diff --git a/notebooks/draft/28_best_performing_model.ipynb b/notebooks/draft/28_best_performing_model.ipynb
index 192de72ee..81064ada8 100644
--- a/notebooks/draft/28_best_performing_model.ipynb
+++ b/notebooks/draft/28_best_performing_model.ipynb
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -39,7 +39,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -148,7 +148,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -178,7 +178,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -187,7 +187,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -309,7 +309,7 @@
        "4    5.0       13.613427  11.188204  2011.0   rmse 2011-05-01  "
       ]
      },
-     "execution_count": 14,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -334,7 +334,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -373,7 +373,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -405,7 +405,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -449,7 +449,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -459,7 +459,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -489,7 +489,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -504,7 +504,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -542,7 +542,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -617,7 +617,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -644,7 +644,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -656,7 +656,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -682,7 +682,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -695,12 +695,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "data": {
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\n",
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\n",
       "text/plain": [
        "<Figure size 864x576 with 1 Axes>"
       ]
@@ -731,7 +731,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -741,55 +741,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 25,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Initialised the Region Analysis for experiment: one_month_forecast\n",
-      "Models: ['ealstm', 'ealstm_ERA5_128', 'ealstm_omf_static_embedding', 'linear_network', 'linear_network_10stopping', 'linear_regression', 'previous_month', 'rnn', 'rnn_10stopping']\n",
-      "Regions: ['district_l2_kenya.nc']\n",
-      "Region Type: administrative_boundaries\n",
-      "* Analyzing for district_l2_kenya *\n",
-      "\n",
-      "** Analyzing for ealstm-district_l2_kenya **\n",
-      "** Written ealstm csv to /Volumes/Lees_Extend/data/ecmwf_sowc/data/analysis/region_analysis/ealstm/ealstm_district_l2_kenya.csv **\n",
-      "\n",
-      "** Analyzing for ealstm_ERA5_128-district_l2_kenya **\n",
-      "** Written ealstm_ERA5_128 csv to /Volumes/Lees_Extend/data/ecmwf_sowc/data/analysis/region_analysis/ealstm_ERA5_128/ealstm_ERA5_128_district_l2_kenya.csv **\n",
-      "\n",
-      "** Analyzing for ealstm_omf_static_embedding-district_l2_kenya **\n",
-      "** Written ealstm_omf_static_embedding csv to /Volumes/Lees_Extend/data/ecmwf_sowc/data/analysis/region_analysis/ealstm_omf_static_embedding/ealstm_omf_static_embedding_district_l2_kenya.csv **\n",
-      "\n",
-      "** Analyzing for linear_network-district_l2_kenya **\n",
-      "** Written linear_network csv to /Volumes/Lees_Extend/data/ecmwf_sowc/data/analysis/region_analysis/linear_network/linear_network_district_l2_kenya.csv **\n",
-      "\n",
-      "** Analyzing for linear_network_10stopping-district_l2_kenya **\n",
-      "** Written linear_network_10stopping csv to /Volumes/Lees_Extend/data/ecmwf_sowc/data/analysis/region_analysis/linear_network_10stopping/linear_network_10stopping_district_l2_kenya.csv **\n",
-      "\n",
-      "** Analyzing for linear_regression-district_l2_kenya **\n",
-      "** Written linear_regression csv to /Volumes/Lees_Extend/data/ecmwf_sowc/data/analysis/region_analysis/linear_regression/linear_regression_district_l2_kenya.csv **\n",
-      "\n",
-      "** Analyzing for previous_month-district_l2_kenya **\n",
-      "** Written previous_month csv to /Volumes/Lees_Extend/data/ecmwf_sowc/data/analysis/region_analysis/previous_month/previous_month_district_l2_kenya.csv **\n",
-      "\n",
-      "** Analyzing for rnn-district_l2_kenya **\n",
-      "** Written rnn csv to /Volumes/Lees_Extend/data/ecmwf_sowc/data/analysis/region_analysis/rnn/rnn_district_l2_kenya.csv **\n",
-      "\n",
-      "** Analyzing for rnn_10stopping-district_l2_kenya **\n",
-      "** Written rnn_10stopping csv to /Volumes/Lees_Extend/data/ecmwf_sowc/data/analysis/region_analysis/rnn_10stopping/rnn_10stopping_district_l2_kenya.csv **\n",
-      "* Assigned all region dfs to `self.df` *\n",
-      "\n",
-      "* Assigned Global Error Metrics to `self.global_mean_metrics` *\n",
-      "* Written csv to data/analysis/region_analysis/global_error_metrics_one_month_forecast_admin.csv *\n",
-      "\n",
-      "* Assigned Regional Error Metrics to `self.regional_mean_metrics` *\n",
-      "* Written csv to data/analysis/region_analysis/regional_error_metrics_one_month_forecast_admin.csv *\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import pickle\n",
     "warnings.filterwarnings('ignore')\n",
@@ -806,7 +760,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -833,7 +787,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -957,7 +911,7 @@
        "4  POLYGON ((36.90575473150634 -1.159051062893981...  "
       ]
      },
-     "execution_count": 45,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -972,7 +926,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -983,7 +937,7 @@
        "       'previous_month', 'rnn', 'rnn_10stopping'], dtype=object)"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1001,7 +955,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -1029,7 +983,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -1057,7 +1011,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -1093,7 +1047,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -1108,7 +1062,7 @@
        "Name: true_mean_value, dtype: float64"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1121,7 +1075,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -1263,7 +1217,7 @@
        "4  19.663117  "
       ]
      },
-     "execution_count": 72,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1283,7 +1237,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -1318,22 +1272,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x12fc95668>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x133bf8e48>"
       ]
      },
-     "execution_count": 102,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1355,7 +1309,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1441,7 +1395,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1478,7 +1432,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
@@ -1532,7 +1486,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1576,7 +1530,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1609,7 +1563,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
@@ -1626,7 +1580,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 595.44x595.44 with 3 Axes>"
       ]
@@ -1662,7 +1616,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 595.44x595.44 with 3 Axes>"
       ]
@@ -1702,7 +1656,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 133,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1773,7 +1727,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1791,21 +1745,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 122,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<xarray.DataArray 'preds' ()>\n",
-      "array(12.35537, dtype=float32)\n",
-      "<xarray.DataArray 'preds' ()>\n",
-      "array(20.161993, dtype=float32)\n",
-      "<xarray.DataArray 'preds' ()>\n",
-      "array(30.18433, dtype=float32)\n",
-      "<xarray.DataArray 'preds' ()>\n",
-      "array(41.598095, dtype=float32)\n"
+     "ename": "NameError",
+     "evalue": "name 'vdi_true' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-44-41378006ec3c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# has it selected the right values? YES\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_da\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvdi_true\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_da\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvdi_true\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_da\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvdi_true\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'vdi_true' is not defined"
      ]
     }
    ],
@@ -1822,32 +1773,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 127,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 1.0, 'How often do we predict the correct Quintiles?')"
-      ]
-     },
-     "execution_count": 127,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig, ax = plt.subplots()\n",
     "(pred_qs == true_qs).mean(dim='time').where(~mask).plot(ax=ax)\n",
@@ -1856,7 +1784,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 132,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1868,7 +1796,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 153,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1891,19 +1819,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 145,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<xarray.DataArray 'vdi' ()>\n",
-      "array(1) <xarray.DataArray 'vdi' ()>\n",
-      "array(5)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from src.analysis import VegetationDeficitIndex\n",
     "\n",
@@ -1918,41 +1836,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 146,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(45, 35)"
-      ]
-     },
-     "execution_count": 146,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "ealstm_base_rmse.load().shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 147,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<xarray.DataArray 'VCI' ()>\n",
-       "array(80.003748)"
-      ]
-     },
-     "execution_count": 147,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# ds.VCI.where(ds.VCI < 80).isnull().mean()\n",
     "# y_test.VCI.where(y_test.VCI < 80).isnull().mean()\n",
@@ -1962,41 +1857,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 154,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig, ax = plt.subplots()\n",
-    "(vdi_e1_pred == vdi_true).mean(dim='time').where(~mask).plot(ax=ax)\n",
+    "(vdi_e1_pred == vdi_true).mean(dim='time').where(~mask).plot(ax=ax, cmap='RdBu_r')\n",
     "ax.set_title('How often do we predict the correct Vegetation Deficit Index?');\n",
     "\n",
     "fig, ax = plt.subplots()\n",
-    "(vdi_e2_pred == vdi_true).mean(dim='time').where(~mask).plot(ax=ax)\n",
+    "(vdi_e2_pred == vdi_true).mean(dim='time').where(~mask).plot(ax=ax, cmap='RdBu_r')\n",
     "ax.set_title('How often do we predict the correct Vegetation Deficit Index?');"
    ]
   },
diff --git a/notebooks/draft/29_tl_predict_delta.ipynb b/notebooks/draft/29_tl_predict_delta.ipynb
new file mode 100644
index 000000000..965093432
--- /dev/null
+++ b/notebooks/draft/29_tl_predict_delta.ipynb
@@ -0,0 +1,2105 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Predict Delta Experiment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/Users/tommylees/github/ml_drought\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pathlib import Path\n",
+    "import os\n",
+    "import warnings\n",
+    "\n",
+    "%load_ext autoreload\n",
+    "%autoreload\n",
+    "\n",
+    "# ignore warnings for now ...\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "if Path('.').absolute().parents[1].name == 'ml_drought':\n",
+    "    os.chdir(Path('.').absolute().parents[1])\n",
+    "\n",
+    "!pwd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xarray as xr\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pickle\n",
+    "\n",
+    "data_dir = Path('data/')\n",
+    "# data_dir = Path('/Volumes/Lees_Extend/data/zip_data')\n",
+    "data_dir = Path('/Volumes/Lees_Extend/data/ecmwf_sowc/data/')\n",
+    "\n",
+    "assert data_dir.exists()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import seaborn as sns\n",
+    "from src.utils import drop_nans_and_flatten\n",
+    "\n",
+    "from src.analysis import read_train_data, read_test_data, read_pred_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Read in the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, y_train = read_train_data(data_dir)\n",
+    "X_test, y_test = read_test_data(data_dir)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds = xr.merge([y_train, y_test]).sortby('time').sortby('lat')\n",
+    "d_ = xr.merge([X_train, X_test]).sortby('time').sortby('lat')\n",
+    "ds = xr.merge([ds, d_])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from src.utils import get_ds_mask\n",
+    "mask = get_ds_mask(X_train.VCI)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "['ealstm', 'linear_network', 'rnn']"
+      ],
+      "text/plain": [
+       "['ealstm', 'linear_network', 'rnn']"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[d.name for d in (data_dir / 'models' / 'one_month_forecast_predict_delta').iterdir()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Standard Experiments\n",
+    "bline_pred = read_pred_data('previous_month', data_dir)[-1].where(~mask)\n",
+    "orig_ln_pred = read_pred_data('linear_network', data_dir,)[-1].where(~mask)\n",
+    "orig_rnn_pred = read_pred_data('rnn', data_dir,)[-1].where(~mask)\n",
+    "orig_ealstm_pred = read_pred_data('ealstm', data_dir)[-1].where(~mask)\n",
+    "\n",
+    "# Predict Delta Experiments \n",
+    "ln_pred = read_pred_data('linear_network', data_dir, experiment='one_month_forecast_predict_delta')[-1].where(~mask)\n",
+    "ealstm_pred = read_pred_data('ealstm', data_dir, experiment='one_month_forecast_predict_delta')[-1].where(~mask)\n",
+    "\n",
+    "# ealstm_pred = read_pred_data('ealstm', data_dir)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explore how perform in each month"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from src.analysis import annual_scores"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "[]"
+      ],
+      "text/plain": [
+       "[]"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print((data_dir / 'models/one_month_forecast_predict_delta/rnn/').exists())\n",
+    "[d.name for d in (data_dir / 'models/one_month_forecast_predict_delta/rnn/').iterdir()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ealstm</th>\n",
+       "      <th>linear_network</th>\n",
+       "      <th>month</th>\n",
+       "      <th>year</th>\n",
+       "      <th>metric</th>\n",
+       "      <th>time</th>\n",
+       "      <th>previous_month</th>\n",
+       "      <th>orig_linear_network</th>\n",
+       "      <th>orig_ealstm</th>\n",
+       "      <th>orig_rnn</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>9.518235</td>\n",
+       "      <td>20.316086</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2011.0</td>\n",
+       "      <td>rmse</td>\n",
+       "      <td>2011-01-01</td>\n",
+       "      <td>9.79577</td>\n",
+       "      <td>22.490418</td>\n",
+       "      <td>13.402106</td>\n",
+       "      <td>11.853916</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>9.518235</td>\n",
+       "      <td>20.316086</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2011.0</td>\n",
+       "      <td>rmse</td>\n",
+       "      <td>2011-01-01</td>\n",
+       "      <td>9.79577</td>\n",
+       "      <td>-0.354995</td>\n",
+       "      <td>0.518841</td>\n",
+       "      <td>0.623585</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>9.518235</td>\n",
+       "      <td>20.316086</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2011.0</td>\n",
+       "      <td>rmse</td>\n",
+       "      <td>2011-01-01</td>\n",
+       "      <td>9.79577</td>\n",
+       "      <td>22.490418</td>\n",
+       "      <td>13.402106</td>\n",
+       "      <td>11.853916</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>9.518235</td>\n",
+       "      <td>20.316086</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2011.0</td>\n",
+       "      <td>rmse</td>\n",
+       "      <td>2011-01-01</td>\n",
+       "      <td>9.79577</td>\n",
+       "      <td>-0.354995</td>\n",
+       "      <td>0.518841</td>\n",
+       "      <td>0.623585</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>9.518235</td>\n",
+       "      <td>20.316086</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2011.0</td>\n",
+       "      <td>rmse</td>\n",
+       "      <td>2011-01-01</td>\n",
+       "      <td>9.79577</td>\n",
+       "      <td>22.490418</td>\n",
+       "      <td>13.402106</td>\n",
+       "      <td>11.853916</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     ealstm  linear_network  month    year metric       time  previous_month  \\\n",
+       "0  9.518235       20.316086    1.0  2011.0   rmse 2011-01-01         9.79577   \n",
+       "1  9.518235       20.316086    1.0  2011.0   rmse 2011-01-01         9.79577   \n",
+       "2  9.518235       20.316086    1.0  2011.0   rmse 2011-01-01         9.79577   \n",
+       "3  9.518235       20.316086    1.0  2011.0   rmse 2011-01-01         9.79577   \n",
+       "4  9.518235       20.316086    1.0  2011.0   rmse 2011-01-01         9.79577   \n",
+       "\n",
+       "   orig_linear_network  orig_ealstm   orig_rnn  \n",
+       "0            22.490418    13.402106  11.853916  \n",
+       "1            -0.354995     0.518841   0.623585  \n",
+       "2            22.490418    13.402106  11.853916  \n",
+       "3            -0.354995     0.518841   0.623585  \n",
+       "4            22.490418    13.402106  11.853916  "
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# load the monthly scores dictionary\n",
+    "experiment = 'one_month_forecast_predict_delta'\n",
+    "monthly_scores = annual_scores(\n",
+    "    data_path=data_dir,\n",
+    "    models=['linear_network', 'ealstm'],\n",
+    "    metrics=['rmse', 'r2'],\n",
+    "    pred_years=[y for y in range(2011, 2019)],\n",
+    "    experiment=experiment,\n",
+    "    true_data_experiment='one_month_forecast',\n",
+    "    target_var='VCI',\n",
+    "    verbose=False,\n",
+    "    to_dataframe=True\n",
+    ")\n",
+    "monthly_scores['time'] = monthly_scores.apply(lambda row: pd.to_datetime(f\"{int(row.month)}-{int(row.year)}\"), axis=1)\n",
+    "\n",
+    "monthly_scores2 = annual_scores(\n",
+    "    data_path=data_dir,\n",
+    "    models=['linear_network', 'ealstm', 'rnn', 'previous_month'],\n",
+    "    metrics=['rmse', 'r2'],\n",
+    "    pred_years=[y for y in range(2011, 2019)],\n",
+    "    experiment='one_month_forecast',\n",
+    "    true_data_experiment='one_month_forecast',\n",
+    "    target_var='VCI',\n",
+    "    verbose=False,\n",
+    "    to_dataframe=True\n",
+    ")\n",
+    "monthly_scores2['time'] = monthly_scores.apply(lambda row: pd.to_datetime(f\"{int(row.month)}-{int(row.year)}\"), axis=1)\n",
+    "\n",
+    "# rename columns and to merge dataframes\n",
+    "monthly_scores2 = monthly_scores2.join(\n",
+    "    monthly_scores2[['linear_network', 'ealstm', 'rnn']].add_prefix('orig_')\n",
+    ").drop(columns=['linear_network', 'ealstm', 'rnn'])\n",
+    "\n",
+    "# merge\n",
+    "monthly_scores = pd.merge(monthly_scores, monthly_scores2)\n",
+    "monthly_scores.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(12, 8))\n",
+    "for model in ['linear_network', 'ealstm', 'orig_ealstm', 'previous_month']: # \n",
+    "    (\n",
+    "        monthly_scores\n",
+    "        .where(monthly_scores.metric == 'rmse')\n",
+    "        .groupby('month').mean().reset_index()\n",
+    "        .plot(x='month', y=model, label=model, ax=ax)\n",
+    "    )\n",
+    "plt.legend()\n",
+    "ax.set_title('Seasonality of Model Performance')\n",
+    "ax.set_ylabel('RMSE');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(12, 8))\n",
+    "sns.distplot(drop_nans_and_flatten(y_test.VCI), ax=ax, label='test [2011-2018]')\n",
+    "# sns.distplot(drop_nans_and_flatten(y_train.VCI), ax=ax, label='train [1982-2010]')\n",
+    "sns.distplot(drop_nans_and_flatten(orig_ealstm_pred), ax=ax, label='Previous EALSTM')\n",
+    "sns.distplot(drop_nans_and_flatten(ealstm_pred), ax=ax, label='Delta EALSTM')\n",
+    "\n",
+    "ax.set_xlim([0, 100])\n",
+    "plt.legend()\n",
+    "ax.set_xlabel('VCI Observed/Predicted');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Model Performance Comparisons"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from src.analysis import spatial_rmse, spatial_r2\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ealstm_rmse = spatial_rmse(\n",
+    "    y_test.VCI.transpose(\"time\", \"lat\", \"lon\"), \n",
+    "    ealstm_pred.transpose(\"time\", \"lat\", \"lon\")\n",
+    ")\n",
+    "\n",
+    "# --------\n",
+    "bline_rmse = spatial_rmse(\n",
+    "    y_test.VCI.transpose(\"time\", \"lat\", \"lon\"), \n",
+    "    bline_pred.transpose(\"time\", \"lat\", \"lon\")\n",
+    ")\n",
+    "\n",
+    "# --------\n",
+    "ealstm_base_rmse = spatial_rmse(\n",
+    "    y_test.VCI.transpose(\"time\", \"lat\", \"lon\"), \n",
+    "    orig_ealstm_pred.transpose(\"time\", \"lat\", \"lon\")\n",
+    ")\n",
+    "\n",
+    "\n",
+    "# calculate mean performance scores\n",
+    "ealstm_mean = ealstm_rmse.mean().values\n",
+    "bline_mean = bline_rmse.mean().values\n",
+    "ealstm_base_mean = ealstm_base_rmse.mean().values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rmses = [bline_rmse, ealstm_rmse, ealstm_base_rmse]\n",
+    "means = [bline_mean, ealstm_mean, ealstm_base_mean]\n",
+    "labels = ['Basline', 'EALSTM', 'Original EALSTM']\n",
+    "\n",
+    "colors = sns.color_palette()[:len(means)]\n",
+    "\n",
+    "assert (len(rmses) == len(means)) &  (len(labels) == len(colors))\n",
+    "assert (len(rmses) == len(labels))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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XI0eOAJCXl0dsbCy33HILDzzwAJs2berxOImJiWRlZfH8888D3uYiW7du7dd7GSk08ywiY87db3kzz49f8vjHjb++3vt4y4tDENGpfemJvwDwi79aPsSRiMhw0FzzDF4S+4tf/AKfz8fnP/95rrnmGpYtW0Z2djZz5swBYPv27fzd3/0dYWFhRERE8Nhjj/Vq3Keffpovf/nL/OM//iONjY2sWrWKxYsX99t9jRRKnkVkzKlv6qROrzH03aUGW12jf6hDEJEuhLK0XH/z+zv/mZCens7atWs7tGdmZnL55Zd3aP/jH//Y8nlOTk5LHzt27Ghpf+CBB1o+z8rK4vXXX+9l1KOHyjZEREREREKk5FlEREREJERKnkVEREREQqSaZxEZWzY8yQXEtnzeIja1Y9uy2wYvrlO4eO64oQ5BRESClDyLyJhza/qyjo0zPzn4gYTozvNnDnUIIiISpLINEREREZEQKXkWkTHntsPPcdvh59o2fvgj7zUM3fjTtdz4047LT4nI2OTz+cjOzm55/eAHP2g5VlhYSEREBD/96U/bXJOZmUlRUVGbtvz8fK6++moWL17MvHnzuPLKK9m+fXtLv6mpqS1bdV9yySXk5ORgZjz00EMtfRQVFREREcF99903sDc9jKhsQ0RERKS3Wj8n0R9CeNYiJiamy22yn3/+ec466yxWr17NXXfddcp+vvOd73DppZfyla98BYBt27axcOHClr5vvfVWrr76aj73uc8B3lrQM2bM4NVXX+Xhhx9uGW/+/Pkh395ooJlnERERkVFi9erVPPLIIxw7dozjx4+f8twTJ04wZcqUlq8XLVrUbf8xMTHMnTuXDRs2APDss8+ycuXKvgU9wih5FhERERlBmrfnbn49++yzAOTm5nLy5EmWL1/OypUrW9q7cu+993L77bdz0UUX8f3vf5+8vLyQxl+1ahXPPPMMx44dw+fzMWnSpD7f00iisg0RERGREaSrso1nnnmmZRZ41apV3H777Xzta1/rsp/LL7+cQ4cO8frrr/P73/+eJUuWsGPHDjIyMk45/hVXXMFDDz3E+PHjufHGG/t2MyOQkmcRGXMuT5rdsXHSksEPJERXL5o41CGIyAiwevVq8vPzefrppwHIy8tj//79zJo1q8trUlNTufnmm7n55pu5+uqref/997n++utPOU5kZCRLly7lkUceYefOnbzyyiv9eh/DnZJnERlzVqVmd2zMPHfwAwnRF87OHOoQRGSY27t3L9XV1W3qnL/73e/yzDPPtFkdo7V33nmHs846i9jYWCorKzl48CDTpk0Labyvf/3rXHDBBaSlpfVL/COJkmcRGXNqA40AxIRFfNzob/A++iKHIKJTq23wAxAT6RviSERkOGiueW52xRVXEB0dzXXXXdfmvOuvv55Vq1a1JM+LFi0iLMx73G3lypVMnDiR++67j/DwcAKBAH/913/NmWeeGVIM8+fPH3OrbDQz59xQxxCyZcuWueanO0VEemXDky1rPD+Z1eoJ8eY1nj/xNx+3DZPtuZvXeH72rrOHOBIR2b17N3Pnzh3qMKSfdfZ9NbONzrkOW9JqtQ0RERERkRApeRYRERERCZGSZxERERGRECl5FhEREREJkVbbEJEx59rkTp4Qn7p88AMJ0eeWTun+JBERGRQhzTyb2RVmttfMDpjZNzs5PsfM1ppZvZk90Kr9dDPb0upVYWZfDR77npkdb3Xsyv67LRGRrn0mZT6fSWmXQE9d4b2GoRuWTeWGZVOHOgwRESGE5NnMfMCPgU8B84CbzGxeu9NKgL8Ffti60Tm31zmX7ZzLBpYCNcBLrU55tPm4c25NH+5DRCRkpU21lDbVtm1sqPJew1BJdQMl1Q1DHYaIDBPHjh3j2muvZdasWcycOZOvfOUrNDR0/jMiLy+Pz33uc932eeWVV1JWVtareL73ve/xwx/+sNP2yZMnk52d3fJqPcZXvvIVJk+eTCAQaGl76qmnuO+++zr09cQTT7Bw4UIWLVrEggULePnll7n33nvJzs5m3rx5xMTEtIzxwgsvcOutt7Zs/tJ6PDOjqKioV/fZLJSyjeXAAefcIQAzewa4FtjVfIJzrgAoMLOrTtHPxcBB59yRPsQrItJnX8v1tpJts87zhie9j63XeR4mvvzrjYDWeRYZjp7f93y/9nfD7BtOedw5x2c/+1m+/OUv8/LLL+P3+7nzzjv59re/zb/927+1ObepqYlJkybxwgsvdDvumjUDM4d5//3388ADD3RoDwQCvPTSS0ydOpX333+fCy+8sMs+jh07xve//302bdpEUlISVVVVFBYWcu211wKQk5PD1VdfzZYtW1quefXVVznttNN4+eWXueWWWwgEArz77rtMnjy5z/cUStnGZCC39T0E23pqFbC6Xdt9ZrbNzJ4ws5TOLjKzO81sg5ltKCws7MWwIiIiIqPDO++8Q3R0NLfd5m3i5PP5ePTRR3niiSeoqanhqaee4oYbbuCaa67hsssuIycnhwULFgBQU1PDypUrWbRoETfeeCMrVqygefO5zMxMioqKyMnJYe7cudxxxx3Mnz+fyy67jNpa7y91P/vZzzjzzDNZvHgx119/PTU1Nb2+j3fffZcFCxbw5S9/mdWr26eHbRUUFJCQkEB8fDwA8fHxZGVldTvGTTfdxLPPPgvAH//4R8455xzCw/v+uF8oybN10tajbQnNLBL4NND617PHgJlANnACeKSza51z/+2cW+acW5aRkdGTYUVERERGlZ07d7J06dI2bYmJiUybNo0DBw4AsHbtWn7xi1/wzjvvtDnvJz/5CSkpKWzbto2HHnqIjRs3djrG/v37uffee9m5cyfJycm8+OKLAHz2s59l/fr1bN26lblz5/Lzn/+823gfffTRlnKKiy66qKV99erV3HTTTVx33XW8+uqrNDY2dtnH4sWLGT9+PFlZWdx222288sor3Y4LMGvWLAoLCyktLWX16tWsWrUqpOu6E0ryfAxo/aTKFCCvh+N8CtjknMtvbnDO5Tvn/M65APAzvPIQEREREemCcw6zjvOardsvvfRSUlNTO5zzwQcftCSQCxYsYNGiRZ2OkZWVRXZ2NgBLly4lJycHgB07dnDeeeexcOFCnn76aXbu3NltvPfffz9btmxhy5YtvPvuuwA0NDSwZs0aPvOZz5CYmMiKFSt48803u+zD5/Px+uuv88ILLzB79mzuv/9+vve973U7NngJ/zPPPMO6des477zzQrqmO6Ekz+uBWWaWFZxBXgX8rofj3ES7kg0zm9jqy+uAHT3sU0RERGRMmT9/fkupRbOKigpyc3OZOXMmAHFxcZ1e61xohQNRUVEtn/t8PpqamgC49dZb+a//+i+2b9/Od7/7Xerq6npzC7z++uuUl5ezcOFCMjMz+eCDD7ot3TAzli9fzoMPPsgzzzzTMhvenVWrVvHQQw9x6aWXEhbWP9ubdNuLc64JuA94A9gNPOec22lmd5vZ3QBmNsHMjgFfA/7ezI6ZWWLwWCxwKfC/7br+VzPbbmbbgIuA+/vljkREunFj6mJuTF3ctjHzHO81DN1y1nRuOWv6UIchIsPAxRdfTE1NDb/85S8B8Pv9fP3rX29ZXeJUzj33XJ577jkAdu3axfbt23s0dmVlJRMnTqSxsZGnn366dzeAV7LxP//zP+Tk5JCTk8Phw4d58803u6yhzsvLY9OmTS1fb9myhenTQ/uZOG3aNL7//e9zzz339Dre9kKqmg4uI7emXdvjrT4/iVfO0dm1NUBaJ+1f6FGkIiL95Iqk0zs2Tjpj8AMJ0TWLJw11CCIyTJgZL730Evfccw8PP/wwgUCAK6+8kn/6p3/q9tp77rmHL33pSyxatIglS5awaNEikpKSQh774YcfZsWKFUyfPp2FCxe2WQauK48++ii//vWvW77+zW9+wxtvvMFPf/rTlra4uDjOPffcllrmp556it/+9rctx//85z/zwAMPkJeXR3R0NBkZGTz++OOE6q677gr53FBYqFP4w8GyZctc+z9ViIj0yIYnOdno/cCfEJHwcXttqfcxptXCP8tuG8TAupZX5j3pPik5ZogjEZHdu3czd+7coQ6jV/x+P42NjURHR3Pw4EEuvvhi9u3bR2Rk5FCHNuQ6+76a2Ubn3LL252p7bhEZcx489nug3TrPm4MzI8Nwnef7n/XWLtU6zyLSFzU1NVx00UU0NjbinOOxxx5T4twLSp5FRERExoCEhIQODxtKz/XPY4ciIiIiImOAkmcRERGRHhhJz4tJ93r6/VTyLCIiIhKi6OhoiouLlUCPEs45iouLiY6ODvka1TyLyJjzpfSlHRtnXtSxbZi447wZQx2CiARNmTKFY8eOUVhYONShSD+Jjo5mypROV1zulJJnERlzLkyY2bFx/ILBDyREl8wbP9QhiEhQREQEWVlZQx2GDCGVbYjImHO4voTD9SVtG6vyvdcwdLCwioOFVUMdhoiIoORZRMagf8h7i3/Ie6tt47bnvNcw9K3/3c63/rdn2+iKiMjAUPIsIiIiIhIiJc8iIiIiIiFS8iwiIiIiEiIlzyIiIiIiIdJSdSIy5tyZsaJj46zLBj+QEP3NJ2cNdQgiIhKk5FlExpyz46d3bMw4ffADCdG5s9KHOgQREQlS8iwiY86e2gIA5sSMg5PbYNfLkDjF22UwJXNog+vEzrxyAOZPShriSERERMmziIw5/3LyjxDw82RZPZzYAjGpcHIrFO6BC74BsalDHWIb//DKLgCevevsIY5ERET0wKCIjC3OQX0llB+F/O1w+lVw0bchfgL4G+BPj0Dh3qGOUkREhiklzyIytmx/ztuG2xcB5/8fmHUphPkgMg6SpkJ0Aqx7HA78wUu0RUREWlHyLCJjx4ltcHQtRCdB4mSIH9/2uC8CzrkfJmXDntdgy2+GJk4RERm2lDyLyNjx3r9AeHSwptk6Pyc8CpZ80ZuFfu8H0NQwqCGKiMjwpgcGRWRsOLEN9rwKs6/gKxMWdTw+5+qPPzeD06+Ev/wUNv8Kzrx98OLsxP+5YvguoyciMtYoeRaRseG9f4GoJMg6n+yI2I7HU7Pafp0xB6aeBe//G2TfDBExgxNnJ5ZOH16rf4iIjGUq2xCR0e/EVm/W+ex7ISKWLTV5bKnJa3tOyWHv1cwMPvn3UHkCNjw5uPG2s/FICRuPlAxpDCIi4lHyLCKj3x//xXtI8Ky7AfjP/A/4z/wP2p6z51Xv1VrWeZB1AXzw71BfNUjBdvSvr+/lX1/X8nkiIsOBkmcRGd1ObIW9r8HZ93kJdE998u+hutCrfxYRkTFPybOIjG5//IGXNK+4q3fXT10Osy6HP/8n1Jb1b2wiIjLiKHkWkdErbwvsXdP7Wedmn/w21JXDRz/pv9hERGREUvIsIqPXR495K2z0dta52cTFMO9aWPsTqKvon9hERGRE0lJ1IjI6NdZ5s87zPt1h1vkbEy7seP78607d3ye+Artehh0vwrLb+i/OEHznmnmDOp6IiHRNybOIjE4H34H6ik6T4jkx4zqenzTl1P1NPgPGzYPNvx705Hn+pD6UnIiISL9S2YaIjE47X4KYFG+puXbWVh1hbdWRto2Fe71XV8xgyS1wfAMU7O7nYE/tg/1FfLC/aFDHFBGRzil5FpHRp7HWK9mYew34Ijoc/u/Cdfx34bq2jfvf9F6nsuhGCIvwZp8H0Y/e2c+P3tk/qGOKiEjnlDyLyOhz4C1oqOq+jrmn4tLh9E/B1megqaF/+xYRkRFBybOIjD47X4KYVMg8v//7XvIFqCmCfa/3f98iIjLsKXkWkdGloQb2vu6tsuEbgGeiT7sYEiYNeumGiIgMD0qeRWR0OfAHaKzu/5KNZmE+yL7JG6cib2DGEBGRYUtL1YnI6LLzJYhNh+nnAvD8vufbHi/ZxvK4qd6xkm0tzfHTswGoatVG+2uD4pJTudIFYOtqOO/r/Rh85/7pswsHfAwREQmNkmcRGfk2POl9bKqHPa/BlDNh86+8ttbJcFB6eFyHtqqYTtZSzvmw0+GqgYLECYxb+xOITvGWsRvAtZ9nZsQPWN8iItIzKtsQkdGjYDf4G2Bi9ilP21tXyN66wjZtE0uOMscyIgYAACAASURBVLHkaMhD5Yyb5T04WHKoV6H2xFu78nlrV/6AjyMiIt1T8iwio8eJzRCVAGkzT3naR9VH+Ki67SYps/N2MDtvR8hDHUvLgvAoyP2oV6H2xM/+dIif/Wngk3QREemekmcRGR2a6iF/F0xYDDbwP9r8vnCYuARObPVmu0VEZExQ8iwio0PBbgg0wqRTl2z0q8lLvcQ5P/QZaxERGdmUPIvI6FCwCyJiIXXG4I2ZNhOiEuH4psEbU0REhpSSZxEZ+ZyDwj2QcfopSzacc9QEGqgP+Kn2N/KX6lyKm2p6P66FwaQlULgbast634+IiIwYWqpOREa+iuNQXwEZczo9XO1vYE3FHg7UF9Po/C3tr1fsxYD50ROoyFrG+PDYno896Qw4/B7sfgXO+EIvb+DUHr1xEEtRRETklEJKns3sCuA/AR/wP865H7Q7Pgd4EjgD+LZz7oetjuUAlYAfaHLOLQu2pwLPAplADrDSOVfat9sRkTGpcI/3sZPk+UB9MS+X7aQu0MSS2EmkhceS7IsmyRdDlPnYWHOc9TXH2FF3kjlRGZwXlsXEiMTQx06e5m3KsuOFAUueJyXHDEi/IiLSc90mz2bmA34MXAocA9ab2e+cc7tanVYC/C3wmS66ucg5V9Su7ZvA2865H5jZN4Nff6OnNyAiY0uHHQOBC/M2EB6Xyls1RyBYhdHkArxdeYB11UfJCI/jltQzGB/hbTays/YkxU3VzI+ZwCWJs/hE/HT2FG7jnfpifl5fxE2p2cyMSgstIDOvdOPg21CZDwnj++tWW7yy1dsG/JrFk3p1/W/Whb5+dbObV0zr1VgiIqNdKDXPy4EDzrlDzrkG4Bng2tYnOOcKnHPrgcYejH0t8Ivg57+g68RbRKRL4Q01pFXmczJ5SktbWVMtPy/6C+uqj3Jm7BT+On15S+IMsKHmGBtqjrV8HRsWyf2FBfympI6M8DieL93GicaK0IOYfAa4AOz6bb/cU3u//ugIv/7oSPcniojIgAsleZ4M5Lb6+liwLVQOeNPMNprZna3axzvnTgAEP47rQZ8iIgCMz99NmHMtyXOTC/BC2XbK/HWsSlnMp5LmEGG+kPpKcHBz6hJiwiJYXbKFsqba0IJImAjj5sOOF3t7GyIiMkKEkjxbJ22uB2Oc45w7A/gUcK+Znd+DazGzO81sg5ltKCws7P4CERlTJuTtpNEXQXGC9/v3WxX7yWus4NrkecyOzuhxfwm+KG5OyabJBXi6ZDM1gRA3QFl4PeSug7Kel0iIiMjIEUryfAyY2urrKUBeqAM45/KCHwuAl/DKQADyzWwiQPBjQRfX/7dzbplzbllGRs//Rygio5hzTDixnfykSbiwMHbXFvCXmlxWxE1lTnTv/5iVERHPjamLKfPX8UzJ1jYrdHRpwfXeR80+i4iMaqGstrEemGVmWcBxYBVwcyidm1kcEOacqwx+fhnwD8HDvwO+BPwg+PHlHsYuIqPZhic7by/Z1vJpYk0psTWl7Jo4j9KmWn5XvotJEYlckjCrz8NPj0zhuuT5vFC2nd+X7+HTyfNPfUFKJkw5E7a/COfe3+fxRURkeOo2eXbONZnZfcAbeEvVPeGc22lmdwePP25mE4ANQCIQMLOvAvOAdOAlM2se6zfOudeDXf8AeM7MbgeOAjf0762JyGg3odR76O9Y0kReLNsOwPXJC/GdYqMUgBtSFnVoW3v6Jzu0zYsZzycaK/iw+ghnxk3tfgm7BZ+D178BhXu9DVv6yWO3LO23vkREpG9CWufZObcGWNOu7fFWn5/EK+dorwJY3EWfxcDFIUcqItLOhLJjlMck82rDCfIaK1iZsoiU8O7XRI4Ni+zQ1hAR3em558ZnsaU2jzcr9vPF1DMITgZ0bv518MaDsP0F+OS3u41DS8iJiIw82p5bREYkn7+R9Ip81qZOZF11LmfGTgm5znlLTR5bato+ujG9YD/TC/Z3ODc6LJwL4mdwpKGUffXtl6tvJ2E8TD8Hdv8u5PsIxcYjpWw8oj2kRESGAyXPIjIijSs/gc8F+HV0GFEWzkUJM0O+dmttHltr2ybPmQX7yewkeQY4I3Yyab5Y3qrYj98FTt353E97Ox4W7gs5nu5sOlrKpqNKnkVEhgMlzyIyIk0oPcauqGi2BipZETeV6LCIARvLZ2FckjiLYn8Nm2qOn/rkOVd5H/e8MmDxiIjI0FHyLCIjj3NMLDvGj9IziDIfK+IGvg54dlQ60yOTea/qEHWBpq5PTJoMk5fBbiXPIiKjkZJnERlx4usqyAvU80GksTxuGjEDOOvczMy4NHE2NYFG/lyVc+qT514DeZuhLPfU54mIyIij5FlERpzxZXn8NDmRKMI4axBmnZtNikhkYcwE1lUfpdxf1/WJc6/xPu55bXACExGRQRPSUnUiIsNJbeVx3oyP5ZxezjrfnLqkQ9sHcy8L6dqL4meyozaf9dW5/HXG8s5PSpsJ4+Z5pRtn3d3xeHADmJlHS7od7+C0G/jS2ZkhxSYiIgNPM88iMrK4AC+F1RKFcVZ872adI8xHhPnatPl94fh93c8nJIfHMDsqnS21eTScqvZ57jVw9EOoKuxVjK1FhocRGa4f1yIiw4F+GovIiNJQcZw3Y6O5MCyp081OQrG+Opf11W3rkWee2M3ME7tDun5p3BRqAo28XXmg65PmXgMuAHvXdH1OiD46VMxHh4r73I+IiPSdkmcRGVE+rMoh2jnOSO799te76vLZVZffpm1K8WGmFB8O6fqZkakk+2J4vmRb1yeNXwApmbDn1V7H2Wz78XK2Hy/vcz8iItJ3Sp5FZMSo8TfwF6vj0romwqMThywOM+OM2MmsrznGofou6pbNYM7VcOiPUKfEV0RktNADgyIypJ7f93znBzqZ1d1edYw6M5b7kmgY4Li6kx0zkferDvF8yTa+MfHClocA2zAf+BvgjW/D5KWDHqOIiPQ/zTyLyIixqzqXKY2NpCROHepQiPdFcUnCLH5XvpO6QGPnJ6VMh6hEOHmK8g4RERlRlDyLyIhQ4a9jn7+aq6prKEqaONThAHBD6kIq/PW8WbG/8xMsDCYshILd3gy0iIiMeCrbEJERYUftSZzBOS6GA76+7Sj4pbRlHdreW3Blj/s5M3YqmZEpPFeylU8nz+v8pAmL4MifoXAfTFjQ4zEA7jhvRq+uExGR/qeZZxEZ9pxzbK/JY1FdPREJw2PWGbwHB29IWcTW2hPsretiPee00yA8Cgp3DW5wIiIyIJQ8i8iwl99URb6/hmuqqilImtTn/j6sOsKHVUfatM0+vp3Zx7f3uK9rk+cRab6ul60L83kJdOG+3oQKwJ/2F/Kn/X3fbEVERPpOybOIDHvbak8Q7uDiugZK4jP63N/++kL217dNRieW5jKxNLeLK7qWFB7D5Ymzea18N/Vd7TiYPgdqiqC6qDfhsudkJXtOVvbqWhER6V9KnkVkWAu4ADtqT/KJ+gYa48fjwobfj60rk+ZQFWhgXfXRzk/ImO19LNo7eEGJiMiAGH7/FxIRaeVQQylVgQauKy8nP7nvJRsDYUXcNOLDInmrq1U34sZBTAoUKnkWERnplDyLyLC2veYEcYRxfk1tv9Q7D4SIMB8XJMzg3cqDNLlAxxPMIP10KN4PAf/gBygiIv1GybOIDFsNgSb21BdwfoPDHxFDRUxyv/Qbbj7CzdemzR/mwx/m6+KK7l2SOIsyfx2bao51fkLG6dBYC+U9r6uO8BkRPut1bCIi0n+0zrOIDFt764todAE+W1rqzTpb/ySQn09d0qHtg3mX96nPT8RnEm3hvFVxgOVx0zqekD4bMK90IyWzR33f+omsPsUmIiL9RzPPIjJs7a8rIs7CWV5TOWzrnZvFhkVwTnwmb1ccIOBcxxMi4yBpih4aFBEZ4ZQ8i8iwFHCOg/XFZAciCAMKE/tvc5T3Kw/xfuWhNm1zc7cwN3dLn/q9OPE0Cpqq2FF7svMTMk6H0hxoqutRv+/syeedPfl9ik1ERPqHkmcRGZaON5ZT6xo5v6aWqugEaqLj+63vww0lHG4oadM2rjyPceV5fer3goQZhBPW9aob6aeDC0DxgR71e7CwmoOF1X2KTURE+oeSZxEZlg7UF2MYl5ecpCBp+GzJfSqJvmhWxE/l7coDuM5KN1KywBepJetEREYwPTAoIsPSgboissJiSWtqYP8wXaKusy25k30x/LnhCD8uWMv4iI9ny29IXQS+cEidqeRZRGQE08yziAw7lQ2VnGiq5MzgbtcF/VjvPNBOj/K2D99TV9D5CRmnQ3UB1JYOYlQiItJflDyLyLBzoMyrCf5kVQXlMcnUR8b0a/8xYRHEhEW0aWsIj6IhPKrPfcf7opgWmXzq5Bl6NPscG+kjNrL3a1CLiEj/UdmGiAw7B0oPkBAWxZmlh8gZd3q/978yZXGHtrVzLu63/udEj+PNin2UNNWQGh7b9mD8BIhKgsI9kDo7pP4+v2J6v8UmIiJ9o5lnERlW/AE/B8sPsjAsloiAf8Q8LNjanFOVbphBxmwo2uetvCEiIiOKkmcRGVZyK3Np8Ddwdn0TDqMwaUK/j/F2xQHermi7XNyCIxtYcGRDv/SfHB7D+PB4DtQXd35C+unQWENsXWhrN7+x8yRv7Oxi7WgRERlUKtsQkWFlf+l+wiyMT5YVUxqXRmM/1CG3d6yxrENbWmUXNcq9NCMqjb9UH6XR+YmwdvXKqTMAiK89Rk1M9zPrR0tq+jU2ERHpPc08i8iwcqDsAJkJU5lSWTAiSzaazYhKxY/jSEPHRJ2YFIhKJL7m+OAHJiIifaLkWUSGjbK6MgprC8n2JRHmAiM6eZ4WmYwP43B9SceDZpA8nfjaY4MfmIiI9ImSZxEZNpqXqDu/poaAGUWJ44c4ot6LMB9TI5M51FXdc8p0YhpKCG9SSYaIyEiimmcRGTb2l+0nJSqF7IKDFMePw++L6P6iXkgMi+7QVhsZ1+/jzIhK5Z3Kg1T7GzoeTMkEIK72OOUJs07ZT1LMqd8H5xwVtU2crKjjZEUdhZX1pMdHMjMjnsnJ/btGtojIWKfkWUSGhaZAE4fLD7M0bQGpe7exa3LHtZj7y3UpCzq0/WX2Bf0+TlZkKnCQww2dlG4kTcVhxNce6zZ5XrlsKnWNfo6X1fLathPkFFdztLiGIyXVnCz3Eua6xs6XvYsKDyMlNpL0hCgy4iPJnppCRkL/P4QpIjJWKHkWkWEhryqPpkAT2YEIzLkRXe/cbGJEItEWzqHO6p7Do6iJHk9CFw8N1vjD2FIex7v5RzlYUEV1g7/N8YyEKKalxrJgchKXzB3PhKRo75UYTXp8FAWV9RwqrOJgYRV/2l/EibJaduWV8+HBYladOY3TJyQMxC2LiIx6Sp5FZFjIqcgB4OyKEvy+CEoSxg3YWG+Ue1tjX5708e6Fiw9/BMDWrLP6bZwwM7KiUjlUX8xzxVsxszbHT4uKY17lUd5r3ANmVDdGkVMxgcPlEzleNQ6/M+Iiq4iN8pEeH8U5p6Vzy1nTmZ4WS1zUqX98Z6bHsTwrFYDfrDsKQHltI79cm8Mv1+Zw1aKJnD0jrUNMIiJyakqeRWRYOFJxhPGx45l5fD9F6acRCPN1f1EvnWyq7NCWXN3J7HA/yIpMZXddASX+WtLabdWdF5PGkvKDpNRX8rvic9helAUYiZHVfGpcCWcmV+GbexU//+AwAAsmJzFvUmKvY0mKieCu82fy3IZcXt12goLKeq5ZNAlfmBJoEZFQKXkWkSHnD/jJrczlzLSFJJetZ/uizwx1SP1mRpQ3+3uovrhD8nwiJg2AotwkttfMYEHaYeanHSY1upILI7xZ8YP9PDMcGR7GzSum8Ydd+by3r5DiqnpuXj6dmMiB+2VFRGQ00VJ1IjLkjlcdpynQxKKA9yOpcPzp3VwxcqT4Ykj2RXe63vORwHjKXSyZDflcOGUz50/ZRlpMJQNdSRFmxuXzJ3D9GVPIKarhsfcOUlxVP7CDioiMEkqeRWTIHak4AsAnykto8kVSkpo1xBH1HzMjKzKVww2lBNzHK2LklCfxwsEL2OpmckH0NualHR302JZOT+Gvzs2ipqGJX350BH/ADXoMIiIjjZJnERlyORU5jI8dz4zCAxRlnIbzDWxFWZovjjRf23Wdq2ISqYrpfT3xqcyISqPeNZHX6NVabzo5gcc3LSUyrJGG1HAmNhUTGWjs8vr0+EjS4yMHJLas9Dg+u2QyhZX1bDgyMHXfIiKjiWqeRWRIfVzvvMCrd168fMDHvDp5boe2jTPPHbDxsqJSADhcX0JO/gJe3n86M5JLuXDyWkrqE7AymFBbwtG4zndUvG7JlAGLDWDuxEQy0+J4a1c+i6ckEx2h+mcRka6ENPNsZleY2V4zO2Bm3+zk+BwzW2tm9Wb2QKv2qWb2rpntNrOdZvaVVse+Z2bHzWxL8HVl/9ySiIwkLfXO/mC987jRU+/cLDYskgnhCeytLeO1g6cxL72QO7I3ER3eyIlo76HBSXVdbOM9CMyMKxdOoLrBz3v7CocsDhGRkaDb5NnMfMCPgU8B84CbzGxeu9NKgL8FftiuvQn4unNuLnAWcG+7ax91zmUHX2t6exMiMnI11zufXVHq1TunZQ74mK+W7ebVst1t2pYe/IClBz8YsDFnRKWS11SKL6yez56+m/Awr7643hdJcWQCk2q7Tp5f2nyMlzYfG7DYAKakxJI9NZk/HyiitKaT7cRFRAQIbeZ5OXDAOXfIOdcAPANc2/oE51yBc2490Niu/YRzblPw80pgNzC5XyIXkVHhSMURxsWO47SCAxRlzMKFDXw1WbG/mmJ/dZu2+NoK4msrBmxMX10mWIAzMteRFNU2Oc2LTmNiXTG4zh/YK6pqoKhq4BPay+Z5ZSNv7jw54GOJiIxUoSTPk4HcVl8foxcJsJllAkuAda2a7zOzbWb2hJml9LRPERnZGv2N5FbmMiNuEknlx0fVEnWtNfrD+OjQWeCMpOR9HY7nxaQR568nqbG6k6sHT3JsJOeels7WY+VsyS0b0lhERIarUKZ4OltxtEfrGZlZPPAi8FXnXPPUzmPAw8G+HgYeAf6qk2vvBO4EmDZtWk+GFZFhbmfxThoDjSwO1jsXDJPk+XBxz5PYrLS4Lo+9nzuNkpoUpoUlc7yxY1Ka16ruuTwyvsdj96cLZmew/kgp339tF8/ddba27xYRaSeUmedjwNRWX08B8kIdwMwi8BLnp51z/9vc7pzLd875nXMB4Gd45SEdOOf+2zm3zDm3LCMjI9RhRWQEWH9yPRBc3zk8itLU6UMcUf8rrYvi7ZwsFmbkMysmnuONFW3WewYoikqiwcJPWfc8WKIifFwydxzrc0p5Q+UbIiIdhJI8rwdmmVmWmUUCq4DfhdK5eVMWPwd2O+f+vd2xia2+vA7YEVrIIjJarD+5nnEx45jZvL7zINQ7A0wIT2BCeEKbtrK4VMriUvt9rFf3zwbgmln7mBqZRKPzc7Kpqs05zsI4GZ3a5YobE5OimZgU3e+xdWXZ9FRmjYvnB7/fQ0NToPsLRETGkG7/T+WcazKz+4A3AB/whHNup5ndHTz+uJlNADYAiUDAzL6KtzLHIuALwHYz2xLs8lvBlTX+1cyy8co2coC7+vfWRGQ4aww0sqVwC4uTZ5NUvoEjWWcP2tiXJ3UsD9madVa/j7OvJJVtheO5YsYBUqLr8fmTAchtKGNSRNsNWfJiUjmzZB9hzk/A2q6zfPWiSf0e26n4woxvXzWXW59cz68+OsLt546eHR9FRPoqpGmeYLK7pl3b460+P4lXztHeB3ReM41z7guhhykio83Oop3UNtWyODA613duChgv7zudtJgazp/qbb2d6IsmyRdNbkMZK+LaPsNREJWCjwBp9RUURg/989MXnj6O82al85N3D/CFs6YTGa4NaUVEQNtzi8gQ2ZC/AYCzy0tpDI+iNHXwHgh+qXQHL5W2rRRbvu89lu97r9/G+CB3GgU1cVw7ay8Rvo9LH6ZGJJHbUI5rtyxdQZQ3Kz2uvuMDhc9tyOW5Dbkd2gfaX52TRXF1A+/sKRj0sUVEhislzyIyJDbkb2Bm0kxmFewftPWdm1UE6qgI1LVpi2moJqahf5aKawoY7x6dzpy0Iuamt61jnhqZTGWgnnJ/2/FLI+NpNB8Z9eUd+iuvbaS8trFD+0A7b1Y64xOjeH4IEncRkeFKybOIDDp/wM+2gm0sSZ1DYsWJUbe+886iDGoaIzl3ytEOx6ZGBuue2y1Z5yyM4sjETmeeh0q4L4zrz5jCu3sLyK+o6/4CEZExQMmziAy6A2UHqGysZImLBKBglNU7r8+bRHJUHbNSSzocGxceT5T5ONrQMUkuiE4mo76sy50Gh8Lnlk4h4OB/Nx0f6lBERIYFJc8iMui2FHiL7ywpL6QxPIqyUbS+c1ldFPtK0lg2MY+wTh6XDjNjcrDuub2CqGTi/PXE+YfPLO+MjHjOzEzh+Q25Heq0RUTGIiXPIjLoNhduJj0mnSnHtlCcfhouzNf9Rf1oSkQyUyKS27QVJ4yjOGFcn/vecGISDmPZxBNdnjM1MpmCpioaXFOb9sLgQ4MZ7Uo3pqXGMi01ts+x9dYNy6ZyqKiaTUdLhywGEZHhQsmziAy6LQVbWJI6HyvYRdG4WYM+/sWJp3Fx4mlt2nZMX8aO6cv61G/AwfoTEzktpYS0mNouz5sWrHsudJVt2ltW3KhrmzxfPn8Cl8+f0KfY+uKqhROJjfTx3PpjQxaDiMhwoeRZRAZVfnU+x6uOkx3c4a8wY/CT54FwuLiaD4/GUFIXS2bCYQ4XV3f5aqwMx4DCQNvkud4XSUV47LB6aBAgLiqcqxZO5NVteVTXN3V/gYjIKKbkWUQG1ebCzQAsqaqEsAhK0gZ/97rnSrfyXOnWNm1n73mbs/e83ad+dxVPIzKskRlJeac8L8J8pFgcBe1mngEKo5I6LFf39LojPL3uSJ9i66uVZ06lusHPmu1dl6OIiIwFSp5FZFBtKdhCtC+aOSd3w+SlBMIjBz2G2kAjtYG26yZHNtUT2VTf6z7r/eEcKp/ErJRjhIcFuj1/nCVQ5KoIuLbnFkQlk9pQgQU+nuGtafBT0+DvdWz9Ydn0FLLS43h+g0o3RGRsU/IsIoNqc8FmFqbNIyJvC0w/e6jD6Tf7S6fgdz7mpoY2Q5xhCfgJUOJq2rQXRCXjwxFTXzgQYfaamXHDsin8JaeEw0X9s5mMiMhIpORZRAZNTWMNe0v2kh2VAYEmmPaJoQ6p3+wumUZadDkZMR2XoOvMuLBgzXe70o3mFTdi64ffltjXnzGFMIMXNmrHQREZu5Q8i8ig2Va0Db/zs6S+HjCYunyoQ+oXRbWJFNamMDf1KNbJ2s6dibUo4oiiIFDRpr15m+7YupMDEGnfjE+M5oLZGbyw8Rj+gNZ8FpGxScmziAyazQWbMYzFBYdgwgKISe7+ogGQFZlKVmRqm7aCpEkUJE3qVX97SqYRZn5mp/RsRnZcWEKHmWdnYRRFJRJbl9/SNjMjjpkZcb2Krb+tXDaV/Ip63t8/vMpKREQGS/hQByAiY8eWgi2cljyTxB0fwZIvDFkc5yfM6NC2e2p2r/ryB8LYVzqFrMSTRIc3dn9BK2kWz2GKqHH1xFpUS3thVDJzq062bNP9yTnjexVbX/xm3dFO25sCAWIjfTzyxl5OlLXdCfHmFdMGIzQRkSGlmWcRGRT+gJ+thVtZEjcVGmtGzcOChysmUOePCvlBwdbSLR6AIlfVpr0gKpkIfy0RTVWdXTakwsPCyJ6azO4TldQO8QogIiJDQcmziAyKA2UHqG6sZknzCmxD+LDg0yWbebpkc5u2c3e9wbm73uhxX3tKphEfUcOUhJ6XMaRaHIZRHGi7ekXLQ4PBuuenPjzMUx8e7nH/A2XxlGT8zrHnZEX3J4uIjDJKnkVkUGwq2ATAkpLjkDoDEga/FKFZk/PT5NrOmvoCfnyBns2k1jVFkFs5jtNTcgkL8UHBNmNaGCkW2+nMM0BsnbfiRqPf0egfPg/oTU6JISkmgh3HQ1tZRERkNFHNs4gMis0FmxkXk8Gk/R/ChIWw4UnvQMm2oQ2sD45WjsdhZCX1fmWMNIsnJ1CEcw4LLtVR74ukPiKJ2Pr8bq4eGmFmzJ+UyF8Ol1Df6CcqwjfUIYmIDBrNPIvIoNhSsIXspJlYY4038zwK5FSMJza8joyYsl73kW7xNOKngrYP39VEjWuz4sZwM39SEk0Bx578jluMi4iMZkqeRWTAnaw+yYnqE5zhgitKpM4c2oD6gd8ZRyvGMz0xP+S1nTuTZt4SdMWBtqUbNdHjiakvIszf+y3DB9L0tFgSosLZqdINERljVLYhIgNuS8EWALLLCyEqEWLThjSeWVEZHdpOpEztUR8nq1NpCEQwPbFvm5kkWSzhhFHkqpjBx3HVRE/AcCRVHWTOhI7xDrUwM+ZNSmTT0VIamgJEhmsuRkTGBiXPIjLgNhdsJiY8mtnHt3slG32Zqu0Hn4if3qFt3+SFPeojp2ICYeZnanzfNgsJMyPV4ih27WeexwGQUrmX82bN69MYA2XB5CTWHS5hX34lCyYnDXU4IiKDQlMFIjLgthRuYWHybCIqjkPayC/ZADhSMZ7J8UVE+Pq+1nGaxVPiqvG7QEtbXWQqfgsnuWJfn/sfKJlpccRG+tiRp9INERk7NPMsIv3u+X3Pt3ze4G9gd/FurorLAuBNXxPlQ7zCxi+KNwDwpbRlLW0X7FgDwHsLruz2+rL6OMrqE1iY3j9rL6dbPLtxlLka0oIbp2Bh1EaPI7lyHz/70yEA7jhveD1o6Qsz5k1MZPvxchr9ge4vEBEZBTTzLCID6njVcRyO7NoaGsOjKI9NGeqQ+uxIhbdG9fSE/lkNIy3MS5g7lG5EjSOp6kC/jDFQFkxOor4prasSkgAAIABJREFUwIGC4bcboojIQFDyLCIDKrcyF4CzS09SkjYDbOT/2DlSMYHU6AoSo2r6pb94oogivMNmKbVRGcQ0lJDohu9OfjMy4oiOCGOnSjdEZIwY+f8XE5FhLbcyl3Ex6UwtzaU4Y+TXOzf4w8mrSuu3Wef/z96dBsd53fee/56n931DoxsbARAESZAUSXHRYtmWLGtPfOXcxInjqhtP6lY5GTuVm6m6tyY1b2Ze3JqbSs3U1J2aVFxxnO2mHNly4lUSaVmyKYuSJVLcSZDEvu9Loxvd6PWZFw2CBAmS2J9u4P+pYhE4/Tz9/CBR1B8H/3MOgFKKkHIzoS8+pjs1vytIQ6Fv3Z613syaxr4qL9eGZsjkpHVDCLH1SfEshNgwuq7TH++n2exF6ToTFeVfPPfFwxTQ1rxF3d0qlJuYniR7x7Hht4rn+kLvuj5rve2v9jGXLfBh54TRUYQQYsPJgkEhxNrdOmr7lvkFgWPZBOl8mkdiowBMJMfBbNvsdPfYZ4/cM9YfalzWvd0zUWymDFHX1LpmCmlu9AJM6rNElBeAjMVL1uTkqGOE7kjpbgW3q9KNzazx1uUhnt5dentSCyHEepKZZyHEhunLFI+tfjwRZ8bhJ1sChTPAcVcdx12LD0XpqGqho6rlgfcVdOiNR9jhGUFT+rpmWjhp8M6+Z6WIuZvYYxrkiZ3GHizzIBaTxt6oh59dGyEnu24IIbY4KZ6FEBumLzuNS7NwIDbKuKfS6DgLsnp+UXsEgCmfw5TPPfC+0WSAVM5Gg3f9+p1vcSgrLqz3LBqMuZvwxdtLvp94f7WPydkMH3dNGh1FCCE2lBTPQogN05eJ0WhyYc+lmSih4vk7k+f5zuT5RWOfbv0Zn2792QPv65mJoChQ5xndkFwh5WaicG/x7MhM8IMPjN0b+2F2Rzw4LCbeurK+veBCCFFqpHgWQmyIRD7NVD7FvvnJ3AlP+ffCds9EqXJNYjdnN+T9KzQ3CdLM6bffP+YpLrKsL+EdNwCsZo3P7Q1z4uowhcL6trQIIUQpkeJZCLEh+jLFfX+PppJkTFbiDr/BidYmnrEzMeejfgNaNm65dbrgnX3PMXexeC7l7epueflAFWPxNJ/0ru9iSiGEKCVSPAshNkRfdhoTGsdjE8VZZ6WMjrQmPTNRgHXfou5OwflFg3f2PSftVSSx01Di29UBPLMnjNWkcVJaN4QQW5gUz0KIDdGXiVFjdlORnGLCc+/WcOWmZyaC1zpLwLZxx1BblRkvDiYLdxyWohQ9Wl3Jt20AeOwWntoV4mfXRtB1ad0QQmxNUjwLIdZdVs8zlJ1hDxYUpdfvfMhRzSFH9aKx7spmuiubl7w+WzDRnwhT7x3e8An0kOZavF0dEPfsoln1b+yD18kL+6P0Tia5Phw3OooQQmwIKZ6FEOtuKBungM6huTQ6ikl3aRXPh53VHHYuLp57KpvpuU/xPJQIktdN1G/QLht3CioXKbLE9OTCmDm6D29uEut8H3kpe64lglJw8qq0bgghtiYpnoUQ6+724SgxYk4/ObPV4ESLJQsZkoXMojFrdg5rdm7J6/sTYTRVoMq18cdP3zospadwe7/kUVs9AL5Ex4Y/f63CHhvH6gOcvLpxCyuFEMJIcjy3EGLd9WWmCZmcNM100FvRZHSce7w+Vdwz+auhYwtjT954F4BTB1655/qBRJiocxKLKX/Pa+stsFA8T3DQVAvAP7Q7eAnwJdr5zkelv3Dwxf1R/usbrfRNJqkLOo2OI4QQ60pmnoUQ60rX9eLhKJoDSz5bcv3OKzWXszCW8lHrGduU5xUXDdrpKYwvjI2qClLY8SU6NyXDWr2wr7gzibRuCCG2IimehRDrqiszSUrP8ki2eJz0eJnvtDGQqAAUNe7NKZ4BgpqLnsLtFhFdaXRrdfgS7ZuWYS12hJzsjXr4mbRuCCG2ICmehRDr6pPZAQAen02QNtuZtXsMTrQ2fYkwFi1LpXN6054ZUm6m9CQzemphrFurw1smM89QbN040zPJeCJtdBQhhFhXUjwLIdbVueQALs3KIzNjjHsqy/5wlIF4mGr3BCa1efsWB+/oe76lR6vDmR7Dki39HTegWDzrOvz8msw+CyG2FimehRDr6nxygAazB+/cTMn2Ox9z1nLMWbtorCO6l47o3kVj8YyDWMZN7Sa2bMC9xfPjjUFsVfsByqbvuaXKQ23AIX3PQogtR4pnIcS6Gc7GGcjO0FIwATDpqTQ40dL2O6Lsd0QXjfVX7KS/YufisXix+N/s4tmqzESUd6F4PljrJ9hwEABfvDz6npVSvLg/yun2CRLpnNFxhBBi3SyreFZKvaSUuqGUaldK/fkSr+9VSn2olEorpf7zcu5VSgWVUm8rpdrmfw+s/csRQhjpXLLY73xkLo0OTLkrjA10H7H8HLH84j2dHekEjvTik/36ExU4zHME7Zt/Wl69FloonqeTGQb0EFmTo2xmnqHYupHJF/jljY0/XEYIITbLQ4tnpZQJ+CvgZWAf8PtKqX13XTYJ/Cnwf63g3j8H3tF1vRl4Z/5zIUQZOzc7gFOz8Gh8ihmHn5zJYnSkJf1w+go/nL6yaOyxtvd4rO29hc91vXg4Sq17zJC27XotxKQ+SzIX4/VP+nn93CAz7p1ls+MGwNH6ACGXVQ5MEUJsKcuZeX4MaNd1vVPX9QzwGvDqnRfouj6q6/oZILuCe18F/nH+438EvrjKr0EIUSLOJQc47KgmnBhjskT7nZdrcs5DKmen1j3+8Is3wA4tBMDQXNvCWMzdVFYzzyZN8fy+CL+4Pko6t/EHzAghxGZYTvFcA/Td8Xn//NhyPOjeiK7rQwDzv5dmc6QQYlli+Tna0+McsQSw5dJMuMu7eO5PzPc7b9LhKHer14IAjMzdPpI75m7CmR7Fkp0xJNNqvLA/QiKd44OOjT/aXAghNsNyiuelfmC53D2b1nJv8Q2U+ppS6qxS6uzYmDH/ExNCPNyF5CA6cCRb/E+83GeeBxIV+KwJPNbUwy/eAE5lI6w8DM/dbtOIuXcB5bPjBsCnmipwWU1yYIoQYsswL+OafqDujs9rgcFlvv+D7h1RSlXpuj6klKoCllxRouv63wB/A3Ds2LHN22hVCLEi55IDmJXGI4kpcpqZGWf5rgEu6IqBRAXNgX5Dc9RrIdrm2rnVOR5zNwHgS7QzHjhsXLD7+M5HvUuO7wy7+cnFQfZXe9HuaiD/yuM7NiOaEEKsm+XMPJ8BmpVSjUopK/Bl4MfLfP8H3ftj4KvzH38V+NHyYwshSs252QH22yPYp/uYcofQVenuhPmEq54nXPWLxm5WH+Bm9QEARpN+sgULdZu8Rd3d6rUQsdwoj+208eldFcw6qoo7bsQ7Hn5zCdlX7SWRztE3mTQ6ihBCrNlDZ551Xc8ppf4EOAmYgL/Tdf2qUuqP51//plIqCpwFvEBBKfVnwD5d12eWunf+rf8C+J5S6j8CvcCX1vuLE0JsjrlClitzw/yH4KMwc5bJaIvRkR5oj/3elpKh4O0Z0GK/s06NQYsFb2mYXzTo8Y+w01VcLlLccaO8iuc9EQ8mTXF1cIb6kMvoOEIIsSbLadtA1/U3gTfvGvvmHR8PU2zJWNa98+MTwOdXElYIUZoup4bJ6QWO6DYo5Et+seB4bhaACvPtQs6dKh57nXD46I+HCTti2M13byC0uW7tuNERu46nsJ+wx0bM3UR0/ENDc62U3WKiKezi6mCMlw9EUWV+ZLsQYnsr3Z+rCiHKxvlkcSnDo6ni4rpSXyz4RqyVN2Kti8aOdpzmaMdpMnmN4WSAGoNbNgBcyobfEuXS2FV+eKF4AE3MvQtnegxLNmZwupXZX+1jKplleGbu4RcLIUQJk+JZCLFm55L97LKF8MUGwOYlZS3fH813Tfsp6KZNP5L7fqL2XeTMtxfi3Vo06C+zvueWKi8KuDpYPtvsCSHEUqR4FkKsSb6Q50JyiKPOWpjuAf8ODDmSb520TQXRVJ4q16TRUYBi8VwwTVJQxVaTac+t7erK56RBALfNTH3IxTUpnoUQZU6KZyHEmtyYusFsIcOj1hDMjkGg/uE3lbD2qSBR5xQWU2mciBe1F4vlnLl43lTSXkXW5Cy7RYMA+6u9DM/MMZFIGx1FCCFWTYpnIcSanB89D8DRbKE44C/f4rmgKwbjHsNOFVzK7eJ5vnVDKWLuXWVZPO+r9gLSuiGEKG9SPAsh1uSTkU+otniJxkcBBb7SP/TiM+5GPuNuXDTWWnuItzxPo6NKpt8ZwGHy4NIiBAK3z5GKeZrwxcurbQMg4LRS7bdzdbC8FjsKIcSdpHgWQqxaQS9wdvgsx111MN0L7ghY7EbHeqidthA7baFFY6P+Gk7OHcFmylHpnDYo2dJ2uHaT0LsWPo+5m3BkJrBlpgxMtTr7q330TaWYSRm7DaAQQqyWFM9CiFXrmO5gKj3FcWfN7cWCZWA4G2c4G1805pudQJtM0BSYQlO6QcmW5qSBWG6UZK44Yxtzzy8aLMPZ531VxdaNa0PSuiGEKE9SPAshVu3M8BkAjik3ZGbLZrHgyZkbnJy5sWjsQMcZ/rTwOrsCpbHLxp3a+vwADM8Vi+Xp+e3qyrHvudJjo8Jtk103hBBlS4pnIcSqnR05S427hprZieJAGS8WTGWLB642l2DxbM7VATA01wZAyh4hY3aX3XZ1AEop9ld76RxPkMzkjI4jhBArJsWzEGJVbvU7H4scK7ZsaBbwVBkda9VSOQsmVSDimjU6yj003YGWCy/MPBd33Ggqy5lnKG5ZV9Dh+nD84RcLIUSJkeJZCLEqC/3O0ePFxYK+WtBMRsdaFV2HVM6Mw5wr2fNdLLkdCzPPUOx79pXZKYO31Pgd+BwW2bJOCFGWpHgWQqzKQr9zxSGI9ZdNv/NSRmZd5HUNh7l0d4Aw53YQz40zmyvusBFzN2HPTmFLTxicbOWUUuyr8tI2EpfWDSFE2ZHiWQixKgv9zskpKOTKqt/5Wc8unp0/5hqKR3L/Zfb3uFJ/1MBUS2vqfZ0/DF3jC875Q2j6/oGm3tdxpgYB2NfxbZp6Xzcw4ersr/aSK+iculE6e2oLIcRySPEshFixW/3ORyNHof9scbBMtqkDqLP6qbP6Fz5vnwrSa68lUxEwMNX97XGn+LTLjQJ6CuMApGxhABzp8iw+60MunFYTJ64OGx1FCCFWRIpnIcSKLep3HjgHVjc4gkbHWra+zDR9meJBKPmComMqwCvuc4RmRgxOtrQbCQc9s14iykd3odimkTW7yZnsZVs8mzRFS5WXd1tHyeQKRscRQohlk+JZCLFit/qdi8XzJ8WWjVJdabeEd+PtvDt/wEhf3Es6b+YPsz/hQO8nBidb2msDYV4bCNOgheiZL55RiqQtjCM9+uCbS9j+Ki/xdI4POsaNjiKEEMsmxbMQYsXOjpyl2lVNjdkN4zfLqmXjbu2TQRQ6DkvpL1xr0CqY1pNMF5IApGyVONNjxe1CylBTpRuX1cTJq6U54y+EEEsxGx1ACFH6Xr95e0GaruucHjhNc6CZUx//d55G5z1zgZHJSwYmXL22qSDV7njJHcm9lHotBEB3YYLDmpOULYw5P4cllzA42epYTBrP7Knk7Wsj/NcvHsCklc9PL4QQ25fMPAshVmQsNUYyl6TeW09wohOASXeFwalWJ5PX6In52BUsvVMFl7JDC6JQW2bRIMAL+yOMJ9Kc750yOooQQiyLFM9CiBXpjnUD0OBtIDjRRdwTIWu2GRtqlbqm/eR1rSSP5F6KTVmoUr6FvuekvfyL58/trcRiUpyUXTeEEGVC2jaEECvSM9ODz+bDb/MRHO9iNNpidKQVe9G7B4CzPUFMqkCjf5oL9scNTnV/X6273RPcoIW4kh9A13VyJhdZkwPnXPkuGvTaLXyqqYKTV0f4315pQZXRwlMhxPYkM89CiGXTdZ2emR4avA04klM45mJMhhqNjrViUYuHqMVD+1SQel8Mq6lAzBUi5goZHW1JDc40Dc508WOtghnmmNKToBQpW2VZzzwDvLg/Su9kkuvDcaOjCCHEQ0nxLIRYtsX9zl0ATJRh8dyZnuDabIzBuIdd8y0bldMDVE4PGJxsaZdmnFyacQK3Fw3e2ffsKOMdNwCe3xdBKaR1QwhRFqRtQwixbF2xYsHc4GsgOPA2ec1MLFAHifIqen6V6CKRsaKjaJ5fLNjSfxGAUX+NkdEWnMrdWPj4h4NPAfBF53lyeh4FvJe7yQxzxCwFXiikccyNkHJEDUq7NmGPjaM7Apy8OsKfPbfb6DhCCPFAMvMshFi2rlgXAVsAv81PcKKL6UAdBZPF6FirksqZsZly1HlmjI6yImZlwq+cTOjF7ekmbD4A/Il2I2Ot2Yv7o7QOzdA3mTQ6ihBCPJAUz0KIZSnoBXpmemj0NUKhQHCypyz7nW9J5Szs9E9h0sqv3SGk3Izrs+i6ztit4jl+0+BUa/Pi/uKsubRuCCFKnRTPQohlGUwMks6nafQ14p0ZxJxLl23xnCtoZPPaQstGualQbjLkmGGOOZONmNlJYOa60bHWZEfIyd6oR4pnIUTJk+JZCLEsi/qd5xcLlmvxnMwWW012lcn+zncLKw8A44Xi7hSj9gCBmVYjI62LF/dHOdszxVg8bXQUIYS4L1kwKIRYlq5YFxFnBJfFRWi8i4zFScITMTrWqjinX8Ic9xBtuj1b+0nTUwYmerCnay8u+tynHFgwMaYnaKKSEZufXRPXMOWS5M1Og1Kuznc+6l34uKDr6Dr8n2+0crwxeN97vvL4js2IJoQQS5KZZyHEQ2XzWfrifTT4GgAITnQVZ53L8ECLfEHRPdZMi1ctip9w+Eg4fMYFe4CAPUHAnlj4XClFhXIzrs/PPNsCKHQCZd73HPXaCTgtXB2KGR1FCCHuS4pnIcRD9Sf6yet5Gn2NmHJpvLGBsm3Z6I75yDpu4g8uns2tmuylarL3PncZqzsWoTu2eJY/rDxM60myep4Rux+g7PuelVLsr/bRMTbLXDZvdBwhhFiSFM9CiIfqinWhUNR76glM9qDpBSYryrN4vj5RgS30K/pNlxeN7x68wu7BKwalerALY7u4MLZr0ViF5kYHxvUEcbOTtMVHIF7exTPAviov+YLOjRE5bVAIUZqkeBZCPFRXrIsadw02s63sFwu2TlRgN+fQVPltUXeniluLBvU4KMWUd2/ZzzxDcdcNl83MtcHy2n9bCLF9SPEshHigeCbOYGKwuL8zEBzvZNYVIm33Gpxs5aZSdkZm3TjNWaOjrJlNmfHiYKxQ7IWe8u7FH29DFcr7a9OUYl+VhxsjcbL5gtFxhBDiHlI8CyEe6JORT9DRFxYLhsY7mahoMjbUKl2fDAHgtJR3gXlLWCsuGtR1nSnPXkyFDN7ZbqNjrdm+Kh+ZXIGOscTDLxZCiE0mxbMQ4oE+GvoIszJT56nDMTuJMzVVvsXzRAVBexKrtjUWo1UoD2lyxJljyrsXKP9FgwBNYRc2syatG0KIkiT7PAshHujXQ7+mzluHWTMTGu8AKMviOZvXaJsM8lj1IJ8LHLjn9Y+bP2tAquX5/I5PlhwPKzcA44UEM64GcpqN4Ewr3TVf2Mx4685s0tgd8dA6HKeg62hluCWiEGLrkplnIcR9jafGaZ9uX+h3Do13kDNZmQ7UGpxs5Tqn/WQLJvaGxvGZ7PhM9kWvp2xuUja3QekezGOdw2Odu2fcp5zzh6XE0TUz057d+GduGJBw/bVUeZlN5+ibTBodRQghFpHiWQhxX2eGzwDQ6L1VPHcyFaxH18rvh1bXJyowa3ma/FNcTQ1zNTW86PXa8U5qxzsNSvdgbVPVtE1V3zOuKUVIuRmbPyxlyruH4Ewr6OW9kwjAnogHTUHrkLRuCCFKS/n9H1AIsWav33x9Wdf9pOMn2Ew2qtxVaPksgakebu55foPTbYzrExXsCkxhMRU4m+wHYL8juvB603CxV7i/Yqch+R7k6kTxm5fmwOA9r4WVmyuFATKFOaa8LTT3fR9XapBZZ81mx1xXDquJnRVurg3FeelAldFxhBBigcw8CyHuqyvWRb23Hk1pxcNRCvmy7HceSzoYTznZGxo3Osq6C2sedGAodXNLLRoEaKnyMJ5IMxZPGx1FCCEWSPEshFjS1NwU0+lpdvqKM7G3FwuW3szsw1yfqACgZQsWzxXziwYH5q4z7WmmgLYlThqEYt8zSOuGEKK0SPEshFhSV6x4kuBO/63iuZOEq4K0w2dkrFVpHa+g0pkg6Lh30V25sykLXuwMpq6TNzmIuxq2zMyz32ml2mfnmhTPQogSIsWzEGJJnbFOPFYPIXsIdJ3QeEdZtmykcyY6pwPsDU0YHWXDVGgeBlLXi4elePcSmGk1OtK6aany0jeZJD63NQ62EUKUP1kwKIS4h67rdMW62B3YjVIKx+wEjtR0WRbP7VMB8rq2qGXjS4GD91z34Z5nNzPWirzYcOaBr4eVm858F9PZYaa8e2kYehNbZoq0NbBJCTdOS5WXd66PcmM4zrGGoNFxhBBCZp6FEPcaTg6TyqUW7e8MMBEuz35nmylHg396YcypWXFq1kXXZSx2Mhb73beXBIc5g8Ocue/rFcoDwECqlcn5RYP+LdK6UeWz43dapHVDCFEypHgWQtyjc7q43/Gt4rlirHg4SsxfXoej6HqxeG4OTmLWbu99fCE5yIXk4m3f6kfbqB9t2+yIy3J9so7rk3X3fd2vnNg0F33JK0zPF8/BLVI8K6VoiXppH02QyRWMjiOEEMsrnpVSLymlbiil2pVSf77E60op9f/Ov35JKXVkfnyPUurCHb9mlFJ/Nv/a/6GUGrjjtVfW90sTQqxWV6yLsCOMx1qc0QyNdzAVaii7w1GGZ11Mp+337LJxMTXIxdTi4rlhtI2Gki2ed3B9csd9X9eUYofzAN3JS6StAWbtkS2zaBCKrRu5gk77aNzoKEII8fDiWSllAv4KeBnYB/y+UmrfXZe9DDTP//oa8NcAuq7f0HX9sK7rh4GjQBL4wR33/T+3Xtd1/c01fzVCiDXLFXL0zvQubFGn5TL4p/rKcou61vHiFnV7tuAWdXerdx5iOjtELDvKlLdlSxXPjRUu7BaNa0NSPAshjLecmefHgHZd1zt1Xc8ArwGv3nXNq8A/6UW/BvxKqbuPhPo80KHres+aUwshNkxfvI+cnqPRX2zZCEz2oOnleTjK5bEItZ4YPtv9+4W3igbXIQB6Zi8y5d2LZ7YbUz5lcKr1YdIUeyIerg/PUNgCR48LIcrbcornGqDvjs/758dWes2XgX+5a+xP5ts8/k4pteSycKXU15RSZ5VSZ8fGxpYRVwixFp2xThSKem89cOfhKOVVPI8lHfTHvRyOjBgdZVNUWOtxmvx0Jy8w6d2PRoFg7KrRsdZNS5WXZCZPz0TS6ChCiG1uOcWzWmLs7m/9H3iNUsoK/Dvg9Tte/2ugCTgMDAH/91IP13X9b3RdP6br+rFwOLyMuEKIteia7qLWU4vNZAOKxXPCHSZt9xqcbGUujkQBOFS5PYpnpRT1zoP0JC8x6i/OQldOnjM41frZHfFgUkpOGxRCGG45q3/6gTuXedcCgyu85mXgnK7rC/8Xu/NjpdS3gJ8uM7MQYoOkcikGZwf5bO1niwO6Tmi8k9Foi7HBVuHCaIRG3xR+e/qe174SfPSesfdbXtiMWKvyGzt/vazrGlyHaI2/xzAJpt27CE9tneLZbjHRVOmidWgGXddRaqk5GyGE2HjLmXk+AzQrpRrnZ5C/DPz4rmt+DPzB/K4bTwAxXdeH7nj997mrZeOunujfAq6sOL0QYl11x7oBFhYLOmcncMzFyq5lYzjhYmTWfd+WDYsyYVGmRWN5k5m8qTR3E7FoeSxa/qHX1TuLM87dyYuMBR6lYvoiSn/4feWipcrLxGyG9tGE0VGEENvYQ4tnXddzwJ8AJ4FW4Hu6rl9VSv2xUuqP5y97E+gE2oFvAV+/db9Sygk8D/zbXW/9l0qpy0qpS8DngP9lrV+MEGJtumJdWDUrNe7ikoVy7Xe+MBJFoXPwPi0bZ2b7ODPbt2isaaiVpqHSPNb6yngDV8YbHnqd3xLFZ66kZ7ZYPFtzCXzx0tx+bzVaosXWoZ9d2x6tOEKI0rSsaZb5beTevGvsm3d8rAPfuM+9SSC0xPh/WFFSIcSG64x1Uu+tx6QVZ2UrxtrImu3E/Hev/y1dul5s2dgVmMRtzS55zbW5YvF13HW726x2oguAjqrSa1Fpny7+8z9Q0f3A65RS1LsOcTP+IcO1XwWgcurcwsEp5c7rsFAbcPD2tRG+8bldRscRQmxTcsKgEAKA6fQ0k3OTC1vUAYRHbzIe3oWumR5wZ2npj3uYSDk5HBmha2J2yV9z2QJz2cJdY3nmsnm6JmaN/hLWpN55iLlCgm6VYtYeITx13uhI62pv1MuFvmlG43NGRxFCbFNSPAshgGLLBtzud7bOxfHFBhmr3G1krBW7MBLFpAocCI8aHcUQ9c6DAPQkLzEWOEJ48lxxOn6LaKkqnnr5i+vb89+vEMJ4UjwLIQDonO7EbXETdhS3hAyP3QRgvIyK54IOF0cj7AlN4LTkjI5jCI8lRMhaN79o8AjO9Ciu1IDRsdZN1Gunxu/g7WtSPAshjCHFsxACXdfpinXR6Gtc2AKsYrSNnMnKZLDB2HAr0BPzEUvbOVw5bHQUQ9U7D9GXvMqQ/xGALdW6oZTi+X0R3m8fI5XZOjuJCCHKR2nuyySE2FQjyRGSuSQ7/TsXxsL955lwh9D7PjYw2cpcGIli0fLsqxh/4HUvWPbfM/bajmc3KtaafXHX6RVd3+A6yLnpn9JqLvCS2UPl1Cd013xhg9JtvudaIvzDB92cbh/nuX0Ro+MIIbYZmXkWQtA53Qnc7ne2ZJL4ZycY90aNjLUi+YLi0mg1CZS/AAAgAElEQVQl+yrGsJm394zkDudBQNGTusxY4DDhya0z8wzwWGMQj83Mz1tlyzohxOaT4lkIQWesk7AjjMdaXIwVGmtHAWNlVDx3TAdIZG0cus/BKHe6mh/kan7xQanHJ69zfPL6RsVbk/OjTZwfXf5e2w6Th6htJz2zxUWDvtlObJmpDUy4uaxmjc/uCfPz1lEKha2zGFIIUR6keBZim8sVcvTO9C7MOkNxi7qC0phwhw1MtjIXRiLYTDn2Biceeu1AYYqBwuJisikxSFNi8D53GKtnJkrPzMq+kal3HWZgrpV+/wEAKrZQ3zPA8y0RxhNpLvZPGx1FCLHNSPEsxDbXO9NLTs/ds7/zpLuCQokeV323XEFxZaySA+FRLKaC0XFKwk7XEfJ6jrPmNHll2VKLBgGe2RPGpCneaZVdN4QQm0uKZyG2uc5YJ5rSaPA2AGDKzhGY7Cmrlo2bkyFSOQuHl9GysV3UOQ9g19zcmD3LhP8RKqfOGR1pXfmdVo43BKTvWQix6aR4FmKb64x1UuuuxWqyAhCa6ETT82VVPJ8fjuI0Z2gOTBodpWSYlJlm9+O0Jz5iyH+IQOwapnzK6Fjr6rmWCNeH4/RNJo2OIoTYRqR4FmIbm83OMjw7vHiLupGb6Eox4S2PLcBm0lYuj1VyJDqMSVve4jETGqa7/vrLaSZyJXoMuVnLY9ZWvoPIbs+TzBVm+dATwqTnCE1f2YB0xnl+fps6mX0WQmym8mhoFEJsiLuP5IbiyYJTgXpyJotRsZbUNTG75PjZkRryusYO9837XnO3z1ta7hn7fu3Ta8q3kX5z569XdV+j6wgWZePXaoI/QhGeOsdo6Pg6pzNOfcjFrko3P28d4Q+fanz4DUIIsQ5k5lmIbaxzuhO7yU61uxoALZ8lON7JWJkcyZ3XFVfHG9jhGcFvW17hvJ1YNBs73ce4PnuOSU/Tlls0CMXWjY86J4mlskZHEUJsE1I8C7FN6bpOZ6yTBl8Dmir+VRCc6MJUyJVN8dw5XcVszsGBiq4V3Xcp38+lfP+isSfHr/Lk+NX1jLduzo7s5uzI6v6d7HF/itn8FKcDu6mcOocpP7fO6Yz1/L5KcgWdUzfHjI4ihNgmpHgWYpuanJtkJjNzz/7OOorx8C4Dky3f5fGdeK0J6j0r63kdLsQYLsQWjdUnR6hPlmbvbH88TH98dXtuN7mPo2HmHZcbcz5FdHx1LSCl6nBdgJDLyjvS9yyE2CRSPAuxTXXGFh/JDRAevUHMX0PW5jYq1rKNJX0MJ0M8UtGFUkanKV12k4sG1yHO5btImz3UjrxjdKR1ZdIUz+6t5BfXR8nmZY9vIcTGk+JZiG2qc7oTv81PwB4AQBVyhMY6yqZl4/JEI2Ytx95gr9FRSt5uz5NMZ0f4oPI4NaO/RBVyRkdaV8/tizAzl+NMt2xVKITYeFI8C7ENFfQC3TPd7PTtRM1P2wYmejDnM2VRPM/lLLRN1bI70IfNtLUKwY2w2/0EoDjhDWHPTm+5hYOfaa7Aatb4+TU5bVAIsfFkqzohtqH+eD/pfHrR/s6R4WvoKMYiew1MtjzXJuvJ6yYeCa1soeAttiX+6kuZbGuNtWHs5sya7neZA9Q69nE2P0xes1I78m5Zb1n3nY/u/WlDY8jFD8730xR2LXxDeKevPL5jM6IJIbYBmXkWYhvqmO5AoWj03d4bNzJ8jangDjIl3u9c0OHKeCM17jFCjviq3uNpyx6etuxZNPajmqf4Uc1T6xFx3b3UcIaXGs6s6T32eJ5kNNPDuYpj1I6+C/ryDpQpF3urPEwls4zE00ZHEUJscVI8C7GdnP17OPv3dIxepNrixdF/Hro/wNxxitB4OyMOH3R/UPxVorpnoiSyTg6sctZ5u9rtfhKANwPVuFODBGauG5xofbVEvQC0Ds0YnEQIsdVJ8SzENjOdSzGYnaHJFlwYC8eG0HSdEX+1gcmW5/L4TtyWJI2+4VW/x7lcD+dyPYvGPjt2ic+OXVprvA3x4VALHw7deyriSvitUSK2Jj5gjAJacfZ5C/E6LNQGHFI8CyE2nBTPQmwzv54t9ovuslUsjEWmB8lpJiY8EaNiLcvknIeBRJgDoW40tfq2g3E9wbieWDRWnRqnOjW+1ogbYmQ2yMhs8OEXPsQB3+cYzHTxUeiRLbdlHcC+Ki/9Uylm5LRBIcQGkuJZiG3m/UQ3dmWm2uJdGIvEBhjzRiloJgOTPdzl8UZMKk9LqOfhF4t7HPA+i4aZ1wMVBOJtuGf7jI60rlqq5ls3hmX2WQixcaR4FmIb0XWdDxPd7LQF0eZ3JHCkZ/GmYoz4awxO92CzWRvXJ3fQ7O/HscbdJ7Yrp9nHbs8TvK8PkoEt17pR6bERdFmldUMIsaGkeBZiG2lLjzOam6XJFloYi0wPADDiK+1+53OjzRR0xdFIm9FRytoh3wukCrP8JNS05Vo3lFLsq/LSMTZLOps3Oo4QYouS4lmIbeSDRLHdYVHxHBskZXEw4wwYFeuhpudsXJ1oYG+wD59tds3v51RWnMq6aCxudhI3O9f83hvBZUnhsqTW5b0aXIfxmsN83+sjPHUBe7o0+7xXq6XKS76gc3M08fCLhRBiFeSQFCG2kdOJbppsIbwme3FA14lMDzIcqIElDpYoFe/0NAKKo5Eb6/J+nzY33zP2RvUT6/LeG+H5+nPr9l6aMnHQ9zynJ/6FIbNGzegv6aj7nXV7f6PtCDpxWk20Ds3wSI3P6DhCiC1IimchtolULsW55ABfDh5eGPPPTmLLzZV0y8Zkys6ZwWpagj14resz+7oVNfW+vuxrY1XP8/7Ev/DdQDW/P/jWliqeTZpib9TDtaEZ8gUdk1a63xQKIcqTFM9CbBNnh8+S0fM85a5nIFNcUBWJzfc7l/BiwXe6i6cgHq28uW7veSZXPGDluPn2CYvPjhZnd9+tPLJuz1kv7w8cAODTNVfue82p3PJn5WstX6LR9Sg/opU/7foYX7ydmGfXmnOWipYqL+d6p+memKUpXNonZgohyo/0PAuxTZwePI1NmTjivF0oR6YHiTn8zFlLs9d3POng7HAVT9T047bOrdv7TulJpvTkorHKuWkq56bX7RnraTzlYzy1vi0Ih3wvMEGKD5xudvf8y7q+t9GaKz2YNcU12XVDCLEBpHgWYps4PXCaY65a7JoFAC2fo2JmpKRnnd/u3ommdJ6t7zY6ypbT7H4Ch8nLP1fspHHwJ1iyW6fQtJo1dlW6aR2aQddXf5iOEEIsRYpnIbaBwcQg3TPdfMrVsDBWER/BpOdL9kju0Vkn54ejfKqmH69N9nVeb2bNwgHv5/jIFCeup9nZ/yOjI62rliov08kswzPr9xMLIYQAKZ6F2BZOD54G4Cl3w8JYZHqQgtIY80YNSvVgb3ftxGLK8zmZdd4wh/wvkCfPP4eb2d37GugFoyOtm71RDwqkdUMIse5kwaAQ28AHAx8QdUXZaQsujEViA4x7KsmbLAYmW9pwwsXF0QjP1HfjtmbX/f09yn7P2JTVs+7PWS9+28bsWRy2NbDD+QjfV938zyO9VI+9z2DlZxdds5JdPDp2fGm9I66ax26hLuikdWiGz++NGB1HCLGFyMyzEFtcNp/lg8EPeKr6KdT8Xs72TJLA7GTJ9juf7GrCasrz9I7eDXn/J81NPGluWvzM6HFORo9vyPPW6pm6izxTd3FD3vt44FUm9Thv+SLs7vnOhjzDKC1VXgan55hOStuPEGL9SPEsxBZ3ZuQMyVySZ+qeWRiLTvUDMBSoNSjV/fXOeLkyVsln6npxWdZ/1lkstsv9GH5LlL8PRqkeP41nttvoSOumpar404TW4bjBSYQQW4kUz0Jscaf6TmEz2Xi86vGFsaqpfpJWJzFn8AF3bj5dhzfam3FZMjy9o2fDnvNhroMPcx2Lxl4cPsOLw2c27Jlr8cu+Q/yy79CGvLemTBwLvEobU1ywOdjd89qGPMcIlR47FW4rrdL3LIRYR1I8C7GF6brOqf5TPFH1BA6zozhYyBGJDTAcqC25I7lbJyronA7wfGMndnN+w54T1+eI64t3YQhk4gQypTlDOZ12M53euMM+Dvqew6Y5+dvILnb2/xBzbnbDnrXZWqq8dI3NMjMnP8UQQqwPKZ6F2MLap9sZSAzwdN3TtwcnO7HkswwF6owLtoR8QfFmxy4qHLM8UT1gdJxtxWZyctD3Ar8yxZlkjqb+Hxgdad3sr/KS13XebR01OooQYouQ4lmILexU/ykAPltzxw4Ko9fIK40RX2nt7/zJcBUjs25eburApMnBFpvtWOAL6MC3K5vZ1/G3mHLJh95TDmqDTrx2M29dGTI6ihBii5DiWYgt7FTfKVqCLURcd2zVNXKNMV9VSW1Rl8lrnOzcyQ7vNI+EZYbQCH5rlN3uJ/iRQ4fs5JY5sltTin3VXk7dHCOZyRkdRwixBUjxLMQWNTk3ycWxi4t22WB2HGZHGfKX1i4bv+rbwUzGzm/uatuUNuyAchJQzkVjo3Y/o3b/xj98FSocMSocsQ1/zvHgqyT1FP8cPci+rr/Dki3NHvCV2l/tYy5b4NSNMaOjCCG2ADkkRYgt6lf9v0JHX9zvPHoNoKT6nRMZC7/oaWB/xSiN/o0vEAGOmxvvGXu38simPHs1Pl1zZVOeU+vYT9S+i9dMM/zHoRn2dv8TSVvlpjx7IzWEXARdVk5cHeblR6qMjiOEKHMy8yzEFnWq/xSVjkr2BffdHhy9Bq4wsw6vccHu8vPunWQLGq80tRsdZdtTSnE88Cqj+VF+UPUke7v+CfMW6H02aYrnWyK82zpKOrdxu7gIIbYHmXkWYgvK5rOcHjjNKztfWThVkMwsTLRD/VPGhrvDWNLBhwM1PF49QKVr84q093NtAHza3Lww9huDvwbgjeonNi3Hcr3dU5wVf77+3Lq83/npt+77WkHPY9NcfNOV5bfyKfKjP4Hq31uX5xrppUeifPdsH6fbx3lWjusWQqzBsopnpdRLwH8HTMDf6rr+F3e9ruZffwVIAv+Truvn5l/rBuJAHsjpun5sfjwIfBdoALqB39V1fWrNX5EQYuFUwadr72jZ6DwFhRxE9gNz9713M3RNFPcRPtndgqYK7PFdpWsivWnPT+r3HtfsKeEZ1tmsY9OepSkT9c5D3Ex8wA8rH+ELY1e5HI6TtXg2LcNG+FRTCI/NzIkrw1I8CyHW5KFtG0opE/BXwMvAPuD3lVL77rrsZaB5/tfXgL++6/XP6bp++FbhPO/PgXd0XW8G3pn/XAixDpY6VZC2k2CyQXCnccHuMJL00xGr4dFwO07L5hXO4uHqnPswKQt/7/ej6QVqxn5ldKQ1s5lNfL6lkrevjZDLF4yOI4QoY8vpeX4MaNd1vVPX9QzwGvDqXde8CvyTXvRrwK+UetiqjFeBf5z/+B+BL64gtxDiPpY8VVDXoe1tCO8BrTS6tT4easFuSnMo3PHwi8Wmsmh2ah0t9GR7OeVvJDx9Dlum/H8w+NKBKFPJLB93TRodRQhRxpZTPNcAfXd83j8/ttxrdOBnSqlPlFJfu+OaiK7rQwDzvy+5pFsp9TWl1Fml1NmxMdlmSIiHWfJUwZErMDMAlXf/0MgYg4kgfYlKjlS2YTXJ3rulqN55GB2dfwgEAEXN6CmjI63Z07srcVhMvHVl2OgoQogytpziealdV+8+/utB1zyl6/oRiq0d31BKfXaJa+9L1/W/0XX9mK7rx8Lh8EpuFWJberf3XeCuUwXbflb8vbLFgESL6Tp8PNyC0zzH/opuQzJUKDcVyr1obNBRwaCjwpA8DxNxTRJxbe5sqdPsJWLbyVUm6Q4epSJ2CcdceR9g47CaeGZPmJNXhykU5BRLIcTqLKd47gfu3BS2Fhhc7jW6rt/6fRT4AcU2EICRW60d87+X99/KQpSIE90nOFJ5ZPGpgjdPQtUhsPuMCzavbSrI4GwFRyM3sWjGbBt2xFzPEXP9orH3wgd5L3zQkDwP82RVK09WtW76cxtdj5Ihz/cCVRQ0K7Wjv9j0DOvtpQNRRuNpzveVfxuKEMIYyymezwDNSqlGpZQV+DLw47uu+THwB6roCSCm6/qQUsqllPIAKKVcwAvAlTvu+er8x18FfrTGr0WIba9tqo326XZebHjx9mBiFPo+hj2vGBdsnq7Dyc4m3JYk+4I9RscRD+G3RgkrDz8rtNMfeoJg/AauZL/Rsdbk2b2VWE0ab12W1g0hxOo8tHjWdT0H/AlwEmgFvqfr+lWl1B8rpf54/rI3gU6gHfgW8PX58QjwvlLqIvAx8Iau6yfmX/sL4HmlVBvw/PznQog1ONF9Ak1pvNDwwu3BmycAvSSK5+sTIXpnfByL3MSkGbfjwansDU5lbywae3XgNK8OnDYo0YOd6D7Oie7jhjx7n6mKMT3Om/5asiYndaPvGpJjvXjsFp7aFeLE1WF0XVo3hBArt6xl97quv0mxQL5z7Jt3fKwD31jivk7g0H3ecwL4/ErCCiHuT9d1Tnaf5Hj0OBV39u5efwN8OyD6CAx8Yli+wvysc9CeZE+w17AcAGnuXaToyJfudnlzOeu6vl94cvl/DgoqSFh5OFm4ySvhT1M//DO8iU5m3KWx5eFqvHygil/86yWuDs5woMb4ViYhRHmR47mF2CJaJ1vpmenh5YaXbw9mZqHzl7D3FVBLrevdPFfGKhlIeHmhsROTkhm/cqEpxbPmvXQURvnI10ja4i3OPpfxrO1z+yKYNMUJ2XVDCLEKpbHhqxBizU50ncCszDxX/9ztwY53ITe3oS0bt04LfJCCDj9ta8Bvi+O3yL7O5eYpczM/zJ7nnfxNjoWfZufgTwjErzPlNX73ltUIuqw83hjkrStD/OcX9xgdRwhRZmTmWYgtQNd1TnSf4MnqJ/HZ7vgx9PU3izts1H/KuHBA+3QNU2kvj0Wvoxk7AS5WwamsfMq8i4/zXXT4dpOyVlA3+gvQy/ekvpcPROkYm+XmSNzoKEKIMiPFsxBbwMWxiwzNDvFy4x0tG/lccbFg84tgshiWraArzgzvJWSP0eS7e5dLY0Q1H1Ftca9rjzNCjzNynzuMVesZo9Zj7CFRnze3kKPAe/l2+iufxpEeJzhz3dBMa/HigSiagp9eLI0/k0KI8iHFsxBbwInuE1g1K5+r+9ztwb6PIDVZ7Hc20M2pWmIZN49Frxvddr3goKmWg6baRWMfVuznw4r9BiV6sGORmxyL3DQ0Q1TzcUCr4Re564x69pCyBqkef79se58rPXaebArx44uDsuuGEGJFpHgWoszlC3lOdp/kM7WfwW2949S862+AyQq7nrv/zRtM1+H8aDNBe4wGryzOKneft7QQ01OcK/QyVPEUrrlhfLPl28P+hYPVdE8kuTIwY3QUIUQZkeJZiDL3ycgnjKfGeanxpduDug433oDGp8HmMSxb90yUqbSHI5VtJTPrDPBOtpV3sotP7Pud/lP8Tv8pgxI92E87n+CnnU8YHYP9Wg0R5eXnuVbGfQfJmD1Uj5Xm3tjL8fKBKiwmxY8vDhgdRQhRRqR4FqLMneg+gcPs4LM1n709ONoKU92GtmzoOpwbbcZrnWWXv7T6SvMUyLN4sZu5kMdcMOa48IfJFUzkCiajY8xvW9dCZ2GMDn2SodCTeJM9uJN9RkdbFZ/TwtO7w/z00hCFgrRuCCGWR4pnIcpYNp/l7Z63eabuGZwW5+0XbrxR/H33y0vfuAkGZ0OMJIMcDrejyb7OW8anzE3YsfBurpXRwBGyJgfV4+U7+/yFQ9UMxeY42zNldBQhRJmQfZ6FKGPv9L3DdHqa39z5m4tfuP4m1BwFb5UxwSjOOjvMc+w1+DRBsXancouPMm/QQnyc76JWC5D27+TTE1e5OvsRrdNuHvUb9w3bajzXEsFu0fjJxUEeawwaHUcIUQZk5lmIMva9G9+jxl3DU9VP3R6cGYTBc7D3NwzLNZb00RePcKiiE7NWvnsBi6XtMUUpoNNWGOVcoJmMMvP4RHluW+eymXmuJcKbl4fI5eXPqhDi4WTmWYgy1TndyZnhM/ynI/8Jk2bi9ZuvA7Cz7RccBU7YzcTnxxZMXtqUbOdGm7FqWfZXdG3K81aqRgvcM9bhrjYgyfLUl9hOJV7loEr5aMuPcMBSwwV/E8embhKYmzQ62qp84VA1P700xOmOCZ7eHTY6jhCixMnMsxBl6ns3v4dZM/Nbu35r0Xht3zninghxg1o2ptMuOmPV7K/owmbKGZLhYfabqtlvWlwsnwnu5Uxwr0GJHuzRyg4erSytLeH2mKIkydCvT3E2uIeCUjwzWJq7lTzMM3vCeOxmfiIHpgghlkGKZyHKUDKb5MftP+b5+ucJOUIL49Z0gvDIDfrrjmDU3nAXRnehqQKHKjoNeb7YHDUqgBMrN/PDJMwOrnobODZ2FmsmZnS0FbOZTby4P8rJK8PMZUtzxxUhROmQ4lmIMvRW11vEs3G+vOfLi8ar+y+g6QX6dxw1JNds1s71qTr2BntxWtKGZFiOn2Wv8rPs1UVjX+59ly/3vmtQogf7YftT/LD9qYdfuIk0pWg2RRjSY8zoKT4JNGMtZGnq/zejo63KvztUTTyd49RNY49BF0KUPul5FqLM6LrOd298l13+XTxa+eii12r6zjHrqmA6UG9ItotjO9F1jcPhdkOeLzZXs1bJpXw/N/MjeG0NtHt30tz7Gtcb/wBdGb8v9Z2+89GDd33JF3RcVhP/37vtTCQyAHzl8R2bEU0IUWZk5lmIMnNl/Aqtk6383p7fQ93RmmHOJIkOX6W/7lFDWjbmchauTjSyyz+Az5bc9OeLzedQVnZoQToKo+T0PKejT+FODVIz+kujo62YSVMcqPFxfXiGdE5aN4QQ9yfFsxBl5rs3vovD7Lhnb+fqgUtohTwDdca0bFyZaCRbMPNoZZshzxfG2K1FyJCnpzDBteA+Zu1V7O7+jtGxVuVQrZ9sXqd1KG50FCFECZPiWYgyEkvHONF9gi/s/AJuq3vRazV9n5By+Jmo2LnpuXIFjcvjO9nhGaHCMbPpzxfGiSgvPhzcKIxQUCZu7vg9opMf44uX3zdRO0JOfA4Ll/qnjY4ihChh0vMsRBn5YfsPSefT/O6e3709ePbvIZemavASnZW7oefXm57r+uQOUjlb2cw612uhe8aue+oMSLI8u/wDRke4L6UUu00RzuS7iWVH6Kj7bR5p/2t293yHMwf+d6PjrYimFAdrfHzQMUEyU5rbLAohjCczz0KUiUw+wz+3/jNHKo+wJ7hn8YujrZgKeQZCDZueq6DDhbEmKp1TVLsmNv35q7HHFGWPKbpo7EKgmQuBZoMSPdiBim4OVHQbHeO+dmphTGj0Jq+Qsfrprv4NGgd+iiVbftvWHazzk9d1Lg+UX3YhxOaQ4lmIMvGDth8wPDvMHx38o3tfHL7InNnOmDey6bk6Y9XMZNw8Gm4zamvpFcvpeXL64kVh5kIOc6E0ZxuzBRPZQmntXnEnqzLTqFUwlGojlY9zs/4rmAtzNPX9wOhoK1btsxPx2jjXM2V0FCFEiZLiWYgykMln+Nblb3E4fJgnq59c/GI+AyPXGAjVg9rc/6R1Hc6PNuOzJmj0DW3qs9fi3dx13s1dXzT2O/3v8Tv97xmU6MHe6HyCNzqfMDrGA+3RohTIcTn2DtPePYwEjrK79zWUXl47VyilOFofpG8qRduILBwUQtxLimchysC/tv0rI8kRvn7464u2pwNg7Abk04a0bLRPBRhL+Xm0sh2tTGadxcYIai58lgjnp99C13Vu1n8Fd2qA6tHyO7L7cJ0fTcHrn/QbHUUIUYKkeBaixKXzaf720t9ypPIIT1QtMfs4dBEsTka9VZue7Ze9DTjMc+wO9G36s0Xp2eF8hMlMPz3JS/RHnmXWHmF3z78YHWvF3DYze6Ne/u1cP9l8weg4QogSI8WzECXu+ze/z2hqdOlZ51wGRq5AZD+6trn/OQ/EPdycDHGoohOzJgWGgKh9F3bNw7npN9A1M+11X6Jq4td4El1GR1uxY/UBxhMZfnF91OgoQogSI8WzECVsLjfHty9/m6ORozwWfezeC7pOQW4Oqg5terZf9tZjM+XYX1F+hZHYGCZl5pD/eW7GPySenaCj7rfJKzPNvd8zOtqKNUc8hD02vndWWjeEEItJ8SxECXv95uuMpcb4xuFv3DvrDHD5+2BxQMXeTc01mbJzcSTCEzX92EyluUPFgzRpYZq08KKxK75GrvgaDUr0YHuDvewN9hodY1kO+19Gp8DF2EnmbBX0RZ9n58CPMOXK68h2k6b490dq+MWNUUbjc0bHEUKUECmehShRqVyKb1/+NsejxzkePX7vBZkkXP8pRA+BaXPPO3qvrx5N6Xymrjx7nZtMlTSZKheNlXbx3MfeYHn8sw5aq2l0HeHC9AkKep62HV/GmovTMPSm0dFW7EtH68gXdH54vnQPqRFCbD4pnoUoUf/j2v9gYm6Crx/6+tIX3HwLMgmoObqpuRIZCx8PVnMkOozPlt7UZ6+XOT3LnJ5dNObIpXHkSvPrSeWspHJWo2Ms2xH/K8RzE7QlPmIs8ChTnt3s7nmtuLdhGdlV6eZofYDvne1HL7PsQoiNI8dzC1GCBhODfOvSt3i+/nmORY8tfdHl74OnGkJNm5rtdH8d2YKJp3d0b+pz19N7uZsAvGDZvzD26uBpAF7b8awhmR7kZHfxJw9f3HXa4CQPd376LQp6Abvm5tTYP5HMx7CFD/Dbnf/GZP836fE0LLr+Uf/LxgRdpt89Vsv/+q+XOd83zZEdAaPjCCFKgMw8C1GC/vLMX6KU4r8c+y9LX5CchLa34cC/39SDUZJZM+/37eBAeJSIq7x6WMXm0ZRGrXM/E5k+ZnPTnKt4lJTJzqeGPzA62or9xsFqHBYTr58tj7YZIcTGk+JZiBLzq+Oh8WcAACAASURBVP5f8U7vO3zt4Neoct9n7+bWH0MhC498aVOzneqtJ5038WJjx6Y+V5SfWsc+FBq9yctkTDbOho9ycOIyrmzC6Ggr4raZeeWRKn5ycYhkpvwWxwoh1p8Uz0KUkHQ+zX/7+L/R4G3gD/b9wf0vvPx9CDVv6hZ1iYyF9/vrOFQ5QtQ9u2nPFeUjPPnJwq+62HV2aAEGk1fwT3zMDbsXs57n2e4fEJ78xOioK/K7x2pJpHOcuDJsdBTx/7d333FyVffB/z/n3qnbe9cWrXpdJCEJkITo1WBscDCuGNfYCST24/b8kl/iJE8cd8f4gbiAwTZgijHIIFEFQqgLCfVdaXvvfaff8/wxI6G62pV2d7Z83y/Na2bO3PKdq5md75w593uEGAckeRZiHPndgd9R21vLd5Z/B4d5jhPEuuuhanO41/ls5etGycbqQgIhk+uKKsZsn2Jim2tkEyBEudVChzOBqphMSrrKUXpiTaqzvCiFgtQYnpahG0II5IRBIcbUM2XPnPOxLm8XD7//MHNT5lLfX3/OZWcdfoXFaNbHxdJX9gx07ButcE/o9jnYUp/HkqxGMibBWOdZRuYZbXuTZkQhkqGZnzoxJ6JJN+JJVXEcCTUx28hiT9IM7mh4l+K+BlpSox3d0CmluGtpHj96tYzq9n4KUmOjHZIQIoqk51mIceKVqldQSnF94fWDLpdftZ2OlEL6Es5MAEfLxuoiLK24rmhiJnGnKzTTKDTTTmk7kpDPkYT8KEU0uJnJDcxMboh2GBdkrpFNL17qdRfH4nLotsWwrLMs2mEN20eX5mEoeGqn9D4LMdVJ8izEOHCo/RClnaWsyVtDojPxnMvFdzeS3FlDTeGKMYut0+tkW30ul2Y3kOr2jNl+R1O/9tGvT63pHB8YID4wPnvVe/0uev2uaIdxQQqMFNzYORJqRCuD95Jnku9pJad/Yk08kp3o5tq5mTy1owaPPxTtcIQQUSTJsxBRNhAY4OWKl8mOzeaynMsGXXZa9Q60UtQWnGXGwVHyRlV41r1rCydHrzPAu8FjvBs8dkrbLY3buKVxW5QiGtwbNUt5o2ZsJ8MZKYYymG1m0ai76bIGeD9xOn5lY03DO9EObdjuW1VE50CA52XGQSGmNEmehYiy9ZXr8Ya83FZ8G8ZgNZu1Jr9qOy2Zc/C6k8YktnaPm52NOazMrSfJNT5n3xPj30wjExPFEasRv+lgX2IRJe17SfB3Rzu0YVlelML8nAQeebdSZhwUYgqT5FmIKDrScYSD7QdZk7eGzNjBxzCnth0jvq+FmoKxG7LxWmURhtJcXTB5ep3F2HMpO0VGOhVWGz4d4L3kWSitubxpa7RDGxalFJ+7oohjLX28c7Qt2uEIIaJEkmchosQT8PBSxUtkxWRxRc4V511++tFNBOzuMRuy0dIfw3tN2VyeW0eC0z8m+xST1xwjixAWR60WuhxxHEyZz8rmbZihiTWO/tbF2aTHO/ntZvlCKcRUJcmzEFGyoWoDnqCH22bchmmYgy5r9/UxrWYn1YUrCdmcYxLfKxXF2M0QVxVUjcn+xOSWbMSSpRIoDTVhaYt3slcTGxygqH5dtEMbFqfN5FMrC3i7rJVjLb3RDkcIEQVS51mIKCjrLGN/237W5K0hKzbrvMsXVG7DtIJUzFgzqnFVtodnDqzqyWRfaybLsw7T2ttF66judezNNc+c9nxnyuwoRDI0JenHzr/QBDDHzOatYClVVjvx8cuojc1ldtUfODbtThhsvP84c8+KfB7ceIxH363iP+5YGO1whBBjbOL8tRJikvAEPfy1/K9kxGSwOnf1+VfQmunlm2hPLaI7edqox+cP2Xi7bjEprh4uST866vuLhmlGCtOMlFPayuNyKY/LjVJEgytMbKYwsTnaYVy0PJVMknKzP1SPRvNO9moS+yvJbns32qENS1qckztKcnnuvTo6+2VIkxBTjSTPQoyxVypfoT/Qz+3Ft593uAaETxRM7G6gYsaVYxAdbGucR3/AxVV5ezGNyVlRoFt76NanjrVN8feQ4u+JUkSD6/TG0emNi3YYF00pxUIjjx48NHnL2Ze6iAFnBnMqH492aMN276pCvAGLJ3fWRDsUIcQYk+RZiDFU2lHKvrZ9rMpdRXbcmUMHzmb60U0EbK4xOVGwsS+FA+1FLEqrIDO2c9T3Fy3bgxVsD1ac0nZ90y6ub9oVpYgG93bdYt6uWxztMEZEvpFKIm7K+3cSVCZlBR8nu30bST1Hoh3asMzJSuCKGak8vqWaQMiKdjhCiDE0pORZKXWjUqpUKXVMKfXtszyulFL/HXl8n1JqSaR9mlJqo1LqsFLqoFLq/pPW+RelVL1Sam/kcvPIPS0hxp9uXzcvVbxEZkwma/LOM3a5agtUbcF+dCPTqndQnVpIqG73ifZTLiMkEDLYWFdCvH2AFVmHR2y7QpzMUIoFZi59wQ6afRUczf8Yfls8C489HO3Qhu2+VUU09XhZf6Ap2qEIIcbQeZNnpZQJ/BK4CZgHfFwpNe+0xW4CZkYuXwQeirQHga9rrecCK4GvnrbuT7XWJZHLyxf3VIQY376/4/sMBAeGVF3juILWY5g6REXW6J/M9kZ1IV2+eK6cthe7KdMPi9FTaKQRYyZS3rcTvy2eI4WfYlrzGxOu93ntrAyK0mL57WaZNEWIqWQoPc/LgWNa6wqttR94Crj9tGVuBx7XYduAJKVUtta6UWv9HoDWuhc4DIzPM3KEGEUbazby14q/hodrxA5tuAZaM725lPa4NLpjU0c1voa+ODZWFzI7uYb8+MlWW0OMN4ZSTI9dRm+wjWN9Oygt/MSE7H02DMW9VxTyfm0X79V0RTscIcQYGUrynAvUnnS/jjMT4PMuo5QqBC4Btp/U/LXIMI9HlFLJQ4xZiAml29fN97Z9j9nJs4dWXSMitbeFRE8XFZlzRjE6sDQ8e3gubluQK3IOjOq+hDguxz0Lt5nAu+1Pndb7XBrt0Iblo0vySHDZ+J+3y6MdihBijAwleVZnaTv996lBl1FKxQHPAQ9orY+fzv4QUAyUAI3Aj8+6c6W+qJTapZTa1doqPWJi4vnBzh/Q5e3i31f9+5CHawBMbz5CwLRTm1Y0itHBO7X51PYm8uFZpbhsgVHd13ix0MxloXlqH8DW1HlsTT19RNr4sDSzjKWZZdEOY0QZymR67FIavWVU9O8+0fu8YIL1Psc6bdy3ajqvHmpmb630PgsxFQwlea4DTi4umwc0DHUZpZSdcOL8R631n48voLVu1lqHtNYW8GvCw0POoLX+ldZ6mdZ6WXp6+hDCFWL82Fy/mRfLX+RzCz/HnJSh9yC7fP1Ma6ukOr2YkGkftfiOdSazvnwGc1NbWZwx8esID1W2kUS2kXRKW3VsFtVDmLAmGqbFtzJtEg6nyXXPIcGWzua2J0/0Puc3vz7hep/vW11EaqyDH2yYWGO2hRAXZigzDO4EZiqlioB64G7gntOWeZHwEIyngBVAt9a6USmlgN8Ch7XWPzl5heNjoiN37wDk92IxqfQH+vne1u8xPXE6X1r0pWGtO6vhAEprSnNGb/ayht44Htu3mLSYAe6edxB1tt+PJqkOKzyTYooRe6ItwxsuzdfiGn8jyNo8CQCkucdnHeoLZSiTVWkf5+Wm/+ZQz9s4Cj/BnKrfs+DYw2xe8tNoh8cT24dew/my4lT+uq+RzUfbWDUzbRSjEkJE23mTZ611UCn1NeAVwAQe0VofVEp9OfL4w8DLwM3AMWAAuDey+hXAp4D9Sqm9kbbvRipr/EApVUJ4eEcVMLzsQohx4JmyZ8752PqK9TT2N3Lvgnt5ofyFIW/T4e2luLmUmvTpDLjiRyLMM3R4XPzm/Utw2oJ8fvEeYuzBUdnPeLUrVAXA9cb8E21Xt+wB4Kn8q6MR0qA214e/RH14xsSaiW8oFiZey3tdL/Nm6yPMnL6C0sJPsvDYQyT1lNKVMH6nTD/d8sIUNh9r4wevHOGKGVegptK3USGmmKH0PBNJdl8+re3hk25r4KtnWW8zZx8Pjdb6U8OKVIgJpLqnmp3NO1mRtYJp8cObUntm2RvYrCBHcheNSmz9fju/ef8SgpbB3y7ZRZLLNyr7EWIoDGVyXeaX+X31N9jS9idiCz/J7Ko/sPDYQ7yz5GfRDm/IbKbBtXMyefa9OjYcaOKmhUOsqiOEmHBkhkEhRlggFGBd+TqSnElclX/VsNa1BTzMKH2TupQCemNGfviAP2Tw230ldHpd3LtoL1lx/SO+DyGGK889lwUJ17Cj83made+JyhtpnXvPv/I4UpKfxMyMOH74ailBmXVQiElLkmchRtjbdW/T4e3g1um34jAdw1q3+OhbOAIDHMkb+amYQ5bi9wcWUdeTwCfmH6AoqXvE9yHEhboq47OYys7rzb/mcNFnGHBmsOTwD0BPnCTUUIpv3DCbitZ+/vxefbTDEUKMEkmehRhBDX0NbG3YSkl6CdOTpg9rXSPoZ9aR12jKmk9n3MiecBSyFM8cmcuR9jTumH2EBemTr3KDmNjibCmsSv045f07KfUeYO/sB0jr3k9hw0vRDm1Yrp+XScm0JH76ehnegMzUKcRkNKQxz0KI8wtZIdaVryPWHsv1hdcPe/2iis24vD0cnn8zeNpHLK4ur5M/HFxIdXcS1xWVc1mu9IiVmPlntG1KH50x5iNhRfahaIcw4tI7dgNQ3NN3oq1AhzikEni7/idc7bydPncOyw79J7WZ1xCyxUQr1GFRSvHNG2dzz6+384dt1Xx+9fC+RAshxj/peRZihGxp2ELzQDO3TL8Fl801rHWVFWT2oQ20pRXTljFrxGI61JbGT3esoKkvjnvm7ef6osoR2/ZElmHEk2GcWsmkwZ1Gg3t8lhjLju0kO7Yz2mGMOpsyuduxghbdy/rQAaqzbsAR7GVe5e+iHdqwXF6cxuqZafxy4zF6vVNj4iEhphJJnoUYAa0DrWyq28S81HnMThl+ea38qu3EDnRweP4tjETB5aClePHoTB7dV0Kyy8v9l27nkqypMwnK+bRYvbRYvae05XjayPG0RSmiwTX2J9PYP/7qT4+GBWYuK8zprAu8z3tOB20J85lb8SgxnqZohzYs37xhDp0DAX79jnxhFWKykeRZiItkaYt15etwmA5uKrpp2OubQR/z971AZ3I+TSMwKUq7x80vdy/jndoCrsir5atLd5Ee47no7U4me0M17A2dOgHGmtZ9rGndF6WIBre9cR7bG8fn1OGj4ZOOlaSrOH7tf5tDGasATUlp9CdNGY6FeYncuiib/3m7nPLWvvOvIISYMCR5FuIi7WzaSV1fHTcU3kCsPfb8K5xmzqH1xA50sHfp3RfV6+wLmrxRVcjPdqygzRPDpxe8z4dnlWI3J061AiEA3MrBl51r6dVeHtL7OFT4aQobX55wpev++UPzcNlN/tcz7xOydLTDEUKMEDlhUIiL0Ont5M2aN5mRNIOFacPvNY7pa2X2oQ3UFCwfdKxzZfu56zEHLYMD7UXsaZmJJ+ikIKGJ1Tn7WZghM5yJiSvfSOVj9kt5IrCdv5jpzLHFcfneb3Jw+n2gzt3vU55/1xhGObiMeBf/ett8HvjTXh7ZXMkX1sjJg0JMBpI8C3GBLG3xYvmLKKW4ZfotFzQdb8l7T6OVwb5L7hz2uiFLcbijgF3NsxkIusiLa2FF1hEyT5xYNvxecCHGk6tscyi1mngu9D5Lsq/g1tpXyOrYQVPqymiHNmS3l+Tw0v5GfvRqKVfPzaA4PS7aIQkhLpIM2xDiAv3+0O+p7qnmhsIbSHQmDnv9zMaD5Nbt4fCCW/HEpAx5PX/Ixr7W6Txx5Fo21S8m0dnP7cWbua1460mJsxATn1KKzzguJ0XF8kN7DeXxxeS1bMTh74p2aEOmlOI/7lggwzeEmESk51mIC3C08yg/f+/nzE6eTUl6ybDXV6EgJbufpC8unbI51w1pnW5fDPvbpnOkIx+/ZSczpoMrp+1lWlzrSBTomFKWmYVntL2ZccnYBzJEq3L3RzuEqIlRTr7sWMsPfBv4WloMv/coihpfpjT/4yNSmWYsyPANISYXSZ6FGCZ/yM933vkO8Y54bi2+9YKGa8wse4OEniY2X/l3WKb9nMtpDRVdybxSOZ/KniwMNMVJ9SxKryAzZuL0vo03KcaZQ1paXOO3FFyauyfaIURVoZnG3zmv4b99r3NfbgGP15aT0nOQjsQF0Q5tyGT4hhCThyTPQgzTL/f+ktLOUn5x9S9o9Qx/mmunp5t5+9fRmLOQxtzFZ13G0rC/NYON1YXU9ybgMn0szShjfloVcXbvRT4D0WiFv3hkG0kn2gr6w3WEq2OzohLTYGp70wGYFj91p1Wfa2bzVefVPOh7gy9k5/Jw06t0xxYTsrmjHdopnthec87HlhUks/loG/c+upMvrpmOEfnifc+KM2e8FEKMXzLmWYhheK/5PR498CgfnflR1k5bO/wNaM2yHY9jWEH2Lrn7jIcDQdh2xOSH2y7nDwcW4QuafHT2YT4971VWZB+RxHmE7A/Vsz906jTll7Uf4rL28TkN9u7mWexuHrmZJyeqBWYuX3FeRZnD4O/SY0lrfjXaIQ1LvMvOhxZnU9MxwLvHxueEPEKI85OeZyGGqNffy3c3f5fcuFy+eek3AdhR2TGsbVze+C459e+zZ8nd9CVknmj3+mHrERvvHLTT61HkxffwqQX7WJDegqGgsn34tZoHK28nxES12JzGlxxreVhv5H/TxFf7j2DGzol2WEO2OC+J/fU9vHqomWnJMRSmSVUcISYaSZ6FGIKQFeLb73yb5v5mHr3xUWLsMcPeRnZ/I7dWv0RjzkKOzb4msl3YXmrj1T12+r2KmTkhPn5lgBm+HRPlXCghxtTbwVIA1poz2OI4ynetd7nM20WyLTxmvbVr/YllL0ka/oyfo00pxUeX5PLQW+X8YXs1X7myONohCSGGSYZtCDEED+59kE11m/j28m9TkjH86hr2kJ97jj6B1+Zi58p70SgO1xr85C8unt/qIDPJ4u8+5OWLN/qYmWNJ4izEeeTa0rlHF+C2NOtDRygLNUc7pCGLcdj4zOWFaA2Pb62meyAQ7ZCEEMMgPc9CnMeGyg38Zv9vuHPWnXxs9scuaBsfql5HlqeZX8/9PDuOKt4/ZtDS6SLOHeDyBR3kpHlp6oOmvvDy6R0y5EKI8wm4c/l6axvP2tt5N6aCdt3HdF2Cocxoh3ZeaXFOPrmygEc2V/KVP+7msc8tx25Kf5YQE4Ekz0IM4nD7Yf7p3X9iScYSvrv8uxdUlm5B+34ua97Om1lX8mTzcioaYnHYLEpmdlGc04chn5djboXtzDq7r2Yti0IkQ3Nl3vvRDmHc2pu2gH+ufp0nAiEeS4Sm9j+xMPFaEu0Z0Q7tvIrSYrljSS7P7q7jn/5ygP/8yMIL+hsjhBhbkjwLcQ7tnnbu33g/Sa4kfrz2x9gHqcd8LsneDu4qf5YK5zQeaPwMvT4ni9PKWZpZhssWACnVHBWJ6szyZh2OhChEMjTJrr5ohzBuWcpkffZKHqh+lTzc/CjZx7b2Z5geu5RFiddiquG/b8fSkvxkshJcPLjxGNPTY/niGhkDLcR4J31eQpxFn7+P+zfeT4e3g59f9XPS3GnD3oY7OMDnDj9CyFJ8rucBME0+MnMTV+QeDCfOImpqrQ5qrVMrpRT31VPcV3+ONaKrqjuTqu7M8y84RbU7E9mUtoi7O+r5Oz2HbNdsyvt38WjVAzR5j0U7vPP6x+tmccuibP5z/RE2HGiKdjhCiPOQnmchTtPt6+Yrr3+Fw+2H+eGVP2Re6rxBl0/v2H1Gm6FDfKhqG6m+dj4V+BYp6b3ckLkL0xh+yTkx8g6HGgGYZqScaLu0I1zFoTwuNyoxDWZv6wwAChMnzklxQ3W8esbF2pU8ixl9DfxN1Us0Lv4HDruKKevbyu+q/oHlKXewOu0e7IZrRPY10gxD8eO7FlPf6eGBP+3hV45lrJmVHu2whBDnID3PQpykw9vB51/9PEc6jvCTtT/h2oJrh70Nf9BkcXkFs/11/Kv6NHnFbazMPiyJsxCjSSlezl6OpUw+Vfp7cux5fL7o/7Io8Vq2dzzHbyr/loq+M7/ojhcuu8lvPrOM6Wlx3PfYTl7a1xjtkIQQ5yDJsxARrQOtfG7D56jsruQXV/+Cq/KvuoBtJJJytJfrQu/xlHMtsTM9ZMR0j0K0QojT9dhjeXLm3eQONPCRyudxG3HcnH0/n8j/Lwxl5091/8wLDT+gP9gZ7VDPKi3OyZNfXEnJtCS+9uR7PLnj3FN9CyGiR4ZtCAE09DXwxde+SMtACw9d+xCXZl06rPW1hv1tRWQ2d/K39nXsiJlNTV4GptKjFLEQ4myOJM/ltdxruK7+Day65yifdif5MQu4r/BBtrY/zdaOp6no2801GZ9nYeK146K6xRPbT02Sb1mYQ48nyHf+vJ+3Slu58ixDOO5ZkT9W4QkhTiM9z2LK21C1gTvX3Um7p51fXferYSfO3qCdDVXLiWvy8l/2X1PhzmJT3kJkphMhouO1addRmjiLZQf/DyndBwGwGXZWp3+C+wofJN1ZwEtNP+Op2v9Np3/8DY9w2Aw+ubKAxXmJvHKwifUHGtFavogLMV6oifSGXLZsmd61a1e0wxCTxEBggP/c8Z/85dhfWJS2iO+v/j7TEqYNaxv3Pv0oO/fHcZW1l5/Zf0mjK4Vn867EfwFl7cTY6dc+AGKV80RbfGAAgN4LmHp9tPX6wye6xTu8UY5k/GtNWQpATKCfB/b9HK0UP1/49wzYY08so7Wm1nOQst4tWNpiZvxyCmJKWJp8S7TCPitLa9a938D2yg6WFSRze0kuphH+Ui49z0KMPqXUbq31GZMAyLANMSUdaDvAtzZ9i9reWr6w8At8peQr2I2hJ7yWpfnVOxW8tSedu+1v8h/2R6h3p/Fs3moCw9iOiI6Tk+bjxmPSfJwkzUN3cvWbdVnLuKf2TT5z6H94Lm81Wn3wY6tKWUqGs5BDPW9T2ruFRs9RprnnkeEqikbYZ2UoxW2Lc4hxmGwsbaWl18fdl04jKcYR7dCEmNJk2IaYUloHWvne1u/xyZc/id/y88gNj/D3S/5+WIlzZ7+fzz++i++vP8IXEl/j/xi/pTYmnWfz1kjiPEFUhdqoCrWd0janp4Y5PePzBK2jnTkc7cyJdhgTTpM7ldczljB9oImbmnaET044icuM45KkmylJuhGv1cejVffzTusfCenxU4ddKcV187L4m2XTaO7x8t9vHuVgg5yELEQ0Sc+zmPSeKXsGX9DHloYtbGvcRkiHWJq5lLXT1lLZU0llT+WQt1XdYvCHjQ56PYofzniFu+oepzImi+dzryBoyNtpoiizwvWSC80PJr8p6QpPpnEkYfz9HH6wPdwbOjO5IcqRTDz7koqJCflY07afgLLxWubSU85HUEqR5ZpBiiOXZm8Fm9ufoLTvXW7OeoAc96woRn6qxdOSyEt289TOWv64vQbTUHz35rm47Ga0QxNiypFPezGp9fp72dKwhS31WxgIDjA/dT5X5V9Fiivl/CufRGvYdNDGyzvtpMf4eWz6Yyyue4tDSXNYn76AkCEfYEKMV9tS5uKwgqzsOEzAsPFW+uIzlnEYbm7L+QbzEtawoelBHq/+OsuSP8TqtE/iNMfHkJ7UOCdfunI6rx5s5vGt1eys6uTBey6hOD0u2qEJMaVI8iwmpdqeWv545I88f/R5BoIDFCUWcU3+NeTEDf+n734vPLPZwcEaG2vyWvmx9XPS6yo4Mu9GfpewlrTOPaPwDIQQI0YpNqUtxG4FWN5Zit+w8ULqGecAATAjbjmfL3qIt1p/x87OFynt3cL1mV9mZvzKMQ767GyGwc0Ls/ncqkK+8cw+PvSLzfzjdbP49GWFOGwyElOIsSDJs5g0QlaIrY1bebr0ad6qfQvTMLmx8EayYrMuKGkGKK0zePodB/0+xQPzD/Cl+gexB31sXfVl6vKXoSs7RvhZCCFGhVK8kbEEuxViVftBeuve5M3cq85aUtJlxnJj1ldZkHg1G5p+wbP1/8bsuMu5NvNLJNjTzrLxsXf1nExe/vvVfPvP+/j3lw7zxPYa/vctc7l6Tsa4qF0txGQmybMYf3Y9OqzFK30dvNB1kHVdh2gJ9pNkuvh82nLuTllMhj2OZ9r2QVsVFF4+5G36g/DyTjvvHraTn+jh4eJ1rCxfR39sKpuu/jo9SbnDfFJCiKhTileylmHTIW6q3UBefx3PFN+Jx3b2YRl57rncW/hztnc8z7ttT1JesZvlKR9mZcqd42IoR1aii9/du5yNR1r4t5cOcd9ju1g9M41/unUeszLjox2eEJOW1HkWo+KZsmeGtfxds+764M55kudnOvbRGfRwxNvCYW8LdYFuFIoZzlRK3NnMdKVjU2f5+XKIyXNtm8GTbzto74ZvTXubT/U9S4ynk9r8Zexe/mkCjg8+NHdUdpxSGktMDN5INQWX+qA6ijsYrv3ssZ1Zxi7aPMFwaTK3zR/lSCYJrZnr6+fmmvV0OxJ5YuY9VMcXDLrKQLCbsr5tNHmP4jDcrE3/LCVJN2KqofVB7elaP6wQL0m6adDHT6/zHAhZ/H5rNT97vYx+f4h7ludz/7UzSYsbf69nISYKqfMsJjRLa4762nirt5xnO/bTFOwFINMWx7XxM1jozibevLgPiZAFG/fZeG2PjWvdB/he2hNktdbQkVLI9iu+QFvG+DnzXlyck5Pm48Zj0nycJM0jTCk25VxJZXwRnzz6R75y4CE25N/I2zlrTqkFfbIYWyIlSTfQHSihtHcLrzY/xM6OF1iZ+lHmJ6zFbrjG+Emcym4afG5VER++JJefvV7GH7fX8KddtdxRkst9q4ukJ1qIESQ9z2JUXGzPs9aa+kAP2/tr2NZXRRIalwAAF3FJREFUw47+GjpCHgDy7ImUmPEswU2upXEEfTgCXkwrhN/uwmd34bO58NmdeO1ufHZ3eFzjID3P1VUe2nceZZFnP9c495ERaqffGcf+gmXUphadc6rtyvb+YT1PMT6Uh1oAKDYzTrQt6A6XLDyQOH4myTjuSEd45ss5KbVRjmTyOD4ToSvo4c6K51jcvo/6mBy2ZF3GnrRLCJjnnohEa02CPZ1Nrb+n2VeOy4hjcdINLEm6hSRH5lnXGe2e59NVtPbx282VPPdeHd6AxeqZady3qogrZ6XLmGghhkh6nsW4FrSClHaWsrdlL3tq/8qegQZagn0AZNhiucKdzcqA4rLudhJqS3FGfmIfCkspvPYYPKVv4YlJwlImphXECAUI+YN4uz18JFCDqTRep5u27Dnscs6hOr0YS2o3T0rlViswkZLncKIkyfPI89rc/GHmJzicPJcrG97mrornuLX6JXanL2Vr5kpaYs5MhpVSzIi7lOLYZdR5DrGr80V2dDzPjo7nKY5bxpz41cyIuxS3Gb3e3unpcfzHHQv5xvWzeWJHDY9tqeKzj+5kZkYcn7qsgFsX5ZASKzMVCnEhJDMQURG0gjT0NVDdU01NTw0/2vkjBoIDAGTZ41kak0uJM5WV3R0U1e1F9R8Or+hKpDI5n9bELAacsfhtzsjFRcgwcAR9OANenAEfzqAXl38A9/GLaSehuxGlLQKGnU6vnXaPA59KoCzjQ9gXzqU7owht2KBqSxSPjhBiTCnF7vSl7E5bQmFvNZc1b2Fl8zZWNb1Lny2WNncaba40Wl1pdDqTsZRBwYBGaYsiNGv0XLqdmbwdPMjWnt38tW8HpoaSkJvLQvEss+JJs7rxmE567TH02dz02Vw0py4fcojFNaf9mmcOUqt+2b0nbibHOvjqVTP4wurpvLS/gd9uruSfXzjI99YdYu3sdD58SS7Xzs2UyVaEGAZJnsWY6fJ2UdZZRllnGTU9NQR1EIAMdwYfKv4QSzOXckl6CVlb/i9UvwuNb4IVgpTpULgK0mZBXCa7Ovefcx9+uxu/3U3v2R4svJw+D+wos7HpoJ0BL1w6K8SNS/3Eu0fnOQshJhClqEoopCqhkBcL+yhp20v2QBNp3jZmdh1lWeDkk4OfOGP1GwEL2O908EZMDK/HBnjQ6QFamO4PsNLjZUW3l0s9XuK0ptf+Cq3udJrdmTS7M2iOyaQpJot++8hPeuKwGdxxSR53XJLH4cYe/rK3nhf2NPD64RbinDZuWpDFbSU5rJyeit2UetFCDEaSZzGqmvubOdh+kLKOMlo84XGmqa5UlmYupSCxgPz4fGLsMdw1/XY48BxsuAua94PNBfmXhy8J2RcVg9ZQ1Z3I1rcd7Ks0CVmKmTkhbrnUT27qB2P+d5xUszm9Q8YyCzGV9dvjeDd71Slt9pCfJH8XSmvmJFwJykBjoJVByHQSMpwU1L+EVgZXKMXlVogGq51DoQa2OMp52t7HE4nxGBoKQgYLLDdXDPSzpm0Pl4e8J/bTbY+nP+lFOuPn0Jkwm86EueE/ZCeNVd4+SI358lDNeZ9fQUosX7t6BpVt/eyt6eLF9xt4ZncdbrvJnKx45uUkMDMjns9eUTj8gyfEJCfJsxhxzf3NbKnfwv62/TQPNKNQFCQUcF3GdcxKnkWqO/XEsnZfH8UHX4YX/z/oa4L0ubDwbyB3CVxk9QNPwMbelky21ufR2BePy65ZOSfIZbODZCZPnBNlhRDRdXo5Sg1kBM9+8ro2Phj+oAyTXCODXFsGjqCbkLZo0700Wt00Gt38VfexzgG2pCzyiGN+0MFKX5Bibw/Te46S1boFAwuAkOGg35VFvzubflc2/e5svI60c57MPBSGUhSnx1GcHsdtJTkcbe7jUGM3hxt72VPbhd1UbK1o4/p5WaydnU6qlL0TApBqG2KE+EI+3qx5kz8f/TPbG7ej0eTG5bIofRHzU+cTYz91QoGkjiqmH3uHgsqt2EJ+mrLmUTbnepqz50P11guKwROwUdmdRHlnMhVdydT3xqNR5MT1cnluLSXLC3GeWaHshFN7nqV282QW1CEAbOqDRMdmhYcRBcfhSaIBKxyn3QhFOZLJ43i1jaEYrb8HPh2kWXfTaHVTb3XST7gkYZqKY41tFpeoXIoDfmK9TcR4m4j1NBDjbcbUx1+rznAy7c6hz51LvzsXvz2B8vy7BtvteYUsTWVbP4cau6lqG6Cpx4tSsCgviatmp3PV7AwW5iZiGFK1Q0xu56q2IcmzuChlnWU8f/R51lWso9vXTU5sDrfNuA1Tmaf0MAPY/AMUVG2nqPwdkjtrCJoOagsu5ejsa+lOnvbBgoOcrBeyFD1+B11eF11eF51eF50+N3U9CSeSZZsRIsPdSU5cGwXxLWTEdKLU+PiwFEKMD+Pt74HWmk49QJ3upM7qoF2Hh45lq0SWmgUssRUwTaWg0Lh9bcR6GojzNIQTal8Thg73UPtsCTSlraQ9aRFtSYvpSJiLdRE18O++dBoHG3p4q7SFjaUt7KntQmtIjXWwZlY6K6ensKIolYLUGCmBJyYdSZ7FiGn3tLO+cj3rKtZxqP0QNsPGNfnX8JGZH2FF1go8Ac0TB/+Cxweqr4f85n3MbH+fmd0HcFh+al35bEq8ki1xl9GrYrGs8AQlIUuFr3vbCIQM/CETv2XiD5kEQuHrvoAdS596MkuMzU9WXD/FyR0UJ3WSn9BDXdeZpwyOtw9LET2loSYAZptZJ9pKOo8CsDd5ZlRiGsyBtkIAFqRVRTUOMXYWGnnsDdWwO1RNmdWMRpOu4lls5rHQnMYsIxN75JcTZQWJ8TYT56kjbqAOV6CTOE8DACFlpzNhLm3J4WS6LamEAVfmkId72JO3n3K/3wtl9SaH60zK6k36veHtJLgtirIs7ly0mEuLUpiRHodNTjwUE5wkz+KCaa1p6OlkQ8VGXqtZz6GunWgsXFY+Mf7lqP6lDHhc9PuC2AI9LFAVLFVHudrcQ4lRDkC9TuWN0BKeCV3Jfl0EKJTSGEpjGERug6E0Nu3FZoSwRy42I3jifozNS5zDQ7zdQ5xjgHi7B7s5tJ+yJXkWx70aOAjA9fb5J9rurnkTgKfyr45KTIP5y7ErAPjwjHejHIkYK1faZp+43aM97A3V8l6wmlKriQAhnNiYZ+Yw38hhuplOjkrGFpkdsTz/LlzeVtK69pHW9T5pXe+T0n0QmxWZgt6RQmfiPNoT5tOROI/OhLkMuLLOmlAPNrmL1tA7YKO1y0lbt5PWLgceX3jYk8NmMCM9jjlZ8czKimd2VjxzsuLJjHeN6HCPJ7af/+TI051vghkhjruoSVKUUjcCPwdM4Dda6++f9riKPH4zMAB8Vmv93mDrKqVSgD8BhUAV8DGtdeeFPDlxYQIhi84BP229flr7fLT0eGnt89HaG75d11dFY3AP/eZ+lKsKpSysQCKB7tWEuktIt+zMczQz23yDeaqSOfaj5Bp1AFgoyp0FPBt/CwcS59IYk41pg0JDM8NowDT1OTs+JHEVQogPJCg3a2yzWGObhU8HOWI1si9Ux/5QHXtCNRAAByb5RipFRhqq0028LZX4xHzi0y4hxkzE1BbJvWWkdu0jpfsQyT0HKGrbSrah6VcG3TY3LbG5NLszaHOl0mmPo8fmoDrUjE8pLB3CItxRYWCglImBgaFM7ElOcpNdFCo3+c5VFCSl09jh5Fizny3l7fx5T/2J52IzFOnxTjISXGTEO8lMcJIR7yLRbcdtN3HaDdx2E7fDxG03sZsGpqEwlMI0FKYRnqTGjNzvGvBjKIVSYCqFYYTbbYaSYSRi1Jw3eVZKmcAvgeuAOmCnUupFrfWhkxa7CZgZuawAHgJWnGfdbwNvaK2/r5T6duT+t0buqU1+Wmu8AYteX4A+b5A+X5Beb5CX9jXiDYTwBkJ4AhbeYAivP8SAP0S/P8iAP8SAP4g3YJ3YljL7MFx1mO567O56TFct2hUe+pAWSmaev4iVQQfL/QOkh7aQZPsTNssfLmpqgd8WR19MLrXuq+hz5/KqvR9fZHrbGGop9tcSORcmaiQpF0JMdE5lY7E5jcXmNLTWtOk+Kq22yKWVt4KlBJoPnbGeQmFEri2HRqdpdFruaUsNAFUQqiKSJ2PTGqfW2LXCRIFSBJQiCASBgLI4+ffrPbwE4aqkxJg2UnJdFOa4cYZiUYF4jGA8ViABX18yTW3J7Aul0es3CGAjgAmMXMJ7PIm2GQqbaWA3DRw2xfN76nDZTWIiCbrbYYtcG8Q4bCcec0USeafd5N2jbdgj27Cb6pTb5jkS9bHq4Zbe97E3lJ7n5cAxrXUFgFLqKeB24OR35+3A4zo8BmSbUipJKZVNuFf5XOveDqyNrP8Y8BYTNHnWWqM1hLTGitwOhCxCliYQ0gQti2BIE7Q0/qCFLxjCF7TwBT647fGHGAiE8ESSW89JyW6/L0i/L0SfL3x7wB++3ecLErI0YGESwkYAQwWxKT925cNu+HAYXlx2L267h0T7ABnuAcz4AQJmLx6zlz5jgC7Di0eF/1IqDQXBEAu8XpZ4vawe8JIVqRkaMGPpd2fTFzeNsszV9MYWEt9XiceZRtCMOeUnP1+wNBr/FUIIMWUopUhX8aQb8SwnPK28pS168NJlDdCpB+jSA/RoDxqNBWg0NVY7CoVdmdgJX2zKxIGJAxtxlkVa0Ed6wENSYIDE4ABO5SA20E9ssJ/YQD8xwQEMNBoYUIpO06DbMOk0DTpMkxbTpMkWvm62mVSbNtrdZ85i6LIsCkIh0kMhUkKaRK1IwCRe2YnDjstwYTdcOMxYbEYsdjMGbHFoMxbLjKW8QxFQboLKhR8XPlz4cODVdnyWiU+beLQZvh0CXwjsCnq9QVp6fAwEgnj8Ft5AuFPJuoCRrAo+SKptkSTdNPjLnvoTPelOu4nTZkQu4cTcYTOwGQrTOH6tsJnhXnZNOLewLI2l+SC3sCwCQU0gZOEPWfiDFocaewhZmpAVzjPCty2ClsayItvROrLN8P0/bq8+0aNvM8I99se/ZDht4diOx+q0GTjtBi6bicsejv3Etc2MLBu+dtjCz91pN7AbBjbzg+2ahsIeeX7hC+f84jHeDSV5zgVqT7pfR7h3+XzL5J5n3UytdSOA1rpRKZUxjLjHzAt76/nms/s++Gatw398tA7X+jz+gh5pTptBjMMkxmEj1mmyxDjK97q+g0KjgE4F18/KCnf8nuWFF4hcBs6x/TjLIjsYZKbPItMyyNJOphNHnpmJ4UzHm5SGx5nGEWc6u93ZDLiyCdjjz9jOGVPGCiGEiBpDGSQRQ5IZQ+E5lnn7fJ0bBvTY4ulxfdB0+jkjSlu4Ql7soQAOy4/DCuAI+ZkTU8KslreYpcPTlysslNagLYL+EB34acNHu/LRhZ8GVxLdtgFCDj9VQQ/dlo8uy08QzQefZL1Aa3jHJzcDnKWQiNI60sseuWhQaP5c30R+MAjdxxc0whcU2BTabqBzltB794sMBMK/znr8IbzBEC/tayQQsgiEdOQ6fDsYSWKDIR25tvCHNIGghc1U9PmCtPb6Ih1n4Q4zb6TjLBC68OTheCLqMA1Clj6RnB7vbTcNdWIYi2Eo7MpAwYkhLlkJLkJan0i6w519Fv3+0IlOvhMxB0J4g+FEfbSEz3kK5zJKhX8hifwjPyWG1/7xylHb94UYSvJ8tq8Ep/+Pn2uZoaw7+M6V+iLwxcjdPqXUeOrSTAPaxmJHrwH/dUZr+1jsejwbs+MvzmrCH/83+KAs4gc/e5VHI5Tz+DUAZ6mAPuH/Dya4KXT8fx3tAM5mWMe/YEhLbYD7HBcaz1QzJq//MkB9fbT3ck5nfdkMJXmuA04qwkse0DDEZRyDrNuslMqO9Dpnc2KU1Km01r8CfjWEOMecUmrX2c7CFGNDjn90yfGPPvk/iC45/tElxz+6pvLxH0oRxp3ATKVUkVLKAdwNvHjaMi8Cn1ZhK4HuyJCMwdZ9EfhM5PZngBcu8rkIIYQQQggxqs7b86y1Diqlvga8Qrjc3CNa64NKqS9HHn8YeJlwmbpjhIfZ3jvYupFNfx94Wil1H1ADXNx8okIIIYQQQoyyIdV51lq/TDhBPrnt4ZNua+CrQ1030t4OXDOcYMehcTmcZAqR4x9dcvyjT/4PokuOf3TJ8Y+uKXv8J9QMg0IIIYQQQkSTTDwvhBBCCCHEEEnyfIGUUjcqpUqVUsciMySKMaSUqlJK7VdK7VVK7Yp2PJOdUuoRpVSLUurASW0pSqnXlFJHI9fJ0YxxMjvH8f8XpVR95D2wVyl1czRjnMyUUtOUUhuVUoeVUgeVUvdH2uU9MAYGOf7yHhgDSimXUmqHUur9yPH/10j7lH39y7CNCxCZdryMk6YdBz5+2pTlYhQppaqAZVrrKVJjNbqUUmuAPsIziS6ItP0A6NBafz/yBTJZaz0hZwkd785x/P8F6NNa/yiasU0FkXKq2Vrr95RS8cBu4MPAZ5H3wKgb5Ph/DHkPjDoVngIwVmvdp5SyA5uB+4GPMEVf/9LzfGFOTFmutfYDx6cdF2JS0lpvAjpOa74deCxy+zHCH2ZiFJzj+IsxorVu1Fq/F7ndCxwmPIOuvAfGwCDHX4wBHdYXuWuPXDRT+PUvyfOFOdd05GLsaOBVpdTuyCyUYuxlRuq5E7nOiHI8U9HXlFL7IsM6psxPptGklCoELgG2I++BMXfa8Qd5D4wJpZSplNpLeEK717TWU/r1L8nzhbnoacfFRbtCa70EuAn4auRnbSGmkoeAYqAEaAR+HN1wJj+lVBzwHPCA1ron2vFMNWc5/vIeGCNa65DWuoTwTNHLlVILoh1TNEnyfGGGMmW5GEVa64bIdQvwPOGhNGJsNUfGIh4fk9gS5XimFK11c+QDzQJ+jbwHRlVkrOdzwB+11n+ONMt7YIyc7fjLe2Dsaa27gLeAG5nCr39Jni/MUKYsF6NEKRUbOWkEpVQscD1wYPC1xCh4EfhM5PZngBeiGMuUc/xDK+IO5D0waiInTP0WOKy1/slJD8l7YAyc6/jLe2BsKKXSlVJJkdtu4FrgCFP49S/VNi5QpCTOz/hg2vH/iHJIU4ZSajrh3mYIz5L5hBz/0aWUehJYC6QBzcD/D/wFeBrIB2qAu7TWclLbKDjH8V9L+OdqDVQBXzo+/lCMLKXUKuAdYD9gRZq/S3jcrbwHRtkgx//jyHtg1CmlFhE+IdAk3On6tNb6e0qpVKbo61+SZyGEEEIIIYZIhm0IIYQQQggxRJI8CyGEEEIIMUSSPAshhBBCCDFEkjwLIYQQQggxRJI8CyGEEEIIMUSSPAshxASilAoppfYqpQ4opdadVH+1UCmllVL/dtKyaUqpgFLqwcj92UqptyLrH1ZK/SrSvlYp1R1pP365NjrPUAghxjdJnoUQYmLxaK1LtNYLgA7gqyc9VgHcetL9u4CDJ93/b+CnkfXnAr846bF3Iu3HL6+P1hMQQoiJTJJnIYSYuLYCuSfd9wCHlVLLIvf/hvAkBsdlA3XH72it9496hEIIMclI8iyEEBOQUsoEriE8Re7JngLuVkrlASGg4aTHfgq8qZRar5T6h+NDPiJWnzZso3hUn4AQQkxQkjwLIcTE4lZK7QXagRTgtdMe3wBcR3jq4j+d/IDW+lFgLvAM4em9tymlnJGHTx+2UT6Kz0EIISYsSZ6FEGJi8WitS4ACwMGpY57RWvuB3cDXgedOX1lr3aC1fkRrfTsQBBaMfshCCDF5SPIshBATkNa6G/h74BtKKftpD/8Y+JbWuv3kRqXUjceXVUplAalA/VjEK4QQk4Ukz0IIMUFprfcA7wN3n9Z+UGv92FlWuR44oJR6H3gF+F9a66bIY6ePeb5zVIMXQogJSmmtox2DEEIIIYQQE4L0PAshhBBCCDFEkjwLIYQQQggxRJI8CyGEEEIIMUSSPAshhBBCCDFEkjwLIYQQQggxRJI8CyGEEEIIMUSSPAshhBBCCDFEkjwLIYQQQggxRP8PHNVa2S6gQvsAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(12, 8))\n",
+    "\n",
+    "for i in range(len(rmses)):\n",
+    "    rmse = rmses[i]\n",
+    "    mean = means[i]\n",
+    "    label = labels[i]\n",
+    "    color = colors[i]\n",
+    "    sns.distplot(\n",
+    "        drop_nans_and_flatten(rmse), ax=ax,\n",
+    "        label=label, color=color\n",
+    "    )\n",
+    "    ax.axvline(mean, ls='--', color=color) # , label=f'{label}_mean'\n",
+    "\n",
+    "plt.legend()\n",
+    "ax.set_xlabel('RMSE');\n",
+    "ax.set_title('Model Performance Comparison (RMSE)');\n",
+    "# ax.set_ylabel('Density')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Baseline EALSTM RMSE:  12.48606477255771\n",
+      "Best performing EALSTM RMSE:  12.754261017040395\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "ealstm_rmse.plot(vmin=5, vmax=20, ax=ax)\n",
+    "ax.set_title('EALSTM Predict Delta')\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "ealstm_base_rmse.plot(vmin=5, vmax=20, ax=ax)\n",
+    "ax.set_title('EALSTM')\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "(ealstm_rmse - ealstm_base_rmse).plot(ax=ax)\n",
+    "ax.set_title('Difference in EALSTM RMSEs \\n[ealstm_rmse - ealstm_base_rmse]')\n",
+    "\n",
+    "\n",
+    "print(\"Baseline EALSTM RMSE: \", ealstm_base_rmse.mean().values)\n",
+    "print(\"Best performing EALSTM RMSE: \", ealstm_rmse.mean().values)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Regional Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Initialised the Region Analysis for experiment: one_month_forecast\n",
+      "Models: ['ealstm', 'linear_network', 'rnn']\n",
+      "Regions: ['district_l2_kenya.nc']\n",
+      "Region Type: administrative_boundaries\n",
+      "GroupbyRegion requires geopandas to be installed\n"
+     ]
+    }
+   ],
+   "source": [
+    "%autoreload 2\n",
+    "from src.analysis.region_analysis import RegionGeoPlotter, AdministrativeRegionAnalysis\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "analyzer = AdministrativeRegionAnalysis(\n",
+    "    data_dir=data_dir, experiment='one_month_forecast', \n",
+    "    models_experiment_dir='one_month_forecast_predict_delta'\n",
+    ")\n",
+    "\n",
+    "r = RegionGeoPlotter(data_dir)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "region_path = analyzer.region_data_paths[0]\n",
+    "admin_level_name = region_path.name.replace('.nc', '')\n",
+    "region_da, region_lookup, region_group_name = analyzer.load_region_data(region_path)\n",
+    "valid_region_ids = [k for k in region_lookup.keys()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(12, 8))\n",
+    "region_da.plot(ax=ax)\n",
+    "ax.set_axis_off()\n",
+    "\n",
+    "# region_da.where(region_da == dict(zip(region_lookup.values(), region_lookup.keys()))['NAIROBI'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_region_mask(region: str):\n",
+    "    lookup = dict(zip(region_lookup.values(), region_lookup.keys()))\n",
+    "    region_id = lookup[region]\n",
+    "    \n",
+    "    return ~region_da.where(region_da == region_id).isnull()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(12, 8))\n",
+    "\n",
+    "region = 'TURKANA'\n",
+    "msk = get_region_mask(region)\n",
+    "sns.distplot(drop_nans_and_flatten(bline_rmse.where(msk)), label='Baseline')\n",
+    "sns.distplot(drop_nans_and_flatten(ealstm_rmse.where(msk)), label='EALSTM Predict Delta')\n",
+    "sns.distplot(drop_nans_and_flatten(ealstm_base_rmse.where(msk)), label='EALSTM')\n",
+    "ax.set_title(f'{region} RMSE Comparison')\n",
+    "plt.legend();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Make region dataframe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from src.analysis import AdministrativeRegionAnalysis\n",
+    "# analyzer = AdministrativeRegionAnalysis(data_dir=data_dir)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Initialised the Region Analysis for experiment: one_month_forecast\n",
+      "Models: ['ealstm', 'linear_network', 'rnn']\n",
+      "Regions: ['district_l2_kenya.nc']\n",
+      "Region Type: administrative_boundaries\n",
+      "* Analyzing for district_l2_kenya *\n",
+      "\n",
+      "** Analyzing for ealstm-district_l2_kenya **\n",
+      "** Written ealstm csv to /Volumes/Lees_Extend/data/ecmwf_sowc/data/analysis/region_analysis/ealstm/ealstm_district_l2_kenya.csv **\n",
+      "\n",
+      "** Analyzing for linear_network-district_l2_kenya **\n",
+      "** Written linear_network csv to /Volumes/Lees_Extend/data/ecmwf_sowc/data/analysis/region_analysis/linear_network/linear_network_district_l2_kenya.csv **\n",
+      "\n",
+      "** Analyzing for rnn-district_l2_kenya **\n",
+      "No matching time data found for rnn\n",
+      "Contents of rnn dir:\n",
+      "\t[]\n",
+      "* Assigned all region dfs to `self.df` *\n",
+      "\n",
+      "* Assigned Global Error Metrics to `self.global_mean_metrics` *\n",
+      "* Written csv to data/analysis/region_analysis/global_error_metrics_one_month_forecast_admin.csv *\n",
+      "\n",
+      "* Assigned Regional Error Metrics to `self.regional_mean_metrics` *\n",
+      "* Written csv to data/analysis/region_analysis/regional_error_metrics_one_month_forecast_admin.csv *\n"
+     ]
+    },
+    {
+     "ename": "PicklingError",
+     "evalue": "Can't pickle <class 'src.analysis.region_analysis.administrative_region_analysis.AdministrativeRegionAnalysis'>: it's not the same object as src.analysis.region_analysis.administrative_region_analysis.AdministrativeRegionAnalysis",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mPicklingError\u001b[0m                             Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-78-f6c87b2ef97e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_dir\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m'analysis/admin_region_analyzer_predict_delta.pkl'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'wb'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m         \u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdump\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0manalyzer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mPicklingError\u001b[0m: Can't pickle <class 'src.analysis.region_analysis.administrative_region_analysis.AdministrativeRegionAnalysis'>: it's not the same object as src.analysis.region_analysis.administrative_region_analysis.AdministrativeRegionAnalysis"
+     ]
+    }
+   ],
+   "source": [
+    "import pickle\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "if (data_dir / 'analysis/admin_region_analyzer_predict_delta.pkl').exists():\n",
+    "    analyzer = pickle.load(open(data_dir / 'analysis/admin_region_analyzer_predict_delta.pkl', 'rb'))\n",
+    "else:\n",
+    "    analyzer = AdministrativeRegionAnalysis(\n",
+    "        data_dir=data_dir, experiment='one_month_forecast', \n",
+    "        models_experiment_dir='one_month_forecast_predict_delta'\n",
+    "    )\n",
+    "    analyzer.analyze()\n",
+    "    \n",
+    "    with open(data_dir / 'analysis/admin_region_analyzer_predict_delta.pkl', 'wb') as f:\n",
+    "        pickle.dump(analyzer, f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The RegionGeoPlotter requires `geopandas` to be installed.\n",
+      "GroupbyRegion requires geopandas to be installed\n",
+      "Reading file: KEN_admin2_2002_DEPHA.shp\n",
+      "Reading file: Ken_Districts.shp\n",
+      "Reading file: Ken_Divisions.shp\n",
+      "Reading file: Kenya wards.shp\n",
+      "Reading file: Ken_Locations.shp\n",
+      "Reading file: Ken_Sublocations.shp\n",
+      "* Read shapefiles and stored in `RegionGeoPlotter.region_gdfs` *\n",
+      "* Assigned the complete GeoDataFrame to `RegionGeoPlotter.gdf`\n"
+     ]
+    }
+   ],
+   "source": [
+    "region_plotter = analyzer.create_model_performance_by_region_geodataframe()\n",
+    "df = analyzer.df\n",
+    "gdf = region_plotter.gdf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "GroupbyRegion requires geopandas to be installed\n",
+      "Reading file: KEN_admin2_2002_DEPHA.shp\n",
+      "Reading file: Ken_Districts.shp\n",
+      "Reading file: Ken_Divisions.shp\n",
+      "Reading file: Kenya wards.shp\n",
+      "Reading file: Ken_Locations.shp\n",
+      "Reading file: Ken_Sublocations.shp\n",
+      "* Read shapefiles and stored in `RegionGeoPlotter.region_gdfs` *\n",
+      "* Assigned the complete GeoDataFrame to `RegionGeoPlotter.gdf`\n",
+      "* Assigned the complete GeoDataFrame to `RegionGeoPlotter.gdf`\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>admin_level_name</th>\n",
+       "      <th>model</th>\n",
+       "      <th>datetime</th>\n",
+       "      <th>region_name</th>\n",
+       "      <th>predicted_mean_value</th>\n",
+       "      <th>true_mean_value</th>\n",
+       "      <th>DISTNAME</th>\n",
+       "      <th>geometry</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>district_l2_kenya</td>\n",
+       "      <td>ealstm</td>\n",
+       "      <td>2011-01-31</td>\n",
+       "      <td>NAIROBI</td>\n",
+       "      <td>34.205864</td>\n",
+       "      <td>56.869999</td>\n",
+       "      <td>NAIROBI</td>\n",
+       "      <td>POLYGON ((36.90575473150634 -1.159051062893981...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>district_l2_kenya</td>\n",
+       "      <td>rnn</td>\n",
+       "      <td>2011-01-31</td>\n",
+       "      <td>NAIROBI</td>\n",
+       "      <td>57.911991</td>\n",
+       "      <td>56.869999</td>\n",
+       "      <td>NAIROBI</td>\n",
+       "      <td>POLYGON ((36.90575473150634 -1.159051062893981...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    admin_level_name   model   datetime region_name  predicted_mean_value  \\\n",
+       "0  district_l2_kenya  ealstm 2011-01-31     NAIROBI             34.205864   \n",
+       "1  district_l2_kenya     rnn 2011-01-31     NAIROBI             57.911991   \n",
+       "\n",
+       "   true_mean_value DISTNAME                                           geometry  \n",
+       "0        56.869999  NAIROBI  POLYGON ((36.90575473150634 -1.159051062893981...  \n",
+       "1        56.869999  NAIROBI  POLYGON ((36.90575473150634 -1.159051062893981...  "
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "orig_analyzer = pickle.load(open(data_dir / 'analysis/admin_region_analyzer_BEST.pkl', 'rb'))\n",
+    "\n",
+    "orig_region_plotter = orig_analyzer.create_model_performance_by_region_geodataframe()\n",
+    "orig_df = orig_analyzer.df\n",
+    "orig_gdf = orig_region_plotter.gdf\n",
+    "orig_all_gdf = orig_region_plotter.merge_all_model_performances_gdfs(orig_analyzer.df)\n",
+    "orig_all_gdf.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "* Assigned the complete GeoDataFrame to `RegionGeoPlotter.gdf`\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>admin_level_name</th>\n",
+       "      <th>model</th>\n",
+       "      <th>datetime</th>\n",
+       "      <th>region_name</th>\n",
+       "      <th>predicted_mean_value</th>\n",
+       "      <th>true_mean_value</th>\n",
+       "      <th>DISTNAME</th>\n",
+       "      <th>geometry</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>district_l2_kenya</td>\n",
+       "      <td>ealstm</td>\n",
+       "      <td>2011-01-31</td>\n",
+       "      <td>NAIROBI</td>\n",
+       "      <td>50.472749</td>\n",
+       "      <td>56.869999</td>\n",
+       "      <td>NAIROBI</td>\n",
+       "      <td>POLYGON ((36.90575473150634 -1.159051062893981...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>district_l2_kenya</td>\n",
+       "      <td>linear_network</td>\n",
+       "      <td>2011-01-31</td>\n",
+       "      <td>NAIROBI</td>\n",
+       "      <td>20.219782</td>\n",
+       "      <td>56.869999</td>\n",
+       "      <td>NAIROBI</td>\n",
+       "      <td>POLYGON ((36.90575473150634 -1.159051062893981...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    admin_level_name           model   datetime region_name  \\\n",
+       "0  district_l2_kenya          ealstm 2011-01-31     NAIROBI   \n",
+       "1  district_l2_kenya  linear_network 2011-01-31     NAIROBI   \n",
+       "\n",
+       "   predicted_mean_value  true_mean_value DISTNAME  \\\n",
+       "0             50.472749        56.869999  NAIROBI   \n",
+       "1             20.219782        56.869999  NAIROBI   \n",
+       "\n",
+       "                                            geometry  \n",
+       "0  POLYGON ((36.90575473150634 -1.159051062893981...  \n",
+       "1  POLYGON ((36.90575473150634 -1.159051062893981...  "
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# join true/preds into a GeoDataFrame\n",
+    "all_gdf = region_plotter.merge_all_model_performances_gdfs(analyzer.df)\n",
+    "# all_gdf['predicted_mean_value'] = all_gdf.predicted_mean_value * 10\n",
+    "# all_gdf['true_mean_value'] = all_gdf.true_mean_value * 10\n",
+    "all_gdf.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# orig_all_gdf.join(all_gdf, lsuffix='_orig')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['ealstm', 'linear_network'], dtype=object)"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.unique(all_gdf.model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Spatial Patterns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 6 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%autoreload 2\n",
+    "region_plotter.plot_all_regional_error_metrics(\n",
+    "    gdf.where(gdf.model == 'ealstm').dropna(),\n",
+    "    **dict(rmse_vmax=10, mae_vmax=10, mae_vmin=0, rmse_vmin=0)\n",
+    ");\n",
+    "plt.tight_layout(rect=[0, 0.03, 1, 0.95])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 6 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%autoreload 2\n",
+    "orig_region_plotter.plot_all_regional_error_metrics(\n",
+    "    orig_gdf.where(orig_gdf.model == 'ealstm').dropna(),\n",
+    "    **dict(rmse_vmax=10, mae_vmax=10, mae_vmin=0, rmse_vmin=0)\n",
+    ");\n",
+    "plt.tight_layout(rect=[0, 0.03, 1, 0.95])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 6 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%autoreload 2\n",
+    "orig_region_plotter.plot_all_regional_error_metrics(\n",
+    "    orig_gdf.where(orig_gdf.model == 'previous_month').dropna(),\n",
+    "    **dict(rmse_vmax=10, mae_vmax=10, mae_vmin=0, rmse_vmin=0)\n",
+    ");\n",
+    "plt.tight_layout(rect=[0, 0.03, 1, 0.95])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Normalise by variance\n",
+    "- explore the patterns independent of how variable the VCI values are over time\n",
+    "- Controlling for how much the VCI changes, how do our spatial patterns of variability look?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "region_name\n",
+       "BARINGO    13.022800\n",
+       "BOMET      19.003933\n",
+       "BONDO      23.879618\n",
+       "BUNGOMA    20.467308\n",
+       "BURET      14.927562\n",
+       "Name: true_mean_value, dtype: float64"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# gdf.head()\n",
+    "variances = all_gdf.groupby('region_name').true_mean_value.std()\n",
+    "variances.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>model</th>\n",
+       "      <th>admin_level_name</th>\n",
+       "      <th>region_name</th>\n",
+       "      <th>rmse_</th>\n",
+       "      <th>mae_</th>\n",
+       "      <th>r2_</th>\n",
+       "      <th>DISTNAME</th>\n",
+       "      <th>geometry</th>\n",
+       "      <th>rmse</th>\n",
+       "      <th>r2</th>\n",
+       "      <th>mae</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>ealstm</td>\n",
+       "      <td>district_l2_kenya</td>\n",
+       "      <td>NAIROBI</td>\n",
+       "      <td>16.416221</td>\n",
+       "      <td>12.831153</td>\n",
+       "      <td>0.309826</td>\n",
+       "      <td>NAIROBI</td>\n",
+       "      <td>POLYGON ((36.90575473150634 -1.159051062893981...</td>\n",
+       "      <td>10.175254</td>\n",
+       "      <td>0.192039</td>\n",
+       "      <td>7.953124</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    model   admin_level_name region_name      rmse_       mae_       r2_  \\\n",
+       "0  ealstm  district_l2_kenya     NAIROBI  16.416221  12.831153  0.309826   \n",
+       "\n",
+       "  DISTNAME                                           geometry       rmse  \\\n",
+       "0  NAIROBI  POLYGON ((36.90575473150634 -1.159051062893981...  10.175254   \n",
+       "\n",
+       "         r2       mae  \n",
+       "0  0.192039  7.953124  "
+      ]
+     },
+     "execution_count": 108,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # \n",
+    "# gdf = gdf.drop(columns=['norm_rmse', 'norm_r2', 'norm_mae'])\n",
+    "gdf_norm = gdf.copy()\n",
+    "gdf_norm = gdf.rename(columns={'rmse': 'rmse_', 'r2': 'r2_', 'mae': 'mae_'})\n",
+    "gdf_norm['rmse'] = (gdf_norm.rmse_ / variances.loc[gdf.region_name].values) * gdf_norm.rmse_.mean()\n",
+    "gdf_norm['r2'] = (gdf_norm.r2_ / variances.loc[gdf.region_name].values) * gdf_norm.rmse_.mean()\n",
+    "gdf_norm['mae'] = (gdf_norm.mae_ / variances.loc[gdf.region_name].values) * gdf_norm.rmse_.mean()\n",
+    "gdf_norm.head(1)\n",
+    "\n",
+    "# variances.loc[gdf.region_name].values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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7eVC7zz/sdwCG0VsdadrtHtmupqx6NPAPYLzfsfRgAnCK30EYRp9YNo8sl2xc/Rcw1+9YeiDANL+DMIzeEiCSwX9+8OWppbH4R9HzkYNcsxtRGouP8zsIw+ithBLbRzoiUikiG0Rko4h8xYPwHVNTVj0cWINuxATZe/wOwDB6zazB6mw+/mz9kIlJFcWVQZs9ZBg26X2w7B5+8HzBZWksfiXwBz+enaEE8IbfQRhGbyjEseQVIjIJuAW4EGgF/i4iq5RSrzjyABfVlFUPQe+rV+p3LDZs8TsAw+i9/rW+qicVxZVzCea0wK62LdmxuNXvIAyjt/wambLL0+iSI1crCd6aq+4kMJvyGSFmqYjtI40S4EmlVJNSqh1YC3zC9W+gj2rKqkegpwVO9jsWm/pH376RvZTYO7JURXHlPOBHfsdhGNlOkMBPEfRsFKk0Fv934F4vn9lHZwG5QHvXE1MjsyYCo4EGdI9+JPn/BHAk+f8Eek+vU4E30aNhQ9DZEsckr1+7xlqRcPsbMfqfjjTtDtkALBKREUAzMB14xqmbu6GmrPoE9LTASX7HkoFzgEe6vlhUUDUSvYVEAbAH3fHT0fN8CP3P3Q4UosubYehMiQXJPw8BRgDrGprn7Xb3WzD6LQXSD9ZXdSe55uo76KmBYTGxpxMzCudcgC43dvNux0870AK0oes3ChiXPJ4FDqLLmQLgRGDHqsalgf6cMMLNr6l/dnnS2CmNxW8Cfk2w11x1dU99PNbcw7lhwH8CH+jjM96cGplVA/xgjbXi9T7eyzCOUthbW9XJSBHp/GH4K6XUrwCUUptE5PvoBssh4Hm66XgIipqy6pOBh9GdJGHRAtzTwzmFTrrz7T4+o7mooOpR4BcNzfP+3Md7Gcbx+uEUwWTj6sdAqNamAr9McW4MOrX8qX15wIzCORuAPwHxVY1L9/flXoZxLDFTBEtj8S8Dy7x4lsP+3tOJNdaKdcA7DjzjFOBWYNPUyKz7pkZmVU6NzMp14L6GkWkWwT1KqQs6Hb/qfC+l1H8rpcqVUpcD+4BArr9K7if1KOFqXAE8Or1u7uHuTjQ0z9uLnpbZVwXozGF/Kiqo2lhUUPXNooKqMI3wGUHXz5JcVBRXRoFfEb7GFcBDPZ1Y1bh0Fe+OkvfFJHRn9JszCuf8eEbhnBsduKdh6CyCKmL78IOrTy2Nxb8GLHXzGS7a0NOJqZFZZ+JsitN84GrgJ8DaqZFZpzl4b8PoMxEZnfz/KcAngd/7G9HxasqqJ6IbV33qdfXJC2nOf9Hh500EFgGriwqqvuDwvQ0j61UUV+aiR50/53csvXAY2NrTyRmFc65CT/1zymAgBtw9o3DOH2YUzilw8N6GEUiuNLBKY/Gc0lj8p8D33bi/R1L93QwABrn03EuAe6ZGZp3t0v2NfkApSKiI7cOGB0TkReCvwJeVUoGa7lFTVn0FsA4o9juWXkr3jzDCpecWA4uLCqr+vaigKtgT2o1gU+gpgnaOkKsorhwCrAI+7XcsvRRdsmNxqrHEQvQadDf8G/CzGYVzTnDp/ka/oNNc2D384PhTS2PxQnSmwDlO39tjJ/Z0Yo21YgPuZgp6L/Di1Misb02NzMp38TlGN0oWxE8uWRC/p2RB/Fy/Y+k9wcrgSEcp9T6l1ESl1HlKqYc9+AZsqymrvhFYDQz1OZS+6LG8SbrJxWfnA/cDDxcVVIVtamXo1U6bH62dNv+O2mnzP1c7bX5YkkB1rx9kEaworhwHPA5M9TmUvhhQUVw5rKeTqxqX/i8plkk44CbgpRmFcz4zo3BOuH8gQmhl+ez3rSyf/dOV5bNP8juWvuhXWQRLY/GxwN+A85y8r08GpDnvdqEQRWclumFqZNZta6wVbhZ2/V7JgrgA7wcq0dM/C4DPlCyI3wt8ZdPC2D4fw8uYArsjU6FVU1YdQf+OfNPvWByQbsqMF5WQKcCGooKqHwMLG5rn9ZTkx3BA7bT5Y9HTyz7PuyOv366dNn/m5NWLnvUvsj7I8iyCFcWVFwF/QWcRDju/6zhDgOXAF2YUzpmzqnFpvcvP69dWls/OAz4O3IaeKRUFPruyfPaPgO/OXL88VHuiSQiSXDjdW1aKs/N2/dSW5vyZnkShn/Pg1Mise4DKNdaKAx49t18oWRAfDtyATjbSdVpmBLgOnXr/8x6H1mcOpmkPqqHo0d5skO7DrQSdudHtEY48YB4wq6ig6oaG5nnrXH5ev1I7bX4EPfLxReCjHD+L5CTgX7XT5p88efWig17H1zfOjU6JyHj0qGqH04EFSqmfdLpGgMXobSOagBuVUusdCaBnl+LedF2v9VjmJEeVTvcojvcBz8wonPNd4HurGpemq3sZGVhZPnscuv7yWY7vGChAJyHZhk7WEip+Ja+wy9Ho6uOxGnQjq9HJ+/pkcKqTa6wV1wCveRQLwPXAS1Mjsz7i4TOzUsmCuJQsiF9csiD+G+BtdIrdVGvebixZEP9wyYL4QE8CdIBCsJT9I4ym183dh94q4f5014ZAyvKmoXneQ3g7UncG8FhRQdVPiwqqQvNzH1S10+aPqp02/+vo7Jt/B2bS8+dvIfC95AhXeChQltg60t5Kqc1KqTKlVBnwHnQD6k9dLrsKnS30LHQF8hcOf0fHWbJjcRy9ZUI2KOzpxKrGpQpvN2jPBe4Anp1ROCcbZkD5amX57OjK8tkfXVk+exWwBfgGqUddv7ayfPalK8tnh2odrv0VWP7Ucdxo/r1F+oxYYVCU6uTUyKzhwCiPYukwBvjr1Misv5kkGJkrWRAfXLIg/nngOeAJ9MhVumkSoAv/B4HakgXx0GSoSxCxfYTV9Lq5CfS/TdilLG+SJrgexfHmAC8XFVR9pqigKrw/KD6onTZfaqfNf2/ttPm/Q3fkVGF/VOCLwJbaafNvrJ02Pzw9IO6swfog8JpS6o0ur88E7lHak8BQEfEiyc1TwF4PnuO2dGXOeE+iONa56EbWL2YUzhnpw/NDbWX57BNWls+eD7yOnso6HXtTPc9AJ4n635Xls0Px9y79MclFfTym0Lt/h9krwL1prrkee5UiN8wANkyNzJo3NTIrVD0OfihZEJ9YsiD+U3Ql55f0fo3gROC5kgXxbJmWli0OA2FfL7Qg1cmigqphwGyPYunqJPRaiTVFBVWn+BRDaNROm19YO23+regNuR8FrqV3GdlygbuBH9ZOm59t+yOOFJFnOh2ppmBfQ/fbQpyE7tDtsC35mtsE2OXBc9z0wJIdi9OtebrNk0iOF0VP2d88o3DOp3yKITRWls+WleWzL19ZPvs+9O/Dd4GTe3m7TwL/XFk+2yQ7coBbzbpPADtcurcXvl8fj6VbE+FH705nucD3gH9Ojcwa53MsgVOyIJ5TsiD+qZIF8X8BG9E98U40iIcBq0oWxEsduJdrFGCpiO0jzKbXzf0D8EO/4+iDWuAfaa45Fb0+yk8fAJ4vKqi6xuc4Aql22vwJtdPm/wzdkfMLdG+8E+YC33boXu6ybB5pNjbvICJ5wMeAFd2d7uY117cxXrJjcQPwIbef47JFNq7xu44zHFgxo3DOb2YUzvGrMzuwVpbPHrSyfPaX0DPG1qL3UnVije65wIMry2cHPitvf5wiCPrDIKz70byO7q1N5xK3A7HpcqB+amTW9VMjs8IzlcQlyYbV9UA9+kP5/S48pgjdyEqXWjulkgVxt/ZSA4REBkeY1ZRVlxPCJCSd3DG9bm66iuGlnkSS3lDg90UFVfcWFVQN9zuYIKidNr80OQ1wE/AlUqxt6YNv1k6bf0tfblA7bX5e7bT57jXSFW5MEbwKWK+Ueqebc9s4tqd+LLC9999ARn7p0XPc8JclOxY/l+qCGYVz8tFr34LgBqB+RuGc9/kdSBCsLJ9dtLJ89tfQa6t+BpzjwmPOAP6+snx2n9oIK8tnu1fHUTrJhd3DD25lpPoecBFwpUv3d9O6+nisJdUFUyOzIvg3PbA7hcD/AP82NTLrljXWirBPX8hYyYJ4BPh3YCF60bPbxgKPlSyIz9q0MPZsyYL4aHSWtwb0uq7x6A/74ejOhhnoNXuJ5Pv3A7eWLIg/iu59qgY+BTy3aWFsQ1+D6xjB6g+m181dX1NW/U30qEHY9o1rBx6zcd0QtwPJ0KeBKUUFVbc0NM/LhjVwGaudNn88emH+1R498ue10+YPA36CnkY1HNg3efWi5tpp809Bl0lbgNPQa71uQCdiOg14CIgB7bXT5tcCX0X/TF04efWi3zgWofObCF9L99MDQa8xmSMi96HrGweVUl7NnLkVeBj/R3l64182rhmCB6OBGTgVWDujcM5PgG+ualx6xO+AvLayfHYBugPnG3iTyfIi9HTB69CdGcXAIXTCmaHoxGCb0NNyTwAqgDeTX29Bl0cfX1k+ezX6d/iv6EyGd89cv7zPGVIFfBuZssuVBlZ9PHakNBa/Cj1VImwjWU/ZuGYivZ/j6qaPARdPjcy6eY21YpXfwXghuX/VDPSUB6+n7Z0GPF2yIP4P9JSRCHrxcxu6wLHjQ8njG8CKTQtjv3UquLCPTGViet3cu2vKqk8Fbvc7lgxtml43N+WHTTK5xDSP4snESUBNUUHVEuCbDc3zDvsdkBdqp80/Fb1m7kbcmwXSnRzg+8At6I6a8UBL7bT5b9HztiEdm+F27uw8DT2Nfx9Q7lx4zm4iLCID0fF/odNrtwIope4EatCL+F9FV/rc3Iz7GEt2LH67orjyA+g6TtjYqeN8CN2IDxJBdxJ8aEbhnOtWNS593u+AvLCyfHYucDO6zOnTrJlemIJuRNWiZwNZ6OU/Rdgfqf9I8mgGvu5E40rTaS6CzM0PhzLC17gCnakvpTXWig2kWZTuo9HA36ZGZi2bGpkVtF5vR5UsiF8BPI7uGfFrTVRH5bfjd2kE9htXnW1HrxNzhFLSb9ZgAdSUVefxbmUyTEbWlFWn7OhqaJ5noRNcpBxZ91EF8GxRQVVQpjG6onba/DG10+YvBl5GV3j8+sU5k3dHTvLp3Z6MucAtk1cvcm5KnUKPYNk57NxOqSal1Ail1MFOr92ZbFyRzB74ZaXUGUqpc5VSzzj2vdgz3ePnOSVtHQc94vCA24H00rnA0zMK53xrRuEct/cF9E0y1fps4CXgTrxvXHUYxLtLLSLojrXeTIN+GFjqUEzJYPrhGqzSWFzQvWw/RPec3e3Gc1xid+j1VwRrCL2rm4DNUyOzsnJBesmC+ALgn8DFfsfiAAu4cdPC2CEnb5pQEdtHFvg0eqrll9EbuIbFSGyUIw3N895GdyQE1Xj0vlm/KiqoyrqOndpp889AT3upwP9kI064e/LqRc7/PCmbR8hVFFeegq7oV6HXRz7pb0QZSVvHSe6DdacHsfRWHvAd4LkZhXOytWPnj8Bv8W7DZzftBT4/c/1yx377O6YIOtXAEpEPi8hmEXlVROZ1c36IiPxVRJ4XkY0iknbE3JWaVX08purjsS/Vx2NfQ7d2U21wFjR25/buI/jTA8YAv58amfXnqZFZYfo3SKlkQbwIvYYgW1RtWhhb5+QNFWAhto+wm1439zfT6+Z+E72eycsNMvuqjY68aumlS6vst46OtY1FBVXZshlrh/8AsmXD5deBSjdu7NRGw0G3ZMfiN5fsWFyJnsnyPsLV6LZbxwnDfqaTgEdmFM6JzyicY2dPy1BYWT67HL3kI1t8fub65Y6vj5QM/kt5H5EoOmHIVeglQNeKyMQul30ZeFEpdR56RK86meW0R652XZfG4iPQ62MuIjz71Lxu56I11gpFeEbmZgKbpkZmXZdM0BF2XyB4i/576xnCkoI5HMrQC4EP+B2ITVtsZBDs8Bd0x07QnQQ8WFRQtayooMqLxdiuqp02fwweru9xmQVcN3n1oka/A8kSZwD/hu7MDMvY3BY7F61qXPoOep1b0EWBr6AzDV7udzAO+ZrfATho2cz1y//odxBpXAi8qpTaopRqBe5D15s7U0ChiAgwGP1Z3J7qpq5Wtuvjsb3onr8TCEcaZQv4ewbX/xD7vc9+Gw7cA9ROjcy6wu9geqtkQTwPl3pffdAEfGbTwlib87eW/jZFsMMadAKa04GdPsdih+0KTEPzvOeBMGXsuwnYUlRQ9fwPl5gAACAASURBVJ2igqowD1vMIXzZKXuyaPLqRY6Olh/D+TTtQfcqOvvrqcBin2OxYzt6A2y7qtwKxAVnoTMN1swonBPajXJXls8eB8zyOw6HvIZb9TWBiIjtg9Sbm9vZtHwpUIL+HXoBqFRKpaz/u75AsD4e25r84/LSWHwUusd+B/AIwUuCcbA+Hnslg+tvx7+Fzr1VDqyYGpl1xRprRRimABzLUp8iIl1/8MNq7qaFsZfduLFO055VFRlbptfN7ZiGcKSmrPq9wPXotNY/RKeIDZpauxcWFVSVEr4P3iLgW0B7UUHVwobmeWHp5QfgT+dXFYwdxRf9jsMhteh1K+7oSHLRjyzZsbidZMWsorjyP9A92r9AN7geQicTCZKNS3YsbrJz4YzCOYLe9iRsrgLGziic84FVjUv3+B1MppSiQiR09cruWMB1M9cvd3RteQfJPHnFHqXUBT3e7nhdP6uuBOqAD6BHrteIyKNKqYaeHuj1P+Kv6+OxR+vjsVfRC0PdK+x7p6g0Frc1l3pqZNbVwOdcjsctI4Cnp0Zm3ZrJm64aP8/X1PRXnn/70AF72z/pZwwOWoXLm1UmiNg+stH0urmvAT+aXjd3P3r+9DXYnALsoZF2LioqqDoBnVgnTGs9Ovs28NdMNie+We478Wa5z9dU0fWvjfkK3uw547YmYPbk1YtcGC3vYHP0Kks7fpbsWJwAvrNkx+ItS3Ys/hd6acS9PofV1agMrv0WOk13GJ0LbJhROOdDdt+wbOItBcsm3pLJ34/j7hh324Q2JdmQuAvguzPXL3/CzQdEMjjSsLNp+U3AH5PZS19F1yUmpLqppyku6+Oxxk5/3loai/8B+E8vY0hjf3081mrz2nLCvQ5oAPCLqZFZeTlnnfEn9PB6A7AHOAw0Ai0Pbq5SV42fl4Nutf/6qvHzLgR2P7i5KnHV+HmTHtxc1edNce248vzbhwCrIy3KVu9bwO0Cbt60MOZab75C+uUIVlfT6+Y2Jv/fAtxfU1b9FfQ+QEFhd1PwgegKW5jNAB6akvOrD5+RKBqPXjvxDrqsOQQ0LVPXJABulvvORG9Y//TNct+P0b2hE4BXlqlrUs57d0ql3D93QF5utiTT+erk1YtcGS0/Rj8bwepqyY7FDZ3+/FxFceXDwGd8DKmrdzK4dgrd9+yHxRjgwY8VffnKj49t3YauNO8F9qPLm0M3v3hXK8CyibcMQq+l+8ayibech95nTgElN79410Yvgr1j3G1nA/9EyVvhWc7Xo6eB77r9EAf3waoFzhKR09DJ665BZybu7E3gg8CjIjIGnTk35XpGv7uu7ezH4KWRpbH450pjcTt/L2HKVJZK3Gps/DN6j4JadKt8FzopycGrxs97HV0oP4Ru4b8B7Lpq/LzVwD+uGj/P9eyEV55/exF6bVy2/J1/dtPCmN2Kda9ZRGwf/Uhv9ihz0001ZdV2pkpf6Hok3rjglWjD3xRqHXqa+GZ0T2EDcORmue/tm+W+14FX0GtbfgAcRE/NeIBOm866qVLurwR+RLgrmB3+ih79dJVS9o9+JGjlzcXJDZJTSk4PzIYyJyeh+GNjm/wDXcepQ9dh9gItyybesnfZxFteRdd5/gfdiXMAPaKxGrh32cRbXP+AvGPcbWcC/wKKs+DX4zAwe+b65S6OlifTtGe2BqtHSql29Frbh9AbK/+vUmqjiNzasbk5esbdpSLyAvpn6etKqZRTUP3epM1uulAv3QVcUhqL31Mfj61Ncd1QrwJyWcTauessycnZLAUF47ucK+T4DeXy0AkzOjZ13XjV+Hk16ClvDz24ueq4XbqvGj8vD8h5cHNVxqNPV55/eyF6YX+2DJvfuWlh7G9uP0QpSJgRrO4Ercz5CPBETVl1JfDY9Lq5e3u4LlvKGw5F2i9+LHfX2ve2jZ7SpQcyh+430xzEuxuJL75Z7puFzqj4IPDSMnXNcXWSm+W+ocvUNb3KJFkp938ZvW4vG+wCPjd59SJv6m2mzOkqaNmThwAPVxRXfhm9HqunOk4O+vcuC8iQf+zM3f/hE1v3FESPm5I9PHl0VpA8OhrHry6beMtf0QmJ/u/mF+86bsP3ZRNvGQI03PziXRn/nt0x7rbT0Y2rjrIv7G2sr8xcvzyTXAa95mTLVylVQ5ekUx2bmif/vB2Ylsk9/W5g+bqmJ4XRpP+3exk434NYvFCY2LZ9ZPTUU96WvNxME0iMRCcSuB5ov2r8vH+g9yIaj56K9Q46o5t11fh51cD9D26uOpp55cpzvzXkoRe+e1yjDI42rlah1+tlg5fRWTU9YaYIHqumrDqH4CXWAf3BugG9/8ajPVyz2btw3PdOtHnKc2rfI+XtIzJNqxxFT12aAlQDL98s961EV2Sno3/HTgIm3Sz33Qn8dJm65ui0qC/JfUNykYbF6upuKzGVcv8X0dmiOoT9l+imyasXuT5aflQ/nyLYjSDXcV5KcV6hR5G7drqGkoWMe2hH3ksfObF1UE6Eggzffhp6g/EK4FCysbURuAw9lRDgHGDDsom3/OTmF+/6Z8cbl028RXYdyS2ct+Xn3SZC6NS4GpthTEG1Evhvrx4maUam/OZbA6s0Fo8R3Fz/VwBjSmPxi+vjsZ7SML4AXO1hTG4bkXjzrdejp53aINFoUS/vkQN8OHl053fAHclG2OsKTiA/d/CV59/+O3RD7BVgzEPP3dGxuPDvdGlcSXj7dtrRKdkP+x1If1RTVn0JOtFCENdN5qIXw/+cnhtYL6IrPcH+RMnAlmjje4eqvKdPTxT2ZSrS2cD/6/R15yxR84Gv3iz3/RnYCjTlwqRc+Fml3J9Ap6tWQP5idfX+Srn/BvS/wVFKBfwTPLWfT169KAz7GGWdiuLKAnQGvi/5HUsPPovu0PlndydXNS5tn1E4ZxNZ0sACSCiZsGZn3tMfLm6dLL1fvDMYuLaHc6cBH1028ZZ1wHrgLUtxRqsV2X/HuNseQq/XeRsYefvWn75zx7jbBqOXZdhO/BNwO4HPzVy/PLy1NIf5OYL1CsGbn9xhENCaonEFen6u64v4PKXUaYk3tz0VHXfKRS72DJyVPEB4imjkIt7dI00BcuX5t5+Orghny8gVwO2bFsae8ephOslFv1pblU4taTL++OwiUqSRb2ie905RQVUd2TNqDkJkfc7eCcOt/K1DVd44l55SQKcKUZ7eHLJrx9j3gG/SfWU4rA2szRzb8HSfAmVGzQFYsmNxc0Vx5SF0MqkgGoveLieVh4CPexCLZ5oScuHT+3LWXjSi3c3siJclD5oTkbUKmQLMS55TwJ47xt1WjC6HumtchbWBctPM9cs9S4sv+J9EIh0/47vMx2fbsS/N+efoofcn1NrbL7K270y19sw5ikkcu1Fbx6fzL9ELzLt5TyjLnnXA971+aAKxffQDpxPM6YGdpStzqj2JwktC0cN52yMtJHq1XipTbd1Pi/pcpdz/I7pZ1K/C2cBqBz4zefUijzOuip4iaOfoH8Jex3kAnRAiq2xrik55tTH6pBfPKohaXRtQgk6V/2s8XC7ggaUz1y//u9cPFRHbhx/8bGA1pr/EVymHbddYK9rR6yayjmpqmpLYs/cxt58jMAil3urm1FR0Osxs0Ahct2lhLJH2Sgd1bDRs9+gHjqArnkE2LM35Ok+i8JgSTvl7/ttvJbDsbpHRa4nufwZGAXO7fYMKZQNrweTVi5715cn9eB+sboS6jrOqceludDa9rPP8gej5O5vF9fqbwLgeBqRupOcZFWHrRd4EfN2PBzu4D5Zr8fnl++iFgkE1uTQW/2VpLN7tRqBTI7M+BlzncUyeUfsPXGIdbKh1/UGW6ilrWrcKt7ScmtuQWIdSx2XyCagvbloY82FzWz1F0O6R7abXzX0TPQ0syO6qKavuNktRUUHVYGCZx/F4pk2scx/Kf/t5t59jZbhRc1t7tHDL9qFrLYvuOoKCaC06tb0vlBJbRz/xOcD1ToM+uLqiuHJRRXFlt78TMwrnzAPe43FMHpH8dXtyT2tokzddfYpQGNHbTNj2dlPBwDZLnlEqFA2tVuDameuXe74/qU7Tbv/wg281q/p4LIEegg6qPPTaoJoe9sVaBSz3NiRPRa2GBtdTWkvCyqiXL6/RGle8tvGykx5qODJ4S8sjklCvuRWbA+7dtDB2r18PtxDbRz/xB78DSONS4MGasurj9ntraJ53CD2lJOi94r3WRGJ8G5arSWAUnKxQtkeTFSKPbzxlyu8ePvfkR+pPWd/QlPekUoEdCd0PXDd59SJPR8uPUujtoO0c/cCSHYv3A2v8jiOFMehOpyU9nP8p8Lh34XhNBm1rirjawAIYELUySlne0JZ33sYDQy/YdLBo+4HW3LVKZbQ5tNe+PnP9ctc7xronGf3nB7+7rn36h8nIZOA7pbH4MftBrbFWJIBK9H4s2elIy2TV3p7RCFPGEtao3rwt2qaGDN/YfPnYmoNnjHry0IbchsTjqEAt0HoD+LJfD+/YB8vu0R9Mr5u7DfBsEW4vRYCf1ZRVn9f1REPzvEfpOYNV+AlF9Tn73J7adpIFveiUEd58Z2j5X9ZNuPiBR0oOvLZ92FpL4V36c3s+P3n1In9H2swUwa7CUMe5paK48saK4spo5xdXNS49jN76YKsvUXlgc0P0JLdrDYNz2nu1F1qrFT1p66HBU57fP3TUW4cHPt1myXNOx9ZHq+m5ce46yWD0qt+NYCW53nvgkG8Cb5TG4itKY/FPdby4xlqhyMZEF+8aYO3e4/Y85Yym7HQlQMHu9knFaxsvHfriEU8WrtqggBs2LYxlNDXAaWaKYLfCMNVrMrC+pqy6tqaselFNWXV+p3PZXN6wNXroLCuDEabeUH2ctnWkNXfkExtPnvLHR0qiCUu2OhRWX/128upFvo/QKktsHf1IGOo4EeBuYGtFceWdFcWVRzPsrWpc2kLPW0eEnoWc9sbhiKtLIUT6Ws+WyN6W/As3HhhyfkNrjjcJyNLbD9w8c/1yX8ejJYPDD37XrN6AwPUC9mQY8CmgpMvr9wKzvQ/HG+rQ4UnKstxc75Tppn89KtrScsng11secep+ffDjTQtjQSkIjWM95XcANkXQezpdT6cGQUPzvGbgPPQ2F1lHCcWvRBuedvUZOuFJnx1pzR3xtyfOzlHK91HRbehNUI3g2YDeBDsMxgJfAAZ2ef0bQJX34XjjhYM5ferkTSfi2HZIwpZDg6c0t0eCMG3zSzPXL3/b7yCCztcGVn08thv4JPBVwpOtpnNvMmusFXvWWCs6NgnNRiOsffvd7OFxtHNh+Ibmy/N3t/nZyPoTAUimoPfBMlkEuzEHXWH4k9+B2JRHl9+RhuZ59ehOnSAvoO+1TTkHhrm8vtuxH/jGpvyxq585Y49S+LWB+B7gI5NXL/IkzX1KdqcH9qPyZsmOxevQybC+AYQlMdMxdZxVjUvfBm4nS0eyWi05b/cR6W77hgASXm4ourDNkvU+BnHHzPXL7/Px+UdFRGwfvsTny1M7qY/H1tXHY3F0xp0w6GkvnW8TvvSatqgDB8a6uLzJ8SHm0U8evjznUOIJp+9rwx+AqzctjAWi4muSXBxvet3cxPS6uVXADYRjm4XRdFNONzTPexr4q/fhuK9d1ITtkWY31644+rm3+8CgCes2nPySD8kvdgFXTF69KDDrfEwWweMt2bH4gSU7FlcBC/2OxaYTu76wqnFpK/BdH2LxxHP7c/a7dW/lcL1QITmbDg45O6HY7OR9bfrPmeuXf9uH5x6nY6Nhk6bdhvp47CHC0SPb7WjOGmvFbvDlB959inGqodHxUSwFbUQjjmcBFKB4beMFkRbLy32Dfgdcu2lhrM3DZ/bI7IOV2vS6uY3A//kdhw110+vm9lRxd32vOr/U5e5167OgPqqnQjlq685h73n+tTFeTj/dCbx/8upFweokMBsNpxKWhFg9fdY/TpZ2Ije2y4WH29nh9H2V4sCh9qjjP/CWksEvHRgyXClPZ37Nm7l+eaAa2SL2Dz8EpoEVIqkaBH4O27rK2rPX0XnKCvaoAbnbVH7ulPRXZ04sck/8Z8MZ0q5eduP+XdwDXL9pYSxQ6ZtNkou0wlBZeDXFuaBllXJMsyQmH5BWR/ePy4G1g6E0gvQqc2k6G14fc9mW7UO9WHu5Hd242uTBszJjpgiGXTs9J+ZoBkIylS5TEl2/L9fRda2WYtP25nwOtedc5OR9O7SpyKjNDUXtSrHPjft3MXfm+uXf9+A5GTFZBDPzZ78DSOMQ8GJpLF7Uw/lKwIsKvfcs6zx1pMWR0SYl8pgakHuYSOQ0J+7Xk0g7hcX/ahhKQm1x8TG/Bm7etDDmz94zPclg9Ko/jmAlBb28AXikpqx6aE1Z9XEdHA3N89YC3/MhJk/U5e51KuPjq/nwZAEyxe39UB7fePKU7XsHP+LiJqFvApdPXr0ocLMllLJ/9FMb6dUWAZ6qAxIVxZWDup5Y1bg0gV5P5tp0Oj/tapHz2q2+J8BRitbm9sjaHc35J1rIUCdi68mRRHTca42Dd7rcyLpt5vrlP3bx/r1msghmJugZBQejk3EcLI3FN5TG4l/ofHKNtWIPcCXhyVSWkcSu3X0aQlfRyFprQN52VZD3XiKRU52KK5WcI2r0yTUHTxuyqflxaVdO9779DPhC4BpXdOz5adZgpbHb7wBsWIKu0GyrKauuqSmr7vqPtQD4Dlm4feseaSlrJbONyLvYOBC2DIaxecjFjgWWkvDP9adf/uDTZ762v3HAOqVwcsrwFnTjKriVdDNFsEdLdixWBL+OcwG6XDxQUVz5REVx5dTOJ1c1Ln0W+Cg6A3SWkSEvNUSf6e27laJpf2vOk9ub8xv2tOZNsZAhTkbXk0PtuRNf2D90wN4jeWuVwsnMfgr4/Mz1y5c6eE/HCGYEK1O/JjyZas4BvlIai+d2fnGNtWIrcBl62tgL6FGvJsKxviy1lpaLervxsILXVH7uFCJy3AJatwnIkFdbLh374MEJw+uaaiOtVr0Dt60Gbtu0MJZ1Fdt+ZBPwQ8LROBkFXAVc0fnFhuZ57Q3N8xYAH0GPnr+J/n583YPNEXrj4V5Puy4AK4qcLsgAJ8OyY1/DwDNXPXn2ZX9ZN37X7gMDH3Ugy+BmdOMqCyu23RORoSLyBxF5SUQ2icglXc6/X0QOikhd8ljgV6wZ+C7h2GIhB7gY+GxFceUx1dNVjUvXAaXoNaAb0XWbg4RjynVKrzRGT+7tCGubkucOtedcbCEjnY0qPQsZ+FbToCn1+4eOead5wGOWoq+zdizghpnrl9/lRHz9lbiYHa5XSmPxk9EjQI3A2T6HY8cu4MfAXfXx2HHDtFMjszpGKE9Hf1/DvQ3PWVJU+Eh0zOjLM3mPisijKj93MuJ9RacnLcOiL71z2eCRSK8Kwx8A8zYtjAXrl6eTYRNGq/f/9yzb1//5vT9/Vil1gYshBVZNWfW96L2lJuLfbAK7EuhslT+eXje32/2iigqqIujvYzlwjYexOS6i2PqJllPHZTi17/Bg/fdU5PaUQLvyctobpk1+bcPQwS2X9uLtL6GzBe50Oi4nlZ88RD32FXsDhYP+Y3Xa8kZE/gd4VCn1axHJAwYqpQ50Ov9+4D+UUh/pQ9ieqyiu/DDwG/R+bJ7M5Oijl9Adiv+zZMfi40ZkZxTOiaAbV1OBvxP8MjSli0e01Z000CrL5D0NbdHHDrblXAzi0J5XfaUYntdae8rgpsm9eLMFzJ65fvnvnY7KSSNzxqiPDbX/8Xb33iWe13GCNoJFfTz2Vn08diIwAYj7HY8No9Gb8HX7w7jGWqHWWCusNdaKV8mCBaKqobFEWVbm85QD1LgCyN+fmCDtqjcVlp8T8MYVmCyCmZheN/cz0+vmTkL32Pq1n5FdUeBq4NGasuoJ3V3Q0DzPamiel0BXdkLNEsa9Gm18MsO3DSBAjSuA1vacoq07h/amzNhGCBpXRzmU5EJEioDLgf8GUEq1dm5chdmSHYv/vmTH4hOAs4B/+R2PDROAu4A7uju5qnGptapxqUJnGQz9+qzn9udk/Huq3xCUxhWAsK81b7JSvZo5dUvQG1eAnppksgj2Tn08purjsa8Cz/odi03TSmPxC9Nck5/mfBiMsvbtz3SeciAbIznNqqEXb3s06I2rDqaBlZnkiNBX/Y7DpjzSx5oN5Q0bc/YPyTBnRCC2SujqwKEBg5XKeDrqq+FpXIGyxNYBjBSRZzodn+9yt9PRa4HuFpHnROTXInJc4gXgEhF5XkQeFJFz3P4WnZQcDZqJXkYQBl+sKK5MNQOnBegpAVhotFhy/p4WySiJjFISyLq0oldp3EOxTMeswXJGmHph56c5H4ZF9Wmp/QdOyWhqqUggGyTDn28a3Yu3BfJ76Uphsgj2UpjKm8/WlFWflOJ8VpQ37aJKtkWaMklJvz9Io1cdtu0ecl5beyTT1OphWB/4LvsjWHuUUhd0On7V5U45QDnwC6XU+eiR5XldrlkPnKqUOg/4KeHICnqMJTsWNwLr/I7DpqHAnJ5Ormpc2kYWjGABPLMvs42HE0oC2JklvNM8oDeJyUJRxwGTRdAJARp2Tevy0lj8zO5OTI3MGgKc4XE8bjlFNTbaHsVSuTndTmXyW+Np+b3JuBOawsdkEeyVqN8BZCACXJ/i/PleBeK2F3L32d5jLiegW2UMzG/dlZtjlWT4ttCUNwBKia3Dhm3ANqVUR0beP6AbXJ2epRqUUoeSf64BcqV3a2r9FqYy5xMVxZXdph6fUTjnbHSm5dA73C6TD7dja+RYKbY3JSIZrdnyyvD81tz0Vx0nNGWOGcHqu/RpTRPqEJZq8iCWdIYCA3s410b2NLCwdu+11fBVIo8TkTFux9Mbw19ougClMu0hDkfho8wUwV5qAdJOHW1riwQl3fLYFOdO8SwKlzWRuOCgtG61cWlLvk5YEjhNLXmjDx7Oz3Q9WXgomynabaRpV0rtBN4SkfHJlz4IvNj5GhE5QUSvrhCRC9H1mV5lufVZ2rIkV6w9AdlB7Cz0hsPdGQkUeBiLiyS6fl+urY6aQ+3RV0EC2Uje1jSwN/8eQfg5S0vQv/B2Dz+EoYH1S9KkNc0/aNUPfbX9jWCUPz2ue2gl+Avo7bOsMqupaWO6y1RetNCLcHoj0s6gnMNWt5nYws4kueid6XVzt6MzZvVIKazX3hidc7gpb4NHYaVy3AbEnWTFdB0ABHk6d0/aEecIPBtxeXPPvqjfMiZLKqDdc3ij4duAe0WkHigD/ktEbhWRW5PnPwVsEJHn0fvFXaOClhbZnjvQWZN7dHrhkRcnDmkKwlTCXHquN7q52a3ndrVIeUuClIlVlKKloS0nkB06AI1tOedaijf9jsMtJslFH9XHY4fRG9v1ONKQf9BCFCU5TSoIFZ67S2Px46aBrLFWtIO9IeewsHbuakl1XsFLRCLnehVPbwx+ozWMH8iGu74DPNzTyUNN+RtAhu/aUxSEvaaurymr/nIP53qzwDmwDkrrxelGsfIDvg3GW7uGnKdURnuU9dvySSlVl1yfVaqU+rhSar9S6k6l1J3J80uVUucopc5TSl2slHrc75h7Y8mOxS8Dt/R8haIwN3HyoBzrPTli+Z1JMQ/4W0VxZXcdp05uchsAMviZfTnPp7qizZJarzYU7h3hcHvOVr+j6K8C38ACqI/HNgM30F3WLKVUpJVJAAW7E0Go8JwD/Cm5n9dRyf2wAvyL2AuJRLl16PBxBZCCBiXUqvxc5VvXgU2D32g5H0tlshA0NBUeM4LVO9Pr5irgc+ge8uOmHu8/MOgAQEtrzuREQnqTidJJecBPa8qqr+vmXGBHcnpFiD6Ru6un39WXcuGRHCSQ6z07KCWRt/cU1mXyFteCcZjC0TVY/c3/Al9Ez9g5RkHU2hoRThOh4MSBrfXeh3acDwAPVBRXdp2tk13lDbDzSOTC5vbjkwUpxe42Sx7f05oX+L1a324qOFkpMtlaJzRljlmD5ZD6eGw58GDH14O2t6/NO2g9i95QsgAg2sJkSaiUQ+0eGQ/c2PVFJbQrkazq5bHeeSe386wMBZYqyMtXBfmTiUYyXdDtuUiCAYPebstkUXwoCh+TRbBvptfN3Tq9bu4DJLeJaG2Nvr3ljZGPKwWW1dFrIHl79mVUWXaLAN+tKas+Zh1AnlCYJ6mnV4fNoUj7JXvkyDEplHPh0UJkwgAkow3Q/VL70klnK9XjOpauQlHeHOXQGqz+ZsmOxWrJjsV30ilF9qShhx8fELG2RUUdrRyPHtB2oi8BHm8qel3cUaJHj3cAe3yJyBVS8OTe3GP2L7UUL29rzh+180j+pQklvclE7KkjiZzTWq1IypG4LkJR5mSSQdBkEbRnM7pnGbFg4M7E2UNfad8iel4wAnlKUq5J8NIxBeGeL17K3lsvLTj48UnZsw4LwFIT1cGGjkxPCESw1Bt+hpSpoZuaJ6GU3QpPaNjtTTY9yil9FDgAqNa23Etf3nLCK0dackv9DqobxXT5HFk+IXpk+YToWQWRY5MDhN2TebuP2Tco4VcgvXT4SF7xwcP5maSdDwdlRrAccD+wFCA3ogrOHXY4b8KQ5gGdzgfpL++YOs7gnGhjYU60OE/kpZ7eEEb7WuWShjY5uo5JYKzoZEih8XZTQfat/cxg9MqMYNlQH4+potfbnogesV5SAgKFojhmiDbaGph5wF0XmP8nMKz9hMKzVU4kkGmEe8vas3dY51EsaU/0Zu8F30Rb1IjC11qewd4C6VD07oCzadpFJCYiG0Vkg4j8XkQGpH1TFnjp1eLGPfsGP0CyYqOUnEWnOfctrYHZRaJxet3co6nM/17+w0uAfwf4wFDJoh5lOCKJ97wTaT6aYMeCs1VAMhzZtfb5cadYlq01cqH6vjLYB8voxpIdi9vPLGz+Z27E2qPAEmF0RBjXcV5glKBafQyxs6N1nGuGg4+rWwAAIABJREFUVeYBVQB5ETkPMpqSFnCS8/TenLeOfiUMHBhNBGGqpm0Nbbmlh9ujdjcQDk2ZE/QsgoGpHdgxZfoPIlFYUfhG4nQk8HtHfLg0Fv8lFidF2/konTZJbDlr1PYBm94J/Nxd25Q6Wx04+IQMG3oJAAnrbJSykGDubt6dYZuOvC+n2Xpy/7kDL05zaSgKH5VM0+4EETkJqAAmKqWaReR/gWuA3zjygGCr2LOv8LP79g96EUi1qa/fhteUVb8flIpE1Ah0JsRxAJ8YIZNW7VNtJEf6s8FTubvbP9pyMskNhce0Q20uTPY7Lrsam/LH3vfPSe3XfnDDHhFS7d0UivJGM6NTfbX83JtPGZ7PXcPy2lHQ3fYmuSp15lAvzawornzhYCunAtcCswBEpDAC6yy4zN/wnHOwTS7Z3ypbhuWp0wGKcts5nAhT9Vl4paHwfacNPrx2SF7blDQXh6LMEfzLDmhXaCrAnYwRyBPFWd2dzD1kBWX05Hxgq1h8jy470DddMHaCSpEVMYysvXuLOwaABIpR4ZuHXbCj7UwslW4ENBSFDzg+RTAHKBCRHPReb9tdDT44TgCwVGRidyePtOQGaWPTfwGrgQfg3V7vohwZPipXryXLFq1inbc90nx0XUF7N8lIgs5SkZxdBwalm74ZmvIGMGuw+i4KjBBhRESO3+NOhIJBOVZQ1lVeB2xOKG4Hbup8Ii8iQWkEOkQitXtz3un4Kiqc6mc0vSPsackfoVTafR5DU+YEfQQrVA2stTVfs4CUey/lNVhREiow65xUlEusCI8p3t2LwBqcf4IqyM2uOfiKcergwXc30RSKfIymV3Ja1MgTH27IQQUiUUofOZfkQin1NvAj9M/wDuCgUmq1B99EELyQ6qRlycjDTXkBWuMkeZYlLynFU51f/djw7KvUrs/dc7STSoK1NsW2Nc+cfvnBw/lP+B2HI2zugRWuyZyeewM4lOqC4oLW7UGqAw/N49z8CGvpNGUwR6ScrEp2AY3tctHBNulYX57y3yioGttyJ209NGiL33E4xeyD5byUFZ5oGxcXvdmeScYUdwm5Kof3WrmMtXQhBEBzaXEWzVHWrN17T1SW1az0AtD2tG8IoJwjasyJ/2hoHll76PlIi1WHUm1dLsmqkcdORorIM52Oz3ecEJFhwEzgNPTC5kEiMtuvQD2WsrwBGf3W9uFj29oiu7wJxw6ZoFTkIsuSLSo5kvyBoVIOGe2/FHgtYp3/erSxNvllKBtYIKx68qz3rHryrC17DxY8ZlnHrSHO1vLG6MbsF5ZZQMr9PIfnt085dVDLIx6FlFZEKBqSx5SR+eRHhXUAIhKNSnYl1wGJrNudu1spUOHLrXPUwba8shcPFL29vang8YTq9t/IlDkOCWMDa1O6C6KtXBppVcHaZFOI0KkS0Fx64mSrIHe9jxG54RRr1+6ngTZgkN/B9FbOETV64M7288aubigburG569Sq0BQ+GU4R3JPc1LPj+FWnW30IeF0ptVvpBucfgUv9+J588DJp/82laOfuIWnLJR8UoadzkhuRvM+MlpSj/2G0PmdvsdI/wUFfk9sjy4rk7W8sOP3Bp8967x8fLSlIWO9mLCNM5Q0mi6BD0pYlowe0TQQVqJ+NiDCQTnWcAZFICZ1m7mSD5oRc8FZT5FmF7W0WAqnVip6068iAS1/YP2xiQ2vO2i6nA/Vz1RMBIqJsH34IYwPLVgrQ/APW624HkimxOi0yz4nkHfzoxOEqSGP9DlCNh84lkbBQ4UrV3pPC11svjrRYnUdEQ/HvpXB0o+E3gYtFZKCICHr/kyA2KBz31UOfPgJsTXddU3N+UPan6Wyb6EoPADNHRC4tjBKEfbsco4SxL+YcWJcI6Yh5V0dac4c//+qYzp2DoShvjjJZBJ2wOd0FIowsyk0EqgxWCpVQ76Zuj4iMyo9k176fAM/tzxkgitPD9qvZkzcPDypV6piR81A0sMDsg+UGW2uXcg9bwenRVFiRNtYC53V+OTFi0DhVkJtVFR5guLVr93MogpJKtk8EOPEfDRMKdrSulXa1KX9PWzimBmSwHiLdmgil1FPAH4D16ClzEeBXKd+UXdKONCvFmYmEBGjtnnpeRBV2ffXfRkrWTU1+KXrw9EQWrex58Y3Rlz5af8r6lrboCw2H895J/46gEJRl7zBSsjWzZVhe+163A7HLUjTub+VxOiXXAcgVuZjjt6wJtXYl57zZFHkxtLOSu2hXkWEbDwzJb2qPrktYbNzfkhuab8zsg+WwtTVf24eNHp5IKxOx/N8vItLG2kgbe0QxRXi3N7lD66nD0mV0CR116PB5qr09aypyEYv8Uc80TTn5wYMlY544HJqpj07ug6WUul0pNUEpNUkpdZ1SKlQbLfaRjSQEIgcaBgZgbxS1QcTaFImo80Q4o+vZS4rkTPQU3qyhhBPrc/YFJrGRE954Z2j5iv8759y/PD4hPNnKzEbDTqnFxhqfEfltQz2IJSWlaNvbwuN7WlDt6vi07KKnPKStr4VN3YGcQpQKZaKL7rSryMiXG4oue+HAsHPeODw4FHtcCiaLoFseT3eBwNC8RuVvhcfiJRTvExjd0yWtpw0f5mVIHhmq3tq+z+8gXBKKhkUm6yFMhSetdXYu2n9wkM+piRUiKiJCSU9XDMuRkTlia4PbUHk52nhJG4ms66wiJOXNUWaKYJ/NfmHZAdJkSwbIiVCaF7F8Ta5zuJ2nEopLoeeswTkivnd0Oy2hpOTtZnsjjSEUjjIngwyCJotgZmz9YOcfsHxdiCiKA5Lm77h9WMEIr+LxSmLIwCebppw9yYpKVu29kxSS3n/n0rQbvICNHuX29sgky/JzaqyAjc2QB0fIqs6PHNg6K3f4W8UDsnJdYEjKG8106DjGVh1n1IC2l90OJJV2lb5cjAj5XsTioZbBOTw2ICrndFm7lC1C0iBWRDI4/BDWBtbTdi6KHlGn+zk1XxKdklr0dE1r4ojKiWTFELrKjW5snlKy+ci00ovVoAEj2wtzsmraTlJWLKY37PvqoU83kSZ1siYFhw4PsHGdW9RTIgxJd9WgKC3AAQ8CclvDxZFBjy3IO+mUsujA0qI8q9TvgFxgypv+yVYdZ0R+W47bgfREKdpaLcbbuNQCXnM7Hi/kCk+dOiiyr7gg+t6IREZYiqxI5tWFKXMc4tsvZx812blI4CRp5x2Vyxi3AzqObtdNSHdZYtTgM/becjEFz25bN/CZt05GqTwUOQIjXY/RIUrY2Tbp5Nfbxp94Seex2NaheafnHmh7W2z0qodIaBbT/3/23jtKruyu9/3+9gmVq7o6q5O6FVtZGuXYGsfxYJtgG4MxBtv3Ag9jLpdrDI8L5pEWfsbP5tqADQ98wTa+YBOMjeO7Hk8rjLJGM5JGGuXUOVYO55z9e39Ud6tDpe6uqq4a9WetWkt9wj67Suf8zv7l107af1mQl8wJhe0hr2dp0g+J8suL+/Rq5cCwwQMfuSOvxGVKzsiUvKmU94FsJf2F92o1m9ykHJrcKAgOmyJPJCxxKNvJFUZFPcXlVTi8oslL3tgEz8mzLBUm4xFSvRGzoguxXyPmuJTdJmMtUoZ9B5DbGFQuEHCj0SHibpX2Tt8es8jpIg4Twb1UcysCFSNzlir0L18q5YU6m+sAjgE4kutAkogt0d3SS0DepZtjO1sOxna2pP5ghjIcue08//CR7d5YV7EmuFgYSFhN/tOJPWt2Q1P2z9mvKy3RNtdd54NIkoAlzk8pGBXzzCyH4hSUzwOYc4/PxpJLVa8IAPIPxanVqOHv1itThqegyeMng3z97we4XQKNxZne4nFCvPQ+rcbZKmxplagWl3XoXoguWkxPlXpuRaJi5E0q8XNZ5hSIbwN4BKAlx3GO1A9f+t/dlBhAHgoWkCp24VCUqbUMM7PFuBqXMsh5yNUlZNSv09UanQ5NtCiZgcG0PWbhtFPlfUsxuSJRETIn1QdrqWeRnYoMEez+9kdNAL+W6zgGWFi8VHlYC6/EQgSrzr069Exnl9HgPl7AORUM6bKdiT2zbShxcH0XNGVOdcSp4+xKR7LGdv411O8rZ9hnOcDzqOi1rIjl5tfD7/kigPO5jpOSKtJz6FWp6i3VYt9/aaY+lGH4oAB63qpWnf5tfcW2VmFbm+k4IqDVbbYBZdZofuFUhLwBAMayvCkU7738hSEAv5/HoRFN8FCx55MOooXfm0REqqBNLkVspjxb75QYy6Hg2Cq3UGtt4nA65WqShKR9hsSxUk6uyFSMzFnOwSoe23MdQAC5H1pNSlzeKsWEpiNMXF70IEQI/MTWw5FdrScZKIsKWayIW/H9ay/Fnt2xlz2OXNY1AECyxnYg1uS4wq+Nfhjl018tB8tFLgrHp9xfsQGZq/NNEk/oR3r6q7pLMKVZ8E3kEZKci/1eseOTHWLcKfLJOSsJsa3C0f0xval2v+Lel2WdM4UqUNvhNb0q8bkSzK/YVIy8AZaLXBSY3GscQsM2fySkkizpu5UZCBuLX7USkceliB0qYQlkZnoUwoVWp7jf4lSOKEQZqyNOJ2zSkZhJJ5lfE4bkivBgActVBIvJm/M5iACf577l14Ky1CU1C3aTxna3HjTr3S8VaryFwMC4sW7FseiP7V5ltdTkFPyzsdzaFtOrlUGfoCeHQjUaXgYAcBBAXj3QwhFH172HNaX2PCfzKXCRD212av/NVrHkL9k6Ul/4iN4YeLdW06WRmFclMoXgbXGbHQAvJ2yXkuUy7YUkrzWOIKzaXh0ZcSrWnWJPaDqyQDlURISJ8MHrhRhvETyot9G5DpfYaVdo1fxOJcQlHZTAmeJMbZnZLPfBKi55xf4CAAE1jiHLLOVKkqmwlVhiT7UsSRIlA9Kq9RyLvn0nkttWHoGgBd8z8Tr7LgYGCjm/JaBiVgfLIYIFJW95AwDxhLYnmVRKGaZWxVy4/iWdDnSqhHuFGm8+6KDrP6fVXv41vfGAn9QF54OpArUOlXP2TCxzKufhZIAl5fVZJi/yljmCsKbJmXxYzMlMh1L9ZwoaSqwRLdXaIOzVqHu1W6zw6WJ3Pl7yTERMamBGpZd6qZgHlIjz/iwFS26lXATzynFSTOzx3jNfSHoFEl6xlhV4IKhoHauJC3uTJlf6t0tNuSYMK2eYUqFgRdyIH90oZLU7ZzGRvFDIZTmVC2rUKn1Vx8JREUaJyXyIZQrGPGUF2e48qFOrqyIn3K5ErU03WhSFi2kkSVAB+80QEZ7x04P/GOVWlC5MjY8qnpOvV7wHxWJWOdOotVttD8MV8chmoqImvyxzCsOXt3xAxTyfu2qb2bWOot3jSdU+llDXGEw1xVwr8zyK6uSDLmidYXE/SlhkRyWca3WKdlVQQYqJWUwdDH6JgG2FGG+JqBiZU+4TLff5ZWPezdCUJA44huWBqjtmXdVNM+m7bZwjgwvfCZ0BMFYUdExBNPrBPRuSzb6SJVMaaxoHZLV7TSHHjDc6NjIQLuSYJaaiciKWKRgL8A5R4+i4+9CDnprOm3cbbK/earw3HnDk1d9m3leiwhd1eF+DOPJHK8VtlKjZrQa68UbVd6hQyhUA2BS0a6KivVjL8ubJhJFHc/PZVOlWV7s7sXdHTaRmd014eIMvekwQF7wfpcUYZqC9kGMKohVuRVSXsuhFg124VUF1hRwzapZ78fCcLMucAlHJCtaikjoJ8AoTu313zCrfbeMcZOGqDZKFEwRkrHS18IEJwbd07mZCb8HHToPVWpN3mfl8YVXURle6hi27cpwXtGhdcirG68vz+CyTk0UmkZPGoPb+oao912819oQjtsUXwZmCgyiSxXSdk9Y95UZJFJRm0ooSItTqNrf7bdaJCq0sWDHyBsCywCkQ7738BQtAYDFjEKHWo1lHdlaHsd4bPVbIbNtAEtcA5FUAYj4Qke5QhB+lMepEHEpejZLnhcG0NWTQZYsrNh+rIhQsIkAQ5/1ZCipSwep69hPVQGE8RAToE4rWPcjClDsliYJaRGagKY7IoVVFj7VmYFxWOYvSxFDalPZom+twtM31kAklr/D4RLBcpr3QbCrcUNT8qM+/aXTc+YOCjEZ8qVAFLtLxa81ij0DxjTqbFWdReuUJgrPGLg+t8prVDkW+lsoplxmFlTdEVEVE/0xE14noGhHtn7WfiOgzRHSLiF4mes30PsOXt3ygEwVa6BLB5dOtI+s8secLoWJJRthkFO23VojaFSq+UUch3CBaeE55NkymLUFD7I2YdJ4Zw8W4xjKpANh8P0tBRSpYAP4AwOZCDkiMRi2CRVfqIwtnUQzv1TTimxr2Sqd2oZjXYKd+o9i1LaVdWRNe7WllgVeKeqEnlWUXVkH4lPsrOwD8XmFHJREIuGBZtEijDt9Dgap5ZcIuyPH+BnpQzGsAwEbhKIpBZxJBcDa7rSM+3VpWsooAo+Bl2v8HgO8ycydSHtprs/a/Bal37VoAvwDgcwX6KkvKl7d8QAD4Agr8XJtMVRbjucWMwQyEDFxAnhVVF4pDiD0oslHHoVDRW98kJe0KGmQuK1nFQVD+n1wQ0TNE9OqEwea3MhxzlIguEdFVIsrZWqDiFKyuZz+xDcAvF3pcFhgTEm8QBs9fcZG4RhZOgzFMFjZTsX9XIgTevrm+mL2xzI76kuRdQJAt0ubyMjBSkus9QSx7sBbPp9xfUQF8BsV5puv7B6penf9pPEYkuwEGEfcSFT+h+k1+2teg4XSxxneALvtIKZ7nfxq1dnmkwvOyypMCVhGkVP+hIwD+FgCYOcnMs6vW/SiAL3KK0wCqiKiwuc9Lw/sA7M951DyJWSImgZ3M6JvPecxA3MKluIULSYmXExIFKQiRDSJy2AXNa57zJF6lUUmKbUlQY8Skh8zzz6lbJjOEwlURJCIFwF8gZbTZCOCniWjjrGOqAPwlgLcz8yYA78o1x4pRsLqe/YTS9ewn/h7ABRTB42c6xCMAUKOoAeefj0UWLggT68kCCROvEuAs9NzSYfkdrdG9bQVvBspAJLGj/YyxseVgocfOeE1daYmscjMDlZAjUTHayHIfrMXxKfdXfgxAD4BDhR+dgwDWG6Z6KBS2zSNWnw0iDgA4AOA0EQ4Ufm5zISJ8vENsIqDgRYHqSX3ho/qKonr9p0MEtLnNAxUSLlgx8gZAIftgrQIwBOB/EtGLRPQ3RDTba9IMYHq4/KOJbRXJl7d8wPvlLR84hZT3quAMxVUA8BmMB/OR+SED3UED2xMWwgGjNOsbANCE2KkQivGM9rQ4xD2HSotuzJ4vBtOOsEk3mBEt1TUXQcXInAL2wdoD4BYz32HmJIB/RMqAM533APhXZn4AAMy5C+RVjIKFlLb4PhQhAU8q6DOdtA4ACGjXIsi/0hcjPOGB3EuMkiklABDb0XxA6krBlCwWdC/+hi0D5prGvYUaM/sF2VQDyXPOu+EXXHfCDgJaSnLdJ4D5hOsse7Ay8kUA9YUflqGr1stE0AFgZNSz0pJ5h6tEidBOBE0I3lf4uWXGpZDnXbV0s4BDWvuE69ivag0HdBJFa5kxnYSFu30Rpft2UL0fs0Rh2k8sM8U85E0tEZ2f9vmFWUOpAJ4C8Dlm3gEgAmB22E46wVXJ5qLfBbAPRVjgjiaUy1FL7Jr4c69E/jlOBqfKsU94rgpaVTgXDiG2o4DRLQrhxQ6XcJROueKwXfBJnyYvuFVeR1Q6BfW1T/7eqwkPVjaZk4+xZh0APxE9T0QXiOh9uWZYSRWKCl6xZpKEX7kLoilLMFk4Qia/wupMF2FaCG4GzhOwK+exhYYIoTeuY9+3Zoemz4SBEQJqsh0jnfq52Bu3roeuFu13njkphvtW6B4xdpfkek8ajHwtxcuk4VPurygoUp6BIH5ZiOleMWocGPQdb2ocP5zH6TFmXAJwmKj0BrIfr6UDXx/hW0nOutBKADCR5fcjYPhn1JreDYqjZErOUEwcCySVI5hn0+hl8mVexpphZs72znwE4BEzT3p3/xlzFaxHAFqn/d2CEhRjKQZf3vIBAuApxtjMwLWAww3QVBEZi9EpgFEiVOc6365ARkzcQomVKyAVKqoRHTOYc8mJEeRY4zgVdDc5xOFiFbaYDYHHfRrrRKU1vD8pEObtIcomc/Ix1qgAdgJ4PQAHgFNEdJqZb2S6YNl7sLqe/YS/69lPfAnAXxVjfAaSrGDP9G0EkBaBBmYz5/kKdkoNmywV15nQzamFRckw2vxbLLd+bs68gCQTeqVTuxB8+6bByK7WE5yyAs4+js222u7Yszt2l0y5AgAiWA61p2TXW2aZPPmU+ys/D2AARZKPqiLneKuSSe1wNKq/nPtsamQWXcx0W0q6NN98isWiENF/aqTZuTCpmQGjDoFXunx0+lOrxIhdzClKAACwg658RG+0NiiOrcWd7Ux8umwv5fWWWTjM3A/gIRFNltF+PTCnGNI3ALxvoprgPgABZi7p81AIvrzlA2sBPA/gF4sxfsBQrjFotlGh2mRcz+d8l4oDNTZU+XXcUginAcjCzzIzNkH7kT59IALgIQGnXYowFEI30nsww3U2OtPsVLpKpVwBAIOqGFhAju0yS0A+xppHSBXdiTDzMIBjyNEepRI8WLUAfqZYg0sN90Cp8MDpELBWjeE504nX5RyE4AChUwp0gjGgGChJ8uQkwbdubPD92+XLrIhk5PAqxfI7qi23rQ6a0gSgCQCMZh+Sq2seVP3TpSQBfgBgYCyxb81dq7W26Emr6UjU2Vaq90uqjxaCinELLedWLZj3Ioc1dDFkshoPDns9K1uHE0SpkJwco6wFJv+P+RxR6TzBXT7a9R+jfHLEgPdNfhrf5qa6Nhua3ApVA4+/29+uE+ZH78gXepKP88TaST/2fq3ugEpU8nePrqBNEL8kmYpeFKSAVIy8QeHzOT8M4B+ISAdwB8D7ieiXAICZPw/g2wCeBXALQBTA+wt69dKxA6mCHkUhkFTShtgxcMBinFcod/SNQqhVCLXVOtbELZwLmaWTN0Sk2YUYiksZFcCALoRXITRQKuSrFRMLY6eiNBpSvhiXvGPqXOBOq1MIm0KlSXuYRdyimFOtuBdxxcicAva3OgdgLRF1IJV3/VNI5VxN598B/Dml3l06gL0APp1t0EpQsAaQqpZXlFLEpkP0IhVbOQdhYD9ZfJeVOdaf9DBCwsAAUFoFy/I720Z/brcBRWhZj6t2toGoB8x+VsW12Bu3etltX7LeIdKmtLPAZZLYslRzWACVIy0rZ6blxk2kLOZFgMcBbEi/jzqGhr3P1dcFcxt1Js8gPoYiVBzLfk3Cn7SLPZqgrPJGI1JX28noSTIAxN6seF88onqXNO+pSpeR0URF9NGcpGKe4sm8z4KNx3wJc0PvPz9tPwP4UMEuuHQUtRfkUELLeMNbjDoBRIjyC4e2GPdDZmF6kM4HTdAOlYRBaYzh01GI2icfGY1wus0ltoi5xVFKRkJip4MRJCpeiksRqBiZUyhpw8wmEf0KgO8hVefhC8x8dbpBh5mvEdF3AbyMlBf3b5g5aw2Esg8R7P72R4MAfqXQ4xIxju6/dv4Xfuz5OWFzU8cADi2CUL5mOWHiEorcAysjOZSrKZgdVpXrRPTtu1az277kFZeSflvRe1E8meRf4GK5yMUcfhsofOidr2Pk0TOf/6eXNHciY++raEzvSiTUPAtJ8DUA24mQ37NfQHIpV5NEJEMAD35Jq3t0RPWWpOJhNqps8qmJCo7LFANJ+X2WmeK9l79wEameXwVFCCvRue7myT3r7gSyHLbSZOTVmoYZciyJUSxRpUaivGROFID0anRspUvsW0rlKgXZLF58f9Vl5kKU8mDl+8kFM3+bmdcx82pm/uOJbZ+f8JZPHvOnzLyRmTcz85/lGrMSPFgw7XiFTLxoOSikh3ImO2ZEVc3o7/yXb5xa1TZUn0yqyWp/ZFc0oV373PczG4yJsVVJ4HnLjqNZB2eACUTAOXDx3P2LJbm59Yq5obls5pf06zv0kUSYAHeRLsHAVGnUJRa2JaZi7FDlxf4918fPXVxz3mFPVoUj9lZm0b7Qsda/48XjB3/3e/7A3Zrxui29e4nQcvUL+24MXWrJUJ2QlP5BX7StZUTmUcTCYKZrAO8iKnx11UKwVtgjz+jVVU4SZWHBFQS7TeFzCYvyKSiyUOJI5eI6UQFGzEKybKxZGJxahJ8BYC2m9YLNlug9fODMHVUx7ZpmelXVOugcrD5+7Frm+hQMHJSMK4KwOescgahTRTRm4qJMJfuXHQTEV9jFZbdGZbPGiVpU5xVFfRmbSBUWUgCUpBpruZCrv9VSU/YKVufvf1pgm/ZbAHZAckKL8HWyYEgVtuqrVlO+C/O66mD/n/3BP4w67EYq9MeVAAAI4py/gZLATkvnfghqzHjQ5HuFSxuuM1+MDc3by+oVKMgpbeK4kpDFWPBMNkt+shQrYCIfoqz+pyuCU6/7GAF4w+6nbr0NACJR271kUr3ssCerX7ne2pNI6ntyDJGCGG/67NeeX/2Wa0cBoH7r43xZ1W5kTTxkFttGRt3HamvCuRYJdURsoowX8XuFxxUtE+Vqklq7rO2JFOUnMwFYeMIWOVMsy5wF8aXNH3QA9BsANgA8xoxrSK0oxgHsJcovEqq2ZuTaof1n/UQz+/apisx1sysmw6EBRjZvOAHuhAUpgZIWp5kPqiDNrRW/8fp8sJg6mfk6EQpdGl4iJXOKVvG23CnbF98EZalgdf7+p38CwG8AWI1UefZU0rcgm+F53L8g4ZfdtjE+AqC3tXXkfm+Pf5+UYs5vvrnz4bXf+/Wv+xXBc8quS6acVRYI8OhhXE96uBGUWdaRxApC6cN15sk4iljyfiEk6uw1zkdF6b9XjP+L8jaZTKdyZrqknHrdx/wAvoRUblQ9phltXM5Eu8uZMsZ0rn9kvnS5Iw4g5qyK3BAOqz3c55uTb6k6krFIgyaOAAAgAElEQVR3ffOvXqrqGD2a7npWQs3pbQpH7Hu83tgDXbPaMh1DxDcBHMx3AbYUqKpVdnOzK7yBwDcY2fM55gkj9T4t9Du1gp7i5XDjfPnS5g9+FKkk+lak8ssnZAL5MVGEKgWfY8Z2APd83mAwGPLsTJd5smbV3VObN17fTgTH7H2mJfKp+rfaYnSrhIwFrxiImIwDKEIv0kIhGV5mWOXm0U9IDNmVgitYAqliC4WmImQOoaBFLopCWSlYez7+Jy3BhP2/IpVzlfPGCaxVukQS/dJGzf5ain/ltz9399s/2Nb7T9/YuzOR1JwA8KNvvvDC+955cmemylx23WhI3U/ZXwzE2K2HcAFgaelIWHY6NPsYqcEnDFygMnWfA4DhoREtxK1URpViLIeykQm3KHtvnWWWKSiXP/pBPXy++d0AfgcZCt1Mx+lIrnxq2+1+XTcbSfDejZ/55rmxHv/N53/r7S2jNxraAcDTMtb77m9/Lqi5jIxNgKs7B8YGX2zNtHsCsvf2+S1FkecEsbGicWynEDNlGDNtBHCBiPPzqi0BdTXBPaGw4xYzlc2zTQR4ddkfSCqFVLDKRp4uU7585amf22Il1d8B8JN5nrIDwDhAa2trxk4fOXj6lavXOkP3HrTsZhYCYN676+KxphWDGRUjvzuaV3NbCRxISpwBoKgEXdBMTxUBaq0N4eEEEihKA/bFw0BVzEK3U82sKC4FcYu22QTHiZ5Q7/YTSlkpWH/3tm98/J3/8s73SBb5vayIIG1oBIBXhxtW/8u17cd/6s0X9vzomy8mzr/c3l3rD6sdbcNZm7yZpjIOUM5mewBAnFKclAQsVvglqc0q90uoRZErAi0Wy0Y7lAR3K8kyEkBESFbpfbaxZNkswl4bLK/5stHyse533/tvb/7d+M2avAvT2GxmKkyYCbf+6Ommzk9+x3r3dz7fFu73nH94bE2k850vbiaR3VIZelSVZzgHdViW0mEB6Bvwn2xqHDs404FOtQA/zHByWSAE21ubh9UHj2rDABUrz3LeVNvljkBSlNWcXgsse7AyY36nWRx+X+2nnv+bXW/I/yxSkWpVg9t32/e1NPce37716uGtm1/pefCo6daKhiGvzZbM+i6PxG3xPC+mIVV6GiZjRAP6iB5XDCSCDQwbgLKWOUEDXTaBc4ooXSn5XDDIazEfVwnFzP184ih3aVMWIYyyf917Zf+6+501I+/q/tkvXlrjH72X45S0fsE/P3P08LdubHqBCN7d2+515VKuAOAv/+N1w/OdLwGKGkULZOU1NQSApIeOlFsDvGSNbRcD2aodLTNfeB6fJ4hAcpUvkFx1Xejykx3/4zvVtT91+WQep835lZIDnubrv/6sYEbQ3RjateEnX+wikb1/VrjPe6735KqsyeTpMAz14OiY61iaXeX+joFNN9urfJEXl3oe01EIHrvCF5d6Hq85mPL7PGGY32n+UwC3VqwffsMzv3bypO5I5nrXpZXK3ScOHA6HnS8Iwc3tbT1dNltyR7rjpgZhJL/43P5c7vJ01BiMADMS0zdOGHjK/o0xnMA6ZmSsEL0URC0qeXn71zb5VxBcqlDCslCwAHwQQBsAvcYR2/GmVbfTde2eTiLTjj859szTv/zNd78STNgu57ooM4LPv7x+QT2YCKjRQxgFc3zWDiPDKeUDEbHA2FJPYwaCHJZTubTU08gDa6knkDfLClYm3gJgPYB6EqipeecrPmQXwIxUMvEckkPuliv/+ccRfKnx+dmLkXTc+OftSZZ5euhnEQo7DkRj+uxnpCI8MHabUXbzrHNYK5d6DnlQMfKGGWCZ3+dJwvxOMwH4RQAdAOBvDh301EYHc5yWUZZ8/7muAxcvbT4jJT3Ide14UrsxGPBmzOPMQafJODu7Sw0BoQWOVzI4ldeWtsHyUmExrZGMsjI0ZaAiZA4hVUUw389SsOQKluxfpyG12JniQzvPb2n2BLN5h7LGsb7U37LxLV/8lS1//+KeE9mO+9iXfuxu3NDnJIXmCwGbtDDOgh+/MliUV9hlJliUnyIYq3esqYC1fmUsDxj5W5OfPIvyDKOK4jY2r/jwme4sxxOyFEyxIrr3zse7jr7y4beOs0X9mY4bu1n7wkt/eSinVz3LNNTBIW+TYYiH0xY9a5hR9n2dVNUquypXNgUrFZLnl3oeOagAkfiY5b57aWkF4Jm+4XW/dJaFaiWznJNljUO496Bt79f/45m20THf8UxHMSPxJ197dlE9qxg4bDGeY3783tNEeSkumWAgvNRzmE3UpGz/5+VCxcgcAc77szTzW3rWADM7gxPB96/v+NqwKqycFf6ysam+L+ti+P5g7aITNYXEET2IIVjcAwAkK0P7hygfRcFSMRRqoe6R7Upi5ClHtoVuOVA2v1sumPP/PGHMiYOvesutg85t/Vm7sudCcSWjpHDGVg6BuzXJxUf0UX1PX03r2LjrODMYwFWi8qoKmg5Vtcpojmx5XLEzLY2j5w6vH3QpQhalhGmBqBh5AywrWBmY025B1eW61//S2VOLG5bgdkV9mfYyEIwkbP5M+/NFAq8zGFeYEWQGkhJzKqeWI4zyCRE0Je6EDRwbSqA5ZKDcjTqVIXMoFbKa72cpKAcFK22vF48tueUff/xfT+WRjwUGm6zKYXZYt7jKOMc1yVMsOPCZu/uj//ftw2dDpp42XDAYtWcUTvOBgAY9DA+ZfByMRVioS0c5vOOYYIZaqXt8PfmTVdQFQavCK/VOpvThWMvMk+UQwTkEkqvcSFPlkwjqyj/+gdez/+Elspk5F9xCsYI2Z/yO7ojfbVzd001CRpU4Dce/13rSvFb1fLpzYkPugsnbYMh5OBB0/ICIF+yBLyWK4LIIEXTYEy+vbB55VOOP7FVVuVtVeEOjL5YznHyZfMhPuXoCFay0RShqVwa69rzzyg+dVbGMXu/HcJzAA4LkHZtinFDJuiJI3rv88qbea1c6zzJjaPYZliUKGcq31WDcCRg4hlkRR+UK89JH6UjGWNDAybCJVSbjCEAtY8mlWu6/tpgs017OOVhLGs4m+9d5APxBpv0ba4cP/9s7v9r/1N/+Z9OQytRcSZERnyva/4f7jz/63u3Vnv8wmoPSzUcxUW0HAFCbvHhT+J+5OeDH9XDtrY+t/eHJDuf4lPJjWuJR0lAX5T6fDgFeLQph2dgEUbn3wgKLpe8TEeygU6aTZr58iBoC620nq64nKkJRLWuevIVMPvwhgLRli0nlttbf627r+8ye02PfXjetzDpDcyT7fW+98aptVQDDn9vR0Lru4fppnqMOpzfyslDkDutqtWrd9gbkkOMF7XDf/uk9qgYuthZUyQgEXSvtdiPidJR/1AkRu1O+0qVbXNhtyVfqa0IbZjdTXdsQcveMlV0EY0XyBCpPWTG/07wNwM9n2r9676OnmzYMXfj6Hz49w/OtqUawyj92d/2mG+OvXN7QZIScEUG0fWL3KmYrBuD2+Ej1M+MAEgnb5c3brgyoqjVVQCcYdQwBWFWo78KMjSrhVPlLmxRyiXOJmIGggT5gptHdYtoZt/imXUHe1WuXSQ+VuXV4qfOFPoAs/RSYgUvRmj4ztZBRAeaa1aMn/G2BTo2kenTlvSOvb79Hqx5sPv5nD2YV01Hx1OQ/r0fq17zn0rtX/q/t//zt1c5RnwLFNAw9oQiulxZ0pBLYF/1bEOOgEkevVKw+1pW0vbBsfdFLnmtj8dF9De3SqWYMJyo2LEhZKtcFAxhfR2elhgPp9gfX2LxV13PWC1gqllcQFUoguWo9gHdmPSik9qkGxyb/tHsjd1bvvR5WNWureaS3j9eGd3j3P3jF/OOnZtwHiiof94yJqz7jfP1+GVVP6T/ygCHYZAJJVU73Ni1a5jDT2v6BqrhqM55rWzH2unTHJCwK/+sd36U1vqTYXR9N+7yVggm1KoQlanLu80RPVHmj62crVwCgKrzJYzduh+La6qWYWw6W5U2FYn6nWQfwYWR5zpnBZp8YnezFqQgrtnPjK2frqsd2Cc2MOVaMdq1o7jfPPb/v5tjwY/vxREPhKWVqZLhmS/cPuhJH33HqOUWVOtXZZWKkKjStx+ei5Q0RdF1BlyZwPWywwwKlLRLTYJcnqzTGqyGxD6AlM+ROzxsrNZIxHDQwCGBjuv1jCepb4eRyVbCWZU6BWDIF69Stg6tbdOd/a9ZnRuNYTPJyrPpa0NISf9a/pd6C2FG3ZrRbSoKvOdQqBB8GABMCz4eazr/e27vrAy2vtH11YG1/b8KdTWHRqoTq9wrHfgBwOYE1jSO3rvXUr0EBfwdhSLfv/GAottJzJlXwItXSVyQsQ8Qtw3U7uIGAWu+V0e7xPfVLo2AxA1SUDuB5kfThgtQpY3NUqYsthkv0ahHZVMp5vdYo8ybnJSX6yWeI3mX/v7guUQd15g9DffYb7LBc6tebHyKo7alHeNhqGTxXt6rfsLniU14o5USdYa4OM2xynXi657j8YXO2niYkQU7Ta05anbHu/eeO3f/GVH2NAskcsr8wriBos59RiAUBggBYDI6YIvnSsEOPmMqhoZg6tLMuKgUtTVj4RJ5fGEugYBE4WeWNthGhLtMxHXWhRy8/rC5HBatymCysswwAwBwWbxVufruwz5Q3Mo5hTlLY6FEfGrfVzQC9sW1F/9naqvHoirqhzUSpkEI2lU6ZVO8I3Vy14+AF47l/f3PW65HCivLmlg5FS1UrbGFExadgSQsKCrjGsSss/Lq8FzSonwF1ModXF0jYFBjVOh8iglKj85mRJO0t1HXni1zC91/MwlVkCA0FgLjExqX15782WFg93tJRcgUr0rfS9q3xVc98aXjH/wtQXYMaPXPI05/81nhbnUsxQ2OmrUUCG6ffer7mUNob9TMDm7fvcQ0FPYqx0qMm7+YqknwqsMLocKbCkpnBD0d8BU/W9F4afEkLJI/q49kL7dgGYjsoaQVZV2YsOMjiQTXC10wntbFKHZOTLdiTyGzaxvkMADDQQ0DBwiTzQaoYDrdQTstNuF2/478aL0cFq8wf6QmesNyqTIRCnQQL64XW9nH1a20/yoLvy6dGH4irPjcUthBXfDBpJQCdQM0AoGrWltat9+aMRSFtj+iuPyafHjxCK8M5f12+41nNDIsoFY4buJlxfb9gRky+PmDQ0YFHnqyKE4PqTvW7ThxcETk0fbuUFA2GHJctKYwaf3hqXyFFTjBkvzA47HNpmjVmGGrJn+kV9ePniLLnxla7kuWauF8Z8gYTImdZwYLxWXtVvLf6p8yg8y8AmEq1PEYaszmqVBPB4CQ6AKwEqH3ynK3rbqYxOFJVfMA/5mwdgqoZOd+ZbJEaG1T73M1mBwBYBg1K6/E1CgOjwS4NQeiq0rOLwCYne0eSUx60KTTCTaeK3qCB7RPl1AuKAEZr7LhvSRAzokTpQ8KLhSFx2ZDpaws8hmoTkq/blexN6ZeIiniICUuXW5UvS+HB+t8/UnXn0A7nwKP/p2/PyR7Dc/BfxlJhwnEz93Tcwrj80zW3IvcSnuQhT3+1WxibAaDFFg68GqnOeu4a5+PefglDvRmO29ZN271oF7o6Fr+uBpKHch+Zytnynxs6FtrgryVVGwIDwkQ1MbYAqFeSHJEaH2MFUOJoNFwYsRy0f0ETY4YwcV0qqLOP8z2SS1eII7KCroIoo2VnkuAa2wrftXhSyKXztFU2T2T59XS8Fwq+KH/mUZBuO8+I47V7lfM1efc/YnBQbglcgs0iqKzInaP7AIB8ydx5lv7EENHjHIj739hUO21vAUIEGWejLJFnsaKLQ472Wod5sRZqLJnQrERS0wxD2QykrMyBoPOC05EIG6ZKpinsK1uGtwrBWVtiZCIe127pNmPl8Ij3VCDo3AOQ3TBK7zxTFatf06ycclNVuLPOE7s0FHJsz3XsMpl50hUs47N2BcAje9OoS9YE70fvNVrWqDK12M5nOUiq+YLqjpFMqknNG52UH4puiw8nE/babOfaqqypcvD9D+yPALRP271omVOl8QlByGuNoxA2NDn4+HgSbl2hoACEJtBEhLUA1toVDMYtdDNADgWto0kkDLlghcPQCPcBoNoGJsIOZYmCE6Mm4shDSRmK01irq7wVhHJnOQdrGpG+lQITFou4VKM9hmfeC/13Vd8Ov7XqwZwX5kF/X+AHo+n76f1o/e1z7Y5goskWsU1us2lmh6pYpmlNFc9Y1G8h4uaQ78VBneah/eujiSP+k/0ytK+lEUQztEMCXIqBI5N1cLQIS6mjj5X5dwPXg3xMGDiElMcqbW5YsWFCLOnBq0kvNuV1vEKrB/e7jjWejOSwBJWcyllBlLfsKRVVAACb9NIZ//wtmXbrjnx6cO49WJXMHN5bE38onhq+D5slMS3JvPHw3dGBE1N/Ll65inG3mSUMZTYS1PLdB96WXXbxfL0qjs7eb1nKzlD48U80MOTrXtEwnvf4k8Ti2quPemtWAugFaImeX2YheLSxfryHCHmFYm9pHW84dt0WNKUoo5LylSVvnrQmwmlwALABgBlyPgBTtjDiNLC01QRXCD3lhZqECLqvOtA71JdOwWKselvwJKlgUh6HwdY2JWpmHbgomWMTfN2n8ZbcRz6m3s6H/Ro/GDGUHbP3EaHeoT7Owa/W0TsQhwXMvwBXvR3XKKVMGkSY/b1LAjOicQsXGdidz/Em0/5Akk/5dCzMcF48KkbmLHuwZvJxTDT59KvxefVneIP3Ufd7am5trFYSc25GZkRvRqps6c4DALdiWP9H65UZVpfvv7TmBdNS5r14mINkqY0nrntfHHQSL6BijypCyOP/gQChhfhWsiq3gqXE+SyZnFAMqMTQIbGHUlbuViC17k56cEWLokVYEwvQIpHw4WK4mXwg+PG4ClJexBu0IyPbHd01l2KL/396Eilv2VN0QqHOTQA+ObVB5wjyLIHFunXVfPcDL9xmeotqUBsE0JF2X0irEQf7FVIwFQ5nxtRb1z53YGva4+dJVHL/izG+MWrlr1xNRyfKS+6HI/btlqRwrhLriYR6N5bQBwMBp8VMwjCVZoDsAKY8hapqjjjsyYFQ2Jk26btQCCEDzQ1jd4jYLQTWAvkvtgRhxcF1g1dO3qhvKzMlq0J4Ikuwz+bfMfE+J82ch/eXTd0fuqg4Ep2k8By5wozheMyeodQlwbcqadZtj8+QB//8mdaC9HcjcMKl8ss1Ok+vnJo3TMhrHkRocqk4GTFzRthYTgXndAUJlaALglMQts26KrtVeSFsil3F1heiJo4lJRqRUvDy8u5NMpqk7QrxBbe2NIbvioYAWlawgEjfymcA7ADwnsltv3r/DeNA5qTj6QjwwC/Xv3JYIRYAYDL1XgnV3DRZYCDpwMfv7No0ZDj3ZTr/H/o69713xaunOpzBKeXs6+c2LLrJMADU/PDhPeL0lWJywQBia6sv5RMyBwDCxAFh8HWpUfpFH7OpB/kFYeBwOk8aAzJWh7ORJvKyQpud/XzC3cvzEgj5wABYwahlw0C4mWohKL1rMQ9CHfoh3/X4kBrnwiewvNYpb9lTNEKhztUA3gDgbUAqxJROVndTWM3rOWMwywPDYfiNKW+rvO8+Tt6kyr3OpHypxsfX/JmTt5OK0/rq6hvqT9+eUrACN+r6k+OONQv9TpO8HJPP3zdwFMjPKzMbn8BNn3hcYTU75Ovtqz7e2jyS0QofCDrODQ77VkyGGc7GYU/caWsd7HU6ErulpKGXrhSnjgSRDCuCQ7XVoZuKwgv2mmkKb16/Ith9tadq2aizAJ5EBcv4rF0D8A4A2wHsBQA2xXCir3pDvmOQIi+p7vhUHpZpKNcScdtYcKwqmYjbxO1X1u0wjcxVLl/6y9r9T/95zy1F5zUAwIzIldPeRVepU4gHWxzSQ5SfV2Y2zODxpMhb0XOr2BQ1EWLAk26/AIaqbbijCqRd7xE46tet815NthNhlxGl6wmZYb20CJghGRgzJe4mJXYCWGCfB3IMJVDvUpmnt/RYJjeE8mjkm42iKliRvpW1AD4F4Gcnt0nmU/eSLstgJc+FPSf+qPnsSNRSR77av/Z+Qiqev+vZsDVo2eb1AvxPV1/f8YPd/zb195rG0cDFu4uv78CqGCVDLqjXRLLRfdqscuT9PQhQtBCPJ714AMBQkhgwHdgHgEUSV7UIR0Wa5EopEI400blYHdaCaEowxWrgdvcuZObpCTfRsYQfHQBcEyGP2ZPi8oFI6Tvqudfy3WBdmUifMplGDp7Qil6hUOcHAPwpJu89xqjtDt0xX3Xl3YOKW2LHrM2BTdaV6u9TWLXJc3XtGHTMK9SHX/Fv56B2nrzGLgBwNQUL0mjJo5AGY2GaMwHygEOpJqK8QyXjCW1TJGq7rKqWJ5HQRnTd8Nt0c6WUFO4frLoUjdk2AjTL+MHweSMXW5uHDV0392IiTFJRuNNuS9yPJ2x558BlQ1XNh80N4yZSIVkNRHADmHcI9WwafbG994ZdDyIJbcGGoQJSMQ/xk1jkwvisfS2A/4lpvY7YEM8nRrwJgLKX/ZuCh2wNo21GQrvYc6dtJBJx+nrvt2yXUsk7/1gapF//h6rRTe8fm9yk1TQm+3tuq4vyxFqMWgABpEIf5824IY5J5GdABgAiVPl0dIcMrBGEsEoYj1nYi5TX6qxbg1sQ5hhzFOL+Gpv5ilPhnUSP10BVujUwEBcFUbCYwWETL1iMNgB+pLzjBQhHpNbBOLobHFwuRp2KeYifdA/WRzFNuQIAQbTfLowbAMcAyvHQcvITrWce/K+e9aGvD3Xo9c6Q36ZaRnvN8IsO1SBBUtwZr3WZUujjcUeHxUrGhYNK0pz+d9xQC5ICySpZC+0XrgbiK2z3x09Ih2ZKuyqkrthZEx4IqgXNXrSkEBL77ONTN9VqNYoAUor8luleKwZgeHA10kgjhhvbQfT03Lljq6VhWDGQNXE2HxJVOJ+oLk6+heUUuwf3u7obTkXKRQAtU4aEQp1rAPztjI2EaqsKl0lIjfMI7ZetkWPG00Pu8K8e9nvah4VqNzRhi94Tq4P3SDNVM+ywjICj2orqLSyFC5ylqfiExx0ApCkK0vRSp4UtdACAAPNKQr7qFmS4BOAQpNkILhXwE9BARGnCrKm6t7960lDSDrAJYAyAD7MWTppmDjXUjV2rqQ7WKwqn9ZI1Nozdu/egcdEKFpGMNDeMG9OLiBQKItj3rh7G8Vcbxg1LFDWEepmK5/OY1UgWqlwviAfyO5177SuGHTdf2BGNRx2jMVP3qMJK1HmCpzTFEpKJA1GXz7CUKsNS6phFxjWOUB9nwBFBj4bUQhh1BIAIUgrFvHGpsk616JjFYItJsRg2mcrDrwMo7Zh2BV32aaLay+gHUE00O1eJTbcqL/k0mdQE7ybCnF6ADoXXTOsFtiiiFo5ZvLCw7NxjU1cgyad9enrP3DKVSbEVrMdWHeZxAIoELlar8a4fqbpx4lvj6zN6sRTIng/XX3n4nYF266sDaw8CkHWu8PF658xF9lONjwAAgYT9xAuPVmUc7021D+4BaJn8+8iGe9FvnM/qwc9ZnUvEzH4RsxYcO6vEzJVKLJR2scEEU+rqgHSoo9KuRqRDS1h2lSy3Xg1VTDUYpGllThmA6cCr0UbqSVRhLYiyF5QgEtF6XPP08DwTcQEGrKQXl5NeCrIAGx7MSWItJLEVWtfAfld3/enIIcpnpbzMk9gH63EjXUYCQBASY3oPraH1g574y00BgNKWBWZwjFfEzyV3BkT00zuaAEVJhpwN7lX3G6f3T1KdBuz1QTBDjl9qMzO+uIktuM2pKqX22sgKzRMPGyF7Jk9aXtUAr8XZzHVMJiSgPzT5QKbYUZ0w6iQMuwQF3YJiLgHpIFK9AgeIJou2kwo8NsgoijVeWxN4qb424FZV66np1uN0VPkiCy7TrmvGLbvNGHTYDVPXTC8RilbxTxDaDqwdvHLxXs1oKK4VXIl7rfIkebAmKgZOLYiZMQxA54R6V3EkD1DYOM6GlvndSnxDrxkPP7yyTiRjju2C2FPjDl2b7PU5idOWShwdi7iOj4S8Gcdr2BWLT/97497AvZPfrMsUSpyXvHGpfJ4Iu3IdlwldYKMuJuXNTLnDjLjJGLQYYyZTxGIYpiQyGCsxrYnxzCI1zA6FX6zSraBN8NZccyNCs13hV+IWzTuNQzICpsTtpEQYAJtFUq4mGU3SFsk44bcVPm3jtckTXKY92NvaKEAdAO4wMJCE3E2pf3cBwJVofQ+AGwBeAPA+pNzQfgBgRs9Wdfzmy4F69a96tkzebOLFgdbDT7fdfFFXrDmL+Xvj1VkTSjscwRkW5MMb7netrB17eH/Y35rhlJzCx31t5AYtMBciF8RQlYTZrCTMGXGM0TXV3UbdXMNUrBZnQi3UAEHrAazP9zrxGvg8PQuYH6BEGykh9dJVCYut0LoevM130zZi9lZdjzfoAatNmKXtccEV5D5/knKw+LlNhJ3YA+AuCB7HVREmE24AfmFQnRm0nUDqF1GQysvqBGDHhAxMCKXbajEc8c9u34pYKqwmMeLZErzVcNK3dmCON1km1XNgkTkPy25FpieEC002H/zLf+l+/md/JtNLOqe8CVh8N8bI2KB7sSQZ1UlG9bh83ERNJ4y8waXOuecVxRrfuP7BbVW1tk02Rs0HIXitw564G4vb0hcIyYKmWoHqquiB3EcWBk3hzXtWDYdDcfXEcMiO+yOuHZYkV2lFQAU9xPxkKVhs0TYINgD0sKnc4pjWBYVvwBIHAIANdRTAAwDHAfwMUp5fPwBYprgUJwoO3+xwBgdrJ7y95I0n9DUOe+IR0WNjMAAwwwpEXFnf67Zqa8Ya6B0fetRx9ns1CSMp0hUAyyt9pVrn3O0oFggR7BqhTQPaHt/njKGEOGMx5hiea21mt1uVm4jyzSFNUaVZw/3W/LN1CPBFLTQCKAx7TiYAACAASURBVFHvPnKNGzgUNHHJLhCotvEahVArCBkLuBUDrhCRQ3iCQwQN8G8SOM6p+PtVAMDApEX3M7/f+vxHapoeGQDwtuMf/jyAuzFD/dhIxNWVsJTGm7Lu6OwxGSTO9K2sPtR8xySaOfcW73ikN5w5moPTvKk+9MyZOx/98jOZFKzsSLa00UTJe6aYXlvaEKFYLRkQ1D7f8VjBFkvDgGJg3o02bWNsxhpK+0JlldbGG7S1/Q0awByrPR89735oLNjCli+GS/QM7XHeNdxKQUK9lik4na5L4mdZICkMqgVmhL0+UqtjP6W983s9ABD95DM1ANYwoz4y4PsdM6H5rYT+DK7PtZVEHtQddLeNnFds5ox7TOjmSlKsIFtKphyHOfdJ0+tvbl5MuMorcX6ITJULi0SdQneQJs/A4Uj0atrCvPeNDWMP7t5vnPf3iMRs22s5HCFaaEL5/CGC2+swD3kdYayqDyMUV0+evV27l5FfJcaFX5eNNfWh0yuqosPFvE5hebKqCMqI7dMQPAYp2gGkDKEWrUPqIf9FR9PI39j/+xiPfWitAuCLAE7EIo4vjwzVbEzGbeuZRZp3OdUYhnpK180ZChYRFLcjdms84s5WnGvGGkcoaNj9xtEzL3yrNrMhKAsa8T1lTnW+4pNOuQIAlypXEM0/ncGu8LqFyF0iQCHcsrhUClYKybQ9agHRKAHgviYHx21K8eV+zMK1oThFrPLWWWYgytwAVcwiHN/lzIuBZuBx5b1vHv7smW8e/uzgw3G/iBr6ZksqGR+iiGFbabG4OXt7jSPaVW2PvJLpvE/c3TnH69XZPJTxRc1AxFJx0dJxMu0BghRpU17NdH6xsPWF0y7wbWMLfLMRUbSBri9oLgEsbRI4kWN4l/OpR2/2nAmss70Qr1EW9D1yEW1QL/e8yeNJ+tVDrFG5F66Zgjj/z2uAm2TR0IRyNRsJ4PX83CYFAJwf+e6I8yPfPTN6s+lhIujaYyX0rNW2or3++OxtRGh0rxl4KeNJMdXPw7bTM84RqHE2BTPmZthtydvNTSPdmboJNWu5clYLz6DJ6yRz3+zt8bhez4wF+L4BnzeywOpCpEi5MFlVKDx28+DTG/ofbmgaP9bgi11QSMYKfQ1FyPChtYOXV9ZGDusqL0lPnwUjKb/PawL63oRyNWcHUuubFQDg/4ublv8vbn7f/xc3o70PWloSMUcG5SqFaSn7mecaaGrcoa2CZDDTeTe/5psz5rqnQnNk1yS6kCOrPNFTjY7EpXT7DUYrMwpYBis/dIEb6bYnLOpfyHhEaHQo/PIC55J3oZHiQCt6Y9TYG6XjQQNnkhIPCn0FZmAsQSf6Y9RpMRW9rH0hIeK8P0tBMReL2R6GdwD4q5Heltmuz/8TwD9mG1QTVkCQTJtHsa2hJ2M5ULdiRJgxQ9g0+CK7vvgrXztZ5w0PzT5earhiOekpqSBjzoNRYy9In4n5oPeF9kHy3dnbHcOYVwPA6cSrF5bAqiTRCuaCP/DzgkiYLmXv2GbHgf4uT2fv69wnpYJFL3qSHvFgdLP9+MNnvRcGD7q3gGjSU1E56ghT/p8Kh1531QQwkmF3G1LFL2b30HsZqdYRWZ9jzRtLKwMUZ3KvsBkPM54o5t4qbzvx54FVP3npfLrD21cOVnvcsU1AeiW+Ucs/9LdQGIDvkclzDFqmqdYmklrm754FIXiNwxGfM2Y+xOJ6xgVmqRACHc3+2JEtLeM7uzYMDFU5E4tW+hQhI01V0XO7OoaPH+0cMG2anAyDqhx5g1SIYD6ffCCie0R0mYguEdGcZ4aIjhJRYGL/JSL6WMG/UHayrXF+DcAfpNn+LgDnsg1KxHeJ5uYZE8Fd7xt/MdN5Qpubn7nt8PjuD/7+nW7NJhNz9lWHrmyqiuzXhczQHZAUkwu/oM+FT5PrAA7N3h40lLSl2/MaU7fGch81F01g0a01Fg85EpIOjyTE3p4oNQ3FqXuxYXzMQNzC9aE4dd+P0PVxgw6lfHap3YuecgkgSjUazvezFBRFwRrpbVkP4B+mbzNYmJJnxHf+15qmRzMe+ld/4mMBAP8dqao1abEpxqig9C5bu2ruanKPp1289CTcTRdDdXME2/qmkYMbmofmWmIppVgRZ1bnRbL00WIEkBJOzFjYJD24OtJJVxc6Jqu0NVaDsws5V4ljjrK3lCSr1IMP3uYbD67ST+c+ei5MsAb2u7p73+htC66zH7bsojIbAPI8PxUMP7eJ+LlNfw48zk/ilKwxJ/8N4Mv0uqsnpp/X+tdfkAC+CuC72cbXPbG0Bggi6O41/X2ZfkDrq6vneB9Up7Fu44dPpFnQsAXAzoyMCkRCYgxpQg+LzQNDzvA4EUmjva3vuzbdmFcuxHRWt/ePLeTGC0XsZeXREYS2ne2ja3Z1DB8XJBdkcKtxxy8f7RwIb2wO7K5yGoeJitv8vVgwF1bBmuBpZt7OzJnCwI9P7N/OzOkUmqIQ/2P/05jexBwASxj8eI0TAfAbs8/b9dy37wP4vWxjK4qc4zGexGVL7NUU81G6ff2nXbuM6Mx1ABGcm/cHDmm2uZ5WTaRciTFLyeBNYyagaDlYmVAIjQTcn77No1qn6uzGPBo3z8QueJMu5LyNOiIVkpgxMqr0kBo2qet+hF6NWQublyUx9ihKp/piojNsUhej8H3CSkP+3qul8mAVK478rwFsYgY+27f3+S7fvbpPPDqEqNTWAhAEBBnkPZY+svXDyNK0LWzYO5KWeElXZNrY4K31vS1DUU/AkMocL9eoYU+7OOkf98zIo2DAkhNiRaqwJd04DYKLJAIs4AWgaSGsNF0aacPxOKWS5UsCAwg3iWCsBcdcffDrQWyM1mNY2hcX6hWrpaRjZP4D2MZZRB1l5gERtGJ0m6MRwCnvneRsr0VWAutsp2MrtGxJ+5WjjhRwpkS0HsA/Tdu0CsDHmPnPCneVBfPTAD4EADKinSVFumXINoSEegRAHCmlxJ5B2K0D8BPZBg8/qA1716SP7FNs1h5bQ/BkYsB3cPY+jqhpKwbGBtxzLM1uV/wqEbYyQB3t/cc11fJLKaLMMFTVWjE47BsIDbtXCOCuRGmtqj7dHNu86cHJ0VEvBoeqNgrBybq6wEbLVJSFhpPourmgYh2JpLqJGePlpIQQQa1yGocPrB06e/JG/S7O4IFMh6rI4Pa2sWy5JZUjb1A5CfKLIf7HfgdShhk/WxSNDvnPKbakI9JX24pU/z0BIAiQpzlV2GKK8697lpCK1MmIaSrbNdUcIZqb+0gEe0vN8PDdwYbmaR6HKayEiGnOOcscIxpUZzwvKsmwV7PqAKDTFxnbXh08T4Aas5SgRzOb4qYYfq6/ep/JCBe71PRsmDnRaDcfEWFkJKE0JyStqbWZGhFiC729iFDrUuW1ZFLMu/mySug3GfOuQlhMGLS+P4Zgk4Nv2hTM6zsNxOmyyVmLk1XMU/zEVREc6W35TSBVqvdz/buf/2Fg1dEfBjoM4HG/GE4JoacBfC/NEFkXOwDQF/aNrfSl9/gSoXFv072TJx6tnrPg2eQeyaS4MRNGDTf6SWKUCQoEpc4XNJUgysrk/JmlikvRtf4jStzqtg1ES9afKdjpPB/cYHsrAIx7ADAnkOoebzDzAwItLC9qgTqSLYDO6KJbexYBIhrd5nhK6nTS92piXz6l3RN+5db4RvuCQy1fyzDzq0CqLDYRKQB6APxb1pNKAD+3qQ0pgw44KW7zmKONgRpgqizvpPHjzeYXD5D6vhdmS+Qfz3WNaH/VGs/qgQAR0oYmO5rGNidHXQNsqDMKxVBH6BHSN75lAFjZOnhMVS1bLK7HfN7YUQAQhA67zewAAGVaPRV/VbhvbMzT/no3hv+/cOleKgTmn99zr1ERaHE2D6OleRjMCBKhQSiyW1rKYmSfBcy35QIppiWua6osu34xdk3u2b926PSFuzWrE6aSto/hdARxYu+q4WuUpnHqMgBSz8n3KWV+/itm/us0x+wnopcA9AL4CDMvOJJjHvw9gFpmyMDdphetpN4F8Iw1DlLP/R5gTohdPWb3zZqLnZkuEqUv160I3l7rCR4bDvlmLZIZNp81t2gXI6rpkqqEeWNnTZDGk+polW7WaII3AUCVbk6NY1NSth+HIpttgkf640pXi8M6q4riVS+djUORp3WFnwEAtyox4dXfCeC+mVf/1PQs1AysC3jMsixtRd7eGOL1dn7RqWDHXHV7LkEDZxOydJWfn3QKGiI40ttCmHCLSwb/7/HVE/2a0jbjPHfkB7/x9jTbM4QHMhPJh9X28Pk271hWAeWxJQ4ebLl9ssk9PiMk8F2Xnl0VtdQZhSkMS5iax3zVcOEGiDayQocgKKvXQ9pwLbgaksEJNZgsaQnNsZ3emeFFRDYQ2UHwQCDACzQ+mA5sCK6kbqbMOWfpECbqIOfmaJQF9P+z9+bxcdz1/f/r/ZmZvQ+d1n1YlmU5tmPFR3zbOQkJScPdFNP+Sgs0X0pLaJuWJC20hYQj5dv0W2gpLUeBUFoIFMIVEhLkO/F9yIdsS5as+17tPTvzef/+GB0raXe1suXEdvx8PPZhaXZmdrTe+ez7fL3JPnKTc1Pbg/7uUKV2wNQokPzuxHOUc+1v8x3tut2z68I7/P3dt3mqk3qt0nF1h0ySuIIiF3cCOM/MbbPueeX5I4xlvOWoowOg4jTrzT4AG4xvbZzu8EgAKXsQiGQfaYkLhbeeu5jOubL2g9+/vMNwFI/sJmWyCZ2P5t0iD+c3Tt9fCatUVjrY6HTqN9ts5rpx5yo9bJZW9Kq5eaMnBs3XtyeiNi9yXBGYrmjms/7lzSBO2ZCeDTfVt++32/U5/z2RqC1Nz8gbj8tmrt9c15ezsnKo0a6avUScNIaesaG2b++ahQM7b1vac+b2pT2G02bO5lxdM+sNMKcSwQIiOpD0+HCK021i5lUA7gXwx0QzDMNDAKqYeSWAfwbwv1f0jwMQezK3ClYfFWRC7TZ125gtknLNOd25fdWdKbanLCUlkrpmj3doWuJlITLPQvK7IptKcoYaHVpy/x9hzyeLI9PLjBNhIVaVBnaszh8VmuDlhY7E1nHnKh0B3f5aqdNoIsi4kjQH8PVAEzzFSSSCjwhEhGpllv61TPg0WeHXzD1zvaU0gWXA3Oyi1w9a0BcTt7SH6UTMxCnJCI0/wwwEdOxrD9P+i2Ha2xqi4cG4yMZRvmbWnDdbiWAJYJVuxKUaYaRU9BrnM7CkS38ybftbAXwRwLutX5ltqrlLU81yIixsKOzqJ5q9Lthnj2/Kd4Z/kyzdPpyw57336L2jq119uxYFYoopBb/cvDB2oqfovmxdTd3Lu/QcrAbBGSlM/LowaqRaQOcd006jXfcXHtHztPTOJWEFCDvAmYd9pj6W8mP52KZ7cSDvJC8VMnspZDIxyleztp6g8oE17nIAIIObwdCJWUqNloFI0e1zuvhrZvGZ45UWTGsk/2qaiDEAPATgvy71suaZydINQ2RaF5YB+AowYyD2s7Dm8X0LY44aCdll90b6SKDBXji6T7GZs2ZLiFDmKAoMxwe8k1/EUqjmr0vrzJ3F7cHF8VZ9yKWxKfTur62+1eeNZjm/jRMLSvu7ibBuxcpz3T/8xTJ+vdoibqse3LW5ciStRDERFEUxYRqKgUuQLXfYE+tvWtI+0HKh5Fhg1H1ztsclDOV1K8m+FIigFXrj2wqX9IEZowmTTjJI2hRZQYQNc2yju3bWm7nJtA9k6KsCADBz19i/fUT0I1gZoR1Jz48m/fxzIvoXIipg5ispbb90/Aczrg1iXJ49Nd8H8LFp20ZgVQL8L4DlAEAk42W1bfvc/mA9gPKLTYs9s+VbiKC4HfFto1HnoeTtsQGlaPdjxYNlW0P7hICXTZIXX/KoC0zj7mxtnL6o89dRQ73dq7Go9Ri/SrDyluyOvDwEuM2lmj2ZMroEbCHwMQZlvV5MHEuozrOb1U5F7uiJqVtSlVimwQ5gAJj7KJvXCwla3h21/hwBPg4ADNgZWAvQHKsEro01hwDQVX6p8+1glWKs5ONCPOciwDUApZO5rAfw+1t//agdwJcALN9x59O/8TjjXinx5YSplKiKlIK4EsBmVcjwLQUd+zVhZj3zyKaYECR1ycIGABKCzkVyFrZ05YfsJ5wrYEWvx/+HEsjCcjEdkCA4ASBeZLsznqe224eMibI88vAoOTki+8W8DiDufmvB4WiZY/ZyHMJaBu8mptnKEFIibbQmXIKd3k5OP4E+Cd2NJtbomhGCYJXqACCDdsn1w9zWnlkNHgAgIhuA38IsfQSvI5UAwCaNgDMa+ZWw+iFeMr618SFYZbU/deaWRwF06mH7f6v2xGIQayT4ViKUOkuHdtqLRufyRa6Rao6yoeRNbAnYS5iBth+ti7EUdbCyZeNrooFZ1mBVNTuJUA0AglDy0Or2xv/Yu2iK01Nb1X/uXFvhvPZlFbj09tuqhzNG0QGACHWKah40DaXhEr7IQYSChVU9w0eOL8r6mDx/+JJUT98IiOCzqbzyGrFZLpv5moNFRG4AgpmDYz+/BdNU+YioGEAvMzMR3QqrIiedkuh8UQVY2QEzbgvNsm89gM93bl+VDyvbZqtf23cSQO5gd+E/KKrxAbszJhzu6AoAWw1d6+g5X3UacxAdsKlmNByfnPFkxoUr0itc576f0yIIGzDVrsnKxombSsG4kqlD4dUJg6cEUOzCbNelKGLQPFbvMFyqOSxmKZclAimMUhN8gkHLL+WVnCpvdSjcFDMpYxZvnJiJPQCysoeuBiToTdLm8MapA2bLvOQdBrvKHYNd5ZUAPgkAo4Z95PG2u+szOFfj1MGSbD/FjI+v/sUTjQBOCIFX7Jq5SRG8hQhVRKAS1+gxh2KsJcreMl7gDt12W9XZkwJJMqQSCbVbG49wCUz2AExZeBgMw86npp/TtCVJmjOHyUzqIRBs+h6JHsl/JhwkJ6dVQpwr3XfnN0YqHdnd4AQnCMtY8D7GpUmoRwtRypbzOSumHTPkVK9zru47eoy5lAfOcY26F8AhZk47z+n1gF9e5uCXl30MsMYTyH73KZizlj/kwvqi/D6AH46eLfoUgH4A+2zu+B8IVW4RCq8nstZFe9HomkylgdMhhZf4lnZ5NH94QkqZGdBjtgtjzhWAKXNVpjhXmk0/M6YmOIHNEZ8yhyYcn+qPLVnY1/zFx35Svu3Wcwezvc7ZqMsPH/vw6otp5+dMhwirFdU8IYS5a/a9Ux3PNTn+UFr56RSvl2nY6vXGNbHeAPOuIlgEYNdYf9VrAH7GzL8kooeJ6OGxfd4N4MTYPv8PwEPMV0ZmI/ZkriP2ZO5aAA8DgB50HY705W3M4tCGsWNOCi3xBwDOAdifX9L/zZzC4W1OT3QLEXKIQENdRa0sxZwU3fK9wU1leUM7pm7lBAHjfUrJds0UG8dkjOqmmFLmzQyWTMmiCUEkfQYFOFztDfbX+kYPz99Hk+FWzb2CrB7f2SBCgQL4VfBu4NLsrHy7Ech2X5Ozs4WuI66NNYeu/hLBeXGw8ks7YrCiscsA4NeBhScBINWwvBQsB1AAK8KctrSt0BG8pHfIrpgNi3IHXh3/Xbtg2yMGldlVVwjxeAEiupf3jG8y7XwKYnK4rqNHP2QLGBMlAu7f0ffYG8ytZMdi7wdjB3wfjzYKv7ykiBoDCC10Hm37neLdwXrXFtAcBtwSckBYD5omoU58htyJRvIkGgGOAswgbpmh/SRoUaQI2cmcvwkSQdcsV2YO1u/gKigPpDuaYrCUDDVmSBhiGcus1hsFlpOlgPD76XZSfZGjRJhzMzURCtzVA/bx7yhpiuDZA/VZrV0ud7QnJy+wUwg5ZG1h3eWJTjhkzIj+4EjFRLliji8y+Pm/fL6ACI6PbN/tvv/2pn333Xby1Zlnzo58p95xz6L+HQ8t7y6yKVw3+xGTEGElCS6eavBwwuGJ7PQvGG4kYY4Fq7gb4JFpxyqV5X1iDt/rN1adqxSWlNVj1vMwtzDzyrHHMmZ+cmz7V5j5K2M/f2nsuZXMvJ558rt6vnE8MRyD5bRUAkB82DcWWORs1pytAIQQ8hbAykZPhxmIRxyXNIDbadO3OjR9ordcAHtgrY0ZMaUY6Yu6eiMJdWLQcDCh7QNoonxZl9SR3F9W7Q0e0gSvtityfb49vrfCHWq8dO1IjmkkD3hU85BCM2YUZoQIFUTYRMCUnnqbMA7mO8ONXlts59hrxAl8YfrxGuFmh5AzAug3uHYgXP1zsOatRDC/tKNrsKv8DwG8snuoKtA96D/AknzF+SPlQmDWfgNTigAz4gDs45WxBE44lER3nhY56VITt1/qteUpUTsPa7vhMkuUHm0BgcpnO4aY7M5eLoqUwCE1brQPYatpwzBosgbbcCk2pUR2eH4vdl6GSDo2W+pfAODYZmwDALNf7Iz+VKuXIyKrRlEGMHKzZ2//ttw6EKWUos8agW3MfBJAPyQ2aJVhEg7Tuq5R7RAYUvEn1sigesTocyZg0FKAPAAQLqVKZz9HhMz8f6dF0soLX69cG9EdYN6vlIhcAO6GJSxxNfAIgHuNoD12cddNw8Ge3NrytWe7c6r6Zy1ZZUaPjKuVAMcAGuvpYUBwzF4wutu+IHjJ2pjMVNvVVvIrtztSGujL7WIWWfUwBIZ92/KLBl/NKxw6OzKYU2QYSrUQcqLHjAhOnyvedceWM63V5UPG0kW9DiKsAgCnw6j/8EP7EI2pp/ceru4bDjgXZOuH2BVz9M82XOiyKbyECLOujekgQq2imgMAjpmGUubyRc5XLG2/HQAKygYGBjvzdxRW9q2VUsQ6m8t3xsOOMmZRAwCqKlcW5I2+NjDkn7UJm5naifiSyoOuQa6d9QbzVyJ4NeJ4YnhH7MnczwJ4GuCYO3/kBDOFI0O+ddnca5or7pKSYkLwRA8hS9JjYeeZoa4FPWBx96VeW44r3DoUomFTilyANmXTE2lTZGWuPdbfH3MW5nB8j9+mb4waWiJ5H0FsutTE6QJ7vJeIpVMxJ8rPSlzRDQAwHDdORE2l3GSR5egENp2K3KUSryZC1i0fqVCABoAPSyAsgbpVJZ31qmA3ALQH/Hv89liezx6vvzjq390V8nkMKVYAJIjgKXQY4YsRDbP936kC6tWpInjFuGbWnDcqM5Ut892D1bhzsOqfdnbXvJtBZQDQO+w/6LAlQrleS8p8bBBfWAiroZwZUk+ozeGYlkgY6j5dV5crijypqpLzHKFRPa45+8nrzLFH26UUhs8RLYskbAMECJ8jVi3A8p2+9pd+Fixb71cSQz2Go3r6DbPvwNIyZirDCBAtwAU1yru1IBYRaPY+KUKB4cE2w43z4KnOROXKvnDefeGydEYJM9h+a0IjB58K/YcjKwer88GiHUrUNGGiH+rMORhzhiwRAHKYLwuHecf4ZsU3OSRUeI0GmzcIM6DtN4fsCehiA4jKB5fjaMExXk5jZZQMSBY4RBIryGr8hBJDJZiHQXTN9EVcJlf3HX0FYeYIMA+fyXmC7mji2A/XPNzyo1s/pY+4NwDQOvbXGkMtxTtKV52rcvijVQBgJpSQopkTM6lkQvTFBrwHAdSTIksBHAHTKMDSWTaMeL8vx5YX0QxdPUWa6SMhnWbU1qH5YjcDgL4oukPt0yoBCIqKPDLElHlXJ3+5Zk93c8WYU8X1iiL3CiEhxGxRWgIRNEWRq/IWDIWkFPuJsHb8WaElhr/+D9/1EGWWeX7iIy+e+cyX71ZGRl2z/l9V+aOn3r20L9E56uiqyYte9sDJsXlOBYpqRkpqOyeCM6rNKCha2LsVAIRiOquWtW0xDTHacaZiZyzkXARQaUV5f31c104EQ64J50kIeZKZcplRMr6ux3V1yOlITH/p65U37XpzlfJlI2JfbXfFHgDITWA4c4K7DF2jRMQ5dl9yAgCPt0gwIxaP206oCUXrvrDoSEK3lTo94QuqluBEzB6KRx25QrCbLEVOScTlzNRBxAVEKNA8sYveyqGWwPnClUIzw4nQ1EyXlDQQi9vucWkJAgBDiibDVAYTUrl1MniUGsmUAKhsJO4oG43bj0qrbWOCcneY7Iqsh9VPNgNmjBa7IqODMceZYd2exbgBNv2acSxuChPEsfGA7qUyVs59iwKg2BV8URU84aRW+gMbk37eVOkPoHUkd2df2O1OSHWVKrCm2GHs6IlpE5VThuTBuESHS8HNRFaoXyOUZV0vfX1wY82ZJ+bVwcov7eA/3/HUV2DNetgKQJdSrI7E7IjGtRMEJCRTLQGdhblBm2lSrH/Y67WaOnkRgARALtNUtpimgu74pM3eE/IDYAnABKhmkWfopc+s2NWaq+hisT34lo2uAZMIvq8P1Rw8Hs+dEsG22xJdsbitDABYQ3VCQ7XuBbt6+IyQtCR5Xwab0WLsdPRhebQIfcCY80RYND3QkZsfcmXqCYs1qjuCX3JmNSfGtFGwf2v+odiYkIVg7pUSr2Ge5k+QXc7agK74E2sVfwJGi/sXMq6uYZVWJrx8whbEcgZ6WMUZVmgbM8cg0QgAwkSBGsaA4eZt00V51BAftQcwIlUgugBzK3O8erlm6rGv8uDOvHD6G7cfBvBTWI6fByxyIgO+red+dcuA5oq/mojaSsGUv2BZ++782q76aFfeaRnXNgF4GylyZKwMsGH8zYp1W2tO+FwRxkpfDKtEhl32D5x6heKkJGpjK3TAB4JCQWW3+6XcKQ5PbkWfq7upauw3Uk1T2WCaCmw2/ddENEN11O0NvSylcBEARbHmOxHBoyhybfJ+rvzgBaIZKogT6AnR+rt/sX2hnlCzcJQYSwsih9+xpG+BIlBW6dNXmKbYIYRcQzR7xcFsEPGIqplpFQgBQFGlr2pZ25bQiOto55nKUSIsLisdGDndXAmAY4rgMzbNrALgY2C/lBRhJhoZdeXabQEpxNQSeGPhQQAAIABJREFUdykxGgw7D4MJTme8yKaZl+0wXgVcM+vNHFUEr0kcTwxHA48s/DqAQliKgnFFMzcL1TQ1Z/wQATYQ17EpDkYD3oXhkLs1HHIvA2hNcNQXBOAGSESDninzKs2JDAmbsJyzem/Z0M98FUMeX9WgW3UlthU0XDQB+M/9YPWQ1NUJIR0izgfQhzGVO1XIZaqQ0GSiP6Q7WNDUuVGmpL7eqOt0kTOybCjumLhHJWZWzNiEzFhq2BF2nwskbNn0ooHAvV7VOC8IGx2KZADnJHhgLqIemfA5YrMGeRfmDG+p9g/jcHdZY8xUlzsUbCBwmEHuuMmtfTHuSTA2ABhwq9wEBnLtVMOM40QzxSNiJu9LSI6rglSncmnCYlch10y+7s2WwQKsmth/B/BlADcD+BcAYBbLx98KBup7h7wxqxZ7PMJCGiabMIcA/ABWHfFdAOBR9JN7Nv93tUsxnBIkmXGHKibFwYmsLIum02BnT97BvgF/aFld+8rBYd/5uK7OlNckHImUoUqJcqNzgLYBgBQ8rOfiiLTh9kgZujBWfpMKIjbHXzMdaqVU1MXmWbNDlHDUitQwAGmjECsUV6NyIsI8tC7ncKjOPemMERUJiSKWvIsVLMFlzaLgqFoYm73ccEQ9ikHbiDqsbGPACcLhSBE5hc57iFEPst4nEDmgwHrPFMDTxREmCrLgVlYQTnhhkgndFsAyAlYCgBLHzlAFb8leGfWq5doxeK7utWdeaHj+6eEjDzz6DVjN2C8CeBmAH6CCRMQxkXHua6raRHFlh92lT0QriZBc0vJtAD5YgaFcABDbzzdRbXA5SzAYqqFgRpmyyWQfaits6j5ZNVByU1uhu2B0wYXX6lN8QTGKSvurQWjv6yqstLJVMqyoZo/bG7kVlrpgBseGIVSZcRaLqkhf/aLe830D3pyeAd+E4URg6XcY3SMxtWw8C6QJjr57ad+Es2YFimirlKJdCNl3uaU7vsLAOViqsmkxDREY7Cw8GhryFCiqrGfGGY8nWllWMvCLoSF/BREm1iwC1gqFATCYERsY8iUAtAnikKaZQU0z1EjEbpcstgFAXFe783ODPYrC86rm+gZwTa0317uDNUYjgAoAvwTwNwAeJoJCxBP2Aqlyg3DEm8I9RUlZa/ImneMUgGOwZN43A4AjN7S79sEjm2BpbEkA95GYDOCO2xusyjPRoI1MKXS3I7Y2ErPvh2VrTaE77Ds5EnfclGePHfPaEusAwJQ01BNxtxgstnaEPV0AVU4/bhI2MYuN6NYSobgU5+OmUsETomYMshRbFcbkXEmvapxXBDaO/S0EYLFgGAC/LIGNs2XbMkGQ5/KdkVnXrOGIa080YTP8tsQ2HydCDJzMsVHoJx1e1WDcAmC8zaMgbFg2TjjCAFAAcA8BfYIwahekJCRHEozNAOwJk8HMO12quGbUBjNwjaw5V7+K4Lw6WIu+95QPwFpYC1A1gNtgmXkzVl0icrA1NG48ShuENSOiCcBDrdsfCyx89rOPY8zBCpla+Z+cuG3/1xte2qaA0+aNNjoHa//h1G2xqKmt7uopAKwJ4DMRAAh5phMNUuUOYVB5pBSdoDE5TspsHNx665kTqmpmlPnUauTmvM9GIEfpaO/D3oXDq/xHR5d5c6WNblKi8mz5cz26FjRKAMDRHfcElrOcnuUh0GaY3M8COyAuYb4VmIU38RopnDmTZtAozrmWjC9yBACMW0CEWD4anYPIS3sskYsAkMTNkIAyNHMXNY4trh40Rgt4KStwwQo+mAwIKJdXJvA6c3Xf0eNc2gDha44jDzx6B4DDsOZYfRwZom+hQZ/D7poyIucirJXgPbAGERfAigQDAOQPq03xwTNdlKeXIt1aaeebWjqr9g4fW3hn17GJFswZfYkeX3gfCawHAF9OsHF0xLfN7Y0cdnkiq7LJGOUv7tqVu6hnaaZ9hED+Zz7+y3xm8COfefA8hVzRu2uGRgtdiZVEKPvvpgVHm4fcKwEgIYXTlGhXkkR7LKhSSqUS4JeFkKvnoqI4cQYhT+WVDM6qCNbZXHFCj9qTHd4lAFBSMkgjI94iTjNcjwiOsRWqTjIhrgvE9enq01QyOOy9kJcb6lSEzGcmHWAFoAQR51xDcZ5r5i5mXP8OVuCRhVWwslf/CcsxSisG43DFlgHcC9B4gHcYQBTAdwH8TcOLL8SO3H3Pc+P7x0bcNX2HK3cVrWrfjAyVMQvWtrmafry6EqDcWNwOpBAHY4YxEncsA6hgOO4YcWuJGAGOjrBHA2hMKIfS2jgCcqTMHR4mmnA4UpJn17fm2XWEEmpjW8izxKWYZzTB5URYZEjaFTSUWwByA4DBlFCmfZyJoAK4QzCaJTiKFFm02eHIwtyh4dnu6YhuOzEU8Uxk24jgIWClS2VUuPX9rSFbJtVrJwAnA8UmAxFz5m0Zk9giTH7VJlAHQCFAZcAgwEZ06c7jG8A1seYQvckyWOcfenwUwK8BYNH3niIAG5G+g1ASYR8zBgDcAeDtsIYIelu3PzYuofkDAA8CuBUg366hspTDUr56omHXrzuq2W+Lyy9te2HTr97+XwPv/Pm7DvRHXbcg3WwWiVwQXoXAOt2HI/YhLgKhEJTde6Io0pNtKY3w8crgY75XRvr9ExFw06Xc0rctb0fZT/tKAMB7LrIqtCjSGF7knuEIEaiQJPs+ueGFL+qGZ+3B/hKzabDA0Rt1lyakqAQQBqAAyaUALH+79tSuF2Ml9rBDmb1M8bS7CaCUPSKGC+tkAF3CyOx0zoYthG3TJ4cwENf9vCNaiK3XQXbr6uLqXnvmhYbnn34ZAI488CgBWAykNwgiAY+SVz7wTSI8CGtt+QiA3Lwnn+sHgKEn3jUE4LMYn/EVVVdyl+vAmIM1QWLYMdT0xw+eUpy6rPrTvQuWfP6Xt5/5xD2/Gd5dXQtTpOzHjISdtfGY7XxR6YDhcMVqR0d8UFXDzHoN0UyDKLv+NyLQZ//kRTT9dF11co/De2/qq/js7uqEyZYq2D/vr3A8su5iurPc4VnW86z3rjZnrCU3L3q6QCS6PHkyotXAWtPN6f0TtsqRPc688HA+Re4dl7pPR2jYc0SP2lOW1BChorR0oLGzc0FW5dXpoeqh4YmkwYSBI4R8NccfLlMVecmCHjdIzfXuYPmfaW0D0AYAgUcWEjIoHxNh1OkJfyMa8twHKzv+NgAdAGINL74wPjrmGQAbAJSAqWSkpfBC0aqZ01XO/GrFK3rIobjyg1i07fTWZb918NjpX67sMHUt5cwjIqg1/qHhwai7KaA7tkUN9ZBNMYszOVXJsGU3VWezLwC4FGNjvl0/aI5lkAFAFbzZLuTeuFQ2AEDEVLdpIrFfJPWVJl1vnU0xmzdsb/1iKOq5tee02xhud3qio2o5SxQDCAGwI2n0DxH31d0+eMLRFFvi0MwZ55xO96jfm+659QWxBW0hLSGTFBMvhYjJ6yIzQ3wXnAq6nAplVUp5g+x5UzlYyZx/6PGzi7731D2wbtKnACRHJt4J4IWW9z0eWfjsZwnAHa3bH3t57Lnh8Z1atz/WvPDZz74TwAEAxUvcwxeASUEJZuBzBzfs/Oapm5cyqAAA7vnxQ6++8OD3Vu1+97eLV/7XHzZFDS1llomAamZrhhMrYMONw6Ds+51GRtwdeXmhrKdjGqYy1eBgDixoHJwS/ZI2kfbbqcw3fGj9wuNbFqmeT3xg6fHnMFbGNCaQ6hmOO4afPLjxtd3d5Z4V+f2h9yw67X9LZevWcPPmwy8OZagCAAATEcRE+kGlRI5IIbd7ui/PwUp5asBuD2CrbRSnTTv3h8qwCsKKeN3gMrm61555peH5p/nIA48+DODnAG4C8Kmkp/8LwJ+xFINEMACsAtCS9+RzBqw5WACAvCefM4eeeNcTsDJQHwI4TIuCU+7RwMHSk2c/dVc0MeDeBABNH3mwa+k//uxk/RdeuK3tX9b9putbq1Ia7dJUCqSpFBC4BQCTkEM2h571+jHamV/mr+yPZN0fxaRgWsnh4R7vMZPptvHfnaqMZDhBoub39r1VFMYejL/1/CdhDXqdIsgceGnha/3fuEWzlQSDjrpBFH7gyHoKaoeMf1whZlPmCvT7RzM97/eH1vb15fYkEtq8l/hJKdYNDXv6ifhIji/i0jRzTpL0N0jP9e5gTWMPrCodN6yAzbgBH4KV3TodDXl0AH8NYGvDiy+cnH6Chhdf2Hnk7nveAWAvAPJVDk1RbzETSuTsr5ftHzxftA0gMdqdI/Wwo7H+3iPb1v7+jui+r94RHs8QTcelGYtH4rIbAEwWsZG41oxZynbHYZBXMh1WiNP2fCZDBI0ZU7I0zDgdl2J6JjvtcOKahRe6a3HQhXfUfwdWa4kydh4A8I722Dp2fKWyJTxocxQuikQb3tlTkVcZW3P2C76gPpC5xTxuqK3I4DAqhKp1BbHGvQPZ9czPkeqoydUxk49pAsNuhbaOC2jc4PJ4U5UIpqAdwGlYTtNmWNFhZezfPgC7W7c/xhjLeqWidftjnQuf/ezXATz+6fo9UwZMdoS8Xd84tXJKzWtn2Lduy3O/e3RzSccI88yBlGxN/bE+3IQRADCduM10zM0cPXWqYuPGjadGpvVxpCQasXWODHnWT9lI5I+WOZrV0+FiAmC4lIF4gc2X+gzA+1bssQFYfd4IRRapnvUAfgagdvw2zXPEcr+46eUZi8MXancX3nFowfCw4UjdAMoAzngOAbQ509/AGq1POPmwFk3fZH85EKNejaHedwFHgpVcySqlL0m8QVZc5WvPvNPw/NOxIw88+gsALwBoBvB3AGphBXTONTz/9CfHdk07lDfvyed46Il3PQXgQygPHyKnOWV9OfvJu6KJQfdk2bEpSk898jbhWdq3U9jNlL1Xk84GSxAqiaAWFg8wUYay22nEA+7F0UHvDldBMKsy4bO/ubkPoOrkbeW+mM2pmoGoofg1IWPLF4QuIo3RYcsPH9K88XWI0R/Airz/M6xekwly7m69NefuqaP2kB9fLzb17pG7i9NGa4NDnsPRoCttfysAEMFVVdV97Ny5yivUQ0WFzFQ4HHDrfm/kqN1uXN5IjBvgzSBykYz/mVY98MjCY/5nWoOBRxa+B1a1zf8B4AHwHwDua3jxhX5YZcuvpDtPw4svvHrk7nt+BfCGBQ3tU1oa+s+UHB88X5z0vU5iqHXBtoPf2fxqTvlQDFYwaYqDxQweF9+KGioBwHDcsXGu86r6Ys7cEleGGEwSUUM9ICGm90CVKoRuk62ZXIK4jZBuSDjLhpVNNwFYg1+efhxvrW+B5bT6x9ccf4le/sDfnZsRwKr54+D505/2l0CmFtEyJY12BXKCmKVEpsaTWHNs2N4TNsUVWXMYuFmXgMm806diHRFlKkm8QRa8aTNYAHD+ocfHC8L2LvreU3FMxtSXwDJ6dmc6XvbUKQAagA/8X4+iFy10jb4n+flzgdx+pIjIDMedK5+/MDlLmIEICAdhRX8LmNEDQIBQNbFThprnVNx8c+urRMjolIxz+lhVO8Zk65Ppu6Ngbd9t+b32fv1sfIHtJhCl7FsQJHu2Vp0ed2z+RBQ3v1/21G0A8L9AZslmTXD5V5e+sus9x++dvNYmz24ABMEmDLIhrmT1d8QK4FcvwhyXbb8SCBMN7i7sCpVxwzXWm3WDq4CG558eGwCK7x554NGPwnKw7AA+duSBR/+u4fmnMyokBfSacuVT0M3PrHwPVYc+Nv15Y9Qx0ymSojjUVJz0pcxwOOPHc/IDQU01iuNxW0co6Ha7PZEQkdU4nUl9NBXOvNEmZ34wqwbq4YsFR/Wwc0Y2foE7sfHP17frIV05SATy2My05yu9t2ncWHmfq1X5q8hC8yMAzgF4GrOkp8RbLy6TR/MHEdLyAWB0wLd/sLNA0eyJMAnmaNC5Kht5ZrvdWO/1ho8Gg+4r6PyQLRB0LcpTQ52qIi9p0OsNxmBkNUT4esL/TGtw7N8XAo8sLIblYAHAGlgzA7+b6Xjzu24vgHJg8x9pnvgzis18e/Lz4QFvSoXw+KhrXe/JyQR1whQD3WFvUzhh80smb6lndFgyGVFDSxK/mFvWpMgZiWezHzPksG6fsS4SwefTDB8zWkymToV4U7rSYZcretDtjo6X+X3AvmrfP8YPrd8EK5BcleqYcVQP31J8X3RHz09dW8eux7wwVHBSU8wAgaVuqnmSxQwRkBTX676jOHLs+U7PFRXGMRlbIibvcCnYSDT7vLI3gGviJibwVe9gvZ6y2ecxWf63C8DnszhGAvi983d+4/1Hb3t2i1/TJzI8XWFPz5/tvCu7CIDAqxDYAsJqEKpAWAqBtaDpDd7ZE49rWUlZxqJadzRiTz//RlBRvMi+GZQ+Y7O6pPWMMqmY+N6zHaUlorh5AJYAyPdmu4Z61/Cm/1N23HJmTUQRFesRVTYirG5BXMlidsUYRDXx3MxO8XygxLHBfwFt4EudEH+DGwCwSosBIA7gvbM5V2NUAPhH5a+PbhZ3dk8EHlgCzX99dyMnlIxN3wCgqubFwuLBpTabsZEEauwO/db8wuGVTlf8kstPEhF7frZOWdtrdWmHRBHB5rWbqz02M30GiXggf33L+PMOAB/y21rYb2v5IoB3Acg4FoYIfvVjx7vHfx/py9VNQ10VCzu3RIOurdk4V+OUl/e5x9TMriDkGRr2FOsJ5cyVfZ0bXOccxaQC29cA/CiLYxQAn1rxgV131b/nwB3JTwS6ck71nCzPSsL8fCCvZVR3bDNZNDBoUWfIV9cd9q0HaM4iNeNIpuDsewERQz0AUFo5dyLUqIK3ZOrLXLHsdPI9/tH4UzmKfdW+JgDrMLmOpyV/a7zBsyRxHACiCdspyWJF3NA2xwzbVski66HkfpvcUOZMHMt2/0slLrFp1OC9V/p1bvDGcsUdLNlTp8ieujV/X7cnDOD9sLJWD55/6PG+WQ6FKG5mWDKoWzFNref/Hr71TNiwZVTVmrwIrAZ4UtuOkLYUL1vOny9d3tZWuJsZGXsJ4nFt5HJf6wO37Eh2BDUAfwQAorg5BmA7gM8gqXdtOkSQK7391nWOqmfSCn9kQcKDBikm+1auBAQoJLHM24arcQG6JqI7AMZkvbJ8XCcE9BoloNd4A3rNCgCfA/AbAL/V8PzTL2RzvN/WsheWYfQnydsTI86hwZdqs3KQDEOt0HVtIhBBBAdlKZ6T9pwxe/HFvUuaWVI7M8KZ9jUT6mVlfn1LepuEyslrxEcSZxZrAOC3tfwIwJ0ATmQ6BykYAkkJAHrMdsliEkJwbXHx4M5LPT57SBkJuItNk7pn3/d15ZpZb8ZVBLN5XE+YeNZv4tkVAI4DeALAdwB82P9Ma3S2Y5X3hUfGjnkYmGqXnP318lEwzRwxk4JK74hnagkgXbaN0xd1lUQNpXE2G8dgkVWmKz08XFd3PjngUwPgPgCwr9rXC6vP7duwRlmkJqIHHTw4BAChuH3wcq5my4Kog654UAeKydgSNuSOK/w61y9k9WBl+3gjuKIOluypq4DlUL19+6qfGrCaPz9y/qHHUwh5p0YUN4/CUvt6FMDfAngNQOKWgp7m2Y51KMa5Qmf44Pa6E4c/uvzAqUv5G9KRSKj5nZ0Fa/bvr4ufPVvy2uioM6UR4PNFliiqmdEYyYRbix2vzh2YHjV/+GxHqR0ARHGzFMXNfwNr0vpfYuYMAwawa5Fz1JKOjioZjbNZIfJFF2BGs+6VQCRQD+aMs39ukIYxmfZsH9cDAb3GBUvc4vuwsiyDAL7V8PzTv5rLefy2lu8BeB+AL8BS+TK13OgAwJnXHCF7FW/suKNyZG/ZP71QA+KBjPvPkdiIp+7CjmVq5/7FbX1NFXuZUxscRfUXL+t1y99xZHrZdTkslVcAgN/WsgfW3J23w5rlMxVJ50gqtfAneljCAM+UrZ8LeXmjtyiKMWtA7vIhf1zXLlz517l+Yc7ucb1g4tk1AF4F0OB/ptWEpTD4Uf8zrVnPElLeFz4P4AOwxDCeANANIOzwRpsyH8lxIczTmk0/uHTZmXBh/tCuS/wzUmKwKBuKO5Z2Rtz6QMxxUHLqoIpX028BOKMTlomiBQPHFEVOr0b60/Ef7Kv2he2r9v0egBUYm6uaDJsyhkOdbQ7NuoSEqVyWB68K1K3KizdezjmyRZeXH+x/M0PEWT/eCOat/nOsX2otgIcAfFcUN78G4BYAzwGIyp66D5y9Dd8CwLLnm0IUN2e9AIni5l4A/zD2On/fa4bW3V772qf+s/mmPRcCBVOaqe+rOte4fUlTUdxUjM2lHROpYcnAj1rrX+sMe7NWCpwNKYVd10Vhb29e4eio++Lq1edm7EMConZph3nmeMYy4pQQeOj/a9iZyiEqgiUaMiEOIoqb+wA8LXvqdsMyDFcDMAAcAbCtxB7Bl6t3vPDHB++/ac4XMg1po22GnY+occw66+ZyICBPC+FgwptmltkNMnMdGTKpCOg1tQBuhRVc+BQsifYQgK8DeGDhc8/9GMB/Ak8joNcIv60l6zXHb2v5bwD/DQDRi7VPKTp9ZMVf/ur+41+4Z8p+wqWfqvv0iwaY2Leqq1ZxJSYizjkfPnJg5N9uuSznYjpG1F5qRO2l0UEf8mq7D6t2Y4boTOnNFzb0nKxsYamkLdtJh6M4sNtdNZSqr/M9sBxXAIDf1sIAfhzQa3ZC0t+D+P0A7JDiBBnKTURw2T5yuuPcH9z3GnB58sRE8FdV9bzS0lKepkF+/ohE7cLlTB8ov0Fm5HWWnUrGxLMeWCVr74UloNMNy8b5AoAaE8++zfPMZ74HgEw8KxRsn4uTdQJjWWHzu+7PG+3K/XUNx/72QPfWsyyVxcn7li1s2+vyhnMUxbS7vJGJEsI780dC//Oze1rkJdz36TBZLACAiCEKBPG+PPvMZJUgeFyq0RgxtDmXQBPJzrVrD6fqfbwr/lROrv3xkYnKHPuqfc3xQ+s/yqZ8BeHEU3BrFdDNAHZf6EFCbvQtGDIdAwM/iw0UXsK80KnU+/RbTozY+uJSXNE1h4EGyTwoiLIawXGDqVztPVjz2WDng2XkvBXAQtlT90FR3PwT2VP3HlgyyQqsL+khAC/JnrrfBfAWUdxsbvidL94H4H5YCjOf3vtff3401Qt0dZaWwmoifUwQlHxHuHEo6j3+Zw2vRvqjrsTXTq5c8JkNjWs8WmKGbKkg4Ptv+eHCjT/6vUDaumQTjRAoAiGruudkYjGtmBmjlKL8MDc/tFQI2SylmJMccIEreP7+JUfWp3k6XeTjVQC2sYcDY1PimcH/tnPtQjBVzOUa0hEthOLpgKQrnAW1DyOSSDu94gYZubrXnssioNcIAA8A+CSsz/rzsGr1u2FJHtfBigj/OKDXfBfAvwb0mvf6bS2Hvrrgm3mwSo9rYZUDfufDfb8/w6rWzy+2A7hXAT0NoNa5INgK8Gjxe04ccdf3u9r/dR2VPHQsnrupPaUD4d52cU3o54t2Ghd9KcUkmHGQJYXEbEPA06CHHEHVHpqxXQhWCmu7L/Y1l8/Z0FrysVcWp3kq5V3ot7UMBYP1ZxhsJ2tI+YSSWLQ5v1UPuLMS5pgNh0O/zeWKHolEnFc0qCMlLZaSBoXgGwbPXOHrXqa9BpYztQpAJ4B/VbD93008+xlYmSfACu4MArCZeJYVbP94/weXEYAPwpoLGgPwucL/aGqbfvL45/wEoB5QHwPwu0IwFEUeVez63tLqDttwf34oHHS7iip6UvZ02zTDc/uG187/enf6lm+TsVsANUQomesfHzeVtM6GT9OXRQw1AlB2YyTGqK66eK4gfzjd+ufBtNYH+6p9HHsydz8s4SI7rGDzxCDn5rO1mwG6bIuBCLl3FEde+UWX54oHdWImN7lUumyncB65Jm5iAkDi6jZy5s3BEsXNw7Kn7j4AxQB6gAn58ndiUnXu3qRtzpNthXd+6ONfXAqrDGecPFiDh6fQ1VlaAGtI38R//odX7uIthaFl6pgAxMcaMvdCFjqjhdsXNzU+e3b5zBuaMUAGNoLQzBqaQEg5PysdzEJLJJTjthTN40KwDTT3mt4tVacz1XBP3Piyp45gGYshWDN8CNMcn45R32vHeouzF7SYDUErdC/vtgczqxheLkocyyA5BHFDUXAuEK6f0r9UjGWj/jGg1/wngAgAl9/WwgG9ZiUm+zVzAfw+gN8G4JQmfeyrC775NQD/BkwEUe4HsBPA2RQv8x0A7x7/RfNFvWt+9K1OrSSyFQAWvG12TYSCv9pX3/PRu4OpvvRNQ0mYprhFg/EbEryJCHMachnqybW58mc6WACgaMac+yyFzWi25UbSBYEKE2cWC23JWQkAwWB9PoBCWHPEymlaXycz4he/uH7e5ksRgSoqeunMmSpc2e9/yovGbDvdrvi8OIZvJvg6l2lXsP2YiWfXA/Ar2D5g4tlxYar3Ju32B7AqRwjASPB/P/wdWM7Vw0n7hGC1PEznTgAvJm+obWjqcjri9xIBuYWzd1aUFfWv9HtHdweCvhnfy8w4LEGbAN4rGAYR5hRsTUhRw4xhIswY+SKIL0FMg83aRRcyBTIWALgIALEnc22wKhT6YQ2Ut2HaQtDRVt6U0G3zNscqzya35drMlmF9/jKCqYhL5DiZcWM01hwhhqCsk8RvCPOafRDFzSyKm7thCTHkyZ66f4fVoDgdZ3NH/vkPPfOOH2GqcwUAxRt+54upPmmDY48JlhX0eNRJdb2seGL1ns12xZhay2eikXTECaQR0zLSUQ+JHeD0whGpMAwlbbOnxxudozAER/9wVWOmqMbfne0oXTL285dgzf3pgqVkNH0eBX55blFaZbFLRc/BIqbMDfeXCwF5vjacguSMymU3SMGbQOTCb2sZ8ttaYgA4oNd8ApOR5GScAPCTVX+2BEAjMCNDnU6Wd4rTZc+NFtgKI6nnyaVByYvzHKF1AAAgAElEQVQVuu9sO5S8jRkn9bjWbpqiASBfIqHdZhjKYU7T45AOI6450j2XW9U356HgpfedyNS7tRrAnwNAMFh/F6z5hqcA9MLq/ZziHHJMaTUGXFk16GeLqsqV+fmBKy58E47Y1ycM0XKlX+d65HoXuVCwPTHmXHkBrDHx7Jdh9SgmowJQor/cdzL20917MNW5ArJcbwDAmx9wzdXuvmvTvsUATygAMiNuMPYbY9cpQRsMwMmM3cyYg11AAJBSWZAIGllrQtbYbHpTWVlPJoW/L8efyimIPZnrhDXb8DQsB+tFpHgP21qq5nW9IYK4szgyeqW/IBm4OWRw4w3R5LlztfdgXanyLiesev0PIsWN0B9wDfzpv9yfLp28FDMNIMCKRncmb2DwnAe1GVIkfqvy7ORCxugjA2soaU4VgRSRoK1kzsngiZ06VVne2ZmfUuwiv3DUVpg3suPtd+7tffude3vz/MFGS/wZEEJeqK3saszxhXYnqR1qx3srMglzFCmgL8ueur2wREAmLz8FNbnD86+KQ1QcLZhdQvVyEQbWOoZwgMxLb6SdR64N6+DNJ3LxIVgDzO+c/oQ0iff/1dt2xAfd6TK475y+QT+/OBdWxnzayTCrMlgyzIB988UiEF8Y32YaSoCZKmGV1I1do3KrHteqmZGtAJARHfRVte1aeoF5ptHjyg3VkGKeXfnhX7Wv/tOfDVTddXQ3iMeEIjjqKR3cmb+svVFoxoRRNHKi1GCZ8dPwheDIki/Amk0z3luWMlNGmvQIRyK7KaVzoKhoaKEQ8ooGdQDSRgLukGnSwFVg81wb680Y17uDlUQ1LCn2j2AsgJOMcaH7fPSHr4xnWqZzf/8Hl9mTN8Q/5x+3xaZ+tmnub5aiSFuuP5Bsh5xm0FqACpNOXGCANjGwZw6njvZEXUbcFCltHJtiDt284uSud7395z0P3Pdii8MRm7ANbJp+fMni841eb3AfYAVLEwmtIBR29WZ4vXXM+AyAM5gM1Ke1Wb2+YGAOf0tWOBRuqPfp++b7vNNJMLYZjJPMPO9r5vXM1e5gXakhZ0XIcCM8+u/3ng5GHemG20pMU8Lr6iy9B1aPxRQDm4FaZmaaJbd6Meht/fbJFW0/b6319kddyxm4AzZIEAQkThFoSlqZwVEAMZjYCAUDoKxUsBzRqL2iqytP83iiO9zuWIGqyglBiaKyobUl/uEdbme8CADu3XKwKK6rgXDUMZjrC9UQoRqADEfsfa+8dvOeQMi15kuv3T341d/6etoXVEEVmCZfn447Fl7YXJc/eKF5ML86m/2zxXRgk6mhVUlg1vlAl4NjGJvtw4jECnhvPJfSF5nf4E1HQK/RYJWrpaR/b/WplmdXZ8oGT4ni6ucXu2AZHjPKXpQYdRk2zvhZl3ElGN5dfmL0l4uMeEvOYkhRr9kSu4Tgama0m6Yy7fPLDCsTVCwlNSrZ9WSp0lBy9aArN9iZ/xubN6o4/JGJsjYioHbbsYuuwuAdAFC67lxB8ZrzidiQt83mieaqzsQWAJIZov2V5Tu79tatDJ0rXJ4I2gdt/lj69Y5wP1IbjVN3U7m8/NF9je2f3jJvJTsAQITi8vLexvb2knk973SYxc2Dwz4IIfcX5AXXzn7EDd5k5CCFYwUAzIzRL3zbhOR0maoIrIxvcsXLJwA8jmm2DwmZg1lgBgaGc5qaztX0tXcXFyYMdRkBVeqYVWTOVBbGmOqfMIGaOUTZnQmp1AR0e9ir6Y0OxVxKNNmqkGuLLysr7unzuCPFcAO//a6f1oTCzl5pKobPF1oxtpvs788799JvNsd1Xas9evSmfZs2HsiUeVoGZFfKePPqYws72spjpqmmzexfCqvy4tXngraIwXPrL5srQYNvAjDgVdGhCZq38upL4LqIgMwVInorgH+CFTT8D2b+XJr91gLYB+C3mfkHmc55pRys9wNYOX1j34i776+/efeZs535M+qDSciLi7a1XWRJHd95+J8nmhu6Okt9AP4e1oI0vV53FMCMD/3RmH//byIFrLOwC0b4az+7fZUpJweEEgAGHwKjjgzMbOomDLKGABhNABaDcZJMjLACG2hm+V0y8bit+PjxhcVVVb27KiomK26EgFJaEJAEZh6bqG63GX67LZRsxAm3K158/237i/WEEqxwDueZTBcEOC+VeIZbqH3I0sG6GPDtbx7MTyeYcekQqdFCHnJ3YeGVvisJcNmHYYvPqUjrTcwbH31/vVgA4C+mb2QJnPjibbtOfWlzyhr68rqLjTUrz3u6zpV9f9pTf4TUWXSwksK5iNOwa6f/FBJkA4Du3XXhwZMVUxwAaQpVCBNSilZgxoBzXdWM00TcQoRlzDjNTL1SkqKqMl0gaoLe49W3kWJGF919JEo0afTllA8uYIWjZJITAITCmqtwNFnOVBABVXec2FK28UxQcei5pivxKhgaaKZzyYABSrFepqHzmVuviCCFxxPdaLfr7fG47ZIHxWeLlNRgGKJNVeXcZWDfbDDA8k1jm30CM+0RGK1dZ0P/8twIdGOGU86ME2BEQXhpwdeaJpon45/z1wD4GIAZ4lwApWw7MHW1kU3FCYYjFHX0/uQ32+4GJvvGGVzDjG4AUQZmKAcT0K5YNtXo2LiHkAlAAUqSnaZUxEx1RcxUUeQM77QrcmJfRXCB3Wa0WV881ufA445Od55EYeFQ3W+/63lpmkpMs+l2ZnQBKCCaPXCTiYsXKk6ZpjrvgRdBKN5UGG1s7HNd0aDOGAUxk5veYAfrmoAwfyqCRKQA+DKAu2FVruwnop8w88kU+30eVsnqrFwpB2tGxOTHe+tfe/p/ttYyKGXz8OI7Wzsq13ZvtJHZOtpVWQGrn6gEQB+Ar8KagTAlYhQw7eHD0aKX7MSOBsfIQhVsezG84GST7tsE0MTftri6a8/ploopSl+k42YInCTQzdOvhZjKkeAoa9BIh41A1gIl+TxraAFh1qbHQMCdm+xgAcBSW0h/q69917+NLNrAAMnJxvDJFWkMm2Z6ew3fTc8FGoIbXK2HKmwjM25ukznrRvZCdyRPFaZpSOWShwyng1Vabbj5gBbO7HzOB8LEajXERw0PzXDgbzCNN4+DNYNIl6/3V/d9sCs+4EnpoDjc0cN3/e5L2wCg+t6TtZG+H5zEmGqVatX4HwRmjgfQTjv7FAd2Sp/pMsrjq7VWx6u24+5CkpNS5EXL2o8MnpwadDVNZT0zHZUy1fpHdiOh3qbZjD1GQrRLqawCUA+wIQQ3EvFWolmiikyKNMU5JSlrzgQtuD560nXM7lVCYhHxFCGKKWuO6kh4AYLSo62HxufMooQPM19ThRXUymp2i3PxUHvoUMmK2fecG0TQKit7es+erbziDhZAWiDo6szPDd1wsLLgOin/y4YpNg5LRuQ7v2iM7ziyEUgjVsPwA7ScFKNb/wfPAgAjAHIBJQDgX2GpME/B1BU2dWUnEQSp5jIwjZhxrQMsJuwBjzOOHE/wwkjIWz15JDkNcJSAEED26edl0HIJ3keAbgJV4/aSCd6vMFQi5E0/ZjpxU4FdmWrqSaccFr5Yoxx1bLPeIxpPkE1Zb4RgIYThAouNYA5CyGNI0TsOIOvWhpy8kTmJBM2Fcpex3quanUFDSSUpP68kGBsk85AgmvX/4E0N8XyqCN4K4BwztwAAEX0PwIPAjJmvfwJr9FRWVQ3z3oMle+q8AH6FpLKbj//bva984X+23cpI94Fhzl84UgoA6xw9HQDaYQlaNHtIXV9a1vU1AOsB/CL5KBuZ+ftjeRt3RQs2f2l4keefhmvRpPu3JTtXAHDL0vMLF+SNTPVEQSrJmc7VxPNMi4VO6wiUm7RtEelwgTFrnayqmjMiT226x1OgJujP85pPP55/evj9vrZGO5lBpEzJMvKE3mlCcR+OVdQywyBL4hUCCOWQtscnNOYsB/E6NaOu2BPKVO98WcTyUMBzWAwvB/sI5r3W+nrkTdSDlQPgf8d/6dmx8Njz6z6mxgc8M2ZEjZNfOjgKAEyckGsD/wmrefoigH82vKIJwBZYs26m9EQo/bYcW7Nri+OAd7X7J/kt9qOedSRpSsmg6kg0VG452Tjdw5VSrEwyOKZBSOjaxjHnanybmtC1bcx4dbY3gCXZhOCpSpsm1YBgjzTEi4Ibo2eD6yOHjBzzBFvXNWPNkRoPQEWQEqIWJg4yWesNA5A2nNYLxA4ge+Gf3Htash4oP1dsNmOtzxc6fKXOn4xpijpmpBUwuoHFuIrg9d6DZeLZQgATpUFsSjnyF//vQHzHkW1I41yxJZhVDgDuir5cWCXBfQD2k2p67J8I/C0sleUjyccRkYsNbYtMaJvMqF03Y7Y8sJgRNLp3/athtyPaM3Ur5TEobcZZgtaboK3J9hKD1prAhczvgIUgnhFIH+nLc5DDLFYKI0eUBeFe4Y01AjyuqjgN1iFkN0BeMDEzTGYYAMCMIWbsQMqsXmq8vuCSKxVVJIL99uJo+xU5+UxscYnZJWpvAEGc9QNAAREdSHp8OOlUZRhTrByjY2zbBERUBuAdAL6S7fVdiQxWGMDBuBSH+ky7fjSWY7Nt6i/GmYoBMKWo7efIyvecOucuiN4MAHlKbNxhGC9ReXG0q/KDpWVd3+7qLH0brBTeTwDYncLIKVZDO3oM71aA/OluLSFQctfGI/nDAc+uk+f/f/bOPDyK60r776mqXrUvSOyrQCwCLDYbBMh2nDh2sGMSJ47Hk4zjZEKSSYKVyThhGHuYMJnkI4mxs2Ec23E8IRknmWAbHHu8BEvs+yZACwgQSALtS+9ddc/3R7VAS7fULXVXC6zf8/RjS11ddVt037rnnnPed5z9Ym12Lyn1cCHQSGi8hxX0aZ7Z1JQ0mxlOIiQww1fTlrLfnqpKgjHHKokyAJmTza7CJ9IrhAqqqlFtlxtUC1qFSRmtuP1jFbc9RVYXOoR8ZKcr3ZWmplemWb1j/RBXzZAWEIU271Q1utzhszSl2TzXsjxlDRm7L7enxE5SnWiiL4V3WdrQb0nTYJE0yGAGhmVN++bGD5zC5QxUnCSnki3VWP3j690TktI6Kjuak4NKAI8YV7/zI4+8p2eSrKIehDG4vjD6CoCRapL0d/as8nW+c1OfB/DfCFhHiImuLPmMnsAhQZ2Z7F4fxNSJ9YVJo5tOtV7Iaqo9mHMbC3nA5S9+n2mB2eIP6rF3HYKnNeGCLd0xHgA0r7JP+GWfVG8eIUb6SiFjEcswueZ4AUab5KJSySWpkotk0kjyZ6ss7DwdjHZTg3zEeyW5Uc515kh+PsgmTADRdIQqm2SovqsJR8zZzjwivWSbGe21m+b3pRA2aEaPbrC0tyfAgJYBOzO5iLhXJmCY7tzowVOYNAHYKVyek2r5pWbfkfIE0epkEDSi3qIvzKgN7GmkAYCkaJ2fo5TA46DvJ4mftHzP8bb3RynvQ5+DfgEAssWfBxKlYCkPoJCle2aTNuvTt5c01zWllxytnDqysTV1wCVmDJonmEskQp/eTA6/eUyiqXO5xu1mWT3VVJPln7rwtB3MEoBRZFdHkU31gVHKfrkJmgRWJRtZVTcp2giSMJP90i5vi9VBmnxOljWnrmxIi4DQ1/ercgUYbDJpnUrKqKqcfBohKqSiQbJJLB5hUcsbvEpu/0cPDsHs/5C2QkUERSbT3sjMoaqsgv2xe66gngHwXWbWwpXUj3qAJY2sEAC8vyv72Ner/Qk/BrDMmu3GnJVlR09tm9ao+eXpACApWoVQ5WkkcUPGpJYZna+3S738W8wAXmmvHZ88ekztLwG8U1szegN0o1CYSQ1rGUkEc0qyc5zLbY1IBawnDAZL/Uub6r5YykmTSZ3Z5rEe+GzqebNd8btZb2ydB6AEQBYRppvAkyeaXJMnmnonxhIlbV6hucW1eNOXbHu//mKF3aT2GSQxQ12++YspFkW1/vWLv694qzynbnRKh3fDroKY1/T6kjHW3AamGM8Mkg+LJD+qhblXL4sRRL3EMibc4PLrkZBirhLOQ/mbTCeTOwD8CIB039feyHj7xXt2Nl9JXwoQkSQus6BkgJJzF5VZJDlg72DVgs0H9wN411Wfe5d9SmWt79zUr0AvFTCDIDPYQ10UAEMhmbTJkklrZCENqreAiIP1bfWioy7NYk1zqGBUO1SrmRY3+iSZ2iWgAMBB6EHkZBBSRAIXiIQgCWcC/NnamPIn7tiT+bFzJ7MfPNPvgqVhW+6eS79ctDz3p2/vlOx+yXMpxeWvTrJrrdbYeuTJPDMpyXWsoyMhpubDANnbHbaDqckuI3owenJjzDcBxIcgwJLxiNCwpab1e899ix3uXwPICXR272TGZNI3bMCMCiJMA1AN0LX+Z5JEz77xEQDe9/0k8ROW7zl2APil90cp9wK4lxkMgjecuZwI6enJHSkuj7VfYYy+YUF6uXSf+ISUw4xmIrZazd4TM28/QqpPYQZGEqENjD0ARhNhIgh5ZNEQrMiFTGJpbXvGiXffXJ712MNbZaLgpuad+FW58oXfPThl/Nja0ruW7Tt57uK45rREp7+8NDf6PeY9WDzC0/LG5dhbcnoFptiZ3UQUVEglxtwQc040e7CgZ6y61vWPhd6m1JUFAP4nEFxlAriXiFRmfg0hoFhq7z95ciVBT6n9HoBF80ue0jem7VPMGnLuuDC7vjzjrD3NY8uY3HqtVO8rqSf3p8i+TjnlrnW7AsBHkkdXf1BbM9ouGCde75hR26AlhrVjIQRVv/rWspFikIsdBvvYDBXUW1yjJ7fccm5Xk9+Glo6EhUeX/iHU7udR6M2pIcf1lb+sKNlbPW75tMzG8/fknr28fNJFaUp6y1RZ4ms7WszoKD4/4ciOcxNtfzk1c9H18YIh42wkzemDwdLM+8wOxHyi8yajxJ0dF/fzVwE8XP5k0ZAOX2wjx/GUL3w77ONP/fjbh/vY3blh8G0uzAPwUwAfA4BTu2ftbarL8I2bXp2UktmWdv7kpEvzP3rk2udGjHUfEgvbur5vgeul07+zZ5V/HgB856aup6uWu+iSbTyB+vWYYgZf3pN7qKVq1KAV6Exmf4kkcb+fdVtG+6nR88+KltPjZlu+fWy/NNIdTJa+HvqNI2RQ0rZ33Imqf/3oKJJFetbKsv0pt11SbRPa0uRE3/SuZsj+VuvRxrdyHFdezZsv3KZr8yFJosRi8Rny3VRV+Wh5+YSQpaDRgytGZLRPIILRWaxWALfcV/r4RYOvGzE51vG8YeITYR376fJv3hTzTf1jeWnQRb1+pv+Ga0GoAEBgzAPhKBgzgc7qHUbKjIs1nUEYus83TQDyzd9xXPL+KCVHqNLbvubkRtbkUPYS3XC4rAf+t7hwUf9H9g2ByxVCWFmasQkdlQlmLzInXFFnffTgjF4HMDQAe8AIuU5TVdn38ubPnnQ67fPHjakrnTrpYuOo7AZrSpJjBnUR2xGC6srPTqqovDA+/VJN195Obhxjd5mlPjP80YEZvL0mobrNL8e8JzNRoRNmKXQbSwx54uXGZ38ch+tGxJz0BN52V/hFEhP/dCDknENECnQv2Y9At4M6CODvmPlUiONfBrA9XiqCXRmNQEOobBLWuZ8uu73ziXHzr/Qq4ZFJdK1h7rodJgH4ydMH77/1hZqvTFuYdvHPI1Pa77LKakd/Ox5C4MrbO+epgw2u9AGRmZn3oZ/0OQCYTKqpvcOa6NDMlnOu5L2Tbe23EvXqe8uHLvk4GQiu3vPz+9/CR174womKxsw5FY2Zk57dfRskEvx4wf6dj84/NndH1cSjvz4wb0zp1exgO6xlIPSe+GKENxVpJkfsk9uKO0Qjcex5CLoIwotxuv4wfWMHrs8HswpOdZNETx/Z0l1i3dSrh7Hr9/PvXfW5P6v7zh3VUuLSM1m3nL/VkuRWEUZjbfPZUSUtVdGRElf98kizpf9WS1uao1V1WvwAoO4cpZgerGqj3mqAWdD7Y4OKeABAyuJLGVmfLT1a/2reXVf/NGvJ1T/p4mTmUR01s55/w8t+2XPp+fnOprdzFgb7ppsUf0jJ/GijKFq+3e4+43LZYjzH0TRmtMchwEoF8Idtec8U3lf6eNTN4ocZNB4AXRbbNBqM6xsw3HOdQEB35eOu800GgCfL7yv8BnDLyKQRTTusdvetJLG/68ZGMHx+pfTNvbflDPA9dIOBXGaUEqGf1SurErGfiDNba0ZkaKp0VlZE9zHoJZPLABSDsRBBVJ8VRTM/+Mj2pldeeLDmUs2ovEs1owAAsqz57vvYjpLR2Q23HDk548Sp8pwpHY7EXnOqWRJnJAodwEUTItCSEe5Lb9UmxjzA8gtuNUtxyQj/16OZq4tfbnz2QDwuHg+YWSWib0BXB5QBvMTMp4joq4Hnw+676kqsM1j3A3g9ktd8JqmiZKK549qkxAz2CKXilHPElbcbp1jfbpqSo7J8LTAzS2rrPRNOX7TIWkhVuctXMoqLD86OWnkHg+vZhNMkYGIFIctgEpJd7zkT6a7ONUheYmPF/857M02ioJ49LugN5D1Vao4DyNMESf/wpwd2Ha8b2W0iybQ7Gzt8lkSvGtr7gSUcgIRB72yFi7WRD5tcwRdv0YIlnG6bjGkgMmKToCduAAvKnyzqqTAzZLCNHMdTPh9BBusnN34Gy7e5UIbeqDoq3Ndwkv+idldTt5slC9RrTbZznpMj/K59o0dqzbYu5bWM9FmXS5ImNCwNslmiH8FoOfm7wrRobjMoJvUDFiTLipZB1Ft2GQBMCe4yW4JLUcyBRY5NbbN89dRJaZQrVF9kMYBg82IVgMkdR0adPvvte3KBbuqDMI/qqPHVJfWhpsWHrDavYZ8ln0/ZX1k5Pqxd/sGQmuI4YzZphm1W9eC/7it9fG2crh0WU6zjecOE74Z17IMV3+h3viGiCwA6oNeVqT2PD/hfPgvgXuj3z0eZ+cgAhj5g6h/L+w8AT0XymuRp1cclRVxbrzDDK1S53NOa1NxxJSPJ3Zw6HV3EHRSzryp9zFWFKHSZ8L5TM/aXV0fvO0DgUkkX/pEkCjpHAGCRaXW9m5XgujvwM0ZMqTmQW3gs+FqDcQGMUUCPTQpCCQjL/T7F/dKvHqr0eKzdsjbJSR017R19zjfqSJv7ikniseG+v8HADP9rlxKbnJoUyucsKsiEXSmm3oImBnEeQP7Ljc8OWUGxOekJvP2js/o/MMCEPx40fI0Ts8XpkydXpiHEDmlf7HaNNjt9Tbucmkn8rXmSdUfLxKltqjUXCJ6y9gklddv5vMQ7xlaWZFhd3XaLvD7lRNWlkW3Hyib3KUgRKQTKIr+ebWKJd0MKHmQ52+13weo5D5NuTHrGkZ5zoiNz99ykxpQgng926KU7B6Av4An6jlY7AFmWGD++991pD/7uMyfavdcnoEZXQv8myAL+6OtFhsaTBiVIO1lUIYGZsgdnNJtx2bku2AC8mrt+46LyJ4sG1dMXS24CdcCw8W0uTIOeuYrsptehTPCdTXmP7KrNW54u3EezR6u1iVNCN5QTmk+NW+5tTjiSmX9hStcMEQvUORtSKmv2TZuILuqj0UD1K7cDgGA6aTYHz2b5nbbprMrFySPa9ADLraSou0aaTZ86X0MyB1ugLAWwE/ouuhl6fygD+s510ry6mekfr/yg+e1pt3d9Ud/BFQBgKjMaKPhGUtQxm9VbrVZvlcdj6dc+YzA4nNam9FRn/wfGhjXb8p7ZcV/p4+/FawDh0FtXbtDcwcyNIZ67B8DUwONW6FLnMQ+0AaD+sTwF+n0g4gWb62p6nSnF6VDdFtVRn57sbk7OZSGHLAVTfebJDRdHt2aOu3JUksW1clhmqG6v5djRyhw+e3lMVN83g/K0wFXoei9ZD0hq9NgXj7C7WLeRILTWjMjxOqwHLYme3qXRhIkAOgNgJ/TKphEArABgMqu2T6x837T11Y9fFOJ6CV7fwRUAkOLwm6rSLD5DAiwimG4b4a54/0pCTAMsjTFHMLdLRDEvfQzCJACbH81c/fDLjc8O2ZVEFHuwYkJMAqwnT668D3qTWERfesFU/ZvzC8Y5/JaIvAYEJOX9y7nL52bUlExLrV8CoL20csLpE+UTl8a6WI38mMhm+BEqhe+SLyNFnQQAGiTpM0c/sWx52uWTL8x+f0IXVTANgA9Anyn+UUmO7J/d/1bLt7fffbTZbQ+/74AQlpR71JBprmrlUsXTX4nB4LA2ocE5Ni4BFqAvQr8LYF2crt8/Q3vuiRq+zYW5AJZA//6oCOVDE4T2S5m72ndn3RXpNZ116fN8Dtv50QVlzSTzJE+bbe/Zt+bPEn4lpr1HLKTZzDhAFDwjrfpM3bJ32sHsRdqxTI/1e0eOUpLadc5wQG9k77O0ZszX98/TXObitpIJtwXz0wmBG+jfRyeajBnTUHfu3NiYBliqKs9kRr/lWjGCALywLe+Z3PtKHx+ikvGGS7B/EsArrJfh7COiVCIaxcx1sbxo/WN5EoBPQ9/07dOUtydC0LGGc+OXgSls+XEAYCGnNlwcPSdtVH2J2eZbrgm6UHJsbmv11ewY78gTaeC20AtFSnapplMJJv8sAPB7LOkH/3hX+vj88l3jbqlc0iXLrwLQQOhTwXnshCsz7vz47oM7/m+JpmlK2N9nkyQMLd0dadUWWCTR4hVSVDfSepDsFdhjk/tWrI4hDwHYAmBbnK7fN4Ro+mDFhFjlNd4EsBUB36Zwcamm2kiDq64cbxqzfMeZGe+/+tYy+4nySTEPrgCAQGMkLxcjVK2liXtJzpS0jJ1914GVHY0+a6ePC6OHiXIo5o+5Mv2/H9qaBXD4f1sypNeuG570/r3CBovixjQIjtuWMoB/zV2/MaZKaQOGI3zc2FQCeBl6SUtEc46jNj38GoMe+Dtsk6rfnW2qfHNeecUbty4WfsWQnUavR0lnRtBdfUnWentP+WWr5wcL5qjHM4qZ4Qj8NjkeK0UAACAASURBVKz5Rkn2JU9a936heZTjWP9HX6MxSIY+plgsvttMJv/l2F6F0t0e8+HYXqNPJgD4xba8Z4amVB/rKoLhPMI/I94hosM9PGs66de7JhZkvVQqAPwJul1MRP8Wfp+5LdLg6jokt9RlLz9QOuOt3/3fRydWX82OsXqmDgMLmbE31PMWWe2VOao+mrv09LsLSzVVqux6aDjXmzWncuFH7tkdkYeeWRJBLTliBRHsizI9x2N9HY/G8VAR7Mrzj2aujvl3aiAQGEQi7Ec8iMnCe/3srQLAFQAPPHly5R0ANgII2SPVSYLiuzXN4qxs8SZErHjHGjq43VRWdyV5GcKQUI4mKZV+e2KNWl2/0HLJmy4tAlFgccF+mEXQMplqT/KY2/d/OvPY0t/XKsQhVckOXh51ZtuZ3PojNaPMm1dun2Qz+c33/fZhU5D36AJwDvpOkR96yWEeA0dAUVL1Y5RAIAeEC4FrEPSFVAL0DNws6DfFoyzBF5Vr9gEBI80dOOBLMa6/rAcmAP+bu37jgvIni2K8uIsMwofHRcO8qrhz9nzGt7nw9wC+D+AfEcYGUub0yyfrSycOKOvk9yqHve4ErxCywTuMUs7Lry9snT+jZuesqXUTZYmvycvKpt4G5wAAQbJ/y7RCkss+kPNabkcIKV7NYXJc+d0tR9sPjJUyPl4hZX321OLKb31ip68uqVemixmV0OcdP/Qs/DwiVs0WX1SCK2Y0eD0WliRxSZKFEwATMSRJjIBezphChMnMOCmE1GK1ejW/3xTTMiGX22Ky22I+tfXFl6Erz/4qnoMIBiMiH6xMIjrU5efnmfn5HscUMHMtEWUBeJeIypi5pMvz4XjXxIRAkHWs/rG8RQAegW4P0a+6qNnine/qSPABFPF3pMNjOX/VkVR7tmnEx42d3QkSCT8BpQJwBTyqAnCNRBw0i9dyOXvOsdeXV8/71Achs77MwJnSqYeqKsc7nQ679KmH31pYX5dx/p3ty3sFj8xoZKCWAZUZfpmQTYSJFlktMcsiKpUDNS7LriaveWyGxXfZKgtNMJBiVhMJMDGgmiWex4wGlyZXgKWYtwcwMFdjrpF1k9t4MBLA1kczVy9/ufHZiDYvjWCoZ7BintlYP3vrjidPrpwP4IsA/hNAduCpZuiu5U0AfgOgnAiYktL060P1kQVYzICotx4VrZZ4yHZDTZBs5nYxYez77gl+G12tv81a7smUliFFPQiFQy6+ss2uRgSfKd0AbJWNaVWP/fmTOQDNAIAVv334kt3kvypYChas1oJwXbpU/9wVg+AFcBQMBjAXPXeWGU0ghLf7w7AS0FMlqev/+gHIBCzR9xfYTWHulA8USytUX0+dNGPJBrA1d/3G5UOuH2tozz0xwbyquB7AV32bC38JfWPnI4GnGLpgzAUAuwG8B6BKsfk2ArwsUtdqoVGN22mbAtAgPWcihwgQQuo4UDph2cFT48WcaTV75s+8NElWRF1ievviPl+b4gvlceIGYDv9hQfL1Gb7MgCo+dWtounN3N2ei2mhygh9AHWZi7gSQBUz2ZjZCSCdqKvCGhDIoJnCUuNjVDNL8zVNytKCWHYFPuBegGYDgN9vKgl2VDQRguYJAackYYBZiKjw7La8Z07dV/p4cRzHEJQIAqy+TD8D5+LawH/riWgrgEXQ/SM7Cce7JqYEAq3/rn8s7y8AngDwL7h+z6uD3nPUCOB5AFVEuNVs8f2bz2uJqLSPGThWN7bO6bPEpWJCAKwQ8mQAzHxW6Ou32ROTWzskCp01tCU7W6H3WfUMsNwAbCePTt+7452Ca3PWS7986LjPZ84AeotXqYyyBq+pi+gDe9LNWrGJWFMFnRAMv0nieXo/WJejGE0U5hqnwWOe4hXyqMsu28RgzxPYy8AIgEZ4NK4y4CYreTQ+l6DELcACgIUAnns0c/UXh1o/1oeyB6sn62dv1QC88OTJlX8EsBb6Ltyi9bO3nut57MoPvvYugDvDPbfWbN4nrlpzgLh4IgEA/AnStdIgk5uzx+xwZ7dOk3c23dt3f0O1J2lMu2ouSTd509A9ELEBQKLFnwDQtYlJFfK4dq88rud5AuSAUYxOxR99iikkICCSDwRMjkshoQlAJhh1YNwKCcXQy09GoXPhw9BAkME4j86bFmNiX++Hek+irYhxgCX5kKc4+aSaQLP7PzpmLADwc+if6yHDEJ97Yop5VfFJ3+bCjwK4D7ov1ivmVcXrex7n/vmdL0P3zAor8yEENTraEmvBmB1pUBZNTLJo9anSOGaSjpePXVJ+PrvlH7/wvr+/0jytLM0vjXNeBLoHPgh8T0nu+qEhyXMxra8FnQXgJoACixeaykxTfd7O2IlBxDWyolUpimoHwB6PdYIkiUqz2adANyHNAPSGfSIogdLHy0JI7UJI/WQgKTAGHUUWBnziidraEw6npTrjdr+Bft/+07a8Z265r/RxQwOK/ohWDxYRJQCQmLkj8P8fg56Z7sobAL5BRP8Dvd+7Ldb9V6HIeqnUCeDf6x/LexF6NmsegAVZL5U6uh5X/1jee8x0LyIQxzheO6a4riN5IUDx6sUBMymdNxQi5EgMJJq9b9pN6if6el17fep4gMoAnovuFQU2AJBl0W0N6vFYQ1Y6KYT8BEXb41Q7KwbI2uxTCpt9Ci4GGhJk4o40s7882+r12BQt65LTdrXJa5qZm+zcZVe0CRLpATnzteJ4EkClU5WvKMSKV0h9/o25Sx+q0tsCIyZ4BSZametkorDVcWPAPwDYD11IZmhAQz/AMlBbDlg/e2v7+tlbvwtgarDgCgC23r7pRwA2hHM+0W46Iq5ab7lu4hcf0sq8vcrDRLoQobL4ibLvFMDtoy3OA41eWxaAriaS1+pPvvvWXeURDmU5GBdCPUmAhYA8EigkgVnEuIuApMDPE8E4DMYpMA5CQIaGehKYRAIFJFBAkde3xzyjQ0Cy/arBIh7B+VLu+o1fiPcghrmOeVUxm1cVvwG9dHVjsGNs3/zbToDuRRifVWa4Ha2J9WCaE8/gqqElocKndje69Kmy3Wr3hdhk4FaYtdOQxXloZAa6zREaAlswjhPZZf6G8MVziJADoLSPI8AsjVH9pmUet22+x21bAKYRQpPzPW7bbI/bqjDjODNO+7zmcrfL2uZxWxM9btstPq9lueo3RSRRLEnCkH8TvyovUVXpghHX6oMR0P2x4mFTYQTZAHYR0XHoyrpvMvPbRPTVTm8aAH+FbilwFsCvAXw9PkO9TtZLpdVZL5X+HYAlPYOrwPNOv8/8VegZ9H45fXVkSV1HSiFAvfyjjIM1hbhbxpkIyLC6Q2ZxTTbfUYB9lmRvqTBbEqFnvDrxAXpWbuffFoW9AUuEhGRFTCVwyLlaY0pq9JoXnGpLWnqoKXXaVY9lmcpSxqm2pKUHm1LHNXtNxarA6Ta/UnKwKUUcbU5uPtSUOu1MW9Lyk63JSyIpvZTIsCz2eJfKFQZdqy+eeTRzdZ8iJcN0Jy6T8/rZW/trYPwegDugpyZDol2xwuh+q55kHPOU2K9qvXwiOmZQkJpj9twz4uL+b004NmmCrd0uEy+SCBr0nYES6Dsq8wFUvXYqt+No7chIm1gJgAsMBwi9xDX6fTFjSY+Md0QKSUFG4zGiTE3SkG9u4b2+NOqzPMoANuWu33hoyPhjDe3NHcMwryr2AaF7Aqe88tzJc1/46regL9JC4veajgMUnX7GAdLaYa1+e09uOtBdunfalLqTREF2xRP8R00PnIc8uykfglRSeBKAclyfbyYBuOCtTZx6/j/udPb0vAqDpQAfA2gADfeU4nHb+u3NDRdZFpGOfYCQ0tKW0Dwio2OiMdcLyXLoWZ1/jfM4AkQkYNEnzFyFIH3bXQ0/A+qB/xSVC0aZrJdKQ65xcrcVc/l9hSsAtKEP4QdmoLo1LZ6VGQAYZuL9RD2V7BhWRZ3U63Diq6PyLpdNWHI2X1Y0SAovZeZ2kHwcggUkUqBIY9jptxS/d1ul1xtZWwcRRmRa1D0NXiWiYKiTyo6Ebms1P9OAxTFIl5fXEKKfNZr4GUtUwVWKRDFVSu0HM4A/PZq5et5Q8cca6j1YhmawwuX0A+sYQP9qTRLi6myfWubdnXzR32uCUBPQKMywg7n1+m/Z8fOZH5z52cziwpyEtvEmiRWJ9J4lAEt8qrQYuuln4sZdt9U8+e4d83suosJkJsL52xkAU+yVBDuxDImvO+wA/py7fmM8+zOu8+FREYwG/X5niDge8tzXcLpNV7bvnCkHy9jnTq11ahpVB0pfAADS1NZi678cnaLMbconCSCFFegiOLnMWC580hIA4331CTmn//4zrDbZI/YtJIIMIB3guGeRjQuwAGaaKwS1GHW9PlizLe+Ze+I9CEAPCMJ9fNjJ3VbsBXCyv+PIgCqQ0DDMxDulXsEVYFf8pwXIyXx9fJKiVc79zMG2nDvLCk1WNVlS2AzAT0TJZFGWaSbLErIoBSBp4h9/f1/Z8cOzBlRma5KwxCZz3Nc4us81Wvs7LkqYPIIv9X9YzJkM4KVHM1fHXUPrQ6siGCV2AvhqqCfZJ9VAHfjuw2BJrvLtSy33Bc2YKE5kTtosMhmAloiLzslU+5H7zzk+PqL6oz0ONQH6Defrr39i7+n6EakJJl/HFUfi7QANJvidC0YHCEmDOMfgkeCFQZ9rYYq9amGYzICeyfqH8ieL4reU4A93D9YAOA6gAwj+nWGG2+8z9Sr5MQq3V2l67YM8F3PwHczX/7qwEAAURW0bO7q56talZfW56/cvJ4voWYKjAICnKu1C+dfuV0kRbvZLFggpiJFoeBBhPDN2QTcujhuyrBl5P2snYsMFTkLwu0A/VtwXYGxMlebNQglC9GIxA61uWzkD4w0eU+cIYCIuljp7unvgUs0zz7ZkAGDNLGmVSWbflcWf3G9JHtXWU9HXBABCEP924/1HvW5zgmLSOlwO2x2DGV2KSbO5tSGRH2gHwhQJGyQKhe1DGGs+BeBbAJ6N6yg+xD5Y0eAD9LG3rp5LFGDq05g3ViRe9B1MP+ldSP38/QiA4sCElBO8+PRPx45parb18q5p95o7bn/+H47uvzR2eYfXMueKI6lgAGU6PUkFcAQcmSdQtGEyLsMoqUNqs+DzAB6L9yCGM1jhM+WV5wSAHaGe9zitB1S/KehiI9Z4fXL71h15TUJI/ZaHqKqScqE6K//V3y+749gf5gXtj6rZtHBn2ZdWjmGfkiNc5tnsVwYcXHVhNMANUTjPgJFkYWS5eArHNbvQjXQAr27LeyauGVZAF7kI5zEMAOBvoZ7wC6l9/6VJuQDFxQfJRFwshwiuukOyTyhTmzz2Zbt35Pv9XqVX1UpzfXL1L9Z97mJHa+Iin9c8y+WwhSqzDvtOJBFm2mXtAMDxSU0EIBhXpaMxD5VNZAD48aOZq2+N9yCIOOxHPBiyAdbpB9bVAvhLqOelLM8lgNsNHBIAwF7rP5Z5zDuHIqy7dbnNMz//pZXiF5sXlrz+Zu6+3/8xb7fPJ/kanfaWZrdtRgyGWgjgMhhxM+JlMm7pTlps1QoHwC9y12+cE88BEIf/GAYA8CJ0pc1emK2+8UTiiMHjgV+VXFt3zL6kaXKEQRCZ31j16YUvLPvazr3PLN1b/IM7d185MfI8M9CyY7K1qzppNCDCZACZAB/q9+AYIfVoxI8xJDSpycDr9cdiAD+M5wAYUTcavtn5G4CyYE/IxJbRya3FAAc1KIglComS8IKr7rTUpxZs+fHKy4fen73r5J7cvWcO5uxnAdRWj7gqNKmnamkwIvpgpJrFoiyLeiCeO4QE4zaRBQ+prVATgD8+mrk6Pa6jkDj8RzyGF5erhs9nAKyEbqDbDTnDt0Qe5TZUWcXaoJ7OOujJoTAdyXsiWMra/lbu8k2/XnjbK7+/peCTDz3c8dKu/LLHFhw7GO2xBsiBbkp500Ma8knliNzfY4wVej/WQProosNwBisiprzy3BsApgN4tedzsiImJaY65gJcY9R4NI18r+3IK/er8qyBnqPmwPhl73z3E4s/+P5HCzYv/Nb47Xc/9tex39inQBZRl/cO+M/Er//Q0HU7kcttqTLyimHwz9vynvlk3K4+3IMVEbnbit0A5kAvt+rWzydLbJkzqrZwZFL7LiPHJIN3K4QBWxCofmXasZK8pfv/b97i3dsX3vrS+s9WtrckusZOurozmuPsRJFwm1niSNWWb0j8jMk8tL484wH89tHM1XGKI4Z+D9aQDrBOP7COTz+w7jXoMsvfga66cw3RZjYsPWtu1c6N3OMeTYhcnS8UmkTm18/mztry7mwB5lj0d7QiRE+JQRi25CGAzO3BdwPjyFQAz+eu3xiXLdvhDFbkTHnluQtTXnnucwAKoMtDX0NT5XIAhgTMQkC8XjzrmMdnCls2vX9ITm61JB/59iecql+OlSxMQxwjdkPvZ16fMlR6sLry8ra8Z3qruxnEcIlgZORuK/bnbiv+OfTN0GeB65YjzECTM8Gwz5gMPqAQR1WJ16/KzgMf5GXXXshCLEJrZlQJjk8ZZRwYp/VhwxMnVgD457hcOdCDFe4jHgzpAKuT0w+s855+YN1Poe8uvwSAWcDDLjnPqDGM2u3ykN7bFDVIwDZyrzMh87i7UHGLfhWFIuA4gGIAtaDeUrcBYv+JI2P3lM0dQOJF3gWOb112Dx5CH2ItwwxNprzy3B7oZVdfBnAFALxuSyNAhmxYnDw7aq/TbenZMD5o9p4enbvz5LilRyqzg5ZCDgRmOJlRzIydAJaH2FeJ+XxDBuewmCmtpTVhl9enRHPuHiyp0PuxhkpD/DBhkLutuDl3W/HjAPIBvAsAHV7Leb9QomZj0DfsU4hnEUV3TSgTJibIIscm8XJ09/scFKrAXrdGxQ1exaQyxUkIBIDBc45L40sdflFs5DXD4IePZq7uy5D+Q8sNEWB1cvqBdVdOP7DuSwDmwSe9DpBh9Z/OUUovgYrBQgxF9nMqEzTVJoVXBsQ4RiqOQqCY/DgLDTvBvTI3cwBkgDCzr8sPdNxDFdmHJYoPS83tQ0OmvgvP5K7fGLEE9qCIpDxwOIMVlCmvPCemvPLciwCmAfiB0CTDRBSmjm/IjYX0udevjACASdltYfavshPg/QDvBPgAwMcB3tO1N4R0w00FoGV9TCsGzDdssNANjferytIOhy1qwWqUWAjgx8ZfNrz+q+EerNDkbisuBXA3gHsbnEkG3sfIzGHIxkd8VkIqERQBXABoYjiv8Wi0s94jH2j1ScWXnMpZh5+KmdFNIVMmzGzzybNUpnHRHnMkEBmlk6yjMpb5GYUa81Ujr9sPMoBXH81cPcLIixKGRS5iwukH1h1Tzyc9DOCTAM4bcc3WXMvUWJ2bGHJamaf/fjIGk4okYuRLGgoJyJEElpGKCaTiBDgQWDC8YLSBUR+rMYcDy4Yt3btdx9wWXKggjpgB/DF3/UZjS4qGA6yoMOWV5zqmvPLcvzFLDwL4oxHXtFvVTJtFPR6r8x+sGJmvCQqnh+gQEW4lwjIiLCLC3IDpaAXAgf4Q9hDxHIBDqqIZgSyLFAMvd21hJQRNE4KM8sMJl29uy3vmQSMvyBjuwYoGuduKOXdb8VuVjVkPQ+/PMuSzpTHFLPKVgIkK9Z95YcbVOpc8t8MvLWryyoU+QTlXPUrhRadiv+KWjwi+tt6zJiraQeb43uslGKbU3C2Q82psqP5AGIwB8N9G92MNlwjGiMo1RVy5pugN6Ma6a6B72MSMjOOes7E8v+2qOt/SpPY9ARGIld56KATYiDGHVOSD0U4qakjFSPKjOnYj7h8hGyad3u3GoHix1NLMewy6drhMBiJXZhoohOEerGiTv33DpfztGx4CcDuAmCoKNrbaz7q9SsxKoFVNTtpzarTKjH56P6mQGb0CPSLMIMJSgEug96qVEfFygOM250gSpwBslKpXl3snJTe1JMbdgyoIhltFDPdgRY/VFZvU1RWbfg49g/4rADFUFGQoMVThJAIsEi8g8IF+jsseY1d72UtoTBlOVZpX41JYMM7WueWKBo9c4BPYF6sxh4MsGaYi2G2t7hFY7BdDLsi6G8Bow65GDEgi/EccuGEDrE4q1xR5KtcU/Qi6oMCLiMV+PDNsDVpMJbcJoIyT7uWSrx8paMIkSMEnFQIk0nARwBTSHwsg4qciyAqM9KXphrk9Mhn9WMOAx/A4ZjiDFRPyt28ohl6G9UUgNlniE5WjaoDYGku2OGzTTl3MDKcsSO0j6zAZwHQiLCSCQsQ18fpABfbf4yKdzizNFoJa+j/SKFgQCWN393lYpj0WrK7Y1LC6YtM/QS/9fzcW1yDgMhFuicW5r12DkGCXRW5/SqwmCbeZJA66YeETmHzFLXvcmjRbgJIFYzEzot6+ES6KgVY0PS/tFVwXp2sHgaGQ6LDKwtgM1hAvERxK5qyDonJN0VUAX576w43PA9gIYEnUTk4EYUK17I+uyEWvywCUdcg1/srihAYQhaxnZQkIpTpJjNndflaRwSa4QLBHd7T9oHGr4oqfBxdxbHs+WDcZbYJevuEC4AddU4BSoJcFJkBXnUsDUALCe7EcU09ouBYnZuRv3yAAvHx0xRNbATwJ4JvQ/82jQlaaw1xTH/uK0qq61MVjMjp2piV5l/Vx2HQANdDLQLpBhLE9fl7MjIPQA1BDcTish6FvtMUF5phmGAQRN0uSaJZk4ZAk4ZElViVJMEmCJInNRGwj4kTo841GZHwf1pCSF7rJWF2x6fSz0752N4AHoPfYTYnWuRkYxYwOotiqDhMhxSqJGo+QRgIUdBOUCFKaWauq9yhB+qsIbo26ZvbNXg3nrQoyYzTkkPgEX/Zo8dtEji0ME3G7VREtNlm02RXhtsua364IzSYLWGWhmCVhNUmcIBFSCMggwvMADM3kx6v0L1xumgCrk8o1RQem/nDjUgCfAvD/EIVJSPaIJslvzI1b9nFm2mnP0ZaZVjuIdE8Z3WCuglQkQl/EhXJC7wUB41lgD+QoBpxhIGloVLzGlcT1hER05PQZaAHhBAAbADv0gCkDemP/2MCjP0oBrKxYW2TcjvJwZsoQ8rdvaAPwnaMrnvgVgP+Crho5aKpqMgz719tzesyCuxecL5MlziGConfJEAG8D7rXSTpR7+AqFERsjkcZWENjihWguPnOCSE1y7I2yIUew2zx7bdZvRpJbCPiJOgBUzoRMoGwF5J3FhSvM7RMWu/BGs5OxZLVFZsYwNZnp33tTQBfA/AUgEGLfUlAKYVWHI4qioSZCnOJyljcaXjODPYL7K5zK5MEYBdMYa8dGMgXjCpJNzs3jA4/LqqMuKnnCY5OBVqiolXnpzvOJ5m0BJPEdpk4NRAwJSN8W5L/WXF4y3eiMZ6w6SwRHMLc8CWCwQj0Z/0v9P6sb2OQTaKp5b5TpC+wDcHeoOaPLnGYwdwExiXSUAZGLQFjCBgR6S2M2Di38U6ECTkcp3IdAAAPzAz6+stRx4QdIJhAKARhEQh5IIwPBFfhUgfgSwB8gxnPMEOb/O0bqvK3b/gcdGn3QfUFeH1yW5vDalgGSBOS7a2Dk6fVt9p3A+wAsBPgYgCjiDCaKOJd2ux4RPc2qy9upUIAIJgGsYHCqtXq2ZmW3laZmOi+VVbEEknifCLkECGDIrO8eAIYckqqw0SR1RWbfKsrNj0L3T/raWBw93iF2NBqE6vMy22yqGGGqjFOXHHLpU5V0lSmMYIpLcLTKYLRHJOB9kGSCRONvmZXeJDr9zSzv/xjo1t23T26ZUy2zV9oV8QCk8QzJX3Oj2T9dADAvw5mLDcrN10GqyuVa4p8ADZO/eHGVwCsg77jE3FvjrVJi8ffickPJxHGAwBpmBGHMQwcIrDMlaQhIx6XZxlNGED2koHzIFwCcBsIdwxyGPUA7q5YWxQXn5xh8Qrjyd++Yd/RFU8sAfA56Bn0iGWEG1oSLwIU057PnjCTJJg1IiQCWD7I0zmgl80aWpacmOROdrnjV7GjyNqoyF/FHpvds99q9U4mQl9lmuHyZEHxujhItAOdMu3DGMfqik0tAP752Wlfew562eAnIz8Lg4CRUR5av/g0kqscsh+Q5gCAa3AFtoYnC8wSjQmI+sTFh0u+3pIQEdlW3/H5GQ6vXRGLAOQOchgnANyz4vAWwwPcTpn2ocxNmcHqSeWaoqbKNUXfBDAbwJuRvr5pjiXSHZVBwzLcpJeGDBoSKISG/dE4VySoNuMzZ9cQkWWwGChjwh4QJoCwHDTofhoVusN5L0Ukw4iyyAURpRLRn4mojIjOENHiWAz7Rid/+wbO377hD9BvXv8G9KfU151Rme3TTUpvJa1YY7doUekh07MufMboLJbd5pkFcNwyxcwUtpItkWhPSHR+kJbe1mGzeQuJIg/Eg/AadLW5+BCmRPtwa2j0WV2xqXJ1xaYHANwJ4FhkryZoeo+loahMHYAUlcogv0CuYIRjOxFVLFL8lJpFRH3mjPEJnkP3j206viy7fW4guBosTQD+KR7BVSfDMu1DiMo1RWcq1xStAPAxRGCqp5nIsPLATiQVKbKXz0TrfKRFVNYWFYTJuLLKnkgCcyG4T4+KQGxxnAmHIGE6CEsQnpN9OEpOPwFQXLG2KG7LiRjItD8L4G1mng5gLoCofT5vRvK3b3Dnb9/wA+jCC78GwjOlJIlNkhR9k+H+qKpLjWb6Zw7QywA9pkgSzIDxpUKduD3mfhu8iURjYpKjODWtHRaL/3YihGPOWQn0u5A7A+A/AcRNyZAxrCIYb1ZXbNoBYAF0if6wVeaYgwtOxJJERcwEoqaEl+DXcDlK5wobkxRLYZu+Ubn/vlgCi6lJ7r0PjGuqWJTpWGCWOdw+u/fDOKYI8VwDEIZl2ocilWuK3gWQD+AfEYbMcsIV1fDdHQAwt6nuKJ5uJhhXoni+fmGKcwlqiN6zWKfR7wAAIABJREFUQGB1AIRSSJgLwoKwz6gHTj1LAn4Lvfy0M1PxBoC1FWuL4uuNE8UMFhElQy8dexEAmNnHzEPNXHVIkr99w5X87Ru+An3O6deMV2iS1+tTDG3YBoDmDutIZrRF41xEMBHx1WicK0LimcEK+Zwka9VJyR07U9Pak8xmtTDQQB4OxQD2ovuccxl6KdjBwM9NAB4uKF53uKB4XVzzQ8MZrPizumKTtrpi02+g+2etRxhmuDIZv6FDBJglvhCt8wlgamyVPHsjxXevIOT6Sib25qU6d60c33R5brpzsSJhWpjndAP4d+j3qq78G4Dvd/l5/YrDW/57xeEt8euzR/gS7fEqJfxQBlgAULmmSKtcU/QC9Enop0DoelZPumx4iSAApJz1FmYddNQnXPbuAg/OQJMACf2aikYXSYuvy3pPGPAzoRiEKkgB0YrQ1EIPnN7B9cDpDwDm43rdsgN6Sc4qAH+BLqhyAMDnK9YWxVfeJoLsVWDuySSiQ10eX+lxxskAGgD8hoiOEtEL1KlyOUxY5G/fcALAXdAVTs+HOk5RhFWWjA9OnB7z2LcOTsYHx8dVaYJCji8CDF1+BBZXxtpR9IOiqKdSUtv3pKR0jDWZtGV9NI/7AfwJwBbo37PO370K4Atdfj4MYGVB8bo3AHwPwE4AjxUUr+tlBh0Pho2Ghw6rKzY5Vldsegq6zcKf+jpWGPxd7SQ32Td/RoqnPlHRSqJwulQY/D60+N7le71XkyTaFmV2/O2BcU0d01PcSyXqsz/sOPTPxVtdfvcfAL6F68qUDdDXNT+CPhf9EMBvoGfL485wieAQp3JNUVvlmqLvQJ+EXgt2jMkhwq6tjyYEQPZxVvJF31JLi7YHzIPanSFhYJ01M8zt0ZFKHygmF84CAAMdgcCqOaAIGE52YDSAj0CvZ/8J9AWxE7jmhfYVACOgNxabK9YW1UPPTvx9xdqi9ii/lYERWQarkZkXdHk83+NsCoB5ADYxcz70v8X3jHgbNxOB/qytAGYA+A6AXnMLM6AJios4jF+VU9qc1sm7SsdqzINTXwUwGTBuZ7ylNXEPQIb74XTi98uBHjaG2ew7kprWdjQ5xTFLlsUS6r/02ATgQeg2HOsAvA29pLQzIHsPug/ZRxEIwAqK1/0NwPpAsDUkEBzeYxjjWF2x6eLqik2fBVAA4FCwYyjM8uVoIxHMZglZkxP9y03EQccWATaNcSQqAwsDjbnNqcXWnLkfxgnmFgCwy9qVZVltJfePbZbHJ3jvDFg69Mdc6OXrbwN4Hvq9yAMgCXrCoQD6nPMaAMuKw1tOA9gK4J9XHN4Sf1XkG6BE8KZWEYyEyjVF5wCsnPrDjcuhy57O73wuqdofP7GGAOln3IVChsOfIF9qzbW6hFma3/+reiAwDQw/CKYYDLEbshfHiY3x1QiFuRUdvmQUA8gHDciTayz0IKsceiBVCz3wOgDgxUCW6sK0H2w0T/vBRgVARyDQijuEqKsIXgZwmZk7xVL+jOEAa8Dkb9/gBfDToyue+C30koxrCqf1zYlnAIqramirw5qzbW+OsFv9NVPHtJwdn9W+mCIUfiHCWGbsB3BrjIZ5DWagrS1xACp+0UMT0myz2bcrIdGVSYR5AzgFAVgG3RJkM/R+qloAFwB8vqB4XWdmq2V34To79ODrg0EPPEoMl/8NbVZXbNrz7LSv3Qpd4fRH6KJwKgHZcRsY9HLBmaneBapAi0OVLlQ7TckMilgF2C8AxaC0QZOXjwK43ZirBYXMkv9gYbbTnmrWbiUakPrrLAB/B1387Y8A/grgGwCeXnF4S6eP3vnt8x+xb5//CAG4uOLwlrj1efZkqKsIDgdYPahcU1Qy9YcbFwH4PPR06CgMEctWSUOipV2bkXXQifbJlhLXSNMCEIVdEkPAKBY4CBkx99gxtxlbjhgMSWAi+k6Rh0Me9JT4Vug9SA8C8AK4a9oPNt4B4NWKtUWd5TlDIriKBcx8hYguEVEuM5dDDzxPx3tcNzr52zc0Avjm0RVPbIJeqvxxHiLzDYMkp8c85ti57DEXryZXFMyqkWSZcyI5BxFLRpSEud2WE8zGStv3hhKsNk8CEaYP4iQjoQfau6Bv5rwOYBsA/+7CdS9DL0V+t6B4nQu6FP4ww4TN6opNAsDvn532tdegl36twRAqq1UkpKWaRVqyyes512He6dKoAKBIQqYFglEvEbJiNkgAzIx2f1SUPwdFlk1NTrNotw3iFCboWaw90Hs9zQDmrDi8xb19/iNfhx4j/HXF4S1nA8fftGucWPChLxEMRuWaIlG5pui3AKYln/N9x9wqejb8xRUCkFLlXZ6936ElXPbuBHMk4hUxz5WSxg2SGrZwRMyQfBgH5sEuQiwAfgEgBXq/w6MAfg7gFeiLoMEspmJLuB3n4W87fxPAFiI6AeAWAP8Vs7F/yMjfvuF0/vYN9wC49/0DU4fMDmEnLQ7btO37p+TsPzPquNurHIjgpYbcY5qakodEsOH1maIhEnIPgDUFxet+BeBu6CaeJdD/lkkYwvftYRXBG4PVFZtcqys2/SeAqSYS/w/6InvIIBGsU5N9y2Yke+tTzdoHQN+KwD2IeVmyQ8UxHoDPZrSpcynRKCVPB/DaisNb3oMeUH15+/xHXoQukHIAiK+hcmgYkCJ4xIEhO1EPBSrXFDkOv/DdnxIwE8Dv4j2enkgakpIv+paNOOzUJJ84DEDfWukbAUYjOHaBlrUZZwiR+VDFAgIIImreGAnQ1brsADZAT6M3VawtejVK54860ZZpZ+Zjgf6sOcz8AAfqv4eJHvnbN7wlhHQ7gH8C0Bjf0fSEcKUlce57RyfMqapLKWEOS7FP1vuwOGbvxeMxVfpVZTC7uFHD7bJG657a2b9K0LPnawH8X0Hxui0FxeucUbpG1BlWEbyxWF2xqfbr5Zu/R4RF0BUrhxRmGSMnJPhvn5Lou0DgwL2830+QKmKomMzMqPfwkKj+avbJE5mjIiZm3j7/kU6bCx+AZ6CvcaoDgdeQg2hY5OKm4J19T116Z99TnwewCHoWY0iheHlM9kHn/Oz9HW0j9zpE4iXvLjAHDaBIwxTyI4NU7I3FWEjlGsmHIWNAK/sH3agP6Dti4yvWFvkq1hY9DaAdwGsVa4sMN28Om0gELoYXPEOKp/kh9Wl+6FcAcqAH8/FvKO6CEJL15Pms5W/un6Jt2zvF996RCTVChPRpmkXEbUSsAhwTn5r6+rQhYxfgdFmjIbKhItAfU1C87vWC4nVHABwvKF63JQrnjinDAdaNyaqyzUcA3AHgAei+a0OKRBNPn53qnTwrxdOWl+r1JCpayGDQq8Hs1ZDl0xANZcJedKg4JNCnArFhMMikMi5E4VQSgKwVh7dcWnF4y2YANSsOb/nDisNbaqNw7tgxxEUuhgOsCHhn31MHARRC78Mx3DW8PyQVKQDgyjJZQcHrlgnIIoDAuA0cfVVBazPOE2IvohEuijviTJ3AdVl2P3RfiAcA8LQfbEya9oONpoq1Ra0Va4viZjAYLiTCfwwz9HiaH2p7mh/6LsKQWY4HmpBsgiVzVqrrsiQF73XU/bCQQYSRRHwu2mNwu82nVU2OeU9puHg85kkDeFnXskIX9JLkbbsL19HuwnWpAFBQvC5uBsrhwoh+iSARyQFbiO1BnrudiNqI6Fjg8VQ038+HjVVlm3lV2ebXoQcPRUBUNiejBhGgSEhhhtepShP7OHQkAEljTIu2L1YgexWuh50hdPjlSL2o3MA1j1A3dCGdzwFI2j7/kZTt8x+RVhzeMuTnGwDDPlg3G+/se4rf2ffU/0IvG/wOEB1jzmghTNQiLFK//U8EyKQhqgseyc8XJD+WRPOcg0X2RNzAq0KXSX4a+r/x56DvJn8ZwHYA1miOL6YMZ7BuCp7mh84/zQ99FsBSAEMua5ozuiXcFfPoaMu219enDi2vPZZsHHl50t8APA7giwAWQreEmAhdHvneqA4wxsRgulkN4Ewfz+9k5lsCj+/3cdwwYbKqbLNvVdnmZ6D3GD0LA3qaIqHdL51l0IQwDh0pGKeiee02P/YxwjbtNYRmnxypyrUXutfVeuhCOt8DkAngRQA/W3F4y42x5UrDPVg3Le/se8r7zr6nfgq9jOfnGAKTkCdNPtE8w1YW9gsElkLD4Whd39qMOhpinyniiMfzN+glEuuhGwr/DHrT+W4A71asLYqLJ9pAiHYP1jDx5Wl+aDeAxQAeBkKW5BmG3eKvnTG+cZfNoub2fzRAhKk0eK+bazidluOakIeUAFGASHbNfdANPF+H3mu1Bfq/bx30/qt3oz66WMHR9cEiorEAPgHghVgOe5jgrCrb3LyqbPPj0Dcat8Z7PILhb/FKh2vdprD7n3wCE0WUKnWYWTR6Oa5y9sEYgK/cb6BXYP0YwGPQTYMLoKsIvh3NscWc4RLBm5t39j3V+M6+p74FPa2+LV7jEBI8rbk2s5ooLw33NQRIpGEEqSgGI9I0czckH5+VVON6r1jmcu94fzhNuZF+s+6G7kEzFrqZ7kcq1hZ9EsD70Bs/bwwYsVARHCbOPM0P8dP80P9ALxtcgyBGxUaRM7rl7LSxLUuJ9NLkMFlEJHYD3FdWol+YgYbGVAPvX6zlzjm7B+Bw5slIvlBmAC9Bz161A/h5QfG6xdA9aZ7o4n015GFQ2I8weQbAE+h7Dl9MRMeJ6C0imjXoNzFML1aVba5cVbb5U9A9nwwz8u2JX1B9tcs0QWOKxNw32auhzS+wmxmDEodp8WEvAwMpAR4QCTLvTVJ4VxiHRnoD/waAX0EvE6wGMAfAP0Df5PljhOeKHzeAyMWQUEK5GXhn31PlAO7/2G3f/wj08jJDPVkkAeuII870+gUJtSAaHe7rCBgPgfEQaGQT2hDZYglg9pkcOGTuCMs5PCowcV3bne5UtnGu0ijvl11SNI1MCXoN93cB2CrWFj0IABVriw5G8RrDDDMonuaH3AB+9G169TfQs61fgsEbZifOj1ienOAtyUj2hG1wSQQJQAERg5l3MUthbwh14vGYypuakhuEiPy1AyVn1vm9OTMvLLXaPYeP78vrTxo50ru5HcA/QpfK/gEAFBSvq4XeG3GzkklEXbOZzzPz850/ENEKAPXMfJiIbg9xjiMAJjCzg4juhV5SOaTkxm8mVpVtLt48fdVCAF+AbtFhqLG3ReYxkxP9p6scpkSAIinVn6kKQAVOWWXMoghdAjTBrS0+Ptnih2EBvEJ84rYR6nxmaB9cVaoEaHIfh0c635igl39uAlC14vCWPwd+HxNRkA8zwxmsKPPOvqfeBzAPwFcBGLr7KPs4K/O4ywPm9khfS0AmqTgekXw7s2prwAFLO5YQG1OXzOCOjmUeJ9s5GwR0FLqnM3HIhYjixnhrA++2NvIuayPvtDZyibWJS2Q37wRzX6Z5KwHsiP47MI7hEsGbn6f5oatP80NfgT7nGPx5JewqHbvM5VUGuvkwBuCIync6OmyHausyxnl9ZsOCq6zRDcW5s6uWAsDYiVfmZ2Q39bkQuXo1vbahMWV3Q2PKrobGlJ2NTSk729oTPtA06suYOwPACNzgQVUEJYKNAcuHzsfzPU5VAOB+IroA4H8A3ElE3axSmLmdmR2B//8rABMRGbbR92FkVdlmsaps88vQ+5D+E0Ak/lSDJskkZo61qycGWHYxS2XsieSVquDmC06+1OLHMuh+UTGHwBeWZqnjZYJZkWBbkKFpAIfsNS1ttY472GjbfajJtutQk23n4SbrzuMt1uJWn7SX+ZpgVzA+DmBf9N+BgQzxRQ71b5s0zED52G3fT4FuEvk49FIQQ/CkK8daplvzQBRxhpKBU2zuZ6eGGbZG7JR8mE1A6oAHGiEM1pzzvcf9Y7V5XX9vuiKfTNhvySNEtjfFAFQ7TntTUe9PwBhIpO9+MleThp+X//u3fxK90RtLYto4vuWO1WEfv3vrvxxm5ribQw8zcL5NrxKATwL4CQw0wZQl4b57YdUFk8wzBvJ6IegsQDl9HeNwWA/XN6RmADRxINcYKAlJzr2F9+5d3HVm0TTyv/uX289rmhzxppKiqPWpqR3lKclOi6Joc4lgYUa7EHRMlvmuguJ1kTasDxlG0xT+shye9/h67XNhzzeBDNZ3mHlFj9+PBHCVmZmIFgH4M/SM1vCixiA2T181AcCPoItBGcZll1LS5FXCzpx3RQJKLAr6fK3G3FHt5HKVkQdDha24eckI1ZGodFdlPdMmlVxyyRG/X4nYPzHBdzIv1evIsqpTJcIoZmgao0pj+uanj/3u/6I3dmOZP076/+3de3xV5Zno8d+z9t7JzoVAIEC4CSgqgtKAICgCbS2Oth57GJnBXj2OPcW2ObXFTp3OTJmpdWbKzGmQkU6lZzpVqC3t1HbGXsXSioIGCXLxBoqAXAJIuCSE3Pbe6zl/rIAh5LJ2si/J3s/Xz/qYnbz7XS9K1l7Pet/3efTFJf7/1+QuaUz5PY4tEUyidZVLa4EHbp714Pfw6tn8aSrOGz4ZLSs82LKp/pLc2T14+0SUYwidb+YUoalYL80/ltoZ0KYrIi9GRscuenIdKY1dExke25BzLDgvnv4ECDUwKdTAJIBYUI86MYpEeR1vjXK/JdjMVLap0EUK/NcS+clv8dbZfx3iXPLbAzHXyfvDtnEl86/dV+0IvpcnnyOih1W7DrAKCpquDZ6KVUajwXE9HmicQjmRnXNu2Xxt+8c2gYCGrv/QluDGp2c2xblUiWg0OKympnhYTU0xIm5DIOAei0YDCvL39xy9u98GV3AuTXtyzyEi9wKo6qN45VI+JyJRvHTTd1pwlVqLd616B/jYqomLHwGW49UKTbpRedG5TVHn+bMxZ06873VhnCotIp0/9A6IDCjJxTnapKkMrpqmDY5VFwYvrrE1scide6zJ2driyrXx9OiqhPbW507bW58LKEUh93B91ClxVX4LrLsjUUNPh9Y9WH2ZLRFMgXWVS99eV7n0DrwMYCmpij3gYMvs3JPRuCuzCwQk0n2aYQ3KqJaixKZA7UrLiOiGpqsinS4LOntd82wNqP8Mip4DtEmzH4hSKspfAR/evHpJQw+H2jfEk+DC7kkySoUuaq7QRd/Gm8X6R7ybz6RqagkOfX7nmAbV+JNuiDAPdF83bRg1suYq6Hw5cCI5Tmzv+z/ywthAwO3wJmxgcf2l46880JOlkTvPfaHq5Eejwd0gk+85ene/Xo58Tkcp2Ts64upT9dlzs1eq+mhrcIWqrlTVyar6PlWdpaovJOrPYeKzeNeqF/Dub+6A5N8XiMBlA1pmh3qWlfSSFrf7pXEDQjItL5CqfUmqVxW5O0tytcMCxiIwqyQ6BjTe+lRvcD7DtVAXCRS7Kv/jqbpH7nuq7pH+/8FvadrNOesql1YCNwO3AbuTfb7iNxrnBRpiL/bgrZNw6fKGByAyQK6PhfCT5aZXokWxTWdnNHc9O+UQPDOnMayon5vJJuDnwD/hJbN4De9z/283r17yyObVS/r/hYc+vzzZJFmFLjpRoYv+Bm+/xJpkn+/02fCEqjdL3+pJcU8R7TbtfCCgA0uHnzoBmuScu/ru3Fsrc3NyI13O/l1V9tac3Lwmv2UuXsZbxfA1vExdbuv3Ftxz9O5+UdTTj0SmaTf9S+v+rJ8DZcDnSPIedBGcKwc2X+Wgcd9LucqVqnRbQ29UntwgXddhS4ixBe7GMQVul7N/4QDDripy9/js8gTwOPBg63EEr4j5J5+qe6T/lH7ojhPHkQa2BytNbp71YAj4LPANvA3OSaFCy7EZhbs0JHFlNVThOQ11vU4ZAFcbC45wVJKUvtTNdbfW3txYhkPAT/u810LPh/fkdLZsoBbvJufRzauXnL+pmfnpCgEGb169pFep6vuSAYNG69S5/vdgPf/Lr9oerAy3RH4yHfg2+Pi97oWJY05svHLMybiSUKhyXFUKQfK6a/vuu4M21J/Ni2s5cBwjqb/hQ1XVxSW1vvZXNTXm1Kz/7zlAp8kV/ht46J6jd1/wpP37pT8YBNTfc/TutNdPTJQRcpneJf72YC1T/3uwTP+0auLigXgPFL4E5CbrPC0u775Rm+uClMbzvqDDiyGn+9IyLTE9+E6DDgIG9HiQXRiS6264dnDM9/WssiawsS7SaQbV/XgB1Y+fqnvkfAKS24v+TwAoeqrukVO9Gmwfcu1YRzf/Vch3+9DnW1J+zbEAK81unvXgIOBv8Spr+//bEodIvrOvZmpBXAGQQq2GyEe6H5PTom/mHWecJDiRhwZ09+k/aRhNiAL/b4KBz+RtcRqdGe1+8p/AFzavXtJv6sr0xoBBo3XaHP8B1nO/sgArG7QmwliAV2Syq9S/vaDcOmPvzpyQG9dDHdeVF0Bu6LZ3Rd85MGyH6wbiqYfjg8amXv/qjpFjj03rvu17Du4duWXnS5PaX28OAf/rnqN3r0/c+Pq2UrlMP+0zwPoXC7CyxqqJi8fjJcL482Sd492mwAtHGkPdXjva2ZkX9FdO52SLvnCiWePtv1sFAX3xhqHR6+NJzxV1afjjseBxRca2+bbi/Td+sG1glcmuHevo5q/FEWB9LvUBli0RTLN1lUtPr6tc+hW8auk/6659T4Qa3PE5tdG41hILDJQYvta0uzlyRaQgsek+VfRw7U2Ng+MKrgAE6uY2XapoTZvvPgAsypbgypjOtBYq/jne9eYrQBKeaAqVu0aG410qKKKj/WwIFEFGj6oZAZrQsV9+9d7KeIMrgDGXVs8YOLj2+TbfqgSuzabgypjOLN61at/iXasWATcCPdmy0K2hubFZAdGd3be8wJSo669w8uAcuSHHYVMPhtapkOiO64dGp8dblyvokD9tcOws6LnraxNw+1N1j/x1tgRX/YUFWH3Eusqle9ZVLv0zvItQwovaDtrdNMlpdtttCFU3MKjlubxrT77iDGy5eC+Vy6V+dyS3DGSuG0jMuBWtPTOvqUXzdGiP3h/WIQ1TWra3vvzq5tVL/jlT9lX5di6ll22KMB1okwjjcuBfOb8ROjFOncm74q3DxRfdTDmB2J5xM3ZvvmTqWy+2D5BEuATY5qf/YNAdXlJS+3aChsuIS45uuOLqfT3JugrArA+8PEVE9wHbgZvvOXp3VzX2MlYyklyYzLB416pNePXNPga8k8i+RXAuLWzJF7T9HqWzjrAh5LCTDq4tURffmTvH5MtUvCV4veage24cFh3v+Fgh1JEhuTqpJFd/h7eX8/an6h75VSLG1e/08T1YFmD1Mesql24CZgGfwltmkhCBiJbkno6ePfdawrEtBTec2Fcw6+Tc0NCWawqvP3ljeFLtZtDT59vAGFyfQZMIjUOZoHSfgbArijafndn8Tmyg26s9XS3jo7s1oH+BVxMoO/m927E7nqzVmgjjPuBq4JeJ7HvP4eL3lt+IHhk9Ze+m6Qufu2z4hOqZIyYeur7s9hebg7nnH4R4zUR9z3oVDWicHg439zrL18Diuo1Tr3+1V3u6gqFYrKT0xBeBj99z9O64MylmCtfnYbLT4l2rdPGuVWuBiXj7sxL2u5If1Akh5/zKlZjA87kOjTkO8wLClFyHsoCwgTYPkxSmu+qvuLcjkj86T5rBf1DWMT16w9BoQcihqDe9vK849mvQT2VU0op4CBZgmfitq1zqrqtc+kPgSrxaNme7eYsveU5jTmBI83N5U0/tGPD+4zMCRdELipHmXNI4M3/Gqeq2Wbok5n+ZjwakuGkw72oPb9kV1YYpLdsjpbG49m50Yu9LP7j/B1k3c9WGZRE0flXoot0Vuuh24EO0SSXeG5FYoGjIpdXPDL2s+rkZC58bPGryO7PF4fyCmNyC5tKp//OFawKhaNu0ztPjWfpXOvzkLBH3zZ6OMZzfuOWG+VtuiHeZTgeOXzdv+6/vOXp30jOO9VWKVYUw/izetapp8a5V38KbQf8eCYq7i0LuKQc25DgcyA0wR4TzyWdEkJAXbLXdzhCIurzlt/+8oFw5MNSbZY5aN6skejY/yIie9+EJCK8/VbfyR73tp19zxP+RjuGl5azGl3WVSxvWVS59CO8i9H16eRHSKS3NBTNOzQ0Nb35fZ22CQ1omhcY0vrdcUOLbRxHLkymxcM9qRzRPiGxsGR+d2ZP3duBjCeqn/7I6WCZOFbpoPTAN+N94qX17rGBAQ82EmbvnX3rd7rlO0O0wi5jjaGDyzVUDQc9l8GwGfxlDvfeTM3LkiRzQuOvWBUORV+d9+MVrHEcT8Tl4JTA1Af30azaDZeKxeNeqY4t3rVqMl9r96d721xxzinMCzHOk86zGQeFG4YL95XHdfQ/NlblBoQf1t7S5rDi2vyjEZd239SWr73FEQBz/RzpYgNUPrKtcemRd5dLP4F2E1vWkD0Vxx7uFftqGJ9XNJeTubH3jBJS4bl6aBjNHhVfjeU/LiOiGxsmRuKuyd8FvfZqMZTNYpicqdFGsQhf9O179rG9AfL//50yasn+vn3Z5RY2jx0zZuwtAhDDwejznyc2JjiseVO9r79Y5juPu+8BtL4wOBt1wPO/rQi3gt0ZNxrIVyaYnFu9a9criXatuAf4E4rt3OMdVIhG9ILNeh0Qgx+EaoAYgpvGlXxcRxuTLpUAc+yxVrypytw8LayJW55wT1zUvIyVwBktEbhGR3SKyR0T+qoOff0JEdrYeL4hIpxMV54fXwz+WSYN1lUtfWVe59E+AW4BX4nlv5MbIC1rkL0WlCBRcd7II9ITAcInFmbxCxGkYxmD1bjq6FR0Y29htIeH4uEB2T53Hs//K7nhMByp0UX2FLvp73ptB9/03RUT5n4s2TvLbfsSkA7NzCxsqW987CrQlnrEOGlQ/OxSK+lu6I3ps7odfzM3JjQyK5xzd+O/Sh5+oS2B//Y5iM1imdxbvWrUO70HyZ4hzBv1wQ+hFEF9L70QYEBL24+1pLMcHAAAgAElEQVTHmhr1u9e8VdCRwSPCUo3Pa2JrIeFErc4Br4jzbxLYX/8jJCzAEpEA8B3gVrwMux8TkfafX/uAeao6Bfgm3tLWLlmA1Q+tq1z6NN5ylLvweRFyR7lxbcwMDIiOK5hT0yQ5sW24TMWNL3uOBmVky0B2dTuuPHfLmblNN8Q3Sd8th15vRDXGAFToouoKXfQZYAo+P9SLBp09JoKvGXPwHupM+chLM4ZNOPycCGNENK59DiIwckTN1aDdJAbSutkf2nKmoLBxZDz9+xDXUmpjTMcW71oVW7xr1ffxHuz8DT5n0Ftcp9si5W0FHKbnOLwNHIy4FKsS1wOSwpCUFQS63w4xNNfdcGWRm8jVOQAF9HBlgenQdcAeVd2rqi3AWuCjbRuo6guq5/cHVwKju+vUAqx+al3l0ti6yqWrgauA73bX3i1xg/GeI1AQG1U4p+ZyCbqHnFBsV6i0cQuivgOXSKHMjIU6rx2hQX2t9oONV+Mk5e9h+IonvylXPPnNXmUj7K8EEFXfhzHdqdBFrwK34RUMPdZV22HDT9V09fOOOI4Gxs94c+7Qy6qfcxx3XPGYYy/nFjQe8TtxFgjogNLhp063TdJzIW2ZPmfHvkFD6ibEOzYfco5+6RPO0S99IiuvN+fYDJZJlMW7Vp1dvGvVPwKTgd911z6mFMd7Dke4MsdLvJMXdXlBlZ2q/v+KjsiT2ULnD5IHBHVjWXEskatz2spZeeW9eSuvvLfXCTP6pzhmr7wZrBIRqWpzfLZNZ6OAg21eH2r9XmfuAX7b3Qjjvuk2fcu6yqW1wOdvnvXgWuAHwKUdtdMB6vtpclsS0sIBN707SYRJAHpVbSRWG3q+8e0BQ2O1ORO7frNGm0bFRuXvDxyWHHcAikPEKQRQR/fXfqhhJEHieuoUh5uADwIPJan/vs/uZEyCVegiBf5zifzk98AjwCc6ajd85MkeL5cbP2P33PEzdiPCWIBoS+C16tfHnj782rjZXe9HVwYOrhtR35izob6u8AMFA+uPnq0tLD3300nT3nx5+KiaWT0dVzcGAE8AX01S//2CPaoxibZ416r9qyYu/jDwaWAFMLB9G/VSF5de9GYfHGF0rgMi52+oT6ryCjChzfc6IbHR+TQdbNCWoHAyqu+NIdfRrTNLoonIUNqRfOBeYDfw46ScoT+IL3tFjWqn22Q6+r/U4eVMRD6AF2Dd2N0JLcDKEOsqlz5386wHrwG+jfeLd6EwQ3rad9sLhAih4KDInMJpJ89qVF4hJo3R2pzGliN5Q9zGwKCcEY37g4NbkJAbcnJjw8VhnE5zKsl1h9LsnIw9O2iI1gVy6+Y2OZob/xOnOFwH3PLmHV/P2jDDZqZMslToolPAJ5fIT36Mt9/xgpoupSNPxrWHqq32NyTBnNjkS8r2MmryO28oaN2x4uONtQWBI7vGTMwvrj9yyZS9tYGcaMAJxIK5Bc0zpsWccfW1BZWFg+rHvfSbmVuOvD1i7JChp98Yf8XBZD1JBrgduKz04ScOdtsyQ53bg2VMoi3etUqBx1dNXPwMsBrvAep5rlILclHg5Ve7a85gYB5wVJXXgVN4e7XG4v01P4CX6TQATMx15KpL8tkcEiY1xNhwokXHxlTrZw+NTnQkqavEcst3P5q9e83P7cFKjEPAmDavR8PF9dFEZArw78Ctqucz33bKAqwMsq5yacPNsx58iHYBVvTq6HYcus14Eg8RCiSk1xBScsJNhIY1NbVmALtoXaoUuN5T46B7WfAjJ2lsDj7rNoffn8jxdOBH2RxcWfIKkwoVuujXS+QnzwMfafv9Kde+nahUxOcFQrGrAAaProHRNYyc9E5z2zo35zgBd3zR4DPjAWbdVlmqLntPv12azOAKYBPwTpLP0ccpahcdk0SLd62qXjVx8b/SLsA63hzcCSR6n1MpF86KNQFhuDgFfI4jcwAKgswrCArDw5HtQYeCBI+nvSeS3H8fJ+AkLH7dAlwuIuOBw8CdwMcvOJvIJcDPgU+pqq/aixZgZZ5q4DRwPkOWW+rWIwlOI9FOa3DlS1M0lIo1w6EUnKMPs/pWJmVeo12AFQy63W4A7i0ROqyt1V6sJXRYpOOl0wmUW/rwE1mf6CJ7n2iZFLoojXuzm5J0Aj7vcbQ5L+j6zqDaC3b/nqAAS1WjIlKOV4stAPyHqr4mIve2/vxRYCkwBPg38aY8o10sOQTsf1DGWVe5VOd/8BtbtVAvc4vdOgTVHO0zH/yq1EZjzhVJPs0Bsj1NO1bfyqRGOK/5tbz85iODBtefHlJSe+b1neMm4C2z6RMiZ8Kp+Jz7VgrO0efZJcekwP6oS3VUpSniSm1QtEA1uQ+Q4+EIbzhCWZJP8zSwPcnn6NsSu0QQVf0N7bLktgZW577+DF75AN8swMpADV9rOEjbKXSlx/shEs1V2Q1yXbzvU6WFmByXoHaz6RSAh9684+tnezC8zGIzWCYFlv7z428CI1oPFnzs+eb0juhCLfXhy+N9jyrEIoH9gVBsnI9N6lWlDz/xZI8Gl0FsD5ZJhcW7VrlLRt53ArjG+45G8FK69wl5AddX/c/2BD0FqCJ+Hk59rXz3o1n+AZ/QJYJJ0bdHZ3pq9wWvhJw0jeMiLdFgUzztNSJ7I8fDG6NH85zo8XCBxsRP+udLejg8Y0z8Lrje+F26lwrqsldjgYv2aXXRvuHsqcINJw8NPVp7bMi4htOF3da5AcYe/dIn+swTdGOyQJtrjoQuTo2TPgNCsThm75UcJ7ZjcG7T1pJwc/GQ3OYTfmsX93R8JnVsBisz7e6+SXo0RYLd7r9SRbUxUBU7kxPAlWm8l3p+kDY7W8iLDZGu95QdSMhg+zMFscfJJgXC5etPNa286TgwNN1jaS/WHDpMJ6Ur2opGAgfOnhqwP9ocKgM5nxCjpSknlOfKGcfRAV28/Z3Sh5/I8qfJHvW7Ltn+a5ne6aP3ONqQF9CrfLRryg9Gq/IDsWEiFyQgmxAQPRxT6W6lTtZmKz1P6PMzWBZgZaZp6R5AR2JROdl8KvcoKkdUxUs4pQKKoIiqOCKqGnHGg8zosI/a3BnU6RuB4mbXyXUnAzFgA17NqzrgK8D3U/aH6stsiaBJgaaVN02j6wJVaXP6YEldc23Bc6qi6gqqAipo6/UGEDfmhNUNXEsHM99uNHj9qcMl9bkFTc8WFJ+ZI0IAb5P9ELwlkT8GlqT0D9WH2TMdk2xLRt6Xw/nlgX1LfiD2emEo0iigIqpC64VRVFrL3QYUokHRa0QurqMkggzObRkVUzafbskZ7aozCqgHdgI3AHuBz5fvfnRrKv9cfZYFWCaVZj/zwAeAL6Z7HB1prAvvds/mdJlK1VdIoHJV7GSu64bcDU5Ry+sS0MclwBeBv3zzjq9fVLsga1l8ZZKsaeVNw4FvwsXp0tNNXdzawyVzQIq6b90VKWw+m/f+5obwrsLBde86jvuFUDhyP/BT4Hc2e+WxPVgm2ZaMvE+Au/DqzvU5lxQ2N+QHY3N7209AmDk4p+Vss+s82xAJ/lcM5zTwB+Cfy3c/eqb3I+3/FEEtwDKpMvuZB/4M74lqIN1j6UhDXTiBn7/iaCQwL3Yib7xTEFkXKIp88s07vm43Om1YoWGTTE0rbxoGrAcmp3ssHWk8Xfg6yNUJ61BlYv2JgZeLE7tz8KgTXy19+InjCes7Q1gdLJNkDwJ/m+5BdGZAKDoyUX2JUBAOuO8PB1qKTjXnfO7eXd97PFF9ZwQBnD55q3ueBVgZYvYzD9yEl5q8T/6NU4Voc3BCEroe6p4NbXj77r+yT/b2LMAySdK08qY8vFTBfTK4AqirHnwiCd0G1A0cs+CqYzaDZZJlycj7vkQfDq5yHPeYIyTjHidUnNuyJQn99nOWRdCkzmHgqXQPojORpuBbIMOT0PWj+8rvP5WEfvu3c+t1/B7GxO/f0j2ArjSeHDAkCd3WYHs8O6R4M1h+/jGmBzYD29I9iM4MDbfsSVLXyxbteMx+ado7l+TC75EGFmBljt14m7R/CPS5NboNtXlHktT1yiT126+1Zg3xfRgTj3D5+kYgD3gBqEzzcC4SiwTq3JgzKQld/8fkJ5c3JKHfjJDo5zkiEhCRbSLyqw5+JiLyryKyR0R2ikifTO5kEmYvMAhYne6BdGRIbiQZ99MngLVJ6DcDiAVYJjU2zV+mwKxN85d9CrgCWJHmIV2gqT6nMAndbt1Xfv/eJPRrjOned8Ll62cDHwD+B3A6zeM5r/7YoNdBkvH59pMk9Gk6dx/wRic/uxWvwOzlwGeB76ZqUCYt3gUur6hecRfePc7v0jyeNlTDAXdiEjr+2aIdj8WS0K9JAduDlUE2zV8Wa/330dnPPPAQ3odT2glu86yJbzmuOjvfmywRDThu4JW9l9Sdacy7IY7u3gWGtX5tH6hdsZkpk0Th8vWx1n83Ab9qWnnTH4A/Te+oPIWXHD+TN/LUTi9RMt7aNUGaTxfUH985dipIns+u2l5vXqIPL1HqC9Rvsn4flyYRGQ18BPgHOk6F/1FgtaoqUCkig0RkhKoma7WESaOK6hWKV5aFiuoVby0Zed9y4Jb0jspTFIzuDjmxFi6sT6VATlSdAMgVPrty8R5UnStW/GgCh5lx1JJcmHTYNH9ZzexnHniNtG1CP/cJKhTnNr4+rOjM1I5a3XD17pqnt5TF0/ER4P8CdwNP9G6MGc4CLJNaG0hrgKWcK8cVHnpmoghj2rcYMOokp/eUvhRpCF/ns9Ni4HN4N/gPTX5yuf1SdcLb9un7P0+JiFS1ef09Vf1euzYPA18FOivyPIoLb2gPtX7PAqzssBloAXLSc/r3rjel+c3HHGFeR60CaFXM95MHHOCPwFFg1KIdj21PwEAzk1iSC5NeaduMPTK/duPUIYe25Qeb95bk1td11i6cEykR0UNxdD0Jr87X8/vK72/q9UAzlSW5MKn3I6A5LWcWrQ0Nr9vjFDRtIhA70FFwdU7esLrGOHoO4RUvHwP8vrfDzHRxXG5qVHV6m+OC4EpEbgPeVdWuCqp2dNdqAXCWqKheUQv8Z7rOP6X4zHNTB9duCjluTXFupNNaew46Ps6uPwh8AvhtrwaYDWwPlkmj7wD/RMo/dJTSvDMDQ447dfKgo2MH5jR1OkWlKjFVBsbReQgYDSRjT1dGsSQXJpXC5etr8PbFxPPAJCGc/OZXRZgQHNA8O1RSX9BV22hDju/Hya0uw7vupOlJeX/hN4egr+vNbOB2EdmPt8n/gyLyw3ZtDsEFgfRowArNZ5cvAj9L9UkD4tYXhaKTCkOx2TNLTueGRDutt6fQ6QPmThTjJfOwe5yuiHh1sPweaWABVgbbNH9Zy6b5y/4amAe8k7ozC9tOjL6ypqlgA9As0nkA5TgaGF5c+zJovLNRv+7dGLOAqv/DmAQIl6//IzAFr+B5yrhnw7Mjp/J3qvKWCF2mZx888fAgcdx4r4cbJz+5vLYXQ8x48Uyad9uX6tdUdbSqjgPuBP6gqp9s1+wp4NOt2QRnAbW2/yq7VFSvOAn8OXAXKcyeHFOncPPxQYEzkcBGoFCEUGdtBcaCvtSD09g9TjdUHN9HOliAlQU2zV/2PPA+UpjeVJGcvWdK5tW25HVbIG/mVXtuBOrj6L4F+K8eDy4rxBFcWYBlEihcvv5UuHz9x/GWuaQsKNHm0JRITWG3ySsGjDo5Jb/09P44u7fsgT64qK+jp0TkXhG5t/Xlb/BSd+8B/h/w+d7/CUx/U1G9QiuqV6zGe7CzMVXnjahTvP3kwBsjrnSZ+EYEJyTuiDi737Fox2O7ezG8LGAzWKaP2DR/We2m+cvuAhYBKSnMK2hkYE5jt0k2GptzjtKaHcin9fvK77daNMb0YeHy9T/Cu+l5NlXnDA5oettPu0h9OAQaz36xi+owmdRQ1WdV9bbWrx9V1Udbv1ZV/YKqXqaq16hqVdc9mUxWUb1iP/B+4GtANBXnzA/EDuQEtNv6ay7yLmg8SwV/2YthmT7CAqwss2n+sp8C15CCDduleXVbRCjprl1BXvOoj86uGj6q5MQGUD+B1poEDC+zKTaDZdIuXL7+AHAT8JdAJLln02bJjU7x0/Kyj2y9YeyHdu5HfC0pe3byk8sPdt/MqPg7jEmGiuoVsYrqFd8CZgG7kn2+y4rO7vfTLiB6bUjcPNAXfXZtGZK7I9gMlul7Ns1fdhj4E+DLJDHr14j82kF+24rAjIl7500ed6i7tcpvksbMQf2KZRE0fUC4fL0bLl//f4EZwGvJOk+gsHmLCMV+2+cPrbtywu0vBcVx93fT9KHejSw7nEvTnswlgsb4UVG9YitwLV6ir6RwRBsGhqK+a8yIEAqJe72gz3XT9KeLdjyW9OCw/xPUcXwf6WABVpbaNH+Zu2n+soeB6cDORPc/INT0RtDRSfG+7/LRR68fWXJyQ5tvHcere/Vu6+uKfeX3p2T6v7+zLIKmLwmXr9+Bd715OPG9K05+87Du210olN8ydPytLwfaJdl5HPhD69fbJj+5fH1ChpgFEphF0Jheqahe0VBRvaIc+DBeXamEGlvQuFWETtOzd0QEguLObZf04hXg221e/0tCBpgNbAbL9GWb5i97FbgOL4hJwCefUhhs2jWh6HiPa1RdM/7AREEPAt/EC6j+Em/G7WPAY70fY5ZI8BJBEdkvIq+IyPZ2RUKN8SVcvr4pXL7+y8B8EpVS23FPBooaN4jDFT15e25R45jwkDNbgHXAUuAf8AomLwLaZ64zXbAJc9PXVFSv+C3eXtAEJcZStyS3+eWR+U1je/Lu1iCrCPQUcD/w7UU7HvsK3v6xBUBXtd/MOSKoE/B9pGWIak+vTavZzzzwAbxMg6PjfW+OEz06Kr9295Bw/WhHuKy3YzlcU/yTn37ou3f2tp9sNTA8Qm8Ye5fv9r97c9lWVZ3eVZvWmjTTVbWml8MzhqaVNw0GHgX+LP53a8TJi2wLFDQpAZ3WVZpkP5rr8g6cOVhy27C/+8UrveknmxXJOJ0Z+DtfbX8f+4turzfGJNKSkfcJ8BfACqDLWnkdKQxG3x5b2HioOCdypQilvRmLKg0u8u2F21cv7U0/2ezayQN000/8X0Lyrnk25decYCpPZvq2TfOX/XH2Mw9MwVu3/LHu2gckVlead2bnsLwzhUFx39fbi04bjaNKTv1HgvrKUpa8wvRt4fL1J5tW3rQIL0PfSmBA1+9QJDe6M1DQXCuh2DUiXJeoseQWNf42d/LBvYnqL1vZFcf0VRXVKxT4/pKR9z0L/BAvEUaXcp3Y0TEFTW8Oy2suDQhXQO8fHgOIcDSAWumHXtDWGay+zJYImgtsmr/s1Kb5yzqtYSNoZFj4zOYpgw9vnjbkUO6ogtobQ45bJkIic0MFgM9+efudffu3p69LfBZBBdaJyFYR+WwSR26yRLh8vYbL13dZw0aC0T3BQWc3hIbXVYeKG6Y4ObE5IvhOoOPTPGCS+7Ppds3pBVfU12FMulRUr3gbmIO3HPiirMVBcevGFjRsnDX01PYZJbXDR+Q3z20NrhJpCLDoF1M/ZdebHrM6WKaf2jR/WZsaNsqgnIYdkwYdeX56yYHGcQNOzgwHojNFyE3S6XOAO4DVX95+5+1JOoe5UImIVLU5OgqgZqvqNOBW4AsiMjfFYzQZKly+fj9ta9g47tFAUcOG0PDat0IlZyc44eg8EUYmcQgTgQewDKXGZLyK6hXRiuoV3wRuAN4SNFKa17R5+pDTm2cNPR2+pLDpxpCjiX5w3NZA4HPAk7+Y+qlEB2+mj7AAy3Rq0/xlB4Cbri4+8oUrBh6/qjDUMiferDm99HFgZgrPl1nim8GqUdXpbY7vXdydVrf++13gF5C4JVrGhMvXx8Ll67/lFDRNDw09UxPIj8wT4fIUDuEOYKb7s+nJenCU0SxNu+lvKqpXvBQSd+qsoacfubyoYWZe0J0pQk6KTl8CfBS4OkXnyzh9PcmFBVimS5vmL3NXTV/zb3g1bF5NwxB+nYZz9n8KuOr/6IaIFIjIgHNfAzeTnr8PJsPl3L1pR+v+qkfScPqnnYVVSasNmOnU52FMX7Hs8CNn/2zH6vuB23ivHEyqnAJeSPE5M4PYEkGTIZaXrd2JF2QtT+FpX1pettYuPj2ioK7/o3vDgY0isgN4Cfi1qv4uqX8Ek7WchVWNzsKqLwK3kIQaNl341xSeK+PYDJbprxZsW/Nr4Brglyk87doF29ak8vqWUWwGy2SM5WVrm5aXrV2CV8PmcJJPFwWeT/I5MlsCk1yo6l5VfV/rMVlV/yEFfwKT5ZyFVU/j3fT8PAWnO0ESiq5nC1siaPq7BdvWvIu3bO+zQEOST2f3OL0hAk7Q/5EGFmCZuC0vW/t7vAQYP03iaYLA40nsP7MleImgMeniLKyqARYCdwP1STzVj52FVVYHtxes0LDp7xZsW6MLtq35f0AZsDmJpzoDPJXE/jOcLRE0GWp52dqTwJ3AJ4G6JJzi+eVla63oZ28kPk27MWnhLKxSZ2HVY8D7gE1JOs13ktRvllDf/xjT1y3YtuYt4Ebg7+kgnXsCPL5g25qzSeg3SwjqBH0f6WABlumx5WVrdXnZ2ifwlvBsSHD3P0pwf9nHAiyTYZyFVXvxalb9Dd4Sm0TZBuxOYH9Zx5YImkyzYNua6IJta74BzAb2JLh7u8fpDcFmsEzmW1629gBwE/BVIJKALtcClkDBGHMRZ2FVzFlY9Y/ALGBXArp8E/gHZ2GV3fkbYy6yYNuazXhLBlclqMuVQFWC+jJ9lAVYJiGWl62NLS9b+y94mQZf60VXdcBjwDuJGFf2imP2ymawTD/kLKzaClxL75f2PYvtheg9AVfU12FMf7Ng25qzC7atuRe4HTjei65eAR5bsG2N/SL0iqAS9H2kgwVYJqGWl63dAUwHHu5hF19YXrb26eVla+3i0xsKuK7/w5h+yFlY1eAsrCoHbqVn6dw3AJ9zFlYlYuY9q3lLBC3JhclsC7at+SVeceCepnO/c8G2NVsTOKQsZVkETRZqTef+ZeCDwCEfb4nipX3/FPCzZI4tq9gMlskSzsKq3+Hd9Pi9fkSAzwDftsyBiWN7sEw2aJPO/TOAn0QVEWA98IkF29a8nsyxZQ1L026y2fKytX/ES+f+4w5+/Drespw9QA1wFTB2ednaptSNMMNZgGWyiLOw6gTw58BdeCmQ2zoEbAf+u/X1XXh1r/albIBZwLIImmzRms79+3iZTV9s92PF2yqxBjgF/AG4AxiZ0kFmMLUsgibbLS9be2p52dqPAx8Halu/fRb4BvCnQB5QCpQA/5yWQWakOGpgWR0skyFa07mvxnuw07aIZyVeUoztra+nAL92Fla9muIhZiz1OXtlM1gmkyzYtuZtYC7wdd7LbFqN9xDnb4CBeHtF64EV6RhjxpKg/yMNLMAyKbG8bO2P8dK5/xF4BPg5XnD19dbXlywvW2v7IBJFQdX1fRiTSZyFVfuBDwBfw0uY8ykggLfn6q+BFtt3lXgWYJls1JrO/SHgBryspF8C3gCagc/jrdYZvWDbGrvmJIotETTmPcvL1h7E24z+A7wp9MDysrU/aH39RjrHZozJLK3p3L8F3IZ3oxN2Flb9EfghXikIY4xJmAXb1mwBZgKb8R7onMLLivxdvCXJJoukJ6wzWWt52dpmvCc80LpkcHnZ2m3pG1EGs6V/xtBmGeDJ1tcH0zicjGazUybbLdi25jRwut23reZVwknaZqb86tujM8b0nCWvMMakiJem3a45xpjkExEkTXur/OrbozPG9Iyq1bcyxqSUK4npR0TCwHNALt59ys9U9e/atXk/XlbIc5kgf66qDyZmBMaYvk3ACaV7EF2yAMuYTGUzWMaYFEnwDFYz8EFVrReRELBRRH6rqpXt2j2vqrcl6qTGmP7CZrCMMWmiNoNljEmZxGUIVFXFS2sNEGo97ImRMeY9FmAZY1LPCggbY1JHgZj/GKhERNpu/P+eqn6vbQMRCQBbgQnAd1R1cwf9XC8iO/DqDn1FVV+Lf+TGmH5HBLEkF8aYlFMsi6AxJqXimMGqUdXpXTVQ1RhQJiKDgF+IyNWq2rYw9MvA2NZlhB8G/gu4vCfjNsb0N31/iaDVwTLGGGNMn6Sqp4FngVvafb9OVetbv/4NEBKRktSP0BhjLta3wz9jTM+p7cEyxqROovZgichQIKKqp0UkD/gQsKxdm1LgmKqqiFyH98DYirkakxX6/gxW3x6dMaZHFFBbImiMSRFFiUnCHuqMAB5v3YflAD9V1V+JyL0AqvoosBD4nIhEgUbgztbkGMaYDCcITgIDLBG5BVgBBIB/V9Vvtfu5tP78w0AD8L9U9eWu+rQAy5hMpGozWMaYlIkzyUXXfanuBKZ28P1H23y9EliZkBMaY/oXAe/5SwK68jr6DjAfOARsEZGnVPX1Ns1uxdvjeTkwE/hu6787ZQGWMRnKZrCMMamUqADLGGO6ltAlgtcBe1R1L4CIrAU+CrQNsD4KrG6dJa8UkUEiMkJVj3TWqQVYxmQqm8EyxqSIAjGxAMsYk3yC4DiJmcECRgEH27w+xMWzUx21GQVYgGVMNjnDqad/rz+LJ6NWTdIGY4zJeK4efvpM09f8XnPsemOM6bGtW/c9HZRPxnOPE+6i9p500L790yI/bS5gAZYxGUhVb+m+lTHGJIZdc4wxqZLg680hYEyb16PxipfH2+YCVgfLGGOMMcYYk422AJeLyHgRyQHuBJ5q1+Yp4NPimQXUdrX/CmwGyxhjjDHGGJOFVDUqIuXA03hp2v9DVV9rVxbiN3gp2vfgpWm/u7t+xcpGGGOMMcYYY0xi2BJBY4wxxhhjjEkQC7CMMcYYY4wxJkEswDLGGGOMMcaYBLEAyxhjjDHGGGMSxAIsY4wxxhhjjEkQC7CMMcYYY4wxJkEswDLGGGOMMcaYBLEAyxhjjNclpNcAAAAMSURBVDHGGGMS5P8Dkrv52sqUOoQAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 864x576 with 6 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%autoreload 2\n",
+    "region_plotter.plot_all_regional_error_metrics(\n",
+    "    gdf_norm.where(gdf_norm.model == 'ealstm').dropna(),\n",
+    "#     **dict(rmse_vmax=10, mae_vmax=10, mae_vmin=0, rmse_vmin=0)\n",
+    ");\n",
+    "# plt.gcf().suptitle('RNN Regional Errors')\n",
+    "plt.tight_layout(rect=[0, 0.03, 1, 0.95])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explore Time series"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x12e3eb518>"
+      ]
+     },
+     "execution_count": 111,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ealstm_ts = all_gdf.loc[(all_gdf.region_name == region) & (all_gdf.model == 'ealstm')].predicted_mean_value.rename('ealstm')\n",
+    "ealstm_bline_ts = orig_all_gdf.loc[(orig_all_gdf.region_name == region) & (orig_all_gdf.model == 'ealstm')].predicted_mean_value.rename('ealstm original')\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(12, 8))\n",
+    "ealstm_ts.reset_index().drop(columns='index').plot(ax=ax)\n",
+    "ealstm_bline_ts.reset_index().drop(columns='index').plot(ax=ax)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import List\n",
+    "\n",
+    "def plot_region_time_series(region: str, ax=None, model: List[str] = ['ealstm']):\n",
+    "    times = all_gdf.loc[(all_gdf.region_name == region) & (all_gdf.model == 'ealstm')].datetime \n",
+    "    obs_ts = all_gdf.loc[(all_gdf.region_name == region) & (all_gdf.model == 'ealstm')].true_mean_value\n",
+    "#     rnn_ts = all_gdf.loc[(all_gdf.region_name == region) & (all_gdf.model == 'rnn')].predicted_mean_value\n",
+    "    ealstm_ts = all_gdf.loc[(all_gdf.region_name == region) & (all_gdf.model == 'ealstm')].predicted_mean_value\n",
+    "    \n",
+    "    # NON-predict delta functions\n",
+    "    ealstm_bline_ts = orig_all_gdf.loc[(orig_all_gdf.region_name == region) & (orig_all_gdf.model == 'ealstm')].predicted_mean_value\n",
+    "    rnn_ts = orig_all_gdf.loc[(orig_all_gdf.region_name == region) & (orig_all_gdf.model == 'rnn')].predicted_mean_value\n",
+    "    lr_ts = orig_all_gdf.loc[(orig_all_gdf.region_name == region) & (orig_all_gdf.model == 'linear_regression')].predicted_mean_value\n",
+    "    ln_ts = orig_all_gdf.loc[(orig_all_gdf.region_name == region) & (orig_all_gdf.model == 'linear_network')].predicted_mean_value\n",
+    "    bline_ts = orig_all_gdf.loc[(orig_all_gdf.region_name == region) & (orig_all_gdf.model == 'previous_month')].predicted_mean_value\n",
+    "    \n",
+    "    if ax is None:\n",
+    "        fig, ax = plt.subplots()\n",
+    "    else:\n",
+    "        fig = plt.gcf()\n",
+    "    df_dict = {'obs' : obs_ts.values}\n",
+    "    \n",
+    "    if 'lstm' in model:\n",
+    "        df_dict['lstm'] = rnn_ts.values\n",
+    "    if 'ealstm' in model:\n",
+    "        df_dict['ealstm'] = ealstm_ts.values\n",
+    "    if 'ealstm_bline' in model:\n",
+    "        df_dict['ealstm_bline'] = ealstm_bline_ts.values\n",
+    "    if 'lr' in model:\n",
+    "        df_dict['lr'] = lr_ts.values\n",
+    "    if 'ln' in model:\n",
+    "        df_dict['ln'] = ln_ts.values\n",
+    "    if 'baseline' in model:\n",
+    "        df_dict['baseline'] = bline_ts.values\n",
+    "\n",
+    "    pd.DataFrame(df_dict, index=times).iloc[1:].plot(ax=ax)\n",
+    "\n",
+    "    ax.set_ylim(0, 100)\n",
+    "    ax.set_title(f'{region} Predicted vs. Modelled');\n",
+    "    \n",
+    "    return fig, ax\n",
+    "\n",
+    "\n",
+    "def plot_region_seasonality(region, ax=None):\n",
+    "    ts = (\n",
+    "        all_df\n",
+    "        .loc[:, ['datetime', region]\n",
+    "        ].set_index('datetime')\n",
+    "    )\n",
+    "    \n",
+    "    if ax is None:\n",
+    "        fig, ax = plt.subplots()\n",
+    "    else:\n",
+    "        fig = plt.gcf()\n",
+    "    ts.groupby(ts.index.month).mean().plot(ax=ax)\n",
+    "    ax.set_title(f'Seasonal Cycle')\n",
+    "    ax.set_xlabel('Month')\n",
+    "    ax.set_ylabel('Mean NDVI');\n",
+    "    \n",
+    "    return fig, ax\n",
+    "\n",
+    "\n",
+    "def plot_region_vs_observed_seasonality(region, model, ax=None):\n",
+    "    ts = (\n",
+    "        all_gdf.loc[\n",
+    "            (all_gdf.model == model) & (all_gdf.region_name == region), \n",
+    "            ['datetime', 'true_mean_value', 'predicted_mean_value']\n",
+    "        ].set_index('datetime')\n",
+    "    ).rename(\n",
+    "        columns={'true_mean_value': 'Observed', 'predicted_mean_value': f'Predicted ({model.upper()})'}\n",
+    "    )\n",
+    "    if ax is None:\n",
+    "        fig, ax = plt.subplots()\n",
+    "    else:\n",
+    "        fig = plt.gcf()\n",
+    "    \n",
+    "    ts.groupby(ts.index.month).mean().plot(ax=ax)\n",
+    "    ax.set_title(f'Seasonal Cycle for {region} District')\n",
+    "    ax.set_xlabel('Month')\n",
+    "    ax.set_ylabel('Mean VCI');\n",
+    "\n",
+    "    return fig, ax"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import r2_score\n",
+    "from typing import Dict\n",
+    "\n",
+    "from scipy import stats\n",
+    "def r2(x, y):\n",
+    "    return stats.pearsonr(x, y)[0] ** 2\n",
+    "\n",
+    "def rolling_average(district_csv: pd.DataFrame) -> Dict[str, float]:\n",
+    "    \"\"\"Over three months\"\"\"\n",
+    "    relevant_districts = ['Mandera', 'Marsabit', 'Turkana', 'Wajir']\n",
+    "\n",
+    "    district_csv['month'] = pd.to_datetime(district_csv.datetime).dt.month\n",
+    "\n",
+    "    output_dict: Dict[str: float] = {}\n",
+    "\n",
+    "    for district in relevant_districts:\n",
+    "        district_df = district_csv[district_csv.region_name == district.upper()]\n",
+    "        true, predicted = [], []\n",
+    "        for i in range(1, 12 - 1):\n",
+    "            min_month = i\n",
+    "            max_month = i + 3\n",
+    "            submonth = district_df[(district_df.month >= min_month) & (district_df.month < max_month)]\n",
+    "            predicted.append(submonth.predicted_mean_value.mean())\n",
+    "            true.append(submonth.true_mean_value.mean())\n",
+    "        district_score = r2(true, predicted)\n",
+    "        print(f'For {district}, r2 score: {district_score}')\n",
+    "        output_dict[district] = district_score\n",
+    "    \n",
+    "    return output_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Performance for EALSTM Predict Delta\n",
+      "For Mandera, r2 score: 0.9285030987864911\n",
+      "For Marsabit, r2 score: 0.9790304574600829\n",
+      "For Turkana, r2 score: 0.8505720902917523\n",
+      "For Wajir, r2 score: 0.9494434497738151\n",
+      "\n",
+      "Performance for EALSTM Baseline\n",
+      "For Mandera, r2 score: 0.9190735851350883\n",
+      "For Marsabit, r2 score: 0.9195482293407499\n",
+      "For Turkana, r2 score: 0.5585825765387414\n",
+      "For Wajir, r2 score: 0.9681868729270298\n",
+      "\n",
+      "Performance for Persistence\n",
+      "For Mandera, r2 score: 0.7418834628995075\n",
+      "For Marsabit, r2 score: 0.8156893342781715\n",
+      "For Turkana, r2 score: 0.6829170118607626\n",
+      "For Wajir, r2 score: 0.7708332053175011\n",
+      "\n",
+      "Performance for RNN\n",
+      "For Mandera, r2 score: 0.8826001126667086\n",
+      "For Marsabit, r2 score: 0.9754361813286556\n",
+      "For Turkana, r2 score: 0.8224845706127023\n",
+      "For Wajir, r2 score: 0.9205917636033555\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Performance for EALSTM Predict Delta')\n",
+    "rolling_average(orig_all_gdf[orig_all_gdf.model == 'ealstm']);\n",
+    "print()\n",
+    "\n",
+    "print('Performance for EALSTM Baseline')\n",
+    "rolling_average(all_gdf[all_gdf.model == 'ealstm']);\n",
+    "print()\n",
+    "\n",
+    "print('Performance for Persistence')\n",
+    "rolling_average(orig_all_gdf[orig_all_gdf.model == 'previous_month']);\n",
+    "print()\n",
+    "\n",
+    "print('Performance for RNN')\n",
+    "rolling_average(orig_all_gdf[orig_all_gdf.model == 'rnn']);\n",
+    "print()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy import stats\n",
+    "def r2(x, y):\n",
+    "    return stats.pearsonr(x, y)[0] ** 2\n",
+    "\n",
+    "\n",
+    "model = 'ealstm'\n",
+    "region = 'TURKANA'\n",
+    "\n",
+    "# TURKANA explore\n",
+    "\n",
+    "def plot_region_performance(model: str, region: str, kind='reg'):\n",
+    "    df = pd.DataFrame(\n",
+    "        all_gdf.loc[\n",
+    "            (all_gdf.model == model) & (all_gdf.region_name == region),\n",
+    "            ['datetime', 'predicted_mean_value', 'true_mean_value']\n",
+    "        ].set_index('datetime')\n",
+    "    )\n",
+    "\n",
+    "    # sns.scatterplot(x='true_mean_value', y='predicted_mean_value', data=turkana)\n",
+    "    # sns.lmplot(x='true_mean_value', y='predicted_mean_value', data=turkana)\n",
+    "\n",
+    "    sns.jointplot(\n",
+    "        x='true_mean_value', y='predicted_mean_value', \n",
+    "        data=df, kind=kind, stat_func=r2\n",
+    "    )\n",
+    "    \n",
+    "    ax = plt.gca()\n",
+    "    ax.set_xlabel(f'{region} - Observation')\n",
+    "    ax.set_ylabel(f'{region} - {model.upper()}');\n",
+    "    ax.set_xlim([0, 100])\n",
+    "    ax.set_ylim([0, 100])\n",
+    "    fig = plt.gcf()\n",
+    "    # fig.set_size_inches(11.7, 8.27)\n",
+    "    fig.set_size_inches(8.27, 8.27)\n",
+    "\n",
+    "    return fig, ax\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def highlight_selected_regions(regions: List[str], scale=1):\n",
+    "    fig, ax = plt.subplots(figsize=(12*scale, 8*scale))\n",
+    "    gdf = all_gdf.loc[(all_gdf.model=='ealstm') & (all_gdf.datetime == '2011-01-31')]\n",
+    "    gdf.plot(ax=ax, color='#23748d')\n",
+    "    for region in regions:\n",
+    "\n",
+    "        gdf_ = gdf.loc[gdf.region_name == region]\n",
+    "        gdf_.plot(color='#57bfd0', ax=ax, alpha=0.9)\n",
+    "\n",
+    "        gdf_.apply(\n",
+    "            lambda x: ax.annotate(\n",
+    "                s=x.region_name, xy=x.geometry.centroid.coords[0], ha='center'\n",
+    "            ), \n",
+    "            axis=1\n",
+    "        );\n",
+    "\n",
+    "\n",
+    "        ax.set_axis_off()\n",
+    "#         fig.savefig(f'/Users/tommylees/Downloads/{region}_maps.png', transparent=True)\n",
+    "#         plt.close()\n",
+    "\n",
+    "\n",
+    "\n",
+    "regions = ['MARSABIT', 'TURKANA', 'WAJIR', 'MANDERA', 'SAMBURU', 'ISIOLO', 'LAIKIPIA', 'MOYALE', 'KAJIADO']\n",
+    "regions = ['TURKANA', 'MANDERA', 'SAMBURU', 'GARISSA', 'MARSABIT', 'WAJIR', 'TANA RIVER', 'WEST POKOT','ISIOLO', 'KITUI', 'MOYALE']\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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I9JMvvhMm/3F37/PQjfCjv7PuTU6dOsWnPvUpvvGNbxAOh/lX/+pf8ed//ue8+93vZmRkhHq9zu233853v/tdbrrppuWve/jhhzl//jyPPPIIAIuLiwwNDfH+97+f97znPZw82dyk7tixY9x777284x3v4M477+Qb3/gGpVKJ66+/nre97W07/jEVjEVERERkx+655x4eeOABfvAHfxCAYrHIgQMHuOuuu/jQhz5ErVbj4sWLPProoyuC8YkTJ3j66af5lV/5FX7sx36MH/mRH1nze/zkT/4kADfeeCO5XI5UKkUqlSIWiy0H6p1QMBYRERHpJxtUdi8Vay133HEHv/3bv7182enTp3nFK17Bt7/9bYaHh7nzzjsplUorvm54eJjvfOc7fOlLX+IDH/gAd911Fx/5yEc6fo9oNApAIBBY/rf/ea1W2/HPoB5jEREREdmx22+/nbvvvpvp6WkA5ufnOXv2LMlkksHBQaampvjiF7+46utmZ2dpNBr81E/9FL/5m7/Jgw8+CEAqlSKbzV7Wn0EVYxERERHZseuuu47f+q3f4kd+5EdoNBqEw2E+8IEP8IIXvIDrr7+eEydOcNttt636uvPnz/NLv/RLNBoNgOWK85133snb3vY24vE4995772X5GYy19rJ8o/WcPHnSrjfHTkRERETWdurUKa699tq9Poyu0+n3Yox5wFp7stPt1UohIiIiIoKCsYiIiIgIoGAsIiIiIgIoGIuIiIj0hW5YN9ZNtvP7UDAWERER6XGxWIy5uTmFY4+1lrm5OWKx2Ja+TuPaRERERHrc0aNHOXfuHDMzM3t9KF0jFotx9OjRLX2NgrGIiIhIjwuHw1x11VV7fRg9T60UIiIiIiIoGIuIiIiIAArGIiIiIiKAgrGIiIiICKBgLCIiIiICKBiLiIiIiAAKxiIiIiIigIKxiIiIiAigYCwiIiIiAigYi4iIiIgACsYiIiIiIoCCsYiIiIgIoGAsIiIiIgIoGIuIiIiIAArGIiIiIiKAgrGIiIiICKBgLCIiIiICKBiLiIiIiAAKxiIiIiIigIKxiIiIiAigYCwiIiIiAigYi4iIiIgACsYiIiIiIoCCsYiIiIgIoGAsIiIiIgIoGIuIiIiIAArGIiIiIiKAgrGIiIiICKBgLCIiIiICKBiLiIiIiAAKxiIiIiIigIKxiIiIiAigYCwiIiIiAigYi4iIiIgACsYiIiIiIoCCsYiIiIgIoGAsIiIiIgIoGIuIiIiIAArGIiIiIiKAgrGIiIiICKBgLCIiIiICKBiLiIiIiAAKxiIiIiIiwA6DsTHmHcaY7xljHjHGfMIYEzPGjBhj/sYY84T3cXi3DlZERERE5FLZdjA2xkwAvwqctNbeAASBnwPeCdxjrb0GuMf7XERERESkq+20lSIExI0xISABXABeDXzMu/5jwGt2+D1ERERERC65bQdja+154D3AWeAisGSt/TJw0Fp70bvNReDAbhyoiIiIiMiltJNWimFcdfgq4AiQNMb84ha+/peNMfcbY+6fmZnZ7mGIiIiIiOyKnbRS/DPgtLV2xlpbBT4D/BNgyhhzGMD7ON3pi621H7LWnrTWnhwfH9/BYYiIiIiI7NxOgvFZ4MXGmIQxxgC3A6eALwB3eLe5A/j8zg5RREREROTSC233C6213zTG3A08CNSAh4APAQPAXcaYf4ELz6/fjQMVEREREbmUth2MAay17wLe1XZxGVc9FhERERHpGdr5TkREREQEBWMREREREUDBWEREREQEUDAWEREREQEUjEVEREREAAVjERERERFAwVhEREREBFAwFhEREREBFIxFRERERAAFYxERERERQMFYRERERARQMBYRERERARSMRUREREQABWMREREREUDBWEREREQEUDAWEREREQEUjEVEREREAAVjERERERFAwXjfm82V9/oQRERERLqCgvE+9thkhh9891f4zrOLe30oIiIiIntOwXgfe3I6h7Xuo4iIiMh+p2C8j00ulQCYypb2+EhERERE9p6C8T42nXX9xdMZ9RmLiIiIKBjvY37FeFoVYxEREREF4/1sMuO1UqhiLCIiIqJgvJ9NZ1QxFhEREfEpGO9T1toVFWNr7R4fkYiIiMjeUjDepzKlGqVqg8ODMSq1BkvF6l4fkoiIiMieUjDep6a8avFNRweB5oQKERERkf1KwXif8idS3HR0CGgG5b710J/D1KN7fRQiIiLSxRSM9yk/CL8kdhaw/T2Zwlr4y38DD3x0r49EREREupiC8T41lSlxo3maW770Wl4W+G5/T6YoZ6FRdR9FRERE1qBgvE9NZcpcE10A4J9Enuzv3e8Kc+5jObO3xyEiIiJdTcF4n5rMlLgqVgDgluDpvugxzpVrfO2JmdVXFOfdx0ru8h6QiIiI9BQF431qKlNiIpoH4Fr7BFNLxT0+op377IPneOOHv7U65BdcZVytFCIiIrIeBeN9aipT4lDAtRakGllCmbN7fEQ7t1Bws5hPz+ZXXrHcSqFgLCIiImtTMN6HavUGM9kyo4EMBMIAHMmf6vnd7/LlGgBn5worr/BbKRSMRUREZB0KxvvQXL5Cw8JQYwmOvIBaIMr1PMliobd3v8t5wfjMfHvF2A/G6jEWERGRtSkY70P+5h7J2gKkD5MZupabAk/3/O53fsX4THvF2G+lqGSh0bjMRyUiIiK9QsF4H5r0FqfFKvOQHKd68GZuNKeZWuztimquXAc6BGO/lQI0mUJERETWpGC8D01nSoSoESovQnKc4NFbSZgyxQu9vWVys2K8xuI7UJ+xiIiIrEnBeB+azJQYC3iV0+QYqatfBEB48uE9PKqdy1dcMM6UaiwWKs0r/HFtoIqxiIiIrEnBeB+aypR5TsKb9ZscJ3rgGjIkSM19Z28PbIdy5RqxsHtIr2inKM5D8oD7tyrGIiIisgYF431oKlPiRNLb0CM5DoEATwav4UC291spnn8oDcCZ+ZZgXJiD4ePu39oWWkRERNagYLwPTS6VuMLb9Y7kOADnEtcyUXkaqr27NXS+XOfawy4Yn/X7jCsFqJVgyA/GqhiLiIhIZwrG+9BUpsREuNljDDCXvp4QdZj8xz08su1rNCz5So3xgQgHUlGe8Vsp/IkUw1e6j5plLCIisnWPfBq+9Ud7fRSXnILxPlOs1MmUahwIZCEQgtgQAIXxHwDAnn9gLw9v2wrVOtbCQCzE8dFEc/c7fyLFsCrGIiIi2/bAR+Gbf7jXR3HJKRjvM/4M4xGTgcQYGANAYuwY03aIytneDMb+qLZkNMQVI8nm7nf+rndDV7iP/RKMZx7XZiUiInL5ZKegtLTXR3HJKRjvM1NeME7XF5f7iwEOpON8p3E1XOjNYOxvBz0QdRXjqUyZUrXebKUYOAShWH8svls6D7//Inj8i3t9JCIisl/kFIylD/nBOFmbX+4vBjiYjvKdxgmii0/15AN/uWIcccEY4Ox8oVkxToxANNUfc4zz02AbkLmw10ciIiL7Qa0MpUWol3t6kf5mKBjvM34wjpTmV1SMD6ZjfNeecJ9c6L2NPnItrRTHR5OAN8vYD8bxYYgM9Ecrhf8zlBb39jhERKSv1BuW3/vKEyzkKyuvyE03/93nrz0KxvvM5FKZRCSIKcyuCMbjqahrpQDowQV4+XId8FopRlzF+Mxc3i2+iw5CMOwqxn0VjPugLURERLrGqYsZ3vuVx/nU/c+uvGJFMO69s8pboWC8z0xlShxPGUw1v6KVIhYOQnyYucgEXHhwD49we5qL74IMJcKkYiFXMS7OQ2LY3Sia7rNg3N9PTiIicnktFqoAfO2JmZVX5Cab/+7z1x4F431mKlPi6tZd71ocTEd5KvJcOP/QHhzZzrQuvjPGcHw04Xa/K8xDYtTdqN8qxv2wkFBERLrGUtEF42+fXqBQqTWvyE01/61gLP1kMlPiqvhawTjGIzwHMufcWJYe0jquDeD4SNLtfleYg/iIu1G0X3qMvUCsVgoREdlFi0XXW1ypN/jm6fnmFa2tFEX1GEufsNYynSlzrG07aN94Ksq3K1e6T3qsnSJfrmEMJCJBAK4YTXBuoYgtzruJFNB/FeM+f9cuIiKXl18xjgQDfO3x2eYVuSkw7vVVi++kbywUqlTqDQ6HvGDV0mMMrmL8jfwRrAnA+d4KxrlynWTEtVEAXDmaoNaw2LxaKURERDZjqVAlEgrwohMjfLW1zzg3DSNXuX/3eVFGwXgfmVxyo9rGA2sE41SUTD1KfezanptMkS/XSEaDy59fMZIkTI1ANdfSSpFyMxhrlTXupUdoKoWIiFwCS8UqQ/EwL7tmnCenc1xY9Fovs5MweAxCcQVj6R9TWReMh+0ihBMQSa64/kA6BkBu9EbXSmHtZT/G7cpVasv9xQDHRxMM4QVIfypFJOU+9vomH2qlEBGRS2CxUGUwHualz3WFs68/4bVT5KZh4CDEBtVKIf1jyqsYD9QXV1WLwU2lAJhKXQfFBVh45nIe3o7kyzUGWoLxoXSMgyGvl7q1lQJ6vwXBD8a1Yu9Xv0VEpGssFasMJcI872CKA6moa6ew1vUYp/xg3N9FGQXjfWQqUwYgXllYtfAO4EDKVYzPxK51F/RQO0W+XCMZaQbjQMDw3JQXGltbKaD3+4xbg32vh3wREekai0VXMTbG8NJrxvn6k7PUi95W0AMKxtJnJjMlRpMRAoWZjsF4POUqxk/aYxCKwYXemWecK9dXtFIAXD3gBeNVFeN+aKVwiwz7/QlKREQun6VChcF4BICXPXeMxUKVJ556yl05cBDiQxrXJv1jKlPiYDoG+dmOrRSxsNs17mKuDgevh8l/3IOj3B7XShFccdkVMdc6YuP+znf9UjHOuScoUMVYRER2zZJXMQa47TkuJ5x64gl35cABVYylv0xlShxMRbxgvLpiDHAwFWM6W4LkAbdrXI9wUylWVoyPRAsAzDYG3AX91GM8OOH+3edPUCIicnlU6w3ylTpDCReMxwai3DCR5uzZ0+4GaqWQfjOVKXFlqgaN6prB+EA66nqR40M9tfI027b4DmA8WCBvo5xZqrsL+qFiXK+6RXeDR93nGtkmIiK7wN/cw68YA7z0mnFysxfcJ63BuIemVm2VgvE+Ua03mM1VOB5zVdQ1g3EqxnSmBLHe6SOq1htUao1VwXiYDAukODPn/czRPhjX5of6tBeMe736LSIiXWGx4IKxXzEGeOk1Y4yySD0QcaE4NgS23tuvoxtQMN4nprNuIsVE2N8OenWPMbiRbdPZMo3YIFSyUK9drkPctnzZHWN7K0WinmHRDnBm3gvGYW9ucy9XjP1jVyuFiIiAO5NYye/4bjpVjG89PsyhwBKZ0AgY48Ix9PVrj4LxPuHvenco6O96t0aPcTpGrWEpBr3qag88+HNeMG6vGAeL8xRCg5yd854wAgG3yUc/BOP0EfdRrRQiIvvb3/0O/NHtO76bpaKb5NQajKOhIM9J5LlYS7sLFIylX0xnXDAeNd6Dec1WCjeybbHhVVd7oM84X3Y9xO0VY4rz1KLDzYoxuHaKXm4/8INxbNCF/D5+chIRkU2YegRmHtvxhk9+xXgoEVlx+ZFQhnPVFGfnCgrG0j8mvWA81PAezP5s3zb+ttBzjbi7oAf6jHPLrRQrx7VRmMckRtwfsy+a6u05xn4wjqbdE1Qvh3wREdm5zHnAQvbiju7G7zFurRgDpOvzzNghvvbkjFuYDz2RDbZLwXifmMqUCQcN8eqCa54Phjvezt8WeqbqBePSwuU6xG3Ld2qlqNegtEhoYIy5fIVsyf3BEx3o8VYKLwhHUxBL9/W7dhER2YSMNzUic35Hd+MH43Ss9bW0SrA4TzE6xtcen1XFWPrHVKbEgVQMk++8653P3/3uYtlVjnvhXWHHxXdeC0hiyP2sKyZT9HQw9ivGKVc17uMnJxHZQK0Cd70Jpk/t9ZHIXqkWoTDn/r20s2C8VKySioYIBVuiYX4Wg2Xk4DG+8dQstbDXa9zHrz0KxvvE5FKJQ4OxdTf3ANdoP5wIc67s9Rj1QI9xx8V33uYk6ZFDAJyd78NgrFYKkf1t6Vl49PNw+qt7fSSyV/xqMUDm3I7uaqlYZTDRdjY5NwXAFXVGzjsAACAASURBVFdcRbZU4zuzDXe5grH0uqlsiUPpGBQ6bwfd6mA6xtm8qxz3bMXYewc9esAF42bFON3b8xfLWcC40XOxtKZSiOxnfjjp45AiG2gNxrtQMW7vL/aD8fOeczXGwFefXPQWfnd/NtguBeN9YmqpxIF0FDZopQDXTnE+byEU64kHf77iT6VoWXxXdBXjxOA4I8kIZ+e9kW2Rgd6uslZyrlocCKiVQmS/85/L9Dywf/l9xeHEypC8DYuFyorNPYDlYJwam+Cmo0Pc+9Rc328LrWC8D+TKNfKVOocHQq7FYINgfDDdW7vf5co1wkFDNNQSjL1WChKjHB9NrO4x7tXtLMuZ5g5+fitFr/4sIrIz/hmjHihgyCXiB+OJW3ellWIovnJUmx+MGTjI8ZEE09mSgrH0Pn9zj2OxEmA30UoRZSZbxsaHeuIJN1+urZ5h7C9GiI9wfKQtGNsGVAv0pHK2JRinoVHr3Z9FRHZmuWLcw2fBZGcyFyA+DKNX70orRXpVK8W0K5KFoqTjITKlmoKx9L4pb4bxkbDXW7tBxfhAyu1+V4uke6ZinIys3tyDYAQiSa4YTXJhqUi5Vm+Gyl6dZbwiGPtjc/SiKLIvldRKse8tnYf0BKSPujVE1dK27sZa6yrGnVopBg4CkI6FyRSr2PhgT2SD7dpRMDbGDBlj7jbGPGaMOWWMeYkxZsQY8zfGmCe8j8O7dbCyPX4wPrDBdtA+f5ZxKdgbDfb5cm3VdtAU5twmJsZwfCSBtXBuodgSjHt0MkVrMI56Y3N6uWdaRLZPPcaS8YPxkebn21Co1KnW7erFd9kpGDgAQDoeptaw1CP9vb5lpxXj3wP+2lr7fOAHgFPAO4F7rLXXAPd4n8se8ne9G2H97aB9/u53+UAKit3/4M+X6x12vVuA+AgAx0cTAG4HvOVg3KNhspx1CwhhXwxaF5F1+H/7vfp8JjuXueBC8eBE8/NtWN4OutNUCq9inPI2/qiEUn39urPtYGyMSQMvAz4MYK2tWGsXgVcDH/Nu9jHgNTs9SNmZqaUSqViIaNlbkLaJcW0AGZI9UTHOdeoxLs5DwgXjK7xgfGYu3ycVY69SrFYKkf1NrRT7W7Xk2if8VgrYdsV4re2gyU2vaKUAKAYGoLwEjfr2jrvL7aRifAKYAf7YGPOQMeb/McYkgYPW2osA3scDu3CcsgNTmbILu/kZMEHXSL+O8QHXSrHQSLhKRJc/+HNrtlK4YDw+ECUeDvLsQrFZbe3VWcYdWyn0oiiyL5Vb5hhrOs3+k/Wqw4MtrRRL25tMsVisuLtq7TEu56Cah5QXjL3QXAj2+JnXDewkGIeAW4A/sNa+AMizhbYJY8wvG2PuN8bcPzMzs4PDkI1MZUuubzg/46rFgfX/t0dCAUaSEWZqcXdBl1cjOk+lmF9upTDGMJaKMJcr93bFuNFYPZUCuv7/j4jsXKPRIfj6FWNNp9mf/LaJ9BGIJNxr3jYrxplih4pxy6g2aLZS5EzSXd6nrz07CcbngHPW2m96n9+NC8pTxpjDAN7H6U5fbK39kLX2pLX25Pj4+j2vsjPTmTIHUhtvB93qQCrKdNULxsWFS3h0O7eqYtxouGNOjC5fNJqMMpevtFRZezAYV/OA1VQKkX3m9//uSX70976Gba8Kt1bs9Dyw//jj2dITzY/bHNnmt1IMJVrmGC8HY2/xnddKkbGuPVHBuI21dhJ41hjzPO+i24FHgS8Ad3iX3QF8fkdHKDtirWUmW/Z2vdt4O2jfeCrKxYrrNe7mPmNr7eqpFOUlsPXlVgqAsYEIs7lKby++88O8/zOEE641pk+fnETEeXI6x/enspyeza+8opRxzwGg54H9yK8O+20UgxM7Xny3XsU4HXevs0vWqxj36ci2nU6l+BXgz40x3wVuBv478DvAK4wxTwCv8D6XPbJUrFKpN7yK8cbbQfuGEhEm/WDcxQ/+UrVBw7KylcLf9S7eDMajySjz+TKEohAI9eYc4/ZgbExz9zsR6Vv5cg2A+56eX3lFOdOsFioY7z+ZC27NUMQLqumJbe9+t1isEgoYkpGWCU8574R/2+K7hT6vGIc2vsnarLUPAyc7XHX7Tu5Xds9Upgy41oittFIMJ8J8r+QW4XVzxTjnvWAMRDtvB+0bHYgwl6tgAeNvC91r/DDvt4OA6zPWKVTpB4/9b7eD1/GX7PWRdJ3ccjCe4xdedEXzilIGjj4Xls72bUiRdfgzjH2DE66NsFJwPcdb4G/uYYxpXpibcoUkr8gUDQWIBAPM1fyzyf35mNPOd31uOutmGB+MW6hkN91KMZSIcM4Pxl1cMfYrKSsqxkU/GDcrxiPJCLWGJVOsuYprTwZjLwD7FWNwIblPn5xkn/nKu+Cr/3Ovj6Ir5cpuMtC9T881+4xrZaiXYfCY+1xnjjZv6Tz8f//drUfpZZnzzfnFsKORbUuFTttBT0HywPKCfWMM6XiIGQVj6WXTXsX40Ca3g/YNJ8Is+n1EPVAx7thKsaLH2IX82XzZhcmeDMZtrRSgVgrpH8WFbS8c6nf5co2AgZlsmaf9PmP/TNGQF4y7+Hm665z6Avz9/4DFM3t9JDvjb+7h28HItqVitcPmHtPLC+98qViYuUoEMH37mFMw7nPTWReMx433JLrpYByhTIRGMNoTFeMVi+8Kc+5ja4/xgFtpO5eruFnGlT4KxmqlkF5nras+Zc5rHm8H+XKNW64YBlw7BdB8QzzoVQn7tHp3SfivEcX59W/XzWplt26ovZUCtrUAb7FY6bAd9ORyf7EvHQuxVKp7rz39+ZhTMO5z09kSA9EQ8Yo3cm3Ti+/cH4jbE72Lg3FljVYKE2yOM8MtvgOas4z7pWKsVgrpB7US1Ctu4x2dAVklV65xw8QgB9PR5gI8/+8+eQCCUT0PbEV+1n0sdPco0nUtzzBubaXwg/HWz7wsFqorR7VBx4pxOh4mU6oqGEvvms6WvYV33iYqm+wxHvb+QCrhdFdXjP3eu1WL7xIjbmqDZ8yvGOcrvR+M/d374JK2UpyezfOjv/c1zi1o4wC5xFqfY9ROsULrSMoXnxjlPr/P2P+7j6W1CHer+qFi3Lq5hy8UdcWvbbZSrKgYN+ouN6QOrbhdOhZ2m4EoGEuvms6UGG8NxomtBeNiMNXdFeOOPcZzK9ooAIaTLa0UPRKMi5U68/lK84JyBkIxCLW8q4+lL9m23R/5+mlOXczwyHm94Mol1voCu82du/pV60jKl5wYZSZb5qmZfDMIR9N9HVIuCX8dSqGXg3Hb5h6+9MSW/4bqDUu2VFsZjAvzbj+AtlaKVCxEtlSD+FBXF812QsG4z01nyxxIezOMQ/HmvMMNDCW9PdEDqa5+8HeeSrGwYuEdQDgYYDAeZi7vt1J0/xzj93z5+/zUH/xD84LW7aB9l2gnv1y5xmcedFWHxUJlg1uL7JCC8ZpaR1K++IQbQXnf03NtFWMF4y3pi4px2+Yevm3sfrf+dtBqpZA+Yq31toOOuieC5PiK9oL1pKIhQgFD1iS7umK8PJUi0jaVomWGsc+fZUw05bZXvgRV1t00lSlxejbPbM4toOwYjP0+6l1up/jsg+cYrEzx95H/i/rc07t63yKrlNRKsZbWN//HRxMcSsdcMFbFePv8YNzTFeML7v97dGDl5dvY/W6x6G8H3RqMJ93HDovvStUG9Uj/PuYUjPtYrlyjWK1zMO21UmyyvxjcvMKhRJglOwDF7n3w58s14uEgwUBL4C/MuY0C2owloy5kLm8L3d3tFKWqC+6PXvBeADsGY69ivItPUNZa/vS+M7xyfI7jgWlSMw/u2n2LdKSK8ZpaR1IaY3jxiRHue3oe6//OomlvBKVanjal0eiPivHS+dVtFOAuKy9t6fWt83bQ/q53q8e1AVRCA11dNNsJBeM+5o9q2+p20L6hRISFRsL9kXVpdTVXrq9so7DWPdm1tVKAqxjP+4vvoOuDcdELxt9bEYzTK2/kV4x3ceHNt07P8/hUjldeHQcgmtveFqMim+a1azWGrlQwbpNrG0n5kqtHmc2VWVqcg3ASgiFVjLeivOR6Z6EZkHtR+653Pn983xbOvPjtcisrxn4rRVvFOO4eh6VQGqoFqPVfq52CcR+b3uZ20L7hRJjZugtH3fqk61Zrt0ykqOTc2Ke1WinyleZUh0p39xkXK34w9n736/YY714w/pP7zjAYD3PLAff0kCwoqMilZb3K0/dqR9VK0aZ9HYXfZzw7O9s8Y6RgvHmt7RO93krR3l8MLSPbNl/QaFaMWxZ256Yhklq1LintVYzzAe/yPjxToWDcx/ztoA+kIltupQBXMZ6p+cG4O0+Z5Mq1zrvexVdXjEeSURYKFephLxh3fcXYbVf66EXviaeyTo/xLr0oTmdKfOmRSV5/61EiVfd9hypbHxYvshVT09MUbJSnamPa5KNN6+I7gCtGEhwejJFdmmu+MY4NulnQtfJeHWbv8KvEyQNuoXYvqpUhP71GK4W/+93m32B2bqWYWtVGAc1WirzxXkf78A2ZgnEf8yvG49Gqq6Juo2I8WfH2RO/SyRSrgrHfM9ahYjw2EMFayFgv7Hf5O92y10pxejbvqkbrLb7bpVaKT377WWoNyy+++Pjym6Hx6sVduW+RtZy7eIEMCZ4qD7rTs136Rnwv5JdntbtA4vqMR6nkF7H+3/8laKnqW34wHrumdyvGWe85uWPF+AhgttSStFjoEIyzU6vaKKDZSpEl4S7o0mywEwrGfWw6WyIaCpCubW3XO99wMsKFktsxrltfqPzB98v8J71OPcbe7ncLDe9n6vqKcZ2RpAvzj01m1m+l2IV37bV6g49/8ywve+44V441p5GM2TmoV3d8/yKdlGt1FudnWLJJF4xB7RQtmq0UzZaxF58YIVrPUQh44WSXzxz1NX/Xu9HnuOlEvVhl96dODHaoGAfDLtBuIRgvFaskIkEioZZIuEbF2G+lWGx4rRRdmg12QsG4j01nyxxMxzAF74lgi60Uw4kIs/XufleYX9VK4b0J6NBKMertfrdQ8YNxl/cYV+vccoWbrnHq3Kyr+kfaRvOEIm7Tj/LOXxC/cmqKyUyJN774uLvAe5EN0sAuPrvj+xfp5G9PTROv5wknh7lovTM9WoC3rNNIypecGCNFkRn/uUzBePOWK8bP9T7vwaqx/8axUysFuMC8pcV3VYZaq8XgbQfdqWLsB2MvG/ThY07BuI8tzzBe3g56660US7a73xXmyvWVi+82aKUAmK56Cwy6vWJcqXP1eJKhRJinn/VOnbVPpQBv4c3OT6H+6X1nmBiK80+f71UJSks0jPvd5qc1y1gujU8/eJ7RYIGhkTEuKBivki/XSESCBFpGUh4biTMUKHKu4IWZ5UW4/RdSdl1hzm125Vdbe3Fk21qbe/jSR7ZcMU63BuNq0T2WUquDcTISJGBgru61WSoYSy+ZypY4kG4NxltffLeEF4y7uGK8upXCuO0q2/itFFMl7/ZdHIwbDUu51iAWDnL9kTTPTnozJdtbKcC9KO7wyenJ6RzfeHKOX3jRFc2Z0MVFMgMnACgrGMslMJcr83ffn+ZgtEx0YIQZhtybMbVSLMtX2s6K4fqM06bAU9kg1lpVjLfC3wDKP6vYixXjzAWIDnZ+PQBIe9NdNrmIdalYaRvV5s8wXh2MjTGkYmFmqn4w7s5ssBMKxn1sJlP2Zhh7rRSJrbdSlIjQCES68sFfb1iK1frqqRSxQQgEV91+MB4mGDDMFhoQTnT14rtyzU2kiEeCXH9kkOkZ781NpyfC2OCOf5Y/u+8MkWCAn/3BY80LS0uUhp9H1QapzT+zo/sX6eQvv3OBWsOStnliqRGsCZCLjKli3CJbanvzD1CrELYVpitRnpzOKRhvRWHWrUHx16H0asV4rWoxuGp4Nb/p1+2lYnWN7aBXB2NwC/DmKiEIhPvyMadg3KeKlTrZco1xf4ZxbND1o27BcCIMGCrhdFdWjPOVlYPvAVcx7tBGARAIGIYTEebyZder28VzjP3NPeJexTjWKLgrOgbj9I5aKQqVGp9+4ByvuvEQYwPR5hWlRQLJEXd6e+GZbd+/yFo+89B5bjg8QLCSIZgYZnwgynxAwbiVW0fR9kbfeyOcJc69T88pGG+F/xrR0xXjDYLx8izjzY3adD3GrTOM/WC8evEdQCoaJluu9e38bAXjPuXPMD6Y3t6ud+BaKQDKoVRXVozbB98Da+565xsbiDCb83a/6+JWitZgfN3hNANmnWC8w1aKzz10gWy5xhtfcrx5YaMBpQyh5AjP2nHCWS2+k931xFSW755b4md+YBhw7QBHhuJMMqZWihb5cn3Fwjtg+e89lBjivqfn3CYMJqhxbZvhB2P/daIXd7/LXOg8kcK3xd3vlopVBjex650vHQ+RKSoYS49pbgcddf1C2wrG7g+lEEh1Z8W4UzD2+8fWsGJb6G4Oxt6ud9FwgBPjAwyHvJFCay2+20Erxd88OsmJ8eTyBAzAbSaCJTYwwrP2APG8toWW3fWZh84TDBh+7DneXPHYIBNDcc7WhrXJR4tc+zoKWA4jE4cOct/T81jwzhz1X0jZdYU5t94mHHctdb22yUet4l7T15pIAc1q8iZ2vytV65RrjbZWimkwgTVzQzoWJlOquteeLswGO6Vg3KeWt4NOR715hJ3f+a0nHAyQiobImmRXVoxzy4PvW04zFuY7jmrzjSajzOXKXR+MSy0V42DA8Jy0FxLWbKXY/gviQqHKxFAcY5qr3v0nu1h6hHOME6/MQyW/7e8h0qresHzuofO87JoxRoNFd2F8iCNDMZ4sp90ubr0WWC6RfKXGQKwtGHtvhE8cPcx8vsITfp+xgvH66lX3O/KLJ/GR3mulyF4E7PqtFAOHXLDdRMXY39xjqL1inBjruFYH3O532VLNLXLvw8ecgnGfmsr420HH3IM8dWhb9zOUDJMh2ZXvCvMd5ntu1Eoxkowwt9xK0b09xsvBOOKemK5Muc9tdGD1jaP+drCVbX2vXLlGqv2F13uyC8SHmA0ddpctnt3W/Yu0u+/pOS4ulXjdLUebL6xeK8XZmvf3u6SzFNBhVjsst0w89wpXNXz42UUF483wQ7D/GpEY7r3Fd37f8HoV42AIUoc31avfeTvozjOMfa6Votq3jzkF4z41nS0TDhqGw1VXXdhGxRjcZIqFRrdWjNtaKapFt53sBj3G2XKNenigq6dStPYYAxxN1KnZAM92OmR/4c02f55sqUoq2jbc3f//HRskG/eegBfObOv+Rdp9+sFzpKIhXnHdwZbH2hCHB+NctN7frxbgAWu0Unh/66khV/nMlmquzaqLn9O6wvLOqD1cMc5ssLmHLz2xqb+hxYIrqKxYfJedXHPhHbhWimy5RiOqYCw9ZDpb4kAqhllnHuFmDCUizNfjrkLRaOziEe6cXzFeftHwn+DWa6Xwpi6UAomubqXwe4xjXjA+GK2SI86jkx1e+GI72xY6W1q7YkxsiELSewJeVDCWncuXa/z1I5P82E2H3eO7pWI8MRTX7nctavUGpWqjw+I79zwQT7l1AYU+nhCwq9qDcWKkByvGG2zu4dvk7nfbqRj7rxfVsLcwv8/WAygY96mZbNmNavNXl3bYwWYzhhNhpmsJwHbdrkqrFt8V206TdTCadO+KC6bLg3F1ZTAeCZXJE+d7FzoF4+2PaqrVGxQqdVKxtopxsVkxDiQPUCKqirHsii99b5JCpe7aKKCtlSLGLIM0TEiTKYC89wZ5rXFt4fggkVDA3S7Wn/2eu2o5GHsz/XuyYnzBnR2IdViI3cqvGG8QWheLbT3G1nrrktapGHshuhhMQb3iWvn6iIJxn1reDjo76S4Y2F6P8XAiwpS/w02X9Rk3F9/5FeO2akAHfsU4a2PQqEKtfEmPcbvK1eYGHwDBSpZKMNk5GC9vB7v106h+O8qaFeP4EEPJCBfNAVWMNytzEZ68Z6+Pomt95sHzHBuJc/K4NwWluAgYiKYZSUYIh0JkwpplDB3OivlKGQgnIRgiGQlSqNQ0lWIzCv5mVy0V49Ji150NXdfSuY2rxeCCca20YfDPeMF4eUvo4oJ7bVxnXVI65k+s8nbG7bPHnYJxn5r2t4PecStFmKlKd279mC/XCBiIhb2H8SZaKcYGXMU40/B+pi6tGrf3GFPOQjTF9y50eALaQStFtrReMDYQSTGciHCmMa6K8WZ98w/g4z/rVsDLCheXinzjqVle+4KjBPytx0tL7jEcCGCMYWIozlxgbNObE/SzVesofOWl5b/7RCREvlx3Z44qOajXLvdh9o72xXfxEbCNrnttW1fmwuaCsT/neIORbYuFKgEDKf8xlr3oPq5bMXa3zRtvMXiXFc12SsG4D5VrdRYKVW8ixSQEQutWUdcznIiwZL13hV324M95q7WXx4xtopVixGulWKj7wbg7F6t0CsahRJqpTJnZXFuVe7mVYus/y9rBeHE5rAwnwjxTH8MuPtN3vWSXRH7OVVxU8VzlO88uYS3c/vyWF93SYvMxDBwZirvdFjWVYjkYd6wYe2eKklG/YryzRbj7QmHOTfEJetXR5W2he2g0YObCxgvvANJHm7dfx1KxSjoebr5RnXvSfRx9ztp37VWMMyTcBaoYS7ebad3cIzsFyQMQ2N7/6qFEmCX80yXdF4wH2jf3gHUrxgPREJFQgLmat/Vxt1aM/Q0+Qt7/t3KW+MAQAI+2t1NEd1IxdlXNVT3GpSXXs4hbgPmsHceUs731ArJX/N+Rxtut4j/ehhMtK+BbHmsAhwdjnKkOuhf0ff5GrOMmRuDCb2vFuFLXttCbUZhbWTjpsW2hba3s+n83E4z9ivEGbzAXi1WGWhfezT7uPm4iGC9Zr2LcZ485BeM+5O96dzAd27CJfiPdXDFeNd+zMA+RAQhF1vwaYwxjyQizFe82XTrLuFSrEw0Fmu/iy1lSg+5JfFWfsb/pxzYqRWtWjIvNKt5wIsI56z2G1Ge8MT8Yq/VklY6Pt9LSqorxU5UhqJd7c7veXbRuj3Frxbhc29Fag33D3/XO10PbQpeqdV7zPz7Nhpt7+JLj7mzxBmeuFguVlRMpZh6HwWNum/E1+K0U83Vv10oFY9lTFx6Cx7+87k38Xe/cVIrJbW/uAS4ULeK/K+yuYJxrD8bF9Xe9840ORJkue08EXVoxLlXqywvvAChniSaHmBiKr+4zDgTdi+J2WinK61SM466KN5wI86z1tgZV2NuYKsZr8oPxip3ciovLjzWAiaE4Fxpe69c+b6dYtcDYp4rx9uRnV7YVLrdSdH/FeCZbJpTzFtMPbqJiHAhC6siG010yxSqDrWdwZh+HsWvW/Rr/8biwHIy7KxvslIJxr/n7/wl/9Y51bzKT9Xa9S3utFNtceAcwnAxTIkI9EO7KivGq7aATwxt+3ehAhIul7g7GxWq92V/cqEM1D9EU1x9Jr26lAC8Y7/LiO++F1rVSqGK8aQrGa8qVq8TDQcLBlpeeDhVjbfLhNFsp2sa1tVaMI209xgrGayvMrwzGPdRKkSlVOWJcZftMdWiDW3sGN97kY0UrhbUw+wSMPXfdrwkFAyQjQWZq3bkwf6cUjHtN9iJkL6y78ngqUyZgYDQecuNpdhKMExHAUA6lu+7Bny/XV28HvYmK8UgywoWi93VdetqxWG2snEgBXjAe5PRcfvkFc1lse7teLVfwVp2qbWmlSIbJkqAcTqtivBFrFYzXkS3VVlaLYVWP8ZGhWMsmH/t7MsXaUylaKsbRlqkUoGDcolCpUam1jGJr7zGODYIJ9kTFOFOscci447z7iU323m9i97ulYrXZSpG54IowG1SMwY13WygbCMX77jGnYNxrslNuvIw/UqWD6WyJsYEoweKsu+02N/cASESCRIIBCoFU11WMOy6+28T0jbGBKGfzXuisdGePcbFSJ9ohGF93JI218Fj7Dnjb3PUqW6oRCQaWNxJZ1hJW/IVSS9EjqhhvpFp0vbGg31UHq3ZZrFfdC/GKYBxnljR1E9r3rRT5co1QwDQX4QLUvA0Voi4Ir5hjDNtqqepXP/OH9/KfP/+I+6RSgFpx5WuEMRAf7omK8VKxymEzT544n/juEtX6JmYvD064sLvGnOZGw7JUrDY39/AX3o09b8O7TsVCrrDShzsuKhj3kkYD8t5c4nVeMKazZbfwboebe4BbrDaUCJMPDHRfxbjSVn0qzq87qs03moywUAtjMV3bSlGu1Yn785n98O61UkCnBXjbbaWorm6jqFWgWlgOK7FwkHg4yFz4MCw8s+Xvsa/41eLUYfeCVKvs7fF0mUypurKfvWXXO18sHGQ4GWMpPK5WivaRlNA8M9TSY1yo1GmEvUW4fRZSduL0TJ7PP3zBnWFb3txjbOWNemRb6EypymEzRzV5mNlcmb///szGX5SecDvT+T97m2y5hrUt20EvB+P1WynATabIlKrub7fLimY7pWDcSwpz0PBOoa8XjP1d73a4uYdvOBEhQ7LrHvwrplLUa96Csc0tvgODjaS6NhgXWxff+ccYSXF4MMZwIsz3zneoGG+zlWK9Xe98w4kwk4GDrj2gl3aJutz8YHz4BwC74XD9/SZbqpFuX3gHK4IxuHaKGaNNPrLtZ8Wg+ffZMpUCoFhn22+Q+1G5VidfqVOs1vnrRybX3hm1R7aFzngV4+TYFYwmI9z9wCaeW9Lrj2xbKrjF1yuCcXRwU5Os0vGWYNxnjzkF416Sm2r+e+nZNW82nS17u955FeMdtFKAm2W82Eh0VcW4XKtTrdvmi4Z/bJupGHu739XCye4Nxq2L7/zAG01hjOH6I4M8erE9GO+kYtw+kWJ1WBlKRDhvx131wX9cyWorgjHqM26TK9dWj2qDFW/CAI4MxjnfGFErRbm2euFdh4qxf1sF46bFQnPnyc8+dH7tYJwY6Yn57Jli4pTaDwAAIABJREFUlUNmntDwUV7zggnueWyK+fwGZ6SWd7/rfOZlqdghGI9d41pMNrDcShEf6rvHnIJxL2kNJGu8YNTqDebyZcZTMdePDLtSMZ6rJ7qqYpz3xhgl/arqJjb38I0l3eYelWB3B+NYhx5jgOuPpPn+ZHZlj5k/rm2LGyJkS+tUpFr6PoeTYU7XvVOQWoC3tvZgrN/VCtlSdeXjrcObMHB9xs9UBtftj9wP8uX66oV3fg9xW8V4eWRbly4ovtz80PjcgwN846lZFme9188erRjniiUOmEXM4ASvP3mUat3yuYc2aDUavsp9nPl+x6sXi+53NOSPa9vERApfOhYmU1TFWPaaH3Sjg2sG49lcBWu9Xe9yUy7chKI7+rbDyTAzNW/laZe8SK3aEWp5O+iNx7WNeBXjUiDRtcG4VFk7GF93JE2l3uDJ6ZaFg7FBsHXXG7wFqyp4sGbF+MmqF4y1qGxtfjA+eL1b7a6K8Qqudae1x9h/rK2sGE8MxXmmNuy21l6jP3I/WLXAGNavGPdhSNmuBS8Yv/m2q7AWHn3qtLsi2V4xHu6JHuNqbo4AFpLjPP9QmhsnBvmLjdop4kMwcrXb/6CDFRXjUsYt6h/fZDCOh8iUatjoYFedTd4NCsa9xK8YT9yyZjCe9mcY78LmHr6hRITpahywXVONyLXvCLWFivFo0gXjguniYFzrPK4N4NrD7gXx8amWY19ekb61F8VVQQU69n0OJ8KcKnrhRVXQtfnBODnuTmMqGC+r1RsUKvXOrRQdKsaT/izjfdxOke/YY9xWMfaCccGvGPdZSNmuBa+V4gVXDHPzsSHOnD3r3qxGVz7WiI+4KR+VrRUVLrdaodlSB/D6k0c5dTHDI+c3eM6fuBXOP9DxKr/dZCgRhrkn3IVbqBjXG5ZaxGvf6aPt2xWMe0l2yj0Zjj137WCcadkOeoebe/iGE2EWbMJ90iVPumtXjDcOxrFwkIFoiJyNd20w7rj4zntCPJByZwBmcy39ZdHtjWrKdJpK0XHxXYSZksEOHFLFeD3FBWwwwlI1DEPHFYxb+G9mN5pKAXB4KMaF5VnG+3cyxapt76GlYux+Z4nlVorattca9KP5gnt+HE6Ged0tEzTyc9RiwxBoiz1+a0WXbwtd85/bI24n2p/8gSNEgoGNF+FN3OoqwR0Wsq6oGM9sfiIFNP+Oi8GUGwvbpaNPt0PBuJfkptxq0cGjUF7q+AQ4nXXB2C2+251gPJSIsGS9fdO7pM941eD7LVSMwS3Ay9hYV/4xW2tX9xiHk26LT9w79YBpnioEmqeit1DRbzQsuXLblABYs5XCWqinj6livJ7SIvlAitvf+1Xs0DG9iWjRcZfF4iIEIxCOr7jtxFBcm3ywRivFWhVjf5MPzTEGms+Pw4kIP37TEUYDWRZIrb5hj2wL3Sj504nca/FQIsIrrj/I5x4+T7lWX/sLJ251HztUjZeKVaIhb4797OMQCMHwlZs6nnTce9wFvGzQR2/IFIx7SW7KzSQePOo+71A1ns6WMAbGkhF3+x1OpAD3xLIcjLumYuyeCAZaK8aB0HJVdSOjyQiL9VhXVozL3k5NMX+OcTmz4ucKBAzDiQgLhdZgvPVWinzFzbBcPZViCYLRFWFl2BsAXxxQ2FtPo7DAVDXObK5MMXHUVWpq5b0+rK6wHIyjba0UscFVq+DHB6Jkg2lqJrxvWymsteQr9c5TKcJJCLrfYyLSWjH2Ft91yVqQvbRQqJCKhQgHA4wkI1yVKPFsKU690XbKv0e2hW6U8+4fLa8Fr7/1KIuFKvecml77Cw/dCIFwx2C8WKis3Nxj5AQEw6tu10nae93Im+4qmu0GBeNekp10QXfwmPu8YzAuM5KIEK5mXd/UDjb38A0nwizRXQ/+ZitFy1SK+MimxswAjCSjzFUjLhh32YtIqepC/4oe47bAP5xsD8Zb3w52uU+74xa9K09t+7vfZeNH3antehVZbWl+mrmG+1uZj3h/e/s02LXLltxjZtXiu7bHGrg3fwcHEyyGxvdtxbhca1Bv2A5TKZaab4RpnjUr+Ivv+uy09nYt5CvLz1sARyJFJmsD3PtUW8tEj1SMjf//1GulAHjpNeMcSsf4i/vXHt9KOAaHblizYtwc1bb5iRTQPPOTxTseVYzlsrO2Q8V49R/DdKbEuD+RAna/laJLKsarFt9tctc739hAhNlqBLBuS9ouUtxEMB5JRFbOsIxuvWLc8dQ2uDc/bWHFryosRg67F9515mjvZ/nFmeW/lSnj/e2pwg6s8Xhr2Xq83ZHBOFOM7tseY//31XEqRbQZjJsV4/q2ngf61XyhynCyGYwH6ovkgmk+81DbG9UeqBhXag3CdW9xoNdKARAMGF53ywR///gMU5nS2ncwcSucfwgaK1suFgtVhuIRV+iYf3pLwTjtBepF/PVH/fOYUzDuFeWsG8WVOujCbqDzKcbl7aD9YLwrrRTdVzFe3WO8sOn+YnA9xjMV70mz3F3VlWLFC8aR9SrGYRbyLVVbv4K0hR7jjhU88HYQXBlWRrwXmJmQVwVVn/Eq2VIVU1okMejG2p1p+OPttAAPWhffdWil6GBiKM65+jAs7c9gvHxWLNKhx7ilYhwNBQgGDAW/lQK6ZnrQXlrIVxjx2wQaDUxhnrHxI/z1I5Pud+WLeyM+u3iTj2ypShIv+EYHVlz307cepWHhMw+u83cycStUsq4q3GKpWHUBd+EZNxpxK8HYe91YqHstd11SNNsNCsa9orUCHAhA+kjnYOxvB728ucfOWykG42GKRKmbUNc8+PPlGpFQgHDQewhvsWI8moySaXh/0F3WZ+xXjJuL73KrK8bJyPKqawDCCddjvYWFN5m1KsYdTm/7A+AvGm+rUFVBV/niI5MMkuPqK44RDBhOl9Pu/4neRADNN2ID7Yvv4mtUjIfiPF0dwmb35yYfa7Y6tVWMjTEkIkG37mIbLVX9aqHQ0kpRXgJb5+rjV1Co1Pny91p2kQ1FIJLq6orxUrFKkqL7JLLyteDE+AAnjw/z2fZKeKs1FuAtFavubODs1iZSQPN1Y245GPfPY07BuFe0t0YMHlsVjBsNy2yubTvoTex5vpFQMEA6FnZjWbqoYrziFGNhvvnOfxNGByJk6c5gXFoVjDtUjBMRFvIVrD870pgtbwfrn6pdPZVi9entdCxEMGA41xhW2FvDFx54hgFT4uDBQxxIRbmQqbq2J1WMgeYbsXT7uLY1KsaH/3/23jtOkrs+E36qOucwO3lm02zS7mq12lUElBO2QBLYL3DYwNn4fMac7TP6nH3Yr02wX6f3bOOEueOMjQNgOIIkokBCEkIorLS72qjNafJM51xdXffHt35V1d3V3dVppnq2n39mp6d7uranwlPP7/k+T9CJmVIYXKkIpOsMF61RpCvtYgwVHmOAVOUMi2tjz7nKEU0XVCuFTHonJyYxHnTha5WNcSYv+UjkivBwOYgWhzJ0qcVbtq7D6YWUcu2owsBWItQ6xDjg0hLjLYa3yWmzwG7lsVSUC8TW0D7XJ8a9gqRMdFlhR7CaGEcyBRRLEoZ8spXC6qx50WkWIY8dad5rKsVYGbyTpBY8xg6kJSd9Y7Jlx5xA6pjqMU7oKsbFkoRkXrMk6PQ39X9JKR5GnYKPiv2G4zgEXTZEsiWZ7PWJsRbTsSxOniffNecKYSTgxFwiCwTX94mxjGSuCJuFg8MqX3YkqS4xHtNGtl2Fdop0ocIuxpArV4wByjKmSuig+pyrGPmiiHRBVCxgSFN7Iu9dh3dcP47nTy8qZVgATF8LncgK8CKLktWj+/MtQ15IEnBusca8DM8D49eXEeN8UUSmICLospHFwjvSNF/wO22I50Cku0+M+1hxVCnGEzStLarEiJnvFSuFd9hwSkMjBN12JOA1kWIsqt67QhoQC015jMMeO1JMMTbZBLfiMbZZiDzUUIyByizj5jJMVY+x5sJbh6wE3TbEMgUqrugrxmX4xsFpBDh5P3KFMBpwYjae6xNjDZI5AT6nDRw7JwkZ8jXWGL4rzzK++ohxSomk1Ilr01OM88W+lUJGWaMboJZ3uAfwyPXjKEnA44c0aSfusKkV43hWgJvLlyVSaLFliB4/s1jnWja+H5g/Cgg55XcCQIBZKdZtbXq7/C4rXUecAdNwg06gT4x7Bck5CsJndoHABCCJqmUC2nIPJz3egUQKhrDbRtP2JlKMvS203jGY2UqhpFLYeUDI0t9ZZ/gOQHUyRZNWCgvPKVPtAOgmQxJ1fZ9k3xCA0Ia+YqyBJEn4+sFp3DoqEz5XCCN+F+biOUiB9XQsCtnV3UgTIJUvGqqDZhgNODHLaqGvQmJc1e4JAMUCxXBW1Bq77ZZ+KoUG7LwYZh5jDTHeMuTFnokAvq61U7jCpm6+S+RIMa4cvGPYOOABzwFnFhoQ41IRmDtCv5MRY6eVWu8Gtze9XT6njSxSzsCa2uf6xLhXkKpQgHVKPhblOughnwNILXQkkYIh5LYjUnKb5q4wXSi23HoH0AkzJZmbGDuslqo6aAZFMa7MMm4ylcLrsKoKHqDbescQZKUiwQ1AepFIdB84Mh3HmYUU7tske+1kxThTEJH11i7judqQzFXMBTQgxj6nDUVnCAJnvyo/P11irNRBVyjGDtljbLUDVpdpBIzVgtJ656kmxgDw9j1jODaTUO0U7gFKNjIpaPguB4tTv8DKabNgMuzG2UaKMaDYKZiqPsgnaDixicE7Br/TSgTbFewT4z5WATIxfvViFD86vaiWfMTUPFl2kA/6HKQwdyCRgiHotmOpaJ4TbqpNxdhq4WFzNR9xthJQCj7sWmJcfiFk3rlIWWRbc3ftyVxRJ5GCkRU9xdhGxJhVhvYtAgAoJslu5XHjsEYxDpB/fckiH4N9hV22UlQkUgA1UykAYCzoRsRydZZ8pPTi2tjxWekxtluoEhpo+gZ5LYIl9oS1xNjqpPQeAJNh+roor7LCHSZyqLEmmgmJbBE+LgeuhmIMAFsGvThbTzH2jwG+UYUYMyvFUF4+N7VkpbCpVoo+Me5jpVGMz+FIwoWf+fsX8P7PvYyvnpHTCOJaYpxHwGWDEwIR2A5aKUJuG5ZEF6Rc3BTRSWVWihYUYwDw+7wQOJvpcozLmu/YBU6n+Q6o8Bg7/E3HtVVNvGdrK8bUtidACq6nB/o+YwhiCU8cnsF91wzDLcqfvawYA8A0+lnGDHQjVpFIAdQd+BkLujCH8FVrpXDZLLDwmhWdWoqx3aoM6601ktIKonoeY/c6ZcVVFRbk8ye7dpg0yziRE+Dl83WJ8dSQF+eW0tWV11qM769SjEOZC/SzVhVjxUphDtGsE+gTY5OjVJLwxZcvIbV8Ba/HHPjQnVO4fesgHn3sLPK2QNkSo5JhzKKNOmilCHqo/Y6TShQUvspI50V1iZGdzJpQjAFgwGNHlnObz0pRoBsPp622lcLnsMLKc9VWikKyqt2oFlJ5oTw6C1AvqDoqXtBtQ6FYQtYj2wP6KiieO7WI5XQB77h+nPZDjgccfkUxvljwUxlP/yaieoVCse3UU4yduFS8Oks+UvmifiIFoJtKUaYYX+3EmFkptB5jzfWhihibvBY6nhXg5bJVGcZabBn0olAs4Uo0U/sXje8DImeBTERRjD3Jc4DNA/jGmt4uv9NGVoo1ts/1ibGJcWI2gZ/9zAv42NdeQxApPHDLXvz2W3fgf75vP+7aPogz+SCmL6pNNvPJHGUYd7DcgyHktiFhkvY7SZKQLhTVaW1FMTaeYwzQAF5KcpmPGAsi7HKbVS1izHGcrOBqiXFz1pD6VgodxZj5mrkgLUn2yR6+dnAaYY8dd2wfJGLsDAI8j2G/ExyHfpaxBsmcUJ1hDDQgxi5cLIYgJWcN3/CtFaTyYvXx2UAxliSJfraGSEoriKQL8DmtagFUZlnxFwMkirDnAVCvHSaNbEvIHmNtHXQlplgyRd0BvBvo68xBxGRi7Iidpfxivnk66HfZkC+WULTLUaFr5BjtE2MTQpIk/Nl3T+Jtf/M8Lixn8KkH6U5u3Qj5ip02Cz7zvv0oeMaQmD+Pf/nJBQCkGA/7nB0t92AIuUkxBrDqSyaZgghJ0gylZCOkoFhs9V9YgQGPA0nJYTpinBNEOFnWaw1iDNAAYVUqBWDYTqFPjGureOrAnyDHkF3dxDieFfD94/N46LoxugBno8oF1mbhMeh1YC6elVM8rm5iLElSdSmPQoz9+i8CRbbNSWFwkqhmuV8lKMtqZ6ijGJckIF8sNR3buBYR07beAVXEOOCyged6RzFOZApwSbVTKQBSjIEGxHhsLwAOmH4NiSx5/rmlU8C65hMpADXqM2eVr09rxNveJ8YmxPmlND79zFm8ddcInn70Dvz0JvnPpFGAHVYL9uzajQ3WCH7vsWP4/AsXsJjMY9DvUDOPfZ0cvrMhLskH5SorxlXT2k223jEMeO2IlVwomZAYu+ya1jtAdwkt5LFRfBoDU3kNK8ZCuecTqDncA9CqASB70/zjV6XvU4vvHJlFoVgiGwUg1xur+2E/y1hFuiCiJKF6+M7urXtDOxZ04WhpE31z7odd3kpzIZUvlg/eARrFuHxFhz0vnV970VmtIJIR1EQKAEiXE2Oe5yhpqdJjbFLFOJfLgEepZo4xQHnE67yO+sTYGSAv8fSrWEzlMeoq0ZxSC/5iQG2xzHDmWE3uFPrE2IRYlg/Wd984iaDbrmm9K/cMW0KTcJfSeGiHBx97/BgKYola75Lz5HX0DHZsm0JuO+Iwh2KcqqxKbbL1jmHA60BKckHMmusuNyuI5a13gL5i7LEr09cAmqqDlSRJXzHOxoiE69SOKgN/mQLddKWuvppeLb52cBpTgx7smZBJikYxBkDtd4wYpxeAQh3v3xqHWiZjrA6aYSzowiFpCin3BPD6l42/oZADzj3byqaaBulKhR3QKMbl5wOWRZ5hWca5OJX1XKWIpgsIs8E7UaDECc+6sueEPfaeUYzFbO2VQy22DHnqR7YBygDe0Ssx3D4gX8tbSKQAqOADANKcTNjXyA1ZnxibEEo4ObvjVawRFQqwnGX85/evwwO7iDSPBmQrhXsdwFcsw7WBMivFqivG5GMqV4ybJ8br5PY7yWTLjtmCSIN3ALXy8TbA6qh6HhVutGalyBdLKJYkePU8xjXis4KKYlwgm05qwRQJJauBQrGEl89H8FO7R9Uc6ApiPBpwycR4Az2gSZC52pCU68erhu8aEONhnwM8x+Fo+H7g/HPG7RQ/+nPgnx/q6aG9tN7wXT5B/v4KlZ09L12QFeOScFWXykTSBU2GsX6cZ8hjV0Qo2L10njWhYixJEko5tnJY22MMUAPemYUUec1rYXwfkF6AELmMm33y/7dFxZjd6CY5c4hmnUKfGJsQVeHkyXkAXLUCLGcZ21Iz+Nv37sNfvWcv7t4x1PFyD4AydRUfkUkUY8V/16JiHPbYkZacpkjZ0CJbaaVw+HSrvcPy8F2JxfM0UQeb0FPw2GtrkJWgS+Mx9g7Txdek8UbdRkxW6ofl9AkAuopxMl9E2i1bLa5iOwUjxt7K5rs6g3cA5Y0P+5141nkXAAk4+tXGbyYKwGv/LL/xbItbvPpIaZN3GHJxXZsTU4zTebFpS9VaRJnHuKLcg2HAoxEWOM60tdBZQYRDkm9y6lgpAGBq0ItErojFVL72k+Sij+v4s9hhm6XV5fDmlraNWSmikI9jNvjf4+gTYxNCCSd3axRjz2D18jYr+Yhfhs3C4+G946Q0drjcg8Hh8kGExQSKcYWVIhNtSTEe8DqQhAu8YK4GNxq+qyDGOgi67ShJKslt5oLIiIpfb/iuBjG2W3l4HVayUrCM7NTaOBE2C5aTynzXKImy2l7uMQaABYs8BHsVDysyK4W/ScUYIDvF4ewQMHqdMTvFG99WV9l6eP8kK0XFql8+oTusyAh0hinGwJpZ1m4WOUFEuiCWl3sAVcQ45KkYXnYPmFIxTmSL8EAmxnWG7wBSjAHg7EKda9rwbhQ5G67nz2C0cIlWtGzO2s+vA2almLfIol18bdz894mxCRFNF+C08apqmFrQL+vwDtPyT2VdKquP7jCCHjvSvM9EirGVmory8ZYU43VeO5KSG1YxZyrlMyeUKhRj/an9sIdImXJyb8JKobu0DTRU8YJuGw3ftUKMZw8DS2eMP9/EYIqxokrl4gCkMhvKiJ8uNlcEP2CxX9Xxdur+plmhyNa27WgxFnRhJpYFrn0XMHsIWDpd/wUHPgc4ZHLYo8RYLEnICnqKccK4YnyVEmOluEJRjJfoq7vcYzxQueLmCpuSGFOGsVxdXSfHGFCJ8Zl6PmOrHRdsU7jFcQHWyBlgsLVECkBVjGOClT7f2Nqwi/WJsQkRSQuqWgyQAqxnjeB5qnnUehdLpa5YKQA60SQ5z6orxmXDd4zQtqAY+502PAO5P/6V/92pzWsb5cN3tRVjNT5NJsZWO2A1VtutOwwF0N+2jooXctsrFOMmBvC+/iHge79j/PkmBlOMAy7581P2w3KPMQDMJgq0utO3UlR4jBsP3wFU8jETz6G0650AOODIV2o/efkscO4Z4JZfoe9Ti61v9CqiasCYoZZibNdTjK9OK0VUuWnVtN4BVYpx2EMrbqzoAu6QKa0UiZycYQw09BiP+J3w2C11q6FLJQkvFTZiR+kssHym5cE7gG7ILDyHRLYIBCfXzBxFnxibENFMoTxqJjVf2xoRmCxXjDPLgCR2xUoR8thoAG+VFeOyuLas/mCFEfA8hwX3Vpzw3Qq8+PdAwRyWirLhu3yiJjFW25u0kW3+pqwU1VPv9VW8oNtGvjyWkZ1qIls2fnnNkENFMWbHKbtZ1BDjIT8NTM7Fc1d9lnEqX3EjVirJJK8xMR4PulAolrDIhYFNt5GdotZw0YHPAbwVuOEX6Wa5RxXjKrsYQy3FWLZcpAtaxXhtDEI1i6oZnRrDd+z8uayNbDOjYpwR4GaKcQMrBcdxmBry1k2mOLeUxiuFzbBLOUDMtzx4x97P57SSnS8w2VeM++geIumC6o8qifUV4MBEOTHuQrkHQ9BtR7TkXnXFmF003DZLy613DANeB77h/Q90Q/Hq5zu1iW0hJ4hw2jQFH40UY61PzmC4f0pPwROLNIjYUDEWaJtsbuOKcT5FRGiNZB9XeYx1FGOnzYIBj72fZQy6EeM4+ZgFyP4EqeHwHQDsWx8CxwF//uQbZKeIngemX61+opADDv0bsONBihP0Dvc8MdZNpainGOf7HmNlRkfrMXYEqpI8dGuhsxHTxdwlcgK8MDZ8B1DRR70s40OXYzgsTakPtEGMAVp5TeaKdI6LXzHd59cK+sTYhIhWTtTWU4ADE0BihkgN0JVyD4aQ24Zl0Q1p1T3GIjx2C3iea0sxBshn9oq4BdjwFuCFvwGKdaZ5Vwg5g1aKsKfCSgGoGaYNoJtKUaM8QIuQ20bvx3FyZJtB4sFitvKJNXHBjmULsFt59e+kQ4wBlmWcpYtGZoluEJpFJtLzsXjJHGXy8rycrlKnerwSu8cD+PCdW/DlA1fwndJNgMWhb6c4/hj9HW74RfreOwik15iVooZizPbDNMsxBtbEcdYK2E0ri5dEekn3+lBFjF1hoFQ0XROqUgcNGCLGU0NezMZzyj5UiUOXo1iyj0Nix16bxNjntCKRlRXjYpY+7x5HnxibEFGtYsyIRy0FODBBxJkpxSwupQvDdyG3HTHJA8kEinFZhjHQkscYoPa75XQBuO0jQHIGOPylDm1la5AkST+uTQduuwV2K19d8tGqlaJOHTRDyGNHMldEUSzRPmY0VzY5o/67h7NlGWJpAUGXrTzDGKgixmr7XYtZxull4C92Aiceb3OLVxeJnKAM6gBoihgDwG/cuxXXrw/it564gMzGeym2Tay48B/4HBCeAjbeTt/3tGJckdUOUAxdMav7mfE8B7fdQoqxzUVD2VdpXJtipdCKSxXlHkANxRgwnc84ni3Cy+UgWew0R9IAU3I19LkadoqDl2LYMxkCN76ffNctikoMfqeNhJYgS8nq/ZWxPjE2GQSxhESuqB7UyQYKsBLZJtspFCtFd4hxHB5wq9yqlCoUy1vvgDYUYweWUwVg6m5g7Hrg+b+svuCuIApiCSWJluFRLADFXE1izHEcwpUlH66woTt2puBZeE0+MrvhaWClAIBYVk6mMGqlSGjyZNeAnaJsVQdQiXHFTcVIwIm5hIYYN2uniJwjMhQ528bWtolj3wAOfbGtX1HVsqh4shtbKQDAZuHx1++5HgDwN0vXkxJ8/hn1CfPHgMsvklrMy5e1ZvZPk6Eqqx3QtN7pp9S47VZSjDnuqq6FjqQL8DmtsFnk/SCzXDV4B2iJsbxKaNJa6EROQMCSB2dALQY0yRQ6dopsQcTJuST2TgaBez4GPPzptrfP77LS8B3jImvAZ9wnxiaDEjUjR3E1JLpy+51KjBfoxGl3d3zb2PAdJ4mrutxUpRjzNkNLTHoYCTiQyhcxn8wDtz1K/sXj3+jg1jaHXIGWzF02C7XeAXVrQCmLUzN8F5wka01JrPs+yZygH9UGNBy+A1j7XROKXJlifKX283oEsYygLtUCRIwd/qqs8dGAC7GMgKynxZKPhPxZrebF+qXPUJNcG0hVEuMmFWMAmAy78Yfv2I3PzW9BzuIFXtfYKQ58jiwWe9+rPuYZBIRMa/aVVYbu8F2+/mfmcVgolQKglaOrlBhHM5oVV4COHR1i7LBa4HVY1fOnaRVjAUFLvuHgHcOGATesPKdLjI/OxCGWJOydDAFje4Htb217+3xOG6UcBdVehV5HnxibDNHKfFS2VF1TMZYvuGxnTM51RS0GaPgujtWvfiRiXNF6p9MMZwR376DP6onDM8D2B4HBHUQCVsnTmRWI0DptFnUptA4xDnts5R7j4HpqpGtgcUjlizr+ReOKsdL+eVFXAAAgAElEQVR+l4sZ82UnZunmhbOsDWKc1VGMdW4oWMnHnOgHrE4geqG5N2K2ExY5tRrILNP5pY1VomReaNq2o4eH947jbfs247HCDRCPPwEUZOJ7+N+BXe8oXznq4RKadEFn+I4pxjrDd4CsGMsWjKtZMY5mBAS1x2ZmueaKYshj01GMzZNpD5DH2G/JGxZ/bBYeGwbcuskUBy/R/23vZHPHXT2QlaJIx7Ld11eM++g8mN+pzGPsCJBvTA8OH+2QimLcnXIPgEhRQpKJ8SoWYqTyIrwOllEZadlfDNCy0+5xPx47NENLsG/5CLBwHDj9vQ5tbXPIycTYZedVpaueYlxppQiup68NlMmqpW1Ao+LV8RhrkzB8TRCP5CzgHwd8o2vESqGjGOsko4zIxHg2kWsty5gd16tKjCNk6WmDYNL+1rrHWItPPLwLP/HcA0sxjfSRJ2gQr5BUh+4YlEjB3rNT6A7f5etbKTx2rWJsLJ1mLSKaLiDMjs1CmqxIOooxAIQ9DjWuzaSKcSInwMcZJ8YAXdf0FONDl2OYCLkw6HN0bPv8LitS+SJECWsmy7hPjE2GqsGB1Hzj6DVtlnFqvivlHrRNNlyU5N+9cLIr72EEZVWp2WjbwwOP7B3Hkek43WHv/hkil8/9j1XxUTPF2GWzqHaVuoqxvXz4TvGy1m9ZIytFRbmHAbKiWimE5ko+krOAf5RWOHpcMZYkCbFMoVyVysV0iTEr+Wg5yzixysS4VFKJQhvNfVU3Yrk4wPEtWaC8Dit+8ed+HnNSGGee+kdIBz4HDO0CJm+qeKJ83kz3IDHOFWHhOTismkt0I8XYIXuMgataMY6kNT0ASrlH9fAdoLbfAZAFAW51b0J1EM8W4eFyhq0UABHji8sZCGL5yuehS7GOqsWAmmyUyhXXTJZxnxibDFUZjMn5xtFrQc3OmKxTBtIm/E4bzmCC/H2XftKV9zCCeFZD6jKRljOMGd5+3Rg4DqQaW6zAm/8rMH0AOP9cB7a2OZRbKRgx1r8QArK9JStQSgSges5bUYyzMbI61GlXCmkj4hRFzoCSmJgltTgw0fOKcaYgQhAlNcMYqK0Yy7XQLWcZK1aKVVKxcjFAkvetFnOYJUmqvhFjDYt8a5egPZNhzK9/ENemXwQ39zpw4y9W26laaWc0CdKy1YnT/p+MKMYsostgbONaRKwy7hSoqRiH3HZEUjIxtlhpnzTb8F1WgAfZpm4ipwa9KJYkXFxWS6vmEznMxHO4fn1718tK+OXriJJMsQby2vvE2GRgirGyTJsy4BlmJR/5JCCku1LuAVAkkM/lwEX37lUjxplCEfGsoCxRKx7jNjDsd+JNUwN47NA0JEkC9v4c3Vy0OXDUCnIFrWJswGPstkHS1praXLS/NFCME5VL24DaelfHr+2xW2CzcKrHGGgc2VYq0X7sGyU7RXy6p0PgmcJkxErhslsQdNswy7KMs5HmBlcTq0yMte8bu9DSr8gXSxBEqaU66Hq49qf+E3hOQo5zUvFHJdwDpEr3oMeY7GI6GcZAzc/Nbbcic5UrxjlBRLoglpd7ADWJMYvrlNj5iJV8mAiJrABXqTlirCZTqMT44CXy9XdaMfa76DwYZ1nG+XjP73t9YmwyRNICPHYLKYaSZEwxDkzQzrh0mr7vQrkHQ8htx0nHbmDx5KpcrOfiFHQ+GnDS59Omx5jh4b3juLicwaHLMcDmBN70X4DzzwJXDrT9u5uBvmJcP5UCQPUAXkPFWC+VItaQrHAch6DbTqkUnkEAXGNFLr1Iwfn+MdpXxXxPh8DHlAIB+eIrSTWJMUCq8RxTjAHjikoxT6TO4qDjWxQav6bT0C4rt2ilSOq1LHaAGPOje3DGvRdf4d+qby/gLbSPrpZi3MbNX9mAMQMjGzXOBx6HRRnagzNI3tpiQfe5axVKqhM7NtP1iXHYY0e+WFLOu2arhRZLEpL5IhxStikrBcsy1g7gHbocg83CYddY7RXIVsDyyan9bm1EtvWJsckQzWj8UfkEndyMKMaAWpPapeE7gFSyw9wO+ubyS117n1qYVYixi+LMSkLbijEAvHX3COxWnuwUALD/F+gk+fQftP27m0FOkOPa7MY9xgAqItvqE+NCsYR8sQRflSIVN5QSoLTfWWx0wWmkyCXlDGOmGAOqd7YHUZUcU0gR8a9BjKtKPowS44S8Lw7vpK+rccFmxNjqbHmJNKm0LFakUjSZSFEFjsP3b/ocfi/9LnXFpBKeodUhxiUR+Ou9wI/+oqWXpwtF/Tpom7uq2pjBbbcio02lYK+5iqAOrzOrHSPG+teIsHwML6c0A3gmUoxTuSIACXYx05Ri7HFYMRpwlg3gHbwUxc5RP4kuHYSvzErRYpGRydA2MeY4zsJx3EGO474pfx/mOO77HMedlr921tCyxhHRtt41KvdgYMHaK0CMQ247XhU2AxY7cPGFrr1PLcxqFeM2W++08DttuPeaIXzz9Rny6zq8wO3/DTj3DHDmqbZ/v1FUD99xgK2O59dd0d4EqJ31NbKM2cS7rsfYgIoXdNsRZUTcSIkCI8Zs+A7o6fY7VZWSL741Wu8YRgIuWTGWLxpGlVdmoxjZQ19XYyiIkYSRaxvac2pBUYwdFakUbSrGALBtmC0Z17CnNFNb3klkoxTN9/QfAtOvNf1y/TjFeN15A4/dgoJYQqFYUhX0Hl/SbhYxxeaksVJwlpo3Ybq10CaKa4tnBTgggJeKdWc/9LBlyKsoxmJJwpHpeMdtFAAQkK0USi000FeMAfwGgBOa7/87gKckSdoK4Cn5+z4MomxwoFEdNEOlYtxFK0XQbcdiFsDYPuDSi+3/wiaXG+fiWQByDFabrXeVeHjvOJZSBTx/Rl7mv/GDRDJ/8LEVyzWuslI4fHUHlNiJPVaVZVxUCWkFVAVPx2NsgKyE3ZpJbt+wWkJTC0z59I1WNzX2INhnHTBIjEcDTiynC8jZQ3STEz1v7I3YZzQqE+PVULIYGR/bV/dmqx50b8SYn71NbBum1ZQ35mqUeHiHycqz0mBWIUkEvv4rgJBt7uX5Ijx2HcW4RiIFQKkUALWbKcfxKubNrwaqhtdZhnGNcyhbnVWSfUymGCdyAjwgMajeyqEepga9OLuQgiRJODWfRKYgYu/6zhNjZqVI5IpkXbI4er4Wui1izHHcBIAHAfxvzcMPA/i8/O/PA3iknfe42hDRtvYoxLgB0fUOA7wVWDpFLXBtpjTUAy2jC8D6W4CZg02f8KvwT28D/uF+w8UHM/Ecwh47EccOKsYAcOf2QfidVtVOYXUAd/8+MHeEslJXAGz4zmnjiRg3WD5TFONKYgzUXPrW9XwChslKyCPvA4BxxZjjaVnbPUDL8j1tpZA9xi75OG2oGNOg6EKyAIQ3A5EmifFqKsaZZbrQDV1DN1vsJqcJsBsxb2UldAcU4/GgCx67BafmaynGg3QeXelhT0bG3/KbwNIbwFPNWbLSebHaSpFLNFSMAbkcRCHGV5eVoirutEYdNMMAI8YpjWJcSJnGm53ICnBzMjFuMtpwy5AX6YKI2XiOZmcAXD/ZeW7AjutkTqAbkMDEVa8YfwrAbwHQymnDkiTNAoD8tTsRCWsU0bSg03rXwBrBW2iwCSCi0mILnBGEPHZkBRGF8VvI38tU6lYQuwRcfJ68yp+5DTj29YYvmYvnlAgshZB0SDF2WC14cM8ovndsTg3K3/0zwOh1tCQq5DryPvWQ0yrGBobhXHYLXDZLRclHfS9rQo+oSJKh9wOgDN9JkqQuVdcjHolZ2i8tVto3/WM9baWIZgrwOqyws4xZA4oxAEqmCG8CIueMvVFimi7UbEVotYixe4AymIGW7BQJ+UaMKUso5ml2ogPEmOc5bB321SHGw4BYWHnlNCMrxte+C7jxl4AXPw1ceN7wy1ParHYGg4pxplBUCfRVZqVQblrdGo9xHWIc9lYML7vlY9gkqnE8K8CrKMbNEWPtAN7BS1GE3DZsGHB3ehNh4Tl4HVYksvI1cw2UfLRMjDmOexuABUmSWmJGHMf9MsdxBziOO7C4uApLXSZEvigilS+qgwOpOVJrjAypsCXqLpV7MLATTnTgenqgndi200/S15//KjC4HfjKfwSe+K91VejZeE4hGp1WjAHgoevGkSmI+P5xWa3neeDeT9DS0IF/6Nj71EJWEGGzcLBZeMMKbthjLx++a5BlnKokKgA1m4kFw8N3xZJES+TeEXpdvSbEpJxhzOAf7+ks47he6x1Q87NTaqETOSLG0QvGLAnxafJkK1W1q0GM5TjEZgcHNahaoTDQsNgMtg176xNjAEit8DWGWSk864D7Pkl/9298yFBUnyRJcipFi4pxXmuluLqIcSRdgM9ppfMnULcOGgB8DitsFk5tv1OONXMQY7JSyNfDFjzGAHBmIYVDl2O4bjJYnovdQfidVkVwWQslH+0oxm8G8BDHcRcAfAnA3RzH/SuAeY7jRgFA/qq7zipJ0v+SJOkGSZJuGBwcbGMz1g6qYqCScoudkZ2ZkaEuDt4B6hLVsugGhnYCF9shxt8HQhuBqXuAX/gOFWu8+o/AZ+8BFt/QfclsPIvRoCbDGOiodeTmTWGMBpyqnQIApu4Cpu4Gnvv/aQm4i8gKojo1bFDBJWuDRjFukGWsa6Vg/y+DijHA2u8M1O5WEmNtU2MPIpop6BPjGjcxI3L73Ww8R1aKkmDsxiB+BfBPUHyg3bt6qRRuplpzLUW2KVYKR7eIsQ9LqQKWUvnqH3rka8tKD+CllwBwRLTsHuCRz9Df83u/0/Cl+WIJxZJUvqIDNFaMZU9ymZXiKkuliGqtiCWRbuSYaKQDjuPKSz6YumwixdijWCma8xiv89oRcNlw6HIMpxdSXbFRMPicNuU4R3A9tU2uwAprt9AyMZYk6aOSJE1IkrQRwHsAPC1J0s8DeBzAB+SnfQDAY21v5VUCNWpGWwdtkOiuEDGeDNFSzNnFFLD+VuDyyy0N5EDIAeeeBbbeT8TfYgPu+wSpx6l54H/dCRz8t7KXZAsiYhlBqdlFJgI4ArRE3yHwPIeHrhvDc6cWy5Me7v0Ekccff6pj76WHnCBSIgXQRHyavXxbAVL4anqMdYbvDNRBa98PYO13TJGrQzwSM5RIwRAYJ7IsFhu+lxkRzWjsTgARY6uLbkh04HVY4XNYKZkivJkeNGKnSFxRj2v3KuWrZiKyL9xBFpgWrBSpXBFuuwVWpuI1sa8ZwfYRIgy6qrGR/bMbyCzRDTs7N62/GXjzbwCv/TNw6nt1X8qGFXULPuopxrL1IpMX6UaK469KxVgRliLnACEDDO+u+5qwx14+fAeYphY6kS3Cx7VmpeA4DlODHjx5bB6ShK4M3jH4XRorRcWA9Uwsi+lYm7NIK4xu5Bj/CYD7OI47DeA++fs+DKBqcKAVYtzFRAoA2DHqg8tmwasXo0SMC0lg/ljzv+ji8+Qz3PpA+eNb7gU+9GNgfD/w2K8Cy2eVH80lNFFtgNx61/m74If3jqNYkvCtI5pUh9E9wJ53AS/+fVf9sdmCRjHOGkuJCGlTIhjqZBnXLFwAjA3fMTuNtv2ulmIsZEn5rrRSSKXGaRYmRTwrqBdfoG65B8NIwEke49AmeqARMc6n6G/C4u1c4dX1GAN1b7bqIZmriB5jqxMdSKUAgO1yMsXpeZ1kCiMrGt1AepFsFFrc+VEiaY//Wt2bnLRMjMtSKUShoS+7TDHmeUoxuMqIcSwjIMxWc+aO0NeRa+u+hqxo5rVSrLPLSmyTw3cA2SlY0tHeiS4SY6dNtVKwko/4JczGs3job3+MX/235iMLVxMdIcaSJD0jSdLb5H8vS5J0jyRJW+Wv5tjDegBVUTPJOeNENyAnEXSpDprBZuFx3WQAr12KUjIF0JrP+NSTpLJtfHP1z3wjwE/9Kf37yivKw7MxTVQb0LHWu0pcM+rDtmEvHjtYQYDv+l0idM/8UcffkyEnlEgxLpXkpVMD8WkePcW4dpZxMl+E08arPjxAHU4yoFCrVoqC6mmvRXJZigEbDgXUm7geHcCLZgoIurRWilhDYjwalLOM/eM0N9AomYJZLfxMMR5YeWJcEon0K8R4fWtWirxQXe4BdEwxHvQ5EHTb8IaeYuwKUVJPeqWJ8bJq42CwOoB3fIbOW996tOZLmWJc5jFm6RJGFONeroWWpJaLZABSjJWCrLkjlNY0uL3ua8rOn0wxNpGVYsAmb1uLxBgANg961HjJLsDvsimCC1OMheWL+M//8iqWUnkcuRJTrRY9gH7znYmgKMYeG1kNcrHGUW0Mw7uI1Izu7eIWEvZvCOHYTAIZt5xL2ywxliTg9PeATbfXXH7G4A7KfNWkXpS13gGyYtx5YsxxHB7eO44DF6O4HMmoPwhtAG76ZeDQF4CFE7V/QRvICiKcdgtVAEMyqODakcwVIYiacJg6WcbJnACvQyfDGDA8fAfI+6vDT/FrtZaqlWQVrZWCEePeG9AQSxLiWUEt9wAMKcajfrn9jufJV99IMWafTWAViXE2BkBSiXFoA5CcaTrKKpkr1rDtdEbB4jgO24Z8ODWnQ4w5Tk5OWWFinFnST0MYuZYsFce+VnNAKS2315Wp7Hn2mRnwGOdZLXSg9+Lazj8LfOpa4LsfbcmiF80UlDY7zB+l64jVUfc1A1pibHORYGMWxTgrIGiRt61JKwWgJlN0o9hDC592+M4/Bonj8fRLr+LIdBz/8U0bUZJAq8w9gj4xNhFYskDIbVeJhtGUCf8o8N8vAuP7urR1KvZvCEEsSTh8OU6q8cWfNJcTunyWJvO33lf7ObwFGLseuHJAeajKStElxRgAHt5LCud3jlYQy9sepSGIH3y8K++bFUS4bHxTPkyWYhI1mGWczBXh12u9M/h+rOkomhHKiMdSKo+/ffo03vvZF/GvL15EviiW10EzKLXQvacYJ7ICJAk6Vor6F56RgBOLqTzdvIQ3NVaMmZrOrBTugZW/WDMizo6x4AZaMWkygzqRK3ZVMQaAbSNevDGfpAjBSqxG+52elYJh73vp64nH9V+qKMaauDYDirHbXqEYO3pQMWbnqxc/DXzpvYZSPBhygohMQSxXjBv4iwGKII1nBVVY8I+aZjg4nhUQtOZp1aMBwdfDjlE/OA64aWN3rpMMficpxpIkARYb0vYhpBbO49H7tuG/PbAdFp7DKxfMcbNhBH1ibCJEM5qoGaZwdHmYrhXsW0/qGNkpbqVldIMFHQDUmLat99d/3sR+OrkVadp8JpZFyG3TeHCjXVGMAWAi5MY6rx3nFtPlP3CHgf0foAEasfNLQ8rwXTMKrnwhiGoj2+rEayUriQrQFBG3Wnj4nValAS5tX4dTZ87gTX/8NP7Hk6dwKZLB//uNo7j9z36Il18/Si/SDt85/XSB70ErRSwr37x6mrRSBJyQJGAhmacBvOj5BtnP0wA49YbCPUB+/pUsHmDEmB1j7GarSTtFKieURwPm4mQnsTk7sJGE7cM+JHNFzCf0kilWWDEuiXQTU2mlYBiYAoavBY7rz6XrDt+xdIk6irHNwsNu5cljDPSmlYLtc/f9AaUWfe6thkmqWtVup1SQ5Cww0pgYs5IPRVgITwGRs3VesXJI5Irw87mmo9oYxoMufPvXb8P/c0PtZI5OwO+yQixJyBREPPPGAk5kA9jjTeDDd22Bx2HF7vEAXj7fJ8Z9tICyqBnm2TQhMQ667dgy5FUH8IDm6qFPP0lLXKw0oBbG91O01RyRq7l4Tom+gijQxaJLijFAJxXdadrQBgCSmlXaQSjDd00ouGzpsMxnXCfLOJkTdOqgY4DNDVjtVc/XQ8hjx0vnI3j4736MH81awKXn8Z6bJvGDj9yBH/3WXfjXD96MDQMeHDlxEhk48ZkXF5ULPoCezTJmF0+l9Q4wPHwHyJXm4c00La9RMR8/PIOPP64ZYo1Pk9feIv+dVqN4QKlc11gpgKY9oLrDdx0avGNQqqF1kylWmBhnoyALSg3FGAB2PUzFRjpNgukWPcYAZRln8lqPcY9VQqeXyMrw5l8Hfu7LtK999m5DRVJqqpPN8OAdPZ+UWEVYGJgCls+tfFuiDhJZAT4u33QdtBbXjPph4btX+gWoCUeHL8fwa188iKRzFFP2qJKbfPOmMA5fjisFVmZHnxibCJF0Qaf1rrspE61i//oQXr0YRWnddlI1L71g7IX5FHDxx/VtFAzj++nrNNkpZuM5jAW603qn+/ahGsSY3ax0YaAnV6xUjI3kGGuG4RhsTvKn68Rr6SvGzVX0DnodODmXRDIrYOOGjZhyZ/DJh3djy5AXHMfhLVvX4cv/+VY8MsUjbl2HP/nuG3jznzytFqcEJnrSY8w+YyXHWMhSWkBDxViTZVyRTLGQyOF3v3YE//TCBdWnF7+s3twAKjldSZ+xohjL7+0bAzhL05FtVftbzljaSjNgxFjXZ+wdJmtDqVT9s26A1UHXslIAwM5H6OvxajuFohg7m1OMASLT5Ypxj3mMWTwgQAlFH3ySLAT/+NPAsW/Ufaly0+q2k78YIGW+Adjqz3JaXm0ITwFCeuXtNzpQcoxbGLxbSbAVoQ9/4TXYLDxu2LMHfGJGieS8cWMYBbGEw5d740atT4xNhHLFeJ5yKGstx60y9m8IIZ4VcG45Qz5jo4rx+WepKa2RjQIgVdE7oqgFs/FseSIF0NFyj0qMBVyYiWWrfYseFgHV+TatbKFEw3dNxKexfSZiMLKtSsEDDGcmM/zRO6/FF37pZvzgI3dgx9Zt4LMR3WX+gdIyRic24RsffjO8Div+6QXZWxsY70krRVQ7BwBooseMKsZy+x2gEOM/+vYJJGUydOSK/HdPTKtebMAcxNhiJbLehJVCEEvICmL18F2HiXHIY8egz1FDMR4GJHHl1HZt610trNsKDO3StVOk9OLaFMW4/ufmsVvLFeNCsrfywjNLgEcztDh0DfCffgiMXgd85QOUA10DUW2q09wRupHz6AxAVmBAVoyVFbcBOWt8eXXtFDlBRL5YghvZlgbvVhJ+F+2ryVwRn/65ffCPTtExl6QVkRs30vmxzGcsFk17DegTYxMhmhbKFWPPIA2hmRD75R2d7BS3AEunjFkLTj9Jw2uTtzR+LseRajz9KnKCiGhGKM8wBrquGOeEUnUUmrd7bVqqx7iZJjpNSkTZD/SJcSpfrLZSZJtTjLcN+/CmLevA85waEainoCdnAN8o9k4GsWvMj6WkvI3+CboI9lg7kuIxVogxa72rT4z9TivcdgtmYjn6u3AWIHIePzm7jG8cmsH7byWbwuErMVrCjU+bQzG2ugC7W30s1FyWcUo3MzvWsUQKLbYP+3BalxivcPsdU4zrWSkAYOfDlOiTLI86TOeLcNks5cvfBhVjt8NSrhhrX9sL0OZmM3jWAe9/HBjbB7z4mZovLesBmDtqyF8MqMKCcv4MT9FXIyU8XQSLP3OVsi17jFcKEyE3eA742EO7cMvmAbXkQ05eCbrt2D7sw0tan/HlF4G/3Amc+cEqbHF99ImxiRBJF5SEAaQWTOkvZti8zoOg2yYT4zfRg41UY0migYqpOw17WTGxH1g+g4V5uniUtd4BXfUYjwXpvarsFJ46RLBNUCW0nErB8YZqQB1WC7wOq5JqokAny1gsSTIx1lGMW/V91moXkyS66MuDd4M+BxZZbW+gN5MpYpkCeE5D9AwSY47jMBJwYi6RJd9wcBKl5bP4/ceOYiLkwkd/6hpsHHDTUmMmQvaMVSfGOnGIwfVNWSl0bQFdUIwBulk7NZ9CqVSxwrPS7Xfsb9RotW/nwwAk4MQTZQ+n8mK5vxigz8zqUj3nNeCxW8tzjNlrewXpJf0bCpsTuObtwMKxmit17PwXtJeApTcM+YsBVVhYZsQ4MEkpEKs8gBeXb8IdUtb0VopN6zw48vEH8L5b5DkENqirscvdtCmM1y5GUWTpHye/RUO4kzev8NY2Rp8YmwTZgoisoImaSc2ZmhhzHIf960M4cDEKjO2lHbxRnvHCcSJClW139SD7jJPnXwaAlVWMGTGOVhBjh5cyljs80COIJYgliRTjbIwGbXhjh2jIY9Nvv6vIMmZEpV2PcRlqtYtllsk246Pou0GfA5F0AYViSSV9PUaMo5kCAi4bKeWAYWIM0L7LsrgR3ozlyydxeiGFj799F1x2C66bDOL1K3E1Dk1rpVAauVYwCzSzrEOMNxLBFIxVvDLPtL+SGHd4+A4Atg1Ty9eVyuNVIcadtz7pIr0ECRze8U8n8O0j1TniCoZ20BByhZ0inS/Cq41qE4ukqrE68Tpw2y3lOcZAbxFjrce4EpvuoK8XntP9sZLqFDlF5z0DUW0ApXkEXDZ1ZdBipZWRVbZSsGPHJmbaGr5bKZTdzCnD3yoxvnFTGOmCiOOzCRJNTn4T2HynKf9vfWJsEjBSo1op5o1nGK8S9m0I4dxiGpG8bHloRIxPfY++brnX+JuMXQ+AAyfnGVd7jLtHjCdCNRRjgJZnO0yMWXWnkw3fNUFUQ+4a7XdA2dJ3UiEqOgUfLRNjeUC0YklYIeQaxRiQh1wY6TNJXqhRRDMauxPQFDEe8cvtdwDS3vVwJC7i3h2DuHcnHed7JoKYjecQm9P4sBmsdlo9WHHFuIKkKMkUxgYn1fpxeX+TpKZtO0axbaRGMoVn5a0UoiOIg1eSePJYg9rznQ/TMLLmXJLOF8tJxmufBxZPAnd9tOFbexw9rBgX8+SJruULHr2OPNbnaxNjxV8MACN7DL91VXtoeGrVrRRMMbYV06ZXjKtgc9FxF1evPSxL+eXzERqOjF0Cdjy4WltYF31ibBJEtP6okkjL9EZb71YJN2wgMnDwUhTYcCswexgopGu/4PT36WSlzbRtBGcAWLcNrsXDACpa7yz2rnqvAi4b3HZL7WSKDlspcvIFzcWG75pQ1UJuu45iXB2vxYhK2WVYMK0AACAASURBVNJ2qUTDPa36PhXiUfF5JMrLPQa9RIwXk1pi3FuKcTwjlFerNqkYLyTzKIolfHfaBT+XwSfuU6uy904SkZm9JCtV/onyX+AOr7zHuJIYKzdbxuwUyUqPcSFNQzldIMZb5frbU5XE2OEjG8KKWSmWkLTQsXRspoG/d+fDVJqisVOktMQ4lwB++EdkV9vxtoZv7bZbkKn0GPcKMa4c9qyExQpsfDNw7lndHyupTnNHKXqSDbkaQBUxHpCJ8SpGtiVkYmwR0qb3GOsiMFl2Az0ScGJ92E3E+OS3AHDA9p9ave2rgz4xNgnKJmrTS3SyNGlUG8OeiSCsPKfmGZeKtfMms1HK7axIo4hlCsgWGmQbju/HYOIogi4rkUZAbb3jupfPyHEcxoOUTFEFT/cUY2X4rgnyUHViB3SzjHWtFPkEAKl1smK109+iknjIE8kKMfZpiLHNSV7CJlvUVhvRTKFaMeZthi5cIwEnxJKErx+cxndmaKBtXFIVxZ2jAVh4Dsn583TTV+lRXelaaF1izG62jBHjVJ4u7opirAyVdt5K4XPaMB50VRNj1s6YXikrxTIWRFKvzy6m6p/fhnYCA1vL7BTpgiY15vm/pCHVB/4/Q+c6j8OqVEr3HDFmw9v1hhY33U7lODoDoHRsyhnGw7uaGlyvVozlrPFkHStMl5HIFWFDEVxJMH0qhS6Ck1WRnDdtCuOVCxFIJ79JQ/vMhmcy9ImxSVAWTt7oztkkcNkt2DXmJ5/x5E0AOODC8/pPPvtDUoo0xFiSJLzz71/A7z92tP4bje+DtxjFdV6N+tLF1jstxmqVfHiHV8BK0aRiXEmMdbKMmZWiKj4LaM/36RupJsaJWVB7G93glRFjQM4y7i3FOJYR1AxjQC33MEBamD/+448fgxDYSA9qqqFddgu2D/tQjF0B/GPV/vKVJMZikUhs5TnIO0zzBI0i2yQJKJXUFQpFATWez90Ktg178YZulvHK1UJL6UVczLkx7HegJAEn5+qoxhxHqvGFHynEMM2G72KXqRr52ncB4/sMvbfbbkFWECGWpN4jxkaue8xnfP5HVT+iVCcbMG+sClqLcKUVbUBOplhFn3EiK8AD+dpjYAjbdAhMklVOo7rftDEMT3Ya3NwR09oogD4xNg3K6ixXoLyiU9i/IYzDl2MQbD4ix8/+KfD5twNH/o9S5QyAYtpcIWDiBuWh47MJnFtME7GuB/k1tzgvqI8xxbjLGA+5KGKrEt4hsnN0sBY6J9C0rjJ815RibEO6IFY3C1VEtlUtbQNNRcPVhB7xSM6Q6ilP0q/z6hHj3lKMY3qKscEsbeaPTxdEfPBtd9KDFT7G6yYDcGZmIWkH7xjcAyuXxatYRCqOMZ4nJaiRYvzl9wF/OIRHnnkrvmL/OMLf/mXge78LHPhH+fd2XjEGyGd8bjGtTr4zdOFGthbE1CLmRS/ef+tGAAbsFLseoRXCk98EQKs6XocVeOqT9PN7ft/we7Ps46wgyi15XO8R43r5z0PXkKJ8vtpOEc0UsMkaof+vwUQKhrCXrGhKZr0JItsSWQFhq3yu7EUrRXA9UMyVrdTctCmM+3l5VXn7T6/ShjVGnxibBJF0ARxHvtZmfIurjf0bQsgXSzg+kwDe8wU6iUcvAl/9IPDnO+hiuPgG+Yu33Fu2vPXkMSJS55fSipKpi6FdyMOG3TijPpaNqDW5XcR40IVIuqD69hjYMncHl2fZkmtLw3dK+51OZJuGGCd0iXEHVDzvsA4xniuzAzltFvidVjWyrcdqoQvFEtIFEUFXpWJsjOSNB13gOOCtu0Zw+6719P+vJMYTQQxJS0g7dXz47rA6dNpt1Et9CTbIMk4vk4dw/S246NuLImywLBwFDnwOeOWz9ByWc9phbB/2oSCWcGE5U/6DlaqFLomw5KKIwI937htHwGVrTIyHd9PSvdzsls4XsU04CRz5MnDrh+lGxCDccppFJl+kmxinv/eIcT3FmOPITnH+uTIlMieIyBREbC5doAeaJMYDHjsEUVLOjwhMkJ1pFSPb4lkBQ055e3rRSlGRZQwAGwbceND+GmYcm1RV3oToE2OTgMVAWS18TxHjfRuIFLx6MUp3+rc9Cvz6IeB9Xwc23Qa89Bng724in1yFv/h7x+YosxfAiVmd5U8ZOcmCo6WNmMqfVB9cKcVYjmyr8hkrEVCdu9gytddtKco1w8ZVtbCsYuomU2iyjHVTKRRi3IaKx4iHdlglMUuWAA0GfQ6NYjxO/uaVqq3NxtpSgJQ6aE9rinHQbcfnf+Em/OnPytPy4c3kl9Rgz5gPI4hgRtLZt91hoJBamVKUeiQltKG+leKNb5ECev8f4ovjv4v/YvsE8GuvAr8zA/z2BeDRN7p2UVSqoauSKYbk+MDOrfDoIhMBBwkl9zqMBlzYOerHsZkGxJTZKc4/BzG1jEyhiPun/5Zuvt/ym029PVOM09pkip4ixlzj42nzHeT9XVaFEiYITObP0O8Y2tnUW7NVIMWOxluout2olSKX6HjDYCInYNAu/85etFKwGzpNMgWXieB66QS+W7yhulHWROgTY5Mgki4o5KaXiPFowIXxoIuIMQPPA1N3A+/6Z+AjJ4B7Pw5c8xCw7a3KUy4tZ3ByLokPyMuNR6drn7znEzkcLk1hKHWSTj6SJCvGK2OlAIDpSjuF0vbWQcWYEeNSih5oxmPM2psaZBknc0VYeQ4Oq+bQz3bCSjFCy2bai7DceqdFGTH2d7nkQ5KA+ePA858C/vGngT/bDPzdzS2rrlHF7qRVjGNNHae3bxukVSEACG2sIurbPGlYuRJO5XT+FoykroSdoh4xDq6nbcjXuJk98QQ9Z/Q6pHKaMhlOJj1dHCreMuQFz6HaZ+wdAiAZa+dsAyU5K3lwmG4Id4/7cXIuCaHS2lGJnY8AkojCsSfwAP8KxhOHgLt+p+mMV7c8nFyWZdwrxDi9RPtHo6G5TbfT13PPKA8xQWAoe5puOJtUWMNeOn8uVw7gGbmRFovA3+wHXvy7pt6zERLZItbZ5O1ZI4oxTn0XPEr4amZvdd64idAnxiZBNFNQyz2yUYC39kx24f4NIRy4GNG/A/QOkerx7n8pqzN98jhN47/35vUY9DlwtI6qMhPL4VBpCtZSDlg8QRfkUnFFFOOxWiUfXchGZVaKVohxuB4xBpSlb0ZUOO2wWCeG7yoV9GKeyFWVYuzUtN8xRaHDPuPLLwPf/E3gU9cCf38r8IOPkaKz8yEqHJk/1tKvjVVmjQNNKcZVCG+mGysNwbSm6AbmcELn2F/J9ru6xLg6BlBBLk6Dttc8BHAckjmhPBqwy3DaLNgw4MHphUpiLO+fXWir1OLyFfpM1k/QcbdrLIBCsYSzi6n6Lxy9DghuAH/0K/jv1i8i7p0Crn9/0+/PYt7ULONg7xDjzHJ9fzFDaBOdOzR5xuy8F4wbb7zTYsCjs+LGIttKDW5q5o/SfrV4qun3rYd4VsAAI8a96DF2Bcnnrk2mOPktCN4xHJM2UmybSdEnxiZBJK0pDshG6YTWxSiyTmL/hhDmE3nMxI0v8T55fB47RnzYMODB7jE/jk3XXk6fS2RxSNpC31w5sCKtdwzDPgcsPKdjpajR9tYGckU5rk1kxLi5gg8A1ckUFSQmmRPKEykAefiOa2+5Tvk85BuFZHmGMcOgt8JKAXSWGEsS8IV3A4e/RJnZb/sU8JvHgQ89Dzzwx/SchRMt/WqmGCuKryhQIUE7xBgoS6ZgF5EXl1zVKuOqEGOdY4yVfOjZKU49CZQEIsagFQqfo36Ncaehm0zRBeuTHs5fvEDbMEV/211jJAbUO78BUOwUjsvPYxM/j+N7fptye5uEohgXelAxziwjZwvinZ/+sVKEowuOo3SKCz9SSGs0U4AXGThTl4CR5hIpgBrnz/BmWgVjsZO1cOUV+trh1JNETkBQIca9IZJVQZtlXEgDZ5+Cdefb4Hfa8MqFPjHuowGi6QJFtQHtqVCrgP1y0cerjdIlZCyn8jhwIYL7d9GS6u7xAE4vJGvmfc7Gc7gkDUFyhSkneQVa7xisFh4jfmd1ZJvdQyerLgzfOUT5ot4EMWYRYpF0hYeSZRnLJCapXdpmyMVJzTdYP60LtjyuEGM5n1fHSpEpiLTU6x0BOL6zVorMMt043f17wH/4AnDDL6gE3DdCN5wLx1v61YpizFZ2FKW9VWIsFxBol2vlz+JiMVTtk1VqoVfCShGhkgSbq/pn9RTjE4/T33XiRgA19rcuY/uwDxeWM+UJLd6Vab+bm6W/38gIHXebB71w2vjGA3gApVMAeE68Ftn1d7b0/opinO9Nj/G86MVrl2L44st1hjsBslNkoxTNBrp+7uDk1zTReMcwoGelMBrZ1i1inBUQ5GURwYS1yYagzTI++zRQzIG75u24cWO4rxj3UR+SJCFSaaXoIWK8Y8QHt92CVw3eAT51cgElCbhfrsLdNRaom/c5G8sh4LKDG99PxHgFFWOABvCqrBSAXPLRuZMhu5A7BJkQNWFtsFl4+JzWaiuFzUnkVFGMdYhKNtZ+4UKlYpyQVRZ/NTEG5Mg2i5W2rZNZxmwgZ2BL9c84eSinTcVY8Ri3OwsQ0iHG8WmUbB4k4MbhyxWEZkUVY506aO122DzVkW2FDHDmB8A1b1NusnRXKLqMrcM+iCUJ5xY1LZyeiv2zC5AkCYnlWZTAKecmC89hx4iBATwAGNuHczf8Hj4q/JIyRNcseloxTi9hTiDLwFcOXKYs5lpgPmPZThFJC7iGl4lxkxnGAOC2W+G08YikNRGjRiPbLr9MXzu4GiFJlJDht7C4th5VjIPrVcX45LfoOrP+TbhxUxjnltLq6qHJ0CfGJkCmIKJQLKnDd7nmBnpWG1YLj72TQbx6yZhi/OSxeYwHXcoy4+5xebmxhqoyG89ROcL4fiI17EBbAcUYoAG8lSj5yAoiLDwHa6G1+DTd9jtAPjkRiUnkBHgrl7YTM+3nyjqDVPzQwEoxxIhxSptlXN6O1BYYMV6nQ4wBykFdPNFS1WssW4DdylPONKAhxi1+dk4/3VxpkykSV8AFJhB02/H6lVj5890rqRgv177x5Dj9ZIqzT1Fb2DVvVx5K5ldBMR4hda3MZ2x3k98x1b32uzMLKbiEKAr2YNkA2a4xP47PJhpP4XMcTm/6eUxjUK2EbhKMUGe0w3eFZMcTEzoOSQIyy7iUd8Np4zETz+FHp+v8rfyjwLptSj10NFPAdbZLdE2omGswCir50Ky4+ccBq7N+ZFt6iY5ftnrYyI9sEOkClbT4uCzAWQCroyO/d8URmATycYpwfOM7VAFtseKmTXRuMaudok+MTQBGZso8xj1EjAGyU5yYTarT0DWQKRTxo9OLuG/nsDIANh50yXmf+srGXCKrEmNItCQDrJhiPBZ0Yi6R0ykNGOyolSInlOQ66NaIcchtr1aMgbIs41S+CL+WqFx5Fbj4PLC9zRYijiu/UUjM0EWlYj+uar/rdJbx8hmqaA6s1//50DX0+bZQ9RpLCwi6bOrgYifSY0KbKjzGRIz3TARx6HIFMbbYAEdg5TzG9fJkK/KxAQDHH6fPYsObAQClkoTUKhDjjQMe2Cxctc+4wys8lXjxfARhLgG+YoBs11gAyVwRlyONp/DTepXtTYDlGJfFtQFy7buJkYsBkoizKQfeuW8CYY8d//5KgxvmTbcDF18ARAHRTAG7+MvkL25xNifstZcrxjwvR7bVUYyZjWLLvdTs2qHEmHiWCLqHy1MiRY/MG1WBRba9/u/0N5bb7naPBeC08aa1U/SJsQkQrfQuNhkBZQbs2xCCWJJwuFLlqsBzp5aQL5Zw/65h5TGO47B73I+jNQZUZmM5jARcMjGGGtPT7vK/QYwH3RBLEhYql308na2ZzQqiWu5hcej7O+ugrmKcmAbEYrWV4ulPEgG69Vfb3HqQnYJ5i1m5R8UJvboWepxIdKcyLZfPkHe31uASyzdtwWcc1Wu9A9o7VisjoeLTQGAceycCOL2Qqi6WcYdNQow30CoE+7sVC8Cp79INltx0mC4UIUmtk7xWYbfy2LzOW+3R7nL73UvnljFqTcHmHyp7XBnAM2CnYMS4VcXYbuFh5Tl1v+mVWmh5FWSu6MWe8QDeef04vn98HkupOkvtm24HhDQw/RpiqSw2ly625C9mCHsc1efPgan6ivGVVyhBatsD9H2HrgcJmRi7pWxvZhgzMIHi5f9JQsnU3QDoGN23PtQnxn3UBvMuhj02mnTPJ3qPGK+XB/Au1LdTPHlsDgGXDTdtLFd7d48F8MZcEoViuSqbE0QspwukGHsGKPs1n6ATfgtT261AzTLWKfnIRokUdAC5ggiXnW+6Dpoh5LZXp1IASpaxlJyRFTzZSnHuWbrJuO3Rzgx3aIlHchbwVS9phtx2WHhOoxhP0OR3p8je0hl9fzHD0DX0tQWfcSwrKEOOADpHjBPTgJCliLv0AuAnxVgsSdX2opWqhW5UoBPaIJezyDfC55+l77U2CqVlcWU9xgCwddiLN6qI8VDX4tokScJL5yMYs6XBVSjG20d8sPCcoQG8pEyMvS0SY47j4LZbkM5XKMZmJ8ZyvnQUPmwf8eHdN06iWJLw9dfqrCZtvA0AB5x/Fq7kBThQaMlfzBB22xCpXHELb6YVnVoWicsv03uygdQOE2OnlO3NqDYGphhHLwBT95T9X27cGMaJuYSijpsJfWJsAkS1Vop2J91XCQGXDTtGfPg/r11BvLKWWIYglvDUyQXcc80QNfxpsGs8gIJYqsofXUgQgRoNOOkBphqvkL8YAMaD9N5VA3jeztZCZwVRtVK04FsNe3RO7ICSZZxbvEC+NaeVlL6nPknE9IYPtrvpBO9Q+fBdxeAdQMNIAx67RjGWUzM64TMuiaS+1mtVc4eJwLdCjDMFHWLMkb2hVbDItuhFdWAxMIE9k/Q7D1faKVZCMRYF8gU2slIAqs/4xOOkbG2+U3lKUq9+fIWwfdiHy5FsubXL29kVHi3Oy4NEQSkOuMuJsdNmwZZBb92sdoZ0vghLZQFPk/A4rOUFH4D5ibG8Ty9LPmwb9mHrsA/7N4TwpVcu1fZmu8OUWXz+OQxnTtNjLWQYM4Q9DkRSOsRYzAMJnUjJkghMv0YJLB2OA2Rk0SFmerPcg8EzSEoxoNgoGG7eFIYkAQcNziatJPrE2ARgyzdhj72nWu8q8cmHd2MmlsWH/u1V3aanV85HEM8KeGBXdfPV7hp5nzNxIqOjAdlWMH4DfV0hfzGgKfmoVIzZpHuHVCjVStGiYuyxIyeUqmPvZDUjv3QBAKhw4Y1vA9MHgDt/m5IrOgHfCFV/i4KsGFcTY0Buv0tVZhl3wGccv0IXsXqKMUCqcUtWCkGn3CPYXsydNrKN5TkHxjHkc2Is4MThKzrJFN0evmPnoHrHmBLZdpEGu05+C9h2f9m+lMrTxb1V9bMdbFhHylRZ/rh3iAhiFyq1XzofAY8SHEJcLf/RYNeY35BinM6L8Ngt5QU8TcJtt2gKPnqFGJNi7AoMKTaSd984ibOL6foxoJtuBy6/hC2FExA5Kw3ktYiwx4Z0QSyP+asX2bZwnKwckzd1PNc+Id9U2kvZ3k2kAMhKF5igWE5N8y1A9sunH70Dd2yrPl5WG31ibAJEMwXwHOB32nqaGN+0KYw/fucevHB2GR97/FjVnf6Tx+fhtPG4fWv1gbBxwAOP3VLlw2NB7yOrqBi77VaE3DZ9KwXQsUn3nNZj3AIxZqkmVaqxrMqKkQsAAJ+DB57+QyKQ1723rW0uA7s4LJ0ie0SN6fDyWmhZMe7EAF69qDYthnYCCyebmiCXJElWjDvUeseglHycUz8D+TO5bjKok0wx0H3FuF7rHYO2UfHST+g1cqkHQ2IVrRQjfjpfzCU0JFhpv+t8MsVL55Yx5SmAg6Tb3rZzzI/FZB4LyfqkPJUvtn0j4XFYy+PagB4gxrTPDY2MKw89eO0oPHYLvlRvCG/znYBYwMN4FhHPFGC1135uA4Q9NP9QNsBcL7KNDd5N3EBWNKurYysSTDG2FtO9m2HMMHY9sPUBskJq4LRZsHnQ29ZNYLfQJ8YmQCRNQz08z/U0MQaAn90/gV+5YwpfeOkS/umFC8rjkiThyWNzuG3rIFx2S9XreJ7DzjE/jlaoKrMyMVasFKN7aNhhBRVjgHzG1e13nS0NyGpTKVoYLGTDm1U+Y6sD8I1CklMEtsx9l9SOu363sz5tRjxmD9NXX/XKAFDRfudZR4OGnWi/Y6qOEcW4mAViFwz/6kxBhCBKaoYx0Bli7AoReYmeVz8D+YZiz0QQF5cz5X9Pd5gi0YTGCQctwwgxdgXl7b5INgqrkybzNWBWCv8qWCmG/URy5hOa4S1P59sqAdVffAfjdDrEePc4EdRGqnE6X2x58I7BbbeUF3wApifGxeQispIdU2OqaOJxWPHQ3jF86/VZJHM1fKjrb4HEW+HnMkgGtre1DWH5/LmstVP4Ronw6hHjy6+QbSa0SU7lGeqcYiwTY15I9bbHGADe+VngPf+22lvRFPrE2ASIVpZ7AO3nyq4ifuuB7bhv5zD+4JvH8cM36ERxbCaBmXhOKfXQw66xAI7PJMqC3WfjWfidVvViYXMB930SuP59Xf0/VEK35KPDVopcQfYYtzh8x07stSLbLInLsKGIqaN/RV68nY+0u8nl8MpEeOYQfdUZvgNIMV5K5VEqSfJS23iHiPEZWnb01t7HAGiSKU4a/tXsM63yGLdLjDlOjmyTrRTuAcrcBXCd7DN+fVpDapSSjy7aKYwQY4DsFNELwIlvEimu8EIyMrMaivGwrBjPlynG3Sn5uBzJYjaew41DMhl16yvGAHC8ATFOdYAYe+waxdjuA8CZnhgnI/NYhh/bR/xlj7/7xvXICiKeOFwjXtHhQ3ZwLwAgv25XW9vAzp9lyRQ8T6s6elaKK6+Qv5gpnt7hzg3f5QT4HFZw+VRvWykA+nz4ajHMzOgTYxMgki6o5R49rhgDpP5+6t17sX3Ej1/7wkGcmk/iyWNz4Dngnmtqk5bd4wFkBRHnl1LKY1TuURFbduuHgU23dWvzdTEWpJKPMnuI3U0Xng5ZKbKCCKeVa3n4jvlfa0W22ZNX8G7LD+FIXQbu+Vh73lg9MOIxKxNjneE7gIhxsSQhxqaRO5VlvHyGPIGNluYGZWWpCZ9xTB4o7biVAlAj2xLT9FnIuHY8AI6rGMBTaqG7aKdQiHGDVZngemoeS86UpVEwpFZx+M5psyDgslUQY2al6Kxi/OJ5+ryuDcpkVEcx9jttWB92N4xsS3fASuF2WFWPMc9TkYzJiXEuvoCo5FXKWRiumwhgx4gP//5K7Yro5aFb6B9tJFIAdYSFgc3VkW2ZCLB8Gpi8UX3MO9Qxm048K8DvsgGFdG8P3/Uo+sTYBIimBYQ8rGZWvgi2oBiaCR6HFf/wgRvgtFnwwc+/giden8WNG8PKyUcPrAFPm2c8F89hNNih4bA2MB50IVMQq6NlvJ0rDcgJIgLWAgXFt6MY1yDGruwsfs36dRTGbq5a9u4IFGL8On2tM3wHoDyZohPDd8tngIGtjZ/n8BGpayKZQska7xYxjl0mWwJL6QAprVOD3nJivBK10Ox3N/LxhzbSsKM2x1WDZK4InlNrilcaI35nOTFmQ3EdtlK8dC6CkNuGEWuy/H0qYGQAL50X2/cY2y3laRw9UAtdSi8higA2DrjLHuc4Du++cRKHr8R11faZWBZ/G7kJXxVvg2PTzW1tw4CelQIgn3H0AqVQMEy/Sl8nKohxx+Laigg5OTq+ejnHuEfRJ8YmQCRTUAljNkonsh5betDDWNCFz75/P+YTeZxfSuN+nTQKLbYMeuGw8jiqWTqejWdVf/EqYlxOprhSFdk23NG4tiCfoW9aIMYBlw0cB0T04vKC68FLIoa5GIp3/V53mpSsDiKKxSyRqho1poNenfa75Gx7tbXFPA2CNfIXMwztbIoYM8VY8RgXC7Ky3wlivIluhpbeKCPGALBnIoDDV+LqSsWKEOMoLd82SithyRSb7tD9HJI5AV6HddWGa4b8DsxpPcZWO21nh60UL51fxk2bwuCzywC4mvvErjE/Li5nkKjll0VnrBRuu0YxBnqCGNtyEYjOUFWMJwA8snccdguPLx9Qh/Bygoi/fuo07v7zZ/CNi3bM3f0pbB5tL90g4LKB53RW3AamALFQHil5+WVKWhjbpz7mHabjUmw/lzeREzDkkH9Pr3uMexB9YrzKkCQJ0XShp+ug6+H69SH85bv2YuuQFw9eq68gMlgtPHaM+BRVJV8UsZQqYMTfXANcN8BKPqoG8DyDHVOgcoKIAMeIcfNWCgvPIeCy1S75APC0uBfOqbe0s5n1wZarayRSABrFOCWreYEJIoapudbfN3IegGScGA/uoPQMgxexmKwYBxgxnjkISKW2clMVsGQKoMxKAQB7J4NYSuUxIw+hrpjH2Mhwa0gmxjo2CgByy+LK+4sZhv1OLCQqUiA63H43HcviSjSLmzcN0A2yO1xT1Ng1Rje7J+qoxpRK0Z4o4nFY5NZB+WbKGVSLWEwKtxgH79UntiGPHQ/sHsHXD04jJ4j47tFZ3PsXz+Ivvn8K9+wYxlOP3oEP32XwuK8DnucQctv1Sz6Acp/xlVeAoV3lNge2YtYBoSSR1RDjvpVixdEnxquMZL6IYkkqV4zXEDEGgAf3jOL7H7lDjVyrg13jARydIYVMKfcwiZUC0Gu/68zymSCWIIgSAkjTAy1aacJ6J3YAGNuH0/5b8FeW91P6SbfALg41bBSAnpVCbkeKtVHyoUS11Sn30GJoJ1AS9IdqdMDaKYMu+Ti9+OP/y957xzl21+f+76M+ajMjTduZnbY7W+1t3mave8XYEDvB2AFCKCEJF9JDEsLNTUgvEPilQLiQC4SWe4OB5U38cQAAIABJREFUJGADXgzYxm1tL7bX3l5m+xRNVRn18/vje466ZjQazUoafd+vl1/yaqQzZ7WSznM+5/k+j7jtu24xe1mYTGGcNzEWJ0iv6nYK/bthOdvvFqqD1llzC9zxUdj6UMEfz+bWj19hutw2xvyRrMW8lUwOADig+Yv3rvGI9rYiNgrIrIYuLIxVVa1QKoUJVYVwTIsjrPGJ8dSMHydz2Fs6ij7m53f3MjMX455/eIr3f+UgDouJr/3yXj71jmtY3Wov+rzF4nFYCpR85ES2JZPCSpHpL4aM+M6lHw9m52J4zfrEWArjK40UxlVmOpizqGcFCuPFcHV3M/5wnPOTc6npbC1YKTwOCzazoUD7XaeYxiyxFloPlXeqmjAuM5Wk1VGkFrqphc+s/jt8tsFyd7E0UhPj4sLYaTVhMxvSwrg1oyyiXBYtjPVq6NIW4E2FojitJix6I9nZZ8TUucBCq0Xj7ASzdnDPmRivbReXUc9PaVcSjCYxAVxuj3EpOeEmK9zw26kUjVwCkVhVhXGn20oiqTIRzIlsq6CV4uiIH7NRYWOXWzuhKP5+6HDbaHNaizbghaIJ4kl16akU2sQ5K8u4hoXx6XNiYV1zW/HvjOvWeFnT5mAiGOXP7ruKR37jBvatrcBnL4dWhyXfSuHqArMjLYzHj4r689V7sh+XigNc+sR4Zi6Gx6TthxTGV5zqfWtJgHQZgye1+G4qLRQakNQCvEszqfa8WhDGiqLQ3dKUauJL4ciohW7uyX9iiejTHedSJ8YOC69fnEmXhWTgD18BoaIL4yJRbSBey6ySD31iPLUUYXxCHJhKfd3a1guPYIk+45lQjOYm7TOaTMC552DrW8vc2Rz0yLax1/Mmxk6riSazMXX1BFj+WujQROmWlHnwh+Op2LRqkIpsm4nQ4dL2o4JrAkBU1ne4bBgNitiuHgVYhKu63QUXkYVjCX7tawcB4StfCnaL+IyHIglwUvPC+OLFc+wEOjuLf2cYDArf/MA+jAZlWe05XoeFE2OB7DsVJTuyLVXskTsxrkwcYDyRJBhN4DFpn3lppbjiyIlxldGneyvVY7xY1ne6MBkUXrs4kyr36MqNa6sSBbOMK/RlqE+MHUntS7kMjzGIS46XZsL85SP5gs8fXnoU1IKkhPH8Cy3bnRm10GabsF5MF49kWpCJU4sTc2abuES6iIlxKjlm5BBE/dB/fRk7WgTPoBDqORaU1ElEIFMYL3P7XWgS7F7G/GG+e+gyf/6dw1kLn0rFX2UrRdEs42gAIoEiz1oco7PhtEUs6FvwCsJV3W5OjAWyaofDsQS/+uWX+NGxcf7qZ7dwY4Fm0MXgsBSYGEcDS1vcuoyMjYqMYrd3/u+MFrtl2T3rnkITY8iObLtwQByjc69OVehYkCrGkRPjqiEnxlVG/xB6HBbhXQpPN7QwtpmNrOt08dqlWQa9dlw20/KLuRLpaWniyGV/9p0Vqpmd0w6Udl0YW93zPLo4t2/q5JdvHORzT51hz6CHN29LT2H8kVgqEWLZKGHxHQif8RlfMH1HS9/SrRTr717cczo2LUIYx9Inr2efEbeV8BfrbLwXjJaCTYQdLmvOxNhbmdznHIZ9QQ6cHOHBqJ9/PTjDXzzxeOpnZqPC7gEPg22lr5C/Ilco5iEljP0FSj6CYxWZxI3OhkX2biIuhhrzWClALMBLJFWOj/rZurqFcCzBL3/pRX5y0sffvmULD+3uW/I+2bXvy1BuLXRk9oo3hpbCjE8IY6UStqQl4nFYmApFSSRVcRUg9YO1cPQR8e984cXsYg8dcxNYm5fsYdcjQZsN2vtWToyvOHJiXGVS+agOi/jiUpMNLYxBy/u8OMOlmXBN2Ch0elqa8AUiWdOedDbq0qYEc1q8UlPCL3Irl1DV/Pt3b+SavhY+/I1XOT2enowFrkRKwOpd0LYBVm2b92FZVgrQWtTKFMZz0+LEZLGX/zs2C99gCfXKM3Ox9DqAs0+LDN8lWGfy2P52eOsXCv6o8MS4sovvTo8HuPXvf8zH/vNZAJqaO/ijezfxnx+8nmc+fBsWo4G/frT0eDtVVaueStHmtGBQYHSmUPtdZewUo5qVQiyGVEuaGINYgDcXTfBL//YCPznp4+/esrUiohgyJsapWmjt6lMNJlMkkyrhGU1ILnBScSXwOCyoKvl59d61kIzD6CHhMc71F+tUYDG2HufnRHvfyonxFUcK4yozGYxiMii4rKb0F1eDC+Oru91MBKO8cn46v/WuinS3FIhsSx1olzYl0CfG1rh/yXXgZqOBf377NVhMBj7w1YMpIX9FLm1718KvHSjBSmFjKhQjGtdWzrf2w+yF8jJA9UucixbGm8SJqO/4gg+dCkVpaTKDqoqJcSVtFAsgJsYZ4s7uqbgwPjcZQlXhk28Wfu933LqD9924hu29LXS3NPGBW4d47PAoz54qzcIRiSeJJ9WqXu0xGQ20Oa2MZk7bK5gcEIjECUQ0H3XQJ+5cQBj3eew4rSZeHJ7ivV98gWdOTfDxB7bx1l29S94fnZTHOHdiXIM+4wtTcziTM6goS/7eqwTpWuhIzg8028SrXxe3q3cV3kAF2u90Ue5QpDCuFlIYVxnhXbSIEHy9DrpMf+lK4eoe8UU+5o/U1sQ4lWWcIVLMTcL2sMQvQ128WuL+irQedrc08YmHtnN0xM9H//t1oPq5spnokW2pxICWfiFSZy4sfmMTSxDGsOACvERSZWYuJso9xo+J6WD/vsXvZ5m0u6zMhuPpKxVNHlGiEg1V7Hfolq61Du3fI+eS+y/dMEhPSxN/8chhkpnxZ0XQp17uKlopALqabYxktd9VxgcKpE5Wupqt6c//AlNPg0Fhc7ebbxy8wPNnJvjkg9t5y87V8z5nsaRSKVIT49oVxkdHZvHgJ25rrYlSK0/R9jstUvHQ1wEFenYW3kAlJsZz4oTGTlisOzDXznCoUZDCuMpMBqN4MhfeQcNPjDetcqfsW6VkH18p0lnGOYLEsfRaaF30mGOzFasDv3VDBx+4ZS3/94Xz/PuBc0QTyap6PjPJyzJORbaVsQBv4qQ4gHgWGUXnWSN8vQv4jGfnYqiqFqmo5xdfYWEM4NPtFMvQfqcLgWZms3+Hhs1s5Pfv3sDrl2b5xsGFT170BUTVPhHrcOXWQreJ90oFsoz1SXSnywYhfWK88MK5nf2tGBT45EPbuX9HBe04GvU0MT464sejzGJ0lJCbfQXQhfFUbha8s0NMboNj4oTaVmQNSAUKZPSTSlsyJGx1VWqObGSkMK4yU8FYerW7FMYAOKwm1miLfLpryErR1WzDoMDF6dw2rY4lexZ1K4U56q/oFYPfuXM9ewY9/PF/vQZQu8K4ZQlZxhMnxeK9IhXURTGaRWzbAhPjae3SZqvDLGwUrlUiXu0KoUeNjfmXTxj7ghEsRgNNsZns35HBz2zrZntvCx/7/rG06CpCWhhXe2JszRbGBqMQL+VcmchB326H2wZB7d+ihAVkv3n7On78oVu5b3vlRTFk5hjX/sT42IifVeYQhhJOKK4EqYlxbjKFHtkG+TFtmTg7xFqhJVzN0a0U1mRI1kFXCSmMq8xkKJrdegcNL4whXZ9aSxNjs9FAp9tWOLItuESPcVR4bY3RmYpNjEH4LP/pbTtwa5O7agsVnTxh7O4BxVjeAryJk1k2irlogriWgb0gHZsWFMb69KjFZhYT4/59V3SKk/daLYMwngxE8TotKLp3uUDBh6Io/K83bWbMH+F/P3F63u35talXtSfGnS7hZY/EMxbMdmwWi6iWiC6MO92alUIxlPTdbTMb6fNWrq0tb/smI4oCoUg9TIxn6TQGayYtI+UxzrVSQDqebV5hrKcUlX88mJ2LYTIoGOMhmUhRJaQwrjLToWh2hjHUxCKEaqMXfXTXQB10Jt0tTQWsFEv3lelWCkNkpuL//p1uG//w8ztoMhsZ8NbGBKLNKd7zKbFnNImUh8VOjFU1L8P4Tf/0FH/2ndJi2OjYBDPnIVy4phfEZxSgI3EZ/JevqI0CxOI7KDAx1r8vKsBEUDtBD00Iz7zJUvBxO/tbedPWVfzvJ09xObfsJoOANjGudtSiHtmWFXfXdbXwii+xrXJ0NoLDYhTiP+QTJxM14JM1GBTsZmN6YmxxCtFeY8I4HEswPBGihZnKNEhWAKvJiMtm4kvPneWj//06z5zypU+y9QV4vUUSKaAi7XdToSgtdgtKNCAX3lUJKYyrSDKpMhWKZUyMp0X15GIvCa9AHtrVx5/fdxVr22vri6GnpSl78R1otdAzEI8UflIJzMUSGEmIL8MKTox1bljXxqGP3sWOvtq4GmE1GWluMmfHkJUT2RYYFeUFmjAORuKcGg/y9RcvpLx686I3lY0fK/qQKa22vXPyJXHHFUykADHFUpTMibE2XauoxziC12nVao3nn979wd0bSarwse8Xf81qxUrR2Vyg5KNrKySiJaWRzMeoP5xu9iuh3ONKYrea0nYXg0Gc7NSYMD45FiCRTNIUnylo3akWn3hwO9tWN/PvB87x9s89z86/+AG/8/9e5kn77URv+H3wriv+5AqUfPgCUTE4iASklaJKSGFcRfzhOImkKlvvCtBsN/PO6wZEWkcN0d3SxOWZueyV+c6MWugyCccSNCvaJHoZhDEIW0UtkZdl3Nq/+Imx74S41S5zDk+I0pC5WIL//GkJJRipZIriE2bdY+waOyCmgm0bFrePS8RkNOB1WNKvla0FUCorjINR2vSJcQEbRSa9Hju/dMMg3zx4kVcvFM7GTadSVNlK4RZDhqzItq4t4nZkaXaK0ZkwHdr2hTCuDZ8siCzjVCoF1GQt9LERP26CGNRETWQY69y5uZN/fddufvrHd/KZX9jJ7Zs6+OGxMX7xv6a46cC1+KOJ4k+uQBygOEm1iBN+q6vs7UjKp7aOlA3GZKrcI2PxnRTGNU1PaxOxhJo96axABNRcNEGHWZtqNUhcX7vTmrYHALQMiNewhMKNFBMnxa02MT47IU4uWuxmvvrcOVR1gWix5j5xlWYen/F0KIpBAcvF54SNwnDlvzbbXTbG9QY3o0nYbSqcSpGyUpQwvfvALWvxOiz8xXeOFHyN9Ymxs9qL77SJblZkm2ctmGww+tqStp01MQ75amrqabeYshdI1qAwPjoyS6dJa7+soddOx24xcffVXXziwe28+D/v4JMPbWNkNszjR+bxDzvaAGVJyRQTwSheh1UIY2mlqApSGFcRPTs0e2LcGKKoXunRPM8XMhfgpaYE5U+M52IJ2kzaNpdpYlxrFJwYA0yfL30jEyfBaAW3yILVJ8a/dusQx0b9HDy3gA/XYID2DfNOjKdCUdbZZlGmhq+4v1gn77WyeysmjEPROHOxhLBSzE2WJFJcNjO/c9d6DgxP8oufP8CJ0eyq9EAkjsNizK7VrQLNTWYsJkN2QYrRJCw0I6+WvV1VVRmdjaSEN8HxmrJSOKy1PzE+OuJnm0cT7zUS11YMk9HAfdt66HLbeOTQ5eIPNJrF52dJE2OxEJZIQC6+qxJSGFeRKU0YZ3mM5cS4pulpEavJL2a13+lWivKnBOFYEq9RO3g3yMmRLvZSE8cWrRJ3MXaKiVPCRqFNcYd9QdpdVt62pw+n1cRXnyshF7lj8wIT4xg3WjTLRpWEcYcrZ7pewVpoPcPY67SIbZY4vXvb7j7++E2beeX8NHf/w1P8yX+9lvpO84djVZ8Wg0jS6HRbsyfGIOwUI4fE4s0ymJkTrY0dbhsk4mKoUUNWinqYGB8b8bO5WVsHUIMT41wMBoW7r+7iiePjqdSVgiyh/S4cSxCIxGlz6hNj6TGuBlIYV5GUlUJ6jOsGPSUjqxa6AlaKcCyBp8Emxh0uK3OxRHr1vJ5lPDVc+kYmTqZjlIDhiRCDXgcOq4n7d3TznUOXU6kSxXdkkzip0Wt9c5gOxdhlOCLC9ju3lL5vFaTdZcUXiKS97ZUUxpqYbbep4mBcYnSWwaDw3hsG+fHv3crb9/Tx5efOcsvHf8wXnj7DVChW9ag2nS53TskHCGE8NwWzl8raZqrcw61N2aGmxJ3DmpFKAcKeVUPCeCoYZcwfYcih/bvUkMd4Pu7duopoPMkPj84zBFlC+51+FbmtyQDxsPjOkVxxpDCuIlkTY1WVVoo6wGUz47aZsrOMzTawNi/ZSuExLO/iu1ojL5/X2SlsEaVOjBNxmDqTtUp82BekX8uIffuefqLxJA+/tECZwwLV0FOhKFvir0PfXnEZvgp0uKzEEmpqISBNnopZKSY0v3yHSXv/LVLgeRwW/vz+q/nub97Elp5m/vTbh9l/eLTqiRQ6HW5bdlwbLHkB3kgqw9iWng7W2sQ4UrsT46Mjwnqz2qZ9j9bQScV87OxrpdNt5ZFX57FTODvLFsb61ZsOm3ZSI60UVUEK4yoyGYpiMRmwW4xiwVEiIifGdUBPqz17YgzCTrEEK8VcNEFLShg3xslRnjA2GISdotRa6OmzkIynFt6FonHG/BEGtNbEzd1udvS18LUDCyzC0yPbii3GCvroiZ2tmo0CCpV8aMK4TCtAJikrheJPb7sMNnS5+PIv7eFff3EX6zqcbFtdG+/jLreNkdlw9nug8ypxW6YwTpV7uGzpKw215DG25E6Mm8XVgMT8jYVXimMjIje8yxgAsx0sy1d4UkkMBoU3Xr2KHx8fJxAp8lo6O8TiuzI+m76g+Hy3WbSrXHLxXVWQwriKTAaieOwWEUkmW+/qhp4WW7bHGLSSjyUI41gCNyHR/tYgvrI8sQdiAV6pWcYTp8RtTiJFZonJO/b2c3o8yHOn57EduLrENh7/M3jtm3k/XjuniacrnF+cSbuzQPtdIgLR4JK3rVspWphNb7tMFEXhjs2d7P+dm/noz1y15H2rBJ1uK6FoIlvIWF2i1rvMBryxVB20VSRSQE3ZAbJyjCF9FSpSvMjmSnJ0xE+r3UxTfLpupsU692wRdorHjxSZCjs7hQ2ijNc6dZJq1oVxYxwLag0pjKuILxChzSXroOuNnpYmLk7NZU+gnEsTxiLHOCisNDWW3bxcpMVehv+zpa90K0VOVNuwT4jE/oy63TdtXYXbZuJrB+aZQisKvPsRcXn94ffAY3+UmqxF40m2JQ8TN1ihe0eJf7PK06E3uOmvVQVroScCEZrMRmyxmextrxD0SLWCPuOyJ8YRWuxmbGZjxsS4dqwUDouRWEIlGtda21K10IVzp680J8YCrO90oZQYD1hL7OpvpcNl5dFi6RRLaL/TbU0tJk0YyxzjqiCFcRXxBaIpcSCFcf3Q67Hjj8SZDmWsTK6AMHYRbBh/MYhFp0aDkt9+Nzc1b0VziomTwnaiXfof1ifGbekpi81s5C07V/O91y7jC8zTTOjqgnd9B3a/D575J/jKz0FwgulQlD2GI/hatla1kTLfSqHXQi99Ad5EUIuH0kV2nQmVhUgL41yf8VaYPA0Rf4Fnzc/IbFjYKEAIY8VQU9/ddovwd6emxilhXBs+45GZMD0tTSXnZtcSBoPCPVtW8aNjRewUS2i/mwhGsZoMNCW1K5LSSlEVpDCuIuP+iIhlASmM6wj9Ur2emQuIKUFkBmLhIs+an7lYAmdyeeqgaxWDQaHNaSmSZVzC1HjipJgWaxP2sxNB2pxWnNbsRV/v2NtHLKEuvAjPZIF7/x7u+xScew4+ezPhEz9ms3KW2Y7di/mrVRyn1YTdYkxHtlWwFloIY2s65WKFfQcVnxhfLW5Hi2dYF2NsNrP1blwshqxC8UsxHFYjQNpnXEPCWFVVxv0R2t1WcVJRZ8IYFrBTLKH9zhcQmkCJBsQd0kpRFWrnk9xgJJMqE8FIahIkhXH9oF+qPzcZSt+pTwnKzK+ciyawq4GGWXink1dckYpsK0UYn0rZKADO+IIMePMX8Qx1uNgz6OFrz5/LrvIuxo5fgPd+D1SVvm8/hFFRifZcu/Dzlpms1yplpajAxDgQwau33lmbRUnBCkKvhS6YZQxlFX1klXuEaqsOGjImxpHamxhPh2JEE0kxcQ9N1tSixVKZ106REsaLv4KYKvfQhbG0UlQFKYyrxMxcjFhClRPjOqTXY0dRYNhXQBiXaacIx5M0JRrLSgHCZ5xlpWgdELcLJVNEgzB7IUsYn50I0e8tPGF5x94+zk2GePpU4aziPHqugV99gon2vUyqTpTePaU9bxlpdxYSxpXwGEfTwrjMRIpaxm4x4bKZ8iPb3D3i+3aRPuNEUlTCp+qggxM1J+5qeWKsX/XocigQ9dfle06kU3Tx42PjBHPtFE2tYDCVaaXQTlJTE2NppagGUhhXCd3vmJoYh6fBaBHRNZKaxmY2sspt42yulQLKimxLJMUimaaEv+FyrPMmxk2tItR+ISvF5Glxq5V7zEUTjMyGGWwr/Pm5++ouPA5LaU14Oo429u/6HDdE/pGWlur/u3S4renFd7Zm4WtdojBWVXHlSlgp6s/vWSpdbhsjMzkTY0WBzquLx/QVYSIQIZFUU5PoWquDhtqeGOvv4VVmPTe7tl67Urlnyyoi8SSP55Z9GAziCkIZxwIxMbaKOmiQOcZVQgrjKqGLgayJcVNrwyQS1Dv9Xke2x3gJE+NwTEx1rAl/402MXVZ8gWja4qAoIpliISuFT6to1qPaJvVEisITY6vJyFt3rmb/kVHOZ1pgFuDSbIQQNlrt1bcXZE2MDUbxfbFEK4U/EieWUMWUaq70Ouh6o9NtY9RfwP/ftRVGX19Uvq++iK8j00pRY+LOoQnj1MTY4hQnUjUgjFOtgUY9N7s+33O7Bjy0u6w8Wqjso4zF2OIkNdNKochBWZWQwrhKjOdOjGUddF0x0GZP5eYCaY9hGcJ4LpbAShRTMtp4wthpJZFUmcqsbW7tX3hifPZpMDugfQOQtrUMthVfrPKe6wcxGRQ+uf94SfsWiMT56nNnuX7Im5rAVZMOt43ZcDx1IiVqoZc2MU7lpjotQmTXqUhZiA63ldHciTEIn3E8DJOnSt6Wvoivy20TgnpuqvYmxpqVIpVKYTCA1V0TwlifGHsMmjCusdeuVIyaneJHx8by7RRltN8FInGi8SRtDm1ibHHKQVmVkMK4SuiTn6y4tgZbeFXP9HkcTASj+MNaZJteC13G5bO5aAI32vS5wd4D7VrkVV5k29TZ4s1RqgonHoM1N6ci1PTpfV+BxXc6Xc023r1vgG+9fJFjIwtHdH3uydNMBKP8/hs2lvi3WV7ySj4qUAut56amrRT15/cshS63jTF/JH/xpZ5MsQifsT557nTb0q9/jYm71MQ4ktN+VwvCeDaCy2rCFtUylev4ZOxezU7xw1w7RRkT46yT1GhA2iiqiBTGVcIXiGIxGnA3aZMoOTGuK/T0g6ypcZlZxpF4Arei10E32MS4WPtdLFhc9PlOiMV5Q3ek7jo7EcTrsOC2zW95eP/Na3FaTHz8sWPzPs4XiPCvT53mni1dbOutjZOVds3TOpa5AG+JVgq99a7NmoBYaMUK4063jXhSZTLzygRA2wYwmBcnjGfCKAq0OS012XoHBSbGINYv1IAwTkW1pXKza+u1WwwpO0VuOoWzUxwLksmStzURzDhJjQbkwrsqIoVxlRAZxlodNMDctBTGdYTuZa2EMJ6LJmnWJ8YNuPgOKBzZVsxOcXK/uF13Z+quM75gVrFHMVodFn7lpjXsPzzKwXNTRR/3zz88STie5EN3bVhwm1eKvImxqxNmLxafrJeAPqVqM2qLfep4ejcfeoJE3gI8kwU6Ni5OGM+KrFmT0ZCOZ6yxibHdrKVS5E6M56rffDc6G6bDpWUYo9T1d55up/jh0Rw7haMD1EQ6baoEfPrE2GHRrBQyw7haSGFcJcYDGRnGICfGdYaeZZy3AK8cK0UsgVtpVCtFkYkxFF+Ad+IxaN8oFulpiKi20haqvPeGQdqcFj72vWPZtd4a5yZCfPX5szy4q5c17bUztenQXyvddtK2XqTZlJmdDWkrRWuy/i9rz0dnatpewGfcuWVRyRSj/nA6w7gG66ABTEYDVpMhe2JcK1YKfyRtQ7F7xELSOuaeQnaKMtrvsq0UQZlhXEWkMK4SPn+GMI5HxaUTKYzrBofVRLvLmh/ZFli8SJmLJXDTmFYKh8VIk9mYMzHWBG+hiXEkAGefybJRhGMJLs+EU42EC/5Oq4kP3jrEs6cn+MnJ/FzjT+w/htGg8Ft3rFvU32W58TqtGBQY14sqtIWHjB8te5sTwSgumwnL+Ovijo7NS9zL2iQ9MS5QC961RQgYf2kiZnQ2ko5qq2E7gMNqIlhjwlhVVcb82sQ4VJ+td7nsHvDgspp4YTjD1lRG+51+kupxWES+s7RSVA0pjKvEeCCjDjqsTWvq+JJSI9LvsTOcZaVoL6sWei6aoLlBJ8aKoogs48zFd1aXWFhWaGI8/BQkorDurtRdup2lFCuFztv39tHT0sTHvp89NT58aZb/euUS77l+MF3gUCMYDQoeR8Zr1aYL4/n90vMxEdTKPS6+JN57njUV2NPao91lRVEK1EJDugFvtDQ7haiD1t4bY4eFgKnBoYbdYiSUZaWovsfYH4kTjiXp0FvvavCEYrEYDQoDbY4cW93i2+/0k1SrySitFFVGCuMqkEiqTAajsg66zun3OjhX6MtwkXaKSLxxJ8ZQoOQDike2nXhMCJG+61J36XaWQnXQxbCajPzWHet49cIM33ttJHX/333/KC6riffftHZxf4krRIfLmm5wc3eLMpSlCOOAVu5x6aBo+1uh8VBmowGvw1pEGOvJFAvbKSLxBBPBqKgzTibg6KPi6oWh9g6lDkuBiXEsCIlY1fZpTHv9O9yax3iFLPbs99qzrx6WYaXwZQ7LZCpFVam9T3MDMBWKkkhm1kHrE2MpjOuJAa+dkdkwc3qIvqO8kg99Ypw0NYnFQA1GVnGFTkt/fi20qsKJH8AHZRq3AAAgAElEQVTgzVmvk35AKlbuUYyfu2Y16zqcfPyxY8QTSZ47PcGPj43zgVuHaK6BQo9CZE3XFQXa14NvKcI4yqqmJIwehp6dFdrL2qSruYgwbmqF5t6SFuDp79OuZitceEGcBG96c6V3tSLYrUZC0ZzFdwDh2ersEKRO6sTEuPaqtMtlwOvgwtQcsYSWQmF1gcm2qCFJqpodhMdYWimqhhTGVSCvDlpOjOuSfu3S/Tm9Sc1ZXsmH8BgHG3JaDORbKUCbGJ/LjjsaPwYz52DdHVkPPeML4XFYaG5anJg1GhR+964NnBoP8s2DF/mb7x6lyy2yjmuVvOl6+8YlWymuMp4VK+i7r6nAHtYunS5bqnUtj64tJQnjrNa7I98GoyXL1lNLOCym7KSElDCuXjKFHjXY4TKvqAryfq+deFLl0vScuENRFp1SNKm33iW16ES5+K5qSGFcBQrWQYMUxnXGQG4yRZlWCpFKEWpoYTwdihGJZ0y3WvqElziQtjmkYtqG7sx6/tmJYMmJFLm84apOtvW28Cf//Tovn5/mt+5Yh81cu6vkOzRhnCqqaFsvLtcuIhZKJ5lUmQxG2JDQ6rV7VrgwbrYVnhgDdF4NEycgNjfvNnQrQKfTCke/I65e2NyV3tWKYLcUmxhXz2esp4J0WCLiZGwFeIwhvb5hONdat5jFd8FIOsMYpMe4ikhhXAXkxHhl0O/Rs4wzhLHZvqhMVIBwTOQYKw36769/DvQcTwBaBsRt5gK8E/uhfRO09GY9f9gXZHCRNgodRVH4/TdsYC6WYG27gwd2ri5rO1eKdpeVeFJlek7zibZrrXzjpdVcZzI9FyOpQn/4CLh7wNVVwT2tPTpdNiaCUaLxAqULXVtATYrFdPMwognj7ugpmBqGTW9ahj2tDM1NZnyBSHpxaQ0I49HZCHaLEWdc24cVNDEGcnzGnSVPjPV1R216hjFIK0UVkcK4CqQnxpqfaG4KFK3LXlI3NNvNtNjN6SmB0QxrboHjjy2qdCEcS9BiCKE06MS4p6UJyDmotOaUfET8IqYtx0YRjiW4NBNetL84k+uH2vjwGzfysbduE6UNNUyHVqGdyuNtXy9uy/AZT2pNW53+wyt+WgwLZBnryRQLnNSOzkYwGxWah78PKLDhngrvZeXYuroZXyDK+UltCl4DwnjMH6HDZUVJVWmvDGHc7rRitxgZ9uUWPpU2MZ4ORUmqeuud9j0orRRVo7aPAiuUcX8Em9mA05pRB21rrsmVzZL5yUumWP8G4YMdO1LyNlJxbQ0qjDeuEgeAYyP+9J3N2lRYnxifeRKSsTw/5/lJPaqtPCuFzvtvXss1fbU/sc8rRGnpF4t8yvAZ+wJRmgngDJ5b8f5iEFYKoLDPuKVfJHwskEwxNhumw2VDOfoI9F2bTh+oQXYPisSHA3q+bi0IY+31S+c/rwxhrCgK/V5Hfq59aLKkFBC9ml2Ue2jfg3JiXDWkEqsCvkCUNqc1ow5att7VKwNee3b7nS7cjn+v5G2kCj4aNMe63WnF47Bw9HKGMDbbwLUqnUxxYr84UPRem/XcMz49qq0x/Hi6ME5FthmM4F1XljCeCETZajgt/rDCEylAWCmgSJaxwSBi2xaaGPvDbHVMiszjjbVrowBY3+GiucnMC2cqIIwnT8PXfj4/KWaRjPsjtLu1cg9YMR5jKHAscHYAarodcR50e6XXYc2wUjTGd1otIoVxFRj3yzrolUK/18Gl6bn0wjF3N6zaBse/X/I2wtEYDhp38Z2iKGzscnF01J/9gxYty1hVhTBec0tenF2q3KNBhHFeLTSIBrxyhHEwwjbllPhD9/ZK7F5N09U8jzAGYacYfU2kAhRhZCbM7bwg/lDD/mIAg0FhV39remJscQrL3mKFcdAHX3kLHP8uHP7vJe3T6GxYnKCssIkxiGPB+ck5EvrC2EW032XXQWvCWOYYV42yhbGiKL2KovxIUZQjiqK8rijKb2r3exRF2a8oygntViq+HHyBCO1OKYxXAgNeO0kVLkxlrGZffzdcOADBiZK2oUT9GEk2XOtdJhu6XBwf8acPKiCSKabOisrj2Quw7s68552ZCNJiN9ds7nClcVhN2C05FdrtG4R9Jxos/sQCTASibDOcRvWub4iTsla7GbNRKR7ZNniTECUH/63oNsZmI+wJPwOdW6B1YHl2tILsHvRwxhcUvmpFWbAWOplU04knANEQfO1BmL0kROy5Z8vel2AkTjCaSJd7mO1gWZoFqpYY8NqJJpJcntGOBYtov5tITYwt6c+xRXqMq8VSJsZx4HdVVd0EXAt8UFGUzcCHgcdVVV0HPK79ub449DB8/u4Fo3vKZdwfoU1OjFcEBVcjr3+DWOF+8gclbcMQ0QL3G0CcFGNTl5u5WCKdCQ1iAd7sBTj2qPjzUL4wPjsRbJhpsU6Hy5rKgwWEMAbwLS6ZYiIQZrvxFEoDLLwDcWWiwzVPZNvGN8HAjfCDj0JgPO/HwUgca2SC3uChmp8W6+zRfMYvDmvJRwsI4z/85iHe+fnnxR8ScXj4vXDpp/DA52H9G8UC2GSBVI8SSGcYW7U66JUzLYZ0wVCqGnoR7XcTwSgGBVrsFrHQGKSVooqULYxVVb2squpB7f/9wBGgB7gP0E+5/w24f6k7ecV55f+KM+PnP1PxTccTSSZD0eyJcXhaCuM6Je/LEGDVDrHwokSfsTGiHagaWBinF+BltHK19IsTjJf+DTo2Q3NP3vOGfaFFVUGvBETJR4a4a9OE8SIj2xIzl2hnuiH8xTpd82UZKwrc+/diSrr/j/N+POaPcKfxRRTUmvcX61zd3YzNbOBAps+4iDCOxBN859VLPHtqgkA4Bo9+SNgn7vkYbLwX+q+DucmymxZTddAum/AYrzBhrC8ATufaa8K4hFx7XyCKx2HBaFCklaIGqIjHWFGUAWAH8DzQqarqZRDiGajdZbuFSMTEWTEKPPWJki+Hl8pkMIqqkp4YJ5OiEloK47rE67DgtJqyhbHBIBbhnXq8pBXJppg2IWjQxXcA6zpcKAocyVyAlxnZVsBGIaLa5pYU1VaPdLhs2RNjzxowmITlZBF4p7WFZg0yMQYR2TZSTBiDmL7v+3V45Wsw/HTWj0ZmwrzB8CJzzj7ovGqZ97QyWEwGdvS28sLwwsL4mVMTBKMJkir4vvtX8NIX4Ibfht3vEw/o3yduzz5T1r6M6nXaDkT6hzv/RLee6XTZsJoM6WOBuUlEsJZgpZgMRsTCO0hbKcyN9b1WSyxZGCuK4gS+AfyWqqoll7ArivIriqK8qCjKi+Pj+ZetqsbFgxALwq0fEWduT328opvXF82kJsaRGUCVwrhOETE9OauRQdgpwjNw/vkFt2GNa2KwgSfGTRYjg15HdmRbS3/6/wvYKC5MhVBVGGxrrANIXi20ySLE8SKtFN2hI8Qxida3BqHTbUsnehTjpt8T/vZHfgfi6dKZyclx9hleIzz0RjFdrhN2D3o4cnkWfzg2rzDef3iUJrORB4xPMvDKJ2DrQ3D7n6Qf0DookmLKFMb6xLjn9MPgvwR73lfWdmoVg0E7FvhykilKXHzn1XsN/CPQ5JHxrVVkSa+8oihmhCj+qqqq39TuHlUUZZX281VAwdMlVVU/q6rqLlVVd7W3ty9lNyrL8JPidvf7YMcvwIHPiaiaCqEf0NpdGeUeIIVxHTPgdWRPjAHW3goGc0l2Cmtc9xg37sQYxAK8o5lWCncPKEaxCKXv2rzHn9HC9Mutg65X2l1W/OE44VhGekL7hkVPjNdGjzHatFZE4zUInW4bgUicQCRe/EEWO7zxY+L1fO7TqbttZx7HoiSwXH3fFdjTyrFnwENShZfOThUVxsmkyv7Do/xq73n+xvw5XrPugJ/55+wTAEWBvuuEMF5EgZHOuD+CyxTH9twnoW8frLl1KX+tmqQ/91hQYvvdRDAqyj0ALrzYUFdxapGlpFIowP8Bjqiq+omMH/038C7t/98F/Ff5u1cFzjwpYnvsHrjlI6LN7PE/r9jm9drbdqd2MJLCuO7p99o5PxkinshYlGJ1wcANJcW22RKap6yBJ8YAG7vcnJ0MEYpqosVogrb1wkZhzE+d0Bc8NuLEGMieGrdtgMkzEF9gGqoRj8fZpJ7C19w402JIt98V9RnrbLhb+Iif+NtUdu+qyz9gXG3Bvib/JK2W2dHXgtGgCDuFraWgMH7lwjTj/gi/OPclZqyreO/cbxBXTPkb698npr3TZ/N/tgBj/gi/3PQjlMCIuCJbR1P3Uhnw2jk7GUwne5Q4MfYFIiKRIjwraslX717mPZXMx1ImxtcD7wRuUxTlZe2/e4C/Ae5UFOUEcKf25/ogFoZzz8PATeLP7lVw3a/B69+ECy9V5Fek6qDlxHjF0O+1E0+qXJrOOdiuv1tc3p44Ne/zbYkAKkrDV4JvXOVCVeH4aCB95zu/BW/+/wo+fngiSHOTWazkbiBSJR9ZyRQbQU0s+F7TmblwBLcyR7Bt63LsYs3S6V4gyziTu7VD13c/DLEwa6af5VnzXhSDcRn3sPI4rCau7nbzwhltYhwL5q192H94lHaDn9apQ4yvuZ+xqDXb76/Tf724LcNOMT09zS8mviVi8QZvLOevUvP0ex2EY8n0Z9PZWTDhJJNIPIE/HKfNaYFLBwFVCuMqs5RUip+oqqqoqrpVVdXt2n+Pqqo6oarq7aqqrtNuJyu5w8vKhQOQiIgPrs71vwGOdtj/v8q6fJTLuD+Cw2LEbtHroKfFbYNfRq9nUskUk7k+Y60F78RjRZ+bTKo4kgEiRmfDe8o2dolkiqOXM+0Uq4pO0hsxkQIySj4ykyna14vbEhMDImdFSUV8VWNdsl2UMG7phZv/AI49At/7MDZ1jtfc9Snodg94ePnCNDE9GzecvRxo/+FR3t1xCgWVtu0icePFswUO3e0bxbHq7NP5P1uAfZPfpCU5Dbf+0aKfWy/o0ZGpNSctfWId0ezlos+ZTNVBW+G8Vh7TQEkxtUhjH4lzOfOU8DTqq29BXBK/5cPii2ARNb/F8AUKZBiDnBjXMekvwxyfsWeNsALM876JxJO4lSBRswxz7221Y7cYOTpSYFJVgOGJIAMNZqOAIlYK7zpAKb0B7+JBgqoVa+fGyu9gDZMWxqVZTrjug0IMvvQF/Njxte9dxr1bPnYPeojGk5wPaZak8HTqZ8O+ICfGAtxtfRUc7bSt20tPS1M6+zgTg0EcH88usugjPMtbI9/ghOta6KvP17AU8nLtdS0x/JOiz9Fb7zwOC1x4QbzfGjihqBaQwjiTM0+KalRbziXta94F3iGRbZmYZ9FGCYz7C7Tegfwg1DEdLis2s4GzvgLNY+vfIGKfIoXFXigap5kgMXNj2yhArOpe35mzAK8IkXiCS9ONF9UG4HVYMSg5VgqLXUynShTGTeMvc0hdg9fdtEx7WZs4rSacVhOXp0ssbzKa4V6xhObHyR20NdfnCezuAVH0cWRSO+Rn+Iz3Hx7FQJKB6edE+ovBwK4BEfGmFrpK2r8PJk+J9IQSiT3zaVoI8PLQB5b096h1uluaMBuV9JCka6u44nXmiaLP8WlJVW0OsxDG0kZRdaQw1okE4OKL2TYKHaMZ7vhT4Rf96ZeX9Gt8gUhq4gMIK4XFVXBxkaQ+MBgU+j2O/IkxCJ9xMganflTwuRen53AroYZfeKezaZWLYyP+wgfkDM5PzpFUaUgrhdGg4HXmRLaBmDSVIozjUdzTR3kluSadndpArO908vqlkpNFYeB6gj/7Jf4m+mDKxlJveBwWhjqcvOLTPlc5wvi+tksYI9OpvPBdAx7G/JHsqnudvkXmGc9NY3zuU+xP7FzxaQtGg0Kvx56eGBuM0H8DDD9V9Dn6xLgzdlEUqEhhXHWkMNY5/xwk44WFMYjmn95r4Ud/JUR0mYwHIrTlToyljaLu6fPas2uhdXr3CtFbJJ3i+GgANyFsLs8y72F9sKHTxVQolj0NLYD+WjeilQJEDnq+MN4AEycXvqo1+hpGNcYhdYjmpsY7Id/e28qhizPEEqVXG1/ovI2LtKesGPXI7gEPL4xof2dNGE8Go7x4dpKHWo4IG+FaEaG2q18ck1LFIJms2irKJ86VaKd49lMYorN8Iv4AHXX8+pXKgNfBsC9jSDJ4I0wNw/T5go+fCIrPsXf6VXFH755l3kPJQkhhrHPmSZE721skikdR4K6/EPWOr/x7Wb8iGk8yHYrlTIynpI1iBTDgtXNuMpSO6dExmmHoDjjxfdFymMPxUT8tSpAm98qqRy2XjauEpWQhn/GB4UlMBoWhjsasTe1wW/NPHto3iMXDC0VpXToIwFnbRgyGlReZtRDb+1qIxJPZZTILoC/W62quX2G3Z7CVyxHt2KMJ48ePjJJUYdvcC+IkXhvSrO904bKZePFsAZ+x0SzEWykT49AkPPcvXO55A0fU/rqduC+Gfm1IkrrqNaAt2CwyNZ4IRrGYDNhGXhLJRHrFu6RqSGGsc+ZJcQnDMs+l2d7dot999LWyfoV+ZignxiuPfq+DSDzJqL/Aavf1d0NwHC79NO9Hx0f9NCshDPLkCCiSTJGDqqo88uplbljXhtvWeBNPKDYx1hbSLWSnuHiQWUMLMefKquQtle2rxWft5fPTCzwyjS6MO131K4x3D3iYRbvCognj/YdH2eIO0TTxWlbtutGgsLO/lRcLTYxB+IxHX0+vkSnG0/8A0QDP9/0yQEMI4wGvg2A0keosoGOzaLI782TBx08EorQ5LCgXXxBpFA2eTlQLyH8BED7fy68Ut1Fk4h0qOSs0F59fK/fImxhLYVzvpJIpfAV8xkN3gGIomE5x6vIUTYRlXJ9Gi91Cl9s278T41QszXJia494tq67gntUW7S4rvkAk+wpF2zpxu1AD3sWDnDCtw9sAIqUQvZ4mPA4LryxCGOvT+Q53/b5mq1vttLibSWCA8AzhWIKnTvh4b5d2PFt3V9bjd/W3cnw0wHQomr+x/n2AKnL/i+EfhQOfhS0PcEztxWxUaG2AzPG8ZAqDQdgpzjxVMPJ1IhChx5EUJxrSRlETSGEMWsVlsrTQce868J0o69eMB8TUoc2Z8eUghfGKIO/LMBO7B1bvgRc+B5+7HT61Fz5xFepf9/GjyM+Lx8iJcYqNq1zzCuNHDl3GbFS4a3PXFdyr2qLDZSWeVJnKFC22ZnB1i0XCxYj4Yfwor6hrG3LhHYCiKGzvbVnUxHhkJkxzkxmbub7KPXLZvcaLHwdqeIafnPAxF0two3pQ1K93XpX12F1aksXBcwWmwj07wWgpnmesqvDo70IyAbf8IWOzIo2pEaw7BeM7B26E2QswdSbv8RPBKDvNw0KDyIV3NYEUxiAucZhspb0pvWshMFI0fms+8ibGqiqF8QohL6Ynl32/JrxjNrfINl5zM+Nrf5bPJN7M8a1/AFseuLI7XMNs6HJxcsxfcHGUbqO4cV07zfbGtFEAtGuX9McDuXaK9fNPjC+/Aqi8GB0QuakNyrbVLZwcD+APxxZ+MMJK0bUCFo7tHvAwnbQTnJlg/+FRWq3gHX1G2ChyKpq3rW7BZFB4oVCesbkJuq8p7jN+/Vtw5Ntw6x+Cdy1j/jDtK+D1K4We1iaMBiV7SKJfjT6T7zOeCETZomons7LYoyaQwhiEKb7vWjCVMEHxDonbMuwU+kEs5TGOBkWUlxTGdY/RoNDbWiSZAmDTm+GXvi8qjh/6Mtz/aX448Lt8PP4Qtpt/S74HMtjU5SaWUDlTIBf6p+enuTjd2DYKSF/SH8stqmjfCOPHi7d0XhTV9s9FBrKvXDUY2/taUFU4dGFm4QcDo/5IXdsodPYMepjFzsyUj8ePjvKevlGUqD/PRgHQZDFydU/z/D7jyy+L41gmgXF49ENCOF/364B4nzaCvxjAbDSwurUpe0jStl7UQ+f4jFVVxReIsD56WFyNtst0olpACuOgTyymGyix6jMljE8u+leN+yO4bKb05ThZ7rGiEKuRi0yMC3B8NECT2cjq1sYqWViIjavEArwjBRbgPfLqZSxGA3ds7rzSu1VT6CVBeQvw2tZDLAgzF/KfFJuD175BvLmfSdyigrZB2bZa5Ib/tEQ7xdhsuK6j2nSG2p3MGZxMTY7jC0S5x3pIpDEN3lzw8bsHWnnlwgyReCL/h/3Xi4jTCy9k3//oh8QV1fs/DUYTAGP+MJ0r4MSiVPq9juwhiaIIjTGc7TMORhNE4glWB6W/uJaQwliPUCnyxZCHZw2glCeMA8Va7+S0cCWgfxkuVE6hc3zUz7pOZ0P47hbDmjYnJoOSF6eVTKo8eugyN61va8j83Ux0O1Z+ZJuWTOHLSaZIJuFb74fLr3Jp90cAGtpK0WK3MNjmKGkB3mQwyuhsmN7W+i+TMRgUTI4WLLFZzEaFwemnYeB6sBaOPdzZL6qkX7tYYLLeu0csKs60U7z+LTj8n3DLh6FjEyBiSqdCMTrqONFjsQx47Zzx5RwLBm+EwGjWGqWJQIQ+ZYym2JT0F9cQUhifeVI0z3XvKO3xZhu09JY9MW7LvJyk99VLYbwiGPDas2N6FuD4qJ91HfVZMbucWEwGhjqceQvwfnp+isszYe7d2tg2CgCH1YTDYixc8gH5kW0//DMhWO78M0633wbQ0FYKILUAb6ETWT3r99aN7Vdoz5YXh9uLWwnxpr4YRt+xgjYKnV0D4tj0YiGfsc0NXVvSwjjog0c+BKu2w77fTD1MtxA2ipUCxJDEH44zHcrwsKfyjNN2Cl8gyg5FE8pSGNcMUhifeQr6r0td8ikJ71BZwtgnJ8Yrmn6tha2ozziD6VCUMX+EDV2NWVCxEBu6XHlZxt959TIWk4E7NjW2jUKn3WVlLDc329EmstYzhfFL/wY/+STsfA/s+/VUBW2jplLobFvdzJg/wuWZAtnjGew/PEqX28aWnpVR2+5t68BNiJ9v1d4j8wjjNqeVNW2OwgvwQNgpLrwA8Sg8+nsiH/n+f8k6nuoZ0CvBo10qelX9cOaxwLNGpH9k+IwnAhGuMZwgYXakJuyS6tPYwnj2EkycKC2/OBM9y7jES+Y64/5IfoYxSGG8Quj36F+GC/uMj4+KWvF1nXJiXIiNXW4uzYSZmRMTF91Gccv6dlwNWuqRS4fLlj8xBpF+ogvjUz+E7/w2rL0d7vk4KAqTQSGMPY0+Me4T37vz2SnmogmePDHOXVd1oigrw/LU3taBXYmwO/wstA6k180UYWd/Ky+dnSw8We+7DuJhePxP4fVvwi1/AJ2bsx6iLxBtJCtFv1cfkmQcCxRFaI3hn6RaUCeDUa4xnCDetQMM9R0FuJJobGGsR6eUI4wjsxAYK/kp4VgCfzguhfEKptdjp8lsLOzHy+H4qLAJrJfCuCD6AjzdZ/zSuSlGZyPSRpGBmBgXEMbtG0Rk29gR+I93Cd/xW7+YmuL5ghEsRgMu6yKukq1ANq1yYTEa5s0zfurEOOFYcmVlZtvE5Ntw5gkxLV5A8O8e8DAVinFqvMCVsP594vbZf4ZV2+D638p7yLi/8SbGvZ4mFCVnYgzCThGagPEjAMzMzrBJOYexb28V9lJSjMYWxsNPisaxzi2Le14ZyRS+VFRbTrmHySYyISV1j9loYPegh2dO+RZ87IlRP06rie7mxpmiLAa9GvrYiLBTPPLqZawmA7dLG0WK9Z0uhieCzIRysnjbN4j1C1+6X3y3vP3/CT+oxkQgitdpWTET0HKxmoxs6nbPK4wfOzyKy2Zi75oVFKOlCWPUxLw2Cp2dKZ9xgdg2R5u4QmEww32fBmP+1ZwxfwSD0ljWHavJSHdzU35KkV4ipg3lLGOvYFKSmPqlMK4lGlsYn3kSBm5YfDd5WcK4QB309DnhB5SsGPat9XJ8NFD4EncGx7REikYXJ8XocttobjJzZMRPIqnyyKHL3LqhA2eDTzkz2bvGg6rCC7mCRV+AF5kVorilN+vHE4EI3ga3Uejs6G3h0MUZEsl8m0A8keTxI6PctrEDs3EFHSp1YWyyiePfAqxpc+BxWIr7jN/wV/DA/4Guqwv+eHQ2TJvTirHB0ncG2uz5E+OWPmjpT/mMPZOvivt7dl3hvZPMxwr6tC+SiB+cXbD2tsU/t3k1GK2LEsa6UEqVe8xNwbHvwoY3Lv73S2qWfWvFic5CU+MTowHWy0SKoiiKwoYuF8dG/LwwPMm4X9ooctne24LFaOBArjDu3gFdW+GBzxdM25kMRvE00PRuPrb1NhOKJlLWpkxeOjvFVCi2smwUkBbGgzeVdLVSURR2aT7jgqy7AzbfV/T5Y/7IisiAXiwivrPAepPBm+DsTyCZoDtwiEvGHnDIAVkt0bjC2OqC9+2H3b+0+OcajGKFaRnCODUxfvU/xKKFne9e/O+X1CxXdTfjtpl49tRE0cf4AhEmglHWd0lhPB+bNGH87VcuYTMbuG1jR7V3qaawmY1s623m+dM577WmVnj/U0VPun2BKG0NnGGcyfbe4gvwHjs8isVo4OYNKyOmLYVD+/uUYKPQ2TXQyvBEaMErYYVopNa7TAa8diaD0dQC4hSDN4n0jpFDrI0c5mzT5sIbkFSNxhXGS8W7tiyPsddhFWkWL31R9KJ3LdLfLKlpjAaFa9d4eWYeYZxeeCej2uZj4yo3gUicbx68yG0bO3BIG0Ueewe9vHZplkAkXvJzJoLSSqEz4LXT3GTO8xmrqspjh0e4fsi78uw7bUPwjocXNZTZPSA81s+fKf69VoyxFVKnvVj0ZIpzuVNjPc/45a/iUacZbd56hfdMshBSGJdL2zqYPAOJ0g5I4/4ILXYzFpNB5D6OHZbT4hXKvrVezk2GOD9ZOLbthBbVJhMp5meDNlGfiyW4d0t3lfemNtkz6CGRVDl4toj/M4dQNE44lpRWCg1FUdimFX1kcnTEz/nJOe66aoXZKHTW3VlwoVwxtvSIK2FPHgo3iyoAABfnSURBVB9f1K+JJ5JMBCO0N1BUm86AJozzfMbuVeAdQj34JQBm20osF5NcMaQwLhfvECRjMHOupIf7ApG0v/ilL4LFCVf93PLtn6Rq7BtqAyhqpzg26qe5ydyQlxcXwwbtxKHJbJQ2iiLs7G/FaFBKnuSlyj3kxDjF9t4Wjo/6CWZM3R97fRRFgds3yfcdgMlo4MZ17TxxfLzkynsQth1Vhc4GnBj3abn2BQufBm9CiYcJqlaSbRuv8J5JFkIK43JJJVOcKunh436t9W5uGl77Jmx5a9F+ekl9s67DSZvTWnQB3olRP+tlIsWCOKwmNq1y88YtXTRZZPh9IRxWE1f3NHPgTJGFUTkUjI1scHb0tpBUycof339khB29LQ1VSrEQN69vZ3Q2wrECCxWLoTczNuLr2GQx0uW2FS580uwUrybX4nE7rvCeSRZCCuNy0YWx70RJD/cFIrS5rHDo6xCfkzaKFYyiKOxb6+XpUxN50xVVVTk24peNdyXyH796LX/1s9KHPx97Bz28cn6GcCyx4GP11rtGypRdiK2rRUqDbqe4OD3HaxdnV66NokxuWi8W7T1xrHQ7Rbr1rjHfb/1ee+GJ8cCNqIqBF9X1ciFsDSKFcbnYvaIcpMQFeOP+CO0OC7z4BVi1Hbq3L/MOSqrJvrVexv0RTo0Hsu4f80eYDcdTNgHJ/LhsZmxmOS2ej72DHqKJJD89V7yoQke3UnjkwTiF12mlz2NPCeP9r48AcNdmWSaTSVezjY1dLp5YhM94tAFb7zIZ8Do44wvl20+c7Tx701f4bPxNeJ2N+drUMlIYl4uiiKlxCcI4FI0TjCa4ihMw9rqcFjcA+9YKn3FuOoWeSLFOJlJIKsSufg+KQkl2Cl9QS8eRVoostve2pCLbHjs8ylCHkzXt8jOay83r23lheDLLjz0fY7MRFCUjv7/B2NHXgi8Q4dM/zrdcnrRdhR+7/CzWIA0vjJMFGo9KxjtUksfY5xdTmmvG/xPMDtjyQPm/U1IX9HntrG5t4umT2T7jYyNCGMuJsaRSNNvNbOxyl7QAbyIQxW4xYressAiyJbKtt4VLM2FOjPp5/syknBYX4eb17cQS6rw57ZmM+SN4HZaV1Ry4CB7c1cv927v52PeP8Y2XLmT9zBeIoijQapfCuNZozHcrMB2Kcv+nnuY/Xjxf/ka8QzB7AaIFPEQZjAciuAjRd+l7QhRbpShqBPat9fLc6cmsutkTowG8Dou8fCapKHsHPRw8N0U0npz3cb5ARNooCrC9twWAT/7gOImkyp1SGBdk50ArdouxZDvFuD/ckFFtOgaDwt89sI19a738wTde5akT6ddtIhDBY7c0XFV2PdCwwri5yUw0nuTzT59ZVPxMFt614nby9LwPG/dHuM/4NMbEHOx8V3m/S1J37FvbxsxcjCOXZ1P3HR/zSxuFpOLsHfQQjiU5lJGskMvMXIwfHhlLiUBJmqu63ZgMCo8eGqHDZWXbavkaFcJqMrJvrZcfHx8r6bg52qCtd5lYTAY+886dDHU4+R9fOcjrl8RndCIQlTaKGqVhhbGiKLz3hkGOjwZ4+uTi23wAUfIBC/qMx/1h3mb8IbH2q6H7mvJ+l6Tu2LfWC5CyU6iqyonRgLRRSCrOnsGFm8m+9Mww/kic/3HL2iu1W3WDzWxk0yo3AHdu7sQgp3hFuXl9O+cn5wrHkOUw5g83ZIZxLm6bmS+8Zzcum4n3fOEFLk7PiQZKmQ5TkzSsMAZ487ZVtDktfP7pM+VtwLNG3C4gjI2XX+Yqw1kMu94tFu1JGoIOt42hDmdqAd6lmTCBSFxGtUkqjtdpZajDWXQBXjAS5/88fYbbN3ZwVXfzFd67+kCfpMuYtvm5eb0oPXni2Ni8j0skVXyBaENmGBdiVXMTX3zPHuZiCd71+QNcmJqTE+MapaGFsdVk5B17+/nh0TFO58RqlYTFAe4e8M0vjIcuPMwcVozbHixzTyX1yr61Xl4YniQaT3JcW3gnq6Aly8GeQQ8vDk8RT+T7jL/2/DmmQzE+eNtQFfasPrh/Rzd3be7kujXeau9KTdPntTPY5ljQZzwZjJJIqg0b1VaIDV0uPvvOXZybCHF5JoxX+v1rkoYWxgDvuLYPi9HAvz0zXN4GvGvnnxiHZ9g6tZ8nzDeBTU5qGo19a72EogleuTCdimpbLz3GkmVg76CHQCTOkcvZzWThWILPPnWa64e8XNPXWqW9q3129nv47C/uwmJq+MPigty8vp1nT0/MWyozOqu33klhnMl1a718/MFtAKxqaary3kgK0fDfAB0uG2/e1s3XX7rAzFxs8RvwDsHECSi2EOGlL2JTw/yk9b6l7aikLrl2jRdFgWdOTnB8NECHy0qLjOeRLAN7B8WkM9dn/PUXzzPuj/DBW+W0WFIZbl7fTjiW5IXh4tnZX3h6GIMir5AV4me2dfOdX7+Bd17bX+1dkRSg4YUxwHuuHyAUTfAfL5QR3eZdB+EZCBX4gohH4bnP8JJhCwHP1UvfUUnd0WK3cFW3m2dO+Tg+6pcHCcmy0dVso99r5/kMn3EskeQzT5xmZ3+rtAhIKsbeNR4sJkPReujvHrrMNw5e4IO3DsmilCJc3dOMwyrzxGsRKYwRb9A9gx6++MxwQX/evHi1KczEifyfvfYw+C/xL7F7aZeXkxqWfWvb+Om5aU6MSWEsWV72DHh4YXgyVVz0rZ9e5OL0HL926xCKXPgrqRB2i4m9g56CPuOx2TAf+dYhtvQ08xu3r6vC3kkkS0MKY433Xj/Ixek59h8eXdwT9SzjXJ+xqsLT/0iifTM/iG1p2EpMifAZRxNJwrGk9BdLlpU9gx6mQzGOj/lJJFX+5cenuKrbzS0b2qu9a5IVxs3r2zkxFuDi9FzqPlVV+b2HX2UuluCTD21v2MY7SX0j37Uad27uZHVrE194enhxT2zpB4M5Xxif2A/jR3iq/W2Awm4tZ1TSeOwe8GDSclFlVJtkOblWs0scODPJI4cuc8YXlNNiybJw83pxsvVkxtT4K8+d5Ynj43zknk0MdcghgKQ+kcJYw2hQePe+AQ4MT3LoQvH2qPwnmsAzmC+Mn/lHVFc3HzmxnmvXeORq8AbGYTWlMlLlxFiynKxubWJVs43nTk/w6R+dZKjDyRtkLq9kGRjqcNLdbEv5jE+NB/jLR49w0/p2uahMUtdIYZzBg7t7cViMfGGxhR/eoews44svwfBTvNzzdi75E3I1uIS37+3jzdu6cdnM1d4VyQpGURT2DHr4/uujHB3x88Fb18oWN8myoCgKN29o5+mTPsKxBL/9/17GZjbysQe2yisUkrpGCuMM3DYzb93Vy7dfvcSYlsFYEt61MHkaklqm49P/iGp185Fz17B1dTM3DLUtzw5L6oafu2Y1//S2HdXeDUkDsHfQSyKp0uex8+at3dXeHckK5ub17fgjcX7lyy/x6oUZ/vpnt9Dplk13kvpGCuMc3r1vgHhS5SvPnyv9Sd4hSERg5gJMnoEj/83Jvgc5MgkfuEX6+yQSyZXjhqE2TAaFX79tCJNc/CRZRvYNtWE0KDx5fJyfu6aHN25ZVe1dkkiWjPzWzGGgzcHtGzv46nNnicZLjG5LRbadhGc/haoY+ZPRGxjqcHLX5s7l21mJRCLJoc9r58D/vIO37uqt9q5IVjhum5m9gx56Wpr46M9cVe3dkUgqghTGBXjH3n4mglF+dGystCd4tazG88/DT7/C5f77eGbMwgdukf4+iURy5fE4ZLui5Mrw6Xdcw7d//Qbccv2EZIUghXEBblzXRrvLysMvXSjtCc4OsLjgmX+C+Bx/PXMHPS1NvHmb9PdJJBKJZOXSYrfIEzHJikIK4wKYjAZ+dkcPPzo6xkQgsvATFEUswIuFmOy5jW9fcvP+m9fIcHOJRCKRSCSSOkIqtyK85ZrVxJMq//XypdKeoPmMPx29hzanVfr7JBKJRCKRSOoMKYyLsKHLxZaeZr5xsEQ7xY5fYGzLr/Kv51fxvhsHsZmNy7uDEolEIpFIJJKKIoXxPDywczWvX5rlyOXZhR+89lb+V+hB3DYz79jbt/w7J5FIJBKJRCKpKFIYz8PPbOvGbFT4RgmL8E6M+vn+66O8e9+AbDeTSCQSiUQiqUOkMJ6HVoeF2zZ28J8vXyKWKJ5prKoqf/3dozSZjbz7+sEruIcSiUQikUgkkkohhfECPLCzF18gwpPHx4s+5uGXLvDDo2N86A0bZGyNRCKRSCQSSZ0ihfEC3LKhHa/DUnQR3qXpOf7s24fZM+jhPfsGruzOSSQSiUQikUgqhhTGC2A2Grhvew8/ODzGdCia9TNVVfmDb7xKQlX5+APbZMudRCKRSCQSSR0jhXEJvGVnD9FEkm+/kp1p/O8HzvPUCR9/+MaN9HntVdo7iUQikUgkEkklkMK4BK7qbmbTKndWRfT5yRB/+chhrh/y8o69/VXcO4lEIpFIJBJJJZDCuETeck0Pr1yY4eSYn2RS5fcffhVFUfjbt2yVFgqJRCKRSCSSFYAUxiVy/44eTAaFh1+6yJefO8uzpyf4o3s3sbpVWigkEolEIpFIVgKmau9AvdDmtHLLhna+/uJ5QtEEN69v56HdvdXeLYlEIpFIJBJJhZAT40XwwM7VTASjmIwKf/OWLSiKtFBIJBKJRCKRrBTkxHgR3Laxk+uHvPzC3n5WNTdVe3ckEolEIpFIJBVECuNFYDEZ+Or7rq32bkgkEolEIpFIlgFppZBIJBKJRCKRSJDCWCKRSCQSiUQiAaQwlkgkEolEIpFIACmMJRKJRCKRSCQSQApjiUQikUgkEokEkMJYIpFIJBKJRCIBpDCWSCQSiUQikUgAKYwlEolEIpFIJBJACmOJRCKRSCQSiQSQwlgikUgkEolEIgGkMJZIJBKJRCKRSAApjCUSiUQikUgkEkAKY4lEIpFIJBKJBJDCWCKRSCQSiUQiAaQwlkgkEolEIpFIgGUUxoqi3K0oyjFFUU4qivLh5fo9EolEIpFIJBJJJVgWYawoihH4FPBGYDPwNkVRNi/H75JIJBKJRCKRSCrBck2M9wAnVVU9rapqFPi/wH3L9LskEolEIpFIJJIls1zCuAc4n/HnC9p9EolEIpFIJBJJTWJapu0qBe5Tsx6gKL8C/Ir2x4CiKMdyHt8MzFRof9qA/7+9e4+Rq6zDOP59ZAGFrRYqbYoiVSzWivaCEVvEVKoYG7H1grZpDAY0qZdEopDYkBqUIgZQQYxiFS1/CNa2EFslVNqALaaSXuiF0pYK1iA2VryExaZe0p9/nHeTcdNqZ/Y9c87MPJ9kMrez7/zeZ9+Z8+45Z/Y8l6ktyFtbzrZyt5cztzr3M3dtzq15fo+2pldy83u0+ragvrnV+XfQK+/R3O2VPdbOPubSEZH9AkwD1jTcXwgsbLKNJRnr2Zy5fzlry9ZWnXOreT9z1+bcKsyszv10btW3lTu3mo+Pnsit5r+DnniP1jm3Zusq61CKTcB4Sa+WdBIwF1jVZBur85eVTc7acvezrrnVuZ91zQycW6vq3E/nVm1budV5fPRKbnX+HeTm3JrXVF1Ks+nsJM0CbgVOAH4QETeU8kLHV8vmiHhzVa/fqZxba5xb85xZa5xba5xba5xb85xZa6rMraxjjImI+4H7y2q/SUuqLqBDObfWOLfmObPWOLfWOLfWOLfmObPWVJZbaVuMzczMzMw6iU8JbWZmZmZGh06MJZ0l6SFJuyXtkvTZ9Pjpkh6UtC9dn5YeH5WWf0HSt4a0dYOkZyS9UEVf2ilXbpJOkfRzSXtSO1+tqk/tkHm8PSBpe2rnjnSWyK6TM7OGNldJeryd/Wi3zGPtYUl7JW1Ll9FV9KkdMud2kqQlkp5Mn3EfrKJP7ZBxnTCiYZxtk/ScpFur6leZMo+1eZJ2StqR1g0vr6JP7ZA5t4+kzHZJuil3rR05MQb+DXw+Il4PvBX4tIpTTn8BWBcR44F16T7AYWARcPVR2lpNcaa+XpAzt1siYgIwBbhQ0ntKr746OXP7cERMAs4DzgAuK7v4iuTMDEkfALr+j1cy5wbMj4jJ6XKw5NqrlDO3a4GDEXEuMBH4ZdnFVyhLbhEx0DDOJgO/A+5tVyfaLEtmkvqA24B3RMSbgB3AZ9rThUrkym0UcDMwMyLeAIyRNDNnoR05MY6IAxGxNd0eAHZTnFlvNnBXWuwuYE5a5u8R8QhF0EPb+nVEHGhL4RXLlVtEHIqIh9LtfwJbgVe2pRMVyDzenk83+4CTGHLim26RMzNJ/cDngMVtKL1SOXPrJZlzuwK4MS13JCJynpyhVsoYb5LGA6OBDSWWXpmMmSldTpUk4KXAH8rvQTUy5vYa4MmI+FO6vxbIulenIyfGjSSNo9hq+SgwZnCSm667dtfhcOXKTdJI4FKKv/S6Xo7cJK0BDgIDwIpSCq2RDJldD3wNOFRSibWU6T36w7Rre1Fa+Xa94eSWPs8Arpe0VdJySWNKLLc2Mq5L5wHLoge+2T+czCLiX8AngZ0UE+KJwJ0lllsbwxxrvwEmSBqXtrrPAc7KWV9HT4zTlqSVwFUNW+Ls/8iVWxqU9wDfjIinc9VXV7lyi4h3A2OBk4GLM5VXS8PNTNJk4LURcV/24mos01ibHxFvBC5Kl4/mqq+uMuTWR7H361cRMRXYCNySscRayrwunUuxXuhqGT7bTqSYGE8BzqQ4lGJh1iJraLi5RcRfKXJbRrFXYj/FYRrZdOzEOA2qlcCPImLwWKY/Shqbnh9LsVXOGmTObQmwLyK68ksWjXKPt4g4THE2yNm5a62LTJlNA86XtB94BDhX0sPlVFwPucZaRDybrgeAu+ny71Jkyu3PFHsmBv8QWw5MLaHc2sj52SZpEtAXEVtKKbYmMmU2GSAinkpb138CTC+p5FrI+Nm2OiIuiIhpwF5gX846O3JinHYJ3gnsjoivNzy1Crg83b4c+Gm7a6uznLlJWgy8DLgqd511kys3Sf0NHwB9wCxgT/6Kq5crs4j4TkScGRHjgLdRHFs2I3/F9ZBxrPUNfsM9rYzeC3Ttf/TION6C4gvZM9JDM4EnshZbIyWsS+fR5VuLM2b2LDBR0hnp/rsojrvtSpnnH6PT9WnAp4DvZy02IjruQrGCDIpdD9vSZRYwiuJY133p+vSGn9kP/IXim+2/Byamx29K94+k6+uq7l/dc6PY1RgUb+LBdj5edf86ILcxwKbUzi7gdoqtK5X3sa6ZDWlzHPB41X3rhNyAU4EtDWPtNuCEqvtX99zS42cD61Nb64BXVd2/TsgtPfc0MKHqfnVKZsACivXoDoo/yEZV3b8Oye0eij9YnwDm5q7VZ74zMzMzM6NDD6UwMzMzM8vNE2MzMzMzMzwxNjMzMzMDPDE2MzMzMwM8MTYzMzMzAzwxNjNrO0nXSbr6fzw/R9LE42jnv5aT9GVJ78xVp5lZr/HE2MysfuZQ/D/ippaLiC9GxNrSqjIz63KeGJuZtYGkayXtlbQWeF167BOSNknaLmmlpFMkTQfeB9wsaZukc9LlAUlbJG2QNOEYyy2V9KHU9n5JX5G0UdJmSVMlrZH0lKQFDXVdk2rYIelLFURjZlYbfVUXYGbW7SSdD8wFplB87m6lODPdvRHxvbTMYuDKiLhd0irgZxGxIj23DlgQEfskXQB8OyIuPspyQ1/6mYiYJukbwFLgQuDFFGfDu0PSJcB44C2AgFWS3h4R60sLw8ysxjwxNjMr30XAfRFxCCBNaAHOSxPikUA/sGboD0rqB6YDyxsmvicf5+sOvs5OoD8iBoABSYcljQQuSZfH0nL9FBNlT4zNrCd5Ymxm1h5xlMeWAnMiYrukjwEzjrLMi4C/RcTkFl7zH+n6SMPtwft9FFuJb4yI77bQtplZ1/ExxmZm5VsPvF/SSySNAC5Nj48ADkg6EZjfsPxAeo6IeB74raTLAFSYNHS5Fq0BrkhbpZH0Ckmjh9GemVlH88TYzKxkEbEVWAZsA1YCG9JTi4BHgQeBPQ0/8mPgGkmPSTqHYtJ8paTtFMcHzz7Gcs3W9QvgbmCjpJ3ACoY30TYz62iKONrePTMzMzOz3uItxmZmZmZmeGJsZmZmZgZ4YmxmZmZmBnhibGZmZmYGeGJsZmZmZgZ4YmxmZmZmBnhibGZmZmYGeGJsZmZmZgbAfwC37yxjAbL5PwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for region in regions[:2]:\n",
+    "    fig, ax = plt.subplots(figsize=(12,8))\n",
+    "#     plot_region_time_series(region=region, ax=ax, model=['ealstm', 'ealstm_bline']);\n",
+    "    plot_region_time_series(region=region, ax=ax, model=['ealstm']);\n",
+    "#     fig, ax = plot_region_performance(model='ealstm', region=region);\n",
+    "#     highlight_selected_regions([region])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Event Hits/Misses"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import confusion_matrix\n",
+    "from sklearn.utils.multiclass import unique_labels\n",
+    "\n",
+    "def plot_confusion_matrix(y_true, y_pred, classes=None,\n",
+    "                          normalize=False,\n",
+    "                          title=None,\n",
+    "                          cmap=plt.cm.Blues,\n",
+    "                          **imshow_kwargs):\n",
+    "    \"\"\"\n",
+    "    This function prints and plots the confusion matrix.\n",
+    "    Normalization can be applied by setting `normalize=True`.\n",
+    "    \"\"\"\n",
+    "    if not title:\n",
+    "        if normalize:\n",
+    "            title = 'Normalized confusion matrix'\n",
+    "        else:\n",
+    "            title = 'Confusion matrix, without normalization'\n",
+    "\n",
+    "    # Compute confusion matrix\n",
+    "    cm = confusion_matrix(y_true, y_pred)\n",
+    "    # Only use the labels that appear in the data\n",
+    "#     classes = classes[unique_labels(y_true, y_pred)]\n",
+    "    if normalize:\n",
+    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
+    "        print(\"Normalized confusion matrix\")\n",
+    "    else:\n",
+    "        print('Confusion matrix, without normalization')\n",
+    "\n",
+    "    print(cm)\n",
+    "    \n",
+    "    fig, ax = plt.subplots(figsize=(12, 8))\n",
+    "    im = ax.imshow(cm, interpolation='nearest', cmap=cmap, **imshow_kwargs)\n",
+    "    ax.figure.colorbar(im, ax=ax)\n",
+    "    # We want to show all ticks...\n",
+    "    ax.set(xticks=np.arange(cm.shape[1]),\n",
+    "           yticks=np.arange(cm.shape[0]),\n",
+    "           # ... and label them with the respective list entries\n",
+    "#            xticklabels=classes, yticklabels=classes,\n",
+    "           title=title,\n",
+    "           ylabel='True label',\n",
+    "           xlabel='Predicted label')\n",
+    "\n",
+    "    # Rotate the tick labels and set their alignment.\n",
+    "    plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\",\n",
+    "             rotation_mode=\"anchor\")\n",
+    "\n",
+    "    # Loop over data dimensions and create text annotations.\n",
+    "    fmt = '.2f' if normalize else 'd'\n",
+    "    thresh = cm.max() / 2.\n",
+    "    for i in range(cm.shape[0]):\n",
+    "        for j in range(cm.shape[1]):\n",
+    "            ax.text(j, i, format(cm[i, j], fmt),\n",
+    "                    ha=\"center\", va=\"center\",\n",
+    "                    color=\"white\" if cm[i, j] > thresh else \"black\")\n",
+    "    fig.tight_layout()\n",
+    "    return ax"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Quintiles of Vegetation Condition Index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calculate_quintiles(da: xr.DataArray, new_variable_name: str = 'quintiles'):\n",
+    "    bins = [0.0, 20.0, 40.0, 60.0, 80.0]\n",
+    "    result = xr.apply_ufunc(np.digitize, da, bins)\n",
+    "    result = result.rename(new_variable_name)\n",
+    "    return result\n",
+    "\n",
+    "mask = get_ds_mask(ds.VCI)\n",
+    "true_qs = calculate_quintiles(y_test.VCI).where(~mask)\n",
+    "pred_qs = calculate_quintiles(ealstm_pred.where(ealstm_pred > 0, 0).load()).where(~mask)\n",
+    "bline_qs = calculate_quintiles(bline_pred.load()).where(~mask)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'How often do we predict the correct Quintiles?')"
+      ]
+     },
+     "execution_count": 154,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "(pred_qs == true_qs).mean(dim='time').where(~mask).plot(ax=ax)\n",
+    "ax.set_title('How often do we predict the correct Quintiles?')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "times = true_qs.time.values\n",
+    "true_np = true_qs.stack(pixel=['lat', 'lon']).values.flatten()\n",
+    "preds_np = pred_qs.stack(pixel=['lat', 'lon']).values.flatten()\n",
+    "bline_np = bline_qs.stack(pixel=['lat', 'lon']).values.flatten()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### test vegetation deficit index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<xarray.DataArray 'vdi' ()>\n",
+      "array(1) <xarray.DataArray 'vdi' ()>\n",
+      "array(5)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from src.analysis import VegetationDeficitIndex\n",
+    "\n",
+    "v = VegetationDeficitIndex(data_dir / 'interim/VCI_preprocessed/data_kenya.nc')\n",
+    "vdi_true = v.vegetation_index_classify(y_test.VCI, 'vdi')\n",
+    "vdi_ealstm_pred = v.vegetation_index_classify(ealstm_pred.load(), 'vdi')\n",
+    "vdi_ealstm_orig_pred = v.vegetation_index_classify(orig_ealstm_pred.load(), 'vdi')\n",
+    "vdi_bline = v.vegetation_index_classify(bline_pred.load(), 'vdi')\n",
+    "\n",
+    "print(vdi_true.min(), vdi_true.max())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "VDI 1 mean: 15.25\n",
+      "VDI 2 mean: 23.35\n",
+      "VDI 3 mean: 33.51\n",
+      "VDI 4 mean: 44.65\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_da = y_test.VCI\n",
+    "test_da = ealstm_pred\n",
+    "\n",
+    "# has it selected the right values? YES\n",
+    "print(f\"VDI 1 mean: {test_da.where(vdi_true == 1).mean().values:.2f}\")\n",
+    "print(f\"VDI 2 mean: {test_da.where(vdi_true == 2).mean().values:.2f}\")\n",
+    "print(f\"VDI 3 mean: {test_da.where(vdi_true == 3).mean().values:.2f}\")\n",
+    "print(f\"VDI 4 mean: {test_da.where(vdi_true == 4).mean().values:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# np.set_printoptions(precision=2)\n",
+    "\n",
+    "# plot_confusion_matrix(true_np, preds_np, classes=None, normalize=True,\n",
+    "#                       title='EALSTM Normalized confusion matrix',\n",
+    "#                       **{'vmin': 0.05, 'vmax': 0.35})\n",
+    "# plot_confusion_matrix(true_np, bline_np, classes=None, normalize=True,\n",
+    "#                       title='BASELINE Normalized confusion matrix',\n",
+    "#                       **{'vmin': 0.05, 'vmax': 0.35})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(45, 35)"
+      ]
+     },
+     "execution_count": 146,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ealstm_base_rmse.load().shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<xarray.DataArray 'VCI' ()>\n",
+       "array(80.003748)"
+      ]
+     },
+     "execution_count": 147,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# ds.VCI.where(ds.VCI < 80).isnull().mean()\n",
+    "# y_test.VCI.where(y_test.VCI < 80).isnull().mean()\n",
+    "y_test.VCI.where(true_qs == 5).min() #.max() .median()\n",
+    "y_test.VCI.where(true_qs == 5).min()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 159,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "(vdi_ealstm_pred == vdi_true).mean(dim='time').where(~mask).plot(ax=ax)\n",
+    "ax.set_title('How often do we predict the correct Vegetation Deficit Index?\\nEALSTM Predict Delta');\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "(vdi_ealstm_orig_pred == vdi_true).mean(dim='time').where(~mask).plot(ax=ax)\n",
+    "ax.set_title('How often do we predict the correct Vegetation Deficit Index?\\nEALSTM');\n",
+    "\n",
+    "# fig, ax = plt.subplots()\n",
+    "# (vdi_bline == vdi_true).mean(dim='time').where(~mask).plot(ax=ax)\n",
+    "# ax.set_title('How often do we predict the correct Vegetation Deficit Index?\\nPersistence');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Red colours show that the Predictions are better than persistence. Blue show that persistence outperforms our models\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "kwargs = {'vmin': -0.3, 'vmax':0.3, 'cmap': 'RdBu_r'}\n",
+    "# kwargs = {}\n",
+    "\n",
+    "print('Red colours show that the Predictions are better than persistence. Blue show that persistence outperforms our models')\n",
+    "\n",
+    "# how different from persistence?\n",
+    "fig, ax = plt.subplots()\n",
+    "((vdi_ealstm_pred == vdi_true).mean(dim='time') - (vdi_bline == vdi_true).mean(dim='time')).where(~mask).plot(ax=ax, **kwargs)\n",
+    "ax.set_title('How different from Persistence?\\nEALSTM Predict Delta');\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "((vdi_ealstm_orig_pred == vdi_true).mean(dim='time') - (vdi_bline == vdi_true).mean(dim='time')).where(~mask).plot(ax=ax, **kwargs)\n",
+    "ax.set_title('How different from Persistence?\\nEALSTM');\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plot the Vegetation Deficit Index (VDI) from Klisch & Atzberger 2016\n",
+    "\n",
+    "```python\n",
+    "dict(zip([0, 1, 2, 3, 4],[[0, 10], [10, 20], [20, 35], [35, 50], [50, 100]]))\n",
+    "\n",
+    "```\n",
+    "\n",
+    "`{0: [0, 10], 1: [10, 20], 2: [20, 35], 3: [35, 50], 4: [50, 100]}\n",
+    "`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Normalized confusion matrix\n",
+      "[[0.08066759 0.16324757 0.24956537 0.17863352 0.32788595]\n",
+      " [0.05922643 0.13807989 0.26424542 0.17860021 0.35984805]\n",
+      " [0.03451673 0.09007406 0.23796205 0.22362775 0.41381941]\n",
+      " [0.02293152 0.06538581 0.20073684 0.24222704 0.4687188 ]\n",
+      " [0.02653035 0.06842889 0.19035525 0.22916397 0.48552155]]\n",
+      "Normalized confusion matrix\n",
+      "[[0.12986787 0.17837274 0.2367872  0.15429416 0.30067803]\n",
+      " [0.09439392 0.16265684 0.25100725 0.16173593 0.33020606]\n",
+      " [0.05485809 0.11194101 0.25140641 0.20573372 0.37606077]\n",
+      " [0.03708295 0.08239507 0.22635403 0.23668354 0.41748442]\n",
+      " [0.0414294  0.08303675 0.21064126 0.22683448 0.43805811]]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x12f7c8518>"
+      ]
+     },
+     "execution_count": 172,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "true_np = vdi_true.stack(pixel=['lat', 'lon']).values.flatten().clip(min=1, max=5)\n",
+    "preds_ealstm_np = vdi_ealstm_pred.stack(pixel=['lat', 'lon']).values.flatten().clip(min=1, max=5)\n",
+    "preds_ealstm_orig_np = vdi_ealstm_orig_pred.stack(pixel=['lat', 'lon']).values.flatten().clip(min=1, max=5)\n",
+    "bline_np = vdi_bline.stack(pixel=['lat', 'lon']).values.flatten()\n",
+    "\n",
+    "\n",
+    "plot_confusion_matrix(true_np, preds_ealstm_np.clip(min=1, max=5), classes=None, normalize=True,\n",
+    "                      title='EALSTM Predict Delta Normalized confusion matrix',\n",
+    "                      **{'vmin': 0.05, 'vmax': 0.45})\n",
+    "\n",
+    "plot_confusion_matrix(true_np, preds_ealstm_orig_np.clip(min=1, max=5), classes=None, normalize=True,\n",
+    "                      title='EALSTM Baseline Embedding Normalized confusion matrix',\n",
+    "                      **{'vmin': 0.05, 'vmax': 0.45})"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/draft/30_tl_feature_exploration.ipynb b/notebooks/draft/30_tl_feature_exploration.ipynb
new file mode 100644
index 000000000..c6ff57538
--- /dev/null
+++ b/notebooks/draft/30_tl_feature_exploration.ipynb
@@ -0,0 +1,10011 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Feature Exploration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/Users/tommylees/github/ml_drought\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pathlib import Path\n",
+    "import os\n",
+    "import warnings\n",
+    "\n",
+    "%load_ext autoreload\n",
+    "%autoreload\n",
+    "\n",
+    "# ignore warnings for now ...\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "if Path('.').absolute().parents[1].name == 'ml_drought':\n",
+    "    os.chdir(Path('.').absolute().parents[1])\n",
+    "\n",
+    "!pwd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xarray as xr\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pickle\n",
+    "\n",
+    "data_dir = Path('data/')\n",
+    "# data_dir = Path('/Volumes/Lees_Extend/data/zip_data')\n",
+    "data_dir = Path('/Volumes/Lees_Extend/data/ecmwf_sowc/data/')\n",
+    "\n",
+    "assert data_dir.exists()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import seaborn as sns\n",
+    "from src.utils import drop_nans_and_flatten\n",
+    "\n",
+    "from src.analysis import read_train_data, read_test_data, read_pred_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Read in the data\n",
+    "\n",
+    "Get all dataloaders from the train/test data\n",
+    "```python\n",
+    "next(iter(dataloader))\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, y_train = read_train_data(data_dir)\n",
+    "X_test, y_test = read_test_data(data_dir)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds = xr.merge([y_train, y_test]).sortby('time').sortby('lat')\n",
+    "d_ = xr.merge([X_train, X_test]).sortby('time').sortby('lat')\n",
+    "\n",
+    "ignore_vars = ['ndvi', 'p84.162', 'sp', 'tp', 'Eb']\n",
+    "ds = xr.merge([ds, d_])\n",
+    "ds = ds[[v for v in ds.data_vars if v not in ignore_vars]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from src.utils import get_ds_mask\n",
+    "mask = get_ds_mask(X_train.VCI)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# What data do we have?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:  (lat: 45, lon: 35, time: 449)\n",
+       "Coordinates:\n",
+       "  * time     (time) datetime64[ns] 1981-08-31 1981-09-30 ... 2018-12-31\n",
+       "  * lon      (lon) float64 33.75 34.0 34.25 34.5 34.75 ... 41.5 41.75 42.0 42.25\n",
+       "  * lat      (lat) float64 -5.0 -4.75 -4.5 -4.25 -4.0 ... 5.0 5.25 5.5 5.75 6.0\n",
+       "Data variables:\n",
+       "    VCI      (time, lat, lon) float64 39.36 18.57 19.15 ... 21.15 60.27 22.19\n",
+       "    pev      (time, lat, lon) float64 -0.006845 -0.006982 -0.006779 ... nan nan\n",
+       "    t2m      (time, lat, lon) float64 296.8 297.3 296.2 294.4 ... nan nan nan\n",
+       "    precip   (time, lat, lon) float64 2.911e-08 2.504e-08 0.2845 ... nan nan nan\n",
+       "    E        (time, lat, lon) float64 14.84 10.96 10.96 7.992 ... nan nan nan\n",
+       "    SMroot   (time, lat, lon) float64 0.2158 0.2149 0.2149 ... nan nan nan\n",
+       "    SMsurf   (time, lat, lon) float64 0.2084 0.2109 0.2109 ... nan nan nan"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "\n",
+    "with open(data_dir.parents[0] / 'one_month_forecast_ALL_ds.pkl', 'wb') as fp:\n",
+    "    pickle.dump(ds, fp)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Build the dataframe"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- https://www.ethanrosenthal.com/2019/02/18/time-series-for-scikit-learn-people-part3/\n",
+    "- https://towardsdatascience.com/time-series-analysis-in-python-an-introduction-70d5a5b1d52a\n",
+    "- https://towardsdatascience.com/four-ways-to-quantify-synchrony-between-time-series-data-b99136c4a9c9"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pixel</th>\n",
+       "      <th>pixel_index</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>lon</th>\n",
+       "      <th>time</th>\n",
+       "      <th>VCI</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>(-5.0, 33.75)</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-5.0</td>\n",
+       "      <td>33.75</td>\n",
+       "      <td>1981-08-31</td>\n",
+       "      <td>39.360001</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>(-5.0, 33.75)</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-5.0</td>\n",
+       "      <td>33.75</td>\n",
+       "      <td>1981-09-30</td>\n",
+       "      <td>50.802499</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           pixel  pixel_index  lat    lon       time        VCI\n",
+       "0  (-5.0, 33.75)            1 -5.0  33.75 1981-08-31  39.360001\n",
+       "1  (-5.0, 33.75)            1 -5.0  33.75 1981-09-30  50.802499"
+      ]
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "VCI_pixels = ds.VCI.stack(pixel=['lat', 'lon']).transpose()\n",
+    "df = pd.DataFrame(VCI_pixels.values, columns=VCI_pixels.time, index=[p for p in range(VCI_pixels.shape[0])])\n",
+    "df['pixel'] = VCI_pixels.pixel.values\n",
+    "df['lat'] = VCI_pixels.lat.values\n",
+    "df['lon'] = VCI_pixels.lon.values\n",
+    "\n",
+    "# pixel index\n",
+    "df['pixel_index'] = (~(df.pixel == df.pixel.shift(1))).cumsum()\n",
+    "pixels = df[['pixel_index', 'pixel', 'lat', 'lon']].drop_duplicates().set_index('pixel_index')\n",
+    "\n",
+    "df = df.set_index(['pixel', 'pixel_index', 'lat', 'lon']).stack().reset_index().rename(columns={0: 'VCI'})\n",
+    "\n",
+    "df.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pixel</th>\n",
+       "      <th>pixel_index</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>lon</th>\n",
+       "      <th>time</th>\n",
+       "      <th>VCI</th>\n",
+       "      <th>pev</th>\n",
+       "      <th>t2m</th>\n",
+       "      <th>precip</th>\n",
+       "      <th>E</th>\n",
+       "      <th>SMroot</th>\n",
+       "      <th>SMsurf</th>\n",
+       "      <th>time_index</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>(-5.0, 33.75)</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-5.0</td>\n",
+       "      <td>33.75</td>\n",
+       "      <td>1981-08-31</td>\n",
+       "      <td>39.360001</td>\n",
+       "      <td>-0.006845</td>\n",
+       "      <td>296.760132</td>\n",
+       "      <td>2.911272e-08</td>\n",
+       "      <td>14.843219</td>\n",
+       "      <td>0.215809</td>\n",
+       "      <td>0.208432</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>(-5.0, 33.75)</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-5.0</td>\n",
+       "      <td>33.75</td>\n",
+       "      <td>1981-09-30</td>\n",
+       "      <td>50.802499</td>\n",
+       "      <td>-0.007329</td>\n",
+       "      <td>297.860565</td>\n",
+       "      <td>1.686568e+00</td>\n",
+       "      <td>8.694020</td>\n",
+       "      <td>0.208080</td>\n",
+       "      <td>0.201334</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           pixel  pixel_index  lat    lon       time        VCI       pev  \\\n",
+       "0  (-5.0, 33.75)            1 -5.0  33.75 1981-08-31  39.360001 -0.006845   \n",
+       "1  (-5.0, 33.75)            1 -5.0  33.75 1981-09-30  50.802499 -0.007329   \n",
+       "\n",
+       "          t2m        precip          E    SMroot    SMsurf  time_index  \n",
+       "0  296.760132  2.911272e-08  14.843219  0.215809  0.208432           0  \n",
+       "1  297.860565  1.686568e+00   8.694020  0.208080  0.201334           1  "
+      ]
+     },
+     "execution_count": 104,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dfs = []\n",
+    "for var in [v for v in ds.data_vars if v != 'VCI']:\n",
+    "    dfs.append((\n",
+    "        ds[var]\n",
+    "        .stack(pixel=['lat', 'lon'])\n",
+    "        .transpose()\n",
+    "        .drop('pixel')\n",
+    "        .to_dataframe()\n",
+    "        .reset_index()\n",
+    "        .drop(columns=['time', 'pixel'])\n",
+    "    ))\n",
+    "\n",
+    "df2 = pd.concat(dfs, axis=1)\n",
+    "df = df.join(df2)\n",
+    "\n",
+    "# time index\n",
+    "times = pd.unique(df.time.ravel())\n",
+    "times = pd.Series(np.arange(len(times)), times)\n",
+    "df['time_index'] = df.time.apply(times.get)\n",
+    "\n",
+    "df.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pixel</th>\n",
+       "      <th>pixel_index</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>lon</th>\n",
+       "      <th>time</th>\n",
+       "      <th>VCI</th>\n",
+       "      <th>pev</th>\n",
+       "      <th>t2m</th>\n",
+       "      <th>precip</th>\n",
+       "      <th>E</th>\n",
+       "      <th>SMroot</th>\n",
+       "      <th>SMsurf</th>\n",
+       "      <th>time_index</th>\n",
+       "      <th>next_month_VCI</th>\n",
+       "      <th>next_month_pev</th>\n",
+       "      <th>next_month_t2m</th>\n",
+       "      <th>next_month_precip</th>\n",
+       "      <th>next_month_E</th>\n",
+       "      <th>next_month_SMroot</th>\n",
+       "      <th>next_month_SMsurf</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>(-5.0, 33.75)</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-5.0</td>\n",
+       "      <td>33.75</td>\n",
+       "      <td>1981-08-31</td>\n",
+       "      <td>39.360001</td>\n",
+       "      <td>-0.006845</td>\n",
+       "      <td>296.760132</td>\n",
+       "      <td>2.911272e-08</td>\n",
+       "      <td>14.843219</td>\n",
+       "      <td>0.215809</td>\n",
+       "      <td>0.208432</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>(-5.0, 33.75)</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-5.0</td>\n",
+       "      <td>33.75</td>\n",
+       "      <td>1981-09-30</td>\n",
+       "      <td>50.802499</td>\n",
+       "      <td>-0.007329</td>\n",
+       "      <td>297.860565</td>\n",
+       "      <td>1.686568e+00</td>\n",
+       "      <td>8.694020</td>\n",
+       "      <td>0.208080</td>\n",
+       "      <td>0.201334</td>\n",
+       "      <td>1</td>\n",
+       "      <td>39.360001</td>\n",
+       "      <td>-0.006845</td>\n",
+       "      <td>296.760132</td>\n",
+       "      <td>2.911272e-08</td>\n",
+       "      <td>14.843219</td>\n",
+       "      <td>0.215809</td>\n",
+       "      <td>0.208432</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           pixel  pixel_index  lat    lon       time        VCI       pev  \\\n",
+       "0  (-5.0, 33.75)            1 -5.0  33.75 1981-08-31  39.360001 -0.006845   \n",
+       "1  (-5.0, 33.75)            1 -5.0  33.75 1981-09-30  50.802499 -0.007329   \n",
+       "\n",
+       "          t2m        precip          E    SMroot    SMsurf  time_index  \\\n",
+       "0  296.760132  2.911272e-08  14.843219  0.215809  0.208432           0   \n",
+       "1  297.860565  1.686568e+00   8.694020  0.208080  0.201334           1   \n",
+       "\n",
+       "   next_month_VCI  next_month_pev  next_month_t2m  next_month_precip  \\\n",
+       "0             NaN             NaN             NaN                NaN   \n",
+       "1       39.360001       -0.006845      296.760132       2.911272e-08   \n",
+       "\n",
+       "   next_month_E  next_month_SMroot  next_month_SMsurf  \n",
+       "0           NaN                NaN                NaN  \n",
+       "1     14.843219           0.215809           0.208432  "
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#### Add the lagged variables\n",
+    "for var_ in [v for v in ds.data_vars]:\n",
+    "    df[f'next_month_{var_}'] = (\n",
+    "        lag.sort_values('time').groupby(['pixel_index'])[var_].shift(1)\n",
+    "        .reset_index().sort_values('index').set_index('index')\n",
+    "    )\n",
+    "\n",
+    "df.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Only if required create the (time, [variable, pixel #]) dataframe\n",
+    "if False:\n",
+    "    df_ts = (\n",
+    "        df.set_index(['time', 'pixel_index'])\n",
+    "        .drop(columns=['pixel', 'time_index', 'lat', 'lon'])\n",
+    "        .stack().unstack(-2)\n",
+    "        .reset_index().set_index('time')\n",
+    "        .rename(columns={'level_1': 'variable'})\n",
+    "    )\n",
+    "    display(df_ts.head())\n",
+    "    df_ts.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Get the times where we have all the data!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(629094, 20)\n",
+      "(629094, 20)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.shape)\n",
+    "df_clean = df.dropna(how='any')\n",
+    "print(df.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3978, 20)"
+      ]
+     },
+     "execution_count": 108,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# get a smaller subset to play with\n",
+    "d = df_clean.loc[np.isin(df.pixel_index, [i for i in range(10)])]\n",
+    "d.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Alternative format\n",
+    "\n",
+    "```python\n",
+    "pd.DataFrame({\n",
+    "    'time': df.time.iloc[:20],\n",
+    "    'variable': np.tile(np.array([v for v in ds.data_vars]), 5)[:20],\n",
+    "    0: np.random.rand(20),\n",
+    "    1: np.random.rand(20),\n",
+    "    2: np.random.rand(20),\n",
+    "})\n",
+    "```\n",
+    "\n",
+    "```\n",
+    "time\tvariable\t0\t1\t2\n",
+    "0\t1981-08-31\tVCI\t0.078587\t0.457228\t0.008751\n",
+    "1\t1981-09-30\tpev\t0.345580\t0.356264\t0.116740\n",
+    "2\t1981-10-31\tt2m\t0.876523\t0.509587\t0.885240\n",
+    "3\t1981-11-30\tprecip\t0.701764\t0.042951\t0.111743\n",
+    "4\t1981-12-31\tE\t0.507153\t0.244528\t0.055843\n",
+    "\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pixels\n",
+    "# df.head(30).set_index(['time'] + [v for v in ds.data_vars]).stack().unstack(-2)\n",
+    "# df.head(30).melt(id_vars=['time'] + [v for v in ds.data_vars])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# What does the data look like?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "import tsfresh\n",
+    "from scipy.stats import pearsonr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x1080 with 7 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot a single Pixel\n",
+    "pixel_id = 3\n",
+    "data = df_clean.loc[df_clean['pixel_index'] == pixel_id]\n",
+    "\n",
+    "vars_ = [v for v in ds.data_vars]\n",
+    "\n",
+    "# get the pearsons correlation \n",
+    "corrs = []\n",
+    "vci_df = data.loc[:, ['VCI', 'time']].set_index('time')\n",
+    "t1 = vci_df.values.flatten()\n",
+    "\n",
+    "for var in vars_:\n",
+    "    ts2 = data[[var, 'time']].set_index('time')\n",
+    "    t2 = ts2.values.flatten()\n",
+    "    mask = np.isnan(vci_df.values.flatten()) & np.isnan(ts2.values.flatten())\n",
+    "\n",
+    "    r, _ = pearsonr(t1, t2)\n",
+    "    corrs.append(r)\n",
+    "#     print(f'VCI - {var}: {r:.2f}')\n",
+    "\n",
+    "# GET LAGGED correlations\n",
+    "lagged_corrs = []\n",
+    "for var in vars_:\n",
+    "    ts2 = data[[var, 'time']].set_index('time').shift(-1)\n",
+    "    t2 = ts2.values.flatten()\n",
+    "    mask = ~(np.isnan(t1) | np.isnan(t2))\n",
+    "\n",
+    "    r, _ = pearsonr(t1[mask], t2[mask])\n",
+    "    lagged_corrs.append(r)\n",
+    "#     print(f'VCI - {var} Lagged 1: {r:.2f}')\n",
+    "\n",
+    "# plot each time series\n",
+    "data.loc[:, vars_].plot(\n",
+    "    subplots=True, sharex=True, figsize=(10, 15)\n",
+    ")\n",
+    "fig = plt.gcf()\n",
+    "pixel = np.unique(df_clean.pixel.loc[df_clean.pixel_index == pixel_id])[0]\n",
+    "\n",
+    "axes = fig.get_axes()\n",
+    "[\n",
+    "    axes[i].set_title(\n",
+    "        f'R2 VCI - {vars_[i]}: {corrs[i]:.2f} \\nLagged 1: {lagged_corrs[i]:.2f}') \n",
+    "        for i in range(len([v for v in ds.data_vars])\n",
+    "    )\n",
+    "]\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### What do the marginal distributions of the variables look like?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x864 with 9 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(nrows=3, ncols=3, figsize=(12, 12))\n",
+    "colors = sns.color_palette()\n",
+    "\n",
+    "xlim_dict = {}\n",
+    "ylim_dict = {}\n",
+    "\n",
+    "for i, column in enumerate(df_clean[[v for v in ds.data_vars]].columns):\n",
+    "    ax_ix = np.unravel_index(i, (3, 3))\n",
+    "    ax = axes[ax_ix]\n",
+    "    sns.distplot(df_clean[column][~df_clean[column].isna()].values, ax=ax, color=colors[i])\n",
+    "    ax.set_title(column)\n",
+    "    xlim_dict[column] = ax.get_xlim()\n",
+    "    ylim_dict[column] = ax.get_ylim()\n",
+    "    \n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# try and wrap into a function\n",
+    "\n",
+    "def plot_variable_distributions(data, xlim_dict=None, ylim_dict=None, vars_=[v for v in ds.data_vars], show_all_data=False):\n",
+    "    xlim_dict = {} if xlim_dict == None else xlim_dict\n",
+    "    ylim_dict = {} if ylim_dict == None else ylim_dict\n",
+    "    \n",
+    "    fig, axes = plt.subplots(nrows=3, ncols=3, figsize=(12, 12))\n",
+    "    colors = sns.color_palette()\n",
+    "\n",
+    "    for i, column in enumerate(data.columns):\n",
+    "        ax_ix = np.unravel_index(i, (3, 3))\n",
+    "        ax = axes[ax_ix]\n",
+    "        sns.distplot(data[column][~data[column].isna()].values, ax=ax, color=colors[i])\n",
+    "        \n",
+    "        if show_all_data:\n",
+    "            sns.distplot(\n",
+    "                df_clean[column][~df_clean[column].isna()].values, ax=ax, color='grey', \n",
+    "                hist_kws={'alpha':0.1}, kde_kws={\"color\": \"k\", \"lw\": 3, 'alpha':0.4}\n",
+    "            )\n",
+    "\n",
+    "        ax.set_title(column)\n",
+    "        ax.set_ylim(ylim_dict[column])\n",
+    "        ax.set_xlim(xlim_dict[column])\n",
+    "        xlim_dict[column] = ax.get_xlim()\n",
+    "        ylim_dict[column] = ax.get_ylim()\n",
+    "    \n",
+    "    return fig, xlim_dict, ylim_dict\n",
+    "\n",
+    "\n",
+    "# plot_variable_distributions(\n",
+    "#     data=df_clean\n",
+    "# )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### That's great but is there any signal in the other features which can give us some information about the status of the VCI? Let's compare the CONDITIONAL distributions of the variables given a VCI of < 10."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, axes = plt.subplots(nrows=3, ncols=3, figsize=(12, 12))\n",
+    "colors = sns.color_palette()\n",
+    "\n",
+    "data = df_clean.loc[df_clean.VCI < 10, [v for v in ds.data_vars]]\n",
+    "\n",
+    "for i, column in enumerate(data.columns):\n",
+    "    ax_ix = np.unravel_index(i, (3, 3))\n",
+    "    ax = axes[ax_ix]\n",
+    "    sns.distplot(data[column][~data[column].isna()].values, ax=ax, color=colors[i])\n",
+    "    sns.distplot(\n",
+    "        df_clean[column][~df_clean[column].isna()].values, ax=ax, color='grey', \n",
+    "        hist_kws={'alpha':0.1}, kde_kws={\"color\": \"k\", \"lw\": 3, 'alpha':0.4}\n",
+    "    )\n",
+    "    ax.set_title(column)\n",
+    "    ax.set_ylim(ylim_dict[column])\n",
+    "    ax.set_xlim(xlim_dict[column])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### What about the distribution of variables LAGGED by 1 month?\n",
+    "- Display the distribution of the variables on the MONTH BEFORE the VCI was low (< 10)\n",
+    "- Visualise the conditional distribution against the marginal distribution (the grey line)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pixel</th>\n",
+       "      <th>pixel_index</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>lon</th>\n",
+       "      <th>time</th>\n",
+       "      <th>VCI</th>\n",
+       "      <th>pev</th>\n",
+       "      <th>t2m</th>\n",
+       "      <th>precip</th>\n",
+       "      <th>E</th>\n",
+       "      <th>SMroot</th>\n",
+       "      <th>SMsurf</th>\n",
+       "      <th>time_index</th>\n",
+       "      <th>next_month_VCI</th>\n",
+       "      <th>next_month_pev</th>\n",
+       "      <th>next_month_t2m</th>\n",
+       "      <th>next_month_precip</th>\n",
+       "      <th>next_month_E</th>\n",
+       "      <th>next_month_SMroot</th>\n",
+       "      <th>next_month_SMsurf</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>(-5.0, 33.75)</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-5.0</td>\n",
+       "      <td>33.75</td>\n",
+       "      <td>1981-09-30</td>\n",
+       "      <td>50.802499</td>\n",
+       "      <td>-0.007329</td>\n",
+       "      <td>297.860565</td>\n",
+       "      <td>1.686568</td>\n",
+       "      <td>8.694020</td>\n",
+       "      <td>0.208080</td>\n",
+       "      <td>0.201334</td>\n",
+       "      <td>1</td>\n",
+       "      <td>39.360001</td>\n",
+       "      <td>-0.006845</td>\n",
+       "      <td>296.760132</td>\n",
+       "      <td>2.911272e-08</td>\n",
+       "      <td>14.843219</td>\n",
+       "      <td>0.215809</td>\n",
+       "      <td>0.208432</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>(-5.0, 33.75)</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-5.0</td>\n",
+       "      <td>33.75</td>\n",
+       "      <td>1981-10-31</td>\n",
+       "      <td>50.107499</td>\n",
+       "      <td>-0.008037</td>\n",
+       "      <td>299.804291</td>\n",
+       "      <td>11.365343</td>\n",
+       "      <td>6.438349</td>\n",
+       "      <td>0.204618</td>\n",
+       "      <td>0.198097</td>\n",
+       "      <td>2</td>\n",
+       "      <td>50.802499</td>\n",
+       "      <td>-0.007329</td>\n",
+       "      <td>297.860565</td>\n",
+       "      <td>1.686568e+00</td>\n",
+       "      <td>8.694020</td>\n",
+       "      <td>0.208080</td>\n",
+       "      <td>0.201334</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           pixel  pixel_index  lat    lon       time        VCI       pev  \\\n",
+       "1  (-5.0, 33.75)            1 -5.0  33.75 1981-09-30  50.802499 -0.007329   \n",
+       "2  (-5.0, 33.75)            1 -5.0  33.75 1981-10-31  50.107499 -0.008037   \n",
+       "\n",
+       "          t2m     precip         E    SMroot    SMsurf  time_index  \\\n",
+       "1  297.860565   1.686568  8.694020  0.208080  0.201334           1   \n",
+       "2  299.804291  11.365343  6.438349  0.204618  0.198097           2   \n",
+       "\n",
+       "   next_month_VCI  next_month_pev  next_month_t2m  next_month_precip  \\\n",
+       "1       39.360001       -0.006845      296.760132       2.911272e-08   \n",
+       "2       50.802499       -0.007329      297.860565       1.686568e+00   \n",
+       "\n",
+       "   next_month_E  next_month_SMroot  next_month_SMsurf  \n",
+       "1     14.843219           0.215809           0.208432  \n",
+       "2      8.694020           0.208080           0.201334  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# # create month+1 VCI variable\n",
+    "# lag = df_clean.copy()\n",
+    "# lag['next_month_VCI'] = (\n",
+    "#     lag.sort_values('time').groupby(['pixel_index'])['VCI'].shift(1)\n",
+    "#     .reset_index().sort_values('index').set_index('index')\n",
+    "# )\n",
+    "display(df_clean.head(2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x864 with 9 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(nrows=3, ncols=3, figsize=(12, 12))\n",
+    "colors = sns.color_palette()\n",
+    "\n",
+    "data = lag.loc[lag.next_month_VCI < 10, [v for v in ds.data_vars]]\n",
+    "\n",
+    "for i, column in enumerate(data.columns):\n",
+    "    ax_ix = np.unravel_index(i, (3, 3))\n",
+    "    ax = axes[ax_ix]\n",
+    "    sns.distplot(data[column][~data[column].isna()].values, ax=ax, color=colors[i])\n",
+    "    sns.distplot(\n",
+    "        df[column][~df[column].isna()].values, ax=ax, color='grey', \n",
+    "        hist_kws={'alpha':0.1}, kde_kws={\"color\": \"k\", \"lw\": 3, 'alpha':0.4}\n",
+    "    )\n",
+    "    ax.set_title(column)\n",
+    "    ax.set_ylim(ylim_dict[column])\n",
+    "    ax.set_xlim(xlim_dict[column])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### What about being conditional on low rainfall values?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x864 with 9 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(nrows=3, ncols=3, figsize=(12, 12))\n",
+    "colors = sns.color_palette()\n",
+    "\n",
+    "data = df.loc[df.precip < df.precip.quantile(0.1), [v for v in ds.data_vars]]\n",
+    "\n",
+    "for i, column in enumerate(data.columns):\n",
+    "    ax_ix = np.unravel_index(i, (3, 3))\n",
+    "    ax = axes[ax_ix]\n",
+    "    sns.distplot(data[column][~data[column].isna()].values, ax=ax, color=colors[i])\n",
+    "    sns.distplot(\n",
+    "        df[column][~df[column].isna()].values, ax=ax, color='grey', \n",
+    "        hist_kws={'alpha':0.1}, kde_kws={\"color\": \"k\", \"lw\": 3, 'alpha':0.4}\n",
+    "    )\n",
+    "    ax.set_title(column)\n",
+    "    ax.set_ylim(ylim_dict[column])\n",
+    "    ax.set_xlim(xlim_dict[column])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### and the same with the precip lagged one month"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pixel</th>\n",
+       "      <th>pixel_index</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>lon</th>\n",
+       "      <th>time</th>\n",
+       "      <th>VCI</th>\n",
+       "      <th>pev</th>\n",
+       "      <th>t2m</th>\n",
+       "      <th>precip</th>\n",
+       "      <th>E</th>\n",
+       "      <th>SMroot</th>\n",
+       "      <th>SMsurf</th>\n",
+       "      <th>time_index</th>\n",
+       "      <th>next_month_VCI</th>\n",
+       "      <th>next_month_pev</th>\n",
+       "      <th>next_month_t2m</th>\n",
+       "      <th>next_month_precip</th>\n",
+       "      <th>next_month_E</th>\n",
+       "      <th>next_month_SMroot</th>\n",
+       "      <th>next_month_SMsurf</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>(-5.0, 33.75)</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-5.0</td>\n",
+       "      <td>33.75</td>\n",
+       "      <td>1981-09-30</td>\n",
+       "      <td>50.802499</td>\n",
+       "      <td>-0.007329</td>\n",
+       "      <td>297.860565</td>\n",
+       "      <td>1.686568</td>\n",
+       "      <td>8.694020</td>\n",
+       "      <td>0.208080</td>\n",
+       "      <td>0.201334</td>\n",
+       "      <td>1</td>\n",
+       "      <td>39.360001</td>\n",
+       "      <td>-0.006845</td>\n",
+       "      <td>296.760132</td>\n",
+       "      <td>2.911272e-08</td>\n",
+       "      <td>14.843219</td>\n",
+       "      <td>0.215809</td>\n",
+       "      <td>0.208432</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>(-5.0, 33.75)</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-5.0</td>\n",
+       "      <td>33.75</td>\n",
+       "      <td>1981-10-31</td>\n",
+       "      <td>50.107499</td>\n",
+       "      <td>-0.008037</td>\n",
+       "      <td>299.804291</td>\n",
+       "      <td>11.365343</td>\n",
+       "      <td>6.438349</td>\n",
+       "      <td>0.204618</td>\n",
+       "      <td>0.198097</td>\n",
+       "      <td>2</td>\n",
+       "      <td>50.802499</td>\n",
+       "      <td>-0.007329</td>\n",
+       "      <td>297.860565</td>\n",
+       "      <td>1.686568e+00</td>\n",
+       "      <td>8.694020</td>\n",
+       "      <td>0.208080</td>\n",
+       "      <td>0.201334</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           pixel  pixel_index  lat    lon       time        VCI       pev  \\\n",
+       "1  (-5.0, 33.75)            1 -5.0  33.75 1981-09-30  50.802499 -0.007329   \n",
+       "2  (-5.0, 33.75)            1 -5.0  33.75 1981-10-31  50.107499 -0.008037   \n",
+       "\n",
+       "          t2m     precip         E    SMroot    SMsurf  time_index  \\\n",
+       "1  297.860565   1.686568  8.694020  0.208080  0.201334           1   \n",
+       "2  299.804291  11.365343  6.438349  0.204618  0.198097           2   \n",
+       "\n",
+       "   next_month_VCI  next_month_pev  next_month_t2m  next_month_precip  \\\n",
+       "1       39.360001       -0.006845      296.760132       2.911272e-08   \n",
+       "2       50.802499       -0.007329      297.860565       1.686568e+00   \n",
+       "\n",
+       "   next_month_E  next_month_SMroot  next_month_SMsurf  \n",
+       "1     14.843219           0.215809           0.208432  \n",
+       "2      8.694020           0.208080           0.201334  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(df_clean.head(2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x864 with 9 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(nrows=3, ncols=3, figsize=(12, 12))\n",
+    "colors = sns.color_palette()\n",
+    "\n",
+    "data = df_clean.loc[df_clean.next_month_precip < df_clean.precip.quantile(0.1), [v for v in ds.data_vars]]\n",
+    "\n",
+    "for i, column in enumerate(data.columns):\n",
+    "    ax_ix = np.unravel_index(i, (3, 3))\n",
+    "    ax = axes[ax_ix]\n",
+    "    sns.distplot(data[column][~data[column].isna()].values, ax=ax, color=colors[i])\n",
+    "    sns.distplot(\n",
+    "        df[column][~df[column].isna()].values, ax=ax, color='grey', \n",
+    "        hist_kws={'alpha':0.1}, kde_kws={\"color\": \"k\", \"lw\": 3, 'alpha':0.4}\n",
+    "    )\n",
+    "    ax.set_title(column)\n",
+    "    ax.set_ylim(ylim_dict[column])\n",
+    "    ax.set_xlim(xlim_dict[column])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### What about lagged LOW temperature?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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O3/bNtGUDPDjHfi8z9xfAgpE1RVEUJUekSgnmsnlOipQYCvVAzabcn291kUlC85L3NcZ8C/gWwM6dO0uuI0rvuFWustARbxHB7/ITiRfnTWZHR4e9vG7duhUfx+FwUFFRYUe7g8EgNTXahmUutHOloiiKMl3eLx/C226io5VNVoBt8xQRD5bNc3ce9i0pUhHvCk/FIiNzj8/pK1qPd1fX9H1XS0tLRsdSn/fSyCjirSiKoqwSxjqtV38erCZ2xFtz55eLMSYmIimbpxP4bsrmmXz/myKyDjgAVAIJEfl94GpjzNhc+xbmX5JbBiYGqPZW250jC4nP5StKq8n4+DgjIyMAOJ1OGhszS6yurKyks9P6HlGf9/wU/i9SURRFKTxj3SBO8OYhQpg6x7gK75WwBJtnD5aNZEn7rkYGwgPU+1dWoSPb+F3+oox4p0QyWG3iXa7MJGF6xHt0dDSjY61m1GqiKIqyyonH44yMjGCl3czDWBdUrLMa3OQaTzkgECq9pD2lNBiIFJfwLrbOlYlEgmPHjtnrmdpMADwej912Pl3UKzPRiLeiKMoqZXR0lOeff56enh5isRj19fV87GMfm9HswibYBRXN+ZmYwwmeAIR683M+Zc0xMDHAe5reU+hpAEmrSbS4hPf+/fvp7bU+fyLCxo0bMz5mejLl0NBQxsdbrWjEW1EUZRUSjUbZs2cPHR0ddmmvgYEBnnjiiRmVDGzGuqAy86jXkvFWwLhGvJXsY4yhd6KXgUhxNGkajgzTPd69+MA8cf78eQ4fPmyv33TTTVRXZ57bUV5ebpcjnJqaYmJiIuNjrkZUeCuKoqxCDhw4MOeFLxqN8vzzzxMOz4rAjXXnWXhXanKlkhPGpsaImzgV7sJXNAHwOD1EE9FCT4NgMMjQ0BA///nPiUQsz/nGjRvZsWNH1s6RLuA16j03KrwVRVFWGQMDAxw5csRef9/73sfHP/5xAoEAkUiE0dFRXnjhhekdImMwFZyu5Z0PPBVqNVFywmB4EIByT/kiI/OD2+EmGi+88AZ4++23GR8fB8Dn8/GBD3zAbnyTDdRusjgqvBVFUVYZL730EuFwmEgkQnNzM1u3bqW+vp7bbrvNHnPmzJnpBKh81vBO4S1Xq4mSEwbClsWkzF1W4JlYuB3uooh4j46OcvToUXt9+/btTE1NZbXmtgrvxVHhrSiKsooIh8MzPNy33HKLvdza2srmzZvt9TfffNNaGE2Oz0e7+BTeZNv4yVD+zqmsCfrD1g1dMTTPgeIR3p2dnXZlo6amJrZu3Zr1c6jwXhwV3oqiKKuIM2fOzLi4zk6auummm+xHyz09PVYDjZTw9uexxXOqlrfaTZQsU3QRb6clvBMmUdB5DA8P28vZqGIyF+nCe3h4eOESpmsUFd6KoiiriJMnT9rLW7ZsueT9srIy2tra7PXjx48nhbfk2WqSaqKjdhMluwyGB3GKE5/TV+ipAFbEGyh4E5104Z0ukLOJ1+vF7/cDVv8AbaRzKSq8FUVRVgkjIyMMDFjRPqfTyaZNm+Ycd/nll9vLJ0+eJDF80RLdDmc+pmlhR7y1somSXQYjg5R7yrOaNJgJtvCOF054G2NmCO/a2tqcnUvtJgujwltRFGWVcOrUKXu5ra0Nr9c757jW1lYCgQAAkUiE9jMn82szgWnhPVEctZaV1cNAeIByd3FUNAGrnCBAOFa4JjpjY2N2PX+/34/Pl7unAXV1dfZyf78+0ZqNCm9FUZRVQnt7u7182WWXzTtORNi2bZu9fvb8BfDnMbESwJP03558Nr/nVVY9A+GBovF3w3TEu5DdK9Mjz7mymaRoaGiwl/v69InWbFR4K4qirALGxsbsR8lOp5P169cvON62oSQSXOzqI+HNs/B2uMAdgCmtaqJkl2KLeLudSeFdwIj34OCgvZxP4d3f308iUdik0mJDhbeiKMoq4Pz58/Zyc3MzLpdrwfG1tbW4XC4iwUGisSjnQu5cT/FSPOUwmb0awooSS8QYjgwXTfMcKA6Pd3rEO5f+boBAIGBb2WKx2AxvuaLCW1EUZVWQLryXWipsw4YNSGTE2n+sAIlo3nKNeCtZZTgyjMEUVcTb4yi8xzufVhO4NOqtTKPCW1EUGxFpE5FfiMgxEXlXRH4vuf2PRKRTRA4mfz6Sts8fishpETkhIvcWbvZrl8nJSbq7u+311tbWJe3X1taGhK1o1PmRAjwO9lRoAx0lqwxGku3ii0h4p6wmE7GJgpw/Go0yNjYGgMPhuKS2fy5Qn/f8LPwsUlGUtUYM+ANjzFsiUgG8KSLPJd/7S2PMf08fLCJXA58CrgFagJ+LyOXGmHheZ73GuXDhgt2oorGx0X7Muxjr1q3DGx3BAMGYi6HRELVVeRQs3nIYOp2/8+WQQ4cOcfz4cW699VauuuqqQk9nzdI/YUVXyzyaXJki3epRWVmJ05n7sqEqvOdHI96KotgYY7qNMW8ll4PAMWChLL37gUeNMZPGmHPAaeDm3M9USefixYtEIhEikQj19fVL3s/hcNAWmCThLgOnmws9ea656ymHqQmIx/J73iwTj8d58803GR8f56WXXir0dNY0qa6VFe7iaBcP0+UEC+XxDgan8ygqKyvzcs66ujomJyeJRCJ0dXURjUbzct5SQIW3oihzIiKbgBuA/clNXxaRQyLyXRFJmQTXAxfTdutgHqEuIg+IyAEROaCev+xhjKGjo8NeX6rNJMUG7xgErLq7nf15ToLyVgAGwqXdZGO2qAiHC+flXev0TVjR1WJMriyUxztlMwGoqMjPDYnb7Z5hadHv/GlUeCuKcgkiUg48Afy+MWYM+AawBbge6Ab+PDV0jt3NXMc0xnzLGLPTGLMz/TGkkhkDAwNEIlYkzefzLbtiQZv0Y/zWPj2Do8RieXQJpcRRibeNj8dn/s5CIfWtF4r+cD9+lx+Xo3ictIUW3ukR73wJb9AEy/lQ4a0oygxExI0lun9ojPkxgDGm1xgTN8YkgG8zbSfpANrSdm8FuvI537XOxYvTDxxaW1uX1yY7PkX5VDdVjdZDing8Qc/gaLanOD+p7pUqvJUs0T/RX1Q2E7AaVrkcLiKxwlhNChHxBmbY3lR4T6PCW1EUG7FU23eAY8aYv0jb3pw27JPAkeTybuBTIuIVkc3ANuD1fM1XmSm8W1palrWvjHUiJkHzxukulh19ebSbeJMR71BpX5RVeBcP/eH+orKZpPA4PEVhNSkvz9/vJl14a4LlNMXzLEZRlGLgNuAzwGEROZjc9h+AT4vI9Vg2knbgdwCMMe+KyGPAUayKKA9qRZP8MTk5OeOCtli3ytk4Rq3a3y2uUQ4nt+VVeKcE0sRA/s6ZA2Z35lPhXTj6w/00BZoKPY1LcDvdBRHeiUSC8fFxez2fEe+amhqcTifxeJxQKEQ4HMbv9+ft/MWKCm9FUWyMMS8zt2/76QX2eQh4KGeTUuals7PTLiPY0NCAz+db1v6O4XYAGtdvxHHiXQCGRkNMRCYJ+LxZneucuP0gDrWaKFkhYRIMTAywpXpLoadyCW5HYYR3KBSyvyMCgUBeSgmmcDgc1NXV2cGB/v5+NmzYkLfzFysqvAvEI/svEInGOdQxyun+ED2jESamYogIbTV+rlxXwa7L6vjQ1U3UlefhAqgoSsnR1TVtp29ubl5g5Nw4hk5jnF5cgRoaaysZGbWSsLr6R9jaloeooTisqPcqE97pyWxK/hiODBMzsaLzeEPhrCbpNpN8lRJMp76+3hbefX19KrxR4V0Qxidj/OxIN/vODhKNG6oDblqq/FT4yjAGAl4nr50d5MmDXfzHnxzm3mvW8aUPbOG61tx3m1IUpXTo7Oy0l5fr7wZwDJ0hUdYIIjTXV9vCu7MvT8IbwFMG46VtNYnFZtYhz3XEW0Q+DHwdcAJ/a4z52qz3Jfn+R4AJ4HOp+vwi8hXgX2HZxg4DnzfGFCbrL8v0h60buApP8QnvQllNCuXvTqEJlpeiwjvPvHJ6gH/7+Dt0j0a4vq2a915WR2uN/5JKBPdc1UT3aIR3Okb4xYk+fnakh2tbKvnodS1U+a3SRL+xS+8cFWWtEgqFGB21KpA4nU4aGxuXfQzH8BniZesAaGmo4e0Tluf7dNcA73/PFdmb7EJ4K0peeM+OeEciEWKxGC5X9i+xIuIE/hq4B6uq0BsistsYczRt2H1Yic7bgF1Y5UB3ich64F8DVxtjwsn8jE8B38v6RAtAz3gPUKTCu0BWk0KVEkyhCZaXosI7Txhj+MG+8/zxnqNsri/jd+64jI1187e0FRFaqv20VPu584pGXjkzwD+d6Odk70k+sr2ZmzbVzLuvoiirn3Sbybp165bv3YxO4Ah2EW28DoD66nLcTifReJzx8CRjoTCV5XlIhFqFVhOwbozSG4hkkZuB08aYswAi8ihWB9l04X0/8H1jmXv3iUh1WmUiF+AXkSgQYBWV/0wJ7ypvVYFncimFEt6FKiWYoqqqCo/H6tw5OTlJKBQqSOS9mFDhnWMe2X8BgOeO9vCLE/1cua6Cf7GzDa976RdJn9vJB69s4sa2Gn7ydidPHuzkVF+Qj1/fQqXPnaupK4pSxJw6dcpunLMSgecYOgtAImBFyh0OB031VXT0Wl0kO/uH8yO8veUwcCr358khs6uaQE6F91zdYnctYcx6Y8wBEfnvwAUgDDxrjHl2rpOIyAPAA0DJ+HJ7xntwiYsy9/xBrULhcXoYmRzJ+3kLHfEGqK2tZWTE+rcPDQ2teeGtdbzzwC9P9vOLE/3s3FjDb92ycVmiO52aMg+fu20T9127jmPdY/yzv3mV9oHxxXdUFGXVkR7xXpG/++APAEiUTXeXa66bFoqd+Sor6CmHqSBES9dmPF/EO0cspVvsnGNEpAYrGr4ZaAHKROS35jpJKXaa7ZnooTHQiEOKT9qs1Yg3MKOb7uDgYEHmUExk9NcpIh8WkRMiclpEvjrH+yIif5V8/5CI3Jj23ndFpE9Ejsza549EpFNEDiZ/PpLJHAvNwYvD7H23h+3rq/jEDetxLKer3Bw4RHjftgb+5W2bGQhNcv9fv8Kb54eyNFtFUUqBkZERwmHrIu7xeKirq1v2MRzjfRiEhH/ag9ncMC28uwZG7DJkOSXlxy3hWt55Ft5L6RY735i7gXPGmH5jTBT4MXBrriaab3rHe1mXzFkoNgqRXDkxMcHU1BQALperYDW0a2qmrbEqvDMQ3mkJHvcBV2M12Lh61rD0BI8HsBI8UnwP+PA8h/9LY8z1yZ956wcXO8d7xvjJ251sqivj13e2ZSy607msoZzdD95ObZmH3/zb/fynnx7hkf0X7B9FUVYv6dHupqam5bWJT+IY78X468A5bVerrSzD67HWI5NRhkbz8EQt1b2yhH3eeRbebwDbRGSziHiwkiN3zxqzG/jtZPDrFmDUGNONZTG5RUQCyconHwSO5Wqi+aZnvIemsuJrngNWOcHJ+CTxRP76i6WL3JXcnGeLdOE9NKSBwkwi3naChzFmCkgleKRjJ3gYY/YBdoKHMeaXwKr9HwhGonzp79/C53Ly6ZvbcDqyJ7pTvHx6gE/fvIG6Mi8/2Heew52jWT+HoijFR6Y2E7CEd6JsZiUUEaG5fjoxrbM/D3YTW3iXbsR7djlByJ3wNsbEgC8Dz2CJ5seSHWS/KCJfTA57GjgLnAa+Dfxuct/9wI+At7BKCTqAb+VkonkmYRJ0j3czOlmc10F38gY3n1HvwcFBIpEIkUikoB0ja2pq7Hn09vbO+XlZS2QivOdM3ljBmLn4ctKa8t2kJ63k+C8/O875wXE+dfMGKnKYAFnudfGF2zfTVhvg0dcvcPBi/pM3FEXJH8aYSyqaLJtEDMfEAPGyS0sQttRPf+V29efh+8RT+sJ7roh3KvE1FxhjnjbGXG6M2ZLsHIsx5pvGmG8ml40x5sHk+9uNMQfS9v1PxpgrjTHXGmM+Y4yZzNlE88hQZIi4iVPpyX+TmKXgcViVPfIpvAcGpj9ThYx4u1wuu3mPMYbh4TzljxQpmQjvFSd4LHLcbwBbgOuBbuDP5zy5yAMickBEDhRbUfZXTw/wyP4LfOH2zWyuz312tc/t5HO3bmJTfRmPH7io4ltRVjHDwwF/mYYAACAASURBVMO2qPN6vTMe4y4VGbmAmDiJOR7Lz/Z5z1WxI6ukPN7jpVvjd67f0VxiXMkdXSHrZrTaV5yN5rIZ8Z6cnOT48eO88sorvPjii3bFkNmkW03SExwLgSZYTpOJ8M4kwWNejDG9xpi4MSaB9Yjs5nnGFWXG9fdeaefBR96irsxDa00gb+f1upx89r3T4nvPO6umNKuiKGnMjnavxN/tHLLK9822mgBUlvkpD/gAiMXi9A3luP25ywsu36rzeKvwzi9d49bnospTfDW8IXsR746ODv7xH/+RZ599ljfffJNDhw6xe/fuS7zT0WjUbrAlIiu6Qc8m6vOeJhPhnUmCx7ykFfkH+CRwZL6xxcg/nexjeCLKP7uxFbczvyWNPC4Hn33vJjbWBfj9fzzIM+/25PX8iqLknnTh3dzcvMDI+XEMnQbmFt4ALfVpZQVz7fMWgbKGVWc1yfmTAmUGdsTbu3oj3keOHOHpp5++xMYUiUR46qmnZkS+06PK1dXVy2+wlWU04j3NipVhJgkeACLyD8BrwBUi0iEiX0i+9WciclhEDgF3Al9Z6RzzzYXBCV46NcD1bdV5sZjMRUp8X9daxZcfeYtfnCjdx7eKoszEGEN393TsYqXCW4bPkfBUgGvuhKt0u0le6nmXNUCodL+r5koW04h3fukKdeF3+fG6vIWeypykIt4TsYkV7X/+/HlefPHFGcmSO3bssLtCRiIRfvnLX9rji8lmMnsOaz3inVHnymSpv6dnbftm2rIBHpxn30/Ps/0zmcypkPzn/3UUhwj3XlPYOqJet5Pvff5mfvNv9/HFH7zJ9//lzey6rHCJFYqiZIfBwUEmJ61cOJ/Pt+LOiI6RdhL++b8TmtMi3n3DQaaiMTzuHDY6LmuA4IIPQ4satZoUnq5QV1G2ik+RScTbGMOrr75qP0Wpr6/n7rvvxu/309bWxgsvvEAikaCnp4dz586xefPmohPe5eXluN1uotEok5OThMPhglZaKSTaMj4LPLL/Amf6Qzx7tJcPXd1ElT97VUxi0SlGhweJRiLEYlEQcIgDcTiQVO6qCCKCy+3G7fESqKikyu/m+/9yF7/+P1/jt7/7Ol+4fTOtNQF+Y1dptP5VFOVSslFGECzhHa9onfd9v9dDfXUFAyNBEokEnX3DbF6fw1yayDAMn8/d8XOMWk0KT1eoq2htJpCZx/v48eOkikg4HA5uv/12W7Q2NDSwadMmjh49CsC+fftobW2d8V1RyIom6VRVVdmVVoaHh1V4KysnYQx7j/RQ7Xdz29b6xXdYAoN93Zx7921GBvsxxuB0e5a1v6/zMjZu3MjXP7GF3/r7ozz8ajtffP+WrMxNUZTCkA1/N9EwjlAP0cbrFxxWW1NFx4CVnHWxdyi3wttTYbWNN8byfJcYc4nsRCKBMWZFya/K8jDG0DXexXUN1xV6KvPidqw84n348GF7efv27ZckSl5//fWcPn2aqakpgsEgjz/+uF1H3uVyFY3wrq6uniG8MwkelDIqvLPA4c5ROkfC/Np7Mk+oDI+HOH5wP8P9vcSjU/b29OXZIjz9vRQ/e/04zrfPArDTX82LI36+96qT39i1gbry4vTAKYoyP4lEYoa/e0X1uwHH65YbMBFY+GLc1lTLoVNWF9wLPTn2ZHorwCQgPAyBwj8WXy7z2UoSiUTBk9rWAsOTw4Rj4eKOeDuTEe/o8oT30NCQnTTpcrm47rpLby68Xi/vec97eO2114CZzZtuvPFG2wdeaNKtcWu5lrcK7wyZjMV59t0emqt87GjL7EPf13mBY2/vJx6LztheXllNWUUlLpcbgyERTwAGy0JvYRIJYrEok5EwE6ExjEkT5NE+thvDa++c5rP/I8KP/+1H8bjyW3FFUZTMGBgYIBq1vhvKy8vthhTLxTFheT8TgYWfzjXUVOBxu5iKxpiITDI4EqKuunxF51wUb6qW98CqEt7xeFyFdx4o9oomsPKI9+nTp+3lDRs24HLNLduuvPJKRkdHOXv2rL1t3bp1XH311SuYbW5IF97z1R5fC6jwzpAf7rvA8ESUz9+6HkcGjxTPHT/MueOHbbEsAi0bL2Pj1qvwBZZXISUeizEy2Edv5wX6ui5ijKG23MuO+jivvfgsH+q7wOc+cQ8er08938oMRKQN+D6wDkgA3zLGfF1EaoF/BDYB7cCvG2OGk/v8IfAFIA78a2PMMwWY+qqns7PTLiPW2jq/P3sxJGw96k34FxbeIkJrYy1nO61qIxd7h3InvO3ulX3QcHluzpFDFhLeSu7pDHUCxS28nQ4nDnEsW3ifOXPGXt68efOCY3ft2kV5eTmHDh3C7/fzgQ98oKisTlVV08mvGvFWVsRYJMr//8IptjaUs62pYsXHOXHwDTrbT9nrvkAZ19x4C5U1K/NlOV0u6ppaqGtqYctV13HhzAk620/TXO5ia72fw0eP8+jkEB+68/2gwluZSQz4A2PMWyJSAbwpIs8BnwOeN8Z8TUS+CnwV+L9E5GqsGv7XAC3Az0XkcmOMKo4skxV/N1bE27gD4F48sWl9Y40tvM/3DHL9FTn6vrAj3qXZRGeucoKgwjtfpCLexVzVBKwEy+UI776+PoJBq4GVx+Nh/fr1i+5zzTXX0NbWhtfrLSrRDVBRUWFH7MPhMJFIBJ/PV+BZ5R/1G2TAt/7pLMMTUe69duXlA8+8e5ALp94lHp0iHp2ipr6Rm+64Z8WiezZef4Bt197Arrs+QkPzeq5qKqOxwstb5/rY9/I/sXfv3hl+MGVtY4zpNsa8lVwOYtXoXw/cDzycHPYw8Ink8v3Ao8aYSWPMOaya/XN2m1VWTjwep6dnuiFWRsI7MkzCt7Qudq2NtXb1pN7BUSYikys+74KkhHeoNIV3usBOtwJoZZP80BnqxOf04XMVt4jzOD3LquOdbhvZuHHjkm1LlZWVeL3Fl8slIjOi3mvVbqLCe4V0jYT525fP8is7WlhfvbKSOBdOH+f8qaP2emNLG9ftugPXMiuYLAV/oIxrd97Gjl3vY9eWBlwO4Y32QY4eO8Fjjz3GkSNHZnjGFUVENgE3APuBplTX2eRrquXheuBi2m4dyW1KFunr67PFXUVFBWVlK2/QJZGRJQtvn9dNU930hbK9K0fdJT1lgFhWkxIkXWCnJ7JpxDs/dIW6qPYVr80khdvhXlbEu6Ojw17euHFjLqaUdzTBUoX3ivmzvccxBv79h69Y0f6Dfd2cPvK2vV7f1MJVN+zC4cjtf0ldUwt33H0f9+zawVgkzvMnBojFYrz66qvs2bOH8fHxnJ5fuZRgMGj/FAsiUg48Afy+MWZsoaFzbJvzDk5EHhCRAyJyIFWTVlka2bKZADgiI5hliJRNLdNe8LOdOfp/E4clvku0e2W6wFbhnX86Q51F7e9O4XEu3WoyMTFhd3h0OBwrrmJUbKjwVuG9It66MMyTB7t475Y6fnly+RGg8HiId994hZQ+qaqp4+obb8m56E7hcnv44B23cesH7uaNnhgnu4aJRCK0t7fzxBNPzLjLVtYeIuLGEt0/NMb8OLm5V0Sak+83AymF1AG0pe3eCnQxB8aYbxljdhpjdjY05LAm9CokXXhndAGeHEPiERLLEN4bm6dtb90Do0QmowuMzgBvZcl6vNMFtts93UBNrSa5xxhT9M1zUiwn4t3R0WG3h6+srJzxd1XKaIKlCu9lE08Y/njPUSq8Lt5/+fLFQyIe58jrLxFLVi/xeH1cs/NWnPOUCMolv3LTNrbe9AFeGKokmrBuAiKRCD/72c/sLljK2kKsbJzvAMeMMX+R9tZu4LPJ5c8CP03b/ikR8YrIZmAb8Hq+5rsWiMVi9Pb22usZ+bvHrOoPy4l4B3xe225ijKG9O0d2E28FhHoXH1dkpItuh8Mxw4erEe/cMxgZJBKPlIbwdrqXXMc7PQC2lKTKUkEj3iq8l80j+8/zzsUR7tu+Dq9r+fVZTx1+i+Co9ccmImzfdQdeX2HapnpdDv70V65mKLCB7qpr7fatxhhefvll9u3bV5B5KQXlNuAzwF0icjD58xHga8A9InIKuCe5jjHmXeAx4CiwF3hQK5pkl76+PjtyWlVVRSAQWPGxJGhFzpfq8U7RWFdDJJYgEktw5FzP4jusBG9FSSZXzk6sVOGdX+xSgiXg8V5qVRNjzKoV3hUVFfbT/YmJCaamLm0AuNpR4b0M+sYi/NneE9y+tZ4drcv/kPdcbKez/ZRdwWTz5VdTlsFFNBt0Do2zs62SJ05EuPzmuygvL7cfbx06dIgXX3xRH5euIYwxLxtjxBhznTHm+uTP08aYQWPMB40x25KvQ2n7PGSM2WKMucIY87NCzn81klV/d1J4LyfiDTN93t39I7mxm3grrOTKEkvyTi8lODvird+duacUmuekWGpVk8HBQbtmv9frpba29JpKzYfD4VjzlU1UeC+DP/1fx5iMJ/jTT1y77PqY48FRjh/cb683NrfSunlbtqe4Iu65opZyr5P/8sJF7v7QvWzYMF2r9+TJk/z85z/XyI2iFIis+bsBGevEiBPjWV7fgfKAj8Yaq1OmwXCuKweRaW8lxCIwuVAub/GRLq6dTueMXB393sw9pdA8J8VSI96zo93FVo87U2pqpp+4qfBW5uXVMwPseaeLL71/C5vrl99J8sgbL5NIfgkHysq5YsfOXExzRfjdTj56dT3He8d55M1e7rzzTi6//HI78n38+HGee+45vYgoSp6Jx+OkV4DJVHg7gp0Yb6VVRWSZbF4/ndNypiMH1UdKtJZ3esTb6XSq1STPdIY6qfXV4nFmvwxvtnE73UxEF494pwvvlpaWXE6pIKx1n7cK7yUQjSf4/UcPUhNwU1vm4ZH9F5a1/5mjBxkb7CcencLEY1zznvfmpFZ3Jly9rpwPXlHHN146z8WRSW677TauueYa+/0LFy7wzDPPzNuhTVGU7NPf32+Lt6qqKjsPY6VIqGdZFU3SSbebdPWPZL+Zji28SyvBMl1czxbeajXJPZ3BTlrKSkOcepwephJTxBLzX0ej0eiMZlmryd+dwuVy2YG99Cd6awUV3kvgB6+dpy84yUe3t+B2Lu9XNtjXTcfZk/b61muup7xqeYlN+eI/3LsVj1P446dPYozh5ptvZseOHfb7HR0d7N27l2g0R+XEFEWZwZkzZ2aUFMsUR6jXinivgIDPy7q6adF+tiPLkWm7bXxp1fKenVypVpP80hnqZH1FaYhTj8MKuC1kN+nu7rZv2GpqajJKpi5W0iPeajVRLiEWT/Dtl86yub6Mq5qX54uMRac4/tZ0ZZC6xmbWb9qa7SlmjcYKL//mg5fxxvlRHn+7G4Abb7yRG264wR7T1dXFnj17CIeX3n1LUZSVkR75ampqyuxgxiChHoy3avGx83BZut0k2810UjcEJWY1WSjircI7t8QTcbrGu1hfXiLCO2mHWchustptJjCzlncoFFpznxMV3ovw3NFeukcj3LalftkJDqcPvcFEcJR4dAq3x8OV19+Uo1lmj1+9fh27NlXzX589w9+90s5P3urkXKKBa665xo68DQwM8OSTT9pdtRRFyT7GmBn+7oyF91QIiU6QWGHEG2Bjcz2O5Pdg7+AowfEs3oB7Apb3/NSz2TtmHliojrdaTXJLf7ifWCJG70Rp2JPcDqsJzkKVTVZrGcF0XC4X5eXlgPU9Nzo6WuAZ5RcV3ovw8GvtrK/2c+Uyo93B0WE6zp2y17ddcwMery/Ls8suP3mrkyff7uLWjdaF+cnD/Zhkaa/rrruO2267zb75CAaDPPnkk5w7d65g81WU1czQ0JBd49bv92dsNXGErOh5JhFvn9dNc0Oa3SSbUW9xgKccJoPZO2YemJ1cmQ+riYh8WEROiMhpEfnqHO+LiPxV8v1DInJj2nvVIvIjETkuIsdE5L05mWQe6AhaIrUUKprAdMR7PqtJKBSyrRdOpzPzm+0iZi2XFFThvQDHe8bYd3aIz7x3ox3lWSon3zlgl6OtqWugqXVjDmaYG2oCbu69so6zg2H2n58u7XX55Zdz5513Eo/HiUQihEIh9uzZw759+zSyoyhZpru7217OxgVYbOGdmYDf3NJoL5/Jus+7suSEd76tJiLiBP4auA+4Gvi0iFw9a9h9WF1ktwEPAN9Ie+/rwF5jzJXADuBY1ieZJ0qplCAsbjW5ePGivdzc3IyrAB2t88VarmyiwnsBfnSgA4/Twb/Y2bas/fq7Oxgdsi5IIsK27e/JxfRyys62Si5vCPDsiUF6g9PVCzZu3MhHPvIRKiqmnwAcOnSIvXv3rskOVIqSK/r6ppMMsyO8rcfxiQwi3gAbm+uIJiASS9AxMMpIcPHyaEvGW15ydbwL0LnyZuC0MeasMWYKeBS4f9aY+4HvG4t9QLWINItIJXAH8B0AY8yUMaZkw40p4V2V4d90vljManLixAnb0rmamubMRbrwVquJYvPKmUF2bqqhpmzppf+MMZw9+o69vn7TVsoqMq9GkG9EhE9sb8DrcvCjd/qIRKcvILW1tXz84x+ntbXV3tbR0cFPf/pTxsfHCzHdkuYnb3XaP4qSord32rfa2Ni4wMil4chSxNvjdrG+cboyU1btJiUe8XY4HDOsJjl6ErgeuJi23pHctpQxlwH9wN+JyNsi8rciMmdjChF5QEQOiMiB9FyDYqIz1EmlpxKXozQiwwtZTeLx+IzSeunX19WIWk2US5iYinO8Z4z3Xla3rP16Lp5jPGjdvTldLjZuuyoX08sL5V4Xn7yugd7gFF979syM97xeL3fffTc7duyw79C7u7vZvXs3Y2OlFbFSlGIjHA4TDFoC1Ol0ZiX6Je0vYVx+yEKjkfRmOtkV3hWW8C6htvEFiHjP5Xuc/Qubb4wLuBH4hjHmBmAcuMQjDmCM+ZYxZqcxZmdDQ8NcQwpOR7CjZGwmsLDVpKenx84XqKiomCFMVyOzhbcpoc98pqjwnof2oTDGwC1bli68E4kE7SeO2Ottl11R9AmVi3F5Qxnvu6yaJw72sOfwzMxxEeHGG2/kjjvusKM8wWCQ3bt3r7lHR4qSTdJtJnV1dTOiqCtFJkczqmiSTltTLc7knIZGQ9mzm3grwcQhXDqez4WqmuRIeHcA6f7HVmB2F5L5xnQAHcaY/cntP8IS4iVJZ6iT6hU2hCoEqTrec1lN0v3dqz3aDeDz+fB4rN9HLBZbU0/LVXjPw7mhCH63kx2tS/9Q93a0ExoZIh6dwgG0XbYtdxPMI3dtq2Xnhir+5OlTHOsJXfL+li1b+OAHP2hfcCYmJnjqqac08q0oKyTdZpKtaKNjcjRjm0kKtytHdhNfqntl6TTRKUAd7zeAbSKyWUQ8wKeA3bPG7AZ+O1nd5BZg1BjTbYzpAS6KyBXJcR8EjuZikrlmKj5F30RfSUa857KaXLgw3RF7LQhvWLs+bxXe89A+FGbnpho8rqX9iowxtJ94115vvWxb0bWFXylOh/DfPnkVVX4Xv/f4uwyNX5pE2drayj333EMsFiMSiTA4OMiePXtUfCvKCkiPeGdLeMvkWEalBGezqSUHdhO7iU5p1GWGhcsJ5sLjbYyJAV8GnsGqSPKYMeZdEfmiiHwxOexp4CxwGvg28Ltph/g/gR+KyCHgeuD/zfok80D3eDcGQ423ODtBz4VTnDjFeYnVJBgMzigjuG7dukJML++sVZ+3Cu856B8cpmc0zC3L8Hf3dV4gPG55Ml0uN62bV0e0O8VLJ/v5xPYG+kNTfOWJo0Tjl15Qmpubufvuu+0SSOPj4xr5VpRlMrtxTlaEdyKOTAUzrmiSzoZ1M+0mo6Es2E28GvFeCsaYp40xlxtjthhjHkpu+6Yx5pvJZWOMeTD5/nZjzIG0fQ8mvdvXGWM+YYwpHV9PGp3BZCnBErKaiAh+l/+SiHd6tHu1lxFMJ703gQrvNcy540d49md7mDj+Mjs3LP0ideHU9NO61s2rJ9qdzvoqH5/Y3sBbF8f446dPzZkM0dzcPMN2EgqFVHwryjIYHh4mGo0CVuOcVIe3TJCJfsQkML7sCe/ZdpP2roHMD2oL79KJeBcguVIBOsdLq4Z3ioArcInwXmv+7hQa8VYwxtB5/jSj4RhEw7z89nEe2X+BR/ZfWHC/ob4egqNW0MDpdLJ+lUW707mupYIvvW8DPz3Uy3deuzjnmJaWFu6++26i0ajdYn7Pnj1rysOlKCslJzaTZCnBbEa8ATasq7eX27sHMz+gyw8OV8kK79kRb20sljs6g524HC4qPMvrKl1oAu7ADKtJLBajs3O6lOxaFd5rSR+o8E5jaGiI6NQUwck4ZR4nQ31Lq6t8/tS0t3td22Y8Xm+uplgUfOl9G7nvmga+/ot2/vPTJ+asQ50S36mL0Pj4OHv27FlzHaoUZbnkQng7gpm3i5+LtqZaJNnVt3dwlHAkwyZaIlbUu0StJrPreGvEO3d0hjppKWvBIaUlY/wu/4yqJl1dXfbfSVVV1YzmdKudyspK+/MSCoVm5EusZkrrLzbHpO46Q1Nxyr1OBnu6Fo1YBEeGGO5PRWeEti1XLDh+NSAi/OnHrmDH+kp+/E4fnSOROce1tLRwzz332H61iYkJ9uzZw9DQUD6nqyglRdb93WSvXfxsfF43VZXlRGIJIrEER89nIVLtrYDx0hHes5Mr1WqSHw4NHCqZxjnpBNyBGcJ7rdpMwLpRTbfSrRW7iQrvNLq6ukgYw/hUnAqvi1h0irHhhX2L7cfeIR6dIh6doq6hEX9gziZgqw6vy8HXf+1qyrxOHnmrh7HI3Heqzc3NfOhDH8LttlrlRiIRnnrqKRXfijIHsViMrq4uuylVXd3yGnjNh4R6MOLAeDL3i89mw7rpOZ7vyYLdxFsBoeLslDgXajUpDCORkZLzd4Pl8R6PTtesTk+sbGtrm2uXVc1a9Hmr8E6SSCTo7u5mfDKOMVDhtb48B7rnt5tEwhP0dqZ9aNZAtBumW5z/8kQ/v/medUzGEvzDWz1zVjoBaGpq4v3vfz+JRMIWFCq+ixcR+a6I9InIkbRtfyQinSJyMPnzkbT3/lBETovICRG5tzCzXh0MDAzYSctVVVV2g4lMcYR6rGh3Dh7Lt6UJ7+7+EWKxDKO83sqS9Xi7XC61muSBiegEE7GJkqpokqLMXWZ7vIeHh+nv7ycSiRCPx2lqairw7PJPemWTtVKEIaNvYRH5cPJie1pELmk7myze/1fJ9w+JyI1p711ycU9urxWR50TkVPI1L0U6+/v7iUajBCetyG15Snj3zC+8O8+enL5I1tZTWZ15W+dSo6nCy6/uaKJzdJLdR/rnbfva0NDAhz70ITweD5FIhJGREX70ox/NeMymFA3fAz48x/a/NMZcn/x5GkBErsZq4HFNcp+/ERHnHPsqSyAXNhNIRryz7O9OUVnmp7oiAEA8kaB7IMMkKW8FTAxAojREayHKCa51OkIdACVVwztFenJlerR7/fr1WelQW2qsxQTLFf8vJy+ufw3cB1wNfDp5EU7nPmBb8ucB4Btp732PuS/uXwWeN8ZsA55Prucc2989GcdVvY4Kf7K1a2iMSPjS+rSxWJSOc6fs9bbL1ka0ey6uairjzq01vNMV4rG3uucdlxLf6baTvXv3rpnHS6WCMeaXwFIfR9wPPGqMmTTGnMNq2HFzzia3ykkX3vX19QuMXB6OYFfWK5qks75xOujQ0ZfhkyxPBZgETJTGEzG1muQfu4Z3iVpNUh7vtditcjbpyaQa8V6cm4HTxpizxpgp4FGsi3A69wPfTxbz3wdUi0gzLHhxvx94OLn8MPCJDOa4ZFJ3WsHJODX1TdTVT0ebRvovfezZde408ZhVazdQVk79upZ8TLNoef/WGrY1BPjas2c41Dn/hyclvlMJl+FwmKeeekrFd2nw5eSTq++mPYlaD6Q/tuhIbrsEEXlARA6IyIF0galMk4uKJpgEMtaJ8efuidz6hunI48XeDAVzidXy1qom+aczVHrNc1KkkisjkQg9PT329rUqvCsrK20LavrvYzWTifBeygV3yRflNJqMMd0AydfGuQZl+yIeiViVOUKTMeqqyqipnz7t8MDMC0AikeDimeP2etuWK+2SWmsVhwi/el0jTRUe/t1PjjEajs47trGx8ZJqJ7t372ZgIAsNOJRc8Q1gC1aL6W7gz5Pb5/rDn9NvZIz5VrJj3s5s2ihWC5FIxI74OBwOamqy8xhdJgaQ+CQJX+4eyzfVVtpdLEeCE4Qm5q50tCRSwrtEKpuo1ST/dIQ68Dg8BFyBQk9l2ZS5y0iYBKfbT9vWzIaGBvx+f4FnVhjKysrsz8zk5CRTUxmWJC0BMhHeS7ngLvmivFyyfRGfnJwkkTCEJuM0VpVRXTctvIf6Z96F9Vw8x2TE6jzl8XpZ17ox4/OvBgIeJ//tk1fRF5zi/3nq5Lx+b4B169bNiHynEi7Xyh1vqWGM6TXGxI0xCeDbTNtJOoD0VPxWoCvf81sNpAcQamtrZwi4TJBRK/aR8OdOeLtcThprp5OkOvsyqNdvR7xL46mIlhPMPx3BDmp8NSUZ8Do+ZAXtTqVZVddqtBus8sRrzW6SifBeygV3JRfl3pQdJfmal7BHJBJhJBwlbqCxupyK6locqbuw8ATh8RBgRbvPn5xumNO6+XJ7nALb11fylbs284uTg/zwjYX/q5uamrj33nvtyg1TU1M8/fTTdHWpbis2Up/JJJ8EUknRu4FPiYhXRDZj5XO8nu/5rQZyYjMBHGNWIprJYcQboDXN552R3SRVa/z4/8pwRvlhoaomxpgFAxDKyugIdpSkvxvA6/RiEoaz7WftbWuxjGA66cJ7LSRYZiK83wC2ichmEfFgVTbYPWvMbuC3k9VNbgFGUzaSBdgNfDa5/FngpxnMcclEIhH6g9bj0aaqAA6HY0bUO2U36Tx3itDIEPHoFGIM6zdtycf0hFIL+AAAIABJREFUSoafvNVJuQuuaAzwFy+c5Wh3cMHxjY2N3HnnnYD1fxCLxdi7d69GvguIiPwD8BpwhYh0iMgXgD8TkcMicgi4E/gKgDHmXeAx4CiwF3jQGKNhvhWQa+GdS6sJQEvj9PG7BkZWLjhdXnC4YbI0Il+zI97pr6BR72yTMAk6QlbEuxTxODzERmOMR6xa3mVlZVmr11+qrLWSgisW3saYGPBl4BngGPCYMeZdEfmiiHwxOexp4CxWpYNvA7+b2n+eizvA14B7ROQUcE9yPackEgmmpqYYDFneoqZqqwlOTcN0Tc2Bnk5i0SnOHT9sb9t4+dW43Nmps7uaEBE+ub2RmoCbf//kccYnF24DW1tby3333Yff7ycSiRAKhXjyySe1zneBMMZ82hjTbIxxG2NajTHfMcZ8xhiz3RhznTHmV9JvoI0xDxljthhjrjDG/KyQcy9VjDE5E94y1oHxVYPLl7VjzkVtZRleT7Ji0WSU0VB4ZQcSAW85TC58014szPZ4p7+CVjbJNgPhASbjk6UrvJ0epvqnmIxPArBxo1pV04W3RrwXwRjztDHm8uRF96Hktm8aY76ZXDbGmAeT7283xhxI2/eSi3ty+6Ax5oPGmG3J15yrr8lJ6wMwEo7icnso91q+49rG6afrA90dHPinZ4lFLXHuC5TRumlrrqdWsgQ8Tj56VT0XhsL8ziOHFh1fXV3Nfffdh89niYOU7SQUCuV6qopScEZHR+3vIa/XO+NClCmOsQ4Slfl5lN1YMz3vnsEMLqDeCpgqfuE9u6JJynOslU1yR0ewdGt4A7gdbqIDUabilpbYsGFDgWdUeDTivQZJVTQZCUcJ+L32l2dFVQ1NaYmTE6HpP4gtV12n3u5F2Fzn5wPJ+t4/PbS4daSqqspusgNWtZO9e/euiSxnZW2Tq2g3gPS9SyJPPuOmukoisQSRWIKz3Zn4vCtKIuI929+dQq0mueNi0EoWLtWI99TYFImpBJPxSXw+35rsVjkb9XivQeyI9/gU5YGZJX2uuP5myipmNp647MpraWxZ28kQS+X9W2vYVOvjob2nOdM/vuj4uro67rrrLjtiNDQ0xM9//nN9XKusanImvE0CR3gI48+PhzQ94t03lEHkylMBk8X/tGsuf/fsZf3uyi7nx84jSMkmVw61WzekE5MT1NfXr8lulbMpLy+3fw/hcHjVB9v0f5yZEe/KwEwfpMvlZvuu9+ELlOH2eLn2ptvZuG12g05lPhwi/PMdTfg9Tr7yxNFF/d4Azc3N3HrrrfZ6R0cHr7zySi6nqSgFJWf+7lAPkoiRCORHeNdXV+BIPjEcGw8TjqzwAuqtgKkQFLlonU94q9Ukd5wfO0+1txqno/SeOBtjGO60Sm1OxafYtGlTYSdUJKy1koIqvLGEdzSeIBiJUVF2aRH7QHklN99xD7fc+WFq67LXxnmtUOlzcf81DbQPhvnC37/Dj9/sWHSfbdu2ceWVV9odrY4dO8ahQ4t7xRWl1IjFYgwODtrrWa1oMtIOQMKfn+8tp9NBQ830BbR3pVFvb7nVNj5S3B1t50qsnL2swju7XAheoC5PT3CyzVDvEPFJ6+/BuAzr1y/WT3DtoMJ7jRGJRBhLdlqsLp+/e1QpFusvFjbX+bn7ilre7Rnnl2eWdjG94YYb2LJlulzjvn37aG9vz9EMFaUw9PX12aX3ampq7ByHbOAYOQ+Qt4g3QEM2Eiw9qSY6xd29cikeb7WaZA9jDOfHzlPrq118cBHS3d6NQxw4cFDRUqE2kzTSEyyDweLP78gE/V/H8niP2MK7rMCzWb3cvrmaHS3lPH9qiL1Hl9aV7rbbbpuRfPLCCy9oa3llVdHT02M/2clmNRMAGWnHiBOTRz9sU910TsyKhbfdNr64u1eq1SS/9If7CcfCJSm8Y9EYfR3WjaRTnJQ1q9ZIRyPea4xIJMLIhCW8a+awmijZQUS4/9pGNtT4+I+7j/N6++KRb6fTyV133YXb7bZrfD/77LOEwyusEawoRUZ6s6hsVzRxjLRj/DWQRz9sutVkcDS0soivt9x6HS+diLdaTXLP+THrCU6tv/SEd8+FHhJx67Pgq/RBeYEnVGSo8F5jTE5OMjJhJQFVVwQKPJvVjcspfPrGdWyo9fPlx45wsGPxD5jP5+Puu++2H8GHQiGee+45fYSrlDyzG+dku7SYY+R83vzdKfxeD2V+LwDxeILhsYnlH8SOeBf3062lVDVR4Z09zo5YbdYb/Nm9Qc01xhjOHz9vr1e1VDEeXbzK11qivHz6TkSF9xogFfGu8LnwJy8YALHIhP2jZI8yj5NvfXo7DeUevvToYd44v3jku7q6mjvuuMNe7+np4aWXXsrlNBUl5wwNDdmls/x+f3atJsbgGGknUYBEtPrq6ehV/8gK/JruAIijZD3e6VYTDRBkjzOjZyhzl1Hpya4lK9f0XuwlnOzk6nQ7qd5QzXhMhXc66RHvUGiFT8pKBBXeJIV3OEq1360t4PNEQ4WX7/zWDhorvHzxHw7z/InFI1ttbW3s3LnTXj9x4oRWOlFKmt7eXns529FuCQ8iUyESgfxXYkoX3gMrEd7iAE95yXq8cx3xFpEPi8gJETktIl+d430Rkb9Kvn9IRG6c9b5TRN4WkaeyPrkccmbkDFuqtpRcoYP2Y+32ctuWNnweHxNRDeil43K5CAQsx8H/Zu/N4yM7qzvv76lFKu271OqW1Jt7td2L3d4NGO8L0CR5mdhDwASCwxuYQMLMBCYzL5k3L++QTCbJZJLgQOLBDmAwNsZtur0v4K3bvW/uTVKrJbXU2nepVKqqZ/64V6WSukoqSbVJOt/Ppz66de/z3Ht06y6/e+55zjHGLOqq1Sq8sYR337CPwuwM3BmZM3dQ4sKy/Ewe/8xWNlbk8kdPf8AT+5pD2R2icfXVV1NVVRUajPbmm29y/vz5JFmsKPElPL477sJ7/z8DpER4lxRMvDbu6JljhoIFILxTEeMtIk7gH4D7gM3AQyIytbjEfcA6+/MI8N0py78KnIqrYUmgrreOtYVrZ26YRrQ3t9PX3kfAF8AEDFVXVJHpzGTYr8J7KkslznvJC29jDF6vl57hMQqz3Sq8k0xhtptPXFXKpooc/vur9fz5C7WMBaZ/xXTzzTdTXl4e+v7666/T2tqaaFMVJe6EC+/wYzoeOIatt0jJjvEGKC2cEN5dfUMEZjinI5KZtyhCTRLg8b4eqDXG1BtjfMBPgJ1T2uwEnjAWe4FCEakEEJEq4AHgn+NtWCLp9fbS5e1aUMI7GAxSe6w29H3F6hVkZGaQ6cjUGO8IqPBeIvh8Pga9Y/iDhpK8LM2rmSSePXQx9MlwOvg32yv40JpCfna4lU/980Ge3NfEs4cuRuzrdDq54447QvGwgUCAl156aVIREkVJdwYHB0OvU10uF8XF8c3U4BjpxCBWVpMkk5nhJs+uAhwMBukZmIPIyEx/j3d4qEn4vSPBebxXAE1h35vtebG2+VvgPwILKoj2XO85AFoGW1JsSey01LcwPGB5tp1uJys3rgQgw5mhMd4RUOG9RAjP4V1RtLAGbCwmHCLctaGEnVeVcb5rhMf2XWRoNLqnyOPxcPfdd5OVZaV/9Pl87N69m97e9K50pyjjhL+lKS8vj/tDv2O4y04l6Jq5cQIoKZxnuElmXtpnNYmlgE4CPN6RApynxuhFbCMiHwPajTEHZ9yIyCMickBEDnR0pP4B6FyPJbwrcuIbkpUoAv4A9SfqQ99XbVhFRqY1hizTkYk34MUf9EfrviQJF96LuYjOkhfe4Tm8yws1oX2qubY6n09fW0nn0BiP7btI+8Bo1LZ5eXncfffdBINB63fs7WXPnj0MD2vsnJL+hAvvZcuWxX39jpHOlISZjDMps8lchHdGLowNgS99PYMpGlzZDFSHfa8CprqBo7W5BfiEiDRghajcLiI/jLQRY8z3jDE7jDE74p1ffi6c7TlLliuLXPfCSIB9sf4ivlErY1FmdibVV0z8HBlOS4BrnPdkwrM6qcd7ERNeLr6iWD3e6cC6smw+s6OSPq+fL/74OH327xOJ4uJi7rrrrpC3aXBwkD179oRStClKupJw4T3cmdRS8VOZd0rBBVC9MkXpBPcD60RktYhkAA8Cu6a02QV81s5uciPQZ4xpNcZ80xhTZYxZZfd73RjzO/E2MBGc6z1HeXb5gshoEgwGJ+XtXrl+JQ7nxDGR6bDGkmm4yWQ01GSJ4PV66ff6cTqgrGBhPEkvBVaXZPHpayu50DXMQ48d5qf7m6K2LS8v56Mf/WjoZtfd3c2rr766qPOAKgub4eFh+vqscupOp5PS0jh7pkd6EP9ISnJ4jxOe2aSnf3j2Aywz7JvwYPoK71R4vI0xfuArwEtYmUmeMsacFJEviciX7GZ7gHqgFvg+8AdxNSLJBE2QU12nqMheGGEmredbGe4bJuAL4BAHy1ctn7Q802kJb00pOBmPx4Pb7QZgbGwMr9ebYosSw5IX3j6fj/6RMfI8brKyPKk2RwljdUkW/9fWCpp7vTx7rH3aVINVVVXcfPPNoTSDtbW1vPPOO0m0VlFiZ2o2k3ChFg8cvVaKzWB26kIEMtwu8nOtMRjBYJDu/lnm5V1gHu9ogysTkcfbGLPHGLPeGLPWGPNte96jxphH7WljjPmyvfxqY8yBCOt40xjzsbgblwBah1oZC45RlsLjeTZcODPh7a5ZVzPJ2w0aajIdSyHcRIW3z0ffyBgFWW4yMzWVYLpxZWUud20o4eSlIf746ZOTsqFMZd26dWzbti30/dSpU1pgR0lLwsNMKisr475+R48lvE0KcniHk5OdjdcfxOsPzj7OO9P2mJ/8RfwNixPhHu9ogyv1zdv8qeutAxZGqfjutu5JmUxWrJmacCYs1ERTCl6GCu8lwOjoKAPeMfKz3GRkaNXKdOSW1QVcXZnLa2e7OdcxvYdg+/btrF07ked17969NDQ0JNjCxYWIPCYi7SJyImxesYi8IiLn7L9FYcu+aVfIOyMi96TG6oVFfX196O1MeFxjvHD0NmCQlIaawNQKlrP0eGfYwtuXvtkNohXQSXAe7yVHfa+VHaQ0hYOFY6W5rjk0vaxmGS735VmFxkNNNMb7cpxOZ+jauFjrc6jwHh2lzztGgcelwjtNERF2XlVGeV4Gzxxto987fQqmW265ZVIVwNdff31SaW5lRn4A3Dtl3jeA14wx64DX7O/YFfMeBK60+/yjXVlPiYLX66WnpwewBFq8C+eA5fE2nsKUpRIcZ1JKwdkOsHS6weWB0fQtHR2Lx1uF9/yp76snx51Dtjs71aZMi8/ro+PiRGjUitWXe7sBMhyW1lCP9+WEe7wXa0rBJS+8ewaH8fkN+R71eKczGS4H/2ZbBf6g4WdH2ggEo8d7O51Obr/9dtxuN16vl8HBQV588cWQ2FGmxxjza6B7yuydwOP29OPAJ8Pm/8QYM2qMOY81mOv6pBi6QAmP7y4pKZkk2OKFo7chJaXipzLvAZaZeQvS463CO77U9dUtCG93y/kWjH1vyi/OJzdKwobxGG/1eF/OUshssuSFd3uP5U3J1xjvtKcsN4OPX1nGhR4vr5+bqgsnM15gZ/w3HR0dZc+ePYv2CToJVBhjWgHsv+Nu2liq6AHpV5AjVYS/Pg1/MxNPHL3n00J4Z7hdkypYdvXNYYBlGnu8o6UTDJ8O94ors8cYw/ne8wsivvti/cTYo0ix3eNkOjIRRD3eEVDhvQTo6LMOfI3xXhhsXZHHtVV5vFXfy9t104vv/Px87r777lB6oqGhIXbv3q0FduJLLFX0rJlpVpAjVSQ6fzcjPYi3L6XFc8KZHOc9ywffjFwYTd+H5Wgl41V4x4+e0R4GxgYoSfF4hZnoae9hZHAEAJfbRfmK6CFkIkKOO4fBsfR9qEwVOTk5oXPJ6/UyNha9jsdCRYV3vyXCClR4Lxju31xKRV4G33zuNJf6p8/zWVpayu233x569dvf38+ePXsYHY1eEVOJSJuIVALYf9vt+bFU0VNsfD4fXV1doe+J8Hg7eqyBaMGc9Hi4mVzBcrYe73wYTV+vV7RQExXe8aOxvxGAIk/RDC1TS8v5icvespplOF3TD3Vx4lThHQGHw0FOzkQV8cX4lnrJC+/xV595HpeGmiwQ3E4Hv729grGA4as/+wDv2PQxlMuXL+fGG29kdHQUr9dLd3c3L7zwQtrcEP3+MZrrz3Lk3Tc4fPhwqs2Jxi7gYXv6YeC5sPkPikimiKwG1gHvp8C+BUFbW1soH31JSUlCHvYd3bUABLPjP2hzLpSGDbBs75mliM7Mg7Fh8KdnJdpogyvDpxejxy6ZNA1YkWzFnuIUWxId/5iflroWAr4AAV+AsmUzP/RmOjMZGFt8ojIeLPaUgkteeHcPDJOT6cTtdKjHewFRmpPBd3Zu5NSlQf5sz7lpi+sA1NTU8KEPfSj0vb29PS2qW3Z2drL3td2cO3mEnq4O9u/fT29vb0ptEpEngfeADSLSLCJfAL4D3CUi54C77O8YY04CTwEfAC8CXzbG6GiyKCQlvru7FuPMwGSlh1ApKcxF7Iiknv4hxmbzwJtp34CH2qdvlyKixXiPh7eBerznS+NAIw5xUJhZmGpTotLW2EbQHjick59DfnH+DD3A4/SoxzsKKrwXMWNjY/SPjJHvceNyuSbF6Cnpz23rS/jKR1ax+0Q7333rwozt165dy4033hj63tjYyNtvv51IE2fkxIkTjPkme/O6u6ePXU80xpiHjDGVxhi3MabKGPMvxpguY8wdxph19t/usPbftivkbTDGvJBK29Od5AjvOoKFq0HS43rmdrkozJtIAzerfN4e+wY8kJ7pQKOVjNdQk/jR2N9IZU4lrhSnxpyO1oawglgrYyuIpR7v6Cz2AZbpcWVOEePl4vM1h/eC5Yu3VPPJLRV8961Gnjo0c2jxpk2bWLduXShB/5EjRzhy5EgSLI1MpOwefX19KbBESTR+v3/S752QgZWAs7uWoDO9rmfh4SazqmA5XjZ+MP2EtzFm0hszFd6JoWmgieq86pkbpoih/iF6O623lCLCsprYzmuP08NAGqfKTCUqvBcxo6OjoXLxKrwXJiLCtx5Yz4evKObbL9ay+8TMr6R37Ngxqbrl+++/T11dXSLNjIjf748YVqLCe3HS0dEREmoFBQV4PJ74byQwhvQ1pk189zhlRROvjtu7Z3EjHQ81Gbw0fbsUEG1gJVwuvGcKhVOik+7CO9zbXVJZQoYnNi2R6czUUJMohAvvxXg/XNLCe3DYy+BogDzN4b2gcTmED68poKbIwzefO82LH8wsvm+99dZJHsc333xzUmGTZNDV1RXxhrwYLzTK5MI5icvf3YAE/QRz0kt4l861gmXI451+Md7RwkzAcghoEZ35M+gbpHe0l25vasPvohEMBidlM4k1zAQsj/eQf4iADom5jHDhPTg4mPKxWPFmSQvvVvuVp3q8FybPHroY+mQ4HXz62kqqizz8yS9O8//uPhNaFgmHw8Htt99OQUEBYN0YX3755aSK3s7OztB0YcnEKHgV3ouT8PjuRJSJB3B0nQEgmJMYYT9XivJzcDqt283AkJcRb4xZShxOyMhJy1CT6TzeoAMs48HFQev6na6pBLsvdeOzj2V3ppuSZbHnGs90Ws4+LaJzOS6XK5RS0Biz6MJNlrTwbumyhLfGeC8OMl0OPrOjkqpCD08faeNYy/SetczMTO68806MMXi9Xnp7e3nhhReSVmAnPN63pGJ56OY9Hn+uLB6MMbS1TYjHhHm8O89ikLTzeDscjsn5vGfl9c5Py8GV0TKaRJqnKQXnRvNAM0DaZjRprm0OTVeurJxVggaP0wo103CTyIRnNllszqh5CW8RuVdEzohIrYh8I8JyEZG/s5cfE5FrZuorIn8mIhdF5Ij9uX8+Nk7HRVt4F2ZnaKjJImFcfK8s9vDM0XaOXpz+Bp+fn89dd90Vukkms8BOuMc7v7CIrOyJ1/GL7Ql/qdPV1cXAwABerxcRmXRTiSeOrrOYwpWQZoMrAcqLJoT3rOO809DjPV2oCegAy3jQPGgJ23T0eI8MjtDZOnENX756+az6jwtvzWwSmfG30aDCO4SIOIF/AO4DNgMPicjmKc3uwyqosQ54BPhujH3/xhizzf7smauNM6GhJouTTJeD39lRyaqSLH5+rJ1dx6a/aZeVlXHbbbchYuUa7u7uTrj4DgQC9PT0hL7n5heSlTshvBfbhWapEx7fnahsJgCOi+8TcOfM3DAFZOdk4/UH8fqD1Lf2zNxhHE96Cu+ZQk1UeM+f5oFmMp2ZZLmyUm3KZTTXNYcK5hQUFZCdmz1zpzDGQ03U4x2ZxZzZZD4e7+uBWmNMvTHGB/wE2DmlzU7gCWOxFyi0y03H0jfhtPUM4XE58LidKrwXGVbM9zJWl2Txn58/w7NHpx84WV1dza233hr63tHRwe7duxMW8hE+sDI7JxeXO4OsnMU9knspk4yBlfhHcQx3EsxJnLCfD+VhmU26egdiz/SRmQ8DlyDNBlhFq1oZaZ6GmsyN5sFmijLTz9sd8Ae4WD8xfqhqbdWs1xEKNfGp8I6EhppEZgXQFPa92Z4XS5uZ+n7FDk15TEQSdta19QxQkG0NgFHhvfgYF983rSniW788yzOHW6dtf8UVV3DttdeGYqw7Ozt57rnnGBiI/6vA8DCT3ALrEM/JnRDeqa5eqcSXcOGdsIGVPfWICRLMTU/hnZvtIdtjefnGAgG6+2IcVOYphOAYDF2e8z6V6ODKxNM00JSWYSatF1rx+6zf1JPtmdWgynE01GR6VHhHRiLMm+rCiNZmur7fBdYC24BW4H9E3LjIIyJyQEQORCpCEgsd/cMUZFkXR43xXpy4nQ7uuKKQK8qy+bM95/jX95unbb9+/fpJnu++vj5+8YtfTBLK8aC9fSI9Wl6hVdrbk62hJouRgYGB0IBdt9tNUVFihISj8xRA2gpvgPLiiYfLtu4Yj/Ese2Bd//TnbrLRGO/E4g/6uThwkWJPcapNmUQwGOTC6YlKyVVrq0JhirNhXHj3+fRaH4n8/PzQfh0cHFxU59B8hHczEJ7VvgqYWjowWpuofY0xbcaYgDEmCHwfKyzlMowx3zPG7DDG7CgrK4vUZEa6+ocozFKP92LH7XTw0PZlbK7I4S9fqecff90w7WvudevW8dGPfpSxsTG8Xi89PT08//zzNDfH78YfLrzzbeGdnbt4n/CXMuHZTMrKymaV+WA2ONtPYhyutCueE05ZYVghnVgrWI4L777IqUFThcZ4J5bWoVb8xk9xVnoJ7/bmdkYGRwBwuV2zHlQ5ToYjA7fDrcI7Cg6Hg9zcxZlwYD53gP3AOhFZLSIZwIPAriltdgGftbOb3Aj0GWNap+trx4CP8xvAiXnYGBXvWIC+wZGQx1uF9+LG5RQ+ta2CnXZ5+W/tPstYIHrM6KpVq7jnnntCx8XY2Bgvvvgi58+fn7ctPp8vFEoiIuTZoSYZmZmhm7Xf78fnizHXsZLWJCPMBMDR/oEV3+24XASmC+XFE8K7rSvGG+l4qEF/+grvSDHeGmoyP5r6rWjUdPN4N5xqCE1Xra3C5b78t48FEaEgo4C+URXe0QgPN1HhDRhj/MBXgJeAU8BTxpiTIvIlEfmS3WwPUA/UYnmv/2C6vnafvxSR4yJyDPgo8EdztXE6LvV5MYExDTVZQjgdwp9/bD1furWGZ4+28aUnj9M9FF3cVlRUcP/994cS+QeDQV599VVqa2vnZUd4aFRxcTHOsJt2dvbEyPhk5RNXEktSBlYag6PjJIG8uXnfkkVJQS4O+/Vx3+Aw3tEYBh1m5IDLA30LN9REB1fOngsDVjhHOgnv9uZ2BnutwZAOp4OqK2Y/qDKcgowC9XhPw2JNKTivd57GmD3GmPXGmLXGmG/b8x41xjxqTxtjzJft5VcbYw5M19ee/xm77RZjzCdsD3ncaekbwQT8FGZbHk31eC8NfnG4heV5bn5zSzlHLw7w248d5vjF6E/SRUVF3H///WRkZOD1ehkZGeGNN97g3Llzc7YhPMxkqgc0XHgPDWlFs4WOz+eju9sqdy0izDUsbiZkqA3HSDfB3PQW3k7n5EI6l7piuJmKQP6KtPN4a4x3YmnsbyTLlUWuO3fmxknAGMPZg2dDKQSXVS0jI3N+ukGF9/SEpxRU4b0IaO31YgJ+zWqyRNm2Io8nPrsVh8BnnzjK4/uao8Z95+bmcv/994cGxRljePPNNzl79uycth2r8FaP98InPL67uLh4UvhBPHG0Wy8Mg3mVM7RMPRUlE16s1s4Ys/eIA1oOJ8iiuaEx3onlfP95VuavnNPAxUTQ2tDKUL/lDHG6nKzcsHLe6yzIVOE9HeEe78WU6WvJCu+L3YNgghRkuXE4HBFj9JTFzZnWfj67o5J1Zdn81av1fPmnJ+mKEnqSlZXFvffee5n4PnnyZMT20zGd8B4PawEV3ouBpISZAM7WQxhxEMib36vvZLAsTHi3xCq8swphJL1uvKksGT/XqtEiUi0ib4jIKRE5KSJfjathceR4x3HcjsQ8qM6WgD9A/Yn60PeadTXz9nYDFGYU0utLr+M6nQgX3uEF5xY6S1Z4N3X1k5PpxO10qLd7CZOd4eTB7RU8sLmU98738LHv7ud/vBI5jMTj8XDvvfdSUjKRs/Wdd97hvffeIxhjcY/+/n5GRqwR8W63m0GTwd6GPvpGLI+YerwXF8kaWOlsPUywdCO40n+sSnlxPmJnlO3qHWTUF4MozSoCbx8E0sdzPFOoSaIGV86najTgB75ujNkE3Ah8OULflNPv66ff1095mmToqT9Zj3fYKqbmznBTva56hh6xkZ+Rz4BvgKBJr+JQ6UJubi7BYBCv10tfX9+iuScuWeF9sWtAB1YqgBV7e8PKAh65aQXXr+SHAAAgAElEQVQel4PH32/lH37VQCB4eeiJx+PhIx/5CHl5eaFCO8ePH2f37t0MDs5cgez06dMT280u5Lf/aS97TnXy129e4OmjbWRlTZRGXiwXmaVKIBCY9HYjcQMrgzhbDxOovCYx648zGW4XJYUTcbsxxXlnFQMG+hoTZ9gsSWGoyZyrRhtjWo0xhwCMMQNYyQ2mFr5LOXW9dQBpIbwHewe5cGYib/cVW66YcyaTqRRmFhIwAS0bPw2FhYWh6fHxMgudJSu8W7sHNJWgMoll+Zn8/s1VbF2Ry6NvN/L7Tx6PGHqSmZnJPffcQ01NTWhea2srP/vZzzhx4kRU77ff7+fUKavISc+wj787OMzwWIDfuXYZO6rzOdYyyOGWCbGtgysXNh0dHSFxlp+fP+ltRjxxdJ1DfAMEll+bkPUngknhJh0xvGrPsQeldtdP3y6JzKZkfJyF93yqRocQkVXAdmBfpI3Eo0jdXDnXY711TLXwDgaDfLD/g1B5v8KyQipXxm8cRX6GlS6v37d4UuXFm/CCYyq8FzitXX2h4jnhXkZlaZPpcvCbWyr45FVlHGzs4+Pf3c/hpss9cm63m9tvv53t27czOjqK1+tlYGCAd999l2eeeYaGhsuL9NTW1jI6Oooxht2nevBmFvPj37uR9eU53L+5lMIsF08caA31S0ePt4g02Ok+j4jIAXtesYi8IiLn7L/pV+M5BYSHmVRWJm7Qo7PFSha1UDzeAMtKwwdYxuDxzim1/nbPP49+vAj3eEcqipTAPN7zqRptLRTJBZ4BvmaMiaj64lGkbq6c7j5NpjMzJExTRcOpBnou9RDwBQiOBdmwfUNc11+YYXlzNc47OuEe78US570khffgqJ+BwREKsixPtwpvZSrXVOfzxZtW4HYKn//hMX74/sXLhLSIsG3bNh544IHLBoG8/PLLPP300xw9epSOjg4aGho4cMASSMcv9lE7VsS/v2cjm5dbNxanQ7h1dSEn2kap77Q83ekovG0+aozZZozZYX//BvCaMWYd8Jr9fclTX18fCkcKT4sVb5wX3iaYW4EpWpOwbcSbiuKCULaKzt6BmfN5Z+aDM2PBerzjPLhyPlWjERE3luj+kTHm5/E0LF4cbj9MVd7cSrHHi4GeAc5/MPGgt+bKNeTk5UzTY/YUZFj3jd5RFd7RCPd4q/BewDT3DBP0j1KYrR5vJTqVdujJFaVZ/MUrdTz02CGe3Nd0WbuysjJ27tzJtddeSyAQCImtnp4e9u3bx7PPPsvLL7/M8PAwI74Au0+0s/3qK3n45lWT1rO9Ko/ywlx+fa4LWFDVK3cCj9vTjwOfTKEtaYExJjnx3cEArsa3CORVw7GfJmYbCSDD7SI3JxuvP4jXH6S5fYZXyCKQXZpWwjuFMd5zrhotlpL9F+CUMeav42lUvOj39VPXW0d1XnwGMM6FYCDIib0nMPY4n/zifGrW18zQa/YcbrNSZHZ6O+O+7sXC1BjvaGl/FxJLUnif7xjC+H2U5lqDKlV4K9HIcjt56Jpl3L2hmFNtQzz6bnPEgjtOp5MtW7bwW7/1W1x11VVR01O+eLqL4PIt/JcHNjA8NMjAwEBomdvp4KHrqqnrGWNo1LpRp6HX2wAvi8hBEXnEnlcxXujK/pv6EVEppru7O/TQlJWVNan0cTxxtB1DvL34S+L7CjwZVC+byA50obVr5g456SW8UxXjPZ+q0cAtwGeA2+1wsSMicn/cjIsDxzqOYTBUpTA1Zu3x2lDObofTweYdmxPifc9xWx70Lm8Mx/8SJSsrK5QAw+/3x5TEIN1Zksmrz3dZwrsk1xpZr8JbmQ4R4dY1RVQXenj6aDufefwIv/+hlfzezdW4nZOfXbOysrjuuuvYunUrFy5c4MKFCwwNDeH3+2kdy+IDTxm//5EruLIycujBHZsq+I7Lw9m2AbbXFDE8PDzpiT8NuMUY0yIi5cArInJ6xh42tlB/BJg0MHUxcvHiRJXFZcuWJWw7roY3MQiB4nUJ20aiqKoo5tDpBgCa2roJBoMRY6VD5JRCw9sQDIDjcg9zshlPCwqRM2OFx3jHO4+3MWYPlrgOn/do2LQBvhyh39tEjv9OG/a17sPlcFGVmxrh3d3WTeOZiew5V1x9Bdl5iRkY7RQn2a5sOkfU4z0dRUVFoTEz3d3dCQ3dSwZL0uPd0DlEniuIx21dvFV4K7GwsjiLP7i1is3LcvnHX1/gnr/fx4HGyLF5GRkZrFu3jjvvvJOdO3dyy10P8PSlIjYuL+JLH4ouOq9eUUBhfi6nL1me8HTzeBtjWuy/7cCzWKnN2kSkEsD+2x6lb8oGayWb5ubm0PTy5Ykr4+6qe5Vg5XZMRnqU1Z4NJQW5ZHssweob89PWPUNmh5wyCPig98L07ZJE+LkZKWONVq6cPcYYXm98nRuW3UCGM/nZxsZ8Y5zcdzJUFr6gqICqtYl9AMh15arHewaKi4tD04shs8kSFd7DlIZpbRXeSqxkuZ18alsFn752GT6/4Xf/9Rh/8JMTHLvYHzX2rKlnhIefOMqoP8i3P7HhMi95OA6HcO3aCs61DRAIBtNKeItIjojkjU8DdwMnsOJJH7abPQw8lxoL04NAIDApo0mihLcMXsLZdhR/duIqYiaammUTN9TGSzOIjzw7M0z7qQRaFBuBQCAUSiQiET3eKrxnT31fPY0DjRRkFszcOAGcPnia0ZFRwCqUs+naTQnfZq47V2O8ZyB8gGVn58LfV0s21KQsc+JtmwpvZbZsKM9hdUkWg2Pw2HtNfPoHR9hUkcu9V5ZxXU0B5XmZjPqDvHSqgx/uv0ggaPjnT29hQ8XMnslbNizn5bcP0Ng1zPY0Et5ABfCsHevoAn5sjHlRRPYDT4nIF4BG4FMptDHltLW1hYRWfn4+ubmJ8Ua76l8FwF92ZULWnwyqKoo53dAKWHHeN1y1NnrjvEpAoO0kbHwgOQZGITzMJCsrK2L8r4jgdDpDgzD9fn/UsR+Kxe763TjEwYbi5I9ZaDrXRFtjW+j7xms3kpmV+OJ6ua5cWkamJqVRwiktLQ1Nt7W1TdNyYbDkrgID3jHa+0dYF3Y+aeVKZS5kOB0UO+Ert1ZzrGWA2q4R/ub1y/MM37CqkG/cvZYrymJLRXXzxuU4HXC6bYA706iIjjGmHtgaYX4XcEfyLUpPwuO7Ex5m4ikimLNwPd6VJYW47DdAvQPDdPYOUFoYJX7TlQnZJdB2IokWRmaq8I6Gy+VS4R0j/qCfJ08/ydqCteRlJDeGt6e9h7NHzoa+V66qpGx5csLhct259PT2EDABnJL6sQvpSFFRUWjMxPDwMIODgwlzaCSDJXcVuNA1PCmjicfjmX5Aj6LMgMft4PqVBfy339hM56CPQ0199Hv9CHBtTQGrSibHfz576GLkFdmUFxWwqiSHs5cG0irURImN8PjuhBXOGRvB2fgWY5XXWan2Figul5NVy8tobrUqI5690BZdeAPkL4e2D5JkXXRmiu8ex+12MzpqhS5ouMn0/Lr51wyODbK9YntSt9vf3c/Rd45OSh24fuv6pG0/151LwAToHe2lxFMyc4cliIhQUlJCb681pqq9vX1BC+8lpzjPd45nNNHiOUp8efbQRd4628HQiA+nCfJb2ysvE92xkJubyxXluVzqH6WlY3EUDFgqeL3eUAyiiCRMeDsb30H8o/hLNydk/clkbdVE9sna5jaCwWD0xnmV0F0HvtQ+kM7G4z2OCu/p+emZn5KXkcf6ouSJ3u62bg6+cZDRwVECvgAOcbBp+yacruR5nnPdloDsGOlI2jYXIuXlE9eJhR5usuSEd0PnEGbMR0mO5vBWEsuzhy6GPrMhNzeXtWXWxfhYw6XphYiSVjQ0NIQG2ZaVlSUsjM1V/yrGnUNgAVWrjEZlaSE5diytd3SMprZpshbkrwATtOK8U0isHu8EVq9cVDQNNPFuy7tcU34NDkmOLGk538LhXx8m4LdCgVxuF1tv3oon25OU7Y8zXja+ZVjjvKdjMQnvJRdqcr5riNIsyHBZJ7cKbyUZzEZ8u1wu1i0vIct9ntq2QQYHBxNWgEWJL+fPT8T4r1y5MjEbMQbXmefxF64Bx8K/hIsI62oqOGLnTj5z4RIrK0sjNy6y92nzfqi+LkkWXo56vOPLrjqr8Ob28sSHmQSDQeqO13Hh9ERaygxPBts/tJ2c/PiWhI+F8bLxLUMqvKcjPAVtZ2cngUAgYsXYhcCS83if7xyiMmfix1LhraQjhYUFrC7Noa5jcnVLJX3x+XyTBlauWrUqIdtxdJ7CMdpHoHRjQtafCirLS0Pl4880dtA/OBK5oacACmugaV9yDZyCCu/4ETRBdtXuYk3BGvIzE+tg8A55Ofyrw1w4fSGUq9vj8bDjoztSIroBPE4PBRkFKrxnwOPxUFBgPaQEg8EFnVZwyQnvhs4hynMm/m0V3ko6kpeXxxXlufQMj3GmKWI9GiXNuHDhQigsqKysLIFpBF8HwL+IhHdhXjbLy6xcvQbD8brm6I2rb7CEd5S8+clgNoMrx9FQk8gc6zhGy1ALW8q2JGwbAX+A8x+c590X3qWnfWLcTMmyEq697dqkh5dMZXnOci4OzS4kcSlSUTGRwamlZeE+qCwp4d0+4KVneIxyLZ6jpDl5eXmhOO93PmhKsTVKLNTX1+P1evF6vZPiEeONs/41AnkrMCkqMpIorgqrEHjmwiVGfVGEavUNMNAKfak7L9TjHT9eaniJDEcGG4oSk7u7ramN9154j7rjdQQDE+NlVm9azZabt+Bypz5ca0XOCvV4x0BBQUHoGtvY2Jhqc+bMkhLeh+3y3jUFE14IFd5KOpKfn09ZXib5Hhf7z6knJN0ZHh6mqWlCCCYqzESGO3G2HFgU2UymsqK8iKI863W/3x/gZH0UIVJzk/X3/FtJsuxy5jK4UoX35QRNkOfqnmNVwSoyXfEdiBzwBzi57yTH3z2Od9g7KbRky41bWL15dcTCR6lgec5yLg1fwh/UY2Q6qqqqQr9ZW1vbpAfghcSSEt6HGntwO4XybA01UdKb/Px8RIT1FbkcrW9lLKCZTdKZDz74IBRmUlFRkbDBsM66VxEM/rKrErL+VHNlmNf76NlGvKMRvN5N70NmPtS9lkTLJvD7/aGwEYfDMW3mmvBQExXel3Os4xgDvgGuLIlv9VXvsJf9r+2n+WxzSHC7M9ys37ae6+64jqKyoplXkkSqc6sJmIB6vWfA4/FMepu4UL3eS0p4H77Qy5XLCwiMjYbmqfBW0pG8PKuIyMbKfIaGBtl/fpoUa0pKCQQCfPDBRFGXTZs2JWxbrrqXCeatIJiXuIqYqWRtVRkFuZYHecwf4FBY5okQIlC2Aepeh2AgyRbGHmYC6vGeiZcaXsIpzrjm7u7v7uf9V95nsHcwNK+ipoKb7r2JqrVVaVkwr6XfEty1/bUptiT9qampCU1fuBDh+rAASL8jMEGMBYIcu9jL9urCSRdOjye1gyoUJRLZ2dk4HA7WlefiDPp58dg0g82UlFJbW4vX6wUgJycncWkER3pwnX8D//r7F3S1yulwOBxcu2lV6PsH51siZzgp2wgjPdByOHnG2cxGeId7w7UK7WQCwQAvX3iZtYVr4xZmcrHuIgdeO4DP6wOsVJUbtm/gyuuuTItY7miUekoRhNpeFd4zUVU18VasubmZQCD5D9/zZckI79OtA3jHgqwvcoR+qKysrEmvAhUlXRAR8vPzyXA5WVuWw6vHJgqzKOlDIBDg8OEJ8bdp06aEedTcZ3YhwTHGNn8qIetPF1ZWllKQn4vXH2TY5+fNg6cvP/bLNoI44fTupNsXa3w3EEp/BtDTo1Vow9nftp/24fa4ZDMZ841x/L3jnHj3BGPeMQK+ABjYdus2VqxZEQdrE4vL4aIks4TaPhXeM1FYWBgK5fP7/ZNqJywUlozwPtxkXfSqcyaejkpLoxRpUJQ0IDzcpLGtm7qOwRl6KMnm+PHj9Pf3A5CRkcH69Qkqd20M7hNPEcitJNh6LDHbSCOuv3ItguXVv9TVx7FzUzKYZOTA6g/DB79IelrB2Xi8CwsLQ9O9vb0Js2kh8nzd8+S6c1lXuG5e6+m61MV7L7xHW+NENcOc/Bx2fHQHReXpFcs9HWVZZZzrO5dqMxYEa9euDU0fPXo0hZbMjSUjvA9d6KEiPxPn6EQxEhXeSjoTEt7L8giODvPqKc3nnU4MDg5y6NChUHqrzZs3J6xEvLPpXZxtRxmruikh6083Sovy2LphIpZz/wcNNE8tJb95J3TXQ9uJpNo2PDwc+s2NMdMWuMrLywtV1xsZGWF0dDRq26VE32gfLzW8xH2r78PtnNtb54A/wOmDpzn8q8Oh0BKAypWVVkGcvNQUxJkrlVmVtI200TmycAvDJIuNGzeGxk90dXVNKly2EFgSwtsYw8HGHrZXF9HdPXHxVuGtpDPjr9MKszNYkw9PH2zWcJM0IRgM8uabb4YGzBUVFbFhQ2LyEGMMGe/9DcGcCsYqU1cmPdlsXVdNWaH18BkMBnlp7wkuhhU/YXQQxAHHnkqqXeNvOGBmj7eIqNc7As/VPsdoYJQiz9w80r0dvex9aS/NtRNZSxw4uPqmq9m0YxNO18IrJV6Taz1oHu5M/riFhYbH45l0vV1oXu8lIbwPNfbS1D3CbRvKJpUZVeGtpDPhaZN2lBlq2wd5p7YrhRYp4+zfv39S5bSbbropYbHdrrO7cTXvxXfDV2CO3sGFiMPh4LYdm8i1qwoGAkH2vHOM/SfPEwgEITMXyq+Eo09CIDlVIYPB4KRMCrHcQ8KFt8Z5gz/o53vHv0d1XjXLcpbNrq/Pz+kDpznw+gFGwgbdli4v5Ya7bqBseVm8zU0aFVkVZDoyOdJ5JNWmLAhWrVrF6OgoXq+X2tpa6uvrU21SzKTvMN848uT7jeRkOLltTR67jliv+jIzM0Ov8hUlHSkvLycjIwOfz8e6YjcFQ6P84N0Gbl2nD4yp5NSpU5M8LNu3b59UyjiujPSQ+ca3COStYIyMxGwjjcnN9vCR667khXeOMewdxeNycPjMBU5faGXDymVUeDaTe/4owfd+wtiaOxgdHQ0Nnne73WRlZVFQUBC3EKCmpiZ8PiusIScnJ6YKperxnsxLDS/RN9rHvavujbmPMYbW863UHq/F5/VZgycBp9vJph2bqFxZmShzk4ZDHFTlVLG/fT/GmLQp7pOu5Ofnc8UVV3DunBUX/9Zbb1FRUUFOTvqHGC164d03MsYvj7Xwm9dUMTIwcdFTb7eS7jgcDlasWMH58+dxOx3cUe3i2dNtNHUPU108fTYFJTEcO3aMvXv3hr7X1NSwdevWxGzMGDzP/A4y3MHIdX8IjoX3+jwe5Odk8cCtW3nr8Bl6+6x46hGvjyNnGq2BlQ358E/fhluHo6ZZzMnJYfny5dTU1LBy5cpJ+bVnQ11dXWh6zZo1MfUpKpoIp1jqwtsf9PPo0UcpyyqLOXd3T3sPZ4+cZaBncix9ybISNlyzAU/W4kkJvK5gHS82v8i5vnOsL0zQQO1FxPXXX09LSwtDQ0OMjo7y+uuvc9999835/E4Wiz7U5LkjF/GOBfm319domImy4Kiurg5Nby/x4xDhb1/Vke/JZnh4mJdffpm9e/eGBtbl5uZy3XXXJcwz5T74fdxtR/GtvZdgftXMHRYxudke7r15C9s2rUGcLrz+IF5/0BLaxauh9wK+lhOh32bqZ2hoiHPnzvHaa6/xwx/+kPfee2/WItjv99PQ0BD6vnr16pj6qcd7gudqn6Ohv4Hbqm+b8bwZ6h/i6NtHOfjGQXrbekOx3E6Hkyuvv5Ktt2xdVKIbYEPBBpzi5JXmV1JtyoIgIyODD3/4w6Hvra2tvPrqq6EqwulKej8WzJN+7xiPvX2eq1cUcNWKAn6xf2LkqwpvZSEQLrxH+7t55Nab+O6vG/jIhjI+sXVxVi9MJ/r7+zl58iSnTp2aVHmwoqKCO++8k4yMBIR/GIP78GNk/urPGSvfgm/lbfHfxgJERNi0ejkbVi6jqa2blo4eegdGGPVsImvwDK7WPbDlv+HMsAY8+nw+RkZG6OvrCxU4Guf48eMcP36c8vJyVqxYQUlJCZmZmfh8PgYHB+nt7WVgYACXy0VmZiaFhYU0NTWFjoGCggJKSkpisrugoAARwRhDf38/fr8/7T1yiaBzpJO/OfQ3XFN+DRuLN0Zs4/P66LrUReuFVrrbuiFsLLnD6WDl+pXUrK9ZkIMnYyHblc2NFTeyu2E3v7vxd8l26ZvNmVi2bBk7duzgxAkru1FjYyN79uzhjjvuSNvK5PM6+0XkXuB/Ak7gn40x35myXOzl9wPDwOeMMYem6ysixcBPgVVAA/BvjDGzHpHiDwT5dz8+THPPCE984WpOnTpFe3v7uF2Ji8lUlDiSk5NDcXEx3d3dBINBPlo2wr6aQv7058fZXJnHFeXpMU5hpmvBQiEYDNLT00NzczMNDQ20tVm5gcOF28aNG7nuuusSIp4cLQfJfOevcDW+xdgV9+Kt/oiVuUMJ4XA4WFlZysrKCeeJc2sB2Ye/j8+8xuhH/nLSPgsGg3R3d9PU1ERdXR1jYxMDMdvb20P3hUiE/+7hVY5nk6/d6XSSl5cXyobS19cXs2iPRiLuvYlkxD/CH7/5xwz5hrih8oaQt3t0ZJSejh5623vp6ehhqH8o1Gc8jhugorqCtVetxZO9uDzckfjcxs/xxTe/yI/P/pjf2/x7qTZnQXD11VczOjrK8ePHAWhpaeGZZ55h69atbNiwITEOknkw5zuHiDiBfwDuApqB/SKyyxjzQViz+4B19ucG4LvADTP0/QbwmjHmOyLyDfv7n8zGtgtdQ/zFi6f51dkO/ttvXs2WCg8/+9lEXOaWLVvIzc2d43+uKMllzZo1oTSYRw4f4g+2XcN/eHWIj/2vt/mTezfyb2+oITOFHqAYrwXTEgwGJ6VpiyVtYnib8WljzGWfYDB42ScQCDA2NobP5wuFIvT399PT0zPJsx1OUVER119/PcuXx/dNgwxewnX2l7hPPo2z/QTGnY13/ScZq75ZRXeMBEo2MHrDvyNz3//CMdCK74avEKi8BlyZOBwOSktLKS0tZdu2bbS0tHDmzBmamppm/UpaRLjqqqu48sorZ9WvsLAwdHz39vbOS3gn8N47Z/r6+ibty/HzMRgMcrLjJH+1768413GO2ytvp+dsDxf7LzLQO8DoyERe83ChPU5xRTFrr1xLXlF6OBiSQX1PPZsKN/GD0z9gZd5K7qq+K9UmLQh27NiB0+nkyBErK8zw8DDvvfce+/bto6ysjNLSUvLy8vB4PGRkZOByuXC5XDgcDhwOByIS+owTPh0eMjZf5uOyuR6oNcbUA4jIT4CdQPgJvBN4wlhn4V4RKRSRSixvdrS+O4Hb7P6PA28yC+H9redO8K97L+ByOPj6Xet56Poadu/eHfJyFBYWsmPHjjn+y4qSfLZu3Upra2uoSED9iUP848dv5ntHhvmvz3/AX754hhvXFPPHd23g6qqCGdaWEGK5FkzL0NAQTz2V3HzM0zHu6XQ4HCxfvpyNGzdOCvuZCRloxVX3sjX4zwQg4EMCPgj4wASRwBgy0o2j/STOjpMABPJW4N3wG4xV7gBXYgrxLGZ8uTWYDZ8ks+ENsp/6FAbB5JRhsooxOeXWJ7uE1Rl5rMrz4F0Prd2DXOoeYMBdhrdgLW63m5ycHPLz88nLyyMYDDIyMkJPTw95eXls3LiR4uLiaYvmRKKoqIjGxkYgLikFE3XvnTO//OUvaehooKG/gaAJMhYco9/XT3N/M63DrWQ4MthStoWAN0AjjaF+U8W2iJBfnE/JshKW1SxbEh7uSNxfdT9Oh5Nv7f8WP6//OVtKtlCUWYTb4cblcLFz9c5Um5iWbN++nfLyct56661JFWbb2tpCby/ngsvl4vOf/3w8TLTWN4++K4DwOr7NWE/WM7VZMUPfCmNMK4AxplVEIuZrEpFHgEfsr4MicmZqmz/8/+EPY/tfwikF0rl0lNo3P9LavofnaN9p4AeTZ62Mi0GxEcu1IKZzNgmk8e/fXwqnOi1/Q1qRrvssBrv6kmLIFOa6v2I9ZxN1751EvM/XBhqiLUq34yut7DnBidIf8aN0sSet9g1JtOcLX/jC1FlzvsfOR3hHGpI89f1wtDax9J0WY8z3gO/Npk8siMgBY0zausTVvvmh9iWEmM7nRJ2zsyGd92+62qZ2zY4k2JWUe2+yztd0+x3Vnuikky2QfvbEynyEdzMQ/u61CmiJsU3GNH3bRKTS9nZXAtFHviiKkg7Eci1QFCU+JOreqyhKEpjPyJ39wDoRWS0iGcCDwK4pbXYBnxWLG4E+O4xkur67gIft6YeB5+Zho6IoiSeWa4GiKPEhUfdeRVGSwJw93sYYv4h8BXgJKy3RY8aYkyLyJXv5o8AerHRGtVgpjX53ur72qr8DPCUiXwAagU/N1cY5ktJX4TGg9s0PtS/OzHA+pxvpvH/T1Ta1a3Yk1K4E3ntTRbr9jmpPdNLJFkg/e2JCYknbpSiKoiiKoijK/NAksYqiKIqiKIqSBFR4K4qiKIqiKEoSUOFtIyL3isgZEam1K2am2p5qEXlDRE6JyEkR+ao9/89E5KKIHLE/96fQxgYROW7bccCeVywir4jIOftvUYps2xC2j46ISL+IfC2V+09EHhORdhE5ETYv6v4SkW/ax+MZEbknWXYuFGI91qKd2yLyKfvcCorIjil9Iu57EbnWPuZrReTvROSy9GxxsCtifxH59JRjOigi2+xlb9rrGl92Wf2DBNq1SkRGwrb9aJrsr7tE5KC9/YMicntYn6j7K9p2wpaL/b/UisgxEblmrjbayxbteS7R72PbRGSvve8PiMj19vyox1KC7dkqIu/Zx8rzIpIf1idhv89s7UnC/vGIyANYmYUAACAASURBVPsictS257/a81Ny/M7WnkTvn7gRqcTyUvtgDTKpA9ZgpVs6CmxOsU2VwDX2dB5wFtgM/Bnw71O9z2y7GoDSKfP+EviGPf0N4C/SwE4ncAkr4X3K9h/wYeAa4MRM+8v+rY8CmcBq+/h0pnpfptMnlmNtunMb2ARswKqOuyOsT9R9D7wP3ISVD/kF4L4E2BVL/6uB+rDvk/6HBO2vaMfqqvBjesr6Ura/gO3Acnv6KuDiTPtruu2Etbnf/l8EuBHYNw8bF/V5TvT72Mvjx4K9P9+c6VhKsD37gY/Y8z8P/Hkyfp852JPo/SNArj3tBvbZx3hKjt852JPQ/ROvj3q8LUIleI0xPmC8jG7KMMa0GmMO2dMDwCmsqmPpzk4mSu89DnwyhbaMcwdQZ4y5kEojjDG/BrqnzI62v3YCPzHGjBpjzmNlJ7g+KYYuHGI51qKe28aYU8aYSNX4Iu57seoK5Btj3jPWVf6JKNucl10x9n8IeDLC/OlIhl0hUr2/jDGHjTHjOapPAh4RyZzO5hm2E27vE8ZiLzBejn0u+25Rn+fT3McMMO5VLiBJucSnsWcD8Gu72SvAb9nTCf195mBPQrGP6UH7q9v+GFJ0/M7BngWBCm+LaOV10wIRWYXlvdlnz/qK/YrzsWivZZOEAV62X+OOlxauMFa+WOy/l73yTgEPMlmkpMv+g+j7K62PyTQhlmNtLvtxunLbzTGsa752xdL/t7lceP9v+/XqfxG5PKQjwXatFpHDIvIrEflQ2LrSZX/9FnDYGDMaNi/S/orleJlNOfaZbFwy5/mU+9jXgP8uIk3AXwHfDGsa6VhKtD0ngE/Yiz7FRJGhpP0+MdoDCd4/IuIUkSNYxQtfMcbsI4XH7yztgSQdP/NBhbfFvEvYJwoRyQWeAb5mjOkHvgusBbYBrcD/SKF5txhjrgHuA74sIh9OoS0REatIxCeAn9mz0mn/TUfaHpPJREReFZETET6xvpGay36csdy2iLyK5Wn5cBLtGt/2DcCwMeZE2OxPG2OuBnqA/wg0JdGuVqDGGLMd+GPgx3ZMarrsryuBvwB+P2z2+P76kP35zCy2M+9y7DGsa1ER4T72fwN/ZIypBv4I+Be7abRjKdH2fB7rHnYQK+TDN940Qve4/z6zsCfh+8cYEzDGbMOqanq9iFw1nemRVpFCe5Jy/MyX+ZSMX0ykZclrEXFjnYw/Msb8HMAY0xa2/PvAL1NkHuOvcY0x7SLyLNYrpjYRqTTGtNqvX9tTZZ/NfcCh8f2WTvvPJtr+SstjMtkYY+6MtkxEYjnW5rIfo/VptqcxxtwpIg8BtxljwgVdPOyaqf/UNzgYYy7afz8qIp/Dil/+SjLssr3Io/b0QRGpA9aTBvtLRKqAZ4HPGmPqIuyvARH5Mda164kZtjOTLdOVY1+y53mk+xhWVeqv2tM/A/4Zpj2WDiTSHmPMaeBue/l64AG7ecJ/n9nYk4z9M44xpldE3gTuJQ2O31jsSeb+mQ/q8bZIuzK69qvPfwFOGWP+Omx+ZViz38B6JZV0RCRHRPLGp7EuEiew9tvDdrOHgedSYV8Yk2Jh02X/hRFtf+0CHhSRTBFZDazDGqimTBDLsTaXczvivrdfaQ6IyI32+fnZKNucr11R+4uIA+vV80/C5rlEpNSedgMfI/JxnRC7RKRMRJz29Bqs/VWf6v0lIoXAbuCbxph3xlc0w/5KVDn2JXmeR7uPYYmzj9jTtwPn7PYRj6VE2yN2Vhv7/PrPwHg2jIT+PrO1Jwn7p8w+bxCRLOBO4DQpOn5na0+i90/cMGkwwjMdPlgjq89ijcr90zSw51asVzbHgCP2537gX4Hj9vxdQGWK7FuDNZr5KNbApT+155cAr2FdSF8DilO4D7OBLqAgbF7K9h/WA0ArMIblKfjCdPsL+FP7eDxDhGwQS/0Tbd8By4E9Ye0inttYD17NWB6SNuClmfY9sANLpNUBfw9W9d842zXdMXEbsHfK9nKAg/YxfRL4n0TILJAou7Dip0/a14JDwMfTYX9hCZYhJq6fR7BiQafdX5G2A3wJ+JI9LcA/2MuPMzkjzlx+00V7nhP9Pnar/RscxYppvnamYynB9nzV/t3OAt8JP04T+fvM1p4k7J8twGHbnhPA/5PK43e29iR6/8TroyXjFUVRFEVRFCUJaKiJoiiKoiiKoiQBFd6KoiiKoiiKkgRUeCuKoiiKoihKElDhrSiKoiiKoihJQIW3oiiKoiiKoiQBFd6KoiiKoiiKkgRUeCtJQ0Q+LSIvp9oORVEURVGUVKDCW0kaxpgfGWPuTrUdirLUEZEGERkRkcGwz9+n2i5FWWyIyK0i8q6I9IlIt4i8IyLXicjnRMSIyF9Paf9Je/4PEmTPD0Tk/0vEupXYUOGtzAoRcaXaBkVR4sLHjTG5YZ+vpNogRVlMiEg+8EvgfwHFwArgv2JVywWr4uNvT7mvfharYmW0deo9eIGjwlsBQh6wb4rIByLSIyL/W0Q8InKbiDSLyJ+IyCXgf9vtPyYiR0Sk136a3xK2rmoR+bmIdIhI17gnzX7CfzusnRGRPxSRehHpFJH/LiJ6TCqKoiiLgfUAxpgnjTEBY8yIMeZlY8wxe/kl4DhwD4CIFAM3A7vGVyAiq+x75RdEpBF43Z7/CRE5ad+D3xSRTWF9Ntnzeu02n7DnPwJ8GviP9luu5xO/C5SpqMhRwvk01gVgLdYF4z/b85dhPa2vBB4RkWuAx4DfB0qAfwJ2iUimiDixnvAvAKuwnvB/Ms02fwPYAVwD7AQ+H99/SVEURVFSwlkgICKPi8h9IlIUoc0TWF5ugAeB55jwiIfzEWATcI+IrAeeBL4GlAF7gOdFJENE3MDzwMtAOfDvgB+JyAZjzPeAHwF/ab/l+njc/lMlZlR4K+H8vTGmyRjTDXwbeMieHwS+ZYwZNcaMAF8E/skYs89+in8c60JxI3A9sBz4D8aYIWOM1xjzdoRtjfMXxphuY0wj8Ldh21QUJbH8wvaIjX++mGqDFGUxYYzpB24FDPB9oENEdolIRVizZ4HbRKQAS4A/EWV1f2bfU0eA3wZ2G2NeMcaMAX8FZGF5y28EcoHvGGN8xpjXsZxhem9NE1R4K+E0hU1fwBLQAB3GGG/YspXA18Nv2kC13b4auGCM8c9zm4qiJJZPGmMKwz7fT7VBirLYMMacMsZ8zhhTBVyFdY/727DlI8BurDfMpcaYd6KsKvxeuRzrfjm+jqC9fIW9rMmeN84Fe5mSBqjwVsKpDpuuAVrsaTOlXRPw7Sk37WxjzJP2sppZDACJtk1FURRFWTQYY04DP8AS4OE8AXwd+NfpuodNt2A5wAAQEcG6l160l1VPGS9VYy+buh4lBajwVsL5sohU2QM8/hPw0yjtvg98SURuEIscEXlARPKA94FW4Dv2fI+I3DLNNv+DiBSJSDXw1Wm2qSiKoigLBhHZKCJfF5Eq+3s1VsjH3ilNfwXchZX9JBaeAh4QkTvsmO6vY4V7vgvsA4awBlC6ReQ24ONMjLVqA9bM/b9S5osKbyWcH2MNyKi3PxFzfRpjDmDFef890APUAp+zlwWwTvIrgEagGSseLRrPAQeBI1iv2/5l/v+Goigx8PyUPN7PptogRVlkDAA3APtEZAhLcJ/AEsohjMVr9viqGTHGnAF+B0uod2Ldcz9ux3T7gE8A99nL/hH4rO1tB+seu9kOE/3FvP9DZdaIMfrWQbHSCQK/Z4x5NYnbNMA6Y0xtsrapKIqiKIqSKtTjrSiKoiiKoihJQIW3oiiKoiiKoiQBDTVRFEVRFEVRlCSgHm9FURRFWSKIyAYRORL26ReRr6XaLkVZKqjHW1EURVGWICLixMrvfIMx5sJM7RVFmT+xFjlJa0pLS82qVatSbYaipA0HDx7sNMaUpdqOaOg5qyiTSdE5ewdQN5Po1vNVUSYzn/N1UQjvVatWceDAgVSboShpg4iktfdKz1lFmUyKztkHgScjLRCRR4BHAGpqavR8VZQw5nO+aoy3oiiKoiwxRCQDq9DKzyItN8Z8zxizwxizo6wsbV+eKcqCQ4W3oiiKoiw97gMOGWPaUm2IoiwlVHgriqIoytLjIaKEmSiKkjhUeCuKoijKEkJEsoG7gJ+n2hZFWWrEJLxF5F4ROSMitSLyjQjLRUT+zl5+TESusedXi8gbInJKRE6KyFfD+hSLyCsics7+WxS27Jv2us6IyD3x+EcVRVEURQFjzLAxpsQY05dqWxRlqTGj8LbzfP4DVjzYZuAhEdk8pdl9wDr78wjwXXu+H/i6MWYTcCPw5bC+3wBeM8asA16zv2MvfxC4ErgX+EfbBkVRFEVRFEVZsMTi8b4eqDXG1BtjfMBPgJ1T2uwEnjAWe4FCEak0xrQaYw4BGGMGgFPAirA+j9vTjwOfDJv/E2PMqDHmPFBr26AoiqIoiqIoC5ZYhPcKoCnsezMT4jnmNiKyCtgO7LNnVRhjWgHsv+Wz2B4i8oiIHBCRAx0dHTH8G4qiKIqiKLExMDAQ+ihKvIhFeEuEeVPrzE/bRkRygWeArxlj+uOwPc0xqiiKoiiKoiwoYqlc2QxUh32vAlpibSMibizR/SNjTPgI6rbxcBQRqQTaZ7G9OTP1yTUvLy9eq1YURVEUJQ3Qe72SrsTi8d4PrBOR1XalqweBXVPa7AI+a2c3uRHoswW1AP8CnDLG/HWEPg/b0w8Dz4XNf1BEMkVkNdaAzfdn/Z8pirIoCX/9q6+AFUVRlIXEjB5vY4xfRL4CvAQ4gceMMSdF5Ev28keBPcD9WAMhh4HftbvfAnwGOC4iR+x5/8kYswf4DvCUiHwBaAQ+Za/vpIg8BXyAlRXly8aYQFz+W0VRFEVRFEVJEbGEmmAL5T1T5j0aNm2AL0fo9zaRY7YxxnQBd0RZ9m3g27HYpiiKoiiKoigLAa1cqSiKoiiKoihJQIW3oiiKoiiKoiQBFd6KoiiKoihTGBoaoqurK9VmKIsMFd6KoiiKoixa+vv78fv9s+ozMDDAc889x65duzh58mSCLFOWIiq8FUVRFEVZlBw/fpxnnnmGp59+Gp/PF3O/EydOMDo6CsCRI0cYGxtLlInKEkOFt6IoiqIoi5JDhw4Bltf76NGjMfUJBAKcPXs29N3n83H69OmE2KcsPVR4K4qiKIqyKAkGg6Hpc+fOxdTn/PnzIW/3OMePH5+0LkWZKyq8FWUJISL3isgZEakVkW9EWC4i8nf28mMico09v1pE3hCRUyJyUkS+GtanWEReEZFz9t+iZP5PiqIokRgZGZn0fXBwkM7Ozhn7nTp16rJ5g4ODNDQ0zMueY6++GPooS5clKbz7+voYHBxMtRmKklRExMn/Ye++o+M+rwPvf+8UDHrvlWAnJVIURYkqVrMkW3IUM3GSXelsnOwmu1qdyLvZbLU3ed83m8R7cpL1ZtdxWzt2HK+LbMuyQssU1SlRjU0kwQqiECB678AAmJnn/WMGgwGEMsB0zP2cg6Mpv3l+D3SImTv3d5/7wFeBx4DdwJMisnvRYY8B23w/TwFf9z3uAv6DMWYXcCfwTMBrPw+8bozZBrzuu6+UUjG11Od8YAnJcq/p6uoCQETYunWr/7nu7u7wTlAlpaQLvDs6Onj++ed57rnnGBwcjPV0lIqmO4BGY0yzMWYGeBY4tOiYQ8D3jNcHQK6IlBljuowxHwIYY8aAK0BFwGv+wXf7H4Bfi/QvopRSqxkdHf3IYw0NDXg3217ajRs3/LfLysqorq723x8eHg7vBFVSSrrA+5VXXgHAGMP7778f49koFVUVQFvA/Xbmg+egjxGRTcCtwAnfQyXGmC4A33+Llzq5iDwlIqdF5HRfX986fwWlVKhEJFdEnhORq77ysbtiPadIWCrjPT09zdDQ0LKvaW9v99+uqqoiJyfHf18DbxUOSRd4B1pc/6XUBidLPLY49bPiMSKSCfwM+HfGmI+mk1ZgjPmmMeaAMeZAUVHRWl6qlAqv/w0cNcbsBG7BewVrwwkMvJ1Op/9nuS/+Ho+Hjo4O//3y8nKys7MREf94a+0HrtRiSR14W63WWE9BqWhqB6oC7lcCncEeIyJ2vEH3D4wxzwcc0yMiZb5jyoDeMM9bKRUmIpIN3Ad8G8AYM2OM2ZCp3MBSk+Li+QtxywXe3d3d/n7dWVlZ5ObmYrFYyMrK8h8zMjISodmqZKGBt1LJ4xSwTURqRSQFeAI4vOiYw8Dv+Lqb3AmMGGO6xJvy+TZwxRjzP5d4ze/6bv8u8I+R+xWUUiHaDPQBfy8iZ0Xk70QkI9aTioTAjHdtba3/9nKBd1vbfJVdVdV8/kHLTVQ4BRV4r7cFme+574hIr4hcXPSaH4vIOd9Pi4ic8z2+SUSmAp77Rqi/5HIslqT+3qGSjDHGBXwOeBnvpeWfGGMuicjTIvK077AjQDPQCHwL+APf4/cAnwU+HvC3+Snfc38JPCIiDcAjvvtKqfhkA/YDXzfG3ApMsEQnokRfk+HxeJiYmPDfr6mp8d8eGBjA7XZ/5DWBCysDF1VmZ2f7b2vgrUJlW+2AgBZkj+C9DH1KRA4bYy4HHBbYguwg3hZkB33PfRf4CvC9wHGNMf804BxfAgKv3zQZY/at9ZdZKw28VbIxxhzBG1wHPvaNgNsGeGaJ173D0vXfGGMGgIfCO9PgDQ4Okp2djc226tuZUsr7Od5ujJlbHP0cSwTexphvAt8EOHDgwPJtQOLUxMSEv3tJWloaGRkZZGVlMTY2hsfjYXBwkMC1Jr29vf5FlzabjbKyMpxOJwC5ubn+4yJVahLY23vvw49G5BwqPgQTea67BRmAMeZtYNm+fb5L2P8E+NF6foFQaOCtVGI7ffo0zz33HM8995wuelIqCMaYbqBNRHb4HnoIuLzCSxLS2NiY//ZcjXZhYaH/scVZ/MuX5/8XbNmyBbvd7r8fiVIT58Q47ZcvrtjaUG1MwaSIlmovdjCIYyqAriDGvxfoMcYE7uVaKyJngVHgT4wxxxe/SESewrvBx4JLQmuhNd5KJbbm5mbAu4iqq6trQV2mUmpZ/wb4gW+tRzPwL2I8n7ALrO8ODLyvX78OLAy8p6en/e8lTqeTqqqqBYH74sDbGOPvdLIegx3t/MN/+hzjA/2Ubt3OA5/9l+seSyWeYFK+IbcgW8WTLMx2dwHVvtqzfw/80LcKe+HgYWhNphlvpRKX2+1mYmLC3yKsqakp1lNSKiEYY875Pj/3GmN+zRizfGPrBBUYOGdmZgILM94dHR3+bHNDQ4P/ill+fj6LY4rU1FRSU1MBcLlcC2rH16q/rZUzR17AnuLgvt/+PcaHBnnuL/6EiRGtHU8WwUSeIbUgW4mI2IDPAD+ee8wYM+2rGcUYcwZoArYHMc9VLb6ko4G3Uolr8a50vb3axVAp5TVXnw2QkeFt2lJUVERKSgrgzYi3trYyPT3N2bNn/cfu2LGDpYSj3MQYQ/OHJ0nLyuazf/m/uf1XP8M/+4svYbXbufrOMS07SRLBRJ7rbkEWxNgPA1eNMf6tokSkyLegExHZjHfBZnMQY61q8Spm/UeuVOJaHHj39/fj8XhiNBulVDyZmZnx356r17ZarQsC6wsXLnDixAn/ZnoZGRls2bJlyfHCscByuLuLkd4eavbeypV3jlH32lGaPzzF5v23M9jZTlfD1XWNqxLLqoF3iC3IEJEfAe8DO0SkXUR+P2D4J/joosr7gDoROY93tfXTxphlF2euxVxj/Dn6Ia1U4gq8lAzeS8D9/f0xmo1SKp4EBt5zWW6AXbt2+a92d3V1cfXqfLB7zz33LFhUGSgw8F5vxrul7kPsqamUb9+54PGKnTeRU1JK4+kPNC5JAkH131pvCzLfc0+uMO4/X+Kxn+HdHS/sNPBWauNYnPEG6OnpWbBDnVIqOQV+3gcG3hkZGWzevJnGxkZgviSlurqagoKCZcezWq3+Yzs7V62k/Yihrg76b7Swef8dWG0Lg3sRoWbPrdS99hID7TeWGUFtFElV5KyBt1Ibx1KXe7u7u2MwE6VUvAn8vF/c43/fvn0LHisvL+fuu+9ecbzAGu+lvvSvpvG0t216xY7dSz5fVL2JlLQ0Oq5uuM6OapGk2nFCA2+lNo6lPvw08FZKwfKlJuDtXPKbv/mbjI6O4nA4/B1LVpKdnY3FYvHviOlyuda0adf1s6fJzC8g1ddhZTGL1UrZtp3cuHCeieEhMnLzgh5bJZakyngv3mBDA2+lEpPL5WJychLwXqad+2Cdmppa0L9XKZWclis1Ae/6EBEhJycnqKAbvF3QMgOC5rXUec9MTdJx9TKFlTUrHlexYzfGeLj01utBj60ST1IF3prxVmpjCMx2Z2ZmhnwZWCm1cRhjFnzeL7dgcq0C32fW0tmk9eJ5PG4XBVUrb/aXkZtHTnEp9e99ZM9AtYFo4K2USjiBwXV2djbZ2fN7bK231ZdSamNYHHSHsstkoPX28m45e4aUtDRyS8tWPbaoppbelibGBrVD00algbdSKuEEBt45OTkaeCul/FZaWBmK9QTexhiunztD9c37sFisqx5fVL0J8NaEq40pqQPvxRvqKKUSQ2AP76ysLLKysvz3tdREqeS20sLKUKyn1OSDnz3L2EAfdocjqOMz8vLJKiyi+UMNvDeqpAq8dXGlUhtDYOCdnZ2tNd5KKb9oBN7Dw8NB7X491O3t+Z1XVhHUOUSEzfvvoPXCWVwBv4faOJIq8NZSE6U2hsAv0Q6HY0HGe2RkJKgPRKXUxhSJhZUAqampOHyZa5fLFVTWe7i7k5S0NNJzclc9do6I4Jqe5u0f/P2656rilwbeSqmEE/i3bLVacTgc/g9Et9vtbzWolEo+gRnvcAbeACUlJf7b169fB7xX4OZ+Fhvu7iK3pGxNCzzzyyuxWG30t7WGPmEVdzTwVkolnMCM99ziKV1gqZSCyJWaANTUzPfingu8lzM22M/U2Ci5peVrOofVZiOvtIzBzvZ1zVHFNw28lVIJZ6lLybrAUikFkc14V1dXY7F4Q6f+/v4ls9xz5rZ/D6aN4GL5FVVMDA0yMTy0vomquJVUgbfL5WLy3Hn/jwbeSiWmwI5EmvFWSgWKVI03eDPoZWXzgXRTU9Oyx3ZcvYTVZieroGjN58kr9y7GvHGpbu2TVHEtqMBbRB4VkXoRaRSRzy/xvIjIl33P14nI/oDnviMivSJycdFr/lREOkTknO/nUwHPfcE3Vr2IfDKUXzDQ4ow3aNZbqURjjNFSE6XUsiJZagKwadMm/+2TJ0/y3HPP0dXV9ZHjOq5cIqek1J8hX4vsgiJsKQ5uXDgfylRVHFr1X4OIWIGvAo8Bu4EnRWT3osMeA7b5fp4Cvh7w3HeBR5cZ/m+MMft8P0d859sNPAHc5Hvd13xzCJkG3kolvsVB99yipcDAW0tNlFqeiLSIyAVf0mvDNYwO/KyPROBdXV2N1TofloyNjfH6668zNDRfFjI9OUFfWyu5JaXrOodYLOSVVdB2SQPvjSaYr2F3AI3GmGZjzAzwLHBo0TGHgO8Zrw+AXBEpAzDGvA0MrmFOh4BnjTHTxpjrQKNvDiHTwFupxBcYeAd++C0OvLWloFIretCX9DoQ64mEW2DGO5w7V85JTU3l4Ycfpry83D/+7Owsr7/+OtPT0wB0NV4DY8gtWXt995z8ikpGensY6e0Oy7xVfAgm8K4A2gLut/seW+sxS/mcrzTlOyKSF+JYq1oq8NbdK5VKLMttBx3YUtDlcjExMRH1uSmlYi/SpSbg7W7y+OOPc+jQIf/70NjYGBcveqtqu65dBRGyi0tWGmZF+eWVALRqucmGEkzgvVTzycWppGCOWezrwBZgH9AFfGktY4nIUyJyWkRO9/X1rXIqL814K5X4AjPeixdO5eXl+W8PDw9HbU5KJRgDvCIiZ0TkqVhPJtyiEXjPKSgo4ODBg/77bW3evGFnw1UKKqqwpwS3VfxSMnLzSM/Jpf3yhZDnqeJHMIF3O1AVcL8S6FzHMQsYY3qMMW5jjAf4FvPlJEGNZYz5pjHmgDHmQFHR6iuG3W73kpeeNfBWKrEEXqVaHHjn5s7vDhdYb6mUWuAeY8x+vOuznhGR+xYfsJ7kVrxY7qpYpAT29u7r68M5NUV3Qz3l23eGNK6IULV7D22XL2jp3AYSTOB9CtgmIrUikoJ34ePhRcccBn7H193kTmDEGPPRJb4B5mrAfX4dmOt6chh4QkQcIlKLd8HmySDmuaKlst2gpSZKJZrFu1YGCgy8NeOt1NKMMZ2+//YCP2eJdVRrTW7Fk2hmvMFb5lZYWAh4uy5dOX8W58Q4ZSEG3gBVN+1lfHCA4e4Vc5kqgawaeBtjXMDngJeBK8BPjDGXRORpEXnad9gRoBnvQshvAX8w93oR+RHwPrBDRNpF5Pd9T/2Vb1V1HfAg8Ee+810CfgJcBo4CzxhjQo6Olwu8NeOtVGJZqpXgnMBSE814K/VRIpIhIllzt4FPMJ/42hCiHXgDlJfP70554eQJ72PbdoU8btVNewBo03KTDSOoazC+Vn9HFj32jYDbBnhmmdc+uczjn13hfF8EvhjM3IKlgbdSG8NKm2NoxlupVZUAP/e14bQBPzTGHI3tlMLHGBPRDXTmLN6xsry8nLo672Y3Vy+cpzwjg/zyCjquXgrpPHllFWTk5dN26QJ7H1quM7NKJEmzc2VgliyQBt5KJZaldq2cM7e5jtPpZHh4GKfTGe3pKRXXfK2Bb/H93ORLdG0Yi4PuuT7/kVZcXOx/P+pqbSW3uhZZx8Y5i114/WWy8gtoPnuK86++FPJ4KvaSJvDWjLdSG8NKXU1EhJycHP99zXorlVyivbByjtVqpbi4GPfsLONDg1hzC8I2dl55JTOTk0yO6PvZRhC9f5UxpoG3UhvDSosrAXJychgYGAC8gXdp6fp2jlNKJZ5Y1HdfOnsBpQAAIABJREFUfvtNAKa62hkdGgZjMOmZYRs/r8y7lclgZ0fYxlSxkzQZ7+VKTbSriVKJZaXFlcCCjLcusFQqucQi8J5TkJfHWL+39eKsNXy15enZOTgyMhjqag/bmCp2kibwXi6zrRlvlUxE5FERqReRRhH5/BLPi4h82fd8nYjsD3juOyLSKyIXF73mT0WkQ0TO+X4+FcnfYaVSE0BLTZRKYtFYWLmc3Oxsxvr7SElPZ9rtYWpqKizjigj5ZZUMdnZoP+8NQANvDbxVkhARK/BVvJtm7AaeFJHdiw57DG/v/G3AU3h3mJ3zXWC5ZfV/Y4zZ5/s5sswxYbFaxls30VEqeQVmvKMdeFutVpgcI7uoGIDu7u6wjZ1XXsGsc4qBttawjaliI2kC7+W+JWqpiUoidwCNvq4GM8CzwKFFxxwCvme8PgBy5za7Msa8DQxGdcZLCKbUxOLrJjA+Ps709HTU5qaUiq1YBt7u2VmYGCO7MPyBd355JQA3Lmk/70SXNIG3ZryVogJoC7jf7ntsrccs5XO+0pTviEjeUgeEawvq1boWWCyWBRvp9Pf3r/tcSqnEEvj+EO0a7/HBAbIcjohkvNOysknLyqbt0vmwjaliI2kC7+Uy3hp4qySyVEPbxX8YwRyz2NeBLcA+oAv40lIHhWsL6tUy3oB/+2aAUIJ8pVRiiWXGe3ygj6zUFIb7+rh+4Twn33id2WUaO6xHXlkF7Zcv4vHolfpEljSBt2a8laIdqAq4Xwl0ruOYBYwxPcYYtzHGA3wLb0lLxKy2uBI08FYqWcVyceX4YB/pmVnk5+f7HjH09g+Ebfz88kqcE+P0tVwP25gq+pIm8NaMt1KcAraJSK2IpABPAIcXHXMY+B1fd5M7gRFjTNdKg87VgPv8OnBxuWPDITDwXqqPN0BBwfzmFRp4K5U8YtlOcGJwgIyCQkoCruh19fSEbfz8Cm+dd+uFc2EbU0Vf0gTeywXYurhSJQtjjAv4HPAycAX4iTHmkog8LSJP+w47AjQDjXiz138w93oR+RHwPrBDRNpF5Pd9T/2ViFwQkTrgQeCPIvl7BJPxzsvL8wfl4+PjYWvrpZSKb7HaudLjdjM1MkxGbj5lJcX+x7t6e8PWAtCRnkFhVQ2tdWfDMp6KjaTZuVIz3kqBr9XfkUWPfSPgtgGeWea1Ty7z+GfDOcfVBPPBarFYyM/PZ2xsDPAusKyqqlryWKXUxhGrxZVTYyMYY0jPzScvJwdHSgrTMzNMz8wwODxMQd6Sa87XLD0nl/YrFzl79EVuffTxsIypoivpM94aeCuVWAKvUq2U0dI6b6WST6wWV0769gxIz81DRBaUm3T29IbtPAWV1Xjcboa7V1x6o+JYUIF3hHa7+2sRueo7/ucikut7fJOITAXsgveNxedbj8CMt9Uy/2trqYlSiSXYxVNa561U8olZ4D08CCKk+3bOLS0ODLzDV+edV1qOWCwMdLStfrCKS6sG3hHc7e5V4GZjzF7gGvCFgOeaAnbBe3qJ167ZgsDbOv9ra8ZbqcSxeGGlyFLdD70CM949PT261bJSSSBWXU0mR4ZIy87BYvVehSsuLMRq8a4zGRsfZ2BoOCznsdrt5JaUMdCugXeiCibjHZHd7owxr/gWewF8gLdtWcQEBtiBGW8NvJVKHMF0NJmTm5tLamoqAE6nk4GB8LX1UkrFp1gF3hNDQ6TnzNdx26xWKspK/febb9wI27kKKqoYH+xnYngobGOq6Akm8I7kbndzfg94KeB+rYicFZG3ROTeNYyzrMBsl80y/4GtgbdSiWMtH6oiQmXl/Pf59vb2iM1LKRUfYtFO0O1y4RwbIT134QLKTVWVzLrczLrcNN9oWzC3UBRWbwKg+eypsIynoiuYwDtSu915Xyjyx4AL+IHvoS6g2hhzK/DvgR+KSPYSr1vT9tMLMt5WDbyVSkTB7FoZKDc3F6fTidPp1MBbqQ3O7Xb7P9MtFsuqV8XCZWrEW0aSkZe/4PH83Fyys7IAb6zR2hGeBZGZ+QWkZmbRdPpkWMZT0RVM4B2R3e4AROR3gceBf+ZrY4YxZtoYM+C7fQZoArYvfu1at59euLhy/nuCLq5UKnGs9TJyeXm5/3Z3d/eCwF0ptbHEqof35LC3mjaw1GRObdX8Vbem1tawrDUREYqqN9Fad5bZaWfI46noCibwjtRud48C/wX4tDFmMuDxIt+CTkRkM94Fm81B/0bLWFjjrRlvpRLRWjPeGRkZ5ObmAt6/9a6uFd+WlEoaImL1lXS+GOu5hEusdq2cGB5CLBZSs3M+8lxVebn/vWp8YiJsrQWLampxzUxz4+L5sIynomfVwDuCu919BcgCXl3UNvA+oE5EzgPPAU8bYz6yOHOtFtR4a6mJUglprYE3LMx6a7mJUn5/iPczfcOIeUcTy0dDKpvNRm3A5l31TU1hOWdeWQUpaek0nT4RlvFU9AT1yRWh3e62LvP4z4CfBTOvtdCuJkolvvVcSq6oqODy5csAtLa2ctddd0VkbkolChGpBH4F+CLetVQbQqwy3lMjI2Tk5y/7/JaaGhpbWgAYHB6mf3CQwhWOD4bFaqV23200nTmJx+PGYolOPbsKXdLsXKkb6CiV+NaT8S4rK/N/CI+OjtLbG75d5JRKUP8L+M/AspmntTYwiAexqPF2u1w4x0dJz85d9pjUVAfVFfON3uqbQq6eBWDbwXuYHBnmxsW6sIynoiNpAu/AzLaWmiiVmNZzKdlqtbJp0yb//YaGhnBPS6mEISKPA72+5gXLWmsDg3gQi4z3WF8PGEPaEvXdgbZuqvG3FrzR2cXQyEjI595y2x04MjK4/NbrIY+loidpAu/lMt4aeCuVONaygU6gzZs3+283NTXp371KZvcAnxaRFrwb4n1cRL4f2ymFRyxqvIe7vQu203KWz3gDZGVmUlE6v6HO1cbQa71tKSnsvPs+Gk6+z/Tk5OovUHFBA2/9AFYqYQQG3mv5YC0tLSUjIwNAe3qrpGaM+YIxptIYswlvl7I3jDG/HeNphUVsAm9v5+S0FUpN5mzfMp8A6OjuZmR0LOTz777vIVwz01w78U7IY6noSJrAWzfQUSrxrbeGU0TYtm2b//61a9fCOi+lVOwFlppEK/Ae6enCnpqGLYjSlpysLMqKi/33L9bXh3z+sm07yCur4NIxLTdJFEkTeC/cMl4XVyqViNazuHJOYODd0tLCpF6aVUnOGHPMGPN4rOcRLrEqNVmtzCTQzm3zDd26envp7R8I6fwiwp6HPknH1Uv0NDeGNJaKjqQJvLWdoFKJL5SuBTabjZycHJxOJ5OTk1y9ejXc01NKxVAsFleOdHeSvsrCykA5WVkLOpzUXbm67t0s6147St1rR7FYraSkpXPqcNg7MasISJrAe0GNt5aaKJWQQsl4A+zYscN/++rV9X/gKaXiT7TbCTrHx3COj60p4w2wa9tWfxwyPDpCw/WWkOZhT3Fwyyc+xbUP3vUv9lTxK2kC78AA2zI5iaenB9BSE6USSaiB96ZNm3A4HACMj49z48aNsM1NKRVb0c54+zuaBLGwMlBaaio7t2zx379Yf43RsdAWWu5/7NNYrBZOv/h8SOOoyEuawHsus2WcTmZfeIHZM2cws7Oa8VYqgQR+UV5LO8HA12zfvt1/f25HS6VU4ot2jfeIv6NJ8KUmc6orK0lPz2DW5WZ6ZoZT5+tCugKXmZfPzQ8+woU3XmGoq2Pd46jIS5rAey7Anv3wDBbfN0vjdGrgrVQCCTXjDQvLTdra2hgdHQ15XvHm0vEO/49SySLagfdwdxcWq5XUzKw1v9ZisbB/z82ICABDIyM0ta7/Clzda0fJKS5FxMLxH/7DusdRkZc0gbc/4z06hi3L+0eigbdSiSXUjDdAVlYWlZWV/vtXrlwJeV7xTINwlSyi3U5wpKeT7OJSxLK+UConK4sdvpKTWZebs5cuMzSy/pITR3oGm27ZT8PJ92i/emnd46jISprA2x9gG8984D01pYG3UglkvTtXLrZz507/7fr6+g271sM146a9fojrdf10NgwzMTLtf04DcrXRxKLGO7e0PKQxttVuIiM9HfC+v10KcY+Bmr37yMwv4M3vfhOPZ2O+ryW6pAm8jTEYtxsM2DIzAfA4pzXwViqBhKPUBKCyspJM3/uA0+mkubk55LnFm87GYd7/eTONp3tpvTDAtZM9fO+P3+PYD+uZmXKtPoBSCSaapSYel4vR3m5yS8tCGsdqtXLL7t3+++1dXUwHfIFY83g2O/f/9u/Re72JulePhjQ3FRlBBd4i8qiI1ItIo4h8fonnRUS+7Hu+TkT2Bzz3HRHpFZGLi16TLyKvikiD7795Ac99wTdWvYh8MpRfcI4xBuP70LZmZGABjHMK0JaCSiWKcJSagHfTiZqaGpxOJ06nk4sXL67+ogTS3TzCtRM9ZBemctujNdz35HZu/5VN7LqrjMvvdPLTvzy9IPutVKJzuVz+klKr1YplneUfwRrt78Pjdoec8QYoLiwgL8e7QNMYQ2t7aFehpicnyS+v5K3vf5tTh7XLSbxZ9V+miFiBrwKPAbuBJ0Vk96LDHgO2+X6eAr4e8Nx3gUeXGPrzwOvGmG3A6777+MZ+ArjJ97qv+eYQEo/HMx94Z2Yi4q3xBm0pqFSiCFfGG2D79u3+4L2vr4/e3t6QxosXHfVDXP2gm9zSdG5+oIKsglQsFiEj18ED/2wnh/5wH9OTs5x7tQ3nxOzqAyqVAKJdZjLX0SS3rGKVI4OzqWp+3UlziG1ORYSd99yP2+Xi2ol3Qp2aCrNgvhLeATQaY5qNMTPAs8ChRcccAr5nvD4AckWkDMAY8zYwuMS4h4C5pbf/APxawOPPGmOmjTHXgUbfHEJijAFfgG3JyMCCYKY0461UojDGhC3jDZCamsrmzZv99y9cuBDSePFgdsbNG//3CmmZdm6+rwKrdeFb/KXjHQz3TnLz/RV4PIaLb3fgdun7n0p80e9o4g28c0IsNZlTUVaGzfeeNj4xQd/AUmFT8DJy89h0y366G6/RUnc2HFNUYRJM4F0BtAXcb/c9ttZjFisxxnQB+P5bvJaxROQpETktIqf7+vpW/SU8Ho+3xhsQmw1Lih3jnPY/p5SKb4uD7rk2XKHYtWuX//b169eZmJgIecxYOnm4mdF+J9sPlmKzL//2npHjYNfdZYwPTnP9fH8UZ6hUZMQi8E7LzsGRnhGW8WxWK5Xl82Urre3tIY9Zu+8A6dk5vP53X2N2RkvL4kUwgfdSn26Lu7wHc0ywghrLGPNNY8wBY8yBoqKiVQcNrPG22GxY7Cl4tMZbqYQRro4mgQoKCigtLQW87wOJXOvd3z7G+dfb2H1vOXkl6aseX1iZSdnWHDquDTHSNxWFGSoVOU5f6Sh4r2ZFmrejSXiy3XOqK+YD767e3pA21AGw2mzs+tiDDPd08cFzPwp1eipMggm824GqgPuVQOc6jlmsZ64cxfffuQLL9Yy1Ko/HM19qYrNhdaRgprTGW6lEEc4yk0C7AzoKXLp0acEHeKIwxvD2s9dwpNu569e2rP4Cn017ChARTr648bq6qOQS/cC7g5zS8NR3z8nLycHhq0+fnplhcHgk5DHzKyq56YGHOfWL5+lt0b/zeBBM4H0K2CYitSKSgnfh4+FFxxwGfsfX3eROYGSujGQFh4Hf9d3+XeAfAx5/QkQcIlKLd8HmySDmuSJjDMZ3KUrsNqwpKdrVRKkEEs6FlYGqq6vJz8/3nyMRa70bT/fS1TjCnb+2mdSM4C+zO9LtVOzI49rJHgY6xiM4Q6UiK5qBt3NslOnx8bB0NAkkIpQEXMHv6u0Jy7hFVZuwO1J54a//HI8mGmNu1cDbGOMCPge8DFwBfmKMuSQiT4vI077DjgDNeBdCfgv4g7nXi8iPgPeBHSLSLiK/73vqL4FHRKQBeMR3H2PMJeAnwGXgKPCMMSbkfymBGW+x2rCkpPi7mmjgrVT8C8x4hzPwFhH27/d3QOXixYtMTydOPeT05CzvPNdAZp4D4zFr3hCnenc+Fovw1o/qdTMdlbDmWoM6nc6IX8Ue7vbmFcNdagJQWhwQePeEp9OSPTWVnXffx1h/H6df/HlYxlTrF1SjS2PMEWPMdmPMFmPMF32PfcMY8w3fbWOMecb3/B5jzOmA1z5pjCkzxtiNMZXGmG/7Hh8wxjxkjNnm++9gwGu+6BtrhzHmpXD8osYYPHPtBG02rA6Hv9REA2+VLKLdkz+cIlHjPaewsBCHw4HT6WRsbIxz586FdfxIev/nTUyNzrD9YAliWfuCU7vDSlFNFr0to7hm9b1QJaZoZryH/a0Ew5vxBiguKMDl9jDrctM/NMzkVHjWXxTXbqF402be+8n36W9rDcuYan2SZufKBRlvu92f8TYejwbeKilEuyd/uEWq1AS8We9bb73Vf//ChQuMjo6G9RyR0NkwxKXjnex9qIrsgrR1j1O+NRe3y9DbGv+/s1JLCQy8HQ5HRM811NmO1W4ns2D1xg5rZbPZKPKVvgF0hinrLSLs+tgDpKSlc/Rrf4PbpbvXxkrSBN7eriZzpSZWLI5UMAYzM6OLK1WyiHZP/rAK/DuNxK50tbW1FBd7u5p6PB5OnDgR9nOE0+ToDL/82gVSM+1kF4SW4csuTCUjJ4XOhuEwzU7FKxFJFZGTInJeRC6JyH+L9ZzCIZoZ78GONvLKKyO2O2ZpcbH/dldPeOq8AVLS0nn4Xz1DT3MjHzz/47CNq9YmqQJv5kpNUlKw+b4Re6amFvT/VGoDi3ZP/gXW2nt/sUhmvOfcccf8Xl3Xr1+noyM+a549bg+vfPsSrhk3N91bjtUW2lu5iFC2LZfxwWldZLnxTQMfN8bcAuwDHvU1RUho0cx4D3a0kV9RHbHxA+u8+wYGmQ1jdnr7wXu46f6HOPH8j+m4ejls46rgJUXgPVdKYtwub5Nwq5XUdG+fW8/UVEItpFIqBNHuyb9wkDX23l8sGoF3UVHRgt0s33777QXnjQfGY3jzB/V01A+x7fYSsvLDk90rrs4CgYbT4cuwqfjju5o19+3K7vsJy994LEUr8HaOjTI1MszM9DSX336Ty2+/GfZzpKelkZ2ZCYDHeOjpC+8mVw/+839NdlERR77yJaYnE3vTsESUFIH3XBN64/IF3jYbDl/gbaamErJvr1LrEO2e/GEVqT7ei91+++14PB6cTid9fX0cO3YsYudaK2MM7zzXwNX3urj9VzZRtiUnbGOnpNnIK0mn4XToG3eo+CYiVhE5h/dv9VVjzEfqqkK9QhVt0So1Gezw7iiZkZe/ypGhWVBu0hu+t9S6145S/97bbL/zY4z19/LTP/9jzr/6EnWvHQ3bOdTKkirwxu1GxFvjnZLh3eZVM94qiUS7J39YRSPjDZCenr6g5OTy5csxLzm5dLyDi2+384//6xx1b7Rzy8eruP3x2rCfp7gmi9G+KfpujIV9bBU/jDFuY8w+vF+s7xCRm5c4JqQrVNE0Ozvrv7Jts9ki+v4w2HEDgIzcSAfe8//Pu8Owi+ViuSVlbDlwkJ7mRjrqteQkmpIi8PaXmgRkvFP9gbdTM94qKUS7J3+4BQbekVrUNGfbtm1UVMyXtr/xxhtMTk5G9JwrMcbQeLqXjvohKnfmkVuaxuV3Qt7Q9yMKq7KwWIWG0xG5aKHijDFmGDjG0t2KEkZ067vbcWRkYk9bfxehYCzexXJgaCjs59h0y23kV1RR/95xxgbDW86ilpcUgXdgqYkF8dZ4++qnzNSkZrxV0oh2T/5witQGOsv52Mc+5r9kPTU1xRtvvBGT1qPGGBrP9NJxbZjKXXls2V+EyNr7dQfD7rBStTufxtM9Wm6yQYlIkYjk+m6nAQ8DV2M7q9BEs6PJUMcN8iuqIvY3OEdEFpSb3OgM/xdtEeHmBx7BlpJC3WtHmZmKXXIhmSRF4D2/uNKNiCAWC460NCQ1FffomGa8lUoA0So1mZOens7999/vv9/Z2cnJkycjft5Axhje+WkDHfXDVO7MY8utkQu652zdX8z40DS9LVpuskGVAW+KSB3e8rNXjTEvxnhOIYlWxtsYw2BHO3kVVasfHAZV5fM7Y7Z3dkXki78jPZ09H/8Ek6MjvPLNr+gX7ihIisDb/w/J5UKs3l85xWbDkp2Fe3RUM95KJYBoZ7wBysvL2bdvn/9+XV0dDQ0NUTn3XNBd90a7N+iOYKY7UO0thVisQuOHWm6yERlj6owxtxpj9hpjbjbG/Fms5xSqaGW8xwf7mXVOkV8ZncC7IC+PdF9Jy8zsLN0RWuSaX17J1gMHqX/vbU6+8NOInEPNS4rAe77G241YvN0QHCl2LFlZeEZHNOOtVAJY65bx9Se6/D+h2LdvH8XFxTid3vUgb7/9Nr1h7DKwFI/HcOz7V/0LKaMVdAM40u1U7cqn6UPtbqISQ9Q6mrR7tzjIL49O4C0iFBcWMetyM+tyc6Mj/OUmczbdchs777mfd579Hg0n3ovYeVSSBN7zXU1cWHwZb4c9BUtWNu4RzXgrlQjWGngHCiUIFxHuu+8+cnNzAW/m/eWXX2Z8PDIbzcxOu3n5Wxe5/G4XBz61iXt+a2vUgm7wdlBJzbQzNuDU7iYqIUSr1KT/xnUQoaCqJmLnWKyqotx/u7Onl5mZmYicR0T4xNP/lrKtO/jll/+KpjPxvXNvIkuqwNu4XP6Mt91qxZKdjWdigumpqZgsmlJKBW89fbynxmZpPDFA/bv9NJ8eZLRvel1ZXLvdzkMPPeT/UJ+amuKll14K+9Wy0y+18IM//YDms31sva2YzDxHRLqXrKawMhMRaDyj5SYq/kUr493X0kxuaTn2CC/gDJSdmUlOdjYAHo+b1gi2NrWnOPjMF/4bRTW1HP7Sf+fKO8cidq5klhSBd+DiSovvA1tESM339uH0jOkCS6Xi3VoXV3Y3jnPhlR6Gu5zMTLkZ7Jjiylt9HP3qFYa61r56Pzs7m4ceesjfynBoaIgXX3wxbO8dzWf7OPNSC87xWfY8WEnlzrywjLsedoeVvNIMGs9ouYmKf9HKeHdfu4o9LT0iu1WuZFNVpf928422iJ2n7rWjXPvgHXZ+7AHKtu3gyN/+D177u6/hilCWPVkFFXiLyKMiUi8ijSLy+SWeFxH5su/5OhHZv9prReTHInLO99Pi20ULEdkkIlMBz30j1F8ysMbbEpApSy8q9D4/MqLlJkrFubUE3kPdk9w4P0x2sYO9j5ay5+ES9j9ezqZbcxnrn+aX//syF97oxHjWFlSWlJRw4MABpqencTqddHZ28sILL9DTs/5t1mecLt78/lVe+j8XSMtM4cBjNRSUZ6x7vHApqslibMCp3U1U3ItGxntieJCZqUmyCqK/mVBVWRk2X+wyNj5O30BEOrb62VMc7Lznfmr23sr5V4/wvf/8b2i9cC6i50wmq6aNRMQKfBXvxhjtwCkROWyMCdzq6DFgm+/nIPB14OBKrzXG/NOAc3wJGAkYr8m3q1ZYBHY1IWDjjdRCb+DtHhnVjLdScS6YUpP6E10YY7hyrA+r3cKWO/KxO7zHWqxCyZZM8ivTaDk7zNmXOuhtGedjT2zGkR58l5QtW7ZgjOH48eMAjI6OcvjwYfbs2cP+/ftJ8W16EYzrdf0cf/Ya40NO9n+ymsy8VCzW6NVzr6SwKpOG00LDmR5KarNjPR2llhWNjHd/y3UAMgsKIzL+Smw2G5Xl5XR0ecvOmltbKSqI7M6ZFouV7QfvoaCymivvHOO5v/gTyrbuYPudH+PAr/56RM+90QWT8b4DaDTGNBtjZoBngUOLjjkEfM+3+cYHQK6IlAXzWvGuGvonwI9C/F2WtWADHev8r5zm2wbXPaYLLJWKd8Eurhxom2JsYIaqPTn+oDuQ3WFl68F8avbl0nVtlKNfvcL44Nr+/rdu3coDDzzgD7KNMdTV1fHss89y7dq1VV/fd2OMX/zteY58rQ6328O+R6rJLkyLm6AbwJ5ipXp3AU1netd8ZUCpaIpGxruvtRlEyMgriMj4q6kNKDfp6O7BGaWYpaCiirt+40lqb72d7uYG3vvp92k89UFUzr1RBRN4VwCBRUXtvseCOSaY194L9BhjApvj1orIWRF5S0TuXWpSIvKUiJwWkdN9q/S2nK/xdi0oNXFfuoTYbUxfa9CMt1JxLtjAu791AkeGlaJN6cseIyKUbs1k+z0FjA9N84v/eZHBjrXVfdfW1nLo0CFyc3P9rQadTifHjh3jl7/8JaOjowuONx7DjUsD/OJvz/OT/36Knusj3P2ZrRz41CZyiiK7/fR6bb3Nu5lOd/PI6gcrFQPGmAWJs4hlvFuvk56Ti9Vuj8j4q8nJzibf11nJYzy0tLVH7dxWm42tBw5y52eeIDUrm3/8H3/B2z/4ezwe9+ovVh8RzPXVpVIwi9Mfyx0TzGufZGG2uwuoNsYMiMhtwAsicpMxZsGnmDHmm8A3AQ4cOLBiOma+1MSNBHxgO+wpSGoaHqdTM95KxblgNtCZnXYz0jtN2fasoFrw5RSnsvvBYuqP9/PSVy6z/Z5Csosc7DhYtuprATIzM3nsscdobm7m1KlTTE56g/empiZaWlooSd1KbeV2+lrH6W8bZ6RvirTsFDbtLaByRx62lLW1RYy22lsKsdotNJzupWxrbqyno9RHTExM+JNrDodjza1Gg2GMoa+lmayikrCPvRYVZeX09A8AcLWpmR1bNke11WhmXj63/+pvMNDeyqnDP2NyZJhPPP1vsVji+30s3gQTeLcDgd3iK4HF/a2WOyZlpdeKiA34DHDb3GPGmGlg2nf7jIg0AduB00HMdUmBGW8JqL9MsdmwpKXicU5pxlupOBfM4sqhTicYKKgMPoOcnm1n94NFXD3ez9XjfWw+kM+Og2ub2+bNm6mqquLDDz/kypUreDwehronqLv8FrOPcUD5AAAgAElEQVSjx6ks2MaWrVvZdXcZRdWZC0re4lnDqR7yy9K5+kEXH/utrQkzb5U8Rkbmr8bk5ORE5BwTQ4NMjY5QtvOmiIwfrIrSEi5evcrM7CxTTifdfX2UFRdHdQ5Wm42H/+UzZOTm895Pf8BQVwe773sIEWHvw49GdS6JKph30VPANhGpFZEU4Ang8KJjDgO/4+tucicwYozpCuK1DwNXjTH+ayYiUuRblImIbMa7YLN5nb8fEFDj7XYv+DacYrcjqWmYqSnNeCsV54LJeA+2T+LIsJKeu7bLwY50G7sfKCIzP4Wmk4N8+FI7Hvfaevu7nFCSuo1Ss5eGY2Ncequb8cFpUnIMswXtdMkZ+mdamHROrGncWCuuyWbW6aajfjjWU1HqIwID7+zsyCwC7m1uBCCrILpB7mJWq5Xqyvlq3aaW1pjMo+61o2Tk5rF5/x10XrtKy7kzMZlHolo1422McYnI54CXASvwHWPMJRF52vf8N4AjwKeARmAS+BcrvTZg+Cf46KLK+4A/ExEX4AaeNsaE1DvHn/GedcGCUhM71owMZqdnmFilTlwpFVur1XhPT7oY7Z2mdFvmui6/2h1Wdt5bRMvZIS6+0UXXtRHu+LUaimoylzzeGMNQ1xQt5wZovzLCcPcUALYUC488+BiurH7qLp1l1j0LeHA6p7hcX8fl+jry8wrZVLWF6ooa7Pbgu6DEQn5FBla7hWune6jaHdlOCkqtVTQy3t2N9VjtdjLyY7OwMlBtVRWN11sA6O7rY3xigsyM2LQf3bz/diZHhmk8/QFZhdFvs5ioguqhZYw5gje4DnzsGwG3DfBMsK8NeO6fL/HYz4CfBTOvYAVuGS8Bl0oddjsW3zfkcd8/ZKVU/AkMui0Wy5KBde/1MYyB3LL1L1S0WIXNB/LZfV8ZJ37WyktfuUJ2sYOCyjQy81PYeqCEsYFp+lrGqf+gF+eYCxHIKnJQdXM22cWp3PqJKl9JxnYqyippammgqaUB5/R8OVt3XyeDQ/3UXf6QrZu2s33LLhyO6O2GtxZWq4XCqkyaz/bxwJM7sNq13ETFj+Hh+Ssxkcp49zRdo7h2y4LmDLGSkZ5OSVERPb5kYXPrDfbu3hWTuYgIu+99kPGhAS68+Qq3f/o3yIpBu8VEE3zz2gQ2307QjdU6/ys7bDasvj/UidbYXLJRSq0umDKT/rYJEMjIC73rQM2efMq351D/Xi8Xj3Vx/UPvh/uF1+a3UM8qTKF0ay75lWkL2hY2nJ7fTMdms7Nj6262bd5JZ08Hbe0t9PR14zEeZlwzzLhmqLt6lsaWa+zcehPbNu8MalfOaCuuyaaneZQbVwap3asfrCp+RLrUxDU9Tf+NFm75xK+Efez12lxd5Q+8r7e1s3vHdv8GO9FmtdvZ+/BjfPD8sxz92t/wm3/854hFv5yvJP7e4SNgrtQEtwsJ+FDz1ng7ELud8bbIbcOqlApNMK0E+29MkJ5jx2oLz5u+3WHl5gfLsKUZpkZdTI3NUlydQ3qOnYLKDFouBF+eZrFYqCyrorKsiumZado6Wrl+o5Gxce+ukC7XLBevnqO5tYFb99xOeWnlKiNGV15pOrYUC2eOtDA5Ms1N9y7uCqtU9Hk8HsbG5ndWjUTg3dvShHG7Kdm6g4mR+GirWVxYSEZaGjOzM8y6ZrnR3sHmmuqYzScjJ5cdd93LleNvcuaXL3DgVz8Ts7kkgqT4WuLxeDDGYFzuBd/EHHY7IFiysxjv6IjdBJVSK1pt10rjMQy0T5CZF5566foTXf4fEfEF2+m4PbOMDU2uKehezJHiYGvtdh6+7zEO7r+b7Mz5YGFyaoJ3Tx7j/dPHmZmJnwXfFotQWJVFf/s47jUuOlUqUkZHR/1XtDMzMyNytain0bshVsmW7WEfe71EhNqAQLsxRossA1Xs2M3W2+/k+I++R8/1plhPJ64lReBtjAHfB3dgjZbNasVlDGRmMdPbw8zMTKymqJRawUqtBOtPdHH+jTZmptxk5Mf3QsVAIkJFWRUfv/eT3LRzL2IRf/lJe2crr751hL7+ntUHipLimizcLg+DnYnVlUVtXIFlJrm5kekz391YT25ZBamZSy+yjpWaigqMgVmXm4HhYX/pSayICI889W9Iz87myJf/mtlpbdG8nKQJvI3vg9uy6EM7PTUVycrETM8wUL/6Vs9KqehbrdRkYmgWgMwECrznWCwWamu28PD9j1FTWet/fHJqgmPvvUrd5Q8XZPxjJbckHZvDSl/r2OoHKxUFke5oYjweepoaKN0aP9nuOXa7fUFrwXjIeqdn5/DoM/+ewa4O3vzuN2M9nbiVFIG3x+PBzH1wLSr6z05PQ7K8l3p7z5+L9tSUUkFYbXHl+OAMFquQlp24y1YcKQ5uu+UO7rztHhD82e/6xsu8cuyXdPUs3rcsuiwWoagyk4GOCdyzWm6SqESkSkTeFJErInJJRP4w1nNar8COJpEIvAc72piZnKB0646wjx0Om6vny026ensZHR+P4Wy8/b1HerqpvWU/F954hbrXX47pfOJVUgTexhjwZcysiz60s9MzsGRngQj9dRdiMT2l1CpWzXgPzpCRZ4/q9smRUl5ayUP3fpKSwlL/Y+MTo7xz4g3efv91BocHYja3wqpM3C4PbVdD2lpBxZYL+A/GmF3AncAzIrI7xnNal0gH3h/+8gUAJkZHufz2m2EfP1SZGRmUFM33z77WFNJeg2Gz5baD1Oy9lTe+83W6GupjPZ24kxSBd2DGWxZ9aGelpYHVhqWwkN5LF2MxPaXUKlZaXGmMYWJ4howwLayMB2lp6dxz8H723XwbBuPPfrd1tfL62y/x5juv0NzayHSU6yjzStOx2ixcP6cbjiUqY0yXMeZD3+0x4AqQcG1qJiYm6OmZXwNRUBD+zW2GuzpJzcrBkRFf9d2Btm+eL0+70dHJ5NRUDGfjJRYLv/Jv/xOZ+QW88Nd/zkhv/KxViQdJEXivVOOdnZEOgK28jMFrDfOb7Sil4sZKiyunJ90YD6Rlh96/O95srtnKIw98ik1Vmxdk8/sHezlz/gMOv/wcbxx/mauNlxkbH434fCxWCwUVGVyv68fj0ffKRCcim4BbgRNLPPeUiJwWkdN9cbizc0PD/Od1eXk56enpYR3f43Ix2tNFbll8fycpyMsjP8+7sNRjPFxrvh7jGXmlZWXz6//l/8M9O8vzf/mnOGNcBhNPkiLw9ng8/lKTxRnv7LS5wLuCkfExZnUjHaXizkqBt3PUu7AyLStx67tXkupIZf/e23n4vkepLKvG5Xb5M+AAA0N9XLj8IUffOMyR117gzPkTtHW2RqwdYWFVJlNjs3Q3Da9+sIpbIpKJd5fof2eM+ci3NmPMN40xB4wxB4qK4m878KtXrwLgdDqprKxc0M87HHqvN+F2zZJbVh7WcSNhe+1m/+3rbW1xkfWue+0oHVcvc/ODjzDU1cEP/usfcfboL6h77WispxZzSRF4B2a8FwfeaQ4HKTYbs+NjzBpo++rXYjFFpdQKVqrxnhrzPpe6QQPvOVmZ2dyx/y4efehX2bt7HwV5hcy6Z/1B+IxrhonJcZpbG/jg9HEOv/Iz3j99nN7+7rDOI788E4tNaD7XH9ZxVfSIiB1v0P0DY8zzsZ7PWnV1dTE66v2ukJKSQk1NTdjP0XHVW3qaUxr/gXdpcRG52d4ad7fbTd2VqzGe0bz88kpufuARhnu6OP/aUTxx0KEp1pIn8J7r471ER4Ss9DQsmVmIzcrg9fi4TKOUmrdSjffUmAtbimXBtu0bWaojla21O7j/7of41MOH2L/3dsqKy7FZrQuCcOPx0N7Zylvvvcaxd19lYCg8gbLNbqFyRz7X6/q1NC8Bibdm6dvAFWPM/4z1fNaqu7ub1157zX9/8+bNEdk4p/PKRTILCrE7UsM+diTs2LqFWZebWZeb9q6umPf1DlS6ZRu77n2QgbZWLrzxMu6AREoySorA2+PxYFwrBN5p6SCCJSeXft06Xqm4s2Kpydjshi0zWY0jxcGmqs3cdfu9PP6Jz3DvwQfYsXUXudl5C4Lwjp423jh+NGw9wWtvKWS0b4qhrskw/BYqyu4BPgt8XETO+X4+FetJBaOzs5MXX3yRKV8phcViYefOnWE/z8zUJD1NDeSUxnd9d6DC/Hyqysv89z+8cInpONoUsHLnTey46156W5o58uW/TurgOyk+rTweD7iXLjWBgAWWBfmM3GjDPTyMNUK7YCml1m61jHdeeVq0pxR3LBYLRYUlFBWWcNOOvYyMDtPc2khr23U8xtt3u77xMj193dx754OkOtb//2zTngLeAq7X9ZFfnhGm30BFgzHmHSCh+m6OjY0xOTnJiy++6H8sNTWVRx55hMwI7CjZduE8Hrebgqrwl7BE0k3bd9DV2wcYJqYmOXn2HB+74/a4abNaffMtGGO49sE7uP/GzeN/+J+xpWycblTBCirjLSKPiki9iDSKyOeXeF5E5Mu+5+tEZP9qrxWRPxWRjqW+cYvIF3zH14vIJ0P9JRfUeNuWCLzTvR8ctuISRt0uJt5/P9RTKqXCaLmMt3PChWvak7QZ75XkZOdy654DPHz/Y5QUlvqz370D3bzxzitMTq5/6/fWiwNk5ju4/E5sN/VRycEYw1tvvYXT6cTp9LbQfPjhhyMSdANcP3uKtKxssgqLIzJ+pKSmOti/52b//Z7+fi7EUb03QM2efXz8956m6fQH/Pyv/owZZ+wXgkbbqoG3iFiBrwKPAbuBJ5dotv8YsM338xTw9SBf+zfGmH2+nyO+1+wGngBuAh4FvuYbZ92MMeDLmFmtH/2Azk7zZn6suTmMWW2Mv/NOKKdTSoXZcoH3aJ/3TXujL6wMRWZGJnffcR97d+/zZ74mJsZ4891XmArhQ6+wMpPRfieTo/FzOVttTJ2dnXR3zy8Svv/++8nKyorIudyzs7RdOEfNvtsQS+JV45aXlLC5psZf732poZHm1huxntYCt37ycR79gz+i7VIdP/2z/8rk6EispxRVwfyrugNoNMY0G2NmgGeBQ4uOOQR8z3h9AOSKSFmQr13sEPCsMWbaGHMdaPSNs24ejwfP3Af3kqUmGVgsAmJhOj+Pwbfe1kVDSsWRmYBaRbt9vl/3SK83+5WWtfF6eIeTiLC1dgcH99+NRbxv+5NTE7x78tiCLzVrUVDhzTa2XNDuJiqy2gLWXu3YsYPy8sh1Gumsv8Ssc4pNt94esXNE2s6tWykrns/Wn710ie7e+FlsCXDT/Q9x6D/+Mf03Wnn2//1PDHV1xHpKURNM4F0BBK44bOeju1wtd8xqr/2crzTlOyKSt4bzram5vzEGZr29fq32j35AWy0WCrKyvbeLiunu6mL6WsOKYyqlome5wHu014lYwJGRHB1NQlVeWsmdBz7mz3wPDQ9w6tz6Susy8xw40m1cP6+Bt4qsGzfmM7a1tbUrHBm6lrOnsTtSKd91U0TPE0kiwm237CUn2xvXGGN4/8MPGRyOj977da8dpe61o0wMDfGb/88XmRof54d/8h9pu3wh1lOLimAC76Wq8heng5c7ZqXXfh3YAuwDuoAvreF8a2ru7+1q4qvxXiLwBijM8fbAtBcXM+ByMX7s2IpjKpWIor1eI1wCA++UgMU4I31OUjNtcbN4KBGUFpexa8fN/prv5hsNNLVcW/M4IkJhZSbtVwaZndHevCoy+vv7mZjwrkdISUmhpKQkYudyu1w0nniPnNJyrr3/bsTOEw02q5W79u8nLdXbDtHtdvPuqdOMTax/bUckDLS1ctunDmG12fjpn/1XPnj+x3g8G/v9JJjAux2oCrhfCSxeUbPcMcu+1hjTY4xxG2M8wLeYLycJ5nxrYozBzHgz3hb70rWghb5vhpKaynBuDsM//Wkop1Qq7kR7vUY4LZfxHhvwBt5qbTbXbGVzzRb//XMXzzAyOrTmcQoqM3HNemi7PBjO6Snl1xqwm3RlZSWWCNZdt54/w6xziuIt2yN2jmhKTXVw94HbQCzMutyMT05x/MRJJqecsZ7aAunZOdx+6Dcp3ryVd3/8f/nJf/sCA+3xVZceTsH8Cz4FbBORWhFJwbvw8fCiYw4Dv+PLlt0JjBhjulZ6ra8GfM6vAxcDxnpCRBwiUos3ADi5zt8P8GW8Z2cQuw2RpX/lIl/GG2Asv4DptjZccdSAXqkwiPZ6jbBZKuNtPIbxgWkcGRp4r8eeXbeSk+V93/N43Jz48N019/jOLUknJc1GS52Wm6jIaGlp8d+urq6O6Lnqj79JSnoGeeWVET1PNGVlZnLn/v1YLd5yvMmpKY6fPLngPTUe2FMc7HnwE9x0/0P0XG/iH/7j53jn2e/hirN5hsOqgbcxxgV8DngZuAL8xBhzSUSeFpGnfYcdAZrxLoT8FvAHK73W95q/EpELIlIHPAj8ke81l4CfAJeBo8AzxpiQrjsYYzCzLsRuX/aSdJrDQZavuwklxQy73Yy9+WYop1Uq3kR7vcYCa1mXsdisb40GzAfek6OzuF1GM97rZLVauf3Wu7D6Mogjo8Ncrq9b0xgWi1BzcwEtF/rxeHRBugovp9PJwMAA4O1TX1ERuQ1txgb6aLtUR8nWHQnZzWQlBXm5HNw/39VobHyc906HZzOtcBIRyrfv4p7f+m1Kt27nxM9/wrc+93sc/+F3Yz21sArqX5cx5ogxZrsxZosx5ou+x75hjPmG77Yxxjzje36PMeb0Sq/1Pf5Z37F7jTGf9mXI5577ou/4HcaYl0L9Jb19vGfBnoJlhVrQuay3NSuLoYwMxl5+JdRTKxVPor1eY+HBa1iXseh1CwLvuVKTsQHv5VLNeK9fdlYON+28xX+/vunymreW37yviKmxWbqb4mPhlto4Ar+gFxQULFjfEW717xwDoGTrjoidI5aKCwu5be8ef5vBrr4+Tp2vi8sObilpadz8wMPsf+zTeNxuTv3ieS4f3ziJ0I31tW4ZHo8HMzOLxWZbMfDOyMhg2u1m2u1hoLKSiXffxVm/9kVHSsWpaK/XCIvF9d3+rM3ANIBmvEO0ZdM2cnJymXHNMD07zamz76+pxeDEyDRiEU6+2BK5SaqkFBh4FxYWRuw8s04nl4+9RtXNt5CaGZn+4PGgsqyMm3bMf7Fo7+qiLs422AlUUFnNwV//J+SWlPHSV77EqV88H+sphUXyBN6uWUhZvtQEoCRv/gp5b2Eh7rQ0Br79d9GYolLREO31GmGxVJkJwPiAN+BzpGsrwVCICPv33o7Nt7nY2PgIF66cC/r1NruF/PIM+tvG4jJ7phLX4ox3pFx68xWcY6Psf/wzETtHvNhWu4nNNfO18g3Xr3Ot+XrsJrSKlNQ09j/2aXbcdS9vf/87XHgz8SsRkiLwnuvjLTY7K3Udy83IICs9HQC31YrzkYcZ/eURnJcvR2mmSkVOtNdrhMtyrQTHBqbJzEtBLNpKMFQZ6Zns3b3Pf7/x+lW6e4NvJlVUlcn0pIu+G2ORmJ5KUtHIeM9MTXL+6C+o2rOPki3bInKOeLNn507KAtoy1l25QlNA95h4Y7Faqdx1MwWV1bzyf/6WN/7+/8R6SiFJmsDbMzOLJcW+bFeTOdUBtacjd9+NrbiYG//yXzHd3BzpaSoVcdFerxEOywfeTjILHOE8VVLbVL2FsuL5HQFPnfv/2bvv8Lju89Dz33caBr03orODpNjFIlqWZNlRcZGdOFk5vms78fMoWtu7ycbZ2Fnn3ps8d9Psm9wkN3G0juO1HceRHdmOZUeyKtXYxE6xEyBBEgSIXmYwmP7bP2YwGELomAbg/TzPPDznzCnvHOAcvvjN77y/Q/h8sys7VlqTBwKtJ7QSlEqMkZERPB4PEOliVlRUlJTjvPPy8/hG3Ox87ONJ2X8mEhF2bL6LgvyCWJ/vt0+dyfjke8v7HyG/pJR39r/IUPftdIc0b8si8Y51NbHZJ31CLF5t3F/Vl196iYJf/mWMz0f75z5POHoTUEqlznQt3gWlznSEtGRt23w3DkfknHq9oxw7fXhW29mzrBRX5tByolu7m6iEmNjNJBmDZA13d3HquZ/StGM35Y2rZt5gCbFZrezdsZ3iuFLKJ8+e48KVljRGNT2r3c7m9z8CBp79yz8j4PelO6R5WRaJd6yrid02Y/H90oICcrIirWhef4A+m43Cj38c//XrdP3Zn6ciXKVUnMkGz/F5gvhHQ+Rri3dCObOcbN64JTaqZVv7VVquXZrVtuUN+Qz3jGp3E5UQye7fbYzhzX/+JharjbKm1Zx/Yz/n31g6lTNmw2azsXfnDoqiAwgCnLt8mcMnThKYwwPWqZRTUMimBz5Ad1srP/nzP+bMy79Id0hztiwS73A4TDgQROyOGVu8RYTauO4mrZ2dZK1cSe6+fQz+27/R9RdfTW6wSqk7TNbiPVZKULuaJF5VxQpWN42P3Hf63IlZjWpZXpePxSK0HO9OZnhqmUhm/+7zb+zn9W9/g1sXzlK3eTtZObkJ3f9i4rDbec+uu6koLY11O7l2s51XDxxkYGgo3eFNqry+kfpNW7h57gw9N9rSHc6cLYvEO9Li7UfsdiyzeBCrKe6hg2tdXXj9fvLuvx9LQQGuF1/Ur1KVSqFJE+/eyFeM+WWaeCfDxnWbKSqI9KkdG9VyphKD9iwrtc0ltBzX7iZq4W7evInX68Xr9SY88fZ5Rmg9epD88kqq1zYndN+Lkc1mY8+O7TTFjQzqcrvZf+AQF1taCYfDaYxucqvv3kteSSnn33gFz9DiGkNgWSTekSHjg5Eh42ds84aywkJKCyK1PEPhMC2dHYjdTt773kfg1i1czy94TB+l1CxNm3iXaOKdDGOjWtqskVKNQ8ODsyoxuGZnBa4+L11tw8kOUS1hHo8Hny9yjdvtdvLzE1db2xhDy8E3MKEQa/fdv+RGqZwvi8XClg3NbL/rrtjw8mET5uylS7zy1gF6+/vTHOGdrDYbdz3wSwR8Pl7+p68vqj/2l8VvXCTxDiB2O9PWE4yztqY2Nn2p/RbGGLK3bMFWUUHP338dk2FDrSq1VE1Wx3u410tOkQObQ2t4J0t+XgF3NW+Lzbdcu0hXz/QFa5q2lmO1Wbjydleyw1MLICLfEpFuEUlozf1EGRsmHqC4uDihD1ZeenM/Ax03adyxm+yCwpk3WGbqa1bwwL695ObkxrqeDLlcvHboMIdPnMQzOrtKR6mQV1LKqh27uXLkIJcOvZnucGZteSTe0RYzsdunHbkyXn1FBVgt+EIhet1urnd3IxYLuffdh7+1FdeLi7+Iu1KLwWQt3sO9Xgq0m0nSNTWsorSkLPaw5dGThwgE/FOu33Ksi5IVOVw42EkolHlfT6uYbwMPpzuIqcQn3iUlJQnbr2dokMPPfJ+Cymqq121M2H6XmrzcXN67Zzcb163DarHe0ff7P155ldbr1zOmhblh8zaqVq/llW89hWc4M/ukT7QsEu+QN/IX2lwSb5vVypoV4zVtT169SjgcxrlhA47Vq+j9+j9gMrDfk1JLzWSJ9+DtUcLhMJeOJLRkuJrEts13k+WI/JEz6vVw8p2j065f2VRIwBfi5vnM+mpajTPGvAFk7A8oWYn3wae/S9DvY/Wee5NSnnApsVgsrGlq5MF791FTVRVbHgyFOHn2HK8fPoJ7ZCR9AUZZLBYeevK38XtGePP73053OLOyPBLvaF8x7NMPGT/R+rp6HLbIMMpDIyO03r6NWCxkb9mK78oV3PuXV+khpdJhYuLt8wQJ+sM482xpjGr5cGY52XbXztj89fZr3Oq8OeX6JdW52LKsXD6yeAe4UCAiT4jIMRE5Fl9hJBWSkXgfePqfuXr0ELUbt5BTmJzBeJainOxs7t66hffsupvc6MjeAL39/bz05ltcvX4jjdFFlNU1sOODH+Xs/pe4delCusOZ0awSbxF5WEQuiUiLiHx5kvdFRP42+v4ZEdk+07Yi8jURuRhd/yciUhRd3igioyJyKvp6aqEfMhxNvC22uSXeWXY7zXFP+Z6+epVQOIxz40asJSX0/r/fyJivW5RaqibW8R7ujXyDpYl36qyoqqW+piE2f/zMkSlHtbRYhYr6fK6e7sU/mpm1gNXMjDHfMMbsNMbsLI8rsZtsoVCIobgydokYsTIcDtN2/AhZefnUbtq64P0tR2UlJbxv3z2saWoiEAwTCIbw+vycOHuWg8eO3/EsTqqdefkX5JWU4szN4+d//Recful5zrz8i4yt8T1j4i0iVuDvgUeADcAnRGTDhNUeAdZEX08A/zCLbV8CNhljNgOXgT+I21+rMWZr9PXkfD/cmFgfb8fsu5qMWVdbS5YjMmiH2+vlUns7YrWSu28f3jNn8Bw6tNDwlFLTmPhw5XCPJt7psHnDdpzObAB8Pi+nzh6fct2qlQWEAmGuHNOHLNXcDAwMxBq08vPzY4NmLUTL4bcYGeijcdvdWKz6QPZ8Wa1WNq5by317d5Oflxdb3tHVxasHD+FKY9cTm93Bmt334O7vpetq5o6+CbNr8d4FtBhjrhpj/MDTwGMT1nkM+K6JOAwUiUj1dNsaY140xow1hxwGakmSkHe8q8lsq5qMsdts3NXQGJs/03YNfzBI9tat2CorIxVOtNVbqaSZ2NVkrJRglibeKeVwONjUvDn2oGXL9Ut03G6fdN38UielNbmcf6sjxVGqxS7R3UyCAT/H/v3fyCsto2yZDQufLMWFhdy3dw8rG+pjD172Dw6x/8BBevrS9+hA5co15BaXcPXE2xn9DN5sEu8aIL5DX3t02WzWmc22AL8JxBfHbhKRkyLyuojcO4sYpxX2j3U1sc25xRtgdU0N+dmRlh6vP8DZ622IzUbpbz3B6PHjjBw8uNAQlVJTmJh4D/d6ycq1zmowLJVYVRUraKhtjM0fP3Nk0ionIkLzvhV0X3fRc1OHkM80IvKvwCFgnYi0i8hn0wbjqPsAACAASURBVB3TmEQn3pfeeg13fy+N23bpA5UJZLNa2dzczI7Nm7FEa6H7AwHePPI2be2T/0GebCLCqh27GBkc4HbrlbTEMBuzSbwn+02d2MQ71TozbisiXwGCwL9EF3UC9caYbcDvAt8XkYJ3BTWHBz9CY/9x2x1zbfAGwGqxsK6hHl8ohC8U4sTVawx7PGDAUljI7f/yXzP6ryulFqtgMBgbNc1isWC1WnH1erWbSRrd1bwNZ7TKidc7yqlzk3c5Wbe7CqvNwgVt9c44xphPGGOqjTF2Y0ytMeaf0h3TmN7e3tj0QhPvUDDI6ed/RuWqtRRWT9bmpxaqbkU19+7eRVZW5J4QNmGOnT7DmfMX0tIboKJxFXklZVw9eTRjeyPMJvFuB+ri5muBiXfSqdaZdlsR+TTwIeCTJnqGjDE+Y0xfdPo40AqsnRjUXB78MHf08Z5fIZeGikpK8sdHszx65TJis5H3wAMEbt1i8Jln5rVfpdTUJrZ2G2MY7tHEO50cDgdbNu2IzbfdaKWz69a71ms90U1pbS7nD3TqQ5ZqVsLh8B2J90KHin/9O/+Iu7+X0saV2tqdRMWFhdy3ZzfZzuxY15NzV1o4eOwEgWBqr30RoWHzVjxDg/Tfmrr6UjrNJgs9CqwRkSYRcQCPA89OWOdZ4FPR6iZ7gCFjTOd024rIw8CXgI8YYzxjOxKR8uhDmYjISiIPbF5dyIcM+sa7msz30hMR7l47nv/f7OnlZk8P2Vu34mhspPtr/51Ad/dCwlRKTTDxwcpRV4CAT0sJpltNdR211eMVn46fPoI/2qUvXu26YkLBMBcOar11NbPBwUGC0UQtJyeHnLjydXMVDodpP3ua3JIyilfUzbyBWpCc7Gzeu2c3VXENoZ3dXbx28BCj3tSOdlnZtBq7M5ub599J6XFna8bEO/oA5BeAF4ALwA+NMedE5EkRGas48hyR5LgF+Efgc9NtG93m74B84KUJZQPfC5wRkdPAM8CTxpgF9dYPx7qazK2c4ERlhYWsjhtU5+3LlwmFwxR85CMYn4+uP/uzhYSplJpgYot3X4cbgOyChVc6UAuzZdN2HA4nEBlY59jpI+9ap6Asm4LybE6/epNwODO/9lWZozuu8WqhJQyvHTuC1zVE3V3btLU7RWw2G7u3b2N1U2Ns2dhw86kcbMdqs1G7fgM9N9oY6s68ykqz6ndhjHnOGLPWGLPKGPMn0WVPGWOeik4bY8zno+/fZYw5Nt220eWrjTF1E8sGGmN+ZIzZaIzZYozZboz52UI/pPFHWs1kgYk3wJaVK3HYI61trtFR3mlrw1ZaStn/9iSu53+B+/XXFxquUipqYuLdezOSeOcUaeKdblmOLDY13xWrcnKr8watbZfftV7d+mJcfV6unUrtICxq8Yl/Xmsh3UyMMZx6/qdkFxRSWt+YgMjUbIkIm9atY9umjbF634PDLl47dIRhtztlcdQ0bwLg9MvPz7Bm6i2PkSujX4GK3R57+na+nA4H21atjs2/c72NoZERLAWFWMvL6fjSlwmn+GsVpZaqdyXe7S5yCu3Ys7QWbyZYUVXLqobx++HJs8fo7buzy11ZbR4FZU6O/+J6xj7spDJDolq828+dpu/mdWo3bdXW7jRpqK1l9/atsZzL6/Py+qEjDA2npspRdl4+5fWNnHvtZUIp7mc+k2WSePsRmxWxWObdxzvequpq8nNz8YVCjAaCvH72LGGLhYIPfpDQ4CB93/pWAo6ilJqsxbtkxfz7farE29S8laKCyOiCJhzmwNuvM+waH3lQLMLdH2yi54aLa6d6p9qNWuaCwSD9/eO9SktLS+e9r1PP/ZTc4hLKm1bPvLJKmuqKCvbu2I7VEmko8fl9vHboMF29qbkP1KzbgGdokKsnj6bkeLO1LBLvsN8PtshX04n461dE2LVuXayOcL/LxdHLl8lqasK5cSN93/hH/GmqY6nUUhKfeAsWBm57KNbEO6NYrVZ2bd9HVlakv7c/4OPNw6/iHhlv2Vq7q5KiyhyO/OwqRvt6q0n09fXFvhEpKirC4XDMaz+3Lpyl8/JFtjz0YR2lMgOUl5Zyz907MAYCwRAer5cDbx/jYktrrFRsspTWNZBbVMzZ/S8l9ThztWwSb4kOOzufAXQmU5yfz44141VOLra3c/X2bfIfegix2bj1xS/GyhgqpeYnPvH2DocwYaMt3hkoLzePnVt3EzYh/EE/g64BXn3rBQaHBgC4cLCT6tWF9HeMcOnt22mOVmWirq7xh+Dm283EGMPRn/yA3JJS1t/3vkSFphaotLiY9+y6s9b32UuX2H/wEH0Dg0k7rsViobyhiWsnjnL02R8l7ThztSwS71AggDgS1+I9Zm1NDfUVFbH5gxcu4LbZKHj0Ubynz3Djt56cZmul1ExcrvFWU1+094Im3pmpuKiE3TvegzXap9Pn87L/wIuxYeUrGvLJL3Vy8Met+LSu97Licrlir6lcvTpeNbi6unpex3nr+9+m+2oL1es2cvnQgXntQyVHUWEB9+/dQ1HB+HiIA0ND7D94kCMnTiat6smKtc0YY+i4fDEp+5+PJZ94G2MwgQCSwK4m8XavX09BtNZoMBTi1TNnYM0acnbtwnPoEMPPPZfQ4ym1nMT3+Qy5bDicVvJKstIYkZpOZXkV+3bfjz16vw0GAxx4+zXOXToDwNq7Kxl1+Xn7ZwsamkEtMS6XK/ZgpcVioampac77CAUCtJ14G2d+IZWr3jXmnsoA2U4n792zmzUrVxIMhWOD7dzs7OTF19/k2OkzCW8Bzy0qprh6BbcuniMcDiV03/O1LBJv/AHEbltwRZPJOGw2djU3EzQGXyhE9/Awr5w+jfPBB7HX19PxlT/Ee+lSwo+r1FJnjGFwcPwm7Bu0Ulqbh1i0SkEmKysp571734fNbouVGjx17hivHXgJS3aQTffW8M7+dm5fHZp5Z2pZaG1tjU3X1tbGuiTMxfFnn2F0aJCVu/YiSfi/XiWGxWJh7cqVvP/e97CiqjK2PGzCtLW3s//gQX720iscPnGSiy2tdHZ34x4ZWVBFpNrmuxh1DdN2+kQiPsKCLfnfznA4TDgYQOyOpJUVKsrLY09zc2y+Z2iIl955B/tjj2EtKKD9c58nODCQlGMrtVS5XK7YKHZ2WxaDHX4qGwtm2EplgsKCIh54zy9RXjreFa+3v5sX9v+crMYhsgttvPT/ncfv1S4ny8Ho6Cjnz5/n8uXLkz5Q19raitfrxev1UllZOW2XlMl0X23h9C9+RuXqdZTU1M+8gUq73Jwcdm3dyr27d5Ofnx9r/YZI9ZP2zk7OXrrEgaPH+MVrr/PvL7zI/oOHOH3+Ardud+GbwzN0FY0rcWTncPrFzOiBsOTHXTbGQCCA5GUlpcV7TGNlJf5gkKPR1u2eoSGeP3+efQ88gO2ZZ2j/3Oep/6dvYlnAELhKLSd3dDNx2wgFwzTcNf9BNVRqZTmy2LfrPi61nOdiy3kAQqEgv/jJfkI+C8HOIl79l2we+s0tWmt5iTLGcOTIEY4dOxZLuNvb23nwwQfJy8sD4Pbt2/T19QGRCjn19XNLnId7unjpqb8mt7iUpp17E/sBVNKVFhfx3t276RsYpO3mTW739BAIBCZdt29ggL6BAa5cuwZEkveCvDzyc3MpKMintLiY/Nzcd21nsVqpWb+RqyePMdTdRWFF5bvWSaUln3iHw2GMP5CQUStnsramBmMMx69cwRiDb3SUVzs72HzvvVS9/jrtX/gCNX/zN1jz85Mah1JLwUDct0SjPYIz20b16kI8ntQNPawWxmKx0Lx2E5Xl1Zw6e4zB4UjXIUeWgwHbDb73z5fpdLXwoU/et+AhwlXmOXfuHGfOnLmjlburq4tnnnmGvXv3UlZWxosvvhh7r66ubk5lBId7uvn51/4fgj4fH/ziV+huu5bQ+FXqlBYXUVpchDGGYbebgcEhBoeHcbnduNxu/BOScbvNyojHw4jHQ2fc8sqyMtatWkVF2Z114GvXb6Tt9HGOP/fvvO8zv5WCTzS1pZ94+3yEBgbIWr8uqS3eY9bV1lKYm8tb587i8wcIhw2nwkGafu3XaPrhD/A99hgr/vRPyd2zJ+mxKLWYjSXeJmwY7jCs21uC1brke8ctSSXFpTzwnl/i2o1WLrWcZ9Q7SkGlHWMML/3kIJ0DN1h7Vz1NTU00NDRQWlqqreCLnN/v5/jx47H54uLi2DMbfr+f119//Y71s7Ky2Llz56z2bYzhysE3OPj0d0GED33xK5TVN2rivQSICIX5+RROaKD0en0MDg/TO9BPX/8Ag8PDk/b77urtpau3l/WrV7Fx7drYfcSZl8emBz7AmZeeZ8ejH01rq/eST7w9ly9jQiHsK1ak7EZeVVzMozvv5s2zZ3FFS+Rcu3WLzm3baDx/Hv+nP0PRhz9M1R9+BWtRUUpiUmqxGetq4ur3YgmUajeTRU5EWNmwmobaJm7eaqO17QrldWHOvXGbc2/cQoj8zI8fP47dbqe0tJT8/Hxyc3NxOBxkZ2eTk5NDfn4+BQUFKWlIUfN34sQJfD4fAHl5eXz4wx+mr6+Po0ePMjw8fMe6FouF+++/n/xZfBvc1XqFt3/8NJ2XzlO1Zh33feZJCiurkvIZVOZwOrOocpZTVRH5ZiwUCuEeGcHt8eByu+kfHKK7ty+2/sWWVgYGh9izfRv26Dgue3/lE1x4Yz+Hnvk+D3/u/0zL54BlkHiPno/0LUxl4g2Q43Ty/u3bOX7lCldu3QLAl+Xk4rbtXLx2jcpnnmH1q6+y4X/8FXn33ZeyuJRaDMLhcKx1rOemm5rsAho2zX8IaZU5rFYrjfWraKxfRW9fN1l7r3LsjfOcff0WTdtLqV9fRiAQ4Pbt29y+PflgOxaLhaKiIioqKigvL6e8vJzi4mKsOlJhRhgaGuLs2bOx+Z07d2K1WqmoqOChhx7ixIkTsbrdBQUFbN26lcrKqVsgz7+xH8/gAG0nj9J/sw27M5tVu/ZRtW4Dty5d4NalC0n/TCqzWK1WCgsKKIyrCz7i8XD63Hm6o88MtN/uYv/Bw9yzczt5ubnkl5ax9eEPceznP2H7o49R0bgyLbEv+cTbe+Ei4nRiLS6OtJCkcLhiq8XCrnXrqCgq4ujlS/gDQbBaYfVqOsrLaD1xkhOf/E+s2bWL5k9/iqoPfEAfvlQKGB4eJhwO4x0J0HvNy/0frcaZa093WCrBykorKCutYPOGbZw51MrFs620DPdStT4bm2P6JNrj8dDf38/Fi+MDY+Tl5eF0OrHZbBhjCIfDhEIhQqFQ5HkfYxARrFYrdrudrKwscnJyyMvLIz8/n8LCQgoLC8nOzk72R1/SDh48GOvXXVFRcUddbpvNxq5du9i1a9es9uV1u2k98hadly9gtdmo37qTmua7sNr1fqDulJuTw96dO7jY0sKl1sgfdsNuFy+9+Rab1q1jUzjMro/+Khfe3M/P/urP+PU//Suy81L/zN2sEm8ReRj4G8AKfNMY8+cT3pfo+48CHuAzxpgT020rIiXAD4BGoA34NWPMQPS9PwA+C4SA/8MY88J8P6Dn/HnsK6oRkWjinfoC6o2VlawoLeXKrVtcbm/H4/NBXj55993H0LVrHDhxnLcOHiTXZmPF6tVUb9lCzb57qHnve8mt0q/QVOKk+lqer7GWzqsnu8m257Hv46sXsjuV4Zw5WXzyy+/j9IvrOPtqJ6E2Hw0786hY4yBEMFZqbmRkBJfLxUi0C5/X6x3fh9OJ2+3G7XYvOB6Hw0FhYSH5+fnk5eWRm5sbe+Xl5ZGTk7Oo+6DPdB9YiOvXr3Pz5s3Y/L59++a1n1AwyIXXXub4z36EzzNC9dpm6rfswO7UP4rU1ESE5jVryM3J5WT0W5dQKMTp8+e58od/wOrGRlbtuZezLz3Hf/zNV/nYl/4LVltq/4ibMfEWESvw98AHgHbgqIg8a4w5H7faI8Ca6Gs38A/A7hm2/TLwijHmz0Xky9H5L4nIBuBxYCOwAnhZRNYaY+acMRu/H++VK9ib1499lrnuImEcNhsbGxrYUF/P7YF+rtzqoL23F9PYiKOhgXBfH8GBfq72dNPyo2cwP/gBALb8fHKqqiioraGoqYn8+gZyq6twFBWRlZuLPSeHLKcTh8OBzRYZJGjsZbVasVgssT86xqYnvuLPTfwytbSk+lqeb5y3b9/mwIEDtF/qp6vNxUMf3UZBmf5nu9RdOXqbnGIrm95fSc+1UW4eHqbzRIi6jUXUbaylYn0eOQWRihd+v5++vj56e3tjr0Qk3BBJ4P1+Pz09PfT09Ey6joiQk5Nzxys7Oxun00lWVhZ2ux2Hw4Hdbo9N22y22D06nWZ5H5gzv99Pa2srhw4dii1rbm6mvLx8TnW5/aMeLh94g3deeR5XTzc1zZuoWL2e3OKShYSnlpn6mhXk5eZw8uw5XNF7g2d0lDMXLmARC9Vbd3PstRfo+d3P8dHf/n2qV61JWWyzafHeBbQYY64CiMjTwGNA/EX6GPBdE3nE9LCIFIlINZEWsKm2fQy4P7r9d4DXiPxn/RjwtDHGB1wTkZZoDONX8yz5WloIBwLYV6wgevy57iLhRITqklKqS0pxj47S3ttLR18fPTYbwbKyyA8kHCY8OIAZGiI87MLV04Pr2jXa9782+T6zHFgLCrEW5CM5uViyHIjdATYbYrUiVktk2m4HiwXik22xjI8EaLGAxXrnyIAikX1E/7MwxoAxEA4jgIn+iwgWhwOx2SKvSc51bNv4J5EtFsRiQSxWIPKeCYchuopYJBJv9CVj08bc8URzbPlsTYyD6CHHjivRz26xYC0uweKcfiS1iU9XJ/J3bbIntysrK3nkkUfmuqtUX8uz5h7w4R7wcruzm5/++7N0XutnqHuUmqZyfvmz75/r51SLWE6hnYatdsoanHRdHaHj0jDXTvbH3isod5JbnEV2vh1HdhkrnJXUNVowEmbUO0IwGCQUCkVudVYLNqsNm92KzWpFbJbI7YMQwWAIf8DLqGeUEc8ILrcLt9vFQNcQwVCI2BUs0Rcg40vJzfczkjv30pYTG0LSYDb3gWm5+noZdUUekHz76DGuXm/DO+rFmDDhYIhwKIhVhOz6FZx84ed43O7Y/d9is2G12rDYbIjFggmH8XtGGBkcoKetle6rLYRDIfLLKtjwvocprqnLiP+71eJTUlTE/Xv30NJ2nda2triShCGkqJS8rXt5681XefXxX6G4spqKxiZyCorIyslh65YtbNq4EbFYyMrJTWgVlNkk3jXAzbj5diItYTOtUzPDtpXGmE4AY0yniIwNcVYDHJ5kX3MW7OlBCgpiiXe6WxomysvOZn1dHevr6giHw/S5XPS7XAy4XAwUFjI0MkIoVv/UYEa9mJERjM8LgWCk20wojAn4CXt9hPr6ofM2hIKYUOS9icmlmruST3+KrFWr0h3GHaYaYGAGqb6WZ+3Uyzc4/cpNzt04woC7h+w8O5vvbeBzv/8ZCgrz5ro7tQTkFjtYucOBCRvcA37cfX48gwFc/T76bnkIekMJvL1lRV9l5AH+gBdvwIMvMBp5Bb34A158AQ++4CiBoJ/yhlxW3z332uNOpzPW7zxNZnMfmNaRn/yA0y89D8C1nn66hu/8tsHpsLG2oozXr5yZ9T6tDgclK+q46wOPsnLnbnpu3JhLSEpNymq1sm7VSlY1NtDe0cn19nYGhoYAqGhsoqjq17l57h162q5x6fDB2Ha33niJU8WFAKy+ew+P/d4fJiym2STek/2pOfF2N9U6s9l2PsdDRJ4AnojOukXk0pR7PHE8fq4M6J0hhlTTmGYnPTF99avTvZup56lhkuWpvpbv3PFcrlmA5+H//tPfmeydTDznM1mMMcPijDs1Mb8JfC9he5vqmk2GxP8fmzqZ+vuYiXFpTLM3fVw//A/4v/7zxKXzvl5nk3i3A3Vx87VAxyzXcUyzbZeIVEdbyKqB7jkcD2PMN4BvzCL+O4jIMWPM7Kr0p4jGNDsa0+xEY2qc5K1UX8t3mO81O1EmnvOZLMaYYXHGvYhjbkzR4ZL6f2wyZerPNhPj0phmL9VxzabvxVFgjYg0iYiDyIOPz05Y51ngUxKxBxiKfvU83bbPAp+OTn8a+Gnc8sdFJEtEmog85PX2PD+fUmpcqq9lpVTmmc19QCmVJDO2eBtjgiLyBeAFIqWHvmWMOSciT0bffwp4jkj5sRYiJch+Y7pto7v+c+CHIvJZ4Abwq9FtzonID4k86BEEPj+fiiZKqTul+lpWSmWeGa5lpVSSzaqOtzHmOSL/Iccveypu2gCfn+220eV9wINTbPMnwJ/MJrZ5yKivzqI0ptnRmGZnyphSfS0nSSae85ksxphhccatMc9gqmt5EcjUn20mxqUxzV5K45LJSpUppZRSSimlEiuz6usppZRSSim1RC2rxFtEHhaRSyLSEh1hL1XHrROR/SJyQUTOichvR5f/kYjcEpFT0dejcdv8QTTOSyLyUJLiahORd6LHPhZdViIiL4nIlei/xamKSUTWxZ2LUyIyLCK/k+rzJCLfEpFuETkbt2zO50VEdkTPb4uI/K3I/EeBmCKmr4nIRRE5IyI/EZGi6PJGERmNO19PxW2TsJjSIV3X8FzN9dpKU4wJ+T3PgJjTeh+dRcxT3f8z+lyn00zXuYisF5FDIuITkd/LkJg+Gb0XnxGRgyKyJUPieiwa0ykROSYi70l3THHr3S0iIRH5eLpjEpH7RWQo7j7yX5IWjImOALjUX0QeImkFVhIpjXYa2JCiY1cD26PT+cBlYAPwR8DvTbL+hmh8WUBTNG5rEuJqA8omLPsq8OXo9JeBv0hlTBN+XreJ1MpM6XkC3gtsB84u5LwQqcazl0jd3OeBRxIc0y8Btuj0X8TF1Bi/3oT9JCymVL/SeQ3PI9ZZX1tpjDEhv+cZEHNa76OziHmq+39Gn+s0/l7OeJ0DFcDdRJ4Fe9fPPk0x3QMUR6cfAY5kSFx5jHcr3gxcTHdMceu9SuRZg4+nOyYioy//PNk/M2PMsmrxjg2Ta4zxA2PD5CadMabTGHMiOu0CLjD9aJyPAU8bY3zGmGtEKkzsSn6ksWN/Jzr9HeCjaYrpQaDVGHN9mnWSEpMx5g2gf5Jjzfq8SKSedYEx5pCJXNXfjdsmITEZY140xgSjs4eJ1OOdUqJjSoO0XcMJMtXvUFok4vc8JYHGmSLmqWRKzFPd/zP6XKfRjNe5MabbGHMUmNcQvkmK6aAxZiA6O+P9OIVxuaP3e4Bc5jjwWTJiivrfgR8xxbgPaYopJZZT4j3VUNgpJSKNwDbgSHTRF6JfA30r7qvGVMVqgBdF5LhERimDCcN/E2lZSGVMYx4H/jVuPp3nCeZ+Xmqi06mIDeA3ibRgj2kSkZMi8rqI3BsXaypjSrSMuIZnaS7XVibJlOt/rtJ9f5iVCff/xXquky0TP/9cY/osd96Pk2VWcYnIx0TkIvAfRP6vSGtMIlIDfAx4itSY7c9vr4icFpHnRWRjsoJZTon3goe8XnAAInlE/sL7HWPMMPAPwCpgK9AJ/OXYqpNsnoxY9xljthP5WuzzIvLeadZN2fmTyKAOHwH+Lboo3edpOkkbYn3WAYh8hUjN+3+JLuoE6o0x24DfBb4vIgWpjClJFlP8c7m2FoNMPveZfH+ImeT+P+WqkyzLlHOdCpn4+Wcdk4g8QCTx/lJSI4oebpJl74rLGPMTY8x6It+q/LcMiOmvgS+Z1I3RMpuYTgANxpgtwP8E/j1ZwSynxHtWw+Qmi4jYidx0/8UY82MAY0yXMSZkjAkD/8j414kpidUY0xH9txv4SfT4XdEuCWNdE8a+Bkrl+XsEOGGM6YrGl9bzFDXX89LOnV81JiU2Efk08CHgk2NfJ0a/ou6LTh8n0rdtbapiSqK0XsNzMcdrK5NkwvU/Jxlyf5jWZPd/FuG5TpFM/PyziklENgPfBB4buwdnQlxjol21VolIWZpj2gk8LSJtwMeBr4tIMrvezRiTMWbYGOOOTj8H2JN1npZT4p22YXJFRIB/Ai4YY/4qbnl13GofA8ae1H8WeFxEskSkCVhD5KG4RMaUKyL5Y9NEHtQ7y9TDfyc9pjifIK6bSTrPU5w5nZfoV8cuEdkT/fl/igQPpS4iDxNpVfmIMcYTt7xcRKzR6ZXRmK6mIqYkWxRDXc/j2sokmXD9z0mG3B+mi2/S+z+L8FynSCZe5zPGJCL1wI+B/9UYczmD4lod/R1ERLYTebgwmX8UzBiTMabJGNNojGkEngE+Z4xJWgvzbGISkaq487SLSH6cnPOUqKc0F8OLyFDYl4m0AH4lhcd9D5GvNc4Ap6KvR4F/Bt6JLn8WqI7b5ivROC+RhMoTRJ7uPR19nRs7H0Ap8ApwJfpvSapiih4jJ/rLXhi3LKXniUjS30nkwZ12Il8bzvm8EPmr/mz0vb8j+mR5AmNqIdJvbex36qnour8S/ZmeJvL12YeTEVM6Xum6hucY45yvrTTFmZDf8wyIOW330VnGPNX9P6PPdZp/N991nQNPAk9Gp6uiP/9hYDA6XZDmmL4JDMT9jI9lyLn6UvQ+dAo4BLwn3TFNWPfbJLmqySzP0xcY/3/zMHBPsmLRkSuVUkoppZRKgeXU1UQppZRSSqm00cRbKaWUUkqpFNDEWymllFJKqRTQxFsppZRSSqkU0MRbKaWUUkqpFNDEWymllFJKqRTQxFsppZRSSqkU0MRbZRwR+ZiI3BQRt4hsS3c8SimllFKJoIm3QkTeIyIHRWRIRPpF5ICI3C0inxERIyJ/NWH9j0aXfztJIf134AvGmDxjzMkkHUMppZRSKqU08V7mRKQA+DnwP4ESoAb4Y8AXXaUV+F9ExBa32aeIDL2a6FjGjtFAZOhWpZRSSqklQxNvtRbAGPOvxpiQMWbUGPOiMeZM9P3bwDvAQwAiQLil0wAAIABJREFUUgLcAzw7tgMRcYrI90SkT0QGReSoiFRG32sTkffHrftHIvK96HRjtOX8syJyA3hTRNyAFTgtIq3J//hKKaWUUqmhibe6DIRE5Dsi8oiIFE+yzneJtHIDPA78lPEWcYBPA4VAHVAKPAmMziGG+4Bm4H3GmLzosi3GmFVz2IdSSimlVEbTxHuZM8YMA+8BDPCPQI+IPDvWYh31E+B+ESkkkoB/d8JuAkQS7tXRVvPj0f3O1h8ZY0aMMXNJ1pVSSimlFhVNvBXGmAvGmM8YY2qBTcAK4K/j3h8F/gP4Q6DMGHNgwi7+GXgBeFpEOkTkqyJin0MINxf2CZRSSimlMp8m3uoOxpiLwLeJJODxvgt8kUiSPXGbgDHmj40xG4j0//4Q411TRoCcuNWrJjvsAsNWSimllMp4mngvcyKyXkS+KCK10fk64BPA4Qmrvg58gEj1k4n7eEBE7hIRKzBMpOtJKPr2KeBxEbGLyE7g40n6KEoppZRSGU0Tb+UCdgNHRGSESMJ9lkjrdoyJeMUY0z/JPqqAZ4gk3ReIJOnfi773n4FVwACRMoXfT8aHUEoppZTKdGKMfsuvlFJKKaVUsmmLt1JKKaWUUimgibdSakoi8i0R6RaRs1O8LyLytyLSIiJnRGR7qmNUSkXo9apU5tPEWyk1nW8DD0/z/iPAmujrCeAfUhCTUmpy30avV6UymibeSqkpGWPeACZ7oHbMY8B3ow/fHgaKRKQ6NdEppeLp9apU5rOlO4BEKCsrM42NjekOQ6mMcfz48V5jTHkKDlXDnQMgtUeXdU5cUUSeINLKRm5u7o7169enIDylFocUXbN6vSqVAAu5XpdE4t3Y2MixY8fSHYZSGUNErqfqUJMsm7RUkjHmG8A3AHbu3Gn0mlVqXIquWb1elUqAhVyv2tVEKbUQ7UBd3Hwt0JGmWJRS09PrVak008RbKbUQzwKfilZL2AMMGWPe9bW1Uioj6PWqVJotia4mSqnkEJF/Be4HykSkHfivgB3AGPMU8BzwKNACeIDfSE+kSim9XpXKfJp4K6WmZIz5xAzvG+DzKQpHKTUNvV6VynyaeC9D7iPj3yzm7dZKUkoppZRSqaCJ9zIQn2gbYwjcchMa9GHJteNcW4yt2JnG6JRSSimllgdNvJeRsD/E6KkeArfcsWXe8/0UPbaKnO0ViExWaUoppZRSSiWCJt6LjMvlwhhDV1cXTqeTurq6mTeK8hzvItjlwbmhlKw1RYTdAXxXhxj4t8sEukYoenRlEiNXSimllFreNPFeZFwuF2+88Qbd3d0AlJWVsW3bNpqbm6fdLtDtIXg7knQ71xUDYC1wkL2lDAD3G7ewFTnJu2dFcj+AUkoppdQypYn3ItLd3c2zzz6L3++PLXO73bz55pu4XC527do16XbGGEbf6cWSYyNrdeEd74kI2VvKCI8GGHy2lUDfKPaKHEAfvFRKKaWUSiQdQGeR8Pv9vPLKK7GkW0Sw2+2x90+dOsXRo0cn3TbQ7iY87Me5sRSxvvtHLiLk3l2FJc/O6KluTCicnA+hlFJKKbWMaeK9SBw4cACXywWAw+Hggx/8II8//jhlZWV4vV68Xi+nTp2KdUGJ57/pwpJjw16TN+X+xWYhe0s54ZEg3ssDSfscSimllFLLlSbei0BLSwtXrlyJzd9zzz2Ul5djs9l48MEHqa6OdAkxxvDGG28QDo+3WIdHgwS7PdhX5M1YtcRekYO9Ng/f5QFC7kByPoxSSiml1DKliXeGGxoa4s0334zNr169mqampti8xWJh37592Gw2vF4vHR0dHDlyBPeRTtxHOhn8+VUwTNvaHS97Uxkg+K5oq7dSSimlVCJp4p3BgsEgL7/8MoFApPW5oKCAPXv2vGu9/Px8tm3bFps/f/48kZGBIdDhRpxWrMVZszqmJduGoz4f/w0XIbd/5g2UUkoppdSsaOKdocLhMC+//DJ9fX14vV78fj979uy544HKeM3NzWRlRZLrkZER2rtuYYJhArPsZhIva3URhA3uQ50zr6yUUkoppWZFE+8M9cILL3D58mW8Xi8Ae/bsobS0dMr1rVYra9asic1fvHaFYLcHQgb7itw5Hdua78BWlcvIoQ7C/tD8PoBSSimllLqDJt4Z6PDhw3c8TLl582bWrVs343bx69zsusVgRx8I2Eqcc47BuaaIsCfI4E9bcB/Rlm+llFJKqYXSxDvFvF7vHVVHJjp27BhnzpyJza9Zs4YdO3bMat8FBQWsWDE+8uTlK1ewFmZNWrt7JtZSJ5ZcG/4brjlvq5RSSiml3k1HrkyysdrbAG+//Tatra3Y7XZqampobm6mrq4OiPTpPnToEOfOnYutX19fzz333DOn461du5aOjg4whqs3rrGtecu84hYRHPUFeC/0E/ZoaUGllFJKqYXSxDtFLl++HEuqvV4vFy9epK2tjYqKCkpKSmhvb8ftdsfWX7FiBffffz8Wy9xaq+vq6rDZbAS8IYY9Llz2UXLmGbOjPh/vhX5t9VZKKaWUSgBNvFOgr6+PQ4cOTfped3f3u0abXLVqFTt37pxz0g1gs9moq6vjyqleAG54uqikfu5BA5YcO7bybPw3hjHGzKkyilJKKaWUupP28U6B06dPx/p1FxcX85GPfIT169ffkVh7vV6MMWzatIm77757Xkn3mKamJsKeIFiEtoFbC4rdUZ9PeCSord5KKaWUUgukLd5JZoyhs3O8Ksi9995LaWkpe/fuZd++fXR1deHxeAgGgzQ0NGCzLfxHUltbi80HJteGy+Omp7+X8pKyee3LXp0Hlh48p7rJaihYcGxKKaWUUsuVtngn2cDAAH5/ZATIrKysO2pxh8NhysvLaWhoYNWqVQlJugEsRqixl2HJiQy2c/lG67z3JXYL9upcRs/0YkImIfEppZRSSi1Hmngn2e3bt2PT1dXVKTlmqNvLqpJ6LNmRRP5aexvBYHDe+7PX5hEeCeBrHUxUiEoppZRSy44m3kkWn3hXVVUl9VgjJ7oYOdHF6PEuKvJKyS7MxRfw4Q8GaOu4Me/92itzEacVz6numVdWSimllFKT0sQ7yeIT78rKypQc04xEhnlftXpVbNnl6y3z3p9YhexNZYye69Mh5JVSSiml5iljE28RsYrISRH5ebpjma+BgQF8Ph8ADoeD4uLi1Bx4JAg5VlbVr4yVALzd103fUP+8dylOK8YXYvDZ+fcXV0oppZRazjI28QZ+G7iQ7iAWIr6aSVVVVcrqYJuRIJJrI8eZTX1VbWz5mUtn571PW1k2kqNDyCullFJKzVdGJt4iUgt8EPhmumNZiIGBgdh0RUVFSo5pjAFPCHIjD1ZuXN2ML+DDF/Bx8foVOnpvz7CHyYkIjrp8gt0eQsO+RIaslFJKKbUsZGTiDfw18PtAeKoVROQJETkmIsd6enpSF9kcDA0NxaYLClJUA9sbhpBBcq0AlBaVsKJivJrK2Zbz8961oy4fAM/JzDzfSimllFKZLOMSbxH5ENBtjDk+3XrGmG8YY3YaY3aWl5enKLq5GRwcL79XWFiYkmOakWjZwNzxmuB3rd4Qm77Wfh23Z2Re+7bmO7AWZzFy7HakZV0ppZRSSs1axiXewD7gIyLSBjwNvE9EvpfekOYuGAzidruBSDeNlLV4RxNviUu8K0rLqSiJ/HESNuGFtXo3FRLsGcXXOjTzymrRE5GHReSSiLSIyJcneb9QRH4mIqdF5JyI/EY64lRKReg1q1Rmy7jE2xjzB8aYWmNMI/A48Kox5j+lOaw5Gx4ejk3n5eVhsaTmVJuRIFgFsu483sbVzbHpy9db8Pq889q/ozYPS44N96GOBcWpMp+IWIG/Bx4BNgCfEJENE1b7PHDeGLMFuB/4SxFxpDRQpRSg16xSi0HGJd5LRTq6mQAwEoJc67sqqNRWrqC4oAiAYCjE+auX5rV7sVrI3VWF93wfwcH5Je9q0dgFtBhjrhpj/ES+gXpswjoGyJfIL1we0A/Mf5hUpdRC6DWrVIbL6MTbGPOaMeZD6Y5jPtLyYCXjpQQnsym+1buthXB4ymdXp5W7O/Kw5sjhzhnWVItcDXAzbr49uize3wHNQAfwDvDbxphJf7EWwwPRSi1yCbtm9XpVKjkyOvFezOIT75Q9WBkyMBq648HKePXVdWRnZQPg8Y1y8/ateR3HVuwke1MZ7kOdhD2BecerMt5khecnPlX7EHAKWAFsBf5ORCb9S3MxPBCt1CKXsGtWr1elkkMT7yRJS1cTb2Q4d8mxTvq2xWJhdX1TbH4hw8gXPFiP8YdwvTm/5F0tCu1AXdx8LZFWsni/AfzYRLQA14D1KYpPKXUnvWaVynCaeCdJOrqamNFo4p09eeINsLp+ZWxAnZb2q/MuLWivyiX7rjLcBzoIjWir9xJ1FFgjIk3Rh68eB56dsM4N4EEAEakE1gFXUxqlUmqMXrNKZThNvJPA6/Xi80VGd7TZbOTm5qbmwNHEm2kS77ycPFaUV8Xmr9xonfNh3Ec6cR/pxFaegwmEcO2/OfNGatExxgSBLwAvABeAHxpjzonIkyLyZHS1/wbcIyLvAK8AXzLG9KYnYqWWN71mlcp8k3cGVguStgcrxxLvrKkTb4i0enf0RIaOb715lW3rN8/reNYCBzk7KnEf7CB3VxX2ipx57UdlLmPMc8BzE5Y9FTfdAfxSquNSSk1Or1mlMpu2eCdBfA3vVCbejIbAaUEskz1fM66mYgUOmx2A4RE3Pf3zb+wofLgRcVgY/FmrjmaplFJKKTUNTbyTYGzESiB13UyItnhP081kjM1mo666NjbfcnP+3fuseQ4K3t+A78og3nN9896PUkoppdRSp4l3EnR1deH1evF6veTn56fuwN7QtA9WxltZ2xibvnbr+rxreruPdIJVsBQ46P/RFcK+0Lz2o5RSSim11GninQTpaPE2YRMpJzjLxLuytILc7EifbK/fN++a3gBiEXK2lmNGgwy/cn3e+1FKKaWUWso08U6C+MQ7Ly8vJcc0rgAYEOfsnpcVkTtavS+1XVnQ8W2l2TgaC3C/eYuhF9siLeFKKaWUUipGE+8kGBkZr42dqsQ7POSPTMyyxRsi1U3GtHd34BpxT7P2zJwbSxG7hdEzvfqgpVJKKaXUBJp4J5jH4yEUivRzdjgcOByOlBx3LPGebR9viNT0rq1YEZtfaKu3xWHF2VxKsHeUQMf8BuZRSimllFqqNPFOsHR0M4H5tXgD1K2ojY1keeVGa+yPhvlyNBZgKXAwerYXE9AHLZVSSimlxmjinWDpS7wD4LAg1ulreE9UU1FNjjMbgFGflwtXLy0oDrEI2ZvLMJ4grgMdC9qXUkoppdRSool3grlcrth0KhNvM+yfc2s3gMViYdPqDbH505fP4g/4FxSLvTwHW1UOrv03CY0EFrQvpZRSSqmlQhPvBEtX4h0e8iPOuSfeAGsaVpGXEyl76Av4eefK+QXHk72xFOMP4Xr1xoL3pZRSSim1FGjinWBpqeFtTKSP9zxavCHS6r113ebY/LnWC7g9C3s40lqQhaO+APfBDoa0trdSSimllCbeiRafeKdq1ErjDUHQIM75/zgba+rJzcnBF/Ax4vXw+om3FhyXc0MJWIXRd3QoeaWUUkopTbwTLL6rScpavIej/ajn2dUEIgPq3L1xe2z+Rmc7Hd0LGwTH4rThXFtC8PYI3isDC9qXUkoppdRip4l3Avl8PgKBSBJss9lwOp0pOW54OFrDewGJN0BFafkdo1kePXdyQfsDyFpdiCXHxuDPrmKC4QXvTymllFJqsdLEO4HSVkrQtfAW7zHbmrdgs0b20zfUz/WOmwvan1gtZG8pJ9jtYehF7eutlFJKqeVLE+8ESseDlRDtaiJA1sJ/nDnObNY2rI7Nn7p4ZsH7tFflkru7Cveb7fiuDi54f0oppZRSi5Em3gmUtlKCrgCSZ0cscxs8ZyobVjePt3oPDyy41Rug8IMrsZVm0/evFwn2ji54f0oppZRSi40m3gmUrhbv8LAfS4E9YfvLznLe0ep9rvXCgvfpOdlN9pZyjC9EzzffITjoW/A+lVJKKaUWE028EyhtXU1cASQ/cYk3QPPKdfiDfnwBH9dv36R/aOFVSawFDnL3rSA8GqTnqdMEbi+sVrhSSiml1GKiiXcCpePhSmMM4eEAlgJHQvebk51DfVVtbP781UsJ2a+tyEn5E5sxYUP3108zeqkf95HO2EsppZRSaqnSxDuB0lLVxBeCQBhLglu8AdY3rY1Nt968itfnTch+HTV5VH5hK7YyJ33fOY//pmvmjZRSSimlFjlNvBMkFArh8XiAyGA0OTk5KTluODp4jiSwj/eYitJySgqLAQiFw7TevJawfVsLsih/YjOOhnw8x7rwt2vyrZRSSqmlTRPvBBkZGe+vnJ2djcWSmlM7lngnuqvJmPiHLK/caE3ovi1OG+W/uQlrqRPPiW5Cw/rApVJKKaWWLk28EyS+m0l+fn7Kjmuig+cko6sJQMOKulhpwf7hQXoG+hK6f7Fbyd1VhdgsjBy+TdgbTOj+lVJKKaUyRUYm3iLiFJG3ReS0iJwTkT9Od0wzSWcpQQDJS07i7bA7aKiuj81fud6y4H3GP0zpPtKJxWkjd1cV4ZEAwzq6pVJKKaWWqIxMvAEf8D5jzBZgK/CwiOxJc0zTSlvi7QogeTbEmpjBcyazqr4JX8CHL+DjwrVLBIOJb5W2lWXjWFmI+1AH/g73zBsopZRSSi0yGZl4m4ix7MsefZk0hjSjtFQ0IdLVxJKfnP7dYypLK8jPiXwmfzDAza5bSTmOs7kEsVvp+94FXIc7knIMpZRSSql0ycjEG0BErCJyCugGXjLGHEl3TNNJZ1eTZFQ0maiptjE2fa29LSnHsDisODeVEur3EmjXVm+llFJKLS0Zm3gbY0LGmK1ALbBLRDbFvy8iT4jIMRE51tPTk54g47hc4+XwUpt4BxI6XPxUGleM9/O+2XULf8CflOM46vOxFmUxeq4PEwgn5Rhq9kTkYRG5JCItIvLlKda5X0RORZ/HeD3VMSqlxuk1q1Rmy9jEe4wxZhB4DXh4wvJvGGN2GmN2lpeXpyW2uFjSM2qlNwT+cNJKCcYrzC+guKAIiNT0vt55MynHERGcm0oxo0HcB5PTpUXNjohYgb8HHgE2AJ8QkQ0T1ikCvg58xBizEfjVlAeqlAL0mlVqMcjIxFtEyqM3B0QkG3g/cDG9UU3N4/EQCoUAcDqdOBzJT4QhrqJJkkoJTtRU0xCbvpqk7iYA9vIcbJU5DO+/ScidnJZ1NSu7gBZjzFVjjB94Gnhswjq/DvzYGHMDwBjTneIYlVLj9JpVKsNlZOINVAP7ReQMcJRIH++fpzmmKcV3M0llDe/wWA3vFHQ1gTu7m3R0307YEPKTyd5UhvGHtbxgetUA8V9ttEeXxVsLFIvIayJyXEQ+lbLolFIT6TWrVIazpTuAyRhjzgDb0h3HbA0PD+P1RpJQmy11p9RER6303hhGekZmWHvhcnNyKS8uY9jtwmC43nmTdY1rknIsa4GDvHtW4D5wi9xdVThqU/cHjYqZrEblxOpCNmAH8CCQDRwSkcPGmMvv2pnIE8ATAPX19RPfVkotXMKuWb1elUqOTG3xXlSGh4dj06ksJTjW1YSs1P0YG1bUxabbOm4k9VgF76/Hkmtn8NlWTDijq0kuVe1AXdx8LTCxzmM78AtjzIgxphd4A9gy2c4y6bkMpZaohF2zer0qlRyaeCdAfFeTgoKClB03PByALAtiSd7gORM1VI/f05Pd3cTitFH4aBP+Gy7cB7WudxocBdaISJOIOIDHgWcnrPNT4F4RsYlIDrAbuJDiOJVSEXrNKpXhNPFOgPgW71T28TYuPzitKTseQE52DhUlZZHjR7ubJPV42ypwri9h+IU2gr2jST2WupMxJgh8AXiByH/MPzTGnBORJ0Xkyeg6F4BfAGeAt4FvGmPOpitmpZYzvWaVynwZ2cd7sYlv8U5tV5MAkp3axBugqqwyNnrl1fa2pPXzhkh5weKPreb2/zhOz7fOkndvDWIR8nZXJ+2Yapwx5jnguQnLnpow/zXga6mMSyk1Ob1mlcps2uK9QMFgEI/HA4DFYknZ4DnGmEhXkxS3eAPUV9fGpjt7u/B4PUk9nrUwi+LHVhPq9+K92J/UYymllFJKJYu2eC/QxBErLZYU/S3jDUEgjDhT/7dTTnYOVaUV3O6LlH+91n6djaubE34c95HOO+Yd9fn4Lg1gK89O+LGUUkoppZJNW7wXKF39u8PRUoLpaPEGaIwbTKc1iYPpxMveUo4lz47naBehYV9KjqmUUkoplSiaeC9QugfPkTQl3vXVtVgk8uvTO9jHkHt4hi0WTmwWcndXYYJh+v7lIiYUTvoxlVJKKaUSRRPvBUpfi3e0hneaEu8sR9b/396dxtaV3vcd//7vfrlTpHZq3zWa0YxGs3jG9jhO3diTFk5QA3VWNE1quE2CBGgAB32RBg0KJG+CIMhiuHaaFkljG4njOrEn08Rjz3ii0TYazWinKFELqY3ifkne9Tx9cakriqbEK4r3nEPd3wcg5h7y8Dw/Xt5H8+dzn/M8rF159wbHC1d7fWk32pKkYd8K8pfHGP22P22KiIiILAYV3o8oqM1z3FihvEdZAHO879g0Y7pJz5ULOOfPJjeJrmaaPryWzIFrTLx3y5c2RURERB6VCu9HNDIyUnnc2trqW7veeB5rimPm3+Y5s3WtXEsqkQQgMzXJtYHr83zH4mn91EYSm1oZ+cZ58tcyvrUrIiIislAqvB9BsVisjHibmb+F92iBSEvct/bmEo1GWbtyNblCjlwhx/vd/u3BYNEIHT+9E0vHGPrLM3jZom9ti4iIiCyECu9HMDo6Wnnc3NxMNOrffGs3mifSlvCtvfvZsn5z5fHVG/013UJ+psyh60ydHqRh73KKQ1kG/scJxg9qW3kREREJLxXej2B4eLjy2M/Rbuc5vLE8kdbgC++25laWt5e3kPecR/flHl/bj3WmSe3uoNCfIX+p9iuriIiIiCyUCu9HMLPwbmtr861dlymABxaCwhtg24Ytlcdne7vxPH+X+UtuayO2PM3UidsUb0/52raIiIhItVR4P4KgCm9vtLyUYBhGvAE2rllPKpkCyjdZXr5+1df2zYyGZ1diEWPo6+dwJX9WVxERERF5GCq8H8HMFU3qufCORqNsW3931Pv0hbO+Z4ikY6T3Lid/ZZzM2/2+ty8iIiIyHxXeC+R53j03V/q6oslIuApvgO0bt1Z2srw5NMDA0G3fM8S7moivbmT09UuM/tNlMof8W95QREREZD4qvBdodHS0smFMU1MT8bh/S/t5owWsMYbFw/Pra0il2bh2feX4xPlTvmcwM9JPL8eixuSxW75t6CMiIiJSjfBUbkvMzPnd7e3tvrbtRvNEWsIz2n3H7i07K48vXb/KyPjoA86ujUiqPOWkNJQl1z08/zeIiIiI+ESF9wLNnN/td+HthWQN79naW9robO+obKhzsudMIDniXU3E1zaRPTNEvm88kAwiIiIis6nwXqChoaHKYz9vrMy8ewNvJEcxW2Di2E3f2q3WE1vvjnr3XLnAZHbS9wyVKSepGENfPaddLUVERCQUVHgv0ODgINlslmw2SzKZ9K/hvFdewzvt3y6ZD2Nlxwo62zoA8JzjbO/5QHJEElEa96+kOJRl6GvncJ7me4uIiEiwVHgvQLFYZGzs7i6Jfo54M1kq/zekhTfA7i07Ko/P9nZTLAYz4hzrTNP2rzeTPTPE2PQqJ3c+RERERPymwnsBRkZGKitmtLS0EIvFfGvbZcuFd1hHvAHWreqiMd0AQDaf40Jfb2BZGl9cTcP+lYy/cVXzvUVERCRQKrwXYHBwsPLY7xsrmZoe8U6Ft/CORCLs3LS9chzEhjp3mBntP7GVxMYWJt+9RXEoG1gWERERqW8qvBdg5o2Vvi8lOFmCuIVqDe+5bF2/mVi0/MfB8PgoNwdvBZbFYhE6fnYXkVSUicM38PKlwLKIiIhI/Qp39RZSQRbeTBahwb+pLQuViCdYs3J1ZWnBs73dgeS4M6d76tQgDc+twmWLTL0/EEgWERERqW8qvBcg6BFvawjvNJOZtq3fUnnc23+ZbC7YaR6xZSlSO5dR6MsweTy4EXgRERGpTyq8H9LU1BRTU1MAxGIxWlpafGvbFb3yHO/G8I94A3S0LbtnacHzVy4EnAiS29uJticZ+fZFre8tIiIivlLh/ZBmb5xjZr617Y3mAZbMiDfA9o1bK4+7L/UEmKTMIkZ673K88QJjb1wNOo6IiIjUkVAW3ma2zsy+Z2ZnzOyUmf1a0JnuGB4erjz2fav4oRwAtgTmeN+xYfU6ErE4AKMT41wfuBFwIoi1p2jYv5LMP/dTGPB/Z00RERGpT6EsvIEi8J+dc7uAF4FfNrPdAWcCymt43+HrxjmAN1we8WYJjXjHYjE2rt1QOe6+HPyoN0Drj23EYhFGX7sUdBQRERGpE6EsvJ1z151zx6YfjwNngLXBpiqbOeLd2trqa9vecA5iBolQ/trua9uGuzdZXrp2JfCbLAGizQmaP9ZF9vQguUujQccRERGROhD6Cs7MNgLPAIdmff5zZnbUzI4ODPi3PNzMEW/fC++hHDTEfJ1XvhiWtbbT0boMgJLnceFqcDtZztT08loiLQlGv9Nb2YlU7mVmnzSzc2bWY2a/+YDznjOzkpl9xs98InIv9VmRcAt14W1mTcDfAL/unBub+TXn3Jecc/udc/uXL1/uS55cLldZ0SQajdLc3OxLu3d4wzmscelMM5lp3Zq1lTW93z9/Mug4ZA5dZ/K9WyS3tJK/Ms7UidtBRwodM4sCfwx8CtgN/NRcU76mz/s94HV/E4rITOqzIuEX2sLbzOKUi+6/dM59I+g8cO80E7/dnLXNAAAdGUlEQVRXNHElhzeSXxKb58xl09oNxGPl7KOZMW7cvhlworLEhhbiqxsZ/XavdrT8Yc8DPc65i865PPBV4NNznPerlPuqFkcXCZb6rEjIhbLwtnJF+xXgjHPu94POc0d/fz/ZbJZsNksikfC1bW80D25pLSU4UzwWv+cmy3OXzgeY5i4zo+3TWyiN5hj/npYXnGUtMPNJ6WPWvRZmthb4SeCL810sqOlhInVk0fqs+qtIbYSy8AZeBn4O+LiZHZ/+eDXoUMGuaDK9lOAS2TxnLmHbyfKO5MZWGp5ZwfhbfRRuTgQdJ0zmektn9mT4PwC+4Jyb9+2CIKaHidSZReuz6q8itRHKKs459zZz/wMSqMBvrIQltZTgbB1ty+hoW8bgyBCec5zt7ebpnU8FHQuA1lc3ke0eZvD/nGXFLz9NJLF0n+dF1Aesm3HcBVybdc5+4KvT0646gVfNrOic+6Y/EUVkBvVZkZAL64h3KI2O3l12zvfC+3YWUtElt5TgbLs27ag8PtPbTakU/LzqzKHrTJ0eJL13OcWbk4x864JWOSk7Amwzs01mlgA+C3xr5gnOuU3OuY3OuY3AXwP/Sf8DFwmM+qxIyC3tKs5HxWKRTCYDlOcF+114lwayRDtTS24pwdk2rFlHQyoNwFQuy4W+cCwtCBBf2UByRzuTR28y9o+X6774ds4VgV+hvPLBGeDrzrlTZvZ5M/t8sOlEZDb1WZHwC+VUkzAaG7u7mmFTUxPRqH9TEZxzeLezxHe24fnWam1EIhF2btrOqZ6zAJzqOcO29VtC8wdFatcyos0Jxt8o35/U8okNockWBOfcd4DvzPrcnDdlOef+nR+ZROT+1GdFwk0j3lWaWXi3tLT42rabKOKmSkSWp3xtt1a2rt9MySuSK+S4MXQrNBvqQPndjHhXE4kNLYy/cZXhr3fjCkv9zx0REREJAxXeVQqy8PYGyqt/RDsfj8I7mUiya/Pdud7HzhwPxVzvO8yM9DPLSe1axuR7t7jx+0cZe1NLDYqIiMijUeFdpZmFt987VpZulwvvx2XEG2D3lp0kE0kAMlOTnLl4LuBE9zIzUjuX0fD8Kkpjeca/d5V833jQsURERGQJU+FdpSALb28gi6WjS3oN79kS8QRPbXuicnz83Akms5MBJppbYm0Tza90gRkDX/qA7LmhoCOJiIjIEqXCu0qBjngPZIk8BiuazLZ941ZaGsvPZb5Y4PCJdwNONLdoa5Lmj3UR60hz+3+dYvI97bIsIiIiD0+FdxU8z2N8/O40Az8Lb+ccpdvZx2Z+90yRSITnn3yWXCFHrpDjzKVu+m/N3ushHCKpGA3PriS2LM3Q184x9NfdQUcSERGRJUaFdxUymUxlTed0Ok0s5t+UD5cpQvbxWdFkttXLV7Fp7YbK8aEPjuJ54VxFxOIRGj+0mmhnismjN5k6NRh0JBEREVlCVHhXIcgVTUp3VjR5TAtvgH27nyYRiwMwkhnjZM/pgBPdn8UiNH1oDdH2JENfO0fhVvjmpYuIiEg4qfCuQqA3Vt4sF3bZGxkmjt30tW2/NKTS7N3xZOX4+LkTZCYnAkz0YBaL0Pj8KgAGvvQB42/3B5xIRERElgIV3lUI9MbK61OQjmKJx/tXtX3jVtqbWwEolkocPXUs4EQPFmmI0/jCKrxMgewZTTkRERGR+T3e1dwiCbLwLl6bxFrjvrYZhEgkwnNPPls5vth/mesDNwJMNL9YZ5rExhZyF0c15URERETmpcK7CkHN8fYmi7jRfF0U3gArO1awpWtj5fjQifDeaHlHancHxCIM/sVpxg9eI3PoetCRREREJKRUeFchqKUES9enR1HrpPAG2LVlByWvSK6Q4/rgTU5fOBt0pAeKJKOkdy6jeGuK4u2poOOIiIhIiKnwnkcul6NQKAAQi8VIpfxbXeRO4W0t9VN4N6QbeGr7nsrxsbPvh/pGS4DEphYsHiF/cTToKCIiIhJiKrznkclkKo8bGxt9bbt0fYpIRxKL19evadfmHbTNuNHy0IkjASd6MItGSGxsoXB9Am+qGHQcERERCan6qugWYGLi7mir/4X3JNHVDb62GQaRSIQXntxfOb58vY/L164GmGh+iU2t4CDXq1FvERERmZsK73ncvHmTbDZLNpulqanJt3a9TAE3XiC6Ou1bm2GyomM5W9dvrhy/8/5h8oV8gIkeLNoYJ7aygfylMVwp3DeEioiISDBUeM8jqKkmpb7ySHt0jb+j7GGyb9deIpEIuUKO4cwIh0+8G3SkB0psbMHlShr1FhERkTmp8J5HUFNNpo7cgqiRvTE+/8mPqWQiyfNP7qscd1+5wNUbfQEmerD4igaIGNnTQ0FHERERkRBS4T2PoApvN5TH2hNYxHxrM4zWr17HhtXrKsdvv3eQbC4bYKL7s1iE2Io0U6cHcc4FHUdERERCRoX3PIKYauJlCpApYssSvrQXds8/uZ90sjzXfSqX5e33Dgac6P7iqxspjeQoXA/3EogiIiLiPxXeD+CcY3Ly7lbgfhXexSvlYt86VHgDpJJJXtz7HLlCjlwhx/mrFzjVcyboWHOKr2oEg+zpwaCjiIiISMio8H6AycnJypSBZDJJLBbzpd3SpQxEDepo45z5dK1cw85N2yvHR04dY2DodoCJ5hZJxUisa2bqjOZ5i4iIyL1UeD/AzGkmfi4lWLySwZZpfvdsz+5+mo62ZQB4zvHdQ28ymZ2c57v8F2mMU+jPMPZWeG8EFREREf+p8H6AQOZ3j+XxBnOa3z2HSCTCR599iWS8/NxM5qb47qE3KZVKASe7V6yzPB+9NDgVcBIREREJExXeDxDEiHehu7wGtK1I+dLeUtPU0MRHnn2JfDFPrpCj79Y1Dhw/FHSse0TbkxAxioPhXH1FREREgqHC+wGCWEqw2D1KpCOJNfkzn3wpWr18Fft3P1M5Pn/1IifOnwow0b0sGiHanqSoEW8RERGZQYX3A/g91cRNFSlezhDf3lrztpa6nZu337Ol/JFT79F/61qAie4V60xTGsnh5YpBRxEREZGQCGXhbWZ/Zma3zOxkkDn8LrwLF8bAg9gOFd7VeOHJ/axYtrxy/P0jbzM+kXnAd/gn1pkGB/nL9bvzqIiIiNwrlIU38OfAJ4MO4XvhfW4Ua4wRXdNQ87YeB5FIhI/uf5mG6c11coU8bxx6k2Ix+FHm2LIUGOQujQYd5ZGY2SfN7JyZ9ZjZb87x9Z8xsw+mPw6Y2d4gcopImfqsSLiFsvB2zr0FBLoQcrFYJJst3xwXiURoaKhtMezyJYoXxolvb8VMywhWK51M8eLTz1EoFsgVclwbvMHBE0eDjoXFIkTbkuR6l27hbWZR4I+BTwG7gZ8ys92zTusFXnHOPQX8DvAlf1OKyB3qsyLhF8rCuxpm9jkzO2pmRwcGBhb9+jNvrEyn0zUvhgvdY1DwiD/RXtN2Hked7R3s33P3Zsvuyz2cv3IhwERlsWUpCn0ZXMkFHWWhngd6nHMXnXN54KvAp2ee4Jw74Jwbnj48CHT5nFFE7lKfFQm5JVt4O+e+5Jzb75zbv3z58vm/4SH5vZRg4cQQ1hInut6f1VMeNzs2bmPT2g2V4wPHD3F7JNht26PtKVzBo3ArfJv8VGktcHXGcd/05+7nF4HX7vfFWv+xLCKL12fVX0VqY8kW3rXm51KCXqZAsXecxJ52TTN5BC88tZ+25vKNqSXP441DbzKVDW5Jv2h7EoDC1SV7g+VcL8Y5h+/N7Eco/0/8C/e7WK3/WBaRxeuz6q8itaHC+z5mjnjXen534dQwOIg/uaym7Tzu4rE4r+z/MM555Ao5BseG+ceD3w/sZstIYxxLx8j3LdnCuw9YN+O4C/ihNRvN7Cngy8CnnXPBvs0gUt/UZ0VCLpSFt5n9FfAOsMPM+szsF/3O4OdUk/yJYaKr0mSvjDJx7CYTx27WtL3HWUtTMy/v+1DlnYPbI4N878gP8DzP9yxmRqKrifzSHfE+Amwzs01mlgA+C3xr5glmth74BvBzzrnuADKKyF3qsyIhF8rC2zn3U8651c65uHOuyzn3Fb8z+LGU4MSxm2S+34d3cwrXHq9JG/Woa+Uantuzr3J89WY/Pzh2AOf8v8kx0dVM4eYEXr7ke9uPyjlXBH4FeB04A3zdOXfKzD5vZp+fPu23gA7gT8zsuJkFv6SMSJ1SnxUJP+1Lfh9+zfH2+qfAwNaka9ZGPdqxcRuTU5Ocv3wRgAt9l4hGorz8zIu+zqNPrGsGDwrXJ0huaPGt3cXinPsO8J1Zn/vijMe/BPyS37lEZG7qsyLhFsoR7zDwY8TbeQ7XP4WtSGEJ/SoW2zO79rJhzTpyhRy5Qo7uKxc48P5hX0e+CwPlFU3G3+4nc+i6b+2KiIhI+Kjam0M+n6dQKAAQi8VIpVI1accN5KDgYV0a7a6V5598lq3rN1eOz106zyEfN9iJpGJYKkppOOtbmyIiIhJOKrzn4NeKJq5vEpIRrDNZszbqnZnx4lPPsblrY+Vzpy+e4+T5075liLWnKA3nfGtPREREwkmF9xz8mGbiTRZxAzlsTRqLaO3uWjIzXnr6BVZ1rqhMO/nB8Xe4fO3q/N+8CKLtSbyJwpK8wVJEREQWjwrvOfixlGDh9Ag4iOimSl+YGS8/8yIrlt3dCOIHxw4wPpF5wHctjmh7eapSaUSj3iIiIvVMhfccZq5oUqupJoWTQ9Acw1q0jKBfotEor+z/ME0N5Xcx8sUCb777ds3X+I62lacSaZ63iIhIfVPhPYfR0dHK4+bm5kW/fmkoR6l/UqPdAUglk3xk30uVJQVvDd3m/e6TNW0zkogSaYxrnreIiEidU+E9h6Ghocrj9vb2Rb9+4UT5+lq7Oxid7R3s3bGnMt/78Ml3GRiu7a7J0fYkRY14i4iI1DUV3rN4nsfY2FjluLW1dVGv75yjcGqY6MYmLBVd1GtL9fZs3V2Z7+05jx+8+88Ui8WatRdrT+GyJUpjGvUWERGpVyq8ZxkdHa3M+W1qaiIeX9w52BNv9OEN53HNKrqDVF7p5Hli0fLvYSQzxjvvH65Ze9H28jzv/NXa38wpIiIi4aTCe5bh4eHK41pMM/GuT0EEbGVtNuWR6jU3NrP/iX2V4/NXL3K2t7smbUVbk2CQ7xuvyfVFREQk/FR4zzIyMlJ5vNiFtys53LVseYv4uJ76MNi2YQtbujZVjt95/wi9/ZcXvR2LRYi2JsldGpv/ZBEREXksqfqbpZYj3sWLY+Ut4nVTZai88NR+OlqXAeBwfP/I21y42rvo7cQ6UuSvjuOKtV2+UERERMJJhfcstSy88x8MQTyCLdcW8WESjUb50DPPk0omyRVyOBxvvvvPHDl5bFHX+I52pqHoabqJiIhInVLhPYPnefdMNWlra1u8a08UKXaPYWu1RXwYNaTSfOKlj9PWfHcVmxM9p/n7t15ncHToAd9ZvVhH+Z0OTTcRERGpTyq8ZxgbG6uMcDY2NpJIJBbt2oWTQ+A5Il2aZhJW6WSKf/nSx+ls76is8d0/cI1vfe81/ung9+ntv0xmcgLn3IKuH0lGia1Ik+8dnf9kEREReezEgg4QJrXaOMc5R/74ENE1DViztogPs2QiyY88/xFO9pzmg3On8JyHw3HlRh9XbvQBYBjJRIKGVJrmxibWrljD+tVdNKQa5r/+plYmjw/gPKd3PkREROqMCu8ZLl68WHm8fPnyRbtuqX8S73aW9KvrKLjabdIii8PMeHLbE6xftY6jp9/j2q3rla8l40kcjmw+x+jEGNcHb9J95QKpeIodG7eyb9deUsn7LxWZ3NjKxKEbFK5PkFjb5MePIyIiIiGhwntaPp/n8uW7y8ht3bp10a6dO3gLUlHiu9sonLq9aNeV2mptbuFHX3iFscw4F65e5PbwIMNjI+QK+R861+E4e+k8vf2X+fAzH2LDmnVzXjOxqTyHPHdxRIW3iIhInVHhDYyPj3P+/HkmJiYAWLZsGbFYjPHxR199onQ7S/HcKMmXV2JJ7Va5FLU0NfPMrr2VY8/zyOZzTGYnGRweorf/MmOZ8mslV8jz3cNvsnvzDl54cj9m904nibUlia9qYOrUIM0f6fL15xAREZFgqfCe1tPTU3m8ZcuWRbtu7p1bEDMSzy3e1BUJViQSoSGVpiGVprOtgx2btnH1eh9HTh1jYmoSgNMXzzGVneKV/R8mErn3Hub0nk7GvnuF0nieaPPi3cArIiIi4aZVTSjvVnnjxg2gPL938+bNi3JdbyRH4eQwtjbN1LlBJo7dXJTrSvisW93Fj3/0x1i3am3lc73XrvDG4bd+aC3w9J5OcDClaUciIiJ1RYU3cOjQocrjNWvW0NAw/+oU83HOMfUP/RA1Ips0l7ceJBNJXtn/YXZu2l5ZjvD81QscPvnuPefFVjYQW55m6uRgQElFREQkCHVfeF++fJlr164B5dHuZ599dlGuWzgzQvHCGKmPrcLSmttdL8yM5/bs44ktOyufO33xHGd7z99zTnpPJ7mLI5QmCkHEFBERkQDUdeHteR4HDx6sHG/fvp2Ojo5Hv+54gez/6ye6Kk1iv+Z216Nndu1lw+q7K5sc/OAwA0O3yRy6TubQdRyAB5PvavqRiIhIvajrwrunp4fR0fIugolEgn379j3yNV22xMRXL+AKHul/tV6bpNQpM+Olp19gWWt5IybPOb535C2yuSxQXt0kubWN8bf68PKlIKOKiIiIT+q28PY8j2PHjlWOn3jiCVKp+298Ug03VWTir3vxBrJEnmoj2z+mGyrrWCwW45X9L5OIlXcrzUxN8sbhtyiVyoV2y4+ux8sUmDh0I8iYIiIi4pO6LbzPnz/P2NgYUB7t3r179yNdr3Rrisz/7KZ0NUPkyTYiy5OLEVOWuKaGJp7d80zlZssbg7d48+jbOOdIbmotj3q/eRUvp1FvERGRx11dFt65XI7Dhw9Xjvfs2UMisbD1lF2+RPZ718h8pRuX92j82a1E1qYXK6o8BtatWsu+GRvwXLp+ldcPfJdsNkvLJzbgTRQY+to5nOcCTCkiIiK1Vpcb6Bw4cICpqSkAGhoaFjTa7XIlMn93Ca93AvIetjZNZEczuYGJxY4rj4Entu5iKpfl4tVLAFwbuMFf/O6XeWr7E2zY1Un29CCjr/XS+uqmH9rtUkRERB4PoR3xNrNPmtk5M+sxs99cjGuWSiXeffddzp+/u7TbRz7yEeLxeFXf75yj2D/B1Ot9jP3hKbxz41hzjOiLHUSfatOW8PJAz+5+mu0bt1amnQxnRjh44ijf6P4njjb0cvybB+j5owPkByeDjloxXz+0sj+c/voHZvbodyiLyIKpz4qEWyhHvM0sCvwx8AmgDzhiZt9yzp1+2GsNDAxw4sQJxsbGGBkZITeVxZUcruSxpWsjq6ydyd5xXMEDz8GMd/udc5At4U0UKd2aotQ/iRsvQNSI72rDa45gbdryW6pjZjy980k62zs4+P4RpnLld10KpSI3V0xxJTPCodc/wF77v7R0ddC8cRmpziYSLSki6TiReLSySk5LSwsvvfRSrfNW0w8/BWyb/ngB+NPp/4qIz9RnRcIvlIU38DzQ45y7CGBmXwU+DVRdeF/7b+/gCh7Xxm5x5Pw75U/OmEPb0dDOls0pBo68X9X1Iu0JYl2NlOIOW57CJSJoQoAsRNfKNfzEx3+cnisXOXfpPGMT4+R6BkjGk6R2LIOCx3j/KKNX5thS3gAz2hta2fCaA4Pl/+EpEuuaaxG1mn74aeB/O+cccNDM2sxstXPuei0CicgDqc+KhFxYC++1wNUZx33M+ovczD4HfG76MGNm53zK9iCdwBzVku/CkgOUZS6Lm+N35vzshkW48rz98D7nrAV+6H/is/pszsxOLkLGWgjL62QuYc4G4c4X5mwAOxbhGovWZ9VfF02Y84U5G4Q734L7a1gL77kGk+9Z8sE59yXgS/7EqY6ZHXXO7VeOu5QlvDmqMG8/rPKc8idn9NkwPwfKtnBhzhfmbFDOtxiXmeNzC+qz6q+LI8z5wpwNwp3vUfprWG+u7APWzTjuAq4FlEWkXlXTD9VXRcJDfVYk5MJaeB8BtpnZJjNLAJ8FvhVwJpF6U00//Bbw89MrJbwIjGquqEhg1GdFQi6UU02cc0Uz+xXgdSAK/Jlz7lTAsaoRlqkvYckByjKXsOR4oPv1QzP7/PTXvwh8B3gV6AEmgV+o8vJhfg6UbeHCnC/M2WAR8tWwz4b5uQtzNgh3vjBng3DnW3A2K9/YLCIiIiIitRTWqSYiIiIiIo8VFd4iIiIiIj5Q4f2QqtiOd6eZvWNmOTP7jYCz/Mz0lsAfmNkBM9sbYJZPT+c4bmZHzezDQeSYcd5zZlYys8/UIkc1WczsY2Y2Ov2cHDez36pVliCEfevqMPWfh80247yav44Xkm36tX3czE6Z2Zt+Zasmn5m1mtnfmdn70/mqvS9hMbL9mZndsvusib0E+kRg+dRfa5svqD5bl/3VOaePKj8o36xyAdgMJID3gd2zzlkBPAf8d+A3As7yEtA+/fhTwKEAszRx956Cp4CzQeSYcd4blG8y+kyAz8nHgL/36/Xr50eVP/+rwGuU1xV+sVavz0fI50v/WUi2GefV9HW8wOetjfJOieunj1eE7Pf6X4Dfm368HBgCEj7l+yiwDzh5n6+HvU8Ekk/9tebPXSB9tl77q0a8H05lO17nXB64sx1vhXPulnPuCFAIQZYDzrnh6cODlNdrDSpLxk2/UoFG7rPJSq1zTPtV4G+AWzXI8LBZHlfV/PyVraudcweBNjNbHZZ8Pvafh842zY/X8UKy/TTwDefcFSj/mxiyfA5oNjOjPCAwBBT9COece2u6vfsJdZ8IMJ/668KFuc/WZX9V4f1w7rfV7lLI8ouU/zILLIuZ/aSZnQW+Dfz7IHKY2VrgJ4Ev1qD9h8oy7UPTb6G9ZmZP1DiTn6r5+YPsT2HqP7OF6XU8WzXP23ag3cy+b2bvmtnP+5auunx/BOyivGnMCeDXnHOeP/HmFfY+EVQ+9deFC3Ofrcv+Gsp1vEOs6u2xfVB1FjP7Ecr/ENVkXnW1WZxzfwv8rZl9FPgd4F8EkOMPgC8450rlP6Brpposx4ANzrmMmb0KfBPYVstQPlrU7eZrIEz954eanONzQb2OZ6smWwx4FvhRIA28Y2YHnXPdtQ5Hdfl+DDgOfBzYAvyjmf3AOTdW63BVCHufCCqf+uvChbnP1mV/VeH9cMK01W5VWczsKeDLwKecc4NBZrnDOfeWmW0xs07n3G2fc+wHvjr9j18n8KqZFZ1z31zEHFVlmfkPh3PuO2b2JzV4ToIS9q2rw9R/FpLNr9fxQrL1AbedcxPAhJm9BewF/Ci8q8n3C8DvTk996zGzXmAncNiHfPMJe58IKp/6a23zBdVn67O/LtYk9Hr4oPyHykVgE3dvBHjiPuf+NrW9uXLeLMB6yruTvRT08wJs5e7NlfuA/jvHQfx+ps//c2p3c2U1z8mqGc/J88CVxX5Ogvqo8uf/ce69MeVwyPL50n8Wkm3W+TV7HS/wedsFfHf63AbgJLAnRPn+FPjt6ccrp/8t6vTx97uR+9+sFfY+EUg+9deaP3eB9Nl67a8a8X4IrorteM1sFXAUaAE8M/t1ynfpLurbItVkAX4L6AD+ZPov7aJzbv9i5niILP8G+HkzKwBTwL91069cn3P4ososnwH+o5kVKT8nn13s5yQoVf78C91u3q98vvSfBWYLRDXZnHNnzOwfgA8AD/iyc27O5biCyEd5mtufm9kJyv/D/ILz6V0mM/sryqsZdZpZH/BfgfiMbGHvE4HkU3+tbb6g+my99ldtGS8iIiIi4gOtaiIiIiIi4gMV3iIiIiIiPlDhLSIiIiLiAxXeIiIiIiI+UOEtIiIiIuIDFd4iIiIiIj5Q4S0iIiIi4oP/D4/QBRqmoQLBAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 864x864 with 9 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(nrows=3, ncols=3, figsize=(12, 12))\n",
+    "colors = sns.color_palette()\n",
+    "\n",
+    "data = df_clean.loc[df_clean.next_month_t2m < df_clean.t2m.quantile(0.1), [v for v in ds.data_vars]]\n",
+    "\n",
+    "for i, column in enumerate(data.columns):\n",
+    "    ax_ix = np.unravel_index(i, (3, 3))\n",
+    "    ax = axes[ax_ix]\n",
+    "    sns.distplot(data[column][~data[column].isna()].values, ax=ax, color=colors[i])\n",
+    "    sns.distplot(\n",
+    "        df[column][~df[column].isna()].values, ax=ax, color='grey', \n",
+    "        hist_kws={'alpha':0.1}, kde_kws={\"color\": \"k\", \"lw\": 3, 'alpha':0.4}\n",
+    "    )\n",
+    "    ax.set_title(column)\n",
+    "    ax.set_ylim(ylim_dict[column])\n",
+    "    ax.set_xlim(xlim_dict[column])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### lagged HIGH temperature?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x864 with 9 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(nrows=3, ncols=3, figsize=(12, 12))\n",
+    "colors = sns.color_palette()\n",
+    "\n",
+    "data = df_clean.loc[df_clean.next_month_t2m > df_clean.t2m.quantile(0.9), [v for v in ds.data_vars]]\n",
+    "\n",
+    "for i, column in enumerate(data.columns):\n",
+    "    ax_ix = np.unravel_index(i, (3, 3))\n",
+    "    ax = axes[ax_ix]\n",
+    "    sns.distplot(data[column][~data[column].isna()].values, ax=ax, color=colors[i])\n",
+    "    sns.distplot(\n",
+    "        df[column][~df[column].isna()].values, ax=ax, color='grey', \n",
+    "        hist_kws={'alpha':0.1}, kde_kws={\"color\": \"k\", \"lw\": 3, 'alpha':0.4}\n",
+    "    )\n",
+    "    ax.set_title(column)\n",
+    "    ax.set_ylim(ylim_dict[column])\n",
+    "    ax.set_xlim(xlim_dict[column])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Current temperature"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x864 with 9 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(nrows=3, ncols=3, figsize=(12, 12))\n",
+    "colors = sns.color_palette()\n",
+    "\n",
+    "# data = df_clean.loc[df_clean.t2m > df_clean.t2m.quantile(0.9), [v for v in ds.data_vars]]\n",
+    "data = df_clean.loc[df_clean.t2m < df_clean.t2m.quantile(0.1), [v for v in ds.data_vars]]\n",
+    "\n",
+    "for i, column in enumerate(data.columns):\n",
+    "    ax_ix = np.unravel_index(i, (3, 3))\n",
+    "    ax = axes[ax_ix]\n",
+    "    sns.distplot(data[column][~data[column].isna()].values, ax=ax, color=colors[i])\n",
+    "    sns.distplot(\n",
+    "        df[column][~df[column].isna()].values, ax=ax, color='grey', \n",
+    "        hist_kws={'alpha':0.1}, kde_kws={\"color\": \"k\", \"lw\": 3, 'alpha':0.4}\n",
+    "    )\n",
+    "    ax.set_title(column)\n",
+    "    ax.set_ylim(ylim_dict[column])\n",
+    "    ax.set_xlim(xlim_dict[column])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Measuring time series correlations\n",
+    "https://towardsdatascience.com/four-ways-to-quantify-synchrony-between-time-series-data-b99136c4a9c9\n",
+    "- Pearsons Correlation\n",
+    "- Pearson's correlation in a window of the data\n",
+    "- Time lagged cross correlation\n",
+    "- windowed time lagged cross correlations\n",
+    "- Dynamic Time Warping\n",
+    "\n",
+    "[Fast FFT based TLCC](https://lexfridman.com/fast-cross-correlation-and-time-series-synchronization-in-python/)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pearson's correlation (global)\n",
+    "corrs = []\n",
+    "vci_df = df_clean.loc[:, ['VCI', 'time']].set_index('time')\n",
+    "t1 = vci_df.values.flatten()\n",
+    "\n",
+    "for var in vars_:\n",
+    "    ts2 = df_clean[[var, 'time']].set_index('time')\n",
+    "    t2 = ts2.values.flatten()\n",
+    "    mask = np.isnan(vci_df.values.flatten()) & np.isnan(ts2.values.flatten())\n",
+    "\n",
+    "    r, _ = pearsonr(t1[~mask], t2[~mask])\n",
+    "    corrs.append(r)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Pixel based correlations\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'df_interpolated' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-117-e6ee665321db>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;31m# df_interpolated = df.interpolate()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;31m# Compute rolling window synchrony\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mrolling_r\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf_interpolated\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'S1_Joy'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrolling\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwindow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mr_window_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcenter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcorr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_interpolated\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'S2_Joy'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m14\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msharex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrolling\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwindow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcenter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmedian\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'df_interpolated' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "# pearson's windowed correlation\n",
+    "\n",
+    "# Set window size to compute moving window synchrony.\n",
+    "r_window_size = 3\n",
+    "\n",
+    "# Interpolate missing data.\n",
+    "# df_interpolated = df.interpolate()\n",
+    "\n",
+    "# Compute rolling window synchrony\n",
+    "rolling_r = df_interpolated['S1_Joy'].rolling(window=r_window_size, center=True).corr(df_interpolated['S2_Joy'])\n",
+    "f,ax=plt.subplots(2,1,figsize=(14,6),sharex=True)\n",
+    "df.rolling(window=30,center=True).median().plot(ax=ax[0])\n",
+    "ax[0].set(xlabel='Frame',ylabel='Smiling Evidence')\n",
+    "rolling_r.plot(ax=ax[1])\n",
+    "ax[1].set(xlabel='Frame',ylabel='Pearson r')\n",
+    "plt.suptitle(\"Smiling data and rolling window correlation\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def crosscorr(datax, datay, lag=0, wrap=False):\n",
+    "    \"\"\" Lag-N cross correlation. \n",
+    "    Shifted data filled with NaNs \n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    lag : int, default 0\n",
+    "    datax, datay : pandas.Series objects of equal length\n",
+    "    Returns\n",
+    "    ----------\n",
+    "    crosscorr : float\n",
+    "    \"\"\"\n",
+    "    if wrap:\n",
+    "        shiftedy = datay.shift(lag)\n",
+    "        shiftedy.iloc[:lag] = datay.iloc[-lag:].values\n",
+    "        return datax.corr(shiftedy)\n",
+    "    else: \n",
+    "        return datax.corr(datay.shift(lag))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# X and y data\n",
+    "For time series:\n",
+    "- https://scikit-learn.org/stable/auto_examples/model_selection/plot_cv_indices.html#sphx-glr-auto-examples-model-selection-plot-cv-indices-py\n",
+    "- https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.TimeSeriesSplit.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import TimeSeriesSplit\n",
+    "\n",
+    "from tsfresh import extract_features, extract_relevant_features\n",
+    "from tsfresh import select_features\n",
+    "from tsfresh.utilities.dataframe_functions import impute"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pev</th>\n",
+       "      <th>t2m</th>\n",
+       "      <th>precip</th>\n",
+       "      <th>E</th>\n",
+       "      <th>SMroot</th>\n",
+       "      <th>SMsurf</th>\n",
+       "      <th>pixel_index</th>\n",
+       "      <th>time_index</th>\n",
+       "      <th>id</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-0.007329</td>\n",
+       "      <td>297.860565</td>\n",
+       "      <td>1.686568</td>\n",
+       "      <td>8.694020</td>\n",
+       "      <td>0.208080</td>\n",
+       "      <td>0.201334</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-0.008037</td>\n",
+       "      <td>299.804291</td>\n",
+       "      <td>11.365343</td>\n",
+       "      <td>6.438349</td>\n",
+       "      <td>0.204618</td>\n",
+       "      <td>0.198097</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        pev         t2m     precip         E    SMroot    SMsurf  pixel_index  \\\n",
+       "1 -0.007329  297.860565   1.686568  8.694020  0.208080  0.201334            1   \n",
+       "2 -0.008037  299.804291  11.365343  6.438349  0.204618  0.198097            1   \n",
+       "\n",
+       "   time_index  id  \n",
+       "1           1   1  \n",
+       "2           2   2  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "id\n",
+       "1    50.802499\n",
+       "2    50.107499\n",
+       "Name: VCI, dtype: float64"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "X:  (351, 9)\n",
+      "y:  (351,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# df['id'] = df.index\n",
+    "# X = df[[v for v in ds.data_vars if v != 'VCI'] + ['pixel_index', 'time_index', 'id']]\n",
+    "# y = df.set_index('id')['VCI']  # [['VCI'] + ['pixel_index', 'time_index']]\n",
+    "\n",
+    "d = d.loc[d.pixel_index == 1]\n",
+    "d['id'] = d.index\n",
+    "X = d[[v for v in ds.data_vars if v != 'VCI'] + ['pixel_index', 'time_index', 'id']]\n",
+    "y = d.set_index('id')['VCI']  # [['VCI'] + ['pixel_index', 'time_index']]\n",
+    "\n",
+    "\n",
+    "display(X.head(2))\n",
+    "display(y.head(2))\n",
+    "\n",
+    "print(\"X: \", X.shape)\n",
+    "print(\"y: \", y.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# df_ts.loc[df_ts.variable == 'VCI']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### play with tsfresh\n",
+    "https://tsfresh.readthedocs.io/en/latest/text/forecasting.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Feature Extraction: 100%|██████████| 10/10 [02:12<00:00, 11.50s/it]\n",
+      "WARNING:tsfresh.utilities.dataframe_functions:The columns ['E__agg_linear_trend__f_agg_\"max\"__chunk_len_10__attr_\"intercept\"'\n",
+      " 'E__agg_linear_trend__f_agg_\"max\"__chunk_len_10__attr_\"rvalue\"'\n",
+      " 'E__agg_linear_trend__f_agg_\"max\"__chunk_len_10__attr_\"slope\"' ...\n",
+      " 't2m__spkt_welch_density__coeff_2' 't2m__spkt_welch_density__coeff_5'\n",
+      " 't2m__spkt_welch_density__coeff_8'] did not have any finite values. Filling with zeros.\n",
+      "WARNING:tsfresh.feature_selection.relevance:Infered regression as machine learning task\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__absolute_sum_of_changes is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__agg_autocorrelation__f_agg_\"mean\"__maxlag_40 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__agg_autocorrelation__f_agg_\"median\"__maxlag_40 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__agg_autocorrelation__f_agg_\"var\"__maxlag_40 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__agg_linear_trend__f_agg_\"max\"__chunk_len_10__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__agg_linear_trend__f_agg_\"max\"__chunk_len_10__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__agg_linear_trend__f_agg_\"max\"__chunk_len_10__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__agg_linear_trend__f_agg_\"max\"__chunk_len_10__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__agg_linear_trend__f_agg_\"max\"__chunk_len_50__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__agg_linear_trend__f_agg_\"max\"__chunk_len_50__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__agg_linear_trend__f_agg_\"max\"__chunk_len_50__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__agg_linear_trend__f_agg_\"max\"__chunk_len_50__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__agg_linear_trend__f_agg_\"max\"__chunk_len_5__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__agg_linear_trend__f_agg_\"max\"__chunk_len_5__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__agg_linear_trend__f_agg_\"max\"__chunk_len_5__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__agg_linear_trend__f_agg_\"max\"__chunk_len_5__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__agg_linear_trend__f_agg_\"mean\"__chunk_len_10__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__agg_linear_trend__f_agg_\"mean\"__chunk_len_10__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__agg_linear_trend__f_agg_\"mean\"__chunk_len_10__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__agg_linear_trend__f_agg_\"mean\"__chunk_len_10__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__agg_linear_trend__f_agg_\"mean\"__chunk_len_50__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__agg_linear_trend__f_agg_\"mean\"__chunk_len_50__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__agg_linear_trend__f_agg_\"mean\"__chunk_len_50__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__agg_linear_trend__f_agg_\"mean\"__chunk_len_50__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__agg_linear_trend__f_agg_\"mean\"__chunk_len_5__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__agg_linear_trend__f_agg_\"mean\"__chunk_len_5__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__agg_linear_trend__f_agg_\"mean\"__chunk_len_5__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__last_location_of_maximum is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__agg_linear_trend__f_agg_\"mean\"__chunk_len_5__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__agg_linear_trend__f_agg_\"min\"__chunk_len_10__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__last_location_of_minimum is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__length is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__agg_linear_trend__f_agg_\"min\"__chunk_len_10__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__linear_trend__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__agg_linear_trend__f_agg_\"min\"__chunk_len_10__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__agg_linear_trend__f_agg_\"min\"__chunk_len_10__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__linear_trend__attr_\"pvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__agg_linear_trend__f_agg_\"min\"__chunk_len_50__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__linear_trend__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__agg_linear_trend__f_agg_\"min\"__chunk_len_50__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__linear_trend__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__agg_linear_trend__f_agg_\"min\"__chunk_len_50__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__linear_trend__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__agg_linear_trend__f_agg_\"min\"__chunk_len_50__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__longest_strike_above_mean is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__agg_linear_trend__f_agg_\"min\"__chunk_len_5__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__longest_strike_below_mean is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__agg_linear_trend__f_agg_\"min\"__chunk_len_5__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__max_langevin_fixed_point__m_3__r_30 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__agg_linear_trend__f_agg_\"min\"__chunk_len_5__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__agg_linear_trend__f_agg_\"min\"__chunk_len_5__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__agg_linear_trend__f_agg_\"var\"__chunk_len_10__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__mean_abs_change is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__agg_linear_trend__f_agg_\"var\"__chunk_len_10__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__agg_linear_trend__f_agg_\"var\"__chunk_len_10__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__mean_change is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__mean_second_derivative_central is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__agg_linear_trend__f_agg_\"var\"__chunk_len_10__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__number_crossing_m__m_-1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__number_crossing_m__m_0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__agg_linear_trend__f_agg_\"var\"__chunk_len_50__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__agg_linear_trend__f_agg_\"var\"__chunk_len_50__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__number_crossing_m__m_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__agg_linear_trend__f_agg_\"var\"__chunk_len_50__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__number_cwt_peaks__n_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__agg_linear_trend__f_agg_\"var\"__chunk_len_50__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__number_cwt_peaks__n_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__agg_linear_trend__f_agg_\"var\"__chunk_len_5__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__number_peaks__n_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__agg_linear_trend__f_agg_\"var\"__chunk_len_5__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__number_peaks__n_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__agg_linear_trend__f_agg_\"var\"__chunk_len_5__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__number_peaks__n_3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__agg_linear_trend__f_agg_\"var\"__chunk_len_5__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__number_peaks__n_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__approximate_entropy__m_2__r_0.1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__number_peaks__n_50 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__approximate_entropy__m_2__r_0.3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__partial_autocorrelation__lag_0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__approximate_entropy__m_2__r_0.5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__partial_autocorrelation__lag_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__partial_autocorrelation__lag_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__approximate_entropy__m_2__r_0.7 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__approximate_entropy__m_2__r_0.9 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__partial_autocorrelation__lag_3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__partial_autocorrelation__lag_4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__ar_coefficient__k_10__coeff_0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__partial_autocorrelation__lag_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__ar_coefficient__k_10__coeff_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__partial_autocorrelation__lag_6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__ar_coefficient__k_10__coeff_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__ar_coefficient__k_10__coeff_3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__partial_autocorrelation__lag_7 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__ar_coefficient__k_10__coeff_4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__partial_autocorrelation__lag_8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__augmented_dickey_fuller__attr_\"pvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__partial_autocorrelation__lag_9 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__augmented_dickey_fuller__attr_\"teststat\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__augmented_dickey_fuller__attr_\"usedlag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__percentage_of_reoccurring_datapoints_to_all_datapoints is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__percentage_of_reoccurring_values_to_all_values is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__autocorrelation__lag_0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__autocorrelation__lag_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__range_count__max_0__min_1000000000000.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__autocorrelation__lag_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__range_count__max_1000000000000.0__min_0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__autocorrelation__lag_3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__range_count__max_1__min_-1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__autocorrelation__lag_4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__ratio_beyond_r_sigma__r_0.5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__autocorrelation__lag_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__ratio_beyond_r_sigma__r_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__autocorrelation__lag_6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__ratio_beyond_r_sigma__r_1.5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__autocorrelation__lag_7 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__autocorrelation__lag_8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__ratio_beyond_r_sigma__r_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__autocorrelation__lag_9 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__ratio_beyond_r_sigma__r_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__binned_entropy__max_bins_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__ratio_beyond_r_sigma__r_2.5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__c3__lag_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__ratio_beyond_r_sigma__r_3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__c3__lag_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__ratio_beyond_r_sigma__r_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__c3__lag_3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__ratio_beyond_r_sigma__r_6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.2__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__ratio_beyond_r_sigma__r_7 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.2__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__ratio_value_number_to_time_series_length is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.2__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__sample_entropy is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.2__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__skewness is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.2__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__spkt_welch_density__coeff_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.4__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__spkt_welch_density__coeff_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.4__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__spkt_welch_density__coeff_8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__standard_deviation is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.4__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__sum_of_reoccurring_data_points is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.4__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__sum_of_reoccurring_values is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.4__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.6__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__symmetry_looking__r_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.6__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.6__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__symmetry_looking__r_0.05 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.6__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__symmetry_looking__r_0.1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.6__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.8__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__symmetry_looking__r_0.15000000000000002 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.8__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__symmetry_looking__r_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.8__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__symmetry_looking__r_0.25 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.8__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__symmetry_looking__r_0.30000000000000004 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.8__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__symmetry_looking__r_0.35000000000000003 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__symmetry_looking__r_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"mean\"__isabs_False__qh_1.0__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"mean\"__isabs_False__qh_1.0__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__symmetry_looking__r_0.45 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"mean\"__isabs_False__qh_1.0__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__symmetry_looking__r_0.5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"mean\"__isabs_False__qh_1.0__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__symmetry_looking__r_0.55 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"mean\"__isabs_False__qh_1.0__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__symmetry_looking__r_0.6000000000000001 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__symmetry_looking__r_0.65 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.2__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.2__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__symmetry_looking__r_0.7000000000000001 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.2__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__symmetry_looking__r_0.75 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.2__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__symmetry_looking__r_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.2__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__symmetry_looking__r_0.8500000000000001 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.4__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__symmetry_looking__r_0.9 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.4__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__symmetry_looking__r_0.9500000000000001 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.4__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__time_reversal_asymmetry_statistic__lag_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.4__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.4__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__time_reversal_asymmetry_statistic__lag_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__time_reversal_asymmetry_statistic__lag_3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.6__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__value_count__value_-1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.6__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__value_count__value_0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.6__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.6__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__value_count__value_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__variance is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.6__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__variance_larger_than_standard_deviation is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__absolute_sum_of_changes is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.8__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__agg_autocorrelation__f_agg_\"mean\"__maxlag_40 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.8__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__agg_autocorrelation__f_agg_\"median\"__maxlag_40 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__agg_autocorrelation__f_agg_\"var\"__maxlag_40 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.8__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.8__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__agg_linear_trend__f_agg_\"max\"__chunk_len_10__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__agg_linear_trend__f_agg_\"max\"__chunk_len_10__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.8__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__agg_linear_trend__f_agg_\"max\"__chunk_len_10__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__agg_linear_trend__f_agg_\"max\"__chunk_len_10__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"mean\"__isabs_True__qh_1.0__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__agg_linear_trend__f_agg_\"max\"__chunk_len_50__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"mean\"__isabs_True__qh_1.0__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__agg_linear_trend__f_agg_\"max\"__chunk_len_50__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__agg_linear_trend__f_agg_\"max\"__chunk_len_50__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"mean\"__isabs_True__qh_1.0__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"mean\"__isabs_True__qh_1.0__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__agg_linear_trend__f_agg_\"max\"__chunk_len_50__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__agg_linear_trend__f_agg_\"max\"__chunk_len_5__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"mean\"__isabs_True__qh_1.0__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__agg_linear_trend__f_agg_\"max\"__chunk_len_5__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.2__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__agg_linear_trend__f_agg_\"max\"__chunk_len_5__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.2__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__agg_linear_trend__f_agg_\"max\"__chunk_len_5__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__agg_linear_trend__f_agg_\"mean\"__chunk_len_10__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.2__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__agg_linear_trend__f_agg_\"mean\"__chunk_len_10__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.2__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__agg_linear_trend__f_agg_\"mean\"__chunk_len_10__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.2__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.4__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__agg_linear_trend__f_agg_\"mean\"__chunk_len_10__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__agg_linear_trend__f_agg_\"mean\"__chunk_len_50__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.4__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__agg_linear_trend__f_agg_\"mean\"__chunk_len_50__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.4__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__agg_linear_trend__f_agg_\"mean\"__chunk_len_50__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.4__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__agg_linear_trend__f_agg_\"mean\"__chunk_len_50__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.4__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__agg_linear_trend__f_agg_\"mean\"__chunk_len_5__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.6__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__agg_linear_trend__f_agg_\"mean\"__chunk_len_5__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.6__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__agg_linear_trend__f_agg_\"mean\"__chunk_len_5__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.6__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__agg_linear_trend__f_agg_\"mean\"__chunk_len_5__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__agg_linear_trend__f_agg_\"min\"__chunk_len_10__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.6__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__agg_linear_trend__f_agg_\"min\"__chunk_len_10__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.6__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.8__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__agg_linear_trend__f_agg_\"min\"__chunk_len_10__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.8__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.8__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__agg_linear_trend__f_agg_\"min\"__chunk_len_10__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.8__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.8__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"var\"__isabs_False__qh_1.0__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__agg_linear_trend__f_agg_\"min\"__chunk_len_50__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"var\"__isabs_False__qh_1.0__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"var\"__isabs_False__qh_1.0__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__agg_linear_trend__f_agg_\"min\"__chunk_len_50__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"var\"__isabs_False__qh_1.0__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__agg_linear_trend__f_agg_\"min\"__chunk_len_50__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"var\"__isabs_False__qh_1.0__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.2__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__agg_linear_trend__f_agg_\"min\"__chunk_len_50__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.2__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__agg_linear_trend__f_agg_\"min\"__chunk_len_5__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.2__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__agg_linear_trend__f_agg_\"min\"__chunk_len_5__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.2__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.2__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__agg_linear_trend__f_agg_\"min\"__chunk_len_5__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__agg_linear_trend__f_agg_\"min\"__chunk_len_5__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.4__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.4__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__agg_linear_trend__f_agg_\"var\"__chunk_len_10__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__agg_linear_trend__f_agg_\"var\"__chunk_len_10__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.4__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.4__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__agg_linear_trend__f_agg_\"var\"__chunk_len_10__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.4__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__agg_linear_trend__f_agg_\"var\"__chunk_len_10__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.6__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__agg_linear_trend__f_agg_\"var\"__chunk_len_50__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.6__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__agg_linear_trend__f_agg_\"var\"__chunk_len_50__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.6__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__agg_linear_trend__f_agg_\"var\"__chunk_len_50__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.6__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__agg_linear_trend__f_agg_\"var\"__chunk_len_50__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.6__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__agg_linear_trend__f_agg_\"var\"__chunk_len_5__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.8__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__agg_linear_trend__f_agg_\"var\"__chunk_len_5__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.8__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__agg_linear_trend__f_agg_\"var\"__chunk_len_5__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.8__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__agg_linear_trend__f_agg_\"var\"__chunk_len_5__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.8__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.8__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__approximate_entropy__m_2__r_0.1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"var\"__isabs_True__qh_1.0__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__approximate_entropy__m_2__r_0.3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"var\"__isabs_True__qh_1.0__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"var\"__isabs_True__qh_1.0__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__approximate_entropy__m_2__r_0.5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__approximate_entropy__m_2__r_0.7 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"var\"__isabs_True__qh_1.0__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__approximate_entropy__m_2__r_0.9 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__change_quantiles__f_agg_\"var\"__isabs_True__qh_1.0__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__ar_coefficient__k_10__coeff_0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__ar_coefficient__k_10__coeff_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__ar_coefficient__k_10__coeff_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__cid_ce__normalize_False is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__ar_coefficient__k_10__coeff_3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__ar_coefficient__k_10__coeff_4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__cid_ce__normalize_True is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__augmented_dickey_fuller__attr_\"pvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__count_above_mean is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__augmented_dickey_fuller__attr_\"teststat\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__augmented_dickey_fuller__attr_\"usedlag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__count_below_mean is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__autocorrelation__lag_0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_10__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__autocorrelation__lag_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__autocorrelation__lag_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_10__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__autocorrelation__lag_3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__autocorrelation__lag_4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_10__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__autocorrelation__lag_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__autocorrelation__lag_6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_10__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__autocorrelation__lag_7 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_11__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__autocorrelation__lag_8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_11__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__autocorrelation__lag_9 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__binned_entropy__max_bins_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_11__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__c3__lag_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__c3__lag_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_11__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__c3__lag_3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_12__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.2__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.2__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_12__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.2__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.2__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_12__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_12__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_13__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_13__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_13__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.2__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_13__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_14__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_14__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.4__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_14__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.4__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_14__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_1__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.4__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_1__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_1__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.4__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_1__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_2__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.4__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_2__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.6__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.6__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_2__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.6__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_2__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.6__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.6__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.8__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_3__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.8__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_3__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.8__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.8__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_3__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.8__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_3__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"mean\"__isabs_False__qh_1.0__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_4__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"mean\"__isabs_False__qh_1.0__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_4__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"mean\"__isabs_False__qh_1.0__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"mean\"__isabs_False__qh_1.0__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_4__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"mean\"__isabs_False__qh_1.0__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.2__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_4__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.2__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_5__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.2__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_5__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.2__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.2__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_5__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.4__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_5__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.4__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_6__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.4__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_6__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.4__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_6__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.4__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_6__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.6__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.6__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_7__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_7__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.6__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_7__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_7__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_8__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.6__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_8__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.6__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_8__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.8__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_8__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.8__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_9__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.8__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_9__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.8__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_9__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.8__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_9__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"mean\"__isabs_True__qh_1.0__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__energy_ratio_by_chunks__num_segments_10__segment_focus_0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"mean\"__isabs_True__qh_1.0__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"mean\"__isabs_True__qh_1.0__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__energy_ratio_by_chunks__num_segments_10__segment_focus_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"mean\"__isabs_True__qh_1.0__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__energy_ratio_by_chunks__num_segments_10__segment_focus_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"mean\"__isabs_True__qh_1.0__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__energy_ratio_by_chunks__num_segments_10__segment_focus_3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.2__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__energy_ratio_by_chunks__num_segments_10__segment_focus_4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.2__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__energy_ratio_by_chunks__num_segments_10__segment_focus_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.2__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__energy_ratio_by_chunks__num_segments_10__segment_focus_6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.2__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.2__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__energy_ratio_by_chunks__num_segments_10__segment_focus_7 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.4__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__energy_ratio_by_chunks__num_segments_10__segment_focus_8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__energy_ratio_by_chunks__num_segments_10__segment_focus_9 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.4__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.4__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_aggregated__aggtype_\"centroid\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.4__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_aggregated__aggtype_\"kurtosis\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.4__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_aggregated__aggtype_\"skew\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.6__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_aggregated__aggtype_\"variance\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.6__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_0__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.6__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_0__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.6__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_10__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.6__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.8__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_10__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.8__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_10__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.8__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_10__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.8__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_11__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.8__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_11__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"var\"__isabs_False__qh_1.0__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_11__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"var\"__isabs_False__qh_1.0__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_11__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"var\"__isabs_False__qh_1.0__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_12__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_12__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"var\"__isabs_False__qh_1.0__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"var\"__isabs_False__qh_1.0__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_12__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.2__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_12__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.2__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_13__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.2__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_13__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.2__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_13__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.2__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_13__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_14__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.4__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.4__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_14__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_14__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.4__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.4__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_14__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.4__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_15__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_15__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.6__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_15__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_15__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.6__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_16__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.6__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_16__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_16__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.6__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_16__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.6__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_17__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.8__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_17__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.8__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_17__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.8__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_17__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.8__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_18__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.8__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_18__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_18__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"var\"__isabs_True__qh_1.0__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_18__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"var\"__isabs_True__qh_1.0__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_19__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"var\"__isabs_True__qh_1.0__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_19__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"var\"__isabs_True__qh_1.0__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__change_quantiles__f_agg_\"var\"__isabs_True__qh_1.0__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_19__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_19__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__cid_ce__normalize_False is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_1__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__cid_ce__normalize_True is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_1__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_1__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__count_above_mean is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_1__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__count_below_mean is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_20__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_10__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_20__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_10__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_20__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_10__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_20__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_21__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_10__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_21__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_11__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_11__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_11__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_11__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_21__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_21__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_12__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_12__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_22__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_12__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_12__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_22__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_13__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_22__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_13__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_22__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_13__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_13__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_23__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_14__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_23__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_23__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_14__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_23__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_14__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_24__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_14__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_1__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_24__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_1__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_24__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_1__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_24__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_25__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_1__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_2__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_25__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_25__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_2__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_2__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_25__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_2__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_26__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_3__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_26__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_3__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_26__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_3__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_26__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_3__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_27__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_4__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_4__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_27__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_4__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_27__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_4__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_5__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_27__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_5__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_28__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_28__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_5__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_5__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_28__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_6__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_28__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_6__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_29__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_6__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_29__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_6__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_29__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_7__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_29__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_7__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_2__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_2__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_7__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_2__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_7__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_2__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_8__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_8__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_30__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_8__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_30__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_8__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_30__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_9__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_30__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_9__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_31__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_9__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_31__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_9__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_31__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__energy_ratio_by_chunks__num_segments_10__segment_focus_0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_31__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__energy_ratio_by_chunks__num_segments_10__segment_focus_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_32__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__energy_ratio_by_chunks__num_segments_10__segment_focus_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_32__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__energy_ratio_by_chunks__num_segments_10__segment_focus_3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_32__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__energy_ratio_by_chunks__num_segments_10__segment_focus_4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_32__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_33__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__energy_ratio_by_chunks__num_segments_10__segment_focus_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_33__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__energy_ratio_by_chunks__num_segments_10__segment_focus_6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_33__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__energy_ratio_by_chunks__num_segments_10__segment_focus_7 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_33__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__energy_ratio_by_chunks__num_segments_10__segment_focus_8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_34__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__energy_ratio_by_chunks__num_segments_10__segment_focus_9 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_34__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_aggregated__aggtype_\"centroid\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_aggregated__aggtype_\"kurtosis\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_34__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_aggregated__aggtype_\"skew\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_34__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_aggregated__aggtype_\"variance\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_35__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_0__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_35__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_0__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_35__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_10__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_35__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_10__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_36__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_10__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_10__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_36__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_11__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_36__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_36__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_11__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_11__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_37__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_11__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_37__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_12__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_37__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_12__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_37__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_12__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_38__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_12__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_38__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_13__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_38__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_13__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_38__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_13__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_39__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_13__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_39__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_14__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_39__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_14__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_39__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_14__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_3__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_14__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_3__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_15__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_15__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_3__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_15__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_3__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_40__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_15__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_16__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_40__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_16__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_40__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_16__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_40__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_16__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_41__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_17__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_41__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_17__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_41__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_17__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_41__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_17__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_42__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_18__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_42__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_18__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_42__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_18__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_42__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_18__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_43__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_19__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_43__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_19__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_43__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_19__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_43__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_19__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_44__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_1__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_44__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_1__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_44__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_1__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_44__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_1__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_45__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_20__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_45__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_45__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_20__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_45__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_20__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_20__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_46__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_46__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_21__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_21__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_46__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_46__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_21__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_21__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_47__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_22__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_47__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_22__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_47__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_22__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_47__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_22__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_48__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_23__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_48__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_23__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_48__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_23__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_23__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_48__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_24__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_49__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_24__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_49__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_24__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_49__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_24__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_49__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_25__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_4__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_4__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_25__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_25__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_4__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_25__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_4__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_50__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_26__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_50__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_26__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_50__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_26__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_50__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_26__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_51__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_27__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_51__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_27__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_27__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_51__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_51__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_27__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_52__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_52__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_52__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_28__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_52__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_53__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_28__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_53__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_28__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_53__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_28__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_53__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_29__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_54__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_54__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_29__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_54__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_29__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_54__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_29__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_55__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_2__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_55__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_2__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_55__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_55__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_2__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_56__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_2__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_56__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_30__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_56__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_56__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_30__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_57__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_57__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_30__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_57__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_30__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_57__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_31__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_31__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_58__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_31__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_31__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_32__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_58__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_32__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_32__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_32__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_58__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_33__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_33__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_58__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_33__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_33__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_59__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_34__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_59__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_34__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_59__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_34__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_59__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_34__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_5__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_35__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_5__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_35__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_35__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_5__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_35__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_5__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_36__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_60__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_36__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_60__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_36__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_60__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_36__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_60__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_37__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_61__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_37__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_61__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_37__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_61__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_37__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_61__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_38__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_38__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_62__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_62__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_62__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_38__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_62__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_63__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_38__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_39__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_63__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_39__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_63__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_63__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_39__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_64__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_39__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_64__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_3__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_64__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_3__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_64__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_3__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_65__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_3__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_65__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_40__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_65__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_40__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_65__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_40__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_66__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_40__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_66__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_41__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_66__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_41__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_41__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_66__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_67__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_41__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_67__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_42__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_67__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_42__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_67__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_42__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_68__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_42__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_68__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_43__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_43__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_68__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_43__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_68__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_43__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_69__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_44__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_69__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_44__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_44__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_69__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_44__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_69__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_6__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_45__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_6__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_45__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_6__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_45__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_6__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_45__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_70__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_46__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_70__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_46__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_70__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_46__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_70__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_46__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_71__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_47__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_71__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_47__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_71__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_47__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_71__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_72__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_47__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_72__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_72__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_72__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_73__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_48__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_73__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_73__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_73__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_74__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_48__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_74__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_48__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_74__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_48__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_74__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_75__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_49__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_75__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_75__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_49__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_75__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_49__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_76__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_49__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_76__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_76__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_4__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_4__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_76__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_4__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_77__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_77__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_4__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_77__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_50__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_77__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_50__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_78__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_50__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_78__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_50__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_78__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_51__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_78__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_51__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_51__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_51__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_79__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_52__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_52__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_52__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_79__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_52__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_53__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_53__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_79__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_53__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_53__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_79__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_54__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_54__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_54__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_7__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_54__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_55__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_7__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_55__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_55__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_7__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_55__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_7__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_56__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_56__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_56__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_80__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_56__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_57__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_57__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_80__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_57__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_57__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_58__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_80__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_58__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_58__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_58__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_80__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_59__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_59__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_81__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_59__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_81__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_59__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_81__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_5__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_5__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_81__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_5__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_5__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_60__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_60__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_82__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_60__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_60__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_61__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_61__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_82__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_61__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_82__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_61__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_62__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_82__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_62__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_83__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_62__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_83__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_83__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_83__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_62__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_84__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_84__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_63__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_84__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_84__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_85__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_85__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_85__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_85__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_86__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_63__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_86__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_86__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_86__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_63__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_87__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_63__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_87__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_87__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_64__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_87__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_64__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_88__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_64__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_64__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_65__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_88__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_65__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_65__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_88__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_65__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_88__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_66__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_66__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_66__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_89__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_66__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_67__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_89__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_67__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_67__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_67__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_68__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_68__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_68__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_89__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_68__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_69__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_69__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_69__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_89__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_69__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_8__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_6__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_8__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_6__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_8__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_6__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_6__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_8__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_70__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_90__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_70__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_90__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_70__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_90__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_90__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_70__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_91__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_91__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_91__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_91__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_71__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_92__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_71__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_92__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_92__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_71__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_92__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_71__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_93__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_72__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_93__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_72__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_93__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_93__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_94__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_72__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_94__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_94__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_94__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_95__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_72__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_95__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_95__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_73__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_95__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_96__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_73__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_73__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_73__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_74__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_96__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_74__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_74__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_74__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_75__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_75__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_96__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_75__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_75__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_76__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_76__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_96__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_76__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_76__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_97__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_77__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_77__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_97__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_77__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_77__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_97__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_78__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_97__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_78__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_98__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_98__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_98__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_98__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_99__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_99__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_78__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_99__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_99__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_78__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_9__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_9__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_79__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_9__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__fft_coefficient__coeff_9__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_79__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__first_location_of_maximum is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__first_location_of_minimum is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_79__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__friedrich_coefficients__m_3__r_30__coeff_0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_79__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__friedrich_coefficients__m_3__r_30__coeff_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_7__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__friedrich_coefficients__m_3__r_30__coeff_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_7__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__friedrich_coefficients__m_3__r_30__coeff_3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_7__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__has_duplicate is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_7__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__has_duplicate_max is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__has_duplicate_min is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_80__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__index_mass_quantile__q_0.1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_80__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__index_mass_quantile__q_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_80__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__index_mass_quantile__q_0.3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_80__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__index_mass_quantile__q_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_81__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__index_mass_quantile__q_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_81__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__index_mass_quantile__q_0.7 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__index_mass_quantile__q_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_81__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__index_mass_quantile__q_0.9 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_81__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__kurtosis is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_82__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__large_standard_deviation__r_0.05 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_82__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__large_standard_deviation__r_0.1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_82__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__large_standard_deviation__r_0.15000000000000002 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_82__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_83__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_83__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_83__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_83__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__large_standard_deviation__r_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_84__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__large_standard_deviation__r_0.25 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_84__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__large_standard_deviation__r_0.30000000000000004 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_84__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__large_standard_deviation__r_0.35000000000000003 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_84__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_85__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_85__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__large_standard_deviation__r_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_85__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_85__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__large_standard_deviation__r_0.45 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_86__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__large_standard_deviation__r_0.5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_86__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__large_standard_deviation__r_0.55 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__large_standard_deviation__r_0.6000000000000001 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__large_standard_deviation__r_0.65 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__large_standard_deviation__r_0.7000000000000001 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__large_standard_deviation__r_0.75 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__large_standard_deviation__r_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__large_standard_deviation__r_0.8500000000000001 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__large_standard_deviation__r_0.9 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature E__large_standard_deviation__r_0.9500000000000001 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_86__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_86__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_87__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_87__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_87__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_87__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_88__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_88__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_88__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_88__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_89__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_89__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_89__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_89__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_8__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_8__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_8__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_8__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_90__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_90__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_90__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_90__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_91__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_91__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_91__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_91__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_92__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_92__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_92__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_92__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_93__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_93__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_93__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_93__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_94__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_94__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_94__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_94__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_95__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_95__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_95__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_95__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_96__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_96__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_96__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_96__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_97__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_97__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_97__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_97__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_98__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_98__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_98__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_98__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_99__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_99__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_99__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_99__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_9__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_9__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_9__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__fft_coefficient__coeff_9__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__first_location_of_maximum is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__first_location_of_minimum is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__friedrich_coefficients__m_3__r_30__coeff_0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__friedrich_coefficients__m_3__r_30__coeff_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__friedrich_coefficients__m_3__r_30__coeff_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__friedrich_coefficients__m_3__r_30__coeff_3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__has_duplicate is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__has_duplicate_max is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__has_duplicate_min is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__index_mass_quantile__q_0.1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__index_mass_quantile__q_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__index_mass_quantile__q_0.3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__index_mass_quantile__q_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__index_mass_quantile__q_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__index_mass_quantile__q_0.7 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__index_mass_quantile__q_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__index_mass_quantile__q_0.9 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__kurtosis is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__large_standard_deviation__r_0.05 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__large_standard_deviation__r_0.1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__large_standard_deviation__r_0.15000000000000002 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__large_standard_deviation__r_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__large_standard_deviation__r_0.25 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__large_standard_deviation__r_0.30000000000000004 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__large_standard_deviation__r_0.35000000000000003 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__large_standard_deviation__r_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__large_standard_deviation__r_0.45 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__large_standard_deviation__r_0.5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__large_standard_deviation__r_0.55 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__large_standard_deviation__r_0.6000000000000001 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__large_standard_deviation__r_0.65 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__large_standard_deviation__r_0.7000000000000001 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__large_standard_deviation__r_0.75 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__large_standard_deviation__r_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__large_standard_deviation__r_0.8500000000000001 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__large_standard_deviation__r_0.9 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__large_standard_deviation__r_0.9500000000000001 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__last_location_of_maximum is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__last_location_of_minimum is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__length is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__linear_trend__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__linear_trend__attr_\"pvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__linear_trend__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__linear_trend__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__linear_trend__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__longest_strike_above_mean is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__longest_strike_below_mean is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__max_langevin_fixed_point__m_3__r_30 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__mean_abs_change is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__mean_change is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__mean_second_derivative_central is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__number_crossing_m__m_-1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__number_crossing_m__m_0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__number_crossing_m__m_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_63__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__number_cwt_peaks__n_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_64__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__number_cwt_peaks__n_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_64__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__number_peaks__n_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__number_peaks__n_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_64__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__number_peaks__n_3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_64__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__number_peaks__n_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_65__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__number_peaks__n_50 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__partial_autocorrelation__lag_0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_65__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_65__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__partial_autocorrelation__lag_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_65__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__partial_autocorrelation__lag_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__partial_autocorrelation__lag_3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_66__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__partial_autocorrelation__lag_4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_66__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__partial_autocorrelation__lag_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_66__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_66__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__partial_autocorrelation__lag_6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__partial_autocorrelation__lag_7 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_67__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__partial_autocorrelation__lag_8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_67__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_67__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__partial_autocorrelation__lag_9 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_67__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__percentage_of_reoccurring_datapoints_to_all_datapoints is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__percentage_of_reoccurring_values_to_all_values is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_68__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_68__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__range_count__max_0__min_1000000000000.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_68__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__range_count__max_1000000000000.0__min_0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_68__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__range_count__max_1__min_-1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_69__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__ratio_beyond_r_sigma__r_0.5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_69__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_69__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__ratio_beyond_r_sigma__r_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_69__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__ratio_beyond_r_sigma__r_1.5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_6__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__ratio_beyond_r_sigma__r_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__ratio_beyond_r_sigma__r_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_6__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_6__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__ratio_beyond_r_sigma__r_2.5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__ratio_beyond_r_sigma__r_3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_6__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_70__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__ratio_beyond_r_sigma__r_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_70__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__ratio_beyond_r_sigma__r_6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_70__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__ratio_beyond_r_sigma__r_7 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_70__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__ratio_value_number_to_time_series_length is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__sample_entropy is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_71__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__skewness is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_71__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_71__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__spkt_welch_density__coeff_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_71__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__spkt_welch_density__coeff_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_72__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__spkt_welch_density__coeff_8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_72__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__standard_deviation is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__sum_of_reoccurring_data_points is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_72__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_72__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__sum_of_reoccurring_values is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__symmetry_looking__r_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_73__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__symmetry_looking__r_0.05 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_73__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__symmetry_looking__r_0.1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_73__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__symmetry_looking__r_0.15000000000000002 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_73__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__symmetry_looking__r_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_74__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__symmetry_looking__r_0.25 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_74__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__symmetry_looking__r_0.30000000000000004 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_74__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__symmetry_looking__r_0.35000000000000003 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_74__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_75__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__symmetry_looking__r_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_75__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__symmetry_looking__r_0.45 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_75__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__symmetry_looking__r_0.5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_75__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__symmetry_looking__r_0.55 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_76__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__symmetry_looking__r_0.6000000000000001 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_76__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__symmetry_looking__r_0.65 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_76__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__symmetry_looking__r_0.7000000000000001 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_76__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__symmetry_looking__r_0.75 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_77__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__symmetry_looking__r_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_77__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__symmetry_looking__r_0.8500000000000001 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_77__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__symmetry_looking__r_0.9 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_77__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__symmetry_looking__r_0.9500000000000001 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_78__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__time_reversal_asymmetry_statistic__lag_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_78__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_78__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__time_reversal_asymmetry_statistic__lag_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_78__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__time_reversal_asymmetry_statistic__lag_3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__value_count__value_-1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_79__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_79__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__value_count__value_0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_79__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__value_count__value_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_79__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__variance is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_7__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_7__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMroot__variance_larger_than_standard_deviation is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__absolute_sum_of_changes is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_7__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__agg_autocorrelation__f_agg_\"mean\"__maxlag_40 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_7__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_80__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__agg_autocorrelation__f_agg_\"median\"__maxlag_40 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_80__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__agg_autocorrelation__f_agg_\"var\"__maxlag_40 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_80__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__agg_linear_trend__f_agg_\"max\"__chunk_len_10__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__agg_linear_trend__f_agg_\"max\"__chunk_len_10__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_80__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_81__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__agg_linear_trend__f_agg_\"max\"__chunk_len_10__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_81__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__agg_linear_trend__f_agg_\"max\"__chunk_len_10__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_81__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__agg_linear_trend__f_agg_\"max\"__chunk_len_50__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_81__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__agg_linear_trend__f_agg_\"max\"__chunk_len_50__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__agg_linear_trend__f_agg_\"max\"__chunk_len_50__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_82__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_82__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__agg_linear_trend__f_agg_\"max\"__chunk_len_50__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_82__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__agg_linear_trend__f_agg_\"max\"__chunk_len_5__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__agg_linear_trend__f_agg_\"max\"__chunk_len_5__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_82__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__agg_linear_trend__f_agg_\"max\"__chunk_len_5__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_83__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__agg_linear_trend__f_agg_\"max\"__chunk_len_5__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_83__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__agg_linear_trend__f_agg_\"mean\"__chunk_len_10__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_83__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_83__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__agg_linear_trend__f_agg_\"mean\"__chunk_len_10__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__agg_linear_trend__f_agg_\"mean\"__chunk_len_10__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_84__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_84__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__agg_linear_trend__f_agg_\"mean\"__chunk_len_10__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__agg_linear_trend__f_agg_\"mean\"__chunk_len_50__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_84__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__agg_linear_trend__f_agg_\"mean\"__chunk_len_50__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_84__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_85__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__agg_linear_trend__f_agg_\"mean\"__chunk_len_50__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__agg_linear_trend__f_agg_\"mean\"__chunk_len_50__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_85__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_85__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__agg_linear_trend__f_agg_\"mean\"__chunk_len_5__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_85__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__agg_linear_trend__f_agg_\"mean\"__chunk_len_5__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_86__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__agg_linear_trend__f_agg_\"mean\"__chunk_len_5__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__agg_linear_trend__f_agg_\"mean\"__chunk_len_5__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_86__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_86__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__agg_linear_trend__f_agg_\"min\"__chunk_len_10__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_86__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__agg_linear_trend__f_agg_\"min\"__chunk_len_10__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_87__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_87__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__agg_linear_trend__f_agg_\"min\"__chunk_len_10__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__agg_linear_trend__f_agg_\"min\"__chunk_len_10__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_87__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__agg_linear_trend__f_agg_\"min\"__chunk_len_50__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_87__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__agg_linear_trend__f_agg_\"min\"__chunk_len_50__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_88__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__agg_linear_trend__f_agg_\"min\"__chunk_len_50__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_88__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__agg_linear_trend__f_agg_\"min\"__chunk_len_50__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__agg_linear_trend__f_agg_\"min\"__chunk_len_5__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__agg_linear_trend__f_agg_\"min\"__chunk_len_5__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_88__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__agg_linear_trend__f_agg_\"min\"__chunk_len_5__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_88__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__agg_linear_trend__f_agg_\"min\"__chunk_len_5__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_89__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__agg_linear_trend__f_agg_\"var\"__chunk_len_10__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_89__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__agg_linear_trend__f_agg_\"var\"__chunk_len_10__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_89__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__agg_linear_trend__f_agg_\"var\"__chunk_len_10__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__agg_linear_trend__f_agg_\"var\"__chunk_len_10__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_89__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_8__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__agg_linear_trend__f_agg_\"var\"__chunk_len_50__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__agg_linear_trend__f_agg_\"var\"__chunk_len_50__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_8__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__agg_linear_trend__f_agg_\"var\"__chunk_len_50__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_8__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_8__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__agg_linear_trend__f_agg_\"var\"__chunk_len_50__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_90__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__agg_linear_trend__f_agg_\"var\"__chunk_len_5__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__agg_linear_trend__f_agg_\"var\"__chunk_len_5__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_90__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__agg_linear_trend__f_agg_\"var\"__chunk_len_5__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_90__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_90__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__agg_linear_trend__f_agg_\"var\"__chunk_len_5__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_91__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__approximate_entropy__m_2__r_0.1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_91__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__approximate_entropy__m_2__r_0.3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_91__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__approximate_entropy__m_2__r_0.5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_91__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__approximate_entropy__m_2__r_0.7 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_92__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_92__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__approximate_entropy__m_2__r_0.9 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_92__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__ar_coefficient__k_10__coeff_0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_92__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__ar_coefficient__k_10__coeff_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_93__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_93__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__ar_coefficient__k_10__coeff_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_93__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__ar_coefficient__k_10__coeff_3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_93__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_94__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__ar_coefficient__k_10__coeff_4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_94__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__augmented_dickey_fuller__attr_\"pvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_94__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__augmented_dickey_fuller__attr_\"teststat\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_94__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__augmented_dickey_fuller__attr_\"usedlag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_95__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__autocorrelation__lag_0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_95__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__autocorrelation__lag_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_95__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__autocorrelation__lag_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_95__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__autocorrelation__lag_3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_96__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__autocorrelation__lag_4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_96__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__autocorrelation__lag_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__autocorrelation__lag_6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_96__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_96__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__autocorrelation__lag_7 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_97__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__autocorrelation__lag_8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__autocorrelation__lag_9 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_97__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__binned_entropy__max_bins_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_97__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__c3__lag_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_97__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_98__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__c3__lag_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__c3__lag_3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_98__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_98__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.2__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_98__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.2__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_99__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.2__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_99__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_99__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.2__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_99__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.2__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_9__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.4__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_9__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.4__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_9__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.4__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_9__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.4__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__first_location_of_maximum is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.4__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__first_location_of_minimum is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.6__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.6__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__friedrich_coefficients__m_3__r_30__coeff_0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.6__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.6__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.6__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__friedrich_coefficients__m_3__r_30__coeff_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.8__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__friedrich_coefficients__m_3__r_30__coeff_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.8__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.8__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__friedrich_coefficients__m_3__r_30__coeff_3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.8__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.8__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__has_duplicate is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"mean\"__isabs_False__qh_1.0__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"mean\"__isabs_False__qh_1.0__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__has_duplicate_max is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"mean\"__isabs_False__qh_1.0__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"mean\"__isabs_False__qh_1.0__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"mean\"__isabs_False__qh_1.0__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.2__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__has_duplicate_min is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.2__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__index_mass_quantile__q_0.1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.2__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.2__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__index_mass_quantile__q_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.2__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.4__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__index_mass_quantile__q_0.3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__index_mass_quantile__q_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__index_mass_quantile__q_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__index_mass_quantile__q_0.7 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__index_mass_quantile__q_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__index_mass_quantile__q_0.9 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__kurtosis is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__large_standard_deviation__r_0.05 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.4__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__large_standard_deviation__r_0.1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__large_standard_deviation__r_0.15000000000000002 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__large_standard_deviation__r_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.4__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__large_standard_deviation__r_0.25 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__large_standard_deviation__r_0.30000000000000004 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__large_standard_deviation__r_0.35000000000000003 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.4__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__large_standard_deviation__r_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.4__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__large_standard_deviation__r_0.45 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.6__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__large_standard_deviation__r_0.5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.6__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__large_standard_deviation__r_0.55 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.6__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__large_standard_deviation__r_0.6000000000000001 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.6__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__large_standard_deviation__r_0.65 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__large_standard_deviation__r_0.7000000000000001 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.6__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__large_standard_deviation__r_0.75 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.8__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__large_standard_deviation__r_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.8__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__large_standard_deviation__r_0.8500000000000001 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.8__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__large_standard_deviation__r_0.9 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.8__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__large_standard_deviation__r_0.9500000000000001 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.8__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__last_location_of_maximum is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"mean\"__isabs_True__qh_1.0__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"mean\"__isabs_True__qh_1.0__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__last_location_of_minimum is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__length is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"mean\"__isabs_True__qh_1.0__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"mean\"__isabs_True__qh_1.0__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__linear_trend__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__linear_trend__attr_\"pvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"mean\"__isabs_True__qh_1.0__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.2__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.2__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.2__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.2__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.2__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__linear_trend__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.4__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.4__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__linear_trend__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.4__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__linear_trend__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.4__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.4__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__longest_strike_above_mean is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.6__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__longest_strike_below_mean is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__max_langevin_fixed_point__m_3__r_30 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.6__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.6__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__mean_abs_change is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__mean_change is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.6__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__mean_second_derivative_central is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.6__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__number_crossing_m__m_-1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.8__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.8__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__number_crossing_m__m_0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__number_crossing_m__m_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.8__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.8__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__number_cwt_peaks__n_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__number_cwt_peaks__n_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.8__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__number_peaks__n_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"var\"__isabs_False__qh_1.0__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__number_peaks__n_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"var\"__isabs_False__qh_1.0__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"var\"__isabs_False__qh_1.0__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__number_peaks__n_3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"var\"__isabs_False__qh_1.0__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__number_peaks__n_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"var\"__isabs_False__qh_1.0__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__number_peaks__n_50 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.2__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__partial_autocorrelation__lag_0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__partial_autocorrelation__lag_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.2__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__partial_autocorrelation__lag_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.2__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.2__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__partial_autocorrelation__lag_3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.2__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__partial_autocorrelation__lag_4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.4__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__partial_autocorrelation__lag_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.4__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__partial_autocorrelation__lag_6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.4__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__partial_autocorrelation__lag_7 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__partial_autocorrelation__lag_8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__partial_autocorrelation__lag_9 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__percentage_of_reoccurring_datapoints_to_all_datapoints is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.4__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__percentage_of_reoccurring_values_to_all_values is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.4__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__range_count__max_0__min_1000000000000.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.6__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__range_count__max_1000000000000.0__min_0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.6__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__range_count__max_1__min_-1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.6__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__ratio_beyond_r_sigma__r_0.5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.6__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__ratio_beyond_r_sigma__r_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.6__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__ratio_beyond_r_sigma__r_1.5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__ratio_beyond_r_sigma__r_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.8__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.8__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__ratio_beyond_r_sigma__r_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.8__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__ratio_beyond_r_sigma__r_2.5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.8__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__ratio_beyond_r_sigma__r_3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__ratio_beyond_r_sigma__r_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.8__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__ratio_beyond_r_sigma__r_6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"var\"__isabs_True__qh_1.0__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__ratio_beyond_r_sigma__r_7 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"var\"__isabs_True__qh_1.0__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__ratio_value_number_to_time_series_length is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"var\"__isabs_True__qh_1.0__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__sample_entropy is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"var\"__isabs_True__qh_1.0__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__skewness is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__change_quantiles__f_agg_\"var\"__isabs_True__qh_1.0__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__spkt_welch_density__coeff_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__cid_ce__normalize_False is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__spkt_welch_density__coeff_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__cid_ce__normalize_True is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__spkt_welch_density__coeff_8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__count_above_mean is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__standard_deviation is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__count_below_mean is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__sum_of_reoccurring_data_points is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_10__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__sum_of_reoccurring_values is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_10__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__symmetry_looking__r_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__symmetry_looking__r_0.05 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_10__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__symmetry_looking__r_0.1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_10__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_11__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__symmetry_looking__r_0.15000000000000002 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_11__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_11__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__symmetry_looking__r_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_11__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_12__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__symmetry_looking__r_0.25 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_12__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_12__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__symmetry_looking__r_0.30000000000000004 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_12__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_13__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__symmetry_looking__r_0.35000000000000003 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_13__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_13__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_13__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__symmetry_looking__r_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_14__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_14__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__symmetry_looking__r_0.45 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_14__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__symmetry_looking__r_0.5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_14__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__symmetry_looking__r_0.55 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_1__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_1__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__symmetry_looking__r_0.6000000000000001 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_1__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_1__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__symmetry_looking__r_0.65 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_2__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_2__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__symmetry_looking__r_0.7000000000000001 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_2__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_2__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__symmetry_looking__r_0.75 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__symmetry_looking__r_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__symmetry_looking__r_0.8500000000000001 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_3__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__symmetry_looking__r_0.9 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__symmetry_looking__r_0.9500000000000001 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_3__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__time_reversal_asymmetry_statistic__lag_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__time_reversal_asymmetry_statistic__lag_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__time_reversal_asymmetry_statistic__lag_3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_3__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__value_count__value_-1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__value_count__value_0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_3__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__value_count__value_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__variance is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__variance_larger_than_standard_deviation is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_4__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__absolute_sum_of_changes is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_4__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__agg_autocorrelation__f_agg_\"mean\"__maxlag_40 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_4__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__agg_autocorrelation__f_agg_\"median\"__maxlag_40 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_4__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__agg_autocorrelation__f_agg_\"var\"__maxlag_40 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_5__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__agg_linear_trend__f_agg_\"max\"__chunk_len_10__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_5__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__agg_linear_trend__f_agg_\"max\"__chunk_len_10__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__agg_linear_trend__f_agg_\"max\"__chunk_len_10__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_5__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__agg_linear_trend__f_agg_\"max\"__chunk_len_10__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_5__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__agg_linear_trend__f_agg_\"max\"__chunk_len_50__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_6__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__agg_linear_trend__f_agg_\"max\"__chunk_len_50__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_6__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__agg_linear_trend__f_agg_\"max\"__chunk_len_50__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_6__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__agg_linear_trend__f_agg_\"max\"__chunk_len_50__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_6__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__agg_linear_trend__f_agg_\"max\"__chunk_len_5__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_7__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__agg_linear_trend__f_agg_\"max\"__chunk_len_5__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_7__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__agg_linear_trend__f_agg_\"max\"__chunk_len_5__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_7__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__agg_linear_trend__f_agg_\"max\"__chunk_len_5__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_7__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__agg_linear_trend__f_agg_\"mean\"__chunk_len_10__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_8__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__agg_linear_trend__f_agg_\"mean\"__chunk_len_10__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_8__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__agg_linear_trend__f_agg_\"mean\"__chunk_len_10__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_8__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_8__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__agg_linear_trend__f_agg_\"mean\"__chunk_len_10__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_9__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_9__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__agg_linear_trend__f_agg_\"mean\"__chunk_len_50__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_9__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__agg_linear_trend__f_agg_\"mean\"__chunk_len_50__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_9__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__energy_ratio_by_chunks__num_segments_10__segment_focus_0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__agg_linear_trend__f_agg_\"mean\"__chunk_len_50__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__energy_ratio_by_chunks__num_segments_10__segment_focus_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__agg_linear_trend__f_agg_\"mean\"__chunk_len_50__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__energy_ratio_by_chunks__num_segments_10__segment_focus_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__energy_ratio_by_chunks__num_segments_10__segment_focus_3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__agg_linear_trend__f_agg_\"mean\"__chunk_len_5__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__energy_ratio_by_chunks__num_segments_10__segment_focus_4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__agg_linear_trend__f_agg_\"mean\"__chunk_len_5__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__energy_ratio_by_chunks__num_segments_10__segment_focus_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__agg_linear_trend__f_agg_\"mean\"__chunk_len_5__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__energy_ratio_by_chunks__num_segments_10__segment_focus_6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__agg_linear_trend__f_agg_\"mean\"__chunk_len_5__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__energy_ratio_by_chunks__num_segments_10__segment_focus_7 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__agg_linear_trend__f_agg_\"min\"__chunk_len_10__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__energy_ratio_by_chunks__num_segments_10__segment_focus_8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__agg_linear_trend__f_agg_\"min\"__chunk_len_10__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__energy_ratio_by_chunks__num_segments_10__segment_focus_9 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_aggregated__aggtype_\"centroid\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__agg_linear_trend__f_agg_\"min\"__chunk_len_10__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_aggregated__aggtype_\"kurtosis\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__agg_linear_trend__f_agg_\"min\"__chunk_len_10__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_aggregated__aggtype_\"skew\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__agg_linear_trend__f_agg_\"min\"__chunk_len_50__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_aggregated__aggtype_\"variance\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__agg_linear_trend__f_agg_\"min\"__chunk_len_50__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_0__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__agg_linear_trend__f_agg_\"min\"__chunk_len_50__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_0__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__agg_linear_trend__f_agg_\"min\"__chunk_len_50__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_10__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__agg_linear_trend__f_agg_\"min\"__chunk_len_5__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_10__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__agg_linear_trend__f_agg_\"min\"__chunk_len_5__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_10__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__agg_linear_trend__f_agg_\"min\"__chunk_len_5__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_10__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_11__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__agg_linear_trend__f_agg_\"min\"__chunk_len_5__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_11__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__agg_linear_trend__f_agg_\"var\"__chunk_len_10__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_11__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__agg_linear_trend__f_agg_\"var\"__chunk_len_10__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__agg_linear_trend__f_agg_\"var\"__chunk_len_10__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_11__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__agg_linear_trend__f_agg_\"var\"__chunk_len_10__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__agg_linear_trend__f_agg_\"var\"__chunk_len_50__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__agg_linear_trend__f_agg_\"var\"__chunk_len_50__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__agg_linear_trend__f_agg_\"var\"__chunk_len_50__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__agg_linear_trend__f_agg_\"var\"__chunk_len_50__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_12__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__agg_linear_trend__f_agg_\"var\"__chunk_len_5__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_12__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__agg_linear_trend__f_agg_\"var\"__chunk_len_5__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_12__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_12__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__agg_linear_trend__f_agg_\"var\"__chunk_len_5__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_13__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__agg_linear_trend__f_agg_\"var\"__chunk_len_5__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_13__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__approximate_entropy__m_2__r_0.1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_13__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__approximate_entropy__m_2__r_0.3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__approximate_entropy__m_2__r_0.5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_13__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__approximate_entropy__m_2__r_0.7 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_14__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__approximate_entropy__m_2__r_0.9 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_14__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_14__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__ar_coefficient__k_10__coeff_0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_14__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__ar_coefficient__k_10__coeff_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__ar_coefficient__k_10__coeff_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_15__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__ar_coefficient__k_10__coeff_3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_15__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__ar_coefficient__k_10__coeff_4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_15__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__augmented_dickey_fuller__attr_\"pvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_15__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__augmented_dickey_fuller__attr_\"teststat\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__augmented_dickey_fuller__attr_\"usedlag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_16__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__autocorrelation__lag_0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_16__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__autocorrelation__lag_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_16__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__autocorrelation__lag_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_16__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__autocorrelation__lag_3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_17__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_17__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__autocorrelation__lag_4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__autocorrelation__lag_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_17__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_17__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__autocorrelation__lag_6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_18__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_18__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__autocorrelation__lag_7 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_18__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_18__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__autocorrelation__lag_8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_19__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__autocorrelation__lag_9 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_19__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_19__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__binned_entropy__max_bins_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_19__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_1__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_1__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_1__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_1__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__c3__lag_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_20__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_20__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_20__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__c3__lag_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_20__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__c3__lag_3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_21__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_21__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.2__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_21__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.2__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_21__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.2__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_22__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_22__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.2__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_22__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.2__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_22__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.4__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.4__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.4__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.4__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.4__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_23__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.6__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.6__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_23__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.6__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_23__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_23__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.6__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.6__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_24__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.8__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_24__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.8__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.8__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_24__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.8__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.8__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_24__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"mean\"__isabs_False__qh_1.0__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_25__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"mean\"__isabs_False__qh_1.0__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_25__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"mean\"__isabs_False__qh_1.0__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"mean\"__isabs_False__qh_1.0__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_25__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_25__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"mean\"__isabs_False__qh_1.0__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_26__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.2__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.2__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_26__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.2__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_26__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_26__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.2__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.2__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_27__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.4__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_27__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.4__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_27__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.4__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_27__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.4__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_28__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.4__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.6__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_28__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.6__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_28__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_28__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_29__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_29__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_29__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_29__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_2__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_2__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.6__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_2__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_2__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.6__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_30__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.6__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_30__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.8__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_30__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_30__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.8__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_31__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.8__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_31__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.8__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_31__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.8__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_31__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"mean\"__isabs_True__qh_1.0__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_32__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_32__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"mean\"__isabs_True__qh_1.0__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_32__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_32__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"mean\"__isabs_True__qh_1.0__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_33__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_33__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"mean\"__isabs_True__qh_1.0__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_33__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"mean\"__isabs_True__qh_1.0__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_33__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.2__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_34__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_34__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.2__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.2__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_34__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_34__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.2__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_35__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.2__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_35__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.4__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_35__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.4__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.4__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_35__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.4__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_36__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.4__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_36__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.6__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.6__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.6__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_36__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.6__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_36__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_37__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.6__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.8__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_37__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.8__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_37__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.8__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_37__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.8__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_38__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.8__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_38__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"var\"__isabs_False__qh_1.0__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_38__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"var\"__isabs_False__qh_1.0__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_38__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"var\"__isabs_False__qh_1.0__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_39__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_39__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"var\"__isabs_False__qh_1.0__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"var\"__isabs_False__qh_1.0__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_39__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.2__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_39__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.2__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_3__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.2__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_3__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_3__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.2__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_3__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.2__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.4__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_40__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.4__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_40__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.4__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_40__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.4__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_40__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.4__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_41__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.6__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_41__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.6__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_41__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.6__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_41__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.6__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_42__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.6__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.8__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.8__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.8__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_42__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.8__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_42__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.8__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_42__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"var\"__isabs_True__qh_1.0__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"var\"__isabs_True__qh_1.0__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_43__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"var\"__isabs_True__qh_1.0__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_43__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"var\"__isabs_True__qh_1.0__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_43__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__change_quantiles__f_agg_\"var\"__isabs_True__qh_1.0__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_43__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__cid_ce__normalize_False is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_44__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__cid_ce__normalize_True is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__count_above_mean is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_44__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__count_below_mean is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_44__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_10__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_44__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_10__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_45__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_10__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_45__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_10__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_45__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_11__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_45__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_11__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_46__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_11__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_46__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_11__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_46__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_12__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_46__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_12__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_47__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_12__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_47__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_47__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_12__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_47__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_13__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_13__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_48__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_48__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_13__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_13__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_48__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_14__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_48__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_14__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_49__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_14__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_14__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_49__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_1__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_49__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_1__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_49__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_1__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_4__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_1__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_4__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_2__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_4__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_2__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_4__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_2__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_50__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_2__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_3__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_50__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_3__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_50__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_3__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_50__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_51__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_3__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_51__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_4__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_51__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_4__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_51__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_4__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_52__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_4__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_52__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_5__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_52__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_5__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_52__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_5__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_53__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_5__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_53__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_6__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_53__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_6__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_53__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_6__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_54__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_6__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_54__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_54__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_7__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_54__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_7__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_55__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_55__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_7__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_55__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_55__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_7__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_56__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_56__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_8__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_56__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_56__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_8__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_57__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_57__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_8__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_57__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_57__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_8__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_58__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_9__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_58__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_9__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_58__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_9__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_58__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_59__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_9__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_59__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__energy_ratio_by_chunks__num_segments_10__segment_focus_0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__energy_ratio_by_chunks__num_segments_10__segment_focus_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_59__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_59__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__energy_ratio_by_chunks__num_segments_10__segment_focus_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_5__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__energy_ratio_by_chunks__num_segments_10__segment_focus_3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_5__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__energy_ratio_by_chunks__num_segments_10__segment_focus_4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_5__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__energy_ratio_by_chunks__num_segments_10__segment_focus_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_5__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__energy_ratio_by_chunks__num_segments_10__segment_focus_6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_60__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__energy_ratio_by_chunks__num_segments_10__segment_focus_7 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_60__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__energy_ratio_by_chunks__num_segments_10__segment_focus_8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_60__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_60__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__energy_ratio_by_chunks__num_segments_10__segment_focus_9 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_61__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_aggregated__aggtype_\"centroid\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_61__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_aggregated__aggtype_\"kurtosis\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_61__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_aggregated__aggtype_\"skew\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_61__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_aggregated__aggtype_\"variance\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_0__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_62__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_0__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_10__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_62__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_10__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_10__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_62__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_10__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_11__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_11__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_11__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_11__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_62__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_12__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_12__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_63__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_12__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_12__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_63__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_13__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature SMsurf__fft_coefficient__coeff_63__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_13__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_13__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_13__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_14__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_14__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_14__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_14__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_16__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_15__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_15__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_15__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_15__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_16__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_16__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_16__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_17__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_17__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_17__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_17__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_18__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_18__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_18__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_18__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_19__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_19__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_19__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_19__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_1__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_1__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_1__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_1__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_20__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_20__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_20__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_20__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_21__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_21__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_21__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_21__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_22__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_22__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_22__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_22__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_23__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_23__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_23__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_23__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_24__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_24__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_24__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_24__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_25__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_25__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_25__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_25__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_41__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_26__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_26__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_41__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_26__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_26__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_41__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_27__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_41__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_27__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_27__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_42__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_27__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_42__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_28__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_28__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_42__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_28__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_42__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_43__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_28__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_43__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_43__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_29__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_43__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_44__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_29__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_44__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_44__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_29__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_44__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_45__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_29__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_45__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_2__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_45__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_2__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_45__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_2__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_46__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_2__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_30__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_46__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_30__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_46__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_46__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_30__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_47__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_30__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_47__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_47__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_31__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_47__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_31__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_48__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_48__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_31__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_48__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_31__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_48__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_32__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_49__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_49__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_32__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_49__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_32__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_32__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_49__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_4__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_33__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_4__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_33__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_4__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_33__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_4__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_33__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_50__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_34__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_50__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_34__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_34__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_34__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_50__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_35__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_35__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_50__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_35__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_51__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_35__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_36__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_51__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_51__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_36__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_51__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_36__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_36__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_52__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_37__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_52__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_52__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_37__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_37__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_52__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_37__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_53__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_53__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_38__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_38__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_53__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_53__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_38__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_38__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_54__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_39__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_54__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_54__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_39__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_54__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_39__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_55__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_39__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_55__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_3__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_55__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_3__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_3__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_55__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_3__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_56__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_40__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_56__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_40__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_56__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_40__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_56__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_40__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_57__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_57__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_57__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_57__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_58__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_58__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_58__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_58__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_59__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_59__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_59__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_59__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_5__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_5__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_5__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_5__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_60__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_60__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_60__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_60__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_61__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_61__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_61__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_61__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_62__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_62__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_62__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_62__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_63__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_63__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_63__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_63__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_64__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_64__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_64__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_64__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_65__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_65__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_65__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_65__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_66__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_66__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_66__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_66__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_67__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_67__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_67__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_67__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_68__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_68__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_68__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_68__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_69__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_69__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_19__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_69__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_69__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_19__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_6__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_6__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_19__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_6__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_1__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_6__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_1__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_1__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_70__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_1__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_70__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_20__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_70__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_20__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_70__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_71__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_20__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_71__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_20__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_71__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_21__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_71__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_21__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_21__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_72__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_21__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_72__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_72__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_22__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_72__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_22__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_22__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_73__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_73__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_22__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_73__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_23__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_73__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_23__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_74__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_74__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_23__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_74__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_74__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_23__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_75__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_75__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_24__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_24__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_24__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_75__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_24__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_25__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_25__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_75__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_25__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_76__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_25__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_76__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_26__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_26__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_76__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_26__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_76__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_26__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_77__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_27__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_27__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_77__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_27__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_77__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_27__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_77__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_28__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_78__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_28__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_78__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_28__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_78__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_28__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_78__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_29__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_29__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_79__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_79__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_29__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_29__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_79__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_2__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_79__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_7__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_2__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_2__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_2__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_7__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_30__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_7__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_30__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_7__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_80__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_80__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_80__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_80__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_30__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_81__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_81__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_30__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_81__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_31__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_81__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_31__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_82__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_31__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_82__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_82__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_31__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_82__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_32__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_83__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_83__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_32__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_83__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_32__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_83__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_32__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_84__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_33__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_84__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_33__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_84__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_33__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_84__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_33__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_85__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_85__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_34__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_85__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_34__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_85__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_34__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_86__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_34__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_86__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_35__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_35__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_86__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_35__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_35__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_86__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_36__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_36__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_87__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_36__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_36__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_37__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_37__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_37__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_37__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_38__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_38__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_87__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_38__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_87__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_38__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_39__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_39__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_87__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_39__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_39__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_88__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_3__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_3__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_88__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_3__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_3__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_88__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_40__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_88__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_89__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_40__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_40__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_89__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_89__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_40__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_41__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_89__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_41__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_8__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_41__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_8__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_41__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_8__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_42__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_8__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_42__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_90__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_90__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_42__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_90__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_90__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_91__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_42__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_91__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_43__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_91__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_43__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_91__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_43__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_92__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_92__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_43__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_92__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_92__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_44__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_44__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_93__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_93__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_44__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_93__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_44__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_93__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_45__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_94__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_45__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_94__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_45__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_94__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_45__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_94__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_95__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_46__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_95__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_46__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_95__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_46__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_46__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_95__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_47__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_96__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_96__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_47__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_47__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_96__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_96__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_47__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_97__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_48__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_97__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_48__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_97__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_48__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_97__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_98__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_48__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_98__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_49__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_98__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_49__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_98__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_49__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_99__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_49__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_99__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_4__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_99__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_4__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_99__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_4__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_9__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_4__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_9__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_50__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_9__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_50__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__fft_coefficient__coeff_9__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__first_location_of_maximum is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_50__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_50__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__first_location_of_minimum is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_51__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__friedrich_coefficients__m_3__r_30__coeff_0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_51__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__friedrich_coefficients__m_3__r_30__coeff_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_51__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__friedrich_coefficients__m_3__r_30__coeff_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_51__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_52__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__friedrich_coefficients__m_3__r_30__coeff_3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_52__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__has_duplicate is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_52__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__has_duplicate_max is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_52__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__has_duplicate_min is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__index_mass_quantile__q_0.1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_53__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__index_mass_quantile__q_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_53__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__index_mass_quantile__q_0.3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_53__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__index_mass_quantile__q_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_53__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_54__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__index_mass_quantile__q_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__index_mass_quantile__q_0.7 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__index_mass_quantile__q_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_54__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__index_mass_quantile__q_0.9 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_54__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__kurtosis is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_54__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__large_standard_deviation__r_0.05 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_55__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_55__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__large_standard_deviation__r_0.1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_55__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__large_standard_deviation__r_0.15000000000000002 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_55__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__large_standard_deviation__r_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_56__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__large_standard_deviation__r_0.25 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_56__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__large_standard_deviation__r_0.30000000000000004 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_56__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_56__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__large_standard_deviation__r_0.35000000000000003 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_57__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__large_standard_deviation__r_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_57__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__large_standard_deviation__r_0.45 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_57__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__large_standard_deviation__r_0.5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_57__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__large_standard_deviation__r_0.55 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_58__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_58__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_58__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__large_standard_deviation__r_0.6000000000000001 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_58__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__large_standard_deviation__r_0.65 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_59__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__large_standard_deviation__r_0.7000000000000001 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_59__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__large_standard_deviation__r_0.75 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_59__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__large_standard_deviation__r_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_59__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__large_standard_deviation__r_0.8500000000000001 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_5__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__large_standard_deviation__r_0.9 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_5__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__large_standard_deviation__r_0.9500000000000001 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__last_location_of_maximum is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_5__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_5__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__last_location_of_minimum is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__length is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_60__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__linear_trend__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_60__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__linear_trend__attr_\"pvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_60__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_60__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__linear_trend__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_61__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__linear_trend__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_61__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__linear_trend__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_61__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__longest_strike_above_mean is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_61__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_62__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__longest_strike_below_mean is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_62__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__max_langevin_fixed_point__m_3__r_30 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_62__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__mean_abs_change is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_62__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__mean_change is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__mean_second_derivative_central is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_63__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__number_crossing_m__m_-1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_63__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__number_crossing_m__m_0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_63__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__number_crossing_m__m_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_63__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__number_cwt_peaks__n_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_64__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__number_cwt_peaks__n_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_64__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__number_peaks__n_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_64__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__number_peaks__n_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__number_peaks__n_3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_64__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__number_peaks__n_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_65__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__number_peaks__n_50 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_65__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_65__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__partial_autocorrelation__lag_0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__partial_autocorrelation__lag_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_65__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__partial_autocorrelation__lag_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_66__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_66__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__partial_autocorrelation__lag_3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_66__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__partial_autocorrelation__lag_4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__partial_autocorrelation__lag_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_66__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__partial_autocorrelation__lag_6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_67__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__partial_autocorrelation__lag_7 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_67__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__partial_autocorrelation__lag_8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_67__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__partial_autocorrelation__lag_9 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_67__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_68__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__percentage_of_reoccurring_datapoints_to_all_datapoints is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_68__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__percentage_of_reoccurring_values_to_all_values is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_68__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__range_count__max_0__min_1000000000000.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_68__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_69__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__range_count__max_1000000000000.0__min_0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_69__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__range_count__max_1__min_-1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_69__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_69__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__ratio_beyond_r_sigma__r_0.5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_6__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__ratio_beyond_r_sigma__r_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_6__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__ratio_beyond_r_sigma__r_1.5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_6__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__ratio_beyond_r_sigma__r_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__ratio_beyond_r_sigma__r_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_6__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__ratio_beyond_r_sigma__r_2.5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_70__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__ratio_beyond_r_sigma__r_3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_70__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__ratio_beyond_r_sigma__r_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_70__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__ratio_beyond_r_sigma__r_6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_70__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__ratio_beyond_r_sigma__r_7 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_71__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__ratio_value_number_to_time_series_length is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_71__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__sample_entropy is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_71__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_71__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__skewness is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__spkt_welch_density__coeff_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_72__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_72__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__spkt_welch_density__coeff_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_72__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__spkt_welch_density__coeff_8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_72__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__standard_deviation is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_73__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__sum_of_reoccurring_data_points is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_73__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__sum_of_reoccurring_values is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_73__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__symmetry_looking__r_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_73__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__symmetry_looking__r_0.05 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_74__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__symmetry_looking__r_0.1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__symmetry_looking__r_0.15000000000000002 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_74__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_74__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__symmetry_looking__r_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_74__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__symmetry_looking__r_0.25 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_75__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__symmetry_looking__r_0.30000000000000004 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__symmetry_looking__r_0.35000000000000003 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_75__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__symmetry_looking__r_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_75__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__symmetry_looking__r_0.45 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_75__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_76__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_76__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__symmetry_looking__r_0.5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_76__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__symmetry_looking__r_0.55 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_76__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__symmetry_looking__r_0.6000000000000001 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_77__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__symmetry_looking__r_0.65 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_77__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__symmetry_looking__r_0.7000000000000001 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_77__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_77__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__symmetry_looking__r_0.75 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__symmetry_looking__r_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_78__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__symmetry_looking__r_0.8500000000000001 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_78__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__symmetry_looking__r_0.9 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_78__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_78__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__symmetry_looking__r_0.9500000000000001 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__time_reversal_asymmetry_statistic__lag_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_79__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_79__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__time_reversal_asymmetry_statistic__lag_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_79__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__time_reversal_asymmetry_statistic__lag_3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_79__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__value_count__value_-1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_7__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__value_count__value_0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_7__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__value_count__value_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_7__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__variance is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pev__variance_larger_than_standard_deviation is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_7__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__abs_energy is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_80__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_80__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__absolute_sum_of_changes is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_80__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__agg_autocorrelation__f_agg_\"mean\"__maxlag_40 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_80__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__agg_autocorrelation__f_agg_\"median\"__maxlag_40 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_81__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__agg_autocorrelation__f_agg_\"var\"__maxlag_40 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__agg_linear_trend__f_agg_\"max\"__chunk_len_10__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_81__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__agg_linear_trend__f_agg_\"max\"__chunk_len_10__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_81__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__agg_linear_trend__f_agg_\"max\"__chunk_len_10__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_81__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__agg_linear_trend__f_agg_\"max\"__chunk_len_10__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_82__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__agg_linear_trend__f_agg_\"max\"__chunk_len_50__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_82__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__agg_linear_trend__f_agg_\"max\"__chunk_len_50__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__agg_linear_trend__f_agg_\"max\"__chunk_len_50__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__agg_linear_trend__f_agg_\"max\"__chunk_len_50__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__agg_linear_trend__f_agg_\"max\"__chunk_len_5__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__agg_linear_trend__f_agg_\"max\"__chunk_len_5__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__agg_linear_trend__f_agg_\"max\"__chunk_len_5__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__agg_linear_trend__f_agg_\"max\"__chunk_len_5__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_82__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__agg_linear_trend__f_agg_\"mean\"__chunk_len_10__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__agg_linear_trend__f_agg_\"mean\"__chunk_len_10__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_82__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__agg_linear_trend__f_agg_\"mean\"__chunk_len_10__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__agg_linear_trend__f_agg_\"mean\"__chunk_len_10__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__agg_linear_trend__f_agg_\"mean\"__chunk_len_50__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_83__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__agg_linear_trend__f_agg_\"mean\"__chunk_len_50__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_83__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__agg_linear_trend__f_agg_\"mean\"__chunk_len_50__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_83__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__agg_linear_trend__f_agg_\"mean\"__chunk_len_50__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__agg_linear_trend__f_agg_\"mean\"__chunk_len_5__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_83__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__agg_linear_trend__f_agg_\"mean\"__chunk_len_5__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_84__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__agg_linear_trend__f_agg_\"mean\"__chunk_len_5__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_84__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__agg_linear_trend__f_agg_\"mean\"__chunk_len_5__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_84__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__agg_linear_trend__f_agg_\"min\"__chunk_len_10__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_84__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__agg_linear_trend__f_agg_\"min\"__chunk_len_10__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__agg_linear_trend__f_agg_\"min\"__chunk_len_10__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_85__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__agg_linear_trend__f_agg_\"min\"__chunk_len_10__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_85__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__agg_linear_trend__f_agg_\"min\"__chunk_len_50__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_85__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__agg_linear_trend__f_agg_\"min\"__chunk_len_50__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_85__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__agg_linear_trend__f_agg_\"min\"__chunk_len_50__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_86__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__agg_linear_trend__f_agg_\"min\"__chunk_len_50__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_86__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__agg_linear_trend__f_agg_\"min\"__chunk_len_5__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__agg_linear_trend__f_agg_\"min\"__chunk_len_5__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_86__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__agg_linear_trend__f_agg_\"min\"__chunk_len_5__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_86__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__agg_linear_trend__f_agg_\"min\"__chunk_len_5__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_87__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__agg_linear_trend__f_agg_\"var\"__chunk_len_10__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_87__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__agg_linear_trend__f_agg_\"var\"__chunk_len_10__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_87__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_87__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__agg_linear_trend__f_agg_\"var\"__chunk_len_10__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_88__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_88__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__agg_linear_trend__f_agg_\"var\"__chunk_len_10__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_88__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__agg_linear_trend__f_agg_\"var\"__chunk_len_50__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_88__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_89__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__agg_linear_trend__f_agg_\"var\"__chunk_len_50__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_89__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__agg_linear_trend__f_agg_\"var\"__chunk_len_50__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_89__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_89__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__agg_linear_trend__f_agg_\"var\"__chunk_len_50__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_8__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_8__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__agg_linear_trend__f_agg_\"var\"__chunk_len_5__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_8__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_8__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__agg_linear_trend__f_agg_\"var\"__chunk_len_5__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_90__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__agg_linear_trend__f_agg_\"var\"__chunk_len_5__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_90__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__agg_linear_trend__f_agg_\"var\"__chunk_len_5__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_90__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_90__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__approximate_entropy__m_2__r_0.1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__approximate_entropy__m_2__r_0.3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_91__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_91__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__approximate_entropy__m_2__r_0.5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_91__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__approximate_entropy__m_2__r_0.7 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_91__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__approximate_entropy__m_2__r_0.9 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_92__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_92__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__ar_coefficient__k_10__coeff_0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__ar_coefficient__k_10__coeff_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_92__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__ar_coefficient__k_10__coeff_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_92__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__ar_coefficient__k_10__coeff_3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_93__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__ar_coefficient__k_10__coeff_4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_93__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__augmented_dickey_fuller__attr_\"pvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_93__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_93__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__augmented_dickey_fuller__attr_\"teststat\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_94__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__augmented_dickey_fuller__attr_\"usedlag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_94__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__autocorrelation__lag_0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_94__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__autocorrelation__lag_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_94__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__autocorrelation__lag_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__autocorrelation__lag_3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_95__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_95__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__autocorrelation__lag_4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_95__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__autocorrelation__lag_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_95__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__autocorrelation__lag_6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_96__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__autocorrelation__lag_7 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_96__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__autocorrelation__lag_8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__autocorrelation__lag_9 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_96__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_96__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__binned_entropy__max_bins_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_97__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__c3__lag_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_97__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__c3__lag_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__c3__lag_3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_97__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_97__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.2__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_98__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.2__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_98__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.2__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.2__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_98__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.2__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_98__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.4__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_99__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.4__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_99__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.4__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_99__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.4__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_99__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.4__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_9__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.6__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_9__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.6__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_9__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.6__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_9__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.6__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__first_location_of_maximum is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__first_location_of_minimum is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__friedrich_coefficients__m_3__r_30__coeff_0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.6__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__friedrich_coefficients__m_3__r_30__coeff_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.8__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__friedrich_coefficients__m_3__r_30__coeff_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.8__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__friedrich_coefficients__m_3__r_30__coeff_3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.8__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__has_duplicate is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.8__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__has_duplicate_max is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.8__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"mean\"__isabs_False__qh_1.0__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__has_duplicate_min is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"mean\"__isabs_False__qh_1.0__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__index_mass_quantile__q_0.1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"mean\"__isabs_False__qh_1.0__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__index_mass_quantile__q_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"mean\"__isabs_False__qh_1.0__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__index_mass_quantile__q_0.3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"mean\"__isabs_False__qh_1.0__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.2__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__index_mass_quantile__q_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.2__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__index_mass_quantile__q_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.2__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__index_mass_quantile__q_0.7 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.2__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.2__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__index_mass_quantile__q_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__index_mass_quantile__q_0.9 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.4__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.4__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__kurtosis is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.4__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__large_standard_deviation__r_0.05 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.4__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__large_standard_deviation__r_0.1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.4__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__large_standard_deviation__r_0.15000000000000002 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.6__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__large_standard_deviation__r_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.6__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.6__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__large_standard_deviation__r_0.25 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__large_standard_deviation__r_0.30000000000000004 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.6__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__large_standard_deviation__r_0.35000000000000003 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.6__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__large_standard_deviation__r_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.8__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.8__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__large_standard_deviation__r_0.45 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__large_standard_deviation__r_0.5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.8__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.8__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__large_standard_deviation__r_0.55 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.8__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"mean\"__isabs_True__qh_1.0__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__large_standard_deviation__r_0.6000000000000001 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"mean\"__isabs_True__qh_1.0__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"mean\"__isabs_True__qh_1.0__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__large_standard_deviation__r_0.65 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"mean\"__isabs_True__qh_1.0__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__large_standard_deviation__r_0.7000000000000001 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"mean\"__isabs_True__qh_1.0__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.2__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__large_standard_deviation__r_0.75 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.2__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__large_standard_deviation__r_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.2__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.2__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__large_standard_deviation__r_0.8500000000000001 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__large_standard_deviation__r_0.9 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__large_standard_deviation__r_0.9500000000000001 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__last_location_of_maximum is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__last_location_of_minimum is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.2__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__length is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__linear_trend__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__linear_trend__attr_\"pvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.4__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__linear_trend__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.4__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__linear_trend__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__linear_trend__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__longest_strike_above_mean is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.4__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__longest_strike_below_mean is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__max_langevin_fixed_point__m_3__r_30 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.4__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__maximum is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.4__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__mean is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.6__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__mean_abs_change is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__mean_change is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.6__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__mean_second_derivative_central is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.6__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.6__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__median is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.6__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__minimum is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__number_crossing_m__m_-1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.8__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__number_crossing_m__m_0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.8__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__number_crossing_m__m_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.8__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__number_cwt_peaks__n_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.8__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__number_cwt_peaks__n_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.8__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__number_peaks__n_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"var\"__isabs_False__qh_1.0__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__number_peaks__n_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"var\"__isabs_False__qh_1.0__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__number_peaks__n_3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"var\"__isabs_False__qh_1.0__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"var\"__isabs_False__qh_1.0__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__number_peaks__n_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__number_peaks__n_50 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"var\"__isabs_False__qh_1.0__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.2__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__partial_autocorrelation__lag_0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__partial_autocorrelation__lag_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.2__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__partial_autocorrelation__lag_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.2__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.2__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.2__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__partial_autocorrelation__lag_3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.4__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__partial_autocorrelation__lag_4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.4__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.4__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.4__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__partial_autocorrelation__lag_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.4__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.6__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.6__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.6__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.6__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__partial_autocorrelation__lag_6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.6__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__partial_autocorrelation__lag_7 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.8__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__partial_autocorrelation__lag_8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.8__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__partial_autocorrelation__lag_9 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.8__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__percentage_of_reoccurring_datapoints_to_all_datapoints is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.8__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__percentage_of_reoccurring_values_to_all_values is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.8__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__quantile__q_0.1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"var\"__isabs_True__qh_1.0__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__quantile__q_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"var\"__isabs_True__qh_1.0__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__quantile__q_0.3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"var\"__isabs_True__qh_1.0__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__quantile__q_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"var\"__isabs_True__qh_1.0__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__quantile__q_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__change_quantiles__f_agg_\"var\"__isabs_True__qh_1.0__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__quantile__q_0.7 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__cid_ce__normalize_False is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__quantile__q_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__cid_ce__normalize_True is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__quantile__q_0.9 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__range_count__max_0__min_1000000000000.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__range_count__max_1000000000000.0__min_0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__range_count__max_1__min_-1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__ratio_beyond_r_sigma__r_0.5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__count_above_mean is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__ratio_beyond_r_sigma__r_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__ratio_beyond_r_sigma__r_1.5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__ratio_beyond_r_sigma__r_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__ratio_beyond_r_sigma__r_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__ratio_beyond_r_sigma__r_2.5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__ratio_beyond_r_sigma__r_3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__count_below_mean is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__ratio_beyond_r_sigma__r_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__ratio_beyond_r_sigma__r_6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_0__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__ratio_beyond_r_sigma__r_7 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__ratio_value_number_to_time_series_length is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_0__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__sample_entropy is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_0__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__skewness is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__spkt_welch_density__coeff_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_0__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_10__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__spkt_welch_density__coeff_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__spkt_welch_density__coeff_8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_10__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_10__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__standard_deviation is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__sum_of_reoccurring_data_points is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_10__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__sum_of_reoccurring_values is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_11__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__sum_values is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_11__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__symmetry_looking__r_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_11__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__symmetry_looking__r_0.05 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__symmetry_looking__r_0.1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_11__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__symmetry_looking__r_0.15000000000000002 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_12__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__symmetry_looking__r_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__symmetry_looking__r_0.25 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_12__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__symmetry_looking__r_0.30000000000000004 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_12__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__symmetry_looking__r_0.35000000000000003 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_12__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__symmetry_looking__r_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_13__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_13__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_13__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_13__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_14__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_14__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_14__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_14__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_1__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_1__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__symmetry_looking__r_0.45 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_1__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__symmetry_looking__r_0.5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_1__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_2__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__symmetry_looking__r_0.55 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_2__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__symmetry_looking__r_0.6000000000000001 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_2__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__symmetry_looking__r_0.65 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_2__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_3__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__symmetry_looking__r_0.7000000000000001 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__symmetry_looking__r_0.75 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_3__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_3__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__symmetry_looking__r_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_3__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__symmetry_looking__r_0.8500000000000001 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_4__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__symmetry_looking__r_0.9 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_4__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__symmetry_looking__r_0.9500000000000001 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_4__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__time_reversal_asymmetry_statistic__lag_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_4__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__time_reversal_asymmetry_statistic__lag_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__time_reversal_asymmetry_statistic__lag_3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_5__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__value_count__value_-1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_5__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_5__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__value_count__value_0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__value_count__value_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_5__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__variance is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_6__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_6__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__variance_larger_than_standard_deviation is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_6__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_6__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__absolute_sum_of_changes is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__agg_autocorrelation__f_agg_\"mean\"__maxlag_40 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_7__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__agg_autocorrelation__f_agg_\"median\"__maxlag_40 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_7__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__agg_autocorrelation__f_agg_\"var\"__maxlag_40 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_7__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__agg_linear_trend__f_agg_\"max\"__chunk_len_10__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__agg_linear_trend__f_agg_\"max\"__chunk_len_10__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_7__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__agg_linear_trend__f_agg_\"max\"__chunk_len_10__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_8__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__agg_linear_trend__f_agg_\"max\"__chunk_len_10__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_8__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__agg_linear_trend__f_agg_\"max\"__chunk_len_50__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_8__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__agg_linear_trend__f_agg_\"max\"__chunk_len_50__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__agg_linear_trend__f_agg_\"max\"__chunk_len_50__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_8__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__agg_linear_trend__f_agg_\"max\"__chunk_len_50__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_9__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__agg_linear_trend__f_agg_\"max\"__chunk_len_5__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_9__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__agg_linear_trend__f_agg_\"max\"__chunk_len_5__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__agg_linear_trend__f_agg_\"max\"__chunk_len_5__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_9__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_9__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__agg_linear_trend__f_agg_\"max\"__chunk_len_5__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__agg_linear_trend__f_agg_\"mean\"__chunk_len_10__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__energy_ratio_by_chunks__num_segments_10__segment_focus_0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__agg_linear_trend__f_agg_\"mean\"__chunk_len_10__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__agg_linear_trend__f_agg_\"mean\"__chunk_len_10__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__energy_ratio_by_chunks__num_segments_10__segment_focus_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__agg_linear_trend__f_agg_\"mean\"__chunk_len_10__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__energy_ratio_by_chunks__num_segments_10__segment_focus_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__agg_linear_trend__f_agg_\"mean\"__chunk_len_50__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__energy_ratio_by_chunks__num_segments_10__segment_focus_3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__agg_linear_trend__f_agg_\"mean\"__chunk_len_50__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__energy_ratio_by_chunks__num_segments_10__segment_focus_4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__agg_linear_trend__f_agg_\"mean\"__chunk_len_50__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__energy_ratio_by_chunks__num_segments_10__segment_focus_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__agg_linear_trend__f_agg_\"mean\"__chunk_len_50__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__energy_ratio_by_chunks__num_segments_10__segment_focus_6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__agg_linear_trend__f_agg_\"mean\"__chunk_len_5__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__agg_linear_trend__f_agg_\"mean\"__chunk_len_5__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__energy_ratio_by_chunks__num_segments_10__segment_focus_7 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__agg_linear_trend__f_agg_\"mean\"__chunk_len_5__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__energy_ratio_by_chunks__num_segments_10__segment_focus_8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__agg_linear_trend__f_agg_\"mean\"__chunk_len_5__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__energy_ratio_by_chunks__num_segments_10__segment_focus_9 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__agg_linear_trend__f_agg_\"min\"__chunk_len_10__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_aggregated__aggtype_\"centroid\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_aggregated__aggtype_\"kurtosis\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__agg_linear_trend__f_agg_\"min\"__chunk_len_10__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_aggregated__aggtype_\"skew\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__agg_linear_trend__f_agg_\"min\"__chunk_len_10__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_aggregated__aggtype_\"variance\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_0__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__agg_linear_trend__f_agg_\"min\"__chunk_len_10__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_0__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__agg_linear_trend__f_agg_\"min\"__chunk_len_50__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_0__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__agg_linear_trend__f_agg_\"min\"__chunk_len_50__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_0__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_10__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_10__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__agg_linear_trend__f_agg_\"min\"__chunk_len_50__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_10__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__agg_linear_trend__f_agg_\"min\"__chunk_len_50__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_10__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__agg_linear_trend__f_agg_\"min\"__chunk_len_5__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_11__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__agg_linear_trend__f_agg_\"min\"__chunk_len_5__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_11__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__agg_linear_trend__f_agg_\"min\"__chunk_len_5__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_11__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_11__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__agg_linear_trend__f_agg_\"min\"__chunk_len_5__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_12__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__agg_linear_trend__f_agg_\"var\"__chunk_len_10__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_12__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__agg_linear_trend__f_agg_\"var\"__chunk_len_10__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_12__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__agg_linear_trend__f_agg_\"var\"__chunk_len_10__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__agg_linear_trend__f_agg_\"var\"__chunk_len_10__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_12__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__agg_linear_trend__f_agg_\"var\"__chunk_len_50__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_13__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__agg_linear_trend__f_agg_\"var\"__chunk_len_50__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_13__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_13__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__agg_linear_trend__f_agg_\"var\"__chunk_len_50__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_13__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__agg_linear_trend__f_agg_\"var\"__chunk_len_50__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_14__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__agg_linear_trend__f_agg_\"var\"__chunk_len_5__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__agg_linear_trend__f_agg_\"var\"__chunk_len_5__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_14__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_14__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__agg_linear_trend__f_agg_\"var\"__chunk_len_5__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__agg_linear_trend__f_agg_\"var\"__chunk_len_5__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_14__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__approximate_entropy__m_2__r_0.1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_15__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_15__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__approximate_entropy__m_2__r_0.3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__approximate_entropy__m_2__r_0.5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_15__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_15__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__approximate_entropy__m_2__r_0.7 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__approximate_entropy__m_2__r_0.9 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_16__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_16__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__ar_coefficient__k_10__coeff_0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_16__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__ar_coefficient__k_10__coeff_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_16__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__ar_coefficient__k_10__coeff_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_17__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__ar_coefficient__k_10__coeff_3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_17__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__ar_coefficient__k_10__coeff_4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_17__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__augmented_dickey_fuller__attr_\"pvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_17__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__augmented_dickey_fuller__attr_\"teststat\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_18__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__augmented_dickey_fuller__attr_\"usedlag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_18__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__autocorrelation__lag_0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__autocorrelation__lag_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_18__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__autocorrelation__lag_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_18__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__autocorrelation__lag_3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature pixel_index__fft_coefficient__coeff_19__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__autocorrelation__lag_4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__autocorrelation__lag_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__autocorrelation__lag_6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__autocorrelation__lag_7 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__autocorrelation__lag_8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__autocorrelation__lag_9 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__binned_entropy__max_bins_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__c3__lag_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__c3__lag_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__c3__lag_3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.2__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.2__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.2__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.2__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.2__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.4__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.4__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.4__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.4__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.4__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.6__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.6__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.6__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.6__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.6__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.8__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.8__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.8__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.8__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.8__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"mean\"__isabs_False__qh_1.0__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"mean\"__isabs_False__qh_1.0__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"mean\"__isabs_False__qh_1.0__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"mean\"__isabs_False__qh_1.0__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"mean\"__isabs_False__qh_1.0__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.2__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.2__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.2__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.2__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.2__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.4__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.4__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.4__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.4__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.4__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.6__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.6__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.6__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.6__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.6__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.8__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.8__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.8__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.8__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_13__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.8__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"mean\"__isabs_True__qh_1.0__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"mean\"__isabs_True__qh_1.0__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_13__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"mean\"__isabs_True__qh_1.0__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"mean\"__isabs_True__qh_1.0__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"mean\"__isabs_True__qh_1.0__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_13__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.2__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.2__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.2__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.2__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.2__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_13__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.4__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.4__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.4__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.4__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_14__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.4__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.6__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.6__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_14__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.6__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.6__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_14__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.6__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_14__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.8__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_1__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_1__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_1__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.8__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_1__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.8__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_2__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_2__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_2__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.8__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_2__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_3__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_3__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.8__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_3__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"var\"__isabs_False__qh_1.0__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_3__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"var\"__isabs_False__qh_1.0__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_4__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"var\"__isabs_False__qh_1.0__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_4__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_4__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"var\"__isabs_False__qh_1.0__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_4__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"var\"__isabs_False__qh_1.0__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_5__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_5__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.2__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_5__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.2__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_5__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.2__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.2__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_6__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_6__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.2__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_6__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.4__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_6__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.4__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_7__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_7__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.4__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.4__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_7__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.4__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_7__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.6__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.6__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_8__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.6__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_8__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_8__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_8__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.6__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_9__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.6__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_9__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.8__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_9__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.8__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_9__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__energy_ratio_by_chunks__num_segments_10__segment_focus_0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.8__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__energy_ratio_by_chunks__num_segments_10__segment_focus_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__energy_ratio_by_chunks__num_segments_10__segment_focus_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.8__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.8__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__energy_ratio_by_chunks__num_segments_10__segment_focus_3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"var\"__isabs_True__qh_1.0__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__energy_ratio_by_chunks__num_segments_10__segment_focus_4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"var\"__isabs_True__qh_1.0__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__energy_ratio_by_chunks__num_segments_10__segment_focus_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__energy_ratio_by_chunks__num_segments_10__segment_focus_6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"var\"__isabs_True__qh_1.0__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__energy_ratio_by_chunks__num_segments_10__segment_focus_7 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"var\"__isabs_True__qh_1.0__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__energy_ratio_by_chunks__num_segments_10__segment_focus_8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__change_quantiles__f_agg_\"var\"__isabs_True__qh_1.0__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__energy_ratio_by_chunks__num_segments_10__segment_focus_9 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__cid_ce__normalize_False is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_aggregated__aggtype_\"centroid\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__cid_ce__normalize_True is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__count_above_mean is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_aggregated__aggtype_\"kurtosis\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__count_below_mean is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_aggregated__aggtype_\"skew\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_10__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_aggregated__aggtype_\"variance\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_10__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_0__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_10__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_0__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_10__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_10__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_11__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_10__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_11__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_11__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_10__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_11__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_10__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_12__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_11__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_12__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_11__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_12__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_11__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_12__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_11__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_12__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_12__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_12__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_12__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_13__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_13__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_13__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_13__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_14__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_14__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_14__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_14__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_15__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_15__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_15__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_15__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_16__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_16__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_16__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_16__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_17__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_17__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_17__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_17__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_18__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_18__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_18__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_18__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_19__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_19__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_19__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_19__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_1__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_1__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_1__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_1__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_20__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_20__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_20__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_20__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_21__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_21__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_21__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_21__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_22__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_22__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"mean\"__isabs_False__qh_1.0__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_22__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"mean\"__isabs_False__qh_1.0__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_22__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"mean\"__isabs_False__qh_1.0__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_23__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_23__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"mean\"__isabs_False__qh_1.0__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_23__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.2__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_23__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.2__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_24__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.2__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_24__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.2__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_24__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.2__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_24__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.4__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_25__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.4__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_25__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.4__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_25__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.4__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_25__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.4__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_26__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_26__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.6__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_26__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.6__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_26__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.6__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_27__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.6__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_27__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_27__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.6__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_27__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.8__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_28__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_28__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.8__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_28__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.8__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_28__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.8__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_29__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.8__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_29__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_29__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"mean\"__isabs_True__qh_1.0__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_29__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"mean\"__isabs_True__qh_1.0__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_2__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"mean\"__isabs_True__qh_1.0__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_2__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"mean\"__isabs_True__qh_1.0__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_2__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_2__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"mean\"__isabs_True__qh_1.0__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_30__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.2__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.2__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.2__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.2__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.2__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_30__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.4__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_30__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_30__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.4__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.4__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_31__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_31__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.4__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.4__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_31__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.6__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_31__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.6__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.6__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_32__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.6__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_32__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_32__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.6__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.8__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_32__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.8__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_33__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_33__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.8__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.8__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_33__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.8__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_33__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"var\"__isabs_False__qh_1.0__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_34__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"var\"__isabs_False__qh_1.0__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_34__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"var\"__isabs_False__qh_1.0__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_34__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"var\"__isabs_False__qh_1.0__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_34__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"var\"__isabs_False__qh_1.0__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_35__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.2__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_35__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.2__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_35__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.2__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_35__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.2__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_36__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_36__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.2__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_36__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.4__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_36__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.4__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_37__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.4__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.4__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_37__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.4__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_37__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.6__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_37__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.6__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_38__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.6__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_38__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_38__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.6__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.6__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_38__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.8__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_39__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.8__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_39__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.8__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_39__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.8__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_39__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.8__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_3__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"var\"__isabs_True__qh_1.0__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_3__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"var\"__isabs_True__qh_1.0__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_3__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"var\"__isabs_True__qh_1.0__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_3__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"var\"__isabs_True__qh_1.0__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_40__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"var\"__isabs_True__qh_1.0__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_40__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__cid_ce__normalize_False is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_40__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_40__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__cid_ce__normalize_True is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_41__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__count_above_mean is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_41__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__count_below_mean is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_41__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_10__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_41__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_42__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_10__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_42__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_10__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_42__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_10__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_11__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_42__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_43__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_11__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_43__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_11__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_11__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_43__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_43__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_12__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_44__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_12__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_44__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_12__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_44__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_12__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_44__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_13__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_45__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_13__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_45__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_13__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_45__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_13__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_45__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_14__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_46__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_14__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_46__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_14__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_14__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_46__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_1__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_46__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_1__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_1__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_47__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_1__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_47__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_2__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_2__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_2__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_47__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_47__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_2__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_48__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_3__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_48__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_3__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_3__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_48__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_48__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_3__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_49__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_49__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_4__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_49__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_4__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_49__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_4__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_4__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_4__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_4__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_5__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_4__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_5__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_4__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_5__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_50__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_50__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_5__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_6__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_50__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_6__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_6__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_6__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_50__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_51__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_7__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_51__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_7__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_7__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_51__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_51__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_7__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_52__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_8__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_52__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_8__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_52__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_52__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_8__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_53__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_8__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_9__w_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_53__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_9__w_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_53__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_9__w_20 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_53__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_54__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_9__w_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_54__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__energy_ratio_by_chunks__num_segments_10__segment_focus_0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_54__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__energy_ratio_by_chunks__num_segments_10__segment_focus_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__energy_ratio_by_chunks__num_segments_10__segment_focus_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_54__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__energy_ratio_by_chunks__num_segments_10__segment_focus_3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_55__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__energy_ratio_by_chunks__num_segments_10__segment_focus_4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_55__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__energy_ratio_by_chunks__num_segments_10__segment_focus_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_55__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_55__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__energy_ratio_by_chunks__num_segments_10__segment_focus_6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_56__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__energy_ratio_by_chunks__num_segments_10__segment_focus_7 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_56__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__energy_ratio_by_chunks__num_segments_10__segment_focus_8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_56__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__energy_ratio_by_chunks__num_segments_10__segment_focus_9 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_aggregated__aggtype_\"centroid\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_56__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_57__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_aggregated__aggtype_\"kurtosis\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_aggregated__aggtype_\"skew\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_57__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_aggregated__aggtype_\"variance\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_57__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_0__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_57__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_0__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_58__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_58__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_10__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_10__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_58__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_10__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_58__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_10__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_59__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_11__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_59__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_11__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_59__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_11__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_59__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_11__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_5__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_12__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_5__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_12__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_5__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_12__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_5__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_60__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_12__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_13__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_60__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_60__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_13__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_60__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_13__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_61__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_13__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_61__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_14__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_61__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_14__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_61__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_14__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_62__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_14__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_62__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_15__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_62__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_15__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_62__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_15__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_15__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_63__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_16__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_63__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_16__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_63__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_16__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_63__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_16__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_64__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_64__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_17__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_64__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_17__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_17__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_64__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_17__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_65__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_18__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_65__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_65__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_18__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_65__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_18__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_18__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_66__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_66__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_19__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_19__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_66__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_19__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_66__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_67__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_19__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_67__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_1__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_67__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_1__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_67__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_1__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_68__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_1__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_68__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_20__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_68__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_20__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_68__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_69__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_20__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_20__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_69__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_21__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_69__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_69__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_21__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_21__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_6__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_21__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_6__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_6__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_22__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_6__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_22__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_22__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_70__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_22__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_70__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_70__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_23__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_70__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_23__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_71__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_23__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_23__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_71__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_24__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_71__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_24__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_71__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_24__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_24__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_72__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_25__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_72__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_25__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_25__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_72__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_25__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_72__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_26__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_73__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_26__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_73__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_26__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_73__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_26__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_73__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_74__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_27__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_74__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_27__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_74__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_27__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_74__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_27__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_75__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_28__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_28__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_75__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_28__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_75__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_28__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_75__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_29__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_76__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_29__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_76__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_29__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_29__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_76__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_2__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_76__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_2__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_77__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_2__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_77__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_2__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_77__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_30__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_77__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_30__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_78__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_30__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_30__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_78__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_31__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_78__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_78__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_31__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_79__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_31__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_79__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_31__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_79__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_32__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_79__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_32__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_7__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_32__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_7__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_32__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_7__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_33__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_7__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_33__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_80__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_33__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_80__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_33__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_80__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_34__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_80__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_81__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_34__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_81__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_34__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_81__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_34__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_81__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_35__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_82__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_35__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_82__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_35__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_82__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_35__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_82__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_36__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_36__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_83__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_83__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_36__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_36__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_83__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_83__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_37__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_84__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_37__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_37__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_84__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_37__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_84__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_38__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_84__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_85__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_38__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_85__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_38__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_85__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_38__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_85__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_39__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_86__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_39__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_86__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_39__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_39__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_86__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_86__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_3__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_87__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_3__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_3__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_87__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_87__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_3__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_87__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_40__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_40__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_88__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_40__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_88__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_40__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_88__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_41__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_88__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_41__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_89__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_41__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_41__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_89__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_89__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_42__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_89__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_42__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_42__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_8__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_42__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_8__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_8__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_43__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_8__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_43__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_90__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_43__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_90__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_43__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_90__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_44__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_90__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_44__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_91__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_44__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_44__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_91__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_91__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_45__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_91__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_45__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_92__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_45__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_92__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_45__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_92__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_46__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_92__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_46__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_93__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_93__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_46__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_46__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_93__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_47__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_93__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_47__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_94__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_47__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_94__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_47__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_94__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_48__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_94__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_48__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_95__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_48__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_95__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_48__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_95__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_95__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_49__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_49__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_96__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_49__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_96__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_49__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_4__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_96__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_4__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_4__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_96__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_97__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_97__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_4__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_50__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_50__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_97__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_97__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_50__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_50__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_51__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_98__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_51__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_98__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_51__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_51__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_98__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_98__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_52__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_52__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_99__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_52__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_99__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_52__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_99__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_53__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_53__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_99__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_53__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_9__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_53__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_9__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_54__attr_\"abs\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_9__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_54__attr_\"angle\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__fft_coefficient__coeff_9__attr_\"real\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__fft_coefficient__coeff_54__attr_\"imag\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__first_location_of_maximum is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__first_location_of_minimum is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature precip__friedrich_coefficients__m_3__r_30__coeff_0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__has_duplicate_max is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__has_duplicate_min is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__index_mass_quantile__q_0.1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.2__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__index_mass_quantile__q_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__index_mass_quantile__q_0.3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.2__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__index_mass_quantile__q_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__index_mass_quantile__q_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.4__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__index_mass_quantile__q_0.7 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.4__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__index_mass_quantile__q_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.4__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.4__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__index_mass_quantile__q_0.9 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.4__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__kurtosis is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__large_standard_deviation__r_0.05 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.6__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__large_standard_deviation__r_0.1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.6__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__large_standard_deviation__r_0.15000000000000002 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.6__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__large_standard_deviation__r_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.6__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__large_standard_deviation__r_0.25 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.6__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__large_standard_deviation__r_0.30000000000000004 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.8__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__large_standard_deviation__r_0.35000000000000003 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.8__ql_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__large_standard_deviation__r_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.8__ql_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__large_standard_deviation__r_0.45 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.8__ql_0.6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__large_standard_deviation__r_0.5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.8__ql_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__large_standard_deviation__r_0.55 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__change_quantiles__f_agg_\"mean\"__isabs_False__qh_1.0__ql_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__large_standard_deviation__r_0.6000000000000001 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__large_standard_deviation__r_0.65 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__large_standard_deviation__r_0.7000000000000001 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__large_standard_deviation__r_0.75 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__large_standard_deviation__r_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__large_standard_deviation__r_0.8500000000000001 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__large_standard_deviation__r_0.9 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__large_standard_deviation__r_0.9500000000000001 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__last_location_of_maximum is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__last_location_of_minimum is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__length is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__linear_trend__attr_\"intercept\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__linear_trend__attr_\"pvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__linear_trend__attr_\"rvalue\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__linear_trend__attr_\"slope\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__linear_trend__attr_\"stderr\" is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__longest_strike_above_mean is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__longest_strike_below_mean is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__max_langevin_fixed_point__m_3__r_30 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__mean_abs_change is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__mean_change is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__mean_second_derivative_central is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__number_crossing_m__m_-1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__number_crossing_m__m_0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__number_crossing_m__m_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__number_cwt_peaks__n_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__number_cwt_peaks__n_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__number_peaks__n_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__number_peaks__n_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__number_peaks__n_3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__number_peaks__n_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__number_peaks__n_50 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__partial_autocorrelation__lag_0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__partial_autocorrelation__lag_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__partial_autocorrelation__lag_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__partial_autocorrelation__lag_3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__partial_autocorrelation__lag_4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__partial_autocorrelation__lag_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__partial_autocorrelation__lag_6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__partial_autocorrelation__lag_7 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__partial_autocorrelation__lag_8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__partial_autocorrelation__lag_9 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__percentage_of_reoccurring_datapoints_to_all_datapoints is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__percentage_of_reoccurring_values_to_all_values is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__range_count__max_0__min_1000000000000.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__range_count__max_1000000000000.0__min_0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__range_count__max_1__min_-1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__ratio_beyond_r_sigma__r_0.5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__ratio_beyond_r_sigma__r_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__ratio_beyond_r_sigma__r_1.5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__ratio_beyond_r_sigma__r_10 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__ratio_beyond_r_sigma__r_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__ratio_beyond_r_sigma__r_2.5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__ratio_beyond_r_sigma__r_3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__ratio_beyond_r_sigma__r_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__ratio_beyond_r_sigma__r_6 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__ratio_beyond_r_sigma__r_7 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__ratio_value_number_to_time_series_length is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__sample_entropy is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__skewness is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__spkt_welch_density__coeff_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__spkt_welch_density__coeff_5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__spkt_welch_density__coeff_8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__standard_deviation is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__sum_of_reoccurring_data_points is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__sum_of_reoccurring_values is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__symmetry_looking__r_0.0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__symmetry_looking__r_0.05 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__symmetry_looking__r_0.1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__symmetry_looking__r_0.15000000000000002 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__symmetry_looking__r_0.2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__symmetry_looking__r_0.25 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__symmetry_looking__r_0.30000000000000004 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__symmetry_looking__r_0.35000000000000003 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__symmetry_looking__r_0.4 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__symmetry_looking__r_0.45 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__symmetry_looking__r_0.5 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__symmetry_looking__r_0.55 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__symmetry_looking__r_0.6000000000000001 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__symmetry_looking__r_0.65 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__symmetry_looking__r_0.7000000000000001 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__symmetry_looking__r_0.75 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__symmetry_looking__r_0.8 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__symmetry_looking__r_0.8500000000000001 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__symmetry_looking__r_0.9 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__symmetry_looking__r_0.9500000000000001 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__time_reversal_asymmetry_statistic__lag_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__time_reversal_asymmetry_statistic__lag_2 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__time_reversal_asymmetry_statistic__lag_3 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__value_count__value_-1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__value_count__value_0 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__value_count__value_1 is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__variance is constant\n",
+      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature t2m__variance_larger_than_standard_deviation is constant\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>variable</th>\n",
+       "      <th>precip__abs_energy</th>\n",
+       "      <th>precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_0__w_20</th>\n",
+       "      <th>precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_0__w_5</th>\n",
+       "      <th>precip__fft_coefficient__coeff_0__attr_\"abs\"</th>\n",
+       "      <th>precip__fft_coefficient__coeff_0__attr_\"real\"</th>\n",
+       "      <th>precip__maximum</th>\n",
+       "      <th>precip__mean</th>\n",
+       "      <th>precip__median</th>\n",
+       "      <th>precip__minimum</th>\n",
+       "      <th>precip__quantile__q_0.1</th>\n",
+       "      <th>...</th>\n",
+       "      <th>SMsurf__quantile__q_0.1</th>\n",
+       "      <th>SMsurf__quantile__q_0.2</th>\n",
+       "      <th>SMsurf__quantile__q_0.3</th>\n",
+       "      <th>SMsurf__quantile__q_0.4</th>\n",
+       "      <th>SMsurf__quantile__q_0.6</th>\n",
+       "      <th>SMsurf__quantile__q_0.7</th>\n",
+       "      <th>SMsurf__quantile__q_0.8</th>\n",
+       "      <th>SMsurf__quantile__q_0.9</th>\n",
+       "      <th>SMsurf__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_0__w_10</th>\n",
+       "      <th>SMsurf__abs_energy</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2.844510</td>\n",
+       "      <td>0.327093</td>\n",
+       "      <td>0.654185</td>\n",
+       "      <td>1.686568</td>\n",
+       "      <td>1.686568</td>\n",
+       "      <td>1.686568</td>\n",
+       "      <td>1.686568</td>\n",
+       "      <td>1.686568</td>\n",
+       "      <td>1.686568</td>\n",
+       "      <td>1.686568</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.201334</td>\n",
+       "      <td>0.201334</td>\n",
+       "      <td>0.201334</td>\n",
+       "      <td>0.201334</td>\n",
+       "      <td>0.201334</td>\n",
+       "      <td>0.201334</td>\n",
+       "      <td>0.201334</td>\n",
+       "      <td>0.201334</td>\n",
+       "      <td>0.055220</td>\n",
+       "      <td>0.040535</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>129.171024</td>\n",
+       "      <td>2.204192</td>\n",
+       "      <td>4.408384</td>\n",
+       "      <td>11.365343</td>\n",
+       "      <td>11.365343</td>\n",
+       "      <td>11.365343</td>\n",
+       "      <td>11.365343</td>\n",
+       "      <td>11.365343</td>\n",
+       "      <td>11.365343</td>\n",
+       "      <td>11.365343</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.198097</td>\n",
+       "      <td>0.198097</td>\n",
+       "      <td>0.198097</td>\n",
+       "      <td>0.198097</td>\n",
+       "      <td>0.198097</td>\n",
+       "      <td>0.198097</td>\n",
+       "      <td>0.198097</td>\n",
+       "      <td>0.198097</td>\n",
+       "      <td>0.054332</td>\n",
+       "      <td>0.039242</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 41 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "variable  precip__abs_energy  \\\n",
+       "id                             \n",
+       "1                   2.844510   \n",
+       "2                 129.171024   \n",
+       "\n",
+       "variable  precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_0__w_20  \\\n",
+       "id                                                                         \n",
+       "1                                                  0.327093                \n",
+       "2                                                  2.204192                \n",
+       "\n",
+       "variable  precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_0__w_5  \\\n",
+       "id                                                                        \n",
+       "1                                                  0.654185               \n",
+       "2                                                  4.408384               \n",
+       "\n",
+       "variable  precip__fft_coefficient__coeff_0__attr_\"abs\"  \\\n",
+       "id                                                       \n",
+       "1                                             1.686568   \n",
+       "2                                            11.365343   \n",
+       "\n",
+       "variable  precip__fft_coefficient__coeff_0__attr_\"real\"  precip__maximum  \\\n",
+       "id                                                                         \n",
+       "1                                              1.686568         1.686568   \n",
+       "2                                             11.365343        11.365343   \n",
+       "\n",
+       "variable  precip__mean  precip__median  precip__minimum  \\\n",
+       "id                                                        \n",
+       "1             1.686568        1.686568         1.686568   \n",
+       "2            11.365343       11.365343        11.365343   \n",
+       "\n",
+       "variable  precip__quantile__q_0.1         ...          \\\n",
+       "id                                        ...           \n",
+       "1                        1.686568         ...           \n",
+       "2                       11.365343         ...           \n",
+       "\n",
+       "variable  SMsurf__quantile__q_0.1  SMsurf__quantile__q_0.2  \\\n",
+       "id                                                           \n",
+       "1                        0.201334                 0.201334   \n",
+       "2                        0.198097                 0.198097   \n",
+       "\n",
+       "variable  SMsurf__quantile__q_0.3  SMsurf__quantile__q_0.4  \\\n",
+       "id                                                           \n",
+       "1                        0.201334                 0.201334   \n",
+       "2                        0.198097                 0.198097   \n",
+       "\n",
+       "variable  SMsurf__quantile__q_0.6  SMsurf__quantile__q_0.7  \\\n",
+       "id                                                           \n",
+       "1                        0.201334                 0.201334   \n",
+       "2                        0.198097                 0.198097   \n",
+       "\n",
+       "variable  SMsurf__quantile__q_0.8  SMsurf__quantile__q_0.9  \\\n",
+       "id                                                           \n",
+       "1                        0.201334                 0.201334   \n",
+       "2                        0.198097                 0.198097   \n",
+       "\n",
+       "variable  SMsurf__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_0__w_10  \\\n",
+       "id                                                                         \n",
+       "1                                                  0.055220                \n",
+       "2                                                  0.054332                \n",
+       "\n",
+       "variable  SMsurf__abs_energy  \n",
+       "id                            \n",
+       "1                   0.040535  \n",
+       "2                   0.039242  \n",
+       "\n",
+       "[2 rows x 41 columns]"
+      ]
+     },
+     "execution_count": 143,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "if (data_dir.parents[0] / 'subset_filtered_features_df.pkl').exists():\n",
+    "    with open(data_dir.parents[0] / 'subset_filtered_features_df.pkl', 'rb') as fp:\n",
+    "        filtered_features = pickle.load(fp)\n",
+    "else:\n",
+    "    filtered_features = extract_relevant_features(X, y, column_id='id', column_sort='time_index')\n",
+    "    with open(data_dir.parents[0] / 'subset_filtered_features_df.pkl', 'wb') as fp:\n",
+    "        pickle.dump(filtered_features, fp)\n",
+    "\n",
+    "filtered_features.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>variable</th>\n",
+       "      <th>precip__abs_energy</th>\n",
+       "      <th>precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_0__w_20</th>\n",
+       "      <th>precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_0__w_5</th>\n",
+       "      <th>precip__fft_coefficient__coeff_0__attr_\"abs\"</th>\n",
+       "      <th>precip__fft_coefficient__coeff_0__attr_\"real\"</th>\n",
+       "      <th>precip__maximum</th>\n",
+       "      <th>precip__mean</th>\n",
+       "      <th>precip__median</th>\n",
+       "      <th>precip__minimum</th>\n",
+       "      <th>precip__quantile__q_0.1</th>\n",
+       "      <th>...</th>\n",
+       "      <th>SMsurf__quantile__q_0.1</th>\n",
+       "      <th>SMsurf__quantile__q_0.2</th>\n",
+       "      <th>SMsurf__quantile__q_0.3</th>\n",
+       "      <th>SMsurf__quantile__q_0.4</th>\n",
+       "      <th>SMsurf__quantile__q_0.6</th>\n",
+       "      <th>SMsurf__quantile__q_0.7</th>\n",
+       "      <th>SMsurf__quantile__q_0.8</th>\n",
+       "      <th>SMsurf__quantile__q_0.9</th>\n",
+       "      <th>SMsurf__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_0__w_10</th>\n",
+       "      <th>SMsurf__abs_energy</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2.844510e+00</td>\n",
+       "      <td>3.270925e-01</td>\n",
+       "      <td>6.541851e-01</td>\n",
+       "      <td>1.686568e+00</td>\n",
+       "      <td>1.686568e+00</td>\n",
+       "      <td>1.686568e+00</td>\n",
+       "      <td>1.686568e+00</td>\n",
+       "      <td>1.686568e+00</td>\n",
+       "      <td>1.686568e+00</td>\n",
+       "      <td>1.686568e+00</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.201334</td>\n",
+       "      <td>0.201334</td>\n",
+       "      <td>0.201334</td>\n",
+       "      <td>0.201334</td>\n",
+       "      <td>0.201334</td>\n",
+       "      <td>0.201334</td>\n",
+       "      <td>0.201334</td>\n",
+       "      <td>0.201334</td>\n",
+       "      <td>0.055220</td>\n",
+       "      <td>0.040535</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.291710e+02</td>\n",
+       "      <td>2.204192e+00</td>\n",
+       "      <td>4.408384e+00</td>\n",
+       "      <td>1.136534e+01</td>\n",
+       "      <td>1.136534e+01</td>\n",
+       "      <td>1.136534e+01</td>\n",
+       "      <td>1.136534e+01</td>\n",
+       "      <td>1.136534e+01</td>\n",
+       "      <td>1.136534e+01</td>\n",
+       "      <td>1.136534e+01</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.198097</td>\n",
+       "      <td>0.198097</td>\n",
+       "      <td>0.198097</td>\n",
+       "      <td>0.198097</td>\n",
+       "      <td>0.198097</td>\n",
+       "      <td>0.198097</td>\n",
+       "      <td>0.198097</td>\n",
+       "      <td>0.198097</td>\n",
+       "      <td>0.054332</td>\n",
+       "      <td>0.039242</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.893875e+02</td>\n",
+       "      <td>4.290356e+00</td>\n",
+       "      <td>8.580712e+00</td>\n",
+       "      <td>2.212210e+01</td>\n",
+       "      <td>2.212210e+01</td>\n",
+       "      <td>2.212210e+01</td>\n",
+       "      <td>2.212210e+01</td>\n",
+       "      <td>2.212210e+01</td>\n",
+       "      <td>2.212210e+01</td>\n",
+       "      <td>2.212210e+01</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.212813</td>\n",
+       "      <td>0.212813</td>\n",
+       "      <td>0.212813</td>\n",
+       "      <td>0.212813</td>\n",
+       "      <td>0.212813</td>\n",
+       "      <td>0.212813</td>\n",
+       "      <td>0.212813</td>\n",
+       "      <td>0.212813</td>\n",
+       "      <td>0.058369</td>\n",
+       "      <td>0.045289</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.767665e+04</td>\n",
+       "      <td>2.578499e+01</td>\n",
+       "      <td>5.156997e+01</td>\n",
+       "      <td>1.329536e+02</td>\n",
+       "      <td>1.329536e+02</td>\n",
+       "      <td>1.329536e+02</td>\n",
+       "      <td>1.329536e+02</td>\n",
+       "      <td>1.329536e+02</td>\n",
+       "      <td>1.329536e+02</td>\n",
+       "      <td>1.329536e+02</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.289586</td>\n",
+       "      <td>0.289586</td>\n",
+       "      <td>0.289586</td>\n",
+       "      <td>0.289586</td>\n",
+       "      <td>0.289586</td>\n",
+       "      <td>0.289586</td>\n",
+       "      <td>0.289586</td>\n",
+       "      <td>0.289586</td>\n",
+       "      <td>0.079426</td>\n",
+       "      <td>0.083860</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2.725624e+04</td>\n",
+       "      <td>3.201842e+01</td>\n",
+       "      <td>6.403683e+01</td>\n",
+       "      <td>1.650946e+02</td>\n",
+       "      <td>1.650946e+02</td>\n",
+       "      <td>1.650946e+02</td>\n",
+       "      <td>1.650946e+02</td>\n",
+       "      <td>1.650946e+02</td>\n",
+       "      <td>1.650946e+02</td>\n",
+       "      <td>1.650946e+02</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.320928</td>\n",
+       "      <td>0.320928</td>\n",
+       "      <td>0.320928</td>\n",
+       "      <td>0.320928</td>\n",
+       "      <td>0.320928</td>\n",
+       "      <td>0.320928</td>\n",
+       "      <td>0.320928</td>\n",
+       "      <td>0.320928</td>\n",
+       "      <td>0.088022</td>\n",
+       "      <td>0.102995</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1.892950e+04</td>\n",
+       "      <td>2.668311e+01</td>\n",
+       "      <td>5.336622e+01</td>\n",
+       "      <td>1.375845e+02</td>\n",
+       "      <td>1.375845e+02</td>\n",
+       "      <td>1.375845e+02</td>\n",
+       "      <td>1.375845e+02</td>\n",
+       "      <td>1.375845e+02</td>\n",
+       "      <td>1.375845e+02</td>\n",
+       "      <td>1.375845e+02</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.355562</td>\n",
+       "      <td>0.355562</td>\n",
+       "      <td>0.355562</td>\n",
+       "      <td>0.355562</td>\n",
+       "      <td>0.355562</td>\n",
+       "      <td>0.355562</td>\n",
+       "      <td>0.355562</td>\n",
+       "      <td>0.355562</td>\n",
+       "      <td>0.097521</td>\n",
+       "      <td>0.126424</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1.580527e+04</td>\n",
+       "      <td>2.438191e+01</td>\n",
+       "      <td>4.876383e+01</td>\n",
+       "      <td>1.257190e+02</td>\n",
+       "      <td>1.257190e+02</td>\n",
+       "      <td>1.257190e+02</td>\n",
+       "      <td>1.257190e+02</td>\n",
+       "      <td>1.257190e+02</td>\n",
+       "      <td>1.257190e+02</td>\n",
+       "      <td>1.257190e+02</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.339140</td>\n",
+       "      <td>0.339140</td>\n",
+       "      <td>0.339140</td>\n",
+       "      <td>0.339140</td>\n",
+       "      <td>0.339140</td>\n",
+       "      <td>0.339140</td>\n",
+       "      <td>0.339140</td>\n",
+       "      <td>0.339140</td>\n",
+       "      <td>0.093017</td>\n",
+       "      <td>0.115016</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>6.601672e+03</td>\n",
+       "      <td>1.575774e+01</td>\n",
+       "      <td>3.151547e+01</td>\n",
+       "      <td>8.125067e+01</td>\n",
+       "      <td>8.125067e+01</td>\n",
+       "      <td>8.125067e+01</td>\n",
+       "      <td>8.125067e+01</td>\n",
+       "      <td>8.125067e+01</td>\n",
+       "      <td>8.125067e+01</td>\n",
+       "      <td>8.125067e+01</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.310640</td>\n",
+       "      <td>0.310640</td>\n",
+       "      <td>0.310640</td>\n",
+       "      <td>0.310640</td>\n",
+       "      <td>0.310640</td>\n",
+       "      <td>0.310640</td>\n",
+       "      <td>0.310640</td>\n",
+       "      <td>0.310640</td>\n",
+       "      <td>0.085200</td>\n",
+       "      <td>0.096497</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>3.612336e+02</td>\n",
+       "      <td>3.686048e+00</td>\n",
+       "      <td>7.372095e+00</td>\n",
+       "      <td>1.900615e+01</td>\n",
+       "      <td>1.900615e+01</td>\n",
+       "      <td>1.900615e+01</td>\n",
+       "      <td>1.900615e+01</td>\n",
+       "      <td>1.900615e+01</td>\n",
+       "      <td>1.900615e+01</td>\n",
+       "      <td>1.900615e+01</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.272203</td>\n",
+       "      <td>0.272203</td>\n",
+       "      <td>0.272203</td>\n",
+       "      <td>0.272203</td>\n",
+       "      <td>0.272203</td>\n",
+       "      <td>0.272203</td>\n",
+       "      <td>0.272203</td>\n",
+       "      <td>0.272203</td>\n",
+       "      <td>0.074658</td>\n",
+       "      <td>0.074095</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>5.702441e-01</td>\n",
+       "      <td>1.464527e-01</td>\n",
+       "      <td>2.929053e-01</td>\n",
+       "      <td>7.551451e-01</td>\n",
+       "      <td>7.551451e-01</td>\n",
+       "      <td>7.551451e-01</td>\n",
+       "      <td>7.551451e-01</td>\n",
+       "      <td>7.551451e-01</td>\n",
+       "      <td>7.551451e-01</td>\n",
+       "      <td>7.551451e-01</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.227428</td>\n",
+       "      <td>0.227428</td>\n",
+       "      <td>0.227428</td>\n",
+       "      <td>0.227428</td>\n",
+       "      <td>0.227428</td>\n",
+       "      <td>0.227428</td>\n",
+       "      <td>0.227428</td>\n",
+       "      <td>0.227428</td>\n",
+       "      <td>0.062377</td>\n",
+       "      <td>0.051724</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>3.311114e-03</td>\n",
+       "      <td>1.115974e-02</td>\n",
+       "      <td>2.231947e-02</td>\n",
+       "      <td>5.754228e-02</td>\n",
+       "      <td>5.754228e-02</td>\n",
+       "      <td>5.754228e-02</td>\n",
+       "      <td>5.754228e-02</td>\n",
+       "      <td>5.754228e-02</td>\n",
+       "      <td>5.754228e-02</td>\n",
+       "      <td>5.754228e-02</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.211096</td>\n",
+       "      <td>0.211096</td>\n",
+       "      <td>0.211096</td>\n",
+       "      <td>0.211096</td>\n",
+       "      <td>0.211096</td>\n",
+       "      <td>0.211096</td>\n",
+       "      <td>0.211096</td>\n",
+       "      <td>0.211096</td>\n",
+       "      <td>0.057898</td>\n",
+       "      <td>0.044561</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>6.549102e-16</td>\n",
+       "      <td>4.963154e-09</td>\n",
+       "      <td>9.926308e-09</td>\n",
+       "      <td>2.559121e-08</td>\n",
+       "      <td>2.559121e-08</td>\n",
+       "      <td>2.559121e-08</td>\n",
+       "      <td>2.559121e-08</td>\n",
+       "      <td>2.559121e-08</td>\n",
+       "      <td>2.559121e-08</td>\n",
+       "      <td>2.559121e-08</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.202564</td>\n",
+       "      <td>0.202564</td>\n",
+       "      <td>0.202564</td>\n",
+       "      <td>0.202564</td>\n",
+       "      <td>0.202564</td>\n",
+       "      <td>0.202564</td>\n",
+       "      <td>0.202564</td>\n",
+       "      <td>0.202564</td>\n",
+       "      <td>0.055558</td>\n",
+       "      <td>0.041032</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>5.970195e-01</td>\n",
+       "      <td>1.498515e-01</td>\n",
+       "      <td>2.997031e-01</td>\n",
+       "      <td>7.726704e-01</td>\n",
+       "      <td>7.726704e-01</td>\n",
+       "      <td>7.726704e-01</td>\n",
+       "      <td>7.726704e-01</td>\n",
+       "      <td>7.726704e-01</td>\n",
+       "      <td>7.726704e-01</td>\n",
+       "      <td>7.726704e-01</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.197926</td>\n",
+       "      <td>0.197926</td>\n",
+       "      <td>0.197926</td>\n",
+       "      <td>0.197926</td>\n",
+       "      <td>0.197926</td>\n",
+       "      <td>0.197926</td>\n",
+       "      <td>0.197926</td>\n",
+       "      <td>0.197926</td>\n",
+       "      <td>0.054285</td>\n",
+       "      <td>0.039175</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>1.023915e+03</td>\n",
+       "      <td>6.205814e+00</td>\n",
+       "      <td>1.241163e+01</td>\n",
+       "      <td>3.199866e+01</td>\n",
+       "      <td>3.199866e+01</td>\n",
+       "      <td>3.199866e+01</td>\n",
+       "      <td>3.199866e+01</td>\n",
+       "      <td>3.199866e+01</td>\n",
+       "      <td>3.199866e+01</td>\n",
+       "      <td>3.199866e+01</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.236978</td>\n",
+       "      <td>0.236978</td>\n",
+       "      <td>0.236978</td>\n",
+       "      <td>0.236978</td>\n",
+       "      <td>0.236978</td>\n",
+       "      <td>0.236978</td>\n",
+       "      <td>0.236978</td>\n",
+       "      <td>0.236978</td>\n",
+       "      <td>0.064996</td>\n",
+       "      <td>0.056158</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>1.736040e+04</td>\n",
+       "      <td>2.555329e+01</td>\n",
+       "      <td>5.110658e+01</td>\n",
+       "      <td>1.317589e+02</td>\n",
+       "      <td>1.317589e+02</td>\n",
+       "      <td>1.317589e+02</td>\n",
+       "      <td>1.317589e+02</td>\n",
+       "      <td>1.317589e+02</td>\n",
+       "      <td>1.317589e+02</td>\n",
+       "      <td>1.317589e+02</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.323990</td>\n",
+       "      <td>0.323990</td>\n",
+       "      <td>0.323990</td>\n",
+       "      <td>0.323990</td>\n",
+       "      <td>0.323990</td>\n",
+       "      <td>0.323990</td>\n",
+       "      <td>0.323990</td>\n",
+       "      <td>0.323990</td>\n",
+       "      <td>0.088861</td>\n",
+       "      <td>0.104969</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>4.271241e+04</td>\n",
+       "      <td>4.008150e+01</td>\n",
+       "      <td>8.016299e+01</td>\n",
+       "      <td>2.066698e+02</td>\n",
+       "      <td>2.066698e+02</td>\n",
+       "      <td>2.066698e+02</td>\n",
+       "      <td>2.066698e+02</td>\n",
+       "      <td>2.066698e+02</td>\n",
+       "      <td>2.066698e+02</td>\n",
+       "      <td>2.066698e+02</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.394276</td>\n",
+       "      <td>0.394276</td>\n",
+       "      <td>0.394276</td>\n",
+       "      <td>0.394276</td>\n",
+       "      <td>0.394276</td>\n",
+       "      <td>0.394276</td>\n",
+       "      <td>0.394276</td>\n",
+       "      <td>0.394276</td>\n",
+       "      <td>0.108139</td>\n",
+       "      <td>0.155454</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>1.299868e+04</td>\n",
+       "      <td>2.211141e+01</td>\n",
+       "      <td>4.422282e+01</td>\n",
+       "      <td>1.140117e+02</td>\n",
+       "      <td>1.140117e+02</td>\n",
+       "      <td>1.140117e+02</td>\n",
+       "      <td>1.140117e+02</td>\n",
+       "      <td>1.140117e+02</td>\n",
+       "      <td>1.140117e+02</td>\n",
+       "      <td>1.140117e+02</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.392315</td>\n",
+       "      <td>0.392315</td>\n",
+       "      <td>0.392315</td>\n",
+       "      <td>0.392315</td>\n",
+       "      <td>0.392315</td>\n",
+       "      <td>0.392315</td>\n",
+       "      <td>0.392315</td>\n",
+       "      <td>0.392315</td>\n",
+       "      <td>0.107601</td>\n",
+       "      <td>0.153911</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>7.890460e+03</td>\n",
+       "      <td>1.722733e+01</td>\n",
+       "      <td>3.445467e+01</td>\n",
+       "      <td>8.882826e+01</td>\n",
+       "      <td>8.882826e+01</td>\n",
+       "      <td>8.882826e+01</td>\n",
+       "      <td>8.882826e+01</td>\n",
+       "      <td>8.882826e+01</td>\n",
+       "      <td>8.882826e+01</td>\n",
+       "      <td>8.882826e+01</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.358581</td>\n",
+       "      <td>0.358581</td>\n",
+       "      <td>0.358581</td>\n",
+       "      <td>0.358581</td>\n",
+       "      <td>0.358581</td>\n",
+       "      <td>0.358581</td>\n",
+       "      <td>0.358581</td>\n",
+       "      <td>0.358581</td>\n",
+       "      <td>0.098349</td>\n",
+       "      <td>0.128580</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>2.052222e+04</td>\n",
+       "      <td>2.778299e+01</td>\n",
+       "      <td>5.556598e+01</td>\n",
+       "      <td>1.432558e+02</td>\n",
+       "      <td>1.432558e+02</td>\n",
+       "      <td>1.432558e+02</td>\n",
+       "      <td>1.432558e+02</td>\n",
+       "      <td>1.432558e+02</td>\n",
+       "      <td>1.432558e+02</td>\n",
+       "      <td>1.432558e+02</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.326832</td>\n",
+       "      <td>0.326832</td>\n",
+       "      <td>0.326832</td>\n",
+       "      <td>0.326832</td>\n",
+       "      <td>0.326832</td>\n",
+       "      <td>0.326832</td>\n",
+       "      <td>0.326832</td>\n",
+       "      <td>0.326832</td>\n",
+       "      <td>0.089641</td>\n",
+       "      <td>0.106819</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>1.274387e+03</td>\n",
+       "      <td>6.923371e+00</td>\n",
+       "      <td>1.384674e+01</td>\n",
+       "      <td>3.569856e+01</td>\n",
+       "      <td>3.569856e+01</td>\n",
+       "      <td>3.569856e+01</td>\n",
+       "      <td>3.569856e+01</td>\n",
+       "      <td>3.569856e+01</td>\n",
+       "      <td>3.569856e+01</td>\n",
+       "      <td>3.569856e+01</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.286577</td>\n",
+       "      <td>0.286577</td>\n",
+       "      <td>0.286577</td>\n",
+       "      <td>0.286577</td>\n",
+       "      <td>0.286577</td>\n",
+       "      <td>0.286577</td>\n",
+       "      <td>0.286577</td>\n",
+       "      <td>0.286577</td>\n",
+       "      <td>0.078600</td>\n",
+       "      <td>0.082127</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>1.026208e+02</td>\n",
+       "      <td>1.964647e+00</td>\n",
+       "      <td>3.929294e+00</td>\n",
+       "      <td>1.013019e+01</td>\n",
+       "      <td>1.013019e+01</td>\n",
+       "      <td>1.013019e+01</td>\n",
+       "      <td>1.013019e+01</td>\n",
+       "      <td>1.013019e+01</td>\n",
+       "      <td>1.013019e+01</td>\n",
+       "      <td>1.013019e+01</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.229519</td>\n",
+       "      <td>0.229519</td>\n",
+       "      <td>0.229519</td>\n",
+       "      <td>0.229519</td>\n",
+       "      <td>0.229519</td>\n",
+       "      <td>0.229519</td>\n",
+       "      <td>0.229519</td>\n",
+       "      <td>0.229519</td>\n",
+       "      <td>0.062951</td>\n",
+       "      <td>0.052679</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>5.835920e-01</td>\n",
+       "      <td>1.481568e-01</td>\n",
+       "      <td>2.963136e-01</td>\n",
+       "      <td>7.639319e-01</td>\n",
+       "      <td>7.639319e-01</td>\n",
+       "      <td>7.639319e-01</td>\n",
+       "      <td>7.639319e-01</td>\n",
+       "      <td>7.639319e-01</td>\n",
+       "      <td>7.639319e-01</td>\n",
+       "      <td>7.639319e-01</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.206650</td>\n",
+       "      <td>0.206650</td>\n",
+       "      <td>0.206650</td>\n",
+       "      <td>0.206650</td>\n",
+       "      <td>0.206650</td>\n",
+       "      <td>0.206650</td>\n",
+       "      <td>0.206650</td>\n",
+       "      <td>0.206650</td>\n",
+       "      <td>0.056678</td>\n",
+       "      <td>0.042704</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>3.310674e-03</td>\n",
+       "      <td>1.115900e-02</td>\n",
+       "      <td>2.231799e-02</td>\n",
+       "      <td>5.753846e-02</td>\n",
+       "      <td>5.753846e-02</td>\n",
+       "      <td>5.753846e-02</td>\n",
+       "      <td>5.753846e-02</td>\n",
+       "      <td>5.753846e-02</td>\n",
+       "      <td>5.753846e-02</td>\n",
+       "      <td>5.753846e-02</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.198387</td>\n",
+       "      <td>0.198387</td>\n",
+       "      <td>0.198387</td>\n",
+       "      <td>0.198387</td>\n",
+       "      <td>0.198387</td>\n",
+       "      <td>0.198387</td>\n",
+       "      <td>0.198387</td>\n",
+       "      <td>0.198387</td>\n",
+       "      <td>0.054412</td>\n",
+       "      <td>0.039357</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>7.157011e-16</td>\n",
+       "      <td>5.188392e-09</td>\n",
+       "      <td>1.037678e-08</td>\n",
+       "      <td>2.675259e-08</td>\n",
+       "      <td>2.675259e-08</td>\n",
+       "      <td>2.675259e-08</td>\n",
+       "      <td>2.675259e-08</td>\n",
+       "      <td>2.675259e-08</td>\n",
+       "      <td>2.675259e-08</td>\n",
+       "      <td>2.675259e-08</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.194428</td>\n",
+       "      <td>0.194428</td>\n",
+       "      <td>0.194428</td>\n",
+       "      <td>0.194428</td>\n",
+       "      <td>0.194428</td>\n",
+       "      <td>0.194428</td>\n",
+       "      <td>0.194428</td>\n",
+       "      <td>0.194428</td>\n",
+       "      <td>0.053326</td>\n",
+       "      <td>0.037802</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>7.639320e-01</td>\n",
+       "      <td>1.695096e-01</td>\n",
+       "      <td>3.390192e-01</td>\n",
+       "      <td>8.740320e-01</td>\n",
+       "      <td>8.740320e-01</td>\n",
+       "      <td>8.740320e-01</td>\n",
+       "      <td>8.740320e-01</td>\n",
+       "      <td>8.740320e-01</td>\n",
+       "      <td>8.740320e-01</td>\n",
+       "      <td>8.740320e-01</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.192386</td>\n",
+       "      <td>0.192386</td>\n",
+       "      <td>0.192386</td>\n",
+       "      <td>0.192386</td>\n",
+       "      <td>0.192386</td>\n",
+       "      <td>0.192386</td>\n",
+       "      <td>0.192386</td>\n",
+       "      <td>0.192386</td>\n",
+       "      <td>0.052766</td>\n",
+       "      <td>0.037013</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>1.847691e+02</td>\n",
+       "      <td>2.636220e+00</td>\n",
+       "      <td>5.272439e+00</td>\n",
+       "      <td>1.359298e+01</td>\n",
+       "      <td>1.359298e+01</td>\n",
+       "      <td>1.359298e+01</td>\n",
+       "      <td>1.359298e+01</td>\n",
+       "      <td>1.359298e+01</td>\n",
+       "      <td>1.359298e+01</td>\n",
+       "      <td>1.359298e+01</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.202848</td>\n",
+       "      <td>0.202848</td>\n",
+       "      <td>0.202848</td>\n",
+       "      <td>0.202848</td>\n",
+       "      <td>0.202848</td>\n",
+       "      <td>0.202848</td>\n",
+       "      <td>0.202848</td>\n",
+       "      <td>0.202848</td>\n",
+       "      <td>0.055635</td>\n",
+       "      <td>0.041147</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>9.575440e+02</td>\n",
+       "      <td>6.001313e+00</td>\n",
+       "      <td>1.200263e+01</td>\n",
+       "      <td>3.094421e+01</td>\n",
+       "      <td>3.094421e+01</td>\n",
+       "      <td>3.094421e+01</td>\n",
+       "      <td>3.094421e+01</td>\n",
+       "      <td>3.094421e+01</td>\n",
+       "      <td>3.094421e+01</td>\n",
+       "      <td>3.094421e+01</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.240059</td>\n",
+       "      <td>0.240059</td>\n",
+       "      <td>0.240059</td>\n",
+       "      <td>0.240059</td>\n",
+       "      <td>0.240059</td>\n",
+       "      <td>0.240059</td>\n",
+       "      <td>0.240059</td>\n",
+       "      <td>0.240059</td>\n",
+       "      <td>0.065841</td>\n",
+       "      <td>0.057628</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>2.573116e+04</td>\n",
+       "      <td>3.110976e+01</td>\n",
+       "      <td>6.221951e+01</td>\n",
+       "      <td>1.604094e+02</td>\n",
+       "      <td>1.604094e+02</td>\n",
+       "      <td>1.604094e+02</td>\n",
+       "      <td>1.604094e+02</td>\n",
+       "      <td>1.604094e+02</td>\n",
+       "      <td>1.604094e+02</td>\n",
+       "      <td>1.604094e+02</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.321999</td>\n",
+       "      <td>0.321999</td>\n",
+       "      <td>0.321999</td>\n",
+       "      <td>0.321999</td>\n",
+       "      <td>0.321999</td>\n",
+       "      <td>0.321999</td>\n",
+       "      <td>0.321999</td>\n",
+       "      <td>0.321999</td>\n",
+       "      <td>0.088315</td>\n",
+       "      <td>0.103683</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>2.198742e+04</td>\n",
+       "      <td>2.875769e+01</td>\n",
+       "      <td>5.751538e+01</td>\n",
+       "      <td>1.482816e+02</td>\n",
+       "      <td>1.482816e+02</td>\n",
+       "      <td>1.482816e+02</td>\n",
+       "      <td>1.482816e+02</td>\n",
+       "      <td>1.482816e+02</td>\n",
+       "      <td>1.482816e+02</td>\n",
+       "      <td>1.482816e+02</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.384347</td>\n",
+       "      <td>0.384347</td>\n",
+       "      <td>0.384347</td>\n",
+       "      <td>0.384347</td>\n",
+       "      <td>0.384347</td>\n",
+       "      <td>0.384347</td>\n",
+       "      <td>0.384347</td>\n",
+       "      <td>0.384347</td>\n",
+       "      <td>0.105416</td>\n",
+       "      <td>0.147723</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>1.364073e+04</td>\n",
+       "      <td>2.265091e+01</td>\n",
+       "      <td>4.530182e+01</td>\n",
+       "      <td>1.167935e+02</td>\n",
+       "      <td>1.167935e+02</td>\n",
+       "      <td>1.167935e+02</td>\n",
+       "      <td>1.167935e+02</td>\n",
+       "      <td>1.167935e+02</td>\n",
+       "      <td>1.167935e+02</td>\n",
+       "      <td>1.167935e+02</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.385320</td>\n",
+       "      <td>0.385320</td>\n",
+       "      <td>0.385320</td>\n",
+       "      <td>0.385320</td>\n",
+       "      <td>0.385320</td>\n",
+       "      <td>0.385320</td>\n",
+       "      <td>0.385320</td>\n",
+       "      <td>0.385320</td>\n",
+       "      <td>0.105683</td>\n",
+       "      <td>0.148472</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>322</th>\n",
+       "      <td>3.888270e-01</td>\n",
+       "      <td>1.209331e-01</td>\n",
+       "      <td>2.418662e-01</td>\n",
+       "      <td>6.235600e-01</td>\n",
+       "      <td>6.235600e-01</td>\n",
+       "      <td>6.235600e-01</td>\n",
+       "      <td>6.235600e-01</td>\n",
+       "      <td>6.235600e-01</td>\n",
+       "      <td>6.235600e-01</td>\n",
+       "      <td>6.235600e-01</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.250397</td>\n",
+       "      <td>0.250397</td>\n",
+       "      <td>0.250397</td>\n",
+       "      <td>0.250397</td>\n",
+       "      <td>0.250397</td>\n",
+       "      <td>0.250397</td>\n",
+       "      <td>0.250397</td>\n",
+       "      <td>0.250397</td>\n",
+       "      <td>0.068677</td>\n",
+       "      <td>0.062699</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>323</th>\n",
+       "      <td>2.282750e-03</td>\n",
+       "      <td>9.266081e-03</td>\n",
+       "      <td>1.853216e-02</td>\n",
+       "      <td>4.777813e-02</td>\n",
+       "      <td>4.777813e-02</td>\n",
+       "      <td>4.777813e-02</td>\n",
+       "      <td>4.777813e-02</td>\n",
+       "      <td>4.777813e-02</td>\n",
+       "      <td>4.777813e-02</td>\n",
+       "      <td>4.777813e-02</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.221636</td>\n",
+       "      <td>0.221636</td>\n",
+       "      <td>0.221636</td>\n",
+       "      <td>0.221636</td>\n",
+       "      <td>0.221636</td>\n",
+       "      <td>0.221636</td>\n",
+       "      <td>0.221636</td>\n",
+       "      <td>0.221636</td>\n",
+       "      <td>0.060789</td>\n",
+       "      <td>0.049123</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>324</th>\n",
+       "      <td>3.975254e-16</td>\n",
+       "      <td>3.866779e-09</td>\n",
+       "      <td>7.733558e-09</td>\n",
+       "      <td>1.993804e-08</td>\n",
+       "      <td>1.993804e-08</td>\n",
+       "      <td>1.993804e-08</td>\n",
+       "      <td>1.993804e-08</td>\n",
+       "      <td>1.993804e-08</td>\n",
+       "      <td>1.993804e-08</td>\n",
+       "      <td>1.993804e-08</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.206155</td>\n",
+       "      <td>0.206155</td>\n",
+       "      <td>0.206155</td>\n",
+       "      <td>0.206155</td>\n",
+       "      <td>0.206155</td>\n",
+       "      <td>0.206155</td>\n",
+       "      <td>0.206155</td>\n",
+       "      <td>0.206155</td>\n",
+       "      <td>0.056542</td>\n",
+       "      <td>0.042500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>325</th>\n",
+       "      <td>6.226304e-01</td>\n",
+       "      <td>1.530319e-01</td>\n",
+       "      <td>3.060639e-01</td>\n",
+       "      <td>7.890693e-01</td>\n",
+       "      <td>7.890693e-01</td>\n",
+       "      <td>7.890693e-01</td>\n",
+       "      <td>7.890693e-01</td>\n",
+       "      <td>7.890693e-01</td>\n",
+       "      <td>7.890693e-01</td>\n",
+       "      <td>7.890693e-01</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.197854</td>\n",
+       "      <td>0.197854</td>\n",
+       "      <td>0.197854</td>\n",
+       "      <td>0.197854</td>\n",
+       "      <td>0.197854</td>\n",
+       "      <td>0.197854</td>\n",
+       "      <td>0.197854</td>\n",
+       "      <td>0.197854</td>\n",
+       "      <td>0.054266</td>\n",
+       "      <td>0.039146</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>326</th>\n",
+       "      <td>7.373860e+02</td>\n",
+       "      <td>5.266406e+00</td>\n",
+       "      <td>1.053281e+01</td>\n",
+       "      <td>2.715485e+01</td>\n",
+       "      <td>2.715485e+01</td>\n",
+       "      <td>2.715485e+01</td>\n",
+       "      <td>2.715485e+01</td>\n",
+       "      <td>2.715485e+01</td>\n",
+       "      <td>2.715485e+01</td>\n",
+       "      <td>2.715485e+01</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.213133</td>\n",
+       "      <td>0.213133</td>\n",
+       "      <td>0.213133</td>\n",
+       "      <td>0.213133</td>\n",
+       "      <td>0.213133</td>\n",
+       "      <td>0.213133</td>\n",
+       "      <td>0.213133</td>\n",
+       "      <td>0.213133</td>\n",
+       "      <td>0.058456</td>\n",
+       "      <td>0.045426</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>327</th>\n",
+       "      <td>6.849918e+03</td>\n",
+       "      <td>1.605128e+01</td>\n",
+       "      <td>3.210255e+01</td>\n",
+       "      <td>8.276423e+01</td>\n",
+       "      <td>8.276423e+01</td>\n",
+       "      <td>8.276423e+01</td>\n",
+       "      <td>8.276423e+01</td>\n",
+       "      <td>8.276423e+01</td>\n",
+       "      <td>8.276423e+01</td>\n",
+       "      <td>8.276423e+01</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.257264</td>\n",
+       "      <td>0.257264</td>\n",
+       "      <td>0.257264</td>\n",
+       "      <td>0.257264</td>\n",
+       "      <td>0.257264</td>\n",
+       "      <td>0.257264</td>\n",
+       "      <td>0.257264</td>\n",
+       "      <td>0.257264</td>\n",
+       "      <td>0.070561</td>\n",
+       "      <td>0.066185</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>328</th>\n",
+       "      <td>2.381396e+04</td>\n",
+       "      <td>2.992834e+01</td>\n",
+       "      <td>5.985669e+01</td>\n",
+       "      <td>1.543177e+02</td>\n",
+       "      <td>1.543177e+02</td>\n",
+       "      <td>1.543177e+02</td>\n",
+       "      <td>1.543177e+02</td>\n",
+       "      <td>1.543177e+02</td>\n",
+       "      <td>1.543177e+02</td>\n",
+       "      <td>1.543177e+02</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.313676</td>\n",
+       "      <td>0.313676</td>\n",
+       "      <td>0.313676</td>\n",
+       "      <td>0.313676</td>\n",
+       "      <td>0.313676</td>\n",
+       "      <td>0.313676</td>\n",
+       "      <td>0.313676</td>\n",
+       "      <td>0.313676</td>\n",
+       "      <td>0.086033</td>\n",
+       "      <td>0.098392</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>329</th>\n",
+       "      <td>3.738296e+04</td>\n",
+       "      <td>3.749762e+01</td>\n",
+       "      <td>7.499525e+01</td>\n",
+       "      <td>1.933467e+02</td>\n",
+       "      <td>1.933467e+02</td>\n",
+       "      <td>1.933467e+02</td>\n",
+       "      <td>1.933467e+02</td>\n",
+       "      <td>1.933467e+02</td>\n",
+       "      <td>1.933467e+02</td>\n",
+       "      <td>1.933467e+02</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.354992</td>\n",
+       "      <td>0.354992</td>\n",
+       "      <td>0.354992</td>\n",
+       "      <td>0.354992</td>\n",
+       "      <td>0.354992</td>\n",
+       "      <td>0.354992</td>\n",
+       "      <td>0.354992</td>\n",
+       "      <td>0.354992</td>\n",
+       "      <td>0.097364</td>\n",
+       "      <td>0.126019</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>330</th>\n",
+       "      <td>1.030475e+04</td>\n",
+       "      <td>1.968727e+01</td>\n",
+       "      <td>3.937455e+01</td>\n",
+       "      <td>1.015123e+02</td>\n",
+       "      <td>1.015123e+02</td>\n",
+       "      <td>1.015123e+02</td>\n",
+       "      <td>1.015123e+02</td>\n",
+       "      <td>1.015123e+02</td>\n",
+       "      <td>1.015123e+02</td>\n",
+       "      <td>1.015123e+02</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.390145</td>\n",
+       "      <td>0.390145</td>\n",
+       "      <td>0.390145</td>\n",
+       "      <td>0.390145</td>\n",
+       "      <td>0.390145</td>\n",
+       "      <td>0.390145</td>\n",
+       "      <td>0.390145</td>\n",
+       "      <td>0.390145</td>\n",
+       "      <td>0.107006</td>\n",
+       "      <td>0.152213</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>331</th>\n",
+       "      <td>3.743301e+04</td>\n",
+       "      <td>3.752272e+01</td>\n",
+       "      <td>7.504544e+01</td>\n",
+       "      <td>1.934761e+02</td>\n",
+       "      <td>1.934761e+02</td>\n",
+       "      <td>1.934761e+02</td>\n",
+       "      <td>1.934761e+02</td>\n",
+       "      <td>1.934761e+02</td>\n",
+       "      <td>1.934761e+02</td>\n",
+       "      <td>1.934761e+02</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.394721</td>\n",
+       "      <td>0.394721</td>\n",
+       "      <td>0.394721</td>\n",
+       "      <td>0.394721</td>\n",
+       "      <td>0.394721</td>\n",
+       "      <td>0.394721</td>\n",
+       "      <td>0.394721</td>\n",
+       "      <td>0.394721</td>\n",
+       "      <td>0.108261</td>\n",
+       "      <td>0.155805</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>332</th>\n",
+       "      <td>1.117129e+04</td>\n",
+       "      <td>2.049833e+01</td>\n",
+       "      <td>4.099666e+01</td>\n",
+       "      <td>1.056943e+02</td>\n",
+       "      <td>1.056943e+02</td>\n",
+       "      <td>1.056943e+02</td>\n",
+       "      <td>1.056943e+02</td>\n",
+       "      <td>1.056943e+02</td>\n",
+       "      <td>1.056943e+02</td>\n",
+       "      <td>1.056943e+02</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.350828</td>\n",
+       "      <td>0.350828</td>\n",
+       "      <td>0.350828</td>\n",
+       "      <td>0.350828</td>\n",
+       "      <td>0.350828</td>\n",
+       "      <td>0.350828</td>\n",
+       "      <td>0.350828</td>\n",
+       "      <td>0.350828</td>\n",
+       "      <td>0.096222</td>\n",
+       "      <td>0.123080</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>333</th>\n",
+       "      <td>2.149044e+02</td>\n",
+       "      <td>2.843083e+00</td>\n",
+       "      <td>5.686166e+00</td>\n",
+       "      <td>1.465962e+01</td>\n",
+       "      <td>1.465962e+01</td>\n",
+       "      <td>1.465962e+01</td>\n",
+       "      <td>1.465962e+01</td>\n",
+       "      <td>1.465962e+01</td>\n",
+       "      <td>1.465962e+01</td>\n",
+       "      <td>1.465962e+01</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.274056</td>\n",
+       "      <td>0.274056</td>\n",
+       "      <td>0.274056</td>\n",
+       "      <td>0.274056</td>\n",
+       "      <td>0.274056</td>\n",
+       "      <td>0.274056</td>\n",
+       "      <td>0.274056</td>\n",
+       "      <td>0.274056</td>\n",
+       "      <td>0.075166</td>\n",
+       "      <td>0.075107</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>334</th>\n",
+       "      <td>5.113095e+00</td>\n",
+       "      <td>4.385396e-01</td>\n",
+       "      <td>8.770793e-01</td>\n",
+       "      <td>2.261215e+00</td>\n",
+       "      <td>2.261215e+00</td>\n",
+       "      <td>2.261215e+00</td>\n",
+       "      <td>2.261215e+00</td>\n",
+       "      <td>2.261215e+00</td>\n",
+       "      <td>2.261215e+00</td>\n",
+       "      <td>2.261215e+00</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.232280</td>\n",
+       "      <td>0.232280</td>\n",
+       "      <td>0.232280</td>\n",
+       "      <td>0.232280</td>\n",
+       "      <td>0.232280</td>\n",
+       "      <td>0.232280</td>\n",
+       "      <td>0.232280</td>\n",
+       "      <td>0.232280</td>\n",
+       "      <td>0.063708</td>\n",
+       "      <td>0.053954</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>335</th>\n",
+       "      <td>2.280952e-03</td>\n",
+       "      <td>9.262431e-03</td>\n",
+       "      <td>1.852486e-02</td>\n",
+       "      <td>4.775932e-02</td>\n",
+       "      <td>4.775932e-02</td>\n",
+       "      <td>4.775932e-02</td>\n",
+       "      <td>4.775932e-02</td>\n",
+       "      <td>4.775932e-02</td>\n",
+       "      <td>4.775932e-02</td>\n",
+       "      <td>4.775932e-02</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.207676</td>\n",
+       "      <td>0.207676</td>\n",
+       "      <td>0.207676</td>\n",
+       "      <td>0.207676</td>\n",
+       "      <td>0.207676</td>\n",
+       "      <td>0.207676</td>\n",
+       "      <td>0.207676</td>\n",
+       "      <td>0.207676</td>\n",
+       "      <td>0.056960</td>\n",
+       "      <td>0.043129</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>336</th>\n",
+       "      <td>3.975258e-16</td>\n",
+       "      <td>3.866781e-09</td>\n",
+       "      <td>7.733562e-09</td>\n",
+       "      <td>1.993805e-08</td>\n",
+       "      <td>1.993805e-08</td>\n",
+       "      <td>1.993805e-08</td>\n",
+       "      <td>1.993805e-08</td>\n",
+       "      <td>1.993805e-08</td>\n",
+       "      <td>1.993805e-08</td>\n",
+       "      <td>1.993805e-08</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.197751</td>\n",
+       "      <td>0.197751</td>\n",
+       "      <td>0.197751</td>\n",
+       "      <td>0.197751</td>\n",
+       "      <td>0.197751</td>\n",
+       "      <td>0.197751</td>\n",
+       "      <td>0.197751</td>\n",
+       "      <td>0.197751</td>\n",
+       "      <td>0.054238</td>\n",
+       "      <td>0.039106</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>337</th>\n",
+       "      <td>7.022859e-01</td>\n",
+       "      <td>1.625264e-01</td>\n",
+       "      <td>3.250528e-01</td>\n",
+       "      <td>8.380250e-01</td>\n",
+       "      <td>8.380250e-01</td>\n",
+       "      <td>8.380250e-01</td>\n",
+       "      <td>8.380250e-01</td>\n",
+       "      <td>8.380250e-01</td>\n",
+       "      <td>8.380250e-01</td>\n",
+       "      <td>8.380250e-01</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.194307</td>\n",
+       "      <td>0.194307</td>\n",
+       "      <td>0.194307</td>\n",
+       "      <td>0.194307</td>\n",
+       "      <td>0.194307</td>\n",
+       "      <td>0.194307</td>\n",
+       "      <td>0.194307</td>\n",
+       "      <td>0.194307</td>\n",
+       "      <td>0.053293</td>\n",
+       "      <td>0.037755</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>338</th>\n",
+       "      <td>8.961427e+01</td>\n",
+       "      <td>1.835927e+00</td>\n",
+       "      <td>3.671855e+00</td>\n",
+       "      <td>9.466481e+00</td>\n",
+       "      <td>9.466481e+00</td>\n",
+       "      <td>9.466481e+00</td>\n",
+       "      <td>9.466481e+00</td>\n",
+       "      <td>9.466481e+00</td>\n",
+       "      <td>9.466481e+00</td>\n",
+       "      <td>9.466481e+00</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.192960</td>\n",
+       "      <td>0.192960</td>\n",
+       "      <td>0.192960</td>\n",
+       "      <td>0.192960</td>\n",
+       "      <td>0.192960</td>\n",
+       "      <td>0.192960</td>\n",
+       "      <td>0.192960</td>\n",
+       "      <td>0.192960</td>\n",
+       "      <td>0.052923</td>\n",
+       "      <td>0.037233</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>339</th>\n",
+       "      <td>6.061048e+03</td>\n",
+       "      <td>1.509874e+01</td>\n",
+       "      <td>3.019748e+01</td>\n",
+       "      <td>7.785273e+01</td>\n",
+       "      <td>7.785273e+01</td>\n",
+       "      <td>7.785273e+01</td>\n",
+       "      <td>7.785273e+01</td>\n",
+       "      <td>7.785273e+01</td>\n",
+       "      <td>7.785273e+01</td>\n",
+       "      <td>7.785273e+01</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.280762</td>\n",
+       "      <td>0.280762</td>\n",
+       "      <td>0.280762</td>\n",
+       "      <td>0.280762</td>\n",
+       "      <td>0.280762</td>\n",
+       "      <td>0.280762</td>\n",
+       "      <td>0.280762</td>\n",
+       "      <td>0.280762</td>\n",
+       "      <td>0.077005</td>\n",
+       "      <td>0.078827</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>340</th>\n",
+       "      <td>4.294590e+04</td>\n",
+       "      <td>4.019090e+01</td>\n",
+       "      <td>8.038180e+01</td>\n",
+       "      <td>2.072339e+02</td>\n",
+       "      <td>2.072339e+02</td>\n",
+       "      <td>2.072339e+02</td>\n",
+       "      <td>2.072339e+02</td>\n",
+       "      <td>2.072339e+02</td>\n",
+       "      <td>2.072339e+02</td>\n",
+       "      <td>2.072339e+02</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.318195</td>\n",
+       "      <td>0.318195</td>\n",
+       "      <td>0.318195</td>\n",
+       "      <td>0.318195</td>\n",
+       "      <td>0.318195</td>\n",
+       "      <td>0.318195</td>\n",
+       "      <td>0.318195</td>\n",
+       "      <td>0.318195</td>\n",
+       "      <td>0.087272</td>\n",
+       "      <td>0.101248</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>341</th>\n",
+       "      <td>2.396394e+04</td>\n",
+       "      <td>3.002244e+01</td>\n",
+       "      <td>6.004489e+01</td>\n",
+       "      <td>1.548029e+02</td>\n",
+       "      <td>1.548029e+02</td>\n",
+       "      <td>1.548029e+02</td>\n",
+       "      <td>1.548029e+02</td>\n",
+       "      <td>1.548029e+02</td>\n",
+       "      <td>1.548029e+02</td>\n",
+       "      <td>1.548029e+02</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.378143</td>\n",
+       "      <td>0.378143</td>\n",
+       "      <td>0.378143</td>\n",
+       "      <td>0.378143</td>\n",
+       "      <td>0.378143</td>\n",
+       "      <td>0.378143</td>\n",
+       "      <td>0.378143</td>\n",
+       "      <td>0.378143</td>\n",
+       "      <td>0.103714</td>\n",
+       "      <td>0.142992</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>342</th>\n",
+       "      <td>1.932424e+04</td>\n",
+       "      <td>2.695989e+01</td>\n",
+       "      <td>5.391978e+01</td>\n",
+       "      <td>1.390116e+02</td>\n",
+       "      <td>1.390116e+02</td>\n",
+       "      <td>1.390116e+02</td>\n",
+       "      <td>1.390116e+02</td>\n",
+       "      <td>1.390116e+02</td>\n",
+       "      <td>1.390116e+02</td>\n",
+       "      <td>1.390116e+02</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.386863</td>\n",
+       "      <td>0.386863</td>\n",
+       "      <td>0.386863</td>\n",
+       "      <td>0.386863</td>\n",
+       "      <td>0.386863</td>\n",
+       "      <td>0.386863</td>\n",
+       "      <td>0.386863</td>\n",
+       "      <td>0.386863</td>\n",
+       "      <td>0.106106</td>\n",
+       "      <td>0.149663</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>343</th>\n",
+       "      <td>2.407531e+04</td>\n",
+       "      <td>3.009212e+01</td>\n",
+       "      <td>6.018425e+01</td>\n",
+       "      <td>1.551622e+02</td>\n",
+       "      <td>1.551622e+02</td>\n",
+       "      <td>1.551622e+02</td>\n",
+       "      <td>1.551622e+02</td>\n",
+       "      <td>1.551622e+02</td>\n",
+       "      <td>1.551622e+02</td>\n",
+       "      <td>1.551622e+02</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.387767</td>\n",
+       "      <td>0.387767</td>\n",
+       "      <td>0.387767</td>\n",
+       "      <td>0.387767</td>\n",
+       "      <td>0.387767</td>\n",
+       "      <td>0.387767</td>\n",
+       "      <td>0.387767</td>\n",
+       "      <td>0.387767</td>\n",
+       "      <td>0.106354</td>\n",
+       "      <td>0.150363</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>344</th>\n",
+       "      <td>2.833313e+03</td>\n",
+       "      <td>1.032320e+01</td>\n",
+       "      <td>2.064639e+01</td>\n",
+       "      <td>5.322888e+01</td>\n",
+       "      <td>5.322888e+01</td>\n",
+       "      <td>5.322888e+01</td>\n",
+       "      <td>5.322888e+01</td>\n",
+       "      <td>5.322888e+01</td>\n",
+       "      <td>5.322888e+01</td>\n",
+       "      <td>5.322888e+01</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.340446</td>\n",
+       "      <td>0.340446</td>\n",
+       "      <td>0.340446</td>\n",
+       "      <td>0.340446</td>\n",
+       "      <td>0.340446</td>\n",
+       "      <td>0.340446</td>\n",
+       "      <td>0.340446</td>\n",
+       "      <td>0.340446</td>\n",
+       "      <td>0.093375</td>\n",
+       "      <td>0.115903</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>345</th>\n",
+       "      <td>6.300866e+02</td>\n",
+       "      <td>4.868185e+00</td>\n",
+       "      <td>9.736369e+00</td>\n",
+       "      <td>2.510153e+01</td>\n",
+       "      <td>2.510153e+01</td>\n",
+       "      <td>2.510153e+01</td>\n",
+       "      <td>2.510153e+01</td>\n",
+       "      <td>2.510153e+01</td>\n",
+       "      <td>2.510153e+01</td>\n",
+       "      <td>2.510153e+01</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.268003</td>\n",
+       "      <td>0.268003</td>\n",
+       "      <td>0.268003</td>\n",
+       "      <td>0.268003</td>\n",
+       "      <td>0.268003</td>\n",
+       "      <td>0.268003</td>\n",
+       "      <td>0.268003</td>\n",
+       "      <td>0.268003</td>\n",
+       "      <td>0.073506</td>\n",
+       "      <td>0.071826</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>346</th>\n",
+       "      <td>4.099368e-01</td>\n",
+       "      <td>1.241725e-01</td>\n",
+       "      <td>2.483450e-01</td>\n",
+       "      <td>6.402631e-01</td>\n",
+       "      <td>6.402631e-01</td>\n",
+       "      <td>6.402631e-01</td>\n",
+       "      <td>6.402631e-01</td>\n",
+       "      <td>6.402631e-01</td>\n",
+       "      <td>6.402631e-01</td>\n",
+       "      <td>6.402631e-01</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.228008</td>\n",
+       "      <td>0.228008</td>\n",
+       "      <td>0.228008</td>\n",
+       "      <td>0.228008</td>\n",
+       "      <td>0.228008</td>\n",
+       "      <td>0.228008</td>\n",
+       "      <td>0.228008</td>\n",
+       "      <td>0.228008</td>\n",
+       "      <td>0.062536</td>\n",
+       "      <td>0.051988</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>347</th>\n",
+       "      <td>2.299501e-03</td>\n",
+       "      <td>9.300016e-03</td>\n",
+       "      <td>1.860003e-02</td>\n",
+       "      <td>4.795311e-02</td>\n",
+       "      <td>4.795311e-02</td>\n",
+       "      <td>4.795311e-02</td>\n",
+       "      <td>4.795311e-02</td>\n",
+       "      <td>4.795311e-02</td>\n",
+       "      <td>4.795311e-02</td>\n",
+       "      <td>4.795311e-02</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.208466</td>\n",
+       "      <td>0.208466</td>\n",
+       "      <td>0.208466</td>\n",
+       "      <td>0.208466</td>\n",
+       "      <td>0.208466</td>\n",
+       "      <td>0.208466</td>\n",
+       "      <td>0.208466</td>\n",
+       "      <td>0.208466</td>\n",
+       "      <td>0.057177</td>\n",
+       "      <td>0.043458</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>348</th>\n",
+       "      <td>3.806335e-16</td>\n",
+       "      <td>3.783732e-09</td>\n",
+       "      <td>7.567464e-09</td>\n",
+       "      <td>1.950983e-08</td>\n",
+       "      <td>1.950983e-08</td>\n",
+       "      <td>1.950983e-08</td>\n",
+       "      <td>1.950983e-08</td>\n",
+       "      <td>1.950983e-08</td>\n",
+       "      <td>1.950983e-08</td>\n",
+       "      <td>1.950983e-08</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.197922</td>\n",
+       "      <td>0.197922</td>\n",
+       "      <td>0.197922</td>\n",
+       "      <td>0.197922</td>\n",
+       "      <td>0.197922</td>\n",
+       "      <td>0.197922</td>\n",
+       "      <td>0.197922</td>\n",
+       "      <td>0.197922</td>\n",
+       "      <td>0.054285</td>\n",
+       "      <td>0.039173</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>349</th>\n",
+       "      <td>7.517965e-01</td>\n",
+       "      <td>1.681578e-01</td>\n",
+       "      <td>3.363156e-01</td>\n",
+       "      <td>8.670620e-01</td>\n",
+       "      <td>8.670620e-01</td>\n",
+       "      <td>8.670620e-01</td>\n",
+       "      <td>8.670620e-01</td>\n",
+       "      <td>8.670620e-01</td>\n",
+       "      <td>8.670620e-01</td>\n",
+       "      <td>8.670620e-01</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.193599</td>\n",
+       "      <td>0.193599</td>\n",
+       "      <td>0.193599</td>\n",
+       "      <td>0.193599</td>\n",
+       "      <td>0.193599</td>\n",
+       "      <td>0.193599</td>\n",
+       "      <td>0.193599</td>\n",
+       "      <td>0.193599</td>\n",
+       "      <td>0.053099</td>\n",
+       "      <td>0.037481</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>350</th>\n",
+       "      <td>1.439186e+02</td>\n",
+       "      <td>2.326619e+00</td>\n",
+       "      <td>4.653239e+00</td>\n",
+       "      <td>1.199661e+01</td>\n",
+       "      <td>1.199661e+01</td>\n",
+       "      <td>1.199661e+01</td>\n",
+       "      <td>1.199661e+01</td>\n",
+       "      <td>1.199661e+01</td>\n",
+       "      <td>1.199661e+01</td>\n",
+       "      <td>1.199661e+01</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.204552</td>\n",
+       "      <td>0.204552</td>\n",
+       "      <td>0.204552</td>\n",
+       "      <td>0.204552</td>\n",
+       "      <td>0.204552</td>\n",
+       "      <td>0.204552</td>\n",
+       "      <td>0.204552</td>\n",
+       "      <td>0.204552</td>\n",
+       "      <td>0.056103</td>\n",
+       "      <td>0.041841</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>351</th>\n",
+       "      <td>1.358893e+03</td>\n",
+       "      <td>7.149234e+00</td>\n",
+       "      <td>1.429847e+01</td>\n",
+       "      <td>3.686316e+01</td>\n",
+       "      <td>3.686316e+01</td>\n",
+       "      <td>3.686316e+01</td>\n",
+       "      <td>3.686316e+01</td>\n",
+       "      <td>3.686316e+01</td>\n",
+       "      <td>3.686316e+01</td>\n",
+       "      <td>3.686316e+01</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.222472</td>\n",
+       "      <td>0.222472</td>\n",
+       "      <td>0.222472</td>\n",
+       "      <td>0.222472</td>\n",
+       "      <td>0.222472</td>\n",
+       "      <td>0.222472</td>\n",
+       "      <td>0.222472</td>\n",
+       "      <td>0.222472</td>\n",
+       "      <td>0.061018</td>\n",
+       "      <td>0.049494</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>351 rows × 41 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "variable  precip__abs_energy  \\\n",
+       "id                             \n",
+       "1               2.844510e+00   \n",
+       "2               1.291710e+02   \n",
+       "3               4.893875e+02   \n",
+       "4               1.767665e+04   \n",
+       "5               2.725624e+04   \n",
+       "6               1.892950e+04   \n",
+       "7               1.580527e+04   \n",
+       "8               6.601672e+03   \n",
+       "9               3.612336e+02   \n",
+       "10              5.702441e-01   \n",
+       "11              3.311114e-03   \n",
+       "12              6.549102e-16   \n",
+       "13              5.970195e-01   \n",
+       "14              1.023915e+03   \n",
+       "15              1.736040e+04   \n",
+       "16              4.271241e+04   \n",
+       "17              1.299868e+04   \n",
+       "18              7.890460e+03   \n",
+       "19              2.052222e+04   \n",
+       "20              1.274387e+03   \n",
+       "21              1.026208e+02   \n",
+       "22              5.835920e-01   \n",
+       "23              3.310674e-03   \n",
+       "24              7.157011e-16   \n",
+       "25              7.639320e-01   \n",
+       "26              1.847691e+02   \n",
+       "27              9.575440e+02   \n",
+       "28              2.573116e+04   \n",
+       "29              2.198742e+04   \n",
+       "30              1.364073e+04   \n",
+       "..                       ...   \n",
+       "322             3.888270e-01   \n",
+       "323             2.282750e-03   \n",
+       "324             3.975254e-16   \n",
+       "325             6.226304e-01   \n",
+       "326             7.373860e+02   \n",
+       "327             6.849918e+03   \n",
+       "328             2.381396e+04   \n",
+       "329             3.738296e+04   \n",
+       "330             1.030475e+04   \n",
+       "331             3.743301e+04   \n",
+       "332             1.117129e+04   \n",
+       "333             2.149044e+02   \n",
+       "334             5.113095e+00   \n",
+       "335             2.280952e-03   \n",
+       "336             3.975258e-16   \n",
+       "337             7.022859e-01   \n",
+       "338             8.961427e+01   \n",
+       "339             6.061048e+03   \n",
+       "340             4.294590e+04   \n",
+       "341             2.396394e+04   \n",
+       "342             1.932424e+04   \n",
+       "343             2.407531e+04   \n",
+       "344             2.833313e+03   \n",
+       "345             6.300866e+02   \n",
+       "346             4.099368e-01   \n",
+       "347             2.299501e-03   \n",
+       "348             3.806335e-16   \n",
+       "349             7.517965e-01   \n",
+       "350             1.439186e+02   \n",
+       "351             1.358893e+03   \n",
+       "\n",
+       "variable  precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_0__w_20  \\\n",
+       "id                                                                         \n",
+       "1                                              3.270925e-01                \n",
+       "2                                              2.204192e+00                \n",
+       "3                                              4.290356e+00                \n",
+       "4                                              2.578499e+01                \n",
+       "5                                              3.201842e+01                \n",
+       "6                                              2.668311e+01                \n",
+       "7                                              2.438191e+01                \n",
+       "8                                              1.575774e+01                \n",
+       "9                                              3.686048e+00                \n",
+       "10                                             1.464527e-01                \n",
+       "11                                             1.115974e-02                \n",
+       "12                                             4.963154e-09                \n",
+       "13                                             1.498515e-01                \n",
+       "14                                             6.205814e+00                \n",
+       "15                                             2.555329e+01                \n",
+       "16                                             4.008150e+01                \n",
+       "17                                             2.211141e+01                \n",
+       "18                                             1.722733e+01                \n",
+       "19                                             2.778299e+01                \n",
+       "20                                             6.923371e+00                \n",
+       "21                                             1.964647e+00                \n",
+       "22                                             1.481568e-01                \n",
+       "23                                             1.115900e-02                \n",
+       "24                                             5.188392e-09                \n",
+       "25                                             1.695096e-01                \n",
+       "26                                             2.636220e+00                \n",
+       "27                                             6.001313e+00                \n",
+       "28                                             3.110976e+01                \n",
+       "29                                             2.875769e+01                \n",
+       "30                                             2.265091e+01                \n",
+       "..                                                      ...                \n",
+       "322                                            1.209331e-01                \n",
+       "323                                            9.266081e-03                \n",
+       "324                                            3.866779e-09                \n",
+       "325                                            1.530319e-01                \n",
+       "326                                            5.266406e+00                \n",
+       "327                                            1.605128e+01                \n",
+       "328                                            2.992834e+01                \n",
+       "329                                            3.749762e+01                \n",
+       "330                                            1.968727e+01                \n",
+       "331                                            3.752272e+01                \n",
+       "332                                            2.049833e+01                \n",
+       "333                                            2.843083e+00                \n",
+       "334                                            4.385396e-01                \n",
+       "335                                            9.262431e-03                \n",
+       "336                                            3.866781e-09                \n",
+       "337                                            1.625264e-01                \n",
+       "338                                            1.835927e+00                \n",
+       "339                                            1.509874e+01                \n",
+       "340                                            4.019090e+01                \n",
+       "341                                            3.002244e+01                \n",
+       "342                                            2.695989e+01                \n",
+       "343                                            3.009212e+01                \n",
+       "344                                            1.032320e+01                \n",
+       "345                                            4.868185e+00                \n",
+       "346                                            1.241725e-01                \n",
+       "347                                            9.300016e-03                \n",
+       "348                                            3.783732e-09                \n",
+       "349                                            1.681578e-01                \n",
+       "350                                            2.326619e+00                \n",
+       "351                                            7.149234e+00                \n",
+       "\n",
+       "variable  precip__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_0__w_5  \\\n",
+       "id                                                                        \n",
+       "1                                              6.541851e-01               \n",
+       "2                                              4.408384e+00               \n",
+       "3                                              8.580712e+00               \n",
+       "4                                              5.156997e+01               \n",
+       "5                                              6.403683e+01               \n",
+       "6                                              5.336622e+01               \n",
+       "7                                              4.876383e+01               \n",
+       "8                                              3.151547e+01               \n",
+       "9                                              7.372095e+00               \n",
+       "10                                             2.929053e-01               \n",
+       "11                                             2.231947e-02               \n",
+       "12                                             9.926308e-09               \n",
+       "13                                             2.997031e-01               \n",
+       "14                                             1.241163e+01               \n",
+       "15                                             5.110658e+01               \n",
+       "16                                             8.016299e+01               \n",
+       "17                                             4.422282e+01               \n",
+       "18                                             3.445467e+01               \n",
+       "19                                             5.556598e+01               \n",
+       "20                                             1.384674e+01               \n",
+       "21                                             3.929294e+00               \n",
+       "22                                             2.963136e-01               \n",
+       "23                                             2.231799e-02               \n",
+       "24                                             1.037678e-08               \n",
+       "25                                             3.390192e-01               \n",
+       "26                                             5.272439e+00               \n",
+       "27                                             1.200263e+01               \n",
+       "28                                             6.221951e+01               \n",
+       "29                                             5.751538e+01               \n",
+       "30                                             4.530182e+01               \n",
+       "..                                                      ...               \n",
+       "322                                            2.418662e-01               \n",
+       "323                                            1.853216e-02               \n",
+       "324                                            7.733558e-09               \n",
+       "325                                            3.060639e-01               \n",
+       "326                                            1.053281e+01               \n",
+       "327                                            3.210255e+01               \n",
+       "328                                            5.985669e+01               \n",
+       "329                                            7.499525e+01               \n",
+       "330                                            3.937455e+01               \n",
+       "331                                            7.504544e+01               \n",
+       "332                                            4.099666e+01               \n",
+       "333                                            5.686166e+00               \n",
+       "334                                            8.770793e-01               \n",
+       "335                                            1.852486e-02               \n",
+       "336                                            7.733562e-09               \n",
+       "337                                            3.250528e-01               \n",
+       "338                                            3.671855e+00               \n",
+       "339                                            3.019748e+01               \n",
+       "340                                            8.038180e+01               \n",
+       "341                                            6.004489e+01               \n",
+       "342                                            5.391978e+01               \n",
+       "343                                            6.018425e+01               \n",
+       "344                                            2.064639e+01               \n",
+       "345                                            9.736369e+00               \n",
+       "346                                            2.483450e-01               \n",
+       "347                                            1.860003e-02               \n",
+       "348                                            7.567464e-09               \n",
+       "349                                            3.363156e-01               \n",
+       "350                                            4.653239e+00               \n",
+       "351                                            1.429847e+01               \n",
+       "\n",
+       "variable  precip__fft_coefficient__coeff_0__attr_\"abs\"  \\\n",
+       "id                                                       \n",
+       "1                                         1.686568e+00   \n",
+       "2                                         1.136534e+01   \n",
+       "3                                         2.212210e+01   \n",
+       "4                                         1.329536e+02   \n",
+       "5                                         1.650946e+02   \n",
+       "6                                         1.375845e+02   \n",
+       "7                                         1.257190e+02   \n",
+       "8                                         8.125067e+01   \n",
+       "9                                         1.900615e+01   \n",
+       "10                                        7.551451e-01   \n",
+       "11                                        5.754228e-02   \n",
+       "12                                        2.559121e-08   \n",
+       "13                                        7.726704e-01   \n",
+       "14                                        3.199866e+01   \n",
+       "15                                        1.317589e+02   \n",
+       "16                                        2.066698e+02   \n",
+       "17                                        1.140117e+02   \n",
+       "18                                        8.882826e+01   \n",
+       "19                                        1.432558e+02   \n",
+       "20                                        3.569856e+01   \n",
+       "21                                        1.013019e+01   \n",
+       "22                                        7.639319e-01   \n",
+       "23                                        5.753846e-02   \n",
+       "24                                        2.675259e-08   \n",
+       "25                                        8.740320e-01   \n",
+       "26                                        1.359298e+01   \n",
+       "27                                        3.094421e+01   \n",
+       "28                                        1.604094e+02   \n",
+       "29                                        1.482816e+02   \n",
+       "30                                        1.167935e+02   \n",
+       "..                                                 ...   \n",
+       "322                                       6.235600e-01   \n",
+       "323                                       4.777813e-02   \n",
+       "324                                       1.993804e-08   \n",
+       "325                                       7.890693e-01   \n",
+       "326                                       2.715485e+01   \n",
+       "327                                       8.276423e+01   \n",
+       "328                                       1.543177e+02   \n",
+       "329                                       1.933467e+02   \n",
+       "330                                       1.015123e+02   \n",
+       "331                                       1.934761e+02   \n",
+       "332                                       1.056943e+02   \n",
+       "333                                       1.465962e+01   \n",
+       "334                                       2.261215e+00   \n",
+       "335                                       4.775932e-02   \n",
+       "336                                       1.993805e-08   \n",
+       "337                                       8.380250e-01   \n",
+       "338                                       9.466481e+00   \n",
+       "339                                       7.785273e+01   \n",
+       "340                                       2.072339e+02   \n",
+       "341                                       1.548029e+02   \n",
+       "342                                       1.390116e+02   \n",
+       "343                                       1.551622e+02   \n",
+       "344                                       5.322888e+01   \n",
+       "345                                       2.510153e+01   \n",
+       "346                                       6.402631e-01   \n",
+       "347                                       4.795311e-02   \n",
+       "348                                       1.950983e-08   \n",
+       "349                                       8.670620e-01   \n",
+       "350                                       1.199661e+01   \n",
+       "351                                       3.686316e+01   \n",
+       "\n",
+       "variable  precip__fft_coefficient__coeff_0__attr_\"real\"  precip__maximum  \\\n",
+       "id                                                                         \n",
+       "1                                          1.686568e+00     1.686568e+00   \n",
+       "2                                          1.136534e+01     1.136534e+01   \n",
+       "3                                          2.212210e+01     2.212210e+01   \n",
+       "4                                          1.329536e+02     1.329536e+02   \n",
+       "5                                          1.650946e+02     1.650946e+02   \n",
+       "6                                          1.375845e+02     1.375845e+02   \n",
+       "7                                          1.257190e+02     1.257190e+02   \n",
+       "8                                          8.125067e+01     8.125067e+01   \n",
+       "9                                          1.900615e+01     1.900615e+01   \n",
+       "10                                         7.551451e-01     7.551451e-01   \n",
+       "11                                         5.754228e-02     5.754228e-02   \n",
+       "12                                         2.559121e-08     2.559121e-08   \n",
+       "13                                         7.726704e-01     7.726704e-01   \n",
+       "14                                         3.199866e+01     3.199866e+01   \n",
+       "15                                         1.317589e+02     1.317589e+02   \n",
+       "16                                         2.066698e+02     2.066698e+02   \n",
+       "17                                         1.140117e+02     1.140117e+02   \n",
+       "18                                         8.882826e+01     8.882826e+01   \n",
+       "19                                         1.432558e+02     1.432558e+02   \n",
+       "20                                         3.569856e+01     3.569856e+01   \n",
+       "21                                         1.013019e+01     1.013019e+01   \n",
+       "22                                         7.639319e-01     7.639319e-01   \n",
+       "23                                         5.753846e-02     5.753846e-02   \n",
+       "24                                         2.675259e-08     2.675259e-08   \n",
+       "25                                         8.740320e-01     8.740320e-01   \n",
+       "26                                         1.359298e+01     1.359298e+01   \n",
+       "27                                         3.094421e+01     3.094421e+01   \n",
+       "28                                         1.604094e+02     1.604094e+02   \n",
+       "29                                         1.482816e+02     1.482816e+02   \n",
+       "30                                         1.167935e+02     1.167935e+02   \n",
+       "..                                                  ...              ...   \n",
+       "322                                        6.235600e-01     6.235600e-01   \n",
+       "323                                        4.777813e-02     4.777813e-02   \n",
+       "324                                        1.993804e-08     1.993804e-08   \n",
+       "325                                        7.890693e-01     7.890693e-01   \n",
+       "326                                        2.715485e+01     2.715485e+01   \n",
+       "327                                        8.276423e+01     8.276423e+01   \n",
+       "328                                        1.543177e+02     1.543177e+02   \n",
+       "329                                        1.933467e+02     1.933467e+02   \n",
+       "330                                        1.015123e+02     1.015123e+02   \n",
+       "331                                        1.934761e+02     1.934761e+02   \n",
+       "332                                        1.056943e+02     1.056943e+02   \n",
+       "333                                        1.465962e+01     1.465962e+01   \n",
+       "334                                        2.261215e+00     2.261215e+00   \n",
+       "335                                        4.775932e-02     4.775932e-02   \n",
+       "336                                        1.993805e-08     1.993805e-08   \n",
+       "337                                        8.380250e-01     8.380250e-01   \n",
+       "338                                        9.466481e+00     9.466481e+00   \n",
+       "339                                        7.785273e+01     7.785273e+01   \n",
+       "340                                        2.072339e+02     2.072339e+02   \n",
+       "341                                        1.548029e+02     1.548029e+02   \n",
+       "342                                        1.390116e+02     1.390116e+02   \n",
+       "343                                        1.551622e+02     1.551622e+02   \n",
+       "344                                        5.322888e+01     5.322888e+01   \n",
+       "345                                        2.510153e+01     2.510153e+01   \n",
+       "346                                        6.402631e-01     6.402631e-01   \n",
+       "347                                        4.795311e-02     4.795311e-02   \n",
+       "348                                        1.950983e-08     1.950983e-08   \n",
+       "349                                        8.670620e-01     8.670620e-01   \n",
+       "350                                        1.199661e+01     1.199661e+01   \n",
+       "351                                        3.686316e+01     3.686316e+01   \n",
+       "\n",
+       "variable  precip__mean  precip__median  precip__minimum  \\\n",
+       "id                                                        \n",
+       "1         1.686568e+00    1.686568e+00     1.686568e+00   \n",
+       "2         1.136534e+01    1.136534e+01     1.136534e+01   \n",
+       "3         2.212210e+01    2.212210e+01     2.212210e+01   \n",
+       "4         1.329536e+02    1.329536e+02     1.329536e+02   \n",
+       "5         1.650946e+02    1.650946e+02     1.650946e+02   \n",
+       "6         1.375845e+02    1.375845e+02     1.375845e+02   \n",
+       "7         1.257190e+02    1.257190e+02     1.257190e+02   \n",
+       "8         8.125067e+01    8.125067e+01     8.125067e+01   \n",
+       "9         1.900615e+01    1.900615e+01     1.900615e+01   \n",
+       "10        7.551451e-01    7.551451e-01     7.551451e-01   \n",
+       "11        5.754228e-02    5.754228e-02     5.754228e-02   \n",
+       "12        2.559121e-08    2.559121e-08     2.559121e-08   \n",
+       "13        7.726704e-01    7.726704e-01     7.726704e-01   \n",
+       "14        3.199866e+01    3.199866e+01     3.199866e+01   \n",
+       "15        1.317589e+02    1.317589e+02     1.317589e+02   \n",
+       "16        2.066698e+02    2.066698e+02     2.066698e+02   \n",
+       "17        1.140117e+02    1.140117e+02     1.140117e+02   \n",
+       "18        8.882826e+01    8.882826e+01     8.882826e+01   \n",
+       "19        1.432558e+02    1.432558e+02     1.432558e+02   \n",
+       "20        3.569856e+01    3.569856e+01     3.569856e+01   \n",
+       "21        1.013019e+01    1.013019e+01     1.013019e+01   \n",
+       "22        7.639319e-01    7.639319e-01     7.639319e-01   \n",
+       "23        5.753846e-02    5.753846e-02     5.753846e-02   \n",
+       "24        2.675259e-08    2.675259e-08     2.675259e-08   \n",
+       "25        8.740320e-01    8.740320e-01     8.740320e-01   \n",
+       "26        1.359298e+01    1.359298e+01     1.359298e+01   \n",
+       "27        3.094421e+01    3.094421e+01     3.094421e+01   \n",
+       "28        1.604094e+02    1.604094e+02     1.604094e+02   \n",
+       "29        1.482816e+02    1.482816e+02     1.482816e+02   \n",
+       "30        1.167935e+02    1.167935e+02     1.167935e+02   \n",
+       "..                 ...             ...              ...   \n",
+       "322       6.235600e-01    6.235600e-01     6.235600e-01   \n",
+       "323       4.777813e-02    4.777813e-02     4.777813e-02   \n",
+       "324       1.993804e-08    1.993804e-08     1.993804e-08   \n",
+       "325       7.890693e-01    7.890693e-01     7.890693e-01   \n",
+       "326       2.715485e+01    2.715485e+01     2.715485e+01   \n",
+       "327       8.276423e+01    8.276423e+01     8.276423e+01   \n",
+       "328       1.543177e+02    1.543177e+02     1.543177e+02   \n",
+       "329       1.933467e+02    1.933467e+02     1.933467e+02   \n",
+       "330       1.015123e+02    1.015123e+02     1.015123e+02   \n",
+       "331       1.934761e+02    1.934761e+02     1.934761e+02   \n",
+       "332       1.056943e+02    1.056943e+02     1.056943e+02   \n",
+       "333       1.465962e+01    1.465962e+01     1.465962e+01   \n",
+       "334       2.261215e+00    2.261215e+00     2.261215e+00   \n",
+       "335       4.775932e-02    4.775932e-02     4.775932e-02   \n",
+       "336       1.993805e-08    1.993805e-08     1.993805e-08   \n",
+       "337       8.380250e-01    8.380250e-01     8.380250e-01   \n",
+       "338       9.466481e+00    9.466481e+00     9.466481e+00   \n",
+       "339       7.785273e+01    7.785273e+01     7.785273e+01   \n",
+       "340       2.072339e+02    2.072339e+02     2.072339e+02   \n",
+       "341       1.548029e+02    1.548029e+02     1.548029e+02   \n",
+       "342       1.390116e+02    1.390116e+02     1.390116e+02   \n",
+       "343       1.551622e+02    1.551622e+02     1.551622e+02   \n",
+       "344       5.322888e+01    5.322888e+01     5.322888e+01   \n",
+       "345       2.510153e+01    2.510153e+01     2.510153e+01   \n",
+       "346       6.402631e-01    6.402631e-01     6.402631e-01   \n",
+       "347       4.795311e-02    4.795311e-02     4.795311e-02   \n",
+       "348       1.950983e-08    1.950983e-08     1.950983e-08   \n",
+       "349       8.670620e-01    8.670620e-01     8.670620e-01   \n",
+       "350       1.199661e+01    1.199661e+01     1.199661e+01   \n",
+       "351       3.686316e+01    3.686316e+01     3.686316e+01   \n",
+       "\n",
+       "variable  precip__quantile__q_0.1         ...          \\\n",
+       "id                                        ...           \n",
+       "1                    1.686568e+00         ...           \n",
+       "2                    1.136534e+01         ...           \n",
+       "3                    2.212210e+01         ...           \n",
+       "4                    1.329536e+02         ...           \n",
+       "5                    1.650946e+02         ...           \n",
+       "6                    1.375845e+02         ...           \n",
+       "7                    1.257190e+02         ...           \n",
+       "8                    8.125067e+01         ...           \n",
+       "9                    1.900615e+01         ...           \n",
+       "10                   7.551451e-01         ...           \n",
+       "11                   5.754228e-02         ...           \n",
+       "12                   2.559121e-08         ...           \n",
+       "13                   7.726704e-01         ...           \n",
+       "14                   3.199866e+01         ...           \n",
+       "15                   1.317589e+02         ...           \n",
+       "16                   2.066698e+02         ...           \n",
+       "17                   1.140117e+02         ...           \n",
+       "18                   8.882826e+01         ...           \n",
+       "19                   1.432558e+02         ...           \n",
+       "20                   3.569856e+01         ...           \n",
+       "21                   1.013019e+01         ...           \n",
+       "22                   7.639319e-01         ...           \n",
+       "23                   5.753846e-02         ...           \n",
+       "24                   2.675259e-08         ...           \n",
+       "25                   8.740320e-01         ...           \n",
+       "26                   1.359298e+01         ...           \n",
+       "27                   3.094421e+01         ...           \n",
+       "28                   1.604094e+02         ...           \n",
+       "29                   1.482816e+02         ...           \n",
+       "30                   1.167935e+02         ...           \n",
+       "..                            ...         ...           \n",
+       "322                  6.235600e-01         ...           \n",
+       "323                  4.777813e-02         ...           \n",
+       "324                  1.993804e-08         ...           \n",
+       "325                  7.890693e-01         ...           \n",
+       "326                  2.715485e+01         ...           \n",
+       "327                  8.276423e+01         ...           \n",
+       "328                  1.543177e+02         ...           \n",
+       "329                  1.933467e+02         ...           \n",
+       "330                  1.015123e+02         ...           \n",
+       "331                  1.934761e+02         ...           \n",
+       "332                  1.056943e+02         ...           \n",
+       "333                  1.465962e+01         ...           \n",
+       "334                  2.261215e+00         ...           \n",
+       "335                  4.775932e-02         ...           \n",
+       "336                  1.993805e-08         ...           \n",
+       "337                  8.380250e-01         ...           \n",
+       "338                  9.466481e+00         ...           \n",
+       "339                  7.785273e+01         ...           \n",
+       "340                  2.072339e+02         ...           \n",
+       "341                  1.548029e+02         ...           \n",
+       "342                  1.390116e+02         ...           \n",
+       "343                  1.551622e+02         ...           \n",
+       "344                  5.322888e+01         ...           \n",
+       "345                  2.510153e+01         ...           \n",
+       "346                  6.402631e-01         ...           \n",
+       "347                  4.795311e-02         ...           \n",
+       "348                  1.950983e-08         ...           \n",
+       "349                  8.670620e-01         ...           \n",
+       "350                  1.199661e+01         ...           \n",
+       "351                  3.686316e+01         ...           \n",
+       "\n",
+       "variable  SMsurf__quantile__q_0.1  SMsurf__quantile__q_0.2  \\\n",
+       "id                                                           \n",
+       "1                        0.201334                 0.201334   \n",
+       "2                        0.198097                 0.198097   \n",
+       "3                        0.212813                 0.212813   \n",
+       "4                        0.289586                 0.289586   \n",
+       "5                        0.320928                 0.320928   \n",
+       "6                        0.355562                 0.355562   \n",
+       "7                        0.339140                 0.339140   \n",
+       "8                        0.310640                 0.310640   \n",
+       "9                        0.272203                 0.272203   \n",
+       "10                       0.227428                 0.227428   \n",
+       "11                       0.211096                 0.211096   \n",
+       "12                       0.202564                 0.202564   \n",
+       "13                       0.197926                 0.197926   \n",
+       "14                       0.236978                 0.236978   \n",
+       "15                       0.323990                 0.323990   \n",
+       "16                       0.394276                 0.394276   \n",
+       "17                       0.392315                 0.392315   \n",
+       "18                       0.358581                 0.358581   \n",
+       "19                       0.326832                 0.326832   \n",
+       "20                       0.286577                 0.286577   \n",
+       "21                       0.229519                 0.229519   \n",
+       "22                       0.206650                 0.206650   \n",
+       "23                       0.198387                 0.198387   \n",
+       "24                       0.194428                 0.194428   \n",
+       "25                       0.192386                 0.192386   \n",
+       "26                       0.202848                 0.202848   \n",
+       "27                       0.240059                 0.240059   \n",
+       "28                       0.321999                 0.321999   \n",
+       "29                       0.384347                 0.384347   \n",
+       "30                       0.385320                 0.385320   \n",
+       "..                            ...                      ...   \n",
+       "322                      0.250397                 0.250397   \n",
+       "323                      0.221636                 0.221636   \n",
+       "324                      0.206155                 0.206155   \n",
+       "325                      0.197854                 0.197854   \n",
+       "326                      0.213133                 0.213133   \n",
+       "327                      0.257264                 0.257264   \n",
+       "328                      0.313676                 0.313676   \n",
+       "329                      0.354992                 0.354992   \n",
+       "330                      0.390145                 0.390145   \n",
+       "331                      0.394721                 0.394721   \n",
+       "332                      0.350828                 0.350828   \n",
+       "333                      0.274056                 0.274056   \n",
+       "334                      0.232280                 0.232280   \n",
+       "335                      0.207676                 0.207676   \n",
+       "336                      0.197751                 0.197751   \n",
+       "337                      0.194307                 0.194307   \n",
+       "338                      0.192960                 0.192960   \n",
+       "339                      0.280762                 0.280762   \n",
+       "340                      0.318195                 0.318195   \n",
+       "341                      0.378143                 0.378143   \n",
+       "342                      0.386863                 0.386863   \n",
+       "343                      0.387767                 0.387767   \n",
+       "344                      0.340446                 0.340446   \n",
+       "345                      0.268003                 0.268003   \n",
+       "346                      0.228008                 0.228008   \n",
+       "347                      0.208466                 0.208466   \n",
+       "348                      0.197922                 0.197922   \n",
+       "349                      0.193599                 0.193599   \n",
+       "350                      0.204552                 0.204552   \n",
+       "351                      0.222472                 0.222472   \n",
+       "\n",
+       "variable  SMsurf__quantile__q_0.3  SMsurf__quantile__q_0.4  \\\n",
+       "id                                                           \n",
+       "1                        0.201334                 0.201334   \n",
+       "2                        0.198097                 0.198097   \n",
+       "3                        0.212813                 0.212813   \n",
+       "4                        0.289586                 0.289586   \n",
+       "5                        0.320928                 0.320928   \n",
+       "6                        0.355562                 0.355562   \n",
+       "7                        0.339140                 0.339140   \n",
+       "8                        0.310640                 0.310640   \n",
+       "9                        0.272203                 0.272203   \n",
+       "10                       0.227428                 0.227428   \n",
+       "11                       0.211096                 0.211096   \n",
+       "12                       0.202564                 0.202564   \n",
+       "13                       0.197926                 0.197926   \n",
+       "14                       0.236978                 0.236978   \n",
+       "15                       0.323990                 0.323990   \n",
+       "16                       0.394276                 0.394276   \n",
+       "17                       0.392315                 0.392315   \n",
+       "18                       0.358581                 0.358581   \n",
+       "19                       0.326832                 0.326832   \n",
+       "20                       0.286577                 0.286577   \n",
+       "21                       0.229519                 0.229519   \n",
+       "22                       0.206650                 0.206650   \n",
+       "23                       0.198387                 0.198387   \n",
+       "24                       0.194428                 0.194428   \n",
+       "25                       0.192386                 0.192386   \n",
+       "26                       0.202848                 0.202848   \n",
+       "27                       0.240059                 0.240059   \n",
+       "28                       0.321999                 0.321999   \n",
+       "29                       0.384347                 0.384347   \n",
+       "30                       0.385320                 0.385320   \n",
+       "..                            ...                      ...   \n",
+       "322                      0.250397                 0.250397   \n",
+       "323                      0.221636                 0.221636   \n",
+       "324                      0.206155                 0.206155   \n",
+       "325                      0.197854                 0.197854   \n",
+       "326                      0.213133                 0.213133   \n",
+       "327                      0.257264                 0.257264   \n",
+       "328                      0.313676                 0.313676   \n",
+       "329                      0.354992                 0.354992   \n",
+       "330                      0.390145                 0.390145   \n",
+       "331                      0.394721                 0.394721   \n",
+       "332                      0.350828                 0.350828   \n",
+       "333                      0.274056                 0.274056   \n",
+       "334                      0.232280                 0.232280   \n",
+       "335                      0.207676                 0.207676   \n",
+       "336                      0.197751                 0.197751   \n",
+       "337                      0.194307                 0.194307   \n",
+       "338                      0.192960                 0.192960   \n",
+       "339                      0.280762                 0.280762   \n",
+       "340                      0.318195                 0.318195   \n",
+       "341                      0.378143                 0.378143   \n",
+       "342                      0.386863                 0.386863   \n",
+       "343                      0.387767                 0.387767   \n",
+       "344                      0.340446                 0.340446   \n",
+       "345                      0.268003                 0.268003   \n",
+       "346                      0.228008                 0.228008   \n",
+       "347                      0.208466                 0.208466   \n",
+       "348                      0.197922                 0.197922   \n",
+       "349                      0.193599                 0.193599   \n",
+       "350                      0.204552                 0.204552   \n",
+       "351                      0.222472                 0.222472   \n",
+       "\n",
+       "variable  SMsurf__quantile__q_0.6  SMsurf__quantile__q_0.7  \\\n",
+       "id                                                           \n",
+       "1                        0.201334                 0.201334   \n",
+       "2                        0.198097                 0.198097   \n",
+       "3                        0.212813                 0.212813   \n",
+       "4                        0.289586                 0.289586   \n",
+       "5                        0.320928                 0.320928   \n",
+       "6                        0.355562                 0.355562   \n",
+       "7                        0.339140                 0.339140   \n",
+       "8                        0.310640                 0.310640   \n",
+       "9                        0.272203                 0.272203   \n",
+       "10                       0.227428                 0.227428   \n",
+       "11                       0.211096                 0.211096   \n",
+       "12                       0.202564                 0.202564   \n",
+       "13                       0.197926                 0.197926   \n",
+       "14                       0.236978                 0.236978   \n",
+       "15                       0.323990                 0.323990   \n",
+       "16                       0.394276                 0.394276   \n",
+       "17                       0.392315                 0.392315   \n",
+       "18                       0.358581                 0.358581   \n",
+       "19                       0.326832                 0.326832   \n",
+       "20                       0.286577                 0.286577   \n",
+       "21                       0.229519                 0.229519   \n",
+       "22                       0.206650                 0.206650   \n",
+       "23                       0.198387                 0.198387   \n",
+       "24                       0.194428                 0.194428   \n",
+       "25                       0.192386                 0.192386   \n",
+       "26                       0.202848                 0.202848   \n",
+       "27                       0.240059                 0.240059   \n",
+       "28                       0.321999                 0.321999   \n",
+       "29                       0.384347                 0.384347   \n",
+       "30                       0.385320                 0.385320   \n",
+       "..                            ...                      ...   \n",
+       "322                      0.250397                 0.250397   \n",
+       "323                      0.221636                 0.221636   \n",
+       "324                      0.206155                 0.206155   \n",
+       "325                      0.197854                 0.197854   \n",
+       "326                      0.213133                 0.213133   \n",
+       "327                      0.257264                 0.257264   \n",
+       "328                      0.313676                 0.313676   \n",
+       "329                      0.354992                 0.354992   \n",
+       "330                      0.390145                 0.390145   \n",
+       "331                      0.394721                 0.394721   \n",
+       "332                      0.350828                 0.350828   \n",
+       "333                      0.274056                 0.274056   \n",
+       "334                      0.232280                 0.232280   \n",
+       "335                      0.207676                 0.207676   \n",
+       "336                      0.197751                 0.197751   \n",
+       "337                      0.194307                 0.194307   \n",
+       "338                      0.192960                 0.192960   \n",
+       "339                      0.280762                 0.280762   \n",
+       "340                      0.318195                 0.318195   \n",
+       "341                      0.378143                 0.378143   \n",
+       "342                      0.386863                 0.386863   \n",
+       "343                      0.387767                 0.387767   \n",
+       "344                      0.340446                 0.340446   \n",
+       "345                      0.268003                 0.268003   \n",
+       "346                      0.228008                 0.228008   \n",
+       "347                      0.208466                 0.208466   \n",
+       "348                      0.197922                 0.197922   \n",
+       "349                      0.193599                 0.193599   \n",
+       "350                      0.204552                 0.204552   \n",
+       "351                      0.222472                 0.222472   \n",
+       "\n",
+       "variable  SMsurf__quantile__q_0.8  SMsurf__quantile__q_0.9  \\\n",
+       "id                                                           \n",
+       "1                        0.201334                 0.201334   \n",
+       "2                        0.198097                 0.198097   \n",
+       "3                        0.212813                 0.212813   \n",
+       "4                        0.289586                 0.289586   \n",
+       "5                        0.320928                 0.320928   \n",
+       "6                        0.355562                 0.355562   \n",
+       "7                        0.339140                 0.339140   \n",
+       "8                        0.310640                 0.310640   \n",
+       "9                        0.272203                 0.272203   \n",
+       "10                       0.227428                 0.227428   \n",
+       "11                       0.211096                 0.211096   \n",
+       "12                       0.202564                 0.202564   \n",
+       "13                       0.197926                 0.197926   \n",
+       "14                       0.236978                 0.236978   \n",
+       "15                       0.323990                 0.323990   \n",
+       "16                       0.394276                 0.394276   \n",
+       "17                       0.392315                 0.392315   \n",
+       "18                       0.358581                 0.358581   \n",
+       "19                       0.326832                 0.326832   \n",
+       "20                       0.286577                 0.286577   \n",
+       "21                       0.229519                 0.229519   \n",
+       "22                       0.206650                 0.206650   \n",
+       "23                       0.198387                 0.198387   \n",
+       "24                       0.194428                 0.194428   \n",
+       "25                       0.192386                 0.192386   \n",
+       "26                       0.202848                 0.202848   \n",
+       "27                       0.240059                 0.240059   \n",
+       "28                       0.321999                 0.321999   \n",
+       "29                       0.384347                 0.384347   \n",
+       "30                       0.385320                 0.385320   \n",
+       "..                            ...                      ...   \n",
+       "322                      0.250397                 0.250397   \n",
+       "323                      0.221636                 0.221636   \n",
+       "324                      0.206155                 0.206155   \n",
+       "325                      0.197854                 0.197854   \n",
+       "326                      0.213133                 0.213133   \n",
+       "327                      0.257264                 0.257264   \n",
+       "328                      0.313676                 0.313676   \n",
+       "329                      0.354992                 0.354992   \n",
+       "330                      0.390145                 0.390145   \n",
+       "331                      0.394721                 0.394721   \n",
+       "332                      0.350828                 0.350828   \n",
+       "333                      0.274056                 0.274056   \n",
+       "334                      0.232280                 0.232280   \n",
+       "335                      0.207676                 0.207676   \n",
+       "336                      0.197751                 0.197751   \n",
+       "337                      0.194307                 0.194307   \n",
+       "338                      0.192960                 0.192960   \n",
+       "339                      0.280762                 0.280762   \n",
+       "340                      0.318195                 0.318195   \n",
+       "341                      0.378143                 0.378143   \n",
+       "342                      0.386863                 0.386863   \n",
+       "343                      0.387767                 0.387767   \n",
+       "344                      0.340446                 0.340446   \n",
+       "345                      0.268003                 0.268003   \n",
+       "346                      0.228008                 0.228008   \n",
+       "347                      0.208466                 0.208466   \n",
+       "348                      0.197922                 0.197922   \n",
+       "349                      0.193599                 0.193599   \n",
+       "350                      0.204552                 0.204552   \n",
+       "351                      0.222472                 0.222472   \n",
+       "\n",
+       "variable  SMsurf__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_0__w_10  \\\n",
+       "id                                                                         \n",
+       "1                                                  0.055220                \n",
+       "2                                                  0.054332                \n",
+       "3                                                  0.058369                \n",
+       "4                                                  0.079426                \n",
+       "5                                                  0.088022                \n",
+       "6                                                  0.097521                \n",
+       "7                                                  0.093017                \n",
+       "8                                                  0.085200                \n",
+       "9                                                  0.074658                \n",
+       "10                                                 0.062377                \n",
+       "11                                                 0.057898                \n",
+       "12                                                 0.055558                \n",
+       "13                                                 0.054285                \n",
+       "14                                                 0.064996                \n",
+       "15                                                 0.088861                \n",
+       "16                                                 0.108139                \n",
+       "17                                                 0.107601                \n",
+       "18                                                 0.098349                \n",
+       "19                                                 0.089641                \n",
+       "20                                                 0.078600                \n",
+       "21                                                 0.062951                \n",
+       "22                                                 0.056678                \n",
+       "23                                                 0.054412                \n",
+       "24                                                 0.053326                \n",
+       "25                                                 0.052766                \n",
+       "26                                                 0.055635                \n",
+       "27                                                 0.065841                \n",
+       "28                                                 0.088315                \n",
+       "29                                                 0.105416                \n",
+       "30                                                 0.105683                \n",
+       "..                                                      ...                \n",
+       "322                                                0.068677                \n",
+       "323                                                0.060789                \n",
+       "324                                                0.056542                \n",
+       "325                                                0.054266                \n",
+       "326                                                0.058456                \n",
+       "327                                                0.070561                \n",
+       "328                                                0.086033                \n",
+       "329                                                0.097364                \n",
+       "330                                                0.107006                \n",
+       "331                                                0.108261                \n",
+       "332                                                0.096222                \n",
+       "333                                                0.075166                \n",
+       "334                                                0.063708                \n",
+       "335                                                0.056960                \n",
+       "336                                                0.054238                \n",
+       "337                                                0.053293                \n",
+       "338                                                0.052923                \n",
+       "339                                                0.077005                \n",
+       "340                                                0.087272                \n",
+       "341                                                0.103714                \n",
+       "342                                                0.106106                \n",
+       "343                                                0.106354                \n",
+       "344                                                0.093375                \n",
+       "345                                                0.073506                \n",
+       "346                                                0.062536                \n",
+       "347                                                0.057177                \n",
+       "348                                                0.054285                \n",
+       "349                                                0.053099                \n",
+       "350                                                0.056103                \n",
+       "351                                                0.061018                \n",
+       "\n",
+       "variable  SMsurf__abs_energy  \n",
+       "id                            \n",
+       "1                   0.040535  \n",
+       "2                   0.039242  \n",
+       "3                   0.045289  \n",
+       "4                   0.083860  \n",
+       "5                   0.102995  \n",
+       "6                   0.126424  \n",
+       "7                   0.115016  \n",
+       "8                   0.096497  \n",
+       "9                   0.074095  \n",
+       "10                  0.051724  \n",
+       "11                  0.044561  \n",
+       "12                  0.041032  \n",
+       "13                  0.039175  \n",
+       "14                  0.056158  \n",
+       "15                  0.104969  \n",
+       "16                  0.155454  \n",
+       "17                  0.153911  \n",
+       "18                  0.128580  \n",
+       "19                  0.106819  \n",
+       "20                  0.082127  \n",
+       "21                  0.052679  \n",
+       "22                  0.042704  \n",
+       "23                  0.039357  \n",
+       "24                  0.037802  \n",
+       "25                  0.037013  \n",
+       "26                  0.041147  \n",
+       "27                  0.057628  \n",
+       "28                  0.103683  \n",
+       "29                  0.147723  \n",
+       "30                  0.148472  \n",
+       "..                       ...  \n",
+       "322                 0.062699  \n",
+       "323                 0.049123  \n",
+       "324                 0.042500  \n",
+       "325                 0.039146  \n",
+       "326                 0.045426  \n",
+       "327                 0.066185  \n",
+       "328                 0.098392  \n",
+       "329                 0.126019  \n",
+       "330                 0.152213  \n",
+       "331                 0.155805  \n",
+       "332                 0.123080  \n",
+       "333                 0.075107  \n",
+       "334                 0.053954  \n",
+       "335                 0.043129  \n",
+       "336                 0.039106  \n",
+       "337                 0.037755  \n",
+       "338                 0.037233  \n",
+       "339                 0.078827  \n",
+       "340                 0.101248  \n",
+       "341                 0.142992  \n",
+       "342                 0.149663  \n",
+       "343                 0.150363  \n",
+       "344                 0.115903  \n",
+       "345                 0.071826  \n",
+       "346                 0.051988  \n",
+       "347                 0.043458  \n",
+       "348                 0.039173  \n",
+       "349                 0.037481  \n",
+       "350                 0.041841  \n",
+       "351                 0.049494  \n",
+       "\n",
+       "[351 rows x 41 columns]"
+      ]
+     },
+     "execution_count": 146,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "filtered_features"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### play with models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(\n",
+    "    X, y, test_size=0.1, random_state=42\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Play with prophet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pixel</th>\n",
+       "      <th>pixel_index</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>lon</th>\n",
+       "      <th>time</th>\n",
+       "      <th>VCI</th>\n",
+       "      <th>pev</th>\n",
+       "      <th>t2m</th>\n",
+       "      <th>precip</th>\n",
+       "      <th>E</th>\n",
+       "      <th>SMroot</th>\n",
+       "      <th>SMsurf</th>\n",
+       "      <th>time_index</th>\n",
+       "      <th>next_month_VCI</th>\n",
+       "      <th>next_month_pev</th>\n",
+       "      <th>next_month_t2m</th>\n",
+       "      <th>next_month_precip</th>\n",
+       "      <th>next_month_E</th>\n",
+       "      <th>next_month_SMroot</th>\n",
+       "      <th>next_month_SMsurf</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>(-5.0, 33.75)</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-5.0</td>\n",
+       "      <td>33.75</td>\n",
+       "      <td>1981-08-31</td>\n",
+       "      <td>39.360001</td>\n",
+       "      <td>-0.006845</td>\n",
+       "      <td>296.760132</td>\n",
+       "      <td>2.911272e-08</td>\n",
+       "      <td>14.843219</td>\n",
+       "      <td>0.215809</td>\n",
+       "      <td>0.208432</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>(-5.0, 33.75)</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-5.0</td>\n",
+       "      <td>33.75</td>\n",
+       "      <td>1981-09-30</td>\n",
+       "      <td>50.802499</td>\n",
+       "      <td>-0.007329</td>\n",
+       "      <td>297.860565</td>\n",
+       "      <td>1.686568e+00</td>\n",
+       "      <td>8.694020</td>\n",
+       "      <td>0.208080</td>\n",
+       "      <td>0.201334</td>\n",
+       "      <td>1</td>\n",
+       "      <td>39.360001</td>\n",
+       "      <td>-0.006845</td>\n",
+       "      <td>296.760132</td>\n",
+       "      <td>2.911272e-08</td>\n",
+       "      <td>14.843219</td>\n",
+       "      <td>0.215809</td>\n",
+       "      <td>0.208432</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>(-5.0, 33.75)</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-5.0</td>\n",
+       "      <td>33.75</td>\n",
+       "      <td>1981-10-31</td>\n",
+       "      <td>50.107499</td>\n",
+       "      <td>-0.008037</td>\n",
+       "      <td>299.804291</td>\n",
+       "      <td>1.136534e+01</td>\n",
+       "      <td>6.438349</td>\n",
+       "      <td>0.204618</td>\n",
+       "      <td>0.198097</td>\n",
+       "      <td>2</td>\n",
+       "      <td>50.802499</td>\n",
+       "      <td>-0.007329</td>\n",
+       "      <td>297.860565</td>\n",
+       "      <td>1.686568e+00</td>\n",
+       "      <td>8.694020</td>\n",
+       "      <td>0.208080</td>\n",
+       "      <td>0.201334</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>(-5.0, 33.75)</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-5.0</td>\n",
+       "      <td>33.75</td>\n",
+       "      <td>1981-11-30</td>\n",
+       "      <td>46.427999</td>\n",
+       "      <td>-0.007395</td>\n",
+       "      <td>299.247986</td>\n",
+       "      <td>2.212210e+01</td>\n",
+       "      <td>16.903798</td>\n",
+       "      <td>0.204654</td>\n",
+       "      <td>0.212813</td>\n",
+       "      <td>3</td>\n",
+       "      <td>50.107499</td>\n",
+       "      <td>-0.008037</td>\n",
+       "      <td>299.804291</td>\n",
+       "      <td>1.136534e+01</td>\n",
+       "      <td>6.438349</td>\n",
+       "      <td>0.204618</td>\n",
+       "      <td>0.198097</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>(-5.0, 33.75)</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-5.0</td>\n",
+       "      <td>33.75</td>\n",
+       "      <td>1981-12-31</td>\n",
+       "      <td>72.150000</td>\n",
+       "      <td>-0.005308</td>\n",
+       "      <td>296.316742</td>\n",
+       "      <td>1.329536e+02</td>\n",
+       "      <td>68.801471</td>\n",
+       "      <td>0.232705</td>\n",
+       "      <td>0.289586</td>\n",
+       "      <td>4</td>\n",
+       "      <td>46.427999</td>\n",
+       "      <td>-0.007395</td>\n",
+       "      <td>299.247986</td>\n",
+       "      <td>2.212210e+01</td>\n",
+       "      <td>16.903798</td>\n",
+       "      <td>0.204654</td>\n",
+       "      <td>0.212813</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           pixel  pixel_index  lat    lon       time        VCI       pev  \\\n",
+       "0  (-5.0, 33.75)            1 -5.0  33.75 1981-08-31  39.360001 -0.006845   \n",
+       "1  (-5.0, 33.75)            1 -5.0  33.75 1981-09-30  50.802499 -0.007329   \n",
+       "2  (-5.0, 33.75)            1 -5.0  33.75 1981-10-31  50.107499 -0.008037   \n",
+       "3  (-5.0, 33.75)            1 -5.0  33.75 1981-11-30  46.427999 -0.007395   \n",
+       "4  (-5.0, 33.75)            1 -5.0  33.75 1981-12-31  72.150000 -0.005308   \n",
+       "\n",
+       "          t2m        precip          E    SMroot    SMsurf  time_index  \\\n",
+       "0  296.760132  2.911272e-08  14.843219  0.215809  0.208432           0   \n",
+       "1  297.860565  1.686568e+00   8.694020  0.208080  0.201334           1   \n",
+       "2  299.804291  1.136534e+01   6.438349  0.204618  0.198097           2   \n",
+       "3  299.247986  2.212210e+01  16.903798  0.204654  0.212813           3   \n",
+       "4  296.316742  1.329536e+02  68.801471  0.232705  0.289586           4   \n",
+       "\n",
+       "   next_month_VCI  next_month_pev  next_month_t2m  next_month_precip  \\\n",
+       "0             NaN             NaN             NaN                NaN   \n",
+       "1       39.360001       -0.006845      296.760132       2.911272e-08   \n",
+       "2       50.802499       -0.007329      297.860565       1.686568e+00   \n",
+       "3       50.107499       -0.008037      299.804291       1.136534e+01   \n",
+       "4       46.427999       -0.007395      299.247986       2.212210e+01   \n",
+       "\n",
+       "   next_month_E  next_month_SMroot  next_month_SMsurf  \n",
+       "0           NaN                NaN                NaN  \n",
+       "1     14.843219           0.215809           0.208432  \n",
+       "2      8.694020           0.208080           0.201334  \n",
+       "3      6.438349           0.204618           0.198097  \n",
+       "4     16.903798           0.204654           0.212813  "
+      ]
+     },
+     "execution_count": 131,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pixel_index</th>\n",
+       "      <th>ds</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1981-08-31</td>\n",
+       "      <td>39.360001</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1981-09-30</td>\n",
+       "      <td>50.802499</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1981-10-31</td>\n",
+       "      <td>50.107499</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1981-11-30</td>\n",
+       "      <td>46.427999</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1981-12-31</td>\n",
+       "      <td>72.150000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1982-01-31</td>\n",
+       "      <td>93.319998</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1982-02-28</td>\n",
+       "      <td>99.632500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1982-03-31</td>\n",
+       "      <td>91.073999</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1982-04-30</td>\n",
+       "      <td>59.599998</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1982-05-31</td>\n",
+       "      <td>33.867999</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1982-06-30</td>\n",
+       "      <td>33.657500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1982-07-31</td>\n",
+       "      <td>25.642499</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1982-08-31</td>\n",
+       "      <td>9.566000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1982-09-30</td>\n",
+       "      <td>27.290000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1982-10-31</td>\n",
+       "      <td>40.044999</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1982-11-30</td>\n",
+       "      <td>79.755998</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1982-12-31</td>\n",
+       "      <td>100.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1983-01-31</td>\n",
+       "      <td>100.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1983-02-28</td>\n",
+       "      <td>89.089996</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1983-03-31</td>\n",
+       "      <td>55.934999</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1983-04-30</td>\n",
+       "      <td>17.190000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1983-05-31</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1983-06-30</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1983-07-31</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1983-08-31</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1983-09-30</td>\n",
+       "      <td>3.672500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1983-10-31</td>\n",
+       "      <td>2.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1983-11-30</td>\n",
+       "      <td>47.694999</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1983-12-31</td>\n",
+       "      <td>84.647499</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1984-01-31</td>\n",
+       "      <td>72.973999</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>629064</th>\n",
+       "      <td>1575</td>\n",
+       "      <td>2016-07-31</td>\n",
+       "      <td>31.837499</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>629065</th>\n",
+       "      <td>1575</td>\n",
+       "      <td>2016-08-31</td>\n",
+       "      <td>41.097999</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>629066</th>\n",
+       "      <td>1575</td>\n",
+       "      <td>2016-09-30</td>\n",
+       "      <td>50.027499</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>629067</th>\n",
+       "      <td>1575</td>\n",
+       "      <td>2016-10-31</td>\n",
+       "      <td>20.607999</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>629068</th>\n",
+       "      <td>1575</td>\n",
+       "      <td>2016-11-30</td>\n",
+       "      <td>16.824999</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>629069</th>\n",
+       "      <td>1575</td>\n",
+       "      <td>2016-12-31</td>\n",
+       "      <td>16.247499</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>629070</th>\n",
+       "      <td>1575</td>\n",
+       "      <td>2017-01-31</td>\n",
+       "      <td>23.346000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>629071</th>\n",
+       "      <td>1575</td>\n",
+       "      <td>2017-02-28</td>\n",
+       "      <td>32.227499</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>629072</th>\n",
+       "      <td>1575</td>\n",
+       "      <td>2017-03-31</td>\n",
+       "      <td>28.485000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>629073</th>\n",
+       "      <td>1575</td>\n",
+       "      <td>2017-04-30</td>\n",
+       "      <td>51.999999</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>629074</th>\n",
+       "      <td>1575</td>\n",
+       "      <td>2017-05-31</td>\n",
+       "      <td>62.641998</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>629075</th>\n",
+       "      <td>1575</td>\n",
+       "      <td>2017-06-30</td>\n",
+       "      <td>53.917500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>629076</th>\n",
+       "      <td>1575</td>\n",
+       "      <td>2017-07-31</td>\n",
+       "      <td>28.609999</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>629077</th>\n",
+       "      <td>1575</td>\n",
+       "      <td>2017-08-31</td>\n",
+       "      <td>35.770000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>629078</th>\n",
+       "      <td>1575</td>\n",
+       "      <td>2017-09-30</td>\n",
+       "      <td>52.912499</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>629079</th>\n",
+       "      <td>1575</td>\n",
+       "      <td>2017-10-31</td>\n",
+       "      <td>53.983998</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>629080</th>\n",
+       "      <td>1575</td>\n",
+       "      <td>2017-11-30</td>\n",
+       "      <td>61.629999</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>629081</th>\n",
+       "      <td>1575</td>\n",
+       "      <td>2017-12-31</td>\n",
+       "      <td>44.902499</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>629082</th>\n",
+       "      <td>1575</td>\n",
+       "      <td>2018-01-31</td>\n",
+       "      <td>27.680000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>629083</th>\n",
+       "      <td>1575</td>\n",
+       "      <td>2018-02-28</td>\n",
+       "      <td>36.744999</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>629084</th>\n",
+       "      <td>1575</td>\n",
+       "      <td>2018-03-31</td>\n",
+       "      <td>39.767499</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>629085</th>\n",
+       "      <td>1575</td>\n",
+       "      <td>2018-04-30</td>\n",
+       "      <td>67.224000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>629086</th>\n",
+       "      <td>1575</td>\n",
+       "      <td>2018-05-31</td>\n",
+       "      <td>77.010000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>629087</th>\n",
+       "      <td>1575</td>\n",
+       "      <td>2018-06-30</td>\n",
+       "      <td>52.010000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>629088</th>\n",
+       "      <td>1575</td>\n",
+       "      <td>2018-07-31</td>\n",
+       "      <td>30.630000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>629089</th>\n",
+       "      <td>1575</td>\n",
+       "      <td>2018-08-31</td>\n",
+       "      <td>40.389999</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>629090</th>\n",
+       "      <td>1575</td>\n",
+       "      <td>2018-09-30</td>\n",
+       "      <td>54.004998</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>629091</th>\n",
+       "      <td>1575</td>\n",
+       "      <td>2018-10-31</td>\n",
+       "      <td>39.747999</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>629092</th>\n",
+       "      <td>1575</td>\n",
+       "      <td>2018-11-30</td>\n",
+       "      <td>37.292499</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>629093</th>\n",
+       "      <td>1575</td>\n",
+       "      <td>2018-12-31</td>\n",
+       "      <td>22.194999</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>629094 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        pixel_index         ds           y\n",
+       "0                 1 1981-08-31   39.360001\n",
+       "1                 1 1981-09-30   50.802499\n",
+       "2                 1 1981-10-31   50.107499\n",
+       "3                 1 1981-11-30   46.427999\n",
+       "4                 1 1981-12-31   72.150000\n",
+       "5                 1 1982-01-31   93.319998\n",
+       "6                 1 1982-02-28   99.632500\n",
+       "7                 1 1982-03-31   91.073999\n",
+       "8                 1 1982-04-30   59.599998\n",
+       "9                 1 1982-05-31   33.867999\n",
+       "10                1 1982-06-30   33.657500\n",
+       "11                1 1982-07-31   25.642499\n",
+       "12                1 1982-08-31    9.566000\n",
+       "13                1 1982-09-30   27.290000\n",
+       "14                1 1982-10-31   40.044999\n",
+       "15                1 1982-11-30   79.755998\n",
+       "16                1 1982-12-31  100.000000\n",
+       "17                1 1983-01-31  100.000000\n",
+       "18                1 1983-02-28   89.089996\n",
+       "19                1 1983-03-31   55.934999\n",
+       "20                1 1983-04-30   17.190000\n",
+       "21                1 1983-05-31    0.000000\n",
+       "22                1 1983-06-30    0.000000\n",
+       "23                1 1983-07-31    0.000000\n",
+       "24                1 1983-08-31    0.000000\n",
+       "25                1 1983-09-30    3.672500\n",
+       "26                1 1983-10-31    2.500000\n",
+       "27                1 1983-11-30   47.694999\n",
+       "28                1 1983-12-31   84.647499\n",
+       "29                1 1984-01-31   72.973999\n",
+       "...             ...        ...         ...\n",
+       "629064         1575 2016-07-31   31.837499\n",
+       "629065         1575 2016-08-31   41.097999\n",
+       "629066         1575 2016-09-30   50.027499\n",
+       "629067         1575 2016-10-31   20.607999\n",
+       "629068         1575 2016-11-30   16.824999\n",
+       "629069         1575 2016-12-31   16.247499\n",
+       "629070         1575 2017-01-31   23.346000\n",
+       "629071         1575 2017-02-28   32.227499\n",
+       "629072         1575 2017-03-31   28.485000\n",
+       "629073         1575 2017-04-30   51.999999\n",
+       "629074         1575 2017-05-31   62.641998\n",
+       "629075         1575 2017-06-30   53.917500\n",
+       "629076         1575 2017-07-31   28.609999\n",
+       "629077         1575 2017-08-31   35.770000\n",
+       "629078         1575 2017-09-30   52.912499\n",
+       "629079         1575 2017-10-31   53.983998\n",
+       "629080         1575 2017-11-30   61.629999\n",
+       "629081         1575 2017-12-31   44.902499\n",
+       "629082         1575 2018-01-31   27.680000\n",
+       "629083         1575 2018-02-28   36.744999\n",
+       "629084         1575 2018-03-31   39.767499\n",
+       "629085         1575 2018-04-30   67.224000\n",
+       "629086         1575 2018-05-31   77.010000\n",
+       "629087         1575 2018-06-30   52.010000\n",
+       "629088         1575 2018-07-31   30.630000\n",
+       "629089         1575 2018-08-31   40.389999\n",
+       "629090         1575 2018-09-30   54.004998\n",
+       "629091         1575 2018-10-31   39.747999\n",
+       "629092         1575 2018-11-30   37.292499\n",
+       "629093         1575 2018-12-31   22.194999\n",
+       "\n",
+       "[629094 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 135,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[['pixel_index', 'time', 'VCI']].rename(columns=dict(zip(['time', 'VCI'], ['ds', 'y'])))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/draft/31_tl_vci_nowcast_experiment.ipynb b/notebooks/draft/31_tl_vci_nowcast_experiment.ipynb
new file mode 100644
index 000000000..08de4c6c5
--- /dev/null
+++ b/notebooks/draft/31_tl_vci_nowcast_experiment.ipynb
@@ -0,0 +1,395 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Nowcast Experiment (NO VCI in X DATA)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/Users/tommylees/github/ml_drought\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pathlib import Path\n",
+    "import os\n",
+    "import warnings\n",
+    "\n",
+    "%load_ext autoreload\n",
+    "%autoreload\n",
+    "\n",
+    "# ignore warnings for now ...\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "if Path('.').absolute().parents[1].name == 'ml_drought':\n",
+    "    os.chdir(Path('.').absolute().parents[1])\n",
+    "\n",
+    "!pwd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xarray as xr\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pickle\n",
+    "\n",
+    "data_dir = Path('data/')\n",
+    "# data_dir = Path('/Volumes/Lees_Extend/data/zip_data')\n",
+    "data_dir = Path('/Volumes/Lees_Extend/data/ecmwf_sowc/data/')\n",
+    "\n",
+    "assert data_dir.exists()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import seaborn as sns\n",
+    "from src.utils import drop_nans_and_flatten\n",
+    "\n",
+    "from src.analysis import read_train_data, read_test_data, read_pred_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Read in the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, y_train = read_train_data(data_dir)\n",
+    "X_test, y_test = read_test_data(data_dir)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds = xr.merge([y_train, y_test]).sortby('time').sortby('lat')\n",
+    "d_ = xr.merge([X_train, X_test]).sortby('time').sortby('lat')\n",
+    "ds = xr.merge([ds, d_])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from src.utils import get_ds_mask\n",
+    "mask = get_ds_mask(X_train.VCI)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "['ealstm', 'linear_network', 'linear_regression', 'previous_month', 'rnn']"
+      ],
+      "text/plain": [
+       "['ealstm', 'linear_network', 'linear_regression', 'previous_month', 'rnn']"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[d.name for d in (data_dir / 'models' / '2020_01_13:151544_nowcast_tommy').iterdir()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Standard Experiments\n",
+    "bline_pred = read_pred_data('previous_month', data_dir)[-1].where(~mask)\n",
+    "orig_ln_pred = read_pred_data('linear_network', data_dir,)[-1].where(~mask)\n",
+    "orig_rnn_pred = read_pred_data('rnn', data_dir,)[-1].where(~mask)\n",
+    "orig_ealstm_pred = read_pred_data('ealstm', data_dir)[-1].where(~mask)\n",
+    "\n",
+    "# Predict Delta Experiments \n",
+    "ln_pred = read_pred_data('linear_network', data_dir, experiment='one_month_forecast_predict_delta')[-1].where(~mask)\n",
+    "ealstm_pred = read_pred_data('ealstm', data_dir, experiment='one_month_forecast_predict_delta')[-1].where(~mask)\n",
+    "\n",
+    "# ealstm_pred = read_pred_data('ealstm', data_dir)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explore how perform in each month"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from src.analysis import annual_scores"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "[]"
+      ],
+      "text/plain": [
+       "[]"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print((data_dir / 'models/one_month_forecast_predict_delta/rnn/').exists())\n",
+    "[d.name for d in (data_dir / 'models/one_month_forecast_predict_delta/rnn/').iterdir()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ealstm</th>\n",
+       "      <th>linear_network</th>\n",
+       "      <th>month</th>\n",
+       "      <th>year</th>\n",
+       "      <th>metric</th>\n",
+       "      <th>time</th>\n",
+       "      <th>previous_month</th>\n",
+       "      <th>orig_linear_network</th>\n",
+       "      <th>orig_ealstm</th>\n",
+       "      <th>orig_rnn</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>9.518235</td>\n",
+       "      <td>20.316086</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2011.0</td>\n",
+       "      <td>rmse</td>\n",
+       "      <td>2011-01-01</td>\n",
+       "      <td>9.79577</td>\n",
+       "      <td>22.490418</td>\n",
+       "      <td>13.402106</td>\n",
+       "      <td>11.853916</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>9.518235</td>\n",
+       "      <td>20.316086</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2011.0</td>\n",
+       "      <td>rmse</td>\n",
+       "      <td>2011-01-01</td>\n",
+       "      <td>9.79577</td>\n",
+       "      <td>-0.354995</td>\n",
+       "      <td>0.518841</td>\n",
+       "      <td>0.623585</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>9.518235</td>\n",
+       "      <td>20.316086</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2011.0</td>\n",
+       "      <td>rmse</td>\n",
+       "      <td>2011-01-01</td>\n",
+       "      <td>9.79577</td>\n",
+       "      <td>22.490418</td>\n",
+       "      <td>13.402106</td>\n",
+       "      <td>11.853916</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>9.518235</td>\n",
+       "      <td>20.316086</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2011.0</td>\n",
+       "      <td>rmse</td>\n",
+       "      <td>2011-01-01</td>\n",
+       "      <td>9.79577</td>\n",
+       "      <td>-0.354995</td>\n",
+       "      <td>0.518841</td>\n",
+       "      <td>0.623585</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>9.518235</td>\n",
+       "      <td>20.316086</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2011.0</td>\n",
+       "      <td>rmse</td>\n",
+       "      <td>2011-01-01</td>\n",
+       "      <td>9.79577</td>\n",
+       "      <td>22.490418</td>\n",
+       "      <td>13.402106</td>\n",
+       "      <td>11.853916</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     ealstm  linear_network  month    year metric       time  previous_month  \\\n",
+       "0  9.518235       20.316086    1.0  2011.0   rmse 2011-01-01         9.79577   \n",
+       "1  9.518235       20.316086    1.0  2011.0   rmse 2011-01-01         9.79577   \n",
+       "2  9.518235       20.316086    1.0  2011.0   rmse 2011-01-01         9.79577   \n",
+       "3  9.518235       20.316086    1.0  2011.0   rmse 2011-01-01         9.79577   \n",
+       "4  9.518235       20.316086    1.0  2011.0   rmse 2011-01-01         9.79577   \n",
+       "\n",
+       "   orig_linear_network  orig_ealstm   orig_rnn  \n",
+       "0            22.490418    13.402106  11.853916  \n",
+       "1            -0.354995     0.518841   0.623585  \n",
+       "2            22.490418    13.402106  11.853916  \n",
+       "3            -0.354995     0.518841   0.623585  \n",
+       "4            22.490418    13.402106  11.853916  "
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# load the monthly scores dictionary\n",
+    "experiment = 'one_month_forecast_predict_delta'\n",
+    "monthly_scores = annual_scores(\n",
+    "    data_path=data_dir,\n",
+    "    models=['linear_network', 'ealstm'],\n",
+    "    metrics=['rmse', 'r2'],\n",
+    "    pred_years=[y for y in range(2011, 2019)],\n",
+    "    experiment=experiment,\n",
+    "    true_data_experiment='one_month_forecast',\n",
+    "    target_var='VCI',\n",
+    "    verbose=False,\n",
+    "    to_dataframe=True\n",
+    ")\n",
+    "monthly_scores['time'] = monthly_scores.apply(lambda row: pd.to_datetime(f\"{int(row.month)}-{int(row.year)}\"), axis=1)\n",
+    "\n",
+    "monthly_scores2 = annual_scores(\n",
+    "    data_path=data_dir,\n",
+    "    models=['linear_network', 'ealstm', 'rnn', 'previous_month'],\n",
+    "    metrics=['rmse', 'r2'],\n",
+    "    pred_years=[y for y in range(2011, 2019)],\n",
+    "    experiment='one_month_forecast',\n",
+    "    true_data_experiment='one_month_forecast',\n",
+    "    target_var='VCI',\n",
+    "    verbose=False,\n",
+    "    to_dataframe=True\n",
+    ")\n",
+    "monthly_scores2['time'] = monthly_scores.apply(lambda row: pd.to_datetime(f\"{int(row.month)}-{int(row.year)}\"), axis=1)\n",
+    "\n",
+    "# rename columns and to merge dataframes\n",
+    "monthly_scores2 = monthly_scores2.join(\n",
+    "    monthly_scores2[['linear_network', 'ealstm', 'rnn']].add_prefix('orig_')\n",
+    ").drop(columns=['linear_network', 'ealstm', 'rnn'])\n",
+    "\n",
+    "# merge\n",
+    "monthly_scores = pd.merge(monthly_scores, monthly_scores2)\n",
+    "monthly_scores.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/draft/32_tl_training_periods_experiment.ipynb b/notebooks/draft/32_tl_training_periods_experiment.ipynb
new file mode 100644
index 000000000..d1ffe8696
--- /dev/null
+++ b/notebooks/draft/32_tl_training_periods_experiment.ipynb
@@ -0,0 +1,231 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 02 Training Periods Experiments"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/Users/tommylees/github/ml_drought\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pathlib import Path\n",
+    "import os\n",
+    "import warnings\n",
+    "\n",
+    "%load_ext autoreload\n",
+    "%autoreload\n",
+    "\n",
+    "# ignore warnings for now ...\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "if Path('.').absolute().parents[1].name == 'ml_drought':\n",
+    "    os.chdir(Path('.').absolute().parents[1])\n",
+    "\n",
+    "!pwd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xarray as xr\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pickle\n",
+    "\n",
+    "data_dir = Path('data/')\n",
+    "# data_dir = Path('/Volumes/Lees_Extend/data/zip_data')\n",
+    "data_dir = Path('/Volumes/Lees_Extend/data/ecmwf_sowc/data/')\n",
+    "\n",
+    "assert data_dir.exists()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import seaborn as sns\n",
+    "from src.utils import drop_nans_and_flatten\n",
+    "\n",
+    "from src.analysis import read_train_data, read_test_data, read_pred_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Read in the data\n",
+    "\n",
+    "Get all dataloaders from the train/test data\n",
+    "```python\n",
+    "next(iter(dataloader))\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, y_train = read_train_data(data_dir)\n",
+    "X_test, y_test = read_test_data(data_dir)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds = xr.merge([y_train, y_test]).sortby('time').sortby('lat')\n",
+    "d_ = xr.merge([X_train, X_test]).sortby('time').sortby('lat')\n",
+    "ds = xr.merge([ds, d_])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from src.utils import get_ds_mask\n",
+    "mask = get_ds_mask(X_train.VCI)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Read in a model for some parameters/Options"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from src.models import load_model\n",
+    "ealstm_eg = load_model(\n",
+    "    data_dir / 'models' / '02_training_periods' / '2020_01_11:175753_one_month_forecast_TRhigh_TEhigh_LEN5' / 'ealstm' / 'model.pt'\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['__annotations__', '__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', '_concatenate_data', '_convert_delta_to_raw_values', '_get_background', '_get_morris_explanations', '_get_shap_explanations', '_initialize_model', '_input_to_tuple', '_make_analysis_folder', '_one_hot', 'batch_size', 'current_size', 'data_path', 'data_vars', 'dense_features', 'device', 'evaluate', 'experiment', 'explain', 'explainer', 'features_per_month', 'get_dataloader', 'hidden_size', 'ignore_vars', 'include_latlons', 'include_monthly_aggs', 'include_pred_month', 'include_yearly_aggs', 'input_dense', 'load', 'model', 'model_dir', 'model_name', 'models_dir', 'num_locations', 'pred_months', 'predict', 'predict_delta', 'rnn_dropout', 'save_model', 'static', 'static_embedding_size', 'static_size', 'surrounding_pixels', 'to', 'train', 'yearly_agg_size'] \n",
+      "\n",
+      "\n",
+      "['ndvi', 'p84.162', 'sp', 'tp', 'Eb']\n",
+      "128\n",
+      "None\n",
+      "10\n",
+      "1574\n",
+      "None\n",
+      "0.25\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(dir(ealstm_eg), '\\n\\n')\n",
+    "\n",
+    "print(ealstm_eg.ignore_vars,)\n",
+    "print(ealstm_eg.hidden_size,)\n",
+    "print(ealstm_eg.data_vars,)\n",
+    "print(ealstm_eg.static_embedding_size,)\n",
+    "print(ealstm_eg.static_size,)\n",
+    "print(ealstm_eg.surrounding_pixels,)\n",
+    "print(ealstm_eg.rnn_dropout,)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Read in results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "\"['level_1'] not found in axis\"",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-16-4c2f383dfa1c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mscripts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mget_results\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mresult_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_results\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_dir\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m'02_training_periods'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/github/ml_drought/scripts/utils.py\u001b[0m in \u001b[0;36mget_results\u001b[0;34m(model_dir, print_output)\u001b[0m\n\u001b[1;32m     67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m     \u001b[0;31m# merge values for that experiment\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 69\u001b[0;31m     df = df.merge(persistence_rmses.drop(columns=\"level_1\"), on=\"experiment\").rename(\n\u001b[0m\u001b[1;32m     70\u001b[0m         \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtotal_rmse_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"previous_month_score\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m     )\n",
+      "\u001b[0;32m~/miniconda3/envs/crop/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   3695\u001b[0m                                            \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3696\u001b[0m                                            \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3697\u001b[0;31m                                            errors=errors)\n\u001b[0m\u001b[1;32m   3698\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3699\u001b[0m     @rewrite_axis_style_signature('mapper', [('copy', True),\n",
+      "\u001b[0;32m~/miniconda3/envs/crop/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   3109\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3110\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3111\u001b[0;31m                 \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drop_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3112\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3113\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniconda3/envs/crop/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_drop_axis\u001b[0;34m(self, labels, axis, level, errors)\u001b[0m\n\u001b[1;32m   3141\u001b[0m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3142\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3143\u001b[0;31m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3144\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0maxis_name\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnew_axis\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3145\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniconda3/envs/crop/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, errors)\u001b[0m\n\u001b[1;32m   4402\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'ignore'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4403\u001b[0m                 raise KeyError(\n\u001b[0;32m-> 4404\u001b[0;31m                     '{} not found in axis'.format(labels[mask]))\n\u001b[0m\u001b[1;32m   4405\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4406\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mKeyError\u001b[0m: \"['level_1'] not found in axis\""
+     ]
+    }
+   ],
+   "source": [
+    "from scripts.utils import get_results\n",
+    "\n",
+    "result_df = get_results(data_dir / '02_training_periods')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/scripts/engineer.py b/scripts/engineer.py
index be023d00c..eab7e6263 100644
--- a/scripts/engineer.py
+++ b/scripts/engineer.py
@@ -6,7 +6,12 @@
 from scripts.utils import get_data_path
 
 
-def engineer(experiment="one_month_forecast", process_static=True, pred_months=12):
+def engineer(
+    experiment="one_month_forecast",
+    process_static=True,
+    pred_months=12,
+    test_years=[y for y in range(2011, 2019)],
+):
 
     engineer = Engineer(
         get_data_path(), experiment=experiment, process_static=process_static
diff --git a/scripts/experiments/01_different_variables.py b/scripts/experiments/01_different_variables.py
index 463e1b1e1..33263ef39 100644
--- a/scripts/experiments/01_different_variables.py
+++ b/scripts/experiments/01_different_variables.py
@@ -5,6 +5,8 @@
 ['pev', 'sp', 't2m', 'tp', 'VCI', 'precip', 'ndvi', 'E', 'Eb', 'SMroot', 'SMsurf',]
 
 # NOTE: p84.162 == 'vertical integral of moisture flux'
+
+Experiment ['VCI', 'E'] Static: False
 """
 from itertools import combinations
 from pathlib import Path
@@ -118,6 +120,17 @@ def run_all_models_as_experiments(
     print(f"Experiment {vars_to_include} finished")
 
 
+def get_already_run(data_dir: Path, vars_joined: str, static: bool):
+    already_run = [
+        m
+        for m in (data_dir / "models").glob(
+            f"*{vars_joined}_{'YES' if static else 'NO'}static"
+        )
+    ]
+
+    return already_run
+
+
 if __name__ == "__main__":
     ignore_vars = None
     always_ignore_vars = ["ndvi", "p84.162", "sp", "tp", "Eb"]
@@ -160,6 +173,16 @@ def run_all_models_as_experiments(
 
         # run experiments
         for static in [True, False]:
+            vars_joined = "_".join(vars_to_include)
+            already_run = get_already_run(data_dir, vars_joined, static)
+            # CHECK if experiment has already been run
+            if already_run != []:
+                print(
+                    f'\n{"-" * 10}\Experiment ALREADY RUN: {vars_to_include} static:{static}\n{"-" * 10}',
+                    "Skipping to next experiment ...",
+                )
+                continue
+
             try:
                 run_all_models_as_experiments(
                     vars_to_include, ignore_vars, static=static, run_regression=False
diff --git a/scripts/experiments/02_different_training_periods.py b/scripts/experiments/02_different_training_periods.py
index 7869c0380..f0371ca88 100644
--- a/scripts/experiments/02_different_training_periods.py
+++ b/scripts/experiments/02_different_training_periods.py
@@ -1,4 +1,49 @@
 """
+
+RERUN:
+chose 8 test years?
+test_hilo: high / train_hilo: high / train_length: 5
+_one_month_forecast_TRhigh_TEhigh_LEN5/test
+
+chose 5 test years
+test_hilo: med / train_hilo: high / train_length: 10
+one_month_forecast_TRhigh_TEmed_LEN10/test
+
+chose 2 test years
+test_hilo: low / train_hilo: high / train_length: 20
+one_month_forecast_TRhigh_TElow_LEN20/
+
+chose 5 test years
+test_hilo: med / train_hilo: high / train_length: 10
+one_month_forecast_TRhigh_TEmed_LEN10/
+
+DIDN'T RUN
+test_hilo: med / train_hilo: high / train_length: 5
+one_month_forecast_TRhigh_TEmed_LEN5/
+
+
+
+
+chose 1981 as a test year
+test_hilo: high / train_hilo: med / train_length: 20
+one_month_forecast_TRmed_TEhigh_LEN20/test
+
+ealstm failed
+test_hilo: low / train_hilo: med / train_length: 10
+*one_month_forecast_TRmed_TElow_LEN10/ealstm
+
+only chose 2 test years
+test_hilo: low / train_hilo: med / train_length: 20
+one_month_forecast_TRmed_TElow_LEN20
+
+
+
+chose 1981 as a test year
+test_hilo: high / train_hilo: low / train_length: 5
+one_month_forecast_TRlow_TEhigh_LEN5
+
+
+=======
 RERUN:
 chose 8 test years?
 test_hilo: high / train_hilo: high / train_length: 5
@@ -41,11 +86,11 @@
 sys.path.append("../..")
 
 from _base_models import persistence, regression, linear_nn, rnn, earnn
-
 from src.analysis import all_explanations_for_file
 from src.analysis import read_train_data, read_test_data
+from src.analysis import all_explanations_for_file
 from src.engineer import Engineer
-from scripts.utils import get_data_path, _rename_directory
+from _base_models import parsimonious, regression, linear_nn, rnn, earnn
 
 
 def sort_by_median_target_variable(
diff --git a/scripts/experiments/06_non_vci_predictors.py b/scripts/experiments/06_non_vci_predictors.py
new file mode 100644
index 000000000..ad1381695
--- /dev/null
+++ b/scripts/experiments/06_non_vci_predictors.py
@@ -0,0 +1,39 @@
+import sys
+
+sys.path.append("../..")
+
+from _base_models import parsimonious, regression, linear_nn, rnn, earnn
+from scripts.utils import _rename_directory, get_data_path
+
+if __name__ == "__main__":
+    # important_vars = ["VCI", "precip", "t2m", "pev", "p0005", "SMsurf", "SMroot"]
+    # always_ignore_vars = ["ndvi", "p84.162", "sp", "tp", "Eb", "E", "p0001"]
+    important_vars = ["precip", "t2m", "pev", "E", "SMsurf", "SMroot"]
+    always_ignore_vars = ["ndvi", "p84.162", "sp", "tp", "Eb", "VCI"]
+
+    parsimonious()
+    # regression(ignore_vars=always_ignore_vars)
+    # gbdt(ignore_vars=always_ignore_vars)
+    # linear_nn(ignore_vars=always_ignore_vars)
+    # rnn(ignore_vars=always_ignore_vars)
+    earnn(
+        experiment="one_month_forecast",
+        include_pred_month=True,
+        surrounding_pixels=None,
+        pretrained=False,
+        explain=False,
+        static="features",
+        ignore_vars=always_ignore_vars,
+        num_epochs=50,
+        early_stopping=5,
+        hidden_size=256,
+        static_embedding_size=64,
+    )
+
+    # rename the output file
+    data_path = get_data_path()
+
+    _rename_directory(
+        from_path=data_path / "models" / "one_month_forecast",
+        to_path=data_path / "models" / "one_month_forecast_NO_VCI",
+    )
diff --git a/scripts/experiments/07_nowcast.py b/scripts/experiments/07_nowcast.py
index f815c94c0..7c7310b0b 100644
--- a/scripts/experiments/07_nowcast.py
+++ b/scripts/experiments/07_nowcast.py
@@ -8,29 +8,33 @@
 
 
 def run_engineer() -> None:
-    engineer(pred_months=12, experiment="nowcast")
+    engineer(
+        pred_months=3, experiment="nowcast", test_years=[y for y in range(2011, 2019)]
+    )
 
 
 if __name__ == "__main__":
+    # run_engineer()
+
     # NOTE: why have we downloaded 2 variables for ERA5 evaporaton
     # important_vars = ["VCI", "precip", "t2m", "pev", "p0005", "SMsurf", "SMroot"]
     # always_ignore_vars = ["ndvi", "p84.162", "sp", "tp", "Eb", "E", "p0001"]
     important_vars = ["precip", "t2m", "pev", "E", "SMsurf", "SMroot"]
 
-    # IGNORE the forecast precip too (to begin with)!
-    always_ignore_vars = [
+    # IGNORE the forecast precip & VCI (to begin with)!
+    ignore_vars = [
         "VCI",
         "ndvi",
         "p84.162",
         "sp",
         "tp",
         "Eb",
-        "tprate_std_1",
-        "tprate_mean_1",
-        "tprate_std_2",
-        "tprate_mean_2",
-        "tprate_std_3",
-        "tprate_mean_3",
+        # "tprate_std_1",
+        # "tprate_mean_1",
+        # "tprate_std_2",
+        # "tprate_mean_2",
+        # "tprate_std_3",
+        # "tprate_mean_3",
     ]
 
     persistence(experiment="nowcast")
@@ -45,11 +49,12 @@ def run_engineer() -> None:
         pretrained=False,
         explain=False,
         static="features",
-        ignore_vars=always_ignore_vars,
-        num_epochs=1,  #  50,
+        ignore_vars=ignore_vars,
+        num_epochs=1,  # 50,
         early_stopping=5,
         hidden_size=256,
         static_embedding_size=64,
+        learning_rate=1e-2,
     )
 
     # rename the output file
diff --git a/scripts/experiments/_base_models.py b/scripts/experiments/_base_models.py
index f4be52f8c..0052aad12 100644
--- a/scripts/experiments/_base_models.py
+++ b/scripts/experiments/_base_models.py
@@ -28,6 +28,7 @@ def climatology(experiment="one_month_forecast", include_yearly_aggs=False):
     )
 
     predictor.evaluate(save_preds=True)
+    print("\n\n")
 
 
 def regression(
@@ -42,6 +43,7 @@ def regression(
     include_latlons=False,
     include_yearly_aggs=False,
 ):
+    print("\n\n** Regression **")
     predictor = LinearRegression(
         get_data_path(),
         experiment=experiment,
@@ -60,6 +62,7 @@ def regression(
     # mostly to test it works
     if explain:
         predictor.explain(save_shap_values=True)
+    print("\n\n")
 
 
 def linear_nn(
@@ -73,11 +76,13 @@ def linear_nn(
     early_stopping=5,
     layer_sizes=[100],
     predict_delta=False,
+    learning_rate=1e-3,
     spatial_mask=None,
     include_latlons=False,
     include_yearly_aggs=True,
     clear_nans=True,
 ):
+    print("\n\n** Linear Network **")
     predictor = LinearNetwork(
         layer_sizes=layer_sizes,
         data_folder=get_data_path(),
@@ -87,6 +92,11 @@ def linear_nn(
         static=static,
         ignore_vars=ignore_vars,
         predict_delta=predict_delta,
+    )
+    predictor.train(
+        num_epochs=num_epochs,
+        early_stopping=early_stopping,
+        learning_rate=learning_rate,
         spatial_mask=spatial_mask,
         include_latlons=include_latlons,
         include_yearly_aggs=include_yearly_aggs,
@@ -98,6 +108,7 @@ def linear_nn(
 
     if explain:
         _ = predictor.explain(save_shap_values=True)
+    print("\n\n")
 
 
 def rnn(
@@ -111,6 +122,7 @@ def rnn(
     early_stopping=5,
     hidden_size=128,
     predict_delta=False,
+    learning_rate=1e-3,
     spatial_mask=None,
     include_latlons=False,
     normalize_y=True,
@@ -119,6 +131,7 @@ def rnn(
     clear_nans=True,
     weight_observations=False,
 ):
+    print("\n\n** RNN **")
     predictor = RecurrentNetwork(
         hidden_size=hidden_size,
         data_folder=get_data_path(),
@@ -128,6 +141,11 @@ def rnn(
         static=static,
         ignore_vars=ignore_vars,
         predict_delta=predict_delta,
+    )
+    predictor.train(
+        num_epochs=num_epochs,
+        early_stopping=early_stopping,
+        learning_rate=learning_rate,
         spatial_mask=spatial_mask,
         include_latlons=include_latlons,
         normalize_y=normalize_y,
@@ -142,6 +160,7 @@ def rnn(
 
     if explain:
         _ = predictor.explain(save_shap_values=True)
+    print("\n\n")
 
 
 def earnn(
@@ -157,6 +176,7 @@ def earnn(
     static_embedding_size=10,
     hidden_size=128,
     predict_delta=False,
+    learning_rate=1e-3,
     spatial_mask=None,
     include_latlons=False,
     normalize_y=True,
@@ -166,6 +186,7 @@ def earnn(
     weight_observations=False,
     pred_month_static=False,
 ):
+    print("\n\n** EALSTM **")
     data_path = get_data_path()
 
     if not pretrained:
@@ -179,6 +200,11 @@ def earnn(
             static_embedding_size=static_embedding_size,
             ignore_vars=ignore_vars,
             predict_delta=predict_delta,
+        )
+        predictor.train(
+            num_epochs=num_epochs,
+            early_stopping=early_stopping,
+            learning_rate=learning_rate,
             spatial_mask=spatial_mask,
             include_latlons=include_latlons,
             normalize_y=normalize_y,
@@ -198,6 +224,7 @@ def earnn(
         test_file = data_path / f"features/{experiment}/test/2018_3"
         assert test_file.exists()
         all_explanations_for_file(test_file, predictor, batch_size=100)
+    print("\n\n")
 
 
 def gbdt(
@@ -213,6 +240,7 @@ def gbdt(
     include_latlons=False,
     include_yearly_aggs=True,
 ):
+    print("\n\n** GBDT **")
     data_path = get_data_path()
 
     # initialise, train and save GBDT model
@@ -230,3 +258,4 @@ def gbdt(
     predictor.train(early_stopping=5)
     predictor.evaluate(save_preds=True)
     predictor.save_model()
+    print("\n\n")
diff --git a/scripts/utils.py b/scripts/utils.py
index daf265e56..7005bff20 100644
--- a/scripts/utils.py
+++ b/scripts/utils.py
@@ -1,6 +1,10 @@
 import shutil
 from pathlib import Path
 import time
+import json
+import pandas as pd
+import re
+from datetime import datetime
 
 
 def get_data_path() -> Path:
@@ -33,6 +37,66 @@ def _rename_directory(
     print(f"MOVED {from_path} to {to_path}")
 
 
+def get_results(model_dir: Path, print_output: bool = True) -> pd.DataFrame:
+    """ Display the results from the results.json """
+
+    def _get_persistence_for_group(x):
+        return x.loc[x.model == "previous_month"].total_rmse
+
+    # create a dataframe for the results in results.json
+    result_paths = [p for p in model_dir.glob("*/*/results.json")]
+    date_regex = r"\d{4}_\d{2}_\d{2}:\d{6}_"
+    experiments = [re.sub(date_regex, "", p.parents[1].name) for p in result_paths]
+    df = pd.DataFrame({"experiment": experiments})
+
+    # match the date_str if in the experiment name
+    date_matches = [re.match(date_regex, p.parents[1].name) for p in result_paths]
+
+    datetimes = []
+    for dt in date_matches:
+        if dt is None:
+            datetimes.append(None)
+        else:
+            datetimes.append(
+                pd.to_datetime(datetime.strptime(dt.group(), "%Y_%m_%d:%H%M%S_"))
+            )
+
+    df["time"] = datetimes
+
+    df["model"] = [p.parents[0].name for p in result_paths]
+    result_dicts = [json.load(open(p, "rb")) for p in result_paths]
+    df["total_rmse"] = [d["total"] for d in result_dicts]
+
+    persistence_rmses = (
+        df.groupby("experiment").apply(_get_persistence_for_group).reset_index()
+    )
+
+    # merge values for that experiment
+    df = df.merge(persistence_rmses.drop(columns="level_1"), on="experiment").rename(
+        columns=dict(total_rmse_y="previous_month_score")
+    )
+
+    #
+    df["outperform_baseline"] = df.total_rmse_x < df.previous_month_score
+
+    if print_output:
+        for i, row in df.iterrows():
+            persistence_score = (
+                persistence_rmses["total_rmse"]
+                .loc[persistence_rmses.experiment == row.experiment]
+                .values
+            )
+
+            print(
+                f"Experiment: {row.experiment}\n"
+                f"Model: {row.model}\n"
+                f"Persistence RMSE: {persistence_score}\n"
+                f"RMSE: {row.total_rmse}\n"
+            )
+
+    return df
+
+
 def _base_increment_folder(data_path: Path, dir: str):
     old_paths = [d for d in data_path.glob(f"*_{dir}*")]
     if old_paths == []:
diff --git a/src/analysis/region_analysis/administrative_region_analysis.py b/src/analysis/region_analysis/administrative_region_analysis.py
index c74b288c1..d99dd474e 100644
--- a/src/analysis/region_analysis/administrative_region_analysis.py
+++ b/src/analysis/region_analysis/administrative_region_analysis.py
@@ -17,12 +17,14 @@ def __init__(
         data_dir: Path = Path("data"),
         experiment: str = "one_month_forecast",
         true_data_experiment: str = "one_month_forecast",
+        models_experiment_dir: str = "one_month_forecast",
     ):
         super().__init__(
             data_dir=data_dir,
             experiment=experiment,
             true_data_experiment=true_data_experiment,
             admin_boundaries=self.admin_boundaries,
+            models_experiment_dir=models_experiment_dir,
         )
 
     @staticmethod
diff --git a/src/analysis/region_analysis/base.py b/src/analysis/region_analysis/base.py
index 66ed0c8ad..5f64621d8 100644
--- a/src/analysis/region_analysis/base.py
+++ b/src/analysis/region_analysis/base.py
@@ -47,6 +47,7 @@ def __init__(
         true_data_experiment: str = "one_month_forecast",
         models: Union[List[str], None] = None,
         admin_boundaries: bool = True,
+        models_experiment_dir: Optional[str] = None,
     ):
         """Base RegionAnalysis class.
 
diff --git a/src/engineer/__init__.py b/src/engineer/__init__.py
index c6c25e926..436c4c4fb 100644
--- a/src/engineer/__init__.py
+++ b/src/engineer/__init__.py
@@ -4,6 +4,7 @@
 
 from .nowcast import _NowcastEngineer
 from .one_month_forecast import _OneMonthForecastEngineer
+from .different_training_periods import _DifferentTrainingPeriodsEngineer
 from .base import _EngineerBase
 
 
@@ -25,6 +26,7 @@ def __init__(
         data_folder: Path = Path("data_path"),
         process_static: bool = True,
         experiment: str = "one_month_forecast",
+        different_training_periods: bool = False,
     ) -> None:
 
         assert experiment in {
@@ -34,7 +36,12 @@ def __init__(
 
         engineer: _EngineerBase
         if experiment == "one_month_forecast":
-            engineer = _OneMonthForecastEngineer(data_folder, process_static)
+            if different_training_periods:
+                engineer = _DifferentTrainingPeriodsEngineer(
+                    data_folder, process_static
+                )
+            else:
+                engineer = _OneMonthForecastEngineer(data_folder, process_static)
         elif experiment == "nowcast":
             engineer = _NowcastEngineer(data_folder, process_static)
         self.engineer_class = engineer
diff --git a/src/engineer/base.py b/src/engineer/base.py
index 6318009b2..859abceca 100644
--- a/src/engineer/base.py
+++ b/src/engineer/base.py
@@ -188,11 +188,14 @@ def _process_dynamic(
         # save test data (x, y) and return the train_ds (subset of `data`)
         train_ds = self._train_test_split(
             ds=data,
-            years=cast(List, test_year),
+            test_years=cast(List, test_year),
             target_variable=target_variable,
             pred_months=pred_months,
             expected_length=expected_length,
         )
+        assert train_ds.time.shape[0] > 0, (
+            "Expect the train_ds to have" f"`time` dimension. \n{train_ds}"
+        )
 
         normalization_values = self._calculate_normalization_values(train_ds)
 
@@ -290,19 +293,19 @@ def _stratify_training_data(
     def _train_test_split(
         self,
         ds: xr.Dataset,
-        years: List[int],
+        test_years: List[int],
         target_variable: str,
         pred_months: int,
         expected_length: Optional[int],
     ) -> xr.Dataset:
         """save the test data and return the training dataset"""
 
-        years.sort()
+        test_years.sort()
 
         # for the first `year` Jan calculate the xy_test dictionary and min date
         xy_test, min_test_date = self._stratify_xy(
             ds=ds,
-            year=years[0],
+            year=test_years[0],
             target_variable=target_variable,
             target_month=1,
             pred_months=pred_months,
@@ -315,12 +318,12 @@ def _train_test_split(
 
         # save the xy_test dictionary
         if xy_test is not None:
-            self._save(xy_test, year=years[0], month=1, dataset_type="test")
+            self._save(xy_test, year=test_years[0], month=1, dataset_type="test")
 
         # each month in test_year produce an x,y pair for testing
-        for year in years:
+        for year in test_years:
             for month in range(1, 13):
-                if year > years[0] or month > 1:
+                if year > test_years[0] or month > 1:
                     # prevents the initial test set from being recalculated
                     xy_test, _ = self._stratify_xy(
                         ds=ds,
diff --git a/src/engineer/different_training_periods.py b/src/engineer/different_training_periods.py
new file mode 100644
index 000000000..47553e42c
--- /dev/null
+++ b/src/engineer/different_training_periods.py
@@ -0,0 +1,208 @@
+import pandas as pd
+from datetime import date
+import xarray as xr
+import warnings
+import pickle
+
+from typing import cast, Dict, Optional, Tuple, List, Union
+
+from .one_month_forecast import _OneMonthForecastEngineer
+
+
+class _DifferentTrainingPeriodsEngineer(_OneMonthForecastEngineer):
+    name = "one_month_forecast"
+
+    @staticmethod
+    def check_data_leakage(
+        train_ds: xr.Dataset, xy_test: Dict[str, xr.Dataset]
+    ) -> None:
+        # CHECK DATA LEAKAGE
+        train_dts = [pd.to_datetime(t) for t in train_ds.time.values]
+        test_dt = pd.to_datetime(xy_test["y"].time.values)
+        assert test_dt not in train_dts, (
+            "Data Leakage!" f"{test_dt} found in train_ds datetimes: \n {train_ds}"
+        )
+
+    def engineer(
+        self,
+        test_year: Union[int, List[int]],
+        target_variable: str = "VHI",
+        pred_months: int = 12,
+        expected_length: Optional[int] = 12,
+        train_years: Optional[List[int]] = None,
+    ) -> None:
+
+        self._process_dynamic(
+            test_year, target_variable, pred_months, expected_length, train_years
+        )
+        if self.process_static:
+            self._process_static()
+
+    def _process_dynamic(
+        self,
+        test_year: Union[int, List[int]],
+        target_variable: str = "VHI",
+        pred_months: int = 12,
+        expected_length: Optional[int] = 12,
+        train_years: Optional[List[int]] = None,
+    ) -> None:
+        if expected_length is None:
+            warnings.warn(
+                "** `expected_length` is None. This means that \
+            missing data will not be skipped. Are you sure? **"
+            )
+
+        # read in all the data from interim/{var}_preprocessed
+        data = self._make_dataset(static=False)
+
+        # ensure test_year is List[int]
+        if type(test_year) is int:
+            test_year = [cast(int, test_year)]
+
+        # save test data (x, y) and return the train_ds (subset of `data`)
+        train_ds, test_dts = self._train_test_split(
+            ds=data,
+            test_years=cast(List, test_year),
+            target_variable=target_variable,
+            pred_months=pred_months,
+            expected_length=expected_length,
+            train_years=train_years,
+        )
+        assert train_ds.time.shape[0] > 0, (
+            "Expect the train_ds to have" f"`time` dimension. \n{train_ds}"
+        )
+
+        normalization_values = self._calculate_normalization_values(train_ds)
+
+        # split train_ds into x, y for each year-month before `test_year` & save
+        self._stratify_training_data(
+            train_ds=train_ds,
+            test_dts=test_dts,
+            target_variable=target_variable,
+            pred_months=pred_months,
+            expected_length=expected_length,
+            train_years=train_years,
+        )
+
+        savepath = self.output_folder / "normalizing_dict.pkl"
+        with savepath.open("wb") as f:
+            pickle.dump(normalization_values, f)
+
+    def _train_test_split(
+        self,
+        ds: xr.Dataset,
+        test_years: List[int],
+        target_variable: str,
+        pred_months: int,
+        expected_length: Optional[int],
+        train_years: Optional[List[int]] = None,
+    ) -> Tuple[xr.Dataset, List[date]]:
+        """save the test data and return the training dataset"""
+
+        test_years.sort()
+        test_dts = []
+
+        months = ds["time.month"].values
+
+        if ds["time.year"].min() == test_years[0]:
+            # if we have the FIRST year in our dataset as a test year
+            # then minimum target month should be `pred_months + 1`
+            # e.g. x = 1984 Jan/Feb/Mar, y = 1984 April
+            init_target_month = pred_months + months[0]
+            print(f"\n* init_target_month = {init_target_month} *\n")
+        else:
+            init_target_month = 1
+
+        # NOTE: the test year is
+        # for the first `year` calculate the xy_test dictionary and min date
+        xy_test, min_test_date = self._stratify_xy(
+            ds=ds,
+            year=test_years[0],
+            target_variable=target_variable,
+            target_month=init_target_month,
+            pred_months=pred_months,
+            expected_length=expected_length,
+        )
+
+        train_ds = ds
+
+        # save the xy_test dictionary
+        if xy_test is not None:
+            test_dts.append(self._get_datetime(xy_test["y"].time.values[0]))
+            self._save(
+                xy_test,
+                year=test_years[0],
+                month=init_target_month,
+                dataset_type="test",
+            )
+
+            # check for data leakage
+            # self.check_data_leakage(train_ds, xy_test)
+
+        # each month in test_year produce an x,y pair for testing
+        for year in test_years:
+            for month in range(init_target_month, 13):
+                if (month > init_target_month) | (year != test_years[0]):
+                    # prevents the initial test set from being recalculated
+                    xy_test, _ = self._stratify_xy(
+                        ds=ds,
+                        year=year,
+                        target_variable=target_variable,
+                        target_month=month,
+                        pred_months=pred_months,
+                        expected_length=expected_length,
+                    )
+
+                    if xy_test is not None:
+                        # check for data leakage
+                        # self.check_data_leakage(train_ds, xy_test)
+                        test_dts.append(self._get_datetime(xy_test["y"].time.values[0]))
+                        self._save(xy_test, year=year, month=month, dataset_type="test")
+        return train_ds, test_dts
+
+    def _stratify_training_data(  # type: ignore
+        self,
+        train_ds: xr.Dataset,
+        test_dts: List[date],
+        target_variable: str,
+        pred_months: int,
+        expected_length: Optional[int],
+        train_years: Optional[List[int]] = None,
+    ) -> None:
+        """split `train_ds` into x, y and save the outputs to
+        self.output_folder (data/features) """
+
+        min_date = self._get_datetime(train_ds.time.values.min())
+        max_date = self._get_datetime(train_ds.time.values.max())
+
+        cur_pred_year, cur_pred_month = max_date.year, max_date.month
+
+        # for every month-year create & save the x, y datasets for training
+        cur_min_date = max_date
+        while cur_min_date >= min_date:
+
+            # each iteration count down one month (02 -> 01 -> 12 ...)
+            arrays, cur_min_date = self._stratify_xy(
+                ds=train_ds,
+                year=cur_pred_year,
+                target_variable=target_variable,
+                target_month=cur_pred_month,
+                pred_months=pred_months,
+                expected_length=expected_length,
+                print_status=False,
+            )
+
+            # Preventing data leakage:
+            # only save if that year is in train_years
+            # and that month is not in test_dts
+            if arrays is not None:
+                if (cur_pred_year in train_years) and (  # type: ignore
+                    self._get_datetime(arrays["y"].time.values[0]) not in test_dts
+                ):
+                    self._save(
+                        arrays,
+                        year=cur_pred_year,
+                        month=cur_pred_month,
+                        dataset_type="train",
+                    )
+            cur_pred_year, cur_pred_month = cur_min_date.year, cur_min_date.month
diff --git a/src/engineer/one_month_forecast.py b/src/engineer/one_month_forecast.py
index cd02b464d..75c5d6cc4 100644
--- a/src/engineer/one_month_forecast.py
+++ b/src/engineer/one_month_forecast.py
@@ -2,7 +2,6 @@
 import calendar
 from datetime import date
 import xarray as xr
-import warnings
 
 from typing import cast, Dict, Optional, Tuple
 
@@ -21,14 +20,15 @@ def _stratify_xy(
         target_month: int,
         pred_months: int = 11,
         expected_length: Optional[int] = 11,
+        print_status: bool = False,
     ) -> Tuple[Optional[Dict[str, xr.Dataset]], date]:
         """
         Note: expected_length should be the same as pred_months when the timesteps
         are monthly, but should be more if the timesteps are at shorter resolution
         than monthly.
         """
-
-        print(f"Generating data for year: {year}, target month: {target_month}")
+        if print_status:
+            print(f"Generating data for year: {year}, target month: {target_month}")
 
         max_date = date(year, target_month, calendar.monthrange(year, target_month)[-1])
         mx_year, mx_month, max_train_date = minus_months(
@@ -42,10 +42,11 @@ def _stratify_xy(
         max_date_np = np.datetime64(str(max_date))
         max_train_date_np = np.datetime64(str(max_train_date))
 
-        print(
-            f"Max date: {str(max_date)}, max input date: {str(max_train_date)}, "
-            f"min input date: {str(min_date)}"
-        )
+        if print_status:
+            print(
+                f"Max date: {str(max_date)}, max input date: {str(max_train_date)}, "
+                f"min input date: {str(min_date)}"
+            )
 
         # boolean array indexing the timestamps to filter `ds`
         x = (ds.time.values > min_date_np) & (ds.time.values <= max_train_date_np)
@@ -68,10 +69,11 @@ def _stratify_xy(
 
         if x_dataset.time.size != expected_length:
             # catch the errors as we get closer to the MINIMUM year
-            warnings.warn(
-                "For the `nowcast` experiment we expect the\
-                number of timesteps to be: {pred_months}.\
-                Currently: {x_dataset.time.size}"
+            print(
+                "For the `nowcast` experiment we expect the "
+                f"number of timesteps to be: {pred_months}. "
+                f"Currently: {x_dataset.time.size} "
+                f"You provided the argument expected_length: {expected_length}"
             )
             return None, cast(date, max_train_date)
 
diff --git a/src/models/data.py b/src/models/data.py
index 2f5caa8b8..6839acae3 100644
--- a/src/models/data.py
+++ b/src/models/data.py
@@ -96,11 +96,13 @@ def to_xarray(self) -> Tuple[xr.Dataset, xr.Dataset, Optional[xr.Dataset]]:
         if self.x.current is not None:
             # NOTE: historical times is one less for nowcast?
             assert (
-                len(self.historical_times) == self.x.historical.shape[1]  # type: ignore
+                len(self.historical_times)  # type: ignore
+                == self.x.historical.shape[1]
             ), "second dim is # timesteps"
         else:
             assert (
-                len(self.historical_times) == self.x.historical.shape[1]  # type: ignore
+                len(self.historical_times)  # type: ignore
+                == self.x.historical.shape[1]
             ), "second dim is # timesteps"
         variables = self.x_vars
         latitudes = np.unique(self.latlons[:, 0])  # type: ignore
@@ -207,8 +209,13 @@ def train_val_mask(
     The train mask and the val mask, both as lists
     """
     assert val_ratio < 1, f"Val ratio must be smaller than 1"
-    train_mask = np.random.rand(mask_len) < 1 - val_ratio
-    val_mask = ~train_mask
+    train_mask = np.ones(mask_len).astype("bool")
+    val_mask = np.zeros(mask_len).astype("bool")
+
+    # We don't want ALL values in train and none in val
+    while all(np.array(train_mask)) and all(~np.array(val_mask)):
+        train_mask = np.random.rand(mask_len) < 1 - val_ratio
+        val_mask = ~train_mask
 
     return train_mask.tolist(), val_mask.tolist()
 
@@ -731,6 +738,7 @@ def ds_folder_to_np(
         if self.predict_delta:
             # TODO: do this ONCE not at each read-in of the data
             y = self._calculate_change(x, y)
+
         assert len(list(y.data_vars)) == 1, (
             f"Expect only 1 target variable! " f"Got {len(list(y.data_vars))}"
         )
diff --git a/tests/engineer/test_different_training_periods_engineer.py b/tests/engineer/test_different_training_periods_engineer.py
new file mode 100644
index 000000000..f86056319
--- /dev/null
+++ b/tests/engineer/test_different_training_periods_engineer.py
@@ -0,0 +1,103 @@
+import xarray as xr
+from src.engineer import Engineer
+from .test_base import _setup
+
+
+class TestDifferentTrainingPeriodsEngineer:
+    @staticmethod
+    def _check_folder(folder_path, expected_length):
+        y = xr.open_dataset(folder_path / "y.nc")
+        assert "b" not in set(y.variables), "Got unexpected variables in test set"
+
+        x = xr.open_dataset(folder_path / "x.nc")
+        for expected_var in {"a", "b"}:
+            assert expected_var in set(
+                x.variables
+            ), "Missing variables in testing input dataset"
+        assert (
+            len(x.time.values) == expected_length
+        ), "Wrong number of months in the test x dataset"
+        assert len(y.time.values) == 1, "Wrong number of months in test y dataset"
+
+    def test_stratify_with_non_sequential_test_train_years(self, tmp_path):
+        _setup(tmp_path)
+        engineer = Engineer(
+            tmp_path,
+            experiment="one_month_forecast",
+            process_static=True,
+            different_training_periods=True,
+        )
+
+        expected_length = 3
+
+        # -------------------------------
+        # test a first test/train option
+        # -------------------------------
+        engineer.engineer_class._process_dynamic(
+            test_year=[1999],
+            target_variable="a",
+            pred_months=3,
+            expected_length=3,
+            train_years=[2000, 2001],
+        )
+
+        for month in range(4, 13):
+            # TEST DATA
+            # because our first year is 1999 (test_year) we can only test from
+            # the 4th month because we don't have any data from before that date
+            # so should X dates = {1999-01, 1999-02, 1999-03}, y dates = 1999-04
+            self._check_folder(
+                tmp_path / f"features/one_month_forecast/test/1999_{month}",
+                expected_length=expected_length,
+            )
+
+            # TRAIN DATA
+            # because of data leakage we should not have 1999-10, 1999-11, 1999-12 in
+            # train data. Therefore, to predict with 3 month lead times we need
+            # to train from y = 2000-04, X = {2000-01, 2000-02, 2000-03}
+            self._check_folder(
+                tmp_path / f"features/one_month_forecast/train/2000_{month}",
+                expected_length=expected_length,
+            )
+
+        for month in range(1, 13):
+            # TRAIN DATA
+            # for 2001 we should have all 12 of the months
+            self._check_folder(
+                tmp_path / f"features/one_month_forecast/train/2001_{month}",
+                expected_length=expected_length,
+            )
+
+        # -------------------------------
+        # test a second test/train option
+        # -------------------------------
+        engineer.engineer_class._process_dynamic(
+            test_year=[2000],
+            target_variable="a",
+            pred_months=3,
+            expected_length=3,
+            train_years=[1999, 2001],
+        )
+
+        for month in range(4, 13):
+            # TEST DATA
+            # because our first year is 2000 (test_year) we can only test from
+            # the 4th month because we don't have any data from before that date
+            # so should X dates = {2000-01, 2000-02, 2000-03}, y dates = 2000-04
+            self._check_folder(
+                tmp_path / f"features/one_month_forecast/test/2000_{month}",
+                expected_length=expected_length,
+            )
+
+            # TRAIN DATA
+            # because of data leakage we should not have 2000-10, 2000-11, 2000-12 in
+            # train data. Therefore, to predict with 3 month lead times we need
+            # to train from y = 2001-04, X = {2001-01, 2001-02, 2001-03}
+            self._check_folder(
+                tmp_path / f"features/one_month_forecast/train/2001_{month}",
+                expected_length=expected_length,
+            )
+            self._check_folder(
+                tmp_path / f"features/one_month_forecast/train/2000_{month}",
+                expected_length=expected_length,
+            )
diff --git a/tests/engineer/test_nowcast.py b/tests/engineer/test_nowcast.py
index 83be81610..66094058c 100644
--- a/tests/engineer/test_nowcast.py
+++ b/tests/engineer/test_nowcast.py
@@ -54,7 +54,7 @@ def test_yearsplit(self, tmp_path):
         engineer = NowcastEngineer(tmp_path)
         train = engineer._train_test_split(
             dataset,
-            years=[2001],
+            test_years=[2001],
             target_variable="VHI",
             expected_length=11,
             pred_months=11,
diff --git a/tests/engineer/test_one_month_forecast.py b/tests/engineer/test_one_month_forecast.py
index 49f103514..c0dfa969d 100644
--- a/tests/engineer/test_one_month_forecast.py
+++ b/tests/engineer/test_one_month_forecast.py
@@ -5,7 +5,6 @@
 import datetime as dt
 
 from src.engineer import _OneMonthForecastEngineer as OneMonthForecastEngineer
-
 from ..utils import _make_dataset
 from .test_base import _setup
 
@@ -54,7 +53,7 @@ def test_yearsplit(self, tmp_path):
         engineer = OneMonthForecastEngineer(tmp_path)
         train = engineer._train_test_split(
             dataset,
-            years=[2001],
+            test_years=[2001],
             target_variable="VHI",
             pred_months=11,
             expected_length=11,
@@ -64,6 +63,21 @@ def test_yearsplit(self, tmp_path):
             train.time.values < np.datetime64("2001-01-01")
         ).all(), "Got years greater than the test year in the training set!"
 
+    @staticmethod
+    def _check_folder(folder_path, expected_length):
+        y = xr.open_dataset(folder_path / "y.nc")
+        assert "b" not in set(y.variables), "Got unexpected variables in test set"
+
+        x = xr.open_dataset(folder_path / "x.nc")
+        for expected_var in {"a", "b"}:
+            assert expected_var in set(
+                x.variables
+            ), "Missing variables in testing input dataset"
+        assert (
+            len(x.time.values) == expected_length
+        ), "Wrong number of months in the test x dataset"
+        assert len(y.time.values) == 1, "Wrong number of months in test y dataset"
+
     def test_engineer(self, tmp_path):
 
         _setup(tmp_path)
@@ -78,24 +92,16 @@ def test_engineer(self, tmp_path):
             expected_length=expected_length,
         )
 
-        def check_folder(folder_path):
-            y = xr.open_dataset(folder_path / "y.nc")
-            assert "b" not in set(y.variables), "Got unexpected variables in test set"
-
-            x = xr.open_dataset(folder_path / "x.nc")
-            for expected_var in {"a", "b"}:
-                assert expected_var in set(
-                    x.variables
-                ), "Missing variables in testing input dataset"
-            assert (
-                len(x.time.values) == expected_length
-            ), "Wrong number of months in the test x dataset"
-            assert len(y.time.values) == 1, "Wrong number of months in test y dataset"
-
-        # check_folder(tmp_path / 'features/one_month_forecast/train/1999_12')
+        # _check_folder(tmp_path / 'features/one_month_forecast/train/1999_12')
         for month in range(1, 13):
-            check_folder(tmp_path / f"features/one_month_forecast/test/2001_{month}")
-            check_folder(tmp_path / f"features/one_month_forecast/train/2000_{month}")
+            self._check_folder(
+                tmp_path / f"features/one_month_forecast/test/2001_{month}",
+                expected_length=expected_length,
+            )
+            self._check_folder(
+                tmp_path / f"features/one_month_forecast/train/2000_{month}",
+                expected_length=expected_length,
+            )
 
         assert (
             len(list((tmp_path / "features/one_month_forecast/train").glob("2001_*")))
@@ -118,8 +124,12 @@ def check_folder(folder_path):
     def test_stratify(self, tmp_path):
         _setup(tmp_path)
         engineer = OneMonthForecastEngineer(tmp_path)
-        ds_target, _, _ = _make_dataset(size=(20, 20))
-        ds_predictor, _, _ = _make_dataset(size=(20, 20))
+        ds_target, _, _ = _make_dataset(
+            size=(20, 20), start_date="1999-01-01", end_date="2001-12-31"
+        )
+        ds_predictor, _, _ = _make_dataset(
+            size=(20, 20), start_date="1999-01-01", end_date="2001-12-31"
+        )
         ds_predictor = ds_predictor.rename({"VHI": "predictor"})
         ds = ds_predictor.merge(ds_target)