Repository: TUW-GEO/ismn

Issue:
ids and ISMN_Interface.metadata.index does not match
values returned by `get_dataset_ids()` and those stored in the index of  `ISMN_Interface.metadata` should match, but they do not.

Discussion:

Patch:
diff --git a/.gitignore b/.gitignore
index f321b1d..46a1803 100644
--- a/.gitignore
+++ b/.gitignore
@@ -52,4 +52,5 @@ MANIFEST
 # ismn metadata files
 **/python_metadata/*
 networks/*
-.artifacts/*
\ No newline at end of file
+.artifacts/*
+.coverage*
\ No newline at end of file
diff --git a/docs/examples/interface.ipynb b/docs/examples/interface.ipynb
index 231deb4..0b233c1 100644
--- a/docs/examples/interface.ipynb
+++ b/docs/examples/interface.ipynb
@@ -62,7 +62,6 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\r",
       "Files Processed:   0%|          | 0/110 [00:00<?, ?it/s]"
      ]
     },
@@ -79,15 +78,16 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Files Processed: 100%|██████████| 110/110 [00:21<00:00,  5.14it/s]\n"
+      "Files Processed: 100%|██████████| 110/110 [00:20<00:00,  5.35it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Metadata generation finished after 21 Seconds.\n",
-      "Metadata and Log stored in /tmp/ismn/python_metadata\n"
+      "Metadata generation finished after 20 Seconds.\n",
+      "Metadata and Log stored in /tmp/ismn/python_metadata\n",
+      "Found existing ismn metadata in /tmp/ismn/python_metadata/Data_separate_files_20090804_20201212.csv.\n"
      ]
     }
    ],
@@ -229,7 +229,7 @@
     {
      "data": {
       "text/plain": [
-       "Sensors at 'SaintFelixdeLauragais': ['PT-100_soil_temperature_0.050000_0.050000', 'PT-100_soil_temperature_0.100000_0.100000', 'PT-100_soil_temperature_0.200000_0.200000', 'PT-100_soil_temperature_0.300000_0.300000', 'ThetaProbe-ML2X_soil_moisture_0.050000_0.050000', 'ThetaProbe-ML2X_soil_moisture_0.100000_0.100000', 'ThetaProbe-ML2X_soil_moisture_0.200000_0.200000', 'ThetaProbe-ML2X_soil_moisture_0.300000_0.300000', 'ThetaProbe-ML3_soil_moisture_0.200000_0.200000']"
+       "Sensors at 'SaintFelixdeLauragais': ['PT-100_soil_temperature_0.200000_0.200000', 'ThetaProbe-ML2X_soil_moisture_0.200000_0.200000', 'ThetaProbe-ML2X_soil_moisture_0.050000_0.050000', 'ThetaProbe-ML2X_soil_moisture_0.300000_0.300000', 'ThetaProbe-ML3_soil_moisture_0.200000_0.200000', 'PT-100_soil_temperature_0.100000_0.100000', 'PT-100_soil_temperature_0.300000_0.300000', 'ThetaProbe-ML2X_soil_moisture_0.100000_0.100000', 'PT-100_soil_temperature_0.050000_0.050000']"
       ]
      },
      "execution_count": 4,
@@ -266,11 +266,11 @@
      "data": {
       "text/plain": [
        "MetaData([\n",
-       "  MetaVar([clay_fraction, 22.8, Depth([0.05, 0.05])]),\n",
+       "  MetaVar([clay_fraction, 23.9, Depth([0.2, 0.2])]),\n",
        "  MetaVar([climate_KG, Cfb, None]),\n",
        "  MetaVar([climate_insitu, unknown, None]),\n",
        "  MetaVar([elevation, 337.0, None]),\n",
-       "  MetaVar([instrument, PT-100, Depth([0.05, 0.05])]),\n",
+       "  MetaVar([instrument, PT-100, Depth([0.2, 0.2])]),\n",
        "  MetaVar([latitude, 43.4417, None]),\n",
        "  MetaVar([lc_2000, 10, None]),\n",
        "  MetaVar([lc_2005, 10, None]),\n",
@@ -278,14 +278,33 @@
        "  MetaVar([lc_insitu, unknown, None]),\n",
        "  MetaVar([longitude, 1.88, None]),\n",
        "  MetaVar([network, SMOSMANIA, None]),\n",
+       "  MetaVar([organic_carbon, 0.97, Depth([0.2, 0.2])]),\n",
+       "  MetaVar([sand_fraction, 39.7, Depth([0.2, 0.2])]),\n",
+       "  MetaVar([saturation, 0.44, Depth([0.2, 0.2])]),\n",
+       "  MetaVar([silt_fraction, 36.4, Depth([0.2, 0.2])]),\n",
+       "  MetaVar([station, SaintFelixdeLauragais, None]),\n",
+       "  MetaVar([timerange_from, 2009-08-04 00:00:00, None]),\n",
+       "  MetaVar([timerange_to, 2019-01-01 00:00:00, None]),\n",
+       "  MetaVar([variable, soil_temperature, Depth([0.2, 0.2])]),\n",
+       "  MetaVar([instrument, ThetaProbe-ML2X, Depth([0.2, 0.2])]),\n",
+       "  MetaVar([timerange_to, 2017-09-01 11:00:00, None]),\n",
+       "  MetaVar([variable, soil_moisture, Depth([0.2, 0.2])]),\n",
+       "  MetaVar([clay_fraction, 22.8, Depth([0.05, 0.05])]),\n",
+       "  MetaVar([instrument, ThetaProbe-ML2X, Depth([0.05, 0.05])]),\n",
        "  MetaVar([organic_carbon, 1.15, Depth([0.05, 0.05])]),\n",
        "  MetaVar([sand_fraction, 43.5, Depth([0.05, 0.05])]),\n",
        "  MetaVar([saturation, 0.44, Depth([0.05, 0.05])]),\n",
        "  MetaVar([silt_fraction, 33.7, Depth([0.05, 0.05])]),\n",
-       "  MetaVar([station, SaintFelixdeLauragais, None]),\n",
-       "  MetaVar([timerange_from, 2009-08-04 00:00:00, None]),\n",
-       "  MetaVar([timerange_to, 2020-01-01 00:00:00, None]),\n",
-       "  MetaVar([variable, soil_temperature, Depth([0.05, 0.05])]),\n",
+       "  MetaVar([variable, soil_moisture, Depth([0.05, 0.05])]),\n",
+       "  MetaVar([clay_fraction, 29.4, Depth([0.3, 0.3])]),\n",
+       "  MetaVar([instrument, ThetaProbe-ML2X, Depth([0.3, 0.3])]),\n",
+       "  MetaVar([organic_carbon, 0.7, Depth([0.3, 0.3])]),\n",
+       "  MetaVar([sand_fraction, 32.0, Depth([0.3, 0.3])]),\n",
+       "  MetaVar([saturation, 0.44, Depth([0.3, 0.3])]),\n",
+       "  MetaVar([silt_fraction, 38.6, Depth([0.3, 0.3])]),\n",
+       "  MetaVar([variable, soil_moisture, Depth([0.3, 0.3])]),\n",
+       "  MetaVar([instrument, ThetaProbe-ML3, Depth([0.2, 0.2])]),\n",
+       "  MetaVar([timerange_from, 2017-10-16 13:00:00, None]),\n",
        "  MetaVar([clay_fraction, 22.4, Depth([0.1, 0.1])]),\n",
        "  MetaVar([instrument, PT-100, Depth([0.1, 0.1])]),\n",
        "  MetaVar([organic_carbon, 0.84, Depth([0.1, 0.1])]),\n",
@@ -293,31 +312,12 @@
        "  MetaVar([saturation, 0.43, Depth([0.1, 0.1])]),\n",
        "  MetaVar([silt_fraction, 37.3, Depth([0.1, 0.1])]),\n",
        "  MetaVar([variable, soil_temperature, Depth([0.1, 0.1])]),\n",
-       "  MetaVar([clay_fraction, 23.9, Depth([0.2, 0.2])]),\n",
-       "  MetaVar([instrument, PT-100, Depth([0.2, 0.2])]),\n",
-       "  MetaVar([organic_carbon, 0.97, Depth([0.2, 0.2])]),\n",
-       "  MetaVar([sand_fraction, 39.7, Depth([0.2, 0.2])]),\n",
-       "  MetaVar([saturation, 0.44, Depth([0.2, 0.2])]),\n",
-       "  MetaVar([silt_fraction, 36.4, Depth([0.2, 0.2])]),\n",
-       "  MetaVar([variable, soil_temperature, Depth([0.2, 0.2])]),\n",
-       "  MetaVar([clay_fraction, 29.4, Depth([0.3, 0.3])]),\n",
        "  MetaVar([instrument, PT-100, Depth([0.3, 0.3])]),\n",
-       "  MetaVar([organic_carbon, 0.7, Depth([0.3, 0.3])]),\n",
-       "  MetaVar([sand_fraction, 32.0, Depth([0.3, 0.3])]),\n",
-       "  MetaVar([saturation, 0.44, Depth([0.3, 0.3])]),\n",
-       "  MetaVar([silt_fraction, 38.6, Depth([0.3, 0.3])]),\n",
        "  MetaVar([variable, soil_temperature, Depth([0.3, 0.3])]),\n",
-       "  MetaVar([instrument, ThetaProbe-ML2X, Depth([0.05, 0.05])]),\n",
-       "  MetaVar([variable, soil_moisture, Depth([0.05, 0.05])]),\n",
        "  MetaVar([instrument, ThetaProbe-ML2X, Depth([0.1, 0.1])]),\n",
        "  MetaVar([variable, soil_moisture, Depth([0.1, 0.1])]),\n",
-       "  MetaVar([instrument, ThetaProbe-ML2X, Depth([0.2, 0.2])]),\n",
-       "  MetaVar([timerange_to, 2017-09-01 11:00:00, None]),\n",
-       "  MetaVar([variable, soil_moisture, Depth([0.2, 0.2])]),\n",
-       "  MetaVar([instrument, ThetaProbe-ML2X, Depth([0.3, 0.3])]),\n",
-       "  MetaVar([variable, soil_moisture, Depth([0.3, 0.3])]),\n",
-       "  MetaVar([instrument, ThetaProbe-ML3, Depth([0.2, 0.2])]),\n",
-       "  MetaVar([timerange_from, 2017-10-16 13:00:00, None])\n",
+       "  MetaVar([instrument, PT-100, Depth([0.05, 0.05])]),\n",
+       "  MetaVar([variable, soil_temperature, Depth([0.05, 0.05])])\n",
        "])"
       ]
      },
@@ -387,7 +387,7 @@
       "                depth_to                       0.05\n",
       "climate_KG      val                             Cfb\n",
       "climate_insitu  val                         unknown\n",
-      "elevation       val                             337\n",
+      "elevation       val                           337.0\n",
       "instrument      val                 ThetaProbe-ML2X\n",
       "                depth_from                     0.05\n",
       "                depth_to                       0.05\n",
@@ -412,7 +412,7 @@
       "                depth_to                       0.05\n",
       "station         val           SaintFelixdeLauragais\n",
       "timerange_from  val             2009-08-04 00:00:00\n",
-      "timerange_to    val             2020-01-01 00:00:00\n",
+      "timerange_to    val             2019-01-01 00:00:00\n",
       "variable        val                   soil_moisture\n",
       "                depth_from                     0.05\n",
       "                depth_to                       0.05\n",
@@ -489,38 +489,38 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2019-12-31 20:00:00</th>\n",
-       "      <td>0.2500</td>\n",
+       "      <th>2018-12-31 20:00:00</th>\n",
+       "      <td>0.2590</td>\n",
        "      <td>G</td>\n",
        "      <td>M</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2019-12-31 21:00:00</th>\n",
-       "      <td>0.2500</td>\n",
+       "      <th>2018-12-31 21:00:00</th>\n",
+       "      <td>0.2590</td>\n",
        "      <td>G</td>\n",
        "      <td>M</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2019-12-31 22:00:00</th>\n",
-       "      <td>0.2500</td>\n",
+       "      <th>2018-12-31 22:00:00</th>\n",
+       "      <td>0.2590</td>\n",
        "      <td>G</td>\n",
        "      <td>M</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2019-12-31 23:00:00</th>\n",
-       "      <td>0.2500</td>\n",
+       "      <th>2018-12-31 23:00:00</th>\n",
+       "      <td>0.2590</td>\n",
        "      <td>G</td>\n",
        "      <td>M</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2020-01-01 00:00:00</th>\n",
-       "      <td>0.2500</td>\n",
+       "      <th>2019-01-01 00:00:00</th>\n",
+       "      <td>0.2590</td>\n",
        "      <td>G</td>\n",
        "      <td>M</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>83939 rows × 3 columns</p>\n",
+       "<p>75204 rows × 3 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
@@ -532,13 +532,13 @@
        "2009-08-04 03:00:00         0.1361                  G                       M\n",
        "2009-08-04 04:00:00         0.1357                  G                       M\n",
        "...                            ...                ...                     ...\n",
-       "2019-12-31 20:00:00         0.2500                  G                       M\n",
-       "2019-12-31 21:00:00         0.2500                  G                       M\n",
-       "2019-12-31 22:00:00         0.2500                  G                       M\n",
-       "2019-12-31 23:00:00         0.2500                  G                       M\n",
-       "2020-01-01 00:00:00         0.2500                  G                       M\n",
+       "2018-12-31 20:00:00         0.2590                  G                       M\n",
+       "2018-12-31 21:00:00         0.2590                  G                       M\n",
+       "2018-12-31 22:00:00         0.2590                  G                       M\n",
+       "2018-12-31 23:00:00         0.2590                  G                       M\n",
+       "2019-01-01 00:00:00         0.2590                  G                       M\n",
        "\n",
-       "[83939 rows x 3 columns]"
+       "[75204 rows x 3 columns]"
       ]
      },
      "metadata": {},
@@ -546,7 +546,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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QFxPN1PlcJtRXcGourbWf+NmrizDsLrXKyoOXHofDunTAqvvPw9CeHU37+L5d5YwBnFcVg8DBprhtvW5JpDBg4mRc83R2orEvEzSt4+F+mltNjSdT+GylXiDegab0yvHnq9uHc+O6v83Bi7M3Gr/FdpJKUUxZvsO/SZkxlmVWK2xotsWI0OAuAFR8a1Yno44c7vSBbgdBJy+4l8HJTi7KFThvtdtTKM1KWB1sctYo519De/BwZwNL2v6z6uLxT9fg8U/XGL+XbT1gRLkzfJzhIssSJjG0hnd6Jyf9qQOZglE+tW1H/uojXPrEDO3jr3n6Sxx/30embazN59K+Z3N65a3FgybiJYckIN88qT8AoDQWQSxiNgf4mB57Dnf+380tL87HN//yJerq5XFCTRntdJnh6zZ3xK8/XIm3Fuqp1RR7psOgIdaFv82oxfV/n4u3F23z5fqGMpmwnVJqxI4Uc58cGtxFAGsgQHafeUnUPJ3j1U50lsmcvOBePNx+Gax23nvlOdx7EwcLHuIEhv/ZWgdSN8guuxW4IEWERz9YiUc/yOoMn/+Hz3HhH78wJTnhJ6hLt+7Ht5+ZbVK9AFqHJ4uXvntaiIMArAaInSJFvsBkv3Qg412z/tCpT+tUHtO6RxG8Et/x5H+dYPotBpXy9cDuPebz3Wza04DVOw4amupNilUsvkqv3H7QpFr0S5ep3DfstuZ2KGajLp+wJu0z/96yN01j84uaZ/RVwn0oBV6YtdFyfLEhNLgLAKttLJK6s3/++F/ZzIniebzntjTqbHCXcgZ35w4llv12WqsqeDlHBhU/3Y6DyfO57Jz3du+trQykTfEkZq6VUx/YRK49DBK5dux8khN+ovazfy/C1FW7TAG3rRE63NTWsOqzesdBW0466w+cjKsTB3TTup8fE/OWRAoziiiOQuwXjxAoJTyX//DqCpsr5a++nPLIpzjrsWm2AgKA2QF1zu+mmVSLXp/vTktb5su6/dVFrmhP7QViO/ErE7Vxvcz/ohoZf99iHuZCg7sAUNZBI/LWgcMNdlx2n0q7m0dpLPu5ZRJZdpHoKvjN4eYv99C7y7UC3lKUWugyX3twCi7+vy8AyCglxHRuW8Ddby7B1X+ZhbVctsABEyfj+89nFSPaxpPa4963lzof5ADGB+bbFEsC8t4SdVKMQuLNBVvw+nyr1KcXyNqETt256w131JVccNZj02xUV/SCyAGgRXNVz49u4qH3luObf/0Siza7C+T0E2cO62H8Lb6hUmEFlPdwl8eiymvGIhEMmDgZd7+Zv+9vl5MC0FPtcoKdg+KVeZulSXCchAnaGsTHVY2nvjFPFQnyeB9BMevC593gJoScSwhZSQhZQwiZKNl/MSFkESFkASFkLiHkZN1zWytE4j9fh/mKlQ1+M2tQin+rUOLgBfeylOzX8rNMR/vP09ZZjuO9b8Tk4TY/2/YDTVi4Oc3JFffxP9tK9sGXM7rq4kTqg6VZhRa3nd6mPQ3Y19DifGAbwy0vpleV+EQpjEryp8/WFqRMTvjxvxbgtpcW+nIt2bxbp+48P2uDct/mvQ3GRObmf3yFgT+f7LV4WtA1uJvjega32pCg+OOU1djqoAADZOuTmH4+n2hoSSKWeTciDbEkZjYH+L7WrpvvnslE/Jwim2choJOXwglichXLflnG5/Zlb1sgvhGVR9orVBxuHsu3FW++hbwa3ISQKIDHAZwH4GgAVxNCjhYOmwJgBKV0JIDvAviri3NbB9QMEgBmz9rGPQ3ccentH2S8bHyHyR+nQpTjOZ/BeToYvNBD/EoCoKuj/emKbPCWrhfDjlKiOzAXM+oDCNRZuf0gTnnkU0NW7mBTHAMmTsa7i/0JfgkKOrEMPNbtqrcsDbc3+UTxjfkWCJ3BlOU7cPLDn2L8w2mP9OTF2wJf9tWtBzxn3w4qg3vtrkP4zUercNML87TLVkhqV0M8ifKStLdafEPb95vpWLwySczGWeNlZTRXMDqR6lX6YfhmHWD2IgdtFfe9swwDJrqbGKveCVshzBU631XM5F1MyLeHewyANZTSdZTSFgD/AnAxfwCltJ5me6RKZOu947mtHVltSTlYZbs3w0t0ayvyg4us0+dVTzqW6QUT+eXhNgzuTLnmcgoBPPglYN7jb+fhFx+Vf2+7Mol1it2QtAP/Lf0YBDbUHcI5v5sGICulxbINPvHZGuV5xQC34+wZv5napjRzt+1vzHkSLDvfi4cqmaLYvr/JSITTkMdMp7pcaZl0pGzAVtmUrL9atHm//AAObieDfuCSkX1Mv3fsbzIm6GJxlm41P0OSe2gxWJiHX3E8XhAkfSDlMCDLxtDW4uGmlOKh95ZjQ506i6wswFqEOBlRTSZfneeN7taSSOGeN5cYq2MqSklrQb4N7sMA8DpFmzPbTCCEXEoIWQFgMtJebu1zWwPESirSKcSOmdEEtgkeCLd8MZ5uIDOUO5RmjeyymF7V8CvAinmaWSf3n5wGrup+xvISddfR8SsIq3ekl59+7zE5SjHAqR7odk5N8STGT/pEGlSUzexV3COKXfl6diqTbm8NSiNA2hDe4RAUOvahT/CnqbnRXmTeTC8D3G8/WomvPTQlp7I44aNl8jTyzItrhw4lUakhKTW4VcF5jnexIp+2QmlM5GVnn1ektlSUmp0surKAbyzQk8wLAkEaXk7Xlu32g8oSBPYcajHGOiCtXPTnqetw2qOfYcDEySZq5frdhzzr7ovVJNfh4p1FW/H3mRvw4LvLsXjzfjRlKGCtNfZKy6oihHTT+NdF51KSbZY3Ryl9nVI6DMAlAO5zc26mvDdm+N9zd+3SE6wvJMRGKnquVcueOoGSPPglw3W7rDPbODf42FEtykuy1UY14LkFn6LVDnxDY7zJjXsabA0t8T3xwUGMZuOUha9Y0dCSEDo1ircXbsV5v//c9bU2723Aln2N0kDV1tK92TWJLh1KpdtbS+f9h09W46QHp5gkzmR4b8k2/PNL7xJZFw7vY9nm5Q1NXRVM38t70b73nDyN/NVj+jle5/j+XaQebllAudLgLk77ysCug80ojUbw+6tGAgB212cnE7wBBlg1pE2ygDa0ET7xSVuC4QBT7PeLThk0tuxrxKj7PsJZj00ztonPtInrUyb8+jOMecDbRNmqUpJbA2GveG9DCy76vy8MZ5Ddmx/BZUgtNuh6uLcCmAtgns2/RRrX2QyA7wn7Zq4tBaV0GoDBhJAaN+dSSp+ilI6mlI7u3r27RrHyC2UQhhGkYT5AFdijSpCjws6D2SQBMllAXms1ZmO58MZtt0q5EeMWRKCUqMDvr8t4ol6as8nW0BKDRXmDe/v+tKEdBA86aGza04Cj7/7AZFxRmg76c6NjnIUdLUe++lIIUErx1sKtUlkuu/KpuMmtxeBmBqyTl3vJlgO44/XFtsfwEO2GYl/FkAVTi6jUoMSVxSJSZ8bD762wbGsltpUFW/c14fQju+OC43ob204c0BUA0KHUvApwZK8q02/eGx5PFP4FyCZCQZZKNR7b3ZsFkBYDNu1pwM6DTfihQJk70BTHGb+ZatrmtQsUX01QXWmj2Nfb3KgY8giooGtwL6eUDqKUDlT9A6DOf5zFHABHEEIGEkJKAVwF4C3+AELIEJKp4YSQUQBKM9d2PLe1glVa5ikQm7cqRXUu46LMWOGXV6OCkcp7lfgB2a8OxvBwOzQWGV8wkUopvf3JFLUk/OGj8Su4gbnYFDma4klc8/SXWLNTHnX9l8/ThscjXAIXwFmN5vZXFxpZuXjY1SeZgk6hMOn9Fbj1xfnSlNV25Vuzs15av4q4fzYhuHdPhV/Ukirdy0ga1PL6O4ucKQwilUJ1jKxvPfuYnpZt6kBH/WcsRNvZuq8Rfbp0QCzTB35jRB/88PQhAIAxggZ51wqz8ySRovjaoPQxOkH5QUPWPwUZgOoUUyW7NXOCVWkmVAoSpzzyKcY8MAXNwlgvU9TxyoUX343Fw+3pqrL7CPaIzbHFnEdA1+Ae68cxlNIEgB8B+ADAcgAvU0qXEkJuIoTclDnsPwAsIYQsQFqV5EqahvRczfIXFcRKyH7/8ZN0QJrYsdgFrHiFLICJD0gUved8xDrvDRYNd0opHpjsLpMXkKWwODUWmTeyJZFS6sQ2xpMWbz3v4ebLL0tpXUh8uX4PPl+9W5m8g3GP+fpBYf5WMrw8d7MRyMbDrnMsJifwn6dmPZy/enuZSXu8yUF1QuYlay1w0h72ijKh7VAK7BZSZhfR59cy5HXe0aHmJA42WVe2ZB5+VbfkyemRp5e5vzGOg80JHNalAwBgxX3n4rErR2LCsB5Y+stzMG5Ijel4cdWT0mywaLVPK5m5QJtTqon5G60ZSs3Xdj8WWZJlFAANLQkTXYj3+G7f34TPVlqpXnbtxc2kJqhxQuVJP1Pw1ANp+kmxQsvgppQ6Muh1jskc9y6ldCildDCl9IHMticppU9m/n6YUnoMpXQkpXQspfQLu3PbAsQlK7HDZ8uex/Tp5HittQ+eb/z903OONP4e0sOcRcyyRAPB4I7IywCkg40YtgvL29sPNOEvnztHN4vIqpTYHyfLU9HQkrQsjzI0tiQt3i7+FjxdZ4YiU2Oh4DSW++3deWa63XdjlBJfb5kznpm+Ht/jJg/vLrZPTBNE+d1KZ+WK5dv91ZmVJa8oBupQkLjzgqOU8o8yr7cfTg8/X+k7i7ZimgNHnnkyD+uaNrjLS6JGvy6j3MhWPRmtxI7DnS/IJkK5dIFvOgR7srHIzXcTc2oUAt9/fp6Jr807sb720BRMklCm2BG884LB6T2Zr2N+cj6XgReoXv3qzKqvLPbKyeFUSDga3Bnv818IIVcRQt4hhPwgHwVry3A7mLEBoLLUeZmKt5NvnjDE+LuL4L2Qefp4nl4sYq4ajZxHfGBNpfH3I++b6QxekU3Tbt9YWjjDf0Am3fBxh3U2BXLyONAUt3i4ea46P8hMb2X6y7I3lcsA9MIs5+CnYjTD3HCwg/LABK2tvHVfI+ZtSHvk/MzomJDMYONJaomJ8PJ4QdnsTtd9dvp6S9tYcd+5pt+b9zZanBAMMl63iv9fiPaQSlH86J/zce0zs22PYwZ3n4yH2wk1Enog844WUvqPQR5bIC+XU33dXd/sWI+MuBXlfvvzC4WZguNIpJTIwJ71yj/PsuwTjeZbXpyPi/6Y9oOKtozoMJvC5c3IBeJ97MYqu/izQkPHw30GgBsB/IhSeiGAEcEWKYTVw50eFHWStBBC8On/no43bx4PAPjB6YNx/ckDpR7tuDDY8oaLeK8415L4jrlvV3Nn7pW3yex7p6QbvPdpQiZ5z5G9qjCqf1djO2/8HGpOGPxFGRgFYVD3SqzddcgxIK0QUL2SfAr8OwUQtRYEFVATNG1w6VZ1EKzMK6WL5kTKUr+SqVTO3zmRTGGlz554BqeS3fv2MkujEWUCd9c343wukJBHkyRIXewr/cCK7Qc8KV3o1uEthsFdrnW8LAifLc8H8fxuIedwe7vW6Ps/dhQdMOJWlEGTFH+euhabJPz2QiY3Eou7db/zmEaRnmiKVDLAWt/eXrgVi7fsRypFLR7moB7baTWHhyrmrRigY3DXZRLRPJz5bf0iIVxBbL6HWsw8QrHBsBmqqI+rqtwDayoxol9apfFn5w7DXRcejWG9rHQUUQaKp5SIaX55yUDeFj9TyFjpdZzWpZTwjYlxT5sTKdOAeojzxtc3JTD8sLRM0GXHp2XbeUOVDa7jB6f5jKJ3oJBQvcuDTXHsb4xLB8GgOvpiCpoUQZH2+m3b7yztGFRATdBKJ3WSgRBIrzzJeIy6kA1O8STN+Tuf9OAU3we+fQ0tGDBxMhZqJJlx+sxV5SWoVNDQZB5uFaXEy8SEgmLp1v0493efG3E7bqBzy8c/XYO731yK0mgENZV6ge0yfw57bp3U9YVALq3OSVaXOlTfHQea8dB7K/BtyUpDIZ3fXpxelAL/UHiNVX3b9LXWFeFCTjQYdLPHFgI6BvfvAYBS+nbm92vBFad9Yp+QgMBicGc6vVyWSmROXlEKjzeqS2xoGDxEb5DXEmZTuzt5uLONiXGzG4QJC6+wsL7ukGGMXzoqbXDzS2Tsekf17oTOHe6/HmgAACAASURBVEpaBa3kuHs/xIhffijdx+vsuoVd9SqCflSJDXUN+NPUtRj70CeOxwblqQva4J74mlzmL9e02rLBKZ5MWVbZ3A6kdQGsvqxw4TF3+s4LNu0zKRTxkHK4PdSbhpZEmt4ioSawlbQFm+wD92TQCe5+NKNc1KdLuXa+BtnkgVFJdCY5hUAuzc6RUuJgNrPJu0xStqD9padBmCrbjMpJIdvutELtFl4ceK3aw00pXQEAGS1sUEq9u1NCAHBfiVgF4uXtEsmU4Rm//dwjpefxkMn3nfzwp6bf/KAietNVA45IVXHj8UmmKF77ajNSKaqtUsIHOTLe9s4DZu/ftv1N6NM5vYy6fNsBo+Mc1D3N2WQBFxWlUWPCECHA2EHVmLG2rihm6Wt21mP+xn0A1MacbOn75n96T1Nup7+c1eH2fPlA8YVmtsigKCWFqjI6NDM7yOpQIkmtqgA53cUfuHnS+ycvt92/u74ZHcvMHu7rxg1At8pSad6DuMrDbXOPB99djnvfXoYpy3eiJZHCpxl1iHRmXL0VPRmuedqeu82jR5UenQSQt3+3E9R8J4PhjeINdYcwYOJkLLOhX/Fw0ptnY5Fj8Dr3d31G9UZG4Sxm2GVrVid9kqj5FIEkXxEM30q4Se3+TGClCGGCWGGYF6qCWwJ9cfZGYym5drc1a6SIXp2zXOu7Ljzasr9bZalp2VTUrlZF/loNbseiGHhuZi1+8vJCvDhnI0cp0edwM3m/yrKYqdNbs7MenTJBorw3mwWOMqdgeUnU0JclBBg/pBpb9jUWhebs1387Fb/9aBWA3NVTdDsgHU8YW65MpahU1aBQkKUjl0EWJOgH8t3Jv7d4G4Dck9Qcak5Yyh5PpSTKJTndpijQq1PW+OxRVWZ5dz07lWPPoRYs2WL15qr6P7vXz1KnN8STllTZuv1drujRST9Pgqz5u52g+u3h5CG7NL+NZT1+Zd4mres5dXfGszsYonwZclXl8ANelSpVVBSVES17f22gmwgUbgzuIvVttT7wFXtjnbNxxwzhDpxKySvzNht/vzx3s+UcEV0rsiolw4XUp9eOPRy9OpWbvBlWg1tFKREMbseSZMG41LsPtmindpdxKcVl8Q171BOQ3p3LjUCPvQ0tKOe46mMzPO5ikwfMF2wpJcLvRz9ciWF3vW9SrwkKX6zejdteWgBAHSCo6+nVNSB0vWQM+c5W+YN/fOUxm6gZsuXwZNIqC1gMniuvgZznHdsLAPDry7Px/tefPFCZJXflDit1xQsViS+tVfo1/X/Q1cbN8rpsws3aS+/Oep5ycYXy0xU7MfQX7wWWyZd/f2zMSqaoViIXp6BJJynIbF9S+LbBwwulws7DrZpESR1TxfUqig5u0iGFrzIAHGpJIELsDc2sLGDWw32Ak/W7/5JjsWlvgyl9r4guHbKDi5gFi8Cadc0ipSc04n7dOqAlkbIk0HEzKPJeHv5vO11jGee0OZ4y9bzvLNxmPOOsdXtwBhfYyT/jwJpK7MhEcBMQDO5eiZ6dyjBjbR2uHtNf+znaCuwGoKTg7XllbtqLdLA5rtRA9wv/9fSXAIDHrhypDBDUjW/QlTeTeTntUIj08GLQsxccbEqAdjaXPZGyygIWgw6zF/ztOydiZN90APnJR9SgdtIFxr5Fm/dZju9aUYIuFVZDXJntV8PF8MnyHZbJUb483H00DWUAGNW/i/H3deMG4NkZtcaKkC5FQnyexz5ehZZkCmt21mNkvy6Ks/SwZKu1TfKGNZt0606qncaqhEEpsff88o9cSMrdE5+tsdArdUFttPdVc833l1hzHhzZq8rT/dsL3BjcoYfbL3BvMpFMpx63m5UyTiFPKRncvSNqM97x84/rrfTWMHThPNwdJcFCpbGIaUYvLrfGhU6sW0UpOnUoMXjGDG4qCa+9zVLJO3WWsvckbtuyrxHDFA2fVygpj0VN3HRCCMYNrsHnq3fZdkCtGXbp6+0oJU9OXQsAmL1+DwBusMqjnWnHrdf1cOsajrf/e5HWcQxBOoBfny9fwfLjlqoEWGL7T3ITlQWb9mFQ90p0Ki8RTw0UXprjyL5d0FXRN4oZNgkBhvaU9xsqD7ddmVgbeUNIHMJ7E3UN7p0HmtCpQ4lF3tAJ/bpVaB977rFZh81pR3bHszNqjXpdoXlf0cNNuD4+V1z+5Ezb/eydvrt4G+684CjH6znRsdg3V3p+JY3eqyxurkilaE45MdKUEvW1ZZC1iRMHdvNchvYAN5SSnwdWinYGvgG3JFMW+oYIpoUa5ZLRrK/L0iaclsYAmAbHrhIPTlksIhif5v2yoCFKremA3QyKPI2E8bFVwUkMoqILAExevA31zWnDYXD3SqNsDFv3yXVIy0oiFm7m2MHV2F3fglU7Cs/FCwKyIDkGWZprhto6M02HfeZ8Mg3sxmyn5d87zh8GIDhZwCADbW97aaF0eypFc6YkNMWTlmskkinL4MuWlVsSKVzy+HTcwGX3zBe8mDJ2/VEHwYjcuq8RpbGI1JDIlVIiQlcGlWHMg1PwX3/90nUZvj1ugOtzAKBMpBRqFtRicGf+D6qf4Osu6wNkY4QM4hxdNCwNg1txPnvWtrD0T6maUqhygs2ttSrsFEPAomiTFBO0DW5K6RIAIIQU79O0QiSSKUfvHOPJ8stn63ZlDaCIxlfsyNFIxLS+hBCUROUDDYNsX79uFRb1EleUEqZMQqlhcDvJb81XyGj99qP07P7rR/UEYOamTlu9K1M2mKgiZbzWeKbY4wZXA2h9WSftwNcbHW6jDDUdzcFXxupEwMPN/I3Z723nDWQKECps2Zvm7S/ctC8Q47gQFGc/btkcT+LL9eaYBVkWWkbFYd97wSbzypZO4Hau8OLhtvvU/avN3t9kKt0PyfogP1K789DNrMtj7gZr39cUlycrKS+J4ILjejs6c1QQ8zDoBhuLBnd2aAu+gbgNIFbJ7zI4pQjPBk0WgZWZIyjUK7oqu0BmiPv9Luw+qepe+xvjgTlWcoWr1kgI+SuAHYSQTYSQLzMp328JqGztAokUdeSf7jhonylKZzm9ssx+SbA0GrEdVGSDUFksYkkb66bP45dVS2LpH06eJJWHlnUWzIjfxQ1C/OSkU4fsZEO2PNu3awUOr64oqsBJNzxMJ3jtD68/eSAAYMKR3QHw386PUqlx6RMzjL9zuVePTv69QxkKweH245ZN8RSem7nBsk2EyGcVB7sgdLf9gK56DZDuD0qiEcQT1hfrycNtc2vWZedqGHznb3Mw+v6PLdtTKXd0EhHimKQb+yAG2OUif+gWKuqQClHBUyXmc0g4UUoyz8Q/WqFYiItcxpxYYBM06UbrP5+Grt1q7ZcaWvWFgNvp7ykAelJK+wG4DMDrACp9L1U7QjRCHA3mzzLeuzcFLiCDzsxe5CuKEDncrN/84emDAchn+2Uxe+65E7JenmyEuY4nScYpY+cNqqk0fp97TC/Lcfxgynu4+Tc4bnANvlxXF5iEnFv4KbXFG4ZuUoKzAZh9M/a+8undycWo7Z7x0Fd3dDco66IwBrfePZlePQA8990xpn1N8aTFuOKPZ0gKg65461z1wPWgf4/Ljj8MC+4+C1UueObJFMWeQy1YueOg5d22eJAFtCutX4boTIlhEU+m0JJMmWJ+dHHigK4AYAmE1o19EA8LWo2Fv26NS4NbfD8ipa7FoJSogglTljIUCpc8Pj2n8ynUq9NOnn4evNebJXcKCgeb1dShb3qgX+UDbg3uWQC6AgCldAul9F1K6ST/i9V+kEhSrcEqlaJYr1i29WOwS1NKzA3ryJ5VuGbs4QDkHh5mcHs1ugwOd4oaWSNlnvQ7LzgKy351Dn5y1lAAwB6bwD+evyXrP/p0yXo6VQFI4wZX42BzAktcSsMFBT+9Bvyn2rG/CT9/bTEefNc+SQiPKSt2AuCDoXwrmiNyuddhXdM69LKkJn6gECIeuq/jw/8+zfh7JKdEEY0QNCWSlmDZeNJKFEoYfNX0/+IEQyeOJJ/47ZUjpWojdmhOpDC7Nh0UvHaXua/1M0Np3aFmo8/2a8LK9xFMOcqLwf3KTeNQO+kCg+LHoGt0id5QQ7c/oI7CTJfjtmvcrlSgzYhjD/PqKxPCZA7nn42Y9heBJa4JSp256jpg33/Nznqc9OAUH0qmxswiWoXWhVuD+ykAUwkh/0sIOYUQ0tnxjBC2aEkmLQYza7+/u3JkdpvNNbwMdtWCN0CUBWRgnmepwZ0xWL2kPQbMgUNlNh7uG04ZhIrSGPpmjKbt+9Uz59ED1FHSBMQ06zZ5uLl3ODbD456xtjh43H5mR+QHohRNJ1B6ato619chAXuuZMhl0D6qdycAwNb9TSYNe78wddVO36/pBN330b+6AovuPRuz7zjTpDZRGo3gQGPC0n/I2roRICZZRgf04khyRdA2PU+PE++lWnmzi1lR7fvZvxdnKSU+NSC+fI2Gwe1GhMwMkfudTFGtyYFl4plHD7dbI1v8Ov+abU6Ys93BQ+v07YLKahsEKNSOPzeTTdZPbLTJheEGew+pvdhlMWunM2PiGcbffk6S/YLbbvIFAC8jLSf4QwAzCCFrfS9VO0JLQs3h5j2wdYfU+po6GQJFHOL4apTStEqJROPajurBKrxXWgmvRWt4uG2uxaQNL/zjF6CUWtI3X35CX1OCH0KA+y451vhdGouYlph5mg3/Bms6lmFYryrMWFMcM+hcPdz8uDBlxQ7jbzcGrHhoVhUwf4OK23r2/PVj8PntE7Dg7rMMmsQfpqzG7a+6k/zTwVcbrJrOQcONEdOpvAQ9OpUjxhlSSUqx82CTpf+QtUHReCgMpSRYNMaTOD6zAqAzCQG8a+UafZ9PNgFP+WB9u1Pcjh1kwZY6Xm4Lhzvzf1D9BN+H6dyjOxf8LdbpZ6avN/1mXn5HWUATiVtetmIHpWrH3a6D+trerI74IY+4vzGOX72zTLn/phe+smzr0yWbUbuuvvjiStwa3JsppfdQSidRSq+mlB4DwJonPIQ2WhxUSr53SjpY7VBz7hn9enJpfnm92YNNCZREialDZZ0X63Rks3XD4LZZprfzimT5fdQY9O1mpeWcgXzWY9OMv08eks4QObxvZ5NXKUIITh/a3fgdjRB8bVB1tmxcTykGpo4dXI05tXukiXbyDT8pJbxkVi4DQiRASklTPInrn51jUcIYdd9Hrq5zyhHd0a9bBbpUlJrqThDwusqTCyhyM2TKYhF0LCuxcFelHm4HY0s38VAucLrDP284KafrN8WTuC4joyf2d6rvy8fPiH2dniygPw0oLvFwi7KHbiBLZqUTPGdVKQmWemYaszTuwVMOnbLkZr+5vedXddszfv0Z1uy0Zi31C28u2IJrnvaPq6yyQ1Zs138GMUFaLjggUUvSAXOytQUP9wJCyI/5DZRSb6mNQgBId5Qxm/VYZiDy0k8vXJ8dWFY/cJ72vV7/4Xi8/aOTAQBPXTPa8Prtqm9OB01aJP6AkkyUv0wfm3mI7YxSO1uRGdn8MXYe7hFcprI1O7MBf09fNxrPfudEXHmiOTtkNELQr1sFXvvhOKy471wAZl4jL4+4R5gNjx9cg+ZEqiCeSxF+Lk3ynVAug31WX9f/kfSL1bsxZcXOnAKB1j54vum3l1UgN/BbNk4HXt/9XRcejVdvGouajmVoSaYsWRDjSau+90tzNxnJj2RwK8nmBU6So+MyE2+vmLdhL5et0Pw9VU4FvlpZJsY2xWX38WsyzRueLAOpKP/qBrIJlEy9RYTF4M4MbUEZ3PzYo3MLPtGTTE6Rh5PBlqVZ8Rzu7Hvbur8Jf/rMPV1PFz/+1wJ8vtof2iOF2uBmTrf3l2wzUTKldcRGu9xpgiPCa5fCJpprXIgC5AtuDe6eAG4ihGwlhLxDCHmAEHJ5EAVrL3DycPeoSgf58bzlwzn9WDf87T5dOuC4vmnafa/O5XjsijRHvL45gUSSIpmilg4zGiEgxNz5sP6lrMSZBmLnFWEDKL8MaecprCyLYfyQasv2kkgEpx/ZwxIEwzJxjurf1aDn8AY37+04hfOEA8CYQd0QIcDMIuBx+xl8w38rN8vZFs6uocPtP3K13cYOqs47xaEQHm6vL//6kwdi9IBuGSlQ6yCoMjQmvbdCaTjlw+C2wxWj+wIA7r3oaPzze9483ZeP7mc4P0QZvGdn1Dqe72ZizBgbfnG4+W/WkDEqZV5qXcjkFHXSu6s83EHRK8zKWtl7qO7HCw8csEn0JV5bhmwgsRr5pNzlgpZESvm8nStKEE+mcNMLX+Gqp2YZ2y8f3c9ybDbQ1Fp/RNlFJ3jN9MyyKd/64nxP5wcJVwY3pfQKSulRAAYCuBvAKgBj7M8KYYd4MmUrVcZoIKZgP062KxfPXU1V+tr1TQm8Nn8LAGDz3gbTMSwpjkUWixCDUmLXcdkZdTylhKHFwYvyjxu+ZtKl/tO3RinfgSwdMR9IxNMMxIlLp/ISDO/bpSj0uN16uA/jeGwiWnzycEPy7fxCrrZbPgL4RFw4vLfzQT4j18G8JEakvNyCTB40YFctvjHiMADAdeMHYtxg957utQ+ej7suPNpY0fPyDqxZFp0DKv2aTM/dsMf4uyFDP6zMIWhSturapGFwq/qUoAzuZomULWCfNZfhxdkbbfezSYzKEBUDiQFr39VaaNzfe24ubv+3PLaF/+6b9jRIj2HI6vXnDp1rdKmwyn4yZTWdOpBveBqaKKXNlNKvKKV/p5T+1O9CtSe0JFI4aWBaWUP00AJAdccyEAKs2pHmUZXFIko5O7dg3t4t+xrxnfEDAMiXlUoVWSjZYG23LGrn4ZYFDukMdPxSKVMUkeH5661zQb6B8u9RZqSNG1yNBZv2GUu0rRX81+GXxv2glAQxoOQacHOgMf/fqxAe3lwD7lTJruyW0lVGfn4oJf5f85snpWlorN9jQaVeqB7ixFinvGwVzk8wT6IXWUAG2Tigw4nNN4d76dZswhe+btoZhrJxliX04sHGt46K4FNRKhOwtoPWFDipQipFpRRCWX1g472s7rttUnbtp6ZjGaorS1FVHsNxh3XGFz+bYCiUOOUcKSQcDW5CyE2ZjJJXZWgkP8hHwdoLeANTVr+ikbT5wYLdbjtrqG8BYJ0yih1RQtCnc9orygZgvp+IRYm0cTHPvN3Sm46HO2XycDt7Ufh3JvNu/+26E/Hd8QNNigwMvJHdoTS7X+bRGT+kBokUNbR5WwtEr/PBpmzwyeZ92YHIiyOT0ZmYTrGoV+wLcjSsFnvMuja4u/ccXm6ysfkFnZUPWRIbBjHZFUM8QV17zwstw+3VsLn/4mNNcTAxjeBtFQqZTvqN+dmkaIz6kYvBLYOOSolq0hEUteKVuVmJT74KyLjN91x0NN655WSszMTz8DisaweLt5R5z2WKLYA88U1vISvwtn3BJn/JB9ikFDAbzTLVKNYG3pIk6XPbpuwcL326lKPuUAsoBY7o0RF9u1aYFEoY9nsMvAwKOh7uMwDcCOBHlNILAYwItkjtC/yA12JEPZs7p8HdO2LT3kYAaVukxEW6YjswT3FjPGkMzCotblXiG77cMthxFPlsa+wwnU79mD6djL9lgRsThvXA3Rc5i+eYKCWSlnDC4V1RGo1gxprC87jdQHyDPB91Oid16MZINPSXhYsHoVVeKD6wrm7x6TbesHwikUrh6Ls/sD3GzgZMU8VSqBEobXb6w8XqsPNarEiEmAwq1p+IHG6dKmmllOQPU1ftMv5mila56HDLEE+mHN+DOPEJ2sN9AUfl4u/Rg1PkYvjO+IE49rDOUm7w3Nq9JgUnIOsljyrGW1ZH7CZ7Xp01zYlkQSdwPHYckAeXNksoRsxO4OujuE8XdnVt0ea0U2XPoRbbhuZV6SQo6BjcdTTtMns48ztUJfERYjCiKeI5U5GiEWJUnAghRodxPJc1zguYBySRoobnVxYYk156tjb+0mgm8Y3HoMmoYXDzHm7nRsnrguZinJm48JLrlJdEMerw4uBxu8E2m8RAPESjQgfiIBAMpSQ3fPI/p3k6T9cz/vS3T7Rs8/Iuc4XOPe049mUZDzeTwjOf564sXpqhW/6yncfLr6X7mCLRF6Xp8n61cS8+WLpdeq7F4C6Q17+xJQFC7Fc3vCCRSjm2TSulJP1/ULYj78Hkb3H3m0tNx/XvVmH63UfwRMuk75jHu0QRFMK8+UFMto+883188y+znA/MAd0q9TKxynjSgJyaw76/rO67VXLSaT4NCuWTNQ+ch7UPno9+wncvNHRa5O8BgFL6dub3a8EVp/1BrIQyb/HQnlXYsi/j4c7UwhX3nYtXvj82p3vzmZqYlE6TpAKXRInUcC4zvOI2soB2lJLM7d0a3HxHkcugxnu49yrSxY8fXINl2w5g76HiE9HXhcoY8UKDSKaoSU4rCI5iLt/0ue+OwaDuHT2dyzKZekEhKCU6HjA7LyeTApV5/FTGvJ9f223dsa0XPhWMrR7K6DotyRQue2IGvv/8POm5hagDMhxqSaKiJOpZ5UGFloQzMUScRBGJU8UN7n5zCT5atkO5nx8v3lu8TXkcU+diGNa7k+n3zRMGW85pysS7qBSP+HvzXHK/8OV6Pe+418B1XWdVUzwp/e41Ha2rCHNq9yqv7TpBnk3xhvToyB1mPTAWjRRlMi5Hg5tSukLYNCOXGxJCziWErCSErCGETJTs/xYhZFHm3wxCyAhuXy0hZDEhZAEhZG4u5Sgk+PYhzo5ls2VZZS8viUo5ym7AOsNTjqgxJKSYh5u/p4pSUmqThZJBK2iSu5lO0OQ5x/Qy/s4lwK6c4ziecLg8Jfy4IdWgFJi1rji93Ly0oaqDUdllbjpANtQmKcXo+z/mtvuPXL7psF5Vyn126i0AMKDaO4e7EBHxOgb3I/85XLlP1a4BoCUpn0T7qUrjlyQeAPTr5n2yxCOqoJQA9gm+AHcqJUGAGX0NLUlU5KDBrYLOhEKcqMiUqNzguZkb8L3n1EM9L/PHlLZkuOHkgabfXSvM3t1ulVbjkTmSVEmdeEWvQipieF09OKq3uq/kIY4TLJuzHUVEZnD7yeE+ooc3p0qh4cpiI4T8FcAOQsgmQsiXmWDKW1ycHwXwOIDzkM5QeTUhRCTbrgdwGqV0OID7ADwl7J9AKR1JKR3tpuzFCrEyy4xXvnL57bWYe+fX8ZdrRxsebp5Swu5VoqKUaKRjtw+atKeUVJRG8edrTrCcd8oRWQ5tLpPYjtyg1FExQA3v2wWVpdGipZXwRvbvrxopPUblXdKR+bJcS+jdT+jf1fU1nBDUUrxTootcJDYf/WCl53O9Qsdg5SenIphKicwYanIwLkV4saf8cgjPmHgGhvTQMx6cUKKglABAs2ISwlBozu2yrekERg0tCd8DJgFNlRIFhzuoV+NU75gndLBgoInGZqmEp83GZpVji/fwsn4xHzEOd7y+2OQA8jqZGaXZd4tZrtl5dvXBF0qJTXfMf5NCB2y7gVsX6SkAelJK+wG4DMDrANy4hcYAWEMpXUcpbQHwLwAX8wdQSmdQSvdmfs4C0NdlGVsVxEorq5R85+l33arpWIbykqhhbMiW70pi9kGTdp5SO6MgG1CTPWYZl/VuWK8qqcHQvSrb0XmZgDD+Hp/6WOUdLolGMGZgN0wvggQ4MuzhqC7nHSvXglZ1yI0tHiglwrU6ltt70hpaEqYy6iCXOm5nKIre3v85ayhe5mhZpTmuGOUbuWYgVamUAOqVJj/tCbcebtUSuEydwCvsVEpk74p/gkJxuJmKxMZMkF9DS9L3gElAj6ssTsiDTnyzTMiSCgCTbz3Z+PtbJ/VH7aQLDEUuBr7tvHnzeOlkaXNGqEDl4ebP8XNC4aSs8c8vN5oS0Hi997s2FBweKolFu/ogq/tuVwHsms8Qj7TBQsPtCDMLQFcAoJRuoZS+Symd5OL8wwBs4n5vzmxT4XoA73G/KYAPCSHzCCE3qk4ihNxICJlLCJm7a5c1WraYkPYwmX+L4DvPoDpxRk2QGUelCllAHQ930pZSkv5fdYiOMe3ldZxxVA8AQKcO8mAQEeMG12DdrkOmbJ/FggF81tEIwaXHp5vTxSP74LjD0rxF1VinkzlORFLoZPcpuO8M5//+c4y67yPX93GDb4zog1vPPAKdymNSdQKGLhXWIKGe3PFlkiAgGQrpUOEniTKVADeYU7sHdYdapAM2T5/g65ifcOsRzgcnk3m4pRxuzcyDDPkyuO+96BgAWcnTgnq4FentvRiFn6/WG7vrhTwJ/KRbpcksTtL+KUmCw/oDWdZNwPy9/ZxQ1NmsxMmcJ14lF8VAUhWqBKcKu5tdQiA/ONx24//JR7hPblUMcGtwPwVgKiHkfwkhpxBCOjueYYbsDUprCyFkAtIG98+4zeMppaOQpqTcTAg5VXYupfQpSuloSuno7t2tEl7FBGvQpHUQDdLDzcAM7jEDrFzmWETl4c6olNjJAtq0MVlqd7fwMqjdc9Ex+OJnE0z8ZzuMy6STD0ICTwVd2+IoIfjn9QyPsVenclzztcMBqAc7N4MEO1T8Vne9sVRydBZBJPYQkaQUPzlrKBbde45tUqgSyUvt2SmrVjBmoJzHX0w4Y1gP4+/fT1ntyzWl3lxuG2/o+umodKtSko8YqCyH2/6dyOCVUpIrL54FejKDJu3h9t/g3qmQh+OhTnzj/hmveXq21nG1u825AHiqnExJA7CuZl0vcLyB7Fir8nDz9Zf1i37ojdsZmrLXuGSL1cuvg4tG9NE6TqUEUlsnz8EQT6akBrfb9ssoUiJ+e8UIDO3JB022Hrg1uF8A8DKAGIAfAphBCFnr4vzNAPpxv/sCsCikE0KGA/grgIsppQZZiVK6NfP/TqTpLK0+rbw42MlmgXzd9ZvDzRCLEERI9v58wy6JSVK7g+vobTxtdoOQLKDG9Kw25X3lprH46TlHenofJdEI05A1/gAAIABJREFU+nZNz+6f/c6JUp44j6N6dULXipK88rjFRDyqAau6o1zaafuBJuNdqgzrgTXugwTF7zkkgOCVJz5z06XocwNLJIMvb6Bfm0kJ7ARZlbsgT6ndeS88T63ygstGpdl6sjbKt2lZUig/4F6lJPihlXkzZcvlTtQoq1dcr7y5UoMIISjhViEbmoMxuHWuaeVwp//3Y6LGxhsx+JnJxLLqMbRnlp+tmtR9uMws7divq9Xby95nVFH/+Wd1O3m0g1sxnjmaWt8XHJfuo6b9dAI++Z/TLEl6VGBZrkVUS1RKgLQNIWuqbuu56rlSFKgMgDKVD7jtSTdTSu+hlE6ilF5NKT0G6eBHXcwBcAQhZCAhpBTAVQDe4g8ghPRHWnrwGkrpKm57JSGkiv0N4GwAS1yWv+ggek3iSWrpnHpUZRtGUF4eQgjKS6Im7wC7VWmUIC6dCBCUxiJotvVwa/D+uEP4Z7cbkE8c0A03TxjieG0nnH5kD9vAMiAdTDd2cDVmrq3zVaXBDuLyuSkjKberUhHsOW3VLkdJLt6LJz4Xn1yIh/g9Lxtlxwjzhi9cJhq66bRBWseJnipxUCCE4K0fjXe8jlxGL4UrnpyJV+ZukpzhH5o4Cc51OWb5tOMrNyXkHm4nCpEbuF3Zkg3iV4z2N8SHaS7L+q0737AfbrxyuL16xh/9z+GonXQBgPS48afP1mLAxMloiCcCMUhySe3uB+WCeaUrhVTrLBj60pGHoW/XDuheVWasVtVUyR0S4vspk2iWL8wkV9HhcPsZMGtXb2Tjz6T3RCE5OR7/1ijUTroA/asrMKh7R1ue/4kDsgGVVeUx6YRJtgoEpJVjZB5ut+9IFbNx4fDepgD3thw0uYAQ8mN+A6VUOxEOpTQB4EcAPgCwHMDLlNKlmfTxN2UOuxtANYAnBPm/ngC+IIQsBDAbwGRK6fsuy190sFBKuN+sHpk6mABrV1ksYhrQGUqiEaUkVFksYiuXZTeosj1qFY3i0LUFgLGDa7BlXyM25IEiAVg7eb5e8JnxROmkSZcdByCdJdPJu5SwGTD4e1CaXSi1+55zavdg4aZ9yv26cDupVEk6ilClaOYxvK9eMinx+ySSFLNr9+Cnry6yHPv8rA244e9ztK7rhMEOwUIv3fg17Wsx776snb08Jztx4Dms0yQps73CD5WSR/7T38THLKtgXFK4HQ4xHF6NLq8ebpXHv6E5aci8+gkdDq7YlxNje+73ZytU4uoD3+bYK3n++jF49jsn4oxhPaXXuuR4s6OAJXHjwb6nKnaAfx++Gtw2Pm6vd5EpWNklRmJ62oD62VQTsB5VZdIncFvPZa/9vouPsaUNFjvcToN7Avg6IeRnAL4CsBDAAkrpK7oXoJS+C+BdYduT3N83ALhBct46tMG08qKHuyWRsizdmYImAyxLWSyKA43WSOK0Xm+2sVDTORHTM4j6tXZBkwzp1O4yabLcgsL8xPjBjMddhwEeqBhuIUrUNSdSYAulpkkZSXt+mMxVVSYaf+u+JkfvEv+tREO6hDOyVCsQgLkzvvzJmQBgeN28IhohSAWQvU3H4BYRIXJjIRohpgHEbjC5y8Ez6gajDldLeT102XE4aVC18fvz2yfYXitrcFvb2bb9jcbfvKeJb6fNiaQyKE0Hbj3c+VhcYh7uuEQG1UmlQ3RK6PbVKk+hE1TXb2hJKle+coGOSonY/7NJQa4rg8fe84ExLqoMLvO4FMXpR/aQHgcAI/uZJ9YyDzeDTrBu3EdZQDuf2ntL5FlOnSALkOysyCApQvXdZTRTQB006baey96DbJJZYDVOV3A1AlFKr6CUHgVgINKe6FVoAzzqQmL+RrNHUBY0yWtEB7l8kqSUS5uercUlUat8WJZuYt733y8tMF/Tpo2xzknVGR8oYDIBEQNrKtGrU3ne5AEtlBLuHfOZNpvjKXz8k9Pw9HXpdOMrt6cDTZZtO8BxuOX3aDFRSsz7eN6unffGz+QlDEGpUZQo1AbsIPKX2aAvljFfWQYnKIyIO84fhqvH9Ddtc0prLNPeZ7hkZNoD+D9nDTV5801SaNwjP/y+3rI2Dz95r36BfVeZo8Bt0KRuX+3Vw606qzGeNKnZ+AWdWAlxcu8Xh7u+OWFIkEopUPEkKKXayYbE9msnCarzHe2MybW76vHgu8t9oSP+e95mT+fJDNWqMl2DW/5sMpopkK7PfnC4ZWWWXfdVj++kEPAUDUMpbaaUfkUp/Tul9Kd+F6o9Q9ap9eB1pwP0cZfFIiZdZVa5S2NyWcD0vgg2cjSLaavMUk56HG7FDNomZXy+QQjBuMHVmLW2Li+Ggp3BzU/Anpm+Hv2rK4wkDJePTsck//sH4xw53Pw35b/TR7edagowbFREqQPBeB3dBOm5Sccuerh1HC5iUZb96lwAQFTo+XW8f0FiG0d3+PM1J+CPVx/veA5bUpZ5uNng2LNzuaku8vWEp128s0hP05dHoRPFyFBiUEr0ysbXf6+Gs9f3YNcP+R00GY0QZfZRHmKbcgrcdoOqTL83SLLCuGZnPSi8O6R0JWJVsPv21z87B09NW4dNexqVx/DQTcClStbGsO7B840Jj+yKdpQSHokUlX6/eFIRHKlQKXFbz2XX1k1HX6zQeuOEkK/8OCaEFaLSgGyZhm+AQUpjVXcskxr8dimg9zXGTSnSRdg3svQ+lXNQJUdUKIwbUoO6Qy1YqYja9hMiR5jnC6p0YYG0V7N20gUCh1vD4OaO6d2lg0lCb+m2/cpr+G00rdpx0KKrq8Lnt0/A5FtP0b62aHDXN9snmACshrWxXfgGhTYe/za91vj7nGN6aUl+2XG4eY89X9/453SbOY6BVS33qyPBv2NCSJou5IHmIRrAus4Rr4a63Xl+p3aPEqJlMIorA4YsoJd7Cn1gr4yqxm+uyDJLJxyZlv1lEpleh0dd41MFRqWRPadTrJIIu2fguyOnFbtIhBirmzKFHTeqP7K6Fk+mpAGl8aQ/Hm6Zcc1v0QlwLzbo1rKjCCGLbP4tBtA6lcgLDNG7pBrEmIRbkDO8slgEzRKvclqHW95YhvaosvVE6wyqqmNylcvyG+M4HnfQEL/z1FU7PV+DvcbzjjWrsfB1TTQWeOPUji3ht6H53Mxa7WP7davQ1lIHrBMVNqF7/79Pwf99U+4RVtFbrEGTwVNK+nXzL6MikDUydhywBgPybY+XRePb6icr3NfJ9PUy9bLI2jdDNEI8rVjoJr4R9d7FZFK6sAtQrvCZUtKSTKGrhPMrPqOVVuNdpURmUA7rVWXEqQDA905JKxTVNyVyWm0rl8QiiAlf7GCn4OJWqUWligKYDU67mJSrTuxn+r3zYG5J22T0kdq6Bmn/mEipPNwuOdySbfx1WWK31gTdGjVM45jicke2Euga3Gx7kCsqZbEIDkm8iyUxouQvVpRFbfVp7QZVnsOdJ7W9nNCnSwcMrKnEzLW7pYkS/IRoHPIJWng3imwQZMhm8kyfIHbQvFEhDpT8/TuURgGFAp3fKZtVHmU/ID4/M7iH9eqEYb3kMogqg1scUIKklMQiBEf17oS3bzlZecyyX53j+rr7GtIe/tU76y37+OC3Ut7DzW2//dVFuGK0eWDXQfrd0aINdiqJEE+c/Lp6PclEsUrJFFF0wCdZqZ10Aaat2oVrn0knixGl83JFTcdSxFPWfjpKCBLcRrEZGH2Qh29dEo0olao+vO1U/HveZmPyMnNdHS4a0cezVruMxuGma7MzuJ1iabxCZnC//sNxmLZqt5FTYM4vvo5H3l+BK0/sbznWDVoUErJp+p9V2ljWbbrtI6WfkvD7CTqWxXDxSL0EPsUALYObUroh6IK0V8ST1OQZUXmLD6+uwJZ9jYEmfyiNRrBHkvim1IZSUlkaU2aEAvS81PwhpTFrgGYxYezgary9YCsSyRRiHlQvdGEXOMh7GZ/4ll3SHrOHW/RytCg43IA5iKhzhxg274UUfhvcuvxFN9xtBtFjVueQyARQJ70QJ6BBBk1SAKcOtV9AtNPUVeHIXmllmz6dy7F1f5NJkYV57AnMA3uuq04pTsGg0DQcFWLRiEVtQwdid6CqyeJze30PZx5llrzjE1l18FmHuyQaUQbJ8RAdLLlkmrQEJnPfZGjPKvz8/KNM++OJlCdKiWqF1U3fZvcNmV6+3zkcRKfMjacOwvH9u+L4/lklo+5VZXj0cmdxt8m3nowL/vCFcv9BhYCBisMt2+GHDre47cs7zgwkQDgoBGcxhNAG7+VuiCeNlKm8cc1oDEEuXZeVRCySc0C6s6VU3mCa4knsb1RzYe0amYzblq/EMl4xbnA1DjYnsHjL/kDvIy4rruW8kPzAZ7fsaXC4M29aDEbkv7UYJMZ35nZcer+ro66H+98/GOf62qJHSAzwlZZH0UMy7zBD0PQnng/83o9PwW+vGIGfnDUUgPcsl8ygYSXn5db4+mA2uHP74ElKs5SSIpQFBNJtz8uKhW75xLrixbgHrAY9r71d6XPQZCxKpHVcbK7iMeIqmxuoVqRUWLbtgCcSd73CmHQTQ8Se22780q1TdkfxNoH4fs4YppZBVKE0GkFFadSSdVhUbVFK9EoKm0hRX3S4ZdQacUtlWUzbSVMMCA3uIgAfEPfI+yvxo3/Otxxz1tFpb4ZqpukHSqMRKXWENWyZl7tHp3JbLpnbTJMm6kQRYuyg/PC4xZn8a/O3AAD2N8SxlVOksPOEG9zBzGcb0c/MeTMFTQqDAf9NbxOkHnn4TinR7Dy91BOxA7/seHmWTP4wXcUUr0aTDsRB/KjenXDZqL645Ywh+PKOM/H4N0d5uq7YrnnVA74+8JOvXFef0hq92b+LEWnj0v1zip5S1Wpkrh5ultr85CPMqx7dKrJyoX4nvimJyFc5xcBQS+IbIY7EDWqE1OENLfZj38Y97pKSXX5COkupHzKkXrJw5gpx3PXyFIvuPRvz7jzL4iUe3MOcYMu0Gso9hmx1IJ5MWd5pLEJcc7hl38WFgFVRwlXxSRr/RQi5O/O7PyEk1OHOEToJXliAAA0wUr9UkTWSLcXLjPFO5THbzsY202RmF98Qi93gru5YhmG9qjAjYD1ucbnwslHpwWHJVrNnXaahzMA6JzYIdulgTnNs9nCbvyE/CVxrk0Lc70EkSMoUf+07zh+GiefLQ1N4I1u3gw9y5YlCnQQil/ZSlpF+3J3hHr/5o/F45D+GAzDXB97bpZNt0A7JFDWoWLmuCgzpYZ910ytiETOlpLqyVEvDXbctiJMztxzuQd0rcXz/LhYaEe/p8zu1eyxKpJNKsX9XaZF7mZiLXvHdCo781J+env3h4ja/uvhY/P6qkUYwvE4ZVNCZcOtO4uxelTlo0lwnvfSd5SVRdCiNWs597EozDYUfK/i+TvbciSS1GMtp5R93dUDWtwUpi5wPuJ0vPAFgLICrM78PAnjc1xK1I4zIZLvSMbhvOGUgbvv6UFx+gvsgJV2UxaKGUc03jVKWUlcy2JZmMk0yL5zY5nU6LL4hMi/b2EHVePWmsW6KnzeMH1KDubV7A82EKXKH2XvkO7KTBnbD0b3lwX5AtnNig53YCcZtOk4xmYBqEPDL4D7nsWn4yUsLlBQOv3HjqYPRo0purPKvXpfioqvb7BVBDDOlMfPL7llVjitO7GeiVBBCTJ60XBc0EilqrDS4naSIS/zPXx+MrycWzeYdmHjeMMy76yytLKW6BoUfHG6n+uC3DncsEpEajE7PkouqVhchIHzLPrks4eHVWTrEut1q54CIDqVRXDzyMFtDVXcyxI6z+5J+BFbzRRXbol9xJIdXV5hWSwCzY4ev57J7JlIpS78Zi8gpSXao7lhq2dbKZbhdG9wnUUpvBtAEAJTSvQCsbyWEFspjLPGEc0OpKI3hx18/Al0rg3vdaQ93tmExgy279GxtMKUZfjdrTBa5NFsOd3pfSzJldFTM4B4/pBqjB3RTnFlYjBtcjeZECl9tVEQS+gCrDnfSsv2l749VpjkG4Dgqt9h0nMf0URvyPPyilKzccRCvzd8SqEqJLvgyqJabxw4ye8V0jEev8QlBcZdFniZ77FjUvPzLG5v8Mv8lHtQBkilqXM/tAHzDc3NNv2VSbn5AZhyoDG5+xdFKp5BfX2xrQdCR7HIjeEFJVI/XbjW40/976SdEWoMdHrz0ONfX14Hut9HycGtOMO1Wsfn+SEx8s/NAs3i4J/zl2tGW+v7CzKxuBl9/ZU24ReLhjkUjvjhnglwBzQfcGtxxQkgUmYkcIaQ7RE2YENowEk9kjKkeQhKcfIN5q0UwI09GHWEZCdmSk+jR8OrhLuaGNWZgN0QjBDMD5HGLhieblPkZIHKwKRv4Jw4YLLW3E/zmcBcDrZd/xyqDW+TIejFGXCGA9kAIkcp3lQi6+2VcUhC+D1C1UdFzziOZyg7Gbg3uXQf9MSicEJPwlUUDRNavic+jmjyKp/pJyzp1aDoRTEcfKCWTLssasbGo3MMtQsVj9/KIUlU4xTsd1rvK/Q0EyIIOxXpw14VHS8/VMaZV0rpuYBdT4hfdNEJgyjQMZFfjAXW7nXxrWra0qSVp6a5iHqQ2ZUNLK4qPlMKtwf0HAK8D6EEIeQDAFwAe9L1U7QRiamVbb2UeUBZLD7TiYMIGUFmHUSoEXrnxcDPwnVqlzxnSgkBVeQmG9+2M6WuC43GLhh7zcJd4iBpRfYH5G7OJM8TOUDTsVZ253xzuYvj+fB1WLYlbtJQ1BlMvr8qgark/VQtdK6wrZtEowaeZpDab9jSYFFn+79M1xt+qtr3onrOV90vmQCnJF2JRgs9Xp9s2m5SWRp37NbEtqCZrYl3xU1Lyue+OQe2kC3yZmF81JqvdHIsQxBPWZxYzJVuybTKlpIAn0rL8EW7xy28cY9kWT5q1x78zbgB6drI6xvTGOU2VEu4w8X3ydYqP8+lYFsM3Rug5SZxACDHiOxh6d87S71TPweI7Hnh3uWViFI0QX8aKJVvUEsStAdqjN0m/wWkAbgfwEIBtAC6hlL4SUNnaPMTUyrmml80VKsPaTqWEeb9YYxM7ep2gSVNARmbw0U3vXSiMG1yNhZv3B1ZOvjMtjWUTQNildRdhHJl5z3ZOUtHDrXsbvw1uuyxrDD88fbCv9xShGtR4iHxiPYPb/K5en78ZL87eqFWmoBZ8ZN7ofQ1xw4D4cn2dsowq1QE7x0GSUuOdBpksKBfw+vobM+nMRY+frN5b2pCiLovnug1Ezady6tVj+uOn5xyJL9fvwezaPZb9oiSsisPtZSXMzRndfKBa9utWgfl3nWXaJmvXMi+zkdrdptA6fYSIqavN0qWq/vGC43r7orYCpFdmxPvw31U1Ue7LqeeIJdl5sBkvzt6Uc9m275fz+FsLtC08mna1vEEpXUEpfZxS+n+U0uUBlq3Ng3EQd9enl0oL7eFmaI6nTHxTg3NpdCrccnPmGZjRLDZUHUoJ3xkz5YEd+3NLRRs0xg+uQTJFMXt9MLQS3rNaHosYqyA6BqkX8B6aFKWIakYv+q0/7TShuHpMP9x+rk7iW++ImDjc8vfw2JUj8XUu8YjOaxCNkdteWoifv7bY9pygjattDu1s1jqzkXXZqKwXzQv3OJmkhsFStLKAXBtLKFbuZI4EcZvaw20+bq9GAiYR+aLcPXTZcbh5whDl/mrB0FXpcAedX+GYPv6k+e5aWYqXbvwarhs3AIC1jhMi/646wZW6Bjd/R1GogHdosVUYIB0D4xd6diq31C/+uyqT4GVWJ4cf1jkwB0GRdhnacOtSnUUIOTGQkrRDMO8wM0rFIKB805X+MSvtyWKSd9nEN1ZZQLaPLT0xyoMlO5hG4hsga1h07pCOTF/rItq8EBh1eFeUxiKYsSYYg5t/j+UlUYsX7L5LjtW+Fk8HGdpTHogkKpacPMQ+syGDrtGkO+A6TSh0AoxzBf/uVfZ/z07l+Ou3R7u6bi5evnzIYcmMuGG9qnD20dmJhcnT5WH04z3culSK7fubcPVTs1zfyytMBnfmGUUOt6hbD1g9/iqDW2wLxeJo8QJLzI7I4c7U23ywhxbefTZW3n9uztc5aVA1ju+f5izLDGlZH6Uz+fQSgyA2Mf7evFe/f7cK19dWQabhzn9XVUKg7MqVNeNnTcdS14l5ZG+0o02it9YAtwb3BAAzCSFrCSGLCCGLCSGLgihYewCbEbJOvazAlJJrxx4OIL20xsPgaUuWPssEpRVxkHGbYeyIHungl0aHJAeFRnlJFKMP7xpYApyYYHAzDzd7m10FySwZZAbUqh31kiPNA8ah5gQG1lTi89snGNtylQXUtTVjDp51p+QXfoA3InQT3+hAZV++Pn+zfAeglNv0C/26dbBs+9qgrDoQIQQ1HE83kavBnUpxQdh65z85dS1mrgs20RQP3rhm/ZdIvZHLoekFTYqe8KCzlPqBq07spxXUr1Ip8RLQJ+sz7JpB54oSY8U1V9jRKGUTKZ3Jo6ibrgI/IRPvLwZ0d6kowT0XHY2J5+W+6vf57RPwh6uPl+7jxwfVl8yuXFn3VVeWWVSR3IA54n52TrCrm0HD7XThvEBK0U7BPNrMe1loT8eg7mnvp9jRMf6ibIAsKzE/g2ig2HO40/v2ckFZ/asrUBqLGGmrixnjBlfj1x+uwp5DLb5wCHnwY3VZLII9mWVn9jrdeDx1jF1+0O+cMebFiZcMugZ3ilJENMrsJAv4wdIdWvdTYfYdZzpqKvNV2M/MZqq2MGvtHlx6fF/bc4Pybw/tUYVNe8y8yEWbs8mVKKWo4eo279nVyRx36xlD8IdPsoGWL8zaaEyqdDPPqQJXg5qE8AZVXOXhltR7S4CbYnnEql3tksMdYPIzFVRBb3z7IMTfTJOFBKtzdZJkO1JKidEu1A/q9jsDwN4G8/15R0x9UwLlJRF8Z/xA19eVoV+3CmWfz397VbZZXgJSdPYQYm8LOOGuC4/GqUNrjLGptcLVcEIp3SD7F1Th2jrKBJUSXqatEOCpI9S03SZoUqCUiAaKW55mRUkUq+4/D+ce29vVeYXAuAztYlbA3rfmRAoHm9KeXTbY6hgb7JCskZ4FSw/NkEil0KtTOUb064JO5fqdmq53TrcaOAX+5JpdsEenckctex0dbi9QUUrsqCZB2ymyST6/ZJyiFNUd5R5u2QT8t1eYM9SJHPhnZ9S69nDnWyGUz+DHjCQxq5/MeND1cIunegkezTfdcMeBJtRJuOb8ZIhAHTgaNIfbbxh0T0m5ZXEmOoo7XmIWxEkcv8rQGE/mjcvP12021osghBiTLrFU0QjJqQ4kUyllorLWBFcebpbSXQSl9Ff+FKd9IRZJ6+Ay7/ARPaosQUqFQG2GP80aDRsgZbKAhsEdV3i4W5trwwWGH9YZHctimL5mN84/LrgJQpeKEkOaTWY8e4FoSO462IzSWASDaiqlx+dKKdHlL8sGsw9vOxXVlaX4/vPz8IsLjtK6Ti7gl21zyZQnQkWvsnszxvcOaFx1WlVLUXOyG95LJ/v24meWfU8jtbuDkUIznrJ8a+/ybYM9rujhlqlS6MoC+pFpMt/4ePlO6Xb+CVPUOhHJJfFNIdElQ2GwBk0SaSC1juNBd17Fvyrxun0ER0m+mgbf7u1UdaIkvRIi9ldRD5km+feg8qq3NrhdMD3E/UsiTTEZ4HOZ2g3SepfZAU9Mx7vjYH6VOlZn+L0vzTXL9xip3aUGN6OUZDzcQkPTbWSFWCbNFbFoBGMGdgs0AQ6QDo4R08i7McD4N8uCJjfuaTAdI2YtY/jGiD4YqDDCARffV5fDLRnMOncoQXXHMrz6g3E4vn9XvQvlAH7Z1k9VGNWSqt27ya5oBDO0yqRIrx7Tz/g7RSlquBTLThxuHaOzRDPxDZ9ePp/g4wiYoSjyT2VGsq7BLRqfrYHDrYJTorPcKCUUVUK/lK+qwPoh2bexC5q0a8tu45kAYFB3dd8LBPs+Hv/mKDxzXTownH8Pds8YiZBMP2cuWK463G6lM4sVbiklv+H+PQDgdAD+qK23U/ADHu9JAhCYAoYK5x7bCwBw6fHmT+pGh1s0mOw6Gb7hBu3JCwrjBldj3e5D2BagPmh5LIpGFjTpos9i75I31lVBk5TKJz0RYu+Bm7ZqF6at2qXcb1xfc0IlM1LyXSV0ZAG9QPUeCznZ3LzXWm/5b0ApTJQS/hlknFTRQJEZJ7qZJlkgWr6/f4nw/IDEwy3jcFPnZ5cd54Xbm28cd5hCdk94RObFjSdT2HWwOScdbkCumJEPGEo6PgZN2nGY9zfGpcl7nOlGwbWOC4b3xvgMbdJpsjBmYDrQOkLkY1QsQjzJiDK0S4NbggoAg/woSHsF7+EWs3aN5NKp5gNseVkcTEpsElUwSgkbuN0kvjEd10q9POMGpzukICdHHUqjBmWHckJxurjlxfmOx7DvJF41EiGOg+W1z8x2vr7m9/WTM+0VJlnAHHrIhpYEnptZa/xWjcm2Hu6Am8VUyWSJnzSnKDUFBPPfcY8koEw0PKQebk1KidHf5LlKyDzcYuIbWZuwKHRoUkpyWWrPF67msk7y4J9wSI+OxuThrjeW4MQHPjZWPlsZo8QY83Q93Do8fLs+cMQvP8SJD3xs2e5kpAbtoGJxCHYT6e+OH4iXvz/WOD6ZohbnXCwSyWmMPyLH2J1igavhhMkAZv4tBbAS6XTvITyC93Dz6VRf/+E43HCKP9HHuig1+NhJUwcppm/nweQAH/1gJQCrgWLXyHjPnioQo9gxrFcVulWWBiYPCKQnQi3JFJIpmtNKACEEt5whT2KhMqqZZyLX8XLZ1taTkteUadKDh5sFBz307grc/eZSY3suUfpBDaxiMCBgHkwrSmMmuhHfnrdKkuaIBoLMONHNNMkMcj959DrgJwLsm4nvSWYI6Xj3gTS9wsT9duH5Y57GfK8Eqp6Fj+thxhYAvL90O4CsZ9ILnYIDKHXgAAAgAElEQVRh9h1nGs6oRoUGtN+IGpQSPQ+3jjHp9A5YsDJfN8RxUexCxEyffoM9q/h8qtiPSIY60kHYH4sSreRAPFg/et8lx+LsY3q5OrdY4XY0uRDARZl/ZwPoQyn9o++lakfgPdwDOK7s8f275p27WCamds/cv8RGh3twhmPG+OdiZL5udq3WumQUiRCMHVSNGWt3BxaJXy6o2QB6Tj+ZdODaXXJKiWowqN3dgO0Hco8lyOn75tm44A08lZfSDmxwEiW9lJQSW+nM9P9BJb6543xrECrv4b3/kmNMGtRO3lhdL6/sWBHsXnX18oQhQb2TNxZsNf42dLg9yALqThTiLozRhnhhHBN88CvP6R/SPet5TBtbmb+FZ/fSM7K636NTuZE0Zl2eEqLZKenIPdxsBVINLxNup2Q5QQcTpoNErQGPqmbN1EhEP0UuHO7BNjFErQ1uDe4fcnKAWyilCULIw24uQAg5lxCykhCyhhAyUbL/W5wXfQYhZITuua0R/KA8tGcV7r3oaMz5xdcLUhY2qIiN2E6Hm00K2OxcHGDtDG7TTD4PGQSDwtjB1di2vwm1dQ3OB3sAM1brmxOch9ubsSHGCTDw3nMes2vTqjk6sld2EKPrVZAZn/lejtbJNGmHFdvTaZbFb6SWBXS+ZlBz70pJsCxvUHTuYJZQ5AdNWSIUXS8v4JwO23GynoeJGHscK4fbWjaxe9SlR7nhcOfLwyuCf5ZvnnQ4PrrtVHz8k1Px6OVZGcgYRz8Tn9wrh5vV+7My2U5lKzJBgE0wZEaiLK5Dx7Gk6+Xn1xNl7RMAhvf1J5W9DviVCwZVgrIISQdN8n3f0b07IRaJ5MThbitwa3CfJdmmnQyHEBIF8HjmnKMBXE0IOVo4bD2A0yilwwHcB+ApF+e2OohRyNeNH2jhcucLkQhBSZRYvJGy1O48BtVU4rSh3QFYPdy6M/CmVkopAWAElkxfszuQ67O0vYeaE1nVCo/Xum7cAOl2w1BSXLiQXWW+jYyoImiySjOtMBt8xVep8vDYGSOs/azfFYxnryxmHQJkUn4M2VTnco+VNb25eohxGoDZftVxQU1Cbj3ziGwZFIlvZN/MkvhG0+B2w+EW1YryBf75CYAjelZhSI8q0/YI5wll34a9gVwoJQBwZiYtuBfNci8wVEokY56M/qhTLi9qNKrxk2VkzgfSHm7n2AwgY3CnrH1fTHKN9ggtg5sQ8gNCyGIAwzjv8yJCyHoAi13cbwyANZTSdZTSFgD/AnAxfwCldAaldG/m5ywAfXXPbY3wKxWtXyiLRdGSEBLfRNQcbiAtWxdX8C3tOiJ+zGIDSb5pNH5gQHUFencuD0wesDJD19m2v8kVh1t2TO/OaU/z14/qadquGgzOzniWvC4HsjLkosNb3dHfLJ5O4N8bb2ctuudsrfNTim+kppSorzUns8IgSnX6BZkxa+eVziaCiUj7A7G9213LadWEDdA85YK/XFA9Bc8/ZYZiScyZw60rC2g5z4UR2SjE1+QLJm1yrgC8xzlKeMNaXN1xf0/+OZfmOQaED5oUI1h+cNpgy/EGpcTmOXUpJfxhqjH3myf1k24PAjGOKsSgqtnRSHqVUrQDYlEPOtyujm4d0PVw/xNp3vabyHK4LwJwAqX0Wy7udxgAfuTYDHtZwesBvOf2XELIjYSQuYSQubt2OUuWFRIyD1MhURqLGDN41mQikTSPS9X4O5RmZet6djZng9L1cO+RZDFrLSCEYNzgGsxcV5ezJ0eGhZlU23e+sSSrUeLR2uhQGkXtpAvwnfEDTNsTydT/t3fmYVJUVxt/T3fPwsAMywybbDMMILIj++KCIKK4xN0kxi1uiXFJPtSgURGXkMQkJibRGOMSNRrXxCjiksQFQRGQRWSHQYZFdhiWmWFmzvdH1e2+XV1VXdXT3dXVfX/Pw0N3dXXNrdtVt84995z3mD4sju+h6V7Hi0+38rwJb3GiBvsD5w5AsYvKl8lANi7kOGGnk8ES3RMeo1Fs0Yd2fWNUx0g2b3+5PWabvFxsPGXx0AxZFLJwY3TGewDXN9iv5qRqci4bkcLot4rhls/AaFA5jTF3Y4iIcTJV8etWyBOnR/67Lvw6WtEnsuohNosza27hGyuVlFQRlq40mQyN7VWGqllTw+/zgwFHHu5Eng3GZ644QvtW6au6qMXmR7fjoImEISA83LGFb0LN0eH2nw/OEkejOTPvZ+YqAK8B2KOXc/8egCeIaKiLv2fWdaa/AhFNgGZw3+72u8z8ODMPZ+bh7du3d9G89FMQp9JbuikIBUyN5LygtY5mYV4wvOzfrijaOLJLlpOP5jfZKCNjK0ux51B9OH63ucj9MUXP0J7cv2PY8E3kgSvbPsbS7vWN5scVFS7jJT1u3mMevy5i+hMdbM20aVNNc6UJjcvpAqs+sPN8tSvSvPupCjO756zYqDwzr7QwOBulEAsnSh12iYPxQkqEsWH8PU7X6wUYjeBkITtB5POVMdXhTvAad3NveFWx0SpmV1bxCYaLnsSubCVua2kHyg+l1+oKCwU4CIPID5mv9hhxmgYjd1V9msOpzLCaXJshYriN931QxXADcB/DfRcz1xDReACnAXgGwGMuvl8NQF4L6Qpgq3EnIhoE4AkA5zDzbjff9Rupemgkyrb9tXhpYXXM9rxgwDKGuyg/aKl+4VSl5IhHsYnJYmyvUgDAvPXJj+Pu3EbzZnQsLowMxk5CSmI2RLaIYwqslvcf+3A9AGCOiSfU4tBRuPVwG/eq9SCZ9uO1kd8w3jLwqvumYPZNJ0RtC3eloU+sjCW70IpWurf8p1P62rYjUczCdcwM7qV6OM0mPTE4LxgwNUaM53K43nrCFM+YqbcIU/vDd47HF3edGqWekkxk49oqhtvsujBucyqm6Sa21asYbqvQICsPd3jirndBcxWcSlp4s8olh/t8MO3kqH3EZenU4E5ksmR33JeuG4NfXTDI9THdEgxQzGTysEVejdjXeLXkBd3HcPvdCWeG2xFL9PJUAI8y878AuAmw/BxAbyKqIKJ8AJcAeEPegYi6Q/Okf4+Z17j5rh8pMCmtnInkBwP4ausBycMa4VBdQ3jZ33iP2IWUyIOw3w3uzq1boGdZy5TocQuP28w3vwpvS8TBIX/HaMTEL7OdmOEbfnA5jl+M3s8LD3dUSEGc8y7MC6Jty2hjQDxYjH1sdSiriSwQ6Y9UFQQyGpIAEDQkyAGxFf9CQQJz/CIudisj8SZhQrno+B6RAmBCpqxty9TF9ct9EvFwG35LBzHcdsjXuRvPnxcTUMA+Fl8gwgmAWA93IsaTPGHpUJy+EAogcr6vLI51PgmEMyEvSJIsoPWJOnY6yM9Fm4TxkRXtcOHw1MdyBwNkK10pD3MB0lYzBhuK9jW3tHu24Nba20JEfwZwEYDZRFTg5hjM3ADgRwDeAbASwEvMvIKIriei6/Xd7gZQCuBPRLSEiBbafddl+zOOTIvh7te5BECs4bP7UD0+27gHz332dcx3Ktu3sjSM7IwJmVqP5K6SyZjKUny2YXfCxqkVIqwDaN6sXx4YjQ/QeG1OtMSw+DNOl9uNf6fGA4P7vKFdw6+dLKUai+MIR46xj60eOEcbbBKL9f9TtYRsZkg5uRetimEZz1GoFwmE4g4Q39Csb9Ta0akkvcaWme640ZvupLS7HfIE1I0hsmXvEcee82Rip1wjCEqygGKyKU6zubKA6UbkB2yykXoV4XKah5vjevETUSl5el6V6+8km2CAHCf2BvSwIqNkaChACSvMpDtfIZW4tfYugmbwTmHmfQDaAbjVzQGYeTYz92HmSmZ+QN/2GDM/pr++mpnbMvMQ/d9wu+/6nUxTKflqm5YNvvdwvelg97FJKei9h+tR19CEmtqjMQahrQ639LrWp4VvZMb1KsOh+kYs37I/qccd0EXWXBUeJPcxJXYJgGEpO8N3xuuSh051tI1YVSqzQl52rGzfEj88OVYRINXIihRO2m0MCwt7uB0a3E4npanA7DoSYUR2hCxKXxvfGw8vjwfxJnlidSzdS8vmHm73pd3tkCcbbgyxsKc9zTaILO84/fTo8KZ3f3wilt4zGQeOHMWy6v1RxVqaOPr/5tD/mBIM05O4MwExWRX3f7zf0bkOt81nHsRZhKTY/HgEKTb8BNByAOzuj5cXbsZv3ltj+Xm24ExYVoeZD0ML9xDvtwHYluxGZT3SdZdpHm7B4bpGtC6Kbdu7X32DE3qXRW0TMd9vLYu9FJyqlKS6RG06GN1Tj+NetwvHd2/+g+HYjsV4/YaxUWV0I5UH3WNno1t5Gy8fW46563ahU2v7pD3LGG6XBrfsBXnhmtHokGbvJgDkByP97cQYMkrGCWPMqElv9dCyD7uK++eTjpMxyUqneN2OGv1z80Qr+VzjhpR4NAmXE/TE+RkN7h0m1VfdhKjKfbNt/5G4+2thC5yy4lrxkFdCjPHUfTpqmtCLv94HAHhmXlV4PBDe+GQYim8ZciVSTXlpETrajD/i/s7XnWZHLZSeBE6MVuP9NLKinYOWppaARThIq4JQjFqJvMohE0+H+9ZXlgEAfnJqn/A2L1ZyUo1THe65+v81RHTA+H9qm5i9EGKXKr3mgXMHAHDndbvplF4AgE6tC2NuEluDW9o1G+K72rXMR7/OJUmL4yYCivKj58RuQgyMS3F2YZhWy33Czoj3rLA6tFhaduohkRMWvUK+J51MGI0hJWLyYoy7tvJw2Xt6XaxoJInWRZH4aKu/Kx60xpjiz6u0EgpWIQjyuBKvpLlXBveugxGJ0nvO6g8gNobbrAKgm9LdsmFV6ECpShj8XoUYbNkXmRQYJ5JGlmzeh+q92v6iSxIpa+510tyeQ/Uoyrf+bcIhJfq1YRcaBjjzcGuhKZH3Tq6NVGM1eTa7x4liNbsBYGn1PtQebUpo4uXD0hyWOJUFHK//X8zMJcb/U9vE7CbTPNwiOcVKZ9OMk461ll70crncC8ZWlmLhpr1JVxMQD/yIh9v9KGT3nZXbDpgOhuI7ic6H3Hq4V27zfv4uP2Q/NAmhMhKTUNdkbnBb9YFdYmhzVjQSpY2FIsR5x0fKHnyje3iXbN4btY+oCJgnTUKWSgWD5MlFvIRUs8lOOpbUv9A9tQAwQT8fY9hQJBkwsdAQeYJrJ52YKcjFgIyhUkbmmlTc9aMEc3lZS1sfa1CK4Qbiq+44mXRo13xkPy9CSIwEAwHzMBGT6yAYiIwNMp9u0Ap4eTWJzhRcW3tENJiIfqT/S70mTZaTaTrcLQu09hysbYga7OSlLWPmdIu8UHi7cXxwGlKSLYztVYr6hiYs3rQ3/s4uuGSEVvghrBLTXJkSAKf1j1SbFLH7xsOG1QbiPDGtPKHCmHCaNBXPCEsHV42vCJ/3sB5t0bpFHibYTCqN5y4erDEGt0UfbN0f+4CK/Rtxd0kaRtUVwWuLt4Rfiwfn+yt3RO0j2il7v1pLBrzswbMKYxITGPE35L3SsRDWt1Ns2exdB+ui3jdXh1teXncjl9a+uMATz+/grhHVCTdKthEdbu8NR7cUhAJhpRwzYgxuaewyW7l24nSob2yKusaNK35e9GIwYH6Nmikn7aypi1kVIAJ+4EEuTibiyuAmopsBPA+gg/7veSK6MRUNyxUyzcMdfhgaBoeXrhuDbu20xLmFBmNS3GBm2py2kmeG4cMHjp64jKwoRTBASZcHFIPbdt174KSrjP1p/M4fvnM83r7ZPi4yz2FCkJUnxq5imxlRf8ej66FVQQiPf0/L1Q4GCEvvmYynrhzp+PviHGIqTSZgLabjATvckIjWuoUUUhLnu8YJkjhFq0IpjU0cnnhYGZrCm+zVZP00vdCUzLtffRP13uy3TDhp0oV6w35dsSjdtwZJP6cbj3zY0PafvY2CUND2+RWO4RaKPQ2RyaRZfQ3nBnf8/dL5rAwGzBMejaF0gKZCZHZ9iFWzeH0QNZ748JqJh1tr7/sARjHz3cx8N4DRAK5JfrNyB7sYMS8wK4QhuPMMrSqdcfYuzuH/Xl4ac48czTEPd6uCEAZ3bY1PklwA55VFWmLqzS8u0TYkMOAaPbF5wQCO61yC4oIQ2lnoGgujJ16oh/y7y8a3MLidergyZUXErZyhzHbdY2185jYnjjWV0ljPXzMKy2ZEwj7aFMUvMiIm34O6Ruvtit8+LxAdAvXqD8bi2yM1zWAxJlg9fMX48tynm5yeQlIxhggBwGSDEW7WdjcGd1RojYvviRXIdJNnqChphtnmcAx3hk427cgPBVDXYB0aKM7XLKTE7Bpy8jsfqmvIuHwmNzHceUHzIkBOnwOHTUIxs8APF8atwU2IFL+B/jqb+iPtZJrBLevkGunUWovvNhpFclEMZqBQKuZjX9Qj0VZmNuN6lWFZ9X7U1CZPeeW7o7tHvU8shtucId3boLy0yPQBt3CTFnv3VTyDm81fi4eS00iRHTV18XdKAwGXEwWZ6a8tB2CiUuLgQbp08z7TB1YqPVoFoSBKCiNGtlUMt8yEY7XY5oqylgAi8ZxCtk14uMU5D+vRFpXtWwGIGJvWibraseREvXRiFg4wyqAWYWaAuAkNkb/vxuDee9gbNSd5TLfycBsTvIGI0ZzIZBNIb7KwkYJQANv2WYd7RXS4IyolArMVHieT9/qGprDaCwAc0zr9Kk1GgmSuUmI28dIq0JrkAonQwji3SDbU47DDrcH9FIDPiGgGEd0L4FMATya/WbmDiH/OFAptdMFbmWTmAwhXmdTgKG3xo42ckJfQz4ypLEVjE2PBxj1JO6bxgZ+ADDdMVgC17fqAyhx7XKfLx7JXW/61wyElLsv6ek1EXSXxYwSNBXHiGB0bdx3COX/8BPdLFUW9kMZq7cDgDvePfm/36Vis66ZrikURnW7Z6ydiXTnqu/FId+JYnkmlTWOCWKI63JeN6QHAYHC7zFvYeTD9k1LZ8LUaE2RJN4H47fz4DNhzqN624E84hlsKKRGYhZSYTayenV+Fd1ZsD7+va2jER1KitpP8jlRjVSXSLGkyL0imq9p5JuOBGX6vOB0PVwY3M/8GwJUA9gDYDeAKZv5tKhqWK2Sah1vOQDd6F5w8iBubOCYu3Sp7239DsDOO794WBaFAUuO4jUv3ieVMmn9LFDZgcMw+YypLHR07OrEtVoEhoaQpDy8QERKx91B9nD2tiQkpiWNXiRWRRV9HciQ8USmRQkrkIUCuGiketrLnsqKsVXj8ECEIshf7Nb1M9rJqzYMXT9XBK8yMJaMOt5l33k1VUtnIdhtCcKiuwdN8F6uQkqvGV8Rs87MsYI/SItt2RJImtf/rG5vCE2SzkBKzMfCuf63Adc8uCr+vPdqE9lKVxpJCg5PLgz4JBSMGt1yDwyyGOy8YMDWqQybjgRlyPY5stA+c6nC/If4BmAHgVP3fTH2bIkEyzeC2o32xffETQCuCYxyQncblZktsUmFeEMPL2+ITE3msRClrFb/v42FXnKah0dzD3bNMCwMY3LW1yTcjyA8T2YAQ2x/57zrX7fVSQmqXbmh/vSfxQiNG+bR4Xj4RymB2v6TTwGpTZB7PL4cVhWMy9XMynlnYwy09YEXC74FaTQYx02JVBWayd0ZPpzHRkcjZ+YgiSYlWmgSAbw54G3a1YedBx/s2J4bba0TJdoHxHjQmTcqTKOMELUCa1O7015aHE1/NqD3aGE7aHdKtjaWBms5QmwARqnYfAmAwuE0mFaFgwLTNYgISr7qs2eTGy7CiZOPUwz0GQFcAHwN4SPr3a/2fIkFaZKDBPbhbm7j7yHFmRoxLjlYGt9cejFQytrIMq7bXYHeCy7/xusbJIOR0oFq38yBWba8BwySkRB8hhIfKSoJX/i3lh6tYItyw85Cjtsh4aXAP6qJNMEotkkmdYFxytfPyNTZx+CEt3y9e3CMlheYrWfLpCKN0897IhES+dkQMq+zFFkVkRNy3G3UOrzF684yGg1VimZGw51/vF6LMkMJ0Q02t8xoNok/8KAuYHwzaJv3HygJGztEYwx0KBPDF1/vwwoKv8Zv3Vlsec8u+I+G+alUQQl1Do+da3Dtr6kwdPuYx3GRrVMcb0/0WeugWpwZ3JwB3ABgA4HfQvNu7mPlDZv4wVY3LBazUIbykSNcGX5SgljSRZrQfq5f8jbeMlI2M1UMxhOB/ItgZzIlM+q2OJ4xhbVyP3kd4cYRXzcqmiPJwm4SUxKPYuHQKZ2oZqaKFjdSlGS9fPyZmm/Hc31q2Ler9iPKIHN/D768Jv44yuCO1RR21IxlYKWHISaBb9YTGB2evMt23sr1mVMsreKJPxUPVTw9XY4iAMCrEpR4MkKM45bDMZqNQcwk49nCfPiBWrtAL4nkpZRqb7BVpMpn8UAB1Nucq7m9ZhzsiCxh9vcjeYLuuKAgFwgZ2i/wgmtj9CkiyqShrafr75ZmFlATMVUqEhv97BnlNI9luKzitNNnIzHOY+XJoUoDrAHygNLibj1lmt9fM3+A+9vgP3xkafk0E/OuGcbjmxJ4AbDzcWRmlpTGwS2sUF4SSKg84+6aIZnZiMdzx4NhlU92LsTOOekiUhzuBKnrGJdgXrx2dlDCaRBEllZ0m8Ywob4fFd51qu49xci3Hy3/x9b6wwSYr+4RjuNO4qiqPSXIb5VALo9PN6IV74FsD8cRlw9G3U6QQcV4g2th06uHOhFHC6LE0qi9ZGRqxx4leWhfhXE7o2raFo/1STWsXE2FxaonJAnr7y+cHCfUNTZarTHv0sLPwypT8+5O54wKwP6+6hqZwroeo7pnsqsVuCUlea3k8EM8G+UxrGxpNQ57OH9YVANC3c2xRKRn5XvDhokhcHCdNElEBEZ0H4DkANwD4PYDXUtUwhb+Y3C/ifTHO/OsbnQ0YPnSCWBIKBjCqZzvMT2LiZL9jIsaLI5USl4ojzGbKJk5VSiKvZY9MmY2uu4xxedIqrCFdtEigAqzRoC40HKN3h1aW321iDl//e0wSNdMZxWiVVyIbz0O7a2Fnk46LVCuV29giP4hJ/TpCJmJsaie6+1A9dpiUgZbJFHULY3iQ0YkQCjoLKRFSh2/qqx1Ov2ekOStnifLc90cBiFVMsqO5Hm4vw3fF88tqJUYkVYb3k4xFo3BAMGg9WZWpb2gK95XQAD9YFwnh8WISEgqYx2WbxXD/a8lW02MIuWEzr7iMHIImztXhI8gXOE2afAbAPADHA7iXmUcw833MvCXOVxVZxu8uGQIgMvgKZO3agCGZpL7BfJDIxhmszJjKMmzcdSi8/J4MxANo3Q7niUvG7xr5zqjuIIJ5DLfDJ54cUiK/Fkbojaf0sm+bw7amCzOVAbfEVJo0eoWlh2dDE0tlsJv9p5uF1WTj1H4dwq9XbatBgMzLoFshekMukz7ywf/gGxuj2+vldPETxqqURBthwUDAkaf6OL2/xvXSQs602G9/hNaM712G1fdPwbAezg1u0Sd+jBQQIXi1FuXdY2QBpWvCuBoR7eG2pq6hKbxaVK7nOpj9/XQOj3lBMs0zOGASyz9Qz30xnqPTpEn5HhK3fi4mTX4PQB8ANwOYR0QH9H81RGRfEUORVZwzpAuqZk3FeClb2cjGXVpMsCyXlIuIh2oy5QGFLbthV/wkxBgj1mK/wlAQrfJDYI6VBXTcLul1IkZSpj2PxSB/5qDOCR/D6I2yS5pcsHGPqReQPXjoREuDRrbLiYMMRn4o4OreluUOZXbYqG5kSuyvcQXG6PHLCzoznEVoirjPQsFAVAhWPDqWeBdmBSCqxoITxO/X6JNJhYwwfK2qTYproiAUG1IypX8n/P7bQ2P2lY9rRl1DU3icaKmHdh3xuBhMKEimxWyWbo4VThArXsYkWdOwGxNkw14cI+c83MwcYOZi/V+J9K+YmUviH0HhBK+zkQWPfvd4AMC3R3aPs6c9+UFtcLaO4c5u+nQoRmnLfMxLYhz36vun4Iqx5fjplL6uv2stC6gZyWYebqO3UyyjGolKmjQxIDLFcHLDqvum4HeXDI2/owXG2zleeITd7Z8Jz5w8aRWLWfPsOZX8BKyNNTs97iNHGzNyJcxoOISChCaO/xsLw0sYVY1NjJo656ofn06f6LKl3hI5T/v9dhyoja0s6vHv3qFEq/JYZ+HhFmFGwUBsSAkR4ezBx8TsC9jf57//z9pwSFlLvdDcoXrn10cqCAUCpuO32fNELAQZ9y8Ixa4CmCEb9hw2uDNh9EsObitNKlJAJ/3GtiookG5OH9gZH982Afd/a0CzjiNUCbweMLwiECCMrizFvHW7kzaZKggFMePs/gl6PM2/EwwEtMI3JjHcwQChbVFe+IFhNfhFx3DHDqrx7O1MNKoK84JJvSfjSaOZfe514phMfymH4H+rd+BAbUOU6kC8S9JqtcDsYS6W6Q9LY8e0yX1wgZ585TVGxRmj3J8V4Yp7uuEhjCun8qF+W14XRmi8icjIB/+DcbP+G7Pdy9MVRqKVlN2+I9F62nbGpF3CsZFV22sAACKqzU63Ox2ELCpNXjqqR8w2cZ5WHm6rPgpXJJY93PrLbDK4M08iIweZP/0UAJk1mHZrZ+7JdEMHvVDOmu01mHBsh9gdMtHKSjLjKsvw1rJt2LjrEHq2t06aSwXGy8nq8gqQtgpRGAqYXoNBSWPYWofbPIbbblu2YzxjY0iJsUvMQk68UCmxoqK0Zfj1roOaoRjjlbTBrIIjEFkB27rvCJZv2Q8gcr5rvqkJ73dK345RicOZRMRgYBTYPFXDlSYNBoybMKybJ/ZGQV5m+soqylqGQwqByOQ7kUqTXiNWZKyUir7Qa1EIVS9ZFtDIvsNyBUVnK12lukqTfO970Y3/WLg5/JoI+MmpfRAMkGnCs3DMxIRcCQ+3RT6XKB8vrxKIayYTxr5kkZl3bY5BRBllbCfKzHP6R70XCXNCgzMXEXrcnyQxjjtRrDwFj324HoB5Em44rngAACAASURBVAwQvfKy3qKAjVUMt3hAfJaA1KSfaWhsink6xg8pMfNwa3g1PMieOfn1FWPLo/ZzYgjkhcwfN3/+aAMA4IRf/i9c5lp4FX/2+pdumps2jMmioWBsWIEZwsNtLJXtJk73x6f2wQ9Ptk9C9orvG8q7Cy9lImozXpvoK7Zqk7+/zt1ou58wMuUwI+Pt2qdjxNkSrysO6J5zoRZk5mFP53hgLHR008TeuGFCLzwzfxMAYNv+iOFt1JmPbNca/P5Kcx3usKEuq5Tog0qmrPwnA2VwK5LGxSO6Rb0XA8ZqyUsl4/WAmg56lBahS5sWmJ/EOO5EsRq25AeAaVyey9HdzOhYWr0/zrfsvb9+w6zAQ7w4VrvPE01mbS5Wz7oT+0SSpv+sT9jitdEorSf4Wi8bLYxQ4T0HgMNHG5FpI4VZkaZwqEickBLh4T5q0CK/+40VUfvtP3zU1epBpnDGwOiwIdEf8UJtrPDqugeAYT3a2n4+oIu22tJKX9Kwm2zJOu5OxzaRP2OVtJkuRvc0V6WZqv/WskEcnngafu8CPZ/rXYvCN+IZc0jKZxDPpWwKKXEqC1gjKZMcUColCjOMSVHZ4LVvLkSEMZWlmL9+tweawtH970i72+QBV+9ARUF+iCQjfMTvIShm2fiHTJLjurTR5MOG9Wgbdc7Cu+N1IrXVw06Whvv52+bVJo1YhZSY6Y4X60aMkBkDvPHym90PJYV5sUmTIqQkzj0utIsXbNRWfI7Rf/+P1uyM2m/ibz4MxzQ/OXej44qnXmPUcBf94bS4TybR/5jWtp//4CRtlWHKgE4IkH0Mt3ztxwspEccROVBWSZvpQtbalxmrq3DJiAqbxmTqwnx7U1PcFwdrZYM7d1VKjOokSqVE4QrT5XL/jcEJMbayFHsPH8XK7d7OTa0MliHd2tjus8thQpcgIVlAYzyzD1VNZMzUO+RYSEF5mZYr8eWW/VEG9z8+34zyn74VWU5O80NHGLxW14yxsIeT5E6rIkpmoUyjdK/ax2u9XxkysmXfEWwwhFaF4iSFCYQ3UIRmWcVii3vuQO1RzHzzq2a1N50Yiz2J+8CPJbvbF9tLME4d1BmL7zoV43qVIRTUJDKt7oMoXf84XSG8wKLia9Xuw84bnQKsHGcLNmrFl3bUREJKrHIUrCbb4c/18USu55HLOtyKZvLKomqs8tjgSgdL756MRT+bFLN9owPd6GxlbKW2/J7MqpOJYLU8+91R3aV9EkOeUMnGcqKPWSdlsjOZX8xZFffc5c/rGpogr8L+9LXlAICfz14JIP2ygJ3bFNp+bjS4AWce6NYtnFUQzbeI985UIuoj9r+6WAUUYSlm/Sjjd6eEMLgTKe7j9eqOEbPxU+Qp5QcDUQmBxntBLpzkdPWuTZF2r8irBl70iJWHuUp/psul3CPJkdG/tzCaT7Co3yEMddlRwbnq4Saiufr/NVIoSU0iISVENIWIVhPROiL6qcnnfYloPhHVEdE0w2dVRLSciJYQ0UI3f9dLdh+sw7SXl2LKwx973ZSU07ooL5xdLXPKrz+MSQzKtAE1VXRqXYie7Vvik3XOvXXJ6BqnKiVyIkuizgTZyN5okViZ6PH8iByHbIf8EDd7EMePfU8NT105EjPO6ocOxeaGd6JeJ6cJUFFFdjLsUjitv7bELksWRjx79oYlEdAyPxiOD47n+fOjsXHTxN7h12KFxmiAOcUvzs14hY/yokJKnJEfDCA/GDCV1U1nbPtai6rGN0zQQmrkYkx54eTH2MlHfjBgOdmIFBkyK3zjk4vAAU5DSsbr/xdLoSTFbkNKiCgI4I8ATgfQD8C3iaifYbc9AG4C8JDFYSYw8xBmHu7073rN13u8XRLyEnlGe99b1kujD104GCf2aZ+OJnnCuMoyLNi4x5XnNtnDjJWRVL33SNx94iEX75g1Jzam99R+5nGAAuMw3N2iwI6f+PW7a6LeC0PNyMiKdhhV0S6j4ta7tGmBK8ZVxGyvbN/SZG/nRrFZvLYZwgMsjx9eP3fXPXA6lt4zGe+s0Cao9725MvzZym2a32lhlXk1TZlQMFIGPhS0Pyk/Lqf/5NQ+4dci4c+sUmE2kRcM2MoCyiElTp0JASIU5AWwfoe3q8P//MJcZawwL1ZFxU5vu76xCZ+sM1/lFT3ykRRClo063K7X7YhoMBH9SP83yOXXRwJYx8wbmLkewIsAzpF3YOYdzPw5AG/V3pOI7K3JNf7w7ePDr//+2ddRn8nDzgXDuuJvV41MU6vSz9jKUhyqb8Sy6thyuOnCatiS1SMSHdo+XhtJ+jIzqpx46oZ2j8SSd27dIsGWZA77DYUxrLxSoQChidnU4D5Lr1aXKYbXO7eciFX3TYnZfrSxKakGMRFQ2jIf3ZNQD6A5HKiN/IahYCAqJEY2RHbUaMvq76zYHnOMy8ZEFwjJC1LYIKmMo83vRw+3zIEj2kS8wechYvHYUVOHFxZE61XLyColVmFH157YM+p9IKAlWhd6rLmeZ7EKIyYOQo8ciEwg3VSgBSITdjFxBSIe7gwZ+pKCq1+SiG4G8DyADvq/54noRheH6AJAzhyq1rc5hQG8S0SLiOham3ZeS0QLiWjhzp07rXZLG/G8GNlM66LomM1cCSMxMrpnKYiAeRYz/FRgvOqsS7tLHyQcUmK+Xfzc8StNsutBOhMxqjTIxBa+iejMHm1kmK1I/3vp1qS2r7mEgoGYxDggsURZmdXbY6VDC0IByyp/6cJsuBLa+nJBFKGmMrw8VkLte6N7YFSFtv1oYxNCgUC4v0LBgO3qj5eyeMlAqLk0sbOxX94nm54UstFqda+UGOQmA0Q4tlMJaqXrzIvHp7jejVTtjvW8b9ITPONplxsxrbIrZAH9PuuUcDt1+j6AUcx8NzPfDWA0gGtcfN+s59xcQuOY+XhoISk3ENGJZjsx8+PMPJyZh7dv732YQl4OG9wA8NilES/3TpeKF9lC25b56Ne5BPM8TJy0engf26kk7j7xiPcwdSKJWGtR0c1PXDS8m+VnZkvJRNrDuLHJ3MPtF6r3HokKTXLLaQ9/FLOtMC/o2TUhjOCubWNXWszi0MfroS9WFXpFHO7hukZsP1CLRZsioSfHdtSK6JjdI4kkG2YqX26Jn+5llFvMlienvIpo9ZsaPcnBAKEoP4idJvkg6fT6Xm4ociU4ySQEtKY2NjBBDGvHdS4Ja4vH7GOyLWdlASUIgDwCNsLdPVENQH4idQXg2IXDzFv1/3cAeB1aiEpGwsxhI0QeR91UFMsWpgyIFEN4Xao66WP7IiHGVpZi0dd7PTMirAZpObY40YH8UJzr2klp5/yQtXfYL/TpWGz52TqL5KP/rtqB5QZZQCOZ+MwRXlvBsiQneObrHm4vhom/XDYcVbOmmnrXZEND/GRCbcRqlaZrG80Qt4ptBYBVJl7+bFj1ETgpfuP1ikZzsbpWZSPbKqTEGKscIK3c+a4ab51Umy1y0MzC/kQInBweKOyfldsORK0KyZgNfTlb+EbiKQCfEdEMIroXwKcA/uri+58D6E1EFUSUD+ASAG84+SIRtSSiYvEawGQAmVn3F0DF9NmomD4bQPQN9p9V5pWWcgWrSlO5wNheZahvaIrybqUSY9yv1bglD2jxhjbZQ/HMVSMxvpfm2YsX+hAvUYgBlGdBouSkfh0sPzMmT6/feShKzcRveWUPnDsgpcc3ergzJbxivJTIKXSXhYyhVVVAIZdmVhBJeMXP+H2sipVfDVCzlYE1JhMKI7X13oZPJAvjtdqxJKL2Y5U4bxyfAwR0KC7wvNLkDguDX8SWyzHmQjtcjut2VvAtdp+cj+Fm5t8AuBKakshuAFcy88Muvt8A4EcA3gGwEsBLzLyCiK4nousBgIg6EVE1gJ8A+BkRVRNRCYCOAOYS0VIACwC8xcxz3LQ/XRiX12XP1QsLvjbunlMs2rQXi7/ei38v3ZpVMXpOGFneDqEAuZIHTCaWSXtSyFO8wU32UJzUp70DXWXtV3ZSwKRDnEIT2cb+I0dNk4TMyMSHTo9Sc8WSZLFk876MLHzTSTKehAax0Ne+83VzH5CQ/6tvaIopqJJns2buVw/3i9eOjtn2hoN8BKMHNFOShZuL7KhwqlISDBCKC/McS4ymCuNKloCI8OsLB+PNG8eHt4VMrmXj6qaZx1zuEtE/ER3u7LgGAOc63COIqBMAMPNiAPsBTAJwJRGZ/xoWMPNsZu7DzJXM/IC+7TFmfkx/vZ2Zu+ryg2301wd0ZZPB+r/+4ruZiHxDlf/0rSjVBitZnGzn4YuHhF+f96d5uPGFLzxsjTe0LAhhSLc2aYvjdpo0KWsBx/MijjQkhcVLaJnrcHLhZ2+WTLui/IS/6zftcSv1gmynjfQbX/Tn+QDiF+rJD+nqDY1N2GnwGNaYVNoUbDRJTPMDXdvGrlY5Gff2Hfa3OJlVLoucaOxUIpGIsOYbbVVAaL47qeiabIRmvGiTzPnDuqJXB+swOiB2XDN7Jsj9JiZd4ns5Z3AD+DOAegDQExV/DuAZaIb346lpmn8xzugue3KBRy3JHKYM6BSz7T4flSxOFmMrS7Gsel+U3Fi6sBq2RLU0IL4ndeqgzlHv7bxzAFB71IXueBYMrKEEjNCrxlWguCDky0lH7w72snbJwA/9UhDndy/RV4KCJtf4eyutw+w+9bg6bboxC7nxkjKTIm52hJ9php9Zjs+3kkgkopgEQVHNcZOhvHs6R0o347LZiqfR4H70g/Ux+8i7rNJX/cQ2s3vGrzh9OgSZeY/++mIAjzPzq8x8F4BeqWmaf/Gbpyod2JUwzuaCN0bG9ipDEwMLNuyJv3OSsRo4e0pawPGGNqNCQzZJNqUDs3jGvCDhaFOT7biRKfHLRuQiJ6lCTE4z+bkbL872lol9cMuk3jh/WNeYz747qnvMNpHPMKRbm5jPshnZw50JT9G7zjzO1f6Lvzavs7BJWqmwK34mhoBfnq+VOLnt9L4AgFcXVbtqh1eYPQ/EuNZDv6bNCgHK4XQiTCscw51FC2mODW4iEiKREwH8V/osZLJ/TrN9f63t53/PwThuu1ny498blsaWeMvQ7m1QEAqkJazE2OVOnK/GYi1AdHVBY4ze2XpWenPJFX12M7WWnTV1qD3aFP5MzvDPdMZJCYSpwg/KTr98Z3X4tdnEqUV+ELdM6oO8YAClLaPDjszuIRGiss/kfsxmrvnbwqj3Xs+xTusfuzLrhltPOxZAdFGboxYqJfK5ikTaJboB/4RLXetU8ZePNrj+jrgf7HJU5I+WbN4btS0XQ0peAPAhEf0LwBEAHwMAEfWCFlaikLCSvhH8wqT0dS5gptsJwLSQRrZSEApiRHk7zFvvRTJY/IHrmfmbYrbdMCGyiNWnU3S8XufWhcbdoxjd01WKBz6YdjLmTz/F1Xf8hFyRU/CaXrFQqDj8bGq/mH3mb8i85EEgOv7fCV/ee5rrv+G2iIYXyJOCD9fssN3XKAtKRJhiYdh5qdufCvw2sZZXZldstTZ1LjBZuQCAH55ciTm3nICTj42oF1klwhJFkivFSmL/YyI1Esw0rtPN9gP2zkQzRKGfH54ceY6YCUsM1ldzxunKVzmrw60nKP4fgKcBjOdIbwUAuKk0mRN0aeP/stSp4JmrRmLDg2dg3QOne90UTxnbqxSrttdgV5qLACU6cBVI+thdDdd2POeDGCniGeZiQCkva+n7su6/OH+g5WdVu6KXU887PlJoV0jAGT2gALB1n/sHXTpwO1luVeB+QXTLvsQL6qSKF66JVuGQl9J3HLC/r+8+K3ZCNUcvCS9CU4RRlmmVRt1wxsDYSYST4jeZhLwy29bkvhRcPqY8+nvS9/tKhcUA+5ASkRQpLqfh5ZGExaue/twX+QxGhOEsO9yMq0DMkTwIY3XiXPRwg5k/ZebXmfmQtG2NrlqikGhTlI+vZrr35OQCgQAhFAzg49smANA8mrnG2EptBj8/xd4rY9xvogNXm6JIIkyhoXR5gUWxmtnLtwGIGNJOqihmy7h68YjYmFzBTD2pShhUr0mFoERxjKL8IFbfPwWr758S/uzYTvZKAF4SXxoyOWTS5THSIJUmYm4B4N5/2yeDd2ljrTcvPOW99GRUMz1rv3DpqB4x22QZTJmyVrHGbKZ5w3vZJAjLY2Q8rBJDCZEkcxF20lKaoH5etTd6Z58gVNqOkZw1xsTJJubwOYlaHTmvw61wTlF+CH/67vHxd8xRurUrQtWsqSgvS62WbyYy4JgSFBeG4iwXJ/9hk6jBPaZnafh1SaH2YBFLnValrMOTCf00vjlQ57AAQm4gPDwXD+8WTpB7RU+MIiIUhIJRk5mzkhQrnwr6dHSnVHLzxN7h2FY7igszNz3ImDzcW+qDeCGFo0zCrL6jJ04Kg6tYv8/SoQKTKswM1PkbzMc8S4nJDDC2Prz1ZNx62rGmK08CNxMjq0rDZnlOPcta4ltDvL/3RZGrZBW7+vV7a6KeB8zADj1cRdQqyVkdbkVinDGwMxb9bJLXzVBkGKFgAKMqSjE/Thx3sseZRI9nzDz/7I6JeOm6MbbfefbT2FhwWy93jtnioi8qO7QMGxvCq2UW+pPJYWrTJmvGs6g6Go8fn9onKi/AinvP7t+sdqUTq5UeM8yMy3f1kJK/f7YpyiD73+rYmH+/0MZEk/71L7aYyuLJQ0OmTcx7lLbEDRN62Sb+u5HOqz3aZBtWYjzuQxcODr/3qme+M7I7/nXDOHzXZNXCiFNvf887ZoeL4DQxY1SF5tg5rnOJvk3bTxncCseUSjqe950T/QA5WGdd8ECR3YytLEXV7sMpjU+NLRVsPXD1bG+/0tCtXQsM6KINhB1LCqOWOu2QCzXEe1hkqvRdKmiUvDdGZRjZe3rysZkvmTmivB1umFAZZRgkg0z26huxkz21QyTKipWj3/93HfreNSfjwikSwaoYULwxr6ZOFHnxL/EM8N53vm3ynUhVR3nclLX9H/8oVsM6HRBROKkxHk9fOdLxcU97+CMAmsEtKrCuDOtw52jSpCI5DC9vh3Ok5aHfvLfGw9YovERkYs9LY5n3gM3d/uTlIwBYe1I/vu0UvHnjCa7/ZpTnyk4WyvWR/Q3rDq4AUVRiFBD9sP7r5SOw6r4pyGQCAcKtp/VFpziJsW4xeoIz2dFVEApEKVV8ucWZeNf/VmuKJheN6Ba1/YM1/vVsy1wxttzRfrKBKf/OGfyT22KsJuoUMfm2kg7cvCfzEoiN2GnHXzQ8Ws3lsJ6zwIg1rIWHOxsKogmUwZ0GFv1sEv5+zSgc17kEv7tkqNfNUWQAfTq2Qlmr/LTKftl5uHuUFuGKseX4y2XDk/o35cfGgo32xX6yaFzFTadYh0y8sqha8nAD3x4ZnWQpe7iDAcpa2Uw7+cc6FxVKvUI2LPKCAVx9QkX4/ZmPzHV0jBVbNW/e1IHRFVz9XuJcMOPs/qiaNTVqm10pewBYutm8eEym8/PzIupEH5pMmM4/PtrYnPPl9qj3BGBCX00+sJ1JOE70vv4cLGeeExsDzsxgjjWsmTmrvNuAMrjTQmmrgrAyhUIBaIPLmMoyzFu/K23Lx3aDFxFhxtn90e+YEuudmomVQkE2cssk6wqM/1u1I+ztDwYoJv432x4yVtjJP+7WlQ0ymYcvHhJ+nRcKoFtba/WReCR7dSDTePn6SM7HmY/MxdHG6Mqq8hD44oLN6Wxa0rhQWuG4xLBiAQCXj42Of/7zR+vRsUQqHU+E/zu1D165fgwGdm0dta+fimHZYeY8EHKoRodQE3NWxW8DyuD2lEnHdfS6CQoPGVtZim8O1GH9zkPxd04K6R+85MnEg7OtCz5lQ8yqTCBAGNy1NQZ2aR3z2VvLt4UTw4gIgwwP12x7yCTCf1eZFY/JrH6RFZZaFYQc5zUAwMhyLVZXVCC0VOnIEoz3Qe8730blHbPD7+W7/y1dUtRvcWZyrPX6nQdjPh/UNdpo/uLrfTG62qFgAMPLY1VshveIDjvL9PHyo1snRFXXtKPvXXMAmIeUZNtYmN13eYZy6WhtCfn2KfGlsRTZy7iwHnd64rjT7Tl9Z8V2LP7a+fJwdg2twL9+NB7/vnE8gFjdZjkD3+j1ybaHTDbz8MVD8MOTK00/swuLmfktLYH+QosKhdlGomFRfo3ftYrBXjZjctT7BsnLb3emHxjUag7V20tPek330iLcccZxAIASg7znshmT8eQVsaGLxp+6qYmzKswQUAa3J9z/rYGomjUVvTtmbjELRerp1q4FurRpkbI4bjcqJcmir1Sg5bpnFzn+Xmb7a5pH1aypMTKKW/dryU9mjs1cCSlxQvREJfOukm8N7YLbpvQ1/ey2V5dZfq9vpxLXz4Bt+zOz2mhz+M9KrchJhjtsHSMK+PSwqE8g1GgEe6TQKbvh+dnvj4p6b3X8TKNq1lQsmxFdBLCkMA+n9I1d3ZcnV+t3HlQhJQqFInkQEcZWlmL+ht1p0Z7NS1C6LB5nDookfBXl23uyjBJ4Mlk2ttpy3p/mATD34IWyPLxA5qaJvW0/v00qkGPlNcxUjOWr49HOprAKAMxNo6JRKnjw3IEx2345Z7Xpvou/3mu6PdNZcMckPPv9kZh4XAfLfe44w3yCZken1oWomjUV153YE09dOSKmNoIf6VBcEPVeNq4n/vpDNHFsgSm/kzsju0KRgYzrVYZ9h4/iqxQkFK79JjqOMD9FhtxvLookj5lloct8YmE0ZIuHyy1B/SEjkqweu3SYl81JO1eNKwdgnmQGAMOk2NXD9Zlft0BOpBTUOKy38Ofv2f/2flcuaWtSEGX1NzX6K8YxUuLoeX+aFyUV6BcCAcIJvdvbhsJcPd48ttmJ8sj0M47DhGOtjXk/YVwZamKOUv5pYhVSolAoksiYSq261rwUxHEbS0xbFaJoLvJx+3W2Vzn54fOLUf7Tt/DoB7EFHPwar9kchDb6rPMHoWrWVEwZ0MnbBqWZNkX5+OjWCZh5zgDMn34KVtx7GjqWFIRLSBMRyvTiYQfrMjtuFUBUnQXBwTgyeIIRJsly2cQgC33m3+r1KE7uG2tIZuOIYOWdfvvLbWluibcY75VH/rsWPaVE5EN1DSqkRKFQJI+OJYXo1aFVSuK427SI9iilysMNAO2LC/CdUd0dL3X+Ys4qU63aXCPbHiiJ0L20CPmhADq3boGWBSF8dsekqBLSL147GuN7lYWVPTKZZE8aNzx4BlbcexouHd0d7/74xKQeO91YFdX63X/WajrMaW5PprEhbWpVmYFRmaf2aBNulELMXlpYnXX5LMrgVig8ZmxlKRZs3IOjjckt9jGud7T2e6o83ADw+Z2TTGM07bj8yQXh135cPnaLXBhDkG0xiqmgV4dWeO7qUWgRJz8gG3j/Jyfh/Z+chBsmVOLT6RMRCBBaFoRw/7cGok8WJ9nvPlQfEz5Q35D9Y4KMH0Kmks0ThkJrFZKHG8g+h4QyuBUKjxlbWYrD9Y1RFdaSEdNsHKwy3bjL7NY1H2NFSQDYtPuwBy1RpBM3t3KvDq3Qq0Mr3Hpa36wshvP3a0ZZfrb/SLTB+f7Kb7Iuhlcw46x+MdsyXeovFZzYp73t59kWZqgMboXCY0b3LAURYsJKmjvWtCoI4ZcXDGreQVJMQ2NTuLRvLpIOdRpFevno1gleNyFjGVtZhrMGx8a5A8C/l27F364ameYWecMV4yrwA4N+e8scWMExIq+63mMyCclwH5FrnJfGyiKOHj2K6upq1NZmn66pXyksLETXrl2RlxebyZ7ttCnKR/9jSvDJul1xZdLcctHwbrjtFWs9YK/pdefbkTdZNria8eNJffDb99eE32eZA0cBLSZdpiHJoWJ+5/eXDMGvLhgUrjAoE8/jmU3cPqVvOHn8jIGdwoVicpXLxpQDAN68cTzOfGQuAGBHTZ2HLUo+OWlwV1dXo7i4GOXl5Vm3ZOFHmBm7d+9GdXU1KioqvG6OJ4yrLMNTn1ThSH1jVsaqHtuxWJIAM+ewD1QomsvNk3obDG41/mQjG39+Biqma6XLu/ukSEm6IJPqqlYczpEwiz99N7fkQGXm3j4B3xyoDYc8DujS2uMWpY6cDCmpra1FaWmpethlCESE0tLSnF5xGFNZivrGJizctCfpx65s3zL+TknkirHlMdvecaCw8Oynm1LQmsxDLue9eY+K4c5GiAhrHzgdH0w7WVUUtuC6k8z1qC8dHcl1qN57JF3N8YQrxpbjr5fHljnPJbq2LcKwHpmvQJQM0m5wE9EUIlpNROuI6Kcmn/clovlEVEdE09x812U7mvN1RZLJ9d9jZEU7hAKUEnnAN340Hp/dMTHpx7XCzOBWRLj/3EhxoFP7xZY4VmQHecEAysvSO9n1E9NPNw+huP9b7tSO/MyMs/tj4nFqDMgV0mpwE1EQwB8BnA6gH4BvE5ExUn4PgJsAPJTAdxUKX1KUH8LQ7m1SYnC3LAihY0n6FA+UkWFPQSiI310yBOcd30U9bBU5zcafn+F1ExSKtJFuD/dIAOuYeQMz1wN4EcA58g7MvIOZPwdgrGMb97sKhZ8ZU1mG5dX7sP+Iv0s4K+JzzpAu+M1FsWXAFYpcgohw62nHAgA+vk2puyg0Ft91Knp3aIV/XDva66YklXQb3F0AbJbeV+vbkvpdIrqWiBYS0cKdO/1fze7qq6/GV199BQAoLy/Hrl3JLwMOAG+88QZmzZpl+fmSJUswe/bslPxtBTCushRNDCzYmPw47nRzzQmR5FezctdmTB3UOVXNUSgUGcoNE3qhatZUdJOSSzuWFHjYIoXXtGuZj/d+chJG9Sz1uilJJd0qJWaBuk6FaB1/l5kfB/A4AAwfPtz3QrdPPPFEWv7O2WefjbPPPtvy8yVLlmDhwoU44wzny4CaxjIjEMjJ/FxXDOneBoV5AXyyLjUTqnRy59R+uHNqPxyub0BBSFMk+MHJlWEZtN/CnAAAE9NJREFULDN+feHgdDVPoVBkMB/eOgFXP7MQf7kstxMKFdlFug3uagDdpPddAWxNw3ctufffK/DV1gPNPUwU/Y4pwT1n9bfd59ChQ7joootQXV2NxsZG3HXXXSgrK8O0adPQ0NCAESNG4NFHH0VBQQFOPvlkPPTQQxg+3H7wqaqqwpQpUzB+/Hh8+umnGDx4MK688krcc8892LFjB55//nmMHDkSe/bswVVXXYUNGzagqKgIjz/+OAYNGoSnn34aCxcuxB/+8Ae8/PLLuPfeexEMBtG6dWu8//77uPvuu3HkyBHMnTsX06dPx8qVK9GqVStMm6bltg4YMABvvvkmAOD000/HhAkTMH/+fPzzn//ESy+9hJdeegl1dXU499xzce+99yans7OIglAQI8rbYf763ejatoXXzUkKRfmRIebcoV3CBvein03CsPvfj9rXqVSYQqHIbgrzgnjuauuqlAqFH0m32/FzAL2JqIKI8gFcAuCNNHw345gzZw6OOeYYLF26FF9++SWmTJmCK664Av/4xz+wfPlyNDQ04NFHH3V93HXr1uHmm2/GsmXLsGrVKvz973/H3Llz8dBDD+HBBx8EANxzzz0YOnQoli1bhgcffBCXXXZZzHFmzpyJd955B0uXLsUbb7yB/Px8zJw5ExdffDGWLFmCiy++2LYdq1evxmWXXYYvvvgCq1evxtq1a7FgwQIsWbIEixYtwkcffeT63HKBsZVlWP1NDXYdzC7BfwCobN8q/Lq0VfSS8fs/OSndzVEoFAqFIm2k1cPNzA1E9CMA7wAIAniSmVcQ0fX6548RUScACwGUAGgiolsA9GPmA2bfbW6b4nmiU8XAgQMxbdo03H777TjzzDNRUlKCiooK9OnTBwBw+eWX449//CNuueUWV8etqKjAwIGarFL//v0xceJEEBEGDhyIqqoqAMDcuXPx6quvAgBOOeUU7N69G/v37486zrhx43DFFVfgoosuwnnnnef6/Hr06IHRo7WEh3fffRfvvvsuhg4dCgA4ePAg1q5dixNPjK/NnGuM66XFrC2t3o8BXUo8bk1yCQYIb998Akpb5QMA5txyAh56ZzWOHG1Erw6t4nxboVAoFAr/kvZKk8w8G8Bsw7bHpNfboYWLOPquX+nTpw8WLVqE2bNnY/r06Zg8eXJSjltQEPEcBgKB8PtAIICGhgYAWly1EaMO9mOPPYbPPvsMb731FoYMGYIlS5bEfCcUCqGpKVK2WC5c07JlRBqOmTF9+nRcd911CZ5V7tD/mNYoLgyhprbB66akhOM6RyYRfTuV4InLR3jYGoVCoVAo0oPKZPOIrVu3oqioCJdeeimmTZuGefPmoaqqCuvWrQMAPPvsszjppNQss5944ol4/vnnAQAffPABysrKUFIS7U1dv349Ro0ahZkzZ6KsrAybN29GcXExamoi5bnLy8uxePFiAMDixYuxceNG07932mmn4cknn8TBgwcBAFu2bMGOHTtScWq+JxggjM6yzGyFQqFQKHKdtHu4FRrLly/HrbfeikAggLy8PDz66KPYv38/LrzwwnDS5PXXX5+Svz1jxgxceeWVGDRoEIqKivDMM8/E7HPrrbdi7dq1YGZMnDgRgwcPRvfu3TFr1iwMGTIE06dPx/nnn4+//e1vGDJkCEaMGBEOhzEyefJkrFy5EmPGjAEAtGrVCs899xw6dOiQkvPzO+MqS/HeV99g0y5V9luhUCgUimyAzMILsonhw4fzwoULo7atXLkSxx1nXlZW4R3qd9FY800NJv9WSyqtmjXV49YoFAqFQqFwChEtYuYYWTkVUqJQZBi9VQKhQqFQKBRZhQop8SG7d+/GxIkTY7b/5z//QWmpiv/1O0SEhT+bhKJ8pUutUCgUCkU2kLMGNzPHKHP4hdLSUlPVED+T7aFNbilrpUobKxQKhUKRLeRkSElhYSF2796tjLwMgZmxe/duFBYWet0UhUKhUCgUiqSTkx7url27orq6Gjt37vS6KQqdwsJCdO1qKr+uUCgUCoVC4Wty0uDOy8tDRUWF181QKBQKhUKhUOQAORlSolAoFAqFQqFQpAtlcCsUCoVCoVAoFClEGdwKhUKhUCgUCkUKyfpKk0S0E8Amr9uRYsoA7PK6ER6T632Q6+cPqD4AVB8Aqg9y/fwB1QeA6gMvz78HM7c3bsx6gzsXIKKFZmVEc4lc74NcP39A9QGg+gBQfZDr5w+oPgBUH2Ti+auQEoVCoVAoFAqFIoUog1uhUCgUCoVCoUghyuDODh73ugEZQK73Qa6fP6D6AFB9AKg+yPXzB1QfAKoPMu78VQy3QqFQKBQKhUKRQpSHW6FQKBQKhUKhSCHK4FYoFAqFQqFQKFKIMrh9AhGR121QeIu6BlQfKBQKhULDb88DZXD7hzyvG5AJEFHQ6zZ4iLpf1X0AIirT/8/Je4GIyr1ug9cQ0XAi6uB1O7yEiCYR0TCv2+EVRNRaeu0rwzOJhLxugBvUAzzDIaIxRPQygIeIqF8uPmT1PpgJAMzc6HV70g0RjSSi5wD8nIgGElHO3be6gfEygF8R0fhcuw9Io4iIXgDwLyD37gUiOp6I3gcwM9d+fwER9SeieQDuAdDG6/Z4ARENJaK3AbwOoJfX7Uk3RDSKiP4F4AkiuoqICjjH1C+IaDQRPQ9tLOjtl/Eg5x7cfkL3YPwBwGxoJUpvBnCV/llOzGiJ6HIAzwD4GRFdpG/z1aw2UYgoQET3AHgCwNvQZvM3ABjsacPSiG5ozgLwGIA3AXwD4EcAunvasDTDGof1t2VE9ANAu0Y8bFZa0K+BOwG8AOBFZr5MTDZyZRyUuBnA68x8FjOvAXKnD4goSESPA/gLgD8D+DuA4/TPsv4+AAAiGgTgjwBeAfAygFOQY5MOIhoA4BFEngfXArhM/yyj74WcuEh9zAAAa5j5KQC/BvAagHOIqA8zc6ZfXEnia2iDyhRofQBmbsiFc2fmJgCbAFzBzM8DeABADwC+mM0nA91z8wGAU5n5GQBPAWAAO71sV7rRjc7O0B4w3wfwAyJqw8xN2W5s6NdAHoC5zPwEEPZyhnLFs6cbm+2gXft/0LedS0RdAbTQ32f1mKhPsuYAOIGZ/wngVQATiKhQHytzgWEA1jHzswDeA1AI7RkJIPuvAZ3RAFYx8wvQJl+HAXyXiMoz3S7K6oHabxDRSUQ0Stq0FMBwIurJzIcAfA5gIYDrgPCDKKsw6YMPAGxn5ncBbCKi+/TtWenlNjn/FwEs0ZcNdwOoAdDZm9alB2MfMPMcZt5LRCcA+BRAOYD7iehUr9qYauQ+IKKA7uHeBu3cqwB8COCnRFSZjcaGyX3wKwBdiOghIvocwH0AniGiC7xpYeqR+0A3Ng8DOBHAKXqI2XUA7gfwsL5P1j8PmPk1Zj6iG1VNANYAKPKsgSnG5D54C8C5RPQAgOUAugL4PRHdDuTGNQDNDuqmj32HoF0H+wFcA2R2HyiDOwMgomIieg1aTNp1RNQWAHQD6x8AbtJ33QfgfQBFurcra7DqAwAEQMSqXgfgJiLqyMxHvWhnqjA5/3b6R3XM3MTMdUSUB22AXe1ZQ1OI1TUgeXD3QPP2j4E2Gf0OEfX1prWpwawPhEFNRH0AbGDmamjerR8CeJmICvRrw/fYjIWHADwLYAiA/2PmMwF8BGCK3i9Zg00f1EJb4fkjgHeYeQqAOwEMIKLTPWtwCrAZC4iISDeqVgGYCM3Lm1XeXZtrYAe0kMIQgDuYeTSApwGMJ6IxXrU3Fdg8E9cDWADgKSL6J4Dh0MJrQkRU6E1rnaEM7sygHsB/AVwKYCuAC6XPXgXQl4gm6g/e3QC6QJvRZROmfaAbm0xEQWZeAe3GmgUAWfaQMZ7/BUDMbP04AN8w8xp9MBqZ/mamFMtrQP9/BTP/T9/3Q2hJYwc9aGcqsRsLtgLoQ0RvQPP4fghgEzPXZdEE1PL89bCqi5j5I33T+wDaI7eugT9BCyFpDwDMvAXAXGhevmzCaixg/XkQ0Ceen8F8rPQ7dvfBKgB9AWzWNy0CsANAXZrbmGqsroGDzHwbtFyep5n5LADrAAzSJ6UZizK4PYKILtOXStowcx20xLj3oS2RDSeiY/Vdl0ILK3iYiHpBm9ETgHwv2p1MHPRBH30/gha7CGa+GsDlRLQXwGA/x6+6OH8RPtMOwGEiugLAPAAD/e7VcXkNyEyGNn7VpLXBKcBpHwAohvbg2QBgmP6g6UY+l0Zzcw0w8x7pq6dCGxd8b3A77QNmPgjgRmhj4BDSkmcnQQsz8jUuroOAnrsQArAWwCHvWp08XIwDAPAugBn6uHgJgP7QnHG+Jk4fDJP7gJmX6bH8gJbn9WmmPw8puyaFmY1+MXSCll3dBG1ppCWAm5l5l75PbwCXQwsluE/67m0AjtX/XcPMK9Pc/KTgsg9qmfl+6XvdAfwWQCmAG5j5y/SfQfNI9Pz17T8HcDu0JcSHmXlZelufHJpxDRQAOAHALwBsAXCb7u3xHYmOBUTUmpn3S8eJeu8XmnENBACMB/A7aMlit+fINWAcCy6GFlrQH1powYo0Nz8pNOc60I3u3wI4yMx3eXICzaQZ40ALAI8D6AAtif4mZv4q/WfQfJp5HwyDJqbQCOBaZl6f5ua7wrfeQb+hh0QwNC/VFmaeCC0Gcw80iSMAADOvhbZE1JmIehFRS31w+SWAHzDzeB8b22774Bi9DwqhefX3ApjFzCf51NhO9PxFUtC/AXybma/ysbGdaB8UQBuMvwFwDzOf7WNDK9GxoAWAWv0YAX0fPxrbzRkHGNpk6x5mPieHroFjpOdBHjP/A8Cdeh/41dhuznXQQv/4Jz42thMZB3oTUREzHwFwJYDLmXmSj43tRK8B8ftXQRsLJma6sQ1kqdJDJqEve80EECSi2QBKoCcBsiZvdxOArUR0EjN/qG9/nYiOgyaB1ArABAArmbnek5NoJknqg1P0QWWBJyfRDJJx/kQ0gZnneXQKzSZZ9wEzL4eWne87kjwW+C5mN8njQMY/XM1I8jXgy+XpXO+DZp7/24g8D1YC2O7NWTSPJD0TxVjwoTdn4R7l4U4hRHQStFlZW2hB/fcBOApNO3QkEE70mAlghvS9C6Fln/8PWiKALz3aQFL7wK8zeHUNqD7I+T7I9XEAUNcAoPog188fyO2xQMVwpxDSdIPLWROpBxH9CZp37giAG5l5mL403AHA76HFI27Uvwdm/tijpieNXO+DXD9/QPUBoPog188fUH0AqD7I9fMHcrsPlIc7tSwC8BIRicqAnwDozsxPQ1tKuVFfGu4KoJGZNwLaBeXni8pArvdBrp8/oPoAUH2Q6+cPqD4AVB/k+vkDOdwHyuBOIcx8mDWNXFG45VRESlJfCeA4InoTwAsAFnvRxlST632Q6+cPqD4AVB/k+vkDqg8A1Qe5fv5AbveBSppMA/pMjgF0BPCGvrkGwB0ABgDYyFoBg6wl1/sg188fUH0AqD7I9fMHVB8Aqg9y/fyB3OwD5eFOD00A8gDsAjBIn73dBaCJmedm20VlQa73Qa6fP6D6AFB9kOvnD6g+AFQf5Pr5AznYByppMk0Q0Who1QHnAXiKmf/qcZPSTq73Qa6fP6D6AFB9kOvnD6g+AFQf5Pr5A7nXB8rgThNE1BXA9wD8hrWSpTlHrvdBrp8/oPoAUH2Q6+cPqD4AVB/k+vkDudcHyuBWKBQKhUKhUChSiIrhVigUCoVCoVAoUogyuBUKhUKhUCgUihSiDG6FQqFQKBQKhSKFKINboVAoFAqFQqFIIcrgVigUigyHiEqJaIn+bzsRbdFfHySiP6Xg7z1NRBuJ6PpkH9vwd04goq+I6MtU/h2FQqHwGqVSolAoFD6CiGYAOMjMD6XwbzwN4E1mfiWFfyPEzA1EVK7/rQGp+lsKhULhNcrDrVAoFD6FiE7WK7SBiGYQ0TNE9C4RVRHReUT0SyJaTkRziChP328YEX1IRIuI6B0i6hznbxTr3m7x/RL9+HlEVKkfexERfUxEffV9ziKiz4joCyJ6n4g6Sm18nIjeBfC3lHaOQqFQZBDK4FYoFIrsoRLAVADnAHgOwP+YeSCAIwCm6kbzIwAuYOZhAJ4E8IDdAZm5BsAH+nEB4BIArzLzUQCPA7hRP9Y0ACK8ZS6A0cw8FMCLAG6TDjkMwDnM/J1mnqtCoVD4hpDXDVAoFApF0nibmY8S0XIAQQBz9O3LAZQDOBbAAADvERH0fbY5OO4T0IzmfwK4EsA1RNQKwFgAL+vHAoAC/f+uAP6he8/zAWyUjvUGMx9J6OwUCoXCpyiDW6FQKLKHOgBg5iYiOsqRJJ0maOM9AVjBzGPcHJSZPyGiciI6CUCQmb8kohIA+5h5iMlXHoFWrvkNIjoZwAzps0PuTkmhUCj8jwopUSgUitxhNYD2RDQGAPQ47P4Ov/s3AC8AeAoAmPkAgI1EdKF+LCKiwfq+rQFs0V9fnqzGKxQKhV9RBrdCoVDkCMxcD+ACAL8goqUAlkALC3HC8wDaQjO6Bd8F8H39WCugxY4Dmkf7ZSL6GMCuJDRdoVAofI2SBVQoFApFFGaygER0AbRkx+8l+W+VQ8kCKhSKLEfFcCsUCoXCyH4A9xFRGTM/RkSPADgdwBnJ/CNEdAI0ZRPlBVcoFFmN8nArFAqFQqFQKBQpRMVwKxQKhUKhUCgUKUQZ3AqFQqFQKBQKRQpRBrdCoVAoFAqFQpFClMGtUCgUCoVCoVCkEGVwKxQKhUKhUCgUKUQZ3AqFQqFQKBQKRQr5f8Ewi9ZW5IJMAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 864x288 with 1 Axes>"
       ]
@@ -600,7 +600,7 @@
        " 'silt_fraction': [(33.7, 0.05, 0.05)],\n",
        " 'station': [('SaintFelixdeLauragais', None, None)],\n",
        " 'timerange_from': [(Timestamp('2009-08-04 00:00:00'), None, None)],\n",
-       " 'timerange_to': [(Timestamp('2020-01-01 00:00:00'), None, None)],\n",
+       " 'timerange_to': [(Timestamp('2019-01-01 00:00:00'), None, None)],\n",
        " 'variable': [('soil_moisture', 0.05, 0.05)]}"
       ]
      },
@@ -776,7 +776,7 @@
     {
      "data": {
       "text/plain": [
-       "Sensors at 'SAA111': ['CS215_air_temperature_-2.000000_-2.000000', '5TE_soil_temperature_0.200000_0.200000', '5TE_soil_temperature_0.400000_0.400000', '5TE_soil_moisture_0.800000_0.800000', '5TE_soil_moisture_0.400000_0.400000', '5TE_soil_moisture_0.200000_0.200000', '5TE_soil_temperature_0.050000_0.050000', '5TE_soil_moisture_0.050000_0.050000', '5TE_soil_moisture_0.100000_0.100000', '5TE_soil_temperature_0.800000_0.800000', '5TE_soil_temperature_0.100000_0.100000']"
+       "Sensors at 'SAA111': ['5TE_soil_moisture_0.050000_0.050000', '5TE_soil_moisture_0.400000_0.400000', 'CS215_air_temperature_-2.000000_-2.000000', '5TE_soil_moisture_0.200000_0.200000', '5TE_soil_temperature_0.050000_0.050000', '5TE_soil_moisture_0.800000_0.800000', '5TE_soil_moisture_0.100000_0.100000', '5TE_soil_temperature_0.400000_0.400000', '5TE_soil_temperature_0.200000_0.200000', '5TE_soil_temperature_0.800000_0.800000', '5TE_soil_temperature_0.100000_0.100000']"
       ]
      },
      "execution_count": 10,
@@ -817,7 +817,7 @@
     {
      "data": {
       "text/plain": [
-       "Sensors at 'SAA111': ['CS215_air_temperature_-2.000000_-2.000000', '5TE_soil_temperature_0.200000_0.200000', '5TE_soil_temperature_0.400000_0.400000', '5TE_soil_moisture_0.800000_0.800000', '5TE_soil_moisture_0.400000_0.400000', '5TE_soil_moisture_0.200000_0.200000', '5TE_soil_temperature_0.050000_0.050000', '5TE_soil_moisture_0.050000_0.050000', '5TE_soil_moisture_0.100000_0.100000', '5TE_soil_temperature_0.800000_0.800000', '5TE_soil_temperature_0.100000_0.100000']"
+       "Sensors at 'SAA111': ['5TE_soil_moisture_0.050000_0.050000', '5TE_soil_moisture_0.400000_0.400000', 'CS215_air_temperature_-2.000000_-2.000000', '5TE_soil_moisture_0.200000_0.200000', '5TE_soil_temperature_0.050000_0.050000', '5TE_soil_moisture_0.800000_0.800000', '5TE_soil_moisture_0.100000_0.100000', '5TE_soil_temperature_0.400000_0.400000', '5TE_soil_temperature_0.200000_0.200000', '5TE_soil_temperature_0.800000_0.800000', '5TE_soil_temperature_0.100000_0.100000']"
       ]
      },
      "metadata": {},
@@ -826,7 +826,7 @@
     {
      "data": {
       "text/plain": [
-       "CS215_air_temperature_-2.000000_-2.000000"
+       "5TE_soil_moisture_0.050000_0.050000"
       ]
      },
      "metadata": {},
@@ -853,9 +853,9 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>air_temperature</th>\n",
-       "      <th>air_temperature_flag</th>\n",
-       "      <th>air_temperature_orig_flag</th>\n",
+       "      <th>soil_moisture</th>\n",
+       "      <th>soil_moisture_flag</th>\n",
+       "      <th>soil_moisture_orig_flag</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>date_time</th>\n",
@@ -866,33 +866,33 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>2012-09-19 11:00:00</th>\n",
-       "      <td>0.1</td>\n",
-       "      <td>G</td>\n",
+       "      <th>2012-10-14 00:00:00</th>\n",
+       "      <td>0.180</td>\n",
+       "      <td>D02</td>\n",
        "      <td>M</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2012-09-19 12:00:00</th>\n",
-       "      <td>0.1</td>\n",
-       "      <td>G</td>\n",
+       "      <th>2012-10-14 01:00:00</th>\n",
+       "      <td>0.180</td>\n",
+       "      <td>D02</td>\n",
        "      <td>M</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2012-09-19 13:00:00</th>\n",
-       "      <td>0.2</td>\n",
-       "      <td>G</td>\n",
+       "      <th>2012-10-14 02:00:00</th>\n",
+       "      <td>0.179</td>\n",
+       "      <td>D02</td>\n",
        "      <td>M</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2012-09-19 14:00:00</th>\n",
-       "      <td>0.2</td>\n",
-       "      <td>G</td>\n",
+       "      <th>2012-10-14 03:00:00</th>\n",
+       "      <td>0.178</td>\n",
+       "      <td>D02</td>\n",
        "      <td>M</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2012-09-19 15:00:00</th>\n",
-       "      <td>0.1</td>\n",
-       "      <td>G</td>\n",
+       "      <th>2012-10-14 04:00:00</th>\n",
+       "      <td>0.178</td>\n",
+       "      <td>D02</td>\n",
        "      <td>M</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -902,70 +902,56 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2020-12-12 19:00:00</th>\n",
-       "      <td>-11.4</td>\n",
-       "      <td>G</td>\n",
+       "      <th>2020-10-17 19:00:00</th>\n",
+       "      <td>0.191</td>\n",
+       "      <td>D02</td>\n",
        "      <td>M</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2020-12-12 20:00:00</th>\n",
-       "      <td>-11.2</td>\n",
-       "      <td>G</td>\n",
+       "      <th>2020-10-17 20:00:00</th>\n",
+       "      <td>0.191</td>\n",
+       "      <td>D02</td>\n",
        "      <td>M</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2020-12-12 21:00:00</th>\n",
-       "      <td>-10.8</td>\n",
-       "      <td>G</td>\n",
+       "      <th>2020-10-17 21:00:00</th>\n",
+       "      <td>0.190</td>\n",
+       "      <td>D02</td>\n",
        "      <td>M</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2020-12-12 22:00:00</th>\n",
-       "      <td>-10.3</td>\n",
-       "      <td>G</td>\n",
+       "      <th>2020-10-17 22:00:00</th>\n",
+       "      <td>0.190</td>\n",
+       "      <td>D02</td>\n",
        "      <td>M</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2020-12-12 23:00:00</th>\n",
-       "      <td>-9.6</td>\n",
-       "      <td>G</td>\n",
+       "      <th>2020-10-17 23:00:00</th>\n",
+       "      <td>0.190</td>\n",
+       "      <td>D02</td>\n",
        "      <td>M</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>61210 rows × 3 columns</p>\n",
+       "<p>52522 rows × 3 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "                     air_temperature air_temperature_flag  \\\n",
-       "date_time                                                   \n",
-       "2012-09-19 11:00:00              0.1                    G   \n",
-       "2012-09-19 12:00:00              0.1                    G   \n",
-       "2012-09-19 13:00:00              0.2                    G   \n",
-       "2012-09-19 14:00:00              0.2                    G   \n",
-       "2012-09-19 15:00:00              0.1                    G   \n",
-       "...                              ...                  ...   \n",
-       "2020-12-12 19:00:00            -11.4                    G   \n",
-       "2020-12-12 20:00:00            -11.2                    G   \n",
-       "2020-12-12 21:00:00            -10.8                    G   \n",
-       "2020-12-12 22:00:00            -10.3                    G   \n",
-       "2020-12-12 23:00:00             -9.6                    G   \n",
-       "\n",
-       "                    air_temperature_orig_flag  \n",
-       "date_time                                      \n",
-       "2012-09-19 11:00:00                         M  \n",
-       "2012-09-19 12:00:00                         M  \n",
-       "2012-09-19 13:00:00                         M  \n",
-       "2012-09-19 14:00:00                         M  \n",
-       "2012-09-19 15:00:00                         M  \n",
-       "...                                       ...  \n",
-       "2020-12-12 19:00:00                         M  \n",
-       "2020-12-12 20:00:00                         M  \n",
-       "2020-12-12 21:00:00                         M  \n",
-       "2020-12-12 22:00:00                         M  \n",
-       "2020-12-12 23:00:00                         M  \n",
+       "                     soil_moisture soil_moisture_flag soil_moisture_orig_flag\n",
+       "date_time                                                                    \n",
+       "2012-10-14 00:00:00          0.180                D02                       M\n",
+       "2012-10-14 01:00:00          0.180                D02                       M\n",
+       "2012-10-14 02:00:00          0.179                D02                       M\n",
+       "2012-10-14 03:00:00          0.178                D02                       M\n",
+       "2012-10-14 04:00:00          0.178                D02                       M\n",
+       "...                            ...                ...                     ...\n",
+       "2020-10-17 19:00:00          0.191                D02                       M\n",
+       "2020-10-17 20:00:00          0.191                D02                       M\n",
+       "2020-10-17 21:00:00          0.190                D02                       M\n",
+       "2020-10-17 22:00:00          0.190                D02                       M\n",
+       "2020-10-17 23:00:00          0.190                D02                       M\n",
        "\n",
-       "[61210 rows x 3 columns]"
+       "[52522 rows x 3 columns]"
       ]
      },
      "execution_count": 11,
@@ -1010,26 +996,18 @@
        "    .dataframe thead tr th {\n",
        "        text-align: left;\n",
        "    }\n",
-       "\n",
-       "    .dataframe thead tr:last-of-type th {\n",
-       "        text-align: right;\n",
-       "    }\n",
        "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th>variable</th>\n",
        "      <th colspan=\"3\" halign=\"left\">clay_fraction</th>\n",
-       "      <th>climate_KG</th>\n",
-       "      <th>climate_insitu</th>\n",
+       "      <th colspan=\"3\" halign=\"left\">climate_KG</th>\n",
+       "      <th colspan=\"3\" halign=\"left\">climate_insitu</th>\n",
        "      <th>elevation</th>\n",
-       "      <th colspan=\"3\" halign=\"left\">instrument</th>\n",
-       "      <th>latitude</th>\n",
        "      <th>...</th>\n",
-       "      <th colspan=\"2\" halign=\"left\">silt_fraction</th>\n",
-       "      <th>station</th>\n",
-       "      <th>timerange_from</th>\n",
-       "      <th>timerange_to</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">timerange_from</th>\n",
+       "      <th colspan=\"3\" halign=\"left\">timerange_to</th>\n",
        "      <th colspan=\"3\" halign=\"left\">variable</th>\n",
        "      <th>file_path</th>\n",
        "      <th>file_type</th>\n",
@@ -1039,18 +1017,18 @@
        "      <th>depth_from</th>\n",
        "      <th>depth_to</th>\n",
        "      <th>val</th>\n",
-       "      <th>val</th>\n",
-       "      <th>val</th>\n",
-       "      <th>val</th>\n",
        "      <th>depth_from</th>\n",
        "      <th>depth_to</th>\n",
        "      <th>val</th>\n",
+       "      <th>depth_from</th>\n",
+       "      <th>depth_to</th>\n",
        "      <th>val</th>\n",
+       "      <th>depth_from</th>\n",
        "      <th>...</th>\n",
        "      <th>depth_to</th>\n",
        "      <th>val</th>\n",
-       "      <th>val</th>\n",
-       "      <th>val</th>\n",
+       "      <th>depth_from</th>\n",
+       "      <th>depth_to</th>\n",
        "      <th>val</th>\n",
        "      <th>depth_from</th>\n",
        "      <th>depth_to</th>\n",
@@ -1058,150 +1036,126 @@
        "      <th>val</th>\n",
        "      <th>val</th>\n",
        "    </tr>\n",
-       "    <tr>\n",
-       "      <th>idx</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>0</th>\n",
-       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>4.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>Dfc</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>unknown</td>\n",
-       "      <td>441.0</td>\n",
-       "      <td>-2.0</td>\n",
-       "      <td>-2.0</td>\n",
-       "      <td>CS215</td>\n",
-       "      <td>68.33019</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>2012-10-14 00:00:00</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>SAA111</td>\n",
-       "      <td>2012-09-19 11:00:00</td>\n",
-       "      <td>2020-12-12 23:00:00</td>\n",
-       "      <td>-2.0</td>\n",
-       "      <td>-2.0</td>\n",
-       "      <td>air_temperature</td>\n",
-       "      <td>FMI/SAA111/FMI_FMI_SAA111_ta_-2.000000_-2.0000...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2020-10-17 23:00:00</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>soil_moisture</td>\n",
+       "      <td>FMI/SAA111/FMI_FMI_SAA111_sm_0.050000_0.050000...</td>\n",
        "      <td>ceop_sep</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0.0</td>\n",
        "      <td>0.3</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>4.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>Dfc</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>unknown</td>\n",
-       "      <td>441.0</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>5TE</td>\n",
-       "      <td>68.33019</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.3</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>SAA111</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>2012-10-14 00:00:00</td>\n",
-       "      <td>2020-12-12 23:00:00</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>soil_temperature</td>\n",
-       "      <td>FMI/SAA111/FMI_FMI_SAA111_ts_0.200000_0.200000...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2020-10-17 23:00:00</td>\n",
+       "      <td>0.40</td>\n",
+       "      <td>0.40</td>\n",
+       "      <td>soil_moisture</td>\n",
+       "      <td>FMI/SAA111/FMI_FMI_SAA111_sm_0.400000_0.400000...</td>\n",
        "      <td>ceop_sep</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>0.3</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>4.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>Dfc</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>unknown</td>\n",
-       "      <td>441.0</td>\n",
-       "      <td>0.4</td>\n",
-       "      <td>0.4</td>\n",
-       "      <td>5TE</td>\n",
-       "      <td>68.33019</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>...</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>SAA111</td>\n",
-       "      <td>2012-10-14 00:00:00</td>\n",
-       "      <td>2020-12-12 23:00:00</td>\n",
-       "      <td>0.4</td>\n",
-       "      <td>0.4</td>\n",
-       "      <td>soil_temperature</td>\n",
-       "      <td>FMI/SAA111/FMI_FMI_SAA111_ts_0.400000_0.400000...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012-09-19 11:00:00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2020-10-17 23:00:00</td>\n",
+       "      <td>-2.00</td>\n",
+       "      <td>-2.00</td>\n",
+       "      <td>air_temperature</td>\n",
+       "      <td>FMI/SAA111/FMI_FMI_SAA111_ta_-2.000000_-2.0000...</td>\n",
        "      <td>ceop_sep</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
+       "      <td>0.0</td>\n",
        "      <td>0.3</td>\n",
-       "      <td>1.0</td>\n",
        "      <td>4.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>Dfc</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>unknown</td>\n",
-       "      <td>441.0</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>5TE</td>\n",
-       "      <td>68.33019</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>...</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>SAA111</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>2012-10-14 00:00:00</td>\n",
-       "      <td>2020-12-12 23:00:00</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>0.8</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2014-07-04 04:00:00</td>\n",
+       "      <td>0.20</td>\n",
+       "      <td>0.20</td>\n",
        "      <td>soil_moisture</td>\n",
-       "      <td>FMI/SAA111/FMI_FMI_SAA111_sm_0.800000_0.800000...</td>\n",
+       "      <td>FMI/SAA111/FMI_FMI_SAA111_sm_0.200000_0.200000...</td>\n",
        "      <td>ceop_sep</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
+       "      <td>0.0</td>\n",
        "      <td>0.3</td>\n",
-       "      <td>1.0</td>\n",
        "      <td>4.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>Dfc</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>unknown</td>\n",
-       "      <td>441.0</td>\n",
-       "      <td>0.4</td>\n",
-       "      <td>0.4</td>\n",
-       "      <td>5TE</td>\n",
-       "      <td>68.33019</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>...</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>SAA111</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>2012-10-14 00:00:00</td>\n",
-       "      <td>2020-12-12 23:00:00</td>\n",
-       "      <td>0.4</td>\n",
-       "      <td>0.4</td>\n",
-       "      <td>soil_moisture</td>\n",
-       "      <td>FMI/SAA111/FMI_FMI_SAA111_sm_0.400000_0.400000...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2020-10-17 23:00:00</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>soil_temperature</td>\n",
+       "      <td>FMI/SAA111/FMI_FMI_SAA111_ts_0.050000_0.050000...</td>\n",
        "      <td>ceop_sep</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1229,207 +1183,202 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>670</th>\n",
+       "      <th>662</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.3</td>\n",
        "      <td>20.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>Dfb</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>unknown</td>\n",
-       "      <td>270.0</td>\n",
-       "      <td>0.3</td>\n",
-       "      <td>0.3</td>\n",
-       "      <td>PT1000</td>\n",
-       "      <td>46.92135</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.3</td>\n",
-       "      <td>38.0</td>\n",
-       "      <td>99</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>2009-08-04 00:00:00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>2013-06-18 16:00:00</td>\n",
-       "      <td>0.3</td>\n",
-       "      <td>0.3</td>\n",
-       "      <td>soil_temperature</td>\n",
-       "      <td>WEGENERNET/99/WEGENERNET_WEGENERNET_99_ts_0.30...</td>\n",
+       "      <td>0.30</td>\n",
+       "      <td>0.30</td>\n",
+       "      <td>soil_moisture</td>\n",
+       "      <td>WEGENERNET/99/WEGENERNET_WEGENERNET_99_sm_0.30...</td>\n",
        "      <td>ceop_sep</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>671</th>\n",
+       "      <th>663</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>Dfb</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>unknown</td>\n",
-       "      <td>270.0</td>\n",
-       "      <td>-2.0</td>\n",
-       "      <td>-2.0</td>\n",
-       "      <td>PT1000</td>\n",
-       "      <td>46.92135</td>\n",
-       "      <td>...</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>99</td>\n",
        "      <td>2009-08-04 00:00:00</td>\n",
-       "      <td>2020-12-12 23:00:00</td>\n",
-       "      <td>-2.0</td>\n",
-       "      <td>-2.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2020-10-26 00:00:00</td>\n",
+       "      <td>-2.00</td>\n",
+       "      <td>-2.00</td>\n",
        "      <td>air_temperature</td>\n",
        "      <td>WEGENERNET/99/WEGENERNET_WEGENERNET_99_ta_-2.0...</td>\n",
        "      <td>ceop_sep</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>672</th>\n",
+       "      <th>664</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.3</td>\n",
        "      <td>20.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>Dfb</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>unknown</td>\n",
-       "      <td>270.0</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>Hydraprobe-II</td>\n",
-       "      <td>46.92135</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.3</td>\n",
-       "      <td>38.0</td>\n",
-       "      <td>99</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>2013-10-23 11:00:00</td>\n",
-       "      <td>2020-12-12 23:00:00</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>soil_moisture</td>\n",
-       "      <td>WEGENERNET/99/WEGENERNET_WEGENERNET_99_sm_0.20...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2020-10-26 00:00:00</td>\n",
+       "      <td>0.20</td>\n",
+       "      <td>0.20</td>\n",
+       "      <td>soil_temperature</td>\n",
+       "      <td>WEGENERNET/99/WEGENERNET_WEGENERNET_99_ts_0.20...</td>\n",
        "      <td>ceop_sep</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>673</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.3</td>\n",
-       "      <td>20.0</td>\n",
+       "      <th>665</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>Dfb</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>unknown</td>\n",
-       "      <td>270.0</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>Hydraprobe-II</td>\n",
-       "      <td>46.92135</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.3</td>\n",
-       "      <td>38.0</td>\n",
-       "      <td>99</td>\n",
-       "      <td>2013-10-23 11:00:00</td>\n",
-       "      <td>2020-12-12 23:00:00</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>soil_temperature</td>\n",
-       "      <td>WEGENERNET/99/WEGENERNET_WEGENERNET_99_ts_0.20...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2009-08-04 00:00:00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2020-10-26 00:00:00</td>\n",
+       "      <td>-1.50</td>\n",
+       "      <td>-1.50</td>\n",
+       "      <td>precipitation</td>\n",
+       "      <td>WEGENERNET/99/WEGENERNET_WEGENERNET_99_p_-1.50...</td>\n",
        "      <td>ceop_sep</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>674</th>\n",
-       "      <td>NaN</td>\n",
+       "      <th>666</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>20.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>Dfb</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>unknown</td>\n",
-       "      <td>270.0</td>\n",
-       "      <td>-1.5</td>\n",
-       "      <td>-1.5</td>\n",
-       "      <td>7041.0000-Rain-Gauge</td>\n",
-       "      <td>46.92135</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>2013-10-23 11:00:00</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>99</td>\n",
-       "      <td>2009-08-04 00:00:00</td>\n",
-       "      <td>2020-12-12 15:00:00</td>\n",
-       "      <td>-1.5</td>\n",
-       "      <td>-1.5</td>\n",
-       "      <td>precipitation</td>\n",
-       "      <td>WEGENERNET/99/WEGENERNET_WEGENERNET_99_p_-1.50...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2020-10-26 00:00:00</td>\n",
+       "      <td>0.20</td>\n",
+       "      <td>0.20</td>\n",
+       "      <td>soil_moisture</td>\n",
+       "      <td>WEGENERNET/99/WEGENERNET_WEGENERNET_99_sm_0.20...</td>\n",
        "      <td>ceop_sep</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>675 rows × 36 columns</p>\n",
+       "<p>667 rows × 62 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "variable clay_fraction                climate_KG climate_insitu elevation  \\\n",
-       "key         depth_from depth_to   val        val            val       val   \n",
-       "idx                                                                         \n",
-       "0                  NaN      NaN   NaN        Dfc        unknown     441.0   \n",
-       "1                  0.0      0.3   4.0        Dfc        unknown     441.0   \n",
-       "2                  0.3      1.0   4.0        Dfc        unknown     441.0   \n",
-       "3                  0.3      1.0   4.0        Dfc        unknown     441.0   \n",
-       "4                  0.3      1.0   4.0        Dfc        unknown     441.0   \n",
-       "..                 ...      ...   ...        ...            ...       ...   \n",
-       "670                0.0      0.3  20.0        Dfb        unknown     270.0   \n",
-       "671                NaN      NaN   NaN        Dfb        unknown     270.0   \n",
-       "672                0.0      0.3  20.0        Dfb        unknown     270.0   \n",
-       "673                0.0      0.3  20.0        Dfb        unknown     270.0   \n",
-       "674                NaN      NaN   NaN        Dfb        unknown     270.0   \n",
+       "variable clay_fraction                climate_KG               climate_insitu  \\\n",
+       "key         depth_from depth_to   val depth_from depth_to  val     depth_from   \n",
+       "0                  0.0      0.3   4.0        NaN      NaN  Dfc            NaN   \n",
+       "1                  0.3      1.0   4.0        NaN      NaN  Dfc            NaN   \n",
+       "2                  NaN      NaN   NaN        NaN      NaN  Dfc            NaN   \n",
+       "3                  0.0      0.3   4.0        NaN      NaN  Dfc            NaN   \n",
+       "4                  0.0      0.3   4.0        NaN      NaN  Dfc            NaN   \n",
+       "..                 ...      ...   ...        ...      ...  ...            ...   \n",
+       "662                0.0      0.3  20.0        NaN      NaN  Dfb            NaN   \n",
+       "663                NaN      NaN   NaN        NaN      NaN  Dfb            NaN   \n",
+       "664                0.0      0.3  20.0        NaN      NaN  Dfb            NaN   \n",
+       "665                NaN      NaN   NaN        NaN      NaN  Dfb            NaN   \n",
+       "666                0.0      0.3  20.0        NaN      NaN  Dfb            NaN   \n",
        "\n",
-       "variable instrument                                 latitude  ...  \\\n",
-       "key      depth_from depth_to                   val       val  ...   \n",
-       "idx                                                           ...   \n",
-       "0              -2.0     -2.0                 CS215  68.33019  ...   \n",
-       "1               0.2      0.2                   5TE  68.33019  ...   \n",
-       "2               0.4      0.4                   5TE  68.33019  ...   \n",
-       "3               0.8      0.8                   5TE  68.33019  ...   \n",
-       "4               0.4      0.4                   5TE  68.33019  ...   \n",
-       "..              ...      ...                   ...       ...  ...   \n",
-       "670             0.3      0.3                PT1000  46.92135  ...   \n",
-       "671            -2.0     -2.0                PT1000  46.92135  ...   \n",
-       "672             0.2      0.2         Hydraprobe-II  46.92135  ...   \n",
-       "673             0.2      0.2         Hydraprobe-II  46.92135  ...   \n",
-       "674            -1.5     -1.5  7041.0000-Rain-Gauge  46.92135  ...   \n",
+       "variable                    elevation  ... timerange_from                      \\\n",
+       "key      depth_to      val depth_from  ...       depth_to                 val   \n",
+       "0             NaN  unknown        NaN  ...            NaN 2012-10-14 00:00:00   \n",
+       "1             NaN  unknown        NaN  ...            NaN 2012-10-14 00:00:00   \n",
+       "2             NaN  unknown        NaN  ...            NaN 2012-09-19 11:00:00   \n",
+       "3             NaN  unknown        NaN  ...            NaN 2012-10-14 00:00:00   \n",
+       "4             NaN  unknown        NaN  ...            NaN 2012-10-14 00:00:00   \n",
+       "..            ...      ...        ...  ...            ...                 ...   \n",
+       "662           NaN  unknown        NaN  ...            NaN 2009-08-04 00:00:00   \n",
+       "663           NaN  unknown        NaN  ...            NaN 2009-08-04 00:00:00   \n",
+       "664           NaN  unknown        NaN  ...            NaN 2013-10-23 11:00:00   \n",
+       "665           NaN  unknown        NaN  ...            NaN 2009-08-04 00:00:00   \n",
+       "666           NaN  unknown        NaN  ...            NaN 2013-10-23 11:00:00   \n",
        "\n",
-       "variable silt_fraction       station      timerange_from        timerange_to  \\\n",
-       "key           depth_to   val     val                 val                 val   \n",
-       "idx                                                                            \n",
-       "0                  NaN   NaN  SAA111 2012-09-19 11:00:00 2020-12-12 23:00:00   \n",
-       "1                  0.3   9.0  SAA111 2012-10-14 00:00:00 2020-12-12 23:00:00   \n",
-       "2                  1.0   8.0  SAA111 2012-10-14 00:00:00 2020-12-12 23:00:00   \n",
-       "3                  1.0   8.0  SAA111 2012-10-14 00:00:00 2020-12-12 23:00:00   \n",
-       "4                  1.0   8.0  SAA111 2012-10-14 00:00:00 2020-12-12 23:00:00   \n",
-       "..                 ...   ...     ...                 ...                 ...   \n",
-       "670                0.3  38.0      99 2009-08-04 00:00:00 2013-06-18 16:00:00   \n",
-       "671                NaN   NaN      99 2009-08-04 00:00:00 2020-12-12 23:00:00   \n",
-       "672                0.3  38.0      99 2013-10-23 11:00:00 2020-12-12 23:00:00   \n",
-       "673                0.3  38.0      99 2013-10-23 11:00:00 2020-12-12 23:00:00   \n",
-       "674                NaN   NaN      99 2009-08-04 00:00:00 2020-12-12 15:00:00   \n",
+       "variable timerange_to                                variable           \\\n",
+       "key        depth_from depth_to                 val depth_from depth_to   \n",
+       "0                 NaN      NaN 2020-10-17 23:00:00       0.05     0.05   \n",
+       "1                 NaN      NaN 2020-10-17 23:00:00       0.40     0.40   \n",
+       "2                 NaN      NaN 2020-10-17 23:00:00      -2.00    -2.00   \n",
+       "3                 NaN      NaN 2014-07-04 04:00:00       0.20     0.20   \n",
+       "4                 NaN      NaN 2020-10-17 23:00:00       0.05     0.05   \n",
+       "..                ...      ...                 ...        ...      ...   \n",
+       "662               NaN      NaN 2013-06-18 16:00:00       0.30     0.30   \n",
+       "663               NaN      NaN 2020-10-26 00:00:00      -2.00    -2.00   \n",
+       "664               NaN      NaN 2020-10-26 00:00:00       0.20     0.20   \n",
+       "665               NaN      NaN 2020-10-26 00:00:00      -1.50    -1.50   \n",
+       "666               NaN      NaN 2020-10-26 00:00:00       0.20     0.20   \n",
        "\n",
-       "variable   variable                             \\\n",
-       "key      depth_from depth_to               val   \n",
-       "idx                                              \n",
-       "0              -2.0     -2.0   air_temperature   \n",
-       "1               0.2      0.2  soil_temperature   \n",
-       "2               0.4      0.4  soil_temperature   \n",
-       "3               0.8      0.8     soil_moisture   \n",
-       "4               0.4      0.4     soil_moisture   \n",
-       "..              ...      ...               ...   \n",
-       "670             0.3      0.3  soil_temperature   \n",
-       "671            -2.0     -2.0   air_temperature   \n",
-       "672             0.2      0.2     soil_moisture   \n",
-       "673             0.2      0.2  soil_temperature   \n",
-       "674            -1.5     -1.5     precipitation   \n",
+       "variable                                                            file_path  \\\n",
+       "key                    val                                                val   \n",
+       "0            soil_moisture  FMI/SAA111/FMI_FMI_SAA111_sm_0.050000_0.050000...   \n",
+       "1            soil_moisture  FMI/SAA111/FMI_FMI_SAA111_sm_0.400000_0.400000...   \n",
+       "2          air_temperature  FMI/SAA111/FMI_FMI_SAA111_ta_-2.000000_-2.0000...   \n",
+       "3            soil_moisture  FMI/SAA111/FMI_FMI_SAA111_sm_0.200000_0.200000...   \n",
+       "4         soil_temperature  FMI/SAA111/FMI_FMI_SAA111_ts_0.050000_0.050000...   \n",
+       "..                     ...                                                ...   \n",
+       "662          soil_moisture  WEGENERNET/99/WEGENERNET_WEGENERNET_99_sm_0.30...   \n",
+       "663        air_temperature  WEGENERNET/99/WEGENERNET_WEGENERNET_99_ta_-2.0...   \n",
+       "664       soil_temperature  WEGENERNET/99/WEGENERNET_WEGENERNET_99_ts_0.20...   \n",
+       "665          precipitation  WEGENERNET/99/WEGENERNET_WEGENERNET_99_p_-1.50...   \n",
+       "666          soil_moisture  WEGENERNET/99/WEGENERNET_WEGENERNET_99_sm_0.20...   \n",
        "\n",
-       "variable                                          file_path file_type  \n",
-       "key                                                     val       val  \n",
-       "idx                                                                    \n",
-       "0         FMI/SAA111/FMI_FMI_SAA111_ta_-2.000000_-2.0000...  ceop_sep  \n",
-       "1         FMI/SAA111/FMI_FMI_SAA111_ts_0.200000_0.200000...  ceop_sep  \n",
-       "2         FMI/SAA111/FMI_FMI_SAA111_ts_0.400000_0.400000...  ceop_sep  \n",
-       "3         FMI/SAA111/FMI_FMI_SAA111_sm_0.800000_0.800000...  ceop_sep  \n",
-       "4         FMI/SAA111/FMI_FMI_SAA111_sm_0.400000_0.400000...  ceop_sep  \n",
-       "..                                                      ...       ...  \n",
-       "670       WEGENERNET/99/WEGENERNET_WEGENERNET_99_ts_0.30...  ceop_sep  \n",
-       "671       WEGENERNET/99/WEGENERNET_WEGENERNET_99_ta_-2.0...  ceop_sep  \n",
-       "672       WEGENERNET/99/WEGENERNET_WEGENERNET_99_sm_0.20...  ceop_sep  \n",
-       "673       WEGENERNET/99/WEGENERNET_WEGENERNET_99_ts_0.20...  ceop_sep  \n",
-       "674       WEGENERNET/99/WEGENERNET_WEGENERNET_99_p_-1.50...  ceop_sep  \n",
+       "variable file_type  \n",
+       "key            val  \n",
+       "0         ceop_sep  \n",
+       "1         ceop_sep  \n",
+       "2         ceop_sep  \n",
+       "3         ceop_sep  \n",
+       "4         ceop_sep  \n",
+       "..             ...  \n",
+       "662       ceop_sep  \n",
+       "663       ceop_sep  \n",
+       "664       ceop_sep  \n",
+       "665       ceop_sep  \n",
+       "666       ceop_sep  \n",
        "\n",
-       "[675 rows x 36 columns]"
+       "[667 rows x 62 columns]"
       ]
      },
      "execution_count": 12,
@@ -1463,7 +1412,7 @@
     {
      "data": {
       "text/plain": [
-       "[464, 465, 466, 467]"
+       "[461, 462, 465, 467]"
       ]
      },
      "execution_count": 13,
@@ -1502,7 +1451,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 864x288 with 1 Axes>"
       ]
@@ -1526,17 +1475,17 @@
    "metadata": {},
    "source": [
     "### Plot station locations\n",
-    "We can define a similar query to plot station locations for a specific variable on a map. If a min/max depth is passed, only stations with a sensor that measures within the passed range are included. Note that this kind of visualisation needs additional (optional) packages installed (use `pip install ismn[plot]` to install them.)"
+    "We can define a similar query to plot station locations for a specific variable on a map. If a min/max depth is passed, only stations with a sensor that measures within the passed range are included. Note that this kind of visualisation needs additional (optional) packages installed (use `conda` to install `matplotlib` and `cartopy`)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 1152x720 with 2 Axes>"
       ]
@@ -1556,6 +1505,77 @@
     "plt.show()\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Network data citations/references\n",
+    "When using ISMN data in a publication, please cite:\n",
+    "\n",
+    "- Dorigo, W. A., Wagner, W., Hohensinn, R., Hahn, S., Paulik, C., Xaver, A., Gruber, A., Drusch, M., Mecklenburg, S., van Oevelen, P., Robock, A., and Jackson, T. (2011). The International Soil Moisture Network: a data hosting facility for global in situ soil moisture measurements, Hydrol. Earth Syst. Sci., 15, 1675-1698, doi:10.5194/hess-15-1675-2011\n",
+    "- Dorigo, W.A. , Xaver, A. Vreugdenhil, M. Gruber, A., Hegyiová, A. Sanchis-Dufau, A.D., Zamojski, D. , Cordes, C., Wagner, W., and Drusch, M. (2013). Global Automated Quality Control of In situ Soil Moisture data from the International Soil Moisture Network. Vadose Zone Journal, 12, 3, doi:10.2136/vzj2012.0097\n",
+    "\n",
+    "In addition, it is required that you cite the networks you are using, information on how to cite a network can be found on https://ismn.geo.tuwien.ac.at/en/networks/ and in the downloaded README file.\n",
+    "The ISMN package provides functions to export citations for a single network, and for all networks in a collection (e.g. in case you don't use all the networks you downloaded before) as plain text."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Kirchengast, G., Kabas, T., Leuprecht, A., Bichler, C. & Truhetz, H. (2014), ‘Wegenernet: A pioneering high-resolution network for monitoring weather and climate’, Bulletin of the American Meteorological Society 95.',\n",
+       " 'Fuchsberger, J., Kirchengast, G. & Kabas, T. (2020), ‘Wegenernet high-resolution weather and climate data 2007 to 2019’, Earth System Science Data Discussions 2020, 1–49.']"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ismn_data['WEGENERNET'].get_citations()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "OrderedDict([('FMI',\n",
+       "              ['Ikonen, J., Smolander, T., Rautiainen, K., Cohen, J., Lemmetyinen, J., Salminen, M. & Pulliainen, J. (2018), ‘Spatially distributed evaluation of esa cci soil moisture products in a northern boreal forest environment’, Geosciences 8(2), 51.',\n",
+       "               'Ikonen, J., Vehvilaeinen, J., Rautiainen, K., Smolander, T., Lemmetyinen, J., Bircher, S. & Pulliainen, J. (2015), ‘The sodankylae in-situ soil moisture observation network: an example application to earth observation data product evaluation’, GID 5(2), 599–629.']),\n",
+       "             ('GTK',\n",
+       "              ['We acknowledge the work of Pekka Haenninen, Raimo Sutinen in support of the ISMN.']),\n",
+       "             ('REMEDHUS',\n",
+       "              ['Gonzalez-Zamora, A., Sanchez, N., Pablos, M. & Martinez-Fernandez, J. (2018), ‘Cci soil moisture assessment with smos soil moisture and in situ data under different environmental conditions and spatial scales in spain’, Remote Sensing of Environment 225.']),\n",
+       "             ('RSMN', ['No reference(s) for network RSMN available.']),\n",
+       "             ('SMOSMANIA',\n",
+       "              ['Calvet, J.-C., Fritz, N., Berne, C., Piguet, B., Maurel, W. & Meurey, C. (2016), ‘Deriving pedotransfer functions for soil quartz fraction in southern france from reverse modeling’, SOIL 2(4), 615–629.',\n",
+       "               'Calvet, J.-C., Fritz, N., Froissard, F., Suquia, D., Petitpa, A., and Piguet, B.: In situ soil moisture observations for the CAL/VAL of SMOS: the SMOSMANIA network, International Geoscience and Remote Sensing Symposium, IGARSS, Barcelona, Spain, 23-28 July 2007, 1196-1199, doi:10.1109/IGARSS.2007.4423019, 2007.',\n",
+       "               'Albergel, C., Ruediger, C., Pellarin, T., Calvet, J.-C., Fritz, N., Froissard, F., Suquia, D., Petitpa, A., Piguet, B., and Martin, E.: From near-surface to root-zone soil moisture using an exponential filter: an assessment of the method based on insitu observations and model simulations, Hydrol. Earth Syst. Sci., 12, 1323–1337, 2008.']),\n",
+       "             ('VAS',\n",
+       "              ['We acknowledge the work of Mike Schwank, Jean-Pierre Wigneron, Yann H. Kerr, Diego Intrigliolo, Jennifer Grant, Ernesto Lopez-Baeza (http://nimbus.uv.es/) in support of the ISMN.']),\n",
+       "             ('WEGENERNET',\n",
+       "              ['Kirchengast, G., Kabas, T., Leuprecht, A., Bichler, C. & Truhetz, H. (2014), ‘Wegenernet: A pioneering high-resolution network for monitoring weather and climate’, Bulletin of the American Meteorological Society 95.',\n",
+       "               'Fuchsberger, J., Kirchengast, G. & Kabas, T. (2020), ‘Wegenernet high-resolution weather and climate data 2007 to 2019’, Earth System Science Data Discussions 2020, 1–49.'])])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ismn_data.collection.export_citations(out_file='/tmp/ismn/citations_for_my_subset.txt')"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1580,7 +1600,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -1595,7 +1615,7 @@
     {
      "data": {
       "text/plain": [
-       "Sensors at 'SAA111': ['CS215_air_temperature_-2.000000_-2.000000', '5TE_soil_temperature_0.200000_0.200000', '5TE_soil_temperature_0.400000_0.400000', '5TE_soil_moisture_0.800000_0.800000', '5TE_soil_moisture_0.400000_0.400000', '5TE_soil_moisture_0.200000_0.200000', '5TE_soil_temperature_0.050000_0.050000', '5TE_soil_moisture_0.050000_0.050000', '5TE_soil_moisture_0.100000_0.100000', '5TE_soil_temperature_0.800000_0.800000', '5TE_soil_temperature_0.100000_0.100000']"
+       "Sensors at 'SAA111': ['5TE_soil_moisture_0.050000_0.050000', '5TE_soil_moisture_0.400000_0.400000', 'CS215_air_temperature_-2.000000_-2.000000', '5TE_soil_moisture_0.200000_0.200000', '5TE_soil_temperature_0.050000_0.050000', '5TE_soil_moisture_0.800000_0.800000', '5TE_soil_moisture_0.100000_0.100000', '5TE_soil_temperature_0.400000_0.400000', '5TE_soil_temperature_0.200000_0.200000', '5TE_soil_temperature_0.800000_0.800000', '5TE_soil_temperature_0.100000_0.100000']"
       ]
      },
      "metadata": {},
@@ -1614,44 +1634,44 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001B[1mMetadata for sensor 5TE_soil_moisture_0.050000_0.050000:\u001B[0m\n"
+      "\u001b[1mMetadata for sensor 5TE_soil_moisture_0.050000_0.050000:\u001b[0m\n"
      ]
     },
     {
      "data": {
       "text/plain": [
        "variable        key       \n",
-       "clay_fraction   val                             4\n",
-       "                depth_from                      0\n",
+       "clay_fraction   val                           4.0\n",
+       "                depth_from                    0.0\n",
        "                depth_to                      0.3\n",
        "climate_KG      val                           Dfc\n",
        "climate_insitu  val                       unknown\n",
-       "elevation       val                           441\n",
+       "elevation       val                         441.0\n",
        "instrument      val                           5TE\n",
        "                depth_from                   0.05\n",
        "                depth_to                     0.05\n",
-       "latitude        val                       68.3302\n",
+       "latitude        val                      68.33019\n",
        "lc_2000         val                           110\n",
        "lc_2005         val                           110\n",
        "lc_2010         val                           110\n",
        "lc_insitu       val                       unknown\n",
-       "longitude       val                       27.5506\n",
+       "longitude       val                      27.55062\n",
        "network         val                           FMI\n",
        "organic_carbon  val                          2.18\n",
-       "                depth_from                      0\n",
+       "                depth_from                    0.0\n",
        "                depth_to                      0.3\n",
-       "sand_fraction   val                            87\n",
-       "                depth_from                      0\n",
+       "sand_fraction   val                          87.0\n",
+       "                depth_from                    0.0\n",
        "                depth_to                      0.3\n",
        "saturation      val                          0.49\n",
-       "                depth_from                      0\n",
+       "                depth_from                    0.0\n",
        "                depth_to                      0.3\n",
-       "silt_fraction   val                             9\n",
-       "                depth_from                      0\n",
+       "silt_fraction   val                           9.0\n",
+       "                depth_from                    0.0\n",
        "                depth_to                      0.3\n",
        "station         val                        SAA111\n",
        "timerange_from  val           2012-10-14 00:00:00\n",
-       "timerange_to    val           2020-12-12 23:00:00\n",
+       "timerange_to    val           2020-10-17 23:00:00\n",
        "variable        val                 soil_moisture\n",
        "                depth_from                   0.05\n",
        "                depth_to                     0.05\n",
@@ -1663,7 +1683,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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U6du9e3dCzjNt2jRMmjQp5PrFixdjxowZCTl3NEjwYiPDpSrWHlGsBUEQBEGIkpdffjkp5zn77LNx9tlnh1y/ePFiFBcX4/TTT7d8TGYGM8Plir99WRTrRgZB/LcEQRAEwcnc//lyrCg7ENdjHtkuD/ee1SfsNlVVVbjoootQWloKr9eLu+++GwUFBRg/fjw8Hg8GDx6MF154AdnZ2RgxYgQef/xxDBo0KOwxS0pKMGbMGJxwwgmYN28eBgwYgKuvvhr33nsvdu7cialTp2LIkCHYs2cPrrnmGmzYsAG5ubmYMmUK+vfvj9deew3FxcV49tln8cEHH+D++++H2+1Gfn4+vv32W9xzzz04dOgQ5syZg4kTJ2LlypVo1qwZxo8fDwDo27cvvvjiCwDA2LFjcfLJJ+OXX37Bp59+ivfffx/vv/8+ampqcN555+H++++PuZ3FFaSRIXER9th3sBZFE6ZjtgR7CoIgCA2cr776Cu3atcOSJUvw+++/Y8yYMbjqqqvw3nvvYdmyZfB4PHjhhRdsH3fdunW49dZbsXTpUqxatQpvv/025syZg8cffxyPPPIIAODee+/FUUcdhaVLl+KRRx7BFVdcEXScBx54AF9//TWWLFmCadOmISsrCw888AAuvvhiLF68GBdffHFYOVavXo0rrrgCv/32G1avXo21a9diwYIFWLx4MRYuXIjZs2fbvjYjYrFuZKieIGAWVxAraBaD52etw/AehSmWRhAEQWgMRLIsJ4p+/fph/Pjx+Oc//4kzzzwTeXl56NKlC3r06AEAuPLKK/Hcc8/hb3/7m63jdunSBf369QMA9OnTB6NGjQIRoV+/figpKQEAzJkzBx999BEAYOTIkSgvL8f+/fsDjjNs2DBcddVVuOiii3D++efbvr7OnTvj2GOPBQDMnDkTM2fOxFFHHQUAqKysxNq1azF8+HDbx9UjinUjQ3MFERdra2RmKJM6kvtbEARBaOj06NEDCxcuxIwZMzBx4kSMHj06LsfNzs72f3e5XP5ll8sFj8cDwNzgZ0x3N3nyZMyfPx/Tp0/HwIEDsXjx4qB9MjIy4PP5/Mv6PNRNmzb1f2dmTJw4EX/+85+jvCpzxBWkkUF+i3Vq5UgXtCwqew/WpVgS56MFgwiCIAjpSVlZGXJzc3HZZZdh/PjxmDt3LkpKSrBu3ToAwJtvvomTTjopIecePnw4pk6dCgCYNWsWCgoKkJeXF7DN+vXrMXToUDzwwAMoKCjAli1b0Lx5c1RU1Bu/ioqKsGjRIgDAokWLsHHjRtPznXbaaXjllVdQWVkJANi6dSt27twZ83WIxbqRoY39GKIAWSEjARHDDZW/v78EFdUevHxl+EAWQRAEwZksW7YMd9xxB1wuFzIzM/HCCy9g//79uPDCC/3BizfccENCzn3ffffh6quvRv/+/ZGbm4vXX389aJs77rgDa9euBTNj1KhRGDBgADp16oRJkyZh4MCBmDhxIv7whz/gjTfewMCBAzF48GC/G4uR0aNHY+XKlTjuuOMAAM2aNcNbb72FNm3axHQd1FAsTIMGDeLi4uJUi+F4nvhmDf773VrcOqo7bjvVvLMJ9azZUYHRTyrBDCWTzkixNM6maMJ0ANJOgiAI0bBy5Ur07t071WIIBsz+L0S0kJlNrUhijmtk1FusBStkuuUWEQRBEATBGuIK0sjwxwE0kJkKQRAEQRBST3l5OUaNGhX0+3fffYfWrVunQKLUIIp1I8OlataiVguCIAiCs2DmoEwY6ULr1q1Ns3SkM9G4S8s8dyNDu119YrG2REOJQRAEQRCcTU5ODsrLy+W94xCYGeXl5cjJybG1n1isGxmSbk8QBEEQnEeHDh1QWlqKXbuk0q9TyMnJQYcOHWztI4p1I4PEFUQQBEEQHEdmZia6dOmSajGEGBFXkEaKWKytIc0kCIIgCIJVRLFuZNQHL4rKKCQGn0/6liAIgtA4SbpiTURjiGg1Ea0jogkm60cQ0X4iWqx+7km2jA0ZkkTWQoKp9nhTLYIgCIIgpISk+lgTkRvAcwBOBVAK4FcimsbMKwyb/sTMZyZTtsaC6NX2EJcZ+1TX+ZCblWopBEEQBCH5JNtiPQTAOmbewMy1AN4FcE6SZWjUaBZrma4XEsWhOrFYC4IgCI2TZCvW7QFs0S2Xqr8ZOY6IlhDRl0TUJzmiNQ4IkhUkWqpFYbTEoVppJ0EQBKFxkmzF2qyckFHHWwSgMzMPAPAMgE9DHozoeiIqJqJiyftoDcljbZf6htqwqyqFcqQPXpkNEQRBEBopyVasSwF01C13AFCm34CZDzBzpfp9BoBMIiowOxgzT2HmQcw8qLCwMFEyN0gkK4h9pM2sIe0kCIIgNFaSrVj/CqA7EXUhoiwA4wBM029ARIeRWsWEiIaoMpYnWc4Giz/dnug+ltC3k7SZNaSdBEEQhMZKUrOCMLOHiG4G8DUAN4BXmHk5Ed2grp8M4AIANxKRB8AhAOOY5VUdL+pdQaRJhcQgXUsQBEForCS9pLnq3jHD8Ntk3fdnATybbLkaC5JuT0g04goiCIIgNFak8mIjg8QVxBb6ZpI2s4a0kyAIgtBYEcW6keF3BRGrom2kzQRBEARBCIco1o0MsVgLiaayxpNqEQRBEAQhJYhi3cgQH2t7SFYQ+4ybMi/VIgiCIAhCShDFupEhBWIEQRAEQRASgyjWjQx/SXPRrG0jLWYd6V+CIAhCY0QU60aGWKztoQ9YFGXROi/O3pBqEQRBEAQh6VhSrImolYVPi0QLK8ROvY+1KIl2kRazzgfFW1ItgiAIgiAkHasFYsrUD4XZxg2gU8wSCQlFLNZCMtCyzwiCIAhCY8KqYr2SmY8KtwER/RYHeYQE40+3l2I50gUZgESHS/RqQRAEoRFi1cf6uDhtI6QYvyuIKIy2kTazzpodlakWQRAEQRCSjiXFmpmr47GNkHrqLdaiJdpH2iwcWW6JhRYEQRAaNxHfhER0AxG9RETjiOgLIroxGYIJiUEs1vaQAjFCIjhU68Xdn/6Oiuq6VIsiCIIgxBErJqaRAK4HcDMznwlgQGJFEhKJS/2PS+o4QUgdr80twZvzNqHffTNTLYogCIIQR6wo1uWsaGGPqss1CZRHSDD+AjEpliMdkTYT4sXysv2pFkEQBEFIAFYU66cBgJk/V5c/Tpw4QqKRdHv2CCwQk0JB0gHJBGKZ7m2ap1oEQRAEIQFEVKyZeRUAEFGBuvxjooUSEo9PtETbeH3SZkJ8kOBhQRCEhomdMP5XEiaFkDRcksc6akSxjoA0j2WGFLUCALRv0STFkgiCIAjxxI5iLRO9DYB6VxDRgqygb6Y6ny91gqQBYoW1jpb2sl2LnBRLIgiCIMQTO4q1vDUbEKJX28frlUYLB8nY2zZyHwqCIDQsxGLdSJEXun3qvGKxDkfTbHeqRUgbNOu+3IaCIAgNCzuK9cSESSEkHQletE+tKNZhyWuSmWoR0ge5/QRBEBoklhVrZv4dAIhI3p5pjKZPy3vdPjUeUazDIWM162hNJbEOgiAIDYsMOxsT0csAzieiKgBlAJYCWMrMzyRCOCFxyPvcGvp2KmyWnTpBhAaJ3IaCIAgNC1uKNYATAbRl5joiag+lvHn/+IslJBqxlNmnxuNNtQhCA8E/cyS3oSAIQoPCrmI9D0BLADuZeSuArQBmxF0qIeHI+9w+bpedkARBCI2kJhQEQWiY2NUUpgD4kYjGE9GJRJSfCKGExCG+nfbQK0C5WZL1QogvchcKgiA0LOwq1m8BeB+KpfsmAHOJaL2dAxDRGCJaTUTriGhCmO0GE5GXiC6wKaNgAXmh20cqL4ZHrLDWYTZ+EQRBEBoCdl1BSpn5Xv0PRGQ5oouI3ACeA3AqgFIAvxLRNGZeYbLdowC+timfYBHREe0jirUQL6QnCYIgNEzsWqwXE9Gt+h+YucbG/kMArGPmDcxcC+BdAOeYbPdXAB8B2GlTPsEi4gpiDX0zeUSxFuKM9CghUVRU16H33V9hT1VtqkURhEaFXcW6LYAbiKiMiL4gooeJ6EIb+7cHsEW3XKr+5kfNNnIegMmRDkZE1xNRMREV79q1y4YYjRdNoRa92j5isRbihdyHQqI565k5OFTnxaCHvkm1KILQqLClWDPzRczcG0AXAPcAWAPFCm0Vs7LoxlfLUwD+ycwRc5sx8xRmHsTMgwoLC22IIYg/rH1EsRbihfQkIdHsP1QHQNz+rFLn9cEnjSXEAbs+1gD87h+L1I8dSgF01C13gFJoRs8gAO8SEQAUADidiDzM/Gk0sgrm/LyuPNUipAX6x6zHJ5UXwyHWVxv4K6BKowmJwe0ys2MJoej+f1/i/KPa44mLB6ZaFCHNiWixJqIbiOglIhqnun/cGMP5fgXQnYi6EFEWgHEApuk3YOYuzFzEzEUAPgRwkyjVghMQi7UQb2QwIiQKF4libZXft+4HAHz829YUSyI0BKxYrEcCuBjAT8x8AhFF9H0OBTN7iOhmKNk+3ABeYeblRHSDuj7qYwtCopHgRSFeiKVaSDRisbbO2ws2p1oEoQFhRbEuZ2YmokfVZTtZQIJg5hkwVGsMpVAz81WxnEsQYkWfPUX87+zh8zFc8nI3RUqaC4lGLNbWWbezMtUiCA0IK8GLTwMAM3+uLn+cOHEEwbmIxdoeXyzblmoRHI/0KCFRiF5tnQUb96RaBKEBEVGxZuZVhp/mJkgWIQmIhSx6xMc6PMa+pWUlEIKpt1hLnxISg1is7VPY3HK9O0EIia10e0T0MoAdRLSFiOarQY1/TZBsgpByArOCiBIUiXMGtvN/93gli0oopCcJiUb0avsUNBPFWogdu+n2TgTQlpnr1EIuAwD0j79YguA8vJJuLyIZrvqxep0o1iHRLNWrtlekWBKhoSIWa/vIDJIQD+wq1vMAtASwk5m3AtgKQyCiIDRUxGJtjzqvtJcVdlZUo03znFSLITQwJG7YPj5RrIU4YLek+RQAPxLReCI6kYjyEyGUkDgkzZc99M9ZryiKthCLdWj0PUne5UIi0M8eCdZYs0OygwixY/fOewvA+1As3TcBmEtE6+MulSA4ELFYW2NAxxYAgM17DqZYEuciyrSQaCSPtSCkBruKdSkz38vMk5j5EmbuA+DIRAgmCE5DsoJEhgj4v9N7AwA+XiRVzAQhVQzt2irVIghCo8SuYr2YiG7V/8DMMRWMEQRnU69Mi8XaGk2z3akWIQ2o70ufLZYBiBB/jihslmoRBKFRYlexbgvgBiIqI6IviOhhIrowEYIJiSHAZ1gURVtIYIs1sjPEtzMS+q40ZfbG1AkiNFgkKYggpAZbWUGY+SIAIKJsAH0A9AMwBMAH8RdNSDR1Xh/cLrEuhkOvAK0oO5A6QdKI7AzpU5EIDF6UAZsQf6RbCUJqsJtuD4Df/WOR+hHSFHFtsMecdbtTLYKj0RREsVjbQ+5DIRHIgE0QUoOlNyARRVSgrWwjOItaj6RDs8PxR7ROtQiOhwBkiWIdEXHJEhKN9CpBSA1WLda9iWhpmPUEQHJapwH6F7rkGY6M/uW0s0LidK0griCR4YCgWLkPhfgjBmtBSA1WFeteFrbxxiKIkHzEYm2PdTuleIAVxGJtDzFYC4lAXEGig5lBEvkpxIAlxZqZNyVaECH51IrFWkgAUpgiMnqdxyeatZAApFdFh8fHyHTLM0yInqiCF4WGgbiCREZTgHIyXWiSKS4O4ZAXuXX0bSXBi0IiEIN1dHh9DHnUC7Egc7aNDP2zVlxBrNOtTTNU1Yq3UyRkBjU6PDLIFeKM6NXRIcHEQqzYUqxJ4TIiukdd7kREQxIjmpBoxGJtnaZZGaj1+KTNLJKbpZh8xM3BHKP/65PfrkmRJEJDRXyso0NmkIRYsWuxfh7AcQAuUZcrADwXV4mEpFEjFuuIaC8nTVHce7A2leKkDbed0gMAUFXrSbEk6cHaHRIYKySGw/NzUi1CWiGzR0Ks2FWshzLzXwBUAwAz7wWQFXephKSw72BdqkVIG3JUpztpM2s0zVbCNzaVH0yxJM7EaEyU6Wch3vjUTiYuf/aQe1GIFbuKdR0RuaG6bxFRIQC5a9MI/fSgPECso1mqv/p9e4olcS56ZbGgmbVAUPMAACAASURBVDLeLq8SC78VZPpZiDfa/SiKtT3kXhRixa5i/V8AnwBoQ0QPA5gD4JG4SyUkhUN1EowXCe0RO+yIAgBAp1a5qRMmDSAo0YsdWirtdEgCPk1hQ2jZ+l3iCiLEF62HicufPcTgJMSK5XR7pGRMnw1gIYBRUKotnsvMKxMkm5BgqmrE/9UqrVQL7MptB3DuUe1TLI3zaaL6pFfL4M0UoytI6d5DqRFEaLD4LdbiM2wLCVAXYsWyxZoVH4JPmXkVMz/HzM+KUp1+6N/nG3dXpUyOdCPTpdwqL87ekGJJ0gMt57fMiphjtCIOKWqVIkmEhop+VkSy81hHLNZCrNh1BZlHRIMTIomQdHIkC35ENKtPh1ZNAAB/OLpDCqVJH/yKtbiCmPLST4EDtAUle1IkidBQ0c+KiDuIdcTHWogVu4r1yQB+IaL1RLSUiJYR0VI7ByCiMUS0mojWEdEEk/XnqMdeTETFRHSCTRkFi8g0vXUIhHb5OfD65AUVCr2FLCdLebSIxdqcDbtktkhIHjUeuQ+tIhZrIVbsljQfG8vJ1IwizwE4FUApgF+JaBozr9Bt9h2AaczMRNQfwPsAesVyXsGcxVv2pVqEtCIzwyXuMxHQKi9muRXFukTaK4hKQ2xDz7bNsXpHBZgZJKUrhTihzwBVUe1Bi1zJjGsF8bEWYsWWxZqZN5l9bBxiCIB1zLyBmWsBvAvgHMM5Krn+idAUUpk1vuhac2np/tTJkSborbAZLkKzHLtj0caJpiDuOyR5v428PT/wkallBHlnwZZUiCM0UPSG14PikmUZSU8oxIotLUErZW6EmR+weIj2APRvj1IAQ03Ocx6AfwFoA+AMOzIK1mmRm5lqEdIGIuCw/BxU18lD1w7frNiRahEcxxGFzQKWLxnSCW/O24TKGhmECPFD72MtFWOtI/7oQqzY9bGu0n28UFxDimzsbzbPGWSRZuZPmLkXgHMBPBjyYETXq37Yxbt27bIhhjCoc0v/dL1gjebZmaioFuVHiA2jK4iWvlFypAvxRD/bJukcrSOxR0Ks2LJYM/N/9MtE9DiAaTYOUQqgo265A4CyMOebTURHEFEBM+82WT8FwBQAGDRokLiM2MBFhJ0VNakWw/noelWL3EwpaR4GY25mwZwte5Qy72//aSj2HaxDfhNl5kgsZUI80d+PG3dLASKrVMt9KMRIrCbLXABdbWz/K4DuRNSFiLIAjINBMSeibmoxGhDR0QCyAJTHKKegolkx2ubnAJBADasQgPzcTPEZjoDE3kWmSZZiz+hzeD5O73c4sjOUx7D4dgrxRD/O3ba/OmVypBtisRZixa6P9TLU369uAIUI46phhJk9RHQzgK/V/V9h5uVEdIO6fjKAPwC4gojqABwCcDGz2MLiTVO1Mt62fdXo1FqmoK3QokkWaj0+VNd5JQe4EDVaysYMtzIK0RRrsVgLcUV9bWa4SAZtNpD7UIgVuykOztR99wDYwcy26mIz8wwAMwy/TdZ9fxTAozblEmzStbApAMlvGgn9iE4L9tx3sA6H5YtiHYlxgzvi+1U7Uy2G49AKULhdmmKt9CV5oQvxhKHMIBU2z5Y0oTb4ctk2XH5s51SLIaQxdl1BbtKl2duqWqBFCU5DtMBFeZlbg4jQQvWF3XdIIuytkJXhQq24GgXh9SqKdaZ6D2ZniiuIEH+YFRe2bfursbzsQKrFcTyHq+6Rc9eXSwl4ISbsKtanmvwWU9EYIbloTjXZqiuDKD7h0TshaUFmEsBojvFVlOV2ibJoQp360nYZiunI7JEQTxgMlwQ9WKKweTZG9Cz0L784e0MKpRHSHUuKNRHdqPpX91LLjWufjQCWJVZEIRFoL3NRfKxBpAQvAqJYh6f+RZ6VIYq1GV6fDxku8hfRcbkImW6S2SMhrvhYeW4df0RrtM3LTrU4aUD9s+vXkj0plENId6z6WL8N4EsoRVsm6H6vYGbpgWlIlmQisI1WEni/uIJYIivDBY+P4fWx359YUHysje2RneGWe1GIK4orCKFrYVOs2l6RanHSivIqecYL0WPJYs3M+5m5BMDHAPaoZcwvB/AyER2VQPmEBCGZCKyhL7LQQlxBbCGDN3O8XkaGQbHOynCJK4gQVxiKk3WTTDcOxbGk+fOz1mHtjoatqC/Zsi/VIghpjF0f67uZuYKITgBwGoDXAUyOsI/gIDQ10e9jLUqPJQhAbpYbmW6SXNYW0bJdSB8LxNxi7UJNnbSTEEfU4MUmWRk4VOeNS0BejceLx75ajfOfnxu7fA7ksQv6AwCO69o6xZII6YxdxVob9p4B4AVm/gxKARchzfD7WHvFSmYVIkJ+kyzsF8XaFGO2ec1iXZPkPnbak7PxyW+lST2nHbw+9mcE0ciWDCpCnNHS7eVmxT+dY0Od6bxoUEe0bpoFr5TOEGLArmK9lYheBHARgBlElB3FMQQHINP01jA+X1vkZmK/uIKERJ+EIDsFAbLMjNU7KnDbe0uSdk67eHy+IIt1llishTjDzCAQmqizkwdrbZWcsExxyR6UpHmebP1zvn3LJvJeFGLCrlJ8EZSqiWOYeR+AVgDuiLtUQsKRMsr20BTG/CaZksfaIjJ4M8dj4mOdneGOysf6+VnrMHP5dv9yrceHOWt3xyyjkP6wmhWkTp0J2RtHg4A+9uSCyb9gxOOz4nbsVKE945eW7sdi8bEWYsCWYs3MB5n5Y2Zeqy5vY+aZiRFNSAT+PNYSvBgVLZpkSvCiRfyKdRJdHNJhBtfrY2SEcAVhZjz97VqsNsnisG5nBQ5UB/a9x75ajevfXOhffnzmalz2v/lYuGlvYoQX0gaG4mPdvkUTAMpMiSAIicdqHus56t8KIjpg/JtYEYVEEK6McmWNB099uwYe8fkMKnqSnyuKtVX8hU+S6OKQBno16nwhsoLU+VBV68WT367BaU/NDtrvlCdm448vzQt7bG1KfldFdfwEFtISZsBFVB/rYLgP67w+PPnNGlsuIm/P3wxASePXULllZDcAQNGE6Xjxx/UplkZIR6ym2ztB/ducmfOMfxMropAIMjOUB6PZNP3jX6/GU9+uxbQlZckWy8Eo7dVCghfDEKjW+kt1J9Vi7XzV2mviY52d4UKNx4eyfYfC7vv71gN4/9ct2FVRg4rq4H6YqSpRdV7nt4OQWHxqTfNQRpQPF5bi6e/W4ulv10Y81py1u3HOs3Nw/+crQm7z2eKtsQnsEH7TuYH868tVKZRESFdsBx4S0QAiuln99E+EUELicZFS7c1M6dFynopvbDAtcjNRWePx+y0K9fgYcOuiF+1W9/xu5Q7srqyJSYZ0UCfrvOYFYg7WepCv5kpv3TR0sqV/fLQUf36zGP3uq/fC0/yzM9XjSv+0xsSPl+L1uSWpFiNhEELHOmh95GCYHNfMjBqPFw9NX4ElpfuD1uvzWd/67mJU16Vrlqn6J0fHVrkBa656dQG+1sUxCEIkbCnWRHQrgKkA2qifqUT010QIJiQGfdBJltu85LSmG8Uh7WnaY7SAtlDLmh9IstX69bkleHfB5qSe0y4+Zuj1RTvBi7UeH659vRiXvjQ/JhnSwGBtmm7Py4zSvYcUKyPgL3ceit2VgQG0G3YpLiBul3Jcj9y8lnhnwRbcO225f3nltgO47OX5DSJ4jZlBRLp4mkClV+thvjA3zb+/Xo2ed30V8nn3n5lrApZ73f0VPv0tPS3XWnuM7XtYwO+zVu/Cn3VxDIIQCbsW62sBDGXme5j5HgDHAvhT/MUSEg1BKRJjlolAe6dzWtj/koM+KwiApBeJuXfackz4eFlSz2kXn48DFMIsGwGy2st9dYwV3dKhz5oViCEogzZNx2FmFE2YjvEfmKcNNF6nS213zXf7Hx8uxUNfrEgL1xgn8OQ3a7By2wHc+NZCzFm3G+c+93OqRYoZLY+13yXLcB9q9yoD2LCrMmC26OWfNmDBxj14v3gLAKCixtwP22z89+3KHbELn0KaZWekWgQhzbGrWBPqi8RA/d5woxgaOKEs1tq/VN7JweRLWfOQaMFSGtm6rCA/rN6J75Lwwk2HPuvx+oKCF9vkZaPOW68ua9k/PlxordCNdji3u/64L8/ZiDnrJPWeFZ7+bi3Oez79lWk9qot1fRBxkGKt/C3bdwgj//Mjhk363r/uoekrcdGLv/j3CXVfuUw060izLU5He8YbYeYGX8pdiA92FetXAcwnovuI6H4A8wC8En+xhEShf0BmZYR3BUkDHSXhGNugRa7i+7pfclkHEeQK4q4vaX71q7/i2teLoz72zgMNJ8tFndcX5AqS6XahzuPzW5jtBh9q96xRYb/tvcXRC9rI8HjZ1P2tus6LfQdrsaeqNq3iTnyszIxkZ2rBi4Gzk+t3Ku5Ds1bvUtcHX1tFdYSMISY6dHqr1UA7NT2hkY8WbcWpT87Gj2t2JVmi9MbnY+ysqMahWm+jCfy3NefBzE8Q0SwAJ6g/XcXM8uROR0hVrE2CnPwPxnQw/yUJrU1aiMU6JD4GXDrFTpuCNr7Qb3xrIYb3KMQlQzqFPNZvm/ciK8OFPu3ysWjzXpz//Fz858IB+MMxHRIjfBKp9TKaZAUq1lkZLtR4fZZvOWO6M81KaLQgVoaYwhcUX3c9PmbTQL5LXpqH3zYrPtejerXB/64anBT5YsXHCPCxNg4KDtkINDT2I+29YWaxTkf0912OOhAxsrRU6QMbd1ViaJdWyHK7Ap53oaiu8yLT7Qpy/2osPPXdWvz3u/rMMyWTzkihNMnBah7radoHwH0ATlU/D6i/CWlIpOBFUauD0YIXRbEOxscc4HMZKivIl79vx8QI/uLnPT8XZ/x3DoD6zAPzN5ZHlCEdxoK1Hp+/bTSyQ7plWUPLxrLBUFo6HdrDKTAQMOOiZbjQlGoA+G7VziRLFT2sziCFinV4x+HB0MlG/+za8MjpQetnLNsGANhZUYNed3+Ff89cbem4ve7+ChM/XhoXGdMRLfd5KO797Hf8kEb3lRWsuoIcB6ADgJ8APK77/Ef9CGlIdqbLdPqPxMe6HkMbNM/JBJESvHj9G8UomjA9NXI5EKOPtZ2sIPq+ZkwVp1ljrWSQS4fgxTqvD1kZgdYrzTXkjV9KLB3DeJ2X/W8+ajxezDZMU8s9bB1mBFgVf0rz0vA+ZhAoYpXdvu2jL0XxuUmtg2lLyvwpW9MVl4vQr30+OraqdwvRMvE8P0spGvOBGthphfeLzWMlZi7fHlHxTHcipVB9/ZdNuPq1XzHx46WWZ9i8PsaCjXviIV5CsKpYHwbgTgB9ATwNxVq9m5l/ZOYfEyWcEH/079kst7lirb1bJKNAPZpy53YR8nIysf9gLWauSO/o93gTKt2e3SILxil6zRprpT/qN3FqTl0zi7XWVi/9tDFo+5XbIhe3Ld17CD3v+iro93Cp1Bo7Zv1Jr1ine+XZeRv2KBZrta9tLj+IognT8cPqQOtg66bZcT/3+zaUTqfiImDLntAFm4wpL6Ph+jcX4s5PnJ3tKVm8s2ALXpi1DkUTpuOteZvCbjv5x/W46MVfMNehwdlWKy96mfkrZr4SSoq9dQBmSQ7r9IVAYYIXlZeLpMI1p0VuZqMJwrCDoljXKybGQDpACWQxUrK7Ch8uqrfoFJfsDVhfn1fdgmKt+/6/OcFKqhMwC17UFGszvl2xI+pBruSztodesW4VpkhPOnCw1gsiAqllzeduUJSQq1/9FVU6y2AiekhDMMqYFcRJFGbPxXTm/s+X42/v/hZy/YHqOtPKseXqYOWZ78NXA9UK9mzb78ygdstZQYgom4jOB/AWgL8A+C+AjxMlmJB4skMo1hoN61aPDjPXgvwmmUnPY50OaMFSGmZptyprg6f6znpmDu7+9Hf/8mX/CywS47Ix0Nu4q97HuMahFmvFFSQ4K0gofBxsxRdix6xF9ZVD012x3l1ZgwEd8wEoz/o9OgurfhbEqATHo2pnuvVWM3lP6d026uPtOFCNzxZvNZ01M1Mo0z33tx5mxqs/l+DTxcFuQhr975sZUDm2ft/6779t3ouTH5/lHwTurqzBF0uVYy5VBz1O7WeWsoIQ0etQ3EC+BHA/M/8eYRchDcjKcIUvENMArA7xQq8i5jfJlOBFA1pfiRT4vnp7cB7YUMUnNIo3Kb50VizWW/cd9H93qi5a47Fnsa6orsN9ny8P+K1snzMtNemOfizo1P5jFSKgbV4OACA7wx1gpb5g8i/+78bbamdFeJ9YPR1aNkHp3tDuEoAy4zKsWwGaZJln23AKxkw7h+fnRH2sS6bMCwokBoC563fjjy/NxxvXDMHwHoX+3+2m13Qy+v5j5muvn+3dUxXoTqN/xp/3/FwAwJLSfTj+iAJc+3oxlmxRvjsdqxbrywH0AHArgLlEdED9VBBRZAdAwTnoOm52hjtEur34pwV6v3gLiiZMx3aHTt2EwkyXa5GbheVlgdOERROm45Z3Qk99NXQ0JSRS+q1orGFvzdusnsPKy6f+/E71L1ZKmge2U3YYxdrjY3xgCH4SC3bsmHUPff+Nh+U2lbiI0DRLsZ1lZ7jgDXE/GGfl3DZS6IWbaQGAVdsP4Lo3ivF/n6afH3G/DvkRt9my56Dp75tMfr/tvcX440vKbJwxw1FDysS3oqxeJTxoMkO5aFO9q58xjuEDk4JYT327Fiu3HcDvW5V3rv6+dKrxz6qPtYuZm6ufPN2nOTNHH1IspAxSg1rMgjPqLdZA6V4l4GXW6tjS4Xys+tBu2FUZ03FShf5d06JJpqmFYZpJhHxjwWfRYu2JwTLjs6DnbCqvtxI5Vfc0+qIDkVxBgi+kqHVu3OVqbJi5eemDuUMNXjaXmytTTkMfTJyd4Qo5b27sXi67ZePCHFOzkpeYWG+dzgidRTkUoZ75ZvfsJ79t9X83rr5x6qIGkx1Eb3QyywsfcF9ZGFAs2LgHY5/+yb+fvm0Xb9kXareUEodbSHAiB2s9uOWd37C3KnTkckVNHZqaTM9pff2Z79fihEd/ABAc5b1y2wEUTZiOjWEemMyMF39cj30HG1aVQi2XtVCP9rCLVM7YOPUXzTmMnPvcz7hLtYjpM5A41Zph9EUHEJQlRI/XF6wCRqyIJ0TFZp2l0RNiJDf83z8kS5yoYWalpLnaz0IVA1O2DVy2Y7EOt+X+g3VYsU1x/XLqIDccrZtFzpYSalYj0qOnpDz4vdlQsoOs0Pnvm/mY62dOQs2O7zgQ2h1J35emOnQwknTFmojGENFqIlpHRBNM1l9KREvVz1wiGpBsGRsCN7y1CNOWlIUtxNG1oBkO1XmDFBDtuXpA9/I2vmM0C/RMNTrXjNlrd+NfX66KWAzEyZg9IPObiGJtRGunSK4gf4uhxHaol/PiLfv87iKB2zvzbc7MQZb9cD7WXh8j07BDeQwDFEHBrHsM6NjC/z3c7IrTXXGM92OoHNZA8ADCVjXFEJsSAQMemOkPSnbqIFcjUurFUERb1GnGsu1+14aGxoqyA35DQUSLdRSkQ470pCrWROQG8ByAsQCOBHAJER1p2GwjgJOYuT+ABwFMSaaMDQWtUITRr06/lJvtho+DH7pmVke9zezHNbv8AQih/PYAoE49rp1gGKehXZ3+ZdMiNzBbgNNfssnAqiuInj+8MBc7D1j3ufda8QXR4dR3uY+DlZdwinWdl8OuF+KH3vU93H1dZeI76iSM92O4WbZfDektrcLMIS3WxnsvHR6R0VRn9zLj9637/dVh7TD+gyX2T+hwKqrrUFJ+EMd0bgkAOGRisS7XFYyJps1PecL5pVOS/bQeAmAdM29g5loA7wI4R78BM89lZu1Onwel4qMQJaGCBQlAs2wlsMVY7cisr2sPxlqPD1e+ssBfScobxqqjrWmRxhbeeheH+t+MFut0D3KKB1q+ZDvWroWb9uLNCIUA9FiJnB9c1NL/PdygL5UYC+kA4X2svb7g9HxCYtBbKet8HLJITJXFCnGpwh9MrF5PoQW3Bg2rd43HxzjlSGsp6Zw6exQrRx6ehzOfmYNTn5xte99VJhmS0h3tmrSZnx0mhpMPdQGKDShmMwBLT2st+4fJx25WkPYA9M66pepvobgWSoq/UHJdT0TFRFS8a9euUJs1akr3hg600ZLSB5UcNentPpPAAWU59LlZp5QmItNIMqhPI6e3WItibWTeeiXK3WUzvN1OARMr0676wznxXW70fdUIlxWkzicW62Shv8+9Pl/IfPWxxAokA6NBIJxie0Rh06jOUef14cjDzXMXGM+WDhbraDDOXsaK1ZLeTmW56t5y/BGtAZgbWs7s387/vYF2C8tZQYzZQKLNCmL21jVtWyI6GYpi/c8wck1h5kHMPKiwMHIEb2Nk78G6kDdr5wLlgVpZbbRYB/+bQilA4ayC+hAFLVvDz+udWYI0FGZp5IwWeL0v5verGk6ifzv8pkZnn9HvcFv72XGjqTEZwBizDSzcFN20drLQbhej/2Y4xbmmzhd2ZkiIDrNHV2BJc8af3ig23dfpfp5GH+tvV4bO6mScCbLqD20n93K6Vhbs0bZZ2PXxLnvf996v09q1cMW2A2jVNAtd1cHawVoPBuriFgBgd1W9Ia+hzmRYKhATR0oBdNQtdwAQlK+GiPoDeBnAWGYuN64X7LFuZ6W/c+v78Ta1yMRHi7ZiUFEr/+9ms/mhbnYrfq/Fm/b4C6o898N63HFaL6uipxwz3+H8MBbra18vxvjRPXHTiCMiZshoSGgvzrZ5gVPO/7lwAG4P40toS7E2+OvVeny4RS2b2699PvYbivY48aEdyhc9nCvI6h0HUBZD/veRj89CTqYbTbLcaJLp1n13Kcvq700y3cjNcgdsa1zfRFuf6Uamm9K6j5ul2+vTLg9z1dmXOi/jt83m6bycblm0E/NgViTMCuFm6ozKuVlbO4lQ0uVmhVeREjFb+cKsdbh5ZPe4HzcZLC87gD7t8vz50w/WeoOMBgdrvKbf9RQ2z4bPx5aCtN+evxl/HNopBqnjj9XKi3OY+QQiqoDSB/W3K9uwWv8KoDsRdQGwFcA4AH80nKsTlFLplzPzGovHFQycf3R7fLxIyZu5ZkdF0KiRiHCcOl1z5OHNA9Z9Z1JeNVTqqXDPFS1tUzpXKdT0Pr0CYfSx1iKfrx/eFdv3V+PfX6/G8rL9+PcFA9A0O9lj19RwoLoOBc2ygxSt/hGKLHh9jAwXWXIJMaYLe+KbNf7Stsu27seABwJL5DpQr/bP8BjbySwtlYZZrnmrjO17GNwuQnWdF4fqvDhY60F5Va2yXKv8dqjOG1V2A7eLdIq6q14Rt6iYa/vk6NYZ98/JdFvKzhANZv2jSWZ9+tG/vL0o5L5XvforSiadkQix4oLPxIUtFHrrOzNbtkR7vBzyHpu/cU/AcjpYYc1aKtIAyhj4P3vNLnSOMcf8xt3pkSfdSK3Hh7U7KnH1sCLkZiv30ZodlUFubktL6wera3ea17XYXVmDw/KsVb6885Nl6alYM/MJ6t/mkbaNcBwPEd0M4GsAbgCvMPNyIrpBXT8ZwD0AWgN4Xn35eJh5UCznbZQw0C4/B7urarEuROfVXBr0Ss2eqlqs2RG8fagHYzirYIY7fa1ZGmalurMzAnN/l+1TFJ/cLDeeHjcQ/drn419frsSGXVWYcvkgdGoExTxWbKtAXk7w46RdiyZh9/P4fMjOcMFjYWq9pq7+Jfbzut14cfZ6XDKkE95ZYJ7L1IlWslBpCdu3DN9O0fLvCwf4g5TD4fWxX/k+VOsN+H6oTr/sq1/WKebVuu+Har3YW1WLMt0+1apSH41+lZXhMlHMXYGKeAjFPFixd/m3N5PFeT0mOswMAqGo1imHj361GpN/XG/pHOGstd+sCDTOpIFebUokFxa9Yl3r8eGKVxYAADq2ahL1gPijRaV45Py+Qe8Zp7N+VyVqvT4c2S7Pn27vnQWbMbJXm4Dt9APlUBN1zEDbvBxsszBT58SqlbbNaWpe6RPVxdnMvNTO/sw8A8AMw2+Tdd+vA3CdXbmEYNxuwhGFzbAmRCqgZqoidP/nK3D1sC4AQlumNeXbqEd7vIy1Oypw6pOzMfW6oRjWrcC/LiMeJbxSjJXCJ1qAk4uUqfE/De+Knoc1x1/f+Q1nPTsHz/3xaJzQvSDk/g2BqhqP6SArksX+rXmb0appFqosKNZb1QFMeWUNbntvMboWNMXdZ/YOqVg78WUeaoo+Lye1mXPcLkLT7IyEzrAwM2q9PlSrynmQ4q5X1EMq7r4AxX9vVV290q/uEy5nc2QZ43jBKcTMIBAK/WzFu79aL7gRquCMxuH59YpROliszdgQoWKkvq/pXWpimWXS9u/WJrx/t9NYrpYy79MuL+B9aSx+pc9tHe69Gm4WT8/6R063I2ZSsPUUJaJbAfwJiqsGAEwloinM/EzcJUsDSnZXoc7rQ/e2MRnyE4L2GOvRthmKdXlK9b5vZn6doaYOtQej0Qro8fmwoESZ9pv843pc+vJ8fHjDcRhU1KpBWKy1adGMMG+oN39RUsbpNxneoxDTbh6G699YiCtemY87T++Na0/oktY+qeGoqK5Dp1bRWeZzwgTuGYPEmBn//Ggp9h2sw6tXDw7rA+lEJcksGLaxQETIznAjO8ONfCRuIOHzMao9RqU9UJnXlPFnv1/nH7BpuF0UURHsUhBdJo1kYexn40f3wOMzI3tW2jGGhCug43aRIRA0PTMnXT2sCK/+XBJyvX5QEuvjZsUDp+HIe74GAEz8eCnuPL03jurUMsJezmFF2QHkZLrQpSBwQGD0sdanGawOY1CJ5Ibz5+FdMbJXG0e+U+2aFK8FMJSZ72HmewAcC0XRbpQ8+MUKnPrk7KirLyUSJXk/oUfb5ti675ClXNVA6HK22kM0yGLtY//D+6e1SsYPrfx5onwjk4l23eGCy37ZoAQ7GW/wzq2b4uObjsfoIw/DQ9NX4rb3FlsePvlERQAAIABJREFUhacbLiJ0aBmdYp2dGXrK0zgt/ea8Tfh25U78c2wv9GkX3n/biRP7ZnnRNfq1j3Q9QHsT15rMMAPY9L8D7eNyEXKzMtC6WTY6tMxFtzbN0a9DPoZ0aYWTehRiTN/DcO5R7XHJkE544qLAwr6a4cCYRu7vp/YIWHb6fWycGbl++BGW9gvzmAsinCvIdSd0CXhmxhJ8m0quH94VRxQ2RdMs82dUuHS2dsl0u3DTCOX/9GvJXvz9/fQqILO8bD96HZbnf+9rLiDhnsI3Tg0dx2BWtVFP8aa9GNq1tW05k4FdxZoA6K/Wi8b57AYAfLdKSWGkVTl0GkTwTyeF8rPW0BK5R7ZYB/L54rIgZVyzljjRYmgXzTXGaH2/ZWS3oG3N2q5pdgZeuOxojB/dA58tKcMFk+cGWcgaCqEUvLevGxp2P70PcAeDr/HT360NWH7g8xUY0bMQ1wwriiiPzUKNSYFVmcwGnYfl1wfr9DrMfBZseI/gtKL3n903YPnknvXbNEbLuB2Gdm2Nf53fz788ZfYGEICWTest6p1b56Jv+0BF26yinJMwurBZzYNux7C8YXeVfyBiHKAA6WVYCfWuOjy/Cb67fQRG9Gxjul6f7tPsGGcNaIdPbjrekgwZLsKo3vUFd+xUpU01zIwV25SMIBraDOZB1aj3+c0n2DrmnqpatG4amCf80qGd/LE8Z/W3l9o1mdhVrF8FMJ+I7iOi+6FURvxf/MVKDx4+T3mhFTa3XtUqWdS7gigvaM3POpSue/Wrv4Y9Xqh81RU1HizdGpiSyuf3x05/zVpzBck0TJHeekqPoG1DvUeICDeP7I6XrxiETbsP4uxn5mD+hoaVRVKpJmjeAMd3K8ANJx2B/11pHoO8TC0qAABDilqhZNIZ+MeYnqbbenyMxy8cYGn6z4nBi+GyNdxz5pH+749fOAAzbxuOh84NVJqvPL5zwPJ9Zx2JPw7thKX3jcYgtYywPuipIbhjJZpcnTVSu9/1fqH3nnUkRvZqi2O7KilJm2S60y6PtVX2HbRe+GaNbkrfzGUhnPucEwn3TOkbYjbph9XhjWrPXHIUuhZY85UmooCsNFW13rR5h5buPYSKag+O1CnWmlFvSel+dC1sin4d8v35ra3Sp30+vrv9JGS6Cbed0gMPn9cPxXedismXHYOr1LgwJ2JLsWbmJwBcDWAPgHIAVzPzU4kQLB3o0lrpJB8tKo2wZfJhVqYSOrXKRVaGy2+xNj5wHzlPsdas2KYEHoRSRur3D14/y/Bw8TJj9fYK7LXxkHYqmm+gUUFxuwg5mYG3T6SX2KjebfHJX4YhPzcTl748H2/8UhLywZkuD1QNn0k1QT0TxvYKsMaE4t6z+gAAbhoRPCOgUWCxPLMTmzBcfuGOrXJRMukMlEw6A33b56NH2+ZB7iG9DsvDa1cP9i+7VQUwLycTbfKC2yXdlJtUcJJhFoAIWLZVeR5eM6wLRvZS+q2mKORmuVHj8Tm66IlZP3v7uqFomRvet91OJcHVOypw23uKu0JQL6OGNVty9kClWuD9ZyvPp+42AgvzmgTGgVw8qKN/MPf7/aehY6v6WbomBpeTkvL0SL2nBS7qXai0FL/6ys63n2puMAGCgxwBYNW2AziisBnWPnw6bj1Fye2dleHCmL6HxUXuRGG1pPlgIjoMAJh5EYD9AE4BcDURtQq7cwOmQp3ieEMNXnMaREoAiT4ziH+KUP3PjxtcX6/nnQWbwyojW/YcNFW7S/cGujb4GDjtqdm44a3Q/lPpgpYNxczyt+rBsQHLa3eaZ1/R061NM3z6l2E4qUch7vlsOSZ8tCyoQEOtx4cuE2dg+GM/xCB5cmFmy2mPRvZqg9/uPtV0nf7F8sVf7U0dAsAJuqw0TtR77KRBA4ABhvzzADCkS/0jV684a5b/r5Zvx9vXDcX5R7V3ZGCP02iRmxWQk5oZ/hmTwUXBllgtL29VrXOLxJgFyR7frQC/TBwVdj+9EhQKbSremFLPiP72O61P5EG1k2nfoglKJp2BK48vwtqHx+Kbv5+Ey44NzJ0cKkDTeA8+ekF/XH6sMvOU6SZ8ctMwfHTjcQAC86gDQEl5+KwkTmFF2X64SBn4a/TQJXXQWmBUb3OXGgAYf1oPnDuwHabqXAd3VkTuj07EqsX6RQC1AEBEwwH8C8DrUBTsKYkRzfnoU8ttdtjIUv9Q69G2Gdaquak1X2nNL9qlezFP/HhZ2GOe+NgPlkorO7HiXbRoATpGVxCNDbpUP+8XW5u5yMvJxEtXDMJfR3bDe8VbMG7KPL+POwAsUIsrbN7jrD4VDh9bs1CteWgsXrpiEFo2VZSZYzrXKy4FzbIDfEFDTb+GQy+CE11BOIwrSCTaqT7Y+kwo+sHM3WfUu5Ic360AT1w8MEopGzceH+OiQR2x7L7RGNuv3o+TDYOiimoHK9Y+8yDZnEx3zIVt/nV+f9PfjQNrZsZpfdqiVdMsR7pLRosWlHnFcUUBv4dTAtc8NBZL7xuNdQ8rxpgJY3th9UNjkJ3hRkGzbBzTud7NSI/TXY40Vmw7gK6FzQIMI/pn+fpdygAhJ0Sg+nFdW+OaYV3w1LijMKxbAZbcOzqxAicYq4q1m5m1UkoXA5jCzB8x890AQs/ZNnCaZWf4pzs2OmxkqWQFUdAygyi5hpXf9C/28aPr/YXL9ocPrPvHR5HTlk9fus22vE7F41Ussa4Q5liXi/DgOcr0YLt8a5WitP1uH90TL1x6NFZvr8BZz8zBos1KWsRElMlNND6LFuusDFdAUNNHNx7vd38ovuuUoO2X3BP4gP3278OtC+U8vdofq2DHQ0Nrn7km1sYV6hQsAIzu4+zpUadzx2mB09TNDbnFNaWnR1vFDWCXg61pkXysNYupXZrnZGBoV/NJ6u9uH+H/Xudhf9yFi8hWUGQqiMb1zugOUl4Z2vUxK8OFvJxMZKhKuZZ60khzdTagp2rtTR+LdWDgosZdZ/QO+u1V1ZVNX5V33JCO/rYBgqsbpxuWFWsi0swkowB8r1vXOGo2h+CxC5TRuyOti+ozVfMNXLBxjz89kN4Ae/PI7v7vZ/x3jv/7xLG9gg4ZafqvoVHn8wXc8GZcdmxn/HNML3x80zDbxx/b73B8ctMw5GS6Me7FeXjv180xFbhIFT4fJ8TtID83EyWTzvArNYXNwg9e9DI4ceYkXnmsF9w5Cscf0Rp/OTnQrtH78DxcNKhDTMdurBjb0shtp/bALaO6449DFaV0j4NjSLTZmlBpqR88ty++/ftJYd2tigwVY3+ZOBLL7jvNNMYhw00Bub1f+XmjEudDSgo/J/ujRwsRBbTfJ79tjfmYLhfh5wkj8fafFHcIBz7CgthbVYuy/dVBKSoB4NKhwQO4k3u2QcmkM/DsJUf7fyvbF5wB5fELB+DNa4fEV9gkYVWxfgfAj0T0GYBDAH4CACLqBsUdpNHSrbAZuhQ0xdR5mxz18NBLMqCDYlW/+rVfMXW+UlnLmCLPzAduUFFLv7Vs+i3mD+Dvbz8pKBVVQ8LjZWRGMC8SEW4ccURAujQ79DysOabdPAxDu7bCPz9aFpRiLh1gi64g0aJZ8bMzwz+y9BI4526sR3OlijUVWZu8HLz9p2PRJi+wz31564l47ILg1GeCNa47IXSmgabZGfj7qT3QWVU4K53sCmJhANetTTP0bZ+Pz/5ibhC44aQjcMExHfDUxQNRMukMHJ5fH2RndCcxU7Z9rAy23UQhs0qlO33b52PVg2MABCcxGBPlDFL7Fk2Qp1ps0yGIXUt8YFZXoEmW269DGOnUOhfXqJk9zLrpBcd0wIndg9OLpgOWFGtmfhjA7QBeA3AC1/+3XQD+mhjR0gOXi/C3U7pj1fYKzPjdQS4QXK9kHJafg+uHd/WvapmbGWSFff7SY4IO0VFX8CNUMY6uhc3wxV9PxPRbTkBBM+sR5emCxxvZYh0PWuRm4dWrBuP64V2xctuBkNvtrHBmblOrriDRogWRmkWO6wnwsXbgO6kuRF50wRncdeaREX2Qten6kgjlrlOJMY91OMwCZAFFKX/8wgE496j2pus1halk0hmmvrPaYNvlIkcZneKN2bW3yM3EExdHP8DVDF+eNGi35WWKbfVIE1eQSNx9Zm+8evXgsAPadMSyxsDM85j5E2au0v22Rs0S0qg5q3879GzbHE98s8ZRpVv1D9U7T++NhXedgtl3nIxFJhkZ3C4KeKGM6tUmyBoWjj7t8lF816loYxKkcrcuP2+6UefjsJXt4kmG24U7T++Np8fVB50ZLRYrt0XOPJIKfBzaDz0evHrVYJx6ZNuI5yDUZ8pwoiuIVsnTTulowVlovtehArGcgKbIWr0l/3B0h6AiL7EO/hjK/eh2NVyLtcbzlx4dsDxr/IiAIGO7uFwEF4UvG+8UVpQdwOH5OWjV1L5hjYhwcs82STFeJZOGdTUpwuUi3HZqD2zYVYXPFpelWhwA5hkRWjfLRqfWuWGtGFcdXwQA2GZSgnbB/wUGT401ySXZNDv4YWIW1JAueLy+pCtB5wxsj9vUAjTG95FT0xIr076JO/7JvdrgpSvMC8zo0VJMAg61WIfIiy6kD1q6PWOaTCfhTxNq8dn1n4sG4PyjOwSkb4zVXUmbxXIT+bNROZVYpdO/C3+7+1Rb+cBDkeF2pYnF+oCpf3VjRhTrOHFan7bo2z4PT323xhFZHZhNkvZbYEBHxeWje9vgBPhtmufgU9Uf76rji/DCZcHuI9kmpXP1lh1jyWqn4/FySpQg7Z1mtPRQVP/VxJNoH2uraBYywJnp9jQFI1mzIEL80ZTPx2euQWWNM/2svX7F2l4/W6ibzdy6L3yGqEhoPtZEzhzkGonl8UVE+GXiSKx6cAxaRmG5NSPDRfD6Uq9LhKO6zov1uyrT2niWCESxjhNEhNtP7Yktew7hA4v5jBOJFpFtl/OO6oBpNw/DUyFy4A7s2ALTbzkB/2eSRgcA/nxS16DfBup8+NLhAatHcQVJ/m3iCmF1dYDuakqifaytomQhcK7F2qP5WIsrSNqin/FbuGlvCiUJjWbccdscwOU3ycSJ3ZX6DJHiGSLh8yn3otvlfIt1PDg8v0lc3YPcLkKdw11BVm+vgI+j869uyDTqVHnxZkTPQhzdqQWe+X4tzj+6vaN98MLRv4N5MItGqEBGQFHMzxnQHnPW7QaREtyox4l+r+FQXEGSrzFq725jezlZsXaGNb3eFcSJfU17UYorSMNAq0LoNKK1WAPAG9cMwU9rd/sV7Gg4qUchVm47gAyXmsfagfeixtLSfY4sxJKRBgMSrZR5OJ2gMSJmkzhCRBg/uie27a/GOws2p1QWRuoUHZeLMLxHIU7sXoj2LQJdP5z+oDBS5+WUBFZobhVBFmtHKK+B+HwMHztDWSSqVyac+C7XgpFSMQsixJ9ah+ac13xzo/GTJlKe37Hkpfcxw+tT3OjcJllBZi7fjqIJ03Ggui7qc8SDnQeqcfazPzvSlzkdfKxXbNuP5jkZaefimWjk6R5nju9WgOO6tsZzP6zHwdrU+d9F6wqSKCad3w9AfX7VdMHj86XEH9aVRhZrzRplzI2eCgJ9rJ2Hlm4v1sAwwRk4tZiT12bwYiLw+BgZLpdpVpBnvl8HANi4K7UpC1eESW2aatLBx1oLXExEcbB0RhTrBHD76B7YXVmDN37ZlGpRHMO4IZ1w2bGd0iLhvR6Pl1PjCgLnujMY0V7idv05EwFRvYLvxL7mt1iLj3WDwKmKdSwW63jAXO9G5zLJCqIFFqdaH9urq57ptNlAt4scnW7P62Os2lYh/tUmyNM9AQwqaoURPQsx+cf1qEjRVJcTb0en+9r9f3v3HR5HdT18/Ht21SxZLnLvcsWVZmNsgwtgU0MgCSQBAgm9hJYCIZTAj/YSQgIESAiQYCCUhE7AGDAxYDDNJgY33GVbLrLlIquX3fP+MbPSqlprr3ZG1vk8Dw/a2dXu1fHszJk7597bkMoELRBTV+SEUzda/jr0O6r2o54z3gIiUb0n3renriqbbu+A4tdSkMiqkF59JxWlKqwEI6UgdY77kYdeJ7MrthZ5+vlNSfZ5Kci6/GJKK0M21V4DLLFuIb+acRC7Syr5x8c5nny+Uwrir5N3QFrfClzbi8o9KgVxe13rnLf9GL3qHmsf9MImBQP4oBmN2rrHmR/eSkFat58fMxjw71zWX21wZitpaF2BRAiHnQvu5ECAoAh1Kxoig968PkU9+uEabxvQBL/PptLUUuZtnY9PQa3bmL4dOWFUD56Yt5bdUbebEslvp+6AiC8HlDVlV3EFZZWJ75VqrMbaD73Cde3PDATxlhwQz3vBmlJa6SRi3drXX6HUtB4/mTAA8G8pSGQawEFdMzz5fMUZvBgMOPNYR9+pfHvxFk/aVJcfS8WiJQXEF2tiNGbp5gKSg8KQ7vXXvGjrLLFuQb+YMYyiiioe+2itB5/uv4NGQOoveOJH7y/P49M1OwiHlV0llfTzYMRzoJEp4/zY01nlowF5fmhDU7btKQec+YJN65Wa5EylWl7pzx7rNduLGN2nQ/VxJNEKSmtKUaJnBXnu8w1c/uxX1a/zssc6z/0uRvhtPIvve6w372FYj0xSGlgUrq2ziLSg4T07cOrBvXnykxzyi8r3/gtx5LdZQYAGa+38pqSiigufWsBZj3/Gks0FQP25uBMhUsZT97jqt/Ie8L7H+syxfat/jvSQ+VUorKQmBTxLeEx8RFaYrQiFCYWV/KJy3/SAhsNKYVkVgz04bkUsd8sE5q3OrzUryI2vLq71Oi/vLq3eVru+eub8HG8a0gg/T7enqiyzpcwbZYl1C7t2+lDKq0I8+kHia7n8lmCIiO+n2zvv719U//zO0q2As/BPokX+6eqerP14YbK1wNu64T+ceQi/Pn4Y4M8Lj2g5O4rp0SHN62aY/RTppSuvDHPSgx8x7s45/M2TO5O1lVWGePZzZzaqXh29n1s4FNYmx9aoh3dWd3pUotlcfl4gZlthOTuKK2wp80ZYYt3CBnVrzw8O78szn62vTkASwY9fR0V9O4o+YoFbmxgQWLPNmWN1tAeDM6oHL9bZ7pdesWh/dS8ae3fy7kQeSaiDgZqLkjnL8zjujx941qaGqEJxuXfz25v4cKaRc2qsV+Y5PZ/3vP2tx61y2nDL60sBfFH7mprU8DzW0c97ZePOEs8+uzmCAakus/ObZe7g05E2cLFBllgnwNXHDSWsyiNzVyfsM9U3S0zX+NuHTo/O4twCj1vSsKKohCeskFdYRvfMVE9u2zc2eNGPHRjbCstplxzkqCH7vgTy/opciARrTbcHazxegKKuylCYQd28GVBm4kdESE0KsiKvsHpbdpd0D1vkKCyrOYZ5MZtRXSlJAXcea+fxIf061XreywVsvt1auPcXeSjJx/NYL3XLJEf0yvS4Jf6U8L1aRE4UkRUislpEbmjg+eEi8qmIlIvIrxPdvpbQLyudHx3Rjxe+3JDQq2S/3hWP3ILbWVzBLa8t8c3trj+/v6rW4/9t2M2YPt5ckQcaqbH243SFO4srPD/ARi5E/F4KUlYZIj3FmynQTHylJAV4b1le9eO6g+G8kJlWs28dP7Knhy1xpAQDBAM1d9rqDvb0srRtce5u+nh4l21vggFhvU971Zdt2cOALulkptkg7IYkNLEWkSDwCHASMBI4S0RG1nnZTuBq4L5Etq2lXXnMUESEh/67au8vjgP/pV81IoPcvvvwxzzz2XoG3zjL8/lg95RVVs/ectqhvau3ezXiOZIf1k2kfZhXo6jndcORC5GA+O0+TW2llSHaJQe9boaJg7plDKWVIc87CUoqqshICbLg5um0S/F+P0sOBmqtvFhWJ7H2Kl6qSs6OEvpGzfhUtzfda/NW5bO9sJyVef7rWV+6eY/VVzch0VnDeGC1qq5V1QrgBeC06Beo6jZV/RLwZsnCFtKzYxrnThjAy19tYu32ll/tSdV/81jf8/0xtR5H9wYfdPPsRDenls/W7ADgmuOGcsxB3au3ezXqubrGus55x0811su37OGipxZQWFZFp/QUT9sS6fnyYJHMmJRWhkj3QcJj9l9qcv2dbUext73WJRUhenRIo6tf5kkXZ+rQSI11aWWIg/vWHPe9mn41UiJ2aFQy7bfzZUTuLn/1WheWVbJ+R4nNCNKERJ+G+gAbox7nutv2iYhcIiILRGTB9u3b97txLe3yaYNJCQZ48P2W77UOq/puSq/h7hcx0jsdXQ8I9XszEmnJJqdm7GeTsjnl4F7V2w/t700vhrSCGuuTHpzHnOV57C6p9LwXNhKXgEi9M2RJRe39bP7q/IQt2rRxZ0n1mAJVpbQiRJol1geElAau4sbf9b4HLamxLr/YFz3VEWlJQXflxUiPdZj+WTW16F7VEOfkO4n1+IFZ1dt8drqstr2w5mJtw46S6nLShjpZyipDvLt0K1UtuLBMpDbdVlxsXKIT64Z23X3+ZqnqY6o6TlXHdeuW+CnRYtW1fSrnH5XNG19vZkULD5yoDIVJ9ln3XZrbwxNZyXBdfjG9O9aUEAy/ZXbC5/uOeHvJViYMyqJzRgrJwQDLbz+Rpy8Yz+Sh3uxXNTXWtb8ey7fs8UWvdd0SlbrJa6JF4tRQjfW9s1fwTe5udhSV8/SnOZz9xOccevt7fJmzs8XbdekzCzn14Y8ZetMsBv52FvlFFb749zP7L3p6yVevmFT9c/YNb7HKg9v3qsrSzXt8tbptl/YptWYFKa0M1ZrvPqzKqrxC/vjuioS2q9g9XmVHrUz5ixnDEtqGvbn5lBEA/OblxTwxby2Db5zFlD/MZfK9c1mZV8jA384i+4a3uOr5//HvBRv5eFU+Fz+9gEueWcjLX+W2WLuWup1QI60UpFGJzrxygX5Rj/sCmxPcBk9dMmUQ7VOSuP+9lS36OZUh9cWo8GjVq5VVhSirDLG5oJQzxvattezuxP/XeI9P3p4yht40i827S+ParlV5hazaVsTJY2p6qtulBJkyzLuLtUh+WPccedes5bzw5cZ6r0+0yADU7pnOLecNHg+y2VLg7BOR0erRZs7P4bsPf8LYO+fwO3cqMoAzH/2Uq57/H4VllXyTu5tFG3fHPeld5i6UURnVM3fs8B5x/QzjjVVRC4wc1r8zh0fd3Zpx/0e8vXgLf3x3BfNWJeZuamTw5Ljszgn5vOYQcOexdi7GK6rC9O1c02O9vbCcGfd/xEP/XZ3QaSiLy527oxkpSUwa3AXwdrrQhvxsUnb1z3e+tbxWPfrx939U/fN/vt7M9S99w0/+/jnzVuUDTjI+7Ka3ue2NpayJQ+lpVSjMMfd9QPYNb3Hbf5YBNcd+U1+ih6d/CQwVkYHAJuDHwNkJboOnOqWncOHkgTwwZxWLcwsY07dlbqdUhcIkp/lr9oHoHuuNO0tQdeb5/u+vpzFnWR4XPb2AypCSfcNbdE5P5tB+nbjw6EH06dyOL3N2cv1L3wAw6Z7/kpYc4Kzx/bnq2KFkZTRe37syr5CyyhDvL9/Gg++v4ukLxjNlWDdKK0IEA0JKUoC3l2xFBE4Y5f0o+oiaGuv6id6qvJav0d+bPaXOEIgTR/fk6U/Xe36Q/XClk7zMW5W/12nP7jh9NPNX5/P2kq385+vN/Ofr+tf2L18+kcP7d27WLCOqSklFiK9zd3P2459z8pieHDWkK49/tJau7VM4rH9nDuqRyYmjezLao1lmTPxFvpo/P2YwAPedeQjH/vHD6uejl+4+vH8nLpkymNSkAOfP/JLTDu3NqQf35rgR3eMyk032DW9V/zxhUJf9fr999fLlE/nBXz+ttS0YcAYpRkr/Qqo8c+F4zv37F1z41ILq14269R0Gds3gvjMPYeyAlrs4UFU+WLENgPTUIA+ffTjvLdvq6UqVDUkKBvj+4X145atNTb5u6rBufLZ2B+V11oioCIWZOT+HmfNzmD6iO0/89AiqQmEembuG3F0ldGmfSmZaEhdNHsg3uQVc+sxCHjrrMLplpjJr8RYemLOK6SN6sHzLHjbV6cw6fmQP38/A5KWEZl6qWiUiVwLvAEHgH6q6VEQuc59/VER6AguADkBYRK4FRqrqnkS2tSVdcPRAZs7P4U/vreDJ88e3yGdUhNTTOUIbkub2WJdVhljn1rgNcJOg6SN78OF105jxp4+oCIXZVVLJ3BXbmbuiprdnaPf21b1EZZVhnvwkhyc/yWHqsG7cfMoIOqYn8+pXm3h9kZMoRXoLo533jy9qPR7UNYO1+cUckd3Z85ktojU23R5AUhN3IgrLKikqr6JLRmqLzWhSFQpzyTMLAZg8tBud01M4I2pZcS90yUhl485S+nRqxy9mDOOaFxbVev6Ug3sxdWg3dpdWcO6EAZw7YQAVVWF+9eLXzFq8pd7sBJHkYHjPTK48dghPf7qeET0zqQoru0oq2FVc6fy/pIJdJZW1Fj6atXgrsxY7q3YGBA7v35nLpw1u4QiYRPvbuWO55bUl/HLGQYDTSfDOtVP4Imcnt7y2pNZrv9qwm8v+ubD68euLNlcfp3p2SOPciQN4d1ke3z+sD+dNHMCLC3I5clAWKUkBgiJ0aJdMQITdpRXOwHT3EPDvLzdy37s1dz8z05KYPNS7+eTHDsiq9TgpKNWlIFc85/z9Q7u3J9hIUrYuv5gf/HU+47OzeOai8RSWVbFow27yCsvISk/hxNE9G03ofj/7Wz5Znc8jZx9Ov6g67nBY2V1aybxV2+sdFzJSkggGhB8d0X9//uwW8/sfHMy3WwpZtmUPM0b24K7TRzP+bueu7gVHDeScCf1rXRB8sjqf4T0z6dAumTnL8qov7uYs38axf/yA3F2l9RZp+8M7NWU45zzxea3n5izPq/V40e9meD5QvTVIeJemqs4CZtXZ9mjUz1txSkQOWB3Skrl0ymB+P/tbFq7fWe9gFA9VoTApSf7nRXrAAAAUl0lEQVS6ooyMoi+vCrN+h1M6MDCqDGRAlwxW3nUSxeVVPP/FBu58a3n1c1OHdePx88ZVJ4tfb9zNaY98Aji9lZEey4i680/fefpobn9zWa2DSrvkICUVzi3Bs8b768Da2AIxAH//eB0ZKUn8/JjBPPv5Bm59Y2m914AzMOf4kT3o2C6Z6SN60DkjhXC46UGtVaEwLy3M5f1vt3HJlEGc88TnPHX+eA7r34nZS7by5jdbah1sxw3ozIyR3pc2jOnTkUUbd3P+UdmcdmifWifQZy5suFY+JSnAQ2cdxkNnHVZr+21vLGXm/By6Z6by7dZCrnzufwB8sW4nXTJS6JSeTFZGCv2y0jmkbyc6ZSSTlZ5C5/QUnv1iA+MGdGZYj/a0T02uNRDWHFhOGNWz3l2ug3pmclDPTMJh5dY3lvLKFZNYtGE33+TupnendnyTW0DOjmJ+c+Jwrnre2a+27imrTm6+3ri70e/z3rx8+aQW7endF5dPG8KNryxme2F59SC8ovKqWvXpEeOzs1i8qYDSyhBf5OxsdKaoSYO7kJoUYHivDvTp1I5nP9/Adw7uVb0C7OR753LjycN5ZO4aCkobn1xsVO8ODbbDT5KDAWZdM7nWtmcuHM/LC3O5+ZQR9Y7l0Yt0nTSmFzn3nELenjKOvPt91rozoWSmJXHKmF4s27KHYEDokpGKqtNhcMHRAwGnbDN3Vwn/55Z9XDFtMNedcJD1UjeTHCgDacaNG6cLFizY+wt9oqSiiin3zmVYj0yeu3hC3N9/2h/mcnDfTvy5TtLgpVBYGXzjLK6dPpRtheXMWryFRb87vtHXqypVYW1yEObO4grunrWcEb06VM8rO3VYN/plpVNWGSI1KVDrYKCqlFeFSYuaxcKPAz1nL9nCZf/8in9dMoHxA7MY+NtZe/+lKNld0snZ0Xjdc0DghpOGU1QeYsXWPSQHA+woquDTtTua/RlzfjnVF8smA+wqruD//rOUO04fTWZaMg/MWclna3dwxbQh+1Ur//GqfHJ2FLO1oIyLJw+iY7otiGCapyoUJqmJ48qWglKuePYrumSk8tuTh/Pesrxay6LfePJw5izbxhdRg2wP79+J5GCAk8f0IhgQCkorGdm7g5ts+mM2kOPv/5CVeUWsuftkggGpVaYCsObuk/kmdzff+8t8AJ676EhG9+1Ih7RkVJUtBWVMuue/tX6nY7vkJpPk5hjZqwPr8ou5+rihXDplkO9mzWpJa7cX8U1uAceP6mELVMWJiCxU1XENPWcR9kh6ShJXTBvC7W8uY/6afCYNju/tO2fwor+SxWBAEIH3luWxvbCcAV2aXtpZRPY6ADMrI4X7zjykwefSGpgCTkTqbfdbnMAZPQ/wo8c+Y/VdJzX52ltPHcnxo3rStX0KAZHqv+fTNTv4asMuoPbtPnBKTO6e9W299xrUNYONu0qoDCnjs7P4ImcnZ43vz2drdzBjZA9+NimbdslBOrZL9tWJqXNGCg/8uOYi8trp8Rnhf/TQrhzt4a1103o1lVQD9OrYjlevOKr68eCp7emflc6o3h2qj42XTHFKiHYUlZORmtTgMc1vnrt4Aks372mwN/jSKYMIBoRhPZyVWn9+zGAmRfWyigi9O7Vj5vlHICJMbeCieFthGS8tzKWgtJI124pZsH4nj507jmWbCzjryP6UVoQorgihqnRtn9oqYtbSBnVrzyCf1ZAfyCyx9tDZR/bnsY/W8qd3VzLxsi5xvc3i9ML6J/GJUHVWbQIsYWnCloKy6p8jU1Vdd8JBqCqvL9rMI+ccTvvUpCZHsk8c3IWJ7oj3y6cOZs32Inp3asdfPljNkO7tKSippCqsHDmwC8GA0L9LOu1T7ZBgjFeiZyaK1sUvC740Q9f2qbUS4jm/nML0PzmzWExwj0cZqUksvHk6Hds1fAdoWtQiXXV1z0zjimlD6m2PzEmdmhSkU9Pjl41pUXYW9VBacpCrjhvCTa8u4Yi73ueCo7MZkJXB4QM6kZmWTGFZJWGlui420gEQDAjd2qc2mYhvKyz3ZU/sTyb055+fbQDgtu+O8rg1/pURdbsu7JaFBwPCZVOHcOWxQ2N+v0BAGOr2El13wvC4tNEYY/ZmSPdM5l1/DE/Nz2HCwJoZS1rTxYIxsbDE2mNnju3H3W8tJ7+onHtnN3+S/KyMFIZ0b09+YTkd05NrJdF5e5zeztQWmhVif9x5+hgunjyIgAgd0qxetTE/OqJf9SCm2Uu3ADQ6kt4YY/ysX1Y6N39npNfNMCYhLLH2WEpSgK9+N4PZS7aSmhTk8Xlrye6SwebdpfTPSmfWki0UllUxqFsGl0wehOJMCB9WZcOOEja7JQNj+nQkI9WpJevVMY1eHdO44pj6t8v8YG+11aZ2ffgv/vU1AFV+Ws/cGGOMMfXYrCCt3M7iCpKC1vt7IFqxtZATHqhZYeupC8Y3OJjHGGOMMYnT1Kwg/qsVMDHJykixpPoAdVDPTBbfVjMd4TifzVFrjDHGmNqsFMQYH8tMS+bNq45GxBlJb4wxxhj/sjO1MT43us4qksYYY4zxJysFMcYYY4wxJg4ssTbGGGOMMSYOLLE2xhhjjDEmDiyxNsYYY4wxJg4ssTbGGGOMMSYOLLE2xhhjjDEmDg6YlRdFZDuw3ut27KOuQL7XjWhFLF6xsXg1n8Uqdhaz2Fi8YmPxaj6LVez2NWYDVLXBpZAPmMS6NRORBY0tjWnqs3jFxuLVfBar2FnMYmPxio3Fq/ksVrFriZhZKYgxxhhjjDFxYIm1McYYY4wxcWCJtT885nUDWhmLV2wsXs1nsYqdxSw2Fq/YWLyaz2IVu7jHzGqsjTHGGGOMiQPrsTbGGGOMMSYOLLE2xhhjjDEmDiyxTgAREa/bYIwxxviNnR9jY/HyP0usEyPZ6wa0JiLS1f1/0Ou2tAYiku11G1oTERknIt29bkdrISLTRWSs1+1oLUSkY9TPlgTtXZLXDWhlLJ+IUaJzCUusW5B7An8R+IOIHG2JYuPEkS4izwOvA6hqyONm+ZqIHC4ic4Dbbd/aOxEZJSLzgVuBTl63x+9E5DAReRt4FRjidXv8TkSOFJHXgSdE5AIRSVWbHaBRIjJBRJ7FOX4NtWNY00RkoptP3CciIy1eTXPjdTskPpewxLoFuEniPcCjwJtAHnAl0N/ThvmYOkrch11F5HIAEbF9tA53/7oJeB54QVXPixw4rIesSdcAr6rqqaq6EixeDRGRoIg8BjwO/A14DhjhPmffxwaIyMHAI8BLwIvAsdjFSKNEZDTwEDXnx0uA89zn7DtZh3uH7WFgFs7y29cAF7jPWbzqEJGfAk8BN4vID91tCbszYgfJFuD2UnwAzFDVp4AnAQW2e9kuP3OTxV44B9kLgctFpJOqhu1kXpu7fyUDH6vqE1Ddu5hkPWT1uYliFs538GF32/dEpC/Qzn1sJyeXe5E2G5isqq8BLwPHiEiaqoa9bZ1vjQVWq+ozwHtAGrAh8qTtX/VMAL5V1edxLuBKgHNEJFtV1eJVzyHASlV9Evgj8ApwmogMs3g1aBPOxe2JOPFCVasSFSdLWOJERKaKyJGRx6o6W1V3ichk4DMgG7hTRGZ41UY/iY6XiATcHustOHHKAT4EbhCRwXYyr79/AX8A+ojIfSLyJXAH8JSInOFNC/0lOl5uolgCTAGOFZF/ApcCdwIPuK9p0xckDRy/XlHVUvdEFAZWAumeNdBnGvg+vgV8T0TuAhYDfYE/i8hvwPavBuL1JdDPPb4X4+xjBcDFYPESkdNF5EYROcXdtAgYFxWvL4EFOMcxi1dNvL7jbpoL5Knqu8B6EbnD3Z6QXmtLrPeTiGSKyCs4dYiXikhnd3sktjuBn6nqROBr4GwRGe5Na73XULwiibOIDAPWqmouTq/PFcCLIpIqIm1ywEZj+5d7cH0GOBT4lap+B/gIONGNY5vURLzKcO4cPQK8o6onAjcBo0XkJM8a7LEmjl8iIuKesL8FjsPphW3Tva9N7F/bcHoVk4AbVXUCMBM4WkQmetVerzUQryz3qTXAF8CTIvIaMA6nhCZJRNK8aa33RKSbG49f4uQOT4rIGaq6HefO0VXuS3cDc4B0905vm9RAvP4hIt9zO1Mix6lLgatFpIeqViaiXZZY778K4L/AT4DNwJkAkWRRVZeq6lz3tR/iDJoq8qCdftFgvFybgWEi8gZOj+yHwHpVLU/UF8KHGo2Xqj4L/FBVP3I3zQG6YftXY/vXX3BKP7oBqOom4GOc3rK2qrHjl7q3mAPuhe7nwBmR57xqrA809X38FhgObHQ3LQS2AeUJbqOfNLZ/Fanq9Thjj2aq6qnAauBg9yK4rRoMfKKqU1T1UeBXwC/c554HhovIcW5+sQPog9PT31Y1FK/rAFS1QkSCqroU56LtHoBEdKRYYr0PROQ899ZWJ1UtB57ASWpW4tyuGea+rm7PzvE4MS9MaIM91tx4AZk4B9+1wFj3YNtP2thUX7HsX6q6M+pXZ+DUEbepxLq58VLVIpwen5+KyKHiDJCdjlN61GbEsH8F3DEOScAqoNi7VnsnhuMXwLvAbe6x/8fAKJwEqM3YS7zGRsdLVb9x6/jBqYn9rK3dEXHjNU1E0nEuxp52tweBZe5/4JQYvQA8KCJDcO4iCZCS+FZ7pxnxWuw+FpzzIap6Ec5xfxdwiLTwuC1p250Pzef+I/XEGSEfxrmVlQFco6r57muGAj8FylT1TndbKjAZ+D1OQf31bs/GAS3GeJWr6h3uto6qWhD1PrUeH6j2Y/8KAEcDD+IMlvqN7V+Nx8vd/iOc2/ajcG7bL01w8xNuf/YvN7m+HyhS1Vs8+QMSbD+OX+2Ax4DuQBC4WlWX1f+EA8t+fh/H4gwwCwGXqOqaBDc/4fYWL7enNSQiPwG+q6o/jPrd64FhOHdHLlbV5Yn/CxJrP+M1ALgf6AL8XFWXtHR7rce6Gdx/NMXpUd2kqsfh1P/uxJmOCgBVXYVzBdVbRIa4SXUYZ6aLW1X1u20k6Yk1Xr3ceLUDytz3CLivaQtJ9b7uX2k4V+SbcPav02z/ajJeGSKSrKr/Am5y49UWkur92b/auU//sg0l1fty/BoqIumqWgqcD/xUVae3kaR6X/evyL6Vg3P8Oq6NJNVNxeuxOi8/HmcKR0SkJ4Cq3gtcoapHt5Gkel/j1c3dVgDco6pTE5FUg6141CT3FujtQFBEZgEdcK6qI1O3XA1sFpGpqvqhu/1VERmBM11Ve+AYVV2Me3viQBaveAHLtQ3MBBKneB3rnrzbwgkpnvvXAX+rzuIVm/2M19tAexE5xk12tnrzVyROPPYvEYkcvz705q9InH2JF05Z3zpxFjr5voicqKq5qlrhxd+QSHGK18mqugFnoGzCWI91I0RkKs7VdWecQRV3AJU487mOh+pBPLcDt0X93pk4sw3MxRmIccBfUYLFK1ZxjNcB3yMGtn/FyuIVG4tXbOz4FZt9iZc4NcMX4PTAdsDppMtNeOM9EMd4baj35glgNdaNEGf+6Wx1JvxHRP6C0+tcClylqmPdcoXuwJ9xalvXub+Hqs7zqOmesHjFxuIVG4tXbCxesbF4xcbiFZt9iNd1OBUFVwFPq+pX3rTcG609XtZj3biFwL/dqyCAT4D+qjoT59bEVW65Ql8gpKrrwDlgtLWDhsviFRuLV2wsXrGxeMXG4hUbi1dsYolXWFXXq+oaVb3W6yTRI606XpZYN0JVS9SZPznkbppBzZLk5wMjRORNnLklPf+H9JrFKzYWr9hYvGJj8YqNxSs2Fq/YxBivhdC2F2Jq7fGywYt74V4xKdADeMPdXAjcCIwG1qmz0ITB4hUri1dsLF6xsXjFxuIVG4tXbGKJl1qdbquNl/VY710YSAbygYPdq6RbcG4/fGwHjXosXrGxeMXG4hUbi1dsLF6xsXjFxuIVm1YZLxu82AwiMgGY7/73pKr+3eMm+ZrFKzYWr9hYvGJj8YqNxSs2Fq/YWLxi0xrjZYl1M4hIX+Bc4E/qLNFqmmDxio3FKzYWr9hYvGJj8YqNxSs2Fq/YtMZ4WWJtjDHGGGNMHFiNtTHGGGOMMXFgibUxxhhjjDFxYIm1McYYY4wxcWCJtTHGGGOMMXFgibUxxviEiHQRkUXuf1tFZJP7c5GI/KUFPm+miKwTkcvi/d51PmeyiCwTkSUt+TnGGOM1mxXEGGN8SERuA4pU9b4W/IyZwJuq+lILfkaSqlaJSLb7WaNb6rOMMcZr1mNtjDE+JyLT3FXHEJHbROQpEXlXRHJE5Psicq+ILBaR2SKS7L5urIh8KCILReQdEem1l8/IdHuvI7/fwX3/ZBEZ7L73QhGZJyLD3decKiKfi8j/RGSOiPSIauNjIvIu8HSLBscYY3zEEmtjjGl9BgOnAKcB/wTmquoYoBQ4xU2OHwLOUNWxwD+Au5p6Q1UtBD5w3xfgx8DLqloJPAZc5b7Xr4FIWcrHwARVPQx4Abg+6i3HAqep6tn7+bcaY0yrkeR1A4wxxsTsbVWtFJHFQBCY7W5fDGQDBwGjgfdEBPc1W5rxvk/gJMevAecDF4tIe2AS8KL7XgCp7v/7Av9ye8NTgHVR7/WGqpbu019njDGtlCXWxhjT+pQDqGpYRCq1ZrBMGOe4LsBSVZ0Yy5uq6iciki0iU4Ggqi4RkQ7AblU9tIFfeQhnqeE3RGQacFvUc8Wx/UnGGNP6WSmIMcYceFYA3URkIoBbJz2qmb/7NPA88CSAqu4B1onIme57iYgc4r62I7DJ/fmn8Wq8Mca0VpZYG2PMAUZVK4AzgN+LyNfAIpxyjuZ4FuiMk1xHnANc6L7XUpzabnB6qF8UkXlAfhyabowxrZpNt2eMMW1UQ9PticgZOIMOz43zZ2Vj0+0ZYw5wVmNtjDFtVwFwh4h0VdVHReQh4CTg5Hh+iIhMxplJxHq1jTEHNOuxNsYYY4wxJg6sxtoYY4wxxpg4sMTaGGOMMcaYOLDE2hhjjDHGmDiwxNoYY4wxxpg4sMTaGGOMMcaYOLDE2hhjjDHGmDj4//m2IAC8SkxHAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 864x288 with 1 Axes>"
       ]
@@ -1701,7 +1721,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -1755,13 +1775,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Sensors at 'Barlad': ['HMP155-Vaisala_air_temperature_-2.000000_-2.000000', '5TM_soil_temperature_0.000000_0.050000', '5TM_soil_moisture_0.000000_0.050000', 'QMR102_precipitation_-1.400000_-1.400000', 'HMP155_air_temperature_-2.000000_-2.000000']"
+       "Sensors at 'Barlad': ['5TM_soil_moisture_0.000000_0.050000', '5TM_soil_temperature_0.000000_0.050000', 'HMP155_air_temperature_-2.000000_-2.000000', 'QMR102_precipitation_-1.400000_-1.400000', 'HMP155-Vaisala_air_temperature_-2.000000_-2.000000']"
       ]
      },
      "metadata": {},
@@ -1780,7 +1800,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001B[1mMetadata for sensor QMR102_precipitation_-1.400000_-1.400000:\u001B[0m\n"
+      "\u001b[1mMetadata for sensor QMR102_precipitation_-1.400000_-1.400000:\u001b[0m\n"
      ]
     },
     {
@@ -1789,20 +1809,20 @@
        "variable        key       \n",
        "climate_KG      val                           Dfb\n",
        "climate_insitu  val                       unknown\n",
-       "elevation       val                           172\n",
+       "elevation       val                         172.0\n",
        "instrument      val                        QMR102\n",
        "                depth_from                   -1.4\n",
        "                depth_to                     -1.4\n",
-       "latitude        val                       46.2331\n",
+       "latitude        val                      46.23313\n",
        "lc_2000         val                            10\n",
        "lc_2005         val                            10\n",
        "lc_2010         val                            10\n",
        "lc_insitu       val                       unknown\n",
-       "longitude       val                       27.6444\n",
+       "longitude       val                      27.64438\n",
        "network         val                          RSMN\n",
        "station         val                        Barlad\n",
        "timerange_from  val           2014-06-06 07:00:00\n",
-       "timerange_to    val           2020-12-12 23:00:00\n",
+       "timerange_to    val           2020-10-31 23:00:00\n",
        "variable        val                 precipitation\n",
        "                depth_from                   -1.4\n",
        "                depth_to                     -1.4\n",
@@ -1814,7 +1834,7 @@
     },
     {
      "data": {
-      "image/png": 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ka2y9+J/4svAc4cgiKb6TxOP7pwTBY134XfeJmzeEYCjELeHn/laScjg7LDfjgOFx84zguNwQ/lxD59EjhpOizGWZH6OT5Md3w3knEDQJSbbeiSScVzKliWC4xyVhfnSMBhPO60MwrGNsxKKfJqx7eFi2GoC3gcOjfg/60U+UH3PP9IRTRKTyhbXkkwk6O95e6vTkg5mNIwgS8/YGvgj7HE4whnUvDzoTioh0C2qeISLdgrsvIxgdZXeLe5OfiIhIFHpDlIh0Gx4Mjza91OnIhpltTjErl05r3ZKZ7Q3MSjH7Y+6+pJjpKTUz+ydBB8xE97r7fxQ7PSKVQs0zREREREQyUPMMEREREZEMFDSLiIiIiGRQEW2ad9llFx8+fHipkyEiIiIiVW7q1Knr3H1o4vSKCJqHDx/OlClTSp0MEREREalyZpb0rbBqniEiIiIikoGCZhERERGRDBQ0i4iIiIhkUBFtmkVEiqmlpYVly5bR2NhY6qR0a3379mXPPfekV69epU6KiIiCZhGRRMuWLWPgwIEMHz4cMyt1crold2f9+vUsW7aMfffdt9TJERFR84yuaGt3PlhdX+pkiEieNTY2svPOOytgLiEzY+edd1Ztv0iCtfVNrK1vKnUyuiUFzV3w17FzOeUv45mrwFmk6ihgLj19ByLbO/IPYznyD2NLnYxuSUFzF7y9ZBMAq+tUEyIi5W/KlClcdNFFaZdZsWIFZ555JgDvvvsuY8aMybjdxOWeeuoprr766q4lVkSkzChoFhGpUG1tbVktP3LkSG644Ya0ywwbNoxHHnkEyD1o/tKXvsSoUaOySpuISLlT0CwiUoYWLVrEgQceyHnnnceIESM488wz2bp1K8OHD+eyyy7j+OOP5+GHH+aFF17gmGOO4eMf/zhnnXUWmzdvBuCtt97i2GOP5dBDD+Woo46ivr6ecePGcfrppwNw6aWXcs455/DpT3+aAw44gFtvvbVjv4cccgjNzc389re/5cEHH+Swww7jwQcf5M033+TYY4/l8MMP59hjj2XOnDlJlxs9ejQ//vGPAVi8eDEnn3wyI0aM4OSTT2bJkiUAfPe73+Wiiy7i2GOPZb/99usI1KU6XfP8bIaPepa2di91UkRyptEzRETS+P3TM5m1oi6v2/zYsEH87osHZ1xuzpw53H777Rx33HGcf/753HTTTUAwFNuECRNYt24dX/3qVxk7diz9+/fnj3/8I9dddx2jRo3i7LPP5sEHH+TII4+krq6Ofv36bbf9adOmMXnyZLZs2cLhhx/Oaaed1jGvd+/eXHbZZUyZMoW//e1vANTV1TF+/Hh69uzJ2LFjueSSS3j00Ue3W2706NEd2/nxj3/Mueeey3nnnccdd9zBRRddxBNPPAHAypUrmTBhArNnz+ZLX/pSR7MQqT63vbYQgNb2dnrU9ChxakRyo6BZRKRM7bXXXhx33HEAfOc73+loWnH22WcDMHnyZGbNmtWxTHNzM8cccwxz5sxh991358gjjwRg0KBBSbd/xhln0K9fP/r168dJJ53Em2++yWGHHZYyPbW1tZx33nl88MEHmBktLS0ZP8Prr7/OY489BsA555zDxRdf3DHvy1/+MjU1NXzsYx9j9erVGbclIlJKCppFRNKIUiNcKImjR8T+79+/PxCMZXzKKadw//33d1pu2rRpkUaeSLX9VH7zm99w0kkn8fjjj7No0SJOPPHEjPtIt88+ffp0/O2ux/YiUt7UpllEpEwtWbKE119/HYD777+f448/vtP8o48+mokTJzJv3jwAtm7dyty5cznwwANZsWIFb731FgD19fW0trZut/0nn3ySxsZG1q9fz7hx4zpqpmMGDhxIff22ITVra2vZY489gM5NMBKXi3fsscfywAMPAHDfffdt9xlERCqFguY8UAWJiBTCQQcdxF133cWIESPYsGED//mf/9lp/tChQxk9ejTf/OY3GTFiBEcffTSzZ8+md+/ePPjgg1x44YUceuihnHLKKUlfEnLUUUdx2mmncfTRR/Ob3/yGYcOGdZp/0kknMWvWrI4OfhdffDG//OUvOe644zqN3JG4XLwbbriBO++8kxEjRnDPPffw17/+NY85JCJSPFYJj8RGjhzpU6ZMKXUytnPO7W/w2gfruPv8o/jkR4aWOjkikifvv/8+Bx10UEnTsGjRIk4//XRmzJhRkO1feumlDBgwgJ///OcF2X6+lMN3Ibm5Y8JCjtp3CIfssSMf+dVzNLe1M+eKU+nTUx0Bu2L4qGcBWHT1aRmWlFyZ2VR3H5k4XW2aRUREJO8ue2YW0Dm4q4B6OpGUFDSLiJSh4cOHF6yWGYKaZpGiyfGN6BPnrePbt73B8/9zAgd+KPkoMCLFojbNUjHeXrKRxeu3lDoZIiJSJM/PWAXAmws3lDglIqpplgry1ZsmAWrHJcXh7pGGbZPCqYQ+NyLSfaimWUQkQd++fVm/fr2CthJyd9avX0/fvn1LnRQREaCANc1mthdwN/AhoB24xd3/amZDgAeB4cAi4OvuvrFQ6RARydaee+7JsmXLWLt2bamT0q317duXPffcs9TJEBEBCts8oxX4mbu/bWYDgalm9iLwXeAld7/azEYBo4BfFDAdIiJZ6dWrF/vuu2+pkyEiImWkYM0z3H2lu78d/l0PvA/sAZwB3BUudhfw5UKloVj0AFdERESkuhWlTbOZDQcOB94AdnP3lRAE1sCuKda5wMymmNkUPSIVERERkVIqeNBsZgOAR4H/cfe6qOu5+y3uPtLdRw4dqrftiYiIVCqNQyPVoKBBs5n1IgiY73P3x8LJq81s93D+7sCaQqZBRERERKSrChY0WzDA6e3A++5+Xdysp4Dzwr/PA54sVBpERERERPKhkKNnHAecA0w3s3fDaZcAVwMPmdn3gSXAWQVMg4iIiIhIlxUsaHb3CaRuxnRyofYrIiIiIpJveiOgiIiIiEgGCprzQK/aFRERyUyXS6lkCpq7IOjrKCIiIunocinVQEGziIiIiEgGCppFRMpMbUMLr89fX+pkiIhIHAXNIiJl5od3T+Gbt05mc1NrqZMiIiIhBc1doA6AIlIIs1fWAdDWpnOMiEi5UNCcB+oQKCIiIlLdFDSLiIhIUTi5PT3Rg10pBwqaRUREpKAs5QuCM6ynB7lSRhQ054HaNouIiIhUNwXNXaC2zCJSSLk+yhYRkfxT0CwiUmZ0Qy4iUn4UNIuIiIiIZKCgWUREREQkAwXNIiIiIiIZKGgWERGRotBgU1LJFDSLiJQpBRhSLdS3VaqBguY80HVNRPJJAYaISPlR0NwFuq6JiIiIdA8KmkVEREREMuiZaoaZ1WVY14CV7v6R/CZJRERERKS8pAyagfnufni6lc3snTynR0RERESk7KRrnvG1COtHWUZERHKgTsZSbVSmpZKlrGl29wXx/5vZoPjl3X1D4jIiItJ16mQs1UZlWqpBuuYZAJjZj4DLgAa23SQ6sF8B0yUiIiIiUjYyBs3Az4GD3X1doRMjIiIiIlKOogw5Nx/YWuiEVDQ10hIRERGpalFqmn8JTDKzN4Cm2ER3v6hgqaoQemuXiIiICCzdsBUz2HPwDqVOSsFECZpvBl4GpgPthU2OiIjEuOsxlgjoWKgEJ1zzCgCLrj6txCkpnChBc6u7/7TgKREREQBMj7GkSmUb/OpIyN2/3lhCzxrj60fuVeqkVI0obZpfMbMLzGx3MxsS+8m0kpndYWZrzGxG3LRLzWy5mb0b/nyhS6kXERGRsqcbweK75PHpXPzotFIno6pEqWn+Vvj7l3HTogw5Nxr4G3B3wvS/uPu1kVInIiIiIlIGMgbN7r5vLht29/FmNjyXdUVEREREykmUl5v0AE4DhtP5jYDX5bjPH5vZucAU4GfuvjHFfi8ALgDYe++9c9yViIiIiEjXRWnT/DTwXWBnYGDcTy7+AewPHAasBP6cakF3v8XdR7r7yKFDh+a4OxGRyqXxAqQaraptZPioZ3l7SdI6M5GyFaVN857uPiIfO3P31bG/zexW4Jl8bFdEpJqoy5RUswnzghcM3zt5MR/fe3CJUyMSXZSa5ufM7LP52JmZ7R7371eAGamWFREREREpF1GC5snA42bWYGZ1ZlZvZnWZVjKz+4HXgY+a2TIz+z5wjZlNN7NpwEnA/3Yp9SIiVWBNfSO/eGQaTa1tpU6KiIikEKV5xp+BY4DpnsWo5O7+zSSTb4+6vohId/H7p2fx7LSVHHfALnzp0GGlTo6IiCQRpab5A2BGNgGziIhkL7Ets866UtUilG8dAlJOotQ0rwTGmdlzQFNsYheGnBMREZFuKpeOrnqjoJSDKEHzwvCnd/gjIiIFpPhARKT8RHkj4O+LkRARke5Oj6JFRMpXyjbNZnZpppWjLNMduC51ItIFqlgWESl/6Wqaf5BhaDkDvgFcmtcUVRBd6ERERKLzFH+LVIJ0QfOtZH5d9q15TIuIiMTRUyypRDeNm8dRw4d0mhZfyaQ2+1KpUgbNasssIiIiqbS2tdOjxrYb2eKa5+eUKEUihRVlnGYRESmCbcPhqypOypu78+FfPcfvn55V6qSIFI2CZhGREtMYtFKpRk9aVOokiBSNgmYREREpumxeNKyXEks5yDhOs5kNBX4IDI9f3t3PL1yypLv73p1vcvCwHfn55z5a6qSIiEgeZfNgRc9gpJxEeSPgk8BrwFigrbDJqUy6Ac6/V+as5ZU5axU0S/emc4tUGV0vpZJFCZp3cPdfFDwlFUjtEEVERCLQ5VKqQJQ2zc+Y2RcKnpIq19LWzqVPzWT95qZSJ0VEykxiPKH7cRHJ1tTFG7l1/IJSJ6OqRQmaf0IQODeaWX34k+5NgZLE8zNWMXrSIi5/RsPziEh6eoQt3YGKeX597R+T+MOY90udjKqWsXmGu2d6K6BE0B5eBdt0lhCRqFTjLFXIVLClQkVp04yZfQn4ZPjvOHd/pnBJEhEREREpLxmbZ5jZ1QRNNGaFPz8Jp4mISB5t1yxDT6ZERMpGlDbNXwBOcfc73P0O4NRwmuRAA7RLNXry3eWcev34UiejYqnjn3QbugQW3YX3v1PqJFSNqG8E3Cnu7x0LkI6qp+HppJr95IF3mb2qvtTJqBo6XUi1SVakVYdUHE+/t6LUSagaUdo0XwW8Y2avEJT7TwK/LGiqKowOfBERkWh0UyiVKmNNs7vfDxwNPBb+HOPuDxQ6YZVAx335mrG8VuNVioiISN6krGk2swPdfbaZfTyctCz8PczMhrn724VPnkhuTr9xAgA//OR+JU6JSHSuBp8iImUrXfOMnwIXAH9OMs+BTxckRVVOl0SpZu6u9vs5SJVjOl8U3jtLNvKVmybxyH8cw8jhQ0qdHBEpYymDZne/IPzz8+7eGD/PzPoWNFUVIpsLmsIIKXdLN2xl+aYGjt5v51InpdvT+aJ4XvtgHQCvzl2roLnIdFMolSbK6BmTIk7rtlSxJtXghGte4Ru3TC51MkRERMpSujbNHwL2APqZ2eFsq/wYBOxQhLSJiHQrGolHqp3a7UslS9em+XPAd4E9geviptcDlxQwTVVpw5bm4A+dL6SKuevJSy7UDrz0dMNSWCrjUg3StWm+C7jLzL7m7o8WMU0VJ8rJ9ndPzSx8QkREJCsK5XKjmwzpjjK+3MTdHzWz04CDgb5x0y8rZMIqgU62IlJICkykmrkKuFSYjB0BzeyfwNnAhQRx4lnAPhHWu8PM1pjZjLhpQ8zsRTP7IPw9uAtpFxGpSnqSLdUsl6YaCq+lHEQZPeNYdz8X2OjuvweOAfaKsN5o4NSEaaOAl9z9AOCl8P+K8/yMlQwf9SzLNzVkv7IuhlLFdGHrGlW8lU5iB7Xho57lUjWry4vF67fkvK7aQks5iRI0xyLDrWY2DGgB9s20kruPBzYkTD4DuCv8+y7gy9GSWV4embocgNmr6rNfWRfFgmtqbSt1ErodXde6RtlXOunK7uhJi4qWjmr23rLaUidBJC+iBM3PmNlOwJ+At4FFwAM57m83d18JEP7eNdWCZnaBmU0xsylr167NcXfSHV01ZnapkyAiIknoaYpUsowdAYFr3L0JeNTMniHoDNiYYZ0uc/dbgFsARo4cqcNMIlu2MYdmMyIiUjB6GiXVIEpN8+uxP9y9yd1r46dlabWZ7Q4Q/l6T43ZEpMDW1jfR0JxdUxf1hs8vvQiieFR0C0fnBakWKYNmM/uQmR1B+EZAM/t4+HMiub8R8CngvPDv84Anc9xOSXXljlkXQakUR/5hLGf+c1KkZVWJlF+mHC0adTQTkagK9kZAM7sfOBHYxcyWAb8DrgYeMrPvA0sIhq+reAqDy4uugfkzc0VdpOV0DIiISLUr2BsB3f2bKWadnO22REREpDKpdYZUi5RBs5l9x93vBYab2U8T57v7dUlWE5FuTNdGqVQqu8WnPJdKk655Rv/w94BiJKSS6Ol/edP3U3xG1y6Ai9ZtYWtzGx8bNihfSRKRMuToHC2VK13zjJvD378vXnKqnx5TiWzvxGvHAbDo6tNKm5ASSzw96Hwh1UKBslSDjEPOmdl+Zva0ma01szVm9qSZ7VeMxImIdAsJEYU6sxbfS++vLnUSqpZGjZJqEWWc5n8BDwG7A8OAh4H7C5koka5QwFE6qhktrPWbm2hs0WviC2Hu6s2lTkL3o/OFVJgoQbO5+z3u3hr+3Es3L+oKykQ601i3xXHEFWM5f/RbpU6GSFZ0My3VIsprtF8xs1HAAwTB8tnAs2Y2BMDdNxQwfWUp8QSQzduOdPIQka6YNH99qZMgkpXE657usaVSRQmazw5//yhh+vkEQbTaN4uI5IFeNywiUr4yBs3uvm8xElLJ/vHqfC64ZyoLr/qCHlOXAb2CuHTU4Sc3qcqsclOqjW4MpZKle7nJp939ZTP7arL57v5Y4ZJV3hLj4neWbAKg3aFHhnhNMbVUIxXr/FJ+SjVxkvd70E22VJp0Nc2fAl4GvphkngPdNmgWEZHqoIqM3GQT7qp2WapFupeb/C78/b3iJaeyBSeG9GdgnTsKTxdBqVQ6PUg1SqxlzqUJna6dUg6ivNzkSjPbKe7/wWZ2RUFTJSLSjehGTyR7dY0tGrdciirKOM2fd/dNsX/cfSPwhYKlqAKo045IcqoNEpFE+WiekezGcsSlL/CZ617t8rZFoooSNPcwsz6xf8ysH9AnzfIiJaVau+JTnheG2oLmz3tLNzFm+srtpmu0ncJLVYrzUbyXbWzo+kZEIooyTvO9wEtmdidB2T8fuKugqapi6i0sIplo6Mr8O+PvEwFYdPVpJU6JiFSqKOM0X2Nm04DPEPRyu9zd/13wlFUgVQqJdB/fue0NGlvaeOQ/j83fRnUOkWpU5DcCHnf1yxy7/8786axDC7sj6XaiNM8AeB943t1/BrxmZgMLmKayp0qg8qbHrV0zfu5aNQuIYMK8dUxZvDEv21KJFcmf5ZsaeHjqslIno6yMn7uW9nad17sqyugZPwQeAW4OJ+0BPFHANIlICZ17x5s8P2NVqZMhUhSqBCk8NUssvXPveJPbJiwodTIqXpSa5v8GjgPqANz9A2DXQiaqUunEINViZW1jqZMgIlVOD7SKa/H6raVOQsWLEjQ3uXtz7B8z64la3uVMJ4kiUM1Rl6mYlgedLwovUx5v3NKcfgGRCqHTSddFCZpfNbNLgH5mdgrwMPB0YZNVmXSBk+5Ox0DX6GlV+Tn88heZv3ZzqZNRVVSvIZUqStD8C2AtMB34ETAG+HUhEyUipZVtR0B1vuwatastjckL1jNp/rqMyy1ev6UIqaleqU4nUxZv5NW5a4ubGJEuSDvknJnVANPc/RDg1uIkSUREpPC+ccvkSMvpCUrXeIq/121u4rw73tTY2UWictx1aWua3b0deM/M9i5SesrOPa8vYvioZ2mLG6pFtULlrTt+Pe3tzvBRzzJ64sJSJ0Wk4mnIxfzrjudlqT5RmmfsDsw0s5fM7KnYT6ETVi6uePZ9AFra2kucEpHUWtqD8nnlmNklTYfa5Gavta2dNxduSDpPN+il0dSq830+vT5/vc4MUhWivEb79wVPhYhUNgV3OfvL2LksCoeCSqzgVIVnaVz+zKxO/+t76Jqn3lvR6X/dDJaKCnJXpaxpNrO+ZvY/wFnAgcBEd3819lOsBFYSnVjLg+mM3GXlVpYfmbqM216rzoH5P1gdbWSGF2bqhTPFMmtlXamTIFJWXp+/nt8/PTPSsnNX1/O/D75LaxU+oU/XPOMuYCTBqBmfB/5clBSVMXenvrFFIwWIFNnPH36vo6lUNUvXDOyCe6YWMSUSr8zuISvalqbWUidBcvDNWydz58RFkZa98F/v8Pg7y5lXhUM1pguaP+bu33H3m4EzgROKlKayNXrSIv7fpS+wdGPyt+pEac+pk68UUr7aFOe6nXKroa4E8Q9GfvNktJocKSxVixTOp/40jrpGBc5SmdIFzS2xP9xdJRx4cdZqAJZs0KsoC2Hphq08+e7yLm8n3QVvzqr6qnzM3dWnH10dLUBBxvbmrannuekrS50MyYPbJyzoNIKSdPbAm0uA4Bz+xDuZz+F1DS0ZlxEpR+k6Ah5qZrGGXUbwRsC68G9390G57tTMFgH1QBvQ6u4jc92WVI8v/m0Cm7a2cMZhexRsH5+7fjyAxgVN8MqcNaVOQtX5zHXRypqae5W/yQs28OjUZXz9yL1KnZSyEX+jPeqx6XzyI0P5yk0TWbc582vH9USqNJTvXZcyaHb3HgXe90nunvlVTGUkU/8yFciu2bRVtQ+l0tDcuS2tyrJIZ1ub9cA1nbZ2jxQw50rnJCkHUcZpFjofsKkO3rcWJR9rNdV2pDC68+AZuZavfOWZivf2Mp0XkuV9dy7D5UplO71szj0az700ih1/VGO8U6qg2YEXzGyqmV2QbAEzu8DMppjZlLVrS/9u+igH+XfvfKuj3bOUTjUeqJnkO8jKNgsV5KV21j9f5/kZ1deOXiRX3fEc3Z1U8/WgVEHzce7+cYKh7P7bzD6ZuIC73+LuI9195NChQ4ufwhwtTzGyhuTP20s2ctVz1T/8WDFV8TmuLCxLc16o5gtMpUo21rsCvfRUe1zZrn5uNlMXb8zLtmLHyvTltXnZXjkpSdDs7ivC32uAx4GjSpGObEQ9Yeq0UXhfvWkSN7+a+kUXCkIya2lrpznNq4KzDRAUUORXQ3NbqZMgUjBRTxfNre20tlffCzJKJd2NzT9fnc/X/jEpL/tpag3OXxc/Mi0v2ysnUV6jnVdm1h+ocff68O/PApcVOx25iNLLXcGDVIKTrh3Hso0NHSM76EajsNKdFxLPK+8u3cT6LYXrUCW50am9+D722+dp1VB/UkZKUdO8GzDBzN4D3gSedffnS5COrOTvsM3/CWD6slomzquogUjS6uqYwd1Z1JxbtrEBgFW1jXkZG7tj/zl8d6/M1nB38d6Oe0QaNTtb2toZPXFhVb62VqpP1HKtgDm/NLxl1xW9ptndFwCHFnu/XRU1GCjFIf7Fv00ANPawZO9bt05mwbotXH/2YZ2mZ9s+sSs11d8b/VbuK1eDPFzHbp+wkKufm01NjXHuMcO7vkGRLKmuo/yp3XnXaci5HOhRdmF19eTbnb+ebD/7ytpGoHxOpqMerb42cNnK5ZuoDd+wVq/XE4t0O89NX8lHf/1cqZOR0vJNDQwf9SwzqqBjoILmiOIvZLqjLqxSZu+WplbmrKovYQqKKxYsJz62K1UZf+CtpaXZcYGluylJvNHJpYlLbBtq2pQfyW4+lbfpOapQKrbGlqDD3R+fn01Tmo7dMbUlen35y2ETvPvD161XMgXNEbnrhFBuCnER+497p/K568fTUqFtQ3PNEb3cpLCyevGDMrHk9BUUVrk82ap0//PAu0DyIRKT+ffM4rxHopq/XQXNeabaiK5LlofJpyVfP+oJJJnJC9an3Xa1aWwJbg7q0jzWf2baCh7KUAOsDibZe2X2Gu6cuDAv24oV+e5SbgvllvHzq6pTdb64O5c9PYt5azanXS7qWUDlND/eDN82Wsiz73UvzuWdJfGdkzN/edX8/Ra9I2DFquJCUAnW1jcVZT+xztrd7anCP8fNTznvx/96B4CvH7lX3vfb1o17x8c6QJ4+YvdO0+NHDIhaIxe7aem+uZkfV46ZDcAR+wzebl41BwKZLNmwlTsmLmTs+6sZf/FJKZczs0gZ1drWjTMzjzrOnwW8Xt3w0gfc8NIHWa0Tf16vtopE1TTnoLsFVMWW7BBLFlsV4lCstgM8qlJ97q/maTD9Spb4ZOSPz8/OYRv5So1IbrI5h/xl7NwCpqT7aG+P9Ukpnihf8/JNDR1/3/jyvAKmpvgUNEcUX+OTrtB005ir4NojNtmArp1AKv3ry1Rz29zazn1vLC7Y/rMt/+8t3VSQdJSbYpWrqPm/dMNWXpi5qrCJqTJqhyvlJvZUqitNEgvtuhfn0hi+4bQajiA1z4go/mLUlVo5BdWpxZ7sJcujYgcd1fo93fzqfP78Yv5recr4nF0W0r8RsOs6Rs+IeKR84a+vUd/UqrHdU1BxlkrQ5iWoac5hnaueez/v6SgV1TRL2Ul24W9PUoNapXFtQW3cGm3Ioe7aTKVihXctUb+2+qbSj+f86NRlHHPVS0mPbalMutkorlL0Ccnl2pBtMmetqOMjv3qOlbUNmRcuMgXNEZXqtO7uVTEgeBTZnnBTHrs6c6dU6hrhBWs380Y4Qkk6M5bXdgRTS9ZvLXSyiqq1rZ1ZK+o6/k/3nVTzvcuox6axsraRlvbKHN5RpNRiAWyhzuulqjy5Z/JimtvaO8Z3LicKmvMs3+3eHp66jNNvnMCLs4ozvmI5SNo8I25aMQK/am2/WPAhoTKs9+k/v8rZt0xOu8zUxRs4/cYJ3PLaAgA++adXckxMefrzi3P5wg2v5XWb25pnVI6aLGvHS61S0lkIUT67Xm5SfLGvpVBDfua7qWQ1FA8FzRG5e8cwOenGtM10csm2wM0N305358SFBe3AVQ7SdWZI2hGwgCHCxi0t/PqJ6TS1tqVcZv3mJn7zxAyaI7yJqdx1tSNJPk+GyzYGj+Sq6QlLfFlN7PyYlzbN214JmIetFUcsaE52bEt5UlBcXmKHTsFqmguz2ej7L3UCklDQHJEDr0d4rJxvsYNh0vz1/OrxGUXffzlob/dOF9ZidNa7csz73Dt5Cc+8tzLlMpc/M4t7Ji/m+SochSBV1ra3O+3tjrsXrD1dLIAvw/Nlzgr9pKQSXy5TEya5UsbqrpBkFlzHOSBheqpO3FK5snmpWKTtdSEt5UJBc57lu1CU81AyxbLfJWP49RPRbxjyEUDEeiWn+z5jw/1UUqe5rhanY65+iZF/GMv1Yz9g/0vG0NCcuiY+Vx1JrJxszUpi+cznMV5JWVYTRs3l2KQ52VeSy/jZ1Wi/S8Zw1s2vbzf9+rFzO72Yp6vGz13b6f9KKtvFVqg4QXm+PQXNEZUqLupOIXO6J8yT5he/lj+qcrixiVo+u5rW1XVNbNjSzL/eXAJAXWPn0TiqtS14PhWkpjmPr9GeOG8dC9dt6fqGMog1z2iroJtOCUxdvHG7ac9MS/1ULhfpOoE9/d4KaiOOBCS5S96mufDHaxlcUlPSOM0iyUTs+FJp8tURsAKb0JatdN9J1OzNdpzmdL592xsABR/DuUdY01wpzTOkuMd7qsBpyfqtXHj/O3zyI0OLl5gyV6gYU5Ug21NNc0T5KjxZP8ov4zuufOuoLYuY17GsrG9sYeQVL3YMZZaPu9S19U0Zl3k2zzUrhfTku8v59LXjUuZstuUy2++qXLZdDgr9ZGLG8loOu+wF1m3OXIaz8cy0FZx07bi8jasca9NczR0BL31qJj996N2k8xqa2/jElWO3a4YggVTN7GKds1dsyn0M3+dnrORTf3qF1rYybBuUg8RTypaI47CnK5+QeSSr7khBc0RLN0Q7QJMVqOVdOLgrsYNPV0U9KGNB1cwVdazb3MyfX8jfm+7eXLQBqJ57lv998F0WrNsSOeDJFLAWcriwWJmvppNz/E3JdmUqz4Xs1tcWsGlrC699kN9g7P8ensbCdVuYt3ZzXrYXu3koRE3zytqGLj2+X74x8zl7yfqtGYOT0ZMW8djby5POW7huC6vrmrhyTOW8La2Yj81rUuzLsrjZWrRuS9J+F794dDqL12+lPs1IWJUk8XuJWqETK58btjRntf1imZ+nc00+KWiO6Gv/mBRpuWTBxnFXv9zxd7a1TOXctiffcr1ByOej6a7sv5zlu3Yz1bjA+Qh089k+txwVuk1zoZvOfPYv4/OynR4FDJqPuepljr/m5cwLprCitjHjMp/80ysdTVlyUe3lvKtSHycdj6IyOvHacfzo3qk57KOyJF47L350GnPC4Wqj+PjlLyadHrsxKVU23TlxUYn2nJqC5ipS7qM43PfGYibNWwcENUFXjnm/c81nknPh1c+l7rEe+7ixXvgdY1ZGSMvMFdUzBnAUNRmuM9kGv7EgPPb9ZRuUl3tZrRQvzlrNk+8u7zRMX65PAd5fWcffXv6g4//fPz2TlgI+vi5084xUtYixz/X3V+Z1eR/vJoy5nY1qCdgKJdU5xaLHzAB5f+JSjpJlVT5eQb1tHGgV1hgFzXmW8eUm2bYdzWLZjWXem/hXj8/gW2HNzM8eeo9bxi9gSpJe2PH++er8lPO2vQ0pkM3F97QbJkRethpkavJQ7Bh2fYbHgdUsn5efH949hZ888G7SHWQbjH757xO5Nq6J050TF/H8jG1jkOf7ulnI5hnp3DlxEWOmr+RP/55T1P2mUq1t97sqVXHbdlMYLd+6Q7iX7DPm45yeeI3N13YrmYLmMpfNhaqSTg6x8Tzbk7T1zL5TWuFehlGON9ibtjYzfNSz3DlxYeR1Yp/jjizWieKEa17hmKteynq9kVeM5a5Ji5LO+6/73u5iqspPfJGuSShU6ZolZXssuMcFFRGWj2/v2ZykVrmQ18ea8OpTio6ALW2lv/JX2mvEy0W2r4xPVktaSU+6Rj06jeGjns16vXTH1Zr6xkjbrKR8KhYFzWUuUzvfxpZtF73Ei3E5S/doduK8aGMyxw7oxLaB2WTDmws3sKiLY9IWO9tX1wWjIvzrjSWR18k2jZlOlfHbWxnX/jObU+wDby3NKk2VLD5fcikv9QnjYa+pa+SVOdvGsY0fcSSbl8PUNgTbXVvflDR4sxR/50PHOM0lGMCgHEbsyDb4KweL128t3s4yFLjIY9NnsY3Zq+q2e819qUU6Tya9MUi9+JKI32NHTbPFT8u9xNY1tPDc9G2dFMfOWs36PI/yU2gKmitc/CNGq6BvM1lzgdiB+R9pOm7E264dbg7p+PrNr3PiteNyWLN0CjG+bbaBXOLyuQRU1VKLkf3oOJ1zK8pF6BePTuv0/5n/fJ3v3flW2nWibDf2YpFv3jo56fz47znf7RprStQ8A8jbsHnpzFgerd9EJRwHpUhhqgqjbIekTFdsE2+eTr3+Nc74+8RI2y0nSZtnpFs+4qG8rZ9Qfo79Z6at5D/ve5vF67ewtbmVH9w9hfPufDPlfstRBYVZ3ct//+tt/v7KPFoyvGN2Vd22Wr6KqmkOS14+Do6axKrmKterR/B5o7yytr3d+fxfX6OxJX052m7orAx5mY+yNjtD7+5Kaes5Z1UdR1/5UuSON4lZ15ThuwFYvqnzaA5LNnSuKeq4qHl2j/1jwWOqoZ1SXSzzEXQWoiPg0g1bGXlF8pEA4hUjTs80HF22HdpKqRSBfaoh5zKV78Qh5lrafLsOrbFVq+W9OslOx+mPq6hRc/ArvulWPopCQ0tbx/Vr8boiPr3IAwXNeZbp5JJq7qraxk6PYJ+dFnRUWbkp/dBH8UU/1UmmHCQORB878XVu05ztCAyx9Qi3ldt20inH+5Bsapqb29p5f2VdxuXqshyvtBjZMn9t4V/lnA93v76YVXWNPP3eipTLtLa1dzQDSsy7eWtSj0W6fFMDDc1tGY/t+ABs2zi2GRLOtjKU6rTVqaY5bnpTa9fbVBSipvmhKUtZt3lbJ9OF67Yk3X55vLo7+Ze6tbmVZRsrK5AohExPs1J9he+v2v58tybFy6rKoZlOPiSrxEj30dKdT1bWNnTc8BWq4iL+mKxvai3oKD35pqA5z5pz7GBy9FUvcer1r203vUcWkXDUF7CUwrFXdx4z1ZIFzdlGYgltmLt6gCe7uEY5pxb7BTQ9w2r61gxPISD3GuHMbZoL/5nnrdnMmws3FHw/+ZIu9rvh5XmceO041tY3bVfO0124z7n9Tb43+s0sXn/uWR0P905enHZ+Ib/lZDfOXZW4qZOuHZd0lIxi1JzWZDh3d5SDhKRccPdUjv/jK4VJVI5KMeRYxuYZKb7DZJPbEq7LseEI/zEu9ehMlaRH0qA5dRlP930ec9XLfPWmSeE2tp+fjyMncbu/f3pmp/8nzV+Xh70UhoLmNG6fsDDrGpWuvJZz+aYG7n59UadpmYKe+MKf79fmFlLs1bH5OADTPa577O1lkbdTCW0LYVstQWsJRwBIVSrznYcLyvCNUKlE+ehLk9QgZmpmM3nBhu1H3EhTC2dZNM94J0Onp/jHsvH7zEegGwsq8xo0JzmjTF6wfcfiQrWjrmts4YpnZtHU2pbxhiPVUJkTwrHs4zt5l1qUp1Vdccv47YPXVJe+XEZL+v3TMztevx1vajjk6ZamVq54ZlYWWywvNUkiua40zpizuj7lNtranCvHvJ/xLYLpJJb5xM7/Re1wmiUFzWlcXoKD6LdPdr7j6pHhGyrnJhmRxHcEzHrVzgdesmvvTx96L5ekVIT4YKuuscRjdBeoHFZCm8NYuYtSs/vH52ZvV4O2MMLoLYm1lok1S0nfCJhxq5kD9gfjeu7HP/XKR/OG2OYK/WQ22Y1coYLm61/8gNsmLOSRqcsyPjlLVdvXu2dw0i+noLnQw0BeOSb1S6wSdZTvFF9hsmviS7PXdCrLMbFj9uZX53PbhIWR01AK6SokklWupbsZjd4RcPttvDR7NbeMX8ClT81MskY07d75klGKzsC56lnqBFSbm8bN5+JTDwTgoN88z+F779RpfrbXmkzNM7JpvlFsP3/4PR6ZuozvHju80/T414p3pZYpcdVZYW1Irk8Skw65lWJbn7nu1dx2kkfxTzXy3Qk0WV5ceP872/4pUrGrhM6AsRTeNWkR1zw/hz0H90vZUa6xpS2n8pl4mAff9/b9ATrtNcKx1RY28TFLvvjgHXp3/B1/rnlx5mq+dsSeGbefTiFGgUn2Gd5bVrvdmLQPTSnMcIex81lzFk8oE5Pcq8ZopjzGki6m9nbvdHOYeJjEjptMzY9S3Ywk+05i5aUSYjb31NejZHFAusM/apPCZJuI5VVDF27q2hKaFi7ZsJXZq+o48EODct5msaimuYAaWtqYND/amMOpZAqGehYpaB47azW1Wb5x8JGpQdOI0QkvsYgfoqvTW7RzDPymLYs2tFM67y7dlFWAFt95q9jN/WKpjK8l7NNz26G8NG5khVyDzmSvB47v6JbuI9dubeHl2atz2m+ixFEiSqWxpY0xceOLJhMbP3vZxgZW1CbvwPvestrtOsVGsd1FLsUXkO3LTWJNfFJdYJvb2pmyaANLN2zt1MHuiXeXR9h6esn6NWTr1blrc2qWNnd1YZr9xHduzHQ+S1VjGguAovRZyNaM5bXMyTBqTam0tjuvzF7Dpq1hOUv1Gu0MbzdNlevJypl7MNrGi7M6n69a2tp56r0VZdVk78n3lqe8wUwWNOenpnn7aR2j3iSkJVnzl1Ra2ra/Mr2xYFv/lXLsgB9TkqDZzE41szlmNs/MRpUiDZUim5rmZG/0yocNW5r5wd1T+OE9U4DgRQtXjXk/q4Mkla6clGJrXvL49C6n48t/n5jzUDq3vbagy/uPYmVtA9f+e07HyTD+BBp/gjzhmq53Ioq1q0wl8WYuPlj48f1vc/7oKaypTz/ySxQ3v1qcvM3k6udm81/3vc3rSW6Csy3D7+Vwk5fYZjHxtNB5POXgd5Rh4Vra2jtecJLMi7NWc+Y/X9+uTOWjPf225hm5bau93Tnvjjc5++bXO6ZlGuat0HrEveUwY5vmFAv0CjfS0pr/gO30GyfwuevH5327+bCpoZnvjX6L798VXGdSXfoyDdWXKl+TFTMH/jBmVkcb3pg7JizkovvfycvNYb7874PvbdfnKSZ5R8DU2+pK84xYzNGSkKHXZvFa+tY25/kZqzpPi9tess9TLooeNJtZD+DvwOeBjwHfNLOPFTsdpfJq2AEundNv3DaKRmJw0t7utLc7w0c9y/BRz/Ly7G1vBeu4Qwdemb2G8+54M/IF/Y4JC/nh3VM45bpXkwwPF/yetSJo/nDDSx9w8/gFPDwleie7VOJrErM9TJK1UYPc71I3J7ngLogw7NnbSzYVpf3hb56Yyd9emdcxokT8SabY9SGJedy3V3Aq+d7ot3jtgyDg3tKU/zyZsqg0o2nExmCubQiOsfhjrRjt8VLdpCS6Y+JCnpkW1IjHUtXU2sbn/jKe4aOe7ej4FDN/7ZacCk8+akFjF8Yo7aNnrqjl1OvHdxqWM7bW/LVbuPGlDzpNK5Xxc4Oyf+WY2TwV92Tmh3dP6XjyFrOtSc22VG9tbmV92MEq0xj91WbuqqD2f+rijQwf9ex2T1cs4XdisVm0bgunXPdqR/4luvq52dw6fkGnpjruzr2Tt3+z6lXPBW2sF5bZGMK/f3oW/52kfXmykVrSVWp1pXlG7KlmU8I1L5vRu75z+xtc/EjnFzbF3+T3690j8raKrRRtmo8C5rn7AgAzewA4Ayirrqtr6nKvJZuYoZYu0/wZy7f1VE7sJDRu7ppOBT72SBiCxxu7DuzL2PdXc+fERQA8P2MVg/r12m4f377tDXYZ0Ie/fuMwAC6L6/T46ydm8P3j9+34PxZMbm5qZeK8dR3NIaYu3si+u/RP+1kyueLZ9zlo96AdU32WtUR/fH42I/bcsdO0ifPWbff4MVV+J06/Zfz2tZo3vjyPo/fbOWNa/j1zFbsM6JNxuai+fdsbANz3g090TBv7fvAI8ScPvNsxLfYZEmvYYtML9fQhsXdz7NH9zBXbyu7r89fn1BQhnW/d9gZ3fvfIvG4zilhznLeXbGJg316dHudOj/jmt65IvHnbmvACh1lx+R7r1T59eS0T561j6YatHTVp597+BrecO7LTum/mcCMye2V9xvNYJrHXr89cXkvvDD2ef/nYdJZs2MrNry7gmP2D4zH+ZuXPL87l4/sMLvgoD8k+c/y0+BrL2DkYghr7F2etZvcd+3ZMiz2JWbqhoWMbb8SN9DFl0QZWpWjm01Vd/e6ALjc9TPS/D73b6f/ZCeMtT5y/ngN2G8imsJlgfLOcifPW8dexH/DBms38/eV5KffxhzHvd/r/gzRjpAO89P5qPrHvkCjJL5pnE5qJxY7xRKm+n4nz1rFofebKoInz1iUdISN2Lpy3ZnOncjR3ddea/by3bFPH9k786K5px70vJSt2mx0zOxM41d1/EP5/DvAJd/9xwnIXABcA7L333kcsXpx+PNF8e+ztZVmNvCAiIiIi+bPo6tNKsl8zm+ruIxOnl6KmOdlzge0id3e/BbgFYOTIkUV/6nbCAUP5x7c/zoC+PXlr0Ub2HNwPPOjEduz+O7OqrpGd+/fhsXeWMaBPTz686wDW1DVxzP47d7RJq21ooWcPo6mljbb2oCnCRz80gAF9tq/5XVXXSGNzG/379KS1vZ0BfXrSu2cNfXr24IM19QzfuT/t7vTpGTy2WLB2My1t7ey/6wDeXryRHjU1HLHPYCB45LSytpH+fXqyY5JaZghqo/r2qmGH3kERaG1rZ2tzG1uaW/nQoL7bdWLZsKWJQf160bOmBndn+aYG9hy8Q9o8bG1r552lm9h/6AC2NLXS1u4M6teLGgu+8Jkr6jhu/5079tXW7qzb3ETPGqO5rZ0VmxrpUQPDdurHjv16MWb6Kj5z0K7MXb2ZDVuaOPGju9KrRw21DS3MXV3PIXvsSL9ePXB3lmzYytKNDXxi3yEd3wcEPagfmbqUr4/ci549atjS1MrbSzYyoE9PDt97MHNW1dG7Zw377jKAOavq+GiK3rxbmlpZsmEr7e7svmNfhvTPXy0zBI//e9QYA/tu+/7qGlrYsKWZoQP7sHZzE3sN3qFTm/Y5q+txd4bv3J++vbY93lqxqYE3Fm5gv13609zWzo79ejGgT08WrN3MAbsN5I2F6/niiGFMX17Lso0NHLbXTuzQuwer6xrZeUCfjr9337Efbe60tjkD+/ZkTX0TA/v2pLXNGdS3Jxu3NuMOPXvUsGzjVg4e1vkpAMCW5la2NrUxfXktR+83hLrGVmatqOPADw1k7up6Pr73YJZt3MpOO/TmlTlr+MxBu9GrRw2zVtTSo6aGo/YdUpIRY5KV+Q/W1LOlqZXD9hrMu0s3MqR/HzZsaeKAXQfS0hYcw/PXbWHogN7stENv3l9Zx+47BsfWqtpGDth1AIs3bKW+sYUaM/r0rGHPwTuwdnMTUxZt4PgDhtKzxhjUtxe9ehjLNjawbnMTB+0+iF49ali3uYkdevdgVV0jB+w6kJW1DfTqUcOgvr1Yv6WJ3Xfs15HWVXWNtLc7uw3qS48aY3VdI6/OXctXP74HPWtqWF3XSM8aC85rA/pQu7WZxpZ2eves6TgXOcErwwfv0Jv/t8eOeXnhxZamVvr3yXwZcndWhWUw3tr6JnrUEKazF+7O7FX1tLtT29DC3kN2YOjAPtQ1tDKgb1Dmh/TvzYcG9WXygg3sMbgfTa1tzFhey4EfGkT/Pj2ZNG8dp3xsN95atJEDdx/IhA/WYQZfHDGs01O79ZubGNi3V8cQcRCcw16YtYoj9hnMTv16s3TjVnbu35vWdmdQwrIQfC9DB/TpVKZnrqhl6MA+7DqwL/m2uamF9naSPn1MJ9b2vbm1nQ/W1DNy+BD69+7Je0s3sbmplea2dg7afRBjZ63miH0G07OH0drmNLS0sa6+ib2G7EBDSxsL1m5mt0F9MWCvITuwy8A+NDa3sWxjAyP23JG1m5uoMaOuoYX9hg5g2rJNbGlqY68h/Tode6vrGtllQHAebG93hu0UlIsVmxoYtlM/VtY2MKR/bxqa22hp847mRLsN6svLs9fQs8bYsV8vDttrJ+oaW1lb30T/Pj1YtG4rA/v2pG+vHqyqbeD4A4bmLe9z1drWzoatzbS1OzvtEHymDVuaaWptY4+d+rFTOMLNonVbmL2qnneXbuRLhw7jY8N2ZNH6Lazf3MzMFbXsNWQHDttrp44RcVbVNTKwT3DeXlPfRGNLG0cOH0JtQ0unslrb0EJTaxs1Zmza2sKHdx3Ahi3NDN6hV6dzgLuzeMNWdhnQG/dgbO+9huxAS5uz1+B+vDxnDUMH9GHZxgb233UAO/Xr1XG979Wjhv2GDuj0uZdu2MoJB+xSpFyOrhQ1zccAl7r758L/fwng7lelWmfkyJE+ZcqUIqVQRERERLqrVDXNpRg94y3gADPb18x6A98AnipBOkREREREIil68wx3bzWzHwP/BnoAd7h77q+WEREREREpsJK8EdDdxwBjSrFvEREREZFs6Y2AIiIiIiIZKGgWEREREclAQbOIiIiISAZFH3IuF2a2Fiju202q2y5A118JJdlQnpeG8r34lOfFpzwvPuV5aRQr3/dx9+0G6q6IoFnyy8ymJBt/UApHeV4ayvfiU54Xn/K8+JTnpVHqfFfzDBERERGRDBQ0i4iIiIhkoKC5e7ql1AnohpTnpaF8Lz7lefEpz4tPeV4aJc13tWkWEREREclANc0iIiIiIhkoaBYRERERyUBBs4iISJkzMyt1Grob5bkkUtBchcxseKnT0B2Z2Ugz27XU6ehOzOwzZnZEqdPR3ZjZjnF/K7Aojp6lTkA31KvUCeiuzKxHqdOQjILmKmJmHzezscBl5VrgqpGZHWxmk4DfATuVODndgpkdbmbPAY8DHy51eroLM/uEmT0J3GZm55tZH1dv8oIys6PN7D6C8/oBOrcXnpkdY2YPA9ea2ceU58UR5vtlAO7eVur0JKOguQpY4FfA/cAD7n5urMCpFqgofgI87u5fdPe5oHwvFDPrYWa3ALcCNwP/Ag4K5+l8VkBmNgL4O/AI8DDwaXTDUlBmdghwI/AMsBq4ADg3nKdzTAGETwv/BowheF3zT4Dzw3nK8wIxs/OAu4Bfm9nXw2ll93RFF5kqENb09AImuPtt0FET11O1QIUTBnBDACc4yWJmXzGzPYF+4f86yeZReDP4PHCCuz8BPAqcZGZ93b29pImrfkcA89z9HuBFoC+wJDZTZb0gjgZmu/v9BDeKW4Fvm9lwd3fleUEcCsx19zuBPwOPAWeY2UeU5wW1nOBG/FSCfMfdW8stvxU0Vygz+5SZfSJu0p+APczsWjN7C7gcuMvMzixNCqtTfL6HAdxW4JPAp83sXuBHwBXA9eEyumnposSy7u6PuXtDeDJtB+YCO5QsgVUqyTnmWeArZvYHYDqwJ3CDmf0CVNbzIUmevwXsZWb7u/sWgvJeC/wQlOf5YGZfNrNLzOy0cNK7wMi4PH8LmEJwblee50lcvp8eTnoFWO3uLwCLzezycHpZ1TYraK4wZjbQzB4jaMv5IzMbDBAe3PcAhwE/c/fTgfHAqWb2kVKlt1qkyfdG4E6Cx9b/dvdTgV8Bh5jZ50uW4CqQKs/D5kgWXrxmAycT1HqqtjMP0pT1NQS1cD2BS9z9aGA0cLyZHVOq9FaDJHk+JJw1H3gTuNPMngBGEjSN6WlmfUuS2CphZkPDPP0psIEgj89097UET7AuDBfdBIwFdjCz3UuR1mqSJN/vMLOvhJVQsfP3j4CLzGw3d28pUVKTUtBceZqBl4HvACuAs2Iz3P0+4OvuPj6cNBYYCmwudiKrUMp8B24iaI4xFMDdlwMTCGqFJHdJ89xDZlbj7suAN4AzY/NKldgqku4cMxs4EFgaTpoKrAGaipzGapOqrG9294uBHwOj3f2LwDxgRHjDLrnbH5jo7p90938CPwP+N5x3P3CgmZ0cNvtaD+xBUMsvXZMs3/8PwN2bzayHu88kuDm8GqCcKqAUNFcAMzs3fGy3k7s3AbcRBMRzCR4jfSRcztx9Q9yqpxC0t1XQnIOo+e7umwlqJc4zs8PM7D+BzwCLSpT0ipVFWa9x9/awo8gHwJbSpbryRc330AvApWGt/jeAgwmCCslChjw/Ij7P3X1a2IYfgnafk/VUJXthnp9oZjsQ3PDdHU7vAcwKfyBofvQA8Fcz+zDB0ywDehc/1ZUvQr5PD/83gpgFd/8BwTV1I3ColUlHb1PFTHkKC8+HCEYHaCd4TNcf+Im7rwuXOQA4D2h09yvCaTXA8cBfCTrp/CKsHZIIcs33cPrZBI+vDyZ4fD2zyMmvSF0p62Hg/Bdgs7v/piQfoEJlme9N7n55OK0fcAuwK9ADuMjdZ22/B0nUxfPLEQQdpNqAC9x9fpGTX5Ey5XlYs9lmZt8BvuTuX49b92LgIwRPV37o7u8X/xNUpi7m+z7AX4Cdgf929xnF/wTJlUXkLp2FhcmBgcBydz8Z+C+C9j83x5Zz9w8I7tqGmdmHwzZuTtAL9XfufoYC5ui6kO/9zayXuz8I/CrMdwXMEXSxrPcLZ/9UAXN2csj33S0YI3gHd28Avgec5+6fUcAcTRfKeqycLyI4r5+sgDmaDHl+S8LinyUYThEz+xCAu18D/Je7H6+AObou5PvQcFotcLW7f6qcAmYos16J3V34qPkyoIeZjQEGEdQqxIZeuQhYYWafcvdXw+mPm9lBBMNwDQA+HV7EdFKNKE/5fhLwvtrURqM8L40u5vtzwAAzOykMIFaV5lNUlnyUdTOLnddfLc2nqCy55DlBM8aFFrxc46tmdqq7L3P35lJ8hkqUp3z/grsvIegAW3ZU01wmzOxTBLULgwk6elwOtBCMQXsUdHRyugy4NG69swhGa3iFoHOIan2ykMd8Vy1ERMrz0lC+F5/O68WXS56HbWvPJ6jxHASc5EEnY4koj/m+ZLuNlxG1aS4TZnYCMNyDFwdgZjcRNI5vAC509yPC9sq7AjcQtFVeGK6Hu79WoqRXNOV78SnPS0P5XnzK8+LLIc//j+Cp+4XA3e7+dmlSXtm6S76rprl8TAUesm3vuJ8I7O3uowkedVzowdA3ewJt7r4QgpOqTqxdonwvPuV5aSjfi095XnzZ5Hm7uy929/nu/j+VEriVqW6R7wqay4S7b3X3Jg8G+IZguLi14d/fAw4ys2cIxo+smAJW7pTvxac8Lw3le/Epz4svyzyfCnopUj50l3xXR8AyE96lObAb8FQ4uR64BDgEWOjByzMkj5Tvxac8Lw3le/Epz4svmzx3tVPNm2rPd9U0l592oBewDhgR3pn9huBxxgSdWAtG+V58yvPSUL4Xn/K8+JTnpVHV+a6OgGXIzI4GJoU/d7r77SVOUregfC8+5XlpKN+LT3lefMrz0qjmfFfQXIbMbE/gHOA6D16vKkWgfC8+5XlpKN+LT3lefMrz0qjmfFfQLCIiIiKSgdo0i4iIiIhkoKBZRERERCQDBc0iIiIiIhkoaBYRERERyUBBs4hIEZjZzmb2bvizysyWh39vNrObCrC/0Wa20Mz+I9/bTtjPCWY2y8xmFHI/IiKlptEzRESKzMwuBTa7+7UF3Mdo4Bl3f6SA++jp7q1mNjzc1yGF2peISKmppllEpITM7MTwrVmY2aVmdpeZvWBmi8zsq2Z2jZlNN7PnzaxXuNwRZvaqmU01s3+b2e4Z9jEwrHWOrT8o3H4vM9s/3PZUM3vNzA4Ml/mimb1hZu+Y2Vgz2y0ujbeY2QvA3QXNHBGRMqKgWUSkvOwPnAacAdwLvOLu/w9oAE4LA98bgTPd/QjgDuAP6Tbo7vXAuHC7AN8AHnX3FuAW4MJwWz8HYk1FJgBHu/vhwAPAxXGbPAI4w92/1cXPKiJSMXqWOgEiItLJc+7eYmbTgR7A8+H06cBw4KPAIcCLZka4zMoI272NIPB9Avge8EMzGwAcCzwcbgugT/h7T+DBsBa7N7AwbltPuXtDLh9ORKRSKWgWESkvTQDu3m5mLb6t40k7wTnbgJnufkw2G3X3iWY23Mw+BfRw9xlmNgjY5O6HJVnlRoLX4D5lZicCl8bN25LNvkVEqoGaZ4iIVJY5wFAzOwYgbJd8cMR17wbuB+4EcPc6YKGZnRVuy8zs0HDZHYHl4d/n5SvxIiKVSkGziEgFcfdm4Ezgj2b2HvAuQROLKO4DBhMEzjHfBr4fbmsmQVtqCGqWHzaz14B1XU+5iEhl05BzIiJVKNmQc2Z2JkEHvnPyvK/haMg5EalyatMsIlKdaoHLzWwXd/+nmd0IfB74Qj53YmYnEIy4odpoEalqqmkWEREREclAbZpFRERERDJQ0CwiIiIikoGCZhERERGRDBQ0i4iIiIhkoKBZRERERCQDBc0iIiIiIhn8f5tAG4Dk9+kxAAAAAElFTkSuQmCC\n",
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4Cy/Qix9h4Qt4DwNux3uItsv3wHsI9Rm8Y2sZcaNBJNl2yv2bIV9jn4v/iS8Lz+GPLJJinyQe3xfiBY+1/r4eEDdvV7yhEBv87/2tJOVwoV9uJgGj4+YZ3nG51f+5lq6jR4wmRZnLMj/GJ8mP8/x5H8PrEpLsc6eQUK9kShPecI+r/fzoHA3GnzcAb1jH2IhFFyZ89hi/bDUB7wDHBN0P+tFPkB9zLtMdThGR6PNbyafhPex4e9jpyQUzm4QXJObsDXwBtjkabwzrfs57mFBEpCyoe4aIlAXnXBXe6Ch7W9yb/ERERILQG6JEpGw4b3i0uWGnIxtmVp9iVk8eWitLZrY/sCDF7MOdc6sLmZ6wmdnNeA9gJrrXOffjQqdHJCrUPUNEREREJAN1zxARERERyUBBs4iIiIhIBpHo07z77ru70aNHh50MERERESlxM2fOrHbOjUycHomgefTo0cyYMSPsZIiIiIhIiTOzpG+FVfcMEREREZEMFDSLiIiIiGSgoFlEREREJINI9GkWESmk1tZWqqqqaG5uDjspZa2yspJ9992Xfv36hZ0UEREFzSIiiaqqqhg6dCijR4/GzMJOTllyzrFlyxaqqqo48MADw06OiIi6Z/TWog116K2KIqWlubmZ3XbbTQFziMyM3XbbTa39Ij20vqaJ7Y0tYSejpCho7oWpS6v51PWvc//01WEnRURyTAFz+LQPRHruhKtf4cRrXgk7GSVFQXMvLK9uAGDButqQUyIiktmMGTP4+c9/nnaZdevWcfbZZwMwa9Ysnn322YzrTVzu6aef5pprruldYkWk1xpb2sNOQklR0CwiElHt7dmdEMeOHcsNN9yQdplRo0bx6KOPAj0Pmj//+c8zbty4rNImIlLsFDSLiBShlStXcthhh3HuuecyZswYzj77bBobGxk9ejRXXHEFH/3oR3nkkUd48cUXOeGEE/jQhz7EV7/6Verr6wF4++23OfHEEznqqKM47rjjqKurY9KkSXz2s58F4PLLL+ecc87htNNO45BDDuG2227r3O6RRx5JS0sLl112GQ899BBHH300Dz30ENOnT+fEE0/kmGOO4cQTT2TRokVJlxs/fjw//elPAVi1ahWnn346Y8aM4fTTT2f1aq8723nnncfPf/5zTjzxRA466KDOQF3KU11zK6PHTeC215eHnRSRlDR6hohIGn/47/ycd8E6fNQwfv+5IzIut2jRIm6//XZOOukkzj//fG688UbAG4ptypQpVFdX8+Uvf5mJEycyePBg/vznP3Pdddcxbtw4vv71r/PQQw9x7LHHUltby8CBA7utf86cOUybNo2GhgaOOeYYzjrrrM55/fv354orrmDGjBn861//AqC2tpbXX3+dvn37MnHiRC655BIee+yxbsuNHz++cz0//elP+e53v8u5557LHXfcwc9//nOefPJJANavX8+UKVNYuHAhn//85zu7hUj5qa73Hli7761V/PDkg0JOjUhyCppFRIrUfvvtx0knnQTAd77znc6uFV//+tcBmDZtGgsWLOhcpqWlhRNOOIFFixax9957c+yxxwIwbNiwpOv/whe+wMCBAxk4cCCnnnoq06dP5+ijj06ZnpqaGs4991yWLFmCmdHa2prxO7z55ps8/vjjAJxzzjlcdNFFnfO++MUvUlFRweGHH87GjRszrktEJEwKmkVE0gjSIpwviaNHxP4fPHgw4I1lfMYZZ/DAAw90WW7OnDmBRp5Itf5ULr30Uk499VSeeOIJVq5cySmnnJJxG+m2OWDAgM6/NXSniBQ79WkWESlSq1ev5s033wTggQce4KMf/WiX+ccffzxTp05l6dKlADQ2NrJ48WIOO+ww1q1bx9tvvw1AXV0dbW1t3db/1FNP0dzczJYtW5g0aVJny3TM0KFDqaur6/y/pqaGffbZB+jaBSNxuXgnnngiDz74IAD33Xdft+8gIhIVCppzQO0jIpIPH/jAB7jrrrsYM2YMW7du5Sc/+UmX+SNHjmT8+PF885vfZMyYMRx//PEsXLiQ/v3789BDD/Gzn/2Mo446ijPOOCPpS0KOO+44zjrrLI4//nguvfRSRo0a1WX+qaeeyoIFCzof8Lvooou4+OKLOemkk7qM3JG4XLwbbriBO++8kzFjxnDPPffwj3/8I4c5JCJSOOqe0Qsadl9E8qmiooKbb765y7SVK1d2+f+0007rbFGOd+yxxzJt2rQu00455ZQuXSoOPfRQbr311i7LjB49mnnz5gGw6667dlv34sWLO/++8sorUy533nnnda7vlVe6v2AhvqUa6Bz1Q8rHzFVbWbShnm99ZP+wkyISiIJmERERKbiv3OR1PYoPmnXnVoqZgmYRkSIU3+KbD5dffnne1i2Srd7cuf3xPTN5fv4GVl5zVuaFRXpBfZolcl5ZuJGaxsxDXYmISOl7fv6GsJMgZUJBs0TK5rodnD9+Bv9z/8ywkyIlTkOghU/7QESKiYJmiZQdbd4T+yurG0NOiZSyyspKtmzZoqAtRM45tmzZQmVlZdhJEREB8tin2cz2A+4G9gI6gFudc/8ws12Bh4DRwErga865bflKh4hItvbdd1+qqqrYvHlz2Ekpa5WVley7775hJ0NEBMjvg4BtwK+dc++Y2VBgppm9BJwHvOycu8bMxgHjgN/mMR15p8YokdLSr18/DjzwwLCTISIiRSRv3TOcc+udc+/4f9cB7wH7AF8A7vIXuwv4Yr7SkG8B3lIrIiIiAakRSopZQfo0m9lo4BjgLWBP59x68AJrYI8Un7nAzGaY2QzdIhURESldaoSSKMh70GxmQ4DHgF8652qDfs45d6tzbqxzbuzIkSPzl0ARERERkQzyGjSbWT+8gPk+59zj/uSNZra3P39vYFM+0yClSaMaiIiISCHlLWg2MwNuB95zzl0XN+tp4Fz/73OBp/KVBik9pnt4IiIiEoJ8jp5xEnAOMNfMZvnTLgGuAR42s+8Dq4Gv5jENIiIiIiK9lreg2Tk3hdSvkz89X9sVEREREck1vREwJ9S/VkREpLeczqdSxBQ094KlbEgXERGRoHQ+lShQ0CwiIiIikoGCZhERERGRDBQ0SySp15uUm/YOx8QFGzVGuYhISBQ0S6So15uUq5tfW8YP7p7Biws2hp0UEZGypKC5F/SUr4gUStW2JgCq63eEnBIRkfKkoDkn1P4pIiLSW+p9JMVMQXNO6CgXERHpKVPbk0SAguZe0LiSIiIiIuVBQbOIiIiISAYKmiWS1O9NypXKvohIOBQ0S6So35uUK5V9EZFwKWgWEREREclAQbOIiIgUBXU/kmKmoFlEREREJAMFzTmgK+PCUV6LiIhIGBQ094IezAmP8l7Kla4bRUTCoaBZRCQCdJ0oIhIuBc0SSeqmISIiIoWkoFkiRd0yREREJAx9U80ws9oMnzVgvXPu0NwmSURERESkuKQMmoFlzrlj0n3YzN7NcXpERESkzOguokRBuu4ZXwnw+SDLiIhIrqhDv4hIKFK2NDvnlsf/b2bD4pd3zm1NXEZERPJDLXEiIuFK1z0DADP7EXAF0MTOIUIdcFAe0xUpavgRERERKW0Zg2bgf4EjnHPV+U5M1KjhJzxOr3gQERGRAgoy5NwyoDHfCREJwnSpIiIiIiEI0tJ8MfCGmb0F7IhNdM79PG+pEhERkbLj1N+xLLy9cisf2n8X+lREqyEsSEvzLcArwDRgZtyPiIgUmEIKKUWmJ13Lxtsrt/LVm9/kn68sCTspWQvS0tzmnLsw7ykREZGU1DVJRPLh6dnr2Fy3g+9/9MCCbG9DTTMASzbVF2R7uRSkpflVM7vAzPY2s11jP5k+ZGZ3mNkmM5sXN+1yM1trZrP8n8/0KvUiIiIi0mM/f+BdrnxmQdjJiIQgLc3f8n9fHDctyJBz44F/AXcnTP+7c+6vgVInIiIiIlIEMgbNzrketdc75143s9E9+WzUaPgzERERkdIW5OUmfYCzgNF0fSPgdT3c5k/N7LvADODXzrltKbZ7AXABwP7779/DTeWXnlsIjx6wFhERibAInseD9Gn+L3AesBswNO6nJ24CDgaOBtYDf0u1oHPuVufcWOfc2JEjR/Zwc/mlwK3wdKEi5U71jpSyWPH+9cOzOfeO6aGmRfIjyufxIH2a93XOjcnFxpxzG2N/m9ltwDO5WG/Y9FS7iORblE80IpkkFu/H3qkKJR0i6QRpaX7OzD6Zi42Z2d5x/34JmJdqWRERERGRYhEkaJ4GPGFmTWZWa2Z1Zlab6UNm9gDwJvB+M6sys+8D15rZXDObA5wK/KpXqRcRKVENO9r430dmU9PUGnZSRESEYN0z/gacAMx1Wbzf0jn3zSSTbw/6eRGRcnb3m6t4dGYVuw3uz8Wf+UDYyRERKXtBWpqXAPOyCZhFRCRHEjp7qioWkVIQxeF6g7Q0rwcmmdlzwI7YxF4MOVdyorjjo045LiIiEj1RHjwhSNC8wv/p7/+IT0+zF56yXMqVyr6UA91IkWIW5I2AfyhEQkREJAkFEVIG1AglUZCyT7OZXZ7pw0GWEcklxQ9SLhREiIgUl3QtzT/IMLScAd8ALs9pikQCUDwhIiISXVHsipMuaL6NzK/Lvi2HaRERkQwieJ4R6WLyks0s3VQfdjIkJFG+i5YyaFZfZhEREemp9g6HARUVXaOkc26fHk6CRHopyDjNIiISkljLskW5eUbK0gcue55T/zYp7GSI5IyC5hyIYr+cqFOWS6lTiCxR19LWwaotjVl9Ru89kGKmoLkXojxAd1Qpx0VESo/OpxIFGcdpNrORwA+B0fHLO+fOz1+yRKCmqZWTr32V2747luMO3DXs5IiIiEgZC9LS/BQwHJgITIj7EcmrOVXbqWlq5YaXl4SdFJHQuIT+X+oOJiISjiCv0R7knPtt3lMiIiKd9NyfiEhxCdLS/IyZfSbvKSkDj86s4uX3NoadDBERESlT767exq2vLws7GZEUpKX5F8AlZtYCtPrTnHNuWP6SVZr+95HZAKy85qyQUyIiIiLl6Es3vgHABScfHHJKoidj0Oycy/RWQJGCU79OKVfqtiGlTHV7+Yjivg7S0oyZfR442f93knPumfwlKXoiuN8jpcu4nQoYpMxE8cQiki1dDJaPKO/qjH2azewavC4aC/yfX/jTJMp7PgI0bqeUs1TlX0G0iEg4gjwI+BngDOfcHc65O4Az/WkiIkn9z30z+ftLi8NOhoiIBPSNW98MOwlFL+gbAUfE/T08HwkRkdLx7NwN/EPja4uIRMa05VvDTkLRC9Kn+WrgXTN7Fa9DwsnAxXlNlQgJfZlFRESkZETxHJ+xpdk59wBwPPC4/3OCc+7BfCdMJKY3fZvnra3htteX5zA1IoUVvdOKiEhqUX7oM2VLs5kd5pxbaGYf8idV+b9Hmdko59w7+U+eSO+uRj/7zykA/PDkg3KVHJGCiPKJRaSndJEoxSxd94wLgQuAvyWZ54DT8pIiEV/6FmZVrVKeVPIL57bXl/PHZ99j3h8+xZABgUZolR7SNaJEQcpawDl3gf/np51zzfHzzKwyr6kSSUHD0BWGc46nZ6/jMx/cm359gj4vLPmklufCu+vNlQBsa2hR0CwigUbPeCPgNBEpEc/O3cAvHpzFja8uCzspZU/jMouIFId0fZr3AvYBBprZMey8ezIMGFSAtIlISLY2tgCwqa45w5IiIr2na8PyE8UGgXT3mz4FnAfsC1wXN70OuCSPaSpJLW0dYSdBRER6IIon9yjp6NiZweqFVA6iu5fT9Wm+C7jLzL7inHusgGkqSb99bE7YSYgsnbBEJAzqR14Y/351KV87dr+wkyGSUcYnG5xzj5nZWcARQGXc9CvymbBS8+qiTWEnQSRrul4pPk5XkVJipq/c2hk0q3RLMcv4IKCZ3Qx8HfgZXpv6V4EDAnzuDjPbZGbz4qbtamYvmdkS//cuvUi7lAm19hSesrx4xMYp18gxUspUustJdC+NgoyecaJz7rvANufcH4ATgCD3UcYDZyZMGwe87Jw7BHjZ/z9y3ltfy+hxE3hL72kXkTwxXS0WjfgXLDW2tDF63AT+M1lvGs2VyUuqw06CSCBBguYm/3ejmY0CWoEDM33IOQrX9N0AACAASURBVPc6kBhVfgG4y//7LuCLAdNZVKYu9Q7wFxdsAIL1udXpr/CaW9vDTkLkqSeAlLNkrftbG7yRZe6curLAqREpFdGNiIIEzc+Y2QjgL8A7wErgwR5ub0/n3HoA//ceqRY0swvMbIaZzdi8eXMPN5df0d3t5eGa5xaGnYTIUiOniIhIV0FecXStc24H8JiZPYP3MGDeB291zt0K3AowduxYtXeVsZ62dq7Z2pjbhIiEQK39IiLFIUhL85uxP5xzO5xzNfHTsrTRzPYG8H9HekgJncvyS62dxaO1vYP1NU2ZF5ScUfEvHrpwERFIEzSb2V5m9mH8NwKa2Yf8n1Po+RsBnwbO9f8+F3iqh+sJVU8e0NFDPRJNXrRw8eNzOeHqV2hsaQs5PeUjMU5TFVJ46fJcQ//lh7K1fERxV+ftjYBm9gBwCrC7mVUBvweuAR42s+8Dq/GGrxPJWqCHLxVk9FjiA1AT39sIwI7WDgb1DyNF5UvlWMqCynnZiHKdlrc3Ajrnvpli1unZrkvKk1ocRCRM6eog3T0UKT8pg2Yz+45z7l5gtJldmDjfOXddko+VlyyCOlWvPRd/btJ5Kly6jik8XTwWJ3XPECk/6bpnDPZ/DylEQqIkMW5zCiWKlCLs3orFBYXOyaWb6nAODtlzaIG3XDxUesOni3QRiZeue8Yt/u8/FC45EaMKtSDUoFN4YQcLn7judQBWXnNWuAkpQjoeioO6Z4iUn4xDzpnZQWb2XzPbbGabzOwpMzuoEIkrejp55ZXOSeFJFZjplnR4dDgUXl2zN1rMiuqGbvN0LIj0ThQPoSDjNN8PPAzsDYwCHgEeyGeiil1PgjkFgIWnPO+9WB6qVa14Nexoo6axNexklKTYK7Ovf3lJyCkpJxGMpCQrUT6bBAmazTl3j3Ouzf+5F5VqkbIQxZaAcnPC1S9z1BUvhp2M0qYDIe8Sh7kUKUZBXqP9qpmNAx7EC5a/Dkwws10BnHNb85i+oqT6M3zaBfmVqmFZ+V44QRv3a5v1wplC0l0XkfIVJGj+uv/7RwnTz8c7h6p/cyCqaHNBuRgO5buIR32ZRcpXxqDZOXdgIRISJYkNDdNXbGX0uAm88uuPc9BIjdCXaz09RynQk1KkIS4LTzkukg/RO7LSvdzkNOfcK2b25WTznXOP5y9Z0VK1rQmAN5dvUdAsJSXxgkWNbIUXa9lUr4DwxJd7dc8Q6Z0oH0PpWpo/DrwCfC7JPAcoaM5iv0e4jIROeVd4iVmufVB4yvLipO4ZIuUr3ctNfu///l7hkhMxaoUragr0RESiRefR0hflC88gLzf5k5mNiPt/FzO7Kr/JKm6KxaRcqT+tlKP4ch/lW8vFLF/ZuqV+B+0dqreKU/SOpSDjNH/aObc99o9zbhvwmfwlKbp0WBZOlK9Uo2RnsBC9yk0kV1TdRFNtcysfvmoiV01YEHZSpEQECZr7mNmA2D9mNhAYkGb5spFNPaqQo+d6PnqGcr2n1JhWPBKLvwK43Hv8nSoWb6wLOxmSY7VN3psyX5y/MeSUSHLRq8yCjNN8L/Cymd2J9w3PB+7Ka6qKXOz2nFo78ytZ3KZboyFTkS+YxLKusp8/Fz48G4CV15yVdL6yPtp0ri4uUa7LMrY0O+euBa4CPgAcAVzpT5NEOjClxMSKdITruIIaPW4C/351adjJkBxT1R5N6YKz0eMm8I+JSwqYGikFQbpnALwHPO+c+zUw2cyG5jFNIjmhQK/nYl1bFm2sY0NNc8ipiZa/vLAo7CSISAB/n7g47CQUnekrttLY0hZ2MopWkNEzfgg8CtziT9oHeDKfiRIB9QQoBnOqajj+6pfDToaIlIl81Ps6lwT3tVve5DePzgk7GUUrSEvz/wNOAmoBnHNLgD3ymaio0oGZH/EtxuqbFi7lfuGpyEs5yMeNQd1s7JmF62sLsp0o1m1BguYdzrmW2D9m1hedO4Fod2aPkmQHlvK+sJTbhZeqiKvyLbxkdVCHg5rG1sInRrIWxeAsTPnOriifT4IEza+Z2SXAQDM7A3gE+G9+kxVN6Q5MxXjZS5dlanGWcqMqJDzJapsNtc0cdcWLbGtoSTJXioHOu5JrQYLm3wKbgbnAj4Bngd/lM1FRocCt8NTCHC4VeSkXT767NtByWxsVNOfTza8to6mlPexkiAAZxmk2swpgjnPuSOC2wiSp+CluE5FC0avLw/HLh2YFWk4Xkvl1zXML2dbQwsWf+UCP16FjKEvKrpTStjQ75zqA2Wa2f4HSU3QembGG0eMm0Nya+Uo3Xcuz3k5XeOV8cTP2qolc89zCnK+3nPO00BKzWuex8OiuYrjqd/RsCDSdd4tbFI+qIN0z9gbmm9nLZvZ07CffCSsWf33RG3N1ux74kAiprt/Bza8t6/kKUj2EFsVaLoKcc7yxbEvSeQoDwlXfrDFs86G63uvmkuwCpbfVjuqt7OT9QcAIV2JBXqP9h7ynQiQN3VoLX5QruSh66O01PDdvQ9J5OhrCdf74txOmaI/kwqeufz3n61S9JbmWsqXZzCrN7JfAV4HDgKnOuddiPwVLYZGID9xSHYfpqk4dvIWnW3Ol66UFG7nupdJ9m9eqrY3dpiUrzWuSLCf5tXZ7U9hJKDtqKY6OtvYOfvnguyzdVBdo+TunruDht9fkOVW5k66l+S6gFZgMfBo4HPhFIRJVzGqb1U2j0JIFv6pDw1Esrf4/vHsGABeecWjIKcm/5taOlPMue2peAVNSvtIFbQrocquhpZ32jtxmqnZRdnrTh3/+ulqenLWO5dUNPP3Tj2Zc/g//XQDA147dr8fbLKR0fZoPd859xzl3C3A28LECpakoGcaL8zcw5vIXmblqG9D9QFTlKaUi8TJFrfaFFZ/bj86sCi0dIoXW0tbBbx/LzWucVWuFp1TjoXRBc2eTqnNOTz5A54M5c9bWhJyS0vbqok3My0Uep6kx125v4vF3FIyk8maKh9Ckd2av2c7rizeHnQzJoTumrtToGhncPmUFza1eC/J/Ji/PuHz3C8Xe5a92T+Fk0xU1isdNuu4ZR5lZ7AXkhvdGwFr/b+ecG9bTjZrZSqAOaAfanHNje7quUPRgP+uKN7jv3ek9aHP/Dz6St2189aY3WFfTzOeOGkW/PkEGkSkvj6d4sUME67ii8oV/TwVg5TVnpV1Oz0AUl3T744Hpq/nk4Xty6mF7FC5BEXPlMwvYWNvM+0YO4aoJ7xVuwzqOQpOuK1+U67eUQbNzrk+et32qc646z9vIGYfLuKMVT+RHPvrRbqrbkfN1lrIoV3KlRhcuhZcpz4OM41/uaptaezzessp8dATpyhfl/akmthyaW7VdlWcu9TJQC/LxKB+8El2x5yJSSXri0YVLaDJduKsaycw5XXxHRS7Kc5Bzq0WwQIQVNDvgRTObaWYXJFvAzC4wsxlmNmPz5vD7AMafxFKVhSdnreOiR5M/wBDFwlHKtDt6RsFBbnzlpjdy029fRKSIlPq5Nayg+STn3IfwhrL7f2Z2cuICzrlbnXNjnXNjR44cWfgUJqYnSbiQrGzMqdqe/8QIAM/PW88dU1b0ah3FMoRasSvxejAUm+tTdxEq9RNPqdEdq8wcLsR6RDsoG+nK81Oz1nLvtFW9WsfOZXYu1NaeemjNYhJK0OycW+f/3gQ8ARwXRjp6K1mZ0KFZGM7Bj+99hyueWZBymXSt+xpCbSfnHE0t6lZUcGkqi2Sls1n7SMpUugAsXd2lej73fvHgLH73ZO/Gh092ap61JhoNjgUPms1ssJkNjf0NfBKIxAj9pd7BvegUIC+1v+C2ycv5wGXPs6m2OeOyURwiqFhlc5ejfkcbd72ZuXVH8iNTsdcdq2By3U1x7fYmPnDZ8xmXU7VVeKWa5WG0NO8JTDGz2cB0YIJzLnOpl7ReWrCRpZvqw05GdKgBotMzc9YDsK6mmfveWkVdkrdeNvkPuBbi5PPc3PX530ixSwgutje2dP6dTYD26qJNLNoQ7HW2klqpBgBRt2pLQ9r56ubUM725CIzleak2sKQbpzkvnHPLgaMKvd3eMizQARhWi0PstcKZxn+NFFV4BTV9xRb+9OxCZq7sPrLDtkYvkJ60eDPnHH9AXtPxk/veyev6y0lszPOSqhckknozeoZa8qVYaMi5gJIdtCV6IVUyFHNnp6nFexBjS0NLymUaezjOak/94b/zC7q9YpFYdlXXhEt38XIjrDpZh4/n6dnrODxAd5be6Gk/8tg+uv+t1Xzoypdyl6AcU9BcILpNFD3vrt5WsreYkgnSmtNR4Oy4c+rKwm6wgLIpWqmW1VCWxaGMqoke600WpSz/GQI0HR2e2MgUf5ywgMYADxRv7sXLv7J6jXaSaZc8MZetaRpuwqagOaD4gzNdmVDlGQ2xfZhqfz0/bwNfuvENHp6xpmBpCsucKm+84EdmVIWckvKSVdCcIuQop4u6Yqa9EFBIF3nlfpz85YVFQPBW4ObW3g//li7Lk6UjKhc4Cpp7IFYW1MhTeA1+94Agr2NNt38y7buV/gMmyzenf9CklKzd3gTA1KWp327f2NLGbx+dQ01j94cFpfemLKnm1teXhZ0MSeKPExaweKMeqkzlny8vYcbKrWEnowvdifHM9t8fkc/seGNpNbe8Vvp1V8EfBIyyIP0MU99GzXlyykZ8nv5n8nIA2nPUTyBVC15HbKNluN/a0uTt/W+tZktDC4MH9OWyzx2e823nar9G1XdufwuAX37ikC7T47OlzBvNQnPb5BU8PXtd0nnl3pIJ8LeXFsNL6R86LcPqtCh0+A3H+cz/b/3Hq7tOPrRnL6Nr0ctNSkvSBwF1Uy6vkt3CyVVMlek21c6YWdV8PNf5Oz9l/yN/mpiX9UbdVWle4iMSBfm4rgjaGFXuZ+p2P/ML0fKezSbWbG3s/Ptbt73VZWjNYqWguUDUEJG9ZIFZRxYZGeTYzbS6Ur9D8GwPx0TOV3muri/+SjNX0mVh4sXayws3JV8uxwX05fc2Zhz7tpypHu+dng8518Pt9fBzpSbd3cN8CdKwsiyh++PdEXiBk7pn9IBuxRVWfEWbzbGfNijJUJvG9nFFCde6be0d/I/GRI60XNdF379rBn0qjGV/+kxO1ysC4d25K/dTdkcBg+ZA+zjFIte9tLjzb+dcUfZJV0tzQL0ZmB1Kv8WyUAp1wVIO3TN6NwRUmZ+F8izM+iLsfuUL1tVy6O+eY31NU6jpkOKResi59HTe9cRamguZH0FOEVHcPwqacywfwcTWhpbOkQ3KXTbZ25vjsRxGSCmGrzZz1bZAr3mev66ms7WkVI6F+LqivcOxYF1t1usoxpaY3rr3rVW0tHUw8b3kXVLCpEvFnnO4nNen2h/BOFe4oLnzNdr531QoFDQH5AjvBPXhq17ipGteCWXbxSA+UM71A2ip1hbrO116IclOvSnPudoLX7npDT51/etpl3l39TbOumEKN/nDGZXisfD3lxbzmRsmd/5fyuUukz5+uYzS3YwIJTWSelvvR6ks5VOx3TktrtQEo6A5C7GWrlVxT3wmSnVo9qawxo73615cxHvrs2+Niqr8jp6Rnotrag6S7/e8uZLJSzbnImkF05MTSaaXwuTDuu3NAMxbW1O4jRZAfBbGxlGVnc8RFLIfphSAK3yQVGxBYlg6uxsWoqW5c5uZj98o3ilT0ByQc47Fm+r9v9Mtl7803PDKUs6+6Y38baDItXe4nOdvqgM7NrWlrYMbXlnKVzLk+6VPzeec26fnNnFFLNbyE+v/2tHh8hbkdN7uK+EYKvHkEcFzSc7E8qK9CPd3qjKYzag+pS5dXRDWdVC5751iGx43ytWbguaAnCuOWzxhDB1TDOZUbefgS57ltcXBW3PTXcVmusJN7AMW9sNRxcY5WLShjoMveZYX52/goEue5Ru3TcvLtqJcwQZVDt8xqD4V0eueceHDs8NOQtE48x+vc8jvnus2fenmei55Ym5Ot/WNW9PXOcUWLIYldvoqRD0TpE9zbF4UR6dS0CyRMH2F93rW6vodOV1vqgO7HF8ImE3rpgNmrdkGwEsLNgI795EEEx8TJuZ9sd22dM7xyIw1NLe2531bnd0zijBoLrLdUpQWb6xP2sgwp6pw3auaW9t5dGZVSd+dysbORqDiKsDpus8U675T0JyFxJ2Y1UgOOSqrRVbm8yq+lSDXB3vGPs0UfoieKIgv8rEKr0jrtkhJLGbpWlnDaIGdtGgzv3l0Dtc+vyjv26qIdc8I8a26KbttqbD3WK/q0izz/U/Pvsf/PjKbyUure7HR0lOY01lpD5+hoDmgoJVlGLeD6ne0FXybhRbkYN9U28yYy19g4YbawJ9JtV9b/Q6VExdkHvZqycbMQ6YVkxXVDXzw8heo2tZ16LZsKtT4ccsLFUiU8q3WxIvCfOTpObe/xb9fXZpyfrpgvM6vYzbVNXebd/3ExXx//Nu9T6Cvwm9qLsaW5lx6bGYVn/z7aynnX/z4XMY9NqeAKSodm+u8O5L1zT0/N76+eDMnXv1yQe6u5FvnkZRQydc1twb6/JPvruW0v00K+HBfdmmLGgXNAa3a2hCoEk+2SP2Otm4BSi6V2qgCMdk++fzSexupbW7jrjdW9nrb1X6lu8gPiNNVBFF49We8B99eTV1zG0/PXteLtex8W1O+Wz5L90HAuDspKefkzuQl1fzlhdQtxen67acbNeX6iUt4eeEmNtV2D6h7oqJzf+c+F5xznRfVPVxDoKWqtjVmDEh+/chsFm+sTzn/gemrefDtNVmlrlRlWxJidUbnOTvNCpZuqqelrfttjSufWcC6mmZWpxktKzJSdDd8/J21gT7+y4dmsXxzA2u2Bo9jAu2zCAbYCpoDOuf26T0+aX/lxjdy9iBZuQ6hE+TqtaIziMtixTnYLVG7sk6VTxUBvkgskHFxw0flP5Yt/W4giVkfxgVCuk0GKePH/enlnKQjn90znp69jjOvn8wL8zekXS5V/je1BGt1/OifX+UL/5qabfJKWiGryWy6jn3iute49Ml53aaXUn2TqsHv90/PZ+aqbRk/Hzv+T/7LqzlJT8ROmV0oaM7Cm8u3ZFwmWdFclOfb91EqgJvqmrnymQW0+WfEJ96tYqL/IFkq1fU7uGrCexnXHcuHzgoiXcb486YuS97nrZQqzETd8skXZGSWLn2a89giWA7SZVtvuiZsrG3mqmcWZH2hnmqbj79TxasLvVFrZqzayp1TV/Q4bUHEguZ8dM9Y6L99cumm5C2897y5ksaWNn7/9Pyk8xsCBs0Ay6sbsk5fKevVy5SyLAuWcGsk06enr0z9EHOUzq+p7HztQPdvU7Utc0t6kAaVnohi3ipojpiotWom+t0T87h9ygomL/GC1V89NJsf3D0j7WeWbW4IFAD05Db+/9z3TvCFU22312sorFy95jTqZbG4JPRp7sWaLnp0Dv+ZsoK3Alzkd9lmio1e+PBsHnunCoCNtTv4w38X9CJ1mVXksdtPphcvXPrUfO6cupJ7phWuy1W5XHQWtKW588Ir4PJ5TEsxSNeOFOTiNJv8ye7lJlmsuEgoaO6FYtnhxTaMTDqx1sx8jHvc2cc2yLI533o4tje2MHrchKxa/2JByQ0vL+nxdr3uGd3ze/S4CTw8I/t+mKPHTeDNZcmDvB/fO7MnSYyUbuOVph09I/26YsdWe5bBWLE8eBfLi2zTH0SQC+tCj8leJNleUjK/8TUh00vghHDqXyfx4StfSrtMshbjjgzdoEaPm5C390OkHXIuL1vsPQXNvZCssgujAly7PToPKmRTNz35brCHFBLXHTv5Z9P/e/HGOt5dvbNvV1RafzbWeg8s3v/W6sCfycX5weFSBiBPBHy4JNGkRZlHKiklybq6ZPV553hl4aYu/z/xbtXOtzRmWYSTLf/O6sz9HXNt5+gZuV93MT4TEo2aJlw9fRBw5zMY6deQLJhM9Zm125uYsqT4hrJbUd3AloaWpPPSDaGa6/KXVeNVmsNxyaa6UOqfTPqGnQDJTrIy9quHovc2qiAH1ENZtlhWZNHvoDZhKKJP/v11AFZec1bQVXQKs6W/b5/YQ1PBU5yL9MafTxJbKIO2WCaelDJ9LiLXMT2SGMwF+aqJz1g8N29Dl7og25bjZMt/+cb0r4/Ph3z2aY4p4aJUtApZTe5sQEk+P5uG5sR0n3HdazS2tHeeK6IgXWtyGA1EQc5BZ14/GaDo8lktzb2wI8kwNbmqjpdvruf4P73MxhwN41Qs8vnwWLdhhnohKgFaX79VrjXTPTbfhQ/P4h+96JYRP2JGqhaFoHmXeEKLja1aCp6ft55P/2MyHWkuZtK9EbAuwPiyiWPQ1jQlDHGWZRl2Ib5MJF7nGwFz3NR83YuL+Jc/TnW+bjf3RFTuavVWbOz7QkismxK3vDFhvPElKR4MTaYxi4dBoyDXe+WzN3jBbqkWawXNRaKtvYMVcU9bj39jJRtqm3lu7vouy0Wp/3Ki1vaOvI552f1VxD1f19aE21zFeFsXoG8f7xBuD3hCCjouZyqdJ6G4IecSa92etjS/lGEUlWWbg5/YwvbLh2bx3vraFBfWnpb2dlZv8Y6HxLKa7rbkloYWtjW0dLulnNgvOhctzWGoyPIhrqBueGXni1021jSzpkjG3832a66obugcfUiSS3wYLTHQfWb2ehKlungppgusnop9t2y6oWRS19zKhprujXrZjDATRQqac6y6PnmfokyufWERp/51UmdF3vm0a4SD5ESXPz2/czD/fFRDuXyt85SIvH51Z0tzYSr2+Pq1c5SDhBwPGnwlJjnTp1ZUNzB5yeZA6w5brCymy4v/fWQOJ//lVZpb27tdlKX73Pg3VnLMlS/RpyIxaO76/1Ozsnt5TdEEzQV4I+BDM9bwsWtfpTVJ8LmjrbAn/Wy+5ua6HZz610l5H8Gk2GRbFDKdN5OVrcSW8HXbvYCwEK+Oz7fYN0usM6DnLcKfuWEyx1+demz2IG9wjWJ0o6A5jTumrOh80Crf3lrhjRP5uyfnsWrLzrcPJinjkRU/OoJzwV/hGdTO7hnd5z04PfiDclES+84Ff+I/zYOAQZOSWKkGqbyXpHmDWjEJ0r0+ts+2NXa/0G4LcOcgMS5IDJqzfVNosTSo5at7RjLJ8jlf3QjaOxxXP/ce1fVdzynZvB4+FtBPfC/9XZlCW7opv+8ieHr2Ot5bH/xNjpm66iWLqa94Zn6Xi6gm//XZsaEbY/svimLZUJEsaO7hOoO8HfDVRZt4albqu5tRbBRU0JzGFc8U7mref56L1xZv5nt3vh33rviuhSp6RWynxAP2n3G3S3Mh3Wudxz0+N6fb6rrdvK06M/+rFvx2bVwWJ2Z30Nt93T4XoPpOPAkWa3/QbMYqvXnSsm4H9trtmU9IiSecxHKY7W3lYsnLfHXPSCZZUJWvfJi8ZDO3vLac/3uia12UzeYG9O0DFF+/2q/dMi3v2zjL7yubL/dOW81z87q/KTK2e6YureaW15bnNQ35Eqtb+yQ5V6W6sOjtceAcfO/Ot/nFg7O6zavIcFFTzDR6Rh79/ql53PVmsEHy41uJmlrb0w5GHiWb6po57o/db+H8+N6ZvG+PIXnZ5jNz1vOvbxUm7yYt2sSdU1cWYEvpFbql+fF31/Lh0bsASVqMA66je7Cd/Wc6XPITQdhiAe0vHpzVOSzc6YftkXTZ5taObmU1yIOAiY1GiS3N2b8RMKvFk1pR3cCBuw/u1ToK0T0j5ojfv8DXxu7bZdptk1fkdZtNrT2/wI0da4U+3jNJfAYkH7L5yhUp7oJlkqzxIYJxXTexO+ZJx2lO8f3yWbf2rfCfxSmychyEWprzKGjADF37GrV3uNQd90MMEN5Zva3Lw4pBrNicevlUr7LtqekrsnsDWjKvZjlW8HNzu7dMhCFVn+bFeXyFe+whtu6BbNcJCzfUMn9d964CPQm2V27pWp7aAo4akg/LNtcze832pPNih238OMovL0xetqYuq076QE0miXVD95bm7PImcb817EgduE9fsZUX52+gPmGZVPmRjVy0Qm2qaw48lu7DM6p6vJ1sxOr49hyU2WR9sXNh2vItge5yhGVrQ0ugOnrnMwXp5ydKFsQ5HO0djufmdX948Pl53Y+BsL04P/U5KVmf5lRXBb2tW9Mdvkv9h7oVNAdkZmea2SIzW2pm48JIQ7GJL8wdLv5BwK7LFaKfXypfvvENTv3rpM7/b3ltWca+bLHRHQrh3mm977f8vTvfThssJOqTcCme7UVFT63Z2sg/Ji7pDDRTdc+IjT+dK/G37GK3/xNLZGJde+b1kznrhind1tXtQcAAQdJ9CS9xCTFm5vS/vcYX/j016bxsrm2rtjUxY1X2g/gnBs2JJ8TsW5q7Ln/pU/NSLvu1W97kgntm8ptHuo4Rn4uTYGf3jF7s26/fMo3v3P5W0XQ5gfigOfu+/DsX9n7la0SHb9w6jZOvfTUv686F88e/HaiO3vlMQfA+zZD6hWX3v7WKB6Z3fW/A6i2N/Pjemfzqoe7dD8J0wT0zU56XkwXNqbr69PZYTnfxdemTXt0SxZFJCh40m1kf4N/Ap4HDgW+a2eGFTkeYkhXG+GnV9Ts6X+yRWDnEHlJZvLGO0eMmMHrchC7zm1t3HgD3v7WacY/NCZyuR2as4Qd3zeDM619nwbr0D120tndw9XML+VKGlx/0LcCTjHOrkj/w1NO+xrOrureWNbUmr1j6JGzk7JsK8zKIXz00i79PXMyiDV7lGEbdEyuz6VqaP3Hdayk/nxjQ9OQBrGfndm/9CcvUuBFXEl+ekw+Jh1Zi61l8fn5//NvdPv/YzCpGj5vABXfPALo+2NPR4Vi/PXPrd2If0Fx0qejT2ac52LrOu3M6T8/uOlJI7OL1tL+91u3Bu7CsrPbuzExbvpW/v7S4c/q/Xl3CZWkuUOKd4jda5LOFrphbXEOoOQAAE8FJREFU/2J3mo74/Qv8+fmFKZfb+XxL1+lTl1bzpRunpgzWLnpsDne/ubLLedU5uOzp+d2WPfkv3sVFMb7J9BcPzup2QQvZBc1RDGgLIYw+zccBS51zywHM7EHgC0BRjaGzqRcvFZmaYbiyCXPXs9vg/l2mxUbPSBQLimJa2juYurSaix5NHgy/tGAjuw7uzz9fWcK05d46PztmVNIA8tv/eYuRQwdw/dePBuA3ceu84J4Z/PkrY5JuY+rS6s4Dqq65Le33XR5wbN1MeZbON2+bxi3nfLjb+hYnjLSQahuJ078/fkbgNCZ2MdnS0NKr75LMt//zFvvtOpBrvrxzf8RaJr97x/S06Us3vSfig8FY2Uxs1Vge19oenz+J6cjFw0y/fmQ2ew2v7PV6eiP2vb79n7cKut1ZCV0hEi/24l92Et81JJbeX/sn1RcXbGTq0mouiXtAbeJ7G3s0pvrsqu2MGjEw68/Fi926XbmlIVDZnbRoM5MWbe5Wp4IXPF/z3EK+dMw+vUpTEMnSGj8tPn/jXzD071eXAfCpI/bKuN74YybX9UyqbYa5jkTbG3eW6ZsmLUu5zSV+t7T4ETemLq3uPEaHDEgd+lz2VNcAuSVDV5jWdpfXfdET89fVMj+h4Wvq0uqkY5NPW568W+MbS6sZWtkv47aCfPdiy5/esELfvjKzs4EznXM/8P8/B/iIc+6nCctdAFwAsP/++3941arg/YNz4fF3qrjw4ei9nlpERESkFIT1Gm0zm+mcG5s4PYyW5mQ3zbtF7s65W4FbAcaOHVvw+wQfO2QkN337Qwyp7Ms7q7az1/AB9KmoYGV1Ax87ZHc21Daz17BKHnx7DXsNr+y8cn3fHkPYZZDX4tHW0cHWhhZ2GdSfbY0tbGtspW+FcfDI7qNGOOeYXbWdvYYPZPjAfjTsaKPCYNfBA2hp66BqWyN7jxhIZd8KzKxz+d0GD2CfXQby/LwNfHCf4ey366DObW+u28HQyr4MGZD8anFrQwsD+/dhYD9vGKP2DkddcytNre3sOayyW5/Jhh1tmMGg/t533VDbzO6D+2fst7x2eyOb63YwZt8RzF6znQN2G8SOtg72GlbJ3LU1DB/YjwN22/nEfU1TK337GLVNrazZ2ki/PhUM6FvBLoP7M3xgP56ft4FPf3Bv5q2toX5HGx8/dCQVZmxt2MGyzQ0cvd8I+vWpwDnHmm1NrNnayLGjd6V/353pbO9wPDJjDZ8/ehSD+veltb2DuWtrqG1q5eRDR7KtoYU12xo5YtRwNtQ0s9uQ/p3fO3G/rd3exMrqBg7dayi7Du7f+WRwrmyqa2ZYZT8q/f0EsL2xhZqmVnYfMoDN9TvYb5dBXW69rdrSQMOONvbfbVCX/V/b1MrkJZsZNrAfuwzqT4dzHLLnUF6Yv4HT3r8HLy3YyJlH7kV7h+Op2ev4+KEj2W1If2qbWtlY28zR++3C+hrvNv7ewweyvqaJvYZVUtPUSv++FdQ2t7HH0AGd3YRqmlppa3f071vBnsO6twhvbWihXx9j6tItfOLwPaja1kTVVq+sL95Yx8mHjGTt9iaGVvbltUWb+fj7RzJkQF/mrq2hvcNx/EG70a+A/ebjNbS0saO1nV0HDwC87gQzVm5l7+EDGTViIO+s3saoEZVsqW9h/10H0d8/dtdvb2LEoH4MH9ifRRtq2XNYJX0qjPU1zRy651BWVDfQ2NKGczB4QF92G9Kf5tZ2piyp5oSDd2PEoH44B0Mr+7G+pom65jaGVvbtsj9mrdnO0fuNYHtTK33MaG5rp1+fCir77TzeW9s7qK7fQd+KCkYOHYBzjmnLt9KnAo47cDecc6za0siQyr5sa2yhb4XR0u5oamljz2GVbG1o4eCRQ1hR3UBdcysjBvXn0D2H5iZvd7QxOE1rYLzq+h0MH9ivSzlo6+hgS30LHc6x9/CBnftr/fZm1m1vYs9hAxgxqD/9+hiDB/RlfU0zA/pW0K9PBUMr+/L2iq3sObySQf37MHlxNR9//0iq61tYs7WRDx2wC++u3sYH9xnOyws3MbBfHz7zwb26HGdbG3YwqH/fLsesc46XF27iqH1HUNmvgqbWdlraOthlcH9a2zoYMahrS3nDjjbaOhzDB+5cb01TKyurGzhyn+HJH+rqpWTpDqK9w7GtsYXW9g5WbWnkkD2GsNuQAWysbWbeuho6OhyH7DGUqu1N1DW3cur792DK0mret8cQFq6v4317DKG6fgd1za1e+TRjWGVf9tt1EM2t7Z11SKy+21TbzGF7DcPheG3RZg7eYwjDB/Zj9yHeseicY0NtM3sPH8iiDbXsu+sgBvfvS4dzbKrbwV7DKlmztZG9h1dS29yGc66zRXmvYZW8umgzfSpgyIB+HLP/CJpa21mztZHhA/uxvbGVLfU7+MDew3hrxVY+dcReedkX2WpoaaO1rYOm1nYG9e9L3wpjzbZG2jsco3cfzOD+fTuP6SWb6nhn9XY+N2ZvDh81nPU1Tazd3sSSjfWMGlHJwSOHsO8uXiyxtWEHlf36sL2xlbrmVjbX7eD9ew1j2MC+NOxo66z/2to7qG1uo73DsXZ7Ix/cZwQdztHc2t6txbq2qZXtTa3st4tXz1f260OfCqOvf/y9/N5GnIMPH7BLwfMxkzBamk8ALnfOfcr//2IA59zVqT4zduxYN2NG8lvmIiIiIiK5kqqlOYwmmreBQ8zsQDPrD3wDeDqEdIiIiIiIBFLw7hnOuTYz+ynwAtAHuMM51/3RVBERERGRIhHKGwGdc88Cz4axbRERERGRbOmNgCIiIiIiGShoFhERERHJQEGziIiIiEgGBR9yrifMbDNQ2LeblL7dgdJ5TU+0KO/Do7wPj/I+PMr78Cjvw9ObvD/AOTcycWIkgmbJPTObkWwMQsk/5X14lPfhUd6HR3kfHuV9ePKR9+qeISIiIiKSgYJmEREREZEMFDSXr1vDTkAZU96HR3kfHuV9eJT34VHehyfnea8+zSIiIiIiGailWUREREQkAwXNIiIiIiIZKGgWERGJMDOzsNNQrpT35UVBc4kys9Fhp6FcmdlYM9sj7HSUKzP7hJl9OOx0lCMzGx73t4KJwukbdgLKWL+wE1DuzKxPobaloLnEmNmHzGwicEUhC5KAmR1hZm8AvwdGhJ2ecmNmx5jZc8ATwPvCTk85MbOPmNlTwH/M7HwzG+D0lHnemdnxZnYfXn1/iOr8wjGzE8zsEeCvZna48r6w/Py/AsA5116o7SpoLhHm+T/gAeBB59x3YwVJLT4F8wvgCefc55xzi0F5Xwhm1sfMbgVuA24B7gc+4M9THZdnZjYG+DfwKPAIcBq6aMk7MzsS+CfwDLARuAD4rj9P9U4e+XcS/wU8i/ea5l8A5/vzlPd5ZmbnAncBvzOzr/nTCnK3RSeUEuG36vQDpjjn/gOdLW991eKTX37Qtivg8CpSzOxLZrYvMND/XxVpnvgXh88DH3POPQk8BpxqZpXOuY5wU1cWPgwsdc7dA7wEVAKrYzNV9vPmeGChc+4BvAvGRuDbZjbaOeeU73l1FLDYOXcn8DfgceALZnao8r4g1uJdnJ+Jl/8459oKke8KmiPMzD5uZh+Jm/QXYB8z+6uZvQ1cCdxlZmeHk8LSFZ/3ftDWCJwMnGZm9wI/Aq4CrveX0YVLDiWWfefc4865Jr/S7AAWA4NCS2AJS1LvTAC+ZGZ/BOYC+wI3mNlvQWU/V5Lk+9vAfmZ2sHOuAa/c1wA/BOV7LpnZF83sEjM7y580Cxgbl/dvAzPw6n3lfY7F5f9n/UmvAhudcy8Cq8zsSn963lubFTRHkJkNNbPH8fpu/sjMdgHwD957gKOBXzvnPgu8DpxpZoeGluASkibvm4E78W5Tv+CcOxP4P+BIM/t0aAkuMany3++eZP7JaiFwOl6Lp1o6cyRN2d+E1/LWF7jEOXc8MB74qJmdEFZ6S0WSfN/Vn7UMmA7caWZPAmPxusf0NbPKcFJbWsxspJ+3FwJb8fL6bOfcZrw7Wj/zF90OTAQGmdne4aS29CTJ/zvM7Et+Q1WsXv8R8HMz29M515rvNClojqYW4BXgO8A64KuxGc65+4CvOede9ydNBEYC9YVOZIlKmffAjXjdMUYCOOfWAlPwWoAkN5Lmv/OZWYVzrgp4Czg7Ni+sxJaYdPXOQuAwYI0/aSawCdhR4DSWolRlvt45dxHwU2C8c+5zwFJgjH8RL713MDDVOXeyc+5m4NfAr/x5DwCHmdnpfjewLcA+eK39khvJ8v83AM65FjPr45ybj3exeA1AvhupFDRHhJl91789N8I5twP4D15AvBjvNtGh/nLmnNsa99Ez8PraKmjuoaB575yrx2t5ONfMjjaznwCfAFaGlPSSkEXZr3DOdfgPhCwBGsJLdWkImve+F4HL/Zb9bwBH4AUSkqUM+f7h+Hx3zs3x+/KD189zmu6u9Jyf96eY2SC8i7+7/en/v737D/WrruM4/nx1nWLOhZi/YtQF7Ye4srgQGo65jCilBjIrUhmrjDBmEWVQSMMVlERiA6lRuUaRZlEMSbPAxlToj5WxHxIh16SV0KDM1Zpr99Ufn8/Nm46+93u/3+/9fM/t9YDL7s7O93POeXF27vt+zud8zgRwoH5BGYp0N3CHpAsod7cEnLz4e710zCP/vfXvotQ22P4Q5efuX4CLNcIHwJVOmPFVT4pzKbMBzFBux50GfMz2obrOq4ENwD9tf74uewlwGXAH5YGcT9eeoJinhWZfl7+Xcrv6Isrt6v2LvPudN8i5Xwvn24HDtm9pcgAd1mf2R21vqctOBbYBZwMTwE22D7x4C3EiA15zpigPRB0HPmz7iUXe/U7rlX3t0Twu6Trg3bbfM+ezNwOvodxpucH244t/BN02YP6vAm4HzgQ+anvfKPc1Pc1jqp4kBk4HDtq+AriRMq7n67Pr2f4d5bexV0i6oI5lM+Xp0s/ZXpeCuT8DZH+apGW27wE+W7NPwdynAc/9U+s/fyIFc/8WkP15KvMDv9T2EWAjsMH221Iwz98A5/zs+f4k5Xp/RQrm/vTIftsLVn87ZWpFJJ0LYPs24Ebbl6Vg7t8A+Z9Vlz0DfNH2mlEXzJC3CI2demv5VmBC0k+AFZTeg9kpVW4C/ihpje1ddfmPJF1ImXZrOfDW+gMrF88+DCn7tcDjGUfbv+TfzoDZ3w8sl7S2Fg1PtzmK7hnGOS9p9nq/q81RdNNCsqcMc5xWeanG1ZLeYfsPtp9rcQxdNqT8r7T9FOWB2EWRnuYxImkNpRfhDMoDHVuAY5Q5Z98M/3mo6VZg85zPXUOZqeEhykMg6eHp0xCzT0/DAiT/dpJ9G7net7OQ7OuY2g9QejpXAGtdHjqOPg0x/6de1Pio9z0dMuND0mpg0uUlAUi6kzLo/QiwyfZUHa98NvBVyljl6fo5bO9utOudl+zbSv7tJPs2kns7C8j+U5Q785uAHbZ/1WbPl4Yu55+e5vGyB/i+nn+H/SPAK21vp9zC2OQytc1K4LjtaSgXz1xAB5bs20r+7ST7NpJ7O/1kP2P797afsP3xFMxD0dn8UzSPEdv/sH3UZeJuKNPF/bl+vxG4UNJ9lPkh8x93iJJ9W8m/nWTfRnJvp8/s90BekjRMXc4/DwKOofrbl4FzgJ118bPAZ4BVwLTLizNiyJJ9W8m/nWTfRnJvp5/snbGsQ9fF/NPTPJ5mgGXAIeAN9TeuWyi3KR7OBXSkkn1byb+dZN9Gcm8n2bfVufzzIOCYknQJ8Gj9usv2Nxvv0v+NZN9W8m8n2beR3NtJ9m11Lf8UzWNK0krgeuArLq9RjUWS7NtK/u0k+zaSezvJvq2u5Z+iOSIiIiKih4xpjoiIiIjoIUVzREREREQPKZojIiIiInpI0RwRERER0UOK5oiIRSDpTEmP1a+nJR2s3x+WdOcItrdd0rSkjwy77RdsZ7WkA5L2jXI7ERGtZfaMiIhFJmkzcNj2l0e4je3AfbZ/MMJtnGT7X5Im67ZWjWpbERGtpac5IqIhSZfXN2EhabOkb0t6UNKTkq6WdJukvZIekLSsrjclaZekPZJ+Kum8Hts4vfY6z35+RW1/maTza9t7JO2W9Lq6zrsk/VLSryX9XNI5c/Zxm6QHgR0jDSciYoykaI6IGC/nA1cB64DvAA/Zfj1wBLiqFr5bgfW2p4BvAV/4Xw3afhb4RW0X4H3AD20fA7YBm2pbnwRmh4o8DFxi+03A3cDNc5qcAtbZfv+AxxoR0Rkntd6BiIj4L/fbPiZpLzABPFCX7wUmgdcCq4CfSaKu86d5tPsNSuH7Y2AjcIOk5cBbgHtrWwCn1D9XAvfUXuyTgek5be20fWRBRxcR0VEpmiMixstRANszko75+QdPZijXbAH7bV/aT6O2H5E0KWkNMGF7n6QVwF9tv/EEH9lKebXtTkmXA5vn/Nvf+zukiIjuy/CMiIhu+S1wlqRLAeq45Ivm+dkdwPeAuwBs/w2YlnRNbUuSLq7rvgw4WL/fMKydj4joqhTNEREdYvs5YD3wJUm/AR6jDLGYj+8CZ1AK51nXAh+sbe2njKWG0rN8r6TdwKEh7HpERKdlyrmIiCXoRFPOSVpPeYDv+iFva5JMORcRS1zGNEdELE3PAFskvdz21yRtBd4JXDnMjUhaTZlxI73REbGkpac5IiIiIqKHjGmOiIiIiOghRXNERERERA8pmiMiIiIiekjRHBERERHRQ4rmiIiIiIgeUjRHRERERPTwby18F1rPyTiPAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 864x288 with 1 Axes>"
       ]
@@ -1852,7 +1872,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -1904,7 +1924,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -1919,7 +1939,7 @@
     {
      "data": {
       "text/plain": [
-       "Sensors at 'Dumbraveni': ['5TM_soil_moisture_0.000000_0.050000', '5TM_soil_temperature_0.000000_0.050000', 'QMR102_precipitation_-1.400000_-1.400000', 'HMP45DX_air_temperature_-2.000000_-2.000000']"
+       "Sensors at 'Dumbraveni': ['5TM_soil_temperature_0.000000_0.050000', 'HMP45DX_air_temperature_-2.000000_-2.000000', 'QMR102_precipitation_-1.400000_-1.400000', '5TM_soil_moisture_0.000000_0.050000']"
       ]
      },
      "metadata": {},
@@ -1938,46 +1958,46 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001B[1mMetadata for sensor 5TM_soil_moisture_0.000000_0.050000:\u001B[0m\n"
+      "\u001b[1mMetadata for sensor 5TM_soil_moisture_0.000000_0.050000:\u001b[0m\n"
      ]
     },
     {
      "data": {
       "text/plain": [
        "variable        key       \n",
-       "clay_fraction   val                            19\n",
-       "                depth_from                      0\n",
+       "clay_fraction   val                          19.0\n",
+       "                depth_from                    0.0\n",
        "                depth_to                      0.3\n",
        "climate_KG      val                           Dfc\n",
        "climate_insitu  val                       unknown\n",
-       "elevation       val                           318\n",
+       "elevation       val                         318.0\n",
        "instrument      val                           5TM\n",
-       "                depth_from                      0\n",
+       "                depth_from                    0.0\n",
        "                depth_to                     0.05\n",
-       "latitude        val                       46.2279\n",
+       "latitude        val                      46.22786\n",
        "lc_2000         val                            10\n",
        "lc_2005         val                            10\n",
        "lc_2010         val                            10\n",
        "lc_insitu       val                       unknown\n",
-       "longitude       val                       24.5916\n",
+       "longitude       val                      24.59161\n",
        "network         val                          RSMN\n",
        "organic_carbon  val                          0.99\n",
-       "                depth_from                      0\n",
+       "                depth_from                    0.0\n",
        "                depth_to                      0.3\n",
-       "sand_fraction   val                            37\n",
-       "                depth_from                      0\n",
+       "sand_fraction   val                          37.0\n",
+       "                depth_from                    0.0\n",
        "                depth_to                      0.3\n",
        "saturation      val                          0.44\n",
-       "                depth_from                      0\n",
+       "                depth_from                    0.0\n",
        "                depth_to                      0.3\n",
-       "silt_fraction   val                            44\n",
-       "                depth_from                      0\n",
+       "silt_fraction   val                          44.0\n",
+       "                depth_from                    0.0\n",
        "                depth_to                      0.3\n",
        "station         val                    Dumbraveni\n",
        "timerange_from  val           2014-06-11 12:00:00\n",
-       "timerange_to    val           2020-12-12 23:00:00\n",
+       "timerange_to    val           2020-10-31 23:00:00\n",
        "variable        val                 soil_moisture\n",
-       "                depth_from                      0\n",
+       "                depth_from                    0.0\n",
        "                depth_to                     0.05\n",
        "Name: data, dtype: object"
       ]
@@ -1987,7 +2007,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 864x288 with 1 Axes>"
       ]
@@ -2039,9 +2059,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.5"
+   "version": "3.8.10"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
\ No newline at end of file
+}
diff --git a/setup.cfg b/setup.cfg
index 995e9ae..6d31571 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -79,7 +79,8 @@ extras = True
 # e.g. --cov-report html (or xml) for html/xml output or --junitxml junit.xml
 # in order to write a coverage file that can be read by Jenkins.
 addopts =
-    --cov ismn --cov-report term-missing
+    --cov ismn
+    --cov-report term-missing
     --verbose
 norecursedirs =
     dist
diff --git a/src/ismn/base.py b/src/ismn/base.py
index f7b2bfb..775f5e0 100644
--- a/src/ismn/base.py
+++ b/src/ismn/base.py
@@ -260,12 +260,12 @@ class IsmnRoot:
         subpath: str, optional (default: None)
             Use linux slashes '/' (and no leading '/') to define a subpath
             in the zip file. If None is selected, the whole archive is searched.
-        fn_templ : str, optional (default: '*.csv')
+        fn_templ: str, optional (default: '*.csv')
             Filename template for files that are searched in the passed dir.
 
         Returns
         -------
-        files : List[str]
+        files : list[str]
             Found files that match the passed template.
         """
 
diff --git a/src/ismn/citations.txt b/src/ismn/citations.txt
new file mode 100644
index 0000000..571ee71
--- /dev/null
+++ b/src/ismn/citations.txt
@@ -0,0 +1,111 @@
+Network;Citation
+AACES;Peischl, S., Walker, J. P., Ruediger, C., Ye, N., Kerr, Y. H., Kim, E., Bandara, R., and Allahmoradi, M.: The AACES field experiments: SMOS calibration and validation across the Murrumbidgee River catchment, Hydrology and Earth System Sciences, Discuss., 9, 2763-2795, doi:10.5194/hessd-9-2763-2012, 2012
+AACES;Ruediger, C., Hancock, G., Hemakumara, H., Jacobs, B., Kalma, J., Martinez, C., Thyer, M., Walker, J., Wells, T. & Willgoose, G. (2007), ‘Goulburn river experimental catchment data set’, Water Resources Research 43.
+AMMA-CATCH;Galle, S., Manuela, G., Peugeot, C., Bouzou-Moussa, I., Cappelaere, B., Demarty, J., Mougin, E., Panthou, G., Adjomayi, P., Agbossou, E., Abdramane, B., Boucher, M., Cohard, J.-M., Descloitres, M., Descroix, L., Diawara, M., Do, M., Favreau, G., Fabrice, G. & Wilcox, C. (2018), ‘Amma-catch, a critical zone observatory in west africa monitoring a region in transition’, Vadose Zone Journal 17(1), 1–24.
+AMMA-CATCH;Pellarin T., J.P. Laurent, B. Cappelaere, B. Decharme, L. Descroix, D. Ramier, 2009 : Hydrological modelling and associated microwave emission of a semi-arid region in South-western Niger, /Journal of Hydrology/, vol. 375, 1-2, 262-272, 2009
+AMMA-CATCH;Mougin, E., Hiernaux, P., Kergoat, L., Grippa, M., de Rosnay, P., Timouk, F., Le Dantec, V., Demarez, V., Lavenu, F., Arjounin, M., Lebel, T. et al., 2009. The AMMA-CATCH Gourma observatory site in Mali: Relating climatic variations to changes in vegetation, surface in press, hydrology, fluxes and natural resources. Journal of Hydrology, 375(1-2): 14-33
+AMMA-CATCH;Cappelaere, C., Descroix, L., Lebel, T., Boulain, N., Ramier, D., Laurent, J.-P., Le Breton, E., Boubkraoui, S., Bouzou Moussa, I. et al., 2009. The AMMA Catch observing system in the cultivated Sahel of South West Niger- Strategy, Implementation and Site conditions, 2009. Journal of Hydrology, 375(1-2): 34-51
+AMMA-CATCH;de Rosnay, P., Gruhier, C., Timouk, F., Baup, F., Mougin, E., Hiernaux, P., Kergoat, L., and LeDantec, V.: Multi-scale soil moisture measurements at the Gourma meso-scale site in Mali, Journal of Hydrology, 375, 241-252, 2009
+AMMA-CATCH;Lebel, Thierry, Cappelaere, Bernard, Galle, Sylvie, Hanan, Niall, Kergoat, Laurent, Levis, Samuel, Vieux, Baxter, Descroix, Luc, Gosset, Marielle, Mougin, Eric, Peugeot, Christophe and Seguis, Luc, 2009: AMMA-CATCH studies in the Sahelian region of West-Africa: An overview. JOURNAL OF HYDROLOGY, 375, 3-13.
+ARM;Cook, D. R. (2016a), Soil temperature and moisture profile (stamp) system handbook, Technical report, DOE Office of Science Atmospheric Radiation Measurement (ARM) Program.
+ARM;Cook, D. R. (2016b), Soil water and temperature system (swats) instrument handbook, Technical report, DOE Office of Science Atmospheric Radiation Measurement (ARM) Program.
+ARM;Cook, D. R. & Sullivan, R. C. (2018), Surface energy balance system (sebs) instrument handbook, Technical report, DOE Office of Science Atmospheric Radiation Measurement (ARM) Program.
+AWDS;We acknowledge the work of Natalie Umphlett (http://www.hprcc.unl.edu/awdn.php) in support of the ISMN.
+BIEBRZA_S-1;Musial, J. P., Dabrowska-Zielinska, K., Kiryla, W., Oleszczuk, R., Gnatowski, T. & Jaszczynski, J. (2016), ‘Derivation and validation of the high resolution satellite soil moisture products: a case study of the biebrza sentinel-1 validation sites’, Geoinformation Issues 8(1 (8)), 37–53.
+BNZ-LTER;Van Cleve, Keith, Chapin, F.S. Stuart, Ruess, Roger W. 2015. Bonanza Creek Long Term Ecological Research Project Climate Database - University of Alaska Fairbanks.
+BNZ-LTER;Bonanza Creek Long Term Ecological Research Project Climate Database. 2015. University of Alaska Fairbanks.
+CALABRIA;Brocca, L., Hasenauer, S., Lacava, T., Melone, F., Moramarco, T., Wagner, W., A, D., Matgen, P., Martinez-Fernandez, J., Llorens, P., Latron, J., Martin, C. & Bittelli, M. (2011), ‘Soil moisture estimation through ascat and amsr-e sensors: An intercomparison and validation study across europe’, Remote Sensing of Environment 115, 3390–3408.
+CAMPANIA;Brocca, L., Hasenauer, S., Lacava, T., Melone, F., Moramarco, T., Wagner, W., A, D., Matgen, P., Martinez-Fernandez, J., Llorens, P., Latron, J., Martin, C. & Bittelli, M. (2011), ‘Soil moisture estimation through ascat and amsr-e sensors: An intercomparison and validation study across europe’, Remote Sensing of Environment 115, 3390–3408.
+CHINA;Liu S., Mo X, Li H., Peng G., Robock A. 2001  The spatial variation of soil moisture in China:Geostatistical Characteristics. Journal of the Meteorological Society of Japan , 79 (2B) 555-574
+CHINA;Robock, Alan, Konstantin Y. Vinnikov, Govindarajalu Srinivasan, Jared K. Entin, Steven E. Hollinger, Nina A. Speranskaya, Suxia Liu, and A. Namkhai, 2000: The Global Soil Moisture Data Bank. Bull. Amer. Meteorol. Soc., 81, 1281-1299
+COSMOS;Zreda M., Desilets D., Ferré Ty P.A., Scott R.L., “Measuring soil moisture content non-invasively at intermediate spatial scale using cosmic-ray neutrons”, Geophysical Research Letters 35(21), 2008  
+COSMOS;Zreda, M., W.J. Shuttleworth, X. Zeng, C. Zweck, D. Desilets, T. Franz, and R. Rosolem, 2012. COSMOS: the COsmic-ray Soil Moisture Observing System. Hydrology and Earth System Sciences 16, 4079-4099, doi: 10.5194/hess-16-4079-2012.
+CTP_SMTMN;Yang, K., J. Qin, L. Zhao, Y. Y. Chen, W. J. Tang, M. L. Han, Lazhu, Z. Q. Chen, N. Lv, B. H. Ding, H. Wu, C. G. Lin, 2013. A Multi-Scale Soil Moisture and Freeze-Thaw Monitoring Network on the Third Pole, Bulletin of the American Meteorological Society, doi: 10.1175/BAMS-D-12-00203.1
+DAHRA;Tagesson, T., Fensholt, R., Guiro, I., Rasmussen, M., Huber, S., Mbow, C., Garcia, M., Horion, S., Sandholt, I., Holm-Rasmussen, B., Goettsche, F.-M., Ridler, M., Boke-Ol´en, N., Olsen, J., Ehammer, A., Madsen, M., Olesen, F. & Ardoe, J. (2014), ‘Ecosystem properties of semi-arid savanna grassland in west africa and its relationship to environmental variability’, Global Change Biology 21.
+FLUXNET-AMERIFLUX;We acknowledge the work of Dennis D. Baldocchi (http://ameriflux.lbl.gov/) in support of the ISMN.
+FMI;Ikonen, J., Smolander, T., Rautiainen, K., Cohen, J., Lemmetyinen, J., Salminen, M. & Pulliainen, J. (2018), ‘Spatially distributed evaluation of esa cci soil moisture products in a northern boreal forest environment’, Geosciences 8(2), 51.
+FMI;Ikonen, J., Vehvilaeinen, J., Rautiainen, K., Smolander, T., Lemmetyinen, J., Bircher, S. & Pulliainen, J. (2015), ‘The sodankylae in-situ soil moisture observation network: an example application to earth observation data product evaluation’, GID 5(2), 599–629.
+FR_Aqui;Al-Yaari, A., Dayau, S., Chipeaux, C., Aluome, C., Kruszewski, A., Loustau, D. & Wigneron, J.-P. (2018), ‘The aqui soil moisture network for satellite microwave remote sensing validation in south-western france’, Remote Sensing 10(11).
+FR_Aqui;Wigneron, J.-P., Dayan, S., Kruszewski, A., Aluome, C., Al-Yaari, A., Fan, L., Guven, S., Chipeaux, C., Moisy, C., Guyon, D. & Loustau, D. (2018), The aqui network: Soil moisture sites in the “les landes” forest and graves vineyards (bordeaux aquitaine region, france), pp. 3739–3742.
+GROW;Xaver, A., Zappa, L., Rab, G., Pfeil, I., Vreugdenhil, M., Hemment, D. & Dorigo, W. A. (2020), ‘Evaluating the suitability of the consumer low-cost parrot flower power soil moisture sensor for scientific environmental applications’, Geoscientific Instrumentation, Methods and Data Systems 9(1), 117–139.
+GROW;Zappa, L., Forkel, M., Xaver, A. & Dorigo, W. (2019), ‘Deriving field scale soil moisture from satellite observations and ground measurements in a hilly agricultural region’, Remote Sensing 11(22), 2596.
+GROW;Zappa, L., Woods, M., Hemment, D., Xaver, A. & Dorigo, W. (2020), Evaluation of remotely sensed soil moisture products using crowdsourced measurements, Eighth International Conference on Remote Sensing and Geoinformation of Environment, SPIE, Cyprus.
+GTK;We acknowledge the work of Pekka Haenninen, Raimo Sutinen in support of the ISMN.
+HiWATER_EHWSN;Jian Kang, Xin Li, Rui Jin, Yong Ge, Jinfeng Wang and Jianghao Wang. Hybrid optimal design of the eco-hydrological wireless sensor network in the middle reach of the Heihe River Basin, China, Sensors, 2014, 14(10): 19095~19114
+HiWATER_EHWSN;Rui Jin, Xin Li, Baoping Yan, Xiuhong Li, Wanmin Luo, Mingguo Ma, Jianwen Guo, Jian Kang, Zhongli Zhu and Shaojie Zhao. A nested ecohydrological wireless sensor network for capturing the surface heterogeneity in the midstream areas of the Heihe River Basin, China, IEEE Geoscience and Remote Sensing Letters, 2014, 11(11): 2015~2019
+HOAL;Bloeschl, Guenter, et al. 'The hydrological open air laboratory (HOAL) in Petzenkirchen: A hypothesis-driven observatory.' Hydrology and Earth System Sciences 20.1 (2016): 227-255.
+HOAL;M. Vreugdenhil et al., 'Towards a high-density soil moisture network for the validation of SMAP in Petzenkirchen, Austria', 2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS, Melbourne, VIC, 2013, pp. 1865-1868, doi: 10.1109/IGARSS.2013.6723166.
+HOBE;Jensen, K. & Refsgaard, J. (2018), ‘Hobe: The danish hydrological observatory’, Vadose Zone Journal 17.
+HOBE;Bircher, S., Skou, N., Jensen, K., Walker, J. & Rasmussen, L. (2012), ‘A soil moisture and temperature network for smos validation in western denmark’, Hydrology and Earth System Sciences 16.
+HSC_SEOLMACHEON;We acknowledge the work of Yeon gil Lee / Minha Choi in support of the ISMN.
+HYDROL-NET_PERUGIA;Morbidelli, R., Saltalippi, C., Flammini, A., Cifrodelli, M., Picciafuoco, T., Corradini, C. & Govindaraju, R. S. (2017), ‘In situ measurements of soil saturated hydraulic conductivity: Assessment of reliability through rainfall–runoff experiments’, Hydrological Processes 31(17), 3084–3094.
+HYDROL-NET_PERUGIA;FLAMMINI A., MORBIDELLI R., SALTALIPPI C., PICCIAFUOCO T., CORRADINI C., GOVINDARAJU R.S. 'Reassessment of a semi-analytical field-scale infiltration model through experiments under natural rainfall events', Journal of Hydrology, 565, 835-845, 2018, https://doi.org/10.1016/j.jhydrol.2018.08.073, Codice Scopus: 2-s2.0-85053026749, Codice WOS: 000447477200069.
+HYDROL-NET_PERUGIA;FLAMMINI A., CORRADINI C., MORBIDELLI R., SALTALIPPI C., PICCIAFUOCO T., GIRALDEZ J.V. 'Experimental Analyses of the Evaporation Dynamics in Bare Soils under Natural Conditions', Water Resources Management, 32 (3), 1153-1166, 2018, doi: 10.1007/s11269-017-1860-x, Codice Scopus: 2-s2.0-85034632795, Codice WOS: 000422982300020.
+HYDROL-NET_PERUGIA;MORBIDELLI R., SALTALIPPI C., FLAMMINI A., ROSSI E., CORRADINI C., 'Soil water content vertical profiles under natural conditions: matching of experiments and simulations by a conceptual model', Hydrological Processes, 28 (17), 4732-4742, 2014, doi:10.1002/hyp.9973, Codice Scopus: 2-s2.0-84905732846, Codice WOS: 000340611500006.
+HYDROL-NET_PERUGIA;MORBIDELLI R., CORRADINI C., SALTALIPPI C., FLAMMINI A., ROSSI E., 'Infiltration-soil moisture redistribution under natural conditions: experimental evidence as a guideline for realizing simulation models', Hydrology and Earth System Sciences, 15, 2937-3945, 2011, doi:10.5194/hess-15-2937-2011, Codice Scopus: 2-s2.0-80052885350, Codice WOS: 000295357100012
+ICN;Hollinger, Steven E., and Scott A. Isard, 1994: A soil moisture climatology of Illinois. J. Climate, 7, 822-833.
+IIT_KANPUR;We acknowledge the work of Tripathi Shivam in support of the ISMN.
+IMA_CAN1;Marcella Biddoccu, Stefano Ferraris, Francesca Opsi, Eugenio Cavallo. Long-term monitoring of soil management effects on runoff and soil erosion in sloping vineyards in Alto Monferrato (North–West Italy). Soil and Tillage Research, Volume 155, 2016, Pages 176-189, ISSN 0167-1987, https://doi.org/10.1016/j.still.2015.07.005. (http://www.sciencedirect.com/science/article/pii/S0167198715001531)
+IMA_CAN1;Capello, G., Biddoccu, M., Ferraris, S. & Cavallo, E. (2019), ‘Effects of tractor passes on hydrological and soil erosion processes in tilled and grassed vineyards’, Water 11(10), 2118.
+IMA_CAN1;Raffelli, G., Id, M., Previati, M., Id, D., Canone, D., Gisolo, D., Bevilacqua, I., Capello, G., Biddoccu, M., Cavallo, E., Id, R., Deiana, R., Id, G., Cassiani, G. & Ferraris, S. (2018), ‘Local-and plot-scale measurements of soil moisture: Time and spatially resolved field techniques in plain, hill and mountain sites’, Water 9.
+IOWA;Robock, Alan, Konstantin Y. Vinnikov, Govindarajalu Srinivasan, Jared K. Entin, Steven E. Hollinger, Nina A. Speranskaya, Suxia Liu, and A. Namkhai, 2000: The Global Soil Moisture Data Bank. Bull. Amer. Meteorol. Soc., 81, 1281-1299
+IPE;Alday, J. G., Camarero, J. J., Revilla, J. & Dios, V. R. (2020), ‘Similar diurnal, seasonal and annual rhythms in radial root expansion across two coexisting mediterranean oak species’, Tree Physiology.
+iRON;Osenga, E. C., Arnott, J. C., Endsley, K. A., & Katzenberger, J. W. (2019). Bioclimatic and soil moisture monitoring across elevation in a mountain watershed: Opportunities for research and resource management. Water Resources Research, 55. https://doi.org/10.1029/2018WR023653
+KIHS_CMC;We acknowledge the work of Yeon gil Lee, Yongjun Lee, Kiyoung Kim (http://kihs.re.kr) in support of the ISMN.
+KIHS_SMC;We acknowledge the work of  Minha Choi, Yeon gil Lee (http://kihs.re.kr) in support of the ISMN.
+LAB-net;Mattar, C., Santamaria-Artigas, A., Duran-Alarcon, C., Olivera-Guerra, L., Fuster, R. & Borvar´an, D. (2016), ‘The lab-net soil moisture network: Application to thermal remote sensing and surface energy balance’, Data 1(1).
+LAB-net	;Mattar, C., Santamaría-Artigas, A., Durán-Alarcón, C., Olivera-Guerra, L and Fuster, R. 2014. LAB-net the First Chilean soil moisture network for Remote Sensing Applications. Procd. IV Recent Advances in Quantitative Remote Sensing Symposium (RAQRS). 22 - 25 September, Valencia, Spain.
+MAQU;Su, Z., Wen, J., Dente, L., van der Velde, R., Wang, L., Ma, Y., Yang, K., and Hu, Z. 2011, The Tibetan Plateau observatory of plateau scale soil moisture and soil temperature (Tibet-Obs) for quantifying uncertainties in coarse resolution satellite and model products, Hydrol. Earth Syst. Sci., 15, 2303–2316, 2011
+MAQU;Dente, L., Su, Z. & Wen, J. (2012), ‘Validation of smos soil moisture products over the maqu and twente regions’, Sensors 12(8), 9965–9986.
+METEROBS;We acknowledge the work of Nazzareno Diodato (http://mistrals.sedoo.fr/HyMeX/Plateform-search?datsId=532) in support of the ISMN.
+MOL-RAO;F.Beyrich and W.K. Adam, Site and Data Report for the Lindenberg Reference Site in CEOP - Phase 1, Berichte des Deutschen Wetterdienstes, 230, Offenbach am Main, 2007, http://www.eol.ucar.edu/projects/ceop/dm/insitu/sites/baltex/lindenberg/falkenberg/
+MONGOLIA;Robock, Alan, Konstantin Y. Vinnikov, Govindarajalu Srinivasan, Jared K. Entin, Steven E. Hollinger, Nina A. Speranskaya, Suxia Liu, and A. Namkhai, 2000: The Global Soil Moisture Data Bank. Bull. Amer. Meteorol. Soc., 81, 1281-1299
+MySMNet;Kang, C. S., Kanniah, K. D. & Kerr, Y. H. (2019), ‘Calibration of smos soil moisture retrieval algorithm: A case of tropical site in malaysia’, IEEE Transactions on Geoscience and Remote Sensing 57(6), 3827–3839.
+NAQU;Su, Z., Wen, J., Dente, L., Van Der Velde, R., Wang, L., Ma, Y., Yang, K. and Hu, Z., 2011. The Tibetan Plateau observatory of plateau scale soil moisture and soil temperature (Tibet-Obs) for quantifying uncertainties in coarse resolution satellite and model products. Hydrology & Earth System Sciences, 15(7).
+NGARI;Su, Z., Wen, J., Dente, L., Van Der Velde, R., Wang, L., Ma, Y., Yang, K. and Hu, Z., 2011. The Tibetan Plateau observatory of plateau scale soil moisture and soil temperature (Tibet-Obs) for quantifying uncertainties in coarse resolution satellite and model products. Hydrology & Earth System Sciences, 15(7).
+NVE;We acknowledge the work of Fred Wenger (https://www.nve.no/hydrology/?ref=mainmenu) in support of the ISMN.
+OZNET;Smith, A. B., J. P.Walker, A. W.Western, R. I.Young, K. M.Ellett, R. C.Pipunic, R. B.Grayson, L.Siriwardena, F. H. S.Chiew, and H.Richter (2012), The Murrumbidgee soil moisture monitoring network data set, Water Resour. Res., 48, W07701, doi:10.1029/2012WR011976.
+OZNET;Young, R., Walker, J., Yeoh, N., Smith, A., Ellett, K., Merlin, O. and Western, A., 2008. Soil moisture and meteorological observations from the murrumbidgee catchment. Department of Civil and Environmental Engineering, The University of Melbourne.
+PBO_H2O;Kristine M. Larson, Eric E. Small, Ethan D. Gutmann, Andria L. Bilich, John J. Braun, Valery U. Zavorotny: Use of GPS receivers as a soil moisture network for water cycle studies. GEOPHYSICAL RESEARCH LETTERS, VOL. 35, L24405, doi:10.1029/2008GL036013, 2008.
+PTSMN;Hajdu, I., Yule, I., Bretherton, M., Singh, R. & Hedley, C. (2019), ‘Field performance assessment and calibration of multi-depth aquacheck capacitance-based soil moisture probes under permanent pasture for hill country soils’, Agricultural Water Management 217, 332–345. https://doi.org/10.1016/J.AGWAT.2019.03.002
+REMEDHUS;Gonzalez-Zamora, A., Sanchez, N., Pablos, M. & Martinez-Fernandez, J. (2018), ‘Cci soil moisture assessment with smos soil moisture and in situ data under different environmental conditions and spatial scales in spain’, Remote Sensing of Environment 225.
+RISMA;Ojo, E. R., Bullock, P. R., L’Heureux, J., Powers, J., McNairn, H., & Pacheco, A. (2015). Calibration and evaluation of a frequency domain reflectometry sensor for real-time soil moisture monitoring. Vadose Zone Journal, 14(3). doi: 10.2136/vzj2014.08.0114
+RISMA;L’Heureux, J. (2011). 2011 Installation Report for AAFC SAGES Soil Moisture Stations in Kenaston, SK. Agriculture
+RISMA;Canisius, F. (2011). Calibration of Casselman, Ontario Soil Moisture Monitoring Network, Agriculture and AgriFood Canada, Ottawa, ON, 37pp
+RU_CFR;We acknowledge the work of Andrej Varlagin in support of the ISMN.
+RUSWET-AGRO;Robock, Alan, Konstantin Y. Vinnikov, Govindarajalu Srinivasan, Jared K. Entin, Steven E. Hollinger, Nina A. Speranskaya, Suxia Liu, and A. Namkhai, 2000: The Global Soil Moisture Data Bank. Bull. Amer. Meteorol. Soc., 81, 1281-1299
+RUSWET-GRASS;Robock, Alan, Konstantin Y. Vinnikov, Govindarajalu Srinivasan, Jared K. Entin, Steven E. Hollinger, Nina A. Speranskaya, Suxia Liu, and A. Namkhai, 2000: The Global Soil Moisture Data Bank. Bull. Amer. Meteorol. Soc., 81, 1281-1299
+RUSWET-VALDAI;Robock, Alan, Konstantin Y. Vinnikov, Govindarajalu Srinivasan, Jared K. Entin, Steven E. Hollinger, Nina A. Speranskaya, Suxia Liu, and A. Namkhai, 2000: The Global Soil Moisture Data Bank. Bull. Amer. Meteorol. Soc., 81, 1281-1299
+SASMAS;Ruediger, C, Hancock, G. R., Hemakumara, H. M., Jacobs, B., Kalma, J. D., Martinez, C., Thyer, M., Walker, J. P., Wells, T. and Willgoose, G. R., 2007. The Goulburn River Experimental Catchment Data Set. Water Resources Research, 43, W10403, doi:10.1029/2006WR005837
+SCAN;Schaefer, G., Cosh, M. & Jackson, T. (2007), ‘The usda natural resources conservation service soil climate analysis network (scan)’, Journal of Atmospheric and Oceanic Technology - J ATMOS OCEAN TECHNOL 24.
+SD_DEM;Jonas Ardoe, A 10-Year Dataset of Basic Meteorology and Soil Properties in Central Sudan, Dataset Papers in Geosciences, vol. 2013, Article ID 297973, 6 pages, 2013
+SKKU;Nguyen, H. H., Kim, H. & Choi, M. (2017), ‘Evaluation of the soil water content using cosmic-ray neutron probe in a heterogeneous monsoon climate-dominated region’, Advances in Water Resources 108, 125–138.
+SMOSMANIA;Calvet, J.-C., Fritz, N., Berne, C., Piguet, B., Maurel, W. & Meurey, C. (2016), ‘Deriving pedotransfer functions for soil quartz fraction in southern france from reverse modeling’, SOIL 2(4), 615–629.
+SMOSMANIA;Calvet, J.-C., Fritz, N., Froissard, F., Suquia, D., Petitpa, A., and Piguet, B.: In situ soil moisture observations for the CAL/VAL of SMOS: the SMOSMANIA network, International Geoscience and Remote Sensing Symposium, IGARSS, Barcelona, Spain, 23-28 July 2007, 1196-1199, doi:10.1109/IGARSS.2007.4423019, 2007.
+SMOSMANIA;Albergel, C., Ruediger, C., Pellarin, T., Calvet, J.-C., Fritz, N., Froissard, F., Suquia, D., Petitpa, A., Piguet, B., and Martin, E.: From near-surface to root-zone soil moisture using an exponential filter: an assessment of the method based on insitu observations and model simulations, Hydrol. Earth Syst. Sci., 12, 1323–1337, 2008.
+SNOTEL;Leavesley et al (2010), ‘A modelling framework for improved agricultural water-supply forecasting’.
+SNOTEL;Leavesley, G., David, O., Garen, D., Lea, J., Marron, J., Pagano, T., Perkins, T. & Strobel, M. (2008), ‘A modeling framework for improved agricultural water supply forecasting’, AGU Fall Meeting Abstracts
+SOILSCAPE;Moghaddam, M., D. Entekhabi, Y. Goykhman, K. Li, M. Liu, A. Mahajan, A. Nayyar, D. Shuman, and D. Teneketzis, A wireless soil moisture smart sensor web using physics-based optimal control: concept and initial demonstration IEEE-JSTARS, , vol. 3, no. 4, pp. 522-535, December 2010
+SOILSCAPE;Moghaddam, M., A.R. Silva, D. Clewley, R. Akbar, S.A. Hussaini, J. Whitcomb, R. Devarakonda, R. Shrestha, R.B. Cook, G. Prakash, S.K. Santhana Vannan, and A.G. Boyer. 2016. Soil Moisture Profiles and Temperature Data from SoilSCAPE Sites, USA. ORNL DAAC, Oak Ridge, Tennessee, USA. http://dx.doi.org/10.3334/ORNLDAAC/1339
+SOILSCAPE;Shuman, D. I., Nayyar, A., Mahajan, A., Goykhman, Y., Li, K., Liu, M., Teneketzis, D., Moghaddam, M. & Entekhabi, D. (2010), ‘Measurement scheduling for soil moisture sensing: From physical models to optimal control’, Proceedings of the IEEE 98(11), 1918–1933.
+SWEX_POLAND;W. Marczewski, J. Slominski, E. Slominska, B. Usowicz, J. Usowicz, S. Romanov, O. Maryskevych, J. Nastula, and J. Zawadzki, 2010: Strategies for validating and directions for employing SMOS data, in the Cal-Val project SWEX (3275) for wetlands, Hydrol. Earth Syst. Sci. Discuss., 7, 7007–7057
+SW-WHU;Chen, N., Zhang,X., Wang, C.,2015. Integrated Open Geospatial Web Service enabled Cyber-physical Information Infrastructure for Precision Agriculture Monitoring. Computers and Electronics in Agriculture.111,78–91. doi:10.1016/j.compag.2014.12.009
+SW-WHU;Chen, N., Xiao, C., Pu, F., Wang, Z., Wang, C., Gong, J. 2015. Cyber-Physical Geographical Information Service-Enabled Control of Diverse In-situ Sensors. Sensors. 15(2), 2565-2592. doi: 10.3390/s150202565
+SW-WHU;Zhang, X., Chen, N., Chen, Z., Wu, L., Li, X., Zhang, L., Di, L., Gong, J. & Li, D. (2018), ‘Geospatial sensor web: A cyber-physical infrastructure for geoscience research and application’, Earth-science reviews 185, 684–703.
+TAHMO;We acknowledge the work of Nicolaas Cornelis van de Giesen and Frank Annor (https://tahmo.org/) in support of the ISMN.
+TERENO;Zacharias, S., H.R. Bogena, L. Samaniego, M. Mauder, R. Fuß, T. Puetz, M. Frenzel, M. Schwank, C. Baessler, K. Butterbach-Bahl, O. Bens, E. Borg, A. Brauer, P. Dietrich, I. Hajnsek, G. Helle, R. Kiese, H. Kunstmann, S. Klotz, J.C. Munch, H. Papen, E. Priesack, H. P. Schmid, R. Steinbrecher, U. Rosenbaum, G. Teutsch, H. Vereecken. 2011. A Network of Terrestrial Environmental Observatories in Germany. Vadose Zone J. 10. 955–973. doi:10.2136/vzj2010.0139
+TERENO;Bogena, H., Kunkel, R., Puetz, T., Vereecken, H., Kruger, E., Zacharias, S., Dietrich, P., Wollschlaeger, U., Kunstmann, H., Papen, H., Schmid, H., Munch, J., Priesack, E., Schwank, M., Bens, O., Brauer, A., Borg, E. & Hajnsek, I. (2012), ‘Tereno - long-term monitoring network for terrestrial environmental research’, Hydrologie und Wasserbewirtschaftung 56, 138–143.
+TERENO;Bogena, H. R. (2016), ‘Tereno: German network of terrestrial environmental observatories’, Journal of large-scale research facilities JLSRF 2, 52.
+UDC_SMOS;Schlenz, F., Dall'Amico, J., Loew, A., Mauser, W. (2012): Uncertainty Assessment of the SMOS Validation in the Upper Danube Catchment. IEEE Transactions on Geoscience and Remote Sensing, 50(5), pp.1517–1529. doi: 10.1109/TGRS.2011.2171694. 
+UDC_SMOS;A. Loew, J. T. Dall'Amico, F. Schlenz, W. Mauser (2009): The Upper Danube soil moisture validation site: measurements and activities, paper presented at Earth Observation and Water Cycle conference, Frascati (Rome), 18 - 20 November 2009, to be published in ESA Special dataation SP-674.
+UMBRIA;Brocca, L., Hasenauer, S., Lacava, T., Melone, F., Moramarco, T., Wagner, W., Dorigo, W., Matgen, P., Martínez-Fernández, J., Llorens, P., Latron, J., Martin, C., Bittelli, M. (2011). Soil moisture estimation through ASCAT and AMSR-E sensors: an intercomparison and validation study across Europe. Remote Sensing of Environment, 115, 3390-3408, doi:10.1016/j.rse.2011.08.003.
+UMBRIA;Brocca, L., Melone, F., Moramarco, T. (2008). On the estimation of antecedent wetness condition in rainfall-runoff modelling. Hydrological Processes, 22 (5), 629-642.
+UMBRIA;Brocca, L., Melone, F., Moramarco, T., Morbidelli, R. (2009). Antecedent wetness conditions based on ERS scatterometer data. Journal of Hydrology, 364 (1-2), 73-87.
+UMSOL;We acknowledge the work of Andrea Pasquali (http://www.arpa.emr.it/sim/) in support of the ISMN.
+USCRN;Bell, J. E., M. A. Palecki, C. B. Baker, W. G. Collins, J. H. Lawrimore, R. D. Leeper, M. E. Hall, J. Kochendorfer, T. P. Meyers, T. Wilson, and H. J. Diamond. 2013: U.S. Climate Reference Network soil moisture and temperature observations. J. Hydrometeorol., 14, 977-988. doi: 10.1175/JHM-D-12-0146.1
+USDA-ARS;Jackson, T.J., Cosh, M.H., Bindlish, R., Starks, P.J., Bosch, D.D., Seyfried, M.S., Goodrich, D.C., Moran, M.S., Validation of Advanced Microwave Scanning Radiometer Soil Moisture Products. IEEE Transactions on Geoscience and Remote Sensing. 48: 4256-4272, 2010.
+VAS;We acknowledge the work of Mike Schwank, Jean-Pierre Wigneron, Yann H. Kerr, Diego Intrigliolo, Jennifer Grant, Ernesto Lopez-Baeza (http://nimbus.uv.es/) in support of the ISMN.
+VDS;We acknowledge the work of Richard de Jeu (https://www.vandersat.com/) in support of the ISMN.
+WEGENERNET;Kirchengast, G., Kabas, T., Leuprecht, A., Bichler, C. & Truhetz, H. (2014), ‘Wegenernet: A pioneering high-resolution network for monitoring weather and climate’, Bulletin of the American Meteorological Society 95.
+WEGENERNET;Fuchsberger, J., Kirchengast, G. & Kabas, T. (2020), ‘Wegenernet high-resolution weather and climate data 2007 to 2019’, Earth System Science Data Discussions 2020, 1–49.
+WSMN;Petropoulos, G. P. & McCalmont, J. P. (2017), ‘An operational in situ soil moisture & soil temperature monitoring network for west wales, uk: The wsmn network’, Sensors 17(7), 1481.
\ No newline at end of file
diff --git a/src/ismn/components.py b/src/ismn/components.py
index 6cc80c3..cb2e974 100644
--- a/src/ismn/components.py
+++ b/src/ismn/components.py
@@ -26,11 +26,10 @@ from typing import Union
 import numpy as np
 import warnings
 import logging
-import pandas as pd
 from collections import OrderedDict
 
 from ismn.meta import MetaData, Depth
-from ismn.const import deprecated
+from ismn.const import deprecated, CITATIONS
 
 logger = logging.getLogger(__name__)
 
@@ -63,7 +62,7 @@ class Sensor(IsmnComponent):
         File handler object to read data.
     keep_loaded_data : bool
         Keep data in memory after loading.
-    data : pd.DataFrame
+    data : pandas.DataFrame
         Container for data in memory (if it is being kept)
     """
 
@@ -123,7 +122,7 @@ class Sensor(IsmnComponent):
 
         Returns
         -------
-        data : pd.DataFrame
+        data : pandas.DataFrame
             Insitu time series for this sensor, loaded from file or memory
             (if it was loaded and kept before).
         """
@@ -153,9 +152,9 @@ class Sensor(IsmnComponent):
 
         Parameters
         ----------
-        variable : str, optional (default: None)
+        variable : str or list[str], optional (default: None)
             Check if the variable name matches, e.g. soil_moisture.
-            One of :const:`ismn.const.VARIABLE_LUT`
+            One or multiple of :const:`ismn.const.VARIABLE_LUT`
         depth : Depth or list or tuple, optional (default: None)
             Check if the passed depth encloses the sensor depth.
             A list/tuple must contain 2 values where the first is the depth start
@@ -189,9 +188,17 @@ class Sensor(IsmnComponent):
         else:
             d = self.depth
 
-        if (variable in [None, self.variable]) and depth.encloses(d):
+        if variable is not None:
+            variable = np.atleast_1d(variable)
+
+            if any([v in [None, self.variable] for v in variable]):
+                flag = True
+        else:
             flag = True
 
+        if not depth.encloses(d):
+            flag = False
+
         if flag and filter_meta_dict:
             if self.filehandler is None:
                 warnings.warn("No filehandle found, can't filter by metadata.")
@@ -222,7 +229,7 @@ class Station(IsmnComponent):
         Latitude coordinate of station and all sensors at station.
     elev : float
         Elevation information of station.
-    sensors : OrderedDict
+    sensors : collections.OrderedDict
         Collection of Sensors and their names.
     """
 
@@ -328,10 +335,10 @@ class Station(IsmnComponent):
 
         Returns
         -------
-        start_date: datetime
+        start_date: datetime.datetime
             Earliest date observed by any sensor at the station after filtering
             for the passed requirements.
-        end_date: datetime
+        end_date: datetime.datetime
             Latest date observed by any sensor at the station after filtering
             for the passed requirements.
         """
@@ -445,7 +452,7 @@ class Station(IsmnComponent):
 
         Parameters
         ----------
-        variable : string
+        variable : str
             variable abbreviation
         depth_from : float
             shallower depth of layer the variable was measured at
@@ -453,7 +460,7 @@ class Station(IsmnComponent):
             deeper depth of layer the variable was measured at
         Returns
         -------
-        sensors : numpy.array
+        sensors : numpy.ndarray
             array of sensors found for the given combination of variable and depths
         """
         return np.array(
@@ -617,6 +624,23 @@ class Network(IsmnComponent):
                 if sensor.eval(**filter_kwargs):
                     yield station, sensor
 
+    def get_citations(self):
+        """
+        Return reference(s) for this network. Users of ISMN should cite the
+        networks they are using in a publication. This information can also
+        be found on the ISMN website.
+
+        Returns
+        -------
+        references : list
+            A list of references / citations / acknowledgements for this Network.
+        """
+        try:
+            refs = CITATIONS[self.name]
+        except KeyError:
+            refs = [f'No reference(s) for network {self.name} available.']
+
+        return refs
 
 class NetworkCollection(IsmnComponent):
     """
@@ -700,7 +724,7 @@ class NetworkCollection(IsmnComponent):
 
         Parameters
         ----------
-        gpi : int or List[int]
+        gpi : int or list[int]
             Point index or multiple indices in self.grid.
 
         Returns
@@ -731,18 +755,18 @@ class NetworkCollection(IsmnComponent):
 
         Parameters
         ----------
-        lon : float or List[float]
+        lon : float or list[float]
             Longitude coordinate(s).
-        lat : float or List[float]
+        lat : float or list[float]
             Latitude coordinate(s).
         max_dist : float, optional (default: np.Inf)
             Maximum search distance.
 
         Returns
         -------
-        station : Station or List[Station]
+        station : Station or list[Station]
             The nearest Station(s) to the passed coordinates.
-        dist : float or List[float]
+        dist : float or list[float]
             Distance in meter between the passed coordinates and the
             actual location of the station.
         """
@@ -750,3 +774,36 @@ class NetworkCollection(IsmnComponent):
         station = self.station4gpi(gpi)
 
         return station, dist
+
+    def export_citations(self, out_file=None):
+        """
+        Returns the references for all networks in the collection.
+        Optionally, they are also written to file.
+        Information on how to correctly cite ISMN networks can be found
+        on the ISMN website.
+
+        Parameters
+        ----------
+        out_file : str, optional (default: None)
+            If a path is passed here, a new file will be generated with all
+            references for the  current collection.
+
+        Returns
+        -------
+        references: OrderedDict
+            Network names as keys and network references as values
+        """
+        refs = OrderedDict([(net.name, net.get_citations())
+                            for net in self.iter_networks()])
+
+        if out_file is not None:
+            with open(out_file, mode='w') as out_file:
+                for name, reflist in refs.items():
+                    out_file.write(f'References for Network {name}:\n')
+                    out_file.write("-----------------------------------------\n")
+                    for ref in reflist:
+                        out_file.write(f"{ref}\n")
+                        out_file.write('\n')
+                    out_file.write('\n')
+
+        return refs
\ No newline at end of file
diff --git a/src/ismn/const.py b/src/ismn/const.py
index 6f4d406..a8763bf 100644
--- a/src/ismn/const.py
+++ b/src/ismn/const.py
@@ -24,6 +24,8 @@ import numpy as np
 from collections import OrderedDict
 import functools
 import warnings
+import os
+import pandas as pd
 
 
 def deprecated(func):
@@ -58,6 +60,15 @@ class DepthError(ValueError):
     pass
 
 
+# Note: At the moment citations are stored in this package, keep them updated.
+#  Once the full list of citations is provided together with the downloaded
+#  ISMN data (in the README file), citations here can be deleted (keep in mind
+#  that people might still use old data, but a new version of the package).
+CITATIONS: dict = \
+    pd.read_csv(os.path.join(os.path.dirname(__file__), 'citations.txt'),
+                             index_col=0, delimiter=';') \
+      .groupby('Network')['Citation'].apply(list).to_dict()
+
 # ==============================================================================
 # Variable short names
 
@@ -227,5 +238,5 @@ KOEPPENGEIGER = OrderedDict(
         ("W", "Water"),
     ]
 )
-
 # ==============================================================================
+
diff --git a/src/ismn/filecollection.py b/src/ismn/filecollection.py
index 73c1e39..c9ecc31 100644
--- a/src/ismn/filecollection.py
+++ b/src/ismn/filecollection.py
@@ -20,10 +20,8 @@
 # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 # SOFTWARE.
 
-import os
 import logging
 
-import pandas as pd
 from tempfile import gettempdir
 from pathlib import Path, PurePosixPath
 from tqdm import tqdm
@@ -154,7 +152,7 @@ class IsmnFileCollection(object):
     ----------
     root : IsmnRoot
         Root object where data is stored.
-    filelist : OrderedDict
+    filelist : collections.OrderedDict
         A collection of filehandlers and network names
     temp_root : Path
         Temporary root dir.
@@ -166,7 +164,7 @@ class IsmnFileCollection(object):
         ----------
         root : IsmnRoot
             Root object where data is stored.
-        filelist : OrderedDict
+        filelist : collections.OrderedDict
             A collection of filehandler stored in lists with network name as key.
         temp_root : Path or str, optional (default : gettempdir())
             Root directory where a separate subdir for temporary files
diff --git a/src/ismn/filehandlers.py b/src/ismn/filehandlers.py
index 4c7f97f..8c81322 100644
--- a/src/ismn/filehandlers.py
+++ b/src/ismn/filehandlers.py
@@ -95,8 +95,8 @@ class IsmnFile(object):
 
         Parameters
         ----------
-        variable : str, optional (default: None)
-            Name of the required variable measured, e.g. soil_moisture
+        variable : str or list[str], optional (default: None)
+            Name of the required variable(s) measured, e.g. soil_moisture
         allowed_depth : Depth, optional (default: None)
             Depth range that is allowed, depth in metadata must be within this
             range.
@@ -113,8 +113,9 @@ class IsmnFile(object):
             Whether the metadata complies with the passed conditions or not.
         """
 
-        if variable:
-            if not (self.metadata["variable"].val == variable):
+        if variable is not None:
+            variable = np.atleast_1d(variable)
+            if not (self.metadata["variable"].val in variable):
                 return False
 
         if allowed_depth is not None:
diff --git a/src/ismn/interface.py b/src/ismn/interface.py
index 3ee91a9..e5de1ef 100644
--- a/src/ismn/interface.py
+++ b/src/ismn/interface.py
@@ -90,16 +90,16 @@ class ISMN_Interface:
 
     Attributes
     ----------
-    climate : OrderedDict
+    climate : collections.OrderedDict
         All Climate classes and their descriptions.
     collection : NetworkCollection
         Contains all loaded networks with stations and sensors.
     keep_loaded_data : bool
         Switch to keep data in memory after loading (not recommended).
-    metadata : pd.DataFrame
+    metadata : pandas.DataFrame
         Metadata for active networks, with idx that could also be passed
         to :func:`ismn.interface.read_metadata`
-    landcover : OrderedDict
+    landcover : collections.OrderedDict
         All Landcover classes and their descriptions.
     parallel : bool
         Switch to activate parallel processing where possible.
@@ -156,12 +156,7 @@ class ISMN_Interface:
         if network is not None:
             network = np.atleast_1d(network)
 
-        if os.path.isfile(meta_csv_file):
-            self.__file_collection = IsmnFileCollection.from_metadata_csv(
-                self.root, meta_csv_file, network=network
-            )
-            metadata = self.__file_collection.metadata_df
-        else:
+        if not os.path.isfile(meta_csv_file):
             self.__file_collection = IsmnFileCollection.build_from_scratch(
                 self.root,
                 parallel=self.parallel,
@@ -169,12 +164,15 @@ class ISMN_Interface:
                 temp_root=temp_root,
             )
             self.__file_collection.to_metadata_csv(meta_csv_file)
-            metadata = _load_metadata_df(meta_csv_file)
-            if network is not None:
-                flags = np.isin(metadata["network"]["val"].values, network)
-                metadata = metadata.loc[flags]
 
-        self.metadata = metadata.dropna(axis=1, how="all")
+        self.__file_collection = IsmnFileCollection.from_metadata_csv(
+                self.root, meta_csv_file, network=network
+            )
+
+        metadata = self.__file_collection.metadata_df
+
+        metadata.index = range(len(metadata.index))
+        self.metadata = metadata
 
         networks = self.__collect_networks(network)
         self.collection = NetworkCollection(networks)
@@ -237,12 +235,12 @@ class ISMN_Interface:
         return self.collection.grid
 
     @deprecated
-    def list_networks(self) -> np.array:
+    def list_networks(self) -> np.ndarray:
         # get network names from list of active files
         return np.array(list(self.networks.keys()))
 
     @deprecated
-    def list_stations(self, network: str = None) -> np.array:
+    def list_stations(self, network: str = None) -> np.ndarray:
         # get station names for one of the active networks
         if network is not None:
             if network not in self.networks:
@@ -257,7 +255,7 @@ class ISMN_Interface:
             return np.array(stations)
 
     @deprecated
-    def list_sensors(self, network: str = None, station: str = None) -> np.array:
+    def list_sensors(self, network: str = None, station: str = None) -> np.ndarray:
         # List sensors names for a specific sensor in an active network
         sensors = np.array([])
         for net in self.networks.values():
@@ -324,24 +322,24 @@ class ISMN_Interface:
         ----------
         variable : str
             variable name, one of:
-                * 'soil_moisture',
-                * 'soil_temperature',
-                * 'soil_suction',
-                * 'precipitation',
-                * 'air_temperature',
-                * 'field_capacity',
-                * 'permanent_wilting_point',
-                * 'plant_available_water',
-                * 'potential_plant_available_water',
-                * 'saturation',
-                * 'silt_fraction',
-                * 'snow_depth',
-                * 'sand_fraction',
-                * 'clay_fraction',
-                * 'organic_carbon',
-                * 'snow_water_equivalent',
-                * 'surface_temperature',
-                * 'surface_temperature_quality_flag_original'
+                * soil_moisture
+                * soil_temperature
+                * soil_suction
+                * precipitation
+                * air_temperature
+                * field_capacity
+                * permanent_wilting_point
+                * plant_available_water
+                * potential_plant_available_water
+                * saturation
+                * silt_fraction
+                * snow_depth
+                * sand_fraction
+                * clay_fraction
+                * organic_carbon
+                * snow_water_equivalent
+                * surface_temperature
+                * surface_temperature_quality_flag_original
         filter_kwargs :
             Parameters are used to check all sensors at all stations, only stations
             that have at least one matching sensor are returned.
@@ -372,8 +370,8 @@ class ISMN_Interface:
 
         Parameters
         ----------
-        variable : str or None
-            Variable to filer out, None to allow all variables.
+        variable : str or list[str] or None
+            Variable(s) to filer out, None to allow all variables.
         min_depth : float, optional (default: 0)
             Min depth of sensors to search
         max_depth : float, optional (default: 0.1)
@@ -509,7 +507,7 @@ class ISMN_Interface:
             Longitude of point
         lat : float
             Latitude of point
-        return_distance : boolean, optional (default: False)
+        return_distance : bool, optional (default: False)
             if True also distance is returned
         max_dist : float, optional (default: np.inf)
             Maximum distance allowed. If no station is within this distance
@@ -553,8 +551,8 @@ class ISMN_Interface:
 
         Parameters
         ----------
-        variable : str, optional (default: None)
-            Show only stations that measure this variable, e.g. soil_moisture
+        variable : str or list[str], optional (default: None)
+            Show only stations that measure this/these variable(s), e.g. soil_moisture
             If None is passed, no filtering for variable is performed.
         min_depth : float, optional (default: -np.inf)
             Minimum depth, only stations that have a valid sensor measuring the
@@ -744,10 +742,10 @@ class ISMN_Interface:
 
         Returns
         -------
-        start_date: datetime
+        start_date: datetime.datetime
             Earliest time stamp found in all sensors that fulfill the passed
             requirements.
-        end_date: datetime
+        end_date: datetime.datetime
             Latest time stamp found in all sensors that fulfill the passed
             requirements.
         """
@@ -863,7 +861,7 @@ class ISMN_Interface:
         """
         return self.get_static_var_vals(variable, min_depth, max_depth, climate)
 
-    def get_variables(self) -> np.array:
+    def get_variables(self) -> np.ndarray:
         """
         get a list of variables available in the data
         """
@@ -895,4 +893,4 @@ class ISMN_Interface:
 
     def close_files(self):
         # close all open filehandlers
-        self.__file_collection.close()
+        self.__file_collection.close()
\ No newline at end of file
diff --git a/src/ismn/meta.py b/src/ismn/meta.py
index 8057187..7e46b0a 100644
--- a/src/ismn/meta.py
+++ b/src/ismn/meta.py
@@ -357,7 +357,7 @@ class MetaData:
         """
         Parameters
         ----------
-        vars : List[MetaVar]
+        vars : list[MetaVar]
             List of MetaVars that build the MetaData
         """
         if vars is None:
@@ -504,7 +504,7 @@ class MetaData:
 
         Parameters
         ----------
-        other: MetaData or List[MetaData]
+        other: MetaData or list[MetaData]
             Other MetaData Collection or a list of MetaData, e.g. from multiple
             sensors.
         inplace: bool, optional (default: False)


Test patch:
diff --git a/tests/test_components.py b/tests/test_components.py
index f5e88d9..b594465 100644
--- a/tests/test_components.py
+++ b/tests/test_components.py
@@ -70,6 +70,9 @@ class NetCollTest(unittest.TestCase):
         assert self.netcol.get_nearest_station(0.1, 0.1)[0].name == "station_1_1"
         assert self.netcol.get_nearest_station(1, 1)[0].name == "station_2_1"
 
+    def test_references(self):
+        refs = self.netcol.export_citations(out_file=None)
+        assert len(refs.keys()) == 2
 
 class NetworkTest(unittest.TestCase):
     def setUp(self):
@@ -131,6 +134,10 @@ class NetworkTest(unittest.TestCase):
                 "Found station but shouldn't ..."
             )  # this should never be reached
 
+    def test_get_citation(self):
+        refs = self.network.get_citations()
+        assert isinstance(refs, list) and len(refs[0]) > 1
+
 
 class StationTest(unittest.TestCase):
     def setUp(self):
diff --git a/tests/test_interface.py b/tests/test_interface.py
index c9e0c58..88b1d70 100644
--- a/tests/test_interface.py
+++ b/tests/test_interface.py
@@ -3,6 +3,8 @@ import unittest
 import os
 from tempfile import TemporaryDirectory
 from datetime import datetime
+
+import numpy as np
 import pytest
 import logging
 from collections import OrderedDict
@@ -13,6 +15,19 @@ from ismn.meta import Depth
 
 testdata_root = os.path.join(os.path.dirname(__file__), "test_data")
 
+def test_metadata_dataframe():
+    # make sure that metadata.index represents same values as get_dataset_ids
+    testdata = os.path.join(testdata_root, "Data_seperate_files_20170810_20180809")
+    metadata_path = os.path.join(testdata, "python_metadata")
+    cleanup(metadata_path)
+    ds_one = ISMN_Interface(testdata, meta_path=metadata_path, network='FR_Aqui')
+
+    assert np.all(ds_one.metadata.index.values == ds_one.get_dataset_ids(None, -np.inf, np.inf))
+    ids = ds_one.get_dataset_ids('soil_moisture')
+    assert ids == ds_one.metadata.index.values
+    assert ds_one.metadata.loc[ids[0], 'variable']['val'] == 'soil_moisture'
+    assert ds_one.metadata.loc[ids[0], 'network']['val'] == 'FR_Aqui'
+    ds_one.close_files()
 
 class Test_ISMN_Interface_CeopUnzipped(unittest.TestCase):
     @classmethod
@@ -61,7 +76,7 @@ class Test_ISMN_Interface_CeopUnzipped(unittest.TestCase):
         assert ids == [0]
 
         ids = self.ds.get_dataset_ids(
-            "soil_moisture",
+            ["soil_moisture"],
             max_depth=99,
             filter_meta_dict={
                 "lc_2010": 210,
@@ -74,7 +89,7 @@ class Test_ISMN_Interface_CeopUnzipped(unittest.TestCase):
         ids = self.ds.get_dataset_ids("novar")
         assert len(ids) == 0
 
-        ids = self.ds.get_dataset_ids("soil_moisture", 0.0, 0.19)  # should get 1
+        ids = self.ds.get_dataset_ids(["soil_moisture", "shouldhavenoeffect"], 0.0, 0.19)  # should get 1
         assert len(ids) == 1
 
         ids = self.ds.get_dataset_ids("soil_moisture", 0.0, 1.0)  # should get 2
@@ -117,7 +132,7 @@ class Test_ISMN_Interface_CeopUnzipped(unittest.TestCase):
         with TemporaryDirectory() as out_dir:
             outpath = os.path.join(out_dir, "plot.png")
             self.ds.plot_station_locations(
-                "soil_moisture", markersize=5, filename=outpath
+                ["soil_moisture", 'precipitation'], markersize=5, filename=outpath
             )
             assert len(os.listdir(out_dir)) == 1
 
@@ -210,6 +225,15 @@ class Test_ISMN_Interface_CeopUnzipped(unittest.TestCase):
         )
         assert station == dist == None
 
+    def test_citation(self):
+        with TemporaryDirectory() as out_dir:
+            out_file = os.path.join(out_dir, 'citation.txt')
+            refs = self.ds.collection.export_citations(out_file=out_file)
+            assert all([net in refs.keys() for net in list(self.ds.collection.networks.keys())])
+            assert os.path.exists(out_file)
+            with open(out_file, mode='r') as f:
+                lines = f.readlines()
+                assert len(lines) > 0
 
 class Test_ISMN_Interface_HeaderValuesUnzipped(Test_ISMN_Interface_CeopUnzipped):
     @classmethod