
-       " 'Final misfit RMSE / L2-norm': '0.0887 /0.2978 mGal',\n",
+       " 'Time elapsed': '0 seconds',\n",
+       " 'Avg. iteration time': '0.14 seconds',\n",
+       " 'Final misfit RMSE / L2-norm': '0.0876 /0.296 mGal',\n",
        " 'Termination reason': ['l2-norm tolerance'],\n",
-       " 'Iteration times': [1.0738378850073786,\n",
-       "  0.277678433994879,\n",
-       "  0.4196646670025075,\n",
-       "  0.2817220750002889,\n",
-       "  0.34515466999437194]}"
+       " 'Iteration times': [0.13145220704609528,\n",
+       "  0.15002987801562995,\n",
+       "  0.14759599702665582]}"
       ]
      },
      "execution_count": 9,
@@ -723,11 +721,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Density contrast: 2644.2598596 kg/m3\n",
-      "Reference level: 484.56202153982827 m\n",
+      "Density contrast: 2857.9296283 kg/m3\n",
+      "Reference level: 487.0251682643297 m\n",
       "Spacing: 1000.0 m\n",
       "Region: (np.float64(0.0), np.float64(40000.0), np.float64(0.0), np.float64(30000.0))\n",
-      "Solver damping: 0.010453883574576146\n"
+      "Solver damping: 0.021017217578137074\n"
      ]
     }
    ],
@@ -752,7 +750,7 @@
    "outputs": [
     {
      "data": {
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       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -766,7 +764,7 @@
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lfaTDda7Dc7XPAh1ilpJ/La0R0DFm0+mnPb5jZ+vI6Y8/2DKSeN0hcTrD0lJ7wolX2eFRiykDQgjHPuW0R1vT25wYScYsJmbMxLnpEJN89raYpdBs+xlz14eEEE4447R9Dz60cuT4p7SPnJAY6ZRe8kUlYhIF6LaYDodNfPSThz3xjNP3PLhj5ciTn9I+EnOco+l9+eDDoZtarabBNFBcStZK1k8krGR9NJn2ESXr1V6CkvWKhJWsf5iwknXHJ+o8omQdQlCyXvvIStY/PIiS9RMjStZHKVkvJ6xk/aMYJevOB1GyXmNEyRpCCImKErSKbNIRQtgwdcKJJ10YVJBXT1gF+UcjKsjLf1VBXjGigvxETGJEBXl5RAX5RzEqyJ1fggryGs+lgvzD9FSQ0zy7CvIyFeQVIyrIazxEBTkEFeQ1DttbBTkx0vcKcqPRqFarWnUAReS/JQQAAAAYMTMzM/Y9AwWlZA0AAAAwYpSsiZT8vUT4kfgmHQAAAAAUy3KrjmFnAZDOajuey+WpwScDAAAAQL/MzMxoLQ0UjpI1AAAAwEhSsiaGPh2sao0mHVObTh1wMgAAAAD07PyNmzuOa9UBFMtqO56nNp26YeqEwecDAAAAQA9OKm/oOG7fM1AsStYAAAAAI0DJmp7p00FnazfpOPGkCwecDwAAAAA9O3lio1YdQNGtseNZyRoAAACgQC6YOs6+Z6DolKwBAAAARoOSNT3Tp4MONOkAAAAAGDFadQCFZsczAAAAwCix7xkoNCVrAAAAgFGiZE1v9OmgnSYdAAAAACNJqw6goOx4BgAAABg9F0wdt1rJ2r5nIM+UrAEAAABGj5I1PdCngxaadAAAAACMMK06gMKx4xkAAABgVK1RsrbvGcgnJWsAAACAUaVkTVr6dPAjmnQAAAAAjDytOoACseMZAAAAYLTZ9wwUiJI1AAAAwGhTsiYVfTr4IU06AAAAAMaEVh1AIdjxDAAAADAO7HsGCkHJGgAAAGAcKFkTT58OQtCkAwAAAGDMaNUB5JwdzwAAAADjw75nIOeUrAEAAADGh5I1kfTpQJMOAAAAgHGkVQeQW3Y8AwAAAIwb+56B3FKyBgAAABg3StbE0Kdj3GnSAQAAADC2tOoAcsiOZwAAAIDxZN8zkENK1gAAAADjScmarvTpGGuadAAAAACMOa06gFyx4xkAAABgnNn3DOSKkjUAAADAOFOyZm36dIwvTToAAAAACFp1ALlhxzMAAAAA9j0DOaFkDQAAAICSNWvQp2NMadIBAAAAwFFadQBDZ8czAAAAAMvsewaGTskaAAAAgGVK1qxGn45xpEkHAAAAAG206gCGyI5nAAAAAFay7xkYIiVrAAAAAFZSsqYjfTrGjiYdAAAAAHSkVQcwFHY8AwAAAJBk3zMwFErWAAAAACQpWZOkT8d40aQDAAAAgDVo1QEMmB3PAAAAAKzGvmdgwJSsAQAAAFiNkjVt9OkYI5p0AAAAANCVVh3AwNjxDAAAAMDa7HsGBkbJGgAAAIC1KVmzkj4d40KTDgAAAAAiadUBDIAdzwAAAADEsO8ZGAAlawAAAABiKFlzlD4dY0GTDgAAAABS0aoDyJQdzwAAAADEs+8ZyJSSNQAAAADxlKxZpk/H6NOkAwAAAIAeaNUBZMSOZwAAAADSsu8ZyIiSNQAAAABpKVkT9OkYeZp0AAAAANAzrTqAvrPjGQAAAIDe2PcM9J2SNQAAAAC9UbJGn45RpkkHAAAAAOukVQfQR3Y8AwAAALAe9j0DfaRkDQAAAMB6KFmPOX06RpYmHQAAAAD0hVYdQF/Y8QwAAADA+tn3DPSFkjUAAAAA66dkPc706RhNmnQAAAAA0EdadQDrZMczAAAAAP1i3zOwTkrWAAAAAPSLkvXY0qdjNK3WpCOEoHwMAAAAQA9Onth4UnlDx39qNBqKyMAaVtvxHJSsAQAAAOjJGiXrNfZPAgQlawAAAAD6Tcl6POnTMZpqtdpq/7Rv712DzAQAAACA0bBtfv/epSMd/6lWq1Wr1QHnAxRIvV6vVCod/0nJGgAAAIAe7Fk8vEbJesDJAMWiZA0AAABAfylZjyd9OkZTtVpd7Xs7f3CXIjIAAAAAqWjSAazTavuelawBAAAASGvb/P57Dj/W8Z+UrIEYStYAAAAA9IuS9djSp2NkadUBAAAAQF9o0gH0hX3PAAAAAKyfkjXQF0rWAAAAAKyfkvU406djlGnVAQAAAMA6KR8DfWTfMwAAAADroWQN9JGSNQAAAADroWQ95vTpGHFadQAAAADQM+VjoO/sewYAAACgN0rWQN8pWQMAAADQGyVr9OkYfVp1AAAAANAD5WMgI/Y9AwAAAJCWkjWQESVrAAAAANJSsibo0zEmtOoAAAAAIBXlYyBT9j0DAAAAEE/JGsiUkjUAAAAA8ZSsWaZPx7jQqgMAAACASMrHwADY9wwAAABADCVrYACUrAEAAACIoWTNUfp0jBGtOgAAAADoSvkYGBj7ngEAAABYm5I1MDBK1gAAAACsTcmalfTpGC9adQAAAACwBuVjYMDsewYAAABgNUrWwIApWQMAAACwGiVr2ujTMXbWbtXx0PbPHjqwc8ApAQAAAJAHysfAUNj3DAAAAECSkjUwFErWAAAAACQpWZOkT8c4WqNVRwjhkYf/VasOAAAAgHGjfAwMkX3PAAAAAKykZA0MkZI1AAAAACspWdORPh1jSqsOAAAAAI5SPgaGzr5nAAAAAJYpWQNDp2QNAAAAwDIla1ajT8f40qoDAAAAgKB8DOSGfc8AAAAAKFkDOaFkDQAAAICSNWvQp2OsadUBAAAAMOaUj4Fcse8ZAAAAYJwpWQO5omQNAAAAMM6UrFmbPh3jTqsOAAAAgLGlfAzkkH3PAAAAAONJyRrIISVrAAAAgPGkZE1X+nSgVQcAAADAOFI+BnLLvmcAAACAcaNkDeSWkjUAAADAuFGyJoY+HYSgVQcAAADAmFE+BnLOvmcAAACA8aFkDeSckjUAAADA+FCyJpI+HfyQVh0AAAAAY0L5GCgE+54BAAAAxoGSNVAIStYAAAAA40DJmnj6dPAjWnUAAAAAjDzlY6BA7HsGAAAAGG1K1kCBKFkDAAAAjDYla1LRp4MWWnUAAAAAjDDlY6Bw7HsGAAAAGFVK1kDhKFkDAAAAjCola9LSp4N2WnUAAAAAjCTlY6Cg7HsGAAAAGD1K1kBBKVkDAAAAjB4la3qgTwcdaNUBAAAAMGKUj4FCs+8ZAAAAYJQoWQOFpmQNAAAAMEqUrOmNPh101rVVxyCTAQAAAGA9lI+BEWDfMwAAAMBoULIGRoCSNQAAAMBoULKmZ/p0sKq1W3UAAAAAUBTKx8BoWGPf85H5Hww+HwAAAAB6oGQNjAYlawAAAIARoGRNz/TpYC1adQAAAACMKuVjoIhW2/e8tDQ/+GQAAAAA6Bcla6CIlKwBAAAARpKSNTFKzWZz2DmQd/V6fWZmpmtYuTy1YeqEEEIplNr/rZQYScSUImLajrNpeuOhQy1tiqY7jsy3tTLqw1OHDi+z1P6oUqfjtI5MT284NL/QMjK1Yb51pNPZS3TYiXkJHV5UslOaVnJaAAAe8ElEQVRPlxc+PTV5aH5x5cjU1OT84faRQ/NLadNrJmKSI22Paib+vZl4W5qJp5raOHHocEt6U1PltpHpjeWDR1pGks/VQcybEJptAR1CWoc2bSgfak1m08buI10PGzq9LR1eQanZLSD5TO2D0xsm5o8sto0cWmgfaYvpkF5Uwt1iOp3zZEybqcmJw4uL7SML3RLukF7Xp+p+nOSlJXmQjZPlI4stH4mNE4mRRMwgE+76XJMT5cXW9CYnyotLGSWcnBe6HSQRNFluT2+yXF5stoxMlMtLS63fqb59bHp44e0D5XL7srBcLjeXmmvHdJpDuz51zDnvlu5yTNujSqXQLb2Ys5fUacHcbIvo/pAOBymFZvuM2Wy2fc7bR+KeK/FUyaHWR8UlnBgplZptX8zESIfjRBw5Kp/2ke4HKZUnmosLXUYm2kd6Sqb7y+ztsKWJyWTCSwsL7TELrQvgQZ3zDglPblg6cnjlSDkxUprc0FxoGel04OGd8w0blw63voQNG5eOzLePHG7dYZPVVzX5iO4vobxxanF+vm1kaf5Q8ulbD9M94agX1eV62SnhqenF1vQmNraPlKemFw+1voSotzfmmt/lIF0fEkKYmJ5eaE1vYnp68WDLuxD1ZiZjOjx9l4GYg0xumj5y8FDakZhyUjN5temWT0zCycNu2DR9uDW9TiNTXV9CzHNFvITkFSDxmIiXsHHT9Hx7whHJPPFkq/V1Xkn5GCi0arXaaDTWjpnadOryH5SsVxxDyTo2PSXr1R6lZJ0y4W4xStarxyhZrxajZL1GjJL10YjuD1GyTnNkJevVRpSs06TXfiAl65UjStarUbKOH1GyXm1EyRpgMJSslayfSE/JenVK1qsMKlmviFGy/uGIkvXqySQTVrJe/SBK1qs+V+KplKyX01OyXiWZXvNRsl4xomS9SoyS9dERJevIfJSsj1KyJj/06SBKZKsOAAAAAPJP+RgYATH7ngEAAADIPyVrYAQoWQMAAACMBiVr4iX7vEIH1Wq1VqsNOwsAAAAA1kv5GBgN9Xq9UqkMOwsAAAAA1kXJGhgNStYAAAAAI0DJmlT06SCWVh0AAAAARad8DIwS+54BAAAACk3JGhglStYAAAAAhaZkTVr6dJDCcqsORWQAAACAIlI+BkaPfc8AAAAABaVkDYweJWsAAACAglKypgelZrM57Bwonmq12mg0hp0FAAAAALGUj4ERpmQNAAAAUCxK1sAIU7IGAAAAKBYla3qjTwc9KpVKw04BAAAAgCjKx8DIO/fccw8cOLBy5JJLLol/+PKe6TPPPDPVo4ZiOdX8/5eMs7Ozc3NzBTqlBUq1KO9+KEKq3v2+K9C7X7jLVFFOaShOqgV69/Ofqnc/C0V590PRLlMFOqUFSrUo734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       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -780,7 +778,7 @@
     },
     {
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       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -791,16 +789,6 @@
       }
      },
      "output_type": "display_data"
-    },
-    {
-     "data": {
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x47TXXnv1WU8p24za45liH+IEAACAmnbUUUdpl1120Q9/+MOBbgoAAEDZMZkGAACATfLJJ5/o6aef1mmnnaY333xTO+2000A3CQAAoOz4midqwo033qihQ4f2+Xfsscc61z///PPzrn/++ef3wxYU7q9//WveNg8dOnSgmwcAKFAlxrK9995bU6dO1S233MJEGgAMoEqMEcBgxifTUBM++eQTffLJJ32mNTU1aeutt7auv2rVKrW1tfWZ1tLSoq222qroNpZae3u7li1bljedX1YDgOpSi7EMABAOMQLoX0ymAQAAAAAAACHxNU8AAAAAAAAgJCbTAAAAAAAAgJCYTAMAAAAAAABCYjINAAAAAAAACInJNAAAAAAAACAkJtMAAAAAAACAkJhMAwAAAAAAAEJiMg0AAAAAAAAI6f8DK/b0WEnM2UoAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 1500x1000 with 12 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
    "source": [
@@ -843,9 +831,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Uncertainty analysis\n",
-    "\n",
-    "## Absolute value of inversion error"
+    "# Absolute value of inversion error"
    ]
   },
   {
@@ -862,7 +848,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -878,12 +864,7 @@
    "source": [
     "inversion_error = np.abs(true_topography - final_topography)\n",
     "fig = maps.plot_grd(\n",
-    "    inversion_error.sel(\n",
-    "        {\n",
-    "            \"easting\": slice(*region[:2]),\n",
-    "            \"northing\": slice(*region[2:]),\n",
-    "        }\n",
-    "    ),\n",
+    "    inversion_error,\n",
     "    hist=True,\n",
     "    cmap=\"thermal\",\n",
     "    title=\"Absolute value of inversion error\",\n",
@@ -900,9 +881,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Starting topography uncertainty\n",
-    "\n",
-    "Here we will estimate an uncertainty distribution for the spline damping value used in creating the starting topography.We then re-create the starting topography a number of times, each using a randomly sampled value for the the damping parameter from within it's uncertainty range. The cell-wise standard deviation of this ensemble will give us an idea of the uncertainty of the starting topography generation. Each repetition of the ensemble will also randomly sample the constraint depths from within their uncertainties."
+    "# Redo starting gravity and regional separation\n",
+    "use the optimal determined density contrast and zref values"
    ]
   },
   {
@@ -918,371 +898,301 @@
        "version_minor": 0
       },
       "text/plain": [
-       "np.float64(3.430469286314926e-07)"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# get best damping value used when creating grid\n",
-    "best_spline_damping = starting_topography.attrs[\"damping\"]\n",
-    "best_spline_damping"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:invert4geom:Sampled 'dampings' parameter values; mean: 6.379332559602493e-07, min: 2.598077917117874e-08, max: 4.529548342955306e-06\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "16c58ad4517f428e81ade13f251548c5",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "starting topography ensemble:   0%|          | 0/40 [00:00<?, ?it/s]"
+       "  0%|          | 0/200 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "width": 500
-      }
-     },
-     "output_type": "display_data"
-    },
-    {
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WNQAAAEBzsO8ZaAJuWQMAAAA0B7esSSTT2dlZ6w7Uu8Dbx5n+Q3qNmRhFUZSJzX+Jr5TPiMlkMpmylSi+EpWsjBszqmPewi2vjB89smPeoiplYiuZ+AibCqfQqywRcgqZ0lPYfvSIOfOXvH5l3KjhZSuZCvXKPnQUn7mTyZRn4icVy5TXy0QtscJD585fXrIyakjZyrhR5Zl44ZaAwi3VH1VeL144EzupsaMGzZu/qnRlYPnKyIHzS1fihUNWelXIlNWLYoXLV0aP6r9w/urXr4wa1W/hgthKaabsA0VR1FK+UGFQU/kTNopaopKfEZlYpvzLMoqiqLMsM2JUv8WlhUeM7Fu2MjyWiRfOROU/sCpkMrFMtXrxk4qizrKFYSP7LVm4umxl6cKSJ0msS9QSKxO/wl04qZBrPmRE/2WLSuoNHdF/WWnhISP6lWW6egoBj4plYp+F8lMYNLz/itJ6g0aUr7TErl6F53DANa/0zC9TXi+KndSg4QNWLl75+pWBw/qXrwwfsKp0pdKVqfocrv6lWjET+xFVflL9hw1YXVpvwLABq5esjGVWlHzorj2rgzJVCse/VPsNHbh2SUm9vsMGlK1Uup4h1zyeqXLNM7F68SfSm4YOXF9ar8/QgeuXLC/PLC37qdq1Kxz/wiz73lL9CsczvYcM2rB0WcnK0EEbS1cqfdGFXM/EmZBTaB0yeNOypa9f2S620jq4fCWT2Vxeptr1jNerVHhz1SdJfKXX4CGbly+JrSwuqTd4aGfZSrUyr/apnilbyXTGvpGVF24ZNLRzRclv45lBw6KqK7EPHcXLtFQ/qdijYlc44BSigcOjlSV/YmQGDo9WlaxEA8pXKvz1EHRS1R5VoXD8b45Ypv/IaPWC8pU1pSvxeiF/ElX4PSB+mrH/rfDzr/R/+42M1i6ostI3thLy87vqH6bxMmG/AZetdL5pZLS+9EnSZ0T5SsAv6GGnUO1JHC8cO6nO7UZGG0vr9R5RaaXkSzWkXmel326rZOL1ooDPS+uIzs2l30l6la9ErcM7N5d8e6xw9Spcz6hctUd1RhWfNqWZ2EqmZfjmqKReJjNsc1TyPT+s8D9XxvS+LVa9XFtbm/vIQIMKvGU9Zvxxr/5HpiV2szR++7Q80xK73xtbyZSvjB0z4pV5S6qvzC/5oyOKl6lQr7V6pnylpfx3uFjhKGopu/88etSQeQtK/lYdM3rIvAUl9wcqFS6vF8UKxx8VVX9US+yXh/KTGj1q4LyFa16/Mmpk/3kL15auDJi3qGSlM/aU6IyV2RzLVFopKbMpU/6iQIWVlvJfA0YP7zNnyfrSld5lK2OG9+4oXemMXc7NIb/uVvg9v/Rv1djLGvGVcUN7z122rnSlT3zlldKVXrE/FlpiZXoFrLSUHid2OaOWgFMYO7jP/BUl9cYM6jN/ZfnKghVlpxAvEysc++2x6qPiheMnFT+FkQP7LF5VUm/kgPKVsMLljSt8Fqqd1Bt8FkqWhvXvs2xNSb1h/SqtrC15nsc/dGvsF+AK9aqdVEtUXq8lU/67djwzqG+fVetK6g16U/lKpatXXqbSKQSslD6q4imUrfTv03vt+g3lKxtKVvr13m7dho1b+ECVVyrUi71AVvb3TqxepVMof6W1z3atGzdtKl/ZGFspzcTLtFS7npVPofyal9ercFKxU2jt1auzs+SeW69evcq2Z4Rczyh277Fzc/mtvM7NJdehs/SyxANRFHVuit8PzGzetLH6ysaSlQpHDvhY8UzUhVOIrbT0at20vuR7S0vrdpvXl/4aUKFevEz1wtUbBjykpc+bNq0pefGrV6WVjaUr8etZ4WMFnWbpNe/SQVr7Dti4quRVjF79+m9cWbrSd8DGlSUvHAQVjj9Jqj2RKj0kVnjA4A2l99hbBwyKrQzeUHqPveqHrrxS7TPVtS+W7QYNW7+k5MZd78HD1i8puVm63eDhZSshR66UiZ1CaabCt6OAg/QeOnLdwnlbXuncHPvWFz/ypvLM5gqPqrLSWeGzVP6QPiNHr51XUq/PiNFrSlfeNLJ8pcL3uQofq/ynQIXM5vj/ZrYQqJjpO2b0qrnzSlfGrJpTshI77yh2OaPNsZe2YmdZ4VGbEgaiKOq//ejlpfUGxFbimU2xn5ixa1MhE//oZZnNUfnLLlUfEkXR4LGjl8x55fUrg7YvXxkcW6lUL35SsUxs82dZpsJhY0/9+GGHjh2zcM7c168M2758JeQ4r9V7ZM3iqJpcLmfANNCgAm9Zt06c/M//ssv6tcPYZf0Ghe2y/v/17LL+J7usX/eoWMYu61dX7LLeUmG7rKPILusqGbuso8gu6y3Xs8v6jTJ2WW+pXmzFLutXP7Rd1q9l7LJ+g8J1ssvaLWsCxb/3QAkzngEAAACaT6FQcAcZaERuWQMAAAA0H29RCDQot6wBAAAAmo9b1gQyp4MtcfsYAAAAoFkZ1QE0nC3csm5p6dPNZQAAAADYhux7BhrOlnZZ99que7sAAAAAsC25ZU0Iczp4Q1u4fZzpP6SbywAAAADQZd/5yREV143qABrIFm5Z9+k7ars+g7u5DwAAAABds2fvARXX7XsGGsiWd1ln3tS/m/sAAAAA0DVvtMvaLWuqMqeDyrZ8+7jXmInd3AcAAACALjutbUejOoCGtuUhHUOH79fNfQAAAADoshG9ehvVATQ0u6wBAAAAmsYWdlm7Zc2WmdNBBW4fAwAAADQZozqAxmVIBwAAAECTMaoDaFx2WQMAAAA0GaM66BpzOijn9jEAAABAUzKqA2hEhnQAAAAANCWjOoBGZJc1AAAAQFMyqoMuMKeDEm4fAwAAADQxozqAxmJIBwAAAEATM6oDaCx2WQMAAAA0MaM6SMqcDv6X28cAAAAATc+oDqBRGNIBAAAA0PSM6gAahV3WAAAAAE3PqA4SMaeDf3L7GAAAAKCHMKoDqH+GdAAAAAD8v/buJslVLV0PMCiVPzuuGw6X43aqyu7VHGoQdsegCA/Ac6oxAG55CG6cCTjCPTccUY3bOLbDP7Uz90/ihu7RRYAAIQSI9TwtbZKED6SzkvWdlW8GQlQHsH5WWQMAAAAEQlQHw8npIIq0jwEAAAACI6oDWDMhHQAAAABBEdUBrJlV1gAAAABBEdXBQHI60D4GAAAACJGoDmCdhHQAAAAABEhUB7BOVlkDAAAABEhUB0PI6Qid9jEAAABAsER1AGsjpAMAAAAgWKI6gLWxyhoAAAAgWKI66CWnI2jaxwAAAACBE9UBrIeQDgAAAIDAieoA1sMqawAAAIDAieqgm5yOcGkfAwAAABCJ6gDWQUgHAAAAAJGoDmAdrLIGAAAAIBLVQSc5HYHSPgYAAADgRFQHsCwhHQAAAACciOoAlmWVNQAAAAAnojq4RE5HiLSPAQAAAKgR1QEsRUgHAAAAADWiOoClWGUNAAAAQI2oDlrJ6QiO9jEAAAAArUR1APMT0gEAAABAK1EdwPyssgYAAACglagOmuR0hEX7GAAAAIAOojqAOQnpAAAAAKBDd1SHljUwLausAQAAAOjQHdWhZR0gOR0B0T4GAAAAoJeoDmAeQjoAAAAA6NUR1aFlDUzIKmsAAAAAelllTZWcjlBoHwMAAAAwkCYycG9COgAAAAAYSFQHcG9WWQMAAAAwkFXWnMjpCIL2MQAAAABX0UQG7kdIBwAAAABXEdUB3I9V1gAAAABcxSprjuR0bJ/2MQAAAAAjaCID9yCkAwAAAIARRHUA92CVNQAAAAAjWGVNJKdj87SPAQAAABhNExmYlpAOAAAAAEYT1QFMyyprAAAAAEazyho5HVumfQwAAADAjTSRgakI6QAAAADgRqI6gKlYZQ0AAADAjayyDpycjs3SPgYAAABgEprIwO2EdAAAAAAwCVEdwO2ssgYAAABgElZZh0xOxzZpHwMAAAAwIU1k4BZCOgAAAACYkKgO4BZWWQMAAAAwIausgyWnY5sutY+jKIr/2b+YsxIAAAAAtuHfJv/63/y7f9X6paIo8jyfuR7ggWRZdulLX778/ZyVAAAAALAN//Lp5Xe759YvFUUxczHAY+loWVtlDQAAAMAI3ausZy6G2cjp2KaODvLnP/y3z//7P+csBgAAAIAN+A///j//p//431u/lGWZsGegQ57nSZK0ful//Y//8v63f5i5HgAAAAAe3X/9+D+/fn5v/VLH+kmAqLNlbZU1AAAAACN0r7KeuRhmI6djm9I0FdUBAAAAwFSEdAA3EtUBAAAAwFS6Qzq0rIFeojoAAAAAmIpV1sGS07FZojoAAAAAmIT2MTAJUR0AAAAA3E5IBzAJUR0AAAAA3M4q65DJ6dgyUR0AAAAA3Ej7GJiQqA4AAAAAbiGkA5iQqA4AAAAAbmGVdeDkdGycqA4AAAAARtM+BiYnqgMAAACAcYR0AJMT1QEAAADAOFZZI6dj+0R1AAAAADCC9jFwJ6I6AAAAALiWkA7gTkR1AAAAAHAtq6yJ5HQEQlQHAAAAAFfRPgbuSlQHAAAAAMMJ6QDuSlQHAAAAAMNZZc2RnI5QiOoAAAAAYCDtY2AGojoAAAAAGEJIBzADUR0AAAAADGGVNSdyOgIiqgMAAACAXtrHwGxEdQAAAADQTUgHMBtRHQAAAAB0s8qaKjkdYRHVAQAAAEAH7WNgZqI6AAAAALhESAcwM1EdAAAAAFxilTU1cjqCI6oDAAAAgFbax8AiRHUAAAAA0CSkA1iEqA4AAAAAmqyypklOR4hEdQAAAABQo30MLEhUBwAAAABVQjqABYnqAAAAAKDKKmtayekIlKgOAAAAAE60j4HFieoAAAAA4EhIB7A4UR0AAAAAHFllzSVyOsIlqgMAAACASPsYWA1RHQAAAAAI6QBWQlQHAAAAAFZZ00FOR9BEdQAAAAAETvsYWBVRHQAAAAAhE9IBrIqoDgAAAICQWWVNNzkdoRPVAQAAABAs7WNghUR1AAAAAIRJSAewQqI6AAAAAMJklTW95HQgqgMAAAA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-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "width": 500
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "starting_topography_parameter_dict = {\n",
-    "    \"dampings\": {\n",
-    "        \"distribution\": \"normal\",\n",
-    "        \"loc\": np.log10(best_spline_damping),  # mean; base 10 exponent\n",
-    "        \"scale\": 0.5,  # standard deviation of exponent\n",
-    "        \"log\": True,\n",
-    "    },\n",
-    "}\n",
-    "\n",
-    "starting_topo_stats = uncertainty.starting_topography_uncertainty(\n",
-    "    runs=40,\n",
-    "    sample_constraints=True,\n",
-    "    parameter_dict=starting_topography_parameter_dict,\n",
-    "    method=\"splines\",\n",
-    "    dampings=best_spline_damping,\n",
-    "    region=region,\n",
-    "    spacing=spacing,\n",
-    "    constraints_df=constraint_points,\n",
-    "    weights=constraint_points.weight,\n",
-    "    plot_region=region,\n",
-    "    true_topography=true_topography,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Regional field estimation uncertainty\n",
-    "\n",
-    "We will do the same thing for the regional estimation procedure. First we will re-separate the regional to see what equivalent source gridding parameter values were determined optimal. We then estimate an uncertainty distribution for these parameter values, and create an ensemble of regional models which each randomly sample both the parameter values and the gravity data. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'method': 'constraints',\n",
-       " 'grid_method': 'eq_sources',\n",
-       " 'constraints_weights_column': 'weight',\n",
-       " 'cv': True,\n",
-       " 'cv_kwargs': {'n_trials': 100,\n",
-       "  'damping_limits': (1e-20, 10),\n",
-       "  'depth_limits': (100, 100000.0),\n",
-       "  'progressbar': False,\n",
-       "  'fname': 'tmp/regional_sep'},\n",
-       " 'block_size': None,\n",
-       " 'constraints_df':          easting      northing  true_upward      upward     uncert    weight  \\\n",
-       " 0    3899.714996  26468.618776   655.102400  656.749723  13.102048  0.005825   \n",
-       " 1   30636.914105   6974.789900   407.011208  405.935844   8.140224  0.015091   \n",
-       " 2   17659.550795  13659.470931   470.155613  476.177579   9.403112  0.011310   \n",
-       " 3   28491.676758  27073.768551   515.239653  516.320627  10.304793  0.009417   \n",
-       " 4   38163.601456   1697.178371   419.197817  414.706788   8.383956  0.014227   \n",
-       " 5   21462.843076  17815.369689   472.257595  475.672915   9.445152  0.011209   \n",
-       " 6   20042.577619  27603.626012   417.272882  428.155359   8.345458  0.014358   \n",
-       " 7    3737.943068   7448.480613   578.101458  589.051636  11.562029  0.007481   \n",
-       " 8   11200.681244  16357.717739   497.531876  490.529262   9.950638  0.010099   \n",
-       " 9   19995.535031  26455.594497   460.766046  449.104781   9.215321  0.011775   \n",
-       " 10  26810.739853   4728.744481   422.504506  417.237780   8.450090  0.014005   \n",
-       " 11  31542.083372  15655.552259   445.703156  446.071538   8.914063  0.012585   \n",
-       " 12  15475.763060  22011.476055   414.325222  395.058844   8.286504  0.014563   \n",
-       " 13   3505.581182  19732.370745   611.906912  609.229309  12.238138  0.006677   \n",
-       " 14  11949.532774  14097.080073   511.165674  498.428336  10.223313  0.009568   \n",
-       " \n",
-       "     inverted_topography  \n",
-       " 0            659.704598  \n",
-       " 1            390.350078  \n",
-       " 2            469.886386  \n",
-       " 3            511.487068  \n",
-       " 4            419.051467  \n",
-       " 5            479.203723  \n",
-       " 6            407.377421  \n",
-       " 7            595.654726  \n",
-       " 8            505.083045  \n",
-       " 9            453.873191  \n",
-       " 10           423.878946  \n",
-       " 11           446.912631  \n",
-       " 12           414.208783  \n",
-       " 13           615.526747  \n",
-       " 14           514.155368  }"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "regional_grav_kwargs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO:invert4geom:Current trial (#10, {'depth': 72203.35621143678, 'damping': 1.347092541549628e-14}) has a worse score (-94.14037938681597) than any of the combinations of parameter value limits, it might be best to stop the study and expand the limits.\n",
+      "INFO:invert4geom:Current trial (#3, {'damping': 1.612573407133368e-10}) has a worse score (-3.3888581411532597) than either of the lower (-3.3568580390225393) or upper (-0.6951917691820506) parameter value limits, it might be best to stop the study and expand the limits.\n",
       "INFO:invert4geom:Trial with best score: \n",
-      "INFO:invert4geom:\ttrial number: 43\n",
-      "INFO:invert4geom:\tparameter: {'depth': 15041.577139067731, 'damping': 0.005076537418909756}\n",
-      "INFO:invert4geom:\tscores: [-0.2066920696630425]\n"
+      "INFO:invert4geom:\ttrial number: 101\n",
+      "INFO:invert4geom:\tparameter: {'damping': 0.03195748382073139}\n",
+      "INFO:invert4geom:\tscores: [0.3425792543858968]\n"
      ]
     },
     {
      "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>northing</th>\n",
+       "      <th>easting</th>\n",
+       "      <th>upward</th>\n",
+       "      <th>upper_surface_grav</th>\n",
+       "      <th>lower_surface_grav</th>\n",
+       "      <th>observed_grav</th>\n",
+       "      <th>gravity_anomaly</th>\n",
+       "      <th>uncert</th>\n",
+       "      <th>starting_gravity</th>\n",
+       "      <th>true_res</th>\n",
+       "      <th>misfit</th>\n",
+       "      <th>reg</th>\n",
+       "      <th>res</th>\n",
+       "      <th>staring_res</th>\n",
+       "      <th>starting_reg</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>1271.000000</td>\n",
+       "      <td>1271.00000</td>\n",
+       "      <td>1271.0</td>\n",
+       "      <td>1271.000000</td>\n",
+       "      <td>1271.000000</td>\n",
+       "      <td>1271.000000</td>\n",
+       "      <td>1271.000000</td>\n",
+       "      <td>1271.0</td>\n",
+       "      <td>1271.000000</td>\n",
+       "      <td>1271.000000</td>\n",
+       "      <td>1271.000000</td>\n",
+       "      <td>1271.000000</td>\n",
+       "      <td>1271.000000</td>\n",
+       "      <td>1271.000000</td>\n",
+       "      <td>1271.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>15000.000000</td>\n",
+       "      <td>20000.00000</td>\n",
+       "      <td>1001.0</td>\n",
+       "      <td>-0.128924</td>\n",
+       "      <td>0.004007</td>\n",
+       "      <td>-0.124917</td>\n",
+       "      <td>-0.124917</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>1.291839</td>\n",
+       "      <td>-1.258972</td>\n",
+       "      <td>-1.416756</td>\n",
+       "      <td>-0.739126</td>\n",
+       "      <td>-0.677630</td>\n",
+       "      <td>-0.677630</td>\n",
+       "      <td>-0.739126</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>8947.792584</td>\n",
+       "      <td>11836.81698</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7.128555</td>\n",
+       "      <td>0.714063</td>\n",
+       "      <td>7.271000</td>\n",
+       "      <td>7.269461</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7.895746</td>\n",
+       "      <td>4.602393</td>\n",
+       "      <td>5.186440</td>\n",
+       "      <td>1.644017</td>\n",
+       "      <td>4.574136</td>\n",
+       "      <td>4.574136</td>\n",
+       "      <td>1.644017</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>1001.0</td>\n",
+       "      <td>-17.179165</td>\n",
+       "      <td>-1.033526</td>\n",
+       "      <td>-16.887669</td>\n",
+       "      <td>-17.040892</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>-9.178955</td>\n",
+       "      <td>-19.592049</td>\n",
+       "      <td>-21.073109</td>\n",
+       "      <td>-3.740570</td>\n",
+       "      <td>-18.444515</td>\n",
+       "      <td>-18.444515</td>\n",
+       "      <td>-3.740570</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>7000.000000</td>\n",
+       "      <td>10000.00000</td>\n",
+       "      <td>1001.0</td>\n",
+       "      <td>-5.448853</td>\n",
+       "      <td>-0.601914</td>\n",
+       "      <td>-5.750137</td>\n",
+       "      <td>-5.739415</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>-5.011978</td>\n",
+       "      <td>-3.001264</td>\n",
+       "      <td>-3.639255</td>\n",
+       "      <td>-2.050330</td>\n",
+       "      <td>-2.446696</td>\n",
+       "      <td>-2.446696</td>\n",
+       "      <td>-2.050330</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>15000.000000</td>\n",
+       "      <td>20000.00000</td>\n",
+       "      <td>1001.0</td>\n",
+       "      <td>-1.160222</td>\n",
+       "      <td>-0.153040</td>\n",
+       "      <td>-0.758131</td>\n",
+       "      <td>-0.713654</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>-0.709894</td>\n",
+       "      <td>-1.444045</td>\n",
+       "      <td>-1.773919</td>\n",
+       "      <td>-0.700769</td>\n",
+       "      <td>-0.664604</td>\n",
+       "      <td>-0.664604</td>\n",
+       "      <td>-0.700769</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>23000.000000</td>\n",
+       "      <td>30000.00000</td>\n",
+       "      <td>1001.0</td>\n",
+       "      <td>3.675021</td>\n",
+       "      <td>0.541706</td>\n",
+       "      <td>3.825804</td>\n",
+       "      <td>3.815224</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>6.245837</td>\n",
+       "      <td>0.461464</td>\n",
+       "      <td>0.742094</td>\n",
+       "      <td>0.665742</td>\n",
+       "      <td>1.222102</td>\n",
+       "      <td>1.222102</td>\n",
+       "      <td>0.665742</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>30000.000000</td>\n",
+       "      <td>40000.00000</td>\n",
+       "      <td>1001.0</td>\n",
+       "      <td>19.599674</td>\n",
+       "      <td>1.599823</td>\n",
+       "      <td>19.781652</td>\n",
+       "      <td>19.936108</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>26.653816</td>\n",
+       "      <td>13.969991</td>\n",
+       "      <td>14.922073</td>\n",
+       "      <td>1.956277</td>\n",
+       "      <td>14.545149</td>\n",
+       "      <td>14.545149</td>\n",
+       "      <td>1.956277</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "0.005076537418909756"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# use the constraints to find the best regional field\n",
-    "_ = regional.regional_separation(grav_df=grav_df, **regional_grav_kwargs)\n",
-    "\n",
-    "# re-load the study from the saved pickle file\n",
-    "with pathlib.Path(f\"{regional_grav_kwargs.get('cv_kwargs').get('fname')}.pickle\").open(\n",
-    "    \"rb\"\n",
-    ") as f:\n",
-    "    study = pickle.load(f)\n",
-    "eq_damping = min(study.best_trials, key=lambda t: t.values[0]).params[\"damping\"]\n",
-    "eq_damping"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "np.float64(5599.156508868462)"
+       "           northing      easting  upward  upper_surface_grav  \\\n",
+       "count   1271.000000   1271.00000  1271.0         1271.000000   \n",
+       "mean   15000.000000  20000.00000  1001.0           -0.128924   \n",
+       "std     8947.792584  11836.81698     0.0            7.128555   \n",
+       "min        0.000000      0.00000  1001.0          -17.179165   \n",
+       "25%     7000.000000  10000.00000  1001.0           -5.448853   \n",
+       "50%    15000.000000  20000.00000  1001.0           -1.160222   \n",
+       "75%    23000.000000  30000.00000  1001.0            3.675021   \n",
+       "max    30000.000000  40000.00000  1001.0           19.599674   \n",
+       "\n",
+       "       lower_surface_grav  observed_grav  gravity_anomaly  uncert  \\\n",
+       "count         1271.000000    1271.000000      1271.000000  1271.0   \n",
+       "mean             0.004007      -0.124917        -0.124917     0.1   \n",
+       "std              0.714063       7.271000         7.269461     0.0   \n",
+       "min             -1.033526     -16.887669       -17.040892     0.1   \n",
+       "25%             -0.601914      -5.750137        -5.739415     0.1   \n",
+       "50%             -0.153040      -0.758131        -0.713654     0.1   \n",
+       "75%              0.541706       3.825804         3.815224     0.1   \n",
+       "max              1.599823      19.781652        19.936108     0.1   \n",
+       "\n",
+       "       starting_gravity     true_res       misfit          reg          res  \\\n",
+       "count       1271.000000  1271.000000  1271.000000  1271.000000  1271.000000   \n",
+       "mean           1.291839    -1.258972    -1.416756    -0.739126    -0.677630   \n",
+       "std            7.895746     4.602393     5.186440     1.644017     4.574136   \n",
+       "min           -9.178955   -19.592049   -21.073109    -3.740570   -18.444515   \n",
+       "25%           -5.011978    -3.001264    -3.639255    -2.050330    -2.446696   \n",
+       "50%           -0.709894    -1.444045    -1.773919    -0.700769    -0.664604   \n",
+       "75%            6.245837     0.461464     0.742094     0.665742     1.222102   \n",
+       "max           26.653816    13.969991    14.922073     1.956277    14.545149   \n",
+       "\n",
+       "       staring_res  starting_reg  \n",
+       "count  1271.000000   1271.000000  \n",
+       "mean     -0.677630     -0.739126  \n",
+       "std       4.574136      1.644017  \n",
+       "min     -18.444515     -3.740570  \n",
+       "25%      -2.446696     -2.050330  \n",
+       "50%      -0.664604     -0.700769  \n",
+       "75%       1.222102      0.665742  \n",
+       "max      14.545149      1.956277  "
       ]
      },
-     "execution_count": 19,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# if depth=\"default\" in EquivalentSources, it will use 4.5 times the mean distances\n",
-    "# between nearest points as the depth\n",
-    "# calculate that mean distance below.\n",
-    "mean_constraint_distance = np.mean(\n",
-    "    vd.median_distance(\n",
-    "        (constraint_points.easting, constraint_points.northing), k_nearest=1\n",
-    "    )\n",
+    "# set the reference level from the prisms to 0\n",
+    "density_grid = xr.where(\n",
+    "    starting_topography >= zref,\n",
+    "    density_contrast,\n",
+    "    -density_contrast,\n",
     ")\n",
-    "mean_constraint_distance"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:invert4geom:Sampled 'depth' parameter values; mean: 22396.626035473848, min: 6019.0932470335965, max: 38774.1588239141\n",
-      "INFO:invert4geom:Sampled 'damping' parameter values; mean: 4.622964070499353, min: 1.6701756069372796e-07, max: 154.30252997677107\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3267e37e20a14f08992cb581d1b72b81",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "starting regional ensemble:   0%|          | 0/40 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
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",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "width": 500
-      }
-     },
-     "output_type": "display_data"
-    },
-    {
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-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "width": 500
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "regional_misfit_parameter_dict = {\n",
-    "    \"depth\": {\n",
-    "        \"distribution\": \"uniform\",\n",
-    "        \"loc\": 1 * mean_constraint_distance,  # lower bound\n",
-    "        \"scale\": 6 * mean_constraint_distance,  # range, 2.5+3.5=6\n",
-    "    },\n",
-    "    \"damping\": {\n",
-    "        \"distribution\": \"normal\",\n",
-    "        \"loc\": np.log10(eq_damping),  # mean base 10 exponent\n",
-    "        \"scale\": 2,  # standard deviation of exponent\n",
-    "        \"log\": True,\n",
-    "    },\n",
-    "}\n",
+    "# create layer of prisms\n",
+    "starting_prisms = utils.grids_to_prisms(\n",
+    "    starting_topography,\n",
+    "    zref,\n",
+    "    density=density_grid,\n",
+    ")\n",
+    "grav_df[\"starting_gravity\"] = starting_prisms.prism_layer.gravity(\n",
+    "    coordinates=(\n",
+    "        grav_df.easting,\n",
+    "        grav_df.northing,\n",
+    "        grav_df.upward,\n",
+    "    ),\n",
+    "    field=\"g_z\",\n",
+    "    progressbar=False,\n",
+    ")\n",
+    "\n",
+    "# regional separation\n",
+    "temp_reg_kwargs = copy.deepcopy(regional_grav_kwargs)\n",
     "\n",
-    "# cant use a cross validation to find damping and depth or there will be no variation\n",
-    "regional_kwargs = dict(\n",
+    "# temporarily set some kwargs\n",
+    "# temp_reg_kwargs[\"cv_kwargs\"][\"plot\"]=True\n",
+    "temp_reg_kwargs[\"cv_kwargs\"][\"progressbar\"] = True\n",
+    "\n",
+    "grav_df = regional.regional_separation(\n",
     "    grav_df=grav_df,\n",
-    "    constraints_df=constraint_points,\n",
-    "    constraints_weights_column=\"weight\",\n",
-    "    method=\"constraints\",\n",
-    "    grid_method=\"eq_sources\",\n",
-    "    # damping=eq_damping,\n",
-    "    # depth=\"default\",\n",
-    "    block_size=None,\n",
+    "    **temp_reg_kwargs,\n",
     ")\n",
     "\n",
-    "regional_misfit_stats = uncertainty.regional_misfit_uncertainty(\n",
-    "    runs=40,\n",
-    "    parameter_dict=regional_misfit_parameter_dict,\n",
-    "    plot_region=region,\n",
-    "    true_regional=grav_grid.lower_surface_grav,\n",
-    "    **regional_kwargs,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Sensitivity Analysis\n",
+    "grav_df[\"staring_res\"] = grav_df.res\n",
+    "grav_df[\"starting_reg\"] = grav_df.reg\n",
     "\n",
-    "Above we perform an uncertainty assessment using all the important parameters and data. Now, we can repeat this stochastic analysis isolated the uncertainty arising from each component. This should give us a sense of which parameters/data are the most important."
+    "grav_df.describe()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1303,14 +1213,8 @@
     "        results=results,\n",
     "        plot=True,\n",
     "        constraints_df=constraint_points,\n",
-    "        weight_by=\"constraints\",\n",
-    "        region=region,\n",
-    "    )\n",
-    "    ds = ds.sel(\n",
-    "        {\n",
-    "            \"easting\": slice(*region[:2]),\n",
-    "            \"northing\": slice(*region[2:]),\n",
-    "        }\n",
+    "        weight_by=weight_by,\n",
+    "        region=inversion_region,\n",
     "    )\n",
     "\n",
     "    try:\n",
@@ -1325,7 +1229,6 @@
     "        mean,\n",
     "        region=vd.pad_region(inversion_region, -spacing),\n",
     "        plot=True,\n",
-    "        region=region,\n",
     "        grid1_name=\"True topography\",\n",
     "        grid2_name=\"Inverted topography\",\n",
     "        robust=True,\n",
@@ -1344,7 +1247,6 @@
     "        stdev,\n",
     "        region=vd.pad_region(inversion_region, -spacing),\n",
     "        plot=True,\n",
-    "        region=region,\n",
     "        grid1_name=\"True error\",\n",
     "        grid2_name=\"Stochastic uncertainty\",\n",
     "        cmap=\"thermal\",\n",
@@ -1370,174 +1272,65 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(2644.2598596,\n",
-       " 484.56202153982827,\n",
-       " 0.010453883574576146,\n",
-       " np.float64(3.430469286314926e-07))"
+       "np.float64(5599.156508868462)"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# parameters used in final inversion\n",
-    "density_contrast, zref, solver_damping, best_spline_damping"
+    "# get mean spacing between nearest constraints\n",
+    "mean_spacing = np.mean(\n",
+    "    vd.median_distance(\n",
+    "        (constraint_points.easting, constraint_points.northing),\n",
+    "        k_nearest=1,\n",
+    "    )\n",
+    ")\n",
+    "mean_spacing"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 23,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "# kwargs to reuse for all uncertainty analyses\n",
-    "uncert_kwargs = dict(\n",
-    "    grav_df=grav_df,\n",
-    "    constraints_df=constraint_points,\n",
-    "    density_contrast=density_contrast,\n",
-    "    zref=zref,\n",
-    "    solver_damping=solver_damping,\n",
-    "    starting_prisms=starting_prisms,\n",
-    "    starting_topography=starting_topography,\n",
-    "    # kwargs\n",
-    "    starting_topography_kwargs={\n",
-    "        \"method\": \"splines\",\n",
-    "        \"region\": region,\n",
-    "        \"spacing\": spacing,\n",
-    "        \"constraints_df\": constraint_points,\n",
-    "        \"dampings\": best_spline_damping,\n",
-    "        \"weights_col\": \"weight\",\n",
-    "    },\n",
-    "    # use cross validation for equivalent sources during regional estimation\n",
-    "    regional_grav_kwargs=regional_grav_kwargs,\n",
-    "    **kwargs,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Density contrast component"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:invert4geom:Sampled 'density_contrast' parameter values; mean: 2644.2598596000003, min: 2448.2634611459944, max: 2840.2562580540052\n",
-      "INFO:invert4geom:No pickle files starting with 'tmp/uncertainty_density' found, creating new files\n",
-      "\n"
-     ]
-    },
     {
      "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e496061735f3450a8dec4875f2c2853f",
-       "version_major": 2,
-       "version_minor": 0
-      },
       "text/plain": [
        "(0.03195748382073139, np.float64(25196.204289908077))"
       ]
      },
      "execution_count": 23,
      "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:invert4geom:starting stochastic uncertainty analysis at run 0 of 20\n",
-      "saving results to pickle files with prefix: 'tmp/uncertainty_density'\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
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-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "width": 500
-      }
-     },
-     "output_type": "display_data"
-    },
-    {
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-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "width": 500
-      }
-     },
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "density_dict = {\n",
-    "    \"density_contrast\": {\n",
-    "        \"distribution\": \"normal\",\n",
-    "        \"loc\": density_contrast,\n",
-    "        \"scale\": 100,\n",
-    "    },\n",
-    "}\n",
+    "# use optimal eq source parameters for regional separation in uncertainty analysis\n",
+    "# re-load the study from the saved pickle file\n",
+    "with pathlib.Path(f\"{regional_grav_kwargs.get('cv_kwargs').get('fname')}.pickle\").open(\n",
+    "    \"rb\"\n",
+    ") as f:\n",
+    "    study = pickle.load(f)\n",
+    "reg_eq_damping = min(study.best_trials, key=lambda t: t.values[0]).params[\"damping\"]\n",
     "\n",
     "reg_eq_depth = 4.5 * mean_spacing\n",
     "\n",
-    "# delete files if already exist\n",
-    "for p in pathlib.Path().glob(f\"{fname}*\"):\n",
-    "    p.unlink(missing_ok=True)\n",
-    "\n",
-    "uncert_density_results = uncertainty.full_workflow_uncertainty_loop(\n",
-    "    fname=fname,\n",
-    "    runs=20,\n",
-    "    # choose what to sample\n",
-    "    # sample_gravity=True,\n",
-    "    # sample_constraints=True,\n",
-    "    parameter_dict=density_dict,\n",
-    "    # choose what to do\n",
-    "    # create_starting_topography=True,\n",
-    "    create_starting_prisms=True,\n",
-    "    calculate_starting_gravity=True,\n",
-    "    calculate_regional_misfit=True,\n",
-    "    **uncert_kwargs,\n",
+    "new_regional_grav_kwargs = dict(\n",
+    "    method=\"constraints\",\n",
+    "    grid_method=\"eq_sources\",\n",
+    "    constraints_df=constraint_points,\n",
+    "    damping=reg_eq_damping,\n",
+    "    depth=reg_eq_depth,\n",
+    "    block_size=None,\n",
     ")\n",
     "reg_eq_damping, reg_eq_depth"
    ]
@@ -1573,25 +1366,57 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO:invert4geom:Sampled 'solver_damping' parameter values; mean: 0.010716700663809675, min: 0.006657041442107438, max: 0.01641625378798854\n",
-      "INFO:invert4geom:No pickle files starting with 'tmp/uncertainty_damping' found, creating new files\n",
-      "\n"
+      "calculated stdev: 0.473439695891782\n",
+      "using stdev: 0.473439695891782\n"
+     ]
+    }
+   ],
+   "source": [
+    "# load study\n",
+    "with pathlib.Path(\"tmp/combining_it_all_damping_CV_study.pickle\").open(\"rb\") as f:\n",
+    "    study = pickle.load(f)\n",
+    "\n",
+    "study_df = study.trials_dataframe().drop(columns=[\"user_attrs_results\"])\n",
+    "study_df.sort_values(\"value\", inplace=True)  # noqa: PD002\n",
+    "\n",
+    "# calculate zscores of values\n",
+    "study_df[\"value_zscore\"] = sp.stats.zscore(study_df[\"value\"])\n",
+    "\n",
+    "# drop outliers (values with zscore > |2|)\n",
+    "study_df2 = study_df[(np.abs(study_df.value_zscore) < 2)]\n",
+    "\n",
+    "# pick damping standard deviation based on optimization\n",
+    "stdev = np.log10(study_df2.params_damping).std()\n",
+    "print(f\"calculated stdev: {stdev}\")\n",
+    "# stdev = .1\n",
+    "print(f\"using stdev: {stdev}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "best: 0.021017217578137074 \n",
+      "std: 0.473439695891782 \n",
+      "+1std: 0.06251941651066213 \n",
+      "-1std: 0.007065379995211915\n"
      ]
     },
     {
      "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9689486b55c243da9fcecc2bdec0ae5a",
-       "version_major": 2,
-       "version_minor": 0
-      },
+      "image/png": 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",
       "text/plain": [
        "<Figure size 500x350 with 1 Axes>"
       ]
@@ -1635,7 +1460,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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0HssrHKZE2XX0Q7NMha32fNyWlu99vUNo+T3G/YWKcmpXS9sTTohWT4GSbrQxr7wVmVX7wJKFzyYHAAC6LO7ieeGFZPmSWttWucDeki43IfCSMPC4t7dgvrLL1VgHazQajUaj9CpNueg9HOVYlC/Lr+zbgfzuMtKx5fu85Jh2c8Fnedvarn1dnlS7pu5clKypex1TAHpI0jPQd0suJWTS6RZdCS96kGrITxvvNLzCShEjrJuZFLjHKOqmXVYuZy8qvEhJS1uKrfJCvdaVfBJwsNK3XtWquVLcsRR31IWsKdft6sbKF9/L55PyhrQ6lZVY/TBe3qZPquEHbpLS+BC3N28s0xVt9HxLLS3f7+qHUPQ9xp18opzaVWpvVDfT6inQ6r/RD7Syv7wAAMDWK7+QrJVvOghT9zouou5H2EyXM8YixtYg0bbt6/r5suFcevxs3BAql58H0g1czRrFopkqHdt8+6Jno4SPn1Ynig4O42Vs66TaqjY6rSdzURfoUgCikPQMsArlD8UMf2TmukSMcMkkoZVp0Mzlj12zp0uGWJSc1PbTjpfRxu9C+KOFl19kDEkIq3UslhH+nOAk9YtZXjLfRaucylodxitYYt6mSXU6nXbn0RdrfDnAyvY7F7Glq29I9D2u5TxqZTtd/ZnhWk6B1iJ87trQBgIAAB3R/fX/VVp9omegtS+ylehybI3N8wsHg8Hu7u70v1l3UJGVfEeQeQD8atZXK3/j9kdXSDyd+v3ts85OqpuiD3MRAGwlSc9A33l7Xfd1uQneXpcR5WA1eF2Ut9eV8+q6Wra4aXNx79BIkmQ6nQ4Gg9FotMbpusG8kaz878u6ZsjozdyINNxYu1vjqV2zUb0y4acZXT6RAwAAiCI8tXF1MUGHzVc8SjILh8PhcDhc2fca7Vn0HcFampZe9053fuGBKHw2ysoe4wKr0Z+5CAC2z+F1BwCwDYbD4f4VUT7rq+7b62JG1oLuR9hMlzNy1v72uvZua55MJvu/INt98/R4PM4sAq74/vtF01Thq+v2S6bDrjV4Wp0oujaMl7SJk+poNBqPx/nU5/1Onh+XlubV/swb/WlpRLF+oSKe2oVY77Hu8ikQAADApkhfSM4fGzn/d/oSMmQNcDabtRFeREtGmO6o/JIpfTAajfKrH/PlxN3d3S0eEukBv64HwOfXycufirJfJnNcKpfluz+VdYdJtYEoA6y3cxEAbAdJzwCt8/a6tM6+aKnLb4jrcmzNzLO7tj6HbzKZ7Kffrf3tdeV3qydeXbex1jWpFuY975tHNRgMhsNhrPXBrs0b7fX8ilu6+iHU2TOBcLFO7ToyqrfvNAMAAGC9ClMbV7MGSGMla7lrFzG29CpxYFVxe2MymaQrjLh42E2Fx64jD4Cf3+effyrKXP7ZKN05TGsfxmyBvs1FnWIJGoAodtYdAMA28PY6CNer10V15+11Xl1HdKPRaDabzX9hF5WZTqdR1gr7M2/0p6Udt4JTO8caAABgmxReSFpDi6LL3Rg9IThibYWxrb0zM7de5x8h3Kq1PJYo/x1B3QfAR1TSA5llrvLjst4HPK19GG86HZisey6KbqODB4BmJD0DxBGS2hj49rq6Vry4sGSE6Y6Sx9NDi14XNc/r2h8wm76+UCg94NfyAPh8cl7gq+sq68nr/lTWHZs+qc5/YWel2c/T6XT/iSYNdHPeaKPn19LS1Q+hVvcYcfKJdWpXqJujGgAAgGUsupBctCXwg4XmF4wre/ZqxAjTl9JdfvZqg44t6ZnAlga+iylibJVHM/qabUtvAIvbw+2ZB9CRx4vMwyi/IT+wZKZ8stqpbPXDeHk9nFSXF3eAbcHbCDc0bACIRdIzQHyFqY3eXtdxJc90XLuIsTVY/ojYG/nXRc1Tu7Y4r6vw2HXhAfDz3i55dd3+v+dlOnV01juMWWQ/+3lRAnSzUdSfeaM/Ld1EcU/tunasu3wKBAAAsEHyS2rpS8jASgJvD56/O2iuZpjLWjLCzGVvSOLaytpYd6m21iV8SOGQ9NPld1frdvRW75CPuwayZA+3J7Pwko5zLd8OpHe66EuBvLpfarQ9lXVnGC9viyfV9sQdYBu69r5ZYW9WtABsBEnPAHF4e117utyN3l5Xy9pfF9Xzt9dtx6vrknUP4y2wgg5MP/55f2OzCXPt80ZE5ZFvUEtXH9hauqK9U7sNOtYltuDbEQAAgLjSF3eZ1MbwbMLpdFq5+Lb6pMm4Eaa3VK6OrvKSOfOohcpmZm5pDi9cqDxzMWJstZ4osd7L/1pHf8kebtWi4bGWB7UuGgD53l70osiQl0a2PZVt0DAu1JNJNa41/q3c3E7rprV/yQjAdpD0DBCNt9elbfqLlry9LrBALe29Lsrb6+qK/uq6ZH1TmbfX7f+7g5Pq8p3f2dfMRe/5dbV09UMo+h7bm3wan9qV6+yonot7CgQAANAri5YcKy+1MilrJddZmdcHrey6MmKEmS0lnZOpqm2j0Si8mQ0yaMNbWvgKtVixZaoqiarttevyThuNRnWP/jI9vDINvh0YHLRkAIU7rdUhgc8Rb3Uq684wbqwPk2p0LQ2w6HPRymS+yAOAfpkBbKPyia7WNJi+ghoOhyUlM/mU6R2VfzB8F4FNqDvPV5aPG2H6R/P3uYdXtcxfrujNzCwDlf80pMKS8hFjq1VVSCvCj07d41hePm4P1w2vVjdm9lsr5rgye9//d/43cdHr4UpiXuVUtsZhHLL3yto2elKtG3zlvhoPgzZ2l1He2Lg9H7eltWqL25C6wS+/x+iTz77Gp3blu4h7rKOUiXsKFBhP470vIzywdFThJVf/lx0AACi0rmuW9IVk3SuFkE8FrojWamB4+YgR5ouVr0/WbVHjZuaPYD6w8Xgc0szC+ANbWrhaEjG2TFXh8RcGVuvoZKpd1NLCvYdEuEwPN25I3TXS8E8VfjbwUyUKHxW8ZMlF0bY3lXVkGC/zdUPEvurspJrfdXjJyr5dptMizkWz9a2FhnfsWg5WYQCWTwGIRdIzsJ3Wde2RuYgqv1ha9MHKwrEup2uVjxhhZaJMSeHAFlU2obBAZomk1lGrbGbdlrYXW6aq8HoKmzmrM95qHcfKcRK3h5cJr1bSba2ZIbrC710WtbewZHnM4SVny01laxzGS0ZeWGazJtVanT9bbdJzSFdEPNZxez5uS6P3W3hDQkTfY/qny08+i2pufGoX/qPyaEvKL1km7ilQYDz7JD0DAADRlZ/8t3rNkuQExrwoYXpfeM11975oj+1FOCvqqJKqwq/Rlm9mYUPKYyvcXWE9IZ1WclUeK7bCqgrryfy3smMrj0J+FA1zSvptPB7v90/0Hq7VkGHNmSEfSeVHCgOr9cHwYBb1Sa1BNbfKqWxdw7jy6C8Kqb2+mq17Ui0vGb67yr6N1WkR56JaXRHSxloCq1rLwWoQJwCEk/QMbKfyc/f2rj0yhZvtJSld1QrJSqnVwMDyESMMuf26sGR4iwrFbWamZGWBWi0tLNlSbCVRBTYh/OgExl+468bhzYX0cHhDZkvMDLX2kg8s/FOBtZXE3yDmVU5l6xrGIbsOqW2jJ9XA3eX3WFlb+U+Xnzcqd1dSZ+Wq9JI9H7eltZq5+iEUfY9xJ59FNdc6tSsJODyAwFFdvrvAMhFPMwLjKaywclINqbBEeD0tre8DAACrUX7yH/eapaR83cuERQ8sKFRy4Vb3GmrRLtqLsFZVmWdthrRoyWaW5LplBC5LlufPBVYYMbbAquY1VHZ++iPle6zVhHlaYUkKY/QertWQVc4Mhc1fUnidDfa+yqlsLcO47lpWSc1bM6mWl4y7zhar02LNRbW6IrCN4QKrCo+wjUXRKOMNADL8UQG2U/m5e3vXHt5eFxhhvlhHXrTk7XXl8S+ZZB+SUBX+uqjoPRzekFn9VYnCUCs/VfjZwE8tEp4sXiutfFG07U1l3l5XXqzVSbVxVIXFyoOJO29U7q5k74EHqHHPx21prWbGbUig6HvMlFnmFyqt8aldScDRR3X57gLLRDzNCIxnn6RnAAAguvKT/7jXLBnhN/ou+nhgDl+JutdQtd4OFyXCfeVV7S8XRMmXqtvM8jZWNjM/cir7LfDFgMvHVhjkohqiJz2HdMX+3kvuh4/ew7Ua0mA1oLCBdT8Y3smxgmkW9iqnstUP48qj35NJNbzToq+zxeq0KHNRra4Ib2OgwL5d48FKl7R2CkBEg9niW80ANtdgMNj/d36iK/9pxmg0mk6n838Ph/9/e3eUpKgOBQBUu2aJxEWCe/R9pF6KAoGACSCe8zHV00WHyzVcJSbQdF2Xv+vMXURd1z0ej8Ev+xcMKYzFllcd4GD7/h4HB1swwtu7RM001TRN+s3md67Mw+y/4pmxve0Vb9tZbO12u7VtG0J4+4elYnvb1Nt24i/nk5/f38ZdaDygMJO0+LcpOcUzXLUyjKPN78mDk+XDD2/jSKYS8raTLB7pnqXskG58y3j1L19UFyMf73FmR/PpKls3bhXO9FKZL3ukaz8J3I5+X/58j/OzSQEAAATxSURBVGVPqPk41364qvpaj3c3VXY2FNXb0osy9aZQr6jmNDgjP7B+VPlb5rxRAgAALOq6rm3btwNflS46xnucv8ApGOFUU4/HY2qcfLO1hxkD2HaYU1eL4za35e2T2GZCvY2Sv2GUKX+/t4xUDAZSUjJrZ7i4epk8rT1L2VHdeMqPFNVjlUrah7XocKl7z3y/fKwU4XmSBsAV1J1TDXCQ+UK3qgyuXXA5mLayas2ip9dNtVN8zfH8YS4u7U1mXt9Bz8lsc7HDFIkts6nx3REWEzu/x/z4m4zHRRXP8OYDqV0ZpjKwWX6D23bt6XVTwdTL1aqmihTVVZHPBz/1J2mDgnXjVeFML5j5gke66jCLH8hReyx4QvVtK+D9P1lsc0pmr37llZ35kFYFdltzE6DFXK0tqosNFgksv5Ov/UgAAADAL7jM1WKpS/LivivD3xXt9Zy2G8PnUnk5Z23pD9ofHQsAl/I39RUmANuM7zKYL4SQ/0Segos18ycJFYwwhPDKe9BSqSPNP8y4OHhxs1ULUruuW8xb27Y5t9EtFdt8SJWW2+bkIe46hLDqbCqV4UpOu3Z51fy2zC33LGWHdONFv1BUY+Q5Wy4Gv9hOvbpRRMHMH3uk+78vF99jpeJTo1MVf63XrkOYCaz4RyAAAADgAvLHh5m3/wAmiW7M9aSSMvXwwGOl0WZnHwCFHTTZGoA5b6fsxHvZ7rbH+X0VjHCqqfxbMH6yr/mA394/OPMwpxbuj9vclrdPYpsJdZz8Sh8bMvMwmHo1yGTVDBdXKZNntmcpO6Qbz/iRojq1u7XBZ6br87pRVcHMH3ukO3ehGnvc/3PUZgVf67VlZ21gZ8hhDOPAAAAAACDT1PD14VYFdtqjeJ07toHBwM7R4VzBZboxFJE6eb0x/M2UPgAqub+mn4QLAGwWQkhraptvviHi/X5PP5/qY8N3Zfi7or2e03ZjAPLFYq6MAwAAcH6nHRDuui7dFnQxsDMPq542w2NfFOq3uEw3hiJSkTlbhTltYABcwN/RAQAA3+HtzVzZwNPrDqQbA3ypEMJNGQcAAIDPxOvr6Pl89v87s6Xr8c26rksznm++HShEN4a+ruti934+n6eaW5yq36miAuAaTHoGgN8SQrj/b2YkKG28T1TXNhjWlNXP6cYAAAAAABu0bZt+jhNGQwhpRlr87/1+749pm6+2Skxg/Lc/y7lpGoPVpejG0JdKTf/UOFYqd9YbAFDDv6MDAAB29Xg8+qM884wHFdEfYnBtX4RuDPBT0vIhZRwAAAA+FEJo2zbNkItX3DPDreeZQvddxil1m+eCdGPoCyE0TfN8PuPNns+wviKej03TGNEFoAZ3egaA3+KxXzvz9LoadGOA35HuiuTbKQAAACgiThhd3KxpmrZtzzB57gJksjjdGPrS3OIzDKKmM863ogBU4k7PAPBzBsvf45Xn4/GIP8R/BwviLcNdJYTwfD7jour+7z29riDdGOB3NE2TKjwAAADwuRDC6/V6O44abx7hSnyzwYMK3ei0Ht0Y+uIXZ2e42XO6zbMTEIBK7q/X6+gYAOCC4rTX+PMJh7S6rstfXHvORfBnznA/tr5zZvJ7XaAbAwAAAAAAAACQ6e/oAACAA3js1/5ksjjdGAAAAAAAAADgd/w7OgAA4Bge+1WPp9ftRjcGAAAAAAAAAPgR/wGR3fvYQA1YuAAAAABJRU5ErkJggg==",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -1649,7 +1474,7 @@
     },
     {
      "data": {
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LITHxeF83/u5/dqHr+iDkGlJGy40q7Cmvbunfkh27XDvdry/WdR15ey5FSlV9XdcTFmfP5+m0AwIAAOzSYFKuJyM0+R0HX5OS2tpi3jLsxk3AYNrnmil4FfQqRhXcBg/g1i8QZeS5KCOf/PqwQQMgMn06AGCfgj/8zzeqscKTL27b9nK5pASqQJbwccVzSnJhbI3d2DaxXdL8wVuMPUiBRM/jrZeF8vvxAzgq/zszbtnNfPf0a6Gnlrr7L+m/ObnobaHYwp5mzqZatcY+bko0lviKAgAAQDRjfwEmWgLhOU82FEjJsPV3IljosPdtNM85fxls9MSfNDNPkv76sdm/Bxmw9Ih1+oO2pxnch/TMc/lIhioGnZN4vN+zo4rXtmPRD+gg5xhK4qXU80+v/qjO/NW+7s4ZZ32Okv4BMe2G1vN56rMGAADYk2J5y6UPMsoW85bTolSg6jhmMI+zz6WMfJQyBbfxA6iMPIUy8iyUkQPAOzcAYJ7E57G6rgu/Y7onx7bcSaUcuf8IiVmklOGlB3PCmc6R6wTHHvPuyJfLpedQoxZhyjjTj3bfc4O8XC6jRth/spsI4NhjPjm8sXFLH16Bu1bGW3HGO8y0syu5sMeOrWdt513V2T9bUw6YeCsAAADYrlzPbtOe9HM9Sqefb96jLXHYmHnOApmcgCc+LS2WK40zeJrpQavyJV56BhZwBsNa9Oy6lbZEMnzCvPS8ZnAlpPxtxoRh4qmN1c3FEjnGhQY86tSWG3b2cBWTuCbnH2GmbeW9R93Tpp1az/F7lnrKeGacNwAAsEO5UhnB85YpB0l/fJs/pIB5yzL13ukBTM/zBAzm2Hiuss+V0RJDGnWy5aug0w94X8yCW2Xkc8Y27U2VkacfZ84IlZEDwGO2CQFgrg316ag/1z1D3lVwPvlIudxJpRx5fgTyPnVPPtnJsg8pfdkkHnZUz93BtEX6odIHmfeUgwcwPYbZ9/YWTbCm37VyDXXUgAePM+F8MwYzewp48ICjVnWWsaXPaZYhAQAAbF36U9tgLmLUM2D6Q1zex7e8R1visDHznAUyOQFPfGzpWEq+bpX8ZN5izTmVdltP0eeScmrFZJ+UKkefjv4PlLu/zbhOshcEPzeSXIu2wGgHz2W5YWcJUXnlP3rmKBaWCSaf1OSC6ecO2D9ZW48zAABQ3irZ2vJ5y4x5wiyHCpi3LFPv3Zk/2uDBTDzHzlr7XBllH9JuqqBHjbPwHS99YCsGUBl5CmXk0w6ojByAY/IRAgBzlS8hnZCqmPDuy51UypHnH2FUeVauZEpGKeMZFf+UA449ZsafcEw8zthBTigmnjnCtQKYfqi8W3GJp1zgrpVyzPSjJcYzy6gmnHL5hV0l31TTl2Ku08w1qo1+0wMAACBd3uzBqAOmP3mlHCf9lPMebYnDphyqfJ4zSCan8ImPOuu8Z1rluzqyZ4yfO2DK3249RZ9LynmVsVDFduKfTH6vsUPK26omi/kNO4oNtecUlhv2nMisJVcavNg6jJz9Lnw6PTHv/9hKGdjkUQEAALu0Sp+O9OElHjDX8FKGlKUrRMpgImeeE09zzunn/T5z4WBuYp8ro+xDWuI0M1ZBp49w7DhzFdxmH9hNGfnsw96Xcsz0oykjf0wZOQA850Xihx8AsFHdE2PbtmsPJJumaQZfU9d1ysvG2m4Y00c+6hybpklMplyv1/TDDlridPoXTPwAJh7ncrmkXBpt25YsU35gwl0r8baQ94DZlV/Y6dJvqk3TFF48KQNLuUDO53OO4QAAAMSVmD2I/Ay4M/vIcy6XyQl74qMqd3NdI4nRGJXfSBnbk2nJrc/gMV0uF9G7r2QO/Hq9Xq/X0+m0StqZ7Nq2TbzZDq6xvHtn/W+0m+VX1/Xku1nbts/FfKGPLQAAgJJWyVtmzDilPCb3v90O8pZx6r1jBtM+1xzxq6DHKlxwGz+AyshTDpv3gNkpI59GGTkAq9OnAwD2bE6t0qaNfU5OjNLY3slxpKenxx45PZmScSti1OuzzFr8AKaksEfti6yVbDrsXWuVhZ1ulUkp/KZqcAEAgN1LLIAb9Ti2bpHWEQTPcy6XyQl74mMrj0vmN0KNLewMHlBd14l1tzP1T3rPAHo+nhb9iCn/EXa0bh11mrWHOc6oJh2hNjuu12uo8cwx+UR6PkR2ExwAAOCwVswNpjzdD44tZTxZ0giR85bRkgmDygfTPtcc8augRylfcBs/gMrIt04Z+epvepztGwCye7n2AACABe2yrWNKImnCc3Jd18V+t6q8JdLTd87nc8rxc5UCjx1kl8EcHGH/C4IHcIk8VGLcspt210r5q1FHLp9rW2VhJxqb/G3b9nQ6zX/fKu3OnHKOg685yGYbAABwZIkPnhOejxIzGzy2gzzntEzOpk98rZT7QqeWErSmaR7cQDY9g4dS1/X5fC6W6txi/WLbtm3bXi6Xkmvver0+vqx2aX/nmN6ko0o4/VHxeVybPmHplmnZs7Tr9Xq9XicU6zdN81y4bBAAAAA7sGKpcJY9gixfNt563jJUvXfAYNrnmil4FfRY5QtugwdQGbky8ucoI098jSwxAHPo0wEAuzW2Q/YmFGub/aR9p2Inxy1yomeUtm3nXDIrBjBxm2psCq/89sPku1bTNBlvd23blj/x5Q4+c2FXW9jH7T/HlNPfX8k4AADAAwtlD6ptfjE7gh3kOadlcrZ+4quUcSdemGXyG1ufweMo3KTj7k2fm6Pn/nv/CAtkJrvccuFuHcdp1bEnPV0eHkv5d1f6L3A+uVTutkXSB7anW+j1ej2dTqO+2tHTpMPFCAAA7MCK7SdS3jrLG/U/vm09bxmq3jtmMO1zFbCJMvIqcMGtMvL5lJEvQRm5MnIAlvZi7QEAAIyQpW32k3b8dJ2YWwnVjfs50waZOLnPra59BHBCCu+Y2w9N05SfylUWdroVV0LiW888R12QAQAAOpOfjzxYTXDYPOdhT3yORYNWD3mQuTKDW3G9Xrs+HZsObLHMZBeo2+3WLfsC79i16ijwRmQxtklHrsm9XC6Dl3DXZSbxgDtbdefzOfEW13PiwXf3AAAANiElndL/TDr/V+7lLTPabjDtcz1nH1XQd8oX3O4jgMrIEykjf0wZOQD006cDAHbrgNnhAnaccppzasUSUuW7FKdbMYDLde0tnHha667VfO50Oq3yg3KRF/bqUs6xf9b29COBAAAAk3k44oH5ec6N5p/nnHjkVMzksbVDoqXEo40nuLtuHWXerj/RvYmbRrfsb7fb5XJZumeHVh2b0LbtqI2DjE060n86svu9zZRX7u8fhCmtOnp+/TLUjxUDAABMtnrecmb14yayRoNmPmDuIwi5PBnM/aU1AtpEGXkVuOBWGfl8ysjHWv3fAAUoIwcgvpdrDwAAILPr9Xo6ndYexcbMTNOkVwrOyaBFziXFD2DwPs0l3UU7SN4t8sKO4Hw+z5mplEvGNicAAEBn8vNR9xXWvIOhOnCe87AnvhtmMJpuRjK2D3hO//GfHEBP6m/dxGlXknv//+z+l7xZ5ev1mrEbznIRO+z+QtM0o2Z87FXWP2Wjwt4tpJTRRmvA9GQQRoX9fD73R77npxTtDgAAAGSR8qTZ86zX8+B2J8sTnLxlRjGDaZ9rjvhV0J2wBbfxA3jYNO9jysi3RRk5APHp0wEAbEmQhAi7tO/VNa3scmZuK4ju3HdwItNs/cRnbj8M7uNKcAMAAEegsiSgrT+wT3bYE58jVNBCDYZRCvTpGGtDH08PhpqxijfjvGwonpuwdJOO7POVuKET7VbwXBxGxb/npHqOk/I1MAAAABLVdT34HPdc78jBP0yp75K3zChgMCW+KCbg+s9FGfkOTmSarZ+4MnIA4nux9gAAgEV4YuROmfTKjpdc8AAuWk9Zslgz+xLqNvZOp9P1et16kvHgdnx7AQAAKMO3EMnO0zpszvV6LZDv7bk5PE7S9n88RS6+bz93u91ut1td15PvinLXAXU/oJo+NXVd32631ZtfrD6AvLrrK/HKul6vz90xnpvHuq53FjEAAIB1nc/nwddM3qpIOfimybcPss81U/Aq6PiCB1AZ+XOUke/Gjm8vAOyDPh0AAOxf5IpesutSq/vo4tzvIAs75TSfm+uUH2qYMiYAAADepzgG1iXFsXW3qUbdflfPl+51od717LhcLhM+EDULCKXbXEh/fV3XcRb2/n5euG3bxGvqya/r9Fxcu/+KFwAAQGGT8xspfyh5QgH2uY4gTh6PpSkj3xll5AAEp08HAACwHwdJrR5NyjbY4x3ZwT1au2sAAAB3PCIBbFHXHiLOPby/nPHBF+l7srhxzmiUpmnatp3WrYPVdaXbozYXLpeLEt6lJUb4yYnrmc3z+XwaI2UMj//KF8kAAIBDGcyHTKvok2YhkaUCdJSR75IycgAi06cDAGAEWRuIbGwRLQAAAMAxHTaFctgT3w0z+KT0TgG+NF5A160jvbjTqo6gbdtRpdt1XV8ul2gX1Pl8XnsIi3jQ4uc50aYDAADgaFIeSx8/uw0+jG/laVeGJyPBBCZTRg4AlKdPBwAAsAdZsqt1Xdd1fbvdsgyJXFK+afB49gfXg586BAAAuKNiCWDT4vzkV89I7n/W9H+jfitfwOgxqlUH6+p+XzH99XVdt20bsCtEwCFlsdfzAgAA2JmmacYmQ1JqtzwVksg+F6CMfMeUkQMQmT4dAAAjqKrcKGmU3Wvbdlp2tcuoXi6X2+12u93att3QatnQUOdLuf3eD8hgcNzPAQAAMlL+uDmHfS7e64kfJ0201xmcb2drYB9fwNjZpOzV2NLty+ViZstLufn75ygAAEB8D57dLpdL/+s3lAzc0FDjWyWYEgtHIK23b8rId08ZOQBhvVx7AAAAmS36zLyD33BbSPAk9eCOzuqCBzC+sT92dz6fd1DqHX9hF3a5XO6mdTA47ucAAMBxnM9nmYctOmye87AnPsf9rMjqzGBkBT4O+j90mqY5VGVkXdc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       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -1663,7 +1488,7 @@
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     {
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       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -1681,7 +1506,7 @@
     "    \"solver_damping\": {\n",
     "        \"distribution\": \"normal\",\n",
     "        \"loc\": np.log10(solver_damping),  # mean of base 10 exponent\n",
-    "        \"scale\": 0.1,  # standard deviation of base 10 exponent\n",
+    "        \"scale\": stdev,  # standard deviation of base 10 exponent\n",
     "        \"log\": True,\n",
     "    },\n",
     "}\n",
@@ -1695,11 +1520,6 @@
     "    fname=fname,\n",
     "    runs=10,\n",
     "    parameter_dict=solver_dict,\n",
-    "    # choose what to do\n",
-    "    # create_starting_topography=True,\n",
-    "    # create_starting_prisms=True,\n",
-    "    # calculate_starting_gravity=True,\n",
-    "    # calculate_regional_misfit=True,\n",
     "    **uncert_kwargs,\n",
     ")\n",
     "\n",
@@ -1723,7 +1543,98 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "calculated stdev: 169.53167981196884\n",
+      "using stdev: 169.53167981196884\n"
+     ]
+    }
+   ],
+   "source": [
+    "# load study\n",
+    "with pathlib.Path(\"tmp/combining_it_all_density_cv_with_cpm_kfolds_study.pickle\").open(\n",
+    "    \"rb\"\n",
+    ") as f:\n",
+    "    study = pickle.load(f)\n",
+    "\n",
+    "study_df = study.trials_dataframe()\n",
+    "study_df.sort_values(\"value\", inplace=True)  # noqa: PD002\n",
+    "\n",
+    "# calculate zscores of values\n",
+    "study_df[\"value_zscore\"] = sp.stats.zscore(study_df[\"value\"])\n",
+    "\n",
+    "# drop outliers (values with zscore > |2|)\n",
+    "study_df2 = study_df[(np.abs(study_df.value_zscore) < 2)]\n",
+    "\n",
+    "stdev = study_df2.params_density_contrast.std()\n",
+    "print(f\"calculated stdev: {stdev}\")\n",
+    "\n",
+    "# manually pick a stdev\n",
+    "print(f\"using stdev: {stdev}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "best: 2857.9296283003237 \n",
+      "std: 169.53167981196884 \n",
+      "+1std: 3027.4613081122925 \n",
+      "-1std: 2688.397948488355\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x350 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plotting.plot_cv_scores(\n",
+    "    study.trials_dataframe().value.values,\n",
+    "    study.trials_dataframe().params_density_contrast.values,\n",
+    "    param_name=\"Density\",\n",
+    "    logx=False,\n",
+    "    logy=False,\n",
+    ")\n",
+    "ax = fig.axes[0]\n",
+    "\n",
+    "best = study_df2.params_density_contrast.iloc[0]\n",
+    "upper = best + stdev\n",
+    "lower = best - stdev\n",
+    "\n",
+    "y_lims = ax.get_ylim()\n",
+    "ax.vlines(best, ymin=y_lims[0], ymax=y_lims[1], color=\"r\")\n",
+    "ax.vlines(upper, ymin=y_lims[0], ymax=y_lims[1], label=\"+/- std\")\n",
+    "ax.vlines(lower, ymin=y_lims[0], ymax=y_lims[1])\n",
+    "\n",
+    "x_lims = ax.get_xlim()\n",
+    "ax.set_xlim(\n",
+    "    min(x_lims[0], lower),\n",
+    "    max(x_lims[1], upper),\n",
+    ")\n",
+    "ax.legend()\n",
+    "print(\"best:\", best, \"\\nstd:\", stdev, \"\\n+1std:\", upper, \"\\n-1std:\", lower)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
     {
@@ -1738,7 +1649,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9c7c1e6b3f724270a1929b70aaff4c5b",
+       "model_id": "56cbc918d0cf4c84b212a7bb19d63ec0",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1760,7 +1671,7 @@
     },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -1774,7 +1685,7 @@
     },
     {
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       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -1788,7 +1699,7 @@
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     {
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kH31oTSvnsYoela4ZPp68fpK5zB2erWRDpibbkj4qpGZDP3tUkZYNOTXZ5zW8ZUO2JnNikb3/NafngKPy7qzN3BgU1E/IUZlr0WxNwPZlTk320QuNMPtYeSMcfkWdU1PZ89q64+zbD9492LI51bJtx+bbD9w95Kjt6aO27jj7jsGWe+zYnG7Znm7ZumPzHQfvWr8mp58dm+8cPGrL9s13DtZs2ZFuucf27FHn3HlruuWudEv6qHOzNeelW87dvjndkqnJPlZ2S6iV/8uQ0k/VZN/6k0w/rbztp6GP1co8Vl5Ntp+Q51VWTciY8/pJd5PzTIPeKIJqAt46cmoCHivnjSL7FlTkzS2sn/HVhL2qhWqKPXrQUekvnhV7rLznle455Ki8luH95C1YmZotW/t33DrQco9tQ1ta99jWv3OwZst5/TsPDrZsS7W0tpyXxLckuTV3Dbacm25Jzt2e3HVgsCbdkmwJqMnt5+7BmnPSLa1zdwytyTnqnB3J4cGa7Oc3rbxPdAJqhl925DxWzlHDb6Ep+OgBNYWO6ic5a2rRnkNWmnRDP+9dO9PP8J6D1rlMP3U7Ku/VyPST82oMOzPIrSnrsUp69LzXJ+Cosvr5l6PO3/jO9OF5bFUD0yRwY/z8ix638h+tufROY6uVaQmpKe2o+fRYA446//zt+/bfPtCy+7xMy7Z9++9Y3bJ793mplvN3b9t3S6Zl/50DR+3auu+WOwdrtu675a7BmnukWs7fdY99twzsAu3enWnZtSWv5XC65cBgy85z81qODB6Vbtm1c/O+A0cHW87Zd/BoumawZfeOzTcfPDZYc/bQltOt9Fn1qZCWufTJzKnMCeqpzGlttqWf+TzkdMjpcU5LZncrc7GQ1zL8qLmclmJHDX/0zIuaJEmyIXMSO5dpCaop1nNJ48n2U+yosDGnDyvWc15NsZ4zR2Vq5gNqwp5XumY+6PXJPnqxxwrop+CjD6/ZvLjx6OBdqmdvXDg2eNfs2YvDW87auHA83bLx+MnBloWFVMumjZmWhYUTmZoTJwe/wrEwf/JUpmVYzcaFhVODLQvz86fSNemWhfn5VDjLwvyGdMuGdM18pmZ+fkMqeGV+w4ZUgMuGDRvS8ShJ+htQc63sJ9SZrwGfTu809k+fSrecyrTk1AT0U6znQo+VlPZYAT2HHJUkSUkvdbGnFjbsTCJbdS9RUD9FXrFij54U+gVOcl75Yv0Umz7Ffl4hr2pIz5l+CvWc7Wfj1h3HD+4faNm241i25cBgy3k7jx3YN1gT0HLerkzLzmO3ZGpSLdt3HR1sWdy+6+j+VMvuo/tvHmjZsTtVs3HH7qP7Bmt27j66b1/qqCODLZt2plsWd+4+cvNgy650y6ad5x9OtezanW7ZvfvwTamW81MtmR/XSsvAQnM6vc7k1JzK1oQclS7JOapYTc5RATWnMktq3lHpmrB+0h+1hByVJMnpzN2k+Z0PH2TAUZnHCuq5n36RCvYc08+njtyaDGNXHJgygRvj8/e9+Nv/1cpsRmRbsrf7Fjoq5LFa1R2Vudk45Fnk9ZzpJ+D1yd4gnfeqhvQ8vCZ7W3XBowJuz872nO3nnru23Lx/4GOCC3bdI9OyZd/+uwdbzs20BNTsPHf/YMv5u87Zv/9wgZZb9qc+kthcrOXAYMuuXWcfuCXTkq3ZP/BBxs5dZx8cPGrnrrMPDh61I1uz86ycowZb8m6ZybvBNd0y/HbWvBttwm46HXabT8gIc2/uyHkWw27qya+p6nnl3iKYrgm55TXvxpaQ+94Djsp59JKOKuk7COW9YsMfK+SmsLCbljOPHvC8iv28ij6vsmqy4wm4RTlzVPZVPXvbuUcPDXxmfdZ556Rbtp17bLBl03nnHEvXpFs2bTv32G3Zfu7I1KRbjg/2s7jt3OOZo44PHrW4Nd2yaduWoTWL2849MdiycWu6Ja9my4nbbh+sSbdkazZu23JysGVha6YlU5O3ZGTfEsNqhr/VFzvqdCmPlffopwscldeSHmHoM82ZhkXuYE/myrmnPeixsjUBj55zVKFnEfLoeTXpkrw/zF7oqJwxZ47KGWG2JuSxMiUBN0gXffSAmkJHhd38HNJzpmnTzuT4wHeok8V0S3/TzuT44LcAFnekW3JrTgy09DfuTLVka5LFdE3OURt3JCdTLTtTLf2FdEveUTuSkwcHj0q3JAs7klMH0jWnBmvmMy0LO/qZlsxR29M18zuS04PjyTv5TrXk1GRasjU5ASWhR8U/Vln9rDoq5I5xG+PEyr6JQr7AHWoAAAAAZtzy8nKn05n0KABKYGMcAAAAgKHsigPTxMY4AAAAAEPZGCeWWBOCrLdDvbh5vGMBAAAAoC4u/v7tue22qoEpsM7G+MLCljEPBgAAAICa2D63kG20Kw5Mh7U2xlubt45/MAAAAADUgdvFKYVYE4Zb59btud3/qjWf8xEdAAAAALPgnhdt/tN3PSr3n2xVA422zsb41vO+Z25+05jHAwAAAEBNPGDx3LWSTVqtVq/XG/+QAEbX6/VardZamSYbzr/v+IcEAAAAQB24XZxSiDVhiPUzTebO2Tbm8QAAAABQK09q38dWNTBl1s802XT27jGPBwAAAIBaWSvZJEmSpaUlySZA4/R6vaWlpdx/kmkCAAAAgNvFGZ1YE9Yj0wQAAACAoWxVA9NEpgkAAAAAQ0k2AaaGTBMAAAAAhnK7OCMSa8KaZJoAAAAAEMhWNTAdZJoAAAAAEEiyCTAFZJoAAAAAEMjt4oxCrAn5ZJoAAAAAEMVWNdB0Mk0AAAAAiCLZBGg0mSYAAAAARHG7OIWJNSGHTBMAAAAACrBVDTSXTBMAAAAACpBsAjSUTBMAAAAACnC7OMWINSFNpgkAAAAAhdmqBppIpgkAAAAAhUk2ARpHpgkAAAAAhbldnALEmjBApgkAAAAAI7JVDTTLWhvji2ftOv+ix8k0AQAAAGAoySZAg8g0AQAAAGBET2rf5+bjT3viU+6d/Se3i5NLrAn/l0wTAAAAAEoh2QRoinUyTbZtf/D4xwMAAABAQ0k2ARpBpgkAAAAAZfmzd/+QZBMCiTXh22SaAAAAAFAiySZA/ck0AQAAAKBEkk2AmpNpAgAAAEC5JJsQSKwJSSLTBAAAAIAKSDYB6kymCQAAAAClk2wC1JZMEwAAAACqINmEEGJNkGkCAAAAQFUkmwD1JNMEAAAAgIpINgFqSKYJAAAAANWRbMJQYk1mnUwTAAAAACol2QSoG5kmAAAAAFRKsglQKzJNAAAAAKiaZBPWJ9Zkpsk0AQAAAGAMJJsA9SHTBAAAAIAxkGwC1IRMEwAAAADGQ7IJ6xBrMrtkmgAAAAAwNpJNgDqQaQIAAADA2Eg2ASZOpgkAAAAA4yTZhLWINZlRMk0AAAAAGDPJJsBkyTQBAAAAYMwkmwATJNMEAAAAgPGTbEIusSazSKYJAAAAABMh2QSYFJkmAAAAAEyEZBNgImSaAAAAADApkk3IEmsyc2SaAAAAADBBkk2A8ZNpAgAAAMAESTYBxkymCQAAAACTJdmEFLEms0WmCQAAAAATJ9kEGCeZJgAAAABMnGQTYGxkmgAAAABQB5JNWE2syQyRaQIAAABATUg2AcZDpgkAAAAANSHZBBgDmSYAAAAA1IdkE84QazIrZJoAAAAAUCuSTYCqyTQBAAAAoFYkmwCVkmkCAAAAQN1INmGFWJOZINMEAAAAgBqSbAJUR6YJAAAAADUk2QSoiEwTAAAAAOpJsgmJWJNZINMEAAAAgNqSbAJUQaYJAAAAALUl2QQonUwTAAAAAOpMsgliTaacTBMAAAAAak6yCVAumSYAAAAA1JxkE6BEMk0AAAAAqD/JJjNOrMk06/V6a2WatDZvlWkCAAAAQP2ttcEFkGudjfGzzto15sEAAAAAwFp2zi+u9U/dbnecIwGabp03jdY5541zJAAAAACwjtxYkyRJlpeX5X1PPbEm06zT6ay1T92/+7ZTN18/5vEAAAAAQNYHl7/x3J/51Fr/6u5tIMo6G+O33foPRw/vG/N4AAAAACDrwKnj//v4Xbn/1G633b0NROn1eu12O/efTu/7p9N3HRrzeAAAAAAga507xrvdbqfTGfN4GDOxJlNOsgkAAAAAdTY008QmNRBLsgkAAAAAdSbTBCidZBMAAAAA6kymCWJNpp9kEwAAAADqSaYJUBHJJgAAAADUk0wToCKSTQAAAACoJ5kmJGJNZoRkEwAAAADqRqYJUCnJJgAAAADUjUwToFKSTQAAAACoG5kmrBBrMiskmwAAAABQHzJNgDGQbAIAAABAfcg0AcZAsgkAAAAA9SHThDPEmswQySYAAAAA1IFME2BsJJsAAAAAUAcyTYCxkWwCAAAAQB3INGE1sSazRbIJAAAAAJMl0wQYs/WTTQ4dvGbM4wEAAABg1sg0AcZMsgkAAAAAk/WcSz8p04TVxJrMHMkmAAAAAEyKTBNgItbZGD92ZL9kEwAAAACqI9MEmAjJJgAAAABMynMu/eQVl38z95/cLj6zxJrMIskmAAAAAIyfTBNggiSbAAAAADB+Mk2ACZJsAgAAAMD4yTQhl1iTGSXZBAAAAIBxkmkCTJxkEwAAAADGSaYJMHGSTQAAAAAYJ5kmrEWsyeySbAIAAADAeMg0AWpCsgkAAAAA4yHTBKgJySYAAAAAjIdME9Yh1mSmSTYBAAAAoGoyTYBakWwCAAAAQNVkmgC1ItkEAAAAgKrJNGF9Yk1mnWQTAAAAAKoj0wSoIckmAAAAAFRHpglQQ5JNAAAAAKiOTBOGEmuCZBMAAAAAKiHTBKgtySYAAAAAVGGdTJNutyvTBJigXq+31sa4ZBMAAAAACpNpQgixJiSJZBMAAAAAyibTBKg5ySYAAAAAlGv9TBO74sDErbMxLtkEAAAAgAJkmhBIrAnftn6yycnrr7ZVDQAAAEAgmSZAI0g2AQAAAKAsMk2ARpBsAgAAAEBZZJoQTqwJ/9c6+9SJrWoAAAA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       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -1817,15 +1728,8 @@
     "\n",
     "uncert_density_results = uncertainty.full_workflow_uncertainty_loop(\n",
     "    fname=fname,\n",
-    "    runs=20,\n",
-    "    # choose what to sample\n",
-    "    sample_gravity=True,\n",
-    "    # sample_constraints=True,\n",
-    "    # choose what to do\n",
-    "    # create_starting_topography=True,\n",
-    "    # create_starting_prisms=True,\n",
-    "    calculate_starting_gravity=True,\n",
-    "    calculate_regional_misfit=True,\n",
+    "    runs=10,\n",
+    "    parameter_dict=density_dict,\n",
     "    **uncert_kwargs,\n",
     ")\n",
     "\n",
@@ -1848,7 +1752,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [
     {
@@ -1862,7 +1766,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "42f4b2e664814c1f94ece924e441ac5a",
+       "model_id": "4e05b18d54a14a3f85ccdfded72a024f",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1884,7 +1788,7 @@
     },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -1898,7 +1802,7 @@
     },
     {
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tbqZ/feifjJ6dnuhbV8OG3qXJ4wPXhe65RVE0deFYeHEUReuvD32PMz01um59DevtyXMHN2wOXT9PTx6/euXChi2hS4XJiZc2bLs/tPPzb1y9fG7D9o+Fdn7qt+t3/H5o5+++Njc7sW7XJwPrL439su+G0IVfFEUzo8/0vPfTgcVXzxzo2h76BiqKornjT695f2jnc+MHuneGvvWLoujq0WfW3hL6lV4ZO9h7Qw2dz7z+zLrbQzufHT04d/HMhg+Grs8nDz278UOhxVMnDl2dPLPpw6H15155dsudocVRFE28/Oz2u0Lrz795aNNNoe/9oyg6ue/Z99wb2vnZo4e23VzD9+jE3mduvD/0e3T6jYM7b62h86O/febWj4d2Pnb44MWzZ25/IPRH/bVfP/2h3w8tPvHagcmJM3d+4jOB9S8/94u7PxlafOzQgfMTp+99KPTd3NFDr9x6R+isG0XR80//7IHPhL77e/3Ayx/4cOiuRRRFv/r5Tx76fOhbxUMv77/jIzW8VXz6Jz/63MNfDCx+ef9Lp0+NP/zF0LeWP/7hD77wpUcCi/fv2zc+fvKRL385sP6p73//0Vreh548efKJx0PfWj7/wgsf/2joFkoURd/89reffDz0Te7zL+y9v5a389/+/lOPPxL6PXph30v3faSGH93v/ugnjz0c+qO7d//LY6dOPfq5zwTWf+9nv/jyZ0InxhcPHDp5+syXPxW6WfT9Z5575JMPBBbvO/T6yYmzj3widPXy1HO//dID9wUWR1H0g1/v/eLHQr+nLx0+dvfNoR92R1H0wxd+94V7Qxck+4+euOv9oQvpKIp+tO/gwx8JXb7uPz76kVo+6vrJy4f/4I7QfdHfnRg/dWHq8x8IfWZ++urxzwdvjL88evrUxenP3RL6YcfPjox+tpbPdH7+xthn3hf65ujAqXfv2LohvPNfvHXm07u3hHY+MfmhTQPhnT/zzrlP7dwYWHzw3NQHN64L7/zZUxc+GfyVHrowPXH56ic2rQ+sf+7cxQc3hn6lr03NnL0698CG0PpfT059bF3oSA7PTp+bm/tof2j9kdnpm3r6A4ujKNo7e/GetaFP47Er0zd219D5/iuTd3bX8NP48tXJ8GLam11xoKPENsZrijWZmj470B+6lpuZnezrq+V4+aUz4cfFZ2YuzM1dHlgffAL84unw4+Iz0+fnrl5evyF0l/7i5Kn114Wun6cvvTt3dTb8xPj01Lv96zcFFkdRdPHdk+s31XKi79xYeHEURes3hx7omp4823dd6E9LFEVTE6MDW2t49zd15u3w4qiW4+Iz58/UegK81uPl4SfGp08eDz8uHkXR9Nibfe8Jrb989tTaWk6Az4we6w0+Xn5lYrx7aw2dX3772Nr3hr7jvnJmfH56KvzE+JW3joYfF587PT5/aaqGE+PH36jhuPipk9GlqRpOjB97I/y4eBRF80ffKASfAJ8/ORbVcrw8euNIdEvwga5aT4Afru0EeK3Hy2s4Lj46Fl2cquHE+KtHwo+Lz709Nj85FX5i/PLBI+HHxa+cGLs2ORV+Ynz2RG0nwKdeObI++Hj59FtjAzfW0PmFl49cH3wCfOr42IZaOj/7uyPhx8UvvDl2+fxU+InxU/uPhB8XP3dsbPb8VPiJ8XdeOhJ+XHzi2Nj0u1PhJ8bPHBvbflMNT+PxFw+/v5YT4Dtr2Yo8uvfwLfeFHukce+OdG26p4Xj56y+8/oH7Q7eLR98YnTx3MfzE+KHnXws/Ln7iyOiFcxfDT4y/8ttXw4+LHz88euHcZPiJ8f2/ORR+XDyKopd+c+je4BPgR18/cUstx8tf+PXBjwafAD/y2onbbq+h89/sOfBA8Anw11976+zEhfAT43t+9Ur4cfFXXz0+MXEh/MT4L5/7Xfhx8YOHjp+ZOB9+YvzpX+4PPy4eRdHPn33pcw/dE1j88qFjH/lQDcfLf/rMiw9/KvRgz/6Db9xVy+9G/fjp57/wmY+Hdn7g8N13hN7RURT98Bd7wo+Lv/TKq+OnJ8JPjP/gZ8+GHxff9/LB8VNnwk+MP/WTX4QfF9+3/5WTp06Hnxjft//le+8O/VGMouj7P/zxl7/0hcDiF/ftv+/e0B/FKIq+99QPHn0k9GP9vfv23X9fDR+kfvd73ws/Lv7C3r1jJ8fCT4x/57vfCT8u/vwLz4+NvRN+YtxxcbIl1oSUkke3AQAAAOhYtqqBTmBjHAAAAIAFdsWBDmFjHAAAAIAFNsZZja5mD4CWZIcaAAAAgKVGRkaKxWKzRwFQRzbGAQAAAFhkVxzoBDbGAQAAAFhkY5zVEGtCzexQAwAAAJBkqxpoYzbGAQAAAIixKw60NxvjAAAAAMTYGCe1wvz8fLPHQCsJ2aFe09XTt3ZDFEV9/b0zM7NL/6mvry/Rkqgpc1XZlsvxltmQqxItZa5a1nOoQvWmgJKwroOuSnaT6rKUVwU11drPfGA/6R69bt+LMpNsumc1oJ+gCT3V8zOf0U9d2hHGpXtWy12V7rFCrkrzU1e/56d/bc/0lSvLWnoSLWt7pi8nWuJXra3aT1/Znqs+eqJlXaqr+nt6pq+muirRMpOqnxRXFaLq39aQWzB1PyGzQtUBlH30oKvq009UblZI2U9Az0FX1a+fgMuCripTk1jMNHXM5S4KWG4lr0r5WNUfPe1XEXBVYtDpei5Xk813uXtNz9W5K7GWuUTL1WtXl7d0z81Vbane85o1PXOxnru6Yy1ryvWcHGGZnufi/Vyr3nO8JVmzZk3Ptao9J7+KruSjlxvh8pYoin/PVpjuqn/vy1WE/AgVVvw/Kz5Q2odKXBNyQ4VVVL3Dy37lIfNCwDRQpiHNVdk9VrKbbMbT2Gcs2U2qF8yQKTrgpzXt1558qFQvEbU8Y9/69jerPsDg4GCpVAoYCUDLCNkY7+3fvvAf5Wb16q8y5ebw6v2ErQrSPHpf39qZmSvxltnlLb1rZ5e39PatnZ25uvyqnpnZREvVmt6e2eUtvX3xlr7enpnZuar9VG/pS/TT212m5XKVmt7e7tnL8ZaZ2WvLr1ozcznRsrymN1GTbImiKPZNTO7fzid+OubjF5XZci/XT7mrstlkSexill2CVH07U/Y2qH39ntyVKtdtfMyhu1Kp1npBX1e6q1LVhMwuaR+9+qK6kV9pc8ec/Jsk2T1Wun6qvxtP91jdXV1z88smtzVdXdeuzS9vKVybn0/UJK5KU1OIPVZXV9f88vF0dXXNx2vihytCa5a3FArJmkRLIX5Vupoy34tCIapWE4WtVGLKnTxJtCRqylwV0DIf0HPYY8UbMhtzop90X2m5nrPrPG/91HGEyZJ8PWNZ/ZiV67iZX2m5b0Xjbt6unt5rl2diLXOXlx0Q6lq79tpsrKX32uxMrGUuoOZavOfeuWRNop9YzZq1fYmr+uKP1ds7t/zgU1dv77WZWE28nzI99/bNLb9qTaKlbM3V5Y++pq/MeObiNX1Xp5f1kxT8TS5UrgnpJ+gHM+Cq+rUkJV/4snv0kOej7CDTDSmjqwJGndl4/sODTVy7kvjHZeyKA20pZGN8oG/z3/1Xqg83gw4QpDuOE/RYqToK+uA73UNl9ZWm+7rq9Wyk6yd5BLevd+3M7OV4y+VYS2+ZmupXlatJfCBS9are8o+1vJ+1gY9epZ/ewH5iNWsT4+ntSXyl8YPEZcacaImi+I9Q0HHoqPrPS/l+qv24rnBVuscKuarm6a7Oz09g71Wvqt5RmQE0cAZK+ZVm9Oj1uyro3E+qftJ9OBXy6KnOKuWun77untnlvwXQ291zeS7eEqspf1XVftb0zCZ6LnNVtZa15XpOjrnMVfGe1yZrrizvZ22i57VrEjXdPVeS/Sxv6SnXc6ylJ9FSWPyfeGMlyc9/Q68q01i9o2w+Qo/K/Eyn67lcTfVP/VbReZWugyaljHoO6afcVfV7frLqOc37jnJfe/WHT/v8pPvuhNxfIWNOXpbqqqDbO/mVBnwVXWvml3+KXb5lftnRo66gq+KfdIc9VuLz8cKaRD9d5Ua4vCXxmXWZ8ZSria7VflWZfpKfj3fFd5CTn48nPsFfaE60JEtCbsN0P67lqqoPJ9UUWMstVvXEuI1xUhBrQg0CU7cH+jbv2HJbFEU7d20/efL00n8q17ItoGb7eJmrzixr2bnt5PjpREuyJtaydXx5y46d22ItUVT25SToyF6iIWCFFPQ+u5H9ZNRz0HHXKo81H3pVmpYysR1BVzWu53IbfyHjiTeU6bmhV6WpCXlWwx6rXiMs/2sD9RpPvCXkmX/Ppo2j755f2nJDoqVMzfUbR8/HWq5PtCT6KXNVmpbdgY8eu2rjxtELy2sSLbs3Jnoud9U7jWopBMy15WsKGdQUMnqs0JZqj9WVUT/leo7fPSFXReV+kWBNoqgr4KpkzZpUV5Xpp8xViRGG9BzST7qrEjXJrz25TAobc8gIM6oJuKrcz082j17u+UnTc8oRBuy/bFh33dT05NKW9f0bLsVbrpuaibckajZcqlYzkKgZ6L9uOtbStyHWsq5MTaKlP3FV34aZmYtLW/r7N8zEe47XlLtqfbylb8NssmU21rI+VtNXpibeT1/fhsvLa6LEj9AKE2myJvkjlKKmkPKqajVRUM/xmtCWVP0k7rGQq6KoEJ/wComWkJp0VyVryr2TzabnQldG/QTV1O2rCPkOJmbSct+vkBE2sp8avtLNWzbE/6kcW9VAOwncGN+5+/ML/1Hoir9RKxQSLSE1mV3VHR9rwFU7d24ZP7VsB2Pnjs2Jlk3jpy4sbdmxY3OsZeeOTeOnEy2nli2qd2y/fvz05PKa68dPX1xeszHWsnP7xvHTU8sfPdGy/bpyLZfiLWeWt2zbUK5levlV8Zbt2wbGz8wsb1k/PjETr1nesmPrwMmJ2eU166q2XCvEF59zIS2JbZe5xFbIXOLdQrJlvivecq38e4yqLfFdzMSQy7ZUv6qrTEu6q6o/enIvK4qiNYkVWXItHFSTrueMxlNm5yrVVWFjjl+WrudyNel6TlyVqOkOqAn7uuI13UHPT/LR0z1WQD8pH716zUDv2plE1vnslWXJU+t6q7f0r+25HG9Ze/nq8paenlhL39pES0/PlUTNlavL86p6uq/OJVqq1azt6Zlb3tLT3T0Xr4m39HR3x8JZerrXxFvWxGu6EzXd3WtiwSvda+JHzdasWROPR0m8K+wqJPc0Er8GfC0ewjV/bS7eMpdoKVMT0E+6nlM9VpTZYwX0HHJVFEUZPdXpvrSwYScS2er3FAX1k+YZS/foUaof4KjMM5+un3S3T7rvV8izGtJzop9UPSf7WXv91ssTp5a1bNo6m2w5s7xl87bZM+PLawJaNm9PtGybPZ2oibVs2T6zvKV3y/aZU7GWHTOnTi5r2bojVrN2646Z8eU123bMjI/Hrppe3tK3Ld7Su23H9MnlLdvjLX3bdl6KtWzfEW/ZsePSWKxlZ6wl8e1aaFn2QnMtcUQmWTOXrAm5Kl5S5qp0NWWuCqiZS7yklrsqXhPWT/zcSshVURRdS5wmLd959UEGXJV4rKCe5+NPUsqea+nnl9Nno2rsigNtJnBj/KYbHvi7/8ro+HH9DlqXO9cc8FiOi9d6VRbHxaNyh2B3bN8yfnrZK/KObVvGz6Ro2XxyecvObVsSLZtPnjmXuOpctZpN8Zqtm09OVGnZkWzZsinFVTu3xq8qWzM28W7lq3Zu2TR29t3KLbu2XB9rSZ7wT8QSBtVEGfWziqtS9RzVbzxRXLV+otBz5tVryt3g6fqp3hI2RYdcVf3BU85SGbWUORqaGGC546PZ1IQcyg3qJ6PHKvNRVKp+yh2rTgTgBly1Y/3G0xfPL2+5/szUspbt6zeWablYvWZiecu2gURLsqbcVWeXt2xdf328pUxNvGXLwMZzsZb118dbBjaeu1Tlqs0D1797Kday8d1EP7GaTcmr1iVrNp6/lPhti+W3SyHx45plTbWroro9epRYSabrOf0IA06Mh/ymQB3Pvefu0QOuKvPoWX0VyUev3nPYV5GqJnEMNaznNMdiy/Wc5qB1ocyj1+tQd/fa/qtXZmMtc8tb1iRakjXlruqbu3J5eU28JVmzpqd37uryq3r6Yi1renrnlqdTdSdaytSsTdasnVv+WfyanrXXYi3da+N/6rV7bfyPyJapSfyh2e6ea8s/d+tKtCRrojK/BZD8UUyeIipTU/7vjlSrSTx6/foJ6nmxn5AT4zbGqVXytQnKC9yhBgAAAKDDjYyMFIvFZo8CIAM2xgEAAACoyq440E5sjAMAAABQlY1xaiXWhCAVdqh7e9Y3eDAAAAAA5MT993+0bLutaqANVNgY7+m5rsGDAQAAACAntnT1JBvtigPtYaWN8YG+zY0fDAAAAAB54Lg4mRBrQnXwp/YTAAAgAElEQVQVjm5v33xr95q1DR4PAAAAADlxw3tu+PrX/23Zf7JVDbS0Chvj12/+cFd3X4PHAwAAAEBOfLB3w0rJJoVCoVQqNX5IAKtXKpUKhcJKmSY7ttzW+CEBAAAAkAeOi5MJsSZUUTnTZH3/lgaPBwAAAIBc+cqTf2irGmgzlTNN+tbtaPB4AAAAAMiVlZJNoigaGhqSbAK0nFKpNDQ0VPafZJoAAAAA4Lg4qyfWhEpkmgAAAABQla1qoJ3INAEAAACgKskmQNuQaQIAAABAVY6Ls0piTViRTBMAAAAAAtmqBtqDTBMAAAAAAkk2AdqATBMAAAAAAjkuzmqINaE8mSYAAAAA1MRWNdDqZJoAAAAAUBPJJkBLk2kCAAAAQE0cFyc1sSaUIdMEAAAAgBRsVQOtS6YJAAAAAClINgFalEwTAAAAAFJwXJx0xJoQJ9MEAAAAgNRsVQOtSKYJAAAAAKlJNgFajkwTAAAAAFJzXJwUxJqwjEwTAAAAAFbJVjXQWlbaGO/t375z9+dlmgAAAABQlWQToIXINAEAAABglb7y5B+enZh88omvJP/JcXHKEmvCfyTTBAAAAIBMSDYBWkWFTJNNW+5s/HgAAAAAaFGSTYCWINMEAAAAgKx84xv/TrIJgcSa8HdkmgAAAACQIckmQP7JNAEAAAAgQ5JNgJyTaQIAAABAtiSbEEisCVEk0wQAAACAOpBsAuSZTBMAAAAAMifZBMgtmSYAAAAA1INkE0KINUGmCQAAAAD1ItkEyCeZJgAAAADUiWQTIIdkmgAAAABQP5JNqEqsSaeTaQIAAABAXUk2AfJGpgkAAAAAdSXZBMgVmSYAAAAA1JtkEyoTa9LRZJoAAAAA0ACSTYD8kGkCAAAAQANINgFyQqYJAAAAAI0h2YQKxJp0LpkmAAAAADSMZBMgD2SaAAAAANAwkk2AppNpAgAAAEAjSTZhJWJNOpRMEwAAAAAaTLIJ0FwyTQAAAABoMMkmQBPJNAEAAACg8SSbUJZYk04k0wQAAACAppBsAjSLTBMAAAAAmkKyCdAUMk0AAAAAaBbJJiSJNek4Mk0AAAAAaCLJJkDjyTQBAAAAoIkkmwANJtMEAAAAgOaSbEKMWJPOItMEAAAAgKaTbAI0kkwTAAAAAJpOsgnQMDJNAAAAAMgDySYsJdakg8g0AQAAACAnJJsAjSHTBAAAAICckGwCNIBMEwAAAADyQ7IJi8SadAqZJgAAAADkimQToN5kmgAAAACQK5JNgLqSaQIAAABA3kg2YYFYk44g0wQAAACAHJJsAtSPTBMAAAAAckiyCVAnMk0AAAAAyCfJJkRiTTqBTBMAAAAAckuyCVAPMk0AAAAAyC3JJkDmZJoAAAAAkGeSTRBr0uZkmgAAAACQc5JNgGzJNAEAAAAg5ySbABmSaQIAAABA/kk26XBiTdpZqVRaKdNkoG+zTBMAAAAA8m+lDS6AsipsjPf3b2/wYAAAAABgJdu6e1f6p+Hh4UaOBGh1FSaNgXWOiwMAAACQF09+5T8p2z4yMiLvu+2JNWlnxWJxpX3qqZmz4xOvN3g8AAAAAJD0zW/97Z/8yT9Y6V+d3gZqUmFj/N2zr8xcGm/weAAAAAAg6czc5VcvXyz7T4ODg05vAzUplUqDg4Nl/+nU2cMXpycaPB4AAAAASKpwYnx4eLhYLDZ4PDSYWJM2J9kEAAAAgDyrmmlikxqolWQTAAAAAPJMpgmQOckmAAAAAOSZTBPEmrQ/ySYAAAAA5JNME6BOJJsAAAAAkE8yTYA6kWwCAAAAQD7JNCESa9IhJJsAAAAAkDcyTYC6kmwCAAAAQN7INAHqSrIJAAAAAHkj04QFYk06hWQTAAAAAPJDpgnQAJJNAAAAAMgPmSZAA0g2AQAAACA/ZJqwSKxJB5FsAgAAAEAeyDQBGkayCQAAAAB5INMEaBjJJgAAAADkgUwTlhJr0lkkmwAAAADQXDJNgAarnGxybuLlBo8HAAAAgE4j0wRoMMkmAAAAADTXV7/692WasJRYk44j2QQAAACAZpFpAjRFhY3x2elTkk0AAAAAqB+ZJkBTSDYBAAAAoFm++tW//61vf7PsPzku3rHEmnQiySYAAAAANJ5ME6CJJJsAAAAA0HgyTYAmkmwCAAAAQOPJNKEssSYdSrIJAAAAAI0k0wRoOskmAAAAADSSTBOg6SSbAAAAANBIMk1YiViTziXZBAAAAIDGkGkC5IRkEwAAAAAaQ6YJkBOSTQAAAABoDJkmVCDWpKNJNgEAAACg3mSaALki2QQAAACAepNpAuSKZBMAAAAA6k2mCZWJNel0kk0AAAAAqB+ZJkAOSTYBAAAAoH5kmgA5JNkEAAAAgPqRaUJVYk2QbAIAAABAXcg0AXJLsgkAAAAA9VAh02R4eFimCdBEpVJppY1xySYAAAAApCbThBBiTYgiySYAAAAAZE2mCZBzkk0AAAAAyFblTBO74kDTVdgYl2wCAAAAQAoyTQgk1oS/UznZ5Ojor21VAwAAABBIpgnQEiSbAAAAAJAVmSZAS5BsAgAAAEBWZJoQTqwJ/1GFferIVjUAAAAAYWSaAC1EsgkAAAAAqyfTBGghkk0AAAAAWD2ZJtRErAnLSDYBAAAAYDVkmgAtR7IJAAAAAKsh0wRoOZJNAAAAAFgNmSbUSqwJcZJNAAAAAEhHpgnQoiSbAAAAAJCOTBOgRUk2AQAAACAdmSakINaEMiSbAAAAAFArmSZAS5NsAgAAAECtZJoALU2yCQAAAAC1kmlCOmJNKE+yCQAAAADhZJoAbUCyCQAAAADhZJoAbUCyCQAAAADhZJqQmlgTViTZBAAAAIAQMk2AtiHZBAAAAIAQMk2AtiHZBAAAAIAQMk1YDbEmVCLZBAAAAIDKZJoAbUayCQAAAACVyTQB2oxkEwAAAAAqk2nCKok1oYqqySZX5y43cjwAAAAA5MfoO6MyTYD2I9kEAAAAgJXINAHakmQTAAAAAFYi04TVE2tCdZWTTWavlP98DgAAAIC298ILz6/0TzapgZZWOdnkyuz5Bo8HAAAAgJyQaQK0q8rJJg0eDAAAAAA58fzzv5VpwuqJNSFI5WQTAAAAAFjKJjXQBipsjF+7NtvgwQAAAACQZ3bFgfbgxDgAAAAAMe+MvVO23cY4NRFrQij71AAAAACEsEkNtA0b4wAAAABUZVccaCc2xgEAAACoysY4tSrMz883ewy0klKpNDQ01OxRAAAAAJBTNqmB9mNjHAAAAICV2BUH2pKNcQAAAABWYmOcFLqaPQBajARuAAAAAFZikxpoSzbGAQAAACjLrjjQrmyMAwAAAFCWjXHSEWtCzexTAwAAAJBkkxpoYzbGAQAAAIixKw60NxvjAAAAAMTYGCc1sSakYZ8aAAAAgKVsUgNtz8Y4AAAAAIvsigOdwMY4AAAAAItsjLMaYk1IyT41AAAAAAtsUgMdwsY4AAAAAJFdcaCT2BgHAAAAILIxzqqJNSE9+9QAAAAA2KQGOoqNcQAAAIAOZ1cc6DQ2xgEAAAA6nI1xVq8wPz/f7DHAMoVCIYoiP5nFYnFkZCTyVHgqlvBULPJULPJ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       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
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     {
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       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -1935,11 +1839,9 @@
     "uncert_constraints_results = uncertainty.full_workflow_uncertainty_loop(\n",
     "    fname=fname,\n",
     "    sample_constraints=True,\n",
-    "    # choose what to do\n",
-    "    create_starting_topography=True,\n",
-    "    create_starting_prisms=True,\n",
-    "    calculate_starting_gravity=True,\n",
-    "    calculate_regional_misfit=True,\n",
+    "    constraints_df=constraint_points,\n",
+    "    starting_topography_kwargs=starting_topography_kwargs,\n",
+    "    runs=10,\n",
     "    **uncert_kwargs,\n",
     ")\n",
     "\n",
@@ -1957,32 +1859,31 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Starting topography component"
+    "## Gravity component"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO:invert4geom:Sampled 'dampings' parameter values; mean: 6.201568177573411e-07, min: 3.592291271776981e-08, max: 3.2759368976582972e-06\n",
-      "INFO:invert4geom:No pickle files starting with 'tmp/uncertainty_starting_topo' found, creating new files\n",
+      "INFO:invert4geom:No pickle files starting with 'tmp/uncertainty_grav' found, creating new files\n",
       "\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "89294c928c0142af81add702e23d8922",
+       "model_id": "5bcba9b404ed445c9caebf68745f6911",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "stochastic ensemble:   0%|          | 0/20 [00:00<?, ?it/s]"
+       "stochastic ensemble:   0%|          | 0/10 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -1992,14 +1893,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO:invert4geom:starting stochastic uncertainty analysis at run 0 of 20\n",
-      "saving results to pickle files with prefix: 'tmp/uncertainty_starting_topo'\n",
+      "INFO:invert4geom:starting stochastic uncertainty analysis at run 0 of 10\n",
+      "saving results to pickle files with prefix: 'tmp/uncertainty_grav'\n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -2013,7 +1914,7 @@
     },
     {
      "data": {
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sjZ+UxAOwbg9zBwAAFGT1HZcHPWnfyQgMmpzD4bCa3HHixFP5XzbY7VoFnt/yZy/N1st8Jgafcuwlz/OgVZ3F1Qjbtt3tdiHHGfq6V3+m5HkDAABYiqUnKNZqidnU6QtpiaOeSWCEKa/f/X5/Op1Cfiz8mOs+iYUInKU5vgKurutz53T6uo21kRGY27ytC777v928db1OosQT3XyBlTxqQmro67ou5AwWvjV52bKmGgAAYBGu5v0uJJ1Gv+LVnwnJni0xNVrs9lCBk2k3baLyK7rTW1NJ/AouECXxURReEh8SnpJ4ANZNWxMA4HNKzi9EEf6YfS5H3Lbt4XAImagEucj71dsh+Yu6roc29h60MLrU/J2XGHqQBOmk+xs8c+wiLGL2BmWZJ85bdBNfPfxauFAX3v1L+HeNzndbSLOqp5iydVfl2C0OmZA5PmsBAACwQUO/9qe0HMUFvS0YQvJ4l3s3zHTY25aYTZ2+DJY46gsmZmMCf35ogvFOki18xjpXJ21lJ3EFwlPc6WeynMLWKbnN2y1OqiK7nMz6Hl3OMLkRfhdd+v0z8J1ivgC2M9UAAABDpUmNJjjIIEtMjY6bpQTl02VO5lp309TDT6QkvrPoevjpASiJv23ukvi56+ErJfEArNsJAEgr8Hmvruv0LxquN7z5xhVy5MtHCMxVhYQXPpkjRjpFrAEOPebNkQ+Hw4VDDVqEIXGGH+22c0EeDodBEV4ebPmzN+KwvREOnbfwCBPctSLejWPdYcYNLeWqHhHkheUdd2FHf3sNOWDgpAEAAHBBrMfDccmEwOfEkEOFDznu0eY4bIHZ1ATJogJHPW7gF7IxQ/NFV8MLLxKNldu5HFWZJ7FMs46uW2lzJCdHnJcLPzMlYXvzu7HSkoHjGqo7ETOlMWeKedDo5gs7+nTFUnLwgy78lNn1q8GMWEvh74BzjKjYqQYAADjFy5YUnhoNOUj4E9n0kApMjaYpXA+fwPDH/wInc+h85tpNi2WOkAYNNn1Fd/gBb1MSP+6Yievhh4Y37nWVxAceZ0qEyerhww+oJB6AJSp3Jx4A1mpZbU3qz+seU29KUXufWucbV8iRp89A9GYQS0/jhi+bwMMOaoR8NTMSfqjwICMOufDZGzSH0XcQZ21rEn7XihXqoIAvH2TEYCPO5BzbJ1ePOWhhR4kt/JxGCQkAAICrwh8Mr2Y8Bj1mDnpOjPuQGPdocxy2wGxqgmRRgaMeOvDA5gXps6BxyzovjLHMk1imkKElE/2kVDHamlx+T7n53Ywf1BmhuwEGzvZVCQK+Opz5wo41S9EVFfzoc5e4PHqOSYv4qaRYQyhhqgEAAE5So0MOEvFQBaZG0xSud6ZHW/hkBo6xk3E3LZboIZVf0R1+tPA4N1USn36kg8arJD5WeHGXYtzw4tbDB4anJB6AJfKWAwCpZSmEHZENGRHAfOMKOfL0Iwyq64qSr4krJJ5B8x9ywKHHjPjtnYHHGRpkrDLE6IGdos7eoKPF3fALDC/BXSvkmOFHC5zP6SGNGG/c4trwCANvqhG/0jDinTkkqoV+ZAUAAKAocRMUgw446OEu5FDho457tDkOG3KoxNnUQpJF6XPIgwYed7BVpCRt9KT0hQOG/PrSNwJiCRlXGjNVnwf+yujXGhrSTFnx0aL0N0kW7YVRzBf2xMmZT1HBJztrE8X9MNJphgr1q5Yy1QAAAKdMbU3Cw4v1VBUxcxjluTUkmPVl9QcNP+5nvxNP5lJ202KJHtIcw4xb0R14qKFxbqQkvvB6+KHHHHTkGyHHDD/aRkriw8NLXw8fPtJYgcknA5DMg8D3SwBgO7qH0rZtcwcSTdM0V3+mruuQHxtqudMYHvmgMTZNE5iyOR6P4Ye9ao7hXFgwi5i9wEMdDoeQS6Nt25Ql13eMuGsF3hbiHjCuxKt6qPCbatM0iRdPSGAhF8h+v48RDgAAwKYFJihKfsxcnxVkU+dLFpU86kFVyFEuk8DZGJRCCQnsXPJzBSdxgw6Hg9m7kTLNfjwej8fjbrdLn9lmy+q6LvaSPx6P4bGN+E7jxEqeagAAgOjSp0arqCnBkGrPyy+3gtRoOYXrZU6m3bQpFlHRPYiS+DvWVA9fKYmP95OxRlH4rVVJPACLo60JAPBFNlvkNPRRPHCWyi9rOyc8CT70yOEpm4gbHoN+fvpZW8TshSTKB+2+5MpnbfOulX5VD5XlpCR+URX/AAAA0wVW8g164stebbYFJWdT50sWFT7qQZmKZFmU0gIr+SRuTV3XgTXEE10+6RcCuPAONd+7TPq3sA02N6nD5A5ztbLsHQTe/Pf7fUh4TdNE/yzKHDa4dwYAAGxTxgxkSALhamwh8UTJVJScGl1cCWj6ybSbNsUiKrrDKYm/bzX18NUC74dRFF4Sn+uMKIkHYK0e5g4AACjLKhtthqSrRjyK13W9iKq1ceZIgt/Y7/chx49V0zw0yC5PejXCCz9Q/uzNkeoKnLfoxt21Qn5r0JETp/PSr+pBhqaY27bd7XaxXjrKMK/+zEa29AAAAGYV+Hg74hEsMH9Cr6VnU8cli7Y56olmGlrIpDVNc/8GsvSTuB11Xe/3+2QJ1cUVYrZt27bt4XBIufCOx2PvZbVKGxlmmY7H4/F4TF+aH76Btd/vL+yvpb82R8s11QAAAOllrHmOshMR5bPZS0+NFlW4XuBk2k2bqPyK7kGUxEd53cty1cNXSuLDpCyJz1gPXymJB2CltDUBAL5gaNvyRUjWy7zXuhO+o+et5HTSIG3bjr5k8s5e4GbY0ERh+k2O0Xetpmki3u7atk058GJX9Y3yd4svDzNkBpQCAwAATDdTgqJa4IfYy7H0bOq4ZNHSR13lKEkPvDCTpVBWcBI3InFPk5sXPXeOzv375Qjnzn926evEDRQ21dmEvI7H4263i/hZjrj2+333fnH7clhQQ5PbCp9qAACAKDJ26wh56SgvdDljs/TUaFGF62VOpt20BBZRD18VXDyccQJXUw9fKYmfwfSS+LyNt5TEA7BKD3IHAAAwryi9zHut+AE+MINTVIv0c8YFGXhye1fXamZvRKJwm5scTdMkPpvpV/VQGVdC4EtPHKa+1AAAAMmMfgTz7DbONrOp2xz1RLNOWn3N/fyYk7gUx+Oxa2uy6IlNk//sZul0OnXLPsErdp1NErwQVFW13+9T3gcGvdbxeOwagtxY9HcXJ55qAACATQlJ2lzOt1x93rz6ElKjES13Mu2mnbOaiu6Okvhx1MOHUxJ/R96VoCQegFXS1gQA+IIN5qATWHFia8rQkuW80reODpR39uYrvkyc28p112o+b7fbpa9kLXZVFyJkmJfP2nKrkwEAAJbF8xf3TcymLjTFPXHUxeZ8RgfWXlNg1r3AkEp209wkzctdzqiXf9/olv3pdDocDnO3ONHZhJQSt9uY74NJ5dPZBAAAWKvsqdGJlZzreFjbZlZ/Jr2TaTctgUXUw1cFFw9nnMDV1MNXSuIHyv43QBpK4gFYn4e5AwAAyK/7xq3cUSzMxGRQYBJq4nc2FpuxWsTsFd48O6WbCS8htVfsqi7HxG9NDLlqbKYCAAAkM/oRrG1bGb+ZbDObus1Rr4yTWJrujBwOh7n7aFw+fm8AFxKMGTO0XWHx7f+3+4+4ievj8Ri3edB8M2YXI7vekztoQe73+wQ3gU7TNHVdl7DRM8KyphoAAGA7Qp6zLjy+hbTgjFKoJjUaUZmTaTdtikVUdFcFFw+XP4EyybcpiV8WJfEArI+2JgDAypWQc2Gt1r26xpU2TkyfFaIb+woGMsIKRj1xk+PqbrE0OgAAQBTqY8q0gszACNsc9USlTVpp8RCuwI/ZL+Ud6k6cEWuR456UpcwnI5w7uU3ThK/DlDeB5X5QZ3FTDQAAsB0hPTTP9ZC9+oshhWpSoxEVOJlyayRT4PqPZcv18JWS+IVTEg/A+jzIHQAAUAoPpdxIk8RZ65Irf/ZmrVlMWRAZfQl124e73e54PK4glblla729AAAArEnIlxDCIBICsETH4zFBVvnC/eF+KvjyO1SxHyRoP+90Op1Op7quR98VpceZqFuHgSvweDymvKwi/gkacqi5/zgpeaoBAAA2Yr/fX/2Z0U+jIQdfNFn9q+ymTVR+RXfhCp/A1dTDV0riOW/FdxgAtklbEwAA6KGwb1O67O1qWmufs51VHTLSc+c65NszxsQEAABAckp8IC9ZlBU4jTXoDpw9K7vKtXrT4uRwOIx4Q0xcsc0qtW0buPZSfj6naZooL3c4HMq5TMqcagAAgI0Y/XgY8ovlPHuyYnbTtmCVOXDOURK/MkriAVgZbU0AAIBN20L2doNCNtvu7/te3Qm2hwcAAJCSpzCAheoaapRzG79cl3nnc/4XcsXljChc0zRt245rbgITBZZEJ96g6TqbTLki6rrurqyIUU1U5lQDAABsxNVnzHGPY5I5BLJUgBtK4ldJSTwAa6KtCQDAvCSGoGRN07hIAQAAAAqxzUTNNke9Mk5ir/DP/Pvi2bl1LRjCS1QtaWK50zbonMQ3gaFXxG2Hw6G7uQUOLZkypxoAAGAL9vv91Z+5/zh2Nf0SctgSyCNFZDKBKZTEAwDl09YEAADYqCgJ3Lqu67o+nU5RQiKWkI9M3D/7V9dDUd++CAAAsHrqrgCWrpyversQye23m8sf+F/Kh0nOGd3HAUYruYlG27an0yn8oqjr+nA4DBpRyptGyVMNAACwbk3TDE25hBShedAjkN00oFISv2pK4gFYE21NAADmpUJ0oWRqVq9t23EJ3C5pezgcTqfT6XRq23Ypq2UpccYScvu9PSdX58f9HAAAYFnUcS7RNp++1zrqTSWj1noSp1vZMljBh0lWdkZYhJA7ZMY/27rmJofDodv9uf8DN6X8bdsOvQkkvmkUPtUAAABbdudx7HA4XP75BeUbFxRq+bJMplzBFkgLr56S+NVTEg/AajzMHQAAQH6zPpYv/bv75lN4KvzqvlFehc/eIgy6Nuu63u/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       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -2027,7 +1928,7 @@
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     {
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       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -2041,169 +1942,47 @@
     }
    ],
    "source": [
-    "fname = \"tmp/uncertainty_starting_topo\"\n",
+    "fname = \"tmp/uncertainty_grav\"\n",
     "\n",
     "# delete files if already exist\n",
     "for p in pathlib.Path().glob(f\"{fname}*\"):\n",
     "    p.unlink(missing_ok=True)\n",
     "\n",
-    "uncert_starting_topo_results = uncertainty.full_workflow_uncertainty_loop(\n",
+    "uncert_grav_results = uncertainty.full_workflow_uncertainty_loop(\n",
     "    fname=fname,\n",
-    "    runs=20,\n",
-    "    # choose what to sample\n",
-    "    # sample_gravity=True,\n",
-    "    # sample_constraints=True,\n",
-    "    starting_topography_parameter_dict=starting_topography_parameter_dict,\n",
-    "    # choose what to do\n",
-    "    create_starting_topography=True,\n",
-    "    create_starting_prisms=True,\n",
-    "    calculate_starting_gravity=True,\n",
-    "    calculate_regional_misfit=True,\n",
+    "    runs=10,\n",
+    "    sample_gravity=True,\n",
     "    **uncert_kwargs,\n",
     ")\n",
     "\n",
-    "uncert_plots(uncert_starting_topo_results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Regional gravity component"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:invert4geom:Sampled 'depth' parameter values; mean: 22396.626035473848, min: 6439.029985198731, max: 38354.22208574897\n",
-      "INFO:invert4geom:Sampled 'damping' parameter values; mean: 2.389253898148588, min: 6.104352757406244e-07, max: 42.21779636559106\n",
-      "INFO:invert4geom:No pickle files starting with 'tmp/uncertainty_regional' found, creating new files\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "72c3515ff3ee40afa640f68e4ae60851",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "stochastic ensemble:   0%|          | 0/20 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:invert4geom:starting stochastic uncertainty analysis at run 0 of 20\n",
-      "saving results to pickle files with prefix: 'tmp/uncertainty_regional'\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "width": 500
-      }
-     },
-     "output_type": "display_data"
-    },
-    {
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-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "width": 500
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fname = \"tmp/uncertainty_regional\"\n",
-    "\n",
-    "# delete files if already exist\n",
-    "for p in pathlib.Path().glob(f\"{fname}*\"):\n",
-    "    p.unlink(missing_ok=True)\n",
-    "\n",
-    "# replace regional grav kwargs which use a cross validation to find the best damping\n",
-    "# and depth values with the regional grav kwargs which randomly sample these values\n",
-    "new_kwargs = copy.deepcopy(uncert_kwargs)\n",
-    "new_kwargs[\"regional_grav_kwargs\"] = regional_kwargs\n",
-    "\n",
-    "uncert_regional_results = uncertainty.full_workflow_uncertainty_loop(\n",
-    "    fname=fname,\n",
-    "    runs=20,\n",
-    "    # choose what to sample\n",
-    "    # sample_gravity=True,\n",
-    "    # sample_constraints=True,\n",
-    "    regional_misfit_parameter_dict=regional_misfit_parameter_dict,\n",
-    "    # choose what to do\n",
-    "    # create_starting_topography=True,\n",
-    "    # create_starting_prisms=True,\n",
-    "    # calculate_starting_gravity=True,\n",
-    "    calculate_regional_misfit=True,\n",
-    "    **new_kwargs,\n",
-    ")\n",
-    "\n",
-    "uncert_plots(uncert_regional_results)"
+    "stats_ds = uncert_plots(\n",
+    "    uncert_grav_results,\n",
+    "    region,\n",
+    "    spacing,\n",
+    "    true_topography,\n",
+    "    constraint_points=constraint_points,\n",
+    "    weight_by=\"constraints\",\n",
+    ")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Total Uncertainty"
+    "## Total uncertainty"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO:invert4geom:Sampled 'density_contrast' parameter values; mean: 2644.2598596, min: 2448.2634611459944, max: 2840.2562580540052\n",
-      "INFO:invert4geom:Sampled 'solver_damping' parameter values; mean: 0.010716700663809673, min: 0.006657041442107438, max: 0.01641625378798854\n",
-      "INFO:invert4geom:Sampled 'dampings' parameter values; mean: 6.201568177573411e-07, min: 3.592291271776981e-08, max: 3.2759368976582972e-06\n",
-      "INFO:invert4geom:Sampled 'depth' parameter values; mean: 22396.626035473848, min: 6439.029985198731, max: 38354.22208574897\n",
-      "INFO:invert4geom:Sampled 'damping' parameter values; mean: 2.389253898148588, min: 6.104352757406244e-07, max: 42.21779636559106\n",
+      "INFO:invert4geom:Sampled 'solver_damping' parameter values; mean: 0.03583141360899327, min: 0.0024811354130749273, max: 0.1780327798309552\n",
+      "INFO:invert4geom:Sampled 'density_contrast' parameter values; mean: 2857.9296283000003, min: 2525.6536416299646, max: 3190.205614970035\n",
       "INFO:invert4geom:No pickle files starting with 'tmp/uncertainty_full' found, creating new files\n",
       "\n"
      ]
@@ -2211,7 +1990,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5b19446c8e9e4092b868b0b65232e0c8",
+       "model_id": "517563d8abc7426ba93e530bd33b0da2",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2233,7 +2012,7 @@
     },
     {
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",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -2247,7 +2026,7 @@
     },
     {
      "data": {
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       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
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     {
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       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -2275,8 +2054,6 @@
     }
    ],
    "source": [
-    "parameter_dict = density_dict | solver_dict\n",
-    "\n",
     "fname = \"tmp/uncertainty_full\"\n",
     "\n",
     "# delete files if already exist\n",
@@ -2286,21 +2063,23 @@
     "uncert_results = uncertainty.full_workflow_uncertainty_loop(\n",
     "    fname=fname,\n",
     "    runs=20,\n",
-    "    # choose what to sample\n",
     "    sample_gravity=True,\n",
     "    sample_constraints=True,\n",
-    "    parameter_dict=parameter_dict,\n",
-    "    starting_topography_parameter_dict=starting_topography_parameter_dict,\n",
-    "    regional_misfit_parameter_dict=regional_misfit_parameter_dict,\n",
-    "    # choose what to do\n",
-    "    create_starting_topography=True,\n",
-    "    create_starting_prisms=True,\n",
-    "    calculate_starting_gravity=True,\n",
-    "    calculate_regional_misfit=True,\n",
-    "    **new_kwargs,\n",
+    "    constraints_df=constraint_points,\n",
+    "    parameter_dict=solver_dict | density_dict,\n",
+    "    # regional_misfit_parameter_dict=regional_misfit_parameter_dict,\n",
+    "    starting_topography_kwargs=starting_topography_kwargs,\n",
+    "    **uncert_kwargs,\n",
     ")\n",
     "\n",
-    "uncert_plots(uncert_results)"
+    "stats_ds = uncert_plots(\n",
+    "    uncert_results,\n",
+    "    region,\n",
+    "    spacing,\n",
+    "    true_topography,\n",
+    "    constraint_points=constraint_points,\n",
+    "    weight_by=\"constraints\",\n",
+    ")"
    ]
   },
   {
@@ -2312,7 +2091,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 68,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2322,8 +2101,13 @@
     "    uncert_constraints_results,\n",
     "    uncert_density_results,\n",
     "    uncert_damping_results,\n",
-    "    uncert_starting_topo_results,\n",
-    "    uncert_regional_results,\n",
+    "]\n",
+    "names = [\n",
+    "    \"full\",\n",
+    "    \"grav\",\n",
+    "    \"constraints\",\n",
+    "    \"density\",\n",
+    "    \"damping\",\n",
     "]\n",
     "# get cell-wise stats for each ensemble\n",
     "stats = []\n",
@@ -2340,7 +2124,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [
     {
@@ -2715,153 +2499,114 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 72kB\n",
-       "Dimensions:            (northing: 31, easting: 41)\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 46kB\n",
+       "Dimensions:            (northing: 29, easting: 39)\n",
        "Coordinates:\n",
        "  * northing           (northing) float64 232B 1e+03 2e+03 ... 2.8e+04 2.9e+04\n",
        "  * easting            (easting) float64 312B 1e+03 2e+03 ... 3.8e+04 3.9e+04\n",
        "Data variables:\n",
-       "    full_stdev         (northing, easting) float64 10kB 51.91 51.03 ... 36.9\n",
-       "    grav_stdev         (northing, easting) float64 10kB 48.95 26.31 ... 18.86\n",
-       "    constraints_stdev  (northing, easting) float64 10kB 64.36 34.61 ... 42.13\n",
-       "    density_stdev      (northing, easting) float64 10kB 22.21 9.916 ... 11.22\n",
-       "    damping_stdev      (northing, easting) float64 10kB 0.4868 1.648 ... 0.5527\n",
-       "    topo_stdev         (northing, easting) float64 10kB 22.94 23.04 ... 15.13\n",
-       "    regional_stdev     (northing, easting) float64 10kB 18.29 13.14 ... 17.23</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-3664982f-e11e-4f51-9f5b-a93b055ff618' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3664982f-e11e-4f51-9f5b-a93b055ff618' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>northing</span>: 31</li><li><span class='xr-has-index'>easting</span>: 41</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-b6871ba8-6c92-45a9-859b-ae1f4013c955' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b6871ba8-6c92-45a9-859b-ae1f4013c955' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>northing</span></div><div class='xr-var-dims'>(northing)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1e+03 2e+03 ... 2.9e+04 3e+04</div><input id='attrs-800625e1-c743-4573-b0d2-1115a255795b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-800625e1-c743-4573-b0d2-1115a255795b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-95a8925d-cf4c-4b4e-a007-48cf5d8da72f' class='xr-var-data-in' type='checkbox'><label for='data-95a8925d-cf4c-4b4e-a007-48cf5d8da72f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([    0.,  1000.,  2000.,  3000.,  4000.,  5000.,  6000.,  7000.,  8000.,\n",
-       "        9000., 10000., 11000., 12000., 13000., 14000., 15000., 16000., 17000.,\n",
-       "       18000., 19000., 20000., 21000., 22000., 23000., 24000., 25000., 26000.,\n",
-       "       27000., 28000., 29000., 30000.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>easting</span></div><div class='xr-var-dims'>(easting)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1e+03 2e+03 ... 3.9e+04 4e+04</div><input id='attrs-fa665a51-4bf1-4002-a148-6be62a0cd332' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fa665a51-4bf1-4002-a148-6be62a0cd332' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-54d63fca-01a0-4f88-bbdf-f31bd903d103' class='xr-var-data-in' type='checkbox'><label for='data-54d63fca-01a0-4f88-bbdf-f31bd903d103' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([    0.,  1000.,  2000.,  3000.,  4000.,  5000.,  6000.,  7000.,  8000.,\n",
-       "        9000., 10000., 11000., 12000., 13000., 14000., 15000., 16000., 17000.,\n",
-       "       18000., 19000., 20000., 21000., 22000., 23000., 24000., 25000., 26000.,\n",
-       "       27000., 28000., 29000., 30000., 31000., 32000., 33000., 34000., 35000.,\n",
-       "       36000., 37000., 38000., 39000., 40000.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-828646f9-1379-41ee-9daa-889b5aa5f86e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-828646f9-1379-41ee-9daa-889b5aa5f86e' class='xr-section-summary' >Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>full_stdev</span></div><div class='xr-var-dims'>(northing, easting)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>51.91 51.03 35.1 ... 23.57 36.9</div><input id='attrs-93ab12d4-9e18-4000-b617-77210f5e2bff' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-93ab12d4-9e18-4000-b617-77210f5e2bff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e295958f-7868-4961-a9eb-fe247baa5eb1' class='xr-var-data-in' type='checkbox'><label for='data-e295958f-7868-4961-a9eb-fe247baa5eb1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[51.90677999, 51.02560493, 35.09884719, ..., 12.79516436,\n",
-       "        16.0802504 , 22.31372757],\n",
-       "       [21.21418128, 40.2965175 , 25.95972607, ...,  9.4516905 ,\n",
-       "        13.25834087, 16.88996728],\n",
-       "       [23.84329642, 33.6624873 , 23.06110001, ..., 18.67373783,\n",
-       "        11.85982528, 18.04834184],\n",
-       "       ...,\n",
-       "       [35.57885621, 30.16607066, 21.49409967, ..., 17.86057751,\n",
-       "        18.6133078 , 24.98177599],\n",
-       "       [74.92512833, 47.07100892, 30.56629145, ..., 15.44579945,\n",
-       "        21.51650499, 26.27798839],\n",
-       "       [97.561704  , 51.36446806, 45.89587986, ..., 26.4112081 ,\n",
-       "        23.56722288, 36.90189851]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>grav_stdev</span></div><div class='xr-var-dims'>(northing, easting)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>48.95 26.31 17.02 ... 11.57 18.86</div><input id='attrs-7e8bf8f1-0c8a-41ab-b39a-7bc563960aae' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7e8bf8f1-0c8a-41ab-b39a-7bc563960aae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-298a6848-c762-4c80-8cad-08eecc1d5912' class='xr-var-data-in' type='checkbox'><label for='data-298a6848-c762-4c80-8cad-08eecc1d5912' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[48.9512608 , 26.31414342, 17.0239965 , ...,  7.03105072,\n",
-       "         7.51017593,  7.11436334],\n",
-       "       [11.78149281, 16.55695971,  7.75905868, ...,  7.16417052,\n",
-       "         8.5411303 ,  5.88143348],\n",
-       "       [11.56119821,  9.9676974 ,  8.58278941, ..., 14.41619045,\n",
-       "        10.08062743,  4.70372002],\n",
-       "       ...,\n",
-       "       [ 4.9293611 ,  4.63051758,  4.13949318, ...,  5.57026965,\n",
-       "        12.35887132, 12.19388776],\n",
-       "       [ 4.61723773, 10.57260241,  4.86305274, ..., 12.63323158,\n",
-       "         9.12185103, 11.08949373],\n",
-       "       [25.01611669,  9.48231253,  6.37805076, ..., 14.24021454,\n",
-       "        11.57241827, 18.8628653 ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>constraints_stdev</span></div><div class='xr-var-dims'>(northing, easting)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>64.36 34.61 41.91 ... 31.71 42.13</div><input id='attrs-03d0f03a-e5c8-4452-bed0-7d72d9908e3e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-03d0f03a-e5c8-4452-bed0-7d72d9908e3e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-413514c7-78d3-4b10-a62f-3cd94387a46e' class='xr-var-data-in' type='checkbox'><label for='data-413514c7-78d3-4b10-a62f-3cd94387a46e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[64.35591893, 34.61082724, 41.91171797, ..., 21.13310321,\n",
-       "        19.88057338, 26.86437745],\n",
-       "       [35.23860454, 28.63072639, 30.5725358 , ..., 11.49659766,\n",
-       "        11.78547514, 18.09852635],\n",
-       "       [42.71700491, 30.82169331, 31.74536355, ..., 11.70192974,\n",
-       "        11.41438963, 17.22546175],\n",
+       "    full_stdev         (northing, easting) float64 9kB 24.81 24.39 ... 37.94\n",
+       "    grav_stdev         (northing, easting) float64 9kB 5.563 2.823 ... 2.796\n",
+       "    constraints_stdev  (northing, easting) float64 9kB 19.04 17.97 ... 44.44\n",
+       "    density_stdev      (northing, easting) float64 9kB 1.859 1.239 ... 1.926\n",
+       "    damping_stdev      (northing, easting) float64 9kB 2.831 0.5222 ... 2.612</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-343db4e5-9e3e-4701-8cd4-081eef80dd07' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-343db4e5-9e3e-4701-8cd4-081eef80dd07' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>northing</span>: 29</li><li><span class='xr-has-index'>easting</span>: 39</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-0b9b25ec-922b-4b0b-9c2b-573c6810c174' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0b9b25ec-922b-4b0b-9c2b-573c6810c174' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>northing</span></div><div class='xr-var-dims'>(northing)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1e+03 2e+03 ... 2.8e+04 2.9e+04</div><input id='attrs-05461616-5e83-44da-bab5-8186a2a3cefa' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-05461616-5e83-44da-bab5-8186a2a3cefa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-35f186b0-7733-42e2-9c22-6fbd0b23b14d' class='xr-var-data-in' type='checkbox'><label for='data-35f186b0-7733-42e2-9c22-6fbd0b23b14d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 1000.,  2000.,  3000.,  4000.,  5000.,  6000.,  7000.,  8000.,  9000.,\n",
+       "       10000., 11000., 12000., 13000., 14000., 15000., 16000., 17000., 18000.,\n",
+       "       19000., 20000., 21000., 22000., 23000., 24000., 25000., 26000., 27000.,\n",
+       "       28000., 29000.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>easting</span></div><div class='xr-var-dims'>(easting)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1e+03 2e+03 ... 3.8e+04 3.9e+04</div><input id='attrs-321de56f-f6f5-47c2-98c6-ca741bb43dcd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-321de56f-f6f5-47c2-98c6-ca741bb43dcd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b49e5bc2-af44-41b5-b58c-b39f5d9fb8ec' class='xr-var-data-in' type='checkbox'><label for='data-b49e5bc2-af44-41b5-b58c-b39f5d9fb8ec' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 1000.,  2000.,  3000.,  4000.,  5000.,  6000.,  7000.,  8000.,  9000.,\n",
+       "       10000., 11000., 12000., 13000., 14000., 15000., 16000., 17000., 18000.,\n",
+       "       19000., 20000., 21000., 22000., 23000., 24000., 25000., 26000., 27000.,\n",
+       "       28000., 29000., 30000., 31000., 32000., 33000., 34000., 35000., 36000.,\n",
+       "       37000., 38000., 39000.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-26fd24fd-08d4-4ba3-9db1-3668f22fb0c6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-26fd24fd-08d4-4ba3-9db1-3668f22fb0c6' class='xr-section-summary' >Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>full_stdev</span></div><div class='xr-var-dims'>(northing, easting)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>24.81 24.39 26.04 ... 35.54 37.94</div><input id='attrs-e26518b4-2f0e-4cba-b299-650c749d28e6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e26518b4-2f0e-4cba-b299-650c749d28e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b95be341-d94e-4f2a-9e69-0b59d9c881fc' class='xr-var-data-in' type='checkbox'><label for='data-b95be341-d94e-4f2a-9e69-0b59d9c881fc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[24.81000173, 24.38854566, 26.03522063, ..., 10.73828306,\n",
+       "        10.65127754, 14.56824295],\n",
+       "       [25.04582245, 26.54919778, 43.09845168, ..., 10.95868588,\n",
+       "        20.49269108, 13.82393482],\n",
+       "       [27.58754183, 24.59049138, 41.30987406, ..., 15.93370969,\n",
+       "        12.48896009, 18.12281869],\n",
        "       ...,\n",
-       "       [17.48337383, 11.03099217, 13.14510474, ..., 22.01278551,\n",
-       "        21.01742117, 29.67227098],\n",
-       "       [16.75607724, 26.59739539, 16.32952923, ..., 21.56670053,\n",
-       "        20.97623585, 29.89655546],\n",
-       "       [70.04110719, 23.39747257, 16.80769872, ..., 32.28498123,\n",
-       "        31.7059724 , 42.12687175]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>density_stdev</span></div><div class='xr-var-dims'>(northing, easting)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>22.21 9.916 8.642 ... 8.032 11.22</div><input id='attrs-e6a46943-5292-42d8-b608-581b90185512' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e6a46943-5292-42d8-b608-581b90185512' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bfd1cca7-ec34-47aa-be45-d5fe2a496d24' class='xr-var-data-in' type='checkbox'><label for='data-bfd1cca7-ec34-47aa-be45-d5fe2a496d24' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[22.20642884,  9.9157848 ,  8.64161052, ...,  2.61562536,\n",
-       "         2.80015983,  4.28596955],\n",
-       "       [ 8.68673617,  9.30020717,  5.93913351, ...,  0.9610215 ,\n",
-       "         1.28382307,  2.2515271 ],\n",
-       "       [ 7.16391377,  5.68670768,  5.12074371, ...,  0.77441482,\n",
-       "         1.05159519,  1.73388732],\n",
+       "       [32.30249487, 32.88526635, 44.32277629, ..., 32.96294578,\n",
+       "        39.26687908, 34.23528669],\n",
+       "       [32.17821876, 31.79196411, 31.6191082 , ..., 35.57342976,\n",
+       "        34.00583218, 41.05129905],\n",
+       "       [33.62223215, 41.61307662, 33.93780391, ..., 40.2765439 ,\n",
+       "        35.54003207, 37.93522301]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>grav_stdev</span></div><div class='xr-var-dims'>(northing, easting)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.563 2.823 1.992 ... 2.668 2.796</div><input id='attrs-f59766d7-6105-49b9-baa0-ad423b8fec4c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f59766d7-6105-49b9-baa0-ad423b8fec4c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3ebc04fb-fbcc-49da-9a1f-768b5fa71ca8' class='xr-var-data-in' type='checkbox'><label for='data-3ebc04fb-fbcc-49da-9a1f-768b5fa71ca8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[5.56316907, 2.82314377, 1.99234042, ..., 2.54010551, 3.20852516,\n",
+       "        4.32459543],\n",
+       "       [1.85264625, 3.17272463, 2.65150144, ..., 3.92964378, 4.52720733,\n",
+       "        4.42654814],\n",
+       "       [3.61543329, 2.99551227, 3.76685824, ..., 1.94710468, 3.16791029,\n",
+       "        2.45787028],\n",
        "       ...,\n",
-       "       [ 6.37036514,  2.5865971 ,  3.89151839, ...,  4.46658973,\n",
-       "         4.67581155,  7.04808559],\n",
-       "       [ 7.64050049, 11.11607224,  5.28699449, ...,  4.9601908 ,\n",
-       "         5.09894771,  7.56940347],\n",
-       "       [29.21342627,  4.58060967,  2.39140748, ...,  7.77914178,\n",
-       "         8.03156082, 11.2235736 ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>damping_stdev</span></div><div class='xr-var-dims'>(northing, easting)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.4868 1.648 ... 0.8085 0.5527</div><input id='attrs-569e8ba2-6ff5-4159-b03d-692e9b8f24c9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-569e8ba2-6ff5-4159-b03d-692e9b8f24c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4dbf6731-4841-4140-993c-d25049203eda' class='xr-var-data-in' type='checkbox'><label for='data-4dbf6731-4841-4140-993c-d25049203eda' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0.48675759,  1.64818382,  1.67517073, ...,  0.33067089,\n",
-       "         0.25911536,  0.12712978],\n",
-       "       [ 0.72680091,  0.93280021,  0.18826659, ...,  0.47312895,\n",
-       "         0.42194769,  0.18532674],\n",
-       "       [ 1.01828065,  0.82496181,  1.55127388, ...,  0.35022899,\n",
-       "         0.32426174,  0.14907828],\n",
+       "       [1.08450524, 0.90327975, 1.53952645, ..., 7.23391874, 2.87878522,\n",
+       "        3.82227096],\n",
+       "       [1.47959223, 2.3565003 , 2.27797228, ..., 2.04827079, 2.86368819,\n",
+       "        4.10039758],\n",
+       "       [2.48984733, 1.1408869 , 1.77042269, ..., 3.90355156, 2.66771859,\n",
+       "        2.79562454]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>constraints_stdev</span></div><div class='xr-var-dims'>(northing, easting)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>19.04 17.97 17.41 ... 47.09 44.44</div><input id='attrs-a4c2fa65-9764-4736-8ac9-4b96b4f9d65c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a4c2fa65-9764-4736-8ac9-4b96b4f9d65c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f10ea778-f86e-423c-9234-d2d68db9be3e' class='xr-var-data-in' type='checkbox'><label for='data-f10ea778-f86e-423c-9234-d2d68db9be3e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[19.03958411, 17.9683512 , 17.40570775, ..., 14.68293704,\n",
+       "        14.1932362 , 13.75020173],\n",
+       "       [18.14626783, 17.07508492, 16.17922031, ..., 13.68902405,\n",
+       "        13.66473418, 13.01259328],\n",
+       "       [17.50785175, 16.47233087, 15.74043011, ..., 12.18291271,\n",
+       "        12.41244268, 11.97652396],\n",
        "       ...,\n",
-       "       [ 4.52231785,  1.81852452,  3.45656689, ...,  0.17293903,\n",
-       "         0.09592628,  0.56339182],\n",
-       "       [11.49101412, 22.98448664, 22.11817499, ...,  0.30315852,\n",
-       "         0.26685808,  0.28338163],\n",
-       "       [33.86303276,  3.6507054 , 21.77199319, ...,  0.3315643 ,\n",
-       "         0.80852303,  0.55269661]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>topo_stdev</span></div><div class='xr-var-dims'>(northing, easting)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>22.94 23.04 20.64 ... 11.5 15.13</div><input id='attrs-4208908c-4111-40be-b409-40bf5066f512' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4208908c-4111-40be-b409-40bf5066f512' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2ebcfc79-ae4a-4ac7-8aa7-2084ad8dc1f7' class='xr-var-data-in' type='checkbox'><label for='data-2ebcfc79-ae4a-4ac7-8aa7-2084ad8dc1f7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[22.93508868, 23.03598312, 20.63778022, ..., 13.54006689,\n",
-       "        12.48122427, 16.31390864],\n",
-       "       [22.43060383, 17.82377625, 15.8803495 , ...,  9.07661528,\n",
-       "         8.3100498 , 11.89635949],\n",
-       "       [20.34787535, 16.15095659, 16.34338955, ..., 27.90997533,\n",
-       "         8.74901457, 11.87064415],\n",
+       "       [20.13904381, 15.87855733, 12.25643501, ..., 45.70571891,\n",
+       "        46.75870559, 45.57601538],\n",
+       "       [21.21626724, 17.1971832 , 13.41014109, ..., 46.73744448,\n",
+       "        47.81738288, 46.2743085 ],\n",
+       "       [21.94578543, 17.90530699, 14.30391175, ..., 45.88619519,\n",
+       "        47.09466012, 44.43746251]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>density_stdev</span></div><div class='xr-var-dims'>(northing, easting)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.859 1.239 1.129 ... 1.788 1.926</div><input id='attrs-d8f45529-c7cd-43f1-b5d5-83c3bc93344b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d8f45529-c7cd-43f1-b5d5-83c3bc93344b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-daa359f7-91b0-4f3d-bb28-3fb12ee5ee47' class='xr-var-data-in' type='checkbox'><label for='data-daa359f7-91b0-4f3d-bb28-3fb12ee5ee47' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.8591103 , 1.23905065, 1.12896383, ..., 0.69954067, 0.63483332,\n",
+       "        0.53950231],\n",
+       "       [1.2757177 , 0.62839846, 0.59997344, ..., 0.40154153, 0.84087888,\n",
+       "        0.33071765],\n",
+       "       [1.72978172, 1.37708036, 0.92416665, ..., 0.65824917, 0.44041618,\n",
+       "        0.33181988],\n",
        "       ...,\n",
-       "       [ 7.44904358,  4.06870666,  3.5660802 , ...,  7.5555706 ,\n",
-       "         7.40929389, 10.54552402],\n",
-       "       [ 8.72300667, 13.62628632,  7.15552496, ...,  7.65131437,\n",
-       "         7.64396589, 11.00114193],\n",
-       "       [33.91685002, 12.24526058,  4.5919248 , ..., 11.38995945,\n",
-       "        11.50309614, 15.12501518]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>regional_stdev</span></div><div class='xr-var-dims'>(northing, easting)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>18.29 13.14 13.51 ... 13.66 17.23</div><input id='attrs-20e7c317-7aec-4a0d-b2e3-539fa53da0c1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-20e7c317-7aec-4a0d-b2e3-539fa53da0c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-60e68924-fa5e-42ed-8973-23876a895fa0' class='xr-var-data-in' type='checkbox'><label for='data-60e68924-fa5e-42ed-8973-23876a895fa0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[18.28791139, 13.13753022, 13.51271572, ...,  9.2572205 ,\n",
-       "         8.69333226, 11.79653908],\n",
-       "       [12.30866735,  9.85877503,  9.40428758, ...,  5.26807301,\n",
-       "         5.00092579,  7.49758447],\n",
-       "       [12.16682765,  9.64356223,  9.44324337, ...,  5.14960462,\n",
-       "         4.63410166,  6.82487808],\n",
+       "       [3.82885253, 0.92646455, 1.30475094, ..., 1.98561982, 2.18639108,\n",
+       "        2.55895825],\n",
+       "       [3.79786973, 1.54845138, 0.93894172, ..., 1.44819664, 1.64808199,\n",
+       "        2.03189355],\n",
+       "       [3.99641781, 1.62419062, 1.05775077, ..., 1.28688141, 1.78767193,\n",
+       "        1.92624113]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>damping_stdev</span></div><div class='xr-var-dims'>(northing, easting)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.831 0.5222 3.682 ... 6.598 2.612</div><input id='attrs-e6843d53-828d-4322-8589-89a802b719be' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e6843d53-828d-4322-8589-89a802b719be' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-271833b9-f223-433d-9c3e-06807c8db401' class='xr-var-data-in' type='checkbox'><label for='data-271833b9-f223-433d-9c3e-06807c8db401' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 2.83076331,  0.52216677,  3.6823876 , ...,  2.45321345,\n",
+       "         2.79970791,  2.4006118 ],\n",
+       "       [ 9.70012282,  1.27390037,  5.95734263, ...,  0.68606149,\n",
+       "         1.36422175,  1.60958914],\n",
+       "       [ 9.18300524,  4.12441195,  1.55573073, ...,  1.36033513,\n",
+       "         0.29766426,  0.52666968],\n",
        "       ...,\n",
-       "       [10.35498332,  8.97825095,  7.12666406, ...,  8.94313936,\n",
-       "         8.79106813, 12.24236411],\n",
-       "       [ 9.94431356, 11.19718219,  6.6998723 , ...,  9.14522112,\n",
-       "         9.12759968, 12.68501035],\n",
-       "       [12.92353339, 10.40554841,  9.78262206, ..., 13.73768062,\n",
-       "        13.66125063, 17.23432802]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4d76ad7d-1ad5-4b50-97a3-c823f3bfbcba' class='xr-section-summary-in' type='checkbox'  ><label for='section-4d76ad7d-1ad5-4b50-97a3-c823f3bfbcba' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>northing</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ac62a912-7808-46bc-b409-feaad6b0c445' class='xr-index-data-in' type='checkbox'/><label for='index-ac62a912-7808-46bc-b409-feaad6b0c445' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([    0.0,  1000.0,  2000.0,  3000.0,  4000.0,  5000.0,  6000.0,  7000.0,\n",
-       "        8000.0,  9000.0, 10000.0, 11000.0, 12000.0, 13000.0, 14000.0, 15000.0,\n",
-       "       16000.0, 17000.0, 18000.0, 19000.0, 20000.0, 21000.0, 22000.0, 23000.0,\n",
-       "       24000.0, 25000.0, 26000.0, 27000.0, 28000.0, 29000.0, 30000.0],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;northing&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>easting</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-dc0f0f92-78d4-42a8-8b9f-c895b08f5299' class='xr-index-data-in' type='checkbox'/><label for='index-dc0f0f92-78d4-42a8-8b9f-c895b08f5299' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([    0.0,  1000.0,  2000.0,  3000.0,  4000.0,  5000.0,  6000.0,  7000.0,\n",
-       "        8000.0,  9000.0, 10000.0, 11000.0, 12000.0, 13000.0, 14000.0, 15000.0,\n",
-       "       16000.0, 17000.0, 18000.0, 19000.0, 20000.0, 21000.0, 22000.0, 23000.0,\n",
-       "       24000.0, 25000.0, 26000.0, 27000.0, 28000.0, 29000.0, 30000.0, 31000.0,\n",
-       "       32000.0, 33000.0, 34000.0, 35000.0, 36000.0, 37000.0, 38000.0, 39000.0,\n",
-       "       40000.0],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;easting&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a4aa4c05-191a-4208-a8af-74ad5745cd4d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a4aa4c05-191a-4208-a8af-74ad5745cd4d' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+       "       [ 9.90173118,  3.06397475,  3.33307194, ...,  3.20567578,\n",
+       "         1.10853966,  1.05261593],\n",
+       "       [ 5.41535876,  2.20580607,  2.05018577, ...,  6.18644543,\n",
+       "         5.76362398,  2.27445939],\n",
+       "       [19.40977591, 23.30648452, 13.58249377, ...,  4.39932154,\n",
+       "         6.59845509,  2.61168522]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-12b0acc4-9f46-4b31-a7c2-83b6b81487a8' class='xr-section-summary-in' type='checkbox'  ><label for='section-12b0acc4-9f46-4b31-a7c2-83b6b81487a8' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>northing</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-267c5562-e65c-4554-bb13-dcbd124ed328' class='xr-index-data-in' type='checkbox'/><label for='index-267c5562-e65c-4554-bb13-dcbd124ed328' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 1000.0,  2000.0,  3000.0,  4000.0,  5000.0,  6000.0,  7000.0,  8000.0,\n",
+       "        9000.0, 10000.0, 11000.0, 12000.0, 13000.0, 14000.0, 15000.0, 16000.0,\n",
+       "       17000.0, 18000.0, 19000.0, 20000.0, 21000.0, 22000.0, 23000.0, 24000.0,\n",
+       "       25000.0, 26000.0, 27000.0, 28000.0, 29000.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;northing&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>easting</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-241168c4-c053-4203-bdd5-3e5893f78814' class='xr-index-data-in' type='checkbox'/><label for='index-241168c4-c053-4203-bdd5-3e5893f78814' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 1000.0,  2000.0,  3000.0,  4000.0,  5000.0,  6000.0,  7000.0,  8000.0,\n",
+       "        9000.0, 10000.0, 11000.0, 12000.0, 13000.0, 14000.0, 15000.0, 16000.0,\n",
+       "       17000.0, 18000.0, 19000.0, 20000.0, 21000.0, 22000.0, 23000.0, 24000.0,\n",
+       "       25000.0, 26000.0, 27000.0, 28000.0, 29000.0, 30000.0, 31000.0, 32000.0,\n",
+       "       33000.0, 34000.0, 35000.0, 36000.0, 37000.0, 38000.0, 39000.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;easting&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-dad0be36-7d8c-4573-bd01-231f75a56ba3' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-dad0be36-7d8c-4573-bd01-231f75a56ba3' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.Dataset> Size: 72kB\n",
-       "Dimensions:            (northing: 31, easting: 41)\n",
+       "<xarray.Dataset> Size: 46kB\n",
+       "Dimensions:            (northing: 29, easting: 39)\n",
        "Coordinates:\n",
        "  * northing           (northing) float64 232B 1e+03 2e+03 ... 2.8e+04 2.9e+04\n",
        "  * easting            (easting) float64 312B 1e+03 2e+03 ... 3.8e+04 3.9e+04\n",
        "Data variables:\n",
-       "    full_stdev         (northing, easting) float64 10kB 51.91 51.03 ... 36.9\n",
-       "    grav_stdev         (northing, easting) float64 10kB 48.95 26.31 ... 18.86\n",
-       "    constraints_stdev  (northing, easting) float64 10kB 64.36 34.61 ... 42.13\n",
-       "    density_stdev      (northing, easting) float64 10kB 22.21 9.916 ... 11.22\n",
-       "    damping_stdev      (northing, easting) float64 10kB 0.4868 1.648 ... 0.5527\n",
-       "    topo_stdev         (northing, easting) float64 10kB 22.94 23.04 ... 15.13\n",
-       "    regional_stdev     (northing, easting) float64 10kB 18.29 13.14 ... 17.23"
+       "    full_stdev         (northing, easting) float64 9kB 24.81 24.39 ... 37.94\n",
+       "    grav_stdev         (northing, easting) float64 9kB 5.563 2.823 ... 2.796\n",
+       "    constraints_stdev  (northing, easting) float64 9kB 19.04 17.97 ... 44.44\n",
+       "    density_stdev      (northing, easting) float64 9kB 1.859 1.239 ... 1.926\n",
+       "    damping_stdev      (northing, easting) float64 9kB 2.831 0.5222 ... 2.612"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 69,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "names = [\n",
-    "    \"full\",\n",
-    "    \"grav\",\n",
-    "    \"constraints\",\n",
-    "    \"density\",\n",
-    "    \"damping\",\n",
-    "    \"topo\",\n",
-    "    \"regional\",\n",
-    "]\n",
-    "\n",
     "# get the standard deviation of the ensemble of ensembles\n",
     "stdevs = []\n",
     "for i, s in enumerate(stats):\n",
@@ -2873,7 +2618,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 73,
    "metadata": {
     "tags": [
      "nbsphinx-thumbnail"
@@ -2882,7 +2627,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -2903,24 +2648,10 @@
     "    \"Uncertainty from constraints\",\n",
     "    \"Uncertainty from density\",\n",
     "    \"Uncertainty from damping\",\n",
-    "    \"Uncertainty from starting topography\",\n",
-    "    \"Uncertainty from regional field\",\n",
     "]\n",
     "grids = list(merged.data_vars.values())\n",
     "\n",
     "grids.insert(0, np.abs(stats[0].weighted_mean - true_topography))\n",
-    "# grids.insert(0, inversion_error)\n",
-    "\n",
-    "inner_region = vd.pad_region(region, -spacing)\n",
-    "grids = [\n",
-    "    g.sel(\n",
-    "        {\n",
-    "            \"easting\": slice(*inner_region[:2]),\n",
-    "            \"northing\": slice(*inner_region[2:]),\n",
-    "        }\n",
-    "    )\n",
-    "    for g in grids\n",
-    "]\n",
     "\n",
     "cpt_lims = polar_utils.get_min_max(\n",
     "    grids[0],\n",
@@ -2933,13 +2664,9 @@
     "    if i == 0:\n",
     "        fig = None\n",
     "        origin_shift = \"initialize\"\n",
-    "        cpt_lims = polar_utils.get_min_max(\n",
-    "            g,\n",
-    "            robust=True,\n",
-    "        )\n",
-    "    elif i == 4:\n",
+    "    elif i == 3:\n",
     "        origin_shift = \"both_shift\"\n",
-    "        xshift_amount = -3\n",
+    "        xshift_amount = -2\n",
     "    else:\n",
     "        origin_shift = \"xshift\"\n",
     "\n",
@@ -2948,7 +2675,7 @@
     "        fig_height=fig_height,\n",
     "        title=titles[i],\n",
     "        title_font=\"16p,Helvetica,black\",\n",
-    "        # cmap=\"thermal\",\n",
+    "        cmap=\"thermal\",\n",
     "        cpt_lims=cpt_lims,\n",
     "        robust=True,\n",
     "        cbar_label=f\"standard deviation (m), mean: {int(np.nanmean(g))}\",\n",
@@ -2960,23 +2687,6 @@
     "        xshift_amount=xshift_amount,\n",
     "        yshift_amount=-1.1,\n",
     "    )\n",
-    "    fig.plot(\n",
-    "        x=constraint_points.easting,\n",
-    "        y=constraint_points.northing,\n",
-    "        style=\"x.2c\",\n",
-    "        fill=\"white\",\n",
-    "        pen=\"1.5p,white\",\n",
-    "    )\n",
-    "    fig.text(\n",
-    "        position=\"TL\",\n",
-    "        text=f\"{string.ascii_lowercase[i]}\",\n",
-    "        fill=\"white\",\n",
-    "        pen=True,\n",
-    "        font=\"16p,Helvetica,black\",\n",
-    "        offset=\"j.6/.2\",\n",
-    "        clearance=\"+tO\",\n",
-    "        no_clip=True,\n",
-    "    )\n",
     "    if i == 0:\n",
     "        # plot profile location, and endpoints on map\n",
     "        start = [region[0], region[3]]\n",
@@ -3027,7 +2737,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 74,
    "metadata": {},
    "outputs": [
     {
@@ -3044,7 +2754,7 @@
     },
     {
      "data": {
-      "image/png": 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nGg899Xdh8ZHBAAAAAADMd4oQMnBtA5HadyOpX19fXRaCr6+v/E+J1zKoHz1dKtIZ5T1eY0Btx7BaAAAAAOAUomEnaW3bVn8cdkOrenYyv7U7AAl/RWq7XMeJpQfTxP/8/HTv/vz8DC6QiODKh0xkcFjt/Otxfw3rRU8NqwUAAAAAtpTu7YwGsbxaZrTLtK7r+/2es7YjbCjTsy93tGBHCxsD1/CfSG0Xox3MdRzGXKdtI4zg9t/t8iGL1/LUtu2yOZDbtu0PqJ28thyG1QIAAAAAqwo7IRNjRdLxxbDfMj3gJFzPhHnlNtvQuzKrsfg74K2/F1jKP0VRDMZoZ0ZnX3m12i5eu+y2OK/+dW7OJbl/fV37OmpYLQAAAACwqjCi+WqsSFVVoyNTw0GlVVWNrmfyvHKbbegtYV/uq/yOXTbm5z+lPgYW9G8/Prp4dHZQt5Xv7+8wSHy73bbZOsfXNE0UnX118U7rD6jdJmjaJeh4/rO7VZIfAwAAAABYSlmWz07Iuq7LsqzruuuE7P7bvRsu1te27TNM0OUc7qc8DP85eVDNZht6V9iXe7/fb7dbumAG1AIL+m+Y9uvr6znj7MbCeO1VI7WDI4avurNL6T/wVZblu1fBvZ5FKHrln1B4PlN30mofgFe0EsAoDQWQppUAXnn2pGkilhJ2TubX6vNToz2KbduOhjO7laQPbtu2TdPkzAvbNM2c4SjzN5RfOflLFnk12a3qGQufszmAp//MU/t4PPYK0xZ/JrLda+scVv969ir7xCv9hbfMQmy2WgAAAABgPVVVpTs8M6OGXTrDdFLfsixnhmm33NC7upocLZi8icDiZBveiDG10ww+ypR/ezF/SO5MhtUygcfbgTStBDBKQwGkaSWAV4ypPa8w13EnMfozoRvzusiqDrKhd/VH/Xbh290LBlyVSO1GRGonG5z0fjTkOfip7Su8H2kWrGWUThMgTSsBjNJQAGlaCeAVkVoA2Ng/excARgxmw0inQR4M026Z9zgsSX/yeZFaAAAAAAAARGo5gVfB2tvtVlVVN7dBt1j3z8ExuHvlpuhnb94lZgwAAAAAAMChvJ39+Pv7+/f3d/L2PjZvhuzHM/WnBwh1cdzEu/sOY+0Hj5umMasBr0hEBqRpJYBRGgogTSsBvCL7MQBsLDdSOxhonOBjr/EitYsYHC+btnuYttMvuaPPKzpNgDStBDBKQwGkaSWAV0RqAWBj49mPv7+/lwrTwkyDaZATDhKmLYZyIBtTCwAAAAAA8MnGx9QuG6b92KexjKldUP7I2kPVsBzIZPJ4O5CmlQBGaSiANK0E8IoxtQCwsZFI7eKjaT/2Gi9Su6xngPNVyPaYQdB+sPY4o345Dp0mQJpWAhiloQDStBLAKyK1ALCxtyO1X19fPz8/axbpmkRqKYqibdt+GmTBWiI6TYA0rQQwSkMBpGklgFdEagFgY+9Fal2hJxOppZNI3SxkS0enCZCmlQBGaSiANK0E8IpILQBs7I1IrcvzHCK1POXPs1sURVmW4T+Fcj+BThMgTSsBjNJQAGlaCeAVkVoA2JhI7UZEankazIGcybjbT6DTBEjTSgCjNBRAmlYCeEWkFgA29s/eBYCPU1XV4/GIBstmut/vt9tNsBYAAAAAAODsjKndiDG19L2VBrnP+XNVHm8H0rQSwCgNBZCmlQBeMaYWADYmUrsRkVoSqqrq/vJW4LZpmucHuRKdJkCaVgIYpaEA0rQSwCsitRMs1UF33ix6zxqo61p35fU4vrC2kUjt9/f37+9v9/evr6+fn59NSnVBIrVMEF75BoO4pq29JJ0mQJpWAhiloQDStBLAKyK1Ewx2/E5w3jp/1oC+yktyfGFt/6bf/vn5eX4PnyHbQxm8EGYGlcM49LufhQ2EV74uahvFa+/3e9u2HmUCAAAAAAA4nX9Glwif5bndbt/f32uW5w232+3V80q/v7+jjzLdbrfB2HPOZ2F7bdvWdV2WZfR6Xde7lAcAAAAAOKyqqm5/7F0WlneZ43uZHYHJxiO1RVF8fX09/94FMneP1+Z8aRPL5MRx3y4TrKyqqsH8EsbUAgAAAMAuHknTlgTgc2RFavvZgLt47QSLFDqKE399fb26pA1uMfr484NhQLq/GBzE4/GIRtZ2OZD3Kg8AAAAAAJf0DKDogoaVZEVqjzbANMxa/Hg8okDy6PNH0ceff//5+Qn/ecx5eaH4e/7aTtM0u5QEAAAAAACAacYjtUcL04aiUbBP0dy64VvhSNnBmO6rdcKhRKHZ+/0uBzIAAAAAAMCJ/Jt+++AZgPtpmUeNjpT9+fl5Bndvt5sZAjimqqrKsrzf789Xwr8DAAAAAJcRDdJYKQ9t27bR+JC6rk89PmROvUW1MaEqojVMPmrP9cw8HJsd321OV7iOadOYpz+1qnDMa2KxV6XN2Ys19vRo1cg1RGdUWZZ7l4gFaB+ANK0EMEpDAaRpJYBX9FsubmZvcNM0ZVkO9i2XZdk0Tf8jr5ZP9x9O+1S0jxM6J8Ptzlmy/1ai3kZLlZhpLvPj7x61zB0py/JQxzdd2vQmMouUWcgJC8NxZM1T+/T19dV97K1PHdM19oIPF13PDKsFAAAAgGuoqqqu61c9fvf7fZEBkW3b3m63dL/i/X6/3W5nGRmZ3qPRWeS6an/1bvfxRFW0bbvgURs9NKM2O74zq/2VZwf4aD2c5fyEvjcitV9fXxOyDS/u5+fnGWd+tcyryWgzkzmbqpaz6F9+Tp2NBAAAAAAoiqKqqig0NTiecnIArNOFFaMXyz+i1xPxy0N5jogtA+ECiUrLqfYu2jr48UR9ZhYgKszoMmlbHt851Z4p/fFFEk3DPtJDbvOXPI4ozhq+NWHHlyrVhMqHHP1r6t4lYi7HEUjTSgCjNBRAmlYCeEX/0uKm9dpFPX6jmWNH0yDnbGgwMW+UyHcwqWz63fw9nbNkWIZXdRJVWnoT/TWMVkWUM3n0qI1W5vMvg4fmCMd38Wof3It08d5dDA7o3+ISbrfb4OuPTVIcv9o6bKBLKxG+ks6/AQAAAACLO12mtyN3oIXDOpum6ddt27bhcMm6rqf1hIcbGqyQbtMnmnOtK+qrSguHzHb/DN8Nd7Nfn1VVVVX17Int10kYqc05avf7PSpDf0fKspxzom52fCdXe46yLLuPJ8p5uvYH/pIO5OYvua/MXZuw45O3DgAAAABwRkv10B7ZhH2cWS2ZHxkd4zi45LQxl+9u6PhjatPFCIOp0WLpyhxcLFxDYs35Zcjfkccxju/8as/ZkZxvRP6JBAd0kTG1g26322P2LcIiKwEAAAAAgFGj4yDDd59DPAeHM6a1bfuMb51lDtociUpLVNGz2suyTCzWT3DYyZ8ktaqqnEGio+sZtfHxnVbtmWseTWsaHr4524JdjERqv76+fn9/u79/f3///PysX6QpnsHU7+/vZ4GLJeKs0ay36a0nDDYlYsAsJZrrfmZaDPbVNRfaB+AVrQQwSkMBpGklgFfM8nYoOTGnzJjfoC6db86SJ8p+PC1Q91ZX6mD4c/FI4fz1bHl8146Pps/zcDev9MwBn+Of9NthaDaMgB7Wz8/P4/EIw6szby8OG5wGAAAAAOBKDjjd5gGLtLhwROxoqK/9Y5Ga2b16dy9AjvCgjA5ZXr84sLDx7MePx+MZ7DxLKuCfnx/Pf/FRohQQ6enoAQAAAGBZko4ubpfRgWGP4omG0i5oQp9qFDs8cq/sSY9vWOx+om+pjzm7rHlqwxzIt9vt6+vr+CNNwwBz+sW+7+/vdQoFK3qmgOhMmJoCAAAAAKYxFdfi3u3cq6pq2lFo27ZpmhPF7Y4mHJJ7wGq8xvF9lQA5/JpohTipkUhtNO1r5/f3d/KI1flDcp+bXi9gfIo8zxCp6zq8Sp390gsAAAAArK2qqkRH4nOQos7Gk7rM8Q17vwfTSRpQy3mNRGqPHLOcWbazZHKGTP2LkwTIAAAAAPAhJmRL7o/I6sJddV2H/Yr6GPOVZXmckZ1XOr6DCZDbtj1+jBlGZWU/PiMZjPlAEiADAAAAwDW8m814Qrbk59/Lsoyid+Rr23ZyFtL1XO/49hMgh3mnjxMgh3f9s3cB1vJqxO3X11f6g2GI16BbziV6bs7zRAAAAABwIltGm8LOQ8n5nkYPQVdXnW2KNM31jm/Y+90dpuc+Sn3MqY2Mqf36+jpaAuTH4/F8PuVVBuMw2hqFZn9+ftIfP9r+Qj4JkAEAAADgcywSqRLlCgfFjuYpbJqmP0ImSnY4urnnYNANYvOXOb5RAuTwrQmpv+E4RsbU/vz8PBa1+A7cbrcwLvv9/X273cJo68/PT/SRMHYbJiXoPvv8pwG1nFF03Y2uWAAAAADAkT379+73ezqMt9kIjW1S96V3drP0gaMbSkfHR49aURR1Xd/v9+MkRDxOSUaF346w69toJU7tlNmPoxjq7+/v7Y9oROxgtDWK3Q5+djRJMhyTBMgAAAAAcF5h/156GEaU3nal8qwaA8vc2Q3icGHYNbG58K1+Mt5O+qi9WsNezhXjDGtM6mMu45SR2iJvwGtimcfjkYjFfn199UfiwilUVRVdmUylDgAAAABnEfbv3e/3wUBaNOXZzEhVYhhoVVWrDgUJ9+JVMdYuQyfc9KtqD0tSlmW0THTURvelv4aV7Hh8FzdYY0cIeMMcZ43UFkWRSKeck2m5S+wcvfj19fV4PIRpuRIJkAEAAADgRKKBg7fbrfpblz73uczoUI3B+FYY363rOgwudpHg2+0WhfFyUvu+K1GMbme7MmwwbjLsR+2irZGwNvrRwSjW2+1LKKrPBeOLRz6+b8kJXffPhHMNC4a+f/cuwFwzZ5M1GS3XE92oAQAAAAAnUlVV0zSDiV4jZVm+CviFnYRduLdbPgzXdS8+lxncShcYe77VbS5cz0xRZ+ZgMbrNhaVdQ1TtiS7WpmkGo4NN0zRNE1b7u2vId5bjO2p0RxLLF1Ifcwn/fH9/712G/zpUYeCkomu8qC0AAAAAnEsXNUxHobpo1quAX3+WtL70JsqybJqmbdtVg3bdniYW2DJqmFPtiSBrN2519KjND9MW5zm+o3J2JFo+/Ke5/7iA/z5Sse/o0ufDHVcd5Dr4vM9Vd5bdRbk4trybYb6uudA+AK9oJYBRGgogTSsBvHL5Ttozats2HKZZ/BlE2OWzHf141E84GCMc3ES0/nCZ/ruL6FaYLsZmXlV7Zi9r9/Fi/d050fFNy9mRp2dLpd+ba4jDh9tfg6MQ5lVvAkRq2ZJI7anpNAHStBLAKA0FkKaVAF4RqQUOrm3bZ4Zq/d5cw+3r6+v39zd6dZsrcT94+fX19fPzs8GmtydSy8Y+5BmIS9JpAqRpJYBRGgogTSsBvCJSCxxcOEhJS8U1/PPz89M/m2+32+12W2nW2O/v72790euPx+OqYVrYnWeLAAAAAAA4tWeY9q3ZbeHI/un+93g8vr6+ovd+f39vf8zf0nNV/SG8X19fnn2AZUUXqm5eBAAAAAAAOKNw8tpnDmQ4u3+ff+vGs74KyoavZ+Yo/v7+7gdl+8RoYQ11XYdT1QIAAAAAwLm0bfschhQOqA2jtnBqt8FA6SKDaEd9VIzWPLVsz1S1J2XKKCBNKwGM0lAAaVoJ4BXz1AJHE05M+9Q0jUgtl/HP4KuPx2MwH/IiulzHLvawtigBsqlqAQAAAAA4NQNquZjhSG3n5+dn2ZBqt7aczMnA4kxVCwAAAADASZVl2TSNIUlczHD241cyp54NZU5qe3myH7O9tm2jadWdcqcgERmQppUARmkogDStBPCK7McAsLH3IrXDqwhikC7hr4jUsgtT1Z6RThMgTSsBjNJQAGlaCeAVkVoA2Fgq+3GmR2D+2oAFRVPVSt8PAAAAAABwEAtEagEAAAAAAAB4ywLZj8kh+zF7kQD5dCQiA9K0EsAoDQWQppUAXpH9GAA2ZkwtXFyUALlt271KAgAAAAAAwNO/excA2FTTNGarBQAAAIAFtW3bNE3397qu3+p/Cxc2ymJLz5p/95CdVHiWFk62T/Vpp/0piNTCxdV1fb/f9y4FAAAAAFxW0zRhF9xb8Q99d5O0PlMAACAASURBVHuZfMjOqKoqZxrFh532ZyH7MVxc1Nq6HgMAAAAAfA5hWjgykVq4vmiqWk/KAAAAAACHVVXV7Y+9yzLLQXYkDNOWf+xYHg7rIGfsp5H9GAAAAAAA4ILC+WjLsjQ9LRyNSC1cX9u24SMwMl0AAAAAAB/u8XjsXYQtNE3z/LswLR9y2p+L7McAAAAAAAAAWxOphY9gqloAAABOpm2L2y3+AwAAFyL7MQAAAADHE2Rr/K+qKmRuBOiNxHg3q23btmFS3Lqu3x3aEa1hclrd53omlOFVYWauLW1mzZ9I5qFZqkIyz6hV6z8sw8xTKKf2tjxvObQHm1D57M7pdxYOEJCmlQBGaSiAtHO0EmX5KIrhP02zd+HgsnQcTRYmtCvL8q3Pprvs+m9FyfPe3W4z+BxM9hqapkkUoEk20el9Kcvy1ZoTxZvwkX55BhdLlza9lcxSZZZzwsLRRwbl7+zgFhc8E16tKtruzDM/7dXKnzsyWG+v3hqtvZmnes4uh8uHh2NwJTnleWvryx6dy5P9GD5F1Npe+GkvAAAAzq1ti/v95bt1bVgt8Jnatr3dbvcXLeT9fh8djVdVVV3Xr97t1pDoNmzbtq7rRAHeGhGY2Jcc6dp4Ful2u83vCJ1f84OeHbaj9bBxX+5oxXYn0iJnQuKkelZs27ZVVS1e/0+Jldd1/W7lp2tv8nn7bjGemyjLcpFxuoc9XS9ApBY+VOLpOQAAANjT6C9WP2mBj/Ts0CsD4QLpkFU/HDW4hleh3C6iFr34bhnCwowuk5AoTH90YCI4nWlmzedIf3yRRNOLlKTIO5GKvDoJj2OiYpumCeOOg4tNrpbR3XkrWJve5ZnnbX6sdO0Uysc5XS9i70G9n0Lls7soNCvtwGFpH4A0rQQwSkMBpB29lWiaON1xWQ4kQ/arFlag33KyDbIfPzfRzysbxXhGS9hfSZR+tr8Lo12L0frT+YSfC3cBqvQeje7O4BpG9+iRnf14Ts2P7ki6DO8uNmi0DI+phybnTBhMg1z8bXQlOYtN6+5On0iDxUjszlu1N+G8zd/fxEFPryF9tmRufeZB+UD/9s8z4JKi51zmpBYBAACAtUQjSMryP7mOqyqVEhnY28ojuJZ3uoFeXW9e0zT90WxRbtjun9ECYWfgoxcqq6qqqqrb7RZuKxRGal+VIRwv2I1xfDXwrlt/WZaTx9uFJRxcSbfpRbpA59T8qLIsu48nirr2+MjQ6KEJy5lzJtR13T/fQoPb6lIEh68MbitMmzztWKdPpGhfMteWWXsTztt+taRLUvSmRJzpaKfrZYxEap9HPf1dAk7h2ZJ2Jtw6AAAAwIr6vZbP7tG2LcLeyfu9aNvzhYbgujxKsYHy9XyTYciqH9OK4qyJ9T9XEk5YGwZ6E2XostQ+w1qDobVwW4ukRU0EoqqqirpD52xlWs2/JT1JcGeDXLKJQxPuXfpMCGs+3Qv9alvhGl5ta8GM06++F++eRZkn9uTz9t1Y6fzU34mtHOF0vYbceWpvt9vtdvv+/l61NMCWTFULAADAsUQ/VMvyr1hs1K3pVy3wYRJhj3TIKifOmlh//qyTXZAp2uhb28rRtu1zas/1AlHRFl+9NX/C3dFlVhof+UqiSKNDQgffTT8c8GbpllxDuDuJ4/jWOZaolkXO2/CD6THr6WWmOeDpeg3vZT/+/f3tRtkaYgtnFD7hBQAAAIcT/WiN+jHr+q8F/MIFPsnksMdbMdHnVsKA0OKhl5nr6XI15yy51IDa+StJrz8xSnKb8ZFhYZZaLGf0Z6a1h2amdyc/2Dm6nvnn7egawrpa49Q91Ol6GbljaiO3P5YtDbAqU9UCAABwXFHnYzSgtlsg6nOU/RhgTDiicTR20v6xyFC8fWdeO8u8b+FBGR2yvH5xDrr1xU17gmEDo/WcHraePwJ+mrOcrucyMqb26+vr9/c3sUAXrP36+vr5+VmyXMA6TFULAADAQU14ntgjyHAY8lyewoSewCgYc9i+xLBg5xqgEs2W+mqwzaFyyV5guOTaEc1ME87bMHFmf7LYtU+YM56uxzcSqX3GX9PDZ59ZkYVs4VxmznIPAAAAy+h3kg52m7ZtEXVSta2RtXAE+0U6WFcY0DpaBLRt26ZpjlaqCV5llA17bneMJva926XcDyh+spnnbeZU0+tF0093uh5fbvbjx+PxeDy+vr7Si3UhW1mR4bCiBvoC9zEAAABcQRAJKIrk6LzoreiDAHyGqqrCwYWR8o+NSzXNaEbZs+wIoxY5b18lQA6fq1hvgJbTdXHvzVP78/OTGbI1kS0ckxG0AAAAHFHUZZkYCBK95RFkgK2UZfl4x3rj6m63WxSj6uJbTdM8N32iUX1RRtnuL23bXmaYzQWyJS9iqfM2rM/B/Mmrxkovf7pu771I7dMzZDu6ZBev/f7+nrYhYHFRMy12CwAAwM76nZKJ36r9t87TFw9wOgeMd4b9mc8oVxfiOm9XZ3+U5EEmUh00IfvxSiVZxDbVu+B5O7j8NqmPO+c6XY9vYqT26fl0THqxZ1ZkIVsAAAAA/pKf+nhwAQmQAfKMRlC6oFFnmyJNEA7dO3V0NtTPKLvN+Mh8Fwu/hRXebHIjsex5m4iVlmW59pfi+Kfrufy71Iqewdp0xuMuZBsuD2ysbdvweyopAQAAADvLT338XMCPWeBIwg63t3rbomF2CxerKIq/y9Y0TTqE0zRNv/xlWebvVNu2YULUNwv7nivFhAYzynZOmjr44KG7t2KZy3ahL1Ih4WS3VVVtnHz4eqfrvuaOqe17ayJbQ2wBAAAA+Mto52m0gKgtcDCHHX04GstJR9fu9/vornUBpIMMDjlIMTKFoyTD6NdxBg2HJUyfCccpc0J/WOqg7fcl57ytqir8koaHY5v25/in64ksH6ktiuJ2u/3+/uYs+cyKvEYxgFdMVQsAAMBRRL9JMweaRIsdNSgCfI6ww62u65xgSVVVUULUVUr2d9kSPYHhW/3spp10nthXa9jF6fo8wxo75oDU/IzB25zYM4W7kzhbNo73Tzhvw1jpZifM8U/XE1kyUnv7Y9pnFywJAJzRczKYw97CAgDAQZmqFthb27ZRsDYRcel6AMII0KozZYb9DPf7fbBgYXn681yG4+dedVyk17CGxMjOqHpPYbDGdg94h8JxnK9OpGgG1iOH7nJ2Z43TeKnzNvped3/Z7IQ5/ul6IgvMU5sfZO1SIr8abnu73UxeC9swVS0c03MymPBbWZZl+tcdAACcW/SbNPOxxWiqWj9sgQMIZ44siuJ+v99ut36kanAi2LV/+DdN8wyiDAalwlL1wy3RRLz3+z3ar2inFgzY9APD4by5dV2HPSfdLLn9Gu5iY/v2ruQ8mt+fEvhoPULhST54hkflP/hohGh3otoOnzyY34W+xnm76gkzesYe/3Q9jcdU+Zv4+vrK/PjgktewbOXDfNEVtCzLvUvEf2gfPlPTNOlnDH1JedJKAKM0FEDa4VqJovjrzwYfBF7Qbznf6A/8d3/y5/cMjC6ZOWy3aZqZu/ZqDfn7MljU8FM5xSjLsl/gwZUMlie/tIklR3ckvfwi3UFhJUzYhdFCJup/zpmQU+z8xebsTnTmT9udaMl0vaXP21e7n1OA0dK+dcaucbp+prezH+enOH6GXX9+fvrvdm91o2yfMme3BYArads2euq2L5EXBQAATmzaJLWDCxvGARxANwotM6LZhbI2+71fVdVotLVpmlej4nJ2rduj+ePqwrS0g9I78qzY3ftSRnekv3z4z93LPyjnRCrLcvdBzJm63Xn17iLnc7i2Zc/baMn5J8xbZ+wpTtdTyE04/FaK48HQbOaaM8tzOoMVeNWd5Sw+5Nt3Ot1xcTg+RxemzVy4u81dtTwcn1YCGKWhANKO1UpU1V+Ji8syN/vxzM8CQ56dRUdpIk6uy2ha9PLBdoGQHX/g91OtvlWkwf1aY/Km/lS+/chQf0eiYoTL7DXD1OiOhJ5fw+P3Ar06kU46jVdX5jDj8XNHwiM4v3k8/nmbf8ae6HQ9spFI7ff3d/4418knaBguuupNgEgtBxQ1uBrTgzhWpwnrG7xAPB9e6w+0XfZRPs5IKwGM0lAAacdqJaL74XdLNfPjwN9EamFf4QP9emuP49mR7qCEnK5L+Tf9dk6Y1mUb4FC6R65cGk+hH3ONnpKLnqgoRGoBAAAALipMw6tzb23RGNbEkuk5yz6W03UpI5HahKUCtPl5lYFltW0bfgFdby4gjOpJPXF8bdv20x/1p5eILpT3+72bGGaLIgIAwKrmTFL7/Eh4U922ZqsF4LzC1Lv7luRD5HSJh71w+VOYfQKn61L+efcDjz+WKsHX19fg34ENRG2ogXrnVVXV7XYbvLcQ2Dus/r3d4N3e4/GIvqqeqwAAgGHB2A4AOJewb1ZEcGOJjvFw5Kj+8yen64JyI7WLB2iffn5+niv/+flZfP0Al9cfmhm53+91XQvWHkr/xi6R1rh/7NwXAgBwBdEPmQm/WfQMAnBmbdtWf4QjFPX8bKBt2+foiG6sS3+B8Lg0H/9AmNN1JTezzG5jMMmzyucIopPTabm77ojkH4hw5vY0aZCPo3/URo9Of8Ja39aP9W4rAXwgDQWQdqBWIuotmVakRVYCFEURdBMdoomAD9Dv8CmST/OzLD2rb3G6ruTt7MfAxUiAfHaDD3OVZdmfHmDw0TB20T9qo7d6htUCAHA18yepnflBADgeIxS3VFVV0zSj06wK077idF3ESKT2FpizmaXWA0DoVd7jtm3D9B1P5jc9gv5RG70dHFzM0QQAgAG6CwE4p7Ism6YREdxYVVVt2w7Ga7vBMI/Hw0Hpc7ouaCT7cRhYnZPyYqn1nJfsxxyZBMiH8lYisn7bEqWbmJBll7VFeULeOiLREXc0P9OB0hUCR6WhANKO0kpU1V/z1DbNxCBrtJ6ynDLfLVAUhezHALA52Y8BCZDPqn+k+rMCVFXVH4gptrej/oDazMkwOobVAgBwHdHd7OSfotEPHDfJAACch0gtEAeKxH5OYTCD7mCUvZ8GeXBqW7YRVf67czmYrRYAgGsy1ywAAB/pv5Ha7+/vW0+4aP/dfJvvF/CGfqTHmMvj6w/ETBy1fjDeId7LnAG1HcNqAQC4gmWfOIwCvX7vAABwEv+N1P7+/u5YDmBfxlyey2De4/TyDvERRAfu3QG1nX6UXdwdAAD+4vcOAAAncXtODr/ZyNfPnI5+sHo/syo4prZto7F9zs+9dM1Fuv6jJqUsy9FYXf8Q9ye1ZW3RgZt8CKqqCofS5pwAXElOKwF8OA0FkHaIVqKq/ppQdmZh2rYIf++UpWG1MM3zd6sbiXe1bft8LL6u67d+74cL+4G/pWfNv3vITio8SwsnG8f2UV/PrSO1H3uNF6nl+MR+DmK00yQ6UumFEx90iDc2+cD1ebTiwx2iaxU4Ng0FkHaIViLqJ5lfmMVXCB9JpHay8Ff/u10uYdexmt/Ss+Y/oZdswY4p2MBHfT3/m/346+tr7Y355sOJmPzysKJDE6U1TujPVrtYmXhf/oHrM7c0AADnFt2+zrg3BgBG9cO0wHH8N1L78/Pz6AkX7b/7rs33DniDyS9PYU6ITnhvR23bRjfEMyvfxMMAAJzYGrevUbj36lnyAK6tqqrbH3uXZZaD7EiUZq+zY3k4moOcqFs61C7/M74I8DHEfo5v8oDaweUd4s1EVT3/btgIaQAAAABGhaMFukSynR2LBIREaoH/iq7Q9/vdNftQ5g+KFd7bS1TV0YGYoKqqKNzbPz0AAOCgol8ii/zwnH2PDcCneWYDvXYXaDh+4Np7ypV8yNezMxKp3WDyWuBQjLk8spkDagvhvZ30K1m1AwDAwqJ7bI+lAgBwBiOR2nDy2m0KBOzLmMvDMsvsZSw1EUh/EPwiqwUAgHVFP2TMkwcAwAf7d+8CAMcyOPhPRPAI5g+o7bRtG06T3uW4NsRzPW3bRsduwS9UWZbhyn1bAQA4gfVSN5WlobTA54g6c97tEGjbNsylV9f1u71D0Rom90g81zOhDK8KM3NtaTNr/kQyD81SFZJ5Rq1a//O/FwsWb86qNjtLc06Szb6ea+/1iut/sAmVz4n0Q4B7l+izDNb5sgclWltZlnPWRtqqte1QfiYtMzBKQwGk7dxKlOWjKP77Z8Gb2PXWDB9DX9Bk4S/0d3+epzt8+m+9enw/c7uJyc5y1tA0TaIATdPk7OngvpRlmR6ZMFi8CR/pl2dwsXRp01vJLFVmOScsHH1kUP7ODm5xwTPh1aqi7c4889MO+714qySJE2+wGG99fdLl3PLrOafGlm1kRg9upvcuuvOnrZ1f4pNSG5xLTgvFSgbbh2WPSP/OY87aSIuqepGLd2L9y66cY3KsgVEaCiBt51Yiiqeut2a/ZOF9fl1Otk2kNhFMytz0aJK2dOBhtADpMkS72f/gW0GUnMJ0Xu1Rusxhad+t+cwdCRd7VWn9nd0gUpveu9EdHC1nuLl03XYrScRBR7eVY+b3YrPaSK9nQjHmRGrTC6/69QxLstJ3c06tTpCb/ThMlQlcXpRS1fyX+1p8htrBFUqAvIb+kVq8niVABgDgZNb7gdm2hf4r4NKeMYkweBB14iV6BqqqGpxdK1rD/X7vh2GKomjbtq7r6MXBNeT0TszsIUkUpuh1ZtZ1PbhH+WbWfI70xxdJNJ1fktEFRk+kIq9OwuM4ePi6lYQvvlpscvfmzO/FgrVRzDjTXhWj//FF+oHTa9jy67nqd3O7Ws0J505f+6TNXZLa4HSi09Ww2s3024c1joWsudvYoJ4dyg/kLgIYpaEA0nZuJVYd9hquXEsI79NvOdkGY2qfmxhNYTpawv5KojGL/V2IBq6NjjxLj1J9LtyNo03v0ejuDK5hdI8e2WNq59T86I6ky/DuYoNyRu5OOzQ5Z8LgiMnib5kDGdOLTesTm/m9WLY2Jp9po1+Ht9IUv6iqiSfJGl/P+TWWs8vzazXf+EW3WNScsp6a2uB0+pfDxbO2MihqH/oHYpGtSIC8jW2+RA7lp3GggVEaCiBtz1Zi7QTFIrUwj5+Wk20QqU33LYQFGIyL5HQdJJZJr39wK/3Fon3JzOP6blEHyzMnUjun5t+KBr3akZkBobeCcKNbyamT0fMtZ1s59Z95Yr8y/3uxeG0k1pM40949i959d7CcmSfJq2XmfD3n11jOLs+v1Xz/FEnf39/pBYCratu2/8jPXoX5WG3bvkqwMFNVVdGqpMxd3AapjzvRoZTIGgCAzxX9YvIzB7ZUne3PCZVl+epXf5hrtN+JF76S6OILexjCDYU9VIkyVFWVuaFuPYt0RiX6yvrdX3O2Mq3m35LTpbNBD17i0IQlTJ8JYc2PJkB+VYzRbc3sBJv5vVijNqadaf3U0Gnzz6LM7+8GX8/1vptb1urIPLW/v7/RK19fXz8/P5O3B5xIXddrZLEnXz+xzHp3Y03TOLjL6h++vUoCAAAH1ZvDbGFNU/iZA5tZbRJqnhL9QulenZw4a7f+29CE3/mTpHbRl25z9+TE5HP6uMIRJv3pMNcwueZz1jxY56F3I0YzJXY2PKaj8dfnfiV6Hefv0fN8m2Dm92Lx2kivZ/6ZNufj+Wvb+Ou5Xo3N3PpbRiK1kcfSyZCBIwtvrTqCeVvqD6hdfP3hTcaq2/pMUZWud2viUAIAcBrRzaofmADZJse03oolDIZYFo8UzlxPVVWZXZSLdJKsHR9Nx7bDPd0g7pW5szmLZcbsc6wxdmX+96L/bnoNo7Ux/0zbZqhVupxbfj23eXZhg1odyX4cEqaFDxRde+73u0jtZqLKX3VAbUcC5AVtlvo4c+sAAPAR3AkD9IQjYkdDfe0fi/Rj7NuReJZuzPCgjA5ZXr84B9364mZ+Lw5VG2HAsq7rqqqO3zd4qAoctGWtvjemls/UFm1TNG1x9O82a+gPq5UDeRv9Gl7joTljptezcepjhxIAgPPZYBiEfDMAf5vQXRAFJw7b4RAW7Fz5xsKS97t0Nk59nGmbpNObmXlW714b/Xx73WnTnTNdlHG3whVFcc6v55a1KlJLShejvRf37u9VcdDLMKuKZqvtXhEHWltU5+nJEibrT0W8+CY+1mapj5/rdyiBC6qq//awP3slDv9oMAAvbfMrsiwFaGEfEjJeVPgw+tE6HNq2bZrmaKWa4FVq3LA/8FCjJN/tqDzFKM/JjlAbTdP0vwvhSVWW5cYh2wt8PTer1TeyHx/T7YWcz35/f/c/+P39vXaZz6IqqrqouzBtURRN0aSX56q6YbXRi9F4Qda20q1M/ypy4XumLW2f+tiTE8DVVFVxu/3Vz36//+cPALzFbxyAi6qqqj/C5Kn8Y+NSTTOaAPksO8Jeuuhv4py/3+9bRmqv8fXcrFZPHKnt4qyv3h2Nud5ut9/f3/7rv7+/mYHey6uLvycoLe4SIH+srj0KXzFh7RqijApPq8bFoyMrBr+IjVMfD27FNxQ4sXAo7eC7AFzDNjFUv3EAllOW5eMd6w0JuN1u/Yx0ZVk2TfPc9IkGJEQJkLu/tG176vGIod3zAx/KerXRnfaPx+NVcPF+v28Q/7rY13ODWj1rpPb7+3swzhpKxFxHa02wtiiKqqjK4u8QjmG1H6x//egmrN2lMBdT/TF4kV470XS00cvc/+1r49THAJfStiMDZ+93wVqAs9rm54bbb4BFHbADMOwre4Z/umjKeZ9cf0aAnt1K4UiAox2FCfl+VyrJERywNp7BxcH44qoFuOTXs7NerY5Eah+P/04vcKjgZRSmDZ/T+fr6Ct/qj6yNXsn/4AfqD6vdqyTsrqqq/mjLI8xGfl7dxamqqvsf/WVWmp425AgubvvUx4PbFXQHzipn5JNgLQAJ0TXCjTFAYDTg9+ywOnKXUdjpcYHwT6efAPm5mwdJEnu0aPGCJnwvTlQbXXxxswmnL/n17Fu2VsfH1Ibxy4MEL8OY8dfXVxhOLori5+cnfKU/9DZ8JVxy9IMfqCrib1H/FT7HYLBWGuTJukz9iRa8aZptLvmy5i5rl9THg9s60S0jwH/08x6XZfF4DIRvdbsDnE50d3qMTmeApYS/wd/qr4/Gny1Zpj/Cso3Oe9U0TX9QwVsF2zLWe5AQ5iKiBMjhUTtptrajRZoj878Xb1mvNjK/cVVVbXwgjnncM21cq+OR2p+fn+fff39/DxKsfQqLF4oGyD6F5Y9CvOkPfqwoAbJhtR9usOkRrH1LVVW32200S8Hj8VCr17DjzbRZh4Hz6Ydpux/PVTXQoe9CCXAuW96dnrlnELiGwz48PRpnSseTcmZDGx2csKWDFCNTmAA57NU5TidhWML0mXCcMueY9r04SG2EUeTDtjyDjvz13LhWs+apjUaa5sQYtpGIqoYR3LC0oyNlX33wQ1VFe+ul8TSs9rMNNkzdpNnnugDv4pgXS1lzlxVV4JbfC4cSOLd+gxk2a20bd7tr5QDI5LcqsIkwhFPXdU4vUDcr1vOf63UchWVL9FSEb/WT8XbSz4W/WsMuTtdXGdbYMQekhiVMnwnbnNgzzfxeHKQ28otxKAf/em5cqyOR2u/v71dx2dsky5b+1YBallEV3QDasj3QxYAj6GbM7r/eDa497KV3d1VVdQ8Vvlqgm199yyI9SYC8lKjqdr+Z9n0ETqNtBwbU9peJuGABnJc7VeBy2raNgrWJDpYutWbYTbRqMCBKzjxYsLA8ZVm+6uJIdACm17CGxHC3qHpPYbDGdg94h8KEi69OpGhq0t07xxJmfi8OUhvh+jO/EdsclFN/PTeu1X/Tb194rta9wiFnUv8nUtvW7e3x3yj7vbi3RWtk7Yfr3012uoQAZVmKD0USl59nIy4+ynxlWUY/85xXwDn0u6UG7yXK8q+A7rF/2gHwly0b7bp2jQB2ET2j32WhG5xKLHplg7hm0zTPmN9gVCksVT862LbtcyDWswPw1ccH1zBZPzAc9n7UdV2W5TMu3rZtl7Y0WkkXa9m3kyRniEvUsVMcr8MwPMkHz/Co/AfvJZ75vThIbfS/Ef1l0jsSmjkW64xfz8FdXrZWRzySpq930uYWFCZGTr+Y+dmZ9q2N6ZrHo3g8ikfZlMWjeP4pH+XeJeMQ0s+JlH/sXcydjT4RGS2/V/twvgbqkHavxuh88wW8JF9Srqko/vrTNLlLauiGaCiAtH1aiagBv9jm4Cp0C8zXNM27I6vSP97zf+OPLpk5bLd5cTeev2uv1pC/L4NFDT+VU4yuZzJnJYPlyS9tYsnRHUkvv0jHTlgJE3ZhtJCJ+p9zJuQUO3+x+buT+F5sUBujS+Y3O4PFyDlRJ5QzXSGTv56L1FjOLs+s1XxZ89Se0eBo4MwhwpIq/1dVFI+iKIq6+etxgHvh0VSKopfUJfKcc/t2ux384an1dA8KDb71bPc3LtIrEiDPd4TUx1EZDp5IBOA/ootOWabSGh84dxYAuTTmwHV1Y7My+wS64M1m/WZVVY1GWxPZuXJ2rduj+d1KYV7ZQekdeVbs7n2SozvSXz785+7lH5RzInU5F0/RwTj/e3GE2shpdhJfz3dP1LRTfD1zdnlmrb4hHcgNR5cuYk5UOV+4xa+vr8HXM9fw7uYOWBsLaB6P4vH3w6hF8//NekaAK8l8ni7x3NCFvaqK0Y9sVsKn6CAaiznBQeowKsYHfu8u72R3EZDj3WGyhkmN0VAAaTu0EmW5dUaEaItAnvP1Wx5b12k2OG5s346Xfm/eW0Ua3K81uv5GuzgGdyRaLFxmr/7Jt/pqdu9ZyvfqRDppZ9Qi34t9a+PVdzOzGMt2Kp7i65mzyzNrNcftcZjhXEt5psvvhDsYvpXe8eeSOfUTbfGymuKvqWnbojjQXOYAAAAAwDKu12kMp9C27XO2y24g5r7lATZwqezHt9stEaadvM6Za7iM/93877/+XRX/p/g//6v4XzsVvzaLwQAAIABJREFUBwAAAAAAriOcRk2YFj7Ev3sXYBnf39/9OWgXefIrJ//z5HG3p3s27Vb8tRf/t/y/1b2Kx9ry8bqZWTNnx1xk4ooDCh9/e8rc2a652KV9qKoqPHAe3HvLoWpv8eeWOJQdWwlYRXSf3DSpSWpnfupjaCiAtB1aiaoqwh+JG2y6bYvwR1lZFn7dQAajVmBfz86lBScNBQ7uCmNqb7dbFKbt5qZdZOU/Pz+LrOcayuLvnN11UxRFUReFHzsEqqpq23Ywz3tfXdeXjNRODtPuK4osZobb6UTV1T8HthR99Y5/7gGfq99AZTZZ0T1G8OA5AEe0/Y8L98AAnE3YgbNvzxKwpXNHar+/v6PnvLoYrfDqSuq/Z6a9V/fnGzCoC9n2Jw8P3e/32+12pUhSf19OEabtiPBN0x8+q+oApsh/bDzqtvB0EQBprhQAHFLbttUf4YBaPUvwOU4cqY0yHufEaDMH2n5/f88t3EVVvTTHbdk+3zOylle6UbY58doL5Npt2zYaW+nW6hM0f4/l2j1BjeHRwGlEDVT+nUD/2nr+uwiAT7HZ3fLet+UAMKqbRa7zfNGAWvgo/3TDUp+iIGX07nxLlTsK0y47jrY/5S1PwwmQi6K4S4PMiGdW5ETItq7rswdrm172xXPtkQjfItxPA2SJoq3v9qdLgAxwFgd5dPVUP80A+FhGfcCn+SeKSqb/eRD9MO2ElSwYNv4odTrTsf4xMqSH2J565tr+gNp+4Pb4JECeIDruR6g0xxG4PgmQAXjLCX+dra5ti6oqqkoYG+AIyrJsmuZcoz6A+U6Z/Xh+mJbJogTI/52q9j//LnoJkmFYF68dDNaed+baaCTlSZ+Ai/bCsNpR0VHePfUxwGlMTn3cqap4WK0eDYBTkIHmINq2aJrifi/u96Kui9vtP38A2FCXg7DTzVm7d4mArZ0yUvv09fW1+EfC/M/CwIOiBMhV8/fFQ7CWd3SDa/cuxTL6N1InTYHb3xGP8p2RRNbA0a1xcTnRTcVzDFP3B+CjbNbu+SGT1oVp+1yYAAA29E8UuUz/8wjCrMUT5qYNPzKYAPmYCZ8P7f/pvSJYyzuqqhoM1t7v9xM9RNbPe3zSAbWdaFToZaLpK4kO/XEC29FxPE7BAIqiF1WdlpDgpAmQq6qo6/+MYer+3G66xYErO0j7fJBiHERVvayQ+12QGwBgM//8/Pw8AlHsM3p3vmVLf8sTfSoMP4fvfn9/h/80oPaVtvh7nNb/ex+YnvZeFO7qyVZV1ePx6OeMPVGwth/LPHVUTALkfCdKfSziDhxLdHGZlojiJPcJf0n0jAPANtp25GLktwMAwFbOnf14miga/QzohqNpDziY+FCiBMht1Rb92ERtZC3vGZy29izB2iiWefaQmATI13DS/NvAh5p8uY9uHg5+25DoGb/fj154gEVs/GjjgZ+k3NPoLwXDagEAtvKJkdqiKB6PRyIW+/X1NSGv8idriqZoi4FgrZG1vOlVsPbgYcJ+XPMU0eU0CZAzHTb1cdE7D42NBg4kulB+Tjd6umdctzhwSYf6cXSowuylXwlNUzwe8eXYc58AAJs4X6R2qZTLXWLn6MWvr69HLwU0fXUxdL8+GKytBWt5z2Cwtq7rQwXAIv0ZavcqyYIkQM4RnZYHPPSmqgWuL2rZjnzB6veM9y8cHo0CYG3RtbIs/3OF6odmBbbhHW3bVn+8++u7CqxUPAZNPmQnFZ6lTrZ3fcjZ8iG7eTT/brOZw87/eqjCnEj1d17je/HnLr/+z7//UhdFIxMyb+huGqLQYF3XTdMc8B7iqomCq6oqyzI8Cq7QfacbanzMLxFp0TW39QAU1xD1ES97fWnbg/Ys93vG27Zo2796xrthtccsP8AiNh6pWdeHfohne/1LzPMqXFVFWaoumKxpmqgXJf+zno/fy+RDdkb97lbe8iFny4fs5tGcb0wtBxFPVdt1HFcvRtAaWcubBkfWniIqdsBRlZP1h9WK1Eb6zxPsVZJXjI0+l7Zoq6Lq/tyKW/fnXtzDP1VRFU1RnKA5hNeiq8n8S2e0hmPeMLzqGe+6xUPHLD/AUjbu8tPDGOk/NhQ6UaYKAN4hTAtHJlLLMpqwz/gxlAZZjxNv6gdr7/f70R7kadv2yNOUztQNqw1fOUWwfDP9Y32087M4ZJEY1MVom6J5RmRfLXkv7kUlUwUnt/jV5HgPygxI9IxH5deBAlzMoZq1QxVme/2fq/1XotitHxTATqqquv2xd1lmOciOhB2Y5R87loczOsjJfElvRGq/v79vU623A+xleKra4O3Y/fWIW3hhMFh7qFBoFLm83i3OSsNqrzEZxlmOflSwC9T8JT1jtPkfuRXurzitqJd8fpw1atkO2Auf7hnvt8zaagDWED0sNfgT5hTPPwHwjrA3ryzL9o8diwSEsiK1XYz29/d37dJwIi+nqv3z9sAg2ntRNIK1vKefTvY4wzr7A2oPmPx2pkWG1fYf37nf7/f7/Xa7nXru28sffTZTFdVbMdqnW3EzbS3n02/2F4lKppM37m60Z/yoj/sALGyX5k4b22nbrHnij//8E8ByHn+ct3sqR9ibd+09Zb4P+VIcTVakVoyWQdFUtVHstqiK4tH7zF0aZN7TjxQeJwdyf0jlQQq2rGnDatu27QbOptMq3O/3uq5PHa99OuzRj+rWrCRH0xZtIkxbFmX359UCdVHH1184uJzRPItvZV85PePmBQSu6oA3yQcs0jbyL8ESIAMAbGg8Urts7uLHox+749L6d/53s+vxnsPmQP6QIZX9YHl6T7sAbdM03cDZnE0847WzCrqtqLSHTX3MwVVF9Wo2gS5A2xZt96cpmlch23txN7KWE1vq6nnkq3Bmz7hucQBWlT/7wJGvqgAAl/PvZlsSo72etmjDSfKGhwS1RdH2pq093Zy1VVXUtf6yHbVtGz010jTNvoG9/tbPFWh8S13XUcz1drsNtur9jND5utHSR4jBX09ZluFxUc8H8SrpcReOjYKvVVF1Y2fboq3bOnrmqS7qx0AiCzik6DKx1NXzyKkaM3vG6/pYxQZYwy4hwLYtFh2EcEpvzT5w5KsqEIh6ot79pd+2bZgubsIz9NEaJnc1PNcz5zn+qDAz15Y2s+ZPJPPQLFUhmWfUqvW/8b5sUKrPOV3P7ZEULfz19RW++/X1lV5V4rOfZkLln0LxKMI/L5crH4+i96fcsKATFEX8pywfTbN3sT5Xf8xis+vhiMpTloud0MdsHwbHjIaHIOOCU5R/e7XMjruZ71zt+XqnK5OVj7J3mSnKvEtjURRFM/GzsLOmiW+uFlSWK658juj7usiS49s8weUJ2NF2rUTUOO9luQZ2Ec8fRN1ftvht++5V8rBXVdZ3lt+5BxT+9H73d3e6h6H/1sxOleb1XCE5a2iaJlGAdJuW3pdEf1GieBM+0i/P4GLp0qa3klmqzHJOWDj6yKD8nR3c4oJnwqtVRdtdtTtxdOU5m5tTJ49Fv+yJMzBdkv4mck7m/IK9u/CHeC9Sm15gdA0LlPe0Bk/ivQu1gKijOdVN3JwqWBv9JvH75BgOFW1a7+t82PYhcUke9dad9L4x+ByHOhUzXe8CdGrNo5kcpn10R7Ms+rFewVpOYNVu32P2Kb9VqmjhGRdErT2Qtl0rcZAQ6TGKkei9Xf1HUFQDo5tb9eEqjs3Pxsm2idQmgqyZmx6dvykdxRktQLoM0W72P/hW2DWnMJ1Xe5Quc1jad2s+c0fCxV5VWn9nN4jUpvdudAdHyxluLl233UoS19DRbY3KOZFy6n/mt+Otakmvas7R6S+QU/Nrn8mXNz5PbVi/+QsPfmrZKW85merFnLUHHG3ftqncPvf7QNYg9rDjbLWfOUdp/u1vqLviDh6ptm0fQxf7uq4l4lhcVM9qeEdt0fbnpu2mpH1jLfc4PXJhwlrOaNkcmFHLdoFUjZOuvAAMOMZPtqZpXk0Ws+6PoLdSHw8ucIGraqBt2+qP2x8Xns+Ii3l2zrxKWtbNLfXq41VVRQ3R4BrqFzfqbdv233q3DGFhRpdJSBRmsK9pzraK2TWfI/3xBVPpzixJkXciFXl1Eh7HRMWG19BXi02rlsyzevQUWvbbUcw45V4dnaVqbNRxzuQzSQdyR5fMWVWYJPljcyBPqPyzyE2A3Dl+GuTouVHDag+mf8nfpRhRGZZ9/PnI7cNbMenuDmB0nYPPxB38iaojnITvOuM44KuaPxb2eeINjs1dp9SwkLWHNB1jyNRf3irScgOYTnSFAnaxUStxnHGZyyUtmFGE8R9Ta42snZZ24gCVtrj0kKy9S3cUKmSyDcbUPjfRby6ic3u0hP2VRF+Q/i5ET/CPDrJMj1J9Ltx1H6X3aHR3BtcwukePoeGD6dJOqPnRHUmX4d3FBuWMd5x2aHLOhMGrW/G30ZXkLDats2v0e5FTjEW+HY8lTrnR78WcL0X6RMo8EDOP11XNjdQ+8q7fOeu5tv73+TJV8XYH8cGDtYm8xwf5hfnZ+lfH7dv0fhmWXf/B24fMYO381R72an3SkOfoLSPbGJye9t2VhN8yOZA5kw3664+WAHlCeRYKNh/8duK8nk+j710QmGujVuI4zfLeJclMULRW8zLt4rJ3pS1uz2D5qUz+Xc8Gkdr0iRoWYDBsmd7KaEnS6x/cSn+x/HYvP8CZ2J3RPKuZ786p+bdCzq92ZGY88q1I7ehWcupk9HzL2VZO/Wee2K/kPOLQXywdwpz87Xgsccq9ezolyvBupHaDM/nC3sh+PEpy489U/p3UuCrGBu+3B06DXFVxSp+yLMqyeDziXEnXyvxzIlVV9RM1bFyGfsRr4wLsq23bxC4/n9V6d7V1XW+WguMzRYlHtv/iUBRFW7T34u/8M0X5KN7+vkTrjC7E0SbgQOTyzRFdZF0K99Plxrz97f5HVVVuVIB8g+kQ++M752fOHBA1Vvk/YJedpOAAcn4Ezc+P+uFuZ/tzRmVZvmoowhO4/4BI+Eri8ZGwXQo31LZtmHv2VRm6LLU5G+rWs8gNVaKfqt+ROGcr02r+LTlXgQ3uQhOHJixh+kwIaz5d5lfvRlG9V5l+E2seFV4aEscufXVY49uRXlXilAtLkl5/Z5cEyKtu+qzSgdycJZda5tomVP5ZRKN5cofyDI6s3f3Jxf7A2fCZFMNqDyP6Km38AE609cUfub1S+/CWIwyYznHeljwn+Qyr6o+mbSZd+frnnmG1nMM2SRQXGpO6W2EWGsB0gotU0zzK8q/9je6995POjdm/XTnmHQukbdRKRG3avvb7OZ9OINR/d+Eb9TlXlkNdVed5q2Hfu7A7e1bFlM+e7c+ythlTm7mefgHyyzbaWI3uTmLh/H1Jb7G7YXuVnndwPTPH1KZLm1jPu5mHpy2Q9u6Y2sSq8uskXeycPcr8Tr11coZGh1xnFmapb8djiVNu5qmSXkP+SPeVzuQLW3JMLZ+p/XswbO5QnsGRtfWuI2v7D3rU9V8vGlZ7GDsOq40eCEo83MS7oifLipWeKJ+nfwLsVZL5Zj7mybv6OSeaohlPRJGnP6y2PUSqCvhbdL0+WAu/vGkPCH/IUJ62Leq6uN//Oivu9/j2ew9VVdV1nX972Q2x9Tw4DDvsr+ZtCxY1KdFApX4Dsu6N+lsXmqtkeqiqqt+wd6Gdfm0f8HcoPE3uhXjrXuUZAQ3H7b07UC9nK3M+3iU46aS/s4t0G67d//Nc/2Bpwx3cYOh/5s7mLJber7escb8dXgJGKzaxwOLfjkVWtcuPlEOdyefyRqT2+/t7vXJwauVA0DVDPRSs3TFw0M97HF3m+02be/ed9C8zfkddwxGyW19YdA+kbrdUFVU/7/FSYdqi99RUURTNnhdUGDI59eK7ojXveIcQ9fxm7nJU4Ku21YkgxP2+11Fr27bLbzzhs3Vdux0FBvUbh9HQ7MI36gs+KXXOZz3DpJSdbgRed2iqqnr0Mid5BIfreSsilRkBzbTvPdJZ7tDCg5Juf9TnSkZ3bY19X2OdYc9q9yNlyyvaWc7kA/o3f9Hf39/RZb6/v39+fmaUhyuoiiprKE9VFFVvUoh7UVRFURfLdV/n6TcNg01JWcYP/rOTsizD31rbxJz6P/D8eFtcfxTLoeaBO/UJ4B5oL4PT0y4+5rUsynAr3bDaBYPBMNc5+3ZniW5OPDL8VFUjd9FdsHbbi2zbtq96LTMfI+vGYAnZwku7Z6OJfs5vpR8j7C/TbzcW+xEUrfndo9DlPzi5/qjZflvdNE10FXiGcuF6JpzbUYt02G9HWLBzPZ4elrzf/qwxZHO+C4yJXOQkOdq3o3v89PnPLgNQ8efkWfvXyhnP5IMYidR+fX2FAdrb7dZ/0CxcZjCaazAuKU1RNMVfPdj3orgXxfDcvlt51Vi0bXG7xa/s3f5+puiqU2wSz4t+4LmorKEbVrt9GD7HBVIfR3V7qCj4hfWHt9bF8r9nurhsGKxdMLsyLG+9X/X9u7WDyL9jjGIJF7vbbNusTv9tg7WvwrRd0r/BCMrg/Un34u69M3AUB7/P3KSRyRlQ2znoj6DzZ3oYHFA72LBHwdru+Rs/l961b2ce6wk7xI7SQP3Rtm3TNEcr1QTPC0F/AMPz74dqlN696T1yozqni++A346mafpfivDsevUzZxGnO5MPYiT7cX+A7O12u91uYfA1WubW6xnJGYzLqU2cqrZTvZibdsv+jX6HUaKxiBruDxwgchjbp8mNNnGBZ8eOSXZrrqSf93i9AGoUADZbLcey1yS1e/1UnjmGKXSxu83+7pRl8XgMvH6/bxbmGbypa5rmVdK/tm0fj8dgh879fu//It5XFdAnwqYu1nxNkjOgtrPWj6D8vo6L6j9v/aolNBcPnFGX0eTVt/U54e7GpZpmNG3sWXaE3XW3/YmT/37//9l7e2zJcWNRN3iWjCf3GLuMLWkcVXccAOfRR28YLb15ADTfGKp7HC21UW3IPSavgZ3cTPwGgMAPmfGtXFJXbiYZAIEAEIEIbO08tdySy0ifU/v29uZ+GXe+Ls8k78YwntP0to7O2qxTxCz9whP3cXT257H7sCdzrpAvnfrYYMk8ScW+Gu3iXCVI6+R4Pq2WmYVuh9R67385dX3vrWDW0KPUxwuS0uPU6eLm8c7oMEkvTQAH/p49MT5msx7fTnByZual6a5d8QG1BvpFEMkIOM8B8Pm4KRDi+6151c8wGIQQew7tjCdmnmPJZg6iPh59IdONlTbW/IebGOC6zBzxQlXJ8/QOeBw4bTaY9txjevuW3Ii0p/bHjx9J96qbEjlyN+SVzLWwTMPZQTxyqLM29xSxq5v/bkRPfx6nPu7JhCvkG6Q+NliSX2jRckW8AbVNn8hhtcykjA2rGvL0mhim66eaDOKGGp+/kdKeZpscyG0l8uQxxp9NaLJlurMCkzOTRsR84sElxiIzfGbFvCLDp52jtWtyBUG/CMralf4CePMeu9ec/8lmZYYxTGg6OHfnwztrXFPXneccKuhQPmdr5GxvoSD7cSNJiiEx7s32XlwOl63Xa9vivVyrJU9C2lMLOGctJliWA2pfhxJLtARwdeMWyI1MSNxghIFzOo2jpz+PUx93hlfIfQgFAzEkWG5aAaL1wbEcVstcg9ZjKI/R05IczbX2OGubreS9hxfi3bSGI7eY9f0oZ23oDF2Lsb5k5lV4+Ql8QUqetvtBy8bHy+YVc5U85hVMuGmYYchJ9gXj7DT0EamAcwe/tHf2jJs29ijmJAED7GO7OsZr2/ps3flb8oSgPLUA8OPHj33fD1er63NNenPf3t44oPbGWEE8hWifs3ZtHFlboIwuu1C5JX38eW485T2mgDMz2wr5BqmPDdYmA3aBt8N1ytKMlSk4rJaZkc6H1I6OmiI4pPbKqSaDuKXwDqbul802FbnblYqTA8/grNVauzkAI2zbdt35DMMU0tE4WJaSh/iwklGHxM9BcVos3jTMFHPuwlktxwoPpZTpwVm25I5tpdRxdEKZYD19vXdy/FhpY89v7aIRIxfyzyXbauSCeXoH8ubu0ey03K8ldwDrqTUYf+2+716fq/mr+/3b21voJ8xtsOzRVhRRBl5nbbvIWtc2gbRWcALkaZjNn8cQMs8K+Tapj4E7SC806P4BtQYOq2Wmo/Mhtd6njJ2q8VrUS6QlWNbDNmG1buxp5Va8kLO2mzc0ZPUQJ9yfsLOW6cSc8+cp2z9ZTsL6bUOh305ZbxZuQC3eNMxGBoaKaQfZpHUl7l3DTG/MQQyTbHSYRAwk57SxZ5/6PIroLGG8Jcwjc4is3RXIvDVje8d5m8VYFTR/S56NPE8tBvdgZPbRMnmEImtb6BaqU1s4fehQWvvzypImMfXwCrkRHU6kYFznaM/AVg6rZeZihmlSZxnqY5ju59zVOuPsXvfAWuoK8eY9rp/geZ21xXG6WSzL4n4phDgObNNar+vqFa+1bMyLcokZZrPRoWYJeYEUODOM7CncgNosVTzPpmHmcpwbD3JHlLV7rJ3F6SxbpEec/+SmMDXEo3JDdxjC5Swe5xqbMyD1LGG8JfRp2JUU9AuXeXoH/u20Zv6WPBv0nlrmZbEieKoih7o5a63ZNl4/cgLkmWjtzytOmsTUM0lts6ueycINqO0c1cphtczU9DHWDDQJkcQQD0/gTE7u/kj3DZLO7lyzCNXgPiSy1jv1dX3PoSN1L2fBZJj5qVlCul2yUIHg98ckGe1oKaA4oNbAm4aZYqyhNr5hy+QpPTfXpp4VK3zQK9hZHneLwzlIzswrcu/QgshEy82hMj/eGhvu8D5zTpwbakjWycGTGPe8WA7FZL8IMUnvON8T2TUavZ35W/Js/Gm0AAwTwHTbzflS0R1b6+qLLP0oxNPKR+tr7Bq+KUKI85BJOw+rXOMxNZgT145/mklG50XynVIfG9xaHSjMLbHcot3yHp9ZYT17i8tPJWCYeoYckjfQ00llX7OmmjcjOZsyYbXnGjA5kCnajzuRoLWKWuOsYV1X71FB9XgtR0qp0HzJNQpHzEkMQ8YkayitwReATk7lEtJa3kZ6dCeutn/IXcEVVGBTIwNzb0xq0+Of27Yty+LdyGV908GvqZQ6NJLXKXWWyruz7ZjkmPSq8QB0QgOaO1c5d1KTOOTwi2utTQ5Y6yZDbEoWmEmXpX9gvs0i50bubeEXCngwjudIcazTmkPDwcDeYeF2DfeaeE9HkmzM87fkuXCTFZ/BX9nnPteloPKvCOxwfMQuCO4o9h2cDxVCnOSFXWQKXPlzhhqrfwmiN+Ja7khuG+Gu+qEYa0pB9WYvJEALrEIppUZLdB/UruBpeAC1E1cvUktYYtCMywyTi1LD5kvWVK2boqN6bt1Uc7rpBDzrpLJfUbQfd2rXaGR3V38tHuS1vGAe5P6QXDZmcpq/97Je34H2gtUvIQkWoeT2imlfqA8q9dtnvJgQHhrqUUrlbvKONzB8O0xeidygFrIS4IsWugO+LF5Rz7/CiCGEcAX23sQrD17ayJXJgsSvJ1E+50ooKEJSyEj917QEjNj4y5B3CHFu/F6Z63vHTtTk8PrHK0nkzlmNuUVLvjGc/ZhpBU34jndLB9Xei8pcQJwAeTIaHSTDqY+HM/ygpgvtBCxm7NkVN2OGgNrj0ed/clgtM4Z51Es3SYbEEE+OVQn42ZT11ratvj7dIa/RyL471kOzQ5/wEe5BmACglMKUyN07zzvcGYaK+iWk2x/H99DrLIRdHVis5/m0WqaY0IkDXowrq5upQUqZ9CdFQvkxRTMlqldc5yy7XuIFOSp2uBknWRD3+vM/h8vvBdOQzGEc44cwBMlWjWnSPXtHHIz+KZMkqzFfoiVPRNyRi7+yz32uS0HlXxGxC/ooIuULq63fgUGyw/RSu0pfAauLkWzV6d9t76ofahgY1XrLgNp9RLD4i9AhoHbP0RIcVsuMZ1Rg694xnFd408CoHdT+/v3jr1kfRSD/XLq9ZuJt/bauUK5ZoWlWidz4ifqbZxWnc20ws9FcS0ybgMrSKg0g6Vm1yxByY8V1korRruAa6fDJ4UUiLcab5Q3rHNuiXDdblkjeckUCKItJpgTzFsS6zIqGHDLnycptdiG1E2pIF51YWq26rJ926x1lYtRLgm/MF2rJw2FPbSfAx2ih6LE8tWQW4RbOWpJlxkDjI+ODPI3bEC/dXfVDDQPdinf11O4jdiG8Aq3GwWfwr8ySByiPEGAYHGO3tZE/XQXO5uj8iXh5P4s+k26veRGWx7rOPWCNfR2G9UbOWqrb3niewyRpqyUGpr5P0tjjSJjor2q6/sKeWqveaI3RE42tLXmpwjLMhJyHEp6eMdeFW3IWn9mPv3z5sjic5yLuX/G4y0iGyUACuLaIDaAmYr4y9bGXefL7vSpuFoXKbBKc3WgSBqb/unHqY8vocIl0NJOjQVsZhld/Ev9+aGekdL9hmIZYOvM6iRM/kY/PArAArDBFHvEt/FkeH4D/hv8GWTdhJqFy6JTSbjnbVnZPd6TrMKybvHDWl9u21Txaa+3mLjbJ5XJv5Z4xcaepDjOSF14aE54qUj5dL845H+EiyoF85UiYS5lhGAbJeShhncNcF27JWXx6av/444+BcjD3wLL/Uh6JJwHcxcVaemYt1brFMZEwwyE8SIaXZFMx5IRga1XPpxQzceY5odYS4/xP5dn6xDDNGG6pt/R2ho374e88PKAX5M/wZ9gA1kdZRnltrWZQMJi6E7D8puUe6dptWPce5rSua9nEktBN65WNj65nmjDVwrmxMJaqcTvsTbjInmYSVc96kmGYzhxDCZuhmEvDLTmL/0pfwjCToBtE1lZi2fsmWatIeZXtri0gDKt181YVysRQYL3ZPjEf9w6qdqt0lCT3YMKAWoMlxgYbh9Uyw5jcWi1PAam31IijvLbW+FLWDCyz+LZl+N0/bmDP63ruwNNakzhrtdZOHPnoAAAgAElEQVSuh6CyLBxW2xytQcoXX6Pldti2tBSGNslT+XS9RQqxi9AiJdKQpSjDMC/LeeC47XYf5gXglpzNkQe58+NejdepDetIPLVTH93qPRss6yHuYVdV8kxzVK0QT8KYf45FqSEyuIawgpNpXCtYt1Pfb6wfKul8lNrAw3G7wafTEdLnhFpDboPsKRvDPDH2kFqMDKLu3FnrjNj37zs8T8aSqOihs+/fd1A7qALZAOAd3lHyt57gUDUDa9adM/EmmRzW4932J4RAChOK4iIXjOcDxLhNd44abju/nUH/R2gmHnlvKtRdjQo4j/UjQDtt9mp68sZLYIaZE6WUePA6qoa5H9ySa/iMqX17e/Mu/AjZe/mDmXmgz7KofWmQsx5Sn4ENf/M+aA3LAtv2tHPW/HPI3m2zbXxZYF1h22BZYFl6SuJGLRSkJ3IDL/gUz+G4MR9NH8dB1UwWcwbUGtyw2lGSMK/FzIfUWsmN8YjHx1ip9fPn9//zdDvMzEc6Nzl/vv0TYP2MhBUSbOv7SaTjg2c7BdoeJ/LSQtgM3PpEb8128x4Pmdd5Y7C2bcPMVL1Jj4EoCSeH1TZESk/iJbNE4kp+AeojSEoWti0OqfXySkmAOy9FGYZ5NZRS24PjSw5DZC4Ht+QaPj21P378cB2550vr3cLdS8cMoIdt2nXWbgALOocbSQY2qp+TEFkgbduA5ZNx0LqSrGs3e4S7jsq1x73K8UKXwj1KramZ9RXaACdApsI6j3aSE2oPJEjrtNqpxGNuyyQG3I+BQwAogL0kubHXNdsHzMTJ9e8aUQH+F/43w3F7nMhLmxuZthm4/gbEXkB3tjBwTPd6WbZtW5YlMquRUobctCRzIT6tthVeN+1Bx8VRb2beqWOwRKJ7EdZ0ur6HsoMQj3seOeGmk85LUYZhGI4YYe4Bt2Q8fE4tQ4xl/G0VuOOdb2PsCe5MvVJZDD+qNr7+B4Bt62oCWJbYX3vZI9x1VFZwgDuE8KAyCd1MFdYbf52JBcfQlDFzQK0XDqtlBtDNN6YfsaEfTln9cLHmqHHXO5ukQwxT5qj3H/jPp+NW5YTbmkDbFlQ2A63tijV7AWO/sA33Y8d0KaXJCeb+ybuzUEq5LIs74TE5kwkLwmG19Gid7rN33AgIMM1OHTxEArsrCPJ7omh3SO3caqFzSiT2mjMM0wgzzeOZGHN1uCXnwp5a5rK4QdobwgTXIvVxsw25KDDLg24mAMwyste63R0J8PETrkWPRqZXRYOWIM+f4lt1c6K/zsKbY2jqcZv0hBGr2nE0ud8wDDGWIm3tG9MP7+yan9PYUOCdvRDyFG6LTJK8UCRDJm8G3pl2KJGslOqnn+wbGDHMZwShAFl4OGvPhCYk67rSToE4rJYe9y171xSvsReQqWHeUM7JVkytUyK5FoaJ3gXDMBdHa30kJdVas3phLgq35Cri+YrPh9fWZz9+ZQoq/7qIXZxPzxJ7s1Oj1b6D84k/zTrbSykCMYR4umfPU7KtRx8lcr/vIJVS7tFpHkk6VpFrYMKcYe76ZTuIeuZO+sHSBsen6p7tX5DbcsgfMQ8vVdhGWM274ah3PLHoTfUbnRlmd2YFJEO/2nfhfNypYO5HpGaPSKyxjgprKpUhDkJReOfS3k8xLarFO+c0zUyIz+m9z9OoWqwFCgvhj6zFoNqI7VZYowe9BKFlmrf1DqrnhhO/UsXVlQbr00aTaktXJJa0ra0Ts77cPoua4baCbty+gAzDMAwzG4mY2vPhtfErGWYA0pfxeAvHQJCnPjYMTFplbWIV4qNE3tRwrYN9vdvG19UTRNtr7603B3J8O483RV4T4e6OBLnAEsqwusBSHHTYYS9z58RZY3FrjzOTZHGhWFVLsA22aUVl7kBxNN6Ru/j4LI/PESx7/pRx3fBZ2iFPorMil8XXNsoILaX/Vtv2kQx5WWBZ3HAq4RZiXSeMrI1Dm/T4DIfVkuHmPT6WaVKCa1q5YD2bvNzmfOUzH3+eLNQyDYXA1hSacAVh3Xmu3D/TxMr0WcFxWC3DMAzDMI1IeGqXZ/rIxFydrpZf6bMurQFbUovUxzDuqFrX+HVeNri+lqYmAHd9ohRo/ZFZzn10r/WM1jrLWeuazNhrVYAGnTwFs8ZL1NR16nrryRNnzQabZWtQzzuGRMY5kAOwxFOoA94ZhoKzIg05YkPuWEokwAJC0ntnOxxS24gjK7JIZUXeiPIhk+DuSnRwJQ1lHB518mLkzFov5GfTurin1bZ71p1xV51WG7MuGNcIC7Dycm/PGN/tZQpDStMpdEYC5HaH1M5NtxWca15o9CCGYRiGYV6LeMht1sVMhILKvzSEmU5ReHPfuUmkGmWl250UQH0SWCVTCoeSbt1emGdCa2YrbZTXUjYk59sN9IM34zFtGuR2byovvdgt4ATINVhNWnnGngYPLX1Nale9B2jmZTl3C1AEOYrLchqbz0/f26aC3Btnmyy6c7k+V4i00kgp2s29P0RV/oM2ANzVlwhcNkkCz7i/tudsZOCj7wNm1TPwAJ0HuVoia6+kOJKNT9uEqLP4WjVAu5DMWKF0ULyjm65L5xXcK+jJo3SjBWEYhmGYVyERU8swZVhRO82jbHUgsvb82J6xDh0i0txdtO5uWTfaoJFgSGEsesXtuZncDMemb8O6rm7eY85lVIA3rbEA4Y01LM6B3CgM9AUDaoETIFdgNWABorhJ98EVb3KBmSuhn8NkP624skcYpjh9zGP16fPPb08Xtwh/6RbD1CF2Rz6m1sn42uEhtlKGgmstucT7uxYClIJ9909BR0/5tNZm16CLUqrnuMzhYrW4bcnbuqwZ5txhtVrrZVmyGsMGoC6VWr6y/lsnxcUmQO5g9BiVUWwmWE8yDMMwDEPOn0YLwLwEClRzW7CxD1oz5PW0o77p7Hldey9R3KOPvFiCtRDSew6TF6WeTBIda0xrfU7SdSa0rBJCsLOqAG/e40MDaNAK1PkCkwO5QD9YnvVt28xbLhX8IapzvtGLeOuFEOf6bJ1c8TZYrX0NZNacCgEimZycYRJo+Myc3bk1nacYWaO0EE8TD3NAAxW3nDDox/+q6FveHk5R9/ARq1ra7ZK03ua2aVfkb98+5TEHhVon+2zbh993HE/HfI7D+OTO30gpeVacAXJlZI5bPl+s1PAdA1601mU7F00Ce/Xvf89YKpe6+u9zSOoZkrVPO4zS6CahtahvrbJYTzIRzNYr89/rumb1gvPF3KJ6ctR87iu7KOdWCtzYMnm11pKEK4SSeMjt29vb+eK3t7c+ob73o6DyL42VX1FgU6RV4+ZqE/uunBQ9LSq/Zwq1rOIk8xJX4iadw1/cN01Q1oJ5SN5jw6X1g5VMUOzCTQbr5hwsSxhLnuTKDcwd2Aw6wwmQCxC7GJVJuPIdDcnYzFye/imLxWMWR0LTGUjr6U1Rik5iZV6QD3ncrM8a0YIzhKxJ7CvhzplHS3QdshqVu6brO/PEvNzIAsrEfGOunHRGTZrF1833QyVm6BF+zdZHp4VHJdMq3KbStA0MObzm9nryruXqwLlt5LbGG7eoyemsQMZye/XVmpdqLRi4QghJZD/+8ePH+Z9//PFH/HqGMVgRcv3Cd9zNvibvkuWEaLHF1bpn0x1JWcVpup/XDajtldO4gCOtXPJKDigsww2NXWF1v1TgOESdbzBYu/tNWG3BfT7FeNWAWuAEyBR483vPiSVqWQdk7o9+5DE2H0IEeJIVuymL9UMGEnqmsm/9rCFjkwmuxeRDDtHrFbgjWjAc0P3+Zcb9OO4cgGcFhcTXHSas9sxkK6lQUiIAMEm5zw1Da70H/LXrus7YhEj7u1VRLRYR857J8iisSZHttplt2+4XZ9M63zXDMEwLIiM7wzDjwbhzC37CWBRX/nUZFWm0K++uf9V8b2nPuIHczdftNmsXlHrozvEdEVnbWR6XScTIxYqkjwfTu8GIw8NqXzmg1jBkN/p1cRt8z6dXaomxwjNTox6RrGRhsmp//04ZGltDu0ij1jFMRZPMVtMJTPMwb3xEuKo7mieGMw6rDcCzgkJym1OHzE9h4lrC7U3IxhDyNlOLTwFR5bvbPQllPJOo0m4WCd+DMPuhWwTXEmcGOiZCCNwi32n9OHXPnRuOqaXlXJ/tntJBgfcpSBJLJ1vpMRgMHVrLteAKISQRU3tokHMa5GVZrCMZGMbFCtnReYeJVSB9kbUgP09UaxRj2m2Lq7tVM7l5s91mbWsrFqYSRu8cPyJrXczKqrM8t8E6oVOAiPR6DZokqo8wrPaVA2oNbmWOkuQSWC32QgG1ACBBWgI3P0uemRz5CJ9dH+cK5vIUJnsOxV3h2z8pQ2NraJT+pMNprFPFUWmAHRKD9mZm4/vj0n4a0jXZJyYG7l9fbPQPQZ655CWwGg9GGxSs7LrgPZvWLJeSLUEGuv2Np9YhrzY5lmtwWJX6wkkxa4dt28jrKnFCsPR9lvDnmAhFrnl89KaPLUoKlAL1l5/+4n/i+cOqlGGYQZxHcCGEfjBQJIZhnog7cq1zals/7sa8YG1YMXP9jqo1qEBgR9Mgzj4bogu2yjbarF22aXfozvFLcEX94IboJWNkC37iheSMjWkDahsd1xhizkqYE5LWW/70ai0x8JBdZiIqI2hDWska5efRJI2CjfrEMOXPnZpPJ7Laz9efG0pySJQbUGvgsNoAHFabTZk2GH2is08iv48ZNTN8rPW87rjpWlHRKeAu3abQiV7ZU5WdHuRtLsdm6NbN4LPaQSlQv339jTQ1SIPPZJ0gxFGxowW5HhxTS8skoaj1zFCQGWS4AfPOapjrk4ip5YNpmWJWX2RrP6R3dSgBRMN9yk1PhD2wNqtidj812qxdFnI3685xpgY3oDYZoudG9ZGE1ULR7vKpAmrPR0Nuz58VYDltyCbHMqZ0iw+4HFbzxjT42XBD3i9XBKaKJT+C1sTLqpNpFhkpO88ob40XjTIHTBX/2hT5OEtYIIJmf/17h1gia0pgYgXSP3Ov4dgCAOBkGwUULNPcy7ZtbAs0R41aXxpbJGp6/Jg9epfj27ZNHVlLJFu7Mlo6bZJe6QpxDtVynbVkMfoa/vXtX8ZBu8MuQUqQf/31ryWpQRiGYRiGYUaDyn7MMAVYNt+t/3xZes1Gup8tusUKrXhJQ+5FrrFq9XFpM71wHTzIjRrWZRtsBWnSpZTW+j938e9m63K9v304Jx+NcDhuyVUM22SRDBjRGnCtjM0MDcdOECRn76zGuWYLkn92o5Hp3FKVjZ4yVU1a6EdK5LiMx56jNhuOXNdIxmg++myOOXGrdGof23BqtN80LdD7irGbHty7AaivX60v75dJ26o0zHGtNVj3/6zMzuPv4/5ui7EajNdZW7Uf9HRkw19//atx0JbfjWEYhmEYZg7+NFoA5s4IEIPN2auGDezlw3bl00GsJQ1+AaY1nI+XNpu1a6wtxZJ4hWGujNXN8fGFJqz2/HMFqmClrbW2Tk9XSiGNiVpryx85KqBW5h8NuT0O5qYS1y241prNshbufoJ+B7GTokEvJ5ed2SrBpq7bYl4sRsuY8ZxQs0yFEE+zDilrY9c6HFIbeu6EylnjBrPjAqOBjjqreBW1ozlPTQMIIc4Vy1u4WrGuT61uUD17jxotdtN+3POXX4RvTyR+rt4Wq+YvyNia1I7K9zYYs6CwlImUMqNp6UeMdovXlTV0X7u9MPfEUgK5Sltrfd48sa5rrlax7lA8ahz3KZAhJEzl3eJU1vyFQL4aqgpBtqim9U9yc9rW2Kdazj+fs++c66GdhMOIJ0fmc2qpeM3aGHxU7X4cPKM8p4M0Ojum9dkwNWe/EZ6BVH/WrCXMPIfYTcC19EPlaZfuabVlYpQdTaeUIjnmthJVcUwk+VFHfChdkvFDG52WmKEsTFvw+kUQaZPJz/skPw+y2wGT+Q8aOZ0Qu3/6jWyH+QezE4zm484KnRyrYvkM+yCV2s/6eZcWeHQW75S4cB7oqwfvzWdpS9XDVuelhLvqOeToOv4q5c2kEPmFN7I28RT8aejJYSV/ZMnCbeHff/r+9PQuYhDSrUnfj3bn1Lp/CgXxI58biW4vNqccP4938HhZQqdcx8Ur+Ikrj/eyuLTxpyClQspZcLH1Ey/4wnqfSNgSkBOSypYfJ3lzzONqWuPepVqOi4+/Ftdq5EqrIDVPifz2aGPu4y7HhUW/Ft6WNFqo5gw2AT95E3s5azt7arOod68e1Buz2BwW5lr6wfKzFnRz6w6qtGcWLP79P1HKbp8tG6pC2xYiZgpCsYLWH+YBVYutkoHo1VBtlWDmomD3B1UrvsTgTjtV67bz7Fqe2v2oZ7GD2t+/V9nZU4Uosf777nKB1luAmdUIUdw4eQsXimsujo536jVWFL7rgI51nzJLW6obFKyZc59C+TVeZ0/tvrsenrjuxe6srdnEOs4b6vV4zbIdoYijFKMFuR59PLXJFOLJRydTtcd9bJgc5hEZrGK6P8xydOETqodKFJf5LG1uzSMLcr4sVGluYTt4auOlSxYwKef5cfG6NTeJeIWTz0qCaUjJ+q9vjX2qxfLUJt8jRtq4p7ZSa8UlNOKdH3dRLiz6tfA2o9FC9WCkp9b2tbQ0TYYeSjs1r1/AU7mdrPuUFbP7YvIqXEg/kLh5CLd0ZI30novf34M+2jaGs5Cb1hvbplL+WjpXC9tkg0wShEqoJSYpEUMJ3ppJziV8XbTTD9K7mTU26lmTe2rdlpAcw/Am+GfKkmr4udPU1HhniaYxXlPItRBRCEpEov26t8CQtYusH6Ws1VNMMq13l/3rAdNmz0NHjL8i/20G20DFGKFAKVACKDpyHV4j8nCpijmKMFqQ69HHU3v2shzgdaz3Ym8b9v7c63HJksFblqQw3ttGhMGXKC6wV1pkzRd4avH+qshloSJ4SRY2JBv+3SVbguUBDVWsVVHey8oUL75VRwpF0hr7VIvVkjGlLtjocC5pjdbCtLRzXSVf97RcWPRr4bbvS7cbPPVenELc+FGl/FN/2plz0zXSJJu1XbtPGe4LYvZ9v5SnlsrHQ6UoQrMizGUQd9A26NcRN23yhyE7BgnuHIjoxndgEr8m4XupTGDOzAXGytk00OQSji7CqVrpreImG2sI+1wSZ1bvSAWerJlKl+1psKSyzqDEvgruOsgqV2YVueabRoK3IKu7GUqaEIn269gC8Rbw3PvGi+A+a7wfq26ntWslbCTmGU8gb3f1Vax7Pw21IIyTtWREEPv39+8T6iX6PjWOQ/jRglwPvMvN5dxsIn89HuH2O6tvJiV0b2IF5yXNKe4FmGmDW5bQEJx0TFrSundIlsiSByNtQc0nCxKXIfcyL0kZ9tJXg2kJ3mECnkneBHNZmcpN9osCMcpa496lWjC+WOQSIHLB+U/eR7hPce8fr9UsF/j8XFj0a+E2mku3GzyWCbhflsiQZ6XAK5KFZRmhnZG38I/W36HZSv5luYp+IMybSuj9Cs1XjhlDyDKlkG7aIiNOoNRVW0e8P6fqRXblDDeizcE8uYJptYRVKA6rvSSYVIGtXyzhIQtNGeqpTWamCiGEsMepFCOnE3jPx5GpsshZa1nqodIafpU2HCfupi2ayVzxZITivlbSkKgWgO0XR6ZaQqESBJ0oVQSkobMrddU+qmvYz+2uu+yXiNQqlRt0TrvN5mpFJ+Kdq3AvyAj6t+rb0MFTawi1pbMAXkcRRmtFronf3/sU9zJ8F8Y7OCPFSWasRf61puazXM6hglT6I7M8tcmnYOok2d4wz8LUP7Jhh8BscXAvizgmIzfB5K/uUC1ILyzmlAdkhYSE3FN9J1mrV1yqhLiw6NcCfIwWqgdjwo/coM+jn4ei2AhptEyiWrpX3ofQhtXUq31lrqIfKN2rpIqiwDAXc9N6kwdWvyCSCH+vPiPpSMhJ26sxSUDtTq0lOKz22iCPc+vAhTZgUenznPvEXSNIhLGG48QeOZ2oqeH4+e2+j4nKUhDNHV0m+UXs6Z8g3bT5PfQqE4P6XnaQ4U0h1H6NWyCmfiofkJQfE8PRlYrXN7Bf2I9uZIII4DGJequu5sRZy0GbFGAm4h1tWv1pUVO3uHFoog9t1fXx1EbuHPcDYfyskVLgD0mNX4nvFFShqOcI0dybkNd8XMLIUzCu1gi5nlqkBx3/BguccPhnYW6F+S3+eOZ4CGnkcfHWiCxLZbXEY4jP1Gx0iFcX5hEFKqugAczDf0ERX758WRzKbsUwxGgN2/b0jRAg5cd/SwBv3tMFQBMJQGcgmBFrUVRT2OOlGKy3xkzPBk+vbIW1+Fb6ufttsOmKDql13m8FgPR8Kz7sA1qDUp6mLj0/QiIB3OauvGKk7uP2wI1Cma3r09vcuHsCAGmbnwq3u8nsxsiMQANIgNWnUAzi5NnrgKUo1rYdREqZq+0/sVR6hT7HIKVclmXbtnpdugFsAIsZL4qLPznyZDxVjzac+IU0n7W+1VltI3Bcwrx4BRbCM43ZtpqWP+fEoFwn+FjXFWvlsGqjRoxmLdCUJf7ijGms6jHW/X1tTGvt+rGUUrSvL4P76dL25oizRVWB+g4KQMEv/wMLPH0iU5Q4RvObvbTas7ZxbeVFj2kFwWDEMCkiOlNGx/djIBBCRK4M3f/c++J6W0p59M346FOj/82YYujT9YprvubOB+fXV/MsJBGRzu80Lvn5r6GTyICiRMV3OEsY7xfnVu29D3lr7FMt+FJv21bcYcv6zrmlRS67z8ib69pF3qr4/neFpPIvStdIHTf6zbtlo10kmisD1ZZJwn2yNZu1aTd6Xyj+piOX0A/kwYX0N8RNaDzRtKF2GAnWz8Grfmo6knerej1XiZ7pxjypj/cGWmKecOEMlNq9B7NdLgYuF0S22O/v33/++rM40UMwwomKj0hMqsjNJUgy/UDcBDMMnUtREHeb3EBdUrQTJldq9s9azO7wSZJruHTGF28WkPhfczoOvu31B5PrWIRJdrTE4wm1X4MU3DRlRIIW3hVpZIsqrfMmdVj89EaKS50mHsXJ6hGf377+dk5uHJPIMfRPmE840temUp4Rahp2fZBr5w9t1eFjDT1VF1Up+NuGrsRHxO6P4cOaZmcVLRLBmSUGiZqNP7Hmr8grMQWJX4MMLoyAkQFZ2J4tAdkGiptKVsVmdaIQyTt0qBbCUpf9CXllwR1yG8BU/AnQlEXNml/tlXszGQaDG00LgZAOE1lr/WUDkHSRtTMjxFNFKYXdU29ddg5WZl6MrXCDdJAV1vM96++vtdZaK6VC+zcFwGqFsQoB6xps1VqDlE99Z10hf3RzVVJBNO2TXL4gXXJlNmf0TE/Uc0IGkQzyuhQa9AKf0zwT1z5jZO3RPUMNcttg20CIG4bIGHQiPEWDVqC23zf4/en7qghUDO4MgY64MgcAE65qNi+jNrNrDed1TQP9JqVMqs1jeRx6NcmCA8C2babI9e83IvN5GZgUu9XkUH/+x7/++a+//vrX4JULgCgdBS+d8cXNKnR+R+40BvJmMkKIOScD8e5mWmwy7idetJgKpdV+bvcxL64IpAGHzFZjVVG0KrTWlnhGmw2LrM1nuKhWl9THgoJKMP3IRtay32vQH1lqfsU2RTegtjKOrQX7vmutvSE+cypS5kU4d59kCBqtlhur4SfUEl7WdT1UhNY6HubYS6jpnt6UZNHqyz5h7XUodRlZSmPapUoWWE9tZXLjZVnYWfuyCBBnp0sr46/WHqdsxJXYzlnbwvxHawJY1yep8BKSJzZsbyplWuB24ZpkxZF7VuoKKaVZDzyt53/5BX7/3SMuxrVj9R0AkDLLGuKWx597ORPXWVuvzFwLWnypcHvumvr4wBqsFagZPbXIYcJk9RxtQiXGGEzDFfDhow1csW3bsiwZvsxpwLg8DYe/tsQMVOALCec7TfqNkG/BjGLwWBuH7mm+N+8XAPA3P/8cz3F97hOp0KDXX1eIq6mtwl9r7Wi8ijJxX4E7Ufc6a9EFtCYGJvvZcENhMp0vRiEc14S2RxgVqpTqUd7iPbXPxEU1nZfYnpWZq1kp5R63McaUb9U5jtkS8FZu/QQ4/b7dotxU0gogwXVkIt++1W6nTXgopTRW0GO4hAkc/AxzUDCiWQ142jXFWbBreW7Okruzjs6pj5FMq4TxNG0k122NM4BPt34fMIG3BbcqftZdeeUK6ZFT0ZvOC5OFwJu9p15A6qRV9FmCS+rKqWQSrNvOl7moP/Prh0adurmucBPKFfSpio7g6hvaErr3r+xO7gKARtALMlXq472NlnDLqGpbECmR/lvfr2dGpZMNKlBZQd5N0u61mCGg89i7pBOCVc6sAj+PC5ydpfkZpRSyOs7JTp+lLkmwjHncU7msyiHlKfUWCAVKgYpl1Cxo7Bc9mwOvACtOc7Aaz9gcnpGWXNnXIq3dc1ty7VedABmTCNoUxPyTQObHg3P7jve8ugHtqqjXp9tGY9zaK1dZ5NmMrYTJAQrWGrw86QbXcDEzZz+uzCRcM4cszmiKlBlzDkLkiTV/RV5JmKs2LkPBzZFFKBYm0ikIH1dcRfj3i7y+sjUiRercos5i46XF1633yiwhqTKlj+W/ko3my5cv7pdvb2/HLby/ent7Q96KmR0pYVmgIqi6eeCRN+kx4CI+j5Q+Z+qD0SyNXL/vIxy3UUi9ka7RHq7MvdhMfzRoK2arPqDW0FZXeMPuAUCIvESp7pWlO0mLUzOG8OZVrsHdszbtttnW3Dv1sUGCtMqlalsQHVKWZHEwkbVXx8TRRkNprezxSUxwGGV3bpP6OBItJ054LzBRnrGNt1aLqt7gbIIL4wJXxp5KKTWAgLQCMuHF5r+XE9uDYhlCj1vX1TQq6YZs0iGlPG/b32BbYVVCwR6uFBNcm1XrVrO5xOZ3b8rcEO6f0NNvNwJyyPZ2KaVpz+6fjA+yMtjXWDm86mVd1xksGDQAACAASURBVOZFltLWouiyxBURPLyJrYKh81esUkq3ns0gRShXC2aYJFc9UT4+C0BtZevPj5Af7m79/An9Mr8areY9VWAZwzCjMFPE5MKhs1RlnOda3inHVQrystypNTIDSPpyrevPPlrrAve3rr+23Kd8ccoqfzB0O8obhiJ5o2lz9obv+74r6sha2s341Rur/WTVWCMZvJK8PJPrh6aRr711RVnfLAqrJQ94RT6l8vXwvnXDbMGmjd7FbKHD+44IpY1E8l19TImG0nrjaIWPyCqALG6pQQyiN9DKK3PoynQBa6aOz7+NyADV4X2h54r2lhp8W3r6VZs+mI69887nz8Mh/iXUtI3+uHoy2QcLfvJgeFhtpLuRBzWGmv3ngxpFYOdPNTHRG+6vQt8XUtr3x0fWWj0C8ejhHcEvBrLaS8Nkf/v6m8lk8NmoqruA226R8UPErZdx4Eou5kViaiv1Hv4+SZnBwUxcrVnBcZ/JY2q9N3lKKlNR8zPE1FrvhfBx9TG1yB9GZCZpjfFHuDcpqxaOqZ2ThOiWq9V10+6p8ZudtQa3r16gKub31JK4aT9uVW3TOZO/2IvK1sUEEL9t00Rwt7GqEzG5fmjqsmrlBiZ003pvmLqVaz1uZ84hdwlbI9coU9RAJvRfttMSVkmbHFiQRWigF2IXwjPcu9dfscWqRLpjr4824qKImO9pHBsNxnGvzBFpSwpYPLd5/mEESh+tV+Z938Nlz+Jwx8afn5HLy3ze30nKHXLO+S6lcNZeKwFy2bkMpTmQ3XfRLelrpPlhWm/xQ0NP3PeWTSXn7SQ3i4QfEuhHBdStf71vtuucM1P4Ub3Awj4rN/k2o7MLfwbjpx1xPv90dRdA+l/ddv6Cq5KexF8HE+HGntosGajuE5fZqm3Mgmh+T617JZULqoWnFtMSIs/NfVzZU4rF8xKSmao1Rh5RIDa5pzbLHYtvJ94rIz5+l5fw1MIz8WtqbnJ7wMdooRAULeBdLO8LgWsnZLqtEJL4zFpCq6VVWKp1oLubPnRn/JVlXMsc1p6Z9UPro2Tp769U0E1b04wzo8xd7dIUWq8w717vcdp6Ju1ehFXYwW7pss5b6n6YhZQV9dwZvVt0gzdOBoeV0SDrhjfCJmtthipgseSnNhZxWjYx3IenTKYxhMXxgPTOhmXBHr9U6bEOeaGibvio7wHxyCtNTYs1nqtdcQyZGETaW2tvTcxZS639PsGtj+KbJ5BGsZ4CR28w1Fmb+Sr7dwE/mM6IOPDe9s4Gat161ocSru4FSBes30/MNGN8874sV/HUJudmZoix5opUfRB/n7j3Ba+KL+SpPWvF87ny9dVO5anNFelFPLVUrTHyiAKxL+2pzYomfzlPrTegdseN3/jGelfAx2ihEBC50IjN3BE3bSWEzlpCwUabAJp7UtlT+8zM+sHqci1ywFJ6iQjD7pM3D7dbN7yntdeIPITXGrxezTLSodlni9RSS1jlHeaZrgmFb2o9b0c0d6t9WHKR66uJs5Z6EK8JmsHnTP6gbHQA2KMHgTdUkqnaPtvUDmEsaF3I8WC+ymqJeKHSpYi7JZJ1cBUdUqPu6MJqW08MQs2AvD2HCDXFRrm+zSOTbzbupsXUjLmSRmCKsWDki855lRO5DJ0q8wiD8dGGvbMHbsf/+ANFL/Du0Do/On4B0wL7RTM5eHoKgqSLF9/+I1fib+J1eyQ9TGe8vl4SMWruM7+n1rqP67gtZpSntrJB9vTUZjXIkKe2sjUib0Xoqa0pdVxa/CPqaxVOxK+cmQxPbfKayvvcG/AxWigERHESVurIcrNvxEdLNVMPOWtzR0Mqw2XTTf2YYNk+wUlXMYd1YVr90CfYjiwEv6mb1r1/uG/SOk2x0pG6h13jyKgkb/0hG79Iaaolpgir9R5Pix8BK45gHEMq3bHlpq18mndSWt6pSXWs1+2Xewe/Q8XbBgpma0Ls/UNpD3KmhT2nE8isyHhX8dnZXFXJxcG1jVLakFO54ij9uft2siXPkDHovWv3ULwkot1AE307kQ6CrxnKd0e0mhvmrM3p8paQI2fFznj0+faRQbRoW0fQP21VXRGhs4qtvUcDu/8LctT2aEEuSZmK6OypTb5c703OQiaLhhGjm6e2pm5JnpLlR7QaA/6H+NsWFCF0q3hLKHbyZYmNv8wLnMBfWe9TnMRTm1XqLGnr66SgmAUNYB7YU9sJ8DFaKBxEts5as28ohWmLhTFJZC2Vp3Z4PGsfY/dVzGFdmFY/WB2vkcuKJgTf6+Yhb1oImxStxzRPOlIPsTV+vY59ZMLUx3t7LdGns0clqB7oe4XVimfAF8JoCN3h+/v3iIP2fCQtoZ06w5eZhLSeqfaFYAtYsCUx7B7ppBjR/WLIdCJQN/7q8naN5K+ym0Q0Wj24KLlExpf6PbUV+1r6tH8ahz0Rwaj9Psu0x/utD6U9MD+hkZZuLBjjrM3p8pZgrUTCAOAqTVSuY0QQrfOoQEsj0pb4DA257ZwpY4oWflkK9nNg3AxnfRi/W+TKpD/YvcySHylG3JOHLwuJp7bSC05e8/Gb7OH9K8kfxiH01OLT0p6LUPa4Dp7agn7hXknVGpG3ovXUIh+U20GQdRK5EtnS4Jn4s2aGPbWdAB+jhcJBNPOu8tTGfbRNjgGjcNaSrFeb+pn2qIHG6x1vxCXMYb2YUz9YkYXZvbjng1pH04Ye5J2ykLpL86QjdRJTuU8uR59mn0trLTE4rNbtwiQeRNKRJWIlx6NAIeNojUOLttN5i5D9CNLh2xWpxh6BLWBOEdRPPwXfZjeViJ6YGcE6SfVAAYhozHENVfapXGdtgwOY6SHpgHRhteS9IOKxo31QvVSiUQtxprIRN21BtZgfEshJnQVqgLMW3RHwls3mPGQ+CxSfWuAjaJ1HhUtNNxNATq5eZA0ynKPCRwtyVfC6wlXsoUaO1zzxK5OCxR0z1l+90lZ6RkO3ipclVG+Yd0ElLbLe4jdxixYvIB5CT+2OeMtW2y6u2w6eWss1nuwX3sswLwupGTpUC3LpjdFOkTtgCpK8MtmK3LLEnzUz7KntBPgYLRQaCm9HSUbTeBxta39eyFmLLz2JfaeDkchrE/fWfLsKb5rk+WrMqR96RhaWu4giGqPJlo6EYcLVIp0RjgKrutvrOWvnTH28d9ESVhfqV3bCxMXN9gDVu2kFCLyPlkpsZEHy+jVdJbc4+dJf+dZt0UWIhPtUypkHemY4QLZT/xUA4v2dZE/D8eJqB514kJn75jtMwishmepgDkMJPp+4z56ECna3sW7aPTfFeiXPCkq8v4eqpax3mN+Sy0ky4A5w1uKKMK2nFuWjLXLT7vFSky7kk6PG7Vcf83DU+WhBrkoyp7fBe03onnjNE7/SdUpFpIo7Zrx3wHRbfFnwbmN4HiMie5tyY3xJaj7pbYoXDSg6I62n1rt8i7SE4sd18NTuvgoPleX4pyUzVWvsUy2RiVboDYaEiVyAKUjyyrjKct9LvEImJ8NT+/b2Fr8GeZ9yYa+Mt/WPFgoNxdIr280Tcrf0dOaFDDrYn1dn9O0TaRrKE9u5wuc3h/ViQv3gxrmWHx+LoMQrHN/Y0W5JH31Q0gjcGjfjY40MLfwokzNn6uO9i5YYFlZLO/Q0UAVJG2IcASJiSz2nOxatz+QLrxgzbkE3diPNOllgl8SpdkIbxFaLNdiFyX6b9fgmrqb2KjsOpZD4ULP6mXxTCDPf0IXVkvSIYJLhaWYdSimv05RewtMyLZIZtvi5ZP2rTWeJ6I0m4yOuQzXUTrmcp6hxH231OiRRalKjQeS9z6MEXoEpGvnFiSerL2jk+L6QvBKZbzykbPFFq48PTuYBxoiRdDvF5cFLG7kyN6ExJsozF1pPrStkpP5rWkIfT+2OW+mfG3/EMRmvjeQUukO1hHycEbFDj4hcg/k55spkS7OUUvxZM5PhqQ2VM1kLmJvcHm8zGi0UGor0X9jIpHgQLTRLdxyiJrK23s/ax1O745y1rat9cnNYRybUD539VfbjbDfxozuEgr97aoxwJx0eUNtCjEY22Wmx2lPTDQpZ9NESVvF7OKrdHl0bQkc5jBZYWyww6Y7Nyq2q1JmU92u6MJqmG0GCrwN3xt50sX3o+ZIRsqdoyZos6EFNKjnpz1Dp4oyHULyKZAbIgAk8E+2KiNNNTiH2qJu2xmdJpiWqbQUh4hqY2F+LKIUbLkMpQC7H/DSu06orKV1q6gYQimHigNqeUGl1Bjn5wbRwvPLBXJmcmCXlif88XiJ8WbwPOv8qXpCzGJGbxOWhqvncZdf5yvhzkZB7andEQ6rff9DNU7unWrVpSxFPLUlr3LtUi/V93BVa/BKRd8BcGZHQei/xCpmchOhvb2/nknvDauO1YN3h0pVVg7cljRYqBwpbgGXz9VyR9BcOmZqHnLVJ6i2YzRa9HuKV34HJzWEdmU0/dA6o3fd9V85RtXHNEPp0aEXhjSykG9krBKSWpEXc27Skh61B9NESvcNqCfMeR+5ZbdH2El9BCSF+/vpzxIr6/f37z19/LhasnsJ+TTdw4+uzjIjvRwghvn71tpP4Slv99BOtkPjCIKt9wHQCF+8rHNyX0nxkUYhkyF9/20FRaiRaaOfqpRG6tNssQj1uxpkGQGhUIN5wEzUZVt6bTEu0XDkmjZ5kT0JosFk8tWLf37938NEa0v2xv+mAaQ+VnmEMoRQj/fdoegUrFslbrhYTOesR7v29BbEus6Ihh8wukgU5M364QRNqSDNO4RBYrTq3n16iNXr9mpUF74Ar4VF1L+Gp3Z1pmeusjdQCu2kPwMdooXKgyK+VMPjGPYWdQ2ktip21NZVGEcqc/URvhGI3vcxrvH3f5/PUdg2ofTRCoZ5ag7IdRggfbTeN4bPvTxJQa6AVxruR7aLz7zjTpj7eO2oJq2u1rYRGQw+RNzHi6kP8OOUQGk1hv/ZpvwL67P/wvr7P9/j+fhREAYhoDigx1mk3s6f2chO5pL8W9idn7TyQHgz5QakSJunC0wWvJ3lUVEhTUOmxptVCoyVatEbPQ8pDPbAgtKv1XIKH5oLSWrsAsrfg1rznIk6OdUdGtnOGYZ7V76RzIebK3MOveSYS63wh0i/D9bbCs7829F7jv3o1vCuK0UJlUm1CDVq94wdMmgSnwylz1tZYrEbFmJrXcf5043IGvjZMpR+6BtSeDD2Wp9afADmiNHri66qzOWJah9XCHTOSsad27xlWS573+IDkBIfMU4U+idtSJ2pT+c5auv1kJRVbRDQyDYsAUMPN0OyppcXNPuFx1qq53A8tlgkVG3O9QUI1P2+tDWp5rqsQ9fOi1kHGNFqi46I1HlxbG/mRKsgUAbUpN+35tHuaB2JKzcmx7sjx0kcLwjAvyv0cacxUXKWBnfMwxa+8wAoCAepleJ21BbQuzMzcoUKqrah+q3fERwujQ2ktQs7aiIA1i5YX3JrKa7x93yfz1PZzVj0b/ZUo8tQO0RiOu2KqgFoDuUghZy2BrNPQyUNZRE8tYdVDKyXg9uV2N8/XEiWtPR71Ep88DCLP19IsWLlQehw1hw1/+Ghn8EHi3GkDphOXnsUhwtT29++7mKP/lrpU826bk/zQ7TJIb2Kwx83chBwFSO5sDoWQfigiosqh0RJ9F61JNV5z66k9tak9Jd/fvyugD8BC9Wtexd8Rgj7FMEwF15gRMZflQp5ajJznmfOlY0iwL6PeWdu0GPNzkzqpzoFsW3vnD6W1yHXW1ixaGplgZobXePu+NzWtZma37hdQ60t+bvvJptq3YWFLPmPUHLlUreM8xmI1/qkCave+Dhg3rJZeD1CcsJBx/8zBpSRWLO7ymbiLZBSWwjRPe8JlzXPjPPlo2/sh0szpqb3HLA7jrx2+2aLdCSmkYbXJFni9jMcHvloidNZG3LS0L51GS4xYtMaDa8vnolElhm/bxGA2kbSRMMM5fQP9zzwzoKkzDPPgrH7vYWBhZuOKntrIPOQqxUmSIX2Ns7ZdAa7CTarFtQtkzsJt14vrNIK5/TF72IITErls7drlvJ8ZGbHUn41W+kGE90YEGlingNqAVOL7ew8nMQmnIgilaKNXqWgR6RsxSl49GfLMqY/37g4Yq3MS10b11KLkERk/zXElmjjauKdnetwe7e/LFOP1wPgkEY6BexLJ8tFOMiWzxs0AvZcb9/DUYnrx8A1ZTau6osHjD0eI75a4wPzBpwDjsZ54/ZY4JJv0vdNoiUHLtyYn14bLMmDAQikitYPav/7cSMhCT+2rruJvxvHeRwvCMK+CmUhYi5TZ964xl+VCrs14j7Bm4BdYR0TJfhmR2bCXFkInZUNenzyCt4VsoyqHDNenkrWAf7Z94z1GcxFKPeTVBmVBJ/ewdhVgtYeLa9gymuiHeJpxX1V3Cqj1RdMaeSZ3lT1xKsVw+22EFrJFJgCXXlRYLXK0ODadZxFttUGfaUDp4JI3eYsbVWfImIoA5ZymmKW43oghy6qjjB+8vwuA42O7aSeZvV/CU3uDKVw8h7nVu49PB5rOluuSHMS3cCV3SFxjm1dUASYPUo0UMBFk3CD/BIGWGL1oJfGOfxKuYevmDRuqOumTpI8WxFlOtwlVCZJ1twnHSqYOklbEMAwe74h2gXkRc00u5KnFJ8S6tBHSUPgykvG1jfydcbJaWFz+drI1fUonKtyr4tni9XT25OShtBahhZNbE2XL16YmmJkZvdqfAXr9EPKGRptZD0epV7CHKpg8/awNwD5xQK2hRVht8rSwa1hdn3Edk6Mlsuk/i3BzIBPd17E7N2otRYNLhisxeSrtpUg7aykG68QjBhIaJeeREFf/vRUFzn98Pb7+vIOaKMS2Xerjg7qWX3wU9GVmC4gOmJubNz6b+ryeWikRaInRa7cjAilU23m3C+ixrkeqJzeIiP3DQetTAoTdKi9C965DwAvTo7UzDHOi5MwdhinlQp7aHWF4vE1/ucDLQJI1b46/2hbNtMMj+hHy+sT5cGM8/ebTU3vF7oR01hbYUzqYYKblZdM+n6DXD8mAWsdH0iOgNhxNe1wyubfsCSF2gPm9M40kRCURvY7Ldv547v6zCFcn0FRLN09Y/sCKzXucjKOdrvmgSFgKqmcpkwTUBumQlLuGOT21d527ftS2enzQ/tpGLbqDY6wudhNjSbnuDGHfsU0dOTVKxhlbN41Mm4uKUq0lpun4NM7aQP/qlPoYo1iE2T4SVAKEolo1meiko332DDl+LcQwTDOsFK9XmhoxF+RanlqDd5Vh5tKjRSPjMi8jjhXjW3ax9T1tWLB32UB4/95ETrtMpcjzeGqvO/yE0iBbdZC7gn3xdc40C/5REOsHb28NnVl72CM6eKoQqdTnd5h9otTkAbWGFmG1hqyEJJNPpKyGN+EZyUNmEfRhtQ0SOcbItG6jpm2hacDxma7tZODWwIe9oHo/Wd7pv4wLbltbb0Vx4/mbPV/COWsb6QFSRx32Kfk9FOmsnX9K4AHd1IvDi2PjDunQSaAlJuv4oTrH2rt9y3DalMKB5+JyrYeFPKCSNtvj++IWjDvSsMEzDMMwDOPjvyoXD8NZlmVZlj/++AP/k/PF+2n+8ePHj/M/s+75cmgN3lXQtsG2wbLAsoCUH/+xbaHbbBJg30HKhqI2RRrns8MGcC6TVVfJ8lo1tq4lsl0Xq7q0HiTHLdDabk5CwL6D1qA1KGXX9raBlBr0Bk+/WoG6EUrpEezS79rp17XGuTa4VUxV6VJKTGgIAGzbtm3bsixyVuVvtX8Jk8rZGe00lqqa8Wqnplj3j+4tcBunZy+ChKBqPA44vXLbcYv88Y31ff6Lc++sL63/+zOr8vykdXcey+GLELjBXgHIZtqgUWNw54eZnVRrve97PC2tUkprfbHub1V4tKmbSih7jnGMef7gLgwHKoSc2uiD1trb5NZ1RbU037rbGrPqHfAfGLWwACwAG0DQZAIgHjrHYE2fnstlJuTWNwXSbWEbjp9XM1kwDMMwDMOQU+DdNT+MhJweN293Wm1xoc6Bs2UXEApMeP8x4FOqnm0azwmQJ4xVyiYZWZuV0feVUx8bXn5DLqV+wOy7dzqp9W/6SFZv3uPAVnfiGL5muGpg2qYrAoqK+CkIl+2ckTSXiOQeNYtww2rLB/HOAbV7xvCajviMh9LO2GQK8edAtt5dZkjf7HmPrwKiMXdVFPeevEXavEIHw5G8ip71TPqs4zDRy2fzK60WyiBjRFoaJLVaYtaOH0r0gmp7jnZtNWYlNYYInKGQ0v/1p+oW3mHKxsAUU9x+GIZhGIYpI2PQtZIDRwZs67IW/tr4Cgf5w5prSASmuvkwQsdMRj/2UbW3sWhGbDR7TmKoWVe8/Xj5o2op9QPGG/pc4VYPrfLEIKWKGvqv4qm13Z9Kzdx0m+ZlPIO3S85jt2VPbeLRz12ysH7c+UOfBoBzMSYMlMmDae+Fx1mLn9I4cN5jMhBvoauiuPf0FVk6hXDZVlZMz3oepagnp0IB7qmjIjKmQ6G1dnZp6rRE3dObEjovOV3Dlu2i2utpg9nbIcJnXeecU1481BYedjtxe2AKIGvzDMMwDMPgwGY/zs0wfOaPP/5YlqXstyFctzEzAClh3zPSHAkBSoEcnxapCTqcAE3nJEB+8dTH4FRObuYl5sDNSOZteFIe7VML2KwfgSBO/erKEBLsIcDTr2fNJWo101WpmZuupauwB8zmY/L+hYxlB9u2retKPlUow0p97Kb8fXHUc3vZYCvpldbQFlUCDfFZzKWUVsa/T8O6fqQo9N8NYKfLJz4NbrrI7VzKzCSQrpdifcF5DgkTZBmNcbPXiiyOjC4HDNvjsjKiWU+JOc0PP7jZax2BlHJ/5B1xg4y11thEtSGPb89U0tazJlNKUkrv+LKua6KSTwXRThLgktTH+pHoWOISHSsA/fhVKTUpmrXWVqkvlqKcYRiGYRjmomDcubm/rXlWMcgHZV1DKHP/CulKPA2ySZR34ipBciWEdsh+/Rm3Gf/lUx8b7h2ZkYJMP7g9MYIQO9i9lz6aMD9d2yUCHD0BtXNHn7ipWzsIirEZDY+sVbuav73tQ2Nq9/ocyK4e6PneU4Os1SY/4kiSoXK3xhsEpvIHaH8uZaYMxEypq6Kw5LkfBfPzZPx9rubrn3XGXZi8eJ+daoUSym5llt7mkxpeq7TEVLURIBTEHBt9TuUS+F8F75YKn82NuUfrouIBtzCgdsdmLmGuwtEGRgvCMAzDMK9COqb2y5cv3u8LolpDt+oGUgAO2M1Ga9h3UOojatZadGjddXvvWEJb6X/9+9MfQsF21mJysr3JzJVwd4vHu+G6aqe5rUAaPCGl3fKFSCoHK6Jxi+1CH4YnoNYQzXE3ENkxrPbAxNfu0dR/JtBh4M591aMmLo8bZ5yhK7T26IEhAbWG58bmxtlorUEDrOEIGJML+NZIKT2Hy5r/Q/dWN0ZHCMFhOvdh4jQSNBTMyVcAEQ6x3QDWzJi5/pMKN6x2215oWTc5UvqbxLZ9ftYVlgWWBaT8/FC9wSskgpJSeqed27Ylw5elO8PHh9cfH4xqFKdQ2jg5cczWwL3htLQ7WJenvph1HcQwDMMwDDMnaU+tm/TYWFp//PgR+snhB07eqjNIASJFY2KYhR/C3nqVdKaFBPOe6SdviHeRfIUVbw+syrm9+a8DSQujlKu19tcgCW1xrnsGbtLCXf0lr9BirarvKfGR+s/7123blFKT+G+INyvcCNeljR3KXbNd53dtNbyTPK518uevP4N0esvnreCW6Y69aK1tmy8APl+51to19XLeY+bCYPaXyEcKUzc07yCrIw1ZJrgq+pV7bs/s0xhcV3qIkPu2GLfsAzddRQlNO7dtW5Yl5K/VztxYCJFw7p69s1sqyzE8vLN7Tq7jHN+nO5fG5Na2HNvpUjMMwzAMwzBEJDy11qFxb29vrv81wr7vVnzqJKfQ0bIgGC0j0xcdirCRn3/AWKt5XXQw3BRyLVy3aKoCXS/Lqki3Qru3QgfSXWtvhzhX9cQuWzestnO1uo6fA3Ns7RBnrRW0PXljG4gEaXVMVLy7N7C+M2Efg+U4FCD+/uvfY6G0LzYuresq3t+tL5dlSXZVrbUb0sTG31qmmhfNfVzlFJjtmqGKWYrG4G49yJ3CceedB5PaqoyaaerVEkGFhirjr3UvdecKicFO4ryzhiOCNrcnZa7vcsNqpZRVAbVTDUwMwzAMwzBXY4l7Xq1pa5ablvYmWU8JPQJzjXUlRlp2xJZwrlfvYohhmG64LoepeqV6NmRMJRtzJ7il5WJNkbjGGIZhGIZhGOZGNDLhMgzDMAxj8Sf8pcXD877v1/VlLkvCmc0QcOtt2f8N//1n+LP3T7/D752FYRg/rnNlKnfL+uwQmkpjfP0Kf/nL5z///W/49ddx0uRjxUBcTn5mLFY4iInUnjeSHEVk1AaA/8B//hf+t6c8DMPcjD/Dn83H+1ejYf4D/+krFMMwWOJd2IU7NcMwDMMwDINij4K/ss99Kh9RUHCT8JlWvNZVcQnELmCH46N2NVqiloh9B+/nVGohTvUB+2s3jw9etUJq9YNSWVVndUbYQVlfCFEuzO5r2yq7v1sSVslDitWnPwp2nabrKifEb4T9TitbyOeNg6nzBNEj4kzbzFzmmUVYlSb2wJty9VKXd+rHacBHS1OgAuP1HirZC/Got1BafCGEOql3N+PxRyXnDwGMH6slO/RTFClJbgLt2KdCq4PzfOLE8KkFuRo30wl3luh+JmFOqfAo5Vb4h5bInZ+3mQd2IzLh9PLxs0iHjcwchK87l8pdtp7CDMTeMZ1ASObK2F2AYRiGYZjGJM6pZX78+DFahJdABe1+t0CH7JoSlkfW2eFH902IVQl8JhaSzIOjrNMlBQhpHaS2beXHDnmPpcx/lXMeVesKMYVYObjv1f+mtQYpQUpYFtg2+51uYh9SLAAAIABJREFUGywLVCfPMIdZ5prPqNCvdugoEdbwvcHm6Z5ae06HHXiY2fOj/7GBAmV8tH7dcpwnxwCA75RrgzlhWkoppVyWxXu4HR9P25CBfWriQ9nnRQLs4ZNrLZ0zw0nAUtrPNaN/7k3MXOKYTmAaj7ne/HaUArnBAZxSgtYf59p6m9C6YotpvbWso0wnQGuNn21+BwF/+QWyWro4zRwKDqMNYVU7ui+4G6fMYG3+W2vtJsATQiQPoUfBIz7DMAzDMAwa9tQyY1jnSq7aHhkOQlkB/vKL/eUNzAH1XG3ZPwvWMj7ally3xAo+M00gNCqB101737b9afK51CYDy1IVCoJDmVOrnbVSSq/5bNu21t4dy+P4/su7fEBjq7opEqS1kWJz0x+7yrxMpTRAA/wNVmP+918hSM2sl+akATRAyMy9bdsW0BVkll/GyzTd6ra0aL2h3ZwbwHrSPJO8XK09Hr5lwdaMmRYW+/UPz67xOHYmcx/k7Gjtb1Trmp61WhcUbcEcjplthgYys3/rO6gd9m+g4fdvqJsaB+1O7aA1uNWe8VPpnVovyyKl9G6r8n7JMAzDMAzDNOW1PLU77sTZL1++tJaEsUyiHsPu/Yjsnf/9G4D6/NvVF/9UWCtSjtjAkGO60qB9AbUSwGmExi6WK4n7ykota1a84wwaQzvHcV7U/2CJvbkFcT3usdsRVEPIWbssSyM3j5Tyl1+edsz8/s/ftwfruhpjVotH3wB369XnEK+1x38/g1X30cD+Airmo+VQ2gPHRmyC4PE3YDftq/A6k1iqeWlkN+cWcPYMdKJ4e3EyFtPEwlLV2LaBUjfe+dcJKf3O2uSc/y4rMq211np/ToZsfLRm/9Y3pK/VzBYOB+2UhMKIvTurlFLlk1528TIMwzAMw5SyxJ2XViIUpKfT4suXL3/88UflTZKcRY084rgMc038sizclDKEN78oy3MWoR1epjb+8kt4Z64GWOG1G8YTllnn1hGZB0ZdFOqHnBqTIC2X51M3dG1qSmX4V1yll/Vz936TaQz57KkVlnHGKv7cnTpWlgLTat2LPj1Z0tuPnjGupo+nWK8oEB48ibepSks0wFUmxtA5b2C9lLCtsYAXxXG0zwQGl6dOFMBYh2foODfkPNY4nauTorDSm0/SxxthdQSi8Q7gEVzr7UkCYHseqMcqf29C+4OzbCZwEzmLcN1IyR/2bGyXmtfh+dAS7h9CdesO6zeoiiO0PWvCa0/9G1PdAkPzaguCie5NO8sLgrGdMgzDMAxDSI+Y2rObdh68rlOmJ3MePNmDf3+Dr/8IrO0kfP2ttzzMjQmb0ryelcRvkeFTbaLoZtMYlqnj0hvILeE/ixZy0woRPOQMyHbThyL21nUlcfmYhG8fRiurKOHbNw3tvS4atNVDFagaN+0SgErcRF5CdtO6BPLqm/TgJibJG69jbL7cZe7MJIl5h0BYdmmyivv+tAGA/vSmDY9adg+sPWPOlDWfdU14W4X4mFHs+8cRqufPvidquCDjSxkznBPcGW/duvlyrlsV8vRZATacm/bIb7z3ddMSZaxRSsUP6J1kPyIzG/rEaFkYhmEY5tbsKXKvj//87e0t9w4FD6q8rKa8mHuS3/yiiF18rHN2gB3ELkZL1JevP++g9g/jhPN5scqIAfD0eQGq9AO6up6vC3RAIewbCkTTLPtV8q4zaQyR7K9WJVDUQFM8JVLKfo/egiAvKxYsYFESFY/wOIDVcwEQtvcaAeqZcBahdmW1AyWchqFU4iZKRc6NO2peCKFSt4oIGhx5TdOfvbOOwOrm0yu0FyI66HdSFFcb76ro0BeEMyQ/faapYe/oj/8IgS2IUh8XR25VPCIguW8j/9ASobd5Lqn3mosS62Lej9p/+j5UYMoW6PprzcyKSlhbWuayeOfho4ViGIZhmDuTHVO7LAvyGNcvX7640Q8/fvzIfSItb29v8QvOpds5y0dLJjx4siv/8zeANbgdN3Qw1QtimeyHn244M+gt/25AqnvS5McN3QNrI3tpTTRtm2Snr64x+rKBLzTW+yq955zF20kOoQiAbdtMMF/u3Uworf2H5w6xy49JUjz4gPeVn5Eg7bBaq5pTaUKPPLrx7HzmgqwTUh8PeITO+Pjl//n/P06l5bfq8spBk0wu91aMlhJrcWCnDgfXAgCoWY6BNKN/bkilEKDUZwQt8kFHiK33cf2PrZ3kFRASObN2WT5egXdaeC00wAKw4LMcm3nDArDCv/7ZVLQ86lrgkQzDTHSVUm1jJe89KDAMwzAMwxCCcedm/TziCqVxLiOErLmykcD9K+QSWPtyR4szCLXv7985uDbIfbexhyjXD+i6QgXUGvARk6H9+HSBDlZYrdobh1AEcEPy/Fwt7MDWPbmRQy0jLSIeU2QQQCJSMzoYRZ5eHtlZx5yziFhYLaKigm8nQF78RzSU9juoS3TSYTTT6kwt0dClToriaoNdLd3KGwn7m6oLRqJdrVkEleoIPbGdarpvI3/SEllx0hdakamcINojtcZUK9BrtcCpqo6pwL9mYhiGYRimGaiY2ojz1T1CLHQqbTKYtRtnSc5Rv1YQ8M4Bte2x4m/0a0aySID/71tw7zwH11r7cFuEL9wGq3ICG67dgNpY1wtFTB5hJVqDlB+nkbmkouhqsA/W7YX11KD3z/LtTb+jPJ5wNi2/207oXr3Z/u91l5rgWkPk55FIzffv7+d/CqcmTBiu9+klkZ33RYIU//p6/uYjrBZxTHXk9YXAxlVHQ2k16AWWb9c+abo7nNxiWvjVdKBbrhcdTsW/zrQ6OA6UNYfOupg4WjNdpHqid/BtNCJbYl8ukBSPlMHAZZf5A4vlZ0BsOojWHEB7Tq0xz7z9dVogwzAMwzDMa4Py1P748aPSbfn29jY87/GBJYnXxzyPX/mlGOV3GY98mGO8K68NYHmsNqdZM3aFEyCX4asoDdrKG+z6pTz3cY0CRz40pfzuc2OYI31Z/hTN3bFKi5VpepeepV02KbV578j81W47ocuBbIg4aw3LspwdfibX8bIskWy6Qohv374lHx1yFRtnIboEd0fr9X9+PX+xSdA/f002A+NKd78/omZDIdHbtq3rGnsFMmai1aBXWD97Jr9KL2wjZvBw86BFAojA7H+bbF1wzlFsfQh9tOfHeaemrMbrwbhgW27ErEKeHLQbLsux8dGuj31dIXivMJ55nNwMwzAMwzDXIisCt8x/SR4I7CX3iZGyvL29NRWvc83MjJUpMZaC9UVIZmcSk2U868OL5VAq1A+4WrISCGdkHcfnQ2uZmKtQeDqE0yOD5GYPngC7dKYIWekE2yfHiyUxzuHwAmY1Ku/T89LwUjDjLEIpo4WEstVB/Hf4Ko0fG+xcHRtMFSizSUVdrZMOwBpcOPXxVEQHmh6K4sVmaB/0zET68RR1jUzInfGmQSbXUddKPJtJUEtEklrPOQqInBTHyDX1JK9+EjGyuKLMjANqvs0wDMMwDB2omNoDE1yL99eaZ2Q9ophzqTDXewOFjY92nvDf22NlYd1QG19vTTy4FgA2gBVgCfz1rnACZCLcgNqMQHZ8PjRoGFUzPGV6RuOzog2u0G7XX345/3MzMStZYRNW6HCDABetddxjh2Hfd5M112pCyRDzUGRtOgfv7VlX08hXNw12OEpFa+1G0wohVl9MTyQJNjxSpPzj2z8+4mnCQUES5ArrBpsQ4nkWcoFOOgCrWuaMo3pZ+HW8CuF8x+tMkbWd8aZBXlfKkD7rVq8TNW4ltTYfpWDfp1M7uRG0ZotWPIL24+IJzjHhtBYMwzAMwzAvQ56n1mB8nAbXa5vrMR2L5bhmH21/hvtdZuTw18aveSk4AXISy5jusyZYflkBIuJB8bCuaQOBMeX0smV0TpnulipRzhlMPDnIf/7T/iZXZm9CQmqMx67gjFghhFLqPD8paEJeZ+G6ri/trD3pZLmBsHbXBKynWmvXI6uUMmmrQ4+Kv/q//fq3iLnWnEpr5BFCaK0v10l7wzbiazF8t8H8R1eS0E1vPHXADUTYWTtVJuSemGM4LAjPm5j+6Iq2HEmtzWeq9Zc++WjjiEwHbYgXbwxZ8GyBYRiGYRgmnxJP7Zmz1/Yq3llmZl73qFoXCRDpT+bw2uVlXLaW7W+4LXI2EFv+3YDa7GNfjb3G+GJdzPe0oQwOY4+qtdRTthFichOP1rBtov71ub21TZOQUuIja42P1nUBlnUKr7O2wHN8E6S0dLJ2atG7DcutMaUU5txfKaX7WwFCgQrtPtGgTSjt8SC/Z/1lXyLDkDCVI6cbPfVGaGmwAaiXWRRYuFvECPN5IPZBMr0xDtrwIfSfiId3tsxBO8PWk3u0wNccGhiGYRiGYTL502gBGIaJYiwPkYXoVrc7+Cq4CzzjNWz9FMO6zr7CRFgJawNqD4baCMamTLcelrbfPPLBXgOlAECv63LadFWiYIzN9FxwpRr1oLOzLeThW9cV4/z7uAm6rMbpe87cu22bCfZF3uEmOG5aAAClBKhz93R9qG7eYyFExpuSct/34xUIEJGcHBr02Qf/9KBrddL+3MNGfG8sfduZ1+w+3fSGtwPuANK3NDDfvGZwrQn3PFeX2SVWOfdgjTcVJuMUMsUxUHSE4as/TmvBMAzDMAzzSiwcBduHZfGcLMqVb1iej13dY5Gkr4rXInOGakU6M5YJhjbFrlkJh+xuZmHcy15j1EWefrAqx3GMSZDuCbWFntrRWGXpVhCrFwpkh7OU/7RqX+sjdEAqtZ3aD7akgbt90MxZW4PVluLePv8dnp21gI4KraRES7TA66Z9KGdrcLe6qjUv+shFXEDq4Hb7LbsPukon7Y/VkTtmtmcyCE8AeiiKl+0+fQoeeYoO7xcrGbavjzvxqFdZTZceczDLdCJCchV80GI5PFbFXbcF8vzhFrANk2EYhmE6U5v9mGGYHmiAHUCF062aY/mm84OQYi3wSFKqmu32ywLbFguPMH9dFnu5Pg+W8I6jyHLTlgfUzke3lOmF4TNXOQXzFJa9PodolxTcTUV406Sy7jGr7je3JRBNezRy6xz6c1d1ndmF9RZVY+ZgveNUWnNEsccfzOegh7hpt705Pd8a4uSF29KhsPFwOhleF5gTUmadbrTCPbC2fqVgjXGvM77PgMnoghmQxXOWY1rGqrXrprWw1Nf82RcOm8DxkZInhAzDMAzDdCbPU/vly5eljkbFYC6NZcy9jQOJHgmwRs/G3O6e9IzW9yNlSf66+ddsjk3B7VO5gYNTMeSoWre+CmtwTs+H1ueOILdN/Otf57+XNPpep9XWYO1gKOgX3gNTO8TUjue5zXwgxFlDWl11g83UcGXe40/CcTa/vP8ihVRCKaGEEMZB+xLvhRb2UjBx5hzRhjBEvZg5f2hdsN59B6eLu0usRmu5kxYeRHqiHhuRIws1AaDaOGhD9GwDnPq4D8Yj69oEzEbt+RYvDMMwDMPcGKyn1vhZ//jjj6bSMAyT4DDKRIJrb2yaofL9uMdZ4dm2Jkfk1pCyGrgBtS2lac6Qo2ota3RGDV7Ct2FZ24WAv/71/MUtw2qp9itIKcVzSbdt+wzcrD8nb0LcJJPgSW0nQXrDal3fdkbe4yPIZgm3SwHf/v1Nn0jd00nYwHi5X0u+B1NNSBhCkOF0EWftls4PfzfcsalYcblTI6Yb8YzH5yBaHpem5RJdxhgEIhO/yRYvDMMwDMPcG5SnlmNhmaZY5vI+fpdrox/5kEMY04y8XYgtie8nGUorxOfHy7ZdaNl2s4Bag+X+6VAiq7lkeF/nT//lBkeuK00M8dxhtVbe7JodDK4vcF1XbZKnGW1jsqjdA5yb1uCG1X77xzf3ZN+MpytEhE19K5upoQ6Do3kuysBR5qU6zlQ7PMyiINRHl9stByK4K4XiuQcnFRhCfCeWoWcQ7aj37k7Or65gJ5Tfmx7GwszhGYZhGIZhupD21LKblmHmZXdi/c4Yc7a6l4Gm0vcTCaUVAvYd9h20/vwo5XfZmsjaSYiu5G8WUOul9VG11psWuTv440fVysBnef5nO9yoESnBMfmWVPH0YbVnKrNqu+7G1dK79/DXhty0AVOmG1b7699+ff5pTt7jSJCNSYG4lw5212moDDMRE+49ug0FWyXiwbXqRmuBOO66oGxb55nn3P5MEzQilHaP7lRuwagNl/cL6Z5tZuWd0Ibg7s8wDMMwTBcSntovX770kYN5cfio2nLkw18bsc4c/tobUOP7CblpjY/W6/E1iY619jtrJ9wd/MwtA2ph0FG1ZJgWa3yxy+kULusDz/9cTr5bQnwBtR9/eb5wu11YrbWJoXLccXMgg9e9PVMNZGM2r1iYaNqwDcvurfJptMLmPY4bcEV1CkQOlnK5WTTPvbmBEf+KWNU+gyk/Elx77+NRLKyhaqrtlYyLGcHX6ChPkjDjQtwgpHtymb3ihbJqXXr2zjAMwzDMdUh4at2Dad/e3vYKmhWEYV4bEzWbjK+9R3Ct6/tJ2l8iB9MqhVp6eZ21MyxBo1EXdw2o7XlUrXYMR9l96KmdKPjlf9KJ3UIcXlsqk2MgoPbjX9a1BfefNazW2rJA0jXccKbN+6Jm0BtlKOXRoin96YbVHq5b17ftIWnAJQkUmz9LeWc49fGlGWJQ5kZCS81WiciKgHYKMS3eHMh47pd4dlrwPtqBb6C/cnPb2xW3GswssyubEB82gSOrlsW6sh5gGIZhGKY1qHNqD/Z9//HjRyNRmFfmHnF+45HRc6rgLsG1uWdQmexGro3GhNLil5FeZ+3Eq9C7BtQauh1Va63Ui6w18pGYdQeQ8Ps3ArG25/TI5feJ7dm3PIqFzqspw2rpM2ZrDdvm+mD9scgT640g3s0uOL97KKw2HVCLMeBS1eWE4XEMU0afDTHcR6Y6qtZCprZvmkQdN8bdFIVvsfdLPDsn+nH2vBfxONFg/JzxmQ6q75YtcCoNaQnjpoeRkp21DMMwDMP0J8NTyxGxTDeaRsjdH2Rw7aVxl0mhtZOUwexGBWst11k7h9vpk1Nh7xpQ66XdUbWWMsoOhzSupkZd7pwquYDUMWyu0CXFmDKs1uodBPm014/X7JZtBcfKdrlMjCE3La4UEuTXf319+mr1nOxr/SYWej6tAfc2cDwZwwyEJKg9uX0Tbu2szd3WeRA+FYKhJLITS1ScOn8PuAU2xZ27hmwIru6dYAnDMAzDMMyNyYupZZh2dIuQewnkI9goxA0yIXtzEVsLrcjBtMV2Z/eHY9dsVgEfi897B9RCr6NqrUrMiN/TKVeThemwR+Tt8Yl35IOyZIaI7e007n03rHYyag9HPxl9vM5aCaC/PrsqZ9vkEcGrSB2/fgKrUkKx4EeYeNJHS87M4XGdsariHtE892YGO/4MMvRn/lj8+PZNuHUyZHd7JaaVRk+FYAjQqZ1Ys02OOs/W7pH62DDh/MHdihERcv4t2gzDMAzD3Av21DKT0i5C7lXQvlMTz2wA65UNNKGDY6UEKWFZYFno3bQGy4gzcM0WtqffPqC251G1eZiMgpGUrQbx8Dntp+hAtzOa7zFeW5PMEN8S3Ubra8bWV/5cvkncbelDrU5W46ntHY7RRwphncC6bZtnSLvExvxQNG2O0pNS/vr//mo1Hc8oL1Nh4p0NuC9rjLtEy2TOzHDQ8nV9CRNCG9RugmuTyZBvqfBycyC7Q95rbkFohwxnp7lKKG1rBXvL1MeGGYYJt3rjCtY1OPAciWEYhmGYZrCnlpmFPhFyL8fh44lw3WTIXmfttsWW0JkOBj/zZHMNPPf2AbUGy8FWGxbpoB2HUawSJfLUt0ewbUHi1sNrGzHarOhkhmhLkPWHwrY+fVhtOT6jj9badtb++qt8f3+6cv6N+W7kAWQkPT7YzE2sLS6wfeqlZAB6HwPuJIp9OJz6mMFwJzVeTItY/EY9TqayZFx6+2YIbw7kyBDmBtvN4Fu6DfGEGS2HGrOP99jNa/4jg27uUnfexUMwIWW5zd0lDKsFhmEYhmHawJ5aZhbmjZC7B3fdTY9fvgpBaXCZc832kOr2AbV9sHpMrBIxzfD9l6dg2xrLy5qKr8V0Z7SpwvpDoWp2O8i4LmN1kKp9DOH4m9Wp0u33321n7cy+QK09rSJfi8rjeici+yOsNl79omMoLcdOARuFL8ud4q5enHYRdRqXn+Pqx6NYePOXeg+qXJydbjwoUBHZj2XO/mjW5Ixr1uzjPXbzmv8wCZhKBr12M9ibBdTONqMoy21efOg1wzAMwzBMJuypZSaCj6pti8RF4yED8uZh39PrWJPaiHBN5V2z9cd3SO2LBNSCUy7y7R3W7TzmuuNMzUiu4+P02W//fPq+xkUnH/G1kXus0cgYyzARNVW4cTiFBqoZugx5dwjH30gplfOWt99/f96UNLGtx22i+anjtdbbuYrcsNp/6ET3KYg+L2aGFLLDuZmZ+GVprVj4MOOD1rn9yZ2Fx/EooZe2XXZFEMKtQ+OmO+Md8mbYhXl1NCKUtk01Hz7aOObknFm4d/Lt4dOq4up1h9SZt1oyDMMwDHNZEp7aff/Mmrq4+0wZpiV8VC05EmTCOvO47mJ40yAbTChtC5Nl7tlXvXjlgFpCJ5z1LoXbLTTiTE1x8pi2sLbED5+LJDavs5UU/hh3Mm5rrJGlqoO4Xf75LfudtVbM85y2Hu/xtPnvyy7+Zle4+lu4+CJ8ml07ZjpQeQz3NhN355Rxs29baq1Y5lRc98C3CY+eY79XckUgL7gusHC3VxpMGlwTWUkx5DE2kb2MjUNpvac3hDCO+9gLb5Hn3GWm9DM3JGeXqgd7QjtHPi2GYRiGYe7Fn5JXvL29/fHHH+a/v3z58uPHj8YiMa/LCisnPabisAlaVbqYAzIBxL8E/AqrWuXmLDOMJ0GkwvKm4lhCH6um1nukjd3nvFbvvFPYXXC+UkCtQYA4t3AFqoc1PBIccMaq+HbhesZf691MdThrz30532OqndsXhkBYXSb/xFNyyo9Id62APqOPlFIIsTmvez1y+pqwWlw9yOfLZOBX67qG/oTCa+Dc4weee5BSWgU3jttzn93kpoX2DEPZT2MocFXB6B56UTRosynk3NoXR02bjQv3Hqbvj5U1t3Jw903tGnI8LST1bZZlZpx1307offFehHqGnkpb0BHXdYZp6Yl75CqwZv4DqdyINnzhzzAMwzDMK7AjOF//9vaG+QljUVz5rwbscP6MFufCWDUZ+QglzGeH3f6IfVejSzI5VnUqgvrC6gchnh4txO68d7GLenlmRu2qRXmV0xU+vhX7Lpy/4buM9cooWsvz/aOChcQQqEoT4fvl0aDL5D2fapSxqjHaZ93I2gOBeAXiQe4cz/xK5VayW7Si12QJLEwxxS6UsMYgioZFB/q13pAi5cCcwc++6Ge87pSg3XLDaiovDqHSGNgH49ObSfRzJcju2H1mMpAmWiLelhrXrjuFOTrT8WJD18T6XOs5vFL3nH5MMq+gql5sc7kJbMNkGIZhmM5gB1oeoSvhWQ4SsT8ZcBX7CfOxfFf4jxLqtqaZdjRYf2L1g7PgtLoPjeV3eloU2eOVVBRGzD7WipCchzYtskj6vdcl4o002VC69jOrEeNnNY5V5MVZZPhr27hpAeD7T99N41TCHqSUUBNtDJrEqjiEF3ZX1OMOwbmf2s1Gzqyg4XJjkEdB7Urswny6PTQNodIYVLEfJOc5V18UuN4a9/NSOp9cSyS3MzbG+4bPPtrkxcGhr/Xc4K5zj0nKRSUG0ST5KnjXFKOFYhiGYZg7kxho397eaG2FfUo1IVwbSCw711yGmCtQaSUEK7wJGSn44lAv2LD6wbXJ0tp8L0ILpWG7ODE+WsxjLZtQO2tFxFlbYaqgCasdGjRA1lqKqjESWduNpIgkNmurpALE9/fv59Zjh9V+FxMNMZNYFftz14CeLtTPvgj0kjsrwPR6omc1xThok/UmdjFmiyeV0ug2SUhye39tJKbyxSDTEvEdjV0WkiE3bfwn3rbg+VXr7mlJcCf/33C1Rju9oZgnX4WSpQTDMAzDMBX8V9yid5xQyzB9KD8y8OXRoOMndAoQ5hO/zyY3qXwn15pzLvkwNRcrdKyPM8Y6TVCIVzuhth1uL5KRc4gEgALQuK7R7qhaCw3+jr4C/PI/xXe1tHOh9O4JYB3PBLPO7S4fcYrenZRSKUUeL5vFsixSSh06mdhVX6kzjL2sp9O/FCgN+tvv354uUE81v33btJxGX1lFfp1zyKy3f48T8tqjQbsH0Locc7DkZGyDDXND3zMGvbKWzzWT2xXWLTXmbLBtsHU6rt6CSmlMsJvnAwUgAhMJwwawOCfYXwUpQWvYd1Dqs0cq9eFVZAqQAGv0VNrVN72mxu1AQiRmMaYtuGybMzltOod3D6ie6LxcUobMqWinN9bPt61sqswwDMMwDOOy7NEFybIQr8Dij7sx3pp82dqIY9mnduBaSqNBh1wOxhq4wno2XZn/jpi9hBZ6Da86+J2c0RrW58qv69pGXST0g5RPa10hFv30NgWI1/HU0ioN+QtsJ6eS0KC9fUsUbVywXpxSDW0x5saeXq4/va6ZbdUayVSZ5c2qhHwxynD1ZGFTqZZfSgkAW6m5Sghhfut1+iJvK4Sw/bVuuYrap9baeGrjWkh+l9u3z8fNpbIcBfsSZjhrptpUO90FCTI2lQJhzb6yfp7tdHQnBtsGLZYbXTpIobv6QbLyibG6T1mdz9kHZXRnVi8nHNMI1KIjwjTNw53CZGkm9+eeO5B0cwetQT0vutevv8lf/k5y8yloU2/lAtSr1peZJbINk2EYhmF6Ew+57fy4G8O1gYcTIBcQSgoXyQJ3nPUVzCP3XfgzIXMyZBfSPEgo/fCcq0v89tV6fTUCXA58s48hPvK22aexliU6Dj6lb2LVYDI6VfZ0mgTI+5gkb2NTH3s9UWC2AAAgAElEQVTvk7WrX5welNQSmODdp8NrqfMeKwgcfP5IxO0eqT7RyfQvmAD5BYtcjduGD92S1ZgjM7G8TuG8xFbLjcatBZPrGPnpt4ggqRNL/HmIJ7Y9pkasNi5IoZYQn9PmyEDfjfopDCoNsnVFxdzVnLYd0lzmr/eBrt4Ink6iWonmzPPDNkyGYRiG6UxioOVzaqng2sDDntpcvPYsfL3F/bWTrMBnh9RqidIPzy9qmGlyDgqVhvJYmsSzd0kop9lXMuQUOn8XVvtP33PvRFYfI9xCVS6QgzaHXR0+VfGMCtiz8LOIpMv24ylElizzrJib9vSqpx7up3WZNII9tZkU7JCLEPFNZvQLZ3xptdxoaXYP+b/dasF7c3vsAqnvQfP3waS/lrdyXpASLYFpBn1xu30BaWdtdSeNO2i9j76D13ascmv09BYO4PlgGybDMAzDdIYH2k7wLCcLa50yWpyp8Zq0CuzdESuh+jlqc2eLjMGqtgrDZa6nVnx/f/EuY/WCdPtXtoP2+NgNXDWwOvVf2Lv+1Yr9FjRhtbT+Thw03YTW4lN6t9xZRNJfq4KGyazSiISP9rm9cVjtRLyAwZGQRh7BSJAuWrKnRtvJU0t446jzNRKsnHTc9tgIUtmJLqFzwtOngV46poYMLTGrq951mRUvwkLO2o8blu62DN0W/xGidyQqJWOVW6PpzWuE1bINk2EYhmE6wwNtJ3iWk8W8ptvJ8Br1aqqrxFnL/loD3So0rR+enzXAHDkfViX4L0rlanOzfTdp26MMFu/fSTqvW3+F9K0HmvBN1y5TaTkr9ViXzSLi/lpx+GsbuWm9v5o2rPYSXhMqXqqw1YQSmZC03tC2OezNn3/XarnRxvAdd7XW36S5eqkMNW7kTmjElB47pgCUlki652FY+usW/rKQGvL8DUGlj/Zcrgs7a0fNMZpObwj3CMwK2zAZhmEYpjP/FTLYMcxABDyZkhWoUZJMzgqr9Y0AIUEW31CDtir/40F/X+UufX8BAIANQAHo4sfegvX5XWxbn8dKp3Pol3wTVrv96AXy8VkAFoDt8Qmgnt+hAAANFf1pMn7/P55eugGseZ3XvbawwQ3qMlWo5/4mBMi69uH+XDfsv1JKrfUesLyYtiDBeTUI/vHtH+u2+kcfERsgrFFsg20WDWa9iEu0z2LuXTpSNOjNGUUECA2apOlKkN77bLChZnd5h19TQPFEDXqBxa1YABDwsQUEfysFyjuVNXXYT8OonMWLNRb0f4+5aIAdYuszM6Is/SRiKNHPU+jo5BkAQADoMStBt5/VT6P23d8FpQT5/t3+KozWIGV6gBWgj0/ksm0DpZpOEntxmymH+zKy1D7DMAzDMIzLaFfxq8CVn0V2LtOXxI0boKqoRP69yK7qF39RRPt20/rhHDHTOWRkVuzIvO9OeCziY33RcF90/9CZz8YZiHrMKS1NAuTdqYeWu+zLwrOcuzTYO1+kN+pnEbHg2vD5uF5++/pbsFshbvOiYbXeZIhDQmZGpCK/KFTnTWAeVBhZa6XcaLHcaHDaOsEZveh7QtNUPcVd6bpx7cmMuNcpymvi0RLJ8NnRcbSfkraMaQzFwioQya6ajKP9OH02cNZp/OcXUg+fjJpmtH7u3cNq2YbJMAzDMJ3hgbYTPMvJhcaqfl9ccyGtrTDkrP0wb7FRxkt3T62wz5Z8yZ7yODXNrgq8j1bsu9iFkxu4If0NFlY/rnPWuufeFtLLNk3jC2wnbX57oJpFhJy1xl+LucN3b1btHAPuvKfVNnJhCrELoUAEjbCgxPt3mmch5bm8DbgH3dy0x+NKnLUdPLXUDSZUTBI9EDzUo5GSKTbZj3JjkIA5vnQOpc64PGkJzKucaa1nj57UUoU8pgKU21uRh9E+7cgKqFNzq8jdrjdQVyaHJ3loo1oLuNvvAdswGYZhGKYznP2YmRQrcdks6RCnwc0ITVtFEqQ36fQKqwT5kfQskgx5uVHOWDx9smU+nqIFbFa2vOAruR3nnGzrR1o2oZ+Vhoj2CHE6ltNkbPtm/70hVjhjZQbdJHZ6rtVfPHQaZOnUT2EB3C4zc1q3dunaOreHEzFFvm3LssiIMBpggW+/f/P8KScRogQ56ZEHDXJT60XKbV02vYZrZwO5/f5tWUBKkLJ9n7Aadn7u61dAg/aeN9Fudmry9LrD+kQZwimQIEPZpGvO8jgIHuoBa5NqtJQ5MhPmzAMfBgmJdQEcufWZWTnPqCOcZ86jcYdo8uErdMMN5AL7Ars8TVfWNT1P3PePlMgAAFrbP3ioAjP0r2swD/q29ZwqNuBOWYLdVnLtd8MwDMMwzEjYU8tcg1nstnPgGrZa1E/IWftpJdQAKuqvlVOs5LvSwePyWNw6h6rmm4yPA1zLPoe7tMNbtkRNnTgLAGr1dQrjoD28s6f3Y93sVi3XPV1VB46X629LLbNrZ2IpzBIDfd8DZTshJUCwLRgOf60549Z8qbX+x7d/OH6rByK7/8x7Wi1d+1wWWBZYQW/oPrZtH+fSNXTZujdlC6OPzm7a83NdR6MCFXx0f71U8UTv2bTkFdvVWVt2/ro7Rl8UHXXWmn2cc6h25gMN7/AOkHLQwunI+WmGCKt7CUE/fEkJ0eQjsIE0g/sSPZJZCFDKuVWq45txfw8cmrttiYcyIUc4MVLab2jyjacMwzAMw8xMPOS28+NuDNdGLm6WudESzULrvMfJx3kSx7m5UOdLkNUJijxLCf0gxA6gnMSZiVR+IufcqfqPeP4gc1xZv8p8qBKO0jhLknpy7zZbminrSIl2Tox2/ib9rCPnWKjn4spPk9qw/RmZNAeft06hllkJNLOI54eK93fkXE6A/xzo7xU5eyc9rZaifSq1i6+/JTMiYlIm0sOpjxG4GXQ7t0+3OcQEOF3YZLlBpLELD+ItBTWbJSE04NL+ZHLiSXTx00KmHY+ZtvHUXiLX8ZnOGWcxaY1DA7d/XHVnF9GO717ep+CUdJ5sdJ7e9JixDYBtmAzDMAzTGfbUdoJrowBrJTJanFlwbVutj/RjZ20e1QvohH4A2J0Tam3jpir3d176U9w1rDv1MCHi2onlkS2xCsVdTSFzKqLPkrm3GxtTCFyA7d3JuZVAMIvwmTmVUiIVzqVC5xzXiTPvabUV7RN5al1W7yZ24rRu2NfH61DsL4YrQ1CVna5qstygaDOd3bSG0GyW+Lm5GqPD+DKK+BT0pZYG8+C8lKCndmKHuttp+vjFCgZ07L1wBQgJcI3dHZ0VXWdP7ahG2Ri2YTIMwzBMZxIDbbYnNkWfUk0I10YBk0bYDKVzQG3kuR57ZXwH/esYZapXhjH9oNTuuGnBuDRe0jX72bTEvqtCpeFuM+gBop3k2oNcE4EQu/r6c7pBFjlryeqtcWSEp7NUStjC+GKZeDp4asMWpZC/NuKj/f5TeTTtwaSDfmn7TLpp/W5XIQQ4Kr6R9Y8DalN4Jz9D9hB4JfH3kdNrbbLcqG4zGWWhpkdkba7n9fbdkJcG8+BbJtie2okdtAedA2rPRGJbrel3rCtnBtSeCc0rLqA5LNFb07+JuO/mGi70GGzDZBiGYZjOJAbaKq8sj+snuDYKmNRoO5SBdZIRi1CR8SwUZjFLcBWG6vCImH4QYgenfr77M5He/ONrS2VZ0wekPt7TxlmqUDwBzw3SazWIpEGO9jzr8vJe2szgRJNIv4/lJUdv1M4icGbOs7825Kb9/v6dSj3PG1ab3z4j/VeAEl9/izUik+IenBz35F2kWb+7BwMdinh5PH2kqaeW5IiH0bXqnWpSapusIaPP+DIcToY8lnD9f3pqLzICVHg56RBiBxCgPj7v39PeWefnxYo05C2efQy3St30tY3aAXOxV5KGbZgMwzAM05kmA+3b2xuP6BY8yymDwMJ+I/rnPcbIAF5PajK+FqzLlffO81hI86gzIcQ9tZ6AWhFORhqxi50/uSjnDk1jeXNELegjZO7GXKw3+SA33XHyI0ApEGmTQeglhmtk/gTIBLtbupl7ch5UO4tA25KEED9//fn7+/dg3yTFHQiIH1BGTvxOPJRWgEqPCI9bGBNw8FaVmZBvnHOVgnmiac+gXIxze2onySbd1lmLr6XbB9Se4cNrh5Can/83/Pe1jBJTdJqaAZTC1RwJ7Z1Xi/R8c6Naye3CatmGyTAMwzCdaTvQ8qB+wLOcMixLynAb2Vistdgot2XIpZoXXPv4YBy0s5lK09StD4P6Qe1K/GRXu0IE1B5uzm415/pxCz5F0uZ65sYE1Bp8Vh58XjWz9scfZCtAqa8/J0QK9dmwm9y6kKw2iEwbBNqjmw2sm6c2N29gRLFQ43GPKXG0+fOnN7hmEDOemg6Ib9in1xT31xYyhbV7XlCTnBG4sybnoPrPVtjcU5upqNPCd6ShsxbvxXnBbphMhswuWxKQGyhF3XSiO1ME1BqK5SDq9deLrM085qOKrFkuLfdy1rINk2EYhmE603ygteJrWz9uWniWUwYnQD6YJ9IoEv/qT1PsGAuUCJ56i/lcIBlyXaySrR9OUQhCOc2ArV3P5Ga7HempdYw1nw1HqI+car4uELkl5mjMBKFMyOBvTpOH1daqzc6HoaGfVTWLwLeJSGNo57B2x5ea9kwmVtr0FnHTfgS151rrTrdQEDu/NhsOqA3jneRMNf9Mi3d0HfLlRkWzaZ5zOJ+GImFUlVJH1/5QES9CZGQh2PbFIFLdnBYI1zJKTLS3oUwUalezd84/r7O2z8Sjp0s4+fSp30catmEyDMMwTGd6DLQ8ru88yymF5ojB6+PWw3CjYTwW1namPnyNQgnX11j2md1ZW7EO/9APjp3FrTo7oPZVvbMWWU1lpG3weTEv3r+blxpy0FpdLKQEjLM2ln81mTQ1kqIQETlPUhv11qv9WqmPMx9XPovICPbK89lTIYQTzqgSJ7Z2Cldwn22JHZLQnBVdIKXzsj60RKAeMpjI2j0dXn07WiibxKTo8fU8nto5s0nv7Zy1py4WP3Pa7cjntBl3JnlOynTdbnowx5E4tXoho8RcwYplG54aDL5XctaWjiB5DJ/huK9k0veRhm2YDMMwDNOZTgMtD+08yylmQrNOf+YJqD2DPFnWfJBGKjde1mvdu0BjKNxqve/icWTUs23FDUT+dNOGM9O+Jnj/3MiAWsNDTAEK6aPN0APhpKkoK3DE2Gc9h8qLR21bqfXU9o877OCpRT4C7aqn5UM6tyO4B3QXNOlKwnESTdy0gVurn6qdtROlj5yLUMqQOScbMTkfzeax8Yuo35YGKk3rpjU0eunxOHj857IWfgRJZy27bPEkI5UDWzkvZJQY7oCzyR1J2wy+kTTI043tdfnzscwww7mLs5ZtmAzDMAzTGfbUdoJnOcVwAmTXwjWPeSviRs39PIWHoh80T1XY5Pp4Tuaqd3h3PbVu2V+vK2DBx+J3DBQMIMR+uGkLek2kETxaYNxSnABnOSVzeFN7RpHNIMgQoyCuBgpnEcgaHuGmtRN3W4JGw2qP99PcHOf6Tb/+HHLTClC1blqDc+vYabioTRizWbun4HLTjJj7s5GntqjleOWcajJPLiHm5Pj/2969KznOZIkdP1TMKGbXnFWQRnXL1DNUT8iSLROs95jWPsa3/SBgmfJlqarfQV5fjKqQq9DGyoAMkCgQyHsmEpnA/xeMiW+6SNyITIB5cE56vYoLtyRnjNe+PHQvDx/FQnaUduzCJY/WePmuZVCixJr9vl3i/IK92LaUdaAGeW4/Ctl/5S1abRjDBAAgMyK1mXCXE4wCyCUPb/UMM9c6vqZVfDWPfivXssYeu3H5OaoaXplHaud1j9um4B0vgMtQ+zz9YAVN04p2PlrHlzqQcF96UVc01T5oYC2j1yQ9kukGNRI85bPKYLnbMFbgXYR14StVPFaEHmfPLvTnubmyd45xsNHKDEVNPyaejD9tNLm85grnHksj3NF1XZGzqFqZbopEujIitYVP+ttLEqy19k7xry1zCTeK4nuqMPaRjktGsq0Pq2VQosTv3et6unztZl3/U9BFPkOktpxn0ZRP1RVx4npgDBMAgMyI1GbCXU6MugbO0pqPcJW8+17x2qZrFAFa22PgVQz5Xel+K7aWAalJpFY1PW19v/Qyc4nSrV/6uP8VrwnTTiqBm5+HUCx6Nk4TXh7NoaResqDefJwpVGxHkW5L/LiljQTeRZhH79ateDx7PbyYyv6vFq+9fUHmuScTb4dmhDdkOupyBjFLUmOYtqe9KRLphp8bqbqvyckTs3nliUmqDojR9jn319fDi/vHt9pk+7IKrXStf7DW1ONtlfXXk/O1u4pBibJmqB1zvKTqZ09YdHOKO1zd7EY0uTwFlsM2psJOnDFMAAAyI1KbCXc5MfZcALmWEa4xQzxpmLb2Y+TLZZKq+/xa5WJX2VO76Q9lp+ffPyK1TdfMYhXSWerZDj8Dd14gbnJIVG9IFFwM1baKxMHhlJ6PDvfBWl37unu/JspoKJoaNW2tdM1LutBeihGNBMUYVhxYcfhuQu4iDOOJhq5p4f3WnZNtq/gS5119YF5ppMY0/eS14rFTo/Kh2b1p1WjzESChdkYXnyv3vuKeNhm0abrkkVrVZcV32wqPfzteju8+or8TG3cLH3n289eNoTlvstUOt6nT4+HwakVxM9QvbeNcfkr4nCFVDErku777crmqKm90Mm5RWQdt6eOgX37fwepeS/WrlQdrGcMEACAzIrWZcJcTY7cFkOchmbW3aDFuRc+GgdNqxv6appOmk7aT1un5d+k66f4qfxWRplGklEkn0tjDtLqf6LsK3Jqf8Fg9ofY6kqL6rqzjwspg7d2n9DE5v7jOdKNN522yyHeKZNbY53vWSqhVrl31xYTcRegGEw1f6xph2vHuulSVMJ/SS3R3ptngxr1tWsaMHHPE+uONJNTe0z37UugdhZ7iivDy0I0jtUm+a5+Tp8YwbaeP3CvfbI2qNk3XPP6wv0m15JXLvC/PfvQc8mu7ndRDdnvQM+yqXf6gxLp3ZHbW7cvegMPv8zNYMue1ffzD5Wlml9c4iBu3TZqb1CK+DDvGMAEAyCzHhZZLe8ddTrTqxnriuaQTbZBbvLZ9qeHgtF338OIao+3n+7z+mvuziKiL4ramepsBP0E3HLU1P+GxbqT2GnNRfcXtH49OS9DkUbVd61I+NypYq2mkTZvukEYPZ8ReMtYdT3H4Br3vInQjiUkHfH0pT8Lpe2ZvUS7KPOifkLbt9Km0i/aqxvqPhoz56xHIVX2xFjHVbkujviK0Ik26SK1PmD/JtK9rcdx4c4u7a50Ruf/mVdTLevTkdpv68uAUoZyk2G6nb2t8HmYN2uvyByVKv3AZrq3KKF2uHQjtdRaW+okx38rzwa+oH87VBmsZwwQAILPFL7TH45FLe8ddTrQdFkDeUULthNuQhGL21nLOCpfQ7H045P5n/l/UPxLThWl1vz83ZrKXw/j7PIcws6bRhGn7f3NciCaz1nEQJHmwdj7rdLi4BIrYzrOE9I3JBsxGhrzvIuZf83qptD3H0Uv3h5bMo3VJ4qfa/vPhpX38I8EKfLdgdshM7dq7qW+ZUxn5qqgDz+0tUhvfjzkPr1cdpu1pL6/DG3wTXnVvddkYY35tdc/bWSs8q29HnZNK29ur+h7OMYk2+i628EGJEu7I7HRxuNBWv+h2SWTQMdIksB3UUIdsV0NPkuHlWD+5bbvm8Ufz8HI3Pbm0VXRSjGECAJCZ5UI7j7NGOh6PeXasNNzlRNpbAeT5/tY7emg1njbmbvhJLI/Sz4O1q+6G56jK/cPv131vWnUqbSfSNrq6x2l/qW4p0Vb3hMe6CbWWMK3PsIV6NHnSeei/zqhpaztFxHt6jrfTSaY9zM9LZ7FP9kSsOhlbUMTvLkIRFK0jTHt9s0MN5J45BhDzTRoaS9YTxCEv1rCp/o18a4YxVd01cO0NjKLbr9vXH7l015KV882oK0zb0x1M6x2Xfomj21z/h+NK6YIiuMRo7d2SY4LpLcu2Mm2OJNqJkgclaqoEYe4XVrryLndTFMW10/xQSGg24at5eFn6MCfBGCYAAJkdOs0FuHc4HCLCsurretoF1kJ5JHd7NMIc5O4YttKe5bzWxiztLOdneR7+byPNRS4rbk9y7l1LI/L1Qb78Vvzp0lyeLk93b35tLt8u8iT5To1+Rc/av1+aS/vUDv/3+Xz3tYrI66v8/iXyKPJZvxQRuXQP8rP/zy+ffsmXLx9/0X3iIm0rIvJsXLBS01yXULtJv9FJJyKTs6/Neb6c5Vkucn/eikgz/remcT/0F7k8iX5pDos6n9VnSNPI05OcrYfmIsP6z608j95/v1Oj97uYb5bzFVP5pbuKWG9Kl4s83X+t95vR31S43kXc7VRj+g6yNAblKde2ppNt8p2ar4m6U9plRXN9RxrVRtKa755ql5QX2UYul765+nQym6HsLQfbuNGa3D0OmotcxPPUn5icUprOZ74BCQ+soTF+rC7d3Yv6hLk00j7dXepSr1e7PbPLwsC3W8vP3C33R8+jO72ItKZ77/ulS9bfBcG8dkrE69bGwO92Iq/JaVP0hcvQPgcrNVRz65P897luV5OBy6E1axr1v/eLbdu7fwz41Ryg6JN5hDFMAAByMwdy065rtwm1Hc+jpbCfAsju+UM1Cn4kVpliO0+rbYfZMoOT+ez7YHravZ2mNCZ47la61j9NV3vwfY9/1TPazvuN+VeXb2OaecbrrYFP/s1rsarUn2vLcHtg3lAezel7H6WATI5tOz/c7j33fGuctqX+hFrlltx/Ex53EbdZkS1zZufaUWVmmPU085243aWjs3633gVOs3E4goZMmlY2e/ukY86jlWGS703QFnZ2mwRdy+EipVxv1ErjUqmsRSmtdJMZh1woE9G264LPX3NWX1Rf6pximzAJNRmvDNpRrvAfjykvQMUOSszvFko+ybvOdqKvuvUuJcfz3dW4VWgI7/n72sIRB3xc7muhRN5ys8PvMYYJAEBm+SK1ew7TdtzlpLCfAsiT3dxGTDrZD5tZ0HISrG3ms2XKaCik8Y/gtn7DKPPgcdzLKUar22PDjhoq6eleNVINs64SnxriZZqykM5TAKoXrpwXsPX4znTng9/gUts1L/eHVzcZqktLDJqdLCpSW9R8aMZTwuMu4rocY2eRa/wwLEzbCyip6hKsVU+Oa6vSufJ4sXNFyObxR4J2XZ62a++nh1O/XK5s27jFmkg/C6/DzILqZ4biOpdUQ+TBIVvd81XSST8hxSpNyfBwVWnMt5rJDp3v/COONyFL8A/NjmO08zYW/6UXOyhR/umtoIvslXHFdewqF6e/vw3o86/d++OP6R8W2OqEsds6zmfGMAEAyM5yoU0yT+3OY7Q97nKSSDsAVKZNJtQu8ThqI92Px65T5bCqg7XmwZr5y38MJXWMtunn2EryMiQSWGMSNf6wHFMd2I8jk4clTNvFRmq7RKPk8eO/88isawNU8tyO2Ad6ijrd5yPcI653EW0pqbS9+QnmdYzn56Y1zObVvzm+ihhxVR7KydFs206klWYDnfn1e+yncm+bh5c0V9tNhml7umTQwF22XaEShmkXnYnQMQ4xO2rqg/n4Y7VUbMPDVeXIOrduUH7q9D5koS8zIJA8is46FuoJPqRlDkrM+4EirrzuJg8OlcExyrj0Vrd/f+nTXvtXTH/+cVZE/4zyNX86zP3AFnNG2DGGCQBAZpZ5apEKczwksfnZW+dzYtW+jy6Tiolqfq9hHh/rhGSXJzl/Oj9/uXtfd8jauOYz5kZo5PVVvv2WS/pd6KfpMUzX1R92x+9LKpnIVjVv33U2TuOMnclcJ1hqFNPTdjL6lj3nbZqv5ixPs7nzvKf01s0h7T4l52QBqv1WruD2H+OvxHPW2KhrRCEz1I5NvozRDGeWieX66e7EOONdc3tnLvMD7DtNl3LaSJcz3LFnsyprXjH1VLSNXC6TP12keVJ902Xtjt7X10sr7e8vKeeO6+eJr/r+yko7Z23AjeWk9d7Ptqhcke+lpxc/H6EX5eSF6o5CcyVb8S5dd4NdSLuOv5cId5HXv8uv3xFT085PDJdDOllfRI81rM29NQQf2ALnqZ33A4Wc1dtgnbZ2TjfPq5ck879q57TW3zAjBmOYAADktmqceEc4+KkkeWC/WFtKqLVmMrk/Ump9UrVtpyeGX1pt3KuxJ/eMs2PVabKPPz4SC6f9w2Rhw2EJz/61ZNl6VXaqguob6SRX3Ttd+cRp6458Hrxpums5xunLN4nKPNOW/eOTM+1H3ExyzlswzyHz6z8DdnVp+lPCchdhPdrZqz4qk6sCKFP3HM9wxz4t8oKVj08J+1YaZeZKcTs10jSKOgSRry3NR+tCk1Dtd1Fomm6c/9RIOz5tYppkbzwjoGNL1GXXDYtKedIML83ZuO4ZpdzZ0upBjDcsW2Zkf2SaW1pqth8FMS9lfeOAl5cCByXmZzWWsEg/ucDL6QaMM2YZjGECAJAZF9pMuMtJJWoOwiINwxbzMEO9e2cdJgsYRTIvc14Lsf2jDQ9jOgeTHk2DKvraxe3tpRpzvOsfnKN3wYXfdCEbx5HTEgMYM6qh5DbPVl/XNztyitYdGam9fVD5RaUqg+y4dZNz7GPdITPJuZbA284MtWOafVffRbgc3jUm54uZnnYuMjLkNUV36f2b+840TddqawwWWFUyVX3jwA5hK0TEN1jrNxWfKnj5+MOjMXoFaN0lnE1wvA2603LFs6u0MsiGY77uhL5tqSHbdhagNT+O4H5n7njASxuUqL7ucT2WfbQlRZfr2o9lL328H4xhAgCQGRfaTOq9yxkGfgsROw3hqizjX/M5LCv8oWFNpV1w+bqBs+gJaOfxzNucU7rZo1Qx2lto1vpbMixSOz1QoTvqd8zvN63kwZRZdKdZemM/xk8N09NOBOtyBBgAACAASURBVLeW+29ImVnrG6y1xoAMJ+M0rXa6aN+WeN9yNCsO39/5rpbT+Wp6AMVdhLV/e/yxSuqX8kSKOcBt1y46K7N4DnOvzLF3Nr69oPO9T+C079LtdZ/qKS/tx4NQo9fS01AWq+8o2kZ9URhaTeB4vXL21rYZNyIdr0hqzPmZJGQ72QD1wVgvXVt3k5a5XZvvGdbqTpVH5uUh7hdBitDsPH3W96KTquEUNShR8u3Yti3xdEtATxv4eNxku+u4e6tDvWOYAABUigttJvXe5Yx/xhZy2zsfHFl7i0xcq7rN8xKaVspP6LmXp+KZNnA4O4bJR83uR3UMo8l3MarHH37jVnf9w3TB3trQ8SjVhrm+CvQYWRrX08dZOhvLNnVZwU+F338B7R+P6m7Gf5cdUzcmDXz+oICad8i2j9oqjsw2E2o7Va/add3QS7i37cc/1tqD+dFNclFTno+F3w8sxWWygRHde1cf23S5vDy8NI8/mseujQyyGOr/b8m1ozAGax9eNFm35peyFHAbtCjjxSUh3/Cwbu26h0X6V8ot9mHYr3XXvvqvGEsYO/I5Toe+5uXhGppd4hCZu3+XXr2oQYmwXUByS4dsm8cfwzNWCbqJ/F3ebtQ7hgkAQKUOneYCjLQOh8P8Hws/+GeR59k/NiJPIucVNufDWc7Po01rpLnIZcXtMTifRUSe58dxornI5enuXy6NPN3tVNPI09N1gQW6XKRttXvaNHJJ/RWp19ie5Zz+3FC1hYvIk+q9bd8+hpZyPqsPSyvnc6v+Rvvuous6uVzkabSW6ON4EWlV7Vpn0t7N3/JE25Z1up5Fnu+/yUW7jmuXP2/aIp3oe/7JNy4iLpeJ+UnWdRe5PKlO0bC91p3Gd0turv/R91STVtOIca39qSnuZ+dZ7pvPQe4usq20Z8cr1XzfSjt3J/cPTSOXy7WXMJxLV7ej3rTpe2EH83ufVJcD3RkuZd8VLEXZO+uPta5FL3GxdjS5tZv62fzlH+W//tPF/frlru+6Vr+5XcKto7g6t/Ks3MlLI+2T5m8zquvabX1pftfkOQ/7RjPmdZttOGMdeuZFrNWuDest5JfL/N5qYLrJCt7yW29iWG8v2QVRvyLrKj5+dKxtfiKteEnCWEwr1n6DkxvE4DNwct5w0iRV4xgmAAB1WzVOvCM1HnzD48VrPx49TbxYq+CYUkgRufm/LfPk9ULMqbSLrnd6nGf5izHnhjEbVZlO0U6KK+pOA/MTxB/9w2Lz7vjmD8xX7D5RVglFRG8pnjmmgjZk0zqdkAFNSPORea34mL3+2Kn+IXjzl9828mNWA9zhbPE5NdtRgfPp9rjuVRWF9hSTtnV/lb9Kv6OWV7PiTim7iLS9gS6zbafJtT7WzcAbM2coStfknFxyY7m219uJ0cvUafev+Rfw8DIkPz206oINpqRdn1ddhWQK7H+0t52p78RcfuwURfdTZbkbVPPBWWK9YasrYVBCmRlcUVeAEKlKFjNJ7ZJqHMMEAKBqXGgzqfEux6Us5VqiCl0uyXFGz/GviYcXdd1j86dWD30NzBXPMmzndPBlFkPyWJRrnEgxNvf3WQROXW9N2nY846bKR/+w/LiOb7x2Ps+f43m+7hnb6L+7tM953J2N7tPT3m2rZyVe4/tTFWkcpth27ttu5/v19RE0dDxh/nh0PTvbPyIC8PN1l6nfreusm10n3YM8aCO1jz+mx3qN5pchTHtdkf60XHHmyCroQhc5xzltvYpq0veMr9r1txNDnFV3bZo2nEm4VKQzPUAoDy9N5ASHq98kBDP3P6tsUoZ2bf2xU+a3mS1Y61ONPvF6A8pQZxiUGO4h56+hD5k8KUK4bftSRVgnJzqSqnEMEwCAqlH9OJN6K4coayCPWWpaLmNe9tCj1uViXAqEyq1GaF+YZ148rS/b6FI2uYTqPoZdzllA9K7u46wu3+PP5vWz/Ug5Vwae1j1upHmSp8nppzsyXWOv0XQtRNa2IYVwQ1lb+lhfK3LYbvczf60z9mD4fyKdpDmwd8fhvhC3eFVkVVW7jXmzuaxoI42IKLetP6tNJUk9NHK5aKrVGj5jP2Tn9vx8f6hdv9C4use+/VvUyX8+y/NdcdZP8um3/FbsaSPy7HP+LEPZJyy3IZbCuZpeGqKvmTm+UVlq1fr61TeuPXMz+5d+uX9/FRH5/Wv0B/9TYHK9q8vhcBD5s8i/Tf9gqGA8vKXf9Z+/5Pt3Q11kZeOaFBZW9gbDO2tXYCX25coRD9+sYbqTQioeKxl6vFRViM2zhOT5cWQ4AWTW6JJXP77Ipb1OaBF6A3lpHh/F5bcbKpZkih9KHy+s3jFMAABqtW6geD9qP/jm1KZ5ml2WTSolrdYxj3Z4fli3C/1rsnDr89FrPbe+VsVjg48DNcsX0R0l5yTaj/y9+R7Pc7bU2bT9lzU/cDPX/mGlak6+hZHHO+CeUpPTZHcel6mBfLfvYdm06mUZj5fzO63psLpEh6SvRl4CK2Q20r08qBrkrMR00zZO16Sgnmuo9xiZPebRdWuqW0xzaoeEd0Wp5NwXCV0HuOxKfc7Y4SRfdpvqka1i6kBXm33oyMz3nMoCDx571wzZ6X6vis6YWzclIn/RNIPWXsfe9iJnfVBacq35t0Nwlem1skUTWq67M/8ezHwpdt+SJIMSC903cqXeuOmV318NpY+Vv7BqObFrH8MEAKA6XGgz2cBdjrUYstfAWYrtSTkjacgGOBc6Vv5qUP6gVe6CdUWZ47VFjURot22yXW0zbnABwcjb0LC9dq79HLCdH9f+YdUj6xXDnjR8QxR/lZNW9VXGRVJHFG1BNfbtv9EunYjfpF7LBF/9X6p5EN1fL3/9P+MYi+JQuwRVlF2Y59tTvRSBW4e5B66R2smkmre217Tt9fXy0nx0X/ZXPGXzzzZ0luQkn4+pbT4cZbisJ//uHGal1V6CYxi+74eX4FuCgoxP2dEOaiO1157H8nWYmsnm24Wv0oK1Lj8fUi1K1r7/92J+2DRgR6xX+VUOjuMztZGDEos920dvswOTbiW+7a3N/YFXIrUAAECJ6seZbKNySF8eVmxVUhfaq3m1qNev599fPrbl4bX58u1a8WbRyjeqL1NBV9tNVyfNWsDZXGM2T70fQ12vDMUSXVy38Glae1bO7cPX8+dP8v2z3wIbkb7qqK5U9XjVTkXVbDVXr4XITIvIyr0w8kcJytveWCt4y8LnzHzj+wN7mNVADqtSeNcbNBd5aqcnXlht9vlJMrlezM82h5MkVTXjviqmspysyyqa25nSH/Cz29lyv4TLV3n81j49n7+P/709t+d5mc6hDV83UdWTzioSupSgd9Xa3yLysYWNw2n437/9l3/9/j+a0VmRZEsH43Ky7q1CW/U9481OX3cxUcnuD3uon7xcxVS53UD+kst3U8Xj++ueiNy33RjWKv2Pf8in/+Z93qxbG9lailbkIPIXkf87+ddxh+dQhnpqD80hjLX/yV8P2Xrm6+7EHM4uyxJKprtj77ns0bjE9+q/zgys89T4Vj8eN/zkl1oDw50nahVZu7iM0sfDCenVHNaqje9rG2OYAADUZN1A8X5s6eA7ZPtE1Yhzz1WVWd1LXV3N4fnGsIeafctdmlekfMrS/cnKtSqPWeswF6Vp5sl26kwdx3SZeQ735CvzS0gyvunaPxR2fL2ybIfT3702+EIJB7p+SVl7M7YZqv4tPA/AcA4oEyXcjmBwBpV4pjW4rEV5wF0723mJ6bZxaNuqRY8O3VDfOOQAjXYsrLaqx6sfSF50FZqOUX1eaVr6il3XQlk+286yXegq33SddI04p9Iud9a4XJKaJmBmhHx1ZTzvSGXIqW2k7V+tKA6w46WhljSgdZmPZP6Crh4/rPxfVQsuWeRyZAq4c78yd+wif3YflAi4sI6rU2i3pO+c3Ja21evvHk1+zvi2mZVKH8f8kqruYrqlMUwAAKpATm0m23serU+PMD866JgPEZBQ9aG9T528NPLk93xi05j+6rVJ1kewdYkLvo9VWp8ET/tEqfV5/Fk2WhH+0/++/K//MDlMreFkNJyuyi+uk2v7NecZq7+L+Vc4OoiKnNqS+gr3FFu57YX1FOolT8vQJdT2lF+rNVVI/XVrsmmjnphWHrU+u23e/j0bYZ/3Iw4PgPdJDMF7cZHLN2m/GxOMDAdce+a0s6R5XUKtcqOunkQ+WqnjWTrRn7SvX6//9/cX7yUE6jd31Z5h6DNb/aHLuYHK7/6XXH5K+3uZvJ9aUiK8GBpCwN3F+ZpHa75hvF6am1zJqdYKJTK6Enld8q5LEJF02cDXzQi8VT6I/Llp/u0i9/tsvGR8dMivr/Lrd/+fl6cC0gPrMa/CMtFJ7t7b/NvB12bOBa8bVHFrgEnqECSnrwj1DyL/2radYYPndWisJtdHaz2qoU9yLM2yvYvvTk3ODPe7xnmPtvAdZ6q6RL1azuHtjWECAFA4IrWZbPUuxzFeO7kPdSwY5aS5yOX+Nt0/WBvP+pvcMGQTdqduiA72UkVPzSsqbTBidkKaQ3UiDiOqynjeUM/WMPJl+Qrmg0O3ozmN1JY6HtbvnFfzzxyynXS7ir7IpxS5V9RQkvwId6+0HnewhsDt+F9iFjhZ1N+l/S1iPlkMh2vaCykP+LmV53PjEx15lZ/yl3+Uf/qnv/12+0C/nY2IyOev8vOLSOqawx4KiNROjb+9Vpq4Jqz8ElMc7Yu+GnXg4vNHXJZmuK45XvQ/yVlEHALk176raXNf5aw3UTKKzTw9STsESv3XFVAheXyEw+6T+41/fr7VNQ2rEnn2iO9izhyvXaUSssT98qqx0LGLsEe15vrjU9QvozHNbv6DyL/2PzsmHYNvaGry7J3j8yW63sj6uEMtsS6YBFcwXr70capC38OcL1Jh+e6tjmECAFAsIrWZbPgux3Hy2kbk9VV+/xLPybAczEftz604ZVYlYB61dJi5RJ1DMk70NQURHaZZktAhlYpSafVPDFxmJ9ztgF9ERB5/yNf/aNkL3fS0hiCa01i28strmuvA6jhSW86BVgl4XMNliPzjs6HDXvYo/fVtppGgTjpLQ1guTCtuKTClBvLHRu3QFHu2TkJ2Pk+nJ7+tYPqAjlfIdthIw5n8+If8/Jt8WTc6O2aN1DqeFP0X04x6yuU7m+ECV8SRVBgfAqdt3OR4sTm5tjfpey4i3+wZtNdlyOVyn9a+AsfA1XAZkrPTHa/V/M4vycTY8+/lYwbKsPSj4GwniIjbtNmr9B4B51tpj2YmFx+sreF2TLmbH5Ha3sPLWUQU91oqk8orXtn/LkeMeO3GBQdcJ5endD+Ww+adHQxx2W2clhsewwQAoExEajOp9y5nuH+2PkbtWiDOv5DcpEDxfKRgUpTp4bX58u0i8Qm7xu0ZH4p5Rpo43d+bivGqV/2xxtHabdEc3x8vLol8hUQPHc6m2Vv+pZV/vtt0w/CTMkwr54u5cKLrYJZmWOguUlvDwI/L4xrKyJnvuNg4w8l6hK0JtR+bYR4GUqbpawK0t3WlGzbKXOt8MaN2OH9+4o4uXqstvqevo+Ddw95Okm+NfE9aurQRkVcREfl1W5Gx0/7Syk8RU67vt/8s3/+ntLe7CJfQmK++VRfQzxfActL2tjdebOmim8vD1/bLF/kl/enqfAq+tvLtLM+ldGCOxQsGw5Xo9at8/iTfP0es2+VZJ7eNUR7Jj0it+IddJ1efQr6tCjkmJq7VgehCa5GPe1bK61HCQXWpxvedwT+I/Ks0rXIWD4OH10ZEPr88ff9n7xsF3+7EfKOuLIQDL8qhjIml8kEDHglarPSx9ckAJevUOfWqdwwTAIBKEanNpN67HN1UiTq/HuXn32xz9fW/zfS19Xruz27rsh4/VhhXb7lp5PXrWUQ+f5JPn0USTFISUAxPu5Ter3+R7/9sfLPxV/FwiFyOz+pnrme4f9b6DoodmEdYlb/WHv+lNQxJeOccqBrYR6S2toFR6/eiDJeGjYtdFzjri/oDZk2oHfcJIppMzbsPzNakGc9KP9iqi5ZUdXrMIl1OjXh4Mt1UQPLc2VPi3FJsz8NlIm6w5brRP+Vym7/WtcO6XRkm56e5XKFiMc6XUb/WN2zSBgejHNljepFTOxfofJZnucjT7ev3Gcq/87ORr09D6yqtA4u5EomkaB2X23Kcc3ztTyyNI7W+qUvL15bcD8e54YdLXv5B/34Lt9RxxXApP15ddHasP7teX10TZ6ciZhqKOW7muYRkW1fenCLLXEev3r/M/gKXp5UPQqnqHcMEAKBSRGozqfcuxzfVQGRUR9F673pLJoj/uWsN1gZwqVoWJKAwp9NCX19FRH7/zfbO+8fk3QtVrTtM51hne+w2B61qLlJVsPbjg40mdGcsrB2YZzxrYx+R2kIylz2Zc4R0ua2xo+QT08CsW931LqC/m695mY59ErursArhJDb7IGePJDyVYZTkemxeX58tTwndpYmmisuKSPMqIvL0IufvoelxhqTvmfNZnp//vcj/ax5/yM9fIiKfP11eYzL7Ppbcs7dE9wv9AvRPi4Xw/8bsk3TXO4TnGFLyX24j7TVMW3gZ1QSFiONbx/01Kzi78S5S65sjyyS1C3D8ZZEw7DRJlfNt1+MZFgmDVSrNdJujPjxAqsC2OZxGcq27VKMcCYr9BoRd01Xmdz8OGytr7KjeMUwAACpFpDaTeu9yQiK1Y87VJ32GqRUuqmhcWLDW95lKZ5YAbZO8emV0Vb3Buk+O2wfFR27R2bsDrXgK2/w8uLK2rfEjUWHs+2Z2jdTW0D8YGFImzY3da44rtXm349iPGWsam22v8Gly83B5KyIRQ0W6oPj5y89r/FJEEbuNvNgMi7mIiG+xwPlSbv/h+fTOXQBmMS7PT4yjbgHP00yXptyM0KV58Z6c1naJfZDmi1zSP5a1pPT3P301gtH4fi3xviQTx14F1GEXEZHHn/Lpc9STfdOOwmt0m0lql+ReY9M6g7vch+J6C/yKudse2Vm4oi5pQrO9WR/uZaHfj+bk2kofk8pjscfQI8LkvqWMEyXUpu2Et6reMUwAACpFpDaTeu9yfCeSnPJJLIhMNVUGa8Xtl8NSSSQiQzlL3wH5i9xNF5NgsxzTCmdWSaX1jTIrA7Rjyt9jD6/N77+5TUGqD9MmPz55YjB56OK1rtVoA6K28zic75kfFK9dKpV2W5RVqX2HjcJGTJI8uNJcLPPLOi9IRKIueHX1Ev3Brytm2XOIPVsfJbp+2WUGKNPf/Pxs5JfIL5lfMeutnus1PYRF0PTP4ycYvA7htKNwH+Cm9PHywiZELM2eYxglGA572nOpn4NW8RPJxr02ezxDC+KefGy56OxcYKR8MlBmfqRr8mb/y9PB7QFeOjepeQwTAIBKEanNZAN3OWHpBeNnad0HyoMHsX0nsEmRPjJPAbpu+PCHhINbwwHJELhdZVzOK32255ggpwvk//21/fntfD2xm4s2RW5WLXm5+o11xWCszPOR+u6kPXYbnFArIqMik69fz58/yffP2yxwupZ5rzNuv+MijcpuOeCAB3QpH6tLkjX7sbjbf6SIWG6sl6iI5oxymXf5+mVFh+kTSDFuO775ue2N6jBUPZujku6673KHPJ5b/fWrfPni/R143VsSqS1fziBKBtQXyWCJ4k+VBqVIrjUL6F4axcjGle8jlR5dgfsVxzcBd7Ieh+djmPN4bANjmAAA1IVIbSbbu8uZzJk4+ZOZ+9D5NR3VfbOWfUTdaXxskUloZ8YZt2mitjcPn+Tzi3z6nmlQLjj2HFDBVBesbaR5fZXfv0QdkLmfmSnDoPMmYzCGOEbMfk46otevMil2+/AqX745LcdsPtbDAFCY+ZkQWhnUziubWtG/J6lssMQzOxvtJepyfxV2L/x8d+1a5uxQsD4G4UaxvQ+vIqLuZp+fS5+SthBhN0IuJ4+io3CpaRw3FI4AC2VGmiUJyVgXTsAj3hLh/CE0+3R4kspvJ3Qpkjt/bsC3uq/jsfIqxeEaL3e/6Pg/ReS+wTs/YZS2N4YJAEDhiNRmwl3OnNcEuF4hW11ALojrmrONtxq4JPX4CgiWm8XEmONTkXzPjYfX5su3j73PM+K81RhM8My1jsz5mijHpPNf6Gty7A8Du7h5P5A3R3KrvUTVvsqllfa3iO3UU5/ySZOu74SO8pu2KPm9AXppK4soOgqXYW4SateT6KGKq3EsNubxsshAMvGPMAnj98OZMD8NtnE7YU6u3dXp55VyHZx57NtT2b8Fl+vOPKBrrpPsfP+zt5P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       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -3069,14 +2779,13 @@
     "        \"cyan\",\n",
     "        \"green\",\n",
     "        \"purple\",\n",
-    "        \"orange\",\n",
     "    ],\n",
     ")\n",
     "\n",
     "fig, df_data = profiles.plot_data(\n",
     "    \"points\",\n",
-    "    start=[inner_region[0], inner_region[3]],\n",
-    "    stop=[inner_region[1], inner_region[2]],\n",
+    "    start=[region[0], region[3]],\n",
+    "    stop=[region[1], region[2]],\n",
     "    num=10000,\n",
     "    fig_height=4,\n",
     "    fig_width=15,\n",
@@ -3111,7 +2820,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.10"
+   "version": "3.12.7"
   },
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {