{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Example of DOV search methods for CPT measurements (sonderingen)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/DOV-Vlaanderen/pydov/master?filepath=docs%2Fnotebooks%2Fsearch_sonderingen.ipynb)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Use cases explained below\n", "* Get CPT measurements in a bounding box\n", "* Get CPT measurements with specific properties\n", "* Get CPT measurements in a bounding box based on specific properties\n", "* Select CPT measurements in a municipality and return depth\n", "* Get CPT measurements based on fields not available in the standard output dataframe\n", "* Get CPT measurements data, returning fields not available in the standard output dataframe\n", "* Get CPT measurements in a municipality and where groundwater related data are available\n", "* Listing techniques per CPT measurement" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import inspect, sys" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import pydov" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Get information about the datatype 'Sondering'" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "from pydov.search.sondering import SonderingSearch\n", "sondering = SonderingSearch()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A description is provided for the 'Sondering' datatype:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'In DOV worden de resultaten van sonderingen ter beschikking gesteld. Bij het uitvoeren van de sondering wordt een sondeerpunt met conus bij middel van buizen statisch de grond ingedrukt. Continu of met bepaalde diepte-intervallen wordt de weerstand aan de conuspunt, de plaatselijke wrijvingsweerstand en/of de totale indringingsweerstand opgemeten. Eventueel kan aanvullend de waterspanning in de grond rond de conus tijdens de sondering worden opgemeten met een waterspanningsmeter. Het op diepte drukken van de sondeerbuizen gebeurt met een indrukapparaat. De nodige reactie voor het indrukken van de buizen wordt geleverd door een verankering en/of door het gewicht van de sondeerwagen. De totale indrukcapaciteit varieert van 25 kN tot 250 kN, afhankelijk van apparaat en opstellingswijze.'" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sondering.get_description()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The different fields that are available for objects of the 'Sondering' datatype can be requested with the get_fields() method:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "id\n", "sondeernummer\n", "pkey_sondering\n", "weerstandsdiagram\n", "meetreeks\n", "x\n", "y\n", "start_sondering_mtaw\n", "gemeente\n", "diepte_sondering_van\n", "diepte_sondering_tot\n", "datum_aanvang\n", "uitvoerder\n", "conus\n", "sondeermethode\n", "apparaat\n", "informele_stratigrafie\n", "formele_stratigrafie\n", "hydrogeologische_stratigrafie\n", "opdrachten\n", "eerste_invoer\n", "geom\n", "datum_gw_meting\n", "diepte_gw_m\n", "lengte\n", "diepte\n", "qc\n", "Qt\n", "fs\n", "u\n", "i\n", "mv_mtaw\n" ] } ], "source": [ "fields = sondering.get_fields()\n", "\n", "# print available fields\n", "for f in fields.values():\n", " print(f['name'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can get more information of a field by requesting it from the fields dictionary:\n", "\n", " * *name*: name of the field\n", " * *definition*: definition of this field\n", " * *cost*: currently this is either 1 or 10, depending on the datasource of the field. It is an indication of the expected time it will take to retrieve this field in the output dataframe.\n", " * *notnull*: whether the field is mandatory or not\n", " * *type*: datatype of the values of this field\n", " * *codelist*: optionally, a codelist that describes the possible values of this field\n", "\n", "Alternatively, you can list all the fields and their details by inspecting the `get_fields()` output or the search instance itself in a notebook:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", "
\n", " \n", "
\n", " pydov.search.sondering.SonderingSearch\n", "
\n", "

In DOV worden de resultaten van sonderingen ter beschikking gesteld. Bij het uitvoeren van de sondering wordt een sondeerpunt met conus bij middel van buizen statisch de grond ingedrukt. Continu of met bepaalde diepte-intervallen wordt de weerstand aan de conuspunt, de plaatselijke wrijvingsweerstand en/of de totale indringingsweerstand opgemeten. Eventueel kan aanvullend de waterspanning in de grond rond de conus tijdens de sondering worden opgemeten met een waterspanningsmeter. Het op diepte drukken van de sondeerbuizen gebeurt met een indrukapparaat. De nodige reactie voor het indrukken van de buizen wordt geleverd door een verankering en/of door het gewicht van de sondeerwagen. De totale indrukcapaciteit varieert van 25 kN tot 250 kN, afhankelijk van apparaat en opstellingswijze.

\n", " \n", "
\n", "

id - Volgnummer (intern en niet-stabiel).

  • type: string
  • notnull: True
  • query: True
  • cost: 1
  • multivalue: False
\n", "
\n", " \n", " \n", "
\n", "

sondeernummer - Het sondeernummer (ook gekend als proefnummer) van de sondering.

  • type: string
  • notnull: True
  • query: True
  • cost: 1
  • multivalue: False
\n", "
\n", " \n", " \n", "
\n", "

pkey_sondering - Permanente URL die verwijst naar de gegevens van de sondering op de website. Voeg '.xml' toe om een XML voorstelling van deze gegevens te verkrijgen.

  • type: string
  • notnull: True
  • query: True
  • cost: 1
  • multivalue: False
\n", "
\n", " \n", " \n", "
\n", "

weerstandsdiagram - URL naar het weerstandsdiagram van de sondering in PDF formaat.

  • type: string
  • notnull: True
  • query: True
  • cost: 1
  • multivalue: False
\n", "
\n", " \n", " \n", "
\n", "

meetreeks - URL naar de meetreeks van de sondering in HTML formaat.

  • type: string
  • notnull: True
  • query: True
  • cost: 1
  • multivalue: False
\n", "
\n", " \n", " \n", "
\n", "

x - De x-coördinaat van de sondering in het Lambert72 coördinaatsysteem (in meter, EPSG:31370).

  • type: float
  • notnull: True
  • query: True
  • cost: 1
  • multivalue: False
\n", "
\n", " \n", " \n", "
\n", "

y - De y-coördinaat van de sondering in het Lambert72 coördinaatsysteem (in meter, EPSG:31370).

  • type: float
  • notnull: True
  • query: True
  • cost: 1
  • multivalue: False
\n", "
\n", " \n", " \n", "
\n", "

start_sondering_mtaw - De hoogte van het aanvangspeil van de sondering in het TAW stelsel (in meter).

  • type: float
  • notnull: False
  • query: True
  • cost: 1
  • multivalue: False
\n", "
\n", " \n", " \n", "
\n", "

gemeente - De gemeente waarin de sondering gelegen is.

  • type: string
  • notnull: False
  • query: True
  • cost: 1
  • multivalue: False
\n", "
\n", " \n", " \n", "
\n", "

diepte_sondering_van - De aanvangsdiepte van de sondering ten opzichte van het aanvangspeil, in meter.

  • type: float
  • notnull: False
  • query: True
  • cost: 1
  • multivalue: False
\n", "
\n", " \n", " \n", "
\n", "

diepte_sondering_tot - Maximumdiepte van de sondering ten opzichte van het aanvangspeil, in meter.

  • type: float
  • notnull: False
  • query: True
  • cost: 1
  • multivalue: False
\n", "
\n", " \n", " \n", "
\n", "

datum_aanvang - Datum waarop men de sondering gestart is.

  • type: date
  • notnull: False
  • query: True
  • cost: 1
  • multivalue: False
\n", "
\n", " \n", " \n", "
\n", "

uitvoerder - De organisatie die de sondering uitvoerde.

  • type: string
  • notnull: True
  • query: True
  • cost: 1
  • multivalue: False
\n", "
\n", " \n", " \n", "
\n", "

conus - Het type conus waarmee de sondering werd uitgevoerd.

  • type: string
  • notnull: True
  • query: True
  • cost: 1
  • multivalue: False
  • codelist:
    • \n", " \n", "
    \n", " \n", "
    \n", " pydov.util.codelists.FeatureCatalogueValues\n", "
    \n", " \n", " \n", "
    \n", "

    E - E

    \n", "
    \n", " \n", " \n", "
    \n", "

    M1 - M1

    \n", "
    \n", " \n", " \n", "
    \n", "

    M2 - M2

    \n", "
    \n", " \n", " \n", "
    \n", "

    M4 - M4

    \n", "
    \n", " \n", " \n", "
    \n", "

    U - U

    \n", "
    \n", " \n", " \n", "
    \n", "

    onbekend - onbekend

    \n", "
    \n", " \n", "
    \n", "
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sondeermethode - De methode waarmee de sondering werd uitgevoerd.

  • type: string
  • notnull: True
  • query: True
  • cost: 1
  • multivalue: False
  • codelist:
    • \n", " \n", "
    \n", " \n", "
    \n", " pydov.util.codelists.FeatureCatalogueValues\n", "
    \n", " \n", " \n", "
    \n", "

    continu elektrisch - continu elektrisch

    \n", "
    \n", " \n", " \n", "
    \n", "

    continu mechanisch - continu mechanisch

    \n", "
    \n", " \n", " \n", "
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    discontinu mechanisch - discontinu mechanisch

    \n", "
    \n", " \n", " \n", "
    \n", "

    onbekend - onbekend

    \n", "
    \n", " \n", "
    \n", "
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apparaat - Het type apparaat waarmee de sondering werd uitgevoerd.

  • type: string
  • notnull: True
  • query: True
  • cost: 1
  • multivalue: False
  • codelist:
    • \n", " \n", "
    \n", " \n", "
    \n", " pydov.util.codelists.FeatureCatalogueValues\n", "
    \n", " \n", " \n", "
    \n", "

    100KN - 100KN

    \n", "
    \n", " \n", " \n", "
    \n", "

    100kN - LOS - 100kN - LOS

    \n", "
    \n", " \n", " \n", "
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    200KN - 200KN

    \n", "
    \n", " \n", " \n", "
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    200kN - GINAF - 200kN - GINAF

    \n", "
    \n", " \n", " \n", "
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    200kN - MAN1 - 200kN - MAN1

    \n", "
    \n", " \n", " \n", "
    \n", "

    200kN - MAN2 - 200kN - MAN2

    \n", "
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    200kN - RUPS - 200kN - RUPS

    \n", "
    \n", " \n", " \n", "
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    200kN - TRACK-TRUCK - 200kN - TRACK-TRUCK

    \n", "
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    200kN - Unimog - 200kN - Unimog

    \n", "
    \n", " \n", " \n", "
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    25 kN - LOS - 25 kN - LOS

    \n", "
    \n", " \n", " \n", "
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    25KN - 25KN

    \n", "
    \n", " \n", " \n", "
    \n", "

    50KN - 50KN

    \n", "
    \n", " \n", " \n", "
    \n", "

    50kN - LOS - 50kN - LOS

    \n", "
    \n", " \n", " \n", "
    \n", "

    onbekend - onbekend

    \n", "
    \n", " \n", "
    \n", "
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informele_stratigrafie - Geeft aan of er aan de sondering minstens één interpretatie van het type 'informele stratigrafie' gekoppeld is.

  • type: boolean
  • notnull: True
  • query: True
  • cost: 1
  • multivalue: False
\n", "
\n", " \n", " \n", "
\n", "

formele_stratigrafie - Geeft aan of er aan de sondering minstens één interpretatie van het type 'formele stratigrafie' gekoppeld is.

  • type: boolean
  • notnull: True
  • query: True
  • cost: 1
  • multivalue: False
\n", "
\n", " \n", " \n", "
\n", "

hydrogeologische_stratigrafie - Geeft aan of er aan de sondering minstens één interpretatie van het type 'hydrogeologische stratigrafie' gekoppeld is.

  • type: boolean
  • notnull: True
  • query: True
  • cost: 1
  • multivalue: False
\n", "
\n", " \n", " \n", "
\n", "

opdrachten - De opdracht(en) waaraan de sondering gekoppeld is.

  • type: string
  • notnull: False
  • query: True
  • cost: 1
  • multivalue: False
\n", "
\n", " \n", " \n", "
\n", "

eerste_invoer - Het tijdstip waarop deze sondering voor het eerst in DOV ingevoerd werd.

  • type: datetime
  • notnull: True
  • query: True
  • cost: 1
  • multivalue: False
\n", "
\n", " \n", " \n", "
\n", "

geom - None

  • type: geometry
  • notnull: False
  • query: False
  • cost: 1
  • multivalue: False
\n", "
\n", " \n", " \n", "
\n", "

datum_gw_meting - Datum en tijdstip van waarneming van de grondwaterstand.

  • type: datetime
  • notnull: False
  • query: False
  • cost: 10
  • multivalue: False
\n", "
\n", " \n", " \n", "
\n", "

diepte_gw_m - Diepte water in meter ten opzicht van het aanvangspeil.

  • type: float
  • notnull: False
  • query: False
  • cost: 10
  • multivalue: False
\n", "
\n", " \n", " \n", "
\n", "

lengte - Geregistreerde sondeerlengte, uitgedrukt in meter.

  • type: float
  • notnull: False
  • query: False
  • cost: 10
  • multivalue: False
\n", "
\n", " \n", " \n", "
\n", "

diepte - Diepte waarop sondeerparameters geregistreerd werden, berekend uit de sondeerlengte en de geregistreerde hellingsmeting, uitgedrukt in meter.

  • type: float
  • notnull: False
  • query: False
  • cost: 10
  • multivalue: False
\n", "
\n", " \n", " \n", "
\n", "

qc - Opgemeten waarde van de conusweerstand, uitgedrukt in MPa.

  • type: float
  • notnull: False
  • query: False
  • cost: 10
  • multivalue: False
\n", "
\n", " \n", " \n", "
\n", "

Qt - Opgemeten waarde van de totale weerstand, uitgedrukt in kN.

  • type: float
  • notnull: False
  • query: False
  • cost: 10
  • multivalue: False
\n", "
\n", " \n", " \n", "
\n", "

fs - Opgemeten waarde van de plaatelijke kleefweerstand, uitgedrukt in kPa.

  • type: float
  • notnull: False
  • query: False
  • cost: 10
  • multivalue: False
\n", "
\n", " \n", " \n", "
\n", "

u - Opgemeten waarde van de porienwaterspanning, uitgedrukt in kPa.

  • type: float
  • notnull: False
  • query: False
  • cost: 10
  • multivalue: False
\n", "
\n", " \n", " \n", "
\n", "

i - Opgemeten waarde van de inclinatie, uitgedrukt in graden.

  • type: float
  • notnull: False
  • query: False
  • cost: 10
  • multivalue: False
\n", "
\n", " \n", " \n", "
\n", "

mv_mtaw - Maaiveldhoogte in mTAW op dag dat de sondering uitgevoerd werd.

  • type: float
  • notnull: False
  • query: False
  • cost: 10
  • multivalue: False
\n", "
\n", "

\n", "
\n", " " ], "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sondering" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example use cases" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Get CPT measurements in a bounding box" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Get data for all the CPT measurements that are geographically located within the bounds of the specified box.\n", "\n", "The coordinates are in the Belgian Lambert72 (EPSG:31370) coordinate system and are given in the order of lower left x, lower left y, upper right x, upper right y." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[000/001] .\n", "[000/001] .\n" ] }, { "data": { "text/html": [ "
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5 rows × 21 columns

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" ], "text/plain": [ " pkey_sondering sondeernummer \\\n", "0 https://www.dov.vlaanderen.be/data/sondering/1... GEO-72/555-SXVIII \n", "1 https://www.dov.vlaanderen.be/data/sondering/1... GEO-72/555-SXVIII \n", "2 https://www.dov.vlaanderen.be/data/sondering/1... GEO-72/555-SXVIII \n", "3 https://www.dov.vlaanderen.be/data/sondering/1... GEO-72/555-SXVIII \n", "4 https://www.dov.vlaanderen.be/data/sondering/1... GEO-72/555-SXVIII \n", "\n", " x y mv_mtaw start_sondering_mtaw diepte_sondering_van \\\n", "0 153008.0 206985.0 NaN 15.8 0.0 \n", "1 153008.0 206985.0 NaN 15.8 0.0 \n", "2 153008.0 206985.0 NaN 15.8 0.0 \n", "3 153008.0 206985.0 NaN 15.8 0.0 \n", "4 153008.0 206985.0 NaN 15.8 0.0 \n", "\n", " diepte_sondering_tot datum_aanvang \\\n", "0 36.0 1973-03-21 \n", "1 36.0 1973-03-21 \n", "2 36.0 1973-03-21 \n", "3 36.0 1973-03-21 \n", "4 36.0 1973-03-21 \n", "\n", " uitvoerder ... apparaat datum_gw_meting \\\n", "0 Rijksinstituut voor Grondmechanica (RIG) ... 100 kN NaN \n", "1 Rijksinstituut voor Grondmechanica (RIG) ... 100 kN NaN \n", "2 Rijksinstituut voor Grondmechanica (RIG) ... 100 kN NaN \n", "3 Rijksinstituut voor Grondmechanica (RIG) ... 100 kN NaN \n", "4 Rijksinstituut voor Grondmechanica (RIG) ... 100 kN NaN \n", "\n", " diepte_gw_m lengte diepte qc Qt fs u i \n", "0 NaN 0.2 NaN 1.6 2.06 NaN NaN NaN \n", "1 NaN 0.4 NaN 3.6 4.26 NaN NaN NaN \n", "2 NaN 0.6 NaN 2.6 3.46 NaN NaN NaN \n", "3 NaN 0.8 NaN 4.0 5.66 NaN NaN NaN \n", "4 NaN 1.0 NaN 3.0 6.53 NaN NaN NaN \n", "\n", "[5 rows x 21 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from pydov.util.location import Within, Box\n", "\n", "df = sondering.search(location=Within(Box(152999, 206930, 153050, 207935, epsg=31370)))\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The dataframe contains one CPT measurement where multiple measurement points. The available data are flattened to represent unique attributes per row of the dataframe.\n", "\n", "Using the *pkey_sondering* field one can request the details of this borehole in a webbrowser:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "https://www.dov.vlaanderen.be/data/sondering/1973-016812\n" ] } ], "source": [ "for pkey_sondering in set(df.pkey_sondering):\n", " print(pkey_sondering)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Get CPT measurements with specific properties" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next to querying CPT based on their geographic location within a bounding box, we can also search for CPT measurements matching a specific set of properties. For this we can build a query using a combination of the 'Sondering' fields and operators provided by the WFS protocol.\n", "\n", "A list of possible operators can be found below:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['PropertyIsBetween',\n", " 'PropertyIsEqualTo',\n", " 'PropertyIsGreaterThan',\n", " 'PropertyIsGreaterThanOrEqualTo',\n", " 'PropertyIsLessThan',\n", " 'PropertyIsLessThanOrEqualTo',\n", " 'PropertyIsLike',\n", " 'PropertyIsNotEqualTo',\n", " 'PropertyIsNull',\n", " 'SortProperty']" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[i for i,j in inspect.getmembers(sys.modules['owslib.fes2'], inspect.isclass) if 'Property' in i]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this example we build a query using the *PropertyIsEqualTo* operator to find all CPT measuremetns that are within the community (gemeente) of 'Herstappe':" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[000/001] .\n", "[000/029] .............................\n" ] }, { "data": { "text/html": [ "
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pkey_sonderingsondeernummerxymv_mtawstart_sondering_mtawdiepte_sondering_vandiepte_sondering_totdatum_aanvanguitvoerder...apparaatdatum_gw_metingdiepte_gw_mlengtediepteqcQtfsui
0https://www.dov.vlaanderen.be/data/sondering/1...GEO-75/194-S1150310.0169796.0NaN56.30.04.51975-05-20Rijksinstituut voor Grondmechanica (RIG)...25 kNNaN1.971.0NaN3.3NaNNaNNaNNaN
1https://www.dov.vlaanderen.be/data/sondering/1...GEO-75/194-S1150310.0169796.0NaN56.30.04.51975-05-20Rijksinstituut voor Grondmechanica (RIG)...25 kNNaN1.971.1NaN2.9NaNNaNNaNNaN
2https://www.dov.vlaanderen.be/data/sondering/1...GEO-75/194-S1150310.0169796.0NaN56.30.04.51975-05-20Rijksinstituut voor Grondmechanica (RIG)...25 kNNaN1.971.2NaN2.7NaNNaNNaNNaN
3https://www.dov.vlaanderen.be/data/sondering/1...GEO-75/194-S1150310.0169796.0NaN56.30.04.51975-05-20Rijksinstituut voor Grondmechanica (RIG)...25 kNNaN1.971.3NaN2.4NaNNaNNaNNaN
4https://www.dov.vlaanderen.be/data/sondering/1...GEO-75/194-S1150310.0169796.0NaN56.30.04.51975-05-20Rijksinstituut voor Grondmechanica (RIG)...25 kNNaN1.971.4NaN3.6NaNNaNNaNNaN
\n", "

5 rows × 21 columns

\n", "
" ], "text/plain": [ " pkey_sondering sondeernummer x \\\n", "0 https://www.dov.vlaanderen.be/data/sondering/1... GEO-75/194-S1 150310.0 \n", "1 https://www.dov.vlaanderen.be/data/sondering/1... GEO-75/194-S1 150310.0 \n", "2 https://www.dov.vlaanderen.be/data/sondering/1... GEO-75/194-S1 150310.0 \n", "3 https://www.dov.vlaanderen.be/data/sondering/1... GEO-75/194-S1 150310.0 \n", "4 https://www.dov.vlaanderen.be/data/sondering/1... GEO-75/194-S1 150310.0 \n", "\n", " y mv_mtaw start_sondering_mtaw diepte_sondering_van \\\n", "0 169796.0 NaN 56.3 0.0 \n", "1 169796.0 NaN 56.3 0.0 \n", "2 169796.0 NaN 56.3 0.0 \n", "3 169796.0 NaN 56.3 0.0 \n", "4 169796.0 NaN 56.3 0.0 \n", "\n", " diepte_sondering_tot datum_aanvang \\\n", "0 4.5 1975-05-20 \n", "1 4.5 1975-05-20 \n", "2 4.5 1975-05-20 \n", "3 4.5 1975-05-20 \n", "4 4.5 1975-05-20 \n", "\n", " uitvoerder ... apparaat datum_gw_meting \\\n", "0 Rijksinstituut voor Grondmechanica (RIG) ... 25 kN NaN \n", "1 Rijksinstituut voor Grondmechanica (RIG) ... 25 kN NaN \n", "2 Rijksinstituut voor Grondmechanica (RIG) ... 25 kN NaN \n", "3 Rijksinstituut voor Grondmechanica (RIG) ... 25 kN NaN \n", "4 Rijksinstituut voor Grondmechanica (RIG) ... 25 kN NaN \n", "\n", " diepte_gw_m lengte diepte qc Qt fs u i \n", "0 1.97 1.0 NaN 3.3 NaN NaN NaN NaN \n", "1 1.97 1.1 NaN 2.9 NaN NaN NaN NaN \n", "2 1.97 1.2 NaN 2.7 NaN NaN NaN NaN \n", "3 1.97 1.3 NaN 2.4 NaN NaN NaN NaN \n", "4 1.97 1.4 NaN 3.6 NaN NaN NaN NaN \n", "\n", "[5 rows x 21 columns]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from owslib.fes2 import PropertyIsEqualTo\n", "\n", "query = PropertyIsEqualTo(propertyname='gemeente',\n", " literal='Elsene')\n", "df = sondering.search(query=query)\n", "\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once again we can use the *pkey_sondering* as a permanent link to the information of these CPT measurements:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "https://www.dov.vlaanderen.be/data/sondering/1976-030128\n", "https://www.dov.vlaanderen.be/data/sondering/1992-000338\n", "https://www.dov.vlaanderen.be/data/sondering/1974-016926\n", "https://www.dov.vlaanderen.be/data/sondering/1992-000336\n", "https://www.dov.vlaanderen.be/data/sondering/1992-000339\n", "https://www.dov.vlaanderen.be/data/sondering/1975-014064\n", "https://www.dov.vlaanderen.be/data/sondering/1971-023323\n", "https://www.dov.vlaanderen.be/data/sondering/1971-023091\n", "https://www.dov.vlaanderen.be/data/sondering/1976-030150\n", "https://www.dov.vlaanderen.be/data/sondering/1971-022775\n", "https://www.dov.vlaanderen.be/data/sondering/1971-022776\n", "https://www.dov.vlaanderen.be/data/sondering/1976-014640\n", "https://www.dov.vlaanderen.be/data/sondering/1976-013899\n", "https://www.dov.vlaanderen.be/data/sondering/1975-014063\n", "https://www.dov.vlaanderen.be/data/sondering/1976-030140\n", "https://www.dov.vlaanderen.be/data/sondering/1974-016927\n", "https://www.dov.vlaanderen.be/data/sondering/1971-023321\n", "https://www.dov.vlaanderen.be/data/sondering/1971-023320\n", "https://www.dov.vlaanderen.be/data/sondering/1976-013898\n", "https://www.dov.vlaanderen.be/data/sondering/1971-023322\n", "https://www.dov.vlaanderen.be/data/sondering/1976-013900\n", "https://www.dov.vlaanderen.be/data/sondering/1992-000337\n", "https://www.dov.vlaanderen.be/data/sondering/1976-014638\n", "https://www.dov.vlaanderen.be/data/sondering/1971-022777\n", "https://www.dov.vlaanderen.be/data/sondering/1980-024720\n", "https://www.dov.vlaanderen.be/data/sondering/1992-000335\n", "https://www.dov.vlaanderen.be/data/sondering/1976-030148\n", "https://www.dov.vlaanderen.be/data/sondering/1980-024719\n", "https://www.dov.vlaanderen.be/data/sondering/1971-023319\n" ] } ], "source": [ "for pkey_sondering in set(df.pkey_sondering):\n", " print(pkey_sondering)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Get CPT measurements in a bounding box based on specific properties" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can combine a query on attributes with a query on geographic location to get the CPT measurements within a bounding box that have specific properties.\n", "\n", "The following example requests the CPT measurements with a depth greater than or equal to 2000 meters within the given bounding box.\n", "\n", "(Note that the datatype of the *literal* parameter should be a string, regardless of the datatype of this field in the output dataframe.)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[000/001] .\n", "[000/021] .....................\n" ] }, { "data": { "text/html": [ "
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pkey_sonderingsondeernummerxymv_mtawstart_sondering_mtawdiepte_sondering_vandiepte_sondering_totdatum_aanvanguitvoerder...apparaatdatum_gw_metingdiepte_gw_mlengtediepteqcQtfsui
0https://www.dov.vlaanderen.be/data/sondering/2...GEO-07/054-S3202348.46213129.09NaN26.271.520.02007-05-10VO - Afdeling Geotechniek...200 kN - TRACK-TRUCK2007-05-10 10:40:00NaN1.551.550.60NaN-1.0NaN0.2
1https://www.dov.vlaanderen.be/data/sondering/2...GEO-07/054-S3202348.46213129.09NaN26.271.520.02007-05-10VO - Afdeling Geotechniek...200 kN - TRACK-TRUCK2007-05-10 10:40:00NaN1.601.600.48NaN0.0NaN0.2
2https://www.dov.vlaanderen.be/data/sondering/2...GEO-07/054-S3202348.46213129.09NaN26.271.520.02007-05-10VO - Afdeling Geotechniek...200 kN - TRACK-TRUCK2007-05-10 10:40:00NaN1.651.650.57NaN0.0NaN0.2
3https://www.dov.vlaanderen.be/data/sondering/2...GEO-07/054-S3202348.46213129.09NaN26.271.520.02007-05-10VO - Afdeling Geotechniek...200 kN - TRACK-TRUCK2007-05-10 10:40:00NaN1.701.700.86NaN0.0NaN0.3
4https://www.dov.vlaanderen.be/data/sondering/2...GEO-07/054-S3202348.46213129.09NaN26.271.520.02007-05-10VO - Afdeling Geotechniek...200 kN - TRACK-TRUCK2007-05-10 10:40:00NaN1.751.750.90NaN0.0NaN0.3
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5 rows × 21 columns

\n", "
" ], "text/plain": [ " pkey_sondering sondeernummer \\\n", "0 https://www.dov.vlaanderen.be/data/sondering/2... GEO-07/054-S3 \n", "1 https://www.dov.vlaanderen.be/data/sondering/2... GEO-07/054-S3 \n", "2 https://www.dov.vlaanderen.be/data/sondering/2... GEO-07/054-S3 \n", "3 https://www.dov.vlaanderen.be/data/sondering/2... GEO-07/054-S3 \n", "4 https://www.dov.vlaanderen.be/data/sondering/2... GEO-07/054-S3 \n", "\n", " x y mv_mtaw start_sondering_mtaw diepte_sondering_van \\\n", "0 202348.46 213129.09 NaN 26.27 1.5 \n", "1 202348.46 213129.09 NaN 26.27 1.5 \n", "2 202348.46 213129.09 NaN 26.27 1.5 \n", "3 202348.46 213129.09 NaN 26.27 1.5 \n", "4 202348.46 213129.09 NaN 26.27 1.5 \n", "\n", " diepte_sondering_tot datum_aanvang uitvoerder ... \\\n", "0 20.0 2007-05-10 VO - Afdeling Geotechniek ... \n", "1 20.0 2007-05-10 VO - Afdeling Geotechniek ... \n", "2 20.0 2007-05-10 VO - Afdeling Geotechniek ... \n", "3 20.0 2007-05-10 VO - Afdeling Geotechniek ... \n", "4 20.0 2007-05-10 VO - Afdeling Geotechniek ... \n", "\n", " apparaat datum_gw_meting diepte_gw_m lengte diepte qc \\\n", "0 200 kN - TRACK-TRUCK 2007-05-10 10:40:00 NaN 1.55 1.55 0.60 \n", "1 200 kN - TRACK-TRUCK 2007-05-10 10:40:00 NaN 1.60 1.60 0.48 \n", "2 200 kN - TRACK-TRUCK 2007-05-10 10:40:00 NaN 1.65 1.65 0.57 \n", "3 200 kN - TRACK-TRUCK 2007-05-10 10:40:00 NaN 1.70 1.70 0.86 \n", "4 200 kN - TRACK-TRUCK 2007-05-10 10:40:00 NaN 1.75 1.75 0.90 \n", "\n", " Qt fs u i \n", "0 NaN -1.0 NaN 0.2 \n", "1 NaN 0.0 NaN 0.2 \n", "2 NaN 0.0 NaN 0.2 \n", "3 NaN 0.0 NaN 0.3 \n", "4 NaN 0.0 NaN 0.3 \n", "\n", "[5 rows x 21 columns]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from owslib.fes2 import PropertyIsGreaterThanOrEqualTo\n", "\n", "query = PropertyIsGreaterThanOrEqualTo(\n", " propertyname='diepte_sondering_tot',\n", " literal='20')\n", "\n", "df = sondering.search(\n", " location=Within(Box(200000, 211000, 205000, 214000, epsg=31370)),\n", " query=query\n", " )\n", "\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can look at one of the CPT measurements in a webbrowser using its *pkey_sondering*:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "https://www.dov.vlaanderen.be/data/sondering/2009-000053\n", "https://www.dov.vlaanderen.be/data/sondering/2010-062407\n", "https://www.dov.vlaanderen.be/data/sondering/2008-077577\n", "https://www.dov.vlaanderen.be/data/sondering/2008-077566\n", "https://www.dov.vlaanderen.be/data/sondering/2015-054999\n", "https://www.dov.vlaanderen.be/data/sondering/2008-077592\n", "https://www.dov.vlaanderen.be/data/sondering/2008-077591\n", "https://www.dov.vlaanderen.be/data/sondering/2008-077581\n", "https://www.dov.vlaanderen.be/data/sondering/2008-077565\n", "https://www.dov.vlaanderen.be/data/sondering/2009-000054\n", "https://www.dov.vlaanderen.be/data/sondering/2009-000052\n", "https://www.dov.vlaanderen.be/data/sondering/2008-077556\n", "https://www.dov.vlaanderen.be/data/sondering/2008-077580\n", "https://www.dov.vlaanderen.be/data/sondering/2008-077545\n", "https://www.dov.vlaanderen.be/data/sondering/2008-077579\n", "https://www.dov.vlaanderen.be/data/sondering/2007-049201\n", "https://www.dov.vlaanderen.be/data/sondering/2015-055496\n", "https://www.dov.vlaanderen.be/data/sondering/2008-077564\n", "https://www.dov.vlaanderen.be/data/sondering/2015-054995\n", "https://www.dov.vlaanderen.be/data/sondering/2007-049200\n", "https://www.dov.vlaanderen.be/data/sondering/2008-077557\n" ] } ], "source": [ "for pkey_sondering in set(df.pkey_sondering):\n", " print(pkey_sondering)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Select CPT measurements in a municipality and return depth" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can limit the columns in the output dataframe by specifying the *return_fields* parameter in our search.\n", "\n", "In this example we query all the CPT measurements in the city of Ghent and return their depth:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[000/001] .\n" ] }, { "data": { "text/html": [ "
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diepte_sondering_tot
02.7
11.4
27.6
311.5
418.6
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" ], "text/plain": [ " diepte_sondering_tot\n", "0 2.7\n", "1 1.4\n", "2 7.6\n", "3 11.5\n", "4 18.6" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "query = PropertyIsEqualTo(propertyname='gemeente',\n", " literal='Gent')\n", "df = sondering.search(query=query,\n", " return_fields=('diepte_sondering_tot',))\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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diepte_sondering_tot
count3875.000000
mean18.765182
std8.516075
min0.660000
25%11.800000
50%19.400000
75%24.800000
max52.600000
\n", "
" ], "text/plain": [ " diepte_sondering_tot\n", "count 3875.000000\n", "mean 18.765182\n", "std 8.516075\n", "min 0.660000\n", "25% 11.800000\n", "50% 19.400000\n", "75% 24.800000\n", "max 52.600000" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'depth (m)')" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = df.boxplot()\n", "ax.set_title('Distribution depth CPT measurements in Ghent');\n", "ax.set_ylabel(\"depth (m)\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Get CPT measurements based on fields not available in the standard output dataframe" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To keep the output dataframe size acceptable, not all available WFS fields are included in the standard output. However, one can use this information to select CPT measurements as illustrated below.\n", "\n", "For example, make a selection of the CPT measurements in municipality the of Antwerp, using a conustype 'U':" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[000/001] .\n" ] }, { "data": { "text/html": [ "
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pkey_sonderingsondeernummerxydiepte_sondering_totdatum_aanvang
0https://www.dov.vlaanderen.be/data/sondering/1...GEO-93/023-SII-E152740.0215493.029.701993-03-02
1https://www.dov.vlaanderen.be/data/sondering/2...GEO-02/111-S1150347.3214036.429.952002-12-17
2https://www.dov.vlaanderen.be/data/sondering/2...GEO-04/123-SKD4-E146437.7222317.54.452004-07-12
3https://www.dov.vlaanderen.be/data/sondering/2...GEO-04/123-SKD6-E146523.9222379.77.402004-07-14
4https://www.dov.vlaanderen.be/data/sondering/2...GEO-04/123-SKD5-E146493.4222298.81.652004-07-16
\n", "
" ], "text/plain": [ " pkey_sondering sondeernummer \\\n", "0 https://www.dov.vlaanderen.be/data/sondering/1... GEO-93/023-SII-E \n", "1 https://www.dov.vlaanderen.be/data/sondering/2... GEO-02/111-S1 \n", "2 https://www.dov.vlaanderen.be/data/sondering/2... GEO-04/123-SKD4-E \n", "3 https://www.dov.vlaanderen.be/data/sondering/2... GEO-04/123-SKD6-E \n", "4 https://www.dov.vlaanderen.be/data/sondering/2... GEO-04/123-SKD5-E \n", "\n", " x y diepte_sondering_tot datum_aanvang \n", "0 152740.0 215493.0 29.70 1993-03-02 \n", "1 150347.3 214036.4 29.95 2002-12-17 \n", "2 146437.7 222317.5 4.45 2004-07-12 \n", "3 146523.9 222379.7 7.40 2004-07-14 \n", "4 146493.4 222298.8 1.65 2004-07-16 " ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from owslib.fes2 import And\n", "\n", "query = And([PropertyIsEqualTo(propertyname='gemeente',\n", " literal='Antwerpen'),\n", " PropertyIsEqualTo(propertyname='conus', \n", " literal='U')]\n", " )\n", "df = sondering.search(query=query,\n", " return_fields=('pkey_sondering', 'sondeernummer', 'x', 'y', 'diepte_sondering_tot', 'datum_aanvang'))\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Get CPT data, returning fields not available in the standard output dataframe" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As denoted in the previous example, not all available fields are available in the default output frame to keep its size limited. However, you can request any available field by including it in the *return_fields* parameter of the search:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[000/001] .\n" ] }, { "data": { "text/html": [ "
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pkey_sonderingsondeernummerdiepte_sondering_totconusxy
0https://www.dov.vlaanderen.be/data/sondering/1...GEO-94/020-SV33.80U110241.6204692.2
1https://www.dov.vlaanderen.be/data/sondering/1...GEO-94/020-SI15.65U110062.5205051.4
2https://www.dov.vlaanderen.be/data/sondering/1...GEO-94/020-SII26.50U110107.0204965.3
3https://www.dov.vlaanderen.be/data/sondering/1...GEO-94/020-SIII16.50U110152.4204876.1
4https://www.dov.vlaanderen.be/data/sondering/1...GEO-94/020-SIV16.70U110197.8204787.0
\n", "
" ], "text/plain": [ " pkey_sondering sondeernummer \\\n", "0 https://www.dov.vlaanderen.be/data/sondering/1... GEO-94/020-SV \n", "1 https://www.dov.vlaanderen.be/data/sondering/1... GEO-94/020-SI \n", "2 https://www.dov.vlaanderen.be/data/sondering/1... GEO-94/020-SII \n", "3 https://www.dov.vlaanderen.be/data/sondering/1... GEO-94/020-SIII \n", "4 https://www.dov.vlaanderen.be/data/sondering/1... GEO-94/020-SIV \n", "\n", " diepte_sondering_tot conus x y \n", "0 33.80 U 110241.6 204692.2 \n", "1 15.65 U 110062.5 205051.4 \n", "2 26.50 U 110107.0 204965.3 \n", "3 16.50 U 110152.4 204876.1 \n", "4 16.70 U 110197.8 204787.0 " ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "query = And([PropertyIsEqualTo(propertyname='gemeente', literal='Gent'),\n", " PropertyIsEqualTo(propertyname='conus', literal='U')])\n", "\n", "df = sondering.search(query=query,\n", " return_fields=('pkey_sondering', 'sondeernummer', 'diepte_sondering_tot',\n", " 'conus', 'x', 'y'))\n", "\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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pkey_sonderingsondeernummerdiepte_sondering_totconusxy
0https://www.dov.vlaanderen.be/data/sondering/1...GEO-94/020-SV33.80U110241.60204692.20
1https://www.dov.vlaanderen.be/data/sondering/1...GEO-94/020-SI15.65U110062.50205051.40
2https://www.dov.vlaanderen.be/data/sondering/1...GEO-94/020-SII26.50U110107.00204965.30
3https://www.dov.vlaanderen.be/data/sondering/1...GEO-94/020-SIII16.50U110152.40204876.10
4https://www.dov.vlaanderen.be/data/sondering/1...GEO-94/020-SIV16.70U110197.80204787.00
5https://www.dov.vlaanderen.be/data/sondering/1...GEO-94/020-SIX27.60U110479.50205240.70
6https://www.dov.vlaanderen.be/data/sondering/1...GEO-94/020-SVI16.80U110288.50204608.80
7https://www.dov.vlaanderen.be/data/sondering/1...GEO-94/020-SVII26.70U110334.30204519.80
8https://www.dov.vlaanderen.be/data/sondering/1...GEO-94/020-SX27.50U110685.00204845.50
9https://www.dov.vlaanderen.be/data/sondering/1...GEO-94/020-SXI25.60U109941.50204346.90
10https://www.dov.vlaanderen.be/data/sondering/1...GEO-94/020-SXII26.50U110412.20204398.10
11https://www.dov.vlaanderen.be/data/sondering/1...GEO-94/096-SIX(CPT9)17.60U105018.00190472.00
12https://www.dov.vlaanderen.be/data/sondering/1...GEO-94/096-SVII(CPT7)26.05U105046.00190550.00
13https://www.dov.vlaanderen.be/data/sondering/1...GEO-94/096-SVIII(CPT8)24.75U104997.00190521.00
14https://www.dov.vlaanderen.be/data/sondering/1...GEO-97/002-S229.90U105376.60189104.30
15https://www.dov.vlaanderen.be/data/sondering/1...GEO-97/002-S35.90U105391.30189083.70
16https://www.dov.vlaanderen.be/data/sondering/1...GEO-97/002-S130.60U105399.30189065.20
17https://www.dov.vlaanderen.be/data/sondering/2...GEO-23/042-S1_U22.98U108697.19203441.72
18https://www.dov.vlaanderen.be/data/sondering/2...GEO-23/042-S2_U22.98U108709.48203436.67
19https://www.dov.vlaanderen.be/data/sondering/2...GEO-23/042-S222.98U108709.48203436.67
20https://www.dov.vlaanderen.be/data/sondering/2...GEO-23/042-S122.98U108697.19203441.72
21https://www.dov.vlaanderen.be/data/sondering/2...GEO-23/042-S3_U22.98U108734.23203429.66
22https://www.dov.vlaanderen.be/data/sondering/2...GEO-23/042-S322.98U108734.23203429.66
23https://www.dov.vlaanderen.be/data/sondering/2...GEO-23/042-S4_U22.98U108747.25203424.10
24https://www.dov.vlaanderen.be/data/sondering/2...GEO-23/042-S422.98U108747.25203424.10
25https://www.dov.vlaanderen.be/data/sondering/2...GEO-24/014-S5_U23.00U108739.04203427.68
26https://www.dov.vlaanderen.be/data/sondering/2...GEO-24/014-S523.00U108739.04203427.68
27https://www.dov.vlaanderen.be/data/sondering/2...GEO-01/162-S118.05U106104.10188699.40
28https://www.dov.vlaanderen.be/data/sondering/2...GEO-01/162-S217.30U106045.30188708.40
29https://www.dov.vlaanderen.be/data/sondering/2...GEO-01/162-S318.70U106100.50188743.80
30https://www.dov.vlaanderen.be/data/sondering/2...GEO-01/162-S517.30U106130.00188712.00
31https://www.dov.vlaanderen.be/data/sondering/2...GEO-01/162-S417.00U106077.50188686.00
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" ], "text/plain": [ " pkey_sondering sondeernummer \\\n", "0 https://www.dov.vlaanderen.be/data/sondering/1... GEO-94/020-SV \n", "1 https://www.dov.vlaanderen.be/data/sondering/1... GEO-94/020-SI \n", "2 https://www.dov.vlaanderen.be/data/sondering/1... GEO-94/020-SII \n", "3 https://www.dov.vlaanderen.be/data/sondering/1... GEO-94/020-SIII \n", "4 https://www.dov.vlaanderen.be/data/sondering/1... GEO-94/020-SIV \n", "5 https://www.dov.vlaanderen.be/data/sondering/1... GEO-94/020-SIX \n", "6 https://www.dov.vlaanderen.be/data/sondering/1... GEO-94/020-SVI \n", "7 https://www.dov.vlaanderen.be/data/sondering/1... GEO-94/020-SVII \n", "8 https://www.dov.vlaanderen.be/data/sondering/1... GEO-94/020-SX \n", "9 https://www.dov.vlaanderen.be/data/sondering/1... GEO-94/020-SXI \n", "10 https://www.dov.vlaanderen.be/data/sondering/1... GEO-94/020-SXII \n", "11 https://www.dov.vlaanderen.be/data/sondering/1... GEO-94/096-SIX(CPT9) \n", "12 https://www.dov.vlaanderen.be/data/sondering/1... GEO-94/096-SVII(CPT7) \n", "13 https://www.dov.vlaanderen.be/data/sondering/1... GEO-94/096-SVIII(CPT8) \n", "14 https://www.dov.vlaanderen.be/data/sondering/1... GEO-97/002-S2 \n", "15 https://www.dov.vlaanderen.be/data/sondering/1... GEO-97/002-S3 \n", "16 https://www.dov.vlaanderen.be/data/sondering/1... GEO-97/002-S1 \n", "17 https://www.dov.vlaanderen.be/data/sondering/2... GEO-23/042-S1_U \n", "18 https://www.dov.vlaanderen.be/data/sondering/2... GEO-23/042-S2_U \n", "19 https://www.dov.vlaanderen.be/data/sondering/2... GEO-23/042-S2 \n", "20 https://www.dov.vlaanderen.be/data/sondering/2... GEO-23/042-S1 \n", "21 https://www.dov.vlaanderen.be/data/sondering/2... GEO-23/042-S3_U \n", "22 https://www.dov.vlaanderen.be/data/sondering/2... GEO-23/042-S3 \n", "23 https://www.dov.vlaanderen.be/data/sondering/2... GEO-23/042-S4_U \n", "24 https://www.dov.vlaanderen.be/data/sondering/2... GEO-23/042-S4 \n", "25 https://www.dov.vlaanderen.be/data/sondering/2... GEO-24/014-S5_U \n", "26 https://www.dov.vlaanderen.be/data/sondering/2... GEO-24/014-S5 \n", "27 https://www.dov.vlaanderen.be/data/sondering/2... GEO-01/162-S1 \n", "28 https://www.dov.vlaanderen.be/data/sondering/2... GEO-01/162-S2 \n", "29 https://www.dov.vlaanderen.be/data/sondering/2... GEO-01/162-S3 \n", "30 https://www.dov.vlaanderen.be/data/sondering/2... GEO-01/162-S5 \n", "31 https://www.dov.vlaanderen.be/data/sondering/2... GEO-01/162-S4 \n", "\n", " diepte_sondering_tot conus x y \n", "0 33.80 U 110241.60 204692.20 \n", "1 15.65 U 110062.50 205051.40 \n", "2 26.50 U 110107.00 204965.30 \n", "3 16.50 U 110152.40 204876.10 \n", "4 16.70 U 110197.80 204787.00 \n", "5 27.60 U 110479.50 205240.70 \n", "6 16.80 U 110288.50 204608.80 \n", "7 26.70 U 110334.30 204519.80 \n", "8 27.50 U 110685.00 204845.50 \n", "9 25.60 U 109941.50 204346.90 \n", "10 26.50 U 110412.20 204398.10 \n", "11 17.60 U 105018.00 190472.00 \n", "12 26.05 U 105046.00 190550.00 \n", "13 24.75 U 104997.00 190521.00 \n", "14 29.90 U 105376.60 189104.30 \n", "15 5.90 U 105391.30 189083.70 \n", "16 30.60 U 105399.30 189065.20 \n", "17 22.98 U 108697.19 203441.72 \n", "18 22.98 U 108709.48 203436.67 \n", "19 22.98 U 108709.48 203436.67 \n", "20 22.98 U 108697.19 203441.72 \n", "21 22.98 U 108734.23 203429.66 \n", "22 22.98 U 108734.23 203429.66 \n", "23 22.98 U 108747.25 203424.10 \n", "24 22.98 U 108747.25 203424.10 \n", "25 23.00 U 108739.04 203427.68 \n", "26 23.00 U 108739.04 203427.68 \n", "27 18.05 U 106104.10 188699.40 \n", "28 17.30 U 106045.30 188708.40 \n", "29 18.70 U 106100.50 188743.80 \n", "30 17.30 U 106130.00 188712.00 \n", "31 17.00 U 106077.50 188686.00 " ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Resistivity plot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The data for the reporting of resistivity plots with the online application, see for example [this report](https://www.dov.vlaanderen.be/zoeken-ocdov/proxy-sondering/sondering/1993-001275/rapport/identifygrafiek?outputformaat=PDF), is also accessible with the pydov package. Querying the data for this specific _sondering_:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[000/001] .\n", "[000/001] .\n" ] }, { "data": { "text/html": [ "
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pkey_sonderingsondeernummerxymv_mtawstart_sondering_mtawdiepte_sondering_vandiepte_sondering_totdatum_aanvanguitvoerder...apparaatdatum_gw_metingdiepte_gw_mlengtediepteqcQtfsui
0https://www.dov.vlaanderen.be/data/sondering/1...GEO-93/023-SII-E152740.0215493.0NaN6.250.029.71993-03-02MVG - Afdeling Geotechniek...200 kNNaNNaN0.6NaN11.60NaN130.069.0NaN
1https://www.dov.vlaanderen.be/data/sondering/1...GEO-93/023-SII-E152740.0215493.0NaN6.250.029.71993-03-02MVG - Afdeling Geotechniek...200 kNNaNNaN0.7NaN6.30NaN100.029.0NaN
2https://www.dov.vlaanderen.be/data/sondering/1...GEO-93/023-SII-E152740.0215493.0NaN6.250.029.71993-03-02MVG - Afdeling Geotechniek...200 kNNaNNaN0.8NaN6.22NaN120.0-4.0NaN
3https://www.dov.vlaanderen.be/data/sondering/1...GEO-93/023-SII-E152740.0215493.0NaN6.250.029.71993-03-02MVG - Afdeling Geotechniek...200 kNNaNNaN0.9NaN4.92NaN120.0-48.0NaN
4https://www.dov.vlaanderen.be/data/sondering/1...GEO-93/023-SII-E152740.0215493.0NaN6.250.029.71993-03-02MVG - Afdeling Geotechniek...200 kNNaNNaN1.0NaN4.40NaN80.0-35.0NaN
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5 rows × 21 columns

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" ], "text/plain": [ " pkey_sondering sondeernummer \\\n", "0 https://www.dov.vlaanderen.be/data/sondering/1... GEO-93/023-SII-E \n", "1 https://www.dov.vlaanderen.be/data/sondering/1... GEO-93/023-SII-E \n", "2 https://www.dov.vlaanderen.be/data/sondering/1... GEO-93/023-SII-E \n", "3 https://www.dov.vlaanderen.be/data/sondering/1... GEO-93/023-SII-E \n", "4 https://www.dov.vlaanderen.be/data/sondering/1... GEO-93/023-SII-E \n", "\n", " x y mv_mtaw start_sondering_mtaw diepte_sondering_van \\\n", "0 152740.0 215493.0 NaN 6.25 0.0 \n", "1 152740.0 215493.0 NaN 6.25 0.0 \n", "2 152740.0 215493.0 NaN 6.25 0.0 \n", "3 152740.0 215493.0 NaN 6.25 0.0 \n", "4 152740.0 215493.0 NaN 6.25 0.0 \n", "\n", " diepte_sondering_tot datum_aanvang uitvoerder ... \\\n", "0 29.7 1993-03-02 MVG - Afdeling Geotechniek ... \n", "1 29.7 1993-03-02 MVG - Afdeling Geotechniek ... \n", "2 29.7 1993-03-02 MVG - Afdeling Geotechniek ... \n", "3 29.7 1993-03-02 MVG - Afdeling Geotechniek ... \n", "4 29.7 1993-03-02 MVG - Afdeling Geotechniek ... \n", "\n", " apparaat datum_gw_meting diepte_gw_m lengte diepte qc Qt fs \\\n", "0 200 kN NaN NaN 0.6 NaN 11.60 NaN 130.0 \n", "1 200 kN NaN NaN 0.7 NaN 6.30 NaN 100.0 \n", "2 200 kN NaN NaN 0.8 NaN 6.22 NaN 120.0 \n", "3 200 kN NaN NaN 0.9 NaN 4.92 NaN 120.0 \n", "4 200 kN NaN NaN 1.0 NaN 4.40 NaN 80.0 \n", "\n", " u i \n", "0 69.0 NaN \n", "1 29.0 NaN \n", "2 -4.0 NaN \n", "3 -48.0 NaN \n", "4 -35.0 NaN \n", "\n", "[5 rows x 21 columns]" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "query = PropertyIsEqualTo(propertyname='pkey_sondering',\n", " literal='https://www.dov.vlaanderen.be/data/sondering/1993-001275')\n", "df_sond = sondering.search(query=query)\n", "\n", "df_sond.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have the depth (`length`) available, together with the measured values for each depth of the variables (in dutch):\n", "\n", "* `qc`: Opgemeten waarde van de conusweerstand, uitgedrukt in MPa.\n", "* `Qt`: Opgemeten waarde van de totale weerstand, uitgedrukt in kN.\n", "* `fs`: Opgemeten waarde van de plaatelijke kleefweerstand uitgedrukt in kPa.\n", "* `u`: Opgemeten waarde van de porienwaterspanning, uitgedrukt in kPa.\n", "* `i`: Opgemeten waarde van de inclinatie, uitgedrukt in graden." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To recreate the resistivity plot, we also need the `resistivity number` (wrijvingsgetal `rf`), see [DOV documentation](https://www.dov.vlaanderen.be/page/sonderingen).\n", "\n", "\\begin{equation}\n", "R_f = \\frac{f_s}{q_c}\n", "\\end{equation}\n", "\n", "**Notice:** $f_s$ is provide in kPa and $q_c$ in MPa." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Adding `rf` to the dataframe:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "df_sond[\"rf\"] = df_sond[\"fs\"]/df_sond[\"qc\"]/10 " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Recreate the resistivity plot:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "def make_patch_spines_invisible(ax):\n", " ax.set_frame_on(True)\n", " ax.patch.set_visible(False)\n", " for sp in ax.spines.values():\n", " sp.set_visible(False)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "image/png": 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+9u/fj/79+6u/Ubp37x5SUlLQqVMnXL16VT2LlI2NDS5cuICrV6+W+f0Lv23csmXLMw3CflL79u3h4OCA9evXq8sePHiAPXv2YMCAAc90TH9/fwQHB2P16tXqsvv372Pnzp0YPHhwmb5NCwkJQePGjdWvPT090bNnT+zevRsFBQXF7rN3717k5uZi/PjxGt0fRo4cCSsrK/z555/P9HnS09MBAJaWluXa7/G2MnDgQFhYWGDz5s1wd3d/pjiKU6dOHY3BmoXfbLdv3x6enp5Fyq9fvw6gfO1UoVCo67OgoAApKSnqLninTp1Sv4eNjQ1u376N48ePV9jna9OmDerUqaN+LYTA77//ju7du0MIoY773r176NSpE9LS0jRiAlTfyD8+6UBwcDCEEBg+fLjGdsHBwYiLi1PPLLRp0yYolUr0799f431cXFzg5+eH8PBwjf0tLCw0rsbJ5XI0a9ZMXeflVdjuirua1rZtWzg6OqqXwm6Jj/v999+xZ88ejWXFihXq9Q8fPnxqmy5cXxhLSRo1aoTg4GB8+eWXWLFiBW7cuIGdO3firbfegrGxMR49elRkn0GDBmHPnj1Ys2aN+orWk9splUrs2rVL3Q3q8W0UCkWRY5qYmBT7XoUOHjyIGTNmoH///uquiY+zs7PDnj17sG3bNsycORMODg4akyyU5qWXXkJkZCR69OiBM2fOYO7cuejUqRPc3d2LdC+sCFOnTi3y892zZ4/GVWIiXWNXKCItLV68GP7+/khLS8Py5ctx8OBBjX9wMTExEEJgypQpmDJlSrHHSEpKgru7O2bOnImePXvC398f9erVQ+fOnfH666+jQYMGJb7/gAEDsGzZMrz55pv46KOPEBoaij59+qBfv35F+hKXhZGREfr27Ys1a9YgJycHCoUCmzZtQl5e3jMnFoDqZG7MmDG4efMmvLy8sGHDBuTl5ZW5O5Gfn1+RMn9/f2RlZSE5OVljNpdCN2/eBAAEBARolMvlctSsWVO9vrysrKwAqE7EyqOwrRgZGcHZ2RkBAQHP9DMqzePJA6DqvgKgSJ/7wvIHDx4AKF87VSqVWLBgAb777jvExsZqJHaPd+uaNGkS9u7di2bNmsHX1xcdO3bEq6++ihYtWjzz5/Px8dF4nZycjNTUVPzwww/44YcfSoz7ceWpI6VSibS0NNjb2+Pq1asQQhTbFgFoJCsAUKNGjSJJs62tbZHpYMuq8KS+uBPbpUuX4uHDh0hMTCyxa2Hr1q2L7e7z+PGL60rzuMI2/3gsj8djaGgIR0dHAKpEZsCAAeqEzdDQEBMnTkRERASio6OLHNvLy0s9k9WgQYMwatQodOjQAdHR0eruUMePH0dycrJGYlG4rrjxMNnZ2UW6UhW6fPkyevfujXr16mHZsmXFbiOXy9GhQwcAQLdu3RAaGooWLVrAyckJ3bp1Q0FBQZHpZ+3s7NTdSJs2bYpNmzYhNzcXZ86cwebNmzF//nz069cPUVFRGkny0zw5LbC1tbXGZ6tfv746ViKpMLEg0lKzZs3UfXt79eqFli1b4tVXX0V0dDQsLCzUVxHef/99dOrUqdhjFPZVbt26Na5du4YtW7bgr7/+wrJlyzB//nx8//33ePPNN4vd19TUFAcPHkR4eDj+/PNP7Nq1C+vXr0f79u3x119/wdDQsNyfaeDAgVi6dKl6CsfffvsNgYGBCAoKKvexHj/mhAkTsHr1anz88cdYtWoVmjRpUuSkXx8EBgYCUM1RX9YpQgHNtqIrJf28SyoX/94noDzt9IsvvsCUKVMwfPhwfPbZZ7Czs4OBgQHGjx+vcdWsdu3aiI6Oxvbt27Fr1y78/vvv+O677zB16lTMmDEDAEq8WlXSVagnTxIL3++1117DkCFDit3nycRcmzqSyWTYuXNnsds+eSXhaccrL2tra7i6uuL8+fNF1hVegdLm5pC1a9dGVFQUbt26VST5KlSYFBWeEP/vf/9T/ywBVXJQGIO7uzsOHTqEq1ev4u7du/Dz84OLiwvc3Nzg7+//1Hj69euHH3/8EQcPHlS3yR07dsDb21vjhLxwEHtCQkKR5DAhIQHNmjUrcuy4uDh07NgR1tbW2LFjR5mvPjZv3hyurq5YvXo1unXrhri4uCLJbnh4eJExGHK5HE2bNkXTpk3h7++PYcOGYcOGDZg2bVqZ3vfxz1loxYoVGDp0aJn3J6oMTCyIKpChoSFmz56Ndu3aYdGiRfjoo49Qs2ZNAKpvM8vybVLhrEzDhg1DRkYGWrdujenTp5eYWACAgYEBQkNDERoaiq+//hpffPEFPvnkE4SHh6NDhw7lHrjXunVruLq6Yv369WjZsiX279+PTz755Kn7lfY+dnZ26Nq1K1avXo3Bgwfjn3/+KdcN6YrrHnblyhWYmZmpvyF9UuG3n9HR0eqfAwDk5uYiNjZW4+dRnjrq0qULDA0NsWrVKq0HcFcV5WmnGzduRLt27fDTTz9plKemphb5Rtzc3BwDBgzAgAEDkJubiz59+uDzzz/H5MmTYWJiAltb22Jn2Cnr1SRHR0dYWlqioKBA59/W1qpVC0II+Pj4lOnEuCzK+7vZtWtXLFu2DMeOHSv2hFkb3bp1w9q1a/HLL7/g008/LbI+PT0dW7ZsQWBgoDrJfOONN9CyZUv1NsVdHfDz81Nf5bl48SISEhLKdEJc2IUpLS1NXfbnn3/i5Zdf1tiuMLk/ceKERp3Ex8fj9u3bGDVqlMb2KSkp6NixI3JycrBv375iZ9cqTXZ2tjomFxcXjVn7ADz1C5jCLxeKuylfaZ58n7p165Zrf6LKwDEWRBWsbdu2aNasGb755htkZ2fDyckJbdu2xdKlS4v9R/L4ZfSUlBSNdRYWFvD19S32En+h4mZ6KfxHW7hf4Y2cyjJFIqBKVPr164dt27bh119/RX5+fpm6QT3tfV5//XVcvHgRH3zwAQwNDUucYao4kZGRGn3l4+LisGXLFnTs2LHEb4Y7dOgAuVyOb7/9VuNb4p9++glpaWka3SnMzc01TmBK4+HhgZEjR+Kvv/4qcgddQPXN9ldffYXbt2+X9eNJrjzt1NDQsMi37hs2bChyx/kn27NcLkedOnUghEBeXh4A1cl6WlqaRveghIQEbN68uUxxGxoaom/fvuqpUkuLW1t9+vSBoaEhZsyYUeTzi39nDyqv8v5ufvjhhzAzM8Pw4cPVd21/Mo5n1a9fP9SpUwdz5szRuHM8oGrTo0ePxoMHDzS+Za9ZsyY6dOigXkrr5qZUKtXxv/322+rykn5GP/30E2QyGRo1agQASExMxKlTpzR+bwHVCXZgYCB++OEHjStdS5YsgUwmQ79+/dRlmZmZePnll3Hnzh3s2LGjxG5tmZmZyMrKKlL++++/48GDB+rkwMTEROPzd+jQQT0jYHh4eLE/jx07dgAo2kXzaZ58n/ImRESVgVcsiHTggw8+wCuvvIKVK1fi7bffxuLFi9GyZUvUr18fI0eORM2aNZGYmIjIyEjcvn1bPf9/nTp10LZtWzRu3Bh2dnY4ceIENm7ciDFjxpT4XjNnzsTBgwfRtWtXeHl5ISkpCd999x1q1Kih/iaxVq1asLGxwffffw9LS0uYm5sjODi4yCX8xw0YMAALFy7EtGnTUL9+fdSuXfupn7twcPW4cePQqVOnIslD165dYW9vjw0bNqBLly7lmsu9Xr166NSpk8Z0swA0umE8ydHREZMnT8aMGTPQuXNn9OjRA9HR0fjuu+/QtGlTjb7ojRs3xvr16zFx4kQ0bdoUFhYW6N69e4nH/uqrr3Dt2jWMGzcOmzZtQrdu3WBra4tbt25hw4YNuHz5crkSp6qgrO20W7dumDlzJoYNG4bmzZvj3LlzWL16tcZVIQDo2LEjXFxc0KJFCzg7O+PSpUtYtGgRunbtqu56MnDgQEyaNAm9e/fGuHHjkJWVhSVLlsDf37/IoOuSzJkzB+Hh4QgODsbIkSNRp04d3L9/H6dOncLevXuLTb6fRa1atTBr1ixMnjwZN27cQK9evWBpaYnY2Fhs3rwZo0aNUt/npTzHLM/vpp+fH9asWYNBgwYhICBAfedtIQRiY2OxZs0aGBgYFHsPlY0bNxY78Pull16Cs7Mz5HI5Nm7ciNDQULRs2VLjzttr1qzBqVOn8N5775W5XRdOMdywYUPk5eVhzZo1OHbsGH7++WeNrlaff/45/vnnH3Tu3Bmenp64f/8+fv/9dxw/fhxjx45VXx3ZsWMHTExM0K5duyLvNW/ePPTo0QMdO3bEwIEDcf78eSxatAhvvvmmxt+uwYMH49ixYxg+fDguXbqkce8KCwsL9OrVC4DqCmmHDh0wYMAABAYGwsDAACdOnMCqVavg7e2Nd99996mff+zYscjKykLv3r0RGBiI3NxcHD58GOvXr4e3t3ex9wvSxt9//43s7Owi5Q0aNCh1nB5RharsaaiIqouS7qYshOqOwLVq1RK1atVS3zX22rVr4o033hAuLi7C2NhYuLu7i27duomNGzeq95s1a5Zo1qyZsLGxEaampiIwMFB8/vnnGtMTPjkN5759+0TPnj2Fm5ubkMvlws3NTQwaNEhcuXJFI6YtW7aIOnXqCCMjI43pLUua7lOpVAoPDw8BQMyaNavI+uKmm83Pzxdjx44Vjo6OQiaTFTv17DvvvFPi3aZLgn+nXVy1apXw8/MTCoVCvPDCCxpTpQpR8p23Fy1aJAIDA4WxsbFwdnYWo0ePLnI33IyMDPHqq68KGxsbAaBMU8/m5+eLZcuWiVatWglra2thbGwsvLy8xLBhwzSmoi2trZTkWaebLW66YhQzbWXhz2/evHka5WVpp9nZ2eK9994Trq6uwtTUVLRo0UJERkaKNm3aaEwDunTpUtG6dWthb28vFAqFqFWrlvjggw9EWlqaxnv+9ddfol69ekIul4uAgACxatWqEqebLWn6zcTERBEWFiY8PDyEsbGxcHFxEaGhoeKHH35Qb1PSHYpL+vmUdMfq33//XbRs2VKYm5sLc3NzERgYKMLCwkR0dLR6mzZt2hQ71W5xv28l/W6WJiYmRowePVr4+voKExMT9d+Lt99+W0RFRRX7OUpanvw9SkpKEhMnThS+vr5CoVAIGxsb0aFDhzLdLftxK1asEEFBQcLc3FxYWlqK0NBQsX///iLb/fXXX6Jbt27Czc1NGBsbC0tLS9GiRQuxYsUKjamo+/XrJ15++eUS32/z5s2iYcOGQqFQiBo1aohPP/20yNSuhVMyF7c8/nNJTk4Wo0aNEoGBgcLc3FzI5XLh5+cnxo8f/9Q7mBfauXOnGD58uAgMDBQWFhZCLpcLX19fMXbsWJ3cebukZdq0aWWKl6giyITQ4ropEVE5TZgwAT/99BPu3r2rvlnW08hkMoSFhWHRokU6jo6IqqL8/HzY29tj9uzZ6ps8ElHVwzEWRFRpsrOzsWrVKvTt27fMSQUR0f379zFhwgT13d+JqGriGAsi0rmkpCTs3bsXGzduREpKSpn6JxMRFXJycsL06dOlDoOInoKJBRHp3MWLFzF48GA4OTnh22+/Lde9H4iIiEg/cIwFERERERFpjWMsiIiIiIhIa0wsiIiIiIhIa0wsiIiIiIhIa0wsiIiIiIhIa0wsnvD555+jefPmMDMzg42NTbHb3Lp1C127doWZmRmcnJzwwQcfID8/X2ObAwcOoFGjRlAoFPD19cXKlSt1H7ye8fb2hkwm01jmzJmjsc3Zs2fRqlUrmJiYwMPDA3PnzpUoWv2yePFieHt7w8TEBMHBwTh27JjUIemd6dOnF2mfgYGB6vXZ2dkICwuDvb09LCws0LdvXyQmJkoYcdV08OBBdO/eHW5ubpDJZPjjjz801gshMHXqVLi6usLU1BQdOnTA1atXNba5f/8+Bg8eDCsrK9jY2GDEiBHIyMioxE9RNT2tbocOHVqkDXfu3FljG9ZtUbNnz0bTpk1haWkJJycn9OrVC9HR0RrblOX3vyznCs+TstRr27Zti7TZt99+W2Mb1mtRS5YsQYMGDWBlZQUrKyuEhIRg586d6vWV2V6ZWDwhNzcXr7zyCkaPHl3s+oKCAnTt2hW5ubk4fPgwfv75Z6xcuRJTp05VbxMbG4uuXbuiXbt2iIqKwvjx4/Hmm29i9+7dlfUx9MbMmTORkJCgXsaOHatel56ejo4dO8LLywsnT57EvHnzMH36dPzwww8SRlz1rV+/HhMnTsS0adNw6tQpBAUFoVOnTkhKSpI6NL1Tt25djfZ56NAh9boJEyZg27Zt2LBhAyIiIhAfH48+ffpIGG3VlJmZiaCgICxevLjY9XPnzsW3336L77//HkePHoW5uTk6deqE7Oxs9TaDBw/GhQsXsGfPHmzfvh0HDx7EqFGjKusjVFlPq1sA6Ny5s0YbXrt2rcZ61m1RERERCAsLw5EjR7Bnzx7k5eWhY8eOyMzMVG/ztN//spwrPG/KUq8AMHLkSI02+/gXiqzX4tWoUQNz5szByZMnceLECbRv3x49e/bEhQsXAFRyexVUrBUrVghra+si5Tt27BAGBgbi7t276rIlS5YIKysrkZOTI4QQ4sMPPxR169bV2G/AgAGiU6dOOo1Z33h5eYn58+eXuP67774Ttra26noVQohJkyaJgICASohOfzVr1kyEhYWpXxcUFAg3Nzcxe/ZsCaPSP9OmTRNBQUHFrktNTRXGxsZiw4YN6rJLly4JACIyMrKSItQ/AMTmzZvVr5VKpXBxcRHz5s1Tl6WmpgqFQiHWrl0rhBDi4sWLAoA4fvy4epudO3cKmUwm7ty5U2mxV3VP1q0QQgwZMkT07NmzxH1Yt2WTlJQkAIiIiAghRNl+/8tyrvC8e7JehRCiTZs24t133y1xH9Zr2dna2oply5ZVenvlFYtyioyMRP369eHs7Kwu69SpE9LT09WZYWRkJDp06KCxX6dOnRAZGVmpseqDOXPmwN7eHi+88ALmzZuncdktMjISrVu3hlwuV5d16tQJ0dHRePDggRThVnm5ubk4efKkRvszMDBAhw4d2P6ewdWrV+Hm5oaaNWti8ODBuHXrFgDg5MmTyMvL06jnwMBAeHp6sp7LITY2Fnfv3tWoR2trawQHB6vrMTIyEjY2NmjSpIl6mw4dOsDAwABHjx6t9Jj1zYEDB+Dk5ISAgACMHj0aKSkp6nWs27JJS0sDANjZ2QEo2+9/Wc4VnndP1muh1atXw8HBAfXq1cPkyZORlZWlXsd6fbqCggKsW7cOmZmZCAkJqfT2yjtvl9Pdu3c1Kh6A+vXdu3dL3SY9PR2PHj2Cqalp5QRbxY0bNw6NGjWCnZ0dDh8+jMmTJyMhIQFff/01AFU9+vj4aOzzeF3b2tpWesxV3b1791BQUFBs+7t8+bJEUemn4OBgrFy5EgEBAUhISMCMGTPQqlUrnD9/Hnfv3oVcLi8yDsvZ2Vn9d4CerrCuimuvj/89dXJy0lhvZGQEOzs71vVTdO7cGX369IGPjw+uXbuGjz/+GF26dEFkZCQMDQ1Zt2WgVCoxfvx4tGjRAvXq1QOAMv3+l+Vc4XlWXL0CwKuvvgovLy+4ubnh7NmzmDRpEqKjo7Fp0yYArNfSnDt3DiEhIcjOzoaFhQU2b96MOnXqICoqqlLb63ORWHz00Uf48ssvS93m0qVLGgMz6dmUp64nTpyoLmvQoAHkcjneeustzJ49GwqFQtehEpWqS5cu6ucNGjRAcHAwvLy88Ntvv/HLAdILAwcOVD+vX78+GjRogFq1auHAgQMIDQ2VMDL9ERYWhvPnz2uMryLtlVSvj4/vqV+/PlxdXREaGopr166hVq1alR2mXgkICEBUVBTS0tKwceNGDBkyBBEREZUex3ORWLz33nsYOnRoqdvUrFmzTMdycXEpMsNO4ch6FxcX9eOTo+0TExNhZWVV7U9ItKnr4OBg5Ofn48aNGwgICCixHoH/6po0OTg4wNDQsNh6Y51px8bGBv7+/oiJicFLL72E3NxcpKamanwLxHoun8K6SkxMhKurq7o8MTERDRs2VG/z5MQD+fn5uH//Puu6nGrWrAkHBwfExMQgNDSUdfsUY8aMUQ9or1GjhrrcxcXlqb//ZTlXeF6VVK/FCQ4OBgDExMSgVq1arNdSyOVy+Pr6AgAaN26M48ePY8GCBRgwYEClttfnYoyFo6MjAgMDS10e78dfmpCQEJw7d07jj/GePXtgZWWFOnXqqLfZt2+fxn579uxBSEhIxX2oKkqbuo6KioKBgYH60nxISAgOHjyIvLw89TZ79uxBQEAAu0GVQC6Xo3HjxhrtT6lUYt++fc9F+9OljIwMXLt2Da6urmjcuDGMjY016jk6Ohq3bt1iPZeDj48PXFxcNOoxPT0dR48eVddjSEgIUlNTcfLkSfU2+/fvh1KpVJ90UNncvn0bKSkp6iSOdVs8IQTGjBmDzZs3Y//+/UW65Jbl978s5wrPm6fVa3GioqIAQKPNsl7LRqlUIicnp/Lba0WMPK9Obt68KU6fPi1mzJghLCwsxOnTp8Xp06fFw4cPhRBC5Ofni3r16omOHTuKqKgosWvXLuHo6CgmT56sPsb169eFmZmZ+OCDD8SlS5fE4sWLhaGhodi1a5dUH6vKOXz4sJg/f76IiooS165dE6tWrRKOjo7ijTfeUG+TmpoqnJ2dxeuvvy7Onz8v1q1bJ8zMzMTSpUsljLzqW7dunVAoFGLlypXi4sWLYtSoUcLGxkZjtgd6uvfee08cOHBAxMbGin/++Ud06NBBODg4iKSkJCGEEG+//bbw9PQU+/fvFydOnBAhISEiJCRE4qirnocPH6r/jgIQX3/9tTh9+rS4efOmEEKIOXPmCBsbG7FlyxZx9uxZ0bNnT+Hj4yMePXqkPkbnzp3FCy+8II4ePSoOHTok/Pz8xKBBg6T6SFVGaXX78OFD8f7774vIyEgRGxsr9u7dKxo1aiT8/PxEdna2+his26JGjx4trK2txYEDB0RCQoJ6ycrKUm/ztN//spwrPG+eVq8xMTFi5syZ4sSJEyI2NlZs2bJF1KxZU7Ru3Vp9DNZr8T766CMREREhYmNjxdmzZ8VHH30kZDKZ+Ouvv4QQldtemVg8YciQIQJAkSU8PFy9zY0bN0SXLl2EqampcHBwEO+9957Iy8vTOE54eLho2LChkMvlombNmmLFihWV+0GquJMnT4rg4GBhbW0tTExMRO3atcUXX3yh8Q9PCCHOnDkjWrZsKRQKhXB3dxdz5syRKGL9snDhQuHp6Snkcrlo1qyZOHLkiNQh6Z0BAwYIV1dXIZfLhbu7uxgwYICIiYlRr3/06JF45513hK2trTAzMxO9e/cWCQkJEkZcNYWHhxf7N3XIkCFCCNWUs1OmTBHOzs5CoVCI0NBQER0drXGMlJQUMWjQIGFhYSGsrKzEsGHD1F/2PM9Kq9usrCzRsWNH4ejoKIyNjYWXl5cYOXJkkS8YWLdFFVenADT+j5fl978s5wrPk6fV661bt0Tr1q2FnZ2dUCgUwtfXV3zwwQciLS1N4zis16KGDx8uvLy8hFwuF46OjiI0NFSdVAhRue1VJoQQ5bvGQUREREREpKlqjbE4cACQyYCSBv8OHapaf+BA5cVERERERERPVbUSCyIiIiIi0ktMLIiIiIiISGtMLIiIiIiISGtMLIiIiIiISGtMLIiIiIiISGv6lVgolVJHQERERERExahaiYVcrnrMyCh+fVxc5cXyhJycHEyfPh05OTmSxVAdsV51h3WrG6xX3WHd6gbrVXdYt7rBetUdXddt1bpBXmwsULMmUL8+cPas5rr79wEfHyA9HQgPB9q2rdTQ0tPTYW1tjbS0NFhZWVXqe1dnrFfdYd3qButVd1i3usF61R3WrW6wXnVH13Vbta5Y+PgAnp7AuXPAli3/lWdmAqNGqZIKIiIiIiKqcqpWYgEA06apHvv2Bdq3B3r0AGrVUl3B6NlT2tiIiIiIiKhYRlIHUMTw4YCBAfDVV8A//wC2tkD37sCcOcB770kWVmGPsXReNalQhfXJeq14rFvdYL3qDutWN1ivusO61Q3Wq+4U1qmuRkJUrTEWVVhycjKcnJykDoOIiIiISCtJSUlwdHSs8OMysSgjIQQePnwodRhERERERFqxtLSETCar8OMysSAiIiIiIq1VvcHbRERERESkd5hYEBERERGR1phYEBERERGR1phYEBERERGR1phYEBERERGR1phYEBERERGR1phYEBERERGR1phYEBERERGR1phYEBERERGR1phYEBERERGR1phYEBERERGR1phYEBERERGR1phYEBERERGR1phYEBERERGR1phYEBERERGR1phYEBERERGR1phYEBERERGR1phYEBERERGR1phYEBERERGR1phYEBERERGR1phYEBERERGR1phYEBERERGR1phYEBERERGR1phYEBERERGR1phY6IHFixfD29sbJiYmCA4OxrFjx6QOSa/Mnj0bTZs2haWlJZycnNCrVy9ER0drbNO2bVvIZDKN5e2339bY5tatW+jatSvMzMzg5OSEDz74APn5+ZX5Uaq86dOnF6nHwMBA9frs7GyEhYXB3t4eFhYW6Nu3LxITEzWOwXouG29v7yJ1LZPJEBYWBoBtWhsHDx5E9+7d4ebmBplMhj/++ENjvRACU6dOhaurK0xNTdGhQwdcvXpVY5v79+9j8ODBsLKygo2NDUaMGIGMjAyNbc6ePYtWrVrBxMQEHh4emDt3rq4/WpVTWl3n5eVh0qRJqF+/PszNzeHm5oY33ngD8fHxGsco7ndhzpw5Gtuwrp/erocOHVqkHjt37qyxDdt12Tytrov72y2TyTBv3jz1NvrarplYVHHr16/HxIkTMW3aNJw6dQpBQUHo1KkTkpKSpA5Nb0RERCAsLAxHjhzBnj17kJeXh44dOyIzM1Nju5EjRyIhIUG9PP4LWlBQgK5duyI3NxeHDx/Gzz//jJUrV2Lq1KmV/XGqvLp162rU46FDh9TrJkyYgG3btmHDhg2IiIhAfHw8+vTpo17Pei6748ePa9Tznj17AACvvPKKehu26WeTmZmJoKAgLF68uNj1c+fOxbfffovvv/8eR48ehbm5OTp16oTs7Gz1NoMHD8aFCxewZ88ebN++HQcPHsSoUaPU69PT09GxY0d4eXnh5MmTmDdvHqZPn44ffvhB55+vKimtrrOysnDq1ClMmTIFp06dwqZNmxAdHY0ePXoU2XbmzJkabX3s2LHqdaxrlae1awDo3LmzRj2uXbtWYz3bddk8ra4fr+OEhAQsX74cMpkMffv21dhOL9u1oCqtWbNmIiwsTP26oKBAuLm5idmzZ0sYlX5LSkoSAERERIS6rE2bNuLdd98tcZ8dO3YIAwMDcffuXXXZkiVLhJWVlcjJydFluHpl2rRpIigoqNh1qampwtjYWGzYsEFddunSJQFAREZGCiFYz9p49913Ra1atYRSqRRCsE1XFABi8+bN6tdKpVK4uLiIefPmqctSU1OFQqEQa9euFUIIcfHiRQFAHD9+XL3Nzp07hUwmE3fu3BFCCPHdd98JW1tbjbqeNGmSCAgI0PEnqrqerOviHDt2TAAQN2/eVJd5eXmJ+fPnl7gP67qo4up6yJAhomfPniXuw3b9bMrSrnv27Cnat2+vUaav7ZpXLKqw3NxcnDx5Eh06dFCXGRgYoEOHDoiMjJQwMv2WlpYGALCzs9MoX716NRwcHFCvXj1MnjwZWVlZ6nWRkZGoX78+nJ2d1WWdOnVCeno6Lly4UDmB64mrV6/Czc0NNWvWxODBg3Hr1i0AwMmTJ5GXl6fRngMDA+Hp6aluz6znZ5Obm4tVq1Zh+PDhkMlk6nK26YoXGxuLu3fvarRja2trBAcHa7RjGxsbNGnSRL1Nhw4dYGBggKNHj6q3ad26NeRyuXqbTp06ITo6Gg8ePKikT6N/0tLSIJPJYGNjo1E+Z84c2Nvb44UXXsC8efM0uvSxrsvuwIEDcHJyQkBAAEaPHo2UlBT1OrZr3UhMTMSff/6JESNGFFmnj+3aSLJ3pqe6d+8eCgoKNP7xA4CzszMuX74sUVT6TalUYvz48WjRogXq1aunLn/11Vfh5eUFNzc3nD17FpMmTUJ0dDQ2bdoEALh7926xP4fCdaQSHByMlStXIiAgAAkJCZgxYwZatWqF8+fP4+7du5DL5UVOCJydndV1yHp+Nn/88QdSU1MxdOhQdRnbtG4U1k1xdfd4O3ZyctJYb2RkBDs7O41tfHx8ihyjcJ2tra1O4tdn2dnZmDRpEgYNGgQrKyt1+bhx49CoUSPY2dnh8OHDmDx5MhISEvD1118DYF2XVefOndGnTx/4+Pjg2rVr+Pjjj9GlSxdERkbC0NCQ7VpHfv75Z1haWmp0Cwb0t10zsaDnSlhYGM6fP6/R7x+ARh/R+vXrw9XVFaGhobh27Rpq1apV2WHqrS5duqifN2jQAMHBwfDy8sJvv/0GU1NTCSOr3n766Sd06dIFbm5u6jK2aapO8vLy0L9/fwghsGTJEo11EydOVD9v0KAB5HI53nrrLcyePRsKhaKyQ9VbAwcOVD+vX78+GjRogFq1auHAgQMIDQ2VMLLqbfny5Rg8eDBMTEw0yvW1XVeprlDfHv0Wdb+rC8UsBWQzZGi7sq3UIUnKwcEBhoaGRWbNSUxMhIuLi0RR6a8xY8Zg+/btCA8PR40aNUrdNjg4GAAQExMDAHBxcSn251C4jopnY2MDf39/xMTEwMXFBbm5uUhNTdXY5vH2zHouv5s3b2Lv3r148803S92ObbpiFNZNaX+XXVxcikywkZ+fj/v377OtP4PCpOLmzZvYs2ePxtWK4gQHByM/Px83btwAwLp+VjVr1oSDg4PG3wy264r1999/Izo6+ql/vwH9addVJrHYdGkT3t31LhIeJqBHQA8MCRqCzr6dn75jNSaXy9G4cWPs27dPXaZUKrFv3z6EhIRIGJl+EUJgzJgx2Lx5M/bv31/k0mFxoqKiAACurq4AgJCQEJw7d07jj2rhP7g6deroJO7qICMjA9euXYOrqysaN24MY2NjjfYcHR2NW7duqdsz67n8VqxYAScnJ3Tt2rXU7dimK4aPjw9cXFw02nF6ejqOHj2q0Y5TU1Nx8uRJ9Tb79++HUqlUJ3ghISE4ePAg8vLy1Nvs2bMHAQEB7C7ymMKk4urVq9i7dy/s7e2fuk9UVBQMDAzU3XZY18/m9u3bSElJ0fibwXZdsX766Sc0btwYQUFBT91Wb9q1pEPHH/P6ptcFpkPsu75P6lCqlHXr1gmFQiFWrlwpLl68KEaNGiVsbGw0ZnKh0o0ePVpYW1uLAwcOiISEBPWSlZUlhBAiJiZGzJw5U5w4cULExsaKLVu2iJo1a4rWrVurj5Gfny/q1asnOnbsKKKiosSuXbuEo6OjmDx5slQfq0p67733xIEDB0RsbKz4559/RIcOHYSDg4NISkoSQgjx9ttvC09PT7F//35x4sQJERISIkJCQtT7s57Lp6CgQHh6eopJkyZplLNNa+fhw4fi9OnT4vTp0wKA+Prrr8Xp06fVMxHNmTNH2NjYiC1btoizZ8+Knj17Ch8fH/Ho0SP1MTp37ixeeOEFcfToUXHo0CHh5+cnBg0apF6fmpoqnJ2dxeuvvy7Onz8v1q1bJ8zMzMTSpUsr/fNKqbS6zs3NFT169BA1atQQUVFRGn+/C2fCOXz4sJg/f76IiooS165dE6tWrRKOjo7ijTfeUL8H61qltLp++PCheP/990VkZKSIjY0Ve/fuFY0aNRJ+fn4iOztbfQy267J52t8QIYRIS0sTZmZmYsmSJUX21+d2XWUSi3Yr2wlMh4h9ECt1KFXOwoULhaenp5DL5aJZs2biyJEjUoekVwAUu6xYsUIIIcStW7dE69athZ2dnVAoFMLX11d88MEHIi0tTeM4N27cEF26dBGmpqbCwcFBvPfeeyIvL0+CT1R1DRgwQLi6ugq5XC7c3d3FgAEDRExMjHr9o0ePxDvvvCNsbW2FmZmZ6N27t0hISNA4Buu57Hbv3i0AiOjoaI1ytmnthIeHF/s3Y8iQIUII1ZSzU6ZMEc7OzkKhUIjQ0NAiP4OUlBQxaNAgYWFhIaysrMSwYcPEw4cPNbY5c+aMaNmypVAoFMLd3V3MmTOnsj5ilVFaXcfGxpb49zs8PFwIIcTJkydFcHCwsLa2FiYmJqJ27driiy++0DgZFoJ1LUTpdZ2VlSU6duwoHB0dhbGxsfDy8hIjR44s8iUm23XZPO1viBBCLF26VJiamorU1NQi++tzu5YJIYROL4k8xfQD0zEjYkax68KHhKOJWxMsPLoQ6y6sw43UGyhQFsDZwhmNXBvhzRfeRCffTpUcMRERERERPUnyWaEaujTEkKAh2BWzC4mZiehbuy8s5BYAAEczR3T4pQOO3jkKBzMHtPVuCxMjE9xOv40dV3fA3NiciQURERERURUg+RWLQm1XtkXEzQjEvhsLbxtvAEB4bDja/9IeTd2a4uCwgzAx+m8qrvScdFxNuYrGbo0lipiIiIiIiApVmVmhipOclQwAaOHRQiOpAAArhRWTCiIiIiKiKqJKJxYNXRrCQGaAFVEr8OPJH5GSlfL0nYiIiIiIqNJV6cTC394fczvMRVZeFkZtHwWn/zkh6PsgTNw9EWcTz0odHhERERER/atKJxYA8F7z93Bt3DV82/lbdPXriltptzD/yHw0/L4hFhxZIHV4REREREQEPUgsAMDD2gNjg8di66CtSP4gGb/2/hWGBob4cO+HePDogdTh6VxOTg6mT5+OnJwcqUOp9ljXlYd1XXlY15WHdV15WNeVh3VdefS9rqv0rFClaf5Tc0TejsTRN4+imXsz3QcoofT0dFhbWyMtLQ1WVlZSh1Otsa4rD+u68rCuKw/ruvKwrisP67ry6HtdV+krFuGx4dh7fS+UQqlRHvsgFpfuXYIMMtSwqiFRdEREREREVEjyG+SV5kziGUzYPQGOZo5o7NYY9qb2SM5KRsSNCOQU5GBss7Fws3STOkwiIiIioudelU4suvl3Q0pWCsJvhOPM3TNIeZQCRzNHtPRsiXeavoPegb0rLRYhBB4+fFhp7/e49PR0jUfSHdZ15WFdVx7WdeVhXVce1nXlYV1Xnsqqa0tLS8hksgo/bpUZY1HVFfZ5IyIiIiLSZ7oaw8HEooykvGJBRERERFRReMWCiIiIiIiqrCo9KxQREREREekHJhZERERERKQ1JhZERERERKQ1JhZERERERKQ1JhZERERERKQ1JhZERERERKQ1JhZERERERKQ1JhZERERERKQ1JhZERERERKQ1JhZERERERKQ1JhZERERERKQ1JhZERERERKQ1JhZERERERKQ1JhZERERERKQ1JhZERERERKQ1JhZERERERKQ1JhZERERERKQ1JhZERERERKQ1JhZERERERKQ1JhZERERERKQ1JhZERERERKQ1JhZERERERKQ1JhZERERERKQ1JhZERERERKQ1JhYSWrx4Mby9vWFiYoLg4GAcO3ZM6pD03vTp0yGTyTSWwMBAqcPSSwcPHkT37t3h5uYGmUyGP/74Q2O9EAJTp06Fq6srTE1N0aFDB1y9elWaYPXM0+p26NChRdpx586dpQlWj8yePRtNmzaFpaUlnJyc0KtXL0RHR2tsk52djbCwMNjb28PCwgJ9+/ZFYmKiRBHrj7LUbdu2bYu027fffluiiPXHkiVL0KBBA1hZWcHKygohISHYuXOnej3b7LN5Wr2yveoGEwuJrF+/HhMnTsS0adNw6tQpBAUFoVOnTkhKSpI6NL1Xt25dJCQkqJdDhw5JHZJeyszMRFBQEBYvXlzs+rlz5+Lbb7/F999/j6NHj8Lc3BydOnVCdnZ2JUeqf55WtwDQuXNnjXa8du3aSoxQP0VERCAsLAxHjhzBnj17kJeXh44dOyIzM1O9zYQJE7Bt2zZs2LABERERiI+PR58+fSSMWj+UpW4BYOTIkRrtdu7cuRJFrD9q1KiBOXPm4OTJkzhx4gTat2+Pnj174sKFCwDYZp/V0+oVYHvVCUGSaNasmQgLC1O/LigoEG5ubmL27NkSRqX/pk2bJoKCgqQOo9oBIDZv3qx+rVQqhYuLi5g3b566LDU1VSgUCrF27VoJItRfT9atEEIMGTJE9OzZU5J4qpOkpCQBQERERAghVG3U2NhYbNiwQb3NpUuXBAARGRkpVZh66cm6FUKINm3aiHfffVe6oKoRW1tbsWzZMrbZClZYr0KwveoKr1hIIDc3FydPnkSHDh3UZQYGBujQoQMiIyMljKx6uHr1Ktzc3FCzZk0MHjwYt27dkjqkaic2NhZ3797VaMPW1tYIDg5mG64gBw4cgJOTEwICAjB69GikpKRIHZLeSUtLAwDY2dkBAE6ePIm8vDyNdhsYGAhPT0+223J6sm4LrV69Gg4ODqhXrx4mT56MrKwsKcLTWwUFBVi3bh0yMzMREhLCNltBnqzXQmyvFc9I6gCeR/fu3UNBQQGcnZ01yp2dnXH58mWJoqoegoODsXLlSgQEBCAhIQEzZsxAq1atcP78eVhaWkodXrVx9+5dACi2DReuo2fXuXNn9OnTBz4+Prh27Ro+/vhjdOnSBZGRkTA0NJQ6PL2gVCoxfvx4tGjRAvXq1QOgardyuRw2NjYa27Ldlk9xdQsAr776Kry8vODm5oazZ89i0qRJiI6OxqZNmySMVj+cO3cOISEhyM7OhoWFBTZv3ow6deogKiqKbVYLJdUrwPaqK0wsqFrp0qWL+nmDBg0QHBwMLy8v/PbbbxgxYoSEkRGV3cCBA9XP69evjwYNGqBWrVo4cOAAQkNDJYxMf4SFheH8+fMcY6UDJdXtqFGj1M/r168PV1dXhIaG4tq1a6hVq1Zlh6lXAgICEBUVhbS0NGzcuBFDhgxBRESE1GHpvZLqtU6dOmyvOsKuUBJwcHCAoaFhkVkdEhMT4eLiIlFU1ZONjQ38/f0RExMjdSjVSmE7ZRuuHDVr1oSDgwPbcRmNGTMG27dvR3h4OGrUqKEud3FxQW5uLlJTUzW2Z7stu5LqtjjBwcEAwHZbBnK5HL6+vmjcuDFmz56NoKAgLFiwgG1WSyXVa3HYXisGEwsJyOVyNG7cGPv27VOXKZVK7Nu3T6PvH2kvIyMD165dg6urq9ShVCs+Pj5wcXHRaMPp6ek4evQo27AO3L59GykpKWzHTyGEwJgxY7B582bs378fPj4+GusbN24MY2NjjXYbHR2NW7dusd0+xdPqtjhRUVEAwHb7DJRKJXJycthmK1hhvRaH7bVisCuURCZOnIghQ4agSZMmaNasGb755htkZmZi2LBhUoem195//310794dXl5eiI+Px7Rp02BoaIhBgwZJHZreycjI0PjmJjY2FlFRUbCzs4OnpyfGjx+PWbNmwc/PDz4+PpgyZQrc3NzQq1cv6YLWE6XVrZ2dHWbMmIG+ffvCxcUF165dw4cffghfX1906tRJwqirvrCwMKxZswZbtmyBpaWlug+6tbU1TE1NYW1tjREjRmDixImws7ODlZUVxo4di5CQELz44osSR1+1Pa1ur127hjVr1uDll1+Gvb09zp49iwkTJqB169Zo0KCBxNFXbZMnT0aXLl3g6emJhw8fYs2aNThw4AB2797NNquF0uqV7VWHpJ6W6nm2cOFC4enpKeRyuWjWrJk4cuSI1CHpvQEDBghXV1chl8uFu7u7GDBggIiJiZE6LL0UHh4uABRZhgwZIoRQTTk7ZcoU4ezsLBQKhQgNDRXR0dHSBq0nSqvbrKws0bFjR+Ho6CiMjY2Fl5eXGDlypLh7967UYVd5xdUpALFixQr1No8ePRLvvPOOsLW1FWZmZqJ3794iISFBuqD1xNPq9tatW6J169bCzs5OKBQK4evrKz744AORlpYmbeB6YPjw4cLLy0vI5XLh6OgoQkNDxV9//aVezzb7bEqrV7ZX3ZEJIURlJjJERERERFT9SDrGYuvWrQgJCYGZmRns7e3Rt29fXLlyBdOnT4dMJsPKlSs1ts/MzMSXX36JJk2awMrKCubm5ggMDERYWBiuXLkizYcgIiIiIiLpxlh8//33GD16NGQyGVq1agVXV1ccOXIEzZo1Q/fu3Ytsn5CQgJdeegkXLlyAra0t2rZtC4VCgevXr+P777+Hn58f/P39JfgkREREREQkSWJx8+ZNTJgwAcbGxti2bZt6QGJeXh6GDRuGVatWFdnn9ddfx4ULF9C/f3/89NNPsLCwUK+7ceMG0tPTKy1+IiIiIiLSJElXqOXLlyM7OxuDBg3SmOXE2NgYCxYsgJmZmcb2x44dw759++Dk5IRly5ZpJBUA4O3tzVH8REREREQSkiSx+PvvvwFo3l22kL29PTp27KhRtnfvXgDAoEGDYGlpqfsAiYiIiIioXCRJLOLj4wEAXl5exa739vbWeB0XFwcAvMU6EREREVEVxTtvSywnJwfTp08v8U6Q9GxYr7rDutUd1q3usG51h3WrG6xX3WHd6o4kiUXh7dJv3rxZ7Ponyz08PAAA165d021gEsjJycGMGTPYuCsY61V3WLe6w7rVHdat7rBudYP1qjusW92RJLFo1aoVAOC3334rsu7+/fv466+/NMo6dOgAAFi7di0yMjJ0HyAREREREZWLJInFsGHDoFAosHr1avXAbEA13eyECROQmZmpsX2zZs3Qrl07JCUlYdSoUUXW37hxA+fOnauU2ImIiIiIqChJ7mPh4+ODr776CmPGjEGnTp3QunVruLi44MiRI3jw4AEGDx6M1atXa+zz66+/IjQ0FGvXrsXu3bvRsmVLKBQKXLt2DVFRUfjqq69Qv359ncUshMDDhw8r/LiF99/gfTgqFutVd1i3usO61R3Wre6wbnWD9ao7rFvA0tISMpms4g8sJLR582YRHBwsTE1Nha2trejZs6e4dOmSmDZtmgAgVqxYobF9enq6mDlzpmjQoIEwNTUVFhYWIjAwUIwZM0ZcvXpVp7GmpaUJAFy4cOHChQsXLly46PWSlpamk/NlSa5YFOrVqxd69epV5u0tLS0xZcoUTJkyRXdBlfLeaWlplf6+REREREQVSVf3hZM0sdAnMpkMVlZWUodBRERERFQl8T4WRERERESkNSYWRERERESkNZkQQkgdBBERERER6TdesSAiIiIiIq0xsSAiIiIiIq0xsSAiIiIiIq0xsSAiIiIiIq0xsSAiIiIiIq0xsSAiIiIiIq0xsSAiIiIiIq0xsSAiIiIiIq0xsSAiIiIiIq0xsSAiIiIiIq0xsSAiIiIiIq0xsSAiIiIiIq0xsSAiIiIiIq0xsSAiIiIiIq0xsSAiIiIiIq0xsSAiIiIiIq0xsSAiIiIiIq0xsSAiIiIiIq0xsSAiIiIiIq0xsSAiIiIiIq0xsSAiIiIiIq0xsSDJzZkzBzKZDOPHj1eXZWdnIywsDPb29rCwsEDfvn2RmJgoXZCkF5YsWYIGDRrAysoKVlZWCAkJwc6dO9Xr2a7oWcyePRtNmzaFpaUlnJyc0KtXL0RHR2tsw7ZFz+LgwYPo3r073NzcIJPJ8Mcff2isF0Jg6tSpcHV1hampKTp06ICrV69KEyzpvcWLF8Pb2xsmJiYIDg7GsWPHKvw9mFiQpI4fP46lS5eiQYMGGuUTJkzAtm3bsGHDBkRERCA+Ph59+vSRKErSFzVq1MCcOXNw8uRJnDhxAu3bt0fPnj1x4cIFAGxX9GwiIiIQFhaGI0eOYM+ePcjLy0PHjh2RmZmp3oZti55FZmYmgoKCsHjx4mLXz507F99++y2+//57HD16FObm5ujUqROys7MrOVLSd+vXr8fEiRMxbdo0nDp1CkFBQejUqROSkpIq9o0EkUQePnwo/Pz8xJ49e0SbNm3Eu+++K4QQIjU1VRgbG4sNGzaot7106ZIAICIjIyWKlvSVra2tWLZsGdsVVZikpCQBQERERAgh+DeLKgYAsXnzZvVrpVIpXFxcxLx589RlqampQqFQiLVr10oQIemzZs2aibCwMPXrgoIC4ebmJmbPnl2h78MrFiSZsLAwdO3aFR06dNAoP3nyJPLy8jTKAwMD4enpicjIyMoOk/RUQUEB1q1bh8zMTISEhLBdUYVJS0sDANjZ2QHg3yzSjdjYWNy9e1ejXVlbWyM4OJjtisolNzcXJ0+e1GhLBgYG6NChQ4W3JaMKPRpRGa1btw6nTp3C8ePHi6y7e/cu5HI5bGxsNMqdnZ1x9+7dSoqQ9NW5c+cQEhKC7OxsWFhYYPPmzahTpw6ioqLYrkhrSqUS48ePR4sWLVCvXj0A/JtFulHYdpydnTXK2a6ovO7du4eCgoJi29Lly5cr9L2YWFCli4uLw7vvvos9e/bAxMRE6nComgkICEBUVBTS0tKwceNGDBkyBBEREVKHRdVEWFgYzp8/j0OHDkkdChFRlcOuUFTpTp48iaSkJDRq1AhGRkYwMjJCREQEvv32WxgZGcHZ2Rm5ublITU3V2C8xMREuLi7SBE16Qy6Xw9fXF40bN8bs2bMRFBSEBQsWwMXFhe2KtDJmzBhs374d4eHhqFGjhrqcbYt0obDtPDm7GNsVlZeDgwMMDQ0rpS3pVWKRmZyJzW9sxleuX2Gm4UzMkM1A1MooqcOicgoNDcW5c+cQFRWlXpo0aYLBgwernxsbG2Pfvn3qfaKjo3Hr1i2EhIRIGDnpI6VSiZycHDRu3Jjtip6JEAJjxozB5s2bsX//fvj4+GisZ9siXfDx8YGLi4tGu0pPT8fRo0fZrqhc5HI5GjdurNGWlEol9u3bV+FtSa+6Qm0dsRVXtl2BcwNn+IT6wMDIAHa+dlKHReVkaWmp7ptcyNzcHPb29uryESNGYOLEibCzs4OVlRXGjh2LkJAQvPjii1KETHpi8uTJ6NKlCzw9PfHw4UOsWbMGBw4cwO7du2Ftbc12Rc8kLCwMa9aswZYtW2Bpaanu325tbQ1TU1O2LXpmGRkZiImJUb+OjY1FVFQU7Ozs4OnpifHjx2PWrFnw8/ODj48PpkyZAjc3N/Tq1Uu6oEkvTZw4EUOGDEGTJk3QrFkzfPPNN8jMzMSwYcMq9o0qdI4pHcrPyRczDGeIb7y/EcoCpdThUAV7fLpZIYR49OiReOedd4Stra0wMzMTvXv3FgkJCdIFSHph+PDhwsvLS8jlcuHo6ChCQ0PFX3/9pV7PdkXPAkCxy4oVK9TbsG3RswgPDy+2bQ0ZMkQIoZpydsqUKcLZ2VkoFAoRGhoqoqOjpQ2a9NbChQuFp6enkMvlolmzZuLIkSMV/h4yIYSo2FRFN9JupeEbr2/g1cYLQw8MlTocIiIiIiJ6TJVJLFJvpGKBzwJ4tfHCoK2DcGD6AVzefBnpt9NhbG6MnLScIvtYe1lj/I3xlR8sERERERFpqHJjLPIf5WNlm5VIvZkK7zbecG3kikubLqF239q49PslmDubw7ezLwDAzMFM4miJiIiIiAiogonFnWN3UCOkBt69/i5MbP67x0HqjVRc+v0SHAId0GtlL+kCJCIiIiKiIqrkdLNdvu2ikVQQEREREVHVVuUSCwtXC7g1cZM6DCIiIiIiKocql1hYe1pLHQIREREREZVTlUssjEyq3LAPIiIiIiJ6iiqXWNDzLScnB9OnT0dOTtHphYm0wbZFusB2RbrAdkW6ouu2xcSCqpScnBzMmDGDf0ypwrFtkS6wXZEusF2Rrui6bTGxICIiIiIirTGxICIiIiIirXGkdBkJIfDw4UOpw6j20tPTNR6JKgrbFukC2xXpAtsV6UphmxJC6OT4MqGrI1cz6enpsLbmVLhEREREpN+SkpLg6OhY4cdlYlFGvGJBRERERNWBpaUlZDJZhR+XiQUREREREWmNg7eJiIiIiEhrTCyIiIiIiEhrTCyIiIiIiEhrTCyIiIiIiEhrTCyIiIiIiEhrTCyIiIiIiEhrTCyIiIiIiEhrTCyIiIiIiEhrTCyIiIiIiEhrTCyIiIiIiEhrTCyIiIiIiEhrTCyIiIiIiEhrTCyIiIiIiEhrTCyIiIiIiEhrTCyIiIiIiEhrTCyIiIiIiEhrTCyIiIiIiEhrTCyIiIiIiEhrTCyIiIiIiEhreplY9O/fH0ZGRpDJZLCwsMCKFSukDomIiIiI6Lmmd4nFu+++iw0bNuCNN97Ali1b4OHhgREjRuDChQtSh0ZERERE9NySCSGE1EGUh4WFBWrWrImzZ88CAPLz86FQKPDSSy9h165dEkdHRERERPR8MpI6gPLIyMhAZmYmunbtqi4zMjKCt7c3zpw5U+w+6enpSE9PV78WQkAmk8Hd3R0ymUznMRMRERERPQ/0KrG4cuUKAMDHx0ej3N7eHomJicXu06NHD0RERBQpT0tLg5WVVcUHSURERET0HNKrxOJZbN26VeOKxcOHD1GnTh0JIyIiIiIiqn70KrHw9/cHAMTGxmqUp6SkwNLSsth9rKysNK5MPJ5kEBERERFRxdCrWaEsLCxgbm6OP//8U12Wn5+PGzduICgoSMLIiIiIiIieb3qVWADAiBEjcO7cOYwcORLbt29H/fr1IYTA//73P6lDIyIiIiJ6bunddLMA8Morr2Dz5s0oKCiAubk5FixYgBEjRpRp3/T0dFhbW3PwNhERERFRBdLLxEIbTCyIiIiIiCqe3nWFIiIiIiKiqoeJBRERERERaY2JBRERERERaY2JBRERERERaY2JBRERERERaY2JBRERERERaY2JBRERERERaY2JBRERERERaY2JBRERERERaY2JBRERERERac1I6gCo+rqRegO7YnZhV8wuvNbgNfSr00/qkKiSKJVK5OTk4NGjR3j06BGys7NLfSzLul9++QVmZmZSf7SKpVQCmZn/LRkZRZ+XVjZ7NuDnJ/WnICIiHUu/nY5ji4/h3sV7GLhloNThlIiJBVWYR3mPcPDmQeyK2YWdMTsRnRKtXmcht2BiIQEhhPoEvzwn8dquy8nJqfDPsmTJEmkSCyGA3NzynfCXdf2jR9rFNnYsEwsiomrszrE7OPLNEVzccBHKfCUAIOFUAlwbuUocWfGYWNAzE0Lg6v2r6kTiwI0DyM7PVq83lBmiuUdzdPbtjG7+3SSMVHpCCOTm5lb6CX52dvbTg9MxQ0NDmJqawtTUFCYmJqU+lrbuqUmFUglkZT3byf/TEoL8fN1WkkwGmJv/t1hYaD6W9LxWLd3GRURElU6Zr8SlTZdw5JsjuB15W13u1cYLL45/Ec5BzhJGVzomFlRumbmZmBI+BX9c/gOxqbEa69wt3dHFtws6+3ZGaM1Q2JjYSBOkDm3cuBF79+4tVxef7OxsCCEkjdvAwOCpJ+/Puq60bYyMSvgzk58PPHwIpKdrLk+WpaSoTvAPHSo9IcjK0n0lyuVlO/kva1nhc1NTVXJBRETPrYLcAhz/7jgiv45Eelw6AMDA2AD1X62P4HeD4fpC1bxK8TgmFlRuO2N2Yv6R+erXRgZG+LD5hxhUfxDqOtaFrBqfIMXGxmLAgAFQKpXPfAyZTFbkJFzbb/PLsr+xsbH2FSCE6gT+yUTg7t3Sk4PiyrTtBlSasnzb/yxlFVGHRERET7i+7zp2jtmJe5fvAQDMHM3QZHQTNB3dFBYuFhJHV3ZMLKjcegb0xIfNP8QPp35AanYq8pX5mH9kPpKzkjGm2Rg0cG4gdYg6s2DBAiiVSjRq1AgDBw58ppN/Y2Pjyk++8vJU3/yX9aS/tDItkqpimZgAlpaAlZXm8niZhUXZT/5NTQEDTnhHRERVX/rtdOyeuBsXN1wEoEoo2n3WDg2HNISRif6dpsuE1P0zKll6ejqsra2RlpYGKysrqcPRa1l5WVh9djUWHluIc0nn1OWtvVpjZtuZaOPdRsLoKoYQAikpKbh27RpiYmLw1ltvITMzE7t370bHjh0r/g2VSiA7+7+uPYVjBkp7/vDh0xODih5rIZOVngiUtczSUtW9iIiI6DmSlZKFk0tP4u8v/kZeZh5kBjI0DWuKdjPbwcTGROrwnhkTC9KaEAKHbh3CouOL8PvF31EgCuBu6Y7bE28/fecqoKCgAHFxcbh27VqR5fr160hPT1dvKwfQtHZt/L17N2SFJ/hlOfkv6fmTZbrsHgSovs3XJhEoXMzMOCaAiIioHIQQuHXoFk4uPYmLGy+iIKcAAODRwgMvL3oZLg1dJI5Qe0wsqEIdvX0UL/70ImxMbPBg0oPKffO8vBJP4nMePEBSbCzu3bqFB3fuIP3uXWQmJeFRSgry09NholTCDIA5ALN/l8efW8hkMBUChpX5eUxMVCfwZmaqLj7FPTczK3riX1JyYGnJMQJERESV7NGDRzjzyxmc+uEUki8mq8tdGrog5L0Q1B9cv9qMT9W/zltUZSmFEhm5Gc9+ACGArVuBq1ef/s1+cc/z8ko8tAKAx7/LM8f2OGPj0k/4n/b8aetNTQHDSk1jiIiIqALdj7mPiJkRuLjhIvKzVdOWG5sZo96gemj8VmO4NXGrNglFISYW9MzuZtzFsTvH1Mvx+ONIzU4FAMgNn6Hf/PnzQK9eWsdVACATQNa/S+HzXENDwNwcRlZWkNvYwNTBARZOTrB2dYWliwsMCgcAl+UqAb/5JyIiolJsHbEVNw/eVL+u8WIN9FnTB7Y+thJGpVtMLKhMMnIzcDL+JI7dOYajd47i2J1jiEuPK7KdiZEJGrs2xugmo8v/Jv7+wPjxwJEjwLVrQHLyU3cpkMlwzcAApwoKcA7AOQCJdnao06ULfGvXRs2aNVGrVi341qoFOzu7avfNABEREVVNIe+FICslC8kXVOczt4/cxiL/RfBp74Pa/Wqjdu/aMHN4ys1f9QzHWFARhXfUDo8NV12NiD+Gi8kXoRSa04zKIEMdxzoIdg9GM/dmaObeDPWc6sHYsIK+zU9PB65fB65dQ97ly8g8exb5V65Adv06bNLTSx/vYGgIeHqq7kxcuHh5ATY2gLW1atyBtbVq4UBkIiIi0pHkS8m49PslXNxwEYlnE9XlMkMZvNt6o84rddBwaEMYKfT/+34mFgRAdUUiPDYcu2J2YWfMziJ31AaAGlY1NJKIxq6NYamwrJD3z8/Px+3btxEbG4vY2FjcuHFD/Tw2Nhbx8fEa2xsDqGtujleDg/FyQAACjY1heOOG6krH9evlm13J0PC/ROPxhOPJ16U9t7ICSrrDNBERERGAlCspuPj7RVzccBF3T99Vlwf0DMCATQMgM9DvLzqZWDynhBC4kHwBu2J2YVfMLvx962/kFuSq1xsbGKOFZws0r9Eczdyboal7U7hZumn1fomJiRrJwuNLXFwc8vPzSz2Gubk5fHx80KBBA7zyyivo3LkzTEyKmetZCCAhQZVkPL7cvg2kpamuhKSlqZaKvNmbuXnJyUdZkxQTE149ISIieg7cv3YfFzdcxIFpB1CQW4B2n7VD609bSx2WVphYPEfyCvKw7co27Ly6E7uu7cLtdM37THjbeKOLbxd09u2Mdt7tnulqRHp6Ovbs2VMkcbhx4wayn3KTNrlcDi8vL/j4+MDb2xs+Pj4ai4ODQ8WOkRBCNZtUYZLxeMLx5OvSnlfkvSeMjMqXiFhbA82bq7p4ERERkd45vfw0to7YCsiAQdsGwb+rv9QhPTMmFs+JpMwk9F7fG4fjDqvLTIxM0Na7LTrX6owufl3gZ+en9Yl769at8ffffxe7TiaToUaNGkUShsLFzc0NBgYGWr2/JHJz/7vD9bMkJoWPz/qrGBQEREVV6EciIiKiyrN99Hac/P4kAMAn1AeN3myEwN6BejfuQr+ipWdyMfkiuq7pihupN2CtsMbQhkPRxbcLWnu1hqmxaYW+V9euXXH8+HGNqxOmpqZ466238P7778Pd3b1C369KkMsBBwfV8qyUStU9OcqTmNy9q5pB6+bNpx+fiIiIqqwuC7ogJzUH59efR+y+WMTui4WpvSmC3ghCozcbwbGOo9QhlgmvWFRzu2N2o//G/kjPSUct21rY/up2BDoE6vQ909PTsWnTJqxZswb79u2D8t9xDIaGhujYsSNeffVV9O3bF6amFZvUPHeuXAECAlTdoB5U8l3OiYiIqMKl3kjF6RWnEbU8Cum309XlHs090GhkI9QbWA9GJlX3ugATi2os/mE8vL/xRp4yD608W2HTgE1wMNPiW/VnkJCQgPXr12P16tU4ceKEurx///5Yv359pcZSrWRkAJ9+CixYwMSCiIiomlEWKBGzKwanfjyFK9uvQBSoTteDhgSh18pe0gZXiqqb8pDWrqZcRZ4yDzWsamDP63ugMFJUegyurq4YMWIE7t+/j3PnziEnJwcAUK9evUqPpVoQAli/Hnj/feDOHVXZK69IGxMRERFVKANDA/h39Yd/V388jH+IpS8sRWZSJozNK+heYTrCxOI5YCG3kCSpyM/Px/LlyzF16lQkJqpuCNOiRQv873//w4svvljp8ei9s2eBceOAiAjV65o1VVcsunWTNi4iIiLSmbjDcaqkwswYrT+p2tPRMrGoxs4mnpXkfW/duoXly5dj+fLliIuLAwD4+vriyy+/RO/evSt2ytjnxaFDQLt2QH4+YGoKfPyx6qpFcffxICIiomrjwLQDAIDg8cGwdKuYGxPrChOLakgIgc8OfoZpB6YBAF72fVnn75mXl4ft27fjxx9/xK5du1A4dMfBwQFTpkzB22+/DblcrvM4qqWCAmDsWFVS0aEDsGwZ4OUldVRERERUCcwczQAA13ZfQ5spbTh4uyqp7oO3s/Oz8ebWN7H63GoAwMQXJ2LuS3NhaGCok/eLiYnBTz/9hBUrVqi7OwFAu3btMHLkSPTu3bv4u2NT2a1cCQwbproZ3tWrgKN+TDlHRERE2ku7lYaljZbiUcojvPDmC+jxYw+pQypR1U156Jm8tf0trD63GkYGRlj88mKMajxKJ+9z5coVfPLJJ9i4caO6zNnZGUOHDsWIESPg5+enk/d9rggB/P478MEHqteffsqkgoiI6Dlj7WmNvmv7YlWnVTi97DQCegQgoHuA1GEVi4lFNRMeGw4A+KXXLxhUf1CFHz8+Ph4zZszATz/9hIKCAshkMnTu3BlvvvkmunfvDmPjqj1bgd64eFE1UHvfPtXrBg1U3aGIiIjouWNqawoDQwMo85VQ5iulDqdETCyqKT/7ir1ikJaWhrlz52L+/Pl49OgRAKB79+744osvOHVsRUpPB2bOVM32lJ8PKBTApEmqRVH5M3sRERGRtHIe5mDjwI1Q5itR55U6COyl2xsda4OJRTWSnpOOzLzMCjmWUqnEhQsXEB4ejgMHDmDfvn1IT1fdATIkJARffvklWrVqVSHvRf9KSQEaNQJu3VK97tkT+Ppr1bSyRERE9Fza8+EePLj2ANZe1uj+Q/cqPbsmE4tq4mbqTXRb2w33H92HpdwStWxrlWt/IQQuX76M8PBwdTJx7949jW1q166N2bNno0ePHlW6UeutzEzg3+l5MW+eajpZIiIieq6l3UwDANR/tT5MbKr2hDhMLKqBo7ePoue6nkjMTISLhQu2DtwKW1PbUvcRQuDq1asaicTjszoBgJmZGVq0aIF27dqhXbt2aNq0KQwNdTO7FAHw9ATeeAP4+Wfgjz+A994DmMARERE91+q/Wh8xO2NwYf0FtP+8fZX+cpfTzeq5Uwmn0GJ5C2TnZyPIOQjbBm2Dh7VHqfts2rQJ48ePV9+8rpCJiQmaN2+ukUjw3hOV7M4dwM8PePQICAlRPff2Bnx8VIu3N1CjBsAEj4iI6LmQm5mLr1y+Qm5GLoYeHAqvVlX3Xla8YqHnNl3ahOz8bDT3aI5dg3fBUlHyHRmVSiWmTp2Kzz//HAAgl8vx4osvqhOJ4OBg3nNCau7uwIwZwIcfApGRquVJRkaAh8d/icaTj66ugIFBJQdOREREuhB3OA55j/IAADlpORJHUzomFnqu8IJTE9cmpSYVaWlpeO2117B9+3YAwIQJEzBr1iyYmZlVSpxUDh98AHTsCFy6BMTGAjduqB5jY4GbN4G8vP9eF0ehUN2Zu7ikw8dHdS+MKnwZlYiIiFTuRd/Dhlc2QBQIBL0RBL+uVfs+YUwsngOpqakICQnB5cuXoVAo8OOPP+L111+XOiwqTVCQanmSUgnEx/+XbDz5GBcH5OQAV66oluKYmamSDH9/oHZtoE4d1WNgIGBurrvPRERERGWWl5WHtd3XIictBx4tPNDth25VenwFwMTiubB69WpcvnwZrq6u2Lp1K5o0aSJ1SPSsDAxUYyxq1ABatiy6Pj8fuH27+KTjxg3VGI6sLNUN+C5eVA0Sf5yXl2ayUfhoW/pkAERERFSxrv11Dfev3oeFiwUGbBoAI0XVP22v+hGS1nbt2gUAGDduHJOK6s7ISHU1wtu7+PU5OaqrGtevA5cvq7pbXbqkSjKSk1VdrW7eBP5tM2rOzkWTjdq1ARcXdqsiIiLSgbjDqkl2/Lv7w9xJP3oUMLGo5nJycrB//34AQJcuXSSOhiSnUAC+vqqlY0fNdffu/ZdoFCYbly6pEpHERNUSHq65j42NKtGYMgXo3LnSPgYREVF1dzvyNgDAo3nps31WJUwsqrnTp08jKysLTk5OaNCggdThUFXm4AC0aqVaHvfwoeoKxvLlwO7dwOMzVKemAocPAz/9xMSCiIioggghkHA6AQDg3sxd4mjKjolFNXfhwgUAQFBQUJUf8ENVxKNHwIkT/013GxmpulrxJFdX1b02QkKA4cMrP04iIqJqKuNuBvIy8yAzkMHO107qcMqMiUU1V5hY1K1bV+JIqMqbNw9Yvx44c0Y1CPxJNWoAXbsCbduqkglPT46vICIi0oH7V+8DAGy8bWAo15+b4jKxqOaysrIAAEePHkV2djZvgEfFy8oCJk8GCgpK3ub2bWDpUuDXXwE7u/8We3vN18WV2dsDbHtERERlkpuZCwDITs1G1r0smDnox33HmFhUc+PHj8e6desQGRmJkSNH4pdffmGXKCrKzAzYuRM4cgRISQHu3/9vefy1UqlKQrKyVIlGeZialj8hsbNTxUZERPQcqRlaE071nJB0Pgk7x+1E3zV9pQ6pTJhYVHOBgYHYuHEjOnfujFWrVqF27dr4+OOPpQ6LqqKXXlItJVEqVQO5n0w2inv9ZFlBgWrsxp07qqU8TEyKT0AaNgTCwtgdi4iIqh1DuSF6ruiJZcHLcH7tedQdUBeBPQOlDuupmFg8B/Lz8+Hg4IDExER8//33TCzo2RgYANbWqsXHRzU7VGYmkJGhSjgePiz+eXo6kJDw3z0ybt5UXfEoq+xs1d3G4+OLruveXXVTPyIiomom614W5JZy5KTl4MK6C0wsSFrXr1/HhAkTsHXrVgCAs7MzFi1aJHFUJJmCAlUiUFoSUJ7nGRmaU89WJHNzwMICsLRULcU9r1dPNYCciIioGsl5mIO/3v8Lp344BQCw97dHq09aPWWvqoGJRTWUn5+Pzz77DF9++SVycnJgZGSEcePGYerUqbC2tpY6PKpIW7aopoZ98uS/uISgPFcJykMm0zzhf1pC8LTn5uaAof7MgEFERFRR7hy7g40DNiL1RioAIPjdYIR+EQpjM2NpAysjJhZ67FLyJaw9vxYAYC5X3epdqVRi+PDh+PXXXwEAoaGh+Pbbb1GnTh3J4iQdSUsDevUq/36Ghv+dyFdEImBmxnEOREREFWD3xN3qpMLM0QxySzmSLybDtbGrXky+IxNCV30Zqqb09HRYW1sjLS0NVlZWUofzzPZe34t+v/VDWk4afGx8cGDoAXhYeeDtt9/GDz/8AENDQ6xYsQKvvfaaXjREekbffgvs3w/cuAHExqrGMzyNu7tqjISPD+DtrflYowZgxO8biIiIpHB973UcnHUQt/6+BaH87xTd0s0S/j38EdgzEN7tvGGkqJr/q5lY6KEfT/6I0X+ORoEoQAuPFtg8YDMczBwwceJEfPPNN5DJZFizZg0GDhwodahUmYQAHjz4L8ko7vFp3aEMDQEPD82Ew8tLdZdtNzfVo50dr1AQERHpUFZKFq7uuIroLdGI2RWDvMw89Tq5hRy+nX0RNDQI/l39JYyyKCYWeiYpMwnuX7sjX5mP1xq8hmXdl0FhpMCuXbvQpUsXAMDy5csxbNgwiSOlKkcIIDm55KTjxg0gN/fpx5HLVQlG4VKYcDz52sFBNZMUERERPbP87HzEhsciems0rmy9gofxD9XrxsaMhV0tOwmj08TEQs9cvncZtRfXhrXCGg8mPVB3cwoNDcX+/fsxbtw4LFiwQOIoSS8plcDdu6pE4/GkIy5ONV1sQoLq3hRlZWQEuLgUn4A8/tzJiYO1iYiIykAoBaJWRmHriK0wNjPG+0nvQ24ulzostarZQYueykBmoE4qTp8+jf3798PQ0BDvvfeexJGR3jIwUJ3wu7kBLVoUv01Ojir5KEw04uOLf56UBOTnq+7O/bQ7dBsYAM7OxScdPj5Aq1a8+zYREREAmYEMiecSAQB+Xf2qVFIBMLHQK0IIrD2nmgXKyED1o7tz5w6GDBkCAOjfvz88Oa8/6ZJCoRpz8bSb0uXlAYmJRROOJ18nJqqulBS+Lo6JCdC+PdCtG9C1K+9dQUREz7XYfbEAgIsbLmKh/0K4N3WHWzM3uDd1h8sLLjA2lW5qWnaF0hM5+TkYtX0UfjnzCwBgVrtZ6GXfC126dEFcXBxcXFxw8OBB+Pn5SRwpUTkUFKiubpSUgERFAbduae7ToIEqyejWDWjWjN2oiIjouXLi+xM4/L/DeHDtQZF1MkMZnOs7w62pG9ybucOtqRuc6jrBwKhyxjwysdAD97Luoc/6Pvj71t8wlBli0cuLUDuzNnr16oXU1FQEBARg165d8Pb2ljpUooolBHDhArB9u2qJjFRd4Sjk4AB06aJKMvr04VS5RET03MhKyUL8iXjcOXYH8cdVj5mJmUW2MzI1gmsjV3WyUX9QfZ3FxMRCD7y++XWsOrsKhjJD/Pnqn/DK90KjRo3w6NEjNG/eHFu3boW9vb3UYRLp3r17wK5dqiRj1y7VTQILDR4MrFolXWxEREQSEkIg+UIyzq05h/Nrz6tvtPekaWKazmLg13t6oHmN5lh1dhUKRAF+OvUTYufH4tGjR2jfvj22b98OU1NTqUMkqhwODsBrr6mWvDzgn3+AbduAb74BVq8GRo8ueeA5ERFRNZCdlo37Mfdx/+p9pFxNwYOYB0i5moL7V+8j617p96uycLHQaWy8YqEnVpxegbe2v4U8ZR5wG7DaYYWLxy7C3d1d6tCIpDdqFPDjj6oxF5GRvH8GERHpNSEEEs8k4l70Pdy/qkoi7seoEoms5NKTB3Nnc9j72cPOzw52vnaw87ODvZ89bGvZQmGp0GncvGKhJ4a9MAwORg7osbYHUANwH+fOpILo3j1g587/BngfOwZERADt2kkbFxERkRYOfnYQB6YdKHkDGeDW2A1O9Zw0Egg7XzudJw+lYWKhJ4QQOBh7EPi3rbSow+4e9BwSAjh37r/B3EeOqMoKeXioFiIiIj3mUNsBJjYmyE7NLn4DAcSfjEdmcibSb6cjLS4N6bfTkX47HXa+drCtaQsjReWf5rMrlB7Iyc/BW9vfws9nflYVHAHiV8bD1dlV2sCIKsOjR0B4+H/JRFyc5vqgoP+mn23alNPPEhFRtSCEQFZylnr8xOPjKu5fvY/cjNwS95UZyGDtaf3flYx/r2bY+9nDIdBBZzEzsdADY3eMxaLji2AoM4T5QXOk70vHoUOH0IKDVKk6UyqBn38GPvpIda+LQiYmQIcOqkTi5Zd5hYKIiJ47QghkJmVqjL14fBB3aUkHZ4V6zh25cwQAsOjlRdh/fj82YANmz56N7du3SxwZkY6cOAGMGQMcPap67eYG9OihSibatQPMzKSNj4iISEIymQwWzhawcLaAZ0tPjXWPJx0pV1PUVzqSLyQj+WKyTuNiYqFHPKw8MGvWLGzevBl//vkn9uzZg5deeknqsIgqzr17wCefqGZ4EgKwsACmTwfGjgXkcqmjIyIiqvJKSjqSLiRhSb0lOn1vzsmoZ/z9/dGnTx8AwKeffipxNEQVKDERqFMH+OEHVVLx2mtAdDTw3ntMKoiIiPQAr1jokYyMDAwfPhy//fYbAEChkG46MaIKFx0NJCcDNjbA1q1Aq1ZSR0RERETlwMRCj4x+ZzQeHH0AmUyGcePG4fPPP5c6JKKK5+LCpIKIiEgPMbHQA3cT7gIAHtx/AH9/fyxfvpwzQlH1IgRw8qTUURAREZEWmFjogeycbECumpN4/br1aNiwodQhEVWcCxdUg7PDw1WvAwOljYeIiIieCQdv6wGPGqp5+oVSoGvXrrh+/brEERFVgLQ0YOJE1Q3uwsNV96eYMQNYs0bqyIiIiOgZMLHQA4ZGqjsJe3p6Ij4+HqGhoUhJSZE4KiItXLoEBAQA8+cDBQVA796qsqlTAVNTqaMjIiKiZ8DEQo98/vnn8PX1xY0bNzB9+nSpwyF6dnv2qKaXrVED2LUL2LQJ8PaWOioiIiLSAhMLPWJra4ulS5cCAJYsWYLLly9LHBHRMxJC9diqFdCpk7SxEBERUYVgYqFn2rdvjx49eqCgoACTJk2SOhyiZ1OYWBAREVG1wcRCD02ePBkAEBERIXEkRM9ACGDHDtVze3tpYyEiIqIKw8RCDz169AgA4OzsLHEkRM9g1y7VGAu5HBg/XupoiIiIqIIwsdBDcXFxAAAPDw+JIyEqp4IC4L33VM/HjQNq1ZI2HiIiIqowTCz00P79+wEAvr6+EkdCVE4xMappZU1MgE8+kToaIiIiqkBMLPRMfHw81vx7A7Hhw4dLHA1RORUUqB4tLAAbG0lDISIioorFxELPLFy4EHl5eWjVqhWaNWsmdThERERERACYWOidQ4cOAQAyMzORnJwscTRE5WRurnp88EDVLYqIiIiqDSYWembevHmwtbXFqVOn0KJFC1y7dk3qkIjKzssL6NxZ1SWK92EhIiKqVphY6JkXX3wRhw8fhpeXF65evYrmzZvjzJkzUodFVHb/+x9gYABs2gQcPCh1NERERFRBmFjoocDAQERGRuKFF15AUlISxvNeAKQPhAD271ddqVAqVWVbtkgbExEREVUYJhZ6ytXVFRs3bgSgGneRlpYmcUREJcjNBX75BXjhBSA0FPjzT0AmA3r2BCZMkDo6IiIiqiBMLPRYzZo14e/vj/z8fPW9LYiqjMxM4IsvVOMqhgwBzpwBzMyAsDAgOhr44w+gRg2poyQiIqIKwsRCj8XFxSEvLw8AcPz4cYmjIfqXEMD69UBgoOomeHfvAm5uwJw5QFwcsGgR4OcndZRERERUwYykDoCezdmzZ9GlSxfEx8fDzc0NQ4cOlTokIuD8eWDsWODAAdVrHx9g5kygf39ALpc0NCIiItItXrHQQ+Hh4WjVqhXi4+NRp04dREZGwt/fX+qw6HmWna0aL9GwoSqpMDEBZswALlwAXnuNSQUREdFzgFcs9NDw4cORnp6OFi1aYNu2bbC1tZU6JHreLV0KfPON6nnv3sDXXwPe3lJGRERERJWMVyz0UEhICABAoVDAxsZG2mCIAKBpU9WjTKa6UsGkgoiI6LnDxEIPffHFF1AoFNi/fz+2b98udThEQPPmQL9+qoHb772neiQiIqLnChMLPeTt7Y0J/87/P2/ePImjIfrXF1+oHvfsAc6dkzYWIiIiqnRMLPTUCy+8AABQFt7BmEhqhfdSsbNT3buCiIiInitMLPTU7du3AQAeHh4SR0IEID0dmDJF9XzaNMDaWtp4iIiIqNIxsdBTCQkJAAArKyuJIyEC8OOPQHIy4O8PjB4tdTREREQkASYWeqrpv7PwrFmzBnfv3pU4Gnru/ZvookcPwNhY2liIiIhIEkws9FS/fv3QrFkzZGRkYOrUqVKHQ8+r3FzgwQMgI0P1WiaTNh4iIiKSDG+Qp6cMDAzw9ddfo2XLlvjpp5/QrFkzvPnmm1KHRVWNEKqT/8zM4peMjJLXlWWb/HypPyERERFVEUws9FiLFi3w1ltvYenSpRg5ciROnz6N+fPnQy6XSx0alceTJ//anuw/uU1Bge4/g5ER4OwMdOum+/ciIiKiKomJhZ5bsmQJPDw8MGXKFHz33Xc4d+4cNmzYAGdnZ6lDq54yMoD79yv+2//KOPk3NgbMzTUXC4uiZc+yDZNZIiKi5x4TCz0nk8nwySefICgoCIMHD8bff/+Njh074vTp0zAw4BCaCiME8PnnwIwZuu3+Y2xccSf7T27DQdVERESkQ0wsqolu3brh8OHDqFevHs6ePYvMzExYWlpKHVb1kJ8PhIUBP/ygei2XV9zJ/pMLT/6JiIhITzGxqEZq1aoldQjVT1YWMHAgsG2basajRYuAd96ROioiIiKiKoeJRTWSzxl6KpZSqRqMHB4OmJgAa9YAvXtLHRURERFRlcTEohr58ccfAQCurq4wNzeXOJpqYO1aVVJhbg7s3g20aCF1RERERERVFkf3VhMpKSmYOXMmAOCzzz7jwG1tZWUBH32kev7JJ0wqiIiIiJ6CVyz0yL2se8WW5+bmIiwsDKmpqQgKCsLQoUMrN7DqaPFi4PZtwNMTGD9e6miIiIiIyqUgtwApV1KQeC4RSeeTcOfoHZ2/JxMLPdDAqQFOxJ/AW9vfgomRCQbUG6Bel5iYiH79+uHQoUOQyWT45ptvYGhoKGG01UR8vOrR1VU1voKIiIioChJKgdSbqUg6l4Sk80nqx3uX70GZr6zUWGRCCFGp7yix9PR0WFtbIy0tDVZWVlKHUyYZuRkY9PsgbL+yHQAws+1MfNr6U5w8eRK9evXCnTt3YGVlhTVr1qBr164SR1tN3LkD+PurukT99hvwyitSR0RERETPucykTCSdT1JfhUg6l4TkC8nIzcgtdnu5pRxO9ZzgVN8JTvWc4FzfGd5tvXUWHxMLPVGgLMCHez7E10e+BgAM9BiIze9sRk5ODgIDA/HHH38gICBA4iirmRkzgOnTAR8f4MoVwIgX+IiIiKjyHf7qMA7PPYzMpMxi1xvKDeFQ20EjiXCq5wRrT2vIZLJKi5NnSnrC0MAQX3X6Ch7WHpiwewK2xm1FTk4OjI2NsW/fPri5uUkdYvXTtKnqMS4OyMwErK2ljYeIiIieS1e2XlEnFba1bOFc3xmO9RzhXN8ZTvWcYOdnB0Nj6bvCc+ogPdPZtzMAQK6Qw9PTE3l5efjss88kjqoaKij4b1aosDAmFURERCSZl/73EgyMVKftrae0xoDNA9D+s/ao278uHOs4VomkAmBiobdkMhlWrFgBAPj++++xb98+iSOqZn7+GTh3DrC1BaZOlToaIiIieo65N3VH25ltAQA7x+xE6o1USeMpCRMLPda+fXu8/fbbAIBFixZJHE01s3On6vHddwE7O2ljISIioudeiw9bwKO5B3IzcnHmlzNSh1MsJhZ6bsSIEQCAffv2IS8vT+JoqhHlv9OzOTlJGwcRERERAANDA9R/rT4A4GbETYmjKR4TCz3XqFEjODo64uHDh/jnn3+kDoeIiIiIdMS7jTcAIC4yDvk5+dIGUwwmFnrOwMBAfe+KsLAwpKWlSRwREREREemCQ20HWLhaIP9RPk58f0LqcIpgYlENzJo1C25ubrh48SL69++P/Pyql8ESERERkXZkMhnaTGsDAAifEo6H8Q8ljkgTE4tqwN3dHdu2bYOZmRn++usvTJw4UeqQiIiIiEgHGo9sDPdgd+Q+zMXuCbulDkcDE4tqolGjRpg1axYA4IcffkBBQYHEERERERFRRZMZyNB2RlsAwNUdVyWN5UlMLKoJpVKJdevWAQBee+01GBpWjRulEBEREVHFunlQNSuURwsPiSPRxMSimli/fj2OHTsGc3Nz9ZULIiIiIqpelPlKRK2IAgA0erORtME8gYlFNbFkyRIAwKRJk+Di4iJxNHquoACIjlY9NzGRNhYiIiKix8SfjEdGQgZM7UwR0CNA6nA0MLGoJmJjYwEAnTp1kjiSauDnn4ELFwAbG6BHD6mjISIiIlJ7eEc1E5R9gD0M5VWr6zsTi2ogPz8f8fHxAAAPj6rV107vZGQAn3yiej5lCmBvL208RERERI/JTMoEAJg7mUscSVFMLKqBlJQUKJVKAICTk5PE0ei5P/4A7t4FfHyAsDCpoyEiIiLSkJWSBQAwtTeVOJKimFhUAzY2NurnDx48kC6Q6iAjQ/XYsCGgUEgaChEREdGTTKxV4z9z03MljqQoJhbVgEKhUF+piIuLkziaauLhQ+DRI6mjICIiItJQ2AUq9WYqcjOrVnJhJHUAVHZKocTy08sBAHJDucY6T09PJCUl4fjx43jhhRekCK96MPr3V2LvXsDSEqhdG2jcWLU0aqS6kmFe9fo0EhER0fPBwtUCABB/PB5zrOfAuYEzarxYQ73Y+dlBJpNJEptMCCEkeWeJpKenw9raGmlpabCyspI6nDLLysvCkD+GYOPFjQCArzp+hYkhE9Xr//e//+GDDz6Am5sbrly5AnOe/D6bpCTgnXeAQ4eAxMSi62UyIDDwv0SjcWPghRdUSQgRERGRjhXkFeDP0X8iZmcMHsY/LLLe1M4U7sHu6kTDvZk7TGwqZ/p8JhZ6IOFhAnqu64nj8cdhbGCMZT2W4Y2gNzS2ycnJQe3atREbG4tp06Zh+vTp0gRbXQgBJCQAJ0+qllOnVI//zr6lQSYD/Pz+Szbat1c9EhEREelQ+u103D5yW70knExAfnZ+ke0cajuoE43GoxrrLB69Sizatm2LiIgIjTK5XI6cnJwyH0MfE4tXf38Va8+vhaHMEHvf2Iu23m2L3e7XX3/FG2+8AXNzc6SlpcHQsGrNbVwt3L37X5JRmHAUN67l6lXA17fy4yMiIqLnVkFuARLPJiIuMg7n15zH7SO3i2wzTUzT2fvr3RgLhUKBY8eOabyu7l6q+RLWnl+LAlGAryK/QmPXxrBUFO16U5h01a9fn0mFrri4AC+/rFoKJSUBp08Dc+cC+/er7n3h7CxdjERERPRcyUnPwZ3jd3D7yG3cOaJ6zLqXVWQ7CxcLncahd4mFTCZDgwYNpA6jUg17YRjM5eYY8scQbL+yHa1WtMK2QdvgYf3fzfDOnDmD5ctVA7u/+uorqUJ9Pjk5AS++CJw7p3o9YwbHXBAREZHOPLj+ALHhsepEIulCEvBEHyRDuSFcG7tqDOy28tBtbx29Syyys7NhaGgIAwMDuLu7Y+3atQgJCSlx+/T0dKSnp6tfP3xYdJCLPuhftz+8rL3Qc11PnEk8g4G/D8Q/w/9Rr//kk08ghMCAAQPQvHlzCSN9Tv3wA5CcDNSqBYwaJXU0REREVE3di76HJfWWQJmv1Ci38bHRSCKcg5xhpKjcU329Sizat2+PRo0aoXnz5oiOjsacOXPQqlUr3Lp1C25ubsXu06NHjyLjMvRVcI1gbBm4BS/+9CIuJV9Sl9+4cQM7duwAAHz22WdShfd8q1lT9Xj3LnDvHuDqKm08REREVC2dX3ceynwlbHxsUG9gPdXMT8HusHDWbTenstCrxGLq1Kkar1977TV4e3vj448/xsqVK4vdZ+vWrUWuWNSpU0eXYeqUtYl1kbLly5dDCIH27dvDz89PgqgIffqoukMdOQJMmQIsWyZ1RERERFQNXd50GQDQZlobNBzSUNpgnqDXd9728vKCmZkZoqOjS9zGysoKNWrUUC/u7u6VGGHl+OWXXwAAQ4YMkTiS55hMBnz9ter58uXAmTPSxkNERETVTvqddCSeVd1ny7+bv8TRFKXXicXdu3fx6NGjErtBPS88PFSDuNeuXQs9mj24+gkJAQYMUN0DY+JE1SMRERFRBVFYKmBsbgwAuPX3LYmjKUqvEosmTZpgwYIFOHToEJYuXYq6desCAGbNmiVxZNL64YcfoFAosGvXLixjFxxpzZkDKBSqaWf//FPqaIiIiKgaUVgp8OL4FwEA+z/dD2WB8il7VC69ukGep6cn7ty5A6VSqZ4VauXKlWjfvn2Zj6GPN8h73Nborei5ridsTWxxf9J9dflXX32F999/HxYWFkhOToaJSeXcup2K8dFHwJdfAvXrA2fPSh0NERERVSPZqdlYUHMBsh9kw97fHh4tPeDZwhMezT1gH2APmUwmWWx6lVhUBH1OLH458wtGbhuJ3IJc9K3dFxv7b1Svi4+PV48fSUlJgZ2dnVRh0s2bgLc3YGQE5OVJHQ0RERFVM1Ero7D1za0QBZqn8ab2pvBo7gGPFh7waO4BtyZuMDY1rrS49GpWqOeVUigxNXwqPv/7cwBA39p98UvvXzS22bt3LwCgcePGTCqkZlx5v8BERET0/Gk4tCH8u/vjduRt3PrnFuL+iUP88Xg8SnmEK9uu4Mq2KwAAA2MDuDV2Q43mNVRXNVp46HRaWiYWemD6genqpGJyy8mY1X4WDGSq4TFKpRK7du3CvHnzAACdO3eWLE56glIJJCWp7sxNREREVIHM7M3g381fPTtUQW4BEk4nIO6fOMQdjkPcP3HIuJuB20du4/aR2zjy9REAwDQxTWcxMbHQAztjdgIAvmj/BSa3mgwAyMrKwq+//or58+erp9s1NzfH4MGDJYuT/uXoCHh4AHFxQPPmwK5dgK+v1FERERFRNWYoN0SN4BqoEVwDIRNDIIRA6o1UxP0Th1v/3MLNiJu4d+meTmPQq1mhnncNnBsgISEBn376KTw9PfH2228jOjoaVlZWmDhxIs6fP4/atWtLHSYZGwN79qjGWVy7pkoujh+XOioiIiJ6jshkMtj62KLBaw3QbUk3vLLhFZ2/J69Y6JHt+7aj97u9kffvgGBvb2+8++67GD58uN4NRK/2AgKAyEjg5ZeB06eBtm2BefOAt94CDA2ljo6IiIiquUcPHuHe5Xu4d+ke7l2+h4STCTp/TyYWeqCeUz2ciD+BZYnLkO+XD7MbZvjll1/Qq1cvGPIktepycQEiIoBXXgF27wbCwoAffwQWLgRatpQ6OiIiItJzQimQFpemTh4eTyQykzIrPR4mFnpgQecFSMxIVI216A9k7c2CQqFgUqEPLC1VN8pbuhT45BMgKgpo1Qp47TVg7lzA1VXqCImIiKiKy8/OR8qVlP+Sh8IEIvoe8h/ll7ifVQ0rOAQ6wKG2g+ox0EGncfI+FnoiX5mPibsnYuGxhQAAu4t2iP8lHgqFQuLIqMySk1XJxbJlgBCAhQWwbh3QtavUkREREVEVFL01Gn+99xfuX7sPlHDGbmBsAHs/+yIJhH2APRSWlXueyMRCz8zaPwtT/p4CZALv5r6Lb775RuqQqLyOHwfGjAGOHVNdsbh6FTA3lzoqIiIiqmL+DPsTJ747AQCQW8rhVNdJM4Go7QBbH1sYGFWN+ZiYWOiZy/cuo/bi2sAjAF8CW7duRffu3aUOi8orJweoXRuIjQWmTQOmT5c6IiIiIqpislKysKLVCty7dA/2AfYYfmg4zBzMpA6rRFUjvaFyk5vIARkwbNgwJCcnSx0OlZdCoRpjAager16VNh4iIiKqUpT5SmQlZ6HxW40BACnRKVjTdQ2UBUqJIysZB2/rGUczR8gN5cgtyIXx68ZIWZeC06dPo2PHjlKHRuXVt69qdqhDh1SPf/4JNGkidVRERERUiQpyC5ByNQXJF5ORfDEZ9y7eUz1G34MyTzOJuHPsDjKTMmHpailRtKVjYqFn7M3s8UuvXzDkjyHIqZkDDANc/F2kDouehUwGbNigea+LDRuALl2kjoyIiIgqWH6Oaman5AvJ6iQi+WIy7l+9D2V+8VchjM2N4VjbEY51HOFY1xHebb2rbFIBcIyF1OE8s0+//xSfX/scsABcLVxxcNhB+Nr5Sh0WPYuHD4F+/YC//lLdPO+XX4BXX5U6KiIiItLSvcv3sP/T/Ug6l4T7MfchlMWfdsst5ark4d8EovC5tYc1ZAaySo762fGKhZ66c/QO8Afg8K4DEjIS8OPJH/HlS19KHRY9C0tLYNs24M03gV9/BUaNAtq3V91gj4iIiPTWX+/9has7/htHqbBWqGZ2quMAp7pO6gTC0t0SMpn+JBAlYWKhp86fPw+kAq2dWmNT8iZk52dLHRJpQy4HVq4ErlwBjh4FpkxR3aWbiIiI9NL9mPu4uvMqIAMGbBoA92B3WLhYVIsEoiRMLPSQUqnExYsXAQCODo4AJ4WqHgwMgK+/Blq0AH76SXXFwtsbcHf/b7G1VY3NICIioirt+JLjgAB8u/gisFeg1OFUCiYWeigtLQ1ZWVkAADPzqjuXMT2D5s2BV15RDeKeNavoehMTwM1NM9ko7jXvyE5ERCSpmwduAgCc6jtJHEnlYWKhh2xtbeHr64uYmBjcunVL6nCooi1ZAjRoANy8Cdy589+SkgJkZwPXr6uW0tjbayYbxSUgDg68+kFERKQjzcY1w5ahW3Bk/hHUf7U+XIKq/9hJJhZ6qnPnzli0aBGuX78OWEgdDVUoe3vg00+LlmdnAwkJmslGfLzm6zt3VHf1TklRLWfPlvw+cjng6qqZbLz8MvDSS7r7bERERM+JoDeCcHnzZURvicYfb/yBYX8Pg8Kqevco4HSzemrnzp14+eWXYdzJGHkheRjXbBwWdFkgdVgkNSGABw+KJhtPJiBJSSUfY+5c4P33eTWDiIhIS5lJmfiu3nfISs6ChasFOn3dCXUH1K22A7iZWOipgoICdO7cGXuVe4HWwIh6I7Cs7zKpwyJ9kZsL3L2rmWwcOwasWaNaP3YsMH++6r4aRERE9MxuH7mNzW9sxv2r9wEAPu190GVRFzjWdpQ4sorHxEKPpaamwneUL1LqpsAp1gmx38XCzIyDuUkLX38NvPee6nm/fqr7apiYSBsTERGRnsvPycfheYfx9+d/Iz87HwbGBgh5LwTtZ7WHgaGB1OFVmOrzSZ5DNjY2eH3Q6wCAJJskzPxupsQRkd6bOBFYu1Y1/mLjRqBjR1XXKiIiInomOQ//z959h0dV53scf09L74V0kpCE3kuQXlS6DVARRLFiAXVXd217XVfd1bXsuoJt7aggYEORJlZ66CVAQgohCem9tzn3j9kMhCSQhCRnZvJ9Pc955sw5M+d8516XzGd+rYrTv56mprwG9+7uABhrjOx4aQfx6+NVrq59SYuFlUstSmXQfwZRoBSgr9Gz5a4tTAqfpHZZwtr9+itcfz0UFUHfvrBxI3TvrnZVQgghhMUrzyvnzPYzpPyewpnfz5BxIAPF2PDrtkuAC5FTI5ny2hQcvRxVqrT9SbCwAbuO7GL0G6MhBPRaPe/Oepc7h9ypdlnC2h09CtOnm8ZfBAaawsXAgWpXJYQQQlgURVGIXx9P4uZEUn5PIfto4wlSPMI9CB0fSuiEUELHh+LZw9MmB3BLsLABiqLQo1cPTg88DQNAq9GS8kgKwW7BapcmrF1qqilcxMaCmxt88w1Mnqx2VUIIIYTFSP4lmRWTVzQ45tPbxxwiuo/rjnuIu0rVdS4ZY2EDNBoNM66eAV+BU50TRsVIZmmm2mUJWxASAtu3w/jxUFwM06bBF1+oXZUQQghhMbr162Zen2LA/AE8lvUYD554kFnvzGLA/AFdJlSABAubMX36dACqKqpUrkTYHA8P2LwZbrwRamrgllvg1VdNa2YIIYQQXZxzN2cmvWAa33pqw6lG4ym6EgkWNsLFxQVCoM6+DgCdRtYfEO3IwcHUUvHII6bnf/qTaYXuEydULUsIIYSwBCMeGEHA0AAqCytZ3ms5G5ZuIPvYRRajtVEyxsIGrF+/ntl/nU3NjBrQw4iAEey6exc6rYQL0QH+8x94/HGoqgK9Hh5+GJ55xjQGQwghhOiiso9ls3r2avNCeAAhY0IYft9w+s7ti95Br2J1nUOChZV79913uW/1fTDB9HxWxCy+uOkLnO2c1S1M2LakJNOaF+vWmZ77+8Mrr8CCBWCDs1wIIYQQLaEYFZJ+SmL/u/s5+e1JlDrT12xHL0cG3zGY4fcPxyvCS+UqO44ECyu2Y8cOxt43Fuaani8ZvoT/zPgPWo30cBOdZONGU4vFqVOm52PGwPLlMHiwqmUJIYQQaqkuqyYnNoeEzQns+OcOaspqzOfsXO14OOlhnHycVKyw49h+m4wN27VrF+SDxqhB0SrsSNtBRkkGQW5Bapcmuorp003Tz77+Ojz/POzYAcOGwX33mZ572e6vMkIIIbo2Y52R/IR8so9mk3Uky/R4NIuCpAJo4md7jVZD0IggDM6Gzi+2k0iLhRV7+OGHeeONN1jwxAK2eG0hpzyHQNdAvr/le4YGDFW7PNHVpKWZBnXXT0fr7Q0vvgh33gk6Ge8jhBDCelXkV3B2/9kGISLneA61lbVNvt7Zzxm/AX50G9ANv4GmR9++vhgcbTdUgLRYWLW0tDQAPEs92fPnPcxaNYvjOceZ/MlkTj9yGg8HD3ULFF1LcDCsWgWLF8PSpXDsGNx7L3z5pWlzdVW7QiGEEKLV8hPzeXfIu1SXVDc6Z3Ay0K1/N7oNMG31YcLZt2uOdZVgYcVGjBjB119/zfLly3F0dGT7s9uJXB5JfkU+CfkJDA8crnaJoiuaOBEOHoS33oKnnoItW2DCBNiwwTTIWwghhLAiBkcDDh4OjYJF1Mwoxj4xlpDRIWi0MnEJSFcotcu5LIqi8OKLL/L0008DcMstt7BtxDbSitPYe89eCRZCffv2wcyZkJ0NYWGwaRP06qV2VUIIIUSr1FXXkbQ1idg1sZz89iRVRecWJHYLcaPvjX3pd1M/gqKD0HTh2RElWNiAFStWcNddd1FbW4vT006UG8olWAjLkZgI06ZBQgL4+ZmmqnWyzdkwhBBC2L7aqloStyQSuzqWuHVxVJeea8mInBbJvO/moTN0zbGFMi+pDbjttttYv349AOXl5SpXI8QFNJpza1soimkTQgghrJTeXk+va3ox+7PZPJb9GDd/czP95/VH76AnYVMCWx7donaJqpFgYSOmTp3K5MmT1S5DiIb274dRo0zrXISGwq+/gnPXHNAmhBDC9hgcDfS+vjdzVs1h7mrTwmIxy2I49MkhdQtTiQQLG3LHHXeY97tYDzdhKQoLTeMonn0Wpk6FsWNN4ysGD4Zdu6BPH5ULFEIIITpGr2t7MXDhQADWL15PcVqxyhV1PpkVykYUFxezbNkyGGN6LsFCdDijEU6cMAWG+u3EicavmzIF1q4FGxnTJIQQQpxPURQSNiaw/aXtnNl2BgCNRkNNec0l3ml7JFjYgPLycmbNmkVMTAzacVqMGNFqpTFKtLPCQtiz51yI2LMHiooavy4yEq64wtQFatQoU2tFF54hQwghhG0y1hqJXRvLjpd2kHUkCwCtQcug2wcx5k9j8O7prXKFnU+ChQ247bbb2LZtG079ndB4aiirLVO7JGErKith5Up48004cKDxeScniI4+FyKuuAJ8fTu/TiGEEKITleeV8/H4j8k5nmM+pnfUM/LhkQQOC6QspwytXouLvwsGJ9tebft8EiysXHFVMb9X/g5LoNynHGrBXmdPoGug2qUJa5aTA2+/bQoU2dnnjkdEnAsRo0bBgAGgl39GhBBCdC0V+RXkncprcKy2opYdL+1o9Fp7N3tcAlxwDXDFJcDl3L6/S4PjDh4OVr8GhqxjYaVO5p7kzZg3+fjwx5RWl5oOVsGciDn84/p/0NO7p7oFCut0/Dj8+9/w6adQ9b/Ff0JC4KGHYOFC0zoUQgghhKAotYic2BxKMkoozSg1P5ZmlFKaaXpeW1Hb4uvp7HXnwscFoeP8487dnNHqLLPLu/zUaGWyy7K5c92d/HDqB/OxPj59cI9zZ/e7u9nrv5dtbKPbnG54eHioV6iwHiUl8O23sGIFbN167viIEfDHP8KcOWDoOs24QgghREu4h7jjHuLe7HlFUagqrmoQOkoySijNLDUHkPrjlYWV1FXVUXi6kMLThRe9r0arwbmbM27BbgQMDyBkdAjdx3THI9xD9RYPabGwIrHZscxaNYvThafRarRc2+taloxYwuTwyRQXFzNo0CBSUlIAsLOzY+bMmSxYsICZM2fi4OCgcvXCotTUwObN8PnnsG4dVFSYjms0cMMNpkAxerQMuhZCCCE6QU1FDWVZZY1aP0oySijLPHe8LLsMxdj0V3cXfxdCRocQMsa0BQwJQGfXuSuAS7CwEpsTNnPTlzdRXFVMhGcE393yHX19+zZ4TXZ2Nh988AGff/45sbGx5uPu7u7MmTOHBQsWMGHCBHS6rrnMfJenKLBzpylMrFkDeef1DY2KggULTN2devRQr0YhhBBCNMtYa6Qsp4zSjFIKkgpI3ZVK6o5UMg5kYKwxNnit3kFP4IhAQsaYWjSCRwXj5O3UofVJsLACa2PXcstXt1Cn1DE+dDxf3/Q13k7NT2GmKApHjhzh888/Z9WqVaSlpZnPBQYG8tprrzFv3rzOKF1YivXrYelSOH363DE/P5g3zxQohg+X1gkhhBDCStVU1HB231lSd6SSutO0VeRVNHqdT28fHjzxYIfVIcHCCoz+YDS70nZxS/9b+Pj6j7HT2bX4vUajkW3btvH555+zcuVKysrKmDBhAr/++mvHFSwsz003mRapA5g4EZ58EiZPlhmdhBBCCBukKAp5cXmk7kzlzI4zpO5IJS/O1FPhr8pfO+y+8q3CCtQYTSs3LhiwoFWhAkCr1TJhwgR69erFypUrAbj33nvbvUZh4V55BY4cgbg4OHQIHB0lVAghhBA2SqPR4NPbB5/ePgy5cwgAabvT+GDUBx16X8ucq0q0q8zMTB544AHKysoYOXIkt9xyi9olic4WGgo7dpjWnigshKuvhi+/VLsqIYQQQnQSO9fW/TjdFhIsbJSiKOzatYsFCxbQvXt3vvnmGwD+9a9/qT4VmVCJtzf89BNcd51pjYqbboI33lC7KiGEEELYCAkWNqaiooKPPvqI4cOHM3r0aFauXElNTQ2jRo3i+++/Z/To0WqXKNTk6AhffQX332+aJerhh+HPfwaj8dLvFUIIIYS4COlkbSOOHj3Kp59+ygcffEB+fj4A9vb2zJ8/nwcffJBhw4apXKGwGDodvPkmBAfD00+bxl+kpsI774B78wv9CCGEEEJcjAQLK3bmzBlWrVrF559/ztGjR83HQ0NDeeCBB7jrrrvw9m5+WlrRhWk08NRTEBQEd98NX3wBP/8ML79sWstCK42ZQgghhGgdCRZWJj8/n7Vr1/L555+zbds283E7OztmzJjBokWLmDVrliyCJ1rm9tshLAzuvRfi42HRInj3XVi+HIYOVbs6IYQQQlgRCRZW5Pnnn+fA6gPU1Jimn9VoNEyYMIEFCxYwZ84cPD09Va5QWKUJE+DoUXj9dXjuOdi1y7Rg3uLF8MILpkHfQgghhLBKiqJQllVGekx6h99LgoUVcDY4A7BHtwd0EBUWxT333MO8efMICQlRuTphE+zsTIO4FywwPa5caRpzsWYN/P3vcM89prEZQgghhLBYVcVVZB/LJvtYNllHs8g5lkPW0awmV+HuCLLythX4JfkXZnw2g0pjJeSC23o3PnjlA+bOnat2acJW/f47LFliaskAGDLE1D1KZhUTQgghVFdbVUvuyVxTiDiabX4sOlPU5Os1Wg1eUV5069+Nm768qcPqkmBhJY5kHWHaimlklGdAObAKbp98O3fffTeDBg3C1dVV7RKFramthbffhv/7Pyj63z9Ut90G//kPeHioWpoQQgjRlRSlFnH4k8PmEJEbl4tS1/RXeNcgV/wG+OHb3xe/AX50698Nnz4+GBwNHV6nBAsrklGSwbWrrmVfxj7IAd40HddoNERFRTFkyBCGDh3KkCFDGDJkCD4+PqrWK2xEdjZMnAgnTpieP/kk/OMfqpYkhBBCdCUfT/iYlN9TGhyzc7XDf7A/3fp3o9uAbqbH/t1w9HRUqUoJFmqX02rHc47T761+uOpdmRQzif3795Oe3vRgnJCQkAZhY+jQoQQFBcnK26LlDh82dYnavt30PCICvvkGBgxQty4hhBCiC4n7Po6tf95K7slc8zGdvY7IaZH0u6kfPa/pib2rvYoVmkiwsDInc0/S580+eDp4kv+4aSG87OxsDh48yMGDBzlw4AAHDx4kISGhyff7+PiYQ8aYMWOYNWuWBA3RWHGxafG8t94yrcrt5GR6/sc/goOD2tUJIYQQXY6iKOTE5hC7JpbYNbHkxeWZz+nsdURNj2LAggH0mdNHte92EiysTFPBoilFRUUcPny4Qdg4fvw4dXV1DV63du1aGQQuGluyxLQ6N8BNN8Grr4LMQCaEEEJYBEVRyD6WTeyaWI6vOU5e/LmQcfM3N9P7+t6q1CXTzdood3d3xo8fzxVXXMGWLVuorq4mISGBiopz042NGjWKK664QsUqhcXK/V9T6zPPwN/+pm4tQgghhGhAo9HgN8APvwF+DLx1IB+N+4jynHJcg1wJig5SrS4JFjZq3759fPDBB6xdu5a8vHMpNioqigULFjB//nyioqJUrFBYtPqGTJkAQAghhLBYZ/ef5fNpn1OeW453T29u3XIrroHqzRQqwcIGxcfHc8UVVzTo9uTt7c0LL7zAXXfdhcHQ8dONCStTWGhau+KXX0zbkSOm41qtqmUJIYQQommKovDlzV9SnluOg6cDi35bhIu/i6o1ybcGG9StWzcmT56M7ryVkvPy8rj//vtxd3dn4sSJPP300/zwww/k5zc/TkPYsJIS2LAB/vQnGD4cvL3huuvg9ddNM0EpCgweDNOnq12pEEIIIZqg0WjocVUPACoLKll9w+oGYy1UqUkGb1uXlg7eBigrK2Pv3r3s2LGDnTt3smvXLgoKChq9rnfv3owePZoxY8YwZswYevbsKTNF2QpFgbIy05iJ+PhzLRL79sEFA/np2RMmTTJtEyeCn58qJQshhBCiZRRF4fCKw2x6aBNVxVXoHfVc9c+riH4wGo2287/LSbCwMq0JFhcyGo3ExcWZg8bOnTuJi4tr9LrQ0FBmzJjB9OnTmTx5Ms7Ozu1Vvrhc5eWmkHD+lpd38WNVVU1fKzzcFCImTzYFiSD1BnsJIYQQou2KzhSx7s51JP+UDMCEv05g4rMTO70OCRZW5nKCRVNyc3PZvXu3OWzs3r2b6upq83k7OzvGjx9vDhq9evWS1oz2UlHRdCi4WFg4b1avVrG3NwWHsWPPtUqEhrbv5xFCCCGEahSjws7XdrL1z1vRO+hZEr8E9xD3Tq1BgoWVae9gcaGysjJ++eUXNm7cyIYNGzh9+nSD82FhYUyfPp0ZM2YwceJEXFzUHSRkMWpqIDu7+VDQ1PHy8rbdy87ONFuTj49pbET9/vnbhcednEACoRBCCGHTFEXhk4mfkPJ7CoNuG8T1n1zfqfeXYGFlOjpYnE9RFOLi4ti4cSMbN27kt99+a9CaodFoiIqKYvDgweZt0KBBBAQE2E6rRlUVZGZCRoZpO3u26f2cnHNTtLaGwdB0OLjYMRcXCQlCCCGEaFL63nTej34fNLD44GL8B/l32r0lWFiZzgwWFyorK+Pnn382B40LWzPq+fr6NggagwcPplevXuj1FjS7cUVF0wHhwud5rZhdQadrfUuCq6uEBCGEEEK0qy9v/pLYNbH0u6kfc1fP7bT7SrCwMmoGiwtlZWVx+PBhDh06ZN7i4uIwGo2NXmtvb8+AAQPMQWPw4MEMHDiw/f9/UFZ2LhhcLDAUFrb8mnZ2EBBwbgsMbHrfx0fWfRBCCCGE6rKOZvHOwHdAAw+eeBCfXp2z4K0ECytjScGiKeXl5cTGxjYIG4cPH6asrKzRazUaDZMmTeL2229n9uzZlz9e4/XX4Y9/bHmXJAeH5kPC+c+9vKRVQQghhBBW5YvrviDuuziG3D2Ea9+7tlPuaUF9U4QtcHJyYsSIEYwYMcJ8zGg0kpSU1CBsHDp0iPT0dH7++Wd+/vlnHnjgAebMmcPtt9/OxIkT0bbll//KypaHCmdn6NsXQkIgOLjxY2CgafyDEEIIIYQVGn7/cOK+i+PM72c67Z7SYmFlLL3FojWSk5P57LPP+OSTT0hMTDQfDwkJYeHChdx222306tWrdRfNzobUVNOWltb4MS3NNIPTpWg04O/ffPAICTG1Zkj4EEIIIYQFKk4v5t/B/0aj0/B0+dPo7HQdfk8JFlamPlg4GZxI/2M6Hg4eapd02RRFYefOnaxYsYLVq1dTVFRkPnfjjTfy6aefYm9v3z43MxpNMzg1FTpaGz60WpgxA777TrpKCSGEEMKi1FbV8pLbS9RV1/FA7AP49vXt8HvKSFMrE+IWgrejN+U15Yz6YBSJ+YmXfpOF02g0jBkzhnfffZeMjAxWr17NzJkz0el0rF27lvnz51NbW9s+N9NqTWMmfH3Bz8/U6hAUZGqF6N4dwsJMW0uCgtEIJ06YHoUQQgghLERVSRUrZ66krroOnZ0Og3Pn9LCQFgsrdCjzENesuoa04jS8Hb35dt63jO0+Vu2y2t2PP/7IrFmzqK6uZsGCBXzyySfodBdpxlMUKCiA9PRz29mzjZ9nZ7d8LIaHhyl4BAWZxl3U79c/79vXtPicEEIIIYQFKMsu4/MZn5OxPwODs4F5386jx1U9OuXeEiysVEZJBtd+cS37zu7DTmfHQ9EP8cCIBwj3DFe7tPajKGxcs4aHFyzAs66OB+bO5faZM01dmeq33Nxz+2fPmgZwt4TBcC4oXBgY6o8FBpoGeQshhBBCWLiqkiqOfn6UHS/voDC5ECcfJxZsXEDg8MBOq0GChRUrrynntm9u46sTXwGgQcOsnrNYGr2UK3tciVZjYT3d6uogP7/pUHD+/vnPz1vpu8W8vRuHhAufy5oTQgghhLABmYcy2ffOPo5+fpTqUtP3JvdQdxZuWYh3T+9OrUWChZVTFIUfTv3AsphlbEncYj7ey7sXD454kNsH346bfQd9zsrKlgeEnBxTqGjDf25lQL5Wi2/fvjgEB5tCga/vua3+ef26Ew4O7f9ZhRBCCCEsRE1FDbFrYtn39j7S96Sbj3v38mb4fcMZfMdgHNw7//uQBAsbEpcbx1t73+KjQx9RUl0CgIudC4+PeZynxj3VthYMRYHERNi1y7QdPAhZWaagUFratkI9PRuHggv3fX15e+1aHv3nP6k1GNi6dSvjx49v2/2EEEIIIWyAoijseWMPv/3tNyoLTN2/tQYtfWb3Yfh9wwmdEIpGxZkqJVjYoJKqEj498inLY5ZzIvcEADf2vZFPrv8ER4Pjxd9cVgZ7954LErt3m0JEc/T6JkNBs4HBy6vFaz+MHTuWHTt28OKLL/LEE0+09OMLIYQQQtgcY62RjQ9tZN/b+wBTd6dhi4cx5M4huPi5qFydiay8bYNc7V15YMQD3D/8fj469BH3rb+PtcfXklKUwrp56/B38Te9UFEgKelciNi1C44cMY2FOJ+dHQwdCqNGwciRpmlZ6wODu3uHreEQHh7Ojh07qLuwHiGEEEKILqS6rJqv5n1F/Pp40MCUV6cw8uGRaHWWNV5UgoUN02g03DnkTiK9Irlh9Q3EpMcQ/V40u2dvJPAvL8HmzU23RgQFmUJE/TZ0KLTXAnUXoSgKWVlZxMfHEx8fT0pKCgCxsbEdfm8hhBBCCEujGBUSf0zk56d+JuNABnoHPbM/n02f2X3ULq1JEiy6gPGh49lz9x6mfDqF5MJksh++m8B1u00nz2+NqN+Cgzu0nsLCQk6dOmUOEPHx8ebnJSUljV5fVVXVofUIIYQQQliSioIKDn18iH1v7SM/IR8AJx8n5n03j5BRISpX1zwJFl1EpFckk8Mns3vTBwz8fo/p4OrVcN11HdIaUVFRQWJiYoPwUL/lXGTMhlarJSwsjJ49e5q3efPmtXt9QgghhBCWJuNgBnvf2svRz49SW1ELgL2bPYMWDWL0o6Nx7+6ucoUXJ8Gii3l1C2iNCsyZAzfd1G7XzcrKYtmyZezZs4f4+HhSU1O52LwAAQEBDcJD/RYeHo59J3S7EkIIIYSwFKm7UvnxsR9J3ZlqPuY30I8RD45gwPwB2LnYqVhdy0mw6EIcyquZlvi/Jy+91C7XzMnJ4ZVXXmH58uVUVFQ0OOfh4dFkeIiMjMTV1bVd7i+EEEIIYc1OfH2Cr+Z/RV1VHVqDlr5z+jLiwRGEjAlRderYtpBg0YXoao3nnvTocVnXysvL47XXXuONN96grKwMgOjoaO6991769OlDz5498fb2trr/QQghhBBCdJa9b+1lw5INoECv63ox651ZuPhbxtSxbSHBQrTaP//5T/7+97+bB1qHhYXxxhtvMGvWLAkSQgghhBAt8OvffuW3Z38DYNjiYcx4c4bFTR/bWhIsuhA3+3MLAh7JOsLAgMGtvkZaWlqjxepOnz7N7NmzCQ4OJiwsjNDQUEJDQxvsh4SEYGdnHf0DhRBCCCE6Um1lLb/9zRQqtHot7t3dqS6pxsHDQeXKLo+svN2FpCcfIajHIADcXnBm1Y2rmdlzZquuoSgKH3/8Mb///jspKSmkpKRw5swZamtrL/o+jUZDYGBgo8Bx/r6j4yVWBRdCCCGEsBGHPj7EL8/8QnFqMQB2LnYMWzyMK/5wBW5B1vkdVYJFV5KXZ1otG9A+AxqdlmXTl/HAiAcu67J1dXVkZGSQkpLC6dOnzYHj/P3KyspLXqdbt24NAsd1113H2LFjL6s2IYQQQghLVVdTx7EvjrHz5Z1kH8sGQGvQMnDBQK7651U4d3NWucLWkWDRlZwXLAYu78fR3FjsdfYUPlGIg77jmt4URSE7O7vJwFG/39TCeB4eHhQUFHRYXUIIIYQQlkBRFBI2JrDjnztI+T0FgPArw1n440KrGr8qYyy6qGPZsaCFh0c+3KGhAkzdoPz8/PDz8yM6OrrR+VOnTvHss8+ycuVK8zE/Pz9efPHFDq1LCCGEEMISaDQaomZEETUjipRtKXx61ack/5RM4pZEIqdGql1ei1n30HPRKoUVheZ9nUbLmzPe5J9X/1O1eg4ePMh1111Hr169zKFi0KBBfPzxx6SkpHDHHXeoVpsQQgghhBpCx4UyYskIALb+eSvGOuMl3mE5JFh0IWs3/wuAOg18cN0Hlz224nJdd911fPfddyiKgp+fH1u2bOHgwYPcfvvtsvq2EEIIIbqsMX8aA0DWkSzSdqWpXE3LSbDoQuatPALAul7wf7/9laNZR1Wt57HHHsPFxbQITFZWFkuWLOHjjz+mpqZG1bqEEEIIIdR0eMVhAJy7OdNtQDeVq2k5CRZdxebNuP68HcVg4J0bwzhTdIbRH45mw6kNqpX00EMPkZKSwrPPPounpyfx8fHceeedREZG8uabb2I0Wk/TnxBCCCFEeyhKLeK350xrXFz9ytU4uFvP2hYSLLqKV18FQPPgg3zx5H4mhU2itLqUeV/Oo7S6VLWyHBwcmDx5MkuWLDF3fzpz5gxLlizhk08+Ua0uIYQQQojOZKw1svftvbw3/D1qymoIGRPCwIUD1S6rVWRWqK4iL8/0OG0aXo5ebLp1E87/cKakuoSCigJc7Fw6vARFUUhOTmbXrl3s3r2bXbt2cfjw4UaL6xkMBqKjoxkzZkyH1ySEEEIIoSZFUYj7Lo6tj28lL870fc0ryotrP7jWqqaaBQkWXZadzg6tpuMbrGJiYvj555/NYSI7O7vRawIDAxk1ahRXXHEFo0aNYujQobIKtxBCCCFs3tl9Z9ny6Bbz2hVOPk5MeHYCw+4dhs6gU7m61pNg0VXUJ94334S+fSEkBA2mY3esu4OHRz7MjKgZ6LTt9x/xli1bmDp1aqPjTk5OzJ8/n6uuuopRo0YREhJidYlcCCGEEOJyZB3N4sMxH1JXXYfeQc8Vf7iCMY+PsaoxFReSlbe7itdfh0cfBaMRnJzg6af5Y/90Xj/4Ngqm/wTCPMJ4YPgD3DX0LrwcvS77lgkJCcydO5fDhw83OqfVaunXrx8jRowgOjqaESNGMGDAAAwGw2XfVwghhBDC0q2Zu4YTX50gdEIoN3x6A+4h7mqXdNkkWHQlhw/DkiWwfbvpeWQkmS88wb+84nj/wPsUVBYA4KB3YMGABSyNXsog/0GXfduCggL2799PTEwMe/fuZe/evaSnpzd6nYODA4MHDzaHjbFjxxIWFnbZ9xdCCCGEsCRZR7J4Z9A7oIH7j95Pt37WM6XsxUiw6GoUBVauhMceg8xM07GvvqL8mmmsOrqKZTHLOJxlamHQoOG3Rb8xLnRcu5dx9uxZ9u7d2yBsFBYWNniNTqfjs88+Y968ee1+fyGEEEIItXx/7/cceO8A/W7qx9zVc9Uup93IdLNdjUYDCxZAXByMH286Fh+Pk8GJu4bexcHFB9l2xzaivKJQUIjPi++QMgIDA7nuuuv4+9//zpYtW8jPzyc+Pp7PP/+cRx55hKFDh1JXV8c999xDQkJCh9QghBBCCKGK/w0tdfZzVreOdibBoqtyc4PIyEaHNRoNY7uPpbdP704tR6PREBUVxfz58/n3v/9NTEwMEyZMoLS0lJtvvpmqqqpOrUcIIYQQoqP0nNkTgPj18dhS5yEJFsIi6XQ6VqxYgZ2dHQcOHGDZsmVqlySEEEII0S7CrwxHa9BSmFxIfkK+2uW0GwkWwiLl5ORw9913U11dDYCX1+XPUiWEEEIIYQnSdqdhrDGiNWixd7NXu5x2I+tYCIuze/dubrzxRtLS0nBycuK9995j/vz5apclhBBCCHHZFKPC1se3AjD8/uG4+LmoXFH7kRYLYVH279/P+PHjSUtLA2DChAmUl5ezY8cO8vNtp6lQCCGEEF3TqY2nyNifAUDk1MbjXa2ZtFgIi1JdXY1Wey7vbty4kY0bN5qf+/n50adPH/PWt29f+vTpQ0BAgKzeLYQQQgiLZ3A0mGaFUmDlzJWEXxnO2CfGEn5luNV/l5FgISzKqFGjyMjI4NixY5w4ccK8HT9+nNTUVLKyssjKyuLXX39t8D43N7cGQaN+CwsLQ6fTqfNhhBBCCCEuED45nPuP3s/Ol3dydOVRkn9KJvmnZAKGBjDmiTH0md0Hrc46OxVJsOjKjMamDytG8iryOrmYczw9PRk3bhzjxjVcmK+0tJSTJ09y/PjxBqEjISGB4uJi9uzZw549exq8x97eHi8vL5ycnHBycsLR0fGS+609Z2dnZ/W/MAghhBCi83Tr143rP7meSc9PYte/dnHgvQNkHMjgy5u+xC3YjbBJYXQf153QcaF49/K2mu8ZsvJ2V1VQAFFRkJdnWon7llsAKKsu49ZvbuXbk98CEHN3DCOCRqhY6KVVVVVx6tSpBq0bJ06cIC4urlPWv9Bqta0KJ20NMQ4ODg26iQkhhBDCNpTnlhOzPIaYZTFU5Fc0OOfk60T3sd3NQcN/sD9avWV+H5Bg0VU99hi89hr07QuHD4NeT3pxOtesuoaDmQex19nz0XUfccuAW9SutM3q6uo4c+YMRUVFlJeXU1FRQXl5+UX3W3Ourq6u0z+Tg4NDp4QYvV4aM4UQQojOVlNew5ntZ0jZlsKZbWdI35NObWVtg9fYudgRPCrYHDSCRgaZxm1YAAkWXVFKiqm1oqaG6vXr+L2PExtPbeTzo5+TVZaFr5Mv3877ltEho9Wu1KLV1NRcdjhpyevUWHVcr9c3CBze3t6EhIQ0uQUEBEgQEUIIITpAbVUtGfszzEEjdUcqlYWVDV6jNWgJmxjG5L9PJmhEkEqVmkiw6IIyP/gP/nc/QkKYG4PuqaW8ptx8rq9vX9bfsp5wz3AVKxTnq6uro7Ky8rLCSUtf15Z/DrRaLYGBgYSEhBAcHNxk+PDz85NuXEIIIcRlUowK2ceySdmWQur2VFK2pVCSXmI+P2DBAK78x5W4d3dXpT4JFl1AWXUZv57+lU0Jm9iUuIlhvyXwxVfwUzhcdTsEuAQwLXIaUyOmcm2va3E0OKpdslCBoihUVVU1Ch3l5eVkZ2eTmpraaEtPT6e2tvaS1zYYDAQFBTUZOurDiI+Pj9UMThNCCCEsgaIo5Cfks+2FbRxecRgAvYOeK/54BWOfGIu9a+eu6i3BwoZV1FRw13d38dWJr6iuqzYfX3gIVnwLp4dGULzhawZ0GyBf6ESb1NXVkZWVRVpaWpPBIzU1lYyMDIzNzEB2PgcHB4KDg7niiiv48MMPMRgso7+oEEIIYQ3O7jvLpkc2kbojFQDnbs7csv6WTu0eJR2jbVheRR6rY1djVM59qRuRBq9tMe0fNOTy8S//R6RnJJFe57bu7t3RaWXtB9G8uro6CgoKyM3NNW8FBQVUVlai0WhwcnLC29sbRVHQ6XScOXPmktesrKwkISGBhIQEXnzxRYKDgzvhkwghhBDWS1EUck/mkvRjEolbEsk8lGk+V5ZdRtKPSZ0aLKTFwsYdzjzMtjPbSMhPwOOnHfz5jf04VSvsC4CZCyDbpfF7DFoD4Z7hpqDhGUmUd5Q5dIS6h2LQyS/JtsRoNFJUVEReXl6DoHCxLT8/v83jMby8vPDx8Wl2GzRoEIMHD27/DyqEEELYgPLccpK2JpH4YyJJW5IoTitucN65mzMRUyKImBpB/3n9O3VqWgkWXcWXX8K8eVBXh3HqVM689yqnqjNIyE8wbQWmx8T8RKrqmp+FSKfREeYRRi+fXjw/6XmGBgztxA8hLldVVRVPPfUU+/btM4eEvLy8Nk+d6+npaQ4E3t7eFw0MPj4+eHh4yEroQgghRBuc2nCKX575hYwDGXDet3edvY7Q8aFETImgx9U98Bvgh0arThd3CRZdxahRsHs3LFgAH30EzfRfNypG0orTzgWOC7aK2nOLtjw66lFenfJqZ30C0Q4SExOJjIxs8pyrq+slg8H5m5eXl0wzK4QQQnSSlbNWcuqHUwB069+NiGkRREyJoPvY7hazjoV8K+gq6tdCWLiw2VABoNVo6e7ene7u3ZkcPrnBuQ8Pfsg939+DUTEyMmgkfx7z546sWHSAiIgIvvvuO9577z02bdpETU2N+VxUVBQ333wzN998M6GhoSpWKYQQQogLTXx2Iklbk6irqqP3Db2Z9NwktUtqRCaW7yous2Hqud+e467v7sKoGOnt05sPr/sQb0fvdipOdKaZM2eyYsUK9uzZw5IlS8wtdwcOHODxxx8nLCyMUaNGsW3bNpUrFUIIIQSYBmm7hbgx+I7BAPz+/O+cXHdS3aKaIF2huoJly+Dhh03hIiYGRoxo1dvrjHW4veTWYCE9ODfIO8orqsGsUjLIu+MpikJxcTEFBQVNbvn5+c2eKywsbNH0r1deeSVbt27thE8jhBBCCDD9fS/LLiMnNofs2GxyjueQE2vaKvIrGry2z+w+3PTVTSpV2jTpCmXLjEZ48kl4+WXT8/vvh+HDW30ZnVbH97d8z/r49eaxFokFiVTXVROfF098Xnzj9/xvkHd90Dg/fIR7hmOns7vcT2f1FEWhpKSkTeGgoKCgReHgYhwdHfH09Gy0eXl54eXlxfz589vpkwohhBDifK0JEGYa8IrwwrevL779fBly15DOLboFpMXCVikK3H47fPqp6fnf/24KGe20EF6dsY70kvQGA7tP5Z8yzyx1/iDvC9WP46ifzvbOIXcyIqh1rSiWSlEUMjIySEpKMm/Z2dnNhoO2zsZUz97eHi8vr2YDQlPH6zcHB4d2+tRCCCGEuJTck7ns/s/uVgcI336++Pb1xae3j8UM0m6OBAtbFRMDI0eCTgfvvw+LFnXarY2KkYMZB9mUsIlNiZvYlbqLOqX5L9ATQifw66JfO62+y1VZWUlycjJJSUkkJiaaA0RiYiLJyclUVDQfqppiZ2d3ySDQ3DlHR8cO+pRCCCGEaE9r5qzhxNcnGhxz9nMmeGSw1QWI5khXKFtV/L/FUvr27ZBQYVSMZJRkNNlikZCfQFlN2UXfH+IWYu4addeQu9q9vsuhKAo5OTkNAsP5j+np6Rd9v1arpXv37kRERBAeHk5AQMBFA4KjoyOadmpJEkIIIYRlmvjcRGqraknYlIBSZ/pdvyKvAjQQMCyAnrN6ore37q/m0mJhq7ZuhauvhgED4MiRy77crtRdfH3i6wYL6bWku1NTA7vDPcJxNFjGL+21tbV89dVX7N27t0F4KC0tvej7XF1diYiIoEePHvTo0cO8HxERQffu3TFcZEpfIYQQQnRdpVmlHPviGEdWHDEtdvc/Dh4O9Lu5HxOemYBroKuKFbadBAtb9dtvMHEi+PhASgo4OV3W5VxfdKW0uvGX7TCPMHr79CbSs2F4CPMIw15vf1n37EiKorBu3TqeeuopTpw40ei8RqMhODi4yeDQo0cPvL29pZVBCCGEEG1mrDNy6ONDbPnjFqqKq8zHBy4cyA0rblCxsraz7vYW0byRIyE01BQq/vUv+MtfLutyr099nWUxyzicdbjB8aLKItzs3RgWOIypEVMJcA24rPt0hm3btvH444+za9cuALy8vFiwYAE9e/Y0B4fQ0FAZ3CyEEEKIdlVTXkPij4nEfRfHqfWnKMs+13Vcq9cSOiGUKx65QsUKL4+0WNiyL76AW24BZ2c4dQoCLv9L/9mSs2xO2MymxE1sSdxCYWVhg/OD/AYxPXI69w67l3DP8Mu+X3tKTEzkkUceYf369eZj8+fP5x//+AchISFotbJepBBCCCHal6IoHPnsCMfXHifpxyRqK2vN5+zd7YmaEUWva3sROS0SBw/r/lHTooLFsmXLeOGFF8jNzcVoNPLEE0/w4osvms8bjUYmTpzIjh07MBqNeHp6snr1aq6++uoW36NLBQtFgT59IC7ONN3sU0+16+XzK/J5a+9b/GvXvyioLGhwboj/EA4sPtCu97tckydP5pdffmn2vJubG+7u7nh4eODu7t5gvyXHZBC2EEIIIS4U/0M8q2atanTcI8yDwXcOxm+AHz69ffCM8ERn0KlQYfuxqK5QBQUFREVFsWjRIl6uX9TtPDNnzmTbtm08+eSTDB06lKVLlzJjxgxycnLw8PDo/IIt3bFjppYKgAkTLutS1XXVHMk6wp60PcScjSEmPYaTuY2XktdpdAzwG8AjVzxyWffrCEuWLMFgMFBYWEhhYSFFRUUUFRVRWVkJmEJncXExqampbbq+Xq9vdRi58JgM+hZCCCFsS9CIIPrf0p/0mHQKkwtRjKbf9AtPF/LrM7+aX6fVa/GK9MKntw/evb3x6e1j3hzcraMlw6JaLM6n0WgatFgYjUYMBgPTp083d2U5c+YMoaGhLF26lDfeeKNF1+1SLRZTp8KWLTB3Lqxd26q3VtZW8tXxr9iTvoeY9BgOZh6kuq660evCPcKJDoo2b0MDhuJkuLyB4p2tqqqKoqKiBmGjfr+lxy53Fex6Tk5OLQ4lYWFhjB49WlpJhBBCCCtRW1lLfkI+uSdzTduJXPN+TXlNs+9zCXBpEDRCx4fiP9i/EytvGYtqsbiY33//HaPRyMKFC83Hunfvjru7O7///nuz76v/FbpeSUlJh9ZpMTZtMoUKOzt46aVWv/2xLY/x5t43GxzzcvQiOiiakUEjiQ6KZkTgCHydfdurYtXY29vTrVs3unXr1qb3K4pCaWlpq0JJYWEhBQUFnD592txiAlBeXk55eTkZGRkXueM569evZ+bMmW2qWwghhBCdS++gp1v/bnTr3/A7h2JUKE4vbjJwlGaUmrfTv5w2vUEDS+OX4hXp1fkf4iKsJljExcUB0KtXrwbHXV1dyc/Pb/Z91157Lb/99luH1mZxamvh0UdN+0uXQkREqy8xJWIK7+5/l1pjLR4OHmyYv4Ergq+QX8eboNFocHV1xdHREYPBgKIoVFdXU1paitFopLKykpKSEnJzc8nJyTFveXl5bWrpcHNzw9fXl8jISAYNGtQBn0gIIYQQnUmj1eAe4o57iDsRV5/73laSUcLuf+9mzxt7qKuqMx+PnBaJS4CLGqVelNUEi7b67rvvGrVY9O3bV8WKOsH778Px4+Dt3eZpZq/tdS2bFmxizpo5FFYWMv/r+fww/wf6+tr4/+3OoygKRUVFpKenk5mZSXZ2doNgcOGWn59PW3oWenh44Ovri6+vL926dTPvN7fZ21vu+iBCCCGEuDx11XUk/5zM/v/uJ+67OPMq3Q4eDgy6fRDD7h2Gb1/L7DFiNcGivqUiLi6OwYMHm4+XlJQQFhbW7Pvc3NwajKU4P2TYrOefNz0++yxcxqD2K3tcye67dzNr5SwSCxIZ9cEo1t64likRU9qlTDUZjUays7NJS0sjPT292ceysrJLX+w8Go0GLy+vZkPBhcHBx8dHBmwLIYQQXZhiVMg8nEnyT8kk/5RMyu8pDcZbhIwJYdjiYfSd2xeDo2V/Z7CaYDF+/Hi0Wi2fffYZN998MwBpaWkUFRUxfvx4lauzMGfPmh7nzr3sS/X26c3uu3cze/Vstp3ZxozPZ7Bs+jLuH3H/ZV+7o1RVVXH27NmLBoazZ89SW1t76YsBnp6eBAQEXLJFoVu3bnh5eaHXW83/rIQQQgjRyRRFIT8h3xwkkn9JpiKvosFrnHyd6D+vP8PuHdZoPIYls6hvQJmZmQ3GQ5w4cYLVq1fTvXt3Ro0axZQpU/jhhx94+umnGTp0KEuWLEGv1/Pcc8+pWLUFa6fxED5OPvy48EcWr1/MJ4c/4YENDxCXF8drU15Dp+38+ZYTEhJITk5uNjjk5OS06DparRZ/f3+CgoIIDg5u8jEoKAgnJ+ua5UoIIYQQlqUsp4zELYkkb00m6ackilMb9qCxc7EjdEIo4VeG0+OqHnTr380qx7Va1HSzr7/+On/4wx8aHY+IiCAhIaHJBfJWrVrF1KlTW3yPLjHdbP1/iJmZ4OfXbpdVFIV/bPsHf/nFNG7jpStf4vGxj7fb9VviP//5D4888shlXSMgIICIiAjCwsJwdnbG3t4eBweHJh/bckxaLIQQQghRr6KgguU9l1OeW97oXLf+3Rh+/3D639IfR09HFaprXxYVLDqDBIu2K68p57ZvbuOrE18BsHL2Sm4ZcEu7Xb8lvv32W+69916Kioqorm68roYl0Ol0zQaQywksFzvW1Dl7e3ur/LVDCCGEsCU15TWsuHIFabvTLvo6lwAXvKO88YrywivKC+8ob7x7euMZ4WnxYyvqSbCwRR0QLDJKMrj2i2vZd3Yfdjo73r/mfRYOWnjpN3Ygo9FIdXU1VVVVVFZWUlVV1WD/wseOOlZZWdluC+S1Nzs7u04JMc0dq990us7vMieEEEJYkqqSKvIT8smLzyP/VD75p/LJO2Xab6o143xuIW6NQodXlBdekV7oDJbzN1aCha2pqgKH/y373g7BIqUwhXf2vcN/D/yX/Ip8vB29+ebmbxgXOq4dirUdtbW1qgWbC49ZIoPBgKOjI46Ojjg4OJj3L/X8cl6r1+ulxUYIIYRVqCioaBA06vfz4vOoKmr+b7uTrxOj/zSaEQ+MwM7ZrhMrbpoEC1vzr3+ZFscLCIAzZ6AN/f0VReGX07+wLGYZ38V9h1Ex/Rrfz7cf6+atI8Kr9Qvuic5RvzifWsHm/HMtnXWro2i12nYJLK15r3Q/E0II0Z4URaE8t7zZ0FFTZpqW1lIChgQLW5KXB5GRUFgIH3wAd97ZqreXVpey4vAKlscs50TuCfPxK8OvZGn0Umb1nKXKLFDCOtXV1VFZWUlFRQUVFRUN9i98frFzrXleWVmp9sdutxaXlr5WupoJIUTXZKw1cuSzI/z+wu8UJBYApoAx5s9jGH7/cFUChgQLW/L44/DyyzBwIBw4AK34spFSmMKI90aQU26aqtXFzoXbB93OgyMepI9vn46qWIh2pSgKVVVVbQ4pbQ00ao+xcXZ2xs/PDz8/P/z9/c37Fz739/fH2dlZ1VqFEEK0r6YChluIG/cdug9Hr86daUrmxbQlBw+aHpcubVWoAKiqq6K0utT83N3enSDXIHycfNqzQiE6lEajMQ8Y9/T07JR7KopCTU1Nu7bCtOS1NTXnVmUtKysjKSmJpKSkS9ZbH0IuFUD8/PwkhAghhBXQ6rUMXjSYgbcOZO9be9n08CaKU4upKqmSYCHaQf3g7Vbo6d2Tk0tO8s6+d3jvwHukl6Tz1M9P8exvzzKv/zyWRi9leODwDihWCOum0Wiws7PDzs4Od3f3TrtvbW2tOXAUFhaSlZVl3jIzM5t8XlFR0eoQ0pIAIiFECCHUp9VrzUHCt58vHqEenV6DdIWyJVOmwI8/wqefwq23tvkylbWVrIldw7KYZew7u898vIdnD8I9wgl1DyXUI5RQ91C6u3cn1COUELcQDDrrmGNZiK5IURRKS0svGjzOf15RUdGq6zs7O3PXXXfxn//8p4M+gRBCiObU1dQR/308vzzzCzmxOYz7yzgmPz+50+uQFgvRiIPegdsG3cbCgQuJSY9h+d7lrD62mqSCJJIKmv6VU4OGQNdAc+A4P3zUPzrbyS+aQqilvsuW0WhEq9ViMBhwcHDAyckJNzc3qqurqaurA0wzaqWmprZqEcqysjL27NnTUeULIYRoQu7JXA58cIAjK45Qll0GgMHZwMAFA1WpR4KFaJZGo2Fk8EhGBo/k9amvcyz7GClFKaQUppge/7d/pugMVXVVpJekk16Szs7UnU1ez9vR+6LBw8vRS6bqFKIFFEWhvLycnJwccnNzG2xNHcvNzSUvL88cHFpDo9Hg7e2Nj4+PefP19W3wvH4bOFCdP2RCCNGVVJdVc3ztcQ68f4DUHanm485+zgxeNJihdw/FK9JLldokWNiiDujd5u3kzYSwCU2eMypGssuyzwWOC4JHSlEKxVXF5FXkkVeRx4GMA01ex9ngTLhnOEMDhhIdGE10UDQD/QZir7dv988jhKUwGo0UFRWRl5d30a0+HNTvt3VqXTc3tyZDQVNhwdfXFw8PD5nOVgghLEDW0Sz2vrWXo58fpbrE1KKs0WqImhnFkLuGEDUjSvVVuGWMhS257TbT+Io5c+DLL9WupoGiyqKGoeOC8JFVltXk++x0dgz2H2wOGiODRxLpFYlWo+3kTyDEpVVXV18yIFwYFAoKCtrUkgBgb29/0VBw4TFvb2/s7SWoCyGEtaitquXE1yfY99Y+zmw/Yz7uGeHJkLuGMPj2wbgGuqpYYUMSLGzJsWMwaBAYjfD77zBunNoVtVhFTQWpxanE58Wz7+w+YtJjiEmPIa8ir9FrPRw8GBE4guigaPPm7+KvQtXCVimKQklJSYtDQn1QKC0tvfTFm+Hs7Iy3t3eTW30ouLBLkrOzs3QfFEIIG1SYUsj+/+7n4PsHzWMnNDoNfW7ow/D7hxM2MQyN1vL+/ZdgYWsWL4b//heio8HKB1IqikJyYbI5ZOxO201Megx1SuNfd7u7d+eOwXfw7MRnO79QYTHq6uooLS2lpKTE/Fi/nf+8qXPFxcXk5+ebg8L560S0hkajwcvLq9mQ0FRY8PLywqEN00QLIYSwLXU1day7Yx3HVh1DMZq+orsGujL03qEMu2eYRbVONEXGWNiS0lJISTHtHzsG1dVg1/nLubdFdV01Z0vOkl5sGgBufjx/vzi9yVABcKboDG/tfYu/Tvir/IJrRWpra1v0pb8l50pKSlo9Reql2NvbNwoBl9pkTIIQQoi2qsiv4NgX50LFjDdnMPSeoaqPnWgpCRa2IisLZs6E/fvB0RFWr7aIUKEoCoWVhY0CwvmhIa04jZzynBZf08fJhyDXIILcgkyP/9ufGDZRQkUHq6mpafOX/qZe29YByJei0+lwdXU1by4uLk3uX/j8wpYGJycn+W9KCCFEp3Hxc2HOqjl8d+d3VJdW8/vzv+Pbz5ewCWFql9Yi0hXKFmRkwJgxkJwMPj6wfj2MHKlKKYqi8Oyvz/L7md9JK07jbMlZymvKW/ReO50dga6BjUJDsFuw+Xmga6DMEtWBsrKyeOqpp0hLS2syELRmXYPWMBgMLfri39Jz9vb2EgiEEEJYrdy4XNbMWUNObA4anYZZ785i6F1D1S7rkiRY2IJPPoFFi0CrNXWB6tNHtVIKKwvx+qcXChf/z8rJ4MTokNFMDJ3IhLAJ9PLuhY+Tj3wZVNmqVauYP39+u13P09OT7t27ExIS0mgLDg7Gw8MDV1dX7CygdU0IIYSwJOV55SyLWkZlQSWBwwO5Z+89apd0SRIsbEFREURGQm4uLF8ODz6oajm/JP/Cr6d/JaEggYR805ZfkX/R9/g5+xHpFdnk5uHg0TmFC6qrq/nyyy9JSUmhsLCQgoKCJh8LCwvbPEXq+dzc3PDw8MDT07PVj46OjhJEhRBC2KSaihpWX7+axC2JGJwMzP9hPmETw9Qu65IkWNiKt94yBQpvb0hIAA8PtStqIL8i3xwyLtwuNb7C29GbSK9IoryjuGfoPYwPHd9JVYvmKIpCaWlpk8GjuTBy/mN5ecu6x12MwWBoMnBERkby5JNP4uTk1A6fVAghhOg8iqKQtjuNX/7yC8k/J5tCxYb5MsbCUtlssKithd69ITHRNHD7ppvUrqjFCisLScxPPBc2zmvpyCzNbPBanUbH8hnLuW/4fSpVK1pLURQqKiooKioyb7m5uaSkpJi306dPk5KSQmZm5qUv2AI//fQTkydPbpdrCSGEEB1JURQyD2Vy7ItjxK6OpSilCACDs4EFGxYQOj5U5QpbTmaFshV6PfToYQoWHTTAtqN4OHgwLHAYwwKHNTpXUlVCYoEpdKw9vpY1sWu4/4f7icuN49Upr6LTWsf0a9bKaDRSVlbWIBRcuBUXF1/yXG1tbbvV5Orqiru7O+7u7ri5uZn367eIiAjGWdHikEIIIbqmnOM55jCRF39uQWA7Fzt6XdeLUY+OImBIgIoVtp4EC2HRXO1dGew/mMH+g5nTZw4Duw3kL7/8hdf3vM6p/FN8ffPX2Olk4G9bnDlzhvfff5/c3NyLBoP2atTUarUNgkBToeD8ranzrq6uskaEEEIIq6UYFWKWx3Dg/QNkH802H9c76Ok5qyf95vUjakYUBkeDilW2nQQLYTU0Gg1Pj3+ant49ue3b2/jh1A98F/cdc/vOVbs0q/Taa6/xxhtvtOi1er2+xYGgude4uLjIYGshhBBdWl58Hpse3gSA1qAlcmok/eb1o9e1vbB3tf7p9CVYCKtzY78b+fTIp3wf/z2FlYVql2O1HnroISoqKjh48CBHjx6lqqqqydeFhYUxatQoBg8ezODBgxk0aBB+fn6dXK0QQghh/bx7etPr2l7EfReH3l7P+P8bT1B0kNpltRsJFsIqyS/fly8iIoL//ve/ANTW1hIXF8fhw4c5dOiQecvJyeH06dOcPn2aVatWmd/r7+9vDhoLFy6kb9++an0MIYQQwmpotBrmrp7LypkrSf45mc+mfcai3xbhN8A2frCTWaFsyZQp8OOP8OmncOutalfToeZ9OY/VsasZHTKadfPW4ePko3ZJNkdRFDIyMhqEjcOHDxMfH99g3MXAgQM5fPiwipUKIYQQ1qW6tJq3B75NYXIh/W7ux9wvbKNbt1btAoRoiz+O+iPu9u7sTN3JyPdHciLnhNol2RyNRkNgYCDTp0/nySefZPXq1Rw+fJjHHnvM/BpnZ2eeeOIJFasUQgghrE/KthQKkwsB6DvXdlr9JVgIqxQdFM2uu3YR7hFOUkESoz4YxdakrWqXZXPq6urIyMhg7969fPHFFwwePJhXXnkFgJkzZxIbG8stt9yicpVCCCGE9chPyOebhd8AMPyB4TYVLGSMhbBafXz7sOfuPcxeM5vtZ7Yz7bNpvDnjTRYPX6x2aVahsrKSs2fPkp6eTlpaWpOPZ8+epa6ursH7/Pz8eOONN7jxxhtlrIsQQgjRAnXVdcSvj+fA+wdI3JyIYlQIGBrA1H9NVbu0diXBQlg1X2dfti7cyt3f381nRz7jvh/u42TuyS69eJ6iKBQXF180MKSlpZGbm9ui62m1WgICAggKCmL06NE888wzeHp6dvCnEEIIIaxfzokcDn5wkMMrDlOeU24+HjYxjOs+vg69vW19FbetTyO6JHu9PSuuX0Fv797mxfMSChJYOXslrvauapfXIWpra9myZQunT59uMjiUlpa26Dr29vYEBwcTFBREcHBwg/36Rz8/P/R6+adCCCGEaIm6mjqOfHqEA+8fIG1Xmvm4i78LgxYNYsidQ/CO8laxwo4j3xaETahfPC/KO4rbv72d9fHrGfvRWNbfsp4Q9xC1y2t3//d//8dLL7100dd4eHg0CgkXPnp5eUl3JiGEEKId/fbcb2x7YRsAGp2GnjN7MuTuIURNj0Krt+3hzRIshE25qd9NhHmEce2qazmSdYTo96NZN28d0UHRapfWrq666irefvttioqKAFOwWrJkCddff705ODg7O6tcpRBCCNH15B43dTUeuHAgV/3zKlwDbLP3RFNsOzaJLik6KJqYe2IY0G0AmaWZTPh4Amtj16pdVru68sorSUxM5NFHH8XOzg5FUVi2bBlvv/02Go1GQoUQQgihktIsU3fkXtf26lKhAqTFQtiYipoKDmUeIiY9hjCPMI5mH6WytpKbv7yZAX4D6O3TW+0S20xRFHJyckhOTiYpKYmkpCQKCwvp0aMHJ0+eBODLL7/k559/JjU1FScnJ5UrFkIIIbqe+kHajl6OKlfS+SRYCKtlVIyczD1JTHoMMekx7Enfw5GsI9Qaaxu9dnjgcKtYnbu8vJzk5GRzeDg/RCQnJ1NWVnbR9zs4ODB27FgcHbveP2ZCCCGEJfCK9CIvPo/Tv50mfHK42uV0KgkWwmpkl2Wz48wOU5A4G8Pe9L2UVJc0el03526MDBpJdFA00UHRDA8cjpejlwoVNy0nJ4fjx483GRwyMzMv+l6NRkNQUBA9evSgR48ehIeHN3j09/eXwdhCCCGEigYsGMCpDac4+tlRJj47sUv9XZZgIaxCQn4CQ94dQml1w2lUtRoto0NGc0XQFeYg0d29u8X+j3j//v2MHj2a6urqFr/HwcGBfv36MXDgQPr164e3tzcuLi64uLjg6upq3tdqtZSXl+Pk5GSxn18IIYSwdb2u64XB2UBBUgGrrlnFyIdG0uOqHmi0tv+3WYKFsAqOeke6OXdrFCyMipHjOcdxsXPB2c4ZZztnnAxO+Dr7qlTpxbm7uxMQEEBKSkqL31NZWcn+/fvZv39/i16v0WjMYaOpANKaY/XPHR0dJawIIYQQLWDnbMe4p8fx81M/c+qHU5z64RTePb0Z8eAIBt0+CAd3B7VL7DAaRVEUtYvoTMXFxbi7u1NUVISbm5va5bSvKVPgxx/h00/h1lvVrqbdGRUjCfkJ5jEVMekxHMw8SHVd41//wz3CzS0YI4NGMiRgCE4GyxnMbDQaKS8vp7S0tMFWUlLSpmMlJSWXHH9xOc4PK20NKecf8/T0xGAwdFi9QgghhNpy43LZ+9ZeDn10iOoS03cVg7OBQbcNYsSDI+jWr5vKFbY/CRa2xMaDRVOqaqs4knXEPO4iJj2Gk7knG73OQe/A/P7zWRK9hCEBQ1SotONdGFbaGlLqj5WUlFBeXt4htfr6+nL48GECAgI65PpCCCGE2oy1RgpPF3J2/1l++9tv5J7IbXB+zqo59J/XX6XqOoZ0hRJWzV5vz4igEYwIGsGDPAhAUWUR+87uM4eNPWl7yCjN4MNDH/LhoQ8ZEzKGJdFLmN1nNnY6O5U/QftQFIXKykrKy8vNW0VFRYOtqWMVFRVUVlY22qqqqlo1DqS1NBqNdK0SQghhE8rzysmLyyM3Lpe8uDzzfn5CPsYaY5Pv0eg0NrkKtwQLYXPcHdy5sseVXNnjSsD0pXtX2i6Wxyxn7fG17EjdwY7UHQS4BLB42GLuHXYvAa6d98t5XV0dZWVljVoJLnfryMZHBweHRt2aLtXt6WKbm5sber388yOEEMI61FXXUZBUYA4P54eI8tzmW/f1Dnq8orzw6eWDdy9vvHt5m/dtcayFdIWyJV2wK1RrGBUjO1N38ofNf2Df2X3m4watge9v+Z6pkVM77N6KorBmzRoef/zxVg3cbgtnZ+d2DQHOzs4SAoQQQnRZx744xre3f0tddV2zr3ELdmsUHHx6+eDe3b1LzAZVT74tCJujKApnis4QmxNLbHYsx3KOEZsdy4ncE5TXNP5VocZYQ0FlQYfVk5KSwgMPPMCGDRsaHNdqtZf8Ut/aEODk5IRWa3tNq0IIIYRaSjNLG4UKrV7L4DsGM+zeYfj09sHOxTa6Vl8uCRbCaimKQkZpBseyTcHhWPYxYnNiOZ5zvMmF8wDsdfb09ulNv2796O/bn37d+jHIbxChHqHtXl9tbS3Lli3jL3/5C+Xl5djZ2fHUU09x33334ebmhoODg4wzEEIIISzcyIdHEjQyiGNfHOP4muOUZpZirDVy4L0DxK+Pp99N/eh/S3+CooO6/N916QplS7pQV6hvTnzDPd/fQ15FXpPn9Vo9vbx7NQgQ/Xz7EeEVgV7b8Xm6rq6OqVOn8tNPP5mPLVq0iKlTpxIWFkZoaCh+fn7SuiCEEEJYEWOdkZTfUji66ignvjpBZUGl+ZxvX18WbFyAe3d3FStUl7RYCKt0JOtIk6FioN9AHhv1GDf1uwl7vb0KlZkoisLRo0cbHPv444/5+OOPzc/t7OwIDQ1tsNWHjtDQUIKCgmRsgxBCCGFBtDot4ZPDCZ8czsw3Z5K4JZFjq45xct1Jco7nsOmRTdz89c1ql6kaabGwJV2oxcKoGNmatJVNCZvYmLCx0doV3Zy7MTViKtMipzElYgo+Tj6dXmNBQQH79+/n9OnTpKSkkJKSYt5PS0vDaGx6Crp6Op2O4ODgRoGj/nlISAj29uqFJyGEEEKYZMdm886gd1DqFBZsWkDk1Ei1S1KFBAtb0oWCxfnqjHXsO7uPTw5/wqpjqyisLGxwXoOGqyOuZs3cNbg7WEbzZE1NDenp6Y0CR/3+mTNnqKmpueg1NBoN/v7+TJ48mRUrVki3KiGEEEJFm/+4md3/3o1biBujHh1Fr2t64dnDU+2yOpUEC1tiQ8FCURRKqkvILM00bxklGab9sswGx7PLsjEqF//1X6vRcuLBE/T07tlJn+DSamtryczM5OzZs5w9e5b09HTzY1paGgcPHiQ/P/+S17G3tyc7O9v2/nsWQgghrEhlUSVv93+b4rRi8zHffr70urYXva7tZRrcbeNTz0oHbtGpqmqryCrLahAMmtsqaitafF0NGnydffF38SfAJQB/F/8G2yC/QZ0WKhRFIT8/v0FQaCo8ZGVltXhRO0dHR4KCgggKCiIwMND8GBgYSHR0tIQKIYQQQmUO7g7cs+8ejq48Svz38aT8nkJObA45sTlsf3E7zt2ciZoVRa9retHj6h7YOdveFLXSYmFLLLDFIqkgiUc2PUJiQSKZpZnkV1z6F/jzudm7NQwJzv6NQoO/iz++zr6dMttTc77//ntefvllc3Coqqpq03VcXFwICwtrsIWGhuLj44OdnR12dnYYDAYMBoN5v6ljOp2uy095J4QQQqipoqCChI0JxH8fz6kNp6gqPvfdQGevI3xSOMGjggmKDiJwRCBO3k4qVts+pMVCdKjfU37n+/jvGxzTarQEuQY1GRDO3/yc/XC2c1ap8tb55JNP2L59+2Vfp7S0lGPHjnHs2LHLvtbFgkdLj7XlPZd7HYPBIONFhBBCWD1HT0cGzB/AgPkDqKuuI2VbCvHfxxP3XRyFyYUkbEogYVOC+fWePTxNISM6kKDoIAKGBGBwMqj4CVpPWixsiQW2WBgVI2/tfYtlMcuIz4s3H58SMYWl0UuZHjkdnVanYoXtIy8vjx9//JHKykpqamqoqamhurq6wWN7HWvqfF1d3aWLtCI6nc5igk5rryOtRUIIIS5GURRyYnNI/iWZszFnSY9JJy++8RT6Gp0GvwF+5qARFB2Eb19ftDrL/fFNgoUtscBgUc+oGPkp6SeWxSxjffx6FEz/2YV7hHN1j6ubbbVwNDiqXLl1MBqNLQogHRluLueYrbmcgBIUFMSzzz6Lt7e32h9DCCFEJ6koqODsPlPIOBtzlrQ9aZRllTV6ncHZQOCwQNOsU9f2UqHSi5NgYUssOFicL7kgmbf3vc37B96noLLgoq91t3e/ZJcpfxd/fJ18baLloytSFIW6ujrVw01bA1ZHtBatWrWKefPmtft1hRBCWAdFUShOKyZtdxq7/72btF1pDc4Hjwrmrp13qVRd82SMheh04Z7hvHz1yzw78Vm+Pfktp/JONZpGNqMkg6q6KoqqiiiqKiIuL+6i19RqtPg6/W9WKNeAiw70drN3k64qFkSj0aDX6612lfH61qLLDS3FxcUsXboUgIEDB6r8qYQQQqhFMSqk7koldnUsx788TmlGqfmco5cjfeb0YfRjo1WssHnW+ZdcNFZXB2WNm8wsmZPBifkD5jd5TlEUiquKm5yGNqM0o8l1LLLKssgqy+Jw1uGL3tdB79AgaAS5BnFD7xuYHD5ZAodoNa1Wi729Pfb29iiKQk1NDeXl5VRUVKAoCrW1tdTW1lJVVUVFRYX53IWPycnJAHTv3p0+ffqo/KmEEEJ0FkVRKDpTRPqedM7sOMPJr082WAvDwcOB3jf0pt/N/QifHI7OYLk9NCRYWLvSUvjoI3j9dUhKMh1zsv7pyjQaDe4O7rg7uNPL5+J9CGuNteSW57ZobYyiqiIqays5XXia04Wnzdd4c++b9PHpw5LoJSwcuBBXe9cO/oSisyiKQnV1daMv8hf7kt+W19bvG40XX6zxUmbMmCEBVwghbFhlUSVn95rGUaTvSSc9Jr3ReAo7Vzt6X28KExFXR6Czs9wwcT4ZY2GtUlNh+XJ4910oKjId8/KCBx+Ev/wF7Gxv0ZX2UF5TTlZpwwX6Dmcd5vOjn1NabWpqdLN3Y9GgRTwY/aBFrdRtSxRFoaqqqsO/5Nc/qvHPnFarxcnJCUdHxxY/enp6cs899+Dr69vp9QohhGh/xlojWUezTAFiTzppe9LIPZkLF/xZ0uq1+A30I2hkEBFTI4icGonewfp+/5dgYW3S0uDPf4Y1a0zdnwB69oRHHoHbbgNn61j3wdIUVxXzyaFPWL53eYNpcadGTOWfV/2TQf6DVKzO8hQUFHD8+HFiY2NJTU2lvLy8VUGgvptQZ9PpdK36ol//2Jb3GAwGaXkQQoguqDSzlF3/2kXarjTO7j9LbUVto9d4hHkQNDKIoJFBBI8Mxn+IPwZH61qzoinWF4W6uueeg1WrTPuTJsEf/wgzZoAsKHZZ3OzdWDhoIX4ufjzzyzPmweKbEzdTWVvJr4t+VbdAleTn5xMbG8vx48fNQeL48eNkZGS02z10Ol2nfNF3cnLCYLD+f7SFEEJYtu3/3M6e1/eYn9u725vXoQgaaXp08XNRscKOI8HC2tQP0P7b3+CZZ9StxcoZFSMHMw6yMWEjmxI2sTttN3XKualDHfQOTAqbxFPjnlKxys6Rl5dnDg3nP2ZlZTX7npCQEPr27UtERATOzs5tDgfyZV8IIYQtsXe1ByB8cjgz3pyBd09vNNqu0YItwcJaWWM3LgtQXVfNupPr+C7+OzYnbCanPKfB+d4+vZkWMY3pUdMZ132cTS7QV1tby8qVK4mJiTGHiOzs7GZf3717d/r160ffvn3Nj3369LHOroRCCCFEB/OK9AKgpqIGjzCPLhMqQIKF6CLOlpzlv/v/y7v73yWzNNN83MXOhat6XMW0iGlMjZxKmEeYekV2ko0bN3L77bdf8nVOTk5cccUVhIWF4e3tjbe3N3V1dZw9e5bq6mrzMS8vL2l1EEIIIf7Hu5c3AGm70njZ+2V6XNWDqFlRRM2Iwi3Itn+Uk2AhbJaiKOxM3cnyvcv58viX1BpNg6f8XfxZOHAhM6JmMDpkNHa6rjWD1tixY1m8eDGJiYnk5eWZt7IL1kEpLy/n559/btE13dzczEHDx8fHvH/+duFxJycnGdwshBDC5gSNCGLUo6M4tuoYJWdLiPsujrjvTGM3/Qf7EzUzip6zehI4IhCtzrbGyMqsUNZmwQJYuRL+/W/TTFCiEaNiZMXhFfxnz384lHnIfHxMyBiWRi/lhj43dLkw0RKVlZUNgsaFW25ubqNjBQUFbZ7dyd7e/pJBJDQ0lF69euHn5ychRAghhFVRFIXMQ5mc+uEUp344RdqetAbTzDr5OBE5PZI+s/vQ67peNvF3ToKFtZFgcUmrj61m3lfzANMA7Pn957MkeglDAoaoXJntqauro6CgoFVhJC8vj+rq6lbdx93dnV69etG7d+8Gj5GRkdjb23fQpxNCCCHaT1lOGQkbEzj1wykSNidQVVRlPjfuL+OY/PxkFatrH9IVStic3PJcAEYFj+L7W77H28lb5Ypsl06nw8fHBx8fnxa/R1EUysrKmg0d9YEkNzeXxMRETp8+TVFRETExMcTExDS4llarJTw83Bw0zg8d3bp1s4lff4QQQtgGZ19nBt02iEG3DaKupo7Unakc//I4e5fvZdsL2/Ab6Ee/G/upXeZlkWAhbIqiKFTWVgIQ7BYsocICaTQaXFxccHFxISws7JKvr6ysJCEhgbi4OE6ePNngsbi4mMTERBITE/nhhx8avM/Dw6NRK8e0adNwdLS9mb6EEEJYF51BR9iEMMImhKG317PrtV2sW7QO7yhv/Af7q11em0mwEFYrtzyX2OxYYnNiic2O5VjOMWKzY8mryANAq7GtAVFdlYODA/3796d///4NjiuKwunTp/n555/ZunUrP/30Ezk556YPLiwsZM+ePezZc26Ronnz5rGqfoFJIYQQwgJc9dJVZB/NJnFLIp/P+Jzbf74dn94t7wlgSSRYCItXWFloDhDHso+Zg0RWWdOLt2nQEOUdxR2D7+jkSkV7qqioID09nbS0NFJTU0lLS2u0f36QuBhHR0dCQkKYNWtWB1cthBBCtI5Wr2XOF3P4ePzHZB/L5qPxH3Hb1tvwG+indmmtJsFCWAxFUdifsZ8jWUcaBIj0kvRm3xPmEUb/bv3p59uPfr796N+tP719etvkwna2pqioiAMHDjQbHHJzc1t0nfrQEBwcbH48fz8kJARPT08ZbyGEEMJiOXo6cvsvt/PplE/JPJjJJ5M+4dbNtxI4PFDt0lpFgoWwCMVVxdzy1S1sOLWhyfPBbsGNAkQf3z642Ll0cqWiPdTW1tK3b1/Onj3b6ve6ubnRp08f+vTpQ+/evenevTuurq64uLiYH+v3nZ2d0el0HfAJhBBCiPbl5OPE7T/fzufTPydtdxofjvmQQbcPYsyfx5hX87Z0EiyE6lIKU5i1ahbHso9hr7NnbPex5hDRv1t/+vr2xd3BXe0yRTvS6XSMHTuWn376idLSUqqqqi79pv8pLi5uNHbiYhwdHRsFjpbuN3VMFvYTQgjRURw8HLh1y618Ne8rTm04xYH3DnDwg4P0vbEvY58ci/8gyx7YLetYWBsbW8did9purvviOrLLsvF38ee7ed8xImiE2mWJTlZTU0NpaSmlpaWUlJRc9n5JSQl1dXUdUuv5s1q1NqhcGFpCQ0MlpAghhGjSmR1n2P7idk79cMp8LGpGFGOfHEv3sd1VrKx5EiysRXU1rF4NTz8Nqak2ESyySrPo8UYPymvKAQhyDaKvb1+C3IIIcv3f5nbusZtzN5npSbSIoihUVVW1S1ApLCwkNze3zSuMX8zcuXNZu3Ztu19XCCGE9VOMCnmn8jj00SF2vrITxXju79CcL+bQ/+b+F3m3OqQrlKXLy4N334XlyyEjw3TM2RnGjlW3rnbgoHcg2C2Y+Lx4ANJL0i86UFuv1RPgEkCwW3Cz4SPINUgGbndxFwaKkpKSZvdbcr6ioqJD6tRqtYSEhHTItYUQQliXupo6co7nkHkwk4wDGWQcyCDzUCY1ZTWNXmtwMuDoZZnfdaTFwlLFxcHrr8Mnn0D9F5vAQFiyBBYvBi/rGMTTEsVVxaQXm0JFo8f/7WeWZqLQsv9UPR08Lxk+fJx8pAuKhaioqKC4uLhdQkBJSQk1NY3/EW4Per0eV1dX81bfnenC8RgtOebq6oqjo6P8NyiEEF1QbWUtWUezzgWIA5lkHc2irqpxF169ox7/Qf74D/UnYGgAAUMD6NavGzo7y5yYRIKFpcnKgieegI8/PndsyBD44x/hppvAzk610tRUU1dDZmnmRcNHekm6uVvVpdw+6HY+vv7jji1aXNIHH3zA4sWLO2Q8hIODQ4u/5LfkvL29fbvXKIQQomvJT8zn/ZHvU5HXuDXc3s0e/yHnAoT/EH98evmg1VtPN3DpCmUpamrgzTfhr3+F4mLTsWuvhT/8ASZMgC7+y6ZBZyDEPYQQ9+a7jiiKQlFVEenF6SQVJLHq2Cq+PvE1VXWNZxyqqO2Y7i2idWpqapoNFcOHD8fPz69NwcDFxQW9Xv55E0IIYWEUqK2oNT+1c7VjyqtTCJ8cjmcPTzRa6/6+Jy0WluCXX2DpUoiNNT0fPtw0pmLkSHXrsjIJ+QlsPLWRTYmb+CX5lwbhQavRckXwFUyLmMa0yGkMCxwmA8EtxIkTJ1i9ejWrV6/m5MmT5uNOTk5cc801zJs3j2nTpuHg4KBilUIIIUT7KEgqYMujWzj5relvnpOPE5P/Ppkhdw1Bq7Pu7yYSLNT27LPwt7+Z9r294aWX4M47QWvd/2F1tqmfTWVL4pYGxwJcApgWaQoSV/e4Gk9HT5WqEy2hKApHjhwxh4ykpCTzOXt7e7y8vHBzczO3UjS1f6nzrq6usmCeEEIIi5D4YyKbHt5E7olcAAKGBXDNf68hYGiAypW1nQQLNR0/DgMHQl0dPPAAPP+8TQ3K7ixGxUjEGxGcLjwNwPwB8/nz6D8z0G+gDI61UoqisG/fPlavXs2aNWtITU1tt2s7OTldMny0NMAYDIZ2q0sIIUTXU1dTx9639vLrX3+lqqgKjVbDFX+4gol/m4ids/WNq5VgoaaZM2HDBrj+evjmG3VrsXJJBUnMWjmLE7kncDI48fnsz7m+9/VqlyXagdFoJCUlhaKiIoqLiykpKTE/tmS//rG6urrda7O3t29zOLlw397eXoKwEEJ0UaWZpWz+w2aOfXEMAPdQd2a+PZOo6VEqV9Y6EizU8ssvMHky6PWmsRU9e6pXi40orCzkprU38WPSj2jQcPfQu1kavZQBfgPULk1YgKqqKnPoaE0gaWq/srKy3eszGAy4urri7u5OYGAgwcHBBAUFNXoMDAyUlhIhhLBRpzac4of7f6DoTBEAE5+byIT/m6ByVS0nwUItzz8PzzwD8+bBqlXq1WFjao21PLTxId7e97b52MSwiSwZsYTrel+HXiszBYnLV1NTQ2lpaasDSVOvLS9v2RTJ9TQaDX5+fk2GjvrHoKAgXFxcOujTCyGE6EjVpdVsfWIre9/ci5OPE3/K+ZPaJbWYfMtSm9rdsWyMXqvnrZlvcXO/m1m+dznfnPiGX0//yq+nfyXYLZj7h9/PPUPvwdfZV+1ShRUzGAx4enri6Xn5EwLU1dU1CCkFBQWcPXuWtLQ00tPTGz3W1NSQmZlJZmYm+/fvb/a67u7uzQaP+kdvb2/pfiWEEBbGzsWO8f83nr1v7qU8rxxjrdFq1rKQYKGWrtVQ1KGMipGSqhIKKgsorCykoKKAgsoCpkdOJ9g1mPcOvEdZTRlpxWk8/fPT/O23v/H8pOf585g/q126EOh0Otzd3XF3d7/ka41GI7m5uc2GjrS0NNLS0igtLaWoqIiioiJi66exboK9vT1BQUH4+vri7OyMk5OTebuc59JVSwghLo+TjxMarQbFqPDTUz8RNimM4CuCcfR0VLu0i5KuUGowGmHUKIiJgaefhhdeUKcOC1JTV2MKBReEg0b7lQUUVDTcL6oqwqgYW3W/EYEjiLknpoM+jRDqKi4ubjZ41D/m5OR02P31en2rgkhbQowsgCiEsHVv9XuLnOMN/6327etL8OhgQkaHEDIqBO9eltXyLMFCDatWwfz54OwMCQng769OHe1IURQqaisafelvcr+Jc2U1ZZddg4PeAQ8HDzwdPPF09Dy37/C/fcdz+5PCJ+Hh4HH5H1wIK1VVVWXuclVQUEB5eTllZWWUl5ebt/OfX+xc/XOjsXUB/3IYDIZ2aV252HMJL0IINZVmlRK3Lo7Unamk7kwl/1R+o9c4ejkSPOp/QWN0CIEjAlWdplaCRWerrIReveDMGVNLxdNPd34NrVRdV83pwtMk5CeYt6SCJPIq8hq0JlTXXf50nq52rg0CQP1+U+Hgwn0HvazMLIRaFEWhurq6VUGkLc/VCC9NBQ8HBwfs7e0vurXkNZd6nyzoKISoV5ZTRtruNFJ3ppK2M430mHRqK2sbvEaj0+A/yJ9BiwYx4oERnb6StwSLzrZ3L0RHg4cHpKeDk1Pn19CEytpKkguSG4SHhIIETuWdIqUopcVdjXQa3cUDwIWtCeftuzu4y6xNQohmnR9eWtua0tLnZWVlWNKfRZ1O16ZA0lHv0ev1FtXtQoiurK66jszDmaTtSjO3ahSnFpvPBwwNYNZ/ZxE4LLDTapJg0dl27zaNrwgPh6SkTr11eU05ifmJjcJDQn4CqUWpKDT/n4KTwYlIr0jT5hlJhFcE3Zy7NQoPLnYu8kdHCGG1FEWhqqrqkkGksrKSqqoqqqqqGuy3ZmvufZb8Z1mj0bRba0x7vM9gMMjfHCHOU5RaRNy6OH75v1+oLKxEo9UwYskIJj8/GXs3+w6/vwSLztaJwaKmroYXfn+B31J+IyE/gfSS9Iu+3tXO9Vx48IokyivKvO/v4i//eAshRAdTFIXa2tpWh5G2hpiWvK8zu5+1RWsDSVBQEH/729/UXctKiA5WmlXKlj9u4ejKowC4Brly05c3EXxFcIfeV/qd2LC04jSe+/25BsfsdHYM9BvYIDTUb75OvhIehBBCRRqNBoPBgMFgsJhFDi8WdNoSZEpKSsjNzW201dbWXrqYJtRftzWuvvpqZsyY0ab7CWENXPxcmP35bAYtGsQ3t35DSXoJMctiJFiItgv3DGfF9St4acdLHM85DpgGYrvZu3Fj3xu5ptc1MqZBCCHEJVVXV1NaWmpePb4124Xvq6mpaff6NBoNLi4uuLq6NtouPB4WFsbVV1/d7jUIYYn8BvpRXWqaXKf/Lf07/H7yrdLGLRy0kFsH3sqvp39lWcwy1sWt4+fkn/k5+We6u3fnqbFPsXj4YrXLFEII0YkyMzPZtm0be/fuJT8//6LBoLKyskNqcHJyavKLf1s2JycntFrrWJlYiM604587qCmvISg6iKiZUR1+PwkWXYBGo2FS+CQmhU/iTNEZ3ox5k5d3vsyZojPc/8P9LBy0ECeDZcxOJYQQon0pisKpU6fYtm0b27dvZ9u2bSQmJrb6OnZ2dpcdAM5vRZCpdIXoeCe/OQnAmMfHdEp3dwkWXYyngyfHc4+bn7901UsSKoQQwobU1tZy6NAhc5DYvn072dnZDV6j0WgYNGgQo0ePJigo6KIBoH7fzk69RbeEEG3jGuhK4elCaqvaNoaptSRYdCGpRanMWjWLI1lHcNA78Mn1n3BTv5vULksIIbo8o9FIdXV1o0HPrXleVFTEnj172LVrF2VlZQ2ub29vT3R0NOPGjWPs2LGMHj0ad3d3lT6tEKKz+PbzJXVnKjmxOZ1yPwkWna3+F5+cHCgoAE/PTrltTHoM131xHZmlmfg5+/HdLd8RHRTdKfcWQghL1NRsR5fzxf5ynrf3gGYPDw/GjBnD2LFjGTduHMOHD8fevuPnsBdCWBbvnt4A5Cfkd8r9JFh0toEDoW9fOH4c/v53ePXVDr/ll8e/ZOE3C6msrWRAtwGsn7+e7u7dO/y+QghxPkVRqKmpUf1LfP1WV1en9v9JmmVnZ9fkOgwXe+7o6MigQYMYN24c/fr1k8HMQgiqSkxTMTt4OHTK/SRYdDa93hQmZsyAN96A+++HiIgOu91L21/iyZ+eBGBm1ExWzVmFq71rh91PCGG9ysrKSE1NJTU1lbS0NEpKStr1i3xVleWuKq3RaFr1Jb4lz9t6DTs7O1lTSAjRLkozSgFwCeicdXEkWKhh2jSYMgW2bIF33oFXXumQ28TnxZtDhYPegZv73Yy9XprCheiKampqSE9P58yZM+bwkJqa2uB5fn7nNJXX0+v1nf6lvbnner1evswLIWxKTXkNKb+lAOAW3DkrzV9WsKhfLVOj0eDj44O3t3d71WXbNBoYO9YULIqLO+w2oe6h3DXkLlYcXkFlbSW3fXsbf/rxTywetpjFwxcT6BrYYfcWQnQeo9FIVlZWk2Gh/nlmZmaLWgtcXFzo3r07ISEhuLu7d8iX+PpNphsVQoiOs2HpBvLi83Dxd6H3db075Z6tChZlZWWsXbuWdevWsXPnTnJzcxuc9/HxYdSoUVx//fXceOONODs7t2uxonXs9fa8f+37vDD5Bd7b/x7v7H+HsyVnee735/jH9n8wu89s7h16L+NCx2Gnk2kEhVBDXV3dJbsPnX+sqQCRlpbWosG/dnZ2BAcHm4NDSEhIg/36MCG/3AshhPVSFIWDHxzk0IeH0Gg1zF45GyefzllaQKO04CesvLw8XnzxRd59910qKysZOHAgw4YNo0ePHnh6eqIoCgUFBSQnJ7N//36OHDmCg4MDixcv5oknnsDHx6czPkuLFBcX4+7uTlFREW5undMs1KQXX4SnnoJ+/eCnn8DPr8NvWVNXwzcnv2F5zHK2ndlmPu5q58qVPa5kWsQ0pkVOI9QjtMNrEUItiqJcciagthxv6zXaawCxVqslICCgycBQv+/r6ysDeoUQwgYpikL20WyOfXGM2NWxFCQVADDh2QlM/OvETqujRcHC1dWVyMhIFi9ezJw5c/D19b3o63Nycvjqq6/473//S0JCAsUd2N2ntSwmWCQlwciRkJtrGry9aRNERnba7Q9lHuKtvW/x7clvySlvOLdxH58+TIs0hYzxoeNx0HfOTALCNl04E1BHfUFv6bXbe1rP9qTRaBp1F7qwG5GPj0+T4SEwMBCDwaD2RxBCCNGJck/mcmz1MWK/iCX35LmeRAYnA4PvGMy0/0xDq+u8H5RaFCw2b97M1KlT23SDy3lvR7CYYAEQH28ayJ2cDD4+8MMPEN25a0sYFSMHMw6yKWETGxM2sittF0bFaD7vqHdkUvgkpkVM48Z+N+Lv4t+p9Yn2YTQaKS0tpaioiOLiYoqLiykrK+u0X+0tdSYgODet56XGA3TGcYPBIN2QhBBCXFRxejGHVxwmdnUsWYezzMd19jqiZkTR7+Z+9JzVEzvnzu/m3qJgYUssKlgAZGXBzJmwfz/4+0NqqmlKWpUUVBTwU/JPrD2+ljWxaxqci/KKIn5pvEqVdU2KolBaWmoOA+cHgwufX2y/pKRE7Y9idv5MQGp/mbezs5OuQUIIIayGsc7Iv4P/TWmmaRpZrV5LxJQI+s3rR+/remPvpu7snzLdrNr8/ODHH8HLCzIzobwcVAg8iqJwNPuoueVi+5ntDc7b6eyYETWj0+uyVoqiUF5e3uoAcOF+SUkJRqPx0jdsIb1ej7u7O+7u7jg5OanyZV5mAhJCCCHapiy7jNLMUjRaDbPenUXvG3rj5N05A7Nbos3BIiUlhU8++YSkpCQKCgoadXXQaDSsW7fusgvsEpzU+Q+itLqUjac2silhE5sSN3G25GyD8xGeEUyPnM60yGlMDJuIs13XmuWrtLSUkydPtikYFBcXt+uqvjqdDnd3d9zc3Mzb+c9bum9vby9dbYQQQggrVZJu6oHg4u/C0LuHqlxNY20KFqtWreL222+ntrYWDw8P3N3dG71GvrxYvokfT2R/xn7zc0e9I5PDJ5sHbkd6dd5gcktTW1tLnz59SEtLu6zraLXayw4Dbm5uODo6yv+mhBBCiC6u5Oz/gkUnraTdWm0KFk8++SS9e/fmyy+/pGfPnu1dk+gkPTx7mIPFnD5z+Gz2ZzID1P/odDpGjBhBZmYmtbW1LXqPv78/Q4YMYfDgwQwZMoQhQ4bQo0cP6cMvhBBCiHZRvx5FcWoxiqJY3I+ObRq87eLiwssvv8wDDzzQETV1KIsbvA1QVQUO//tCX1TUaWMsaupqeHDDg7x34D0A7ht2H29MfwODTqasrFdbW0tqaipJSUkkJiY2eiwqKrro+52cnOjRo4d5i4iIMO/37NlTQocQQgghWqy2spYX3V7EWGPkocSH8OzhqXZJDbSpxWLkyJGcOXOmvWsRncygM/DurHfp5d2LP/34J97Z/w7uDu68dNVLapdmMfR6PeHh4YSHh3PllVc2Op+fn98gbJy/n5qaSnl5OceOHePYsWON3nvDDTfw9ddfd8bHEEIIIYQN0DvoCRgSQHpMOml70mwjWLz++utMnz6d4cOHM3fu3PauSXQijUbDo6MfBeCxHx9jd9pulSuyLl5eXnh5eTF8+PBG56qrq0lJSTGHjZ9//pmvvvrKfN7BQbqdCSGEEKJ1NDrL6v50vjYFiwEDBvD3v/+defPm4ezsTHBwcKMpJDUaDYcPH26XIkXHC3EPUbsEm2NnZ0dUVBQREREkJCSwadMmwBQo/v73v/Pwww+rXKEQQgghrIliVMg+mg2A/yDLW7S4TcHirbfeYunSpTg4OBAREdHkrFCiFWpq1K7A7HDWYR7b8hjTIqcxrvs47PXqLrRiCx566CHefPNN83MXFxfWrl3L5s2bWz1LlIuLi8UN1BJCCCFE5yg8XUh1aTU6ex3ePb3VLqeRNgWLf/zjH4wePZr169dLqGgP//2v6TEwEFzUmT5sQLcBOBucKaws5LVdr/HartdwMjgxKWySeS2LCK8IVWqzdhfOj5Cbm0tubm6brqXRaNpl+lonJycJKEIIIYSVqS6rBsDezR6t3vImgGnTrFCurq688sor3HfffR1RU4eyuFmhcnMhMtI0G9SHH8Idd6hWSkFFAVuTtrIxwbRoXkZpRoPzkV6RTIuY1mUXzLsc2dnZ5gX02roKd3svuNceAcXBwUECihBCCNFJSjJK+Ffgv9BoNfxfzf+h0VrW3+A2BYtZs2YRGhraoHuHtbC4YPHYY/DaazB4MOzbBxeMVVGLoigczT7KpoRNbEzYyPYz26k1nlvPwV5nzyfXf8LN/W9WscquQ1EUKioqLiuY1G9Go7Hd6tLr9QQGBnLVVVcxbdo0rrrqKjw9LWuGCiGEEMJW1NXU8YLdCwD88ewfcQ1wVbmihtoULFJTU5k+fTq33XYbd911F97eltfHqzkWFyzGj4dt2+CTT+C229SuplklVSX8lPwTSzcuJa3YtBr185Oe5y/j/6JyZaI1FEWhrKyszcGkfr+kpKRRFy8wrTR+xRVXMG3aNKZNm8awYcNkrQ4hhBCiHb094G2yj2XT76Z+zPlijkX1HGhzVyij0UhlZSVgmuWmqVmhLrV4mBosNlh8+SXMmaN2Nc2qrqtm8frFfHzoYwAein6I16a+hl7bpmE6wsoZjUZKS0spLi7mxIkTbN68mU2bNhEbG9vgdT4+PkyZMoVp06YxZcoU/Pz8VKpYCCGEsA1pe9L4aOxHGGuNXPPeNQy9e6jaJZm1KVgsWrSoRenoo48+alNRHUmCRdtc98V1fBf3HTqNjjemv8EDI6xv1XXR8VJTU9m0aRObNm1i69atFBcXNzjfo0cPHBwcMBgMjTa9Xt/k8YttrX1PW+5hSb8ECSGEEAA7Xt7B1se3onfUc8OnN9Bndh+L+HvVpmBhzSRYtJ5RMdL9391JL0mnu3t3frn9F3p49lC7LGHhampq2L17tzloHDhwQO2S2kSr1Vpk4Gnre6RrmhBCWD/FqLBy5koSNiUAEDk9khnLZ6i+ErcEC7VZQbAAOJhxkGtWXUN6STo+Tj58e/O3jOk+Ru2yhBXJysri1KlT1NTUmLfa2toGz1u6teV9LXlPe868ZalaE5Q6M/C09T0SlIQQXVVNRQ3bX9zOjn/uoK66Dr2DnrFPjWXMn8egt1enq3qLgsWqVauYN29eq5tYFEXhiy++4JZbbmlzge3NYoPF2rUwd67a1VzU2ZKzXLvqWvZn7MdOZ8fGBRuZHD5Z7bKEaDdGo7FBALG04NPa90hQahhEnJyc8PLywtvbGx8fH7y9vZvcvLy8MBgMan80IYRokdy4XDY8uIHkn5IB8O7lzaJfF+Hi3/lro7UoWPj5+eHm5sY999zDjTfeSHh4+EVfn5CQwJo1a/jggw8oKysjMzOz3Qq+XBYXLGbPhm++gbvugvffV7uaSyqvKWfumrlsTNjI/AHz+Xz252qXJIRoxoVBydKCT2vf15lByc3NzRw0LhZCzt+cnZ0too+zEKLrURSFY18cY/MfNlOWVcaEZycw8a8TO72OFgWLsrIyXn/9dd544w1yc3MJCwtj6NChhIeH4+npiaIoFBQUkJyczL59+0hNTcXb25uHHnqIP/zhDzg7W85CahYXLHbtgtGjQaOBgwdh0CC1K7qk5THLWbpxKTf1u4nVc1erXY4QootoLii1NMSUl5eTl5fXaMvNzTXvFxYWNjmVckvY2dm1Koh4e3vj6enZaFZFIYRoq/3/3c/6xesJHR/Kot8Wdfr9W9QBy9nZmaeffprHH3+c77//nnXr1rFz506+/vpr8z/AGo2GiIgIJkyYwHXXXcc111wjTcktMWoU3HwzrF4Njz4KW7eqXZEQQlgkrVaLnZ0ddnZ2HXaPuro6CgoKmgwgzYWRvLw8qqurqa6uJiMjg4yMjBbfT6PR4OnpaQ4aISEhvPjii0RERHTYZxRC2K7wyaZeRam7Uqkuq8bOueP+vWxKq0Z26PV6brjhBm644QbA9A9wfn4+AF5eXvKrS1v9/e+mYPHTT1BWBhbUwiOEEF2JTqfDx8cHHx+fFr+nfuHJ84NGVlYWR44c4cCBAxw4cICCgoJm35ufn09+fj6nTp1i9+7djBs3jqVLl7bXRxJCdCGeEZ4YnA3UlNVQlFKEb1/fTr3/ZQ0Z1+l0+Pp2bsE2KTj43H4XGGwphBDWrqqqijNnzpCSkkJKSgqnT59usJ+enn7JMSFarZbg4GDCwsIIDQ0lNDSUXr16ceONN3bSpxBC2JqK/ApqymoAcA917/T7y7LJQgghRDMURSE+Pp5Nmzaxe/duc4BoSXcnOzs7unfvbg4N5weIsLAwgoKC0Ovlz7AQov3kxeUB4Bbs1undoECChRBCCNFASUkJP//8s3lxx9OnTzf5Oicnp2ZDQ2hoKP7+/rLOhhCiUxUkm7pdekV6qXJ/CRZCCCG6NEVROHLkiDlIbN++ndraWvN5Ozs7xo8fz+TJk+nZs6c5QPj4+Mj0skIIi1KeWw6Aczd1xutKsBBCCNHlFBUVsWXLFjZs2MDmzZsbdW2KiIhg+vTpTJs2jYkTJ1rUtOlCCNGcirwKABy9HVW5vwQLS6DTgV4PtbUQG2uaglYIIUS7io+PZ/369fzwww/8/vvvDVolnJycmDRpEtOmTWPatGlERkaqWKkQQrRNRb4EC6HXw623wscfw2OPwfbtpgXzhBBCtFl1dTXbtm3jhx9+YP369Zw6darB+V69ejFz5kymT5/O2LFjcXBwUKlSIYRoH+YWCy8rCxabN2/mgw8+ICkpiYKCgkYrlWo0GhITE1t1zWXLlvHCCy+Qm5uL0WjkiSee4MUXXzSfj4yMbHRNb29vcnNz2/oxLMcLL8CaNbBzJ3z5Jch0g0II0Sa//PILb731Fps3b6akpMR83GAwMH78eGbNmsXMmTOJiopSsUohhGh/9S0WTt5Oqty/TcHilVde4YknnsDPz4/o6GgGDBjQLsUUFBQQFRXFokWLePnll5t8jY+PDz/99JP5uaura7vcW3VBQabWiueeg7fflmAhhBBtNGvWLMrLyxsd79GjB15eXpw9e5YtW7YQHx9vnsHJxcVFhUqFEKJ9VRVXAWBwNqhy/zYFi//85z9MnjyZDRs2YDC0X+HPPPMMzzzzDECzwUKv1zNw4MB2u6dFGTHC9FhWpm4dQghhxd58802+/fZbTp8+TXJyMsXFxQDExcURFxfX5Hu8vb0JCwszb/XTxtbvu7m5deZHEEKINnHv7k7a7jQKkgpUuX+bgkVBQQFz585t11DRUpmZmWi1WvR6PVFRUXz77bfSnC2EEMJs0aJFLFq0yPy8sLDQvCJ2/Xb+84KCAvLy8sjLy2P//v1NXtPT07NR6Jg/fz6+vr6d9KmEEOLSfPuZ/k3Kic1R5f5tChbR0dHN/urTkWbMmIGrqytDhw5l3759/Otf/2LIkCHk5+djZ9f06oLFxcXmX6uABv1tRdvY6Uz/t95+ZjtHs44ywK99usIJIURH8PDwwMPDg0GDBjV5vri4+KLBIy8vj4KCAgoKCjh48KD5fVu2bOGHH37orI8hhBCX5BHmAUBBohW1WLz11ltMnz6d4cOHM3/+/PauqVlvvPGGeX/OnDlMnTqVSZMm8Z///Ic//elPTb7n2muv5bfffuusEruEOX3m8OrOVzmVf4oxH47hi7lfMCNqhtplCSFEm7i5uTFgwIAG4wXPnj3L5s2b2QGOyVIAANwgSURBVLRpExs3bmz0o9TAgQN58MEHO7tUIYS4qDM7zgDg21+d1tQWBYumxjTU1taycOFC7r//foKDg9HpdA3OazQaDh8+3D5VNmPixIloNBoOHTrU7Gu+++67Ri0Wffv27dC6bJ23kze7797NnDVz+PX0r1yz6hpem/IaS6OXotPqLn0BIYSwQPv372ft2rVs3LiRI0eONDjn4eHBlClTmDZtGlOnTiUwMFClKoUQommKUSH+u3gAel/XW5UaWhQsvLy80FywroK3t7fqYxv27t2LoiiEhYU1+xo3N7cGg+7ODxmi7bwcvdh862buX38/Hx76kD9s/gPLYpbxwPAHuHPInXg6eqpdohBCtNiZM2cYMWKEeep0jUbD8OHDmTZtGtOnT2fEiBHo9bL0kxDCcqX8nkJpZil2rnaETQpTpYYW/Sv566+/dnAZJpmZmQ26LZ04cYLVq1fTvXt3wsPDueaaa1i8eDG9e/dm165d/PWvf8VgMPD44493Sn2iITudHe9f+z4D/Abw3G/PkVSQxGM/Psb//fJ/3DrwVpZEL2Ggn43O4CWEsCnHjx9HURQCAgJ49dVXufrqq2VgthDCqmx/aTsA/W/pj95enR9CtG1504oVKzh9+nSz51NSUlixYkWrr/vFF18wb9485s2bB8C6deuYN28eCxcuxM7OjuTkZO69917GjRvHk08+SWhoKAcOHJBpAFWk0Wh45IpHSPtjGu9d8x4D/QZSUVvBewfeY9A7gxj/0XjWxq6loqZC7VKFEKJZKSkpAAwdOlRmexJCWJ2z+8+SuDkRjU7D2MfHqlaHRrlwyewW0Ol0fPrpp80O3F69ejXz58+nrq7usgtsb8XFxbi7u1NUVGR5geSHH2DWLOjVC06cgAu6n1kDRVHYfmY7y/cu56vjX1GnmP4bcNA7MDFsItMipjEtcho9vXs26l4nhBCdoaSkhAMHDrB//3727dvH/v37iY839Uu+7777ePvtt1WuUAghWq62spYVV60gdUcqA28dyA2f3qBaLW1qJ7lUFikrK5O+qG0xbBg4OkJcHHz9NcyZo3ZFrabRaBgXOo5xoeNIL07nv/v/y0eHPiK1OJVNCZvYlLAJNkO4RzjTIk0hY1LYJFztbWQFdSGERSktLeXgwYMNQkRcXFyTf8ciIiJYuHChClUKIUTbKEaFb277htQdqdi72TP+mfGq1tPiFosjR46YZ19atGgRixcvZtSoUY1eV1hYyDvvvINOp+Po0aPtWmx7sOgWC4C//hWeew569IDjx8HeXu2KLpuiKJzIPcHGUxvZlLiJ31N+p7qu2nzeoDUwtvtYpkVOY2bUTPp166ditUIIa1VXV8fu3bvNAWLfvn2cPHmyyRAREhLC8OHDGT58OMOGDWPYsGH4+PioULUQQrTdpj9sYs/re9AatNy6+VbCJ4WrWk+Lg8Xf/vY3/va3v5nepNFctNXCw8ODFStWMGvWrPapsh1ZfLAoLYWePSEjA957D+6+W+2K2l1ZdRm/nv6VjQkb2ZiwkaSCpAbnv7/le2b1tLz/doQQlu3+++/nnXfeaXQ8ODiYYcOGNQgR3bp1U6FCIYRoP/E/xLNq1ioAZq+czYBb1F+wuMX9le69915mzZqFoihER0fz3HPPMX369Aav0Wg0ODs7ExERIV2h2srFxdQFavlyuMgAeWvmbOfMzJ4zmdlzJgC/p/zOtM+mUVFbgZ3ODl8nGTQphGi9yMhItFotRqPRfCwoKIhFixYxZ84cBg0aJGO7hBA2Q6szzcHk6O1Ivxsto7dHmwZv//bbb/Tp08cqf/Gx+BYLgIcfhjfegKefhhdeULuaDnUg4wDXrrqW9JJ0fJx8+PbmbxnTfYzaZQkhrFR2djbr1q3j66+/5qeffqKmpsZ8rkePHsyePZvZs2czcuRItNo2TYwohBAWoa6mjtcCXqMir4JbN99KxJQItUtq23SzEyZMsMpQISxLfkU+Ez+eSHpJOn18+rDn7j0SKoQQl6Vbt27cc889bNy4kezsbD799FOuv/56HBwcSEpK4tVXX2X06NH07NmTV199lby8PLVLFkKINtEZdPS7ydRSEbsmVuVqTNrUX2ny5MkXPa/RaHBwcCA4OJhJkyYxd+5c6RolGtFpdNjr7SmpLqG8ppzymnK1SxJC2BAPDw9uvfVWbr31VsrKyti0aRNfffUV69evJzExkT/96U/85S9/4eabb+aBBx4gOjpaukoJIayKZ4QnANWl1Zd4ZedoU4uF0WgkNTWVX3/9lcOHD1NUVERRURGHDx/m119/JTU1lezsbL766ivmz5/P8OHDyc3Nbe/ahZVzd3Bnx507iPSKJKUohdEfjDZNRyuEEO3M2dmZOXPmsHLlSjIyMnjvvfcYMmQIVVVVrFixgiuuuILhw4fz/vvvU1ZWpna5QgjRIvmn8gHwivJSuRKTNgWLF154gYKCAj755BOys7PZv38/+/fvJzs7m48++oiCggKWLVtGTk4OH374IbGxsTz55JPtXbuwAT29e7L7rt1MCJ1ASXUJM1fOZHnMcrXLEkLYMGdnZ+6++27279/P7t27ue2227C3t+fAgQPcc889BAQEMH/+fL766isJGUIIi5YXb+rO6d3TW+VKTNoULB577DHuuOMOFi5ciE6nMx/X6XTcfvvtLFq0iD/84Q9oNBoWLVrEnXfeyQ8//NBuRQvb4u3kzZaFW7hj8B0YFSNLNy5lyYYl1Bpr1S5NCGHDNBoNI0eO5JNPPiE9PZ1XXnmFiIgISkpKWLVqFXPnzsXX15fZs2fz+eefU1RUpHbJQgjRgDlYRFlxsDhy5AhhYWHNng8LC+Pw4cPm58OGDSM/P78ttxJdhJ3Ojg+u/YCXrnwJgDf3vsk1q66hqFL+kAshOp63tzePPfYY8fHx7Ny5k8cee4zw8HAqKir45ptvuPXWW/H19WXGjBl88MEH5OTkqF2yEKKLqy6rpiS9BLDyFouAgAC+/PLLBnOF1zMajaxZswZ/f3/zsby8PLy8LKPvl7BcGo2GR0c/yqOjHgVgU8ImJq+YLC0XQohOo9VqGTVqFK+88gqJiYkcPHiQv/zlL/Tp04eamho2btzI3Xffjb+/PzNnzmTjxo1N/i0UQoiOlp9g+tHe3t0eRy9HlasxadNUTX/84x9ZunQpY8aM4Z577iEiwjRvbkJCAu+99x579+7ljTfeML9+7dq1REdHt0/FwuakFaexKWETmxI2sTVpK0VV51opUotSMSryR1sI0fk0Gg2DBw9m8ODBPP/885w4cYKvv/6ar7/+mgMHDrBhwwY2bNhAZGQkDzzwAHfccQceHh5qly2E6CIcPR3RaDVUFVVx6ONDDF40WO2S2rZAHsDbb7/NM888Q15ennl6PkVR8Pb25tlnn+XBBx8EoKqqit27dxMWFkZoaGj7Vd5GskCe+qpqq9h+ZrspTCRu4lj2sQbnvR29mRIxhWmR05gZNRNvJ8to3hNCiHqnTp3i7bff5sMPPzSPvXBycuLWW2/lwQcfZODAgSpXKIToCn57/jd+feZX9I567om5h2791V1nrs3BAqCmpoZ9+/aRkpICQGhoKMOHD8dgMLRbge1NgoW6/rXrXzzzyzOU1ZybaUWDhpHBI5kWMY3pUdMZFjAMnVZ3kasIIYT6ioqKOH78OC+++CLff/99g3M33ngjq1atajDBiRBCtDfFqPD59M9J3JKITx8f7j96P1pdm0Y6tIvLWrXOYDAwatQoRo0a1V71CBv3Y9KPDUJFX9++rJy9kkH+g1SsSgghGlIUhezsbFJSUprdLjZL1ObNm7mM3+2EEKJFNFoNPa/pSeKWRPIT8qkuqcbBw0G1ei4rWBw/fpykpCQKCgqa/Af0tttuu5zLCxu0cvZK3tn3Dm/ve5vU4lSO5xxn2H+HcX3v61kSvYQJoRNk5VshRIerqakhPT292dBw5swZqqqqLnkdLy8vQkNDzVv37t0JDQ1l9OjR6PWX9SdWCCEuKfdkLlsf3wrA5L9PVjVUQBu7QiUmJnLrrbcSExPT7C8yGo2Gurq6yy6wvUlXKMtQa6zlu7jvWB6znF9O/2I+PqDbAL6++WsivSJVrE4IYQuMRiOpqamcOHGC48ePc+LECeLi4jh9+jTp6emXnM1Jo9EQGBjYIDicHx5CQ0NxcXHppE8jhBANKUaF90a8R8aBDMKvDGfhloVotOr+ONumn1MWL17M0aNHef311xk3bhyenp7tXZewcXqtntl9ZjO7z2yOZR/j+i+uJ7EgkaPZR9mZulOChRCixWpra0lMTDSHh/O38vLyZt9nZ2fXICRcGByCg4Oxs7PrxE8ihBAtV5RaRMaBDLR6LTesuEH1UAFtDBY7duzgqaeeYunSpe1dj+hiaupqeGPPGyQWJAKweNhi5g+Yr3JVQghLVFFRQVxcnDk01AeJU6dOUVNT0+R7DAYDPXv2pE+fPvTp04fevXvTo0cPQkND8fPzQ6tVb5CjEEJcDq3+f/9+acA10FXdYv6nTcHCx8cHd3f39q5FdDFGxcjMlTP5MelHNGj419R/8fDIh2WMhRDCLCkpiT/96U8cOnSI5OTkZrvfOjs707t3b/r06UPfvn3NQaJHjx4WPVOhEEK0ld7B9DXeWGPEWGdUdTaoem0KFvfddx+fffYZDz74oEylJ9ps39l9/Jj0I/Y6e7686Utm9ZyldklCCAuzbt26/2fvvuOjqvL/j7+mJ5PeOykQCITeRYqgYkWwrn3ta0HXXd39+lV/67rlu6u7llVXV2zr2taGgiiIIiAq0hEIEEIIIYX0Xqbf3x8DA0MKSUhyZ8LnuY/7mDv33rnzmawk9z3n3HNYvHhxu/umT5/Orbfeypw5c0hOTpbWByHEacUYdKyrpqXOgjnKrGI1bj0KFkOHDsXpdDJmzBhuueUWUlJS2g0Yl1122SkXKAYuq8M94kpaeJqECiFEuxYuXIjRaGTt2rV8//33lJaWevZ99913fPfdd2RkZHDmmWd6lhEjRkjIEEIMePoAPaHJoTQUN1CTV+O/weJnP/uZZ/3BBx9s9xhfHRVK+A4FGeNdCNE5g8HAPffcwz333IOiKBQWFvL99997lp07d3LgwAEOHDjAW2+9BUB4eDhnnHEGM2bMYPbs2UycOFGGfhVCDEhRQ6NoKG6gKreK5KnJapfTs2CxevXqkx8kRCcONx7mgZUPABAZGKlyNUIIf6DRaEhLSyMtLY3rrrsOcM9+/eOPP3qCxoYNG6irq2P58uUsX74cgODgYE/ImD17NuPGjZNuvEKIASF6RDQF3xRQvL6YsT8fq3Y5PZvHwp/JPBbq2162nXnvzaO4oZiowCiWXbuMqclT1S5LCDEAOBwOfvrpJ77//nvWrFnD2rVrqamp8TomNDSUmTNneoLGmDFjpOuUEMIvHVh1gLfOeYuAiAAeOPwAepO6rbOn9O5Wq5WtW7dSUVHBmWeeSXR0dG/VJQaoL/d/yeUfXE6zvZms6CyWXbOMwZGD1S5LCDFA6PV6JkyYwIQJE7jvvvtwuVzs2LGDNWvWsHr1atauXUt9fT3Lli1j2bJlAERERDBr1iwmTJhAUlISiYmJniUyMlJGqhNC+Ky0s9IISQqhsaSRvM/zGH7ZcFXr6XGweO655/j9739PfX09AF999RVz5syhqqqKrKwsnnzySW655ZZeK/S0YjK5H3fvVreOPvDIN4/QbG8GICs6i5rWGgYjwUII0Te0Wi1jx45l7Nix3H///TidTrZv387q1atZvXo169ato7a2lk8//ZRPP/20zeuNRqNX0Dh+SUhI8KyHhYVJABFC9DutTkvWpVlsemETBd8UqB4setQV6o033uDWW2/l6quvZu7cudxyyy18/fXXzJkzB4CrrrqKuro6Vq5c2esFnyq/6Aq1YweMHQuKAt9/D9OmqV1Rr1ldsJrffv1bNpdu9mybnDSZeyffy5UjrsSkN6lYnRDidONwONi6dStr1qwhNzeX0tJSDh8+TGlpKZWVlV0+T2BgYIcB5PglODi4Dz+NEOJ09PG1H7PrvV2c/Zezmf7QdFVr6VGwGDlyJJmZmXzyySdUV1cTExPjFSyeeOIJnnvuOUpKSnq94FPlF8EC4Pbb4dVXYfJkWL8eBlj/340lG3l+4/N8kPMBNqcNgBhzDL+Y8AsenvEwgYZAlSsUQpzubDYbZWVllJaWdrrU1tZ2+ZwhISFtWj3S0tKYN28egwYN6sNPI4QYqF4a9RIVuyq4Ztk1DL1oqKq19ChYBAQE8Nxzz3HHHXe0GyxeeeUV7r33XiwWS68XfKr8JliUlcHgwdDSAps2wcSJalfUJ8qbynl166u8tPklShrdQfQf5/+D+6bcp3JlQgjRNa2trZ5WjvaWo/saGho6Pc/MmTO57rrruOKKK4iMlNHyhBBd8yfTn3DanNyXfx8RGRGq1tKjeyzCw8OpqqrqcP/u3buJj4/vcVECiI+H1FTYsweam9Wups/EBcfxyMxH+O2Zv+Wqj67i072f0mhtVLssIYTossDAQDIyMsjIyOj0uKampnYDyJYtW/j22289y8KFC7ngggu47rrrmDdvHoGB0oIrhOiYPkCP0+bE5XCpXUrPgsWFF17IokWLuPvuu9vsy8nJ4ZVXXpEbt0W3GHQGYswxapchhBB9Jjg4mMzMTDIzM9vsKyoq4r///S/vvPMOP/30E0uXLmXp0qWEhIRw2WWXcd999zF+/HgVqhZC+LrAqECsDVZaa1rVLoUeddz/05/+hNPpZOTIkTz66KNoNBrefPNNrr/+eiZOnEhsbCy/+93vertWMYDl1+TzTcE3ABh1RpWrEUKI/hUYGMjEiRO59dZbOfPMMz3bGxsbefPNNznrrLM4zaadEkJ0UWCku1XTF4JFj1osEhMT2bJlCw8//DDvv/8+iqLw1ltvERISwjXXXMNf//pXmdNCdNm6wnVc+v6lVLdWkxSSxDWjrlG7JCGE6BPNzc3k5OSwa9cudu7c6XksLy9v93i9Xk9WVhZXX321DGcrhGiX3wcLgNjYWF599VVeffVVKisrcblcxMTEoNVqaW5uprS0lMTExN6sVQwwiqLw5k9vcsdnd2B32ZmYOJElVy8hMUT+uxFC+L+ioiJ++OEHrwBRUFDQYctDeno6o0aNYuTIkZ7HoUOHYjRKK64QomMDIlgcLybGu2/8s88+y+9+9zucTmdvnF4MME22Jt766S1e2PQCuyvdkwBeNvwy3rr0LcwGs8rVCSFEz1RVVbF69WpWrVrFN998Q15eXrvHxcbGtgkQ2dnZMseFEKJHjgaLhpLOR57rD70SLIToin3V+3hx04u8sf0NGqzu//iDDEH8Ztpv+H+z/h9azcCaq0MIMbA1NTXx7bffeoLE9u3bvfZrtVrGjx/P2LFjPQFi5MiRxMbGqlOwEGJASp6azOaXNpP7aS7n/PUcVbtNSrAQfUpRFJbvX87zG59nxf4Vnu2ZkZncM+kebhp7E2EBYSpWKIQQJ+dyuSgrKyM3N5c1a9awatUqNmzYgMPh8DouOzubs88+m7PPPptZs2YRFia/34QQfSvr0iz0d+qp3lfN4S2HSZyoXpdyCRaiT72x/Q1uXXorABo0XDT0IhZOWsi5g8+VFgohhM+w2WwUFRVRWFjoWQ4dOuRZLyoqwmaztXldWlqaJ0jMmTOHuLg4FaoXQpzOTCEmhl0yjJz3c9jzyR4JFmLgKqgtAGDGoBm8Mf8NBkcOVrkiIcTpqLGxscPQUFhYyOHDh086nKtWqyU5OZlp06Z5wkR6eno/fQIhhOjY0e5P+gB1L+27/O5bt27t8klLS0t7VIwYuMbGj5VQIYToMy0tLeTk5LQbGgoLC6mtrT3pOQICAhg0aBCpqaltlkGDBpGUlITBYOiHTyOEEN1T9EMRAIPOHKRqHV0OFhMnTuzyzSCKosh420IIIfrc0XmUfv3rX1NdXd3pseHh4e0GhqPrsbGx8rdLCOF3GksbqT9Uj0anIWlykqq1dDlYvPHGG31ZhxBCCNEt+/fv584772TVqlUAREdHM2TIkDaB4egSGhqqcsVCCNH7GksbAQhJCMEYrO68N10OFj//+c/7sg4hhBCiS+x2O0899RSPP/44FouFgIAAHnvsMR544AHpqiSEOO3YmtwDSxhD1J9MU27eFn3GpbjIq2l/gighhOip2267jf/85z8AnH322fzrX/9iyJAhKlclhBDq8ASLIPWDhYz3KfpEq72Vqz+6mvdz3gdgdtpslSsSQgwUWq37T5dGo+G2226TUCGEOK0FxQUBUJ1XjcvhUrUWCRai15U1lXHWm2fx4e4PMWgNvH7J61w6/FK1yxJCDBAvvvgil112GYqicM011/D000+rXZIQQqgmYXwCgVGBWOutFK0vUrUWCRb+4IMPoLxc7Sq6ZEf5Dia/MpmNJRuJDIzkqxu+4uZxN6tdlhBiAAkMDOSDDz7gvvvuA+CBBx7ghRdeULkqIYRQh1anZch57pbb/cv3q1uLqu8uOjd2rPvxxRchNRVuvRV27VK1pM58vu9zznz9TIoaihgaNZQfb/2RWWmz1C5LCDEA6XQ6nn32WaZPnw7AwYMH1S1ICCFUlDE3A4CDqw+qWocEC1/2n/+4WyumTAGrFV5/HUaNgvPOgy+/hJPMEttfFEXh2R+f5ZL/XkKTrYnZabNZf+t6MqMy1S5NCDGAVVZWsmHDBgBuuOEGlasRQgj1pM5MBaB0cym2ZptqdUiw8GV6PVx5Jfz4I/zwA1xxBWi1sHIlnH8+TJ0KDQ2qlmh32rn787v51Ze/wqW4uG3cbXx5/ZdEBkaqWpcQYmByuVzs2LGDZ555hssuuwy73c6kSZMYM2aM2qUJIYRqwtPCCU0OxeVwUfxjsWp1SLDwF2ecAR9+CPv3w/33Q3AwbNwITzyhWkl1ljouevci/rXlX2jQ8Pdz/86ieYsw6GQceSFE71AUhfz8fF555RWuvvpq4uPjGTNmDL/+9a/5/vvvATz3WgghxOlKo9GQfEYyAIe3HlatDpnHwt+kp8Mzz8BZZ8GCBfDUU3DHHe57MPrRgdoDXPzuxeyp2oPZYObdy95lftb8fq1BCDEwlZWV8c0337Bq1SpWrVpFYWGh136z2czMmTOZM2cOc+fOldYKIYQAYrJj4EOo2lOlWg0SLPzVJZe4w8WaNfDoo/DWW/3ytnWWOt7Y9gb/993/UdVSRVJIEp9d8xnjEsb1y/sLIQYORVEoKSlhx44d7Ny5k507d7J161b27NnjdZxer2fq1KmcffbZnH322UyZMgWjUf2JoIQQwpfEDI8BJFiIntBo4He/cweLb77p87fbWb6TFza+wNs736bF3gLAhIQJLL1mKYkhiX3+/kII/9bY2MiuXbs8IeLoY11dXZtjNRoNY8eO9QSJ6dOnExwc3P9FCyGEH4kfFw+4b+BuKmsiOL7/f29KsPBnkUdukO6j0aEcLgdL9i7h+Y3Ps7ZwrWf7yNiR3Dv5Xm4ccyMB+oA+eW8hhH9yOBzk5eV5hYcdO3Z0OBysTqcjKyuLUaNGMXr0aEaNGsUZZ5xBVFRU/xYuhBB+LioziuQzkileX8y217cx4+EZ/V6DBAvRrrUH13L9J9dT3HBsZIFzM87lkRmPMDN1JhqNRsXqhBC+wmq18u2337Js2TLWrVvH7t27sVqt7R6bmJjoCQ9HH7OysjCZTP1ctRBCDEzjbx9P8fpitizawvSHpqPR9u/1mgQL0a6Pdn/kFSoAvjrwFXuq9pAdk012TDYjY0eSHZvNiJgRBBulm4IQp4uysjK++OILli1bxldffUVTU5PX/qCgIEaNGuVZjoaIyEgZhloIIfrKwbUHWfendQA0lTVhb7FjDO7f+9EkWIh2PXnuk0xMnMjOip3sqthFTmUOxQ3FnuXL/C+9jk8NS3UHjZhssmPdoSMrOguzwazSJxBC9BaXy8W2bdtYtmwZy5YtY/PmzV77ExISuOiiizjvvPMYP348aWlpaLUymrkQQvQHW5ONr//3aza9sAmA0JRQ5r8xv99DBYBGUXxk+uZ+0tDQQFhYGPX19YSGhqpdzqn56ScYOxYSEqC0tM/frs5Sx+7K3eRU5HjCRk5lDmVNZe0er0FDRkQG4xLG8cQ5T5ARkdHnNQoheo/L5eJ3v/sdr732GmVl3v/ODQYDF110ET//+c85++yzCQ4Oli6SQgjRz6rzqnn7vLepK6gDYPwd45n7t7mYQtXpYirBwp/1c7DoSHVLNTmVR8JGRQ7fHvqWXRW7vI75+7l/54FpD6hUoRCiJ6qqqoiJienSsUajkcjISKKiojyPx6939BgQIANACCFET637v3V884h7dNCLF13MhNsnqFqPdIUSpyzKHMXM1JkE6gPZULKBfdX7PPuizdHcMf4O7pl8j4oVCiF6Ijo6mrVr17J+/Xqqq6upqamhurq6zbrdbsdms1FWVtamZeNkAgMDOw0e7W2LiIiQeSyEEAKYcMcEdry9g6o9VXz3f98x5PwhhKWEqVaPtFj4Mx9psVhftJ5fffkrNpRs8GyblDiJeyffy5XZV8qQtEIMYIqi0Nzc7AkaJz62t62mpoaamhqcTmeP3zckJKRN4IiPj+fMM89k9uzZREdH9+KnFEII39V4uJF/z/w3NftriMyM5Nb1t2KOUuceVwkW/sxHgsXF717M53mfA3D+kPN5/KzHmZw0WbV6hBC+z+Vy0djY2Gn4aC+g1NXV0ZU/W8dPsDdjxgyZYE8IMaDVH6rntTNeo7G0kUtev4RxN49TpQ7pCiVO2eNnPc6m0k1UNFfwU9lPaDUyGowQonNarZawsDDCwsLIyOj6wA5Op5O6urp2w8eBAwf45ptvyMnJYfv27Wzfvp2nnnoKvV7PlClTPEFj6tSp0pVKCDGghA0KIyQxhMbSRgIjAlWrQ1os/JmPtFgAFNYVcvF7F7OrYheB+kDeuvQtLh9xuao1CSFOT2VlZaxevZpVq1axatWqNrN+m81mpk+fzvTp0xk8eDBpaWmkp6cTFxcnw+QKIfySoig8Ef4E1gYrd+fcTcyIrg280dskWPgzHwoWAA3WBq7+6GqW718OwP/N+T8emv6QDEEphFBVQUGBJ2R88803VFRUtHtcQEAAqampnqBx/GNaWhoxMTHy+0wI4ZNaqlv4W/TfAHjE8gh6kzqdkiRY+DMfCxYADpeDB758gOc2PgfAz8f8nEXzFmHUSbcDIYT6FEUhJyeHVatWsW3bNg4ePEhBQQHFxcW4XK5OX2s2m9sNHUcfIyIiJHgIIVRRV1jHP9L+gT5QzyMtj6hWh9xjIXqVXqvnHxf8g2HRw7hv+X28+dObFNQVsOTqJYQHhKtdnhDiNKfRaBg5ciQjR4702m632ykuLqagoMATNo5/LC0tpaWlhd27d7N79+52zx0SEuIVNoYPH84NN9yA2azO6CxCiNOHvcUOgMFsULUOCRaiT9w96W4MWgN3LLuDbwu/5bWtr8kEeUIIn2UwGEhPTyc9Pb3d/VarlUOHDrUbOgoKCigvL6exsZEdO3awY8cOz+s+/vhjvvjiC/R6+XMrhOg7Tuux4bsVRVGt9VR+04k+8UPRDzzyjbspLjEkkflZ81WuSAghes5kMpGZmUlmZma7+1taWjh06JAnbBw4cIAXX3yRr776ivvuu49//vOf0k1KCNFnIjMj0eq1tFa3Unewjoj0CFXqkGAhet17O9/j5iU3Y3VaGRc/js+u+Yyk0CS1yxJCiD5jNpvJysoiKyvLs23q1KlcccUVvPTSS2RlZXHfffepWKEQYiAzBhlJnJRI8fpiCtcWSrAQA0N1SzU3LbkJm9PGgqwFvH3p2wQZg9QuSwgh+oTT6aSwsJC8vDzPsn//fvLy8igoKPAc9+CDD3L77bcTGKje+PJCiIEt5cwUitcXU7KxhLE3jVWlBgkWoldVt1Zjc9oIMYbw8VUfy2R5Qgi/53Q6KSoq8goNR5cDBw5gt9s7fG1gYCBDhgzh/PPPl1AhhOhT1gYrAAERAarVIMFC9AmdViehQgjhVxRFYf369ezcubNNeLBarR2+zmQyMXjwYM89GMcviYmJMumeEKJfVO2pAiBmuDqT44EEC9FLbE4bH+3+iOc2uOevkFAhhPAXFouFt99+m2effZacnJx2jzEYDAwePJghQ4a0CQ/JycnodLp+rloIIbzV5tcCEJYaploNEizEKSlpKOHlLS+zaMsiypvLATBoDSyctFDlyoQQonPl5eW89NJLvPjii1RWVgIQHBzMzJkz24SHQYMGSXgQQvi02FGxNJY2krs0l9QZqarUIMFC9Mj3h77nuY3PsXjPYhwuB+AeVvauiXdx+/jbiQuOU7lCIYRoX05ODk8//TRvv/02NpsNgEGDBnHfffdx2223ERam3rd9QgjRU5MXTib/y3y2vbqNs35/FsYgY7/XIMFiIGhtBbsdDP0z2+JX+V8x9+25nuczU2eycNJCFmQtwKBTd8ZHIYToTGFhIRMmTPDcMzF8+HAef/xxLr30UpnETgjh1zIvzCRicAS1+bXkLsll1LWj+r0G6QjvzzIzIToa6upg0aJ+e9vk0GRCjCGAu5Vi0cWLuDL7SgkVQgifFxYWxrBhwzzP9+3bxyeffMK2bdtUrEoIIU6dRqshJNF9faa4FFVqkGDhz8xm+MMf3OuPPeYOGP1geMxwfrj1B1LDUiltLOWM185gdcHqfnlvIYQ4FeHh4Wzbto2lS5dy1lln4XQ6ee+995g8eTIzZsxg8eLFOJ1OtcsUQohuUxSFip0VgPt+CzVIsPB3t98Ow4dDdTU891y/ve3I2JFsuG0DU5OnUmupZe7bc3l166v99v5CCNFTWq2WefPmsXr1arZu3coNN9yAXq/nu+++4/LLLycuLo6bbrqJxYsX09TUpHa5QgjRJY0ljVjqLGh0GqKzolWpQYKFv9Pr4cYb3ev5+f361nHBcaz++WquGXkNDpeD2z+7nd+s/A1Ol3zbJ4TwD+PGjeM///kPhYWFPPzww0RFRVFdXc2bb77J5ZdfTnR0NBdddBGLFi2itLRU7XKFEKJD5Tvdo3NGD4tGb1LnnjEJFgOBikMgBugDeOeyd3hs1mMA/H3937n8g8tpssm3fEII/5GYmMif//xnDh8+zOrVq7n//vtJT0/HarXyxRdf8Itf/IKkpCQmT57Mn//8Z3bu3ImiqNOHWQgh2qN2NyiQYCF6gUaj4fdn/Z53LnsHo87IktwlXL/4erXLEkKIbjMYDJx11lk888wz5Ofns2vXLv785z8zZcoUADZt2sSjjz7K6NGj+c1vfqNytUIIcUzFriPBYqQECzEAaI78D0BBvskTQvg3jUZDdnY2Dz/8MD/++COlpaW88sorhIaGAlBWVqZyhUIIcYxW776sd1gd6tWg2juLAUNRFB5f8zjXLr4Wq9PKJcMu4Z3L3lG7LCGE6FUJCQkMHjyYhoYGdDodjzzyiNolCSGER9KUJABKfixRrQYJFuKU/fHbP/L7tb8H4MEzHmTxVYsJNgarW5QQQvSBhx56CIC77rqL4cOHq1yNEEIckzw1GYDiDcUyj4XwX2VN7u4AWo2WrOgsdFr1biYXQoi+1NDQAIDNZlO5EiGE8GYIdE9U7LQ5VRtcQoKFOGVPzX2Kq0dejUtxcdtnt/Hbr36LS3GpXZYQQvS6F198EYBFixbx5ZdfqlyNEEIcU7yhGIDECYlodepc4kuwEKcs0BDIu5e9y+9n/R6Av/3wN+794l51ixJCiD4we/Zs7rvvPgBuvfVWLBaLyhUJIYRb8Y/uYHH0Xgs1SLAQvUKj0fDYWY+x6OJFALy36z2VKxJCiL5xwQUXAO5RoY52jRJCCLU1HHL/PooZEaNaDRIsRK+alTYLkOFmhRADk9Pp5H//938B9w3csbHqjRcvhBDHcznc3dC1BvUu7yVYiD7hcDmwOeXmRiHEwPLOO++wfft2AOLj49m8eTNWq1XdooQQguOChV6ChRggYoNiMRvMNNmaOO/t86hprVG7JCGE6DV79+71rD/66KNMmjSJkJAQJkyYwC9+8QsWLVrE1q1bZdQoIUS/szW7f+8cHR1KDXrV3lkMSOEB4Xx05Uf87KOfsebgGqa+OpVl1y5jaNRQtUsTQohT9vjjjzN58mQ2bdrE5s2b2bJlC9XV1WzdupWtW7d6jjMajYwePZoJEyYwceJEJk6cSHZ2NgaDen/whRADW0Ox+x6L0ORQ1WrQKGoNdKuShoYGwsLCqK+vJzRUvR98r/rb3+C3v4Ubb4Q331S7GgB2Vezi4ncvprC+kIiACP4858/cMOYGmThPCDGgKIpCYWGhJ2QcfaytrW1zrMlkYsyYMVx66aXcdtttREdHq1CxEGIgcjld/Mn0JxSnwq9Lfk1IYogqdUiwGAh8MFgAlDeVM/+/89lQsgGAUFMoN4+9mXsm3UNmVKbK1QkhRN9QFIWCgoI2YaO+vt5zjMlk4uqrr2bhwoVMnDhRxWqFEANB0foiXp/2OgazgYcaHpJ5LMTAExccx5qb1vDsec+SGZlJg7WBf2z4B0NfGMoF71zAF3lfyER6QogBR6PRkJGRwVVXXcUTTzzBqlWrqKmpIS8vj1deeYUJEyZgtVp58803mTRpElOnTuWdd96Rm8CFED227fVtAIy4coRqoQKkxULtcnqHj7ZYHM+luFiZv5IXNr7AF3lfeIajHRwxmLsn3c3NY28mIjBC5SqFEKLvKYrCxo0beeGFF3j//fex2+0AxMbGcskll5CamkpycjIpKSmkpKSQnJyM2WxWuWohhK+yNdl4KuEpbE02blp7E6kzU1WrRYLFQOAHweJ4+TX5vLjpRV7f/jp1ljoAzAYz14+6noWTFzIqbpS6BQohRD8pLy/n1Vdf5aWXXqKkpKTD4yIjIz1B42jYOP55UlISAQEB/Vi5EMJX7Fu2j/fmvUd4ejj35d+HRqNRrRYJFgOBnwWLo5ptzby7812e3/g8Oyt2erbPSp3FwskLmT9sPgadjKAihBj47HY7n3/+Odu2baO4uJiioiLP0tzc3KVzxMTEdBg8kpOTSUpKwmg09vEnEUL0t7V/WMuax9Yw+vrRXPrWparWIsPNCtUEGYO4fcLt3Db+NtYdWscLG19g8Z7FrC1cy9rCtSSFJPGrqb/iV2f8Cq1GbgcSQgxcBoOBBQsWsGDBAq/tiqJQX1/vCRnHh47j11tbW6msrKSystJr2NvjaTQa4uLiPEEjLS2NWbNmMXv27IHzRZsQp6GybWUAxI+PV7kSCRbCB2g0GmamzmRm6kyKG4p5efPLLNq6iJLGEh786kG+K/qOty99myBjkNqlCiFEv9JoNISHhxMeHs6oUe13E1UUhZqamg5Dx9HnVquVsrIyysrK2LRpEwDPPPMMOp2OM844g7lz5zJ37lwmTpyITqfrz48phDgFFbsqAIgfo36wkK5QA4GfdoXqjNVh5Y3tb/DLFb/E5rQxPmE8S69eSlJoktqlCSGE31EUhaqqKq/QkZOTw9dff01eXp7XseHh4ZxzzjnMnTuXc889l7S0NHWKFkKclKIo/J/5/3BYHNyXfx8RGeoOhCMtFsInmfQm7px4J6PjRrPgvwvYengrk1+dzGfXfMb4hPFqlyeEEH5Fo9EQExNDTEwM48d7/w4tKCjgq6++YuXKlaxatYq6ujo++ugjPvroIwAyMzOZO3cuY8aMIT09nfT0dAYNGiSziAvhAyy1FhwWB4Bqk+IdT4KF8GnTUqax4bYNXPzexeyu3M2MN2bwzmXvsCBrgdqlCSHEgJCens4dd9zBHXfcgcPhYPPmzZ6gsX79evLy8tq0ami1WpKTkz1BIyMjw7Oenp5OfHw8Wq3cGydEX2sqawIgMDIQfYD6l/XqVyDESaRHpPPDLT9w1UdXsTJ/JZe9fxlPnPMED057UNUh1YQQYqDR6/VMnTqVqVOn8v/+3/+jvr6eNWvWsHr1avLy8igoKKCgoACLxcKhQ4c4dOgQa9eubXMek8lEWlpah8EjIkLmLRKiN7ic7omGtQbfCPISLAaSAXy7TFhAGJ9f+zm/XP5LXtz8Ir/9+rfsrdrLU+c9RXhAuNrlCSHEgBQWFsb8+fOZP3++Z5uiKJSXl3PgwAFP0Dh+KSoqwmq1kpubS25ubofnPT5ojB49muuuuw69Xi5LhPBn8i94IDjaz/Xdd8FqhV//GqZMUbemPqDX6vnnRf8kKzqL+7+8n9e3v85/c/7LDaNv4J5J98jEekII0Q80Gg3x8fHEx8czbdq0NvvtdjtFRUXtho6CggLKy8upr69n+/btbN++3eu8N954Yz9+EiH8X3O5e54brc43WixkVKiB4PBhuOkmWLny2LZp0+BXv4IFC2AAfgP05f4vefCrB9lVscuz7ay0s1g4aSHzs+aj1w68zyyEEP6oubmZb7/9lpUrV7Jy5Up2797ttT84OJjzzjuPF198kdjYWJWqFMI/vXvRu+R9kce4W8dxyauXqF2OBIsBZedOeOYZeOcdsNnc29LS4Je/hLvvhgE246qiKHxb+C0vbHqBT/Z8glNxApAcmsydE+5k4eSFhAWEqVylEEKcfsrKynjzzTdZuXIl3333Hbajf5Nw3/g9adIkzj33XObOncvUqVNlhCkheqBsexkvj3sZjVbDwtyFRA6JVLskCRYDUlkZvPiie6mudm/7y1/goYfUrasPFdUX8fKWl1m0ZRGVLZUAZEZm8vm1n5MZlalydUIIcfpobW0lOzubgoICz7ZBgwZx3nnnMXfuXObMmUNkpPoXQEL4u6W3L2Xbq9sYec1ILn/3crXLAcA3OmSJ3hUfD3/4AxQVwRVXuLdVVqpbUx9LCUvhT3P+RNGvivjPgv+QEppCXk0eU16dwpqDa9QuTwghThtPP/00BQUFJCYm8vzzz5Obm8vBgwdZtGgRV1xxhYQKIXpJ0fdFAIy61nfuMZVgMZAFBkJGhtpV9CuT3sQNY25g4+0bmZI0hVpLLXPfmsvLm1+mxd6idnlCCDGglZWV8Ze//AWA+++/nxtvvJHMzEwZGlyIXmapt1C1twqApMlJKldzjNzhKgak+OB4Vv98NTctuYkPcj7gzs/v5J4v3CNHTUmawuSkyUxOmszw6OHotDq1yxVCiAHhpZdeornZPUrNb3/7W37729+i0+kIDw8nIiKCyMhIIiIiPMvxz9vbZzabJZQI0Y6SjSWgQFhqGEGxQWqX4yHBQgxYgYZA3rv8PcbEjeH5jc9T1lTG9rLtbC/bzstbXgYgyBDExMSJnqAxJWkKyaHJ8odMCCF64KyzzmLJkiWUl5dTW1uL1WrF6XRSXV1N9dF7/rrBYDB0OYSc+DwgIKAPPqEQvmH/8v0ApJ2Vpm4hJ5BgIQY0rUbLwzMe5n+n/y/FDcVsLNnoXko3srl0M022JtYWrmVt4bGZY+OD491BI9EdNmakziBAL3+ghBDiZGbPnu01N0Vrayu1tbXU1NRQW1vrWbry3OFwYLfbqaiooKKiotu1BAQEdDmEREREMHz4cMLDw3vvhyFEH1EUhX2f7QNg6LyhKlfjTYKFOC1oNBpSwlJICUvh8hHukROcLid7q/ayoWSDJ3DsKN9BWVMZS3OXsjR3KQBDo4ay7JplMrqUEEJ0U2BgIIGBgSQmJnbrdYqi0Nzc3K0gcvxzl8uFxWLh8OHDHD58uEvvGRUVRXFxsbR0CJ9XsbOCmv01aA1aBs8drHY5XiRYiNOWTqsjOzab7Nhsbhl3CwAt9ha2l233BI2vD3zNvup9THl1Cot/tpiz0s5St2ghhDgNaDQagoODCQ4OJiUlxWufw+GgoqKC0tLSdpfi4mLy8vKwWCxdfr+AgACmTJmCcYDN9yQGpu/++h0Awy4ZhinEpHI13iRYCHEcs8HMtJRpTEuZBkBZUxkL/ruADSUb3KNLXfwyN4+7WeUqhRBiYGpoaODAgQMdhobS0lLKy8txuVxdOp/BYCAhIYHExMROl/DwcLm3TviFqr1V7PrvLgBmPjpT5WrakmAhRCdOHF3qlqW3sLdqL3855y9oNTJasxBC9Ja8vDwmTpxIQ0PDSY/V6XTEx8e3GxKODxJRUVFotfK7WgwcPzz1AygwbP4w4sfGq11OGxIshDiJo6NLDYsaxh+//SNP/vAkeTV5vHXpWwQZfWeINyGE8Gcmk4mAgIA2weLyyy9n7ty5XuEhJiYGnU6GChenn7KtZQCMvWmsuoV0QGK8EF2g1Wj5w+w/8Nalb2HUGflk7yfM+vcsShtL1S5NCCEGhEGDBrF//35efPFFRowY4dn+8ccf895772G1Whk9ejTx8fESKsRpq7G0EYCwQWEqV9I+abEY6Ox2tSsYUK4ffT3p4ekseH8BWw5vYfg/h3Pe4PM4f8j5nDf4PJJCfWf2SyGE8DchISHcdddd3HnnnaxZs4YXXniBTz/9lDVr1rBmzRqMRiPJyckMGjSIlJQUUlJS2qyHhfnmBZcQp8ppd9JU3gRASGKIytW0T6MoiqJ2Ef2poaGBsLAw6uvrCQ0NVbucvtXQAJmZUFEBr7wCt92mdkUDxoHaA8z/73x2Vezy2j4qdhQXDLmA84ecz5mDzsSokxFGhBDiVBw6dIiXX36ZV155hcrKypMeHxIS0m7gOLqekpIiQ8oKv1S6pZRXJr6CKczE/9T8Dxqt7w04IMFiIHv4YfjLX2DoUNi1CwwGtSsaUJwuJ5tKN7E8bzkr8lewqWQTCsf+OQUbg5mTPofzB5/P+UPOJz0iXcVqhRDCvzkcDoqLiykqKuLQoUMUFRW1Wa+pqenSuWJiYjps8UhJSSEhIQG9Xjp1CN/y47M/8uWvviTzwkyu/fxatctplwSLgaqsDNLSwGqFTz+F+fPVrmjAq2qp4qv8r1i+fzlf5n9JRbP3TLFJIUlkRmUyJGIIQyKHuNcjhzA4YrDcBC6EEL2gubnZEzJODB1Hn7e0tJz0PDqdjtDQUAICAggICPDcWN7e8+6ud+U4o9Eow9+KNj644gP2fLyHs/9yNtMfmq52Oe2SOD5QNTa6QwVAN2c8FT0TbY7mmlHXcM2oa3ApLraXbWfF/hUs37+c9UXrKWksoaSxhDUH17R5bUJwAkMih3gtmZGZDI4cTKhpAAdgIYToRUFBQWRlZZGVldXufkVRqK2t7TB0FBUVUVxcjMPhoLa2tp+r99bfYaa9dRmq17c0VzQDEJ4erm4hnZAWi4HsxhvhrbdgxgxYuxbk2w/V1Fvq2VO1h/01+8mrzmN/7X7217iXmtbOm+5jg2KPBY4TWjvCA8L75wMIIcRpwuVyUV5eTn19PRaLBavVisVi6XS9t47zNQaDod/DzImvly5px/x3wX/JXZLLRS9dxMQ7J6pdTrskWAxkRUUwbBi0tsKSJXDJJWpXJNpR01pDfk2+J2jsrz0SPmr2U9nS+Y2KUYFRpISlEB8c716C3I8JIQnHtgXHE2IMkWZ1IYTwYYqiYLPZ+iy0HF1vbW2lsbGRxsZGmpqaaGpqwuFwqP3xO6TT6bxCR1paGosXLyYuLk7t0vqFoihU76vm4JqD/PjMj1TnVjP7T7OZ+YjvzboN0hVqYIuLcy8HD0J+vtrViA5EBkYSmRTJpKRJbfbVW+rJrz0udBy3HG46THVrNdWt1Sd9j0B9oFfQOH5JCD4WQuKC42QkKyHEaU1RFBwOB3a7vc1jb23ry3Of7P387ftkp9NJc3Mzzc3ubkClpaWUlJQM2GBxfJAoXFPIwTUHaSpr8jomOD5YpepOTlosBrJ//APuvx9iY2H/fgjxzTGPRc802ZrIr8mntLGUsqayY0tzmdfzBmvDyU92nMjASO/wEXRcCDmuJSQyMBKtRvrfCnE6cjqdA/bC2+l0qv3j7XdarRa9Xo/BYMBgMHjW29t2sv19fZ7k5GTS0tLU/pH1mq4ECZ1JR8oZKaSelUrG2RmknJnis70QJFgMVHV1kJEBtbWwaBHcfrvaFQmVtNhbvINHJ4vd1fUJFfVaPXFBccQGxWI2mDHpTZh0Jow6o2fdpDNh0h/ZdmS9zTFd2N/eNoPW4LO/WIU4VQ6HA6vV6lmOdmdpb+loX3df050L/dPs0gHAZy6s++LccpN271NcCq01rTRXNNNc2UxLZUubx5bKFip3V3YaJNLOSiN5SjL6AP/oZCTBYqD65hs4+2xITnZ3hdLp1K5I+DhFUai11LYJG4cbD7dpBalqqVK7XACvQNLtcNJLAefE1+i1egk8fsjlcvXqRfqpvsblcqn9I+k2rVbrtxfWJ9um0+nk3/VpzuVw0VLd4gkGzRVtQ8LxwaG1uhXF1bVLbH8OEifyz6rFyR39oxQRIaFCdIlGo3Hf7xEYyYiYEZ0ea3faqWiuoKypjIrmClodrVgdVmxOG1anFavDitV55PmRda9tXTnmuG1HX+Nwed9gaHPasDltNNoa+/JH0y0aNF7ho1vhpBcDzon7dRrfujBSFKXfLtK7+m29r9JoNJ6bV9tbemOf0Wj0XFT35KJdvvUW/sZpc9JS5R0GOgsLrTWtPXqfgPAAzDFmgmKCCIoNwhxj9jw3x5gJGxRG0qQkvw0SJxoYn0II0a8MOgNJoUkkhSb16/s6XU5PmDg+iHQr1JzsNT04r0s59u2yguLe5/StoSM1aLocTq4YcQV3TLijw3PZbDYOHz5MSUmJZykuLqakpISampouXdjb7V3vdqeG3rpg7419er20gglxKlxOFysfWEnJxhJPWLDW9+B3tAYCIwM7DAknPpqjzegMp9eXuxIshBB+Q6fVEagNJNAQqHYpXpwuZ7cCzKm07lgcFiqaKzjceJiqlioUutbUrqBgcViwOCxwkr+n+yr2kVad5hUcjg8QFRUVnZ+gB4xGo89czBsMcv+OEANJc0UzG5/f2KZrkkarwRxt9oQETyDoICwERgWi1UnLXGckWAghxCnSaXWYtWbMBnOXX+N0Oamz1FHTWuO1VLdWU9Nag7XVSpOtqc3+WkutVwtJdxk0BoJ1wQQoAejsOmgFR4MDa62Vpqom7A12Cg8Ucl7NeZ2ex2g0kpiYSHJyMklJSZ4lOjraa3KrrlzMG41GuZAXQvSZkIQQFry5gM/v+hxbkw2NTsPcp+Yy5d4paLTyu6c3SbAYqCwWtSsQQgA/Fv/I8xufp6qlyisg1FnqTum8QYYgzz0x7S2Hcg+xad0mGsoaqCmpoaqoClezC7vdTi21nZ47IiKCpJFJXoEhKSnJK0RER0dLGBBC+I3R148mZVoK/53/Xyp2VfDl/V9yePNhFry5QMJFL5JgMdDYbPDcc/D44+7nwb47iYoQp4On1z/Nh7s/7LXzRZujSQ5NJjIwkiBDEGaDmSBDEEHGIM+jQWtgydIlFBcUgw2wAyFAAGCD8OBwzpx0JqmJqaQmp7YJEGZz11tehBDCVzVXNFO6udS9bHI/Hj+06463dzDzdzOJyoxSscqBRYabHUi++gruuw/27nU/nzIFXnsNsrPVrUuI09ih+kN8mPMhDdYGmu3NtNhbaLY302xr9npssbd4beuvm7916DDrzYQGhhJkbD+oBBmCjoWYE7d38ppAQ6BMoiiE6BetNa3HQsSRpaGo7QSxGp2G2OxYEiYmkHlBJsMvHy6tr73Ip4LFeeedx3fffUdLSwsajYa4uDhef/11LrjgAs8xdXV1zJgxg5ycHBRFITExkS+//JKRI0d26T0GZLCoq4PbboOPP3Y/j42FJ56AG28EGf5PCL/kcDk8YaOrYaTNviPPGy2NVDdW02hppNXZilPrhH761RCoD+xSGJmTPofLhl/WP0UJIQaE/JX5bHttG6WbS6k90E4XTw1EZ0WTNCmJhIkJJE5MJH5MPAazof+LPU34VLCIjo5mwYIFzJs3D6vVysKFC6mpqaG0tJTY2FgAsrOzyc3N5YknniAuLo677roLjUZDQ0PbVNqeARksnn/e3VKh08HChfD730N4uNpVCSF8lNVq5cfNP7Lm+zV8t+E7Nm7fSENrAxgAI57HoIggktKSiEuOIzIuktDoUHQBOndgORJa2gs9rY7uj/ceYgyh7qE6aeEQQnTZc0Oeozb/WKCIzIwkcWKiZ4kfF48pxKRihacfn7rHoqrKezbfUaNGMWLECN5//33uvfdeDh06xO7du/n1r3/NAw88AEBkZCQXXXQRr776KrfddpsaZavv6I3a11wDzz6railCCN9nMpmYdeYsZp05C3DPOr1nzx7WrVvHxo0b2bp1Kzk5OTQ7mtl35H9HhYeHM378eCZMmOB+PHMCgwcP9poczaW4aLW3ttuScjSMlDSW8Og3j9LqaEWDhr+c/RcJFUKIbjn/2fNZcvMSWqpa0Gg1ZC3I4qzfnyUtEiryqWBxotLSUgCSk5MB+PBD9w2Q9913n+eYCy+8EJ1Ox7Jly07fYHGUdHsSQvSAVqslOzub7Oxs7rzzTgAsFgs7d+5ky5YtbN26lS1btrBz507q6ur45ptv+OabbzyvDw0NZdy4cYwfP557772X9PR0dxcnYxAEtX2/Q/WHOO/t82h1tGLUGfngig+YnzW/vz6uEGKAGHrxUO7efTcrfrmCXe/t4oe//cDeT/Zy2buXkTSpfydwFW4+GywcDgfXXnstISEhXHrppQAcOHAAgNTUVK9jAwICOHz4cLvnaWho8Oom1djY2EcVCyHEwBEQEMCkSZOYNGmSZ1tZWRnvv/8+b775Jtu2bfNsb2hoYO3ataxdu5Z9+/axbNmyTs/99Pqn2VvlHmTC5rRx7eJrGREzguyYbLJjshkZO5Ls2GxSQlPkpkohRKe0Oi3pc9IpXFtIY2kjNftrWPHLFdz6w61ql3Za8tlgMWbMGKqrq1m/fv0pneeSSy5h7dq1vVSVEEIMfM3NzezevZtdu3Z5lp07d3b4BY5WqyUzM5ORI0fym9/85qTnv3PinVS3VrOzfCd7q/bSYm9hc+lmNpdu9jouxBhCdmy2J3Bkx7pDR0JwggQOIU5j1kYruUty2fXfXeR/mY/LcWzS0OSpyZz1+7PUK+4055PBYvTo0eTm5vLNN994fVuWkZEBQGFhoVerhcViISEhod1zLV26tE2LxYgRI/qociGE8D8bNmxg6dKlnhBRUFBAR+N6pKamMnLkSK8lKyuLgICALr9fVnQWb136FuAe/Sq/Jp+cyhxyKnLYVbmLnIoccqtzabQ18mPxj/xY/KPX68MDwo+1bBwXOGKDYnv+QxBC+Ly8L/LY/sZ29i3bh8Pi8GyPGxPHyKtHkv2zbCLSI1SsUPhUsHC5XIwdO5acnBxWrFjBzJkzvfZfeeWVPPjgg7zwwgv87W9/A2D58uU4nU4uvvjids8ZGhrqNfpTV0ePEkKI04HFYmHatGm4XC6v7XFxcW0CxIgRI3p9ND29Vs+w6GEMix7mNdyszWkjrzqPnMocdlXs8gSPvJo86ix1fF/0Pd8Xfe91rmhzdJvAkR2TTZRZJr8Swp/ZW+x8sfALtr+x3bMtamgUI69xh4mY4THqFSe8+NRwsyNHjiQnJ4dnn32W2bNne7YnJycTGRkJHBtu9sknnyQ2Npa77roL6Pq9EwNyuNm//Q1++1v3vBVvvql2NUIIP+JyucjKyiIvL48LL7yQ3/zmN2RnZxMT45t/qC0OC7lVuW0Cx4HaAyi0/+csPji+3cARFhDWz9ULIbqrck8lH175IZU5lWi0GiYtnMTYm8YSPzZeukT6IJ9qscjJyQHg/vvv99p+66238uqrrwLw/fffM2PGDB588EEURSEhIYEVK1b0d6lCCDEgaLVafv3rX3PXXXexe/du9Hq9Z+jviIgI9Hqf+jNBgD6AMfFjGBM/xmt7i72FPZV7vALHropdHKo/RFlTGWVNZawqWOX1muTQZE/gGBk7kitGXEGwMbg/P44QohM73tnBsjuWYW+xExQXxOXvXU767HS1yxKd8KkWi/4gLRZCCOGtpaWFQYMGUV1d3WZfeHg40dHRREVFERUV1aV1k8l3JqRqtDayu3L3sdaNI4GjtLG0zbFXj7ya9y5/T4UqhRAnqi2o5bnBz3G0ITJ9TjojrhpB+ux0IjMjpbXCR/nWV1FCCCH6ndls5qWXXuLpp5+msrKS6upq6urqAKirq6Ouro79+/d3+XzBwcGekNHVMBIUFNQnFwohphCmJE9hSvIUz7Zth7fxp3V/YvGexZ5tkYGRXDH8il5/fyFEzwTHBzPyZyPJXZqLvcVOwTcFFHxTAEBIUgjps9NJm5NG+px0wlPD1S1WeEiLxUAgLRZCiF7mcDioqamhurqa6upqqqqqTrpeXV3d5ibwrjKZTCcNH1FRUQwaNIjhw4d3u4uWoii8n/M+L2x8weum73Hx47h38r1cPfJqAg2BPapdCNF3nDYnJZtKKPimgIPfHKRofRFOq9PrmPD0cNLnpJM2O42sBVkYg4wqVSskWAwEEiyEED7A5XJRX1/f5SBydN1ms3XrfQICAhg9ejQTJkxg/PjxTJgwgezsbIzGji8mFm1ZxC+W/cJr26CwQVw54koyIzPJjMpkSOQQkkOT0Wq0Pfr8Qoi+Z2+1U7y+2B00Vh+kZGOJ1zwWWZdm8bPFP1OxwtObdIUSQgjRK7RaLREREURERDBkyJAuvUZRFJqbm9u0fLQXQKqqqsjPz6ehoYGNGzeyceNGz3mMRiOjRo3yBI3x48czatQoz/waY+PHMiJmBLlVuTgV97edh+oP8dT6p7zqMelMZERkMCRySJtlUNgg9Fr5symEmgyBBtLnpJM+x30Tt63JxqHvDrHygZVU7q7EEGhQucLTm/yGFEIIoRqNRkNwcDDBwcGkpaWd9HiXy0V+fj5bt25ly5YtbN26la1bt1JbW8uWLVvYsmULr7zyCgB6vZ7s7GxP2HhtwmuMmzCO4sZi9tfsZ3/NfvJq8jzrB2oPYHVa2VO1hz1Ve9q8t0FrID0i3R00Io4FjsyoTNLD09Fpdb394xFCnIQx2Mig6YOoL6oHYNxt41Su6PQmXaEGgscegz/8QbpCCSFOS4qicPDgQa+wsWXLFs+wucd74IEH+Pvf/97ueZwuJ0UNRZ6gcXzwyK/Jx+q0dlhDgD6A4dHDPXNkHJ0zIzU8VbpWCdGHmiuaWXbnMvZ+spfIIZEs3LdQRoxSkbRY+LuGBvjXv9zrU6Z0fqwQQgxAGo2G9PR00tPTufzyywF32CguLuatt97ikUce8RzbWauITqsjLTyNtPA0zsk4x2ufS3FR0lDSbujIq8nD4rCwrWwb28q2eb0uyBDEiJgRbQJHcmiyXPwIcQoURWHnOztZ8csVtNa0otFpmP3H2fLvSmXSYuHvHn4Y/vIXyMyEXbugk5sXhRDidKEoCv/85z/5zW9+g8ViITo6mtdff5158+b1+ns5XU4K6grcc2VUHJsrI7c6F5uz/RvTQ02hjIgZwciYkZ7QMTpuNHHBcb1enxADTf2hepbduYz9y93DYMePjeeS1y4hYXyCypUJCRb+rKYGkpLAYoFPP4X589WuSAghfMITTzzBQw895Hm+YMECzjrrLEaMGMHw4cNJSkrqlW827U47DdYGz1JvrXc/Wuqpaa1hy+Et/FD0A3k1eSc9l1aj5ZOffcIlwy455bqEGKgsdRaeH/o8LZUtAATFBnHOE+eQfEYyUZlRaLTSYqEm6Qrlz3Q6ODqWe1OTurUIIYQPiYqKQqvVeubV+PTTT/n00089+4NDgskcmcng4YNJHpJMfGo8MckxBIYH0mRv8goIDbaGY+snhIdWR2uv1RyoDyRQL3NpCNEZrUFLYESgJ1g0VzSz5OYlAJjCTCROSCRhYgJJk5JInJRI2KAw6R7Vj6TFwt/95S/u7lDJyZCbC2az2hUJIUSfURSFFntLmwv89loMqpurKakqobyunJrmGhqsDbS6WnHoHWDq3brMBjOhplDCTGHux4Aw7+cnbG9vW7AxWG70FqILFJdCdV41pZtKKdlUQummUsq2leGwONoca44xkzgxkcRJiSROTCRpUhLB8cEqVH16kGDh71pbISsLDh2Cv/8dHnhA7YqEEKJPOFwOJr8yuc0N0qdChw69Q49iVbA32lEsClgA63GLBbBBRGAEceFxJEUnMShuEBlJGQwdNJShaUMZkj6E4GC5WBFCLS6Hi4qcCk/YOLz5MOU7yr0mzztq2m+mce6T56pQ5cAnXaH8nU537IZta8dDIQohhL9zKS4arA29ek6dTkdwYDCh0e5WBINiACvYm+y01rfSWNlITVkN1gYrtU211FbXsnf3XrDhHT5sEBkWSWpqaodLVFSUdMkQoo9o9Vrix8QTPyae8beNB8BhcbBv2T6+uOcLmiuaPce21vReF0bhTVos/N0//gH33w+xsbB/P4SEqF2REEL0GbvTTnlzOQ3WBhqtje5HW2P7z488trevxd7S+8U5aBM2jn+ud+kJDwwnKiSKuPA4EqMSSYlNIT0xnSEpQxicPJhwczghxhAMOpk9WIhTUbm7ku+f/J6d7+z0tFokjE/gzIfOZPhlw9HqpNthX5Bg4c+ammDQIKithUWL4Pbb1a5ICCH8gsPloMnW1Gk4qbPUUdlcSWXLkeXIelVLFQ5X277cvcmkNTE1ZSpf3/g1eq10LhCiO9Y/s56Vv155bIMGJt41kUl3TyJySCR6k/yb6isSLPzZTz/B2LEQEQGVle5uUUII4eccLgcWhwWrw4rVafWsWxyWnj/v5LiO9vV1eDgZjUXDLfW3cPm8y5k9ezYBAQGq1iOEv/hi4Rds+uemdvdptBrCUsOIGhrltURmRhI2KExaMk6RBAt/djRYJCRAaana1Qgh/JiiKNhd9h5dwHd6rLP7IcCltL3ZUm0aNAToAzDpTe5Hnclr/cR9nW5v57lBa6CpromcHTms/3Y969etp/Vwq7s7FRAUFMR5553H/PnzufDCC4mOjlb3ByKED1NcCoe3HqZ6XzXVedXU7Kuhel81VblV2Brbn7QSQGfUETkk0h00hkZ6BY+g2CC5R6oLJFj4s+3bYdw4CRZCiC47VH+Iaz6+hpKGkjYX9wq+9+dAq9ESoA/o3oX6cdu7c3Hf2XO9Vt+vFxUWi4U1a9awZMkSli5dSulxv+O1Wi3jx49n9OjRjBw5klGjRjFq1ChiY2PlwkeITiiKQnNFsztwHFlq9tW4w8f+GpxWZ4evNYWaGDpvKJe+dan8O+uEdDLzZx9/7H5MkCnshRBdc6D2AD8U/aB2Gd3idDmxO+2Ae2Sooy0rBp0Bo86IQWvAoDO0eWyzr6Ptnb3myKNRZ+zW8cfv60koCQgI4Pzzz+f888/nxRdfZOvWrSxdupQlS5bw008/sXnzZjZv3uz1mujoaEaNGuUJGyNHjmTkyJGEyKAeQgCg0WgIjAgkJCEEp9WJ4lLQ6rXoA/UYg4wc3nq43eFpAawNVvI+z8Nld6EzStfzjkiLhb8qKoKhQ8FigcWL4dJL1a5ICOEncqtyOdx0GJvTht1px+6yez3anLY22+wue4fHe/Z14zUdHT9Q6bX6Xgs21hYr1ZXV1NXUUVNZQ1VFFbVVteDEvbjwWo+KiCI1OZW0QWmkD0onMyOT9NR0zCZzt95fp5WLKeH7XA4XDcUN1B2so7aglrqDddQV1HkeG0oaOFnjrDHYSHh6OBHpEYSlhRGRHkF4WjjJU5Nlcr2TkGDhr37+c/jPf2DWLFi9GqRZTgjh5xRFweFytBs4ehJ4unV8L76HU+m4O4U/06Dpchgy6UyEBYQRZgojPCD82GPAsefHr4cHhGM2mKWLiegWRVGo2FVB/pf5FKwqoCq3ivpD9SjOzi9t9YF6wtPaBofw9HDC08IJjAyU/xZ7SLpC+aujTeBz5kioEEIMCBrNkQtXnQH8eBoHl+LC4XL0WotQR8e3u++E7S2WFsoqyjhYdJCmlibQ4V607sfg0GDCIsPafY8T77lRULA5bdicNuiDxiWdRtcmfHiem9rffuK6zP8x8LVUt3DgqwPkf5lP/sp8Gksb2xyjM+oISw3rMDjIjdh9R1os/NUbb8Att0BYmHtiPBkhRAghBNDU1MTatWtZuXIlX331FXv27PHaHxISwpw5c5g7dy433ngjwcHtd+1wupw9bhGyOqzUW+upt9RTb62nzlJ37NHS9nlvtfKYDeYOW0g63X4kmAQbg+WC08e4HC6Kfyxm/5f7yf8yn9LNpV5dmfSBetJmpTH4vMEkTkwkPD2ckIQQNFr5/1ENEiz8ldMJEye6R4a691547jm1KxJCCKGi/fv384tf/IJ169Zht3s3KQwdOpSf/exnnHfeeUyePBmDwXe+2VcUhWZ7c7uB4+jzNvtOOKbZ3twrteg0OkJNoV7hY0jEEJ467ylCTX58zeCHbE02vlj4BXs/2Yu1weq1L3ZkLIPPG8zg8waTOiMVfYB0wPEVEiz82eefw8UXQ0oKHDqkdjVCCCFU9OSTT/I///M/He4PCAggMTGRpKQkz3L888TERBITE/1yIj6Hy+HdOmKpp7y5nNyqXHKrc9lbtZd91ft6HEDW3rSWmakze7lq0ZmSTSW8OvlVr236AD1TfzWVsTePJXJIpLQu+SAJFv5MJsgTQghxhM1m47333iM/P5/S0lJKSko8S01NTZfPExUV1SZwnBhGYmJi0Gr7f4Zil+KiqqWKsqayky61ltpunTvUFEp8cLxnSQhOID44nhExI7h46MVoNTIjc3/bs3gPuz/azYGvDtBS1eK1Lzw9nMFz3a0W6XPSCQjzv0A8EEmw8GcSLIQQQnSBxWLxhI3jQ8eJAcRqtZ78ZIDBYCAhIcETOiZMmMCNN95IUlJSu8fbnDaabc0025s9jy32ljbbmm1Hth9Zb7I3Ud5U7gkLFc0V3bofw6A1HAsKIQnEB8V7hYejS1xwHGaDucvnFf1LcSmUbS9j/5f7ObDyAIe+P4TLfmy+CY1OQ/KUZNLPTmfQ9EEkn5GMKcSkYsWnLwkW/kyChRBCiF7gdDlptjVTUllCQXEBhYcLKS4vpqSyhLLqMirrKqlqqKK2qZZGa6N71C4DYMRrPSIugvCYcHQBumPhwd6Mw+Xo1XqjzdGeFoXOloiACOkuMwDZmmwcXHOQ/JXukaGqc6u99mu0GuLHxpMyPYVB0wcx6MxBhCTKRJH9QYKFP5NgIYQQpwVFUbA4LF3+pt/rsQuvsTgs/fI5dBodQcYgggxBbR7NBvOxbcdtjw2K9QoLsUGxMqys8FJ3sI78r/I5tO4Qh747RF1BXZtjIjIiGDR9kCdsRGdFS+jsAxIs/JkECyGE8FuvbX2Nn8p/6jAQHB8GWuwtuBTXyU96ijRoPBf4ZoO5TQDwbGtne31lPd+v+Z41X62hsboRbIADZk6dydQJU4kKiSIyLJLQ0FBCQkIIDQ31LCEhIYSEhPjUaFXCfzWUNFD0fRGHvjvEoXWHKN9RjuLyvtwNjApk0JmDGPPzMQy/bLhKlQ48Eiz82bvvwnXXQXo6HDigdjVCCCG6qKShhORnkk/5PCadidTwVOKC4rwv9PXmzlsE2gsLxiAC9ac+47DVauXTTz/llVdeYdWqVd16bWBgYIfBozvPg4JkAjRxjKXeQvGPxRz67hBF3xVRvKEYR+ux7nk3f3czg84cpGKFA4cEC39lscDw4XDwIPz+9/DYY2pXJIQQoosURWHRlkVsLt1MRUsFlc2VVDRXUNFcQaOt7UzCJxOgDyA2KJbYoFhizDHtrwfFeLYFGgL74FO1lZ+fz3//+19KSkpoaGjwLI2NjV7PLZbe7Yql0Wg8YeNUQ4rJJDcBDzROm5PD2w7z/V+/Z++ne0mclMhtP94mk+r1AgkW/urJJ+F//geSkiA3F4KC1K5ICCFEL7A4LJ6gUdlyLHBUNldS0XLc+pHtrY7Wbr9HsDG43fDRXhiJMcf0+T0NdrvdK2ycGDy689zp7J1ZvI8yGAxdCiITJ07kwgsv7NX3Fn2rqbyJ5zOfx9ZoY8F/FjDmhjFql+T3JFj4q/R0d2vFG2/ATTepXY0QQgiVNNuaj4WPLgQRu8t+8pOeICIgwtPicbJWkajAKHRaXR980pNTFIXW1tZTCiZHnzc1NXX7/W+55RZeeOEFAgP7p0VInJrD2w7z/qXvU19YT/rZ6dz49Y1ql+T3JFj4q9hYqKyEXbsgO1vtaoQQQvgBRVFosDacNIgcfV7ZUtntm8Y1aIg2R3c5iPjqkLBOp5OmpqYuBZGysjLeffddXC4XY8eO5eOPPyYjI0PtjyA64LA4WPuHtXz/5PcoToXAyEAue/cyhpw3RO3S/J4EC38lwUIIIUQfcykualprvFo8vMLICcGkurX65Cc9gV6rJ8Yc024QSQpJ4vIRlxNsDO6DT9e7vv76a6655hqqqqoICwvj73//O1OmTCEzM5OAAJkV2leUbCzhkxs/8cx9MeLKEVzw/AUEx/n+f2P+QIKFv5JgIYQQwsc4XA6qWqq6HETqrfUnPeejMx7lj3P+2A/Vn7ri4mKuuuoq1q9f79mm0WhITU1l2LBhXsvQoUNJTk72ydaageyZlGdoKG4AYP6/5zP252PVLWiAkWDhryRYCCGE8EO1rbVsKt3ExpKNrDu0jjUH12Bz2tocF2IMYXLSZJ6a+xRj4v3nplqbzcb//d//sXz5cnJzc6mv7zg8mc1mhg4d6hU2jq6HhMhM0X1h3V/W8c0j34AC4WnhLHhzAakzU9Uua8CQYOGvJFgIIYTwAYqiYHPaaLG30OpopdXe6llvsbfQam9lf81+NpZuZGPJRvZV72tzDr1Wz5i4MUxOmuxZhkUNU+0m8N6iKAqVlZXk5uZ6Lfv27SM/Px+Hw9HhaxMSErwCx8iRI5k9e7ZMItgLCtcV8unPP3XP0K2BM359BtMenEZwvHSHOlUSLPxRc7M7WLS0wO7d7vkshBBCiCMURcHisHhd3Hd24d/ecd05XqF7lxJDIoe4A0TiZKYkT2Fs/FgC9KfXfQh2u52CgoI2gSM3N5fy8vJ2X5OQkMAdd9zB7bffTlJSUj9XPLBYG618+esv2fbqNs+26OHRpJ2VRtrsNNLOSiMoRoby7y4JFv7o8cfdk+Klp7vnsJBvL4QQwue5FFebi/KuXvh79nUjKKhBq9FiNpgxG8wE6gMJNARiNphJCE5gctJkpiRNYWLiRKLMUarU5y/q6urYt2+fJ2jk5uaydu1aKioqANDpdMyfP5+7776bOXPmyH0ap2Dfsn2seWwNh7cd5sR8HDsy1h0yZqeRNiuNwEgZRvhkJFj4m5ISGDrU3Vrx4YdwxRVqVySEEANGq72VksYSypvKO764b+db/a4EBavTqspn0mv1ngt9s8FMoCHQa91r33FhoL1jTrbPoDXIRW4fsdlsLF68mJdeeolvv/3Ws33o0KHcddddnH/++aSkpBAkE+b2SGtNK4XfFlKwuoCDqw9SsbPC+wANxI2OI212Gumz08k4NwNDoHyxeyIJFv7mwQfhqafgzDNh3TqQX+BCCHFSiqJQ1VJFSWMJJQ0lFDcUe9ZLGks867WW2n6px6gzdu3C/SRh4GQX+oH6wD6fNVv0v127dvHSSy/x1ltv0djY6LUvIiKClJSUDpfk5GRMJpNKlfuP5spmCtceCxpVe6q89gcnBDPj4RmMv308epNepSp9jwQLf3PddfDuu/D00/CrX6ldjRBCqM7qsFLaWNphWChpLKG0sbTdkYfaE6gPJD44nmBjcPsX7vqTfOvfhX3+flOy8A2NjY288847vPHGG+zZs6dNyOhIbGxsm7Bx/PPExES5SfwETWVNHFxzkILVBeR9nkdjiftnHZocyoxHZzDu5nHojPLvWoKFvzkaLJ55Bu6/X+1qhBCiTymKQk5lDkX1RR2GhqqWqpOf6IgYcwxJoUkkhyaTFJLkXkK9H8MDwqU7j/BL9fX1FBUVdbpYLJaTnker1RIfH99hq0d2djbBwafvCEpOm5Ntr2/j2z996wkYYalhzPx/Mxlz4xh0htM3YEiw8DcLFsCSJRIshBCnhV8u/yXPbXyux683aA2kR6QzOGIwGREZhJnCCNAHEKAPINAQeGxdH9il7SadSUKH8FuKolBTU9Np8CguLsZut3d6noiICD755BNmzZrVT5X7prrCOj654RMOrTvk2Tbl/imc/8z5KlalLukU5k/27IFly9zrkyapW4sQQvSDoVFDCQ8Ip9nWjN3V+cVOe+wuO/uq97U7d0JPmXQmr/DR5WDSxfDS0Xa5MVqcKo1GQ1RUFFFRUYwdO7bdY1wuF5WVlR0Gj/3791NRUcHcuXN56623uOqqq/r3Q6jIUm+hcG0hB1YdoGBVAZU5lV77NToNkUMiVarON0iLhT+5+GL4/HOYPx8+/VTtaoQQol85XU4sDotnaXW0Hlu3t3Zvezde2+poxaW41P74aND0LJScQqAx6ozotDr0Wj16rR6dRifh5jTX2trK9ddfz+LFiwF4+umn+dUAvefT3mKnaH0RBasKKFhVQOnmUhSX92Vz3Jg40s9OJ+PsDAbNGIQp5PS+MV6Chb9Yvx6mTQO9HnJy3EPOCiGE6Bd2p713Ao29FYuze6/1NVqN1hMyPIHjhPDRG9v1mr45b19s12q0p1Xgcjqd/OpXv+L5558H3BP3TZgwgfHjxzN+/HgmTJhAUlKSz/9MrA1W6grrqDtYR31hPXWF7sf6wnrqDtbRXNHc5jWRmZGkn51O+px00menY442q1C575Jg4S/+/W+4+WY491xYuVLtaoQQQvQDRVGwOW29E2gcFizOrr+2q6NoCbejQaO7oaRHAcdHwtRrr7zGk399ErvVDi7ci+J+jI6MZuKEiZ6gMX78eFJTU/stbCiKQmt1K3UH6zyBoa6wjvqDxwKEpe7kN7KHJIa4g8SRMBGWEtYP1fsvucfC38jwb0IIcdrQaDSY9CZMehNh9O8FjUtx4XA5PIvT5Ty2rjhV3+61T+mfOpyKs8Ofl1Nx4nQ6oeNDBqZftr+5iipWKCtY4VoBW4HN7u58eq0eo96I0WAk0BhIgCmgZwEHPQH1AZiqTZiqTBgrjegr9RgqDegqdGgrtGisJw8xmjAN+gQ9+gQ9hkQDpkQTpmQTAUkBBCYHYgw3YtAZqNZWU+esQ39If8rBzNdbck6FBAshhBBCtKHVaDHqjBh1RrVL8RmKorgDRFfCji8FsD5+P4UOOr9oAN2RBVBQsB/5X7OjmVpHLbR0//+H2PJYbvr3TZhbT94NqTG4kbrwOurD6qkLr/Narw+rx2Zqp2Wu9siyq/u1dUVUYBTLr1vOpKSBNxCPBAshhBBCiHYcbbU52lpx/AX18QHjxPXees3JXt/paxT3uktxeRZFUdyPR4LA0d7wGtzfoGs0GveiaNBqtGg1WhSNglajdU/q6AJFo4DW+zV2p73jcNEHDHYDRlvXAq/OqcNgN2CymghsDcRusOPUOXFp3QMyNIQ2YDVZoR8bERqsDTTZmvrvDfuRBAshhBDiNHH0G/f+vkDutdecyoV2D14jeocGd1hxOVyeezA0aDAHmAk2B2PQG9rco6LT6LzWj+7TaXToM/Rsm7SNoKogTLUm91JjwlhjxFBjwFBjQFelQ2vVYm41Y241E18e33GBgaCJ1aCL1bmXOB36WD3GeCOGeAMB8QEYIg1tujd1WmM760ePCzGFEGryo/t8u0GChRBCCDFAHKw7yLz35lHaWNruRXV/fqs80J14Udmdi80eXZRqeuk8fVljO6958oknefSRR73+2zv77LN56KGHGDZsGImJieh0vT9TtaIoWOutNBQ3HFtK3I+NxY2ebZY6C7SCUqjgKHTgoP1AqTPpGHXdKOa/Nr/Xax1IJFgIIYQQA0RZUxm7K3f7xLwbHQkyBBFqCiXUFOr55jbEGOLZFqgP7PRCtb8uiDs77nQbXvZU5O7NbbNt1apVrFq1CgCDwUBqairp6emeJS0tzbMeExPTo5+1RqMhIDyAgPAAYkfGdnicrdlGY0ljmwBSsqGEw1sOe45zWp2Ubirtdh2nGxlu1l8cHW72wgvdk+QJIYQQ7ShtLKWkoYQWewst9haa7c3H1m3N7W8/yTH9OfSsTqPDbDATZAzCbDC71w3H1j379OauHdfOPoNORljsL06nk02bNlFQUNBmOXToEA5H513OgoKCvILGieEjLKz3RktTFIVD3x1i43Mb2fPJHhSn+xI5NCWUSXdPYvzt4zFHybwVnZEWCyGEEGIASQxJJDEksVfP6XA5PGGj04DSheDS3nHNtmbPUK5OxUmjrZFGW2OvfobjGbQGrwAyO202i+YtklaIPqDT6Zg6dSpTp05ts8/hcFBSUkJBQQEHDhxg165dbN++ne3bt1NbWwtAc3MzOTk55OTktHv+iIgIZsyYwUcffYThFIbkL9textJbl3J467FWirSz0ph872SGXTIMrV7b43OfTiRYCCGEEKJTeq3e01Wpr9id9q6FlB62ujTbmj39/O0uO/XWeuqt9QDUtNbw0sUvodfIZVFP2Gw26uvraWhooL6+vkfrjY2N9KQTTW1tLV988QW1tbXExnbc5akjiqKw4bkNfP3br3HanOgD9Yy+fjSTF04mbnRct893upN/QUIIIYRQnUFnIFwXTnhAeK+cT1EUdpTvYMX+FazIX8F3h77zGunJpDMxK20W5w8+nytGXIFee/pdEimKQlNT0ymHAqvV2ms1GQwGwsLCCA0N9XrsbH3EiBE9ChXNlc0suXkJeZ/nATBs/jDmvTKPoJigXvs8p5vT71+REEIIIfqFoig4XA5sThtWpxWb0+Zed1g73Hb89va2dXjsCfv3Vu3lcNNhr3oyIzO5YMgFnD/kfGalzcJsOH36y69Zs4bHH3+cqqoqTyhoaGjoUStBR4KDg7sdCk5cDwgI6JcuadV51fx71r9pOuyeTyLzwkzOeeIcAiMC+/y9BzIJFkIIIYSfUhQFu8vevQvwkxzbZpure2HgxO1qDnFrNpg5O/1szh9yPucNPo/BkYNVq0VtH3/8MWvWrDmlc2i1WpKTk8nIyGhzM3VqaioxMTGYTKbeKbiPHVxz0BMqAPK+yCPvizy0Bi2RQyKJzor2LFHDoojOiiYgLEDFiv2DjArlL2RUKCGE6HcuxYXdae/3b9u7eq7+HK2pN2jQYNKbMOqMmHTuR6PO6Nl2/Pb2tnVp/5HtcUFxTEuZhknvHxe6fa2lpYUvvviCiooKqqurqampoaamxrN+/DaXq+fDFQcFBREZGUlUVJTXY0frUVFRREREYDR2bSbt3qK4FPYt20fZ9jKq9lZRtbeK6txq7C32Dl8THB/sDhpZUV7BIywlDI1WbvwHCRZql9N1EiyEEKJdFoeF2tZaai211FnqOlyvt9ZjcVi6/G271Wn1u9mXdRpdzy7W9SaM2pNfrHfnwr69bTqNTkZe8nEul4uGhoZOg0d722pra08pkAQHB3sFj+joaOLj472WuLg44uPjiYmJQa/v/U43ikuhoaTBEzSq9lZRvbeaqr1VNJZ2PEqZPlBP9LBo4sbEMfqG0aTPTj9tg4YEC38hwUIIMUApikKjrZHa1iNhwFLbZt0TFNrZZ3X23o2jJ6PX6rt9Me11Md4HF+vHb9dpe38GYyG6wuVyUV9f360wUl1dTV1dXbfv89BoNMTExLQJHicGkPj4eCIiInolzFobrFTltg0c1XnVuOzegSpicATjbx/P2JvGEhwXfMrv7U8kWPgLCRZCCB/mcDk8LQQdBQDPejutCac6U7QGDeEB4UQERhARENF2PSCCsIAwAvWBPb6wN2gNcuEuRC9zOp3U19e3CR6VlZWUl5dTVlbmtVRUVHQriBgMhk6Dx/FLUFD3R4NyOVzUFtRStaeK/Sv2s+PtHdga3V0UtXotWQuyGH/HeDLOzjgtWjEkWPgLCRZCCJXUW+rZVLqJjSUbyavJa7c1ocnWdPITnYRRZyQiIKLTcBAR2P56iCkErUYmsBJioHM4HFRVVbUbOk5c6urqunXu4OBgr/CRlpbG9OnTmT59OtHR0V06h63ZRs4HOWxdtJXiH4s928PTw5n+0HQm3DGhWzX5GwkW/uK+++D55+GKK+DDD9WuRggxQFkdVnaU72BjyUY2lm5kY8lG9lbt7fLrg43B7QcA07GQ0FE4CDTIMI9CiN5jsVioqKg4aQApKyujtbW103NlZ2cza9YsZs6cyYwZM0hMPPns9uU7ytnyyhZ2vLUDa727y+bl/72ckT8b2SufzxdJsPAH+/ZBdjY4HPDVV3DOOWpXJIQYAFyKi/01+90homQjG0o2sL1se7sjDaWHpzM5aTKjYkcRbY5ut9tReED4aTnJmBDCvx2dKPDEsJGTk8O6devYvXt3m9cMGTKEmTNnMnPmTGbNmkVqamqH93LYW+ysengVG/6xAWOwkds33070sK61gPgbCRb+YMECWLIELroIli1TuxohhB+zOCw8s/4ZVh9czabSTdRZ6jo8NiMig2kp0zgj+QwSghPQa/WexaAzeD0/2WLQeh+v1WhldCAhhF+orKxk3bp1fPvtt3z77bds3769zX0eKSkpzJw5k3PPPZdLLrmEiIgIzz7FpVB3sI63zn2L2gO1xI6M5bYNt2EwG/r7o/Q5CRa+rroajvbr270bhg9Xtx4hhF9bmruU+f+dr3YZAF0OIScNLe2FHE03ju1GIOp2He0sci+IEP6trq6OH374gbVr1/Ltt9+yefNmHA4HIYQQRRQx2hjGpowlIzwDc6uZhsIGnFan1zmu/eJaMi/IVOkT9B0JFr6upASSk8FgAJt/TYQkhPA9LfYWnl7/NOVN5ThcDveiuB/tTvuxbSdZ7K6uHWt32lWdedkXadB0O+R0O2z1QSA61XPLHBbC3ymKQnNFMzV5NVTnVbsf91VTlVtF9f5qXJZORrfTQVhaGHEj4kgYn8CZvz1zQLZYSGdYIYQ4jZgNZh6d+Wi/vqdLcXU5hPR2sDnpeZW+raE9Cgp2lx27q+MZfgcqnUbXayHnVMJWbFAsV4+8WmblFp2q2FVBzgc5XkHC2tDxvDkanYbwtHACEgOocFbwU/FPbDu0jRpqqHfW48p3MTFiIleHXM104/R+/CT9R4KFEEKIPqXVaD3zQfQGRVFwKS5cigun4sTpcnrWXYqr0+e9fezJ9tmddqxOK1aH1TO79/Eze3s9Hjmm02OPPHcqzpP/oHyQU3HidDr7dVLDjpgNZq7MvlLtMoQPW3z9Ysp/Kj/pccHxwQyaPoikKUmEJocSFBdEcFwwQXFB5Jfkc8/Ce1i3bh0AmzdvZvPmzSQkJHDttdf29Ufod9IVytdJVyghVKcoSq9caPb1sb1+gY2K793JsdK1yj9p0KDT6tBqtOg0Oq91rUbb6fPePjbGHMNjsx4jyhyl9o9F+LC9n+5lx1s7aCpvorm8mabyJs/kd13lxEkLLTTRRDPNWA1WEoYkMO+aecSmxxIUG+QJIuYYM1qdf9+DJcHC10mwECqrbqlmxf4V2Jy2Xvsmt8Nj++Fb5J4cK/yTGhesXT5W5Qvr/j5WRgETA4W91e4JGY2HGyndV0r5gXKqD1VTtr+M2qJatBYtwQQTSDfn5tGAOdrsae04PnSEJoeSdWkWxqDeafntK9IVyteVlrofdTp16xCnrbu/uJsPcj5Quwy/pNfqMelMGHVGDDpDv13cee3DNy4se/UzdeFYuYgVQpwKi8VCSUkJJSUlFBcXt/t4+PBhnM62Xz4ZDAZmzZrFRedfxJzJc4g2R3vCSHN5M80VzcfCSUkjVblVoAAKtFS20FLZArva1jT94emc/eez+/7DnwIJFr5MUeCRR9zr8+apW4s4bd089mYarA1YHdY2N6wef6Prybafjo7+DJrtzWg12nZvIj3xZlWD1oBWcX+7q9PqTvoar+1dOaYftstwqkIIX6UoCg0NDRQXF3cYGIqLi6muru7S+XQ6HQkJCSQlJTFixAguvvhizjnnHK9eMdZGK1qdFnurnZbKFuwtdpormqk9UEv9oXo67d2pgbCUMKKGRTH8Mt+fckC6Qvmy5cvhwgvBaHTPYTF4sNoVCdEjR2+27Wko6ZPtSt+91+muveFU+yTEnOI5h0cPZ3Ck/F4Vwl8pioLVaqW5uZnm5maampo86/X19R2Ghubm5i6dPzAwkKSkJJKTkzt8jIuLQ3ekV0l1XjVFPxRRm19LbX4tNfk11B6odbdAdEIfqCciI4LIwZFEDI4gIiOCiMHu5+Fp4eiM/tNrRVosfNlf/+p+vO8+CRXCr2k0Gk+Xl4HuaIhqbzhS1cJUH71XRyHKX4ZTNeqMVP6mklCTj3/JJISfs9vt7V78n+rzpqYmXK5O5o7oRERERKeBISkpiYiIiC53q2yubOblsS9jb2n/9545xuwdHo4Eh4iMCIITggdM900JFr6sstL9eNFF6tYhhOiy40OUiYE/Rr5LcfVJMLI5bVS2VFLcUExRQxHFDcUUNxRT0VxxyjWbDWYSghOYPmg6IcaQXvgpCOH/nE4nLS0tPbq4P9mxdnvff8lgMpkICgryLKGhoSQmJnYYGsxmc6++f+7SXOwtdoLjgxk2f5hXq0NERgSm0IH/9wAkWPiHAZJihRADj1ajxaQ3dStEKYrCptJNFDcUU9ZU1uHSnRYPvVZPXFAc8cHxJ12CjcE9+ahC9DtFUbDb7dhstg6Xo12BTvXi32Kx9Pnn0ev1ngv/4OBgryBwqs/1evUuae0tdnZ/uBuASfdMYuajM1WrRW0SLPxBQ4PaFQghRK95bM1j/PHbP/b49WaDmSGRQ9xLhPsxLTyNUFMowcZgr8WoMw6YLgaidyiKgtPp7PRi3VeW/vim/0QajaZPLv6Dg4MxGn17qNT2KIpCa3UrdYV11BfWU3+o/tj6kectVcfuoci6NEvFatUnwcKXTZ8Oe/bAX/4CF18sLRdCiAFhYuJEMiIyqG2tpdHW2O1Rw1rsLewo38GO8h0nPVav1bcJG16Lof3tIaaQDl8TqA+UsNIOp9N50m/XfWXx13FrNBoNJpMJo9HotXTn4v5kxwYEBJxW/327HC4aSxs7DQ4d3TdxPGOIkeGXDSdmREw/VO27ZFQoX1ZSAkOHQksLvP8+XHWV2hUJIUSvszltNNmaerw02hrbbLM4+q5bhwZN52GlvaBibD+oDI4c3O3hee12O4WFhapfnJ+4tDeev7848ULdVxedzGnVI9V51e6hXQvdoaHhUIMnPDSUNKA4T34pHBwfTFhqGGGDwghLDSM8NdzzPDw1nIDwgH74JL5PgoWve/xx+P3vYdgw2LtX7WqEEAPI0RGsjs5oPiAej6zbnDbqrfXUW+q9HptsTWr/2L1cmnUpi3+2uMvHK4rC+PHj2b59e98V1Ut0Ol2bC+P2vm1Xe9Hr9afVN/Snm7V/WMuax9Z06zVBcUHEjYojdnQscaPjiBsVR1BsEAHhARiCDPLfSyekK5Svu/lmd7A4cEDtSoRQlS9eBDtcjl67EFbj0aX0bJhGcWqOjhqm1+rJjMzs1ms1Gg3Dhg1j9+7d2Gy2Pqqwd+j1egwGAyaTqd0lICCgS9u6u729bWre2CvUFZoSijHYiK2p6/9emsubOVB+gANft7320ug0mEJNBIQFEBAegCnMvW4KM3k9b2/f0XV9wMANs9Ji4etKSiA5GQwG8PE/IqJjLsVFg7VB9QthuQgW3XX0Iri3H/VafdeO7aP3V+uxN2clVxTFMyrQ0cVisXg978n2np7D4ejevTL9SavVnnI4OdWAYzabCQmR4Y3V4rA6sNZbsdRbPI+Wurbr7e2z1lux1FlwOXrn75DWoPUKGscHkRNDSHshxRxt9tlgIhFeiD7mUlyc8doZbCzZqHYpA5oGjfoXjj5y8eqLF8Gi9x29kddk8o3x8V0ul08EnKPL8ROnuVwuWlpaaGnpfAbkvvboo4/yxz/2fEQ00XN6kx59rJ6g2KAevV5RFBytjg5Dx/GBxVrX/j5rgxXFpeCyu2ipbDnpjNwdyTgngxu+uqFHr+1rEiyE6GMaNOi1vv9PzaA1EGIKIcQYQqgp1HvdGEKI6dh6gD7AJy58j3/01W9vhDhdaLVaAgMDCQwMVLsUABwOR4/CSUtLC8XFxRQWFnqWw4cP98pIUtIly39pNBoMZgMGs4GQhJ61PCkuBVuTrcNWE2u9lYbiBvYv30/tgdqOa9H57t876Qrl66Qr1ICgKApNtiaa7c202FtosbfQbDtuvSvbj1tv77juDtl5KgxaA2aDGbPBTJAx6Ni6oYP1jo7pYLvZYEanldFPhBC9w+VyUVNTQ1lZGeXl5ZSVlbVZP/q8srKyWyFCr9cTFxdHfHy85/H49eO3hYWF9eGnFP6otbaVQ+sOUfhtIYXfFnJ46+E2o1QFJwSTOjPVvcxKJWZ4DBqtb4YLic5C9AONRuNuATD1Xf9au9Pe9ZDShfBy/DFH9x29z8HusrtH2rHW99nnCdQHEhd83EzKQfEkhCS0mUU5LigOk943uoIIIdTncrnYu3cvGzZsYMOGDWzcuJFdu3Z1a7I5rVZLTExMh2Hh+PWIiAi0Wuk2KLqndEspax5bQ94XeXBCjg1PC/eEiNSZqUQMjvCbVnkJFkIMEAadgTBdGGEBffONmKIo2Jy2PgsvR7cf1epo5WDdQQ7WHTxpbREBEW0CR0Jw2xASZY6S+waEGGAOHz7sCRAbNmxg06ZNNDY2tntsVFRUl8JCdHS0zBkh+kT5znLW/G4Nez89NoVA1LCoY0FiRiphg/y3ZUuChRCiSzQaDSa9CZPeRERgRJ+8h6IoWBwWWuwt1FvrKW8qp6ypzHtp9n5uc9qotdRSa6llT9WeTs+v0+jatIJ4gsgJrSHBxuA++YxCiJ5rbm5my5YtXq0RRUVFbY4zm81MnDiRKVOmMHnyZCZOnEhSUhIGg0GFqoWAqr1VrPn9GnI+yHG3UGhg9HWjmfm7mURlRqldXq+Reyx8ndxjIUSHFEWh1lLbNny0s1S2VHbr3EGGIEbEjOCOCXdw7ahrMRvMffQphBAnoygKV1xxBZ9++qnXaE/g7raUnZ3N5MmTmTJlClOmTGHEiBFyo7TwGT/+40dW/nolist9yZ19VTazfj+LmOExKlfW+yRY+DoJFkL0CrvTTmVLJYcbD5+0FeTE2ZkjAiK4ddyt3DXpLjIiMlT6BEKcvrZt28b48eMBSEpK8gSIyZMnM2HCBJkfQvgsW5ONpxKewtZkY9glw5j9x9nEjY5Tu6w+I3FeCHFaMOgMJIYkkhiSeNJjm2xNHG48zJLcJfxz0z85WHeQv6//O0+tf4qLh17MwskLOSfjHLlfQ4h+smLFCgDmzZvH0qVLVa5GiK7L+TAHW5ONyCGR/OzTn/nNTdg9JX8V/cXp1bAkhKqCjcFkRmXy4LQH2X/vfj668iPSw9NRUPhs32ec9/Z5jH5pNCUNJWqXKsSAVlZWxuuvv86rr74KwAUXXKByRUJ0z7bXtgEw7rZxAz5UgLRY+L6wMDAa3d2gVqyA889XuyIhBiy7087+mv3sqthFTmUOOZU57KrYRV51Hk7F6XVsTmUOudW5JIUmqVStEAOPy+Vi27ZtLFu2jGXLlrF582bPPr1ez0UXXaRidUJ0X0NRA+DuEnU6kHss/MGDD8JTT8GIEfDTTyA3pAlxSpwuJ/m1+eRUHAsPOZU55FblYne1P9Z9mCmM7NhsRsaMJDs2m6nJU5mcNLmfKxdi4GlqauLrr79m2bJlfP7555SVlXntnzhxIhdffDFXXnklI0aMUKlKIXom58McPrrqIzQ6Dbf9eBuJE0/eHdefSbDwB3V1MGQIVFfDv/4Fv/iF2hUJ4XcO1h3kpU0vsfLASvZW7cXisLR7XLAxmBExIzwBIjsmm5GxI0kMSTwtmrGF6AuKolBTU0NBQQEHDhygoKCAgoIC9u3bx/fff4/tuMFJgoKCmDt3LhdffDEXXHABCQkJKlYuxKn76GcfkfNBDjEjYrhzx51odQP3TgT56tsfhIfDtGnw2Wfwww8SLIToIkVR+PrA17yw6QU+y/0M5bjpTQP1gQyPGc7I2JFkxxwLEClhKXJTthA90NzczMGDBz2h4fgAUVBQ0OGkdQAZGRlcfPHFXHzxxcycOROTydSPlQvRt85/7nxyPsihcncljaWNhKX47wR4JyPBwh9s3OgOFRoN/PKXalcjhM9rsDbw5vY3+eemf5JbnevZPnfwXG4ZewsTEyeSFp6GTisz6wrRVXa7naKiIq+wcHyIqKioOOk54uPjSU9P91qmTZtGVlaWtAiKAau5vBkAY4iRkMSBPTSyBAtfpyjwwAPu9RtvhCPjeAsxECiKgsPlwOKwYHFYaHW0etZ7ujTaGlmxf4VnLooQYwg3jb2JuyfdTVZ0lsqfWAjfV19fz86dO/npp5/YsWMHeXl5FBQUUFRUhNPp7PS1YWFhXqEhIyPDs56WlkZgYGA/fQohfEfxhmIAAiMDyf8yn4TxCQTHB6tcVd+QYOHrKirgu+/crRV//rPa1YgB6OiFfav91C/qLQ4LFmf3jncprpMX2QNZ0VksnLSQG8fcSIhpYH9DJERPOJ1O8vPzPQFix44d/PTTTxQWFnb4GpPJRFpaWofhISIioh8/gRD+obWmFYD6wnrevehdAILjg0kYn0D8+HgSxieQMC6BsNQwv2+5k5u3fZ3MvD3gHf+NvRrLicOoqsmkMxGgDzilxaQzMSZ+DLPTZvv9L2ghekttba1XeNixYwe7du2itbW13eNTUlIYM2YMo0ePJisryxMe4uPj0WrlHiQhusPeamfrK1sp2VjC4a2Hqc6tRnG1vfwOiAhwh4zxCcSPcweOqMwoNFr/+VsmwcLXSbDwCzanjb99/zcO1h3s9jf2DpdD7fI9jDpj9y7kdV0/NtAQ2Ol+o84oN00LcYpsNhu5ubns2rWLnTt3esJEUVFRu8cHBgYyatQoRo8ezejRoxkzZgyjRo2Slgch+pCt2Ub5jnIObz3M4a2HKdtWRsWuClz2ti34xmAjcWPiSJ+TzoyHZ6AP8O3ORhIsfJ0EC7/wQ9EPnPn6mX36HlqNlmhzNHFBccQGxRIbFEuIMeSUv+H3fNOvN8mFvRB+wuVycfDgQXbu3OkJEbt27SI3NxeHo/0vK9LS0rwCxOjRoxk8eDA6nQxiIITaHFYHlTmVFP9YzMYXNlK1p6rNMTeuupH0OekqVNd1vh17hPATk5Mm86+L/kVeTR4N1gbqrfXuR0u91/NGa6PXkKfd4VJcVDRXUNF8bOSVQH0goaZQwgLC3I+msGPPjce2n7jPoDOg1+oxG8yEmEIkUAjhoxRFoaKiok2AyMnJobm5ud3XhIaGMmrUKEaOHMmoUaM8rRBhYQN3iEsh/FndwTrylueRvyKfA6sOYG8+bqJWDSRPSWb45cNJOytNtRq7SlosfJ20WAwoLsVFk62p3dBx9LnXtg4CSou9pVfrCjGGtB9CjjzGBsUyPmE8k5ImER4Q3qvvLYS/URQFp9OJ3W7Hbrdjs9k86yc+72j9ZMdVVVV5gkRVVdtvLsF9I/Xw4cM9AeLoY3JystxfJIQPUhSFprImavbXULO/hvKfytm/Yj/VudVexwXFBjHk/CEMuWAIGedmYI4yq1Rx90mLhRD9SKvRei7ek0OTe3weu9NOo62x04By9HlH++ot9dhd7m9FGm2NNNo6nrzqeMOihjE5abJnGRM3BpNeJrMSXacoCg6Ho9cuwvvquM5e05/fyWk0GoYMGdImQAwZMgS9Xv6MC+FLXE4XDcUN1OyvoTa/1hMijj63t9jbvEaj05AyLcUTJuLHxPvVDdvHkxYLXyctFqIPWRyWY+Gjk4ByqP4Qm0o3caD2QJtzGHVGxsaPZXLisbCRGZUp3av6kMvl6vCC1x8u1ju6B8CfaTQajEYjBoMBg8HQpfXO9oWGhpKdnc2oUaMYPnw4ZrP/fGMpxEDntDupL6w/Fhrya6jd7w4RtQdqcdo6Hm1Ro9UQnhZO5JBIIjMjSZudRsbZGQSEB/TjJ+g7Eix8nQQL4SOcLicljSWsK1zHt4XfsrZwrdes1scLDwhnUuIkJidN5qaxNzEkckg/V9u5jrqx+NpFeEf7TjZJmT/S6XRdvujujQv33j5OboAWYmCqK6xj76d7qck71vJQd7AOxdnx5bPWoCUiI8IdHo5bIgZHEJ4ajs44cH9fSLDwdRIsxBEuxYXVYe3VWaq7sxztNtVdI0NH8syoZ3zqYt3l6ptJ+dTkyxfdJztOr9fL3AhCCJ+huBTyV+az6cVN5H2e1+6cE/oA/bHAMORIiBjsfh6aEopWd3r+TpPOmUJ00YkX9j1dWh2ttNhaaLW10mpvpcXegsXuHRSsDitWpxWry4rNacPmsmFXenZh3ydcgOPIYj9uvZ1l1/ZdnJt3rlqVdolGo/GpC+3uHqfX6+VmXSGEOEUt1S1sf2M7m/+1mdr8Ws/2tLPSSJqa5NX6EJIQ4rf3QfQlCRZiQHC6nFS3VnuGY61qqXJfsB9/QW9vpaquipLyEhpbG90X7kcWm+vYxfvRxXH8/zQOXBof+pb7+At7NZYTfhQ6na7zi99Rvn1xLt1YhBDi9FW1t4rv/vodu/67C6fV3dXUFGZi7E1jmXjnRKKzolWu0H9IsBA+yaW4qLPUeYJCZXPlsfWWtuvVLdU9nh+iDc2RpTMKJ/2mvjuLTtGhQ4f+yP8MGoNnMWqNnsWkNblnqdYbO76ANhkwBPffhbvBYJBuLEIIIfzWOxe+Q11Bnde2+LHxKIrCga8PUFdYR1RmFGGpYadtF6eukmAh+oWiKDTaGrsUEo4e41S6f4OqyWmCZrDWWsFGmwt4jVNDVFgUoUGhGDXGthfuOpNnCdAHEKgPJMDgnpnabDQTaAjEbDATYAzotRFgpBuLEEIIoZ7JCyfz05s/UbO/xjMcbOHaQgrXFnodd/Sm7KihUURmRhKVeewxNDlUukYhN2+rXc7J+cHN2zanjVUHVlHWVNZpULA6rd0+d5gpjNigWGKDYokJiiFEG0J9aT0NZQ1UHayiaG8R9aX10Ay04tVFJzIykjFjxniW0aNHM2LECAICBsaQbkIIIYToPYqi0FjaSE1eDdV51e7Hfe7HmvwaTzep9ugD9EQMjvCEjcjMSFLOSCF2ZGw/fgL1SbDwdX4QLG5ecjP/3v7vLh0bbAwmxhxzLCwcWY8Jarst2hzdZuK1rKwscnO9hzjVarUMHTqU0aNHewWJpKQkaQkQQgghxCnzTHx3JHR4Akeee+4Kl6PtfZgarYa7c+4+re7RkK5Q4pSdN/g8lu1bRlVLVZt9w6OHc+OYG7ls+GUkhyZjNpzaJE9nnnmmJ1jcdNNN3H333WRnZ8vkUUIIIYToM1qdlvDUcMJTw8k4J8OzXXEpFKwuYN2f13Fw9UGv1wyaMYjghOB+rlRd0mLh6/ygxQLcozJtPbyVFftXsCJ/BT8W/4hLOZbezQYzs9Nmc8GQC5g3bB6Dwgb16H1sNhuXX345y5YtIygoiK+//pqpU6f21scQQgghhOiUw+qgak8VOR/msPOdndQX1nv2hQ0KY9R1oxh9w2hihseoWKU6JFj4ur17Yfhwnw8WJ6ppreHrA1+7g8b+FRxuOuzZp9fq+ddF/+LW8bd2eg5FUaioqGDfvn1eS05ODnl5eQDEx8dTXFwsw4UKIYQQote4nC7qD9VTve9Yt6ej6/WF9V6T5plCTYy4cgSjrx9N6szU0/ombukK5ev++lf347Rp6tbRTZGBkVyVfRVXZV+FoijsrNjJ8rzlfJr7KT8W/8htn91GbnUufz3nrzQ2NJKXl+cVHo4+b2ho6PA9jEYjM2fOlFAhhBBCiG5TFIWmsiav0OC5d2J/DU5bxzdrG4ONpM5KZcyNYxg6byiGQEM/Vu67pMXCl23ZAhMnutc3bIDJk9Wt5xQoisLu3bvJzc3lxd0vssq5CgDTARPW96zuOSHaodFoSEtLY+jQoQwdOpTMzEzP+qBBgyRUCCGEEKJT9hY7Fbsqjt10ve9YkLA1ddwbRGfUETkk0j287FD3Y1RmFFFDowiKC5IBYtohLRa+7Pe/dz9ee61fhwqAO++8k0WLFh3bcD4wFawZVpgP8d/FewLD8QEiIyNDhocVQgghRI/YW+08N+Q5mg43tbtfo9UQnhZ+bG6KoVGeJTQlVCbE6yYJFr7saItKdbW6dfSCmJgY939to4DJQMKxfTfMv4H/fPgflSoTQgghxECl1WsJSQjpMFgYggyEJIYQMTiC2JGxxI2OI3ZkLMZgYz9XOjBIVyhflp/vvnHbbofly+H889WuqEcO1h3kpU0vsWjLIuqsde6NdmAnsBEog7Fjx3L55Zdz2WWXMWLECPWKFUIIIcSAoigKzeXNlP1URvmOcip2VFC+o5zKPZW47G3nnwCIGBxB3Og49zLG/RiRHnFa35jdFRIsfN2DD8JTT8HYsbBtm9rVdIuiKPx53Z95bM1jnqFn08LTuHvi3VyQcAHfrviWxYsXs2bNGpzOYzdIzZkzh3/9619kZmaqVboQQgghBjinzUlVbhXlP5VTvuPY0lHrRmhKKFPum8L428YTEC7dtNsjwcLXFRRARobfDTdrcVi4beltvLPzHQDOyTiHeyffy0WZF6HTet9wXVVVxWeffcbHH3/MypUrsdvtmEwmfve73/Hggw9iNEpzpBBCCCH6R3NlMxU7K46FjZ/KqcipwGl1fwlqDDYy7tZxTPnlFCLSI1Su1rdIsPB1fjJB3vEqmyu59P1L+b7oe3QaHS9e9CJ3TLijS689cOAAd911FytXrgRg5MiRvPLKKzIJnhBCCCFU47A42PneTn58+kcqdlUA7hu/sy7N4oxfn0HyGckyShQSLNQu5+T8LFjsrtzNxe9eTEFdAWGmMD666iPOyTinW+dQFIV3332X+++/n6qqKjQaDe+//z5XXnllH1UthBBCCHFyiqJw4KsDrH96Pflf5nu2D547mMveuQxztFnF6tQnwcLX+VGwWJm/kis/vJIGawMZERl8fu3nZEVn9fh81dXV3HrrrSxZsoR58+axdOnSXqxWCCGEEKLnKnZV8OOzP7LjrR04bU7C08O5esnVxI2KU7s01cjgvKJXvLTpJS5850IarA3MGDSDDbdtOKVQARAVFcXtt98OQFFRUW+UKYQQQgjRK2JHxnLJq5dwx5Y7CE8Pp66gjtfOeI09n+xRuzTVSLAQp+y5Dc9x9xd341Sc3DjmRr664SuizdG9cu7k5GRAgoUQQgghfFPsyFhu33Q76XPSsTfb+eCyD9j+7+1ql6UKCRbilL27810AHjzjQf49/9+Y9KbeO/e77nOHh4f32jmFEEIIIXqTOcrM9V9eT8qZKQAU/XB6fiEqwUKcMgX3bTqz0mb16ogIP/zwA3/7298AeOaZZ3rtvEIIIYQQvc3ldFG5uxKAEVeenpP9SrDwFy6XT968rSgKrfbWXj9vQ0MDN910E4qi8POf/5x58+b1+nsIIYQQQvSWfcv2Yam1EJIUQvqcdLXLUYUEC18XEQGhoeB0woUXQn292hV5WB1Wbvz0RnZW7AQgPbx3/hE5HA6uueYa8vLySE5O5tlnn+2V8wohhBBC9JWGogYAXHYXLZUtKlejDgkWvs5shg8/hOBgWLUKZs6E0lK1q6KyuZKz/3M2b+94G51Gx78u+hfZsdm9cu4HHniAL774gsDAQBYvXiz3VwghhBDC5024YwKxI2Nprmjmkxs/QXGdVjM6ABIs/MPcubB2LcTFwY4dMHUqlJWpVk5lcyVTXp3C90XfE2YKY8X1K/jFxF/0yrlfffVVnnvuOQD+85//MGnSpF45rxBCCCFEXzKYDVzx/hXoA/Uc+OoAP/z9B7VL6ncSLPzF+PGwfj2kpUFREXz8sWqlfLL3EwrqCkgOTWb9reu7PbN2ZxYvXgzATTfdxBVXXNFr5xVCCCGE6GsxI2I4/x/nA7DhuQ0qV9P/JFj4k/R0mDbNvW63q1aGzem+iXxayjSGxwzv1XMvWLAAgPXr13OaTQovhBBCiAFg1LWj0Og0NJY00lDSoHY5/cqngsV5551HUFAQGo0GrVZLQkICy5cv9zomPDwcjUbjtYwYcXoO6aW26pZqrA5rr57zmmuuISgoiNzcXNatW9er5xZCCCGE6GvWeishCSEAlGwoUbma/qVXu4DjbdmyhWuuuYZ58+ZhtVpZuHAh8+bNo7S0lNjYWM9xWVlZvP/++57nkZGRapR72goxuv+xrCpYxaBnB3HH+Dv4xcRfkByafMrn/uyzz3A6nQBs3bqVmTNnnvI5hRBCCCH6iqIoVOysIHdpLrlLcindfGyQnabyJhUr638+FSyqqqq8no8aNYoRI0bw/vvvc++993q2BwQEMHr06P4uTxxxzahrKG0s5Z+b/klJYwl/Wvcn/vLdX7hs+GUsnLyQGYNmdHuiPKfTyUMPPcTf//53AC644AJuvfXWvihfCCGEEOKUOO1OCtcWusPE0lzqC4+bDkADyVOSybo0i3G3jFOvSBX4VLA4UemRYVWTk72/Cf/pp5/QarUYjUbGjRvHZ599RnR0dLvnaGhooKHhWP+2xsbGviv4NGHUGfnfGf/Lg9MeZEnuEl7Y+AJrC9fy4e4P+XD3h4yKHcXvZv2OK0Z07eZri8XC/PnzWblyJQD/+7//yx//+Ed0Ol1ffgwhhBBCiG6p3lfNmsfWkLc8D2v9se7g+kA9g88dzNBLhjL0oqEExwerWKV6NIqP3iHrcDhISkqitbXVKxhcf/31DB8+nKysLFatWsXLL79MXFycJ4Sc6KyzzmLt2rVtttfX1xMaGtpn9feZ666Dd9+FZ56B++9XuxqPneU7+eemf/LWjrdosbsnhXnozIf489l/Rqvp/FaeFStWcMEFFwDw9NNP86tf/arP6xVCCCGE6A57i52XRr9EbX4tAEGxQQydN5Rhlwwj45wMDGaDyhWqz2eDRXZ2Nrm5uaxfv77TuQyeeuopHnzwQVatWsWcOXPa7G+vxWLEiBESLPpIbWstf/3urzz5w5MAXDb8Mt669C3MBnOHr2lpaWH69Ols27aNESNG8MMPPxAWFtZfJQshhBBCnNTKB1ey/qn1hCaHcsUHV5A8JRmNtntdvwc6nxoV6qjRo0eTm5vLN998c9IJ0m644QbAPTxpe0JDQ0lOTvYsSUlJvV6vOCYiMIInzn2C/yz4D0adkcV7FjPzjZlUNFd0+Bqz2cxnn31GYmIiu3fv5qqrrvLcwC2EEEIIobaSjSX8+MyPAFz0r4tIOSNFQkU7fCpYuFwuRo8eTU5ODsuXL+/SiEAfH5kobtiwYX1dnugip8tJRGAEQyKHALDl8BZe2fJKp69JSkris88+IzAwkJUrV8pQs0IIIYTwGT/95ycUl7uTz+aXNpP7WS4up0vlqnyPT928fTRUPPvss8TFxbFjxw7AffN2ZGQk33zzDX/+85+5+eabSUtLY/ny5Tz55JOEhYXJLM0+oKa1hje2vcGLm1/kQO0BADRouGjoRdw45saTvn78+PGMHj2aDRs2eHVfE0IIIYRQ05RfTqFqTxUF3xSQ93keeZ/nEZoSyvjbxjPu1nGEJvlh9/o+4FPBIicnB4D7T7h34NZbb+XVV18lKCiILVu2sHr1ahRFwWAwMG7cOBYvXqxCteKo/Jp8/vrdX3ln5zu0OloBiAiI4NZxt3LXpLvIiMhQuUIhhBBCiJ6LyozixlU3Ur2vmi2LtrD939tpKGpgzWNrWPuHtQybN4wzHjiDQdMHqV2qqnwqWJzsPvIpU6ZQV1fXP8WILvn6wNdc8cEV1Fvd4zePjhvNvZPv5dpR13Z6w3ZH7HY7QLfnwRBCCCGE6GtRQ6OY+/e5zPnTHHZ/vJstL2/h0LpD7P10L7lLczn/ufOZfM9ktctUjU8FC+FfXt78Mvd8cQ9OxckZyWfwxDlPMH3Q9B6HAkVRyMvLAyA9Pb03SxVCCCGE6DX6AD2jrxvN6OtGU5FTwXf/9x07393J8oXLqTtYx7lPnHta3tztUzdvC/9gcVj49Ze/5s7P78SpOLl+9PWs/vlqZqR2f8bt4xUVFdHY2Iher2fo0KG9WLEQQgghRN+IzY7l0rcvZc6f3dMerP/7ej66+iOsDdaTvHLgkRYLf6M9kgXLy/v9rQ/VH+Jfm//FK1tfoaqlCoA/zf4TD894uFe6Lu3evRuAzMxMjEbjKZ9PCCGEEKI/aDQaZjw8g7BBYSy5ZQm7P9zN/uX7GXXdKCbeNZH4MfFql9gvpMXC3xyZoZp//rNfwoWiKKwuWM1l719G+j/S+ct3f6GqpYqU0BQ+vPJDHpn5SK/dD1FYWAjA4MGDe+V8QgghhBD9afT1o7lh5Q1EZ0Vja7Kx5eUtvDz2ZV6b9ho/vfUTDotD7RL7lAQLf3P11TBxIjQ2wmOP9dnbWBwWXtr0EiNfGsmc/8zhk72f4FJczEmfwyc/+4QDvzzAFSN6d4jf4uJiAFJSUnr1vEIIIYQQ/SXtrDTu3n03P1/9c7Kvykar11K8vphPb/yUp5OeZuWDK6k9UKt2mX1Co5xsKKYBpqGhgbCwMOrr6wkN9dMxh9etg5kz3d2iSkshLq7X3+Kez+/hxc0vAhBkCOLnY37OPZPvYUTMiF5/r6Nuv/12Xn31VQwGA5MnT2bWrFnMmjWLadOmERwc3GfvK4QQQgjRV5rKmtj2+ja2vLyF+kPuUTR1Rh3TH57O9IemozcNnDsTJFj4q5gYqKqCXbsgO7vXT3/Je5fw2b7PuH387fzt3L8RFhDW6+9xopUrV3LLLbdQUlLitV2v1zNhwgTOOussZs2axZlnnunf/98JIYQQ4rTjcrrYv3w/Pz77IwWrCgCIGRHDvFfnkXLGwOitIcHCX8XGQmVlnweLV+e9yq3jb+3183dEURTy8/NZu3atZzl06JDXMVqtlvHjx3taNM4880wiIyP7rUYhhBBCiJ5SFIWcD3JYcd+K/9/efcdVXff/H38cpmxkyVCGAi7EvUfOHKmlZWZl2bKlXXk1tauu1u9qel0Ny8q2lX41y8wUM01zheRExIUIhuBAhux1fn+QpwgcyIHDeN5vt3M78Pl8zuf9Oh6Q8zyf9yD3VC4YoOeDPRn2n2HYu9hburwaUbBoqOooWPg5+3FN2DWMCh3FsNbDcG/mbva2LuXYsWMVgsbRo0crHRMaGkrv3r1Nt86dO2Nv37B/OUVERKTxyj+bz5pZa9jz+R4AwseGM+X7KRauqmYULBqqWg4Wn+/5nHtX3ktBSYFpm7XBmr6t+jI6dDSjQkfRxbcLVoa6H///+++/Vwgahw4dqnSMnZ0dXbp0oXfv3vTq1YvevXsTGhqqFb1FRESkXsg6nsXy25ZzbMMxAPo+0perX7/askXVkIJFQ1XLwQIgvzifX5J+IepIFFEJURw4c6BiCU4+jGwzktGhoxnWehg+Tj61UselnD17lu3bt7N9+3aio6OJjo4mPT290nHNmzc3hYzevXvTp08fdaESERGROmUsM7L/6/2svHclBZkF2DrZMvrt0XSZ1qXBfwCqYNFQ1UGw+LtjmcdYc2QNq4+sZl3iOnKKcirsb96sOaEeoZVuYR5heDl61dkvi9FoJDEx0RQytm/fTkxMDCUlFeeOdnV1ZceOHYSGhtZJXSIiItL4lZWUkZ2STVZSFpnHMslMyiTzWKbp++zj2ZQWlQLg39Of67+6Ho/QxvFBZ+OZ30pqXbB7MPf2uJd7e9xLUWkRW49vJepIFKuPrGbvyb1kFGQQcyKGmBMxlR7rau/6Z9hoXjF4+Dr7mjV05OXlkZKSUuH291AB4OLiohW+RUREpFpKi0rJ/j27ytCQlZRF1vEsjKUX/9ze2t6avo/0ZfCzg7G2ta6jymufrlg0VBa4YnExuUW5HM04ypGzR/68ZRzhcPphjmcfv+hjnWydqrzSEeoRir+L/0XHcZSWlhIfH2+6MhEdHc2+ffsoLS2tcJzBYKBjx44Vxlx07NgRGxtlaxEREflTSUEJWclZlULD+a+zU7LhEu+erWytcAt0wz3YHbeg8nv3IHfT964BrljZNL51qhUsGqrzwWLXLujSxdLVXFR+cT6JmYkVQ8cft6SsJMqMZRd8bCefTuy8dyc2VpUDQFFREd27d2ffvn2V9gUEBFQIEd27d8fFxcWsz0tEREQalyNRR/i/Cf9HSUHlng5/ZdPMpjwwBLnjFuxWITS4B7vj4ueCwaphj5e4Evq4tqFq27Y8WHzwAbz7rqWruSgHWwc6eHegg3cH0vPSiTsdR9ypOPad2seutF1Ep0RfMFzkFeddcF9ZWRkFBQWVtoeGhjJt2jRGjx5Nly5dsLJqfJ8IiIiIiPmVFJZQWlx60WOauTejZZ+WeLb1xCPMA89wTzzDPXFr5dYkw8Rf6YpFQ7VxIwweDFZWsHdvvegO9VdZBVkVAkTc6TjiTseRlpNW5fEGDLRu3poInwg6eneko09HInwiaOfVDjvrC4+DyM/P55dffiEqKoqoqCgOHPjbzFU+PowcOZJRo0YxYsQIvL29zfo8RUREpHHJP5vPmYNnSD+UTvqhdM4ePmu6L84rvuDjrO2t8Qj1wDPME4/wPwJHWHnocGrh1OBnfLocChYN2cSJ8O23cO21sHy5pashsyCTT3Z9wvs73udg+sELHhfkFlRlgHC0daxxDceOHWPlypW8++67xMfHV9hnMBh4+umnee6552rcjoiIiDQtRqORcyfOmYJG+uF0zh46W35/5CxlxRfu2u3g6cDkbyYTNCioDiuue+oK1ZA9+mh5sNi+3aJlxJ6MZd72eXwR+wV5xXmm7QEuAZUCRHuv9rjY13ysg9FoJDU1lUOHDnHw4MEK90ePHq00ePv8Y6patVtERETkUgwGA64BrrgGuBI8OLjCvrLSMrKSsypc4Ug/lM6JmBPkn80nPz2fzGOZChZSjzk5WazpkrISlh9Yzrzt89iYtNG0PcIngpm9ZjKpwySaOzSvcTvnzp3j0KFDVQaInJycCz7O0dGR8PBw2rZtS3h4uOnWvXv3GtckIiIi8ldW1lY0D2lO85DmMLJ8W8bRDD7s/SEA7a9vT+StkRassG4oWEi1HU4/zIiFI0jKSgLA2mDNhPYTmNFzBoOCBpmlD+F7773Hiy++SEpKygWPsbKyIiQkpEKAOH8fEBDQJPoyioiISP1zfOtxvrvjO/LO5OHXzY/rPruuSQzsVrCQajs/TSyUd3fafOdmgt2DzXb++fPn88ADD5i+9/HxqTI8tG7dGnt7e7O1KyIiIlITGYkZrHtyHXFL4gBw8XfhphU3YefUNBbkVbCQahsVOoq5V8/l0R8fJeVcCtO/n86SSUtwb+Ze43N/9tlnplDx+OOPM3v2bNzda35eERERkdpSkFXApv9sIvqNaEqLSsEAXe/qytAXhuLs62zp8uqMZoVqyPbsKV8cz88PTpyo8+ZXHFzBzctuJrc4l/Ze7dkxfQcOtg5XfL61a9cyatQoysrKeOihh3jjjTfUnUlERETqtdSdqXwx8gvyzpRPYBMyLISr516Nb2dfC1dW97RymFyx8W3Hs+mOTXg4eBB/Jr7CIO4rsXXrVsrKyqdqO3LkCAkJCeYoU0RERKTWHP3pqClUDHlxCFPXTm2SoQIULKSGuvp1JdQjFCifKaomHn/8cZ544glsbW1ZtWoVHTt2ZM6cOeTm5pqjVBERERGz6/lATwIHBAKwbe420nZXvRhwU6BgITVW00BxnoODAy+//DKxsbGMGjWKoqIiXnrpJdq2bcu3335rljZEREREzMnO2Y6bV91My74tKcgoYOHwhRxaeYgmNtoAULCQGigtK+WxHx9jZ+pOAPxd/M1y3rZt27Jq1Sq+++47QkJCSElJYeLEiUyePJlTp06ZpQ0RERERc7F3sefWqFsJ6B1A/tl8Fo1bxKdXfcrxbcctXVqdUrCQK5JTlMPEJRN5fdvrADw3+Dm6+XUz2/kNBgPjx48nLi6OOXPmYG1tzZIlS+jYsSOLFy9ukp8CiIiISP1l72rP1LVT6fd4P2ya2ZC8KZmP+33M4usWc3r/aUuXVyc0K1RDZqFZoTLyMxj6+VB2p+3G3tqeT6/7lJsibqrVNnfu3Mkdd9zB3r17ARgxYgR9+vTB39+/ws3HxwcbG82iLCIiIpaT/Xs2G57dwO5PdmMsM2KwMtDlji4Mf2U4jp6Oli6v1ihYNGQWChaL9y1myrIpAHg4ePBQr4eY3n06fi5+tdpuUVERr7zyCi+88ALFxcVVHmNlZUWLFi3w8/OrFDr+evP29sbKShfsREREpPacjj/N+qfWc+DbAwA4+ThxzXvX0H5CewtXVjsULBoyCwWL4tJiXtr8EvN/m09aTvnMBzZWNtzQ4QZm9ppJ35Z9a3X9ifj4eJYtW8aJEycq3NLS0igtLb2sc9jY2ODr63vR8OHv74+Hh4fW0hAREZEaOb71ON/f872pS1TETRGMfns0jl6N6+qFgkVDZuEF8opKi1i2fxnzYuax9fhW0/auvl15dcSrDG89vE7rKS0t5cyZM5UCx99vJ0+evOwxGnZ2dhWCRsuWLXnggQcICwur5WcjIiIijUlJQQkbn9/Ille2YCwz4uTjxIQvJtBmRBtLl2Y2ChYNmYWDxV99sOMDHlz1oGnq2XDPcA7OOGjRmv6uoKCAkydPkpKSQmxsLLt27WLXrl3ExsaSn59/2ee5++67WbBgQS1WKiIiIo1RZlImK6evJOHH8kWAW3RuwX2777NwVeajUa5yxQpLClm6fynzts8jOiXatL27X3deHfFqrbdvNBrJzs7m1KlTnDx5kpMnT1b4+u/fnzt3rlrnNxgMeHt74+PjQ4sWLUxjN+67r/H8ByAiIiK172TsSba+upXYRbEYS8s/0/ft4svIN0ZauDLzUrCQaispK+HFX15k/m/zOZVbvq6ErZUtkyMmM6PnDHoF9DLruISNGzeyZs2aKgNDYWFhtc5la2trCgl/DQx//97HxwcvLy+sra3N9jxERESkaTkVd4qfHv+Jw6sOm7aFDA2h/xP9aT2idaMbx6lg0ZDZ2pbfZ2bCmTPg5VUnzS4/sJznNj5n+r5XQC++ufEbAlwDzN5WWVkZI0aMuOAsUADOzs6VQsGFAoObm1uj+yUWERGR+qfwXCELRywkJzUHg5WB9te3p//j/fHvYZ4FhesjjbFoyMrKoFu38rEWM2fCW2/VSbPnCs9x3w/3sSRuiWlMhZ+zH/f1uI/p3afj6+xr1vYWLFjAW2+9xb59+ypsd3Jy4sYbb+TGG29k8ODBNGvWzKztioiIiFypn578iS2vbKF56+bcuuZWPEI9LF1SrVOwaOjWrYPhw8HaGvbtg3bt6qzpE+dO8P5v7/P+jvc5mXsSKO8SdUOHG/jXoH/RwbuDWdtLSkrihx9+YOXKlaxfv75CNyhHR0eGDx/ODTfcwK233qqrEiIiImIx6YfTebfju5QVl3HTiptoO66tpUuqEwoWjcHYsfDDDzBjBrz9dp03f37a2be2v8Wvv/8KQFvPthyYcaDW2szNzWX9+vW89dZb/PTTTxX2rVq1itGjR9da2yIiIiIXs3zacvZ8tofQUaHcvOrmJvOBp5Yebgyuuqr8PjvbIs3bWdsxvu34Cl2g7ul2T621l5yczPz583n++ecrhApbW1smTJhAnz59aq1tERERkYspKSwxrbQ98KmBTSZUgAZvixmkZKcwbtE4dqXtws7ajo/Hf8wtkbeYtY38/Hw+/PBDFi9ezNatfy7GZ2VlxbBhw5g8eTITJkzAw6Px918UERGR+sloNBL/TTyF2YW4BLjQql8rS5dUpxQspMb++eM/2ZW2CyjvFjVz9Uxe3/Y6QW5B5Tf3ivdejl7VTu9z5szhjTfeqLDN19eXp556ipEjR9KmTRusrHQBTkREROpOaVEpqTtTSd6SzO9bfyd5SzK5J3MB6HBDBwxWTedqBShYiBnc2OFGDqUfIikziYyCjPJbWga703ZXebyDjYMpZAS6BVYKH/4u/thYVfzRHDduHOvWrWP//v2UlpYCkJaWxsyZMwFwcXGhc+fOdOvWja5du9K1a1c6dOiA7fkpeUVERERqKC89j+Nbj5ffthznRMwJSgpKKhxjbWdNyz4t6f2P3haq0nI0eLsxeO01ePxxuO02+Owzi5aSXZhNclYySZlJJGUl/Xn/x9epOamXPIe1wZqWri3/DBt/BI5g92B6+fTiYPxBdu7cya5du9i5cyd79+6tcqE8e3t7IiIi6Nq1K926dWPgwIFERETUxtMWERGRRij3VC6HfjjE8S3lQeLMgTOVjnHwdKBVv1a06t+KwP6B+Pfwx6ZZ0/zsvmk+a6k1rvauRPhEEOFT9Rv4wpJCjmcfrxg+/ggdO1J3kF2YTamx1LT97+7scicfXfsRPXv2NG0rKSnhwIEDFcLG7t27yc7OZseOHezYsQMAg8HAr7/+Sq9evWrnyYuIiEij8mGfD8lMzKy03a+bH92mdyN4cDCe4Z5NaoD2xShYSJ0xGo2k56dz4twJjmUe48jZIxVu54rOXfTxLV1bMiRkSKXtNjY2REREEBERwW233QaUr9idmJjIF198wbPPPguAj48PQUFBZn9eIiIi0jiFjg5l9ye7Kcmv2N0pdWcqUQ9F4RPhg29XX/y6+eHb1ZcWkS2wc7KzULWWp2AhZncm7wyxJ2P/DA0Zf4aHvOK8Cz7OgIFAt0BCPUIr3Vo3b42jrWO16khMTDQN+G7bti1RUVG0aNGiJk9NREREmpBr3rmG0W+NJv1QOqk7U0nblUbarjRSd6ZSkFlA6s5UUnemsuuj8klsDFYGPNt64tfVD99uvvh19aNVv1ZNpmtU03iWTUVursWaNhqNbDm+hXnb57EsfhklZSVVHmdlsCLYPbg8MDT/MziEeYYR4h6CvY19tdpMTU3l0KFDHD58uMItISGBgoICAPr168eKFSvw9PQ0y3MVERGRpsPK2grv9t54t/cm8pZIoPw9SFZSVnmw2JVK2s40UnelkpOaw5n4M5yJP0PsV7EABA4I5I5Nd1jyKdQZBYvGoPcfsw589x0cOgTh4XXWdF5xHotiFzEvZl6FWaDaNG9DuGc4YR5hFa48BLkHYWd9+ZcIjUYjp0+fNgWGv4aII0eOkHuRMGVra8tNN93E+++/j4ODQ02epoiIiIiJwWDAPdgd92B32k9sb9qek5ZD6s5UfnriJ07tOwWAa8tGMlnQZVCwaAwGDYJrroEffiifHWr58lpvMjEjkXdj3uWjXR+RUZABlE8je0unW5jRawadfTtX63xZWVkcOHCgwlWH8yEi+yIriltbWxMcHExYWJjpFh4eTlhYGIGBgdjY6EdcRERE6oaDpwMHvz9oChXdpndjzLwxFq6q7mi62cYiPh46dYLSUoiOhlqc+Wjb8W0M+nSQqbtTiHsID/R8gDu73omHQ/VXvt61axf9+/cnPz+/yv0Gg4FWrVpVCA3nbyEhIdjZNd1BUiIiIlI/GI1Gvr312/IuUAa4+vWr6TOrT5OaMUof5zYW7dvDwIGwYUN5d6haDBa21n8uOhfgEsCWO7fg5+J35eeztaW4uBgon+Hp9ttvNwWI8PBwWrdura5MIiIiUq/F/V8csV/FYmVjxaSvJ9Hu2naWLqnOWVm6ADGjOlpluod/D3689UeaN2tOyrkU+n7Ul32n9l3x+SIiIvjqq6+wsrKipKQEJycnHnvsMSZMmEDHjh0VKkRERKReO3fiHD888AMAA/81sEmGClCwkCs0JGQIv979K6EeoSRlJdFrQS+uXXwt7/32Hscyj1X7fJMmTeKTTz4B4K233uLdd981c8UiIiIi5mE0Gjl75Cy7Pt7F8mnLWdBrAQUZBfh182PgnIGWLs9i1BVKrli4Zzi/3vUrk5ZO4udjP7Pi4ApWHFwBQFvPtowOHc2o0FEMChqEg+2lrzrcfPPNPPvssyQmJpKenl7b5YuIiIhcFmOZkdP7T5P0S5LplpOaU+EYRy9Hrvv8OqxtrS1UpeUpWEiNeDp6su62dexK20XUkSiijkSx9fhWDqYf5GD6Qd6IfoNmNs0YHDzYFDTCPMKqHMi0YMECEhMT8fT05KGHHrLAsxERERGBspIyUnelkvRLEsm/JJO8OZn8sxUnmbG2syagVwCBgwIJGhREq36tsHe5/PW4GiPNCtWYXH01rF0LCxfCrbdarIysgizWJa5j9eHVRCVE8Xv27xX2h3uGc2/3e7mjyx00d2gOQH5+PoGBgZw5c4Z58+bx4IMPWqJ0ERERacKyf89m8yub2fPpHopyiirss3WypVW/VgQNCiJwYCABvQKwdaib8a0Nha5YiNm5NXNjYvuJTGw/EaPRyP7T+8uvZiRE8UvSLxxKP8QjPz7C0z8/za2dbmVGrxn42/iTlZUFQHBwsGWfgIiIiDQpWcez2PzSZnZ9tIvSolIAmjVvRuCA8qsRQYOC8O3q26S7OV0OXbFoTOrJFYuLySnKYVHsIt7e/jaxp2JN268KugrXA658/9r3dGjXgT179mhxOxEREalVmUmZ5YHi412UFZcBEDQoiEHPDCJkSAgGq6azBoU5KFg0Jg0gWJxnNBrZlLyJedvn8U38N5Qayz8dsDpnRdnCMt6Y8wb/+Mc/LFyliIiINFYx82OI+keUKVAEDw7mqn9fRfDgYMsW1oBpulmxCIPBwKCgQSyZtITEfyTSpnkbAMpcysAHHn30UTZs2GDZIkVERKTRStqYZAoVLgEuDHxqoEJFDSlYiEXlFuXy8JqHSchIAOCpgU9xY8cbKSkpYeLEiRw+fNjCFYqIiEhjNO6DcQz810BsnWw5l3KOhSMW8vmwz/n9198v/WCpkrpCNSYNpCtUWk4a21O2sz1lO98e+Jb9p/djZ23Hh+M+ZGrnqeTn5zNkyBCio6MJCwtj9+7dODo6WrpsERERaYRyTuaw+aXN/Db/N9PA7ZChIQQODMS/pz8BPQNw8nGycJUNg4JFY1IPg0VOUQ47TuwoDxInysNEclZyhWM8HTxZftNyBgQOMG07efIkXbp0IS0tje+++47x48fXdekiIiLShGQmZbLx+Y3s+XQPxrKKb4/dAt3w7+lvChp+3f1o5tbMQpXWX5p2R8ympKyEfaf2ma5GbE/ZTtzpOMqMZRWOM2Cgg3cHegX0oldAL65tey1+Ln4VjmnRogXDhg3jyy+/JC4uTsFCREREapV7kDvXfnQtA54cQMKaBE7EnCAlJoUzB86QlZxFVnIW8cviTcd7hntWCBu+XX2b/LoWChZSY4v3LWbe9nnsTN1Jfkl+pf0tXVvSO6C3KUh09+uOi73LJc8bEREBwNKlS/H29iYyMpKOHTvi5KTLkSIiIlI7PMM88QzzNH1fmF1I6s5UUmJSOBFzghMxJ8g8lkn6oXTSD6UT+2X59PkGawM+ET606t+KsNFhBA8Jxs7JzlJPwyLUFaoxqeOuUGXGMp5a9xQvb3nZtM3V3rU8QPiXh4ieAT3xd/G/ovOvW7eO4cOHV9hmMBho06YNnTp1olOnTkRGRtKpUyfatGmDtbUWrREREZHal3s6l9QdFcNGTlpOhWOs7a0JHhxM2JgwwsaE4RHqYaFq646CRWNSh8EirziPqd9O5Zv4bwB4vN/j3NH1DsI9w7EymGeyMaPRyDfffMPWrVuJjY1l7969nDx5sspjHRwc6NixY4WwERkZibe3t1lqEREREbkQo9HIuZRzpGxP4ei6oxz+4TBZSVkVjvEI8zCFjKBBQdg0a3wdhxQsGpM6ChZZBVkMXzic3078BsDtnW9nzsA5BLoF0symdgcynTp1itjYWNNt7969xMXFkZ9fuQsWlI/VOH9Fw8nJqcLN0dHxktscHBywstKszCIiInL5jEYjZw6c4fCqwxxZdYSkX5IoK/lzzKmtoy0hQ0MY+v+G0iKyhQUrNS8Fi8akjoLF1/u/ZtLSSVXua+HUgiD3IILc/rj98XWgWyBB7kG4N3M3ez2lpaUkJCRUCBuxsbEkJCRgjh9vBweHKwoll3Osvb09BoPBDP8KIiIiUl8VZheWX8n4I2icO3EOKJ9t6sH4B7F1bByDvhUsGpM6ChYFJQV8tPMj9p3aR1JWUvktM4nc4txLPtbV3rVC4KjwtXsQLZxamO2Ndm5uLnFxcezdu5eUlBRyc3PJy8sjNze3wq2qbRe6AmJuVlZW1Q4r1Qk1NjaN7zKriIhIQ2Y0Gjm55ySLr11MVnIWA+YMYNj/G2bpssxCwaIxseA6FkajkbP5Z00ho8L9H1+n56df8jz21vamqxtBbkG0bt6ajt4difCJIKR5iNnGb1xKWVkZ+fn5lxVCLnfbX7cXFRXVyfOwtbW97GAyfPhwxowZUyd1iYiINHXx38azZOISrGytuD/2frzaelm6pBrTx5liFgaDAU9HTzwdPenm163KY3KLcknOSq4ydCRlJXHi3AkKSws5fPYwh88ervR4BxsH2nu3J8Ingo7eHU2BI9At0Ozdif56JaE2lJSU1CiYXGpbWVl5P87i4mIyMzPJzMy8ZE2ffvop6enp6polIiJSi07vP83eL/ey76t9AJQVl3Ho+0MKFiLV4WTnRHvv9rT3bl/l/uLSYn7P/r1C2DiUfoi403HEn44nvySfnak72Zm6s8LjnO2c6eDdgQjvCDr6/Bk4/F386+2bZBsbG9zc3HBzczP7uY1GI0VFRZcVQNatW8eyZcsAmDFjRr399xIREWnIzp04R+yiWGK/jCVtV5ppu52LHR2u70DXu7pasDrzUbCQesPW2paQ5iGENA+ptK+0rJSEjATiTsURdzqOfaf2EXc6joNnDpJTlGNa6fuv3Ozd6OjTkQjvCAYEDuCWyFvqrCuVJRkMBuzt7bG3t8fZ2Zns7Gzs7OywtrbGaDRSUlJCYWEhZWVl/PzzzwBMmjSJZ5991rKFi4iINDIHvjvA9re3k7g+Ef4YfGBlY0Xo6FAib40kfFx4o1qtW2MsGhMLjrGoa+e7VR05e4R1ietYdXhVld2n/mr73dvpGdCzjiq8ckajkYKCArKzs6t1y8rKqrStsLDwku317NmTDRs24OjoWAfPTkREpGk4+tNRFo5YaPq+Vf9WdLqlEx0ndcTRq3H+zdUVC6l3amMgeFffrkS2iKz1unNzc6sdCKoKBiUlJWatzdHREVdX10q3oKAg5syZo1AhIiJiRqVFpayeuRqAjjd2ZNjLw2ge0tzCVdU+BQuxCKPRSHJWMjEnYjhy9kil4GCOqWt9nHyuuOvT2bNnTWthVHUl4EK384OmzcXFxaXKQHD+5ubmdtH9rq6uuLi4aNpZERGROhT9djRnDpzB0duRse+PpZl77S4gXF/o3UZjYm1dfn/ihGXrqEJGfgYxJ2KI/j2a7SfKx0Ocyj110cf4OPlcNDiYY7G9oqIiDhw4YFpU7/x9SkrKFZ/Tysrqkm/2LycQODs7a9VvERGRBmj72+XjPr07eEMTmhdFYywak/nz4YEHwMsLDh8Gd3eLlFFQUsCetD1Ep0SbBlVXNf7BxsqGyBaRdPDuUOVK3Q62DmaryWg0cvz48QrhYe/evRw8ePCC3Y6CgoJo164dHh4e1QoEjo6Oml1JRESkCdvy6hZ+evInMJavrn3tJ9cSMrTy5DSNjYJFY1JSApGREB8Pjz0Gr75aJ81m5Gew4uCK8hBxYjt70vZQXFZc6bhQj1B6B/SmV0AvegX0ootvF5rZmP/SoNFoZO/evWzbts0UImJjY8nKyqryeDc3NyIjI+nUqROdOnUiMjKSiIiIxvfzISIiInUmaVMS3037joyjGQD0mtmL4a8Mb1SzQP2dgkVjs2oVXHMN2NvD2bNQy4Ny96TtYeyisfye/XuF7d6O3vRu2Zte/uUhomdATzwcPGq1ltOnT/Pll1/yySefsHfv3kr7bWxsaNeuXaUQ0bJlS11hEBEREbMryinix0d/ZMf7OwAYMHsAw/4zzMJV1R6NsWhsRo8uvy8shHPnajVYfH/we6Ysm0JucS6tm7dmQrsJ9AroRe+A3rWyGnZViouLWbVqFZ9++ikrV640dWuys7NjyJAhdO7c2RQk2rVrh52dXa3XJCIiIgJg52zH2PfGYtPMhug3oynILLB0SbVKwaKxqYM380ajkf/9+j8e/fFRjBgZFjKMr2/82iyDqS+3/T179rBw4UK++OILTp36cxB4jx49uOOOO7jpppvw8KjdKyQiIiIil8PKpnwyFjvnxv0Bp4KFVEtxaTEzVs3gg50fAHBv93t5e/Tb2FrXbn/Bo0ePsn79etatW8f69esrhAkfHx+mTp3KtGnTiIiIqNU6RERERKorK6l8nKetU+MdXwEKFlINGfkZTFo6iXWJ6zBgYO7Vc3m4z8O10uUpLS2N9evXm8LEsWPHKux3dHRk5MiR3HHHHYwaNQpb28b9iyoiIiINU+LPiez/ej8Aba5uY+FqapeChVyWhLMJjF00lgNnDuBk68Si6xcxru04s50/JyfHFCLWrVtHXFxchf02Njb06dOHoUOHMmzYMPr06aPxEiIiIlKvFeUW8f3d3wPQ/b7utOrbysIV1S4FC7mkMmMZI78YSUJGAn7Ofqy+ZTWdfTubtY1evXoRHx9fYVuXLl0YNmwYw4YNY+DAgTg7O5u1TREREZHa9MuLv5BxNAPXVq6MeGWEpcupdQoWckkGDAS4BpCQkUBGQQYH0w+aNVgYjUb8/f1NwaJt27asWbOGoKAgs7UhIiIiUpdyT+ey/a3yFbhHvzUae1d7C1dU+6wsXYDUfwaDgZVTVjI2fCwFJQVM/noyL/7yIuZaAsVgMLBq1Soef/xxDAYDBw8eZMSIEcTExJjl/CIiIiJ17df//UpxXjF+3fxoe21bS5dTJxQs5LK42LuwfPJyZvWZBcDTPz9N34/68uXeLykqLarx+e3s7HjllVdYt24dLVu25PDhw/Tr14/vvvuuxucWERERqSvF+cVsfX0rv77xKwCDnhnUZBbi1crbjdH5H960NGjRwuynf/+393ko6iFToGjh1ILp3adzX4/78Hfxr/H5MzIyuOuuu/j2229xdHRk8+bNdO3atcbnFREREaktZSVl7PpkFxuf28i5lHMAhAwLYeraqQoWjZWChXmczDnJgp0LmP/bfE6cOwGAjZUNE9tPZEbPGQwIHFCjX6KSkhKuueYafvzxRwICAoiOjiYgIMBc5YuIiIiYhbHMyP6v97P+X+s5e/gsAG6Bbgx+bjCRUyOxsm46HYQULBqjOggW5xWXFrP8wHLmxczjl6RfTNsHBw9myQ1L8HbyvuJzZ2Vl0a9fP/bv30/Pnj3Ztm0b1tbW5ihbREREpMZKCktYNHYRR386CoCjlyMD/zWQHvf1wMa+6c2RpGDRGNVhsPirPWl7eCfmHb7Y+wX5JfmEuIew8uaVdPDucMXnTExMpFu3bmRmZrJgwQLuvvtuM1YsIiIicmWMRiPf3/M9uz7aha2TLf0f70+fWX2wd2n8sz9diIJFY2ShYHFe/Ol4xi4ay9GMo7jZu7F00lJGtLnyuZvfeOMNZs2aRYsWLTh8+DAuLi5mrFZERESk+ra/s53VM1ZjsDJw86qbCR0ZaumSLK7pdPpqisrKLNJse+/2RN8dzYDAAWQVZjH6y9FsOLbhis/3wAMPEBoaysmTJ3n55ZfNV6iIiIjIFTgZe5I1D68BYOh/hipU/EHBojFq9cdy8YsWWawEDwcP+rfqD0CpsZTD6Yev+Fx2dna89tprAMydO5ekpCSz1CgiIiJyJXJP5lJWUv4BbvzX8Zw5cMbCFdUPChaN0bPPlt+/8AKkp9d58/nF+UxZNoVXtrwCwJP9n+SubnfV6JzXXnstgwcPprCwkDlz5pijTBEREZEr0np4a65fdD3N3Jtx4rcTvN/1fbbP2262xYMbKo2xaIxKS6FbN9i7F2bNgv/+t9abLDOWsSdtD1FHovgy9kviTsdha2XL+2Pf546ud5iljZ07d9KjRw+MRiMxMTH06NHDLOcVERERuRLZv2fz3Z3fcXRt+axQrfq1InJqJO0mtMO5hbOFq6t7ChaN1ZIlMHkyREbCnj210kR6Xjo/JvxIVEIUa46s4WTuSdM+DwcPvrnxG64Kvsps7eXm5hIUFER6ejqfffYZt912m9nOLSIiInIljGVGtr+znZ8e/4mSgpLyjQYIHBBI++vb035ie9xauVm2yDrS9CbYbSo8PMrvzZgbS8tKiTkRQ9SRKKKORLE9ZTtG/jy/s50zw0KGMSp0FBPaTaCFs3lnpHr11VdJT08nJCSEyZMnm/XcIiIiIlfCYGWg98zetLu2HbGLYolfFs+JmBMkb0omeVMyax5eQ0CvgPKQcX17PNp4WLrkWqMrFo3VTz/BiBHQqVN5l6gaevKnJ1mwcwFn889W2B7ZIpJRbUYxKnQU/QP7Y2dtV+O2/q60tJTly5czdepU8vPzWbp0KTfccIPZ2xERERExh6zkLOK/iSd+WTzJW5L5y+ew+Hbx5dpPr8W3s6/lCqwlumIhl5RTlGMaiO3ezJ0RrUcwKnQUI9uMJMA1oNbazc7O5uOPP+att94iMTERgEGDBnH99dfXWpsiIiIiNeUW6Eafh/vQ5+E+5KTlEP9tPNFvRJN+KJ203WkkrElQsJCmqbSs1PR1yj9TcLR1rNX2kpKSePvtt1mwYAHZ2dkAeHp6cv/99/PII49gOL8AoIiIiEg95+zrjEeoB1nHswAIGRpCzwd6Wriq2qFg0diZYZG8pKw/142wsaq9H5nCwkKmT5/Ol19+SWlpeZhp164ds2bN4tZbb8XRsXYDjYiIiIi5Ja5PZNG4RZQWlhI4IJApK6dg62Br6bJqhdaxaKx8fMrv4+LK17Oo5lCaMmMZqw+vZsyXY+j8XmcAnGydsDZYm7tSk3379vH555+bQoWVlRWRkZHY29ubrlyIiIiINCR7F+6ltLD8vU3y5mTe6/wePzz4AweWH6Agq8DC1ZmXBm83Zk89Bf/5T/nX99wD774LNhe/4pBZkMknuz7hnZh3SMhIAMCAgTFhY5gzcA79WvWrtXKNRiNff/01S5YsYe3atWRlZVXY36VLF0aNGsWoUaPo168ftraNM+2LiIhI45F7OpeYd2M4uvYov//6O8bSP996G6wNBPQKoPWI1rQZ0YaA3gFY29beh7i1TcGisXv3XZgxo/yKxbhxsHw5WFW+UJVZkMmcdXP4bM9n5BXnAeUDte/scif397yfUI/QOi27pKSE6OhooqKiiIqK4rfffquw38XFheHDh/PCCy/QsWPHOq1NRERE5EoUZBVwbMMxjq49ytG1R0k/lF5hv52LHSFDQrjq31fh183PQlVeOQWLpmD5crjpJigshE2bYMCACruPZhxl7FdjiT8TD0Ann07M6DWDWzrdgpOdkwUKruzUqVOsXbuW999/n02bNpm233HHHXz88ccWrExERETkymQlZ5GwNoGjPx7l6Lqj5KfnA+Ds58xDRx7C1rFh9c7Q4O2m4LrroEMH2LULcnMr7NqSvIXr/u86zuSdIcAlgM+u+4yhIUPr3cxL+fn5bNiwgW3btpm2jRkzhmeeecaCVYmIiIhcObdAN7rd1Y1ud3XDWGYkdVcqS29YSuaxTH5981cGzh5o6RKrRYO3m7ClcUsZ+vlQzuSdobtfd7bfs51hrYfVq1CRn5/PAw88QFhYGB9++CElJSWMHDmSX3/9lR9++IHg4GBLlygiIiJSYwYrA/7d/Rny4hAAtryyhbz0PAtXVT0KFk3Y7HWzKSotwsvRi7VT1+Lv4m/pkir5+uuvmT9/PsXFxYSFhbF582aioqLo3bu3pUsTERERMbvwa8KxtremMKuQI1FHLF1OtShYNGH/GvQvrA3WnMk7Y+oOVd+MHz+e/v37A3DkyBE2bdpEExsWJCIiIk2E0Wjk++nfU1pYimsrV8JGh1m6pGpRsGjCpnWZxg83/4CrvSu/JP1Cnw/7cORs/UrGbm5urF+/nnvvvRej0cjs2bOZPHky+fn5li5NRERExKxi3o1h/9L9WNlYMWnJJBw8HCxdUrUoWDRxI0NHsvXOrQS7B5OQkcCcdXMsXVIldnZ2vPfee7z33nvY2tqydOlSPv/8c0uXJSIiImI2RqOR9U+tB2D4q8Np2aelhSuqPgULoaNPR14a9hIAp3JPWbiaC7v33nsZNGgQAA4ODSvBi4iIiFxMaVEphVmFAHS9s6uFq7kyChYCgJWhYfwoHD9+HIBWrVpZuBIRERER8ynOKzZ9bevQsNavOK9hvJuUWmdrVf4DHHsqlt9O/HaJoy3nzJnyAebFxcWXOFJERESk4Ti24RgAVrZWWNk2zLfoDbNqMbuRoSOJ8IngbP5ZBn0yiGX7l1m6pCqNHz8egIceekgDuEVERKRRSIlJ4dtbvwWg+/Tu9WpNsepQsBAAHG0d2XLnFkaFjiK/JJ8blt7AS5teqndTu/73v//F39+fgwcP8tRTT1m6HBEREZEayUjMYNHYRRTnFdNmZBtG/m+kpUu6YgoWTcX55JuefsFDXO1d+X7K99waeSsAc9bPYXPy5rqo7rIYjUb27NlDQEAAAG+88QYZGRkWrkpERETkykX9I4rcU7n4dvFl0tJJWNtaW7qkK6Zg0VQMH15+///+H5SUXPCw7SnbWXNkDQD+Lv6082pXF9VdVE5ODu+99x6dOnViyJAhxMTEAHDdddfh5ORk4epERERErpyjpyMAQYODsHext3A1NaNg0VTMng2enrB/P3z4YZWHfLn3S4Z8NoTTeafp5teN7Xdvx9vJu44L/VNCQgKzZs2iZcuW3H///cTFxeHo6Mh9991HbGws33zzDXZ2dharT0RERKSmwseHA3Dwu4P1rgt6ddlYugCpI+7u8OyzMHMmPP883Hvvn92jgF2pu7j12/IuUO7N3Fk4YSEBrgGWqRVIT08nMjKSvLw8AEJDQ3nwwQeZNm0a7u7uFqtLRERExFyKcoo4ueckAJmJmWQczcCjjYeFq7pyumLRlNx0U/l9air8LRG3cG5BqEcoAJkFmXSa34kJ/zeBdUfXWSQ9Ozs707Jl+YqTXl5e/PLLLzz88MMKFSIiItLgFecXs+2/23iz9ZtsfG4jAL5dfHHyadhdvA3Ghn7NpZqys7Nxc3MjKysLV1dXS5dTt9LTwcur/OvSUrCqmCtLy0pZeWgl82Lm8dPRn0zb23u1Z0avGdzS6RbcmrnVWbnJyckMHDiQ5ORkIiIi2LBhA56ennXWvoiIiIg5lRSUsGPBDjb/ZzM5aTkAeIR6cNW/ryJiSgRW1g37M38Fi6bkEsHir+JPx/NOzDt8tuczcorKf/CtDFZ08O5AL/9e9AroRe+Wveno3RFb69pbHTIhIYGBAweSmppKZGQkq1evxt/fv9baExERETGX0uJSUnekkrg+kWM/HyN5SzIl+eWT6LgFuXHVM1fR+bbOWNk07EBxnoJFU1KNYHFedmE2n+/5nHdj3iX+THyl/Q42DnTz60avgD/CRkBvgt2DzbqwS3x8PEOGDOHkyZMEBgYSFRVF+/btzXZ+EREREXMoKy0jbXcax34+RuL6RJI3JVOUU1ThGLdANwbMHkDXO7tibddwp5atioJFU3IFweKvUs+lsj1le/ntxHZiUmLIKsyqdJyXo1d50PjjykbPgJ54OXrVqPTExERGjRrFoUOHaN68OStWrGDAgAE1OqeIiIhITRjLjJyKO2UKEkkbkyjILKhwjIOHA8GDgwkeGkzIkBC82ns12JW1L0XBoimpYbD4uzJjGYfTDxOdEm0KHLvTdlNcVlzp2C6+XRjVZhSjQkfRr1W/K+o+debMGcaPH8+2bduws7Nj1qxZPPHEEzRv3rxGz0NERESkOkoKS9i7cC9bXt3C2cNnK+yzd7UnaFCQKUi0iGyBwapxBom/U7BoSswcLKpSWFLInpN7TEEjOiWaQ+mHKhzjYufCsNbDGB06mpFtRhLkHnTZ58/Ly2Pq1Kl88803ADRv3pzZs2czY8YMHBwczPpcRERERP6qKKeIHR/sYNvcbZw7cQ4AW0dbAgcGEjwkmJChIfh19Ws0YyaqS8GiKamDYFGVU7mnWJuwlqiEKNYcWcPpvNMV9rf3as+o0PKrGYOCBtHMptlFz2c0Gvnhhx948skniYuLA6Bly5Y899xz3HbbbdjYaHkWERERMZ+89Dy2v72d7W9vJ/9sPgAu/i70fbQv3e/pjp2zFuwFBQtLl1O3LBQs/qrMWMbO1J1EHYki6kgU237fRpmxzLTf0daRKRFTmNlrJp19O1/0XKWlpXzxxRc8/fTTHD9+HID+/fuzadOmRtt3UUREROpOdko22+ZuY8cHOyjOLe/q7RHmQf/H+xM5NRIbe32Y+VcKFk1JPQgWf5eRn8G6xHWsPryaqIQoTpw7Ydo3IHAAM3rOYGL7iRcdk1FQUMBLL73E888/j42NDQUFBVhbN65ZFkRERKTupB9OZ8srW9jz+R7Kiss/APXt4suA2QNof337Br/eRG1RsGhK6mGw+Cuj0cjm5M28E/MOy+KXUVJWPs+zn7Mf9/W4j+ndp+Pr7FvlY1evXs2YMWPo0KGDqXuUiIiISHWk7kply8tbiFsaB3+8Qw4aFMSA2QNoM7KNekRcgq7fSL1hMBgYGDSQgUEDOXHuBO//9j7v73if1JxU/r3h37z4y4tMjpjMp9d+irVVxSsS58NERESEJUoXERGRBiznZA7fTfuOI1FHTNvCx4YzYPYAWvVrZcHKGpb69ZG1yB/8Xfx5bshzJM9K5quJX9G3ZV+Ky4rJKsiqFCoA9u3bB0DHjh3rulQRERFp4Bw8HDgdfxqDlYFON3fivj33MeX7KQoV1aSuUE1JSQns2VP+dbdu0MAu5+04sQMbK5sqB3WfO3eO+Ph4fHx8CA4OrvviREREpEE7tuEYrq1c8WjjYelSGiwFCxERERERqTF1hRIRERERkRpTsBARERERkRqrV8FiypQpODg4YDAYMBgMuLi48Pzzz5v2Z2Zm0qlTJ6ysrDAYDAQEBJgG7YqIiIiIiOXUq2ARGhrKk08+yY8//khUVBSRkZH8+9//5rvvvgPKV1WOj4/ntddeY+HChWRnZ9OvXz8LVy0iIiIiIvV+8LaVlRW33XYbzz//PEFBQfzzn/9k7ty5AKxatYprrrmGBQsWcPfdd1/W+TR4W0RERETE/OrVFYu/Kioq4qGHHsJoNDJx4kSWLl0KwEMPPWQ6ZsyYMVhbW7Ny5coLnic7O5vff//ddEtJSan12kVEREREmpp6t/L2smXLuOGGG0zfP/vss4wfP54HH3wQgKCgoArHN2vWjNTU1Aueb/z48WzcuLF2ihUREREREaAeBouRI0fy008/kZqayjvvvMNzzz1H165dr/h8K1asIDs72/S90Wg0DQwXERERERHzqPdjLDw8PGjRogV33303jz76KMeOHatw1cLGxoaxY8eyfPlyyxUpIiIiItLE1dsxFucZjUaKi4uZNGkSAPPmzTPtW716NaWlpYwdO9ZS5YmIiIiICPWsK1Tfvn25+eab6dq1KydPnuT1118nMzOTJ554gsDAQDp06MD//vc//Pz88PHx4f7778fZ2fmyZ4QSEREREZHaUa+6QoWHh3P06FFKS0sxGAy4u7vz+OOP8+STTwLlC+QNHDiQuLg4jEYjfn5+pvUuRERERETEcupVsBARERERkYap3o+xEBERERGR+k/BQkREREREakzBQkREREREakzBQkREREREakzBQkREREREakzBQkREREREakzBQkREREREakzBQkREREREakzBQkREREREakzBQkREREREakzBQkREREREakzBQkREREREakzBQkREREREakzBQkREREREaszG0gWI1IXS0lKKi4stXYaIiDRRtra2WFtbW7oMkVqlYCGNmtFoJC0tjczMTEuXIiIiTZy7uzu+vr4YDAZLlyJSKxQspFE7Hyp8fHxwdHTUf+YiIlLnjEYjeXl5nDp1CgA/Pz8LVyRSOxQspNEqLS01hQpPT09LlyMiIk2Yg4MDAKdOncLHx0fdoqRR0uBtabTOj6lwdHS0cCUiIiJ//j3SmD9prBQspNFT9ycREakP9PdIGjsFCxERERERqTEFC5Em5ODBg/j6+nLu3DmLtF9UVERwcDC//fabRdpvCIxGI9OnT8fDwwODwcDu3buv6DxPP/0006dPN30/ePBgHn74YfMUWYX9+/fTsmVLcnNza62NpqioqIjQ0FC2bt0KwLFjx2r0c3E5nnzySWbOnFlr52+s0tLSGDFiBE5OTri7u1u6HBGLULAQaUJmz57NzJkzcXFxAWDDhg0YDAaaN29OQUFBhWNjYmIwGAwVLt2fP/78rUWLFlx//fUcPXr0stq3s7Pj0Ucf5YknnjDfk2pkoqKi+PTTT1m5ciWpqalERERU+xxpaWm8+eabPPXUU5f9mGeffdb0utrY2BAcHMysWbPIycm5rMd36NCBPn368N///rfa9cqFvffee4SEhNCvX7/LfkxwcLDptXRycqJbt24sXbr0sh//6KOP8tlnn13277WU+9///kdqaiq7d+/m0KFDli5HxCIULESaiOTkZFauXMm0adMq7XNxceHbb7+tsO2jjz4iMDCwynMdPHiQEydOsHTpUuLi4hg3bhylpaWXVcctt9zC5s2biYuLq/ZzaAoSEhLw8/OjX79++Pr6YmNT/cn7PvzwQ/r160dQUFC1HtexY0dSU1M5duwYr7zyCh988AGPPPLIZT/+jjvuYP78+ZSUlFS3ZKmC0Whk3rx53HXXXdV+7PPPP09qaiq7du2iZ8+eTJ482XTV41K8vLwYOXIk8+fPr3a7TVVRUREJCQl0796dsLAwfHx8LF2SiEUoWIjUQ7m5udx22204Ozvj5+fH3LlzK3VlKSws5IknnqBVq1bY29sTGhrKRx99dMFzLlmyhM6dOxMQEFBp3+23387HH39s+j4/P5/Fixdz++23V3kuHx8f/Pz8GDRoEM888wz79+/nyJEjxMTEMGLECLy8vHBzc+Oqq65i586dFR7bvHlz+vfvz+LFi6v5r9L4TZs2jZkzZ5KcnIzBYCA4OBiAr7/+mk6dOuHg4ICnpyfDhw+/aJejxYsXM27cuIu29cMPP+Dm5saXX35p2mZjY4Ovry8tW7Zk8uTJ3HLLLaxYsQKAhQsX0qNHD1xcXPD19eXmm282zcl/3ogRIzh79iwbN268wn+Bxis4OJg33nijwrYuXbrw7LPPXvAxO3bsICEhgWuuueaCx5SWlnLnnXfSrl07kpOTTdvPv07h4eG88847ODg48P3331NaWspdd91FSEgIDg4OtG3bljfffLPSeceNG6ff0YsYPHgwM2bM4OGHH8bLywt7e3uWLVvG559/jsFgqPIDHJGmQOtYSJNyfpEiS6jOAn2PPfYYGzdu5LvvvsPHx4c5c+awc+dOunTpYjrmtttuY9u2bbz11lt07tyZxMREzpw5c8Fzbtq0iR49elS5b+rUqbz22mskJycTGBjIsmXLCA4Oplu3bpes9fzc7EVFRZw7d47bb7+dt99+G6PRyNy5cxkzZgyHDx82db8C6NWrF5s2bbqsfwtzMRqN5BVb6LW3vbzX/s0336RNmzZ88MEHxMTEYG1tTWpqKlOmTOHVV19lwoQJnDt3jk2bNmE0Gqs8x9mzZ9m/f/8FX2uAr776ivvuu4+vvvqKsWPHXvA4BwcHioqKgPLpMV944QXatm3LqVOn+Oc//8m0adNYtWqV6Xg7Ozu6dOnCpk2bGDZs2CWfr1kYjWCh32kcHaEWZ/nZtGkT4eHhFX53/qqwsJApU6Zw7NgxNm3ahLe3d5XH2djYYGtrS1FREWVlZbRs2ZKlS5fi6enJ1q1bmT59On5+ftx4442mx/Tq1Yvff/+dY8eOmQJubTMajRTnWWYaVltH22rP2PTZZ59x//33s2XLFjIzM3n22WdxdXXlzTffNP2/KNLUKFhIk5KXl4ezs7NF2s7JycHJyemyjvvoo4/44osvTG/OPvvsM1q2bGk65tChQyxZsoS1a9cyfPhwAFq3bn3R8yYlJV3wzaaPjw+jR4/m008/5ZlnnuHjjz/mzjvvvGStqampvP766wQEBNC2bVs6depUYf8HH3yAu7s7GzdurPAG1t/fn6SkpEue35zyivNwfslCr/3sHJzsLv3au7m54eLigrW1Nb6+vgDs3LmTkpISJk6caOra9Pd/579KTk7GaDTi7+9f5f533nmHp556iu+//56rrrrqgufZsWMHX331FUOHDgWo8PPQunVr3nrrLXr27ElOTk6F36k6f23z8sBCv9Pk5MBl/E5fqaSkpAu+jjk5OVxzzTUUFhby888/4+bmVuVxRUVFzJ07l6ysLIYOHYqtrS3PPfecaX9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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Determine Lengte or Depth\n", "# If diepte is available, the y-axis will be Diepte\n", "# Else the y-axis will be Lengte\n", "if df_sond['diepte'].isnull().values.any():\n", " # IsNan\n", " y_type = \"lengte\"\n", " y_axis = \"Length (m)\"\n", "else:\n", " y_type = \"diepte\"\n", " y_axis = \"Depth (m)\"\n", "\n", "\n", "fig, ax0 = plt.subplots(figsize=(8, 12))\n", "\n", "# Prepare the individual axis\n", "ax_qc = ax0.twiny()\n", "ax_fs = ax0.twiny()\n", "ax_u = ax0.twiny()\n", "ax_rf = ax0.twiny()\n", "\n", "for i, ax in enumerate([ax_qc, ax_fs, ax_u]):\n", " ax.spines[\"top\"].set_position((\"axes\", 1+0.05*(i+1)))\n", " make_patch_spines_invisible(ax)\n", " ax.spines[\"top\"].set_visible(True)\n", "\n", "# Plot the data on the axis\n", "df_sond.plot(x=\"rf\", y=y_type, label=\"rf\", ax=ax_rf, color='purple', legend=False)\n", "df_sond.plot(x=\"qc\", y=y_type, label=\"qc (MPa)\", ax=ax_qc, color='black', legend=False)\n", "df_sond.plot(x=\"fs\", y=y_type, label=\"fs (kPa)\", ax=ax_fs, color='green', legend=False)\n", "df_sond.plot(x=\"u\", y=y_type, label=\"u (kPa)\", ax=ax_u, color='red', \n", " legend=False, xlim=(-100, 300)) # ! 300 is hardocded here for the example\n", "\n", "# styling and configuration\n", "ax_rf.xaxis.label.set_color('purple')\n", "ax_fs.xaxis.label.set_color('green')\n", "ax_u.xaxis.label.set_color('red')\n", "\n", "ax0.axes.set_visible(False)\n", "ax_qc.axes.yaxis.set_visible(False)\n", "ax_fs.axes.yaxis.set_visible(False)\n", "for i, ax in enumerate([ax_rf, ax_qc, ax_fs, ax_u, ax0]):\n", " ax.spines[\"right\"].set_visible(False)\n", " ax.spines[\"bottom\"].set_visible(False)\n", " ax.xaxis.label.set_fontsize(15)\n", " ax.xaxis.set_label_coords(-0.05, 1+0.05*i)\n", " ax.spines['left'].set_position(('outward', 10))\n", " ax.spines['left'].set_bounds(0, 30)\n", "ax_rf.set_xlim(0, 46)\n", "\n", "ax_u.set_title(\"Resistivity plot CPT measurement GEO-93/023-SII-E\", fontsize=12)\n", "\n", "ax0.invert_yaxis()\n", "ax_rf.invert_xaxis()\n", "ax_u.set_ylabel(y_axis, fontsize=12)\n", "fig.legend(loc='lower center', ncol=4)\n", "fig.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualize locations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using Folium, we can display the results of our search on a map." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "# import the necessary modules (not included in the requirements of pydov!)\n", "import folium\n", "from folium.plugins import MarkerCluster\n", "from pyproj import Transformer" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "# convert the coordinates to lat/lon for folium\n", "def convert_latlon(x1, y1):\n", " transformer = Transformer.from_crs(\"epsg:31370\", \"epsg:4326\", always_xy=True)\n", " x2,y2 = transformer.transform(x1, y1)\n", " return x2, y2\n", "\n", "df['lon'], df['lat'] = zip(*map(convert_latlon, df['x'], df['y'])) \n", "# convert to list\n", "loclist = df[['lat', 'lon']].values.tolist()" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Make this Notebook Trusted to load map: File -> Trust Notebook
" ], "text/plain": [ "" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# initialize the Folium map on the centre of the selected locations, play with the zoom until ok\n", "fmap = folium.Map(location=[df['lat'].mean(), df['lon'].mean()], zoom_start=11)\n", "marker_cluster = MarkerCluster().add_to(fmap)\n", "for loc in range(0, len(loclist)):\n", " folium.Marker(loclist[loc], popup=df['sondeernummer'][loc]).add_to(marker_cluster)\n", "fmap\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Listing techniques per CPT measurement" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "While performing CPT measurements, different techniques can be used. Since these can have an impact on the results, it can be interesting to download this additional information in order to better comprehend the CPT data.\n", "\n", "Different CPT techniques can be applied at various depths, so in pydov this is modelled using a subtype `Techniek`. The result will be that one can then choose to query CPT measurements and either retrieve a dataframe with the measurements themselves, or a dataframe with the techniques applied. The user can subsequently compare or merge the two dataframes at will." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To check the available subtypes for a the `Sondering` type, you can use:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'Meetdata': {'name': 'Meetdata',\n", " 'class': pydov.types.sondering.Meetdata,\n", " 'definition': 'Subtype listing the CPT measurement results. It has the following fields: lengte, diepte, qc, Qt, fs, u, i.'},\n", " 'Techniek': {'name': 'Techniek',\n", " 'class': pydov.types.sondering.Techniek,\n", " 'definition': 'Subtype listing the different techniques used to perform the CPT. It has the following fields: techniek_diepte_van, techniek_diepte, techniek, techniek_andere.'}}" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from pydov.types.sondering import Sondering\n", "\n", "Sondering.get_subtypes()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To retrieve the techniques instead of the measurement results, we can instantiate the search class with the `Techniek` subtype:" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "from pydov.search.sondering import SonderingSearch\n", "from pydov.types.sondering import Techniek\n", "\n", "sondering_search = SonderingSearch(\n", " objecttype=Sondering.with_subtype(Techniek)\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The extra fields are now available, and should be included in the output of `get_fields()`. E.g. to get more details about the `techniek` field:" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", "
\n", "

techniek - De gebruikte techniek.

  • type: string
  • notnull: False
  • query: False
  • cost: 10
  • multivalue: False
  • codelist:
    • \n", " \n", "
    \n", " \n", "
    \n", " pydov.util.codelists.XsdType\n", "
    \n", " \n", " \n", "
    \n", "

    B - B - sondeerbuizen door een harde laag geduwd of geboord

    \n", "
    \n", " \n", " \n", "
    \n", "

    D - D - dissipatieproef uitgevoerd

    \n", "
    \n", " \n", " \n", "
    \n", "

    E - E - sondeerbuizen op en neer bewogen

    \n", "
    \n", " \n", " \n", "
    \n", "

    S - S - uitvoering sondering tijdelijk onderbroken

    \n", "
    \n", " \n", " \n", "
    \n", "

    V - V - plaatsing van voerbuizen

    \n", "
    \n", " \n", " \n", "
    \n", "

    andere - andere - een andere dan de standaard voorziene technieken

    \n", "
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\n", " " ], "text/plain": [ "{'name': 'techniek', 'type': 'string', 'multivalue': False, 'definition': 'De gebruikte techniek.', 'notnull': False, 'query': False, 'cost': 10, 'codelist': , , , , , >}" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sondering_search.get_fields()['techniek']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And the technique data is returned when querying:" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[000/001] .\n", "[000/010] cccccccccc\n" ] }, { "data": { "text/html": [ "
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pkey_sonderingsondeernummerxymv_mtawstart_sondering_mtawdiepte_sondering_vandiepte_sondering_totdatum_aanvanguitvoerdersondeermethodeapparaatdatum_gw_metingdiepte_gw_mtechniek_diepte_vantechniek_dieptetechniektechniek_anderetechniek_label
0https://www.dov.vlaanderen.be/data/sondering/2...VLA08-3.2-S16218343.8211622.951.1551.150.012.802009-02-17Labo Devlieger - Van Voorendiscontinu mechanisch200 kNNaTNaNNaNNaNNaNNaNNone
1https://www.dov.vlaanderen.be/data/sondering/1...GEO-92/114-S4105658.0188808.0NaN12.450.02.701992-10-15MVG - Afdeling Geotechniekdiscontinu mechanisch25 kNNaTNaNNaNNaNNaNNaNNone
2https://www.dov.vlaanderen.be/data/sondering/2...GEO-01/169-S23189313.7203475.9NaN21.401.215.152002-03-06MVG - Afdeling Geotechniekcontinu elektrisch200 kN - MAN22002-03-06 14:45:001.255.501.2VNaNplaatsing van voerbuizen
3https://www.dov.vlaanderen.be/data/sondering/2...GEO-01/169-S16189633.5203331.5NaN21.221.430.002002-03-11MVG - Afdeling Geotechniekdiscontinu mechanisch200 kN - MAN22002-03-11 13:50:001.2210.409.2VNaNplaatsing van voerbuizen
4https://www.dov.vlaanderen.be/data/sondering/2...GEO-01/169-S16189633.5203331.5NaN21.221.430.002002-03-11MVG - Afdeling Geotechniekdiscontinu mechanisch200 kN - MAN22002-03-11 13:50:001.2213.009.2VNaNplaatsing van voerbuizen
5https://www.dov.vlaanderen.be/data/sondering/2...GEO-01/169-S10189907.3203208.5NaN20.921.515.352002-03-13MVG - Afdeling Geotechniekcontinu elektrisch200 kN - MAN22002-03-13 14:20:001.249.503.2VNaNplaatsing van voerbuizen
6https://www.dov.vlaanderen.be/data/sondering/2...GEO-01/169-S11189862.4203231.2NaN21.301.515.202002-03-13MVG - Afdeling Geotechniekcontinu elektrisch200 kN - MAN22002-03-13 12:50:001.559.504.2VNaNplaatsing van voerbuizen
7https://www.dov.vlaanderen.be/data/sondering/2...GEO-01/169-S12189815.8203249.5NaN21.271.615.352002-03-13MVG - Afdeling Geotechniekcontinu elektrisch200 kN - MAN22002-03-13 11:00:001.429.555.8VNaNplaatsing van voerbuizen
8https://www.dov.vlaanderen.be/data/sondering/2...GEO-01/169-S13189770.0203269.7NaN21.331.315.452002-03-12MVG - Afdeling Geotechniekcontinu elektrisch200 kN - MAN22002-03-12 14:30:001.504.502.2VNaNplaatsing van voerbuizen
9https://www.dov.vlaanderen.be/data/sondering/2...GEO-01/169-S14189724.6203290.6NaN21.261.315.452002-03-12MVG - Afdeling Geotechniekcontinu elektrisch200 kN - MAN22002-03-12 13:00:001.3010.454.2VNaNplaatsing van voerbuizen
10https://www.dov.vlaanderen.be/data/sondering/2...GEO-01/169-S15189679.0203311.1NaN21.261.415.352002-03-12MVG - Afdeling Geotechniekcontinu elektrisch200 kN - MAN22002-03-12 11:00:001.459.608.2VNaNplaatsing van voerbuizen
\n", "
" ], "text/plain": [ " pkey_sondering sondeernummer \\\n", "0 https://www.dov.vlaanderen.be/data/sondering/2... VLA08-3.2-S16 \n", "1 https://www.dov.vlaanderen.be/data/sondering/1... GEO-92/114-S4 \n", "2 https://www.dov.vlaanderen.be/data/sondering/2... GEO-01/169-S23 \n", "3 https://www.dov.vlaanderen.be/data/sondering/2... GEO-01/169-S16 \n", "4 https://www.dov.vlaanderen.be/data/sondering/2... GEO-01/169-S16 \n", "5 https://www.dov.vlaanderen.be/data/sondering/2... GEO-01/169-S10 \n", "6 https://www.dov.vlaanderen.be/data/sondering/2... GEO-01/169-S11 \n", "7 https://www.dov.vlaanderen.be/data/sondering/2... GEO-01/169-S12 \n", "8 https://www.dov.vlaanderen.be/data/sondering/2... GEO-01/169-S13 \n", "9 https://www.dov.vlaanderen.be/data/sondering/2... GEO-01/169-S14 \n", "10 https://www.dov.vlaanderen.be/data/sondering/2... GEO-01/169-S15 \n", "\n", " x y mv_mtaw start_sondering_mtaw diepte_sondering_van \\\n", "0 218343.8 211622.9 51.15 51.15 0.0 \n", "1 105658.0 188808.0 NaN 12.45 0.0 \n", "2 189313.7 203475.9 NaN 21.40 1.2 \n", "3 189633.5 203331.5 NaN 21.22 1.4 \n", "4 189633.5 203331.5 NaN 21.22 1.4 \n", "5 189907.3 203208.5 NaN 20.92 1.5 \n", "6 189862.4 203231.2 NaN 21.30 1.5 \n", "7 189815.8 203249.5 NaN 21.27 1.6 \n", "8 189770.0 203269.7 NaN 21.33 1.3 \n", "9 189724.6 203290.6 NaN 21.26 1.3 \n", "10 189679.0 203311.1 NaN 21.26 1.4 \n", "\n", " diepte_sondering_tot datum_aanvang uitvoerder \\\n", "0 12.80 2009-02-17 Labo Devlieger - Van Vooren \n", "1 2.70 1992-10-15 MVG - Afdeling Geotechniek \n", "2 15.15 2002-03-06 MVG - Afdeling Geotechniek \n", "3 30.00 2002-03-11 MVG - Afdeling Geotechniek \n", "4 30.00 2002-03-11 MVG - Afdeling Geotechniek \n", "5 15.35 2002-03-13 MVG - Afdeling Geotechniek \n", "6 15.20 2002-03-13 MVG - Afdeling Geotechniek \n", "7 15.35 2002-03-13 MVG - Afdeling Geotechniek \n", "8 15.45 2002-03-12 MVG - Afdeling Geotechniek \n", "9 15.45 2002-03-12 MVG - Afdeling Geotechniek \n", "10 15.35 2002-03-12 MVG - Afdeling Geotechniek \n", "\n", " sondeermethode apparaat datum_gw_meting diepte_gw_m \\\n", "0 discontinu mechanisch 200 kN NaT NaN \n", "1 discontinu mechanisch 25 kN NaT NaN \n", "2 continu elektrisch 200 kN - MAN2 2002-03-06 14:45:00 1.25 \n", "3 discontinu mechanisch 200 kN - MAN2 2002-03-11 13:50:00 1.22 \n", "4 discontinu mechanisch 200 kN - MAN2 2002-03-11 13:50:00 1.22 \n", "5 continu elektrisch 200 kN - MAN2 2002-03-13 14:20:00 1.24 \n", "6 continu elektrisch 200 kN - MAN2 2002-03-13 12:50:00 1.55 \n", "7 continu elektrisch 200 kN - MAN2 2002-03-13 11:00:00 1.42 \n", "8 continu elektrisch 200 kN - MAN2 2002-03-12 14:30:00 1.50 \n", "9 continu elektrisch 200 kN - MAN2 2002-03-12 13:00:00 1.30 \n", "10 continu elektrisch 200 kN - MAN2 2002-03-12 11:00:00 1.45 \n", "\n", " techniek_diepte_van techniek_diepte techniek techniek_andere \\\n", "0 NaN NaN NaN NaN \n", "1 NaN NaN NaN NaN \n", "2 5.50 1.2 V NaN \n", "3 10.40 9.2 V NaN \n", "4 13.00 9.2 V NaN \n", "5 9.50 3.2 V NaN \n", "6 9.50 4.2 V NaN \n", "7 9.55 5.8 V NaN \n", "8 4.50 2.2 V NaN \n", "9 10.45 4.2 V NaN \n", "10 9.60 8.2 V NaN \n", "\n", " techniek_label \n", "0 None \n", "1 None \n", "2 plaatsing van voerbuizen \n", "3 plaatsing van voerbuizen \n", "4 plaatsing van voerbuizen \n", "5 plaatsing van voerbuizen \n", "6 plaatsing van voerbuizen \n", "7 plaatsing van voerbuizen \n", "8 plaatsing van voerbuizen \n", "9 plaatsing van voerbuizen \n", "10 plaatsing van voerbuizen " ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = sondering_search.search(max_features=10)\n", "\n", "df['techniek_label'] = df['techniek'].apply(sondering_search.get_fields().techniek.codelist.get_definition)\n", "df" ] } ], "metadata": { "kernelspec": { "display_name": ".venv (3.13.5)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.13.5" } }, "nbformat": 4, "nbformat_minor": 4 }