{ "cells": [ { "cell_type": "markdown", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "# DMI API Tutorial\n", "\n", "```{post} 2022-02-22\n", ":tags: open science\n", ":author: Adam R. Jensen\n", ":image: 1\n", "```\n", "\n", "This tutorial gives an introduction on how to use the [Danish Meteorological Institute's (DMI) API](https://confluence.govcloud.dk/display/FDAPI) to download meterological observation data (v2).\n", "\n", "The tutorial uses the Python programming language and is in the format of a Jupyter Notebook. The notebook can be downloaded and run locally, allowing you to quickly get started downloading data. Part 1 of the tutorial provides some background basic information on how to work with the API, whereas a complete example is provided in Part 2.\n", "\n", "\n", "If you're new to the DMI observation data, I recommend that you check out some of the following links:\n", "1. [Meterological observations data](https://opendatadocs.dmi.govcloud.dk/en/Data/Meteorological_Observation_Data)\n", "2. [Meterological observations API](https://opendatadocs.dmi.govcloud.dk/en/APIs/Meteorological_Observation_API)\n", "3. [Station list](https://opendatadocs.dmi.govcloud.dk/Data/Meteorological_Observation_Data_Stations)\n", "4. [Station list explained](https://opendatadocs.dmi.govcloud.dk/Guides/Station_Lists_Explained)\n", "5. [FAQ](https://opendatadocs.dmi.govcloud.dk/en/FAQ)\n", "6. [Terms of use](https://opendatadocs.dmi.govcloud.dk/en/Terms_of_Use)\n", "7. [Operational status](https://statuspage.freshping.io/25721-DMIOpenDatas)\n", "8. [User creation](https://confluence.govcloud.dk/pages/viewpage.action?pageId=26476690):" ] }, { "cell_type": "markdown", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "
\n", "\n", "First, in order to retrieve data it is necessary to create a user and obtain an api-key. This api-key grants permission to retrieve data and allows DMI to generate usage statistics.\n", "\n", "A guide to creating a user profile and getting an api-key can be found [here](https://confluence.govcloud.dk/pages/viewpage.action?pageId=26476690)." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [], "source": [ "api_key = 'xxxxxxxx-yyyy-zzzz-iiii-jjjjjjjjjjjj' # insert your own key between the '' signs" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [ "hide-cell" ] }, "outputs": [], "source": [ "# Delete this cell if you run the notebook locally\n", "import os\n", "api_key = os.environ[\"DMI_API_KEY\"]" ] }, { "cell_type": "markdown", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [ "hide-cell" ] }, "source": [ "An easy test to see if your api-key works is to paste the following url into your browswer followed by a question mark and your api-key, e.g.: [https://dmigw.govcloud.dk/metObs/v1/observation?api-key=xxxxxxxx-yyyy-zzzz-iiii-jjjjjjjjjjjj](https://dmigw.govcloud.dk/metObs/v1/observation?api-key=xxxxxxxx-yyyy-zzzz-iiii-jjjjjjjjjjjj) (the example API key error).\n", "\n", "If you have obtained an api-key and pasted it correctly, a page with data will be shown.\n", "\n", "
\n", "\n", "The following code blocks retrieves a list of all the DMI stations (both in Denmark and in Greenland) and plots them on a map using the Python package Folium." ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "editable": true, "scrolled": true, "slideshow": { "slide_type": "" }, "tags": [ "hide-cell" ] }, "outputs": [ { "data": { "text/html": [ "
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Make this Notebook Trusted to load map: File -> Trust Notebook
" ], "text/plain": [ "" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import folium\n", "from folium import plugins\n", "\n", "EsriImagery = \"https://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/{z}/{y}/{x}\"\n", "EsriAttribution = \"Tiles © Esri — Source: Esri, i-cubed, USDA, USGS, AEX, GeoEye, Getmapping, Aerogrid, IGN, IGP, UPR-EGP, and the GIS User Community\"\n", "\n", "# Create Folium map\n", "m = folium.Map(\n", " location=[75, -5],\n", " zoom_start=2, min_zoom=2, max_bounds=True,\n", " control_scale=True, # Adds distance scale in lower left corner\n", " tiles='openstreetmap',\n", ")\n", "\n", "\n", "# Add each station to the map\n", "for index, row in stations.iterrows():\n", " folium.CircleMarker(\n", " location=row['coordinates'][::-1], # switch latitude/longitude\n", " popup=f\"Station ID: {row['stationId']}\\n{row['name']}, {row['country']}\",\n", " tooltip=f\"{row['name']}, {row['country']}\",\n", " radius=5, color='blue',\n", " fill_color='blue', fill=True).add_to(m)\n", "\n", "folium.raster_layers.TileLayer(EsriImagery, name='World imagery', attr=EsriAttribution, show=False).add_to(m)\n", "folium.LayerControl(position='topright').add_to(m)\n", "\n", "# Additional options and plugins\n", "folium.plugins.Fullscreen().add_to(m) # Add full screen button to map\n", "folium.LatLngPopup().add_to(m) # Show latitude/longitude when clicking on the map\n", "\n", "# Show the map\n", "m" ] }, { "cell_type": "markdown", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "## Part 1: Retrieving data\n", "Part 1 of this tutorial will show how to request data and convert it to a table format. Part 2 will deal with how to request specific data and more advanced data handling.\n", "\n", "First, the necessary libraries have to be imported:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "import requests # library for making HTTP requests\n", "import pandas as pd # library for data analysis\n", "import datetime as dt # library for handling date and time objects" ] }, { "cell_type": "markdown", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "\n", "In the following code block, data is retrieved using the ``requests.get`` function. Further information on REST APIs and HTTP request methods can be found [here](https://restfulapi.net/http-methods/).\n" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "DMI_URL = 'https://dmigw.govcloud.dk/v2/metObs/collections/observation/items'\n", "r = requests.get(DMI_URL, params={'api-key': api_key}) # Issues a HTTP GET request\n", "print(r)" ] }, { "cell_type": "markdown", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "
\n", "\n", "The [response status code](https://en.wikipedia.org/wiki/List_of_HTTP_status_codes) indicates whether the request was successful or not. A 200 code means that the retrieval was successful. \n", "

\n", "\n", "\n", "Next, we extract the JSON file containing the data from the returned request object. [JSON](https://restfulapi.net/introduction-to-json/) is a human-readable format for data exchange.\n" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dict_keys(['type', 'features', 'timeStamp', 'numberReturned', 'links'])\n" ] } ], "source": [ "json = r.json() # Extract JSON data\n", "print(json.keys()) # Print the keys of the JSON dictionary" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "When inspecting the json object, it can be noticed that the measurement data is contained within the features:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[{'geometry': {'coordinates': [8.0828, 55.5575], 'type': 'Point'},\n", " 'id': '00000001-30ad-ae74-5b33-7ef0a1a6ef92',\n", " 'type': 'Feature',\n", " 'properties': {'created': '2023-07-08T04:22:44.246708Z',\n", " 'observed': '2015-09-11T10:10:00Z',\n", " 'parameterId': 'temp_dew',\n", " 'stationId': '06081',\n", " 'value': 11.4}},\n", " {'geometry': {'coordinates': [11.3879, 55.3224], 'type': 'Point'},\n", " 'id': '00000005-79f9-4ab8-6905-bec39ce37f54',\n", " 'type': 'Feature',\n", " 'properties': {'created': '2023-07-07T12:46:35.469876Z',\n", " 'observed': '2010-08-10T03:30:00Z',\n", " 'parameterId': 'humidity',\n", " 'stationId': '06135',\n", " 'value': 100.0}}]" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "json['features'][:2]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", "The JSON object can be converted to a convenient table (pandas DataFrame) using ``pd.json_normalize``:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idtypegeometry.coordinatesgeometry.typeproperties.createdproperties.observedproperties.parameterIdproperties.stationIdproperties.valuegeometry
000000001-30ad-ae74-5b33-7ef0a1a6ef92Feature[8.0828, 55.5575]Point2023-07-08T04:22:44.246708Z2015-09-11T10:10:00Ztemp_dew0608111.4NaN
100000005-79f9-4ab8-6905-bec39ce37f54Feature[11.3879, 55.3224]Point2023-07-07T12:46:35.469876Z2010-08-10T03:30:00Zhumidity06135100.0NaN
200000006-ffbe-6f2f-fe2a-4ed40a6fa65aFeatureNaNNaN2023-07-08T10:03:52.695118Z1960-12-16T06:00:00Zcloud_cover06190100.0NaN
30000000e-638e-5ce8-2dab-0a16387eb3e9Feature[8.6705, 56.383]Point2023-07-08T00:17:49.896354Z2005-08-22T06:00:00Ztemp_max_past12h0605616.6NaN
40000000e-fa0d-c953-0901-0856867a22bbFeature[11.6035, 55.7358]Point2023-07-07T14:01:51.083752Z2014-04-08T17:10:00Zpressure_at_sea061561006.3NaN
\n", "
" ], "text/plain": [ " id type geometry.coordinates \\\n", "0 00000001-30ad-ae74-5b33-7ef0a1a6ef92 Feature [8.0828, 55.5575] \n", "1 00000005-79f9-4ab8-6905-bec39ce37f54 Feature [11.3879, 55.3224] \n", "2 00000006-ffbe-6f2f-fe2a-4ed40a6fa65a Feature NaN \n", "3 0000000e-638e-5ce8-2dab-0a16387eb3e9 Feature [8.6705, 56.383] \n", "4 0000000e-fa0d-c953-0901-0856867a22bb Feature [11.6035, 55.7358] \n", "\n", " geometry.type properties.created properties.observed \\\n", "0 Point 2023-07-08T04:22:44.246708Z 2015-09-11T10:10:00Z \n", "1 Point 2023-07-07T12:46:35.469876Z 2010-08-10T03:30:00Z \n", "2 NaN 2023-07-08T10:03:52.695118Z 1960-12-16T06:00:00Z \n", "3 Point 2023-07-08T00:17:49.896354Z 2005-08-22T06:00:00Z \n", "4 Point 2023-07-07T14:01:51.083752Z 2014-04-08T17:10:00Z \n", "\n", " properties.parameterId properties.stationId properties.value geometry \n", "0 temp_dew 06081 11.4 NaN \n", "1 humidity 06135 100.0 NaN \n", "2 cloud_cover 06190 100.0 NaN \n", "3 temp_max_past12h 06056 16.6 NaN \n", "4 pressure_at_sea 06156 1006.3 NaN " ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.json_normalize(json['features']) # Convert JSON object to a Pandas DataFrame\n", "df.head() # Print the first five rows of the DataFrame" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", "The timestamps strings can be converted to a datetime object using the pandas ``to_datetime`` function." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 2015-09-11 10:10:00+00:00\n", "1 2010-08-10 03:30:00+00:00\n", "2 1960-12-16 06:00:00+00:00\n", "3 2005-08-22 06:00:00+00:00\n", "4 2014-04-08 17:10:00+00:00\n", "Name: time, dtype: datetime64[ns, UTC]" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['time'] = pd.to_datetime(df['properties.observed'])\n", "df['time'].head() # Print the first five timestamps" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "Last, we will generate a list of all the available parameters:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['temp_dew' 'humidity' 'cloud_cover' 'temp_max_past12h' 'pressure_at_sea'\n", " 'wind_speed' 'temp_soil_max_past1h' 'weather' 'wind_dir' 'temp_dry'\n", " 'wind_dir_past1h' 'precip_dur_past10min' 'temp_grass'\n", " 'leav_hum_dur_past10min' 'temp_min_past1h' 'precip_past1min'\n", " 'precip_past1h' 'pressure' 'radia_glob' 'wind_speed_past1h'\n", " 'humidity_past1h' 'sun_last10min_glob' 'precip_past10min' 'visibility'\n", " 'visib_mean_last10min' 'leav_hum_dur_past1h' 'temp_soil'\n", " 'temp_soil_min_past1h' 'wind_min' 'temp_grass_min_past1h'\n", " 'wind_min_past1h' 'cloud_height' 'temp_min_past12h'\n", " 'wind_max_per10min_past1h' 'temp_max_past1h' 'temp_soil_mean_past1h'\n", " 'wind_max' 'radia_glob_past1h' 'temp_grass_mean_past1h'\n", " 'precip_dur_past1h' 'wind_gust_always_past1h' 'sun_last1h_glob'\n", " 'temp_mean_past1h' 'temp_grass_max_past1h' 'snow_depth_man']\n" ] } ], "source": [ "parameter_ids = df['properties.parameterId'].unique() # Generate a list of unique parameter ids\n", "print(parameter_ids) # Print all unique parameter ids" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

\n", "\n", "## Part 2: Requesting specific data\n", "\n", "The above example was a heavily simplied example to illustrate how the API can be accessed. For most applications you probably want to specify query criterias, such as:\n", "1. Meterological stations (e.g. 04320, 06074, etc.)\n", "2. Parameters (e.g. wind_speed, humidity, etc.)\n", "3. Time frame (to and from time)\n", "4. Limit (maximum number of observations)\n", "\n", "*Click the \"View to show\" button below to see a list of a all stations and parameters.*" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "tags": [ "hide-cell" ] }, "outputs": [], "source": [ "all_stations = [\n", " '04203', '04208', '04214', '04220', '04228', '04242', '04250',\n", " '04253', '04266', '04271', '04272', '04285', '04301', '04312',\n", " '04313', '04320', '04330', '04339', '04351', '04360', '04373',\n", " '04382', '04390', '05005', '05009', '05015', '05031', '05035',\n", " '05042', '05065', '05070', '05075', '05081', '05085', '05089',\n", " '05095', '05105', '05109', '05135', '05140', '05150', '05160',\n", " '05165', '05169', '05185', '05199', '05202', '05205', '05220',\n", " '05225', '05269', '05272', '05276', '05277', '05290', '05296',\n", " '05300', '05305', '05320', '05329', '05343', '05345', '05350',\n", " '05355', '05365', '05375', '05381', '05395', '05400', '05406',\n", " '05408', '05435', '05440', '05450', '05455', '05469', '05499',\n", " '05505', '05510', '05529', '05537', '05545', '05575', '05735',\n", " '05880', '05889', '05935', '05945', '05970', '05986', '05994',\n", " '06019', '06031', '06032', '06041', '06049', '06051', '06052',\n", " '06056', '06058', '06065', '06068', '06072', '06073', '06074',\n", " '06079', '06081', '06082', '06088', '06093', '06096', '06102',\n", " '06116', '06119', '06123', '06124', '06126', '06132', '06135',\n", " '06136', '06138', '06141', '06147', '06149', '06151', '06154',\n", " '06156', '06159', '06168', '06169', '06174', '06181', '06183',\n", " '06184', '06186', '06187', '06188', '06193', '06197', '20000',\n", " '20030', '20055', '20085', '20228', '20279', '20315', '20375',\n", " '20400', '20552', '20561', '20600', '20670', '21020', '21080',\n", " '21100', '21120', '21160', '21208', '21368', '21430', '22020',\n", " '22080', '22162', '22189', '22232', '22410', '23100', '23133',\n", " '23160', '23327', '23360', '24043', '24102', '24142', '24171',\n", " '24380', '24430', '24490', '25045', '25161', '25270', '25339',\n", " '26210', '26340', '26358', '26450', '27008', '27082', '28032',\n", " '28110', '28240', '28280', '28385', '28552', '28590', '29020',\n", " '29194', '29243', '29330', '29440', '30075', '30187', '30215',\n", " '30414', '31040', '31185', '31199', '31259', '31350', '31400',\n", " '31509', '31570', '32110', '32175', '34270', '34320', '34339'\n", "]\n", "\n", "all_parameters = [\n", " # Cloud cover and height\n", " 'cloud_cover', 'cloud_height',\n", " # Humdity\n", " 'humidity', 'humidity_past1h',\n", " # Precipitation\n", " 'precip_past10min', 'precip_past1h', 'precip_past24h',\n", " # Pressure\n", " 'pressure', 'pressure_at_sea',\n", " # Radiation\n", " 'radia_glob', 'radia_glob_past1h',\n", " # Temperature\n", " 'temp_dew', 'temp_dry', 'temp_max_past12h', 'temp_max_past1h',\n", " 'temp_mean_past1h', 'temp_min_past12h', 'temp_min_past1h',\n", " # Visibilty and weather\n", " 'visib_mean_last10min', 'visibility', 'weather',\n", " # Wind speed and direction\n", " 'wind_dir', 'wind_dir_past1h', 'wind_gust_always_past1h', 'wind_max',\n", " 'wind_max_per10min_past1h', 'wind_min', 'wind_min_past1h',\n", " 'wind_speed', 'wind_speed_past1h',\n", "]" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "hide-cell" ] }, "source": [ "
\n", "\n", "Due to poor design of the API, it is only possible to request one station or all stations, and similarly, it is only possible to request one parameter or all parameters. To be able to select a subset of stations or parameters it is therefore necessary to loop as shown below. This also avoids hitting the rather low maximum amount of data that can be transferred for each request. The implementation below is most suitable for downloading a few stations and a few parameters, and will incur a significant performance penalty if downloading data for all stations." ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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stationId0425006188
parameterIdradia_globwind_speedradia_globwind_speed
time
2022-01-01 00:00:00+00:000.03.60.04.9
2022-01-01 00:10:00+00:000.04.00.05.5
2022-01-01 00:20:00+00:000.03.80.04.8
2022-01-01 00:30:00+00:000.03.80.05.3
2022-01-01 00:40:00+00:000.03.80.05.9
\n", "
" ], "text/plain": [ "stationId 04250 06188 \n", "parameterId radia_glob wind_speed radia_glob wind_speed\n", "time \n", "2022-01-01 00:00:00+00:00 0.0 3.6 0.0 4.9\n", "2022-01-01 00:10:00+00:00 0.0 4.0 0.0 5.5\n", "2022-01-01 00:20:00+00:00 0.0 3.8 0.0 4.8\n", "2022-01-01 00:30:00+00:00 0.0 3.8 0.0 5.3\n", "2022-01-01 00:40:00+00:00 0.0 3.8 0.0 5.9" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Specify the desired start and end time\n", "start_time = pd.Timestamp(2022, 1, 1)\n", "end_time = pd.Timestamp(2022, 1, 15)\n", "\n", "# Specify one or more station IDs or all_stations\n", "stationIds = ['04250', '06188']\n", "# Specify one or more parameter IDs or all_parameters\n", "parameterIds = ['radia_glob', 'wind_speed']\n", "\n", "# Derive datetime specifier string\n", "datetime_str = start_time.tz_localize('UTC').isoformat() + '/' + end_time.tz_localize('UTC').isoformat()\n", "\n", "dfs = []\n", "for station in stationIds:\n", " for parameter in parameterIds:\n", " # Specify query parameters\n", " params = {\n", " 'api-key' : api_key,\n", " 'datetime' : datetime_str,\n", " 'stationId' : station,\n", " 'parameterId' : parameter,\n", " 'limit' : '300000', # max limit\n", " }\n", "\n", " # Submit GET request with url and parameters\n", " r = requests.get(DMI_URL, params=params)\n", " # Extract JSON object\n", " json = r.json() # Extract JSON object\n", " # Convert JSON object to a MultiIndex DataFrame and add to list\n", " dfi = pd.json_normalize(json['features'])\n", " if dfi.empty is False:\n", " dfi['time'] = pd.to_datetime(dfi['properties.observed'])\n", " # Drop other columns\n", " dfi = dfi[['time', 'properties.value', 'properties.stationId', 'properties.parameterId']]\n", " # Rename columns, e.g., 'properties.stationId' becomes 'stationId'\n", " dfi.columns = [c.replace('properties.', '') for c in dfi.columns]\n", " # Drop identical rows (considers both value and time stamp)\n", " dfi = dfi[~dfi.duplicated()]\n", " dfi = dfi.set_index(['parameterId', 'stationId', 'time'])\n", " dfi = dfi['value'].unstack(['stationId','parameterId'])\n", " dfs.append(dfi)\n", "\n", "df = pd.concat(dfs, axis='columns').sort_index()\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", "If the request was succesfull, the dataframe ``df`` now contains the requested data. The dataframe is a MultiIndex dataframe and has two column levels (station and parameter). The index is the observation time.\n", "\n", "MultiIndex dataframes are extremely convenient and versatile, though they do take some time getting used to. As an example, the below command demonstrates how to get the wind speed from the station 04250 for four days in December:" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "time\n", "2022-01-05 00:00:00+00:00 9.2\n", "2022-01-05 00:10:00+00:00 7.5\n", "2022-01-05 00:20:00+00:00 6.1\n", "2022-01-05 00:30:00+00:00 4.4\n", "2022-01-05 00:40:00+00:00 4.4\n", " ... \n", "2022-01-14 23:20:00+00:00 5.5\n", "2022-01-14 23:30:00+00:00 4.7\n", "2022-01-14 23:40:00+00:00 4.8\n", "2022-01-14 23:50:00+00:00 5.0\n", "2022-01-15 00:00:00+00:00 4.4\n", "Freq: 10T, Name: (04250, wind_speed), Length: 1441, dtype: float64" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc['2022-01-05':, ('04250', 'wind_speed')]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", "The last step is to visualize the data. As an example, we'll visualize the wind speed and global horizontal irradiance (GHI) for the station 04250." ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 0, '')" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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IzMxEUlIS5s6dG+AREgTBciW3BC//fJBvryvVfl0c0faEQE6WlRFXEW0XOpwgiCoONyfPebgDYsJDHUQ2oMzHzTGwRU2Hx1AS0Q51GdEmoc0zb948TJgwAQMHDsR3330HKzPTJyUloW/fvliyZEkAR0gQhJjxP+7HT3su4a4vtwKQi2i7iDiItvY2/ncdn204JThmpOokXsVVRLtMYsFEEATBwQnY5Bj5CkbubIx9Oupmh2NKNjG5iPbHI6Utia4Sv32J6qwjs2bNwh133IHFixcjKyvL4fb27dvj008/DcDICCL42HY6E5dySnBvR/mKH95gf3ouAKC0vNqIZDKkznkDgkKDCfGRYfzvo+ftdjinzGQBcwpRQVytW6jsIkEQzigx2hbj+lB5Na0kIs0htfN5W4ua2Hku2+n9OKF9Z9taqBkbgbOZhXh95RH+dksAt+dUJ7RPnz6N8ePHy96ekJAgKcAJgnDk/jk7AQANkqPQQUGJJE8RRwuUJEOKKSgVCm0pSPh5F1fWEan3kSAIArDtQuYU25qTOavJ747QluLhrnURHxmK7KIy3NaipmRiPGcd0Wg06NowEUUGk+D2glITftx1EbfeVB3VY8IrNB53UZ11JD4+HpmZ8mb5Y8eOoWbNmn4cEUEEP/sv5vr1+QwKrSPxkXYv3qoDlwVWMSlI+HkX19YRer0JgpAmq6gMVqutO2Q1J0ESue6RSgnRaTG8XW083rMB0hIiJc8R+8DFyZFzNp/FpBWHcdcX2yo0Fk9QndAePHgwZs+ejdzcXIfbjh49im+//RbDhg3z/8AIQiVYrVbsvZCNQtGK3RnX8kt9OCJHDEZHb69UBYtfxnXnf/7wr5O8BUUOEn7exVVE20A7CARByHCjwAAASIgKcyqmKyq0xSRGufYPisv9bT9rc0Jczi3x6liUoDqhPX36dJjNZrRs2RKTJ0+GRqPBggUL8OCDD6JDhw6oXr06pkyZEuhhEkTAWLb3EkZ8tR33zd7u9Dw2udDfeSBSEW0p6iVFoRoT1b7uYkFgDGDmeGWEi2g/06cRxvZq4HB7mcmC6wWlyC4q8/fQCIJQOZw9IybcuQu5otYRMQsf64wxvRrgxQFNZM8RR7TjIxyrofgL1Qnt1NRU7N27FwMHDsTSpUthtVqxcOFCrF69GqNGjcKOHTuopjZRpVmx7xIA4MjlfKfnsX5mf5eeViq0AUDHRB6u5jkX2hTR9i5cxYB+zWtg0uCbHG7PKzGi0zsb0G7aOpfRb4IgqhZcImREqPNEd6XXnxCFke/mqbF4bfBNiAyTF/jOHsvfFUhUJ7QBoHr16pgzZw6ys7Nx7do1ZGRkICcnB3PnzkX16tUDPTyCCChhIc4nNQ5WlPq7xYtUHW052Alx6upjyMiT39qjZEjvwmXiy7VRPpdZxP+spA0yQRBVh1KFQltpwQ93ey04e1ixVZHV1qUS1kZfoiqhXVxcjPbt2+Prr7/mjyUnJ6NGjRrQuihG7g6FhYV48803MXDgQCQkJECj0WD+/PmS5x4/fhwDBw5EdHQ0EhIS8NBDD+HGjRteGwtBuEuYwrbYrND2dyzSYHQnoi38e/46ek02KZIi2t6Fi+zITa9sEi3pbIIgWIrLyoV2mLLgjxQd6lbjf3a3e7Cz5HlxRJtN/K7SQjsyMhLnzp3zeYvlzMxMvP322zh+/DjatJEubg4Aly5dQq9evXD69GnMmDEDL774In7//Xf0798fZWXkWSQCg9LJiLVviJvB+Bp3rCNiL12oTiu7tffboSsVGhchhI9oK9iypYg2QRAsk3+x1akuKHWemG+VCfU0qh6Nn5/qxv/urtB2hvi6wibol/hZaKuujvbAgQOxdu1ajB071mfPkZKSgoyMDNSsWRN79uxBx44dJc+bMWMGioqKsHfvXtSpUwcA0KlTJ/Tv3x/z58/HmDFjfDZGgpBD6fbaP/9d538u83MSoTsRA6nIg1yr9cxCQ4XGRQjhFjRy1hHBuZSIShAEAxfRPuCiWpRc4Fk86yjdreW4p0Ma5mw+hz7Nkh1uE19XWHFd6saOqzdQVUQbAN544w2cPHkSDz30ELZs2YLLly8jOzvb4V9F0Ov1impxL1++HEOHDuVFNgD069cPTZo0wU8//VShMRC+Y92xa7jji604e6Mw0EPxCUpX/VNWHeV/9mfrcrPF6l5EW+RbyCsukxXaF7KKKzQ2QojdOmK7KL3Q35bFf59EJ1G594QgCMIZJplFunh9H+qmRzsuIhTbXu2LmcNbO9wmvk6yFauqtHUEAFq0aIFjx45h0aJF6N27N+rUqYPk5GSHf77m8uXLuH79Ojp06OBwW6dOnbB//37Z+xoMBuTn5wv+Ef7jie/34GB6Lp5feiDQQ/EJ7iaMAJ5bR/JKjHh8wW6sPujcshHGTGqFBpNbyZAv3dZU8HuBwSQ7Xn/XA6/scNqZi2g/e2tjHH97IHo0dqzs5O9MfYIggoO7bq7l9HaTQtuZJ9YRcaMaDmfXSX8LbdVZR6ZMmeJzj7YSMjIyANhsJmJSUlKQnZ0Ng8EAvd6x7ejMmTPx1ltv+XyMVZkv/jkNAHi6TyPZczJclIoLVlhRa7ZYZf21dRMj+Qiwp/Wnv9x4GuuPX8f649dxe5tU+ROZIRSUGvmI9uhu9TB/23n0lBBuHP2a1xD8bjTJW0f8veVX2eGtI8xnKCJMJ1n31p+7IgRBqJ+k6DBkFpZhbG/HGvwsrq4/t7WogbVHr2GMRC1/T9E7qc5V5T3aU6dODfQQAAAlJbYSY1JCOjw8nD9H6vZJkyZh4sSJ/O/5+flIS3PciiU8o7jMhA/W/gcAaFM7XjL6Bvh/1eov2FbmpUYzovTSX+Na8RG80Pa0LF5mgeuk37wSo6AaSEGpia86MqB5DTzYpQ5qV5Num8sxsX8TfLTuZPlYzbJbjaUmM59p/smGU2hWMwYDWzouhgllcJn44qhQSZnjd4ci2gRRubFarfh4/Sm0TI3FgBau7bVcQCXMRSTaLBPRfrBLXQDA5/e3w/nMIjSqHu3miOVxFtGetOIwVozrhuox4V57PmeozjqiFiIiIgDYbCBiSktLBeeI0ev1iI2NFfwjvAcb7Xzwu52y51XW6CdbH9TZypzNBPfUOqJkJ2/mmuOC34sY64g+VItG1WMQ7qLO6rhbGqJXE5slzGiyykZPrVbbomHH2Wz83/pTePKHfQr+CkIOi0wyZLHE54o82gRRudl48gY+3XAKYxbudXlufqmRv8a4snxIBU4e61EfD3auy9+/cY0Yr7oZnIn/SzkleOGng157LleoTmhrtVrodDqX/3wNZxnhLCQsGRkZSEhIkIxmE75HafWDytrcxMKkcEtFHjm49riA59YRJa1zd50XJieXmS38IsfZ9h1LiE6Lno1sOxOrDl52iJ7WSbBHxEuNFkFLcGe1VAk7FosVP+66iFPXCvhj9oi28NxGyY6RJaU+S4IggpNrbtgt2QCL3kXekNQi/ZamybL+am8Q6qKCyc5zFSuq4Q6qs45IebTNZjPOnz+PX375BU2bNsXQoUN9Po5atWohOTkZe/bscbht165daNu2rc/HQEhT0Xq+WYUGbD+bhQHNa3qUWBho2IXGwUu5SEuQtmWw0W5PFx3sRFhqNEtGpsXR5zKTxR7RduP15d6LUqMF/zFiELDVRNVobBFtg9EseNziMnn7DGFnye50vLbyMADg/LtDYLVa+bJb4oh214aJmDK0Od7+7Rh/TM7OQxBE5cCdgDIrVF1FtKVsZ666SVYUV9Fxf1rhVHd1cubRzsjIQJcuXdCkSRO/jGXEiBFYsGAB0tPTeY/1hg0bcPLkSTz//PN+GQPhSEW/ICNn78Dp64V4vl8TPNevsZdG5T/YDlfPLN6Poa2lkxRZj7qniWxswOGPIxm46+baLu9jE9ruRbQBYYOBC1lFwhutQHiIDiVGM0qNFsEFIa/ESEKbwWS2YOe5bLRJi0c087r8efSq4Dz2eySVUPu/HvXx/fbzOF/u8yePNkFUbjRMVrvRbHEqoNndTlcBK6nrT0W6SXqDKi20nZGSkoInn3wS06ZNw6hRoyr0WJ9//jlyc3Nx5YqtbNnq1atx6dIlAMCzzz6LuLg4vPbaa1i2bBn69OmD5557DoWFhfjggw/QqlUrPProoxX+ewjPYCNrrlbgZ24UIjEqDPGRYfyx09dt9bX/OJIRnEJb4QTBetSdWUycwbZSv5LruK1osVhxPV+Yx2A02+to60OVR7TZP0vcwt0KIDxUixKjGTcKDYK/La/EiNR46XyJqsiC7Rcw7bdj6NogET+O6cIf33Y6U3Aeu2BTUiKLrCMEUblhr6enrxfiphT5/DJ2ynAltKWsI3ERoW6PL1gJKqENAFFRUTh37lyFH+fDDz/EhQsX+N9XrFiBFStWAAAefPBBxMXFIS0tDZs2bcLEiRPx6quvIiwsDEOGDMGsWbPInx1AWKHpaqfr1lmbUCNWjy2v9HVYnet9vHXlK5QIbavVilKmlnVRmfMWuXKwE+SNAsfE4NwSeym/9nWrYe+FHBSXmfgxumMdMTKVS85lCiPaVqsVOcVGAMD/5u/GlKHN+dvySoyKn6MqsGKfLWCw/WwWf+z09QKHix2rm+U6Q7I7EmQdIYjKDZvuMuiTzdj/Rn9EhOlQaDAhKVqoediItrgLo5jqMY56iQ1+VXaCSmgfOXIEn376qVesI+fPn1d0XosWLbB27doKPx/hPVjBYLEKa0kXSwjKa/kGjF24F3NHdxQcd0cEqgklQttgsggmzWKDZxFtV0L77q+38T9zNgW22omraiMs7Pbiiv2XBbexf3FeiVGwiMgnoS2gVnwEjl4RNsn692Smw3lsRFuuFnuN2HAcvpxnO5+sIwRRqSkVNRo7npGPV1YcQnp2Cba92lewc8jO7a780C/d1hQ5xWWICgvh5/aoAFtH/InqlEb9+vXRoEEDh38JCQlo06YNrl27ho8++ijQwyQCiPiCz9Vwzio0oPkU6UXR3yeuOxwLVqGtpMya2HrhaUSbrX8qLiVYZDDh7A175JlLbmGFtqv6qizO/i6rFVj8RGf+9/TsEv5nimgLkdqSlRLSbFKtXHWZaXe24H82ktAmiEqNuPfE/XN28nPtxv9uCG5rUztO8eMmRuvxzUMd0LOJveeFPxoTTr29ueuT/IDqItq9e/d2eAM0Gg2qVauGhg0b4r777kNCQkKARkeoAbFXtMxkQUSYDuuOXXN531+YSKk7iXpqwiISPFar1eE7I45MlBotTrtIysHaBcQJLVwzHI7Q8oVLfqlN+IaFaN0q3+SsTJ8VVnRrmIRmNWNw4moBvt50hr+NhLYQ1i9pMJmhD9FB6m1gI9pyW78pcRFoUzsOBy/lyTadIAiicuCs94R4/ucCIxPcyHMa2CIF82ufR/dG8p2Cvcno7vVRu1okHv/esXocABxMz0WbtHifj0N1Qnv+/PmBHgKhchwi2uUTgBJRN2HpAf5nV74ytSKO/JotVkHFDsAemQjRavjzb521EZ3qJ+D9u9sofi72tRa/7jnFwq6RXPSaE76Rbm4NPtC5Lj7866TkbZwmbFg9GieuCkv/kXVECJuLUGywCW2prGH2/XT23eEWZ+TRJojKjbNuyg5Cu3w+cFXajyUiTIdVz/TwbHAe0q95DZyZMRh3f70N+y/mCm6buvooVo7r7vMxqG7v/OLFi3z7cylKSkpw8eJFP46IUBtiockJbbmErsqGWPBKWS64STE6PITXWOezivHTnktuPRf72GKhJZ6UuUgqJ7TdrZNaLSoMnepJ71ZxQvue9o7lBSmiLYRtaNTrg39w5HIeSiWqznDnudrl4C6klbUBFEEQNpx1GmavBX8czsDSPekAXDeGUQM6rQYrnuqGVwc1ExwXV8zyFaoT2vXr18fKlStlb//1119Rv359P46IUBtyHm1ngkFK9FkRnBE68d9vkbBccJpIp9G4FXFw9lxiy454mzGsfMLlhbYHyS5yZaI4W4lUciUJbSFsF9CCUhMeW7DbwaNvtVr599bVApWrziP2/RMEUblwZh0xMQvtpxbt43+uyPXFn2g0GocqZbX8VBZWda+Qq3bKRqMRWnG/YKJKIY6sHkjPAeB8+zsxuvKUEpLzygmP2e007iQkyj2O1POIox+R5VVHMsurk3jS+UtsgeHgnpmEtmvEn49r+QYUlppE59iFtqvplEsaNphIaBNEZcbgJKJdJmMdCxahDTg66PwlJVXh0c7Pz0dubi7/e1ZWlqQ9JDc3F0uWLEFKSoofR0eoDXFE9/mlB3HXzbUlE744xAmEALD26DUUGkyC7nlq5etNZxAZpsPDXes5JDpK/W0WQUTb86099rU2urCOJJfXWb2YbUuS9ERoy03aj3avB8DWtEYMCW0hUl3YuBrk7Dm8dcRVRJsX2p6ViCQIIjgQX1tY5LoLB4N1RI4yPwUPVKEwPv74Y7z99tsAbOH9CRMmYMKECZLnWq1WTJ8+3Y+jI9SGXIc6Z8laZpmdkqW70/FYD3VbkfJLjXj3jxMAgD5Nq2Pr6SzB7SaLFTlFZfhuyzmMaF8b9ZOi+L9Xp3W0jlgsVkWJo/O2nsPu8zn87+KqE6zQTogKQ3J5UwIu8u2JdWRo6xRB9Zh3h7dC05oxaFM7HoCtDbuYi9nFiv+myszFrGL8tCcdqw5ccbgtr0SYuGo0W5iItiuhXW4doYg2QVRqlFhHxIGuYIpodxDlAPlrTlOF0B4wYACio6NhtVrx8ssvY9SoUWjXrp3gHI1Gg6ioKLRv3x4dOnQI0EgJNSDXOMNZspZcCeBig2f1pf0Jm8j25A97HW63WKx4deVhrD9+Dcv3XcL2Sbfa/bdaDTQa4URotFig1zoXwQfSc/HW6mOCY+KFDDdJpcaFY9UzPXDmRqHgdk8i2sPapOK5JQf43+snReHmOtXsj8mI96ToMOSXmpBZWIbLuSVIS4h0+/kqE/d8sw3XZJJ7ckUR7eIys+JkSG4XgTzaBFG5cVZ15NvN5/B8/yb4Zb9wIR9MQrtdnWr48YkuuJxbgheXHaxaEe2uXbuia9euAICioiKMGDECLVu2DPCoCLUi19hEbmsLsInRIglRrQuCbS92ASHu+AfYovXrj9uiwBl5pQCEFSVCRCkgJrMVrtwymRJdIMWvOzcp929eA8kxeofOke6W9wOETQzqJUaiU31hBIKNaA9tnYrfDmUgs9DgcUOeyoScyAYc7TWZhQY+Uu3aOmI7b+/FHKfnEernUk4x9l7IwdDWqW7X1CcqP86ENgC898cJhx2w6HBVyEjFdG2YiL0XsgH4r5KS6l6hN998M9BDIFSOOKLdp2kyAOd+K7PVijd/PepwvKBU/QJN7I0WI2WZ4W0BGiBElPGhpLOkVFKiSTQplZRH2rkExRjRhOuJdQQA/n6hNzb+dwP3d67j0IgnPMz+t2g0QET578US5esIO6euC3cbbhQYkBJny7h3ZR05n2Xr/vnvyRtOzyPUz62zNsFgsqC4zIxRneoEejiEynBmHQGALacz0bNxsuBYbJAJbQAI0/m3kpIqX6HS0lIsX74c+/btQ15eHiwib6hGo8F3330XoNERgUYsqDkhykW0h99cCyuYDpCATXiu2OdYQ7qgVP2JdM4i9YB0eT8LYx0JDRFHtF1PLlKVShwi2uWJM3o5oR3q2fTSIDkaDZKjJW9ju3nmFRsRWf4cO85moXlKrGRVEsJOrfgIXM4twY0CA2rEhgNwHdE+kJ7rh5ER/oCze207k0VCm3DA1bVGqm16THior4bjM7gyslU2on3hwgX06dMH58+fR3x8PPLy8pCQkIDc3FyYzWYkJSUhOlr6IkxUDS7l2KpaRIXpUFRm5qshGJ10qrJYpKtmBzKiXWo0I7uoDKkuanm68pFJRai5ZEitRB1tOY87S6hEPWtH64htXJwXW1y9JSLMt969alFhCC+Pmr//53/Ycz4Hc0d39OlzBhOvDGyG9/48IThWPVaPy7klyC81Cnz8zkiJC0d2UZnTc4jgQi9Tr56o2ijZ7RQTDFW7xHCff395tFX3bXvppZeQl5eHHTt24OTJk7BarVi6dCkKCwvx3nvvISIiAmvXrg30MIkAci7TJrSb1IwBYP+ycP+LI7iATXhKFR4pCaDl4NZZm9Dt3b9xWrStL8ZlRFtKaLMRbZ04GVKBdURCfInHwfn5uGS5EJ1WkAAZGeabCXj+ox0xpHUKnu7TCBFMub+/T1z3yfMFIzH6EDzR07GaDleCsaDUZF+MubgKvHNXK/5nV59FIjggoU2IsVqtyCx03ikxv8To0OtEvJMZDIT5uWSp6r5tf//9N8aNG4dOnTrxjWmsViv0ej1eeukl3HrrrbKl/4iqwYVyz2jTGjahzW2HclYGqRJwFqv0xaU0QCXL/vnvOi7nlgAA/jp21em5ciWIksqb8LBRCG5nj02GFNtAlFhHpKKc4kg4F9Fm7RqsL9tXNo5bmlbHF/e3Q0JUmM/EvFLKTBaXTba8jdlidXgvxImoOp0GIRI7O1wJxoJSk91e5MI6Uj8xiv/5ie/3eDRmIvCwiW5yHViJqssLPx3kd3jlPh/XCwxYsP2C4FhUgOdgT+D+PqPZKhmo8jaq+7YVFxejXr16AIDY2FhoNBrk5eXxt3ft2hVbtmwJ0OiIQGOxWPnkrCY1hBFtLjotVe3CYrFiYMua/O/t69pKxpUGKKL96Lzd/M8xTrberFYr7pu9w+H4vR1q82KYFV2caOK0tFaiYY2S7UEp7ShOuhRHtAFh8wJPqo64i7iEoD8mTY7LuSVoP20d3lh1xG/PabZYMfD//sWdX2wVCPxNokRF7nV4tm8jwXFOaLPWEVfJkOwO0cb/KCEyWGF377QuFldE1YPNawp3YyEWjP0L2KCbP3zaqhPaderUwaVLtqS1kJAQ1KpVCzt22IXGsWPHEB4eHqjhEQFk25lMNHhtDTILbX7RJqKINl8FgxF4HcoFtdlq5UXopEHN8EwfmwARtxEPBM4iv/kyHvJQnZavJsIKba1IfOu0GofqH84a+3BIJViKGwXxQpvZQWAjIbF+SJJJLI/qczR4bQ3f3MfXfPnPaRQYTPhhh2MXW19xJbcEp64X4vDlPBQy5SoTo4SvA/e5GdGutuB4Yrl1ZMW+y1i6Jx2A64h2MNXJJeRhBYW/vKlEcKKv5Enl7HXKH01rVDeD9u3bF6tWreJ/Hz16ND7++GM88cQTeOyxx/DFF1/g9ttvD+AIiUDx93G7B1ersUfnOLHM/c9GObnIjdVqv9CE6LTQl0dhAyW06zDNVZytqK+U20vEhOq0vLeW9c1y3mq2vbbYXiHXWZNFKjBssQojxrxVh3m9WVFWPVbv8nkqSl3G1sDx9aYzXnt8q9WKsQv34Inv9zhEy+UWQb6EXVSxYknOOy1u4hOtt79XK/bZIliukiGl/PpE8MF+XtQQYCDUS2X38LN2Sn8sOlVnrnn11Vexe/duGAwG6PV6vPbaa7hy5Qp+/vln6HQ63H///fjoo48CPUwiALCBN4sViCoXDVxkT8o6wt7nt0MZAIAwnYYX464K9PuD11cewd3tawtK13Fk5EkL7bAQe0SbjWzarSP2RLcoDyLakKzRYrOdhJULL86jrWesI8UG++vJJd75ktQ43+5uFRhMWHvU1gxof3oubzkCgNUHHVud+xq2XjgrluSEk06rQdcGidh+NgsAJD9jroS2VEkvIvhgE7/Enn6CYKnsZVI1Gg3CQrQoM1lQZrbgzVVHkFtixEf3tvXJ86lu2VKnTh2MGDECer3tIh0eHo45c+YgJycHmZmZmD9/PmJjYwM8SiIQiFeeXFmhMpMFRrOFFxvhEhFtlhCdlrdTBEpoi4XRX+ViTsyV3FLJ46E6DR91ELTXlkiGjNQ7RrQvZhXjie/38B2yxMhZndlouH1hY3/8q/n28abE+97i5WlTHKWwn49T1wqQW1yGJxfuxex/vRc1d4dipgMm21zCWeOF0d3rAQAe7FJHMlJFHQKrBuwW+fmsImw9nYnHF+yRXcwTVRelEe3qMb4PpvgKfXlUu9hgwoLtF7DqwBVsO5Ppk+dSldAuLi5G+/bt8fXXXwd6KIQKYS0Wrw5qhihGQBYZTNLWEYlPeIjWHtEOVHk/8fM+t2S/5HkLtp2XPB6q0/LWGa7tOmCPVts7Q2ocItoGowXPLd2PdceuYcRX2yUfXy6pkO1SmV/e7EeuvJNU9NTbiJMhvQ0rYF9dcRht316HP49exYw1jj7wFfsuYdpvxxTVKfeUIuZzwy4CSp2UqbqtRU1sfrkPpt7eQvI9ocS4qgEbqLiUU4IH5uzE+uPX8NqKwwEZz/fbz+PTDacC8tyEcx7qUlfReV8/1N7HI/EdnE/7601n+WPnM4t88lyqEtqRkZE4d+4cbVUSknCiZ2jrFDzRswFCdVp+5V1QapIsNyclIkJ1Wv6c0gCUZ7NarQ4RbYsVSM8udjjvougYR6hOi+oxtojxVSYixS1G2GRIsUe70GByeC4xclqRe1yLxcpbVgJZR9XX5f3c2fGY+NNBfLflHH4qTzL0NkazBdN+O8b/zloBXI0zLSESITqtoEIMh7gqjSv8/X0hKo7RbMFMZnHILgaPXMn3+3isViumrDqKj9ad9Jm4IeRZdeAyvttyTvb2oW1SFT1OMC/Ss8qbcC1nOkYbFdkq3UdVQhsABg4cSA1pCEkM5SKyfd1q/HY3Zx8pKjMJ2o5zx29pWt3hcVihbbZYffblkqPMbOEvdDWYhEGH2siFBtmM6MgwHV9x4wbTZICrsSxMhhRGMQtKTS7tAtJ9NO01uIuNZr4EYIzesbqIu+LNU3zdfbLUiSWDha1NvrPcD+1tft57SdDciB2b0nF64tEW48uIvRKOXsnDd1vOKaoHT9hYujsdu85L28SKDf5P6mXntUB2562qPLfkAKb9dkzWKhGq06B2Necdi4HKt+j2VQUS1QntN954AydPnsRDDz2ELVu24PLly8jOznb4R1Q9uK1PVixw9pEig0nQdnz9xN748oF2eKBzHYfHCWGSIQGhX/r3Qxk45uMID3th6d+8Bv+zeMq6nCP0TsZF2AVtRKiOz5wWi6wyk8VeR1urQXykUAgXGkwuS7rJzZ9cDe6CcttIiFYjGSUNcdVu0EtE+CCiXWo0Y+nuiziekY952+SjPoL7MBO0r3So+PPARrGLFVqg9F54rzxp0+xNhny6BdN+O4Ylu32zc1AZcRY1LgqAfY797JormVhTO9nlkVwA2HFGOigQqtVCSbA6wFOB1/FVp0jVVR1p0aIFAFu97MWLF8ueZzYHvloE4V+2l08KbA1MLnL9y/4ruFaeiKfTalAzLhyDW6VIeo1DdbYmLjqtBmaLFaVGM+IiQrHnfDaeXrwPAHD+3SE++zsOXMwFAKTEhUMD+2wmLtEmtpf0bVYdK/fbS7KFlEeNxZNDmcnCX7x0Gg2qRQprLBeUGl02GZCqow3YPeBF5VGw6PAQSauXvyLa7jRWUMqX/5zGp3+fdus+haKo3OXcEuy/mIMQrQa3NK3ulSx+sUXn0KU83HqTbaGWV2KUuosD3kiGNJotkn/PmRuFuFFgQJcGiW49nqccy/C/5SFYkeoSylJqNPu10oQwkdeMzaduoG5CFOokRjq5F+ENHltgb5Ymt8jRajWCa5Mc4vr9wY7SnUF3UZ3QnjJlCnm0CQeyCg28J5gVcZzQXrjD3haWvaZICcoQrRYajQbhIVoUlZn56MqJqwW+GLoDC7afBwCkVYtEvSR7HWix0OYi+OGhWoy7pREe7lqXF9q2jo+2P1RcccJgNgtsNKnxwi3AvBKjy9rI4vmXK4XEVR3httjEzUy489gyeL5EruqI2WL1qJqG1Wp1W2QDQE5xmeD37u/+zf88plcDvDb4JrcfU4y4eswnG05hQr/GyC814d+Tyjo2Sr0mShZFS8Z04TuUypWHvHXWJgDAhhd6o2FytKLxVAS6SijH1Xt8KacYjarH+Gk0wiDCxpM38NVGWxUfXwY4CBv7ywM9ALD3Qg6u5ZdKlntsVStONkcIALo1TBRcvyoDVSaiPXXq1EAPgVAhbMQun/k5Su+6ioJWI9zi4uwm+lAdisrMKCqv/ewsscNgMuNyTgkaeEFAcJNa14aJeKhLXT7BTewV54R285RYjL+1scAPp9XaG4mIK07YrCP29tppCZGYMrQ5ftqTjhNXC3Axu9ilCBVHtMN5oW07zomtUNHjrH6mB37cdRFP9xG2/vYVkWEheOeultj43w2sO2YvkWg0W6DTuh+hW880RXIH9oIkbgi0bE86Xht8E8wWK87cKETj6tEeBROkGitkFZXhlZ8P4bJMYyMxYR5GtLs0SIRGY1uAyTXH4Th5tcA/QpuUtmJc2YPG/3gAa57r6afRCK0jfx656rfnrSilRjMy8kpRv5IIzB1ns9F5xgbJ26bd2RLVY/W4p30a3lp9FDvPCS27o7vV88MI/UuV8WhzGAwGbN++HatWrUJmpm9qG1Y1zBYrMguDs1EBK0xYCRild1wrigWzWEjElm/Bc141rsQUe5o4yeP+b3ei76xN+OeEZ0KMw2S28JHzYW1TERaiRb3y7VJxchf3pefEEfsaaDUafjtYHNEuM1mYZEjbsf/1qI9XBjUDAFzIKnZ54RVHtLmWvJzI4gS3eEu6ac0YTB3Wgi896A8e6FwX3zzYHvd1TOOPuRKDcvx2SLoJDeull2Lswr38z2K/NPdaTPvtGAZ8/C++8rBzJSdO+t1kT/A9ea0AG5jPpNiPL6Z6TDgm9GuMW5vZH0OpRzu0/Dxj+XufVWhAXnkN90DUo1eytU3YCGEi2uLkaMD/Nhz28yLX/VaNDP9yG/p8uFG2/0BlIiEqDG/e3gLNU2Nxd/vaDrcHOim6ojRIdlwsZReWSZxZcVQptD/99FOkpKSgR48eGD58OA4dOgQAyMzMRFJSEubOnRvgEQYnL/x0AB2mr/d5sp8vMDNRwhHt7F96qdJyYmEdJhKDMeFCMfLnUVtEhRXo4ujy3gs5AIBFOy8qHrPVanUQIB+tOwnAJojqlbcP5+wX4lbsZbzQdrwwRoTq+Ii22J/LVjVhrTOcV9uTqiNc5NrMR7S5dvbqEDtarQbv3NWK/92dSjLseyRXqrB3k2TFjyeukc4l3s4vr4n+/p//KX4sFkP5OFPjI9CmdhwA4ESG0O60+PEu6NM0GXMe7iD7OBP6NcGEfk3435VabDj7gclswdW8UnSesQHd3t2A/FKjIMFXSXKb1WpFXomxQlULKKKtHPY9lgpO+MvqxSHwaPuhBba34BYkvx7wf1dYbzB/q+vk7u//18nhmFrmeW8iNUf+efSqTyqpqE5oz5s3DxMmTMDAgQPx3XffCf7opKQk9O3bF0uWLAngCIOXX8onh28C1NWuInAR1KRoveBCUSPWsfugOKIt9vFGl4spLtLIRwiZu8k1AHEnUvrCTwfRfto6Qee19PLKEbWrRfAXPy4qLPa+vrDsIAAgu8i+C/FMn0bo1SQZfZtV5ye/6yJ/HWvxYKuLcIlwBpPFtXVE9GdyY+QELPf4oX6qLqIEnVbD/11KS799vekMWry5FttOZyI9uxg/7JBeSOlDtJjYvwkvcDmiJURLUZkwMdJb03Yp79nX8YvFMzfs5f7G9mqA5qmxmPdoJ/RzEYFnq48ovYiyn4FT1wtgslhRVGbGldwSPn8CAIoNrqPbI2fvQJu3/sKD3+1U9Nwc7PWg8l36fQdrO7o5Ld7hdqnKQb7EWYOlYCDcxx1pfUGp0Yypq485PSc1Lhy9JIIKUmVBuUTsYEXOBuqLhZ96rpLlzJo1C3fccQcWL16M22+/3eH29u3b4+jRowEYWeVhVRCuxjkRKk7i46LCLGIRKZ4kuE6JT/VuCEC6RJzcVrg7QnvF/ssoKjPjl/321zu3PGnu0W71+WNh5UJH7rGPXLbvQLx4W1N8/79OCNFpZUVumckiSIbksAttsyDSLSVKxR7tEFFE26iyiDYHN04lk2VWoQHv/nECZosVr608jOm/y1+E9KE6jL+1MZaO7So4Xi3K0arBvl/ehIuUh4doeYF/9oatbFv7utUwyY2ES7b6iKvEWI5Q5nPKRrAH/t9mfLL+JP871zHUGbvK/Z5bTyuvOf7T7nS0fusv/ndKmlcOJ7RHd6uHUAmfvth+5muMMt9PNddlZscWGaq69DaXHFdgD5Kz/Ik78DatESOZ71EZqBJC+/Tp0xg0aJDs7QkJCcjK8k1DiMpMsSjK9vTifXji+z2yrbbVhklCOAKQLKovrmTFRmtqxUfwF2huoigrj66wUR+5C49cxQUxnHcVsEfQAbvNg/XShvKRQve+4HIi18CW92OFdvlkaTBZBOKqVGJiEf+VITphpFhu4RNo+PdUwWvJWYYAWy3hnGJ5gcgJU3GVFba2ua/hFn/6UB1vcTlx1XbxTIlz3NlxBrv41CncleC83Caz1aGc4S/M4t2VX9tTMfXy8kPU3MRDuO+DPkTrYKUD/GvfKDKYMIbJaWBR8r0NFOyujSeNshbvvIg+H27EhSz/dcIsNZox9LPNmPbbMcH45YiLlC7XJ94Vrsxr3DIf7LaoTmjHx8c7TX48duwYatas6ccRVQ7EzS5+P5SBdceuOS3foyY4j7ZYXHJtyFnE1hG2PuyC/3Xkf2atFD/tScfkX47wt4lrWHMYxZ4KGTIZu8cXTLk4rgxcPDOhcdaPn/faSvdZLFa88NNBl88hVxu3pMzsNKJdZrIItt3FnmLAUQxxYmzmHyfw5MK9KC5/fVzV5/U33HutJDmP/ZzEuxDM3OPqtBpB0qxUV0wxUtVCWMuHUqSsI9ziwN0qCOziU+lFMzSkPKJtseCDv+R95q788d6qVVuZL/behvP3h4VoJRfH3i5rtvnUDQz7fAuOXslzuG3ZHvlGQ2r2a7NJzjPWnJCcN53x2srDOJdZhKm/+m9H/rdDGThyOR/fbTmHh77b5fL8SJla6uKIdjC3XneFL3Z31HWVBDB48GDMnj0bubm5DrcdPXoU3377LYYNG+b/gQUphy/l4e6vtqH/x/9K3v7jbuXJfYGEi6CKI9pSW13OhDZbYSGMEZ4v/3xIcJ98mQYgSqPO7HlXyxvpAEBusWNEm1vsrD9uK0936HIelu+75PI5xKX1OGasOc7XgmZfC/Z1YKMbUqJUvNHB2QYOX87Dn0ev8qX01BbRjuCFtuv3ie2Qdup6IW9nkIKNArJRbbnkSRYp8cDVnHYH7n2KCNUJdkkAoHEN92ogsxFts8JdmlAmoi1Vd5fD1Xckt8T9zH6pzyhVHVFOQfn3PTIsRHJx7G2B+9B3u3DoUh7u/9bRg+9sISa1KFUL4rGtlqlQ5Ip//lNW894byAWM5JCqSCN13JMeBcGCwQfNEFUntKdPnw6z2YyWLVti8uTJ0Gg0WLBgAR588EF06NAB1atXx5QpUwI9zKBh3tZz2FNeMUMKcdUCtcJ5g8XCLiJM5yB2xJMAW4aIjYizEW0xcmUQjSZlokQ8KVutVpgYb6tcBHXp7ou4/9sdgmPDb64lea7cZHeWabcsFdEGhA1WpESMeHdf/Lqfumb73IitFIGGi9QaFFxgstwo5cT++azojmXex7qJkWhc3THBRm4sSrZyWbj3KTxUy5eoBGyR3R6Nktx6LNZfqbSlutg+JIczoZ1VaMCDc+ziS+lC7UB6rsOxJbsvYvpvx1Tt61UL3MKoeoxe0LyGW5j6yqMt1bHUWTBUzUJb/Ll+/88Tqv/sKU0K55BL8hR3DXXVWTiYqRIR7dTUVOzduxcDBw7E0qVLYbVasXDhQqxevRqjRo3Cjh07kJTk3kWlqnHyWgHeWn0UNwoMfCRDDmeRKTVh92g7fmQ3vdRHsHUungTYizQrDLmonrirHyD/uii1jogvGKVGC/IZfynr7WW35V5ZftihDrNckpsSkcsK7RDG9sD6kbmoR3p2Mab+ehTp2cUSyZDC5yrlrSPqmnB564jJjL+OXsWsv/6TvBhezSvFXJlSV39O6ImvHmgn+xzs38wu8hokRUlGhOSihQUKkgZZ7EJbJ6h2clPNWCS42QqZ/VyYFX6muc9AmdnitE66s4jlB2v/w5kb9oWgyWJVlCcitRgsLjNjzpZz2H1ePpBA2ODms+QYveC73KSGbWHojSogP++9hDmbzwKwJ5y7i5qtI+IFaWZhmcfdhH2RG7XvYg6m/XYMRcw1f5PCjrEcctYR8bVGZdO+V6kSyZAAUL16dcyZMwfZ2dm4du0aMjIykJOTg7lz56J69equH6CKsHzvJcmuWoM+2Yx5W8/j9ZWHXTaouREkDWzkItqArbB+Q6b4vE4UMqnG2DTY+3NRvbM3HJNTbshEO5VaR8RJPTnFZbygjwkXbt/Oe9TmG68pUaoQEJZiY1EiclnriEaj4RcX7EKA8xo+vmAP5m87b0uSFQtt0XNx1gylzU78RXj537d0dzrGLNyLz/4+LfkdeX7pAdnHiIsIxaBWKVj9TA/J29lqF7FMTfZIfYggaej9u1sDcCa0lUe0rVYrLyjDQ7WCWvDVYyvWHEhpyXF7HW2rw44Hi7PvSEZeqcMxJXOQs4sfawEipMksn88So8Ow6sBl/jjXdr2iUTyr1YoXlx3E9N+PI91F51n2+9MwOQpLx3ThF4pqjmhLdmZVsCt2o8CAL/45LTimdBfJHYZ/uQ3fbTmH2z/bAgBYdeAyNrppUxEnPXKIk74rs3XEF59BdV0lGY4cOYL3338fb775Jt566y3MmjULR44ccX3HKsK1/FK8sOwgnvxhr8PqmBOlR6/kCyKzUtnmWYUG1W9/AWwnQukvuJ5ZiYu131cPtud/ZktbOStPJK6qwI9DoSoRf1n/PnEd6eVe7ERR9LF6eXRQXH+ZQy8zTiUi11kFFg4uye6/cjuIVJRGPLFyETC1ebQ51h61t2NfIyG0pawIHFzprlZMzezUeHt1G/YvZq0j+hCtwPvcPCUWgHyimTtCm21/HB4itEu5SuR0BVun3RlcVMtksTi0mWdxJrSlPO0vLnOd+Ovs4udsLIQNbvckNjwUHerZm9NwOxMlRjMOX3JMXARsZeF+2p3u9DrBLoSKy8yKrQU3pcSic4NEfh5R83sp9bm+Uei4cBTz5q9H8MFaYfKwL//Os5lFMJjMeG7JAbfvKye0I8J0+PWZ7vzvlSUZ8nmmcReHwQeVb1QntA0GA0aPHo02bdrg1VdfxezZszF79my88soraNOmDR5++GGUlVEEg/W+yUV7LueW4BJTbWRomxSHcyxW4M8jV3E+038lhzyBrzoiM4GHs+XKRJPATTVj+Z/ZxYY4k5pFbltfaatpsTCYueY4Piyv1NChXoLgNs4GICe8pBZIgHykm0X8WoiT6ADpqiNsRHvthF4OW4dqtY5I5cetPuiYtOTstQtnSnctf6ob5jzcAWkJkZLnsrsl+hCtoCwiF6Uzmq2SXkl3PNri5FxW4MfLlORyBZeQy34/nMG913sv5PBJvVI4W4yKu7ICwOZT8lWmOJwJ7QtZwVE5KVCUmSz8NSI2PBRTbm/B3xatt8+BD8zZ4XBfwLZD+vLyQ/hDYsHKwUbEQ3QagRD79eAV5MjsOnDzOTe//LDjAq4XuBavgWAvk+vENXU5n+n6s7dJIqrsTudaT3C3IgqHnHUEAFrXjud/rixCu16S47xeJTzar7zyCr7//ns89dRTOH78OEpLS2EwGHD8+HE8+eST+OGHH/Dyyy/7ZSwbN26ERqOR/Ldjh/SkFAhY8bf7vHTlhPG3NsbUYS34ZC0u2gYATy3ahz6zNvp0jBVFro42BxudFk8CcZGh+PrBdvjqgXaCpI4mTio1yIleZ7WWWbiJlIvgFZWZ+UYmadWEX26plsgsco05asU71hAXI44sRUuUo5OsOlI+1/RqkoymNWMcFjjchVttyZByCzF3PJHswqZ93WoOXRbZt6OjaNHEPg9bWSZf4vPkjkc7kmmqpA/VCerHR+k988OuGd8TL93WFM/0baTofO69/naz8zbOzmohy9UdP33deblDZ9YRcbQwEBxIz8Xl3BLXJwYA9nMWHR6CVKbmOmthk/qMshxIz0VOURm2n8lyiG6zHm8NhMnD43/cjye+3yO4nYPLueHm9R93peMVUQUoNWC2WDH99+MAgBqxetzWwjYn7LkgX6mIQ2p+53adbxQYsOOs4+tZUZwt4t+5qyW+lWg/DgC1q0kHFMRUFuuI1N/hizraqmtv9MMPP+Chhx7C559/LjjetGlTfPHFF8jPz8cPP/yA//u///PbmMaPH4+OHTsKjjVqpOziJOZCVhGMZgvvjfMUNmrETnKnrjlesMLK20cDwNzRHfH99vN4pFs99HjvH/4ctbtH7B5tmeguK7QlvjwDWzpG88NCtLgpJVayY5a7FSHElJWXCGqbFo/jGQUCrzwrwAD5kkquqO4kIY1DHNGW2rqXKgHFfRy4e4sj19znRW3WEblAS3pOMeqWdxG9klsiiMj2b16DL1doewzlfxPbKMZotqJ5aiy2nLZFaFnBnssk3PZolIQtpzPdipyw29bt6sQLbmumMCItJjU+Ak/3UT6PKX2vnUWYxZ99jn4fbcK5mYNlX3tfXPwqSpnJglPXC6AP0eLOL7YCAM6/OyTAo3KkkC/tp3MQFjXc8PcbjGYM/nQzMvJKMe2OFnioaz3+NnaxbrFaHd5HucpXVjhaAv1Z/k4pbMO3vBIjbyVztrPjDJPZglKjGR3fWQ/AtnPWvm41F/dSjrhvBssDnesCsF0LxAGl+snK6vFXkoC2w/UR8E0ypOqEttFoRJcuXWRv79atG1avXu3HEQE9e/bE3XffXeHHySkqQ+9PbKWt/n2pD+ok2lePZosV1/JLBV5QZ7C+T7ZmcGK04zYye8FPS4jE60OaA7BdONmkDIPJzHtMjWYLcouNTqsL+BO5OtocbMkqd1bbtzarLim0nUUbbxQYXL4uZUzEV5yQKo7qedpKWqPR4IO7W+MlJxEg8aIjRiK64sw6wt1dboET68fOiEqQ29LccTYLdROjYDCZ0e3dv/njHepWw8zhrbDtdCaKFG63ZjIJUGxE0GK14pm+jZBbXIY729ZCiE7Lf8e4nZAwnZZfWLkzoXPntq9bjf+8fDbqZpy6VoAhrRwXkb5A6e7F4ct5KC4zCaLwHGypzZEd0rCUaV5islgF32MWZ1FydyuuSFFqNCOvxIgaMgnJUry47CB+PXgFbRgvP4fVasXl3BJBJ9pAcCmnmM+5YCvVfDyyDY5czkffpjXk7uqAwWThk1nfWHVUJLTt74/JYoWzKVjq5VDbgl0MG4ywWu2vpbhClBRSf6/JYsV9s+274gfSc70qtEfOdr3jfn+nOvjmX1uVmJvrxKNbw0Q0UNj4qlJHtKuCR/u2227D2rVrZW//888/MWDAAD+OyEZBQQFMpopFOa8xjUsu5QqjPpNWHEK3d//Geiay5gz2Is1GE6T8kXJfCXEyICsgJq88go7vrHeaNOZPTC482qwIkFqlyiE3YYhX+mwCY8d31iMjz/k2cVn5+yDlr/bmdfeeDmnY/Xo/DGheA/2bO140xX+fVERbqrQXt5XJiQS5172iiXjeRk5oc0nB4sZE93Wqg6RoPQ6+OQDD29XCtDtaSN1dEVarzQP7/t1t0K28rjX3et8o953qQ7V84q5Svz9gn/zZz9PtbVIxcUBTv9W0dccmJGex4oT2qE51MKZ3A8FtzpIonXm05aLk7vD4gj3oPGMDXx9eCb+We/8PMkmEeSVGWK1WzP73LHq89w8W7rhQ4bG5g9Vq5YMEW05losd7/2BsebtzNj/jrptr442hzR12qjx9D8TXIKlmQmaLFVarVVKcVqR6kcViFZS08wVsMMJgsvCLZSXPK3VNNpmtgmurp7uaFYGdN94b0Rov3dZM8aLQnWusmpG6/ivpweAuqhPa06ZNw7lz5zB8+HBs2LABFy5cwIULF7B+/XrcdddduHDhAqZNm4bs7GzBP1/y6KOPIjY2FuHh4ejTpw/27Nnj+k4SsIJGnAzx0x5bJ8CP1p1U9Fis0GZLB0lWOJD5ToirWXCThsVi5SNNixReKMw+KFfE4sqjzYoAd+ZsuQiauP64OInwj8PyiUGA/aIUFqLF5CH2OthdGiTgthY1FY2tQ91qmP9oR5fnJcfoMfvhDhgo8bjiCZGtisFRKtmC3fY/H9GWeZ3ivRBN9CZy8z+3cFp1QJgYyX0HQnRafHRvW0GUzhW9yxOiOKS+A1yi4pVcm9AOD9Uh3EmjJDmM3A6Jk0o5vsadxFc57c+W6RQv+px183T2WnkjeYmz+wz4P+kOukpp89ZfmPnHCcz84wQAYMoqW7ttTmT6mtdWHka7aetw9EoeZpfXtOaIkthhEM+nxQbbXCD1WXb2HrARX7NMRHvcor14+edDQk89Z0ETfbbmiMbujMe/34NWU9fiqkTpSG8httdxr6WSiLZULwtxPwZ3a+qL8aQkHXttcDe5sbI0rJGSLSVlVcA6ctNNNlFy+PBhrFq1SnAbN1E1b97c4X5mH7TNDAsLw4gRIzB48GAkJSXh2LFj+PDDD9GzZ09s27YNN998s+T9DAYDDAa7XSA/32ZNYC8Ich2blDYOYFddvx3KwOf3lx+X+MLJfYnEEe13fj+OBf/rJHgMqSoBYhbtvIB3fj+O+Y92Qqf6CS7P9wSzi/J+bJKjexFtaeEinvjE10hX9cm5yFCoTov/da+PHWezoA/R4fP7b1YcNfj5qW6KzuOQKs0knhClJsgSo9nhwsr9yo1V7nUKloi2XKJXvgcXuOl3tsT8befxzl0tBcfFtccBe7T1xFXbHJAcrecrnsiV/ZNCKqLtb9y5tMpVHjEzC+YYUWJuu2nrsO+N/pJWEGdCwptbvUq18CgnW/Oz/xWKxIJSI/p9tAmd6ifis1HS1wxvYLZY8eMuW4Dk90MZDsJeKmoqFtpt3v4LrWvHIT27GGsn9EJ1xkrjbAdGENG2OHq0AWHJTTHiHbPpvx/H4z0byJwt5O8T1wHY6kaP7d1Q0X3cRSyouQTHojITrBKedA6DySz52b111ibB7+6U+pQiV6LpmivY995d3VxJdLZkftLnoprn3kB1QnvKlCkB9bSxdOvWDd262cXOsGHDcPfdd6N169aYNGkS/vzzT8n7zZw5E2+99ZbDcVbAslt0rA3h7I0ip19cjlKZC4/Ul1ruocRCm+sixQoAV1tjf5+4htdX2uqbj1u0D3sm93N6vqfYPdrSQoMtU+XO6lwuol1qtMBotvCRciuEF60vN55Bcowej3avL3l/NqKt1Wow5xHnkekXBzTB5/+c5qN6zyqsBMEiVSNb/OdJWUBKjGaHCYcTjdzZcq+T2jzacu98ocHkUHmkZmw4+t2k3KPK8WCXuniwS12H45JCu/z14baJ6ydF8bsK7kS0Z6yxVTzw9Ra5t5Db4TJb7UI7PFSLno2TBOX9lu+9hCd6OQos5xFt7wZZ2FwVKXKKyrD9bJbix/v7xHVcyzdg9cErPhXal3LsdsTU+AgcEtXFlqp+IRWU4O63YPt5vHRbM/64sy7D7A6t2WJVvKvI3StEtICUyiVxhbO+CBVlxb5Lgt+5Sj9Wq61fRctajj59QLmArqjQvq6ww/Org+zvJyu03fVcVxaPdolM7wpvozqhPXXq1EAPwSmNGjXCHXfcgRUrVsBsNkOnc5yQJ02ahIkTJ/K/5+fnIy0tTXBBKGMmpvlbzwvuP3rebpgtVsx/tKPDBMQhvrhw4lwqSiYnPOUuJqxYzytxHvH733y7jcaXH1pnnSEBIILZFnVnW8tZEk5hqQnVyqNrUv0FvvjnjKTQvpxbwluA5JrNiHmmb2NBBQhPFpvhEjVQxROi1AT5w46LDtUn7MmQnEdb+u+Q8nwHErnXbfXBKwL/9cnpgxCq03h1US8lLqPLd4TOl1fiqBkXzn8m3LE8cBdidwSet3HntZLrfMdGtDUaDb7/Xye0fHMtn4gq9911JrTzS0349eAVDGuT6nRM645dw6y//sP/3ddWUKlF7Eu+lmcQJKqLOZvpvBShGDYptMhgclnO01POM9VeZq457pDcq2THi0VsbxTXwrZYrPz92R3a+2Zvl9ySl4KLuovn4WoKLWmsd9rZ4giwVfwau3AvxvZugLturq1sgAC2ncnEDzsu8r+/PLCpoAfD3gs5skJbrvGZmIouoG+4ENpxEaHY9NItgpr7woi2m9YRlQRDK0qRwT/VjFTn0Q4G0tLSUFZWhqIi6SYver0esbGxgn+A0BbCTkziC9imkzew5XSm0zJH4gsPtzUudfGWtY7IiXjmsf88elXx9nqxD5IIOFx5tKM8TCbROdmKv8Y0TpDyV0pFMAEIElrd8a6zddo9QepCI76Qyr1+60RJuNxWMKev5Sw7sSoT2s7WWFzZuagwHcJCtF7fOZN6q7mdAG7xGqUPYSLa6itZ5wx3Xi3ZiLboe6zRaASRSNmqIy6i/+N/3O9y+/yJ7/fgxNUC/G/ebsFxsS3AVT1sJW23xc/L8e9J35WuY8WWVAWdUDejkGL7j9gDbWbmPyPzfnuSriMOKClNcM0tsb8XrlxVk385ghNXC/D80oNuje3+b3cyz6HBuFsaQaPR4P7OdQAAb/56VPa+SiPVy/ZewtErea5PlIF9HcScnTEYu1/v59DYSiC03fxsVBahPaiVsnypikJC2wPOnj2L8PBwREdHu3U/Vsy8svwQLBYrSo1mfL3pjOT54lWuxWLFaysPY/7Wcw5Cm5tkpfyKD0lscwPyiVXix166K13yPDHezvX57dAVPLdkP0qNZpedISM9jBI5u/iwXb/YP40TAyVlZkkBLq4p6y+kRIrYLsG+fuz2rLjzILdg46oHyCVyKfHw+5OHnSQzXi2v+uNpJ0U5epRXGJH6nokXs1FhOt6jvXjXRYfzVY0b11a5FtO80Ja5UMtdwJX4sLnEQ1dcEQnGlSJbgKuKQtnlkV0ldezFPLVoH7Jc5Hd4iqvFiLtiymIVJnCKrSPsYsrsYUtxrsOieF4/dClPUYIfK2RddUM8nyUdGFu+9xJeXHZQsuLKRVFN+K8eaMf/zDZZkltYupPk+PB3uxSfK8ZZUqZWq5G01bDfQXeriFQW60hKXARuL98J69LAN/llgAqtIwCwZcsWzJ07F2fPnkVOTo7DRV6j0eDgQfdWpZ5w48YNJCcLKwscPHgQv/76KwYNGgStmyWJ/j5xA1q9bUvSaLbi5PUCzFhzgr89KVovSLITR7yOZeRj8U7bxXncLcKkj+sFpWhUPdpBJH/5QDvJahSAvMgUT3ByUSZf88zi/QBsDTlcRbTZTpfu4Ozic5W54HKC+duHO6BNWhw6vbMBJUYzSo0Why3ZkgAJbanXRlzJgT0nSh/CXzzFW/3c7gs3/8pt3Uf7aBvcU3o0TkKD5CicvWG/qEaF6VBUZsbRy7aIkTtNOpQw79GOuJpXKtmmXVwSL1Ifwm87W63Af1cL0LSm8uZVfZtVr9hg/YSc7hJHtAGhRUHu+63Eh73tjGe2mqmrjwl+dxWxzioX2s1SYnG9wP0I9ax1JzHjrlZu388Vrpr6uFur2myxylqAAOHc5qyleLOaMXwtb5auDRJx1821ZMe2Yt8l3NepjtMxskGNEhdWLLnP5AvLbFqibVq8Q+7Fs0v2C35nF+mP96iPXedsFc8KS02Ik4jCO/O1i8mSaVOvBG6RcUvTZGxkdsKdWRe1AuuIe89XWSLaAPDJyLZ4oX8T1E2MxPCvtmHvKfmGW56iuoj2Rx99hN69e2Pp0qXIz89HQkICEhMTBf8SEny38mAZOXIkhgwZgnfeeQfffvstnn/+eXTr1g2RkZF49913K/z4OUVGwVaiuFyYWCSxUR1xq3Uuoi3OeL+labKsmJS6qL29+hjWHhWWrnOniYO3YBdX5zOLXHq0k2P0+PuF3tj52q1uPY9UdQQu0ssmnHLXm1rxEUhgJlupTHzWl+dhoMcjpOoci8fHvudsFQJxRIT77HETqpQlSafVBKT+qyvqJwqbLnDe9U//tmWTi9umV5RQnVZSZHO3sUTrdby4AICH5+4U38UB9rsw7c6WTs70LVK1keWQi2hLLZjZeU0uMqgkop3nZPscUJ5PIC69Juazv08BABoq7KInxlXk1VNcJdfKJZLLYbJYnVrfTIKItvx5EWE6yXmiac0Ye51+iWBOrov8IEB4jfznP1v1EavVii/+OY1tZzIF57Lvv9R4z2c6RrxPihYIbLMxtm9BgUF6rBVNclQKN88nRwuDCBte6C17n5AKWUfcOl3VaLUa1EuKgkajQctUaa99RVFXOArABx98gO7du2P16tWIi/PNH62UO++8E4sWLcJHH32E/Px8JCcnY/jw4XjzzTc9bsHOIvYUiiNt4oh2MWPcF0cIbhQYBOL7rptr4cneDSW7s3FITW5zt55zOBaI7uxsEqaJiaw4u1g0SHbPygNIl/fRMdYQDk7saLU2P2GoTgOj2SpZjrGISQo1+zGiLbUIEY+PFThsJF6c6GQQRbQbVncUFdH6ENVUCGIxii6i4iTRpGj/dTsNDRG+PpFhIYL3QEmyFBstjHbyffY17lxc5YSXRUpoMwJRTiwqsRE4i6oCts+rEuEjV5oQAK7klvDiLjEqDOsn9oLFCkz77ZigeoozpJISvYGr18jdiHaZyeK0gQ1rS5MrVwvYGzmJF/Ps7kyhRFKa3HitVlsZwxapsYL5e9e5bOSXGrHlVCZfq/v8u0P429nPXHGZycH2pkTYx0bYv38ajYbfhZb7XHHWkSGtUjCyYxpe/+Uw0rOdW5M8gXtt2UTbqDAdaleTT+oVVB2potYRMZF633w3VRfRLi4uxgMPPBBwkQ0A48ePx86dO5GVlQWj0YgrV65g4cKFXhHZgOMXW/zFF0cRixkBJ/5iZxaW4ewNu2fsppQYl1vSSrtxedKMJiOvBN9vP4/rBZ41EeAafAAo92g7r6PtKc4i0qxItTdwsT1/eAjX3c/xAlMgiGj706Pt+H6Ki++zFy+25a/4syiOaD/UpR5m3NUKD3S2b+X6829zB/EWul5U9tCflVIcPdq25368h61aTc/GyQ73EcN+DsPDAlhHW+Kr9/7drSXPlbIcZBeVYdleW76H3IX6v/J649z5K/ZdQkmZYy3ixY93lnxuZ8KQtTlxn909THCC+z44E43sQqDEaEaj6jFoUiMGn49q53Du7+N7YOurfR2O58l0zawIVqsVK/ZfdnqOu3NnmdnidO5nb3NmMQGAlrUcrX29mB3cHRK2H7nPyMaTN/DaysO444utDvP3jN+P45xEZFqM1K7Cz3sv4YooEVYciIkVXaO5uUROaHN5VtH6EPRqkowm1ZXbxNyBGye7cyDVKZhFENGuog1rxPRukozn+nlH37GoTmj36dMHhw8fDvQw/ALbkh1wrIMsju44S3goNZrxO9OtUEljC6WrUk98xi8uO4gpq47itRVH3L4vIExIyi0pY+poe/cLXixRkrBWtQgAwLI9l/jIuriutLM22oWML8+fHm2pC6k4os1OkHe0TeWjlOLXgfu7uPk3IkyH+zvXEYjzQj/VIHUXsTjILxGO058XCUePtu1z0zzVJjyKFLyG7HsRyIY1UtzbIU3yuJRAe3juTt6CJRet5DrkAsCDc3Zi4k8HMWPNcYe5sGH1aNzbIc2hadCJDPkW6my0r7DMhDKTBXd/vZ0/lhpv+96Ld0RYWCFfN8G+yxMXGYrFj3fmf+/ZOAktUuNQKz4CB6cMwOuDb+I7xGYVeT8ZcvWhDJcCU2lE+572ttJ3RpPF6S4Bt1uXkVfitJqKFY5JyhGiXaa2afGKx3vsin0xJp5/l+xOx9bT0jsL7GJN7lra6/1/JI9ziC0wnNAulLGOcK8ft7Plq1rf3MKBfV3jXPQ4EFYdce/5KksLdjHdGibhiZ7eb3qkrlkbwGeffYYNGzbgww8/9Hlr9UAjLpeUEhch+F08iUhdlLkL74+7LgomOyVb+koj1Z5EtLeetkUo1h+X7wbmjKvMImTP+Rze8+nu9qcrxP7zF/o3wfDyGqvZRWX4sHwLUtwpkVsUufJou9jN9ipSOxSpceGic+yvX1xEKEZ3s0VWxfVEOWEj9uUObpXC/+zHNYRbsB/XuIhQhy6ennyePUUstLmIdqSohbPJbMG+izmSEVluZys8RBdQq069JOWeZKkI55HLdoHkLIK26eQNXMktwbEM2/m/HbrCi6SRHdLw/ojW/Pe2sShC6KzuPysscouMDovLuHJbgNOINrODNbxdLcFt3cqrzwBAo+p2G1tcZCie6NUAN9exLVLFlSy8waIdFwS/38/sPHHUkckjEMMtSFxFtDkr+4CP/3VaihZWq6BjcLOaMfh9fA/BKe/f3RpP9m6IL+637wzI9ZBgyZZIIJRLimWDDnILXJPFKgiUiBF//1xFtO25Rba/RXyNF+PsuZ3BzSOsLclVvXY2MONuRLuyWkd8heqEdlpaGsaOHYtXX30VycnJiIqKcqhJrQZbiTe4wEy4z/ZthO6NEgW3i6M4UttdXIcq8blKIqlyCUti/ClMONgLmsFkQXq27bVSandRyn0dhRekZ29tLKjHuvmU7QJib+BiO875fqU83uxkWdPLFS6cwVaHCdVpMLpbPTzWQ9hlj51QY8JDeVuF+LMljmhzhIfq+LJmDdwQXv6E/bz+/GRXB4HhKtLjTcTfQ+77yv3PbS1/8Nd/GP7lNkz7TVgBA7B/xqQ6f/qT/3Wvj9Hd6ik615WtiL3It64tnM8fmbsL3d79W3CMS4a8u0Nt3NvRHkUXV0RyJlRY8Z9bUuZQa5qz7hnNVmTklQg6LdrHYbtPnYRISSG47MmueLhrXUzs38ThtnrlTXCu5JV6vYb6znP2oNTz/ZrgJqYK0z3ta+N/3eu7rODBwX02y0wWp9cILqKtxPceHqrDO3e1xEu3NcWfE3o55NPUS4rCq4OaCTzQcmKOXSD9J1HNhIVNJGavKdx8J/U5Zcsv1q7mXBhzdqR8mdfAKAoQPdGrvsMCjeXh71wnR0vB7ZIkRtsT9V0JbTbfyV3hXEkD2j5DdcmQU6ZMwTvvvINatWqhQ4cOlUZUS8F2eXthQFOH6Gi2KFlSqotRlD4EORKePyXRRqNJmYAORORSLFC4RYm3PdpSW3nX8u0TLR+RFHu0Q+W7+3EX+zoJkXj21sbeHK5T2At/5/qJmDqshcM57OsaGabjd0TEER7uQiRV2vGje9ti9uazeFKiVbYaYC+ujWvEYNqdLfHGL0dwMbsYzVNicZtMuUtfII64iSPa3Ov+zSZbtaDvt1/A23fY7BD5pUZEhur4eUGq86c/CQ/VYeqwFni8Z33MXHMCj/W07YZwicEsrjy77ILvi/vb4c1fj+LvE9dlz+cEn1gQiHcMpKxg/GMwkercYqNgm12rsT/WjUIDen+wEWUmC7a+2he14u1iiwtoyFkAOtZLkK1qU42pVpRfYkJyjG/eT32oFnrmdenfvAYGuPGZ5wRaZqHBaYKlWeF2HXfWA52l+zmwCD43Mg/PXgeX7LZ5/vvdVENy99RgsiA8VAer1SpoU841YZOqZpNTbETdRNs8wlXz6lQ/AfdLLFS4xZlcUjO3COES7FPiIvDRvW2xYp+0n37fxVyUGs1uf9evl1+zqsfYdzBd1XlndzfdFc6V1TriK1QntL/++msMGTIEv/zyi9t1qoOBbx5qj+v5pXhDormC+KLx+6EMfHyvhZ/Ui42OX+a4iFBcynHMYlYyBboqY8UhrpxRZrL4zGvGP6fYZ1s+Mfpjy4oVo5ztgPdocxHtENce7bmjOzgkz/gSduKU29FgBVF4qL15itizyP0u9Xr3aJyEHo2THI6rBbHG690kGf++3CcgYxE3rOA82s4WaoCtLn6ndzagY71qeOm2ZuX3UUcpxdrVIvEF07hDp3UU2q4amLCfq7SESMwd3RH1Xv1d9nzu4cQXePGWtzhKzc5VbDWRnOIyxDI7G3UTo/i+AheyiniBufdCjkBoc8ed1SeWQ6vV8DXdiwwmJHvQ8EYJYTqtsNumm2ONLP+c2booHpA9z1lFpe6NEnn7oDuBGvb9lVusSc25yTF6PN6jPuZsEVbNMhhtQltc9vZ/8/fg/LtDJK1ab60+ipXjuqPAYOIXVgse7SRZLcZuHbF/z8tMFpSZLdBpNHzfi1AnWubWZtWxgVlkXswuRpMaypMmrVYrn++VFK3H5CE3Yde5bNx5s3zkHKhY1RFPG8RVVVSnZMvKyjBkyJBKKbInDWqK21rUlN3SkRI1bNWOYomIdjWZLndynfxYnJWxYmFF7y/7L+OmKX/ij8MZiu7rCVar1WES5xLavO3RlmJcn0aoVt58gBOcFlFEO5qbYEVb1Varlck092/XRPa1ka1HzESoQnVavh24uAspFxn0tlXHHwTC6iSH+DvG7SBwQk2unN1fR23Rud3nc3hh4Ym48wfsRbpVLdsOpKuIttT3WO7vs4K1bjn//hczn+MzNwrR5q2/MGPN8fIx2V/rvBKj4Fx9iJbfEWKjk+N/3C+ox+wqou2KaD55zneJxNHhIYLX0p0E2t5NklGXsYQdvJQney73PZOyNElVQFJCt4Z2+6ScbUVKaIeHajF5aHMHqwfny575xwmH+1itVslkz/0XcwHYPwdhOq1sSUau50JBqQlWq83f3WTyH2j55lr0fN9ufxInYM+6pw3/8xtDmwtuU1I1heXktULkl5oQHqpF3cRIPN6zAWY/3MHle8B+B5UGsCYPuQlt0uLxePluFqEM1c3cQ4cOxebNmwM9DJ/wQJd6ABy76Y3vK19Ohp0IpDKlq0XJCW3X43FWCgsAEsofm42OTlh6AGaLFU8t2qdIzLvLn0cy0H76eocsdm6Lzx8R7VrxEfhzQi8AdsFghTCizUWqHVqXM5n6UT6qySkH+9rIvTXii5ec4OM+a/5Y2Hgbf1Z6ccV4kXWIS6biFjjirXnOzsDaGrjSi2qJaIthI6acx9bVYkdKBDRz0t2VF9ourlhsRPvDtf+hxGjmo5kCj3axUSB29SFa3iYlrunMtXb/aXc6xi7cC8Dz6i9ckMWnQlsfIlgIuPoOP9u3EeolRuLAlP5Y8L9Oir/z3HsiFQ1lF+hWNzoxhOi0GFbeEluu4olUSVXuu/HJfTcLjm86eQNvrXbcPQaA+2bvwJBPbVpDp9VgUEubvUYfosVzS/aj9we2CiTi8qAsnHXkxNV81J+0Bi3fXMvflsl2GBXNScPb1ULfZtXRrWEi6iZG8n8zYG8+p5RzmbayvjelxLo1R+gE1hFl7/njPRtg1dPd/bpTWxlQndB+8803cezYMYwbNw579+7FjRs3kJ2d7fAvmBEL7QdEbV9Zvtl0BkM/24yP/vpP0n9YTaLta1J0GO4uL9HkDFdCmyvlJnfRdNUgwhOe/GEfsovKsOOs9HvsL+HHPY/ZYoXVanWIaHOCQpwEw15Ao/zcXISdLOW2dcURVrkoIi+0VVZOTglqEtr1kqLw3K2NEaMPwfqJvfnjnBASe0Q5IcZe92aWR2TFDa7UwtcPtkdkmA7vDm/FJ1i52i2TEtrJ0dJBAwDgXiZ3ItriRGWxdeQaI2jCQrT8d168u8NNOS8vP8Qf03u46OHmfvFzsBQZTLj/2x346K//PHqOiFCdIALryjrywoCm2PhSH769uFQlDym464KUz/lGQSkmD7kJUWE6zLxLuta6HFwejlz1F6kmYZyVr33dajj/7hC+2tLW05mYt/W85OPsPJeNjPLKX6E6Dcb2tpV1S4rWY9WBK/z1zZl45awju8/nOP2bxJdQjUaDuaM7YvETXaDRaPDpqJsxsrxUprPKOVJw3nNXnmwxwbhbGayozmjTtGlTAMCBAwfwzTffyJ5nNvumja0/EFtHxMKbhUv2OHI536FFOwB+cuR4/+7WGNGutqLIr6vtXS6qJie0fdVK2BnuthF277Htrxk7CZnKxTZgF0Bc5YpPN5xCZJgOT5ZP0tx2Y7Q+JKBF/eXEpviiKCcYuAWDVDKk2nljaHM89N0u/j0JNM/3b4LxtzYWfL64iKjZYsXS3Rf545mFBvyw44Lg/ePEwHkflIXzBl0aJOLw1Nug02rw1zGb5cVVRFsqqfml25ph/XHHhEir1W6FE89rDatHITJMxy8M2aTejaKScwLrSLERF5gt+lcHNcPx8hrcYoEeotU6RKA9jWhHK4hoz91yDtvOZGHbmSxMHNDU5WOKX2t9qBYxjG3NmT9YCiVNlLjnlbNfHLyUh1XP9MCj3eu7vQvJjdcdj3aEqJHT1GEtMGbhXsU2jFCdlg9aXRY1rRHX/GaJVtj8SsniP778+cVdel1xo8AxEVIJVKLPf6hOaE+ZMkWVbZ29ifjLKS6CL4e4wQ3gWKosRKtR/AVyFXXiHoebI1YfvCK4Xaq0na/xZUQ7inkfWCFgtkhEtJmts3f/OGEX2kwnsEAip3PEuxiuBEMwTsY9GyfjyFu3Bfw9YBG/jux29CvLhQ26Jv9yBC/d5lpgqQnu7+NyRs5lORc4Ugu4pjVjcOSt2wTb7xxm3qMtPK4P0WHfG/2xYNt5zPzjhGRlJsAm1Nn5LrfEyAu55/s1Qfu6CTh9vVDyvscy8h3GJNegxBVKrCOs0LNYrLIL9uyiMkxacQjD2giT3lrWikM2Y1vgmqUoJSEqDL8+0x3DPt/q9DyLjMgG7Luhnswf3Nwrt+PqzDrCwX33lQaDrFbHngr2x3ZtHXGFkrSRuHKhraQVPGBrFPTGL0dxttw6ImcjlYNrmuXrwgaECoX21KlTAz0En8MKgKm3N1e8sDgrsToXr7bdmdhcWUc4Ucld5J79cb/gdimhbbVaJf+eHWez8Mv+y3htyE1u+bvYaBXgW+HHvi+s0DaaLQ4R7ViZWsxcS2d/+7PFyPnnHawjLmoze5rUFGjUJLKlcLXAkbKJNGaaoKiVTvWrYfm+SzhyWT6JDpDftpZ737iorZR1JDxUxwccOHud+PNfXGYWREhzist4sc/VHhZ7s53hqTtJiXUkkrGcFRhMsnXfP153EmuPXsPao/aydjsm3YrY8FCB99+TUmxySfaAbQ42W6wwWayCHbKejZOw+ZQtcfTVQc3cfk4Obs6RCwRJJkOGCOdbzjrjrJsyS6HBhPBQHTrWq+ZgA1FiHXGFEp8695rnSpTrleLt1ccEJQ2VBuw44iJCsf+N/i6vAUTFUe0rbDAYsH37dqxatQqZmdItVYMV1jriThkfqZqm4i0zdzo8ubKOcFpAbhtYasKTe8z7Zu/Akt3peP9Px+xvZ9RNFDZF8XYdbQDo0sBW9/ZhphkHKwSkItpyFz9ukg60S1huq7Jvs+oA7N5+sUc7XuT5D8aIdjDgyvsuVbLzo3vb+mg03oPrfOcqocud73FeiZGvRyw3v3FzKmc9Ec9DeSVGgec3r9jIR0Y5ISXVoEaOUQqbv4jhrSNOmrywH408J6JLqpU7VzKQFYBylW2c4SzKyc19FosVRuaxGzJNaJzZLVzBzTly5WfFCeiAY8CAE9rs7kCb2nGyhQce7V4PAFAv0bEJl1jEs8QoXNArWZjFl7+u649fcxkEAxx3uN0V2oAtCh7p51yiqogqhfann36KlJQU9OjRA8OHD8ehQ7YklMzMTCQlJWHu3LkBHmHFiGQmoUY1KhalEk8C7lgrjC4mYG7Ck+vyduaG41arVBSCjS6dvCq9PStHXIRwEvBFguGcRzpi0eOd8URPewMWnVbDR6/ZqA336oqj8mUmW9T79/Kyh492D2z5I7l5un/zGlg6pgs2vHALAHv1C454CSsS4X9OSHS8Ey+C1Ej18k6o1wsMMJkt+Pbfszh6xTG67SyiP3N4K9nb5BZ+rP4+c6PQIQiQX2oUfI9zS4x8d0Zuscm1SHdGh7rV8MNjnXFH21SX50rBLQg4L7sURoHFRd6vK577NRr768N+rz2p1+2slCQn4s0WKy8ItRrhfTwRfeLnlqoxX2o040qeo4VSLBYjQx2vEz+O6YJnb20s2DVJig7Dj090wcvlteqlvmNKqo64wlWnVMBuHQGAFfsuuTw/WvTcFVncEL5FdUJ73rx5mDBhAgYOHIjvvvtOINKSkpLQt29fLFmyJIAjrDharQarnu6On8Z2dTuBQYx4a9+dBLz+zWsAgKBFdYe61fDtwx2w6aVbBNYRqeg1V9s6Jc7+N0hloLPRJYOTlbqU3UE8kblqK+sJ0foQdG+U5HAR50Qme+HjXhNxRK6kzIyjV/L537s2kO4O5y/krCMajQadGyTypRvFF1Tx6xus1pFgIyk6DHNHd+B/l0ri8sVn39skRdtEXXZRGeZvO4931hzHkE+3OJznLKJfP8kxqsght2GXHG0Xk4WlJgcfb26xUShgi8tQYhQ2nxnSKkX2eTkm9m+CHo2TPM4julDuXT9xtUDe3sVEcuU859lFZVh5QNhdUPxd/e3ZHvjxiS6y3mNnOItoc6+XmbGOhIVoBfOnXN1pJXARc6nINRehjtaHCKLJSaKKNeFhjuOPDAtBqE6L90bYq6CseqYHujZM5McrLi4AuG8d6d4o0eFaomSHk90lzZBYTDg8t2g+qMhrTvgW1V1FZ82ahTvuuAOLFy/G7bff7nB7+/btcfSodF3MYKJNWjw61a+4GBMLPnf8eG/d0QLT7miBpWO78Mc0GpsAr5sYJYhoS016nM8wIkzHJykZJAQ5az2Rup1DynYinsj86X3m7CNs5J8T2uKJvdhoErTVbVRduSXIFyht2iJO9BFHosg64h9iwkPRt1kN1E2MlD2nIlFCf8GOcctpecufs50S8XeLRe7zyM6lRQYTH63mEJess1iBrPKur1zlHZ1Wg1Gd0vhzXh7omJBa18kiQAkNku33lyvjZhL0TpC2mDz5w14HO0Ko6LVpWSsOXZkGMO7gbMeBi5YbzBbezhiq0wrEpFREWSlcVHnNEcemaFxFjsToMEHZwuqixYQzO8SQ1in44v52+HNCT0HXT/a5WZwJbanv5F031xbsWgPKqo6wvng5a6Kz5w6G+aGqojqhffr0aQwaNEj29oSEBGRlZflxROqD/RKKoxg6N7yPMeGheKhrPdRkJikN7PfnRKXFCuRIeAW5zPkwnZbfttp+Nsthm4z1m0lFvDmkEoTEFg1/+sk4MSCwjpS/3I2qx2DGXfYt7pPXCjF3q63974DynYJAorSWtNg64nDxDsLyfsHC/Z3tPl9uO5vd1k6ICkOHunY7g1ob1rCwdoasQnnbg7OdEmeLVLlAgkajQdu0eAC2eUkc0c4stPuZOT16Pb9caDOCbfKQ5ni+XxOsn9gb425x9PM6WwQoYSxTcnL1wSuSNavZyLu4pTzHrnOOfQa8WfM+RKeVXdSw1g5urGE6rSAgIRVRVgo355caLbgiKrXHJQrGR4QKFiHi90Vso3jnrpaC34e0TkGzmo4NkuIjJCLaTqL74p2N525tjCGtUlBL1KFSyXScyoh+JRW9xIEpFbUPIESoTmjHx8c7TX48duwYatas6ccRqQ921S2eDD3JMJfbBuWE9rnMIocJD7AL4xCdht+2em7JAWw8KayFe/aGfRtcKqGT44O1jg0aAhrRLheZ7JjZV+r+znX4Rcpu5sKntLaqL1HahVy8RSzefvRl3fKqzuCWdqsC9zlnP+8RobqA1mL3BK1Ww0dDxSKSFT+ukiF7Nk6SPO7MssEtUg6k5zqUz2OTCjm7HreAZoV2lD4Ez/VrjEblFV5WjuuGJkwejXhh6i6x4aFoWp4A/8aqoxjw8b8O57DWkWI3Okh6e1EsF9XmPMsGk5kPooTqtIKAhKd1xgEgnJmD2AUSAGSUJwDGR4YJFlPi90Wn1aABs/swop3rBm6AdAM4dywZz/dvgogwnUNSpdIuyqPLE/ILnCTLcohLFwbbXFGVUN1VdPDgwZg9ezZyc3Mdbjt69Ci+/fZbDBs2zP8DUwnVY/SCC5bYY1vhrX7m7txc+fvhDDw6f7fDqVxziBCtVjDRrdwvrLd9xxf2eqzOLA2Ldl50OCaO4vkzos2JTDYiL656wG3Xff7Paf6YGiKPyiPaws+PeAeBItq+g92Z4qJZbE5CZJgOdRPkrSRqhRNiYqHN2pRcNVGRS8ZzNr9xQvvLjWfwZHmrdI78UpvQDtFqHOoNOxPPN9ephj+f64VHutbF+4y3tyL0YBYRYiEJCK0j7rRq93anP3YR3r1RIu5pXxvfPtzBbh0xWviKJmEhWsE8WZFeGL2ZhjniaWzO5rMAlFkrlj3ZFUNapeC1wc0Uz8nuerTlaM/sRAFAnJNyiSyx5QttKaummAJRLfdeChsNEf5HdUJ7+vTpMJvNaNmyJSZPngyNRoMFCxbgwQcfRIcOHVC9enVMmTIl0MP0Gc6yvQFb+SH2HKmVvLdwtULmVt1hOq1g1Z8lcfHgcFVS0GEMognb1evjTUIlItri8UhFO1LjKpbg6g08tY4kx+gFF1hq0+s7YpmKOvXKvdlsglNEmA4vD2yGPk2T8c1D7f0+Pk/hhIl4+5sNELjSYXLJeM6mJDZZ9Kqo9Nm3m222LpPFijTRtr6rOVOr1eCtO1ri3o5pTs9TytheDQS/W0XJ5qxg5ZIhS41ml1FRb9dDFkT6w0LwwT1t0L95Dbt1RBDR1nhURlAKrVbDJ8SKy9xx7xW7yyBXyzoxWo8vHmiHMb2Ud4htWtPRtuTMOiLHTSlCW8oY0XsuR83y8pji7pRizBYr9l/MBQDUiNXj12e6Uz6NilHdVTQ1NRV79+7FwIEDsXTpUlitVixcuBCrV6/GqFGjsGPHDiQlSW8rVgYWP9EZrWvHYfHjnSVvT4oJE/gbxZOrN79srmwohax1hLmIbjsj76FXUuaIJUSrwbA2qYLf/YVOouqI+CWRKjf4CFOPO1DIlKB1QCxoxt/aWHBhocnbd7C7B7WrlQttkXUkOUaPeY92wm0tgscuJ9dJj7UEuErWlfNwO1v8Ryu0lbHeeAC8TcRfiMvtTVh6AK3f+ouv4y2o0mQyI6eoDK2n/oWH5+4CIG9D8HZ5N8GCm9nZ4oR2iVFoHZHrEukJ4iCH1Wpr987VZx/I2K68mQQoNd/pPXhduTKXADBvdEfFDbTqJdnmgfMuWscXlpr4ZjybX+6L1rXj3R4j4T9UJ7QvXryImJgYzJkzB9nZ2bh27RoyMjKQk5ODuXPnIiYmBhcvOloMKgvt6ybg1/KSQ1JEhoUIJkCvW0fceCy7R1vrtE0ti9lJVKZ5imNyilarEWSz+1P4cRd7toKB+PnZiHbLWrE4/+4QxbVVfYnSiDb793Sqn4CEqDDB3+TPHYSqBiuque6E7GcnWMt1yTX44BbuIVqN0+oqgPznzllDLqW5EV0a2OfWZU929ftiUmyrWHXgCspMFizcfgGAMIprMFmw9uhVlJktfNdFuQRJTwShM+R2trgdixlrTuDt1cf4c8tMyrowKoGbeznf99OL96HzjA38Liq7WPG2nXDB/zoJfvfEOpLElJsUJ0Y6gytT6aoNO2cbCQvRUgv1IEB171D9+vWxcuVK/vfk5GTUqFED2vIv+q+//or69QPbDMQfaDQaye3VyDCdc+tIBbxxUmNwBteyOFSrUdyRMrfYiHUyzRqkxKFOI4zgVMT75y5c9PyxBXscjnGwlVKkFgqBQqnQFlB+F0EOALXn9RkhOi16N0lGkxrRfHk6ViwGa7kuOWHCLbIXPd7Z5fdYTjw4m9+U1hkPD9Xh/LtDcP7dIehYLzD17r9+sJ3DMU4om0URbXEUP0eiUgngmcXBGey1RS64c+q6rQFZmNcj2lx+jO0x1xy+iuvl0WyNxu5lBrwfye/dJBnLnuzK/640iMRSLTIUneoloFWtOEFSpiu4745U3woWzlKkNFJOBBbVXUVd+dCMRiMvuis7Ui9FRJhOMOmJJwF/Wke48kqhOq1bpYWe+H6P5HGpiiQ6UUTbn7QTdYoL1WkcBMKeCzn8zz1VlIyioIOvLKxQUkNiZ2Vm/qMdsXZCL17UsFHuYH3tpdpHW61W3jamZI6Ss444m5KCSXQMbJniENXXh2jx4rKDAuudwWhxmIelSgIC3v+8sNcZ9rWVipzbrCPe8WgD9qolRrMFJtHjRobqoNFo8Hy/JgCAt+9o4bXn5WC/h652KGfd0wYA8Omom/ljGo0GS8d2wa/PdHer7KJdaFtwLrMId3yxFX9K1BPnbJvB9JmvyqjiXcrPzxdUGcnKypK0h+Tm5mLJkiVISXHdwauyEhkagrAQdmvfu8mQ7L2lSnA906cRMvJKsXzfJX7VHaLTOLWEKEUqmSaQJYveHdEKG05c5ysDSCUGvja4GWasOQEA6N1UPUJbaTkpwX3KlzTsBZva+voW8cJNXHUkGHmmbyM8s3i/4JjRbOVLTirZlZKrBKKk6ogzbq4T7/IcfxEfEYoLzO9S5U1LTRbBPGwyW5BdLCe0vRzRZgRipAs7WWiIFmN7NcDmU5mCnBpPCQ3h8mMsDt0xI8qtIs/1a4yxvRv4ZEHKfg+ru2hhP6J9bQxpneIwDk92X9n38KVlB3EwPRdP/rAP598dIjiPE9rB0C2WUInQ/vjjj/H2228DsH04J0yYgAkTJkiea7VaMX36dD+OTl1E6XUY1TENqw9eQZu0eIfya96MaEslHuaWlCGsfBLk6piG6rSKOxE6Q0po6zQaZf1rfYBGY6vFygttiYXHEz0boH/zmkiKDlOFN5vDk4UPdxd2sg/WqGqwIqg6EqSvPVenmsVotvBzhJI5Sr7qiPx9q7kooabTarBsbFen5/gTqVJyYgxGs+BvLjVZ5K0jXv68sLYxVtBJCe0wnRbdGyVhz+R+SFBYys4ZvEfbZOFLM3JEhPl+fmIj2kpKCXprHOzjiCvnsHCWRXEbdkKdqOJdGjBgAKKjo2G1WvHyyy9j1KhRaNdO6GHTaDSIiopC+/bt0aFDhwCNNPBEhOnQrVES/n6hN1LjIxxWzUq90kqQ2vLKKTKiWpRt4uFaCIdoNR5FUMVIJdMEugj/rvP2RjRS29kajb0UlZpwt7oLS4TAOlI1bFpqQVB1xI81472J1PehzGTh8waU5JH0aZqMTzeccjjubDqo4yLBMjU+3KvdEytKrAIBZzBZBHaZUqOZt470apKMTvWq4cO/TgKQT0L1FLbpTFSY/C4qAD74wiYBeuO5jWarQ/OWirR3V0o0892r7UYyY0UJLe/IabZYBfXUxRSWchHt4FyMVzVUMZN37doVXbvaIg1FRUUYMWIEWrZs6eJeVRNuC69BsnRJKm+WvwuTiOD2bpKMo1fyBMeMZoviToTOkItoWwPm0rZtNXP1SoOheUunegnYdT4bd7f3vOYvK0a8ffEmnCOoOhKkEe3kGD3CRJ0CP1p3Ehl5tgidkhSbm+tUw6/PdIfFCtxZ3vBKo3G+He9KEKnts6wkedFgMgsE1xPf7+Hno7oJkWhf157MGell0cXuKkTq2dwBCeuIlxcwoSFcRNvMN0bjn98PliqtVoNNL90Co9nq953K8BAtisrMgg6hYsg6ElyoZ3lfzptvvkki2wlSpYye7mMvyO9V64jE5DmifW2Htty3t0mtsHXEarXyF+ZOTCWAEJ3GrURLbzPtDvtnMRiat8wZ3QHfPNQerwxq6vFjsJ8gso74l8pQdQQA2qbFC35fuMPuRla669a6djxqMPWIXUXC9SE6/PZsD6wc1433YrOttwM4jUiipKKPwWQRLFg4kQ3YPh9sCUhvRZP58Yka1tiPSydDehMuor1s7yXM/ves4LZIP81JdROj/F5jHbDPuWwVl/0XcwTn8NYRhSUticCifuVQhblFIrlO6uLLlqiqqNWiQz17pQ2p6LhOq3HwKifH6D0rJ8dgslh5Qd0mLY4/Hq0PCajQTmG6PAaBzkZseChua1HTaVtpOaReZqqj7V9YP6i4s2Iw4Szq6E4wgBVwSvR5y1pxuLlONcwf3Qnv3NUSU25vzt+WW+y6rbU/Yb+jteKlo/FFBpNsNQ99qE6w6yFuhFNR2Ig2a1GQWiB4u5Yzd+05eiXfoRxssNaXVwq3M8F+/++bvUNwDh/RDlJ7WVWDrqIq5tNRN+Ol25pi3C32iLWU0E6Msk+wnlpH1k/sjdcGN8OzfRvzx+SiFA5NW0J1aF07TvJcOQ6k5wKwrdQv55YIbCNs1D4mPDSg1hF225BL/qystEi11QFnO9MF2iNf1WArZwRztMpZ1NGdPBJ2oWdyY9csLjIUD3SuK1i45JVIJxEGCvZve65fY8lzTl4rRIlMg5rwUK3g70uN866XWCi0nSdD+nOWCObvhRK4RRdb7tZgsmDJrovYcTYLK/dfwg/lO0RkHQkO6F1SMbHhoXi6TyP8tCedPxYhkQiSEG3P8vY0GbJR9WiHbTKx0F7+VDcAjmI+IkyH14c0R7WoMHyzyb7Np9FI1wIHgEOXcnE+swgTlh4AAOx7oz9/Gxs9CXSdUPZiI3fBC3Z+e7YHfjuUwVuQvFkPl3Cf5U91w/rj1zD85tquT1YpzqKOYR7UFQbk5xKleLOhijcIVxiN/nGXdCfkiFAdasaF442hzZFVaECXBt5tvhOms48v0kUypDdydJQS6GuCr6mXGIUdZ7Mdjr+64rDDscq+6Kgs0LsUBOhltvA42HJK3qj+wcFaREZ3q4f2dW22EqmIdlxEKCYNugm3NquBe7/ZDsC2UMiTaSW7cv9lgd+Q2yYL0WoEF+KY8MBaR1iKRUk5lYWWteLQspZ9R8JZtjvhe9rXrcZ/14IVZ0LbnW6j3vb+qgl2Xk+W8Ffb2ppbcOZGkeT9OaH+WA/fdEoWlPcLcx7R9uZ1BwBSZaw0QOUX2kqq0XAkRle8lCLheyrvLFaJYCc2qWTIiDAdJvRrjMd71Ef1WMcatp7CVtm4kGWf7KUi2hyCTHUnF1tWZAPAI3N3ASj3ajPHY8JDVJPE5M+oTSDxRk10omrjrGKKOxFtbyDnfw407LyeEOUomJ67VdpOwuHr0pvsIoetaCJl4fF2MOSJXg1kb/O2H1xtKKlGw5EQ5V1fPuEbVPWJLS4uRvv27fH1118HeiiqokuDRHSsVw3D29VCkswKdkK/Jpg8tLnkbZ7CVtlg2wKLS2yFCzpV2u/DTtSta8fh5YHylTBOXy/kf2aj4LEqagJTVajsyUaE73H2vfW3UPpudAd0qFsNix/v7NfndUUo8zpIRYkHt3LeAdmThGd3YKPUbES7U/0E9GqSjJdus8/nFU2GFxOtD0E9mbrolb0SklSLezncEeVE4FDVHkxkZCTOnTvnUevSykx8ZBiWPdnN78/LCuWZw1vxP7NJGkNbpwgS5sIEQtt+fNXT3VFmtuD9Px3bDIthO59ptRpFDS58CbeFW1WYOqwFxny/B0/2buj6ZIKQIDZC/tLib6HdrGYsfn7K//OnK9h5LVTiNamTEImoMB2KZHJDCkt9a2XjKlsAwt3J8FAdvv9fJwD2tvG+2AST+pzUTYx0uQAJdpRUehrVqQ7OZxYFvcWsqqC65dDAgQOxdu3aQA+DAATVPm5tVoP/mU2W+/S+mwX3YbeFw5iIi0ajgT5Eh/F9G7l83nzRBWTS4GaoHqPHpEHNlA/ei0y9vQX0IVpMHnJTQJ7f39RPisK6ib0xon3wJuMRgcVZgNObTbWCma4NE5EYFYYuDRIkW2nrtBrJZimd6icgJS4ctzRzLP/qTdidRVfBL19UhhIL7Ue718Oml/qoshOvN3EltN8d3gozh7fCj2O6qKrTKSGPqiLaAPDGG2/gnnvuwUMPPYSxY8eifv36iIhw9NglJHg3w5pwxMCUs2MbaTgr/8Ym0EhNGEraSo+7pSHWHr2Kh7rWBWBrHLDztVsDttNxf+c6uK9jGpW6IwiFOKtc4+n3uLJVWKibGIXdr/dz2vEyOjwEyBcem3FXSzRMjvb5fJjnRt1xXySsixuENa0R4/0nUSHOLEHn3x3ix5EQ3kJ1y6EWLVrg2LFjWLRoEXr37o06deogOTnZ4R/he9iC+WylEWcXUT1TEmpgy5oAgJtSYvljEQoSeBrXiMGBKQMwaZA9ghxoOxGJbIJQzrC2qV57rJEd0gAAzyrYDQs2tFoNP7eN7e2YACi1uAgP1fllPny6/PW+t4PrnS1ve7QBoUe8RqweIzumef051Ig7VXmI4EB1IYIpU6YEXFQRNjqU+79iRZO9s/Jv7HZfSlw4Dk8dIKhA4CqRZVibVIfHIQgiuEiJi8DBNweg1/v/yJb4VMrM4a0wvl9j1VYP8RavDmyG5XsvIbPQnqMilVTqr2TAdnWq4cCU/oKmOHL4wqOdXWx/Hf59uU+V0QXUjbfyoTqhPXXq1EAPgSinemw4dr52q0NUxWSRj2iHiTLpxR5DZ22lV47rhta14z0bLEEQqiIuIhTR+pAKC22tVlPpRTZg27V7vn8TvL7yCPrdZMuJSZQo++esdKK3iY90Xqe5UfVonL5eyAdIvEnvJsn4YcdFDGpZ0+cVVtREVfpbqwoBF9oXL9q6XtWpU0fwuyu4832JwWDAlClTsHDhQuTk5KB169aYPn06+vfv7/rOlYQaEnW5nXVY08lUIOGQy5Qf1akObq5DGdQEUZmo7M1FvM2ojnXQPCWWt9tJ1ddWU3m7X57ujnM3itCyVqzrk91k8pDmGNC8JtpVscoacm3VR7Sj5PRgJeCzYL169aDRaFBSUoKwsDD+d1eYzb5vhz169Gj8/PPPmDBhAho3boz58+dj8ODB+Oeff9CjRw+fP79aMSls0d0gKdrhWMva9g6Eu167FYcu5SEsRIvujZK8Nj6CINTB7W1S8N9fBaiTEIn3726NOgnStZEJG1qtRhBwSJDomyDuzBtIovUhaMXM6d4kPFSHXk2qXj4W+x2ZN7oj30CsR2O6RgYrARfac+fOhUajQWhoqOD3QLNr1y4sWbIEH3zwAV588UUAwMMPP4yWLVvi5ZdfxrZt2wI8wsDRJi0evxy4Inv76md6IKvIgHoSZZhuaZKML+5vh5tSYlA9Nhz9mnuvkyVBEOpibO+GqFUtAt0aJknujhHOkbKOEJWbGrH2bo8Nk6NRR6ZxDxE8BFxojx492unvUuTk5PhmMAw///wzdDodxowZwx8LDw/HY489htdeew3p6elIS6saWdBiHuxSF1qNBt0bJUre7izCodFoMKR15W44QBCEjVCdFnfdTFvenkIttqseGo0Gi5/ojBsFBhLZlYSgSW81GAxYtmwZ7rzzTqSk+F6o7d+/H02aNEFsrNB71qmTrSPWgQMHfD4GtRKq0+KRbvXQqHrVqGtKEAQRCKQ82kTlp1vDJNzRtlagh0F4iYBHtJ1htVqxYcMGLFq0CCtXrkR+fj6Sk5Nx//33+/y5MzIyJAU9d+zKFXnrhMFggMFg4H/Pz8+XPZcgCIIgpEiOpog2QQQ7qhTae/fuxaJFi7BkyRJcvXoVGo0G9913H5555hl06dLFLx7ukpIS6PWOk1x4eDh/uxwzZ87EW2+95bOxEQRBEJWftIQIPNq9HjJyS6HVAkNaeb+MHkEQvkU1Qvvs2bNYtGgRFi1ahFOnTqFWrVp44IEH0KlTJ4wcORIjRoxA165d/TaeiIgIQVSao7S0lL9djkmTJmHixIn87/n5+VXWz00QBEF4hkajwZu3twj0MAiCqACqENpdu3bFrl27kJSUhLvvvhtz5szhy+edOXMmIGNKSUnB5cuXHY5nZGQAAFJT5SMLer1eMhpOEARBEARBVB1UIbR37tyJ+vXr46OPPsKQIUMQEhL4YbVt2xb//PMP8vPzBQmRO3fu5G8nCIIgCIIgCDlUUXXk888/R0pKCu666y7UrFkTY8eOxT///AOrVb4Doa+5++67YTabMXv2bP6YwWDAvHnz0LlzZ7KCEARBEARBEE4JfOgYwLhx4zBu3DicO3cOixYtwuLFi/Htt9+iZs2a6NOnDzQajd+b2HTu3Bn33HMPJk2ahOvXr6NRo0ZYsGABzp8/j++++86vYyEIgiAIgiCCD401kGFjJ3CVR5YuXYqMjAzUqFEDt99+O4YNG4Z+/frx1T98SWlpKd544w388MMPyMnJQevWrTFt2jTcdtttbj1Ofn4+4uLikJeX51CXmyAIgiAIggg8vtBrqhXaHBaLBX///Td++OEHrFy5EgUFBYiMjERhYWGgh6aYvLw8xMfHIz09nYQ2QRAEQRCECuGqxOXm5iIuTr7LtTuoXmizlJaWYtWqVVi8eDFWrVoV6OEo5uzZs2jYsGGgh0EQBEEQBEG44MyZM2jQoIFXHiuohHawkpubi2rVquHixYteWyH5mo4dO2L37t2BHoZiaLy+J9jGTOP1PcE2Zhqvbwm28QLBN2Yar2/Jy8tDnTp1kJOTg/j4eK88piqSISs7Wq2tuEtcXFzQWEd0Ol3QjBWg8fqDYBszjdf3BNuYaby+JdjGCwTfmGm8/oHTbV55LK89ElGpePrppwM9BLeg8fqeYBszjdf3BNuYaby+JdjGCwTfmGm8wQdZR/wAVR0hCIIgCIJQN77QaxTR9gN6vR5vvvkmtWUnCIIgCIJQKb7QaxTRJgiCIAiCIAgfQBFtgiAIgiAIgvABJLQJgiAIgiAIwgeQ0CYIgiAIgiAIH0BCmyAIgiAIgiB8AAltgiAIgiAIgvABJLQJgiAIgiAIwgeQ0CYIgiAIgiAIH0BCmyAIgiAIgiB8AAltgiAIgiAIgvABJLQJgiAIgiAIwgeQ0CYIgiAIgiAIH0BCmyAIgiAIgiB8AAltgiAIgiAIgvABJLQJgiAIgiAIwgeQ0CYIgiAIgiAIH0BCmyAIgiAIgiB8AAltgiAIgiAIgvABJLQJgiAIgiAIwgeQ0CYIgiAIgiAIH0BCmyAIgiAIgiB8AAltgiAIgiAIgvABJLQJgiAIgiAIwgeQ0CYIgiAIgiAIH0BCmyAIgiAIgiB8AAltgiAIgiAIgvABJLQJgiAIgiAIwgeQ0AbwzjvvQKPRoGXLlg63bdu2DT169EBkZCRq1qyJ8ePHo7CwMACjJAiCIAiCIIIJjdVqtQZ6EIHk0qVLaNq0KTQaDerVq4cjR47wtx04cABdu3bFTTfdhDFjxuDSpUv48MMP0adPH/zxxx+Kn8NiseDKlSuIiYmBRqPxxZ9BEARBEARBVACr1YqCggKkpqZCq/VOLDrEK48SxLz44ovo0qULzGYzMjMzBbe99tprqFatGjZu3IjY2FgAQL169fDEE0/gr7/+woABAxQ9x5UrV5CWlub1sRMEQRAEQRDeJT09HbVr1/bKY1XpiPa///6Lvn37Yv/+/Xj22WeRmZnJR7Tz8/ORmJiI559/Hu+//z5/n7KyMiQmJmLkyJGYM2eOoufJy8tDfHw80tPTecFOEARBEARBqIf8/HykpaUhNzcXcXFxXnnMKhvRNpvNePbZZ/H444+jVatWDrcfPnwYJpMJHTp0EBwPCwtD27ZtsX//fsXPxdlFYmNjSWgTBEEQBEGoGG/afKus0P76669x4cIFrF+/XvL2jIwMAEBKSorDbSkpKdi8ebPsYxsMBhgMBv73/Pz8Co6WIAiCIAiCCDaqZNWRrKwsTJkyBW+88QaSk5MlzykpKQEA6PV6h9vCw8P526WYOXMm4uLi+H/kzyYIgiAIgqh6VEmhPXnyZCQkJODZZ5+VPSciIgIABJFpjtLSUv52KSZNmoS8vDz+X3p6esUHTRAEQRAEQQQVVc46curUKcyePRv/93//hytXrvDHS0tLYTQacf78ecTGxvKWEc5CwpKRkYHU1FTZ59Dr9ZKRcIIgCIIgCNVz5YDt/9S2gRxFpaDKRbQvX74Mi8WC8ePHo379+vy/nTt34uTJk6hfvz7efvtttGzZEiEhIdizZ4/g/mVlZThw4ADatm0bmD+AIAiCIAjCVxhLgdm9bf/KigI9mqCnykW0W7ZsiZUrVzocnzx5MgoKCvDJJ5+gYcOGiIuLQ79+/fDDDz/gjTfeQExMDABg4cKFKCwsxD333OPvoRMEQRAEQfgWc5n954KrQGLDwI2lElCl62iz3HLLLYI62gCwb98+dOvWDc2bN+c7Q86aNQu9evXC2rVrFT92fn4+4uLikJeXR+X9CIIgCIJQL6V5wLt1bD8/8TdQq31gx+NHfKHXqpx1xB3atWuH9evXIyIiAs8//zxmz56Nxx57DD///HOgh0YQBEEQBOF9rBb7zyU5gRtHJaHKWUfk2Lhxo+TxHj16YOvWrf4dDEEQBEEQRCBgjQ6leYEbRyWBItoEQRAEQRCEDTaibTYGbhyVBBLaBEEQBEEQhA0S2l6FhDZBEARBEEDWGcBU5vo8onLDCm0LCe2KohqPdnZ2doXuHxcXB51O56XREARBEEQV4uRfwOJ7gKaDgVE/Bno0RCARCG1z4MZRSVCN0E5KSoJGo/H4/uvWrUPfvn29OCKCIAiCqCJs/9z2/39rAjsOIvCQdcSrqEZoA8Cdd96J1q1bu3WfoqIizJo1y0cjIgiCIAiCqEKQdcSrqEpojxgxAvfff79b98nKysKHH37ooxERBEEQBEF4keJsQBsChKu0gR1FtL2KIqH9/fffe/TgDz/8sOJzP/74Y3To0MHt54iOjsbHH3+Mpk2bun1fgiAIgiBEZBwCUtzbXSYUkncJ+KQtEBYFTDxm+19tCCLapsCNo5KgSGiPHj3a7QfWaDRuCe3nnnvO7ecAAL1e7/F9CYIgCIIAoGGKkF0/TkLbV2SestkxSnOB/AwgqVGgR+QI27CGItoVRpHQPnfunK/HQRAEQRBEoChhKn8ZiwM3jsoOW8VDrf5nVmhTRLvCKBLadevW9ekgiouL8d9//6FRo0aIiYkR3LZ161Z0797dp89PEARBEF7HVAaEhAV6FMoozrH/bCoN3DgqO6xwVWu0mLWOGEtswrsCVeGqOgFvWLNjxw7UrVsXQ4cORY0aNTB9+nTB7YMGDQrQyAiCIAjCQ3Z8DcysBZzfEuiRKIMi2v6BFdqqjWgzQnvXN8AS94pUEEI8rjpy9epVfPfdd9i3bx/y8vJgsVgEt2s0GmzYsMHl40ycOBGff/45Ro4ciVOnTuGhhx7CyZMnsWDBAmg0GljZLQyCIAiCCAb+fMX2//IngBeOB3YsSigrsv9sLAncOCo7VtY6otJmMFahnqPa6hXDo4j2oUOH0Lx5c0yfPh1nzpzBP//8gxs3buDUqVPYuHEj0tPTFQvkY8eOYeTIkQCAxo0bY+PGjcjOzsZdd92FsjJqBUsQBEEEMcEQHbaYATDXbBLaviPYrCNEhfFIaL/66quIjo7Gf//9h/Xr18NqteKTTz5Beno6li5dipycHLz77ruKHisuLg6XL1/mfw8PD8cvv/yCiIgI3HbbbQ6RcoIgCIIIGoJBtIoT3oJhzMFKUCRDku7yJh4J7a1bt2Ls2LGoU6cOtFrbQ3CC+J577sEDDzyAl156SdFj9evXD/PmzRMcCwkJweLFi9GoUSOUlNAXniAIgghSzIZAj8A14shqMEThgxVBRFulFT2khLZao+9BgEdC22KxoEaNGgCA+Ph46HQ6ZGfbEylatWqFvXv3Knqsr776ChMnTnQ4rtFo8O233+L8+fOeDJEgCIIgCCWII6tUdcR3BFsyJActvjzGI6Fdv359vra2VqtF/fr1sX79ev72bdu2IT4+XtFjhYWFITIyUvb2OnXqeDJEgiAIggg8Wo9rDvgPcWTVRPlRPoO1jqg1SiyVY0d2Io/xaAYYMGAAli1bhnfeeQcA8NRTT+GFF17A2bNnYbVasXHjRrzwwgsVHtyWLVswd+5cnD17Fjk5OQ4JlhqNBgcPHqzw8xAEQRCET9DpAz0C11BE238IItpBZB0hoe0xHgnt119/HaNGjYLRaERoaCgmTJiAoqIiLF++HDqdDm+88QZee+21Cg3so48+wksvvYTw8HA0bdoUCQkJFXo8giAIgvA7wdCwRhxZNQWBrzxYESRDktCuCngktKtVq4b27dvzv2s0GkyePBmTJ0/22sA++OADdO/eHatXr0ZcXJzXHpcgCIIg/IY2NNAjcI1Y8AVDAmewEqzl/cxkJ/IUjzzaffv2ddqM5p9//kHfvn09HhRga8v+wAMPkMgmCIIgghetLtAjcI1YaJN1xHcEazIkqHmgp3gktDdu3Ihr167J3n79+nVs2rTJ40EBQJ8+fXD48OEKPQZBEARB+B02n0gTBEKbrCP+I1gj2lRb22M8EtqAzS4ix+nTpxETE+PpQwMAPvvsM2zYsAEffvihoHQgQRAEQagaVkBpPL7M+g+HZEgS2j6DFazB5NGmgLbHKPZoL1iwAAsWLOB/nz59Or799luH83Jzc3Ho0CEMHjy4QgNLS0vD2LFj8eKLL+KVV15BeHg4dDphZECj0SAvL69Cz0MQBEEQXoUVUE6CUqrBobwfCW2fEQxVRyRVNSltT1EstIuLi3Hjxg3+94KCAr4rJIdGo0FUVBSefPJJTJkypUIDmzJlCt555x3UqlULHTp0IK82QRAEERywEeKg8GhTeT+/IRDaZvnzAolkRJuEtqcoFtpPPfUUnnrqKQC2hjWffPIJhg0b5rOBff311xgyZAh++eUXB0FPEARBEKqFjRAHg3WEs7qERto6AFJE23ewQlutvmdKhvQqHs0A586d86nIBoCysjIMGTLE6yL76NGjuOeee9CgQQNERkYiKSkJvXr1wurVqx3OPX78OAYOHIjo6GgkJCTgoYceEkT1CYIgCMIBNkIcDJFATvyFRdv+NxuCY9zBCBvFtqo1oi3x3tPnwWMq1Bu2oKAAFy5ckOzaCAC9evXy+LGHDh2KzZs3Y+zYsRUZogMXLlxAQUEBHnnkEaSmpqK4uBjLly/HsGHD8M0332DMmDEAgEuXLqFXr16Ii4vDjBkzUFhYiA8//BCHDx/Grl27EBYWBE0ICIIgCP/DJkOqNWrJwo1XHw0UXbeN2WICdEFQAzzYEFhHVPrZoIi2V/FIaGdmZuLZZ5/F8uXLYTY7rsisVis0Go3kbUp58803MXLkSIwbNw6PPfYY6tSp45AMCcDtjpGDBw92SNR85pln0L59e3z00Ue80J4xYwaKioqwd+9e1KlTBwDQqVMn9O/fH/Pnz+fPIwiCIAgBliAT2sZi2/8R1ezHTKUktH0B2/hFtRFt8mh7E4+E9pgxY7B69WqMHz8ePXv2RLVq1VzfyU2aNm0KADhw4AC++eYb2fMqIuY5dDod0tLSsHv3bv7Y8uXLMXToUF5kA0C/fv3QpEkT/PTTTyS0CYIgCGkE9oAgENpc8mN4PHOsDNAHZDSVm2DY7aCItlfxSGj/9ddfeP755/H+++97ezw8U6ZMcVqru6IUFRWhpKQEeXl5+PXXX/HHH39g5MiRAIDLly/j+vXr6NChg8P9OnXqhDVr1jh9bIPBAIPBnkySn5/v3cETBEEQ6iUYxBQLF9EOi7S1jLcYqfKIr2A/G1R1pErgkdCOjIxEvXr1vDwUIVOnTvXp47/wwgt8pFyr1WL48OH4/PPPAQAZGRkAgJSUFIf7paSkIDs7GwaDAXq99HJ/5syZeOutt3w0coIgCELVWIJATLEYS2z/h0YCIeFAGQltnyGwFan0s0ERba/iUUmPBx98ECtXrvT2WPDhhx/ixIkTXn9cKSZMmIB169ZhwYIFGDRoEMxmM8rKbN6pkhLbpCMlpMPDwwXnSDFp0iTk5eXx/9LT033wFxAEQRCqxBwEJdxYjOWiOiQcCClP9KcSf76B9WirdRFGEW2v4lFE++6778amTZswcOBAjBkzBmlpaZKJiu3atXPrcd9//3288sorqFu3LoYMGYIhQ4agT58+spHjitCsWTM0a9YMAPDwww9jwIABuP3227Fz505EREQAgMD+wVFaapuQuHOk0Ov1PhkzQRAEEQQEazIkF9EGbCX+CO8jWISpVLxSRNureCS0e/Towf+8bt06h9s9rTpy7do17Ny5E3/88Qd+//13fPnll4iIiECfPn0wdOhQDBo0SJCc6E3uvvtujB07FidPnuQtI5yFhCUjIwMJCQkkpAmCIAhpzEFgD2DhrSMRQEj5tY0i2r4hKKqOUB1tb+KR0J43b563xwHA1sK9S5cu6NKlC9566y1cu3YNv//+O/744w+88sorGDduHJo3b44hQ4Zg6NCh6Natm9ca2nBWkLy8PDRt2hTJycnYs2ePw3m7du1C27ZtvfKcBEEQRCWEjQYHQ0TbxArt8t3asqLAjacyEwz+fYpoexWPhPYjjzzi7XFIUqNGDfzvf//D//73P5hMJmzevBlr1qzB6tWr8f777yM+Ph4DBgzA888/j86dOyt6zOvXr6N69eqCY0ajEd9//z0iIiLQvHlzAMCIESOwYMECpKenIy0tDQCwYcMGnDx5Es8//7x3/1CCIAii8lCcY/9ZrU1JWNiINldLuyRH/nzCc4Jht0NqAUARbY+pUGdIACgsLOST/dLS0hAdHV3hQUkREhKCPn36oE+fPvjggw9w/vx5/P7771izZg02b96sWGiPHTsW+fn56NWrF2rVqoWrV69i0aJFOHHiBGbNmsWP/7XXXsOyZcvQp08fPPfccygsLMQHH3yAVq1a4dFHH/XJ30gQBEFUAkqy7T8HQ0SbE9oh4UAkCW2fEhTl/aSEdhB8jlWKx0J79+7dePnll7FlyxZYylfsWq0WPXv2xPvvvy9Zg9oVPXr0QM+ePdG9e3d0797daSOcevXq4emnn8bTTz/t1nOMHDkS3333Hb766itkZWUhJiYG7du3x3vvvYdhw4bx56WlpWHTpk2YOHEiXn31VYSFhWHIkCGYNWsW+bMJwpeUFQP7vgeaDgSq1Qv0aAjCfYpZoa1SMcXClveLTLT9zP4NhPcQeLRVKl7JOuJVPBLaO3fuxC233IKwsDA8/vjjuOmmmwAAx48fx48//ohevXph48aN6NSpk1uPe/HiRbz33nvQaDTQaDRo1qwZevTowf/zRu3u++67D/fdd5+ic1u0aIG1a9dW+DkJgnCDv6cDO74A/p4GvHY50KMhCPdho8FqFVMsktYREto+IRiaGUlaR/w/jMqCR0L79ddfR61atbBlyxbUrFlTcNvUqVPRvXt3vP7665IVSZxx8eJFXLp0CVu2bMGWLVuwbds2fPfdd5g9ezY0Gg1SU1PRvXt3Xni3adPGp90jCYIIAGf/sf1fVhjYcRCEp7DNXtQqpljYZMjQKNvPXMk/wrsERTKk1LhIaXuKxxHtKVOmOIhswJbAOGbMGEybNs2jAdWuXVsQdS4sLMS2bduwdetWbN26Fb///juWLVsGAIiNjUVODvnICKJSQUk3RLATDE1JWNiIdmiE8BjhXYKhvB+XwNv8DiDnPJBxkOblCuCR0NZqtTCZTLK3m81mr5Xdi46OxoABAzBgwABkZGTgn3/+wRdffIHt27cjPz/fK89BEARBEF5DUIPaahMpat59ZRvWkND2LcHQNZQbl0YHgPvcktD2FI+Edrdu3fDFF1/g/vvvR926dQW3Xbx4EV9++SW6d+9e4cEdOXIEW7Zs4aPZFy5cgF6vx80334wXXnjBK89BEITaoAmdCHLYqCVgEy4ax+7JqoFvwa4noe1rgmG3g4u0a7T2BSJFtD3GI6E9Y8YM9OrVC82aNcNdd92FJk2aAAD+++8/rFq1CiEhIZg5c6bbj7tp0yZs3boVW7ZswY4dO5Cbm4saNWqgW7duePrpp9GtWze0b98eYWFhngybIIhggCZ0ItgRd1W0WgCoWGhzvmFdmF1osz5zwntYgqiOtpYi2t7AI6F98803Y8eOHZg8eTJ+/fVXFBfbtp0iIyMxcOBATJ8+nW/84g59+vRBaGgo7rnnHnz22Wfo2rUrGjRo4MkQCYIIWmhCJ4Ics0hoW8yALjQwY1ECZ2fQhtrsIwAlQ/oKQR1ttVpHuIi2jiLaXsDjOtotWrTAypUrYbFYcOPGDQBAcnJyhbzZrVq1wtGjR/Hjjz/i8OHD6NatG3r06IFu3bqhfv36Hj8uQRBBBDuhX9wB1OkSuLEQhCeYJKwjaoaPaIfYmtYAZB3xBaYy4QJGrZ8LblxaLSiiXXE8UsX//vsvjh49ansArRY1atRAjRo1eJF948YN/Pvvv24/7sGDB5GTk4M//vgDd955J06dOoUnn3wSjRo1QkpKCkaMGIGPPvoIO3bsgNFodP2ABEEEIcyEPvc24Mr+wA2FIDxBHNFWq0WAg4uyCiLaJLS9zt9vC39X6+eCi7RrtLZ/AEW0K4BHQvuWW25B27ZtMWPGDMnb//rrL/Tp08ejAcXExGDAgAF46623sH79euTm5mLXrl2YNGkSQkND8fHHH6N79+6Ii4tDr169PHoOgiBUjHhCv7gzMOMgCE8RR7Qt8lW6Ao7VykS0QykZ0pds/0L4u+qTIRnrCEW0PcZj60ibNm0wefJk7N27FwsWLEB0dLQ3x8Wj0+nQvn17tG/fHn369MHmzZuxaNEibN++HVu3bvXJcxIEEUjEEzpN8ESQIU4kNKtYaLNiTxtCQtuXiK0iqo1oSyRDUkTbYzwW2hMnTkRoaCgee+wxdO7cGb/88gsaN27stYEZDAbs3LmT7xK5Y8cO5OXlAQD0ej169uyJHj16eO35CIJQCcE6od84CUQlAZEJgR4JEWjE5f0sKrY6smNjhbaJhLbPyb0Y6BFIw9bRpoh2hfFYaAPAPffcgxYtWmD48OHo2LEjFi5ciNtvv93jx1u1ahUvrPfv3w+j0Qir1YrExEReWPfo0QMdOnRAaKiKM7gJgqgAogk9GIR39lngi442P+Ob1K22yiMu76dm6wg7NtY6YjHZvNtqrpYS7GSftdmMQlRWsthKEW1vUiGhDQDNmzfH7t278cgjj+Cuu+7C5MmT0bBhQ48e66677gIA1K9fHyNHjuSF9U033VTRYRIEESwE44R+aa/tf7VWESD8izgZ0qziiDY7Nm0oEMJ0sDQWA7o4/4+pKlFWCISobBfMItGwhiLaHlNhoQ3YEhhXrFiBd999F2+88QaqV6/u0eMsXboUPXr0QEpKijeGRRBEpSAIJviIePvPaoxQEf4lmJIh2bFpdUwU02rrGBlOQtunqDGwYGWqjvARbQoieIpHVUfq1q0rmfz46quv4o8//kBZWZnEvVxzzz33kMgmiCpPEFpH9DH2n0vIOlLlCcaItjbUFr3UaKhpjT9Ro4Dl62hTwxpv4JHQPnfuHIYNGyZ5W79+/XDjxg2Yze5l07Zu3Rpr1qxxeyx5eXlo3bo1du3a5fZ9CYJQIQ4TehBM8OyYi7MCNw4i8JhNdqHCCdZgSIZkvdih1LTG93ACVoWVRyxMeT+iwnjextHLHDlyhK8q4g4mkwlHjhxBYWGhD0ZFEITfCcbICRuVInFStWGj2bzQVqGY4uDbrzNOUm7cVHnEu1RvYfu/Trdyiw5UGtGW8GgH47ysEhR5tPv06QOtVou1a9ciJCQEffv2dXkfjUaDDRs2uDWYCRMm4PXXX3frPhaLBRqNxvWJBEEECUFoHWGjUmLbAFG1YCuOhEUBxZnqto5YpIQ21dL2DeVz2S2vAIvuKT+kQqEtVUc7GHYWVYoioW21WmGx2D8MSsSt1c2L4yOPPOLW+WJSU1MrdH+CqBIYCoR+YsI7sBdLcbMSomrB19DWMKXy1Cy0JawjIWQd8QkCP3x5RFuNux1SdbSDIeChUhQJ7Y0bNzr93RvMmzfP649JEATD3vnA6ueA2z8B2o8O9GjkCUaPNnuxFFecIKoWXEQ7RG8TVIC6I9qs+OPgkyFJaHsVdlGjKXfuqjGizdfRZqqOBMM8rFLc9miXlJRg4sSJWL16tS/GQxCEr1j9nPD/YCEYIinsGMk6UrXhIto6PaArj2WpubwfuzDgIOuIb2D98GoW2hamvB8/ziCYh1WK20I7IiIC33zzDa5du+aL8RAEQQQfAusIRbSrNLxwDQuOiDaX8MiJa/ZnKu/nXbiItjakPFoMdQptK1N1hBrWVBiPqo60b98eR44c8fZYCIKo6litEhHhIJjg2WRI8mhXbbjPr05vTzBUc0Tb6ERo02fZu3CfA9VbR5g62tSCvcJ4JLT/7//+D0uWLMGcOXNgMql4AiEIIrhY8oBjHepgmODZiyVZR6o23I5GSFhwWEecCW2KaHsX3jqi8mRIC0W0vYlHLdhHjx4NrVaLsWPHYvz48ahVqxYiIiIE52g0Ghw8eNArgyQIoorw3+8SB4NggqdkSIJDENEOAusIJ7RDmGt4CHm0fQKfDKlyj7ZVorxfMAQ8VIpHQjshIQGJiYlo2rSpt8dDEAQhxKLCC5EYimgTHIKIdrnQVnN5P6cRbRLaXoWt8MI3rFFzRFtDEW0v4JHQ9kV5PzkMBgP27duH69evo3v37khKSvLbcxMEoQLUvO3OIUiGJKFdpZHyaKs5os0nQ0baj1F5P+9jtQZReb9yUa2hiLY3UE0Ldik+/fRTpKSkoEePHhg+fDgOHToEAMjMzERSUhLmzp3r9mPu3r0bzzzzDFq0aIGoqCjUqVMH9957L06ePOlw7vHjxzFw4EBER0cjISEBDz30EG7cuFHhv4sox1gCfNkN+KIzUFYU6NFUIYKsk+qmd4Hv71D3RP/PDPvPJLR9Q/4V4JM2wNZPAz0S5wjqaHMebRVGLTn4iHa4/Rj3MyVDeg/2M6D28n6sdYQi2hXGY6FtNpuxYMEC3HvvvejcuTM6d+6Me++9F99//z3M5opPKvPmzcOECRMwcOBAfPfdd4JOk0lJSejbty+WLFni9uO+9957WL58OW699VZ88sknGDNmDP7991+0a9dOUEnl0qVL6NWrF06fPo0ZM2bgxRdfxO+//47+/fujrIw8mF7h+nHg+lHgxgng2rFAj4YINM4sImc3AkUqXuRmnbL/bKb5wSf8PR3IOQ+seyPQI3EOX0c7SKwjhgLb/2HR9mN8RJuSIb0GuzPHCm01WuPYZEiKaFcYj6wjeXl5uO2227B7927ExMSgQYMGAIB169Zh+fLl+Oqrr7B27VrExsZ6PLBZs2bhjjvuwOLFi5GVleVwe/v27fHpp+5HNiZOnIjFixcjLCyMPzZy5Ei0atUK7777Ln744QcAwIwZM1BUVIS9e/eiTp06AIBOnTqhf//+mD9/PsaMGePhX0bwsNuSJE78iEonTJcWkSCJxFNE2zcES3Q12DpDclV+IhPtx8ij7X3YxZbqrSOc0NYyLdhVOM4gwaOI9uuvv469e/fis88+w40bN7Bv3z7eR/35559jz549eP311ys0sNOnT2PQoEGytyckJEgKcFd069ZNILIBoHHjxmjRogWOHz/OH1u+fDmGDh3Ki2wA6NevH5o0aYKffvrJ7eclJBAIbRInVR5XSUFqjgqy0GfZN6jZfsEiiGhz1hEVf3aLs23/RybYj1HVEe/DLrbUngwpqKPNHwzIUCoDHgntlStXYty4cRg3bhxCQ0P546GhoXjqqafw1FNPYfny5RUaWHx8PDIzM2VvP3bsGGrWrFmh5+CwWq24du0an2h5+fJlXL9+HR06dHA4t1OnTti/f7/TxzMYDMjPzxf8IyQwsUJbxReiyohZhQmGriLaav2M7F8k/J0i2r5BjYJECsmItgq/bxwl5UI7ghHaFNH2PgLriE7dEW2LVESbhLaneCS0s7KynJb2a9asGbKzsz0eFAAMHjwYs2fPRm5ursNtR48exbfffothw4ZV6Dk4Fi1ahMuXL2PkyJEAgIyMDABASkqKw7kpKSnIzs6GwSB/MZ05cybi4uL4f2lpaV4ZZ6WDrCOBI/O/QI/AEVdCW63VR1aNE/5OQts3qNHLKgVfdSQsODpDGgpt/+tj7MdIaHsfE/O50GjsDWvUKLSlOkMSHuOR0G7UqBF+/fVX2dt//fVXNGzY0ONBAcD06dNhNpvRsmVLTJ48GRqNBgsWLMCDDz6IDh06oHr16pgyZUqFngMATpw4gaeffhpdu3bFI488AgAoKbFNLnq93uH88PBwwTlSTJo0CXl5efy/9PT0Co+zUsIm2pA48T3h8fafjSr0u7oSUmoWKyy0aPQNahQkUvB1tPXBkQzJtgXn4Fuwk9D2GlyOAffa8smQKtypEUS0ucg7RbQ9xSOhPW7cOPz1118YPHgw/vrrL5w/fx7nz5/H2rVrMWTIEKxbtw7PPPNMhQaWmpqKvXv3YuDAgVi6dCmsVisWLlyI1atXY9SoUdixY0eFa2pfvXoVQ4YMQVxcHH7++WfodLYVJtflUipqXVpaKjhHCr1ej9jYWME/QgJW7KnVFlCZYCd0NVYT4IW0TAQlWD4jtGj0DcFiHZGso63iRSL3vdMwflyKaHsfbs7lKrqo2TrCjYlasHsFj6qOjBs3DtevX8e7776LtWvXCm4LDQ3FlClT8NRTT1V4cNWrV8ecOXMwZ84c3LhxAxaLBcnJydBqK17+Oy8vD4MGDUJubi42b96M1NRU/jbOMsJZSFgyMjKQkJAgGe0m3IQVe5RA5nvYiLAaL6Dc+LQh0hFANUcFWUho+wY1Rv6kYDtDckJ7xxfAwBny9wkk3OuqZeQAlffzPlxgKaS8RrlWzUKbWrB7E4+ENgBMnToVzzzzDNavX48LFy4AAOrWrYt+/fp5pXujyWRCcXExHw1OTk4W3J6fn4/IyEiEhLj/J5SWluL222/HyZMnsX79ejRv3lxwe61atZCcnIw9e/Y43HfXrl1o27at289JSMAKkmCJVgYzrNBW45YwO7lLiWo1RwVZaNHoG4Ixon31UGDHogR+gctEtDkxqEaLWbASTBFtto42RbQrjMdCG7A1jrnvvvu8NRYB48ePx7///itoIsPSvXt39O3bF5988olbj2s2mzFy5Ehs374dq1atQteuXSXPGzFiBBYsWID09HQ+mXHDhg04efIknn/+eff+GEIa1stKvlbfE0wRbcnbg2QxRhFt36BGQSIFG9EuZSpOmY1CH7RakPrecWLQVGLLnfDCLnKVx8GjreZkSMajTRHtClMhoV1QUIALFy4gJydH0LmRo1evXh4/9p9//omHH35Y9va7774bP/zwg9tC+4UXXsCvv/6K22+/HdnZ2XyDGo4HH3wQAPDaa69h2bJl6NOnD5577jkUFhbigw8+QKtWrfDoo4+6/wcRjrDCicSJb7FYIIhIqHFL2CJVu5UhWHY9aNHoG4Kt6khIuHCOMxYDurjAjMkZVinrCJODZCoFwiL9O6bKCB/RDoJkSE7PabUU0fYCHgntrKwsPPPMM1i+fDnfbt1qtUJT/oZwP1ekFfuVK1dQq1Yt2dtTU1Nx+fJltx/3wIEDAIDVq1dj9erVDrdzQjstLQ2bNm3CxIkT8eqrryIsLAxDhgzBrFmz1O/PvrwX+G0ikHEAGP07UK9HoEckDSuczEbg+Grgvz+BIbOA0PDAjasyIt52V+OWsFRSltTtaidYOhgGG8FiHWHLuLGfBWMJEK5CoS0Z0Sah7XW4XUSx0FZjRJtasHsVj4T2E088gdWrV2P8+PHo2bMnqlWr5u1xITExEf/9J1/r9/jx4x5V89i4caPic1u0aOGQ7BkU7J5rE9kAMH8IMDUvoMORRSC0DcBS2yIHNVsC/9/eecdHUad//LPZ9IQkRCCEkgIIaKiilLOAEgRFUboUBQ5F5UREyiknKIogYD/1AEFByknNz1M8ERCUIp2jCAgIoQZCSSGkZ+f3x+Q7O7s7s7tJdneexef9evHaNtl8Msx8v8/3+T6lfdWTaRkV9kYqSY+2q9ARfzG02aPtFSgaJFqUqcr7tX4SyNgvv6Z4zwGqZEjVAjfALDfbsZSU647V/FGmAhTfkB9FWE6AH4SOBHCMtieolKH9448/YsyYMZg5c6an9Sh069YNc+bMwaBBg9C6dWubz/bu3Yu5c+eib9++Xvv9fk3JDaMVuIdejHZ+1ZodMRrYG6kUwxtstrBNcBjYKYaOaIUzcDKkd6C4xa5FqSoZstVg4Ptx8muKu0iA/gI3MAQoLqE5Vvgj9q3uFY82weuaPdoepVKGdnh4OJKSkjwsxZa33noLP/zwA9q2bYsePXogJSUFAHDo0CF8++23qFWrFt566y2vavBbKBokWthUwVAN5rxN6XnsDW2KMfHq6gcmk+PATjEZUssIYY+2d6BokGhRpi7vFwBEJwA5Z2gmIAPaVUcAa+Kmv8wn1Mm/Kj+G2RvaBA1YpY42x2h7gkqlEg8ePBhpaWme1mJDnTp1sHv3bgwcOBAbNmzA1KlTMXXqVPz0008YNGgQdu3ahXr16nlVg99ib0RRNKoAWyOlQOXFDo70vZabHXtvIEUvldYWthqK5f20vNfs0fYO/ujRBqz5JhRDRyRJ36Mt9FMcK/yRAh2PNsXr2qbUavnzrR8bp8fPccujvXfvXpvXffv2xc8//4xu3bphxIgRqF+/vtJVUc0dd9xRJXHx8fFYuHAhJEnC5cuXAcj1tE0mnc5xjIz9wHjqF+DWLsZocYbaU5KXaX3O/7+exy882hrblTafE/SsaZ3HsmIuieYN/MWzqlQdCZYfRUxuMcGQPnV8sIOhXa6fd2g8Q2F5rlRojPyoxGgTNLQtKo/2mV/l5/lXgOsXgWq1jdPlp7hlaN95550Oxq0o57du3TqH4z1RdUSNyWRCrVq1PPJdfwrsJ6RCP0iGLMy2PueB3fM4eLQpGtp+mAypNrQfnw3833Py87JiIIAr53gUquOYPUp1iXIDW3gwCwjmnqjHBd3QER6PPYIYKwLLdwrEOEfdo62uBU9xV8YPcMvQ/vLLL72twyl5eXm6tboTEhIMUEQceyOKbGyg2tBWTaJcHs3zOHi0CU6eakPbZHIMCaTo0RRGSEgU0Ky3ytAu4hKVnka9GKeMfattEZNLMclbPS5oJUMCNBfl/oi6Gg2gMrQJOhDUMdrq+Zhi+J4f4JahPWTIEG/rcKCwsBBTpkzB/PnzcfXqVd3jPOU1v2nIzZDraAPy5F+US9dwVXtK1IY2e1A8j0PVEYKTp+JFUXUjU0NxQlLXTFZ3/aO4kPFncjP8x5tm32o7/Bb5kaRH24mhzcmQnkXMayIkR4nRJjiuqcP41HMFxfA9P6BKnSG9yciRI7Fw4UI8/vjjXqvVfVOyaZr1eWi0bGhTnaDUq2P2aHsX++1JioagRVXer1kv4MAy288pTvhlqu1gkwkIDJPbVlO95/yVHbONVuA+9o1JwsrnroJsQ+Q4xamhXW4QsuPDM5TaGdr+EjqihuIY7AeQNbRXr16Np59+GnPmzDFain8hmiMActJFzlm6oSPqAVydlEMxUc/fsfdgU/Roq70o3d8DYhvaLhwpelPUHm0ACIsBrhcABVlA9UTDZN10XM+wPg+uZpwOV0iSvNACrIa2KFdKcRxWG3kmu+RdUXWEx2PPYO/RFoY26WRIO0ObovfdDyCbFm8ymapcteRPT0h5mTyKAzygbzjxwO557L1SJD3aqhjtkGrAXU/bfk4xPlBJcCqPx6UcJuDPqOObKXbSE6h344ShHVj+SHGXQ9xzJrNjtScOHfEsZXaLcr+I0ba7Jihq9QPIGtqPPfYY1q9fb7QM/0Pt0RbJo1QNbb0BnA1tz5J9Btgx1/Y9kh5tu6ojZrsNN4oebXVzEsAaJkAx8c2fKfATQ1s91goDWxjcFMdhZ5V+OHTEs4j5TowVIiyDmvEqSUDxdfk5h454BLKG9qRJk3Dy5EmMGDECe/bsweXLl3Ht2jWHf4yK7LO2r6PLG/pQ9KQA+gM4x2h7ls/+Ahz42vY9iosZYUCJ+tNiohdQm5AAx+YkSim3LGP03Kz4i0dbGNMBQdaFojC0S/3M0BbVMXg89gz2YWZUY7TP7rQ+D44Eat5mfU3R2eEHkI3RvvXWWwEA+/btw/z583WP46ojKtRen0c/BorzgEMr6Q6Uuh5tonr9FeGdUEPRS2U/6QcE2X5O0ZsirlVhlARFyI8Um5P4M/5maIuKIwBtj7ayuNUwBcJi5EdeNHoGMX5RDx25cdn6PKIGMHgV8MHt8muK4Xt+gFuG9ldffVWpL3/qqacq9XMAMHnyZO4AWVHEjRyTCLQZAuz+Qn5NcYAHrHobdwOO/aB6n6AReLNB0aPtYGj7QSKOfW1cxXvJi0WPUVYKFKmqEpE2tEVpvzDre5QNbeWec+zsTLr+tz+ilwxJbVwT12lyR/kxui5Q907g/G56Wv0EtwztoUOHVviLTSZTlQztN954o9I/+6fFfmtKeFWoho6IbajIONv32UjxHkERQMkNmosZmxbscEzEIenRtgsdCSKc+Oav2HtUKVZpEIixS92sKNAfDG0NU8CfEnuPrwNuaQjENjBaiTaSZFsKFKAbo621WBSJsRw6UincMrRPnTrlbR2MJ7BfMYtKCBQHeMCqt1pt2/cpeltvFkIiZUOb4jl25l0DaA7y9smQiqHNi0WPoRW6IEmOCzEK2DerAazXBMVwImeGtr8k9qZvBZb0kZ+/keP8WKNQOzaE0aoY2sQWjspiUWVoi+uDorPDD3DL0E5MNKYebGFhIVatWoW9e/ciJycHFovtlqHJZHIav/2nw2HSJ1y/FbDGe0XWsn2fohF4sxBSDci7RNujrTa0+3wBfPey3H6bYnygmJTYo+09hCcwIMi62JIsjjV+KWDfrAawxjpTbCHvNBmy3FFDcaxQc3aH0QpcozQrMskdmwHCoSMai0WqWv0EssmQp0+fxv3334/09HTExMQgJycHsbGxyM7ORllZGWrUqIHIyEijZdLC3qNNOTYQsOqNZI+2zwguv2conmNJ1RlS0Kw3kHMeWDeJ5iBfahejTTlMwF9RyqKFAMUqQxuEDe1AlaEtQjBKC4HifGsDGwooi1uNAmRmNq48hgi/CatudSRQrToiduMCVeFPXFO9SlTa0L548SLmz5/v1Nu8YcOGSgsbP348cnJysH37djRo0AC1atXCsmXLcPfdd+Pjjz/GJ598grVr11b6+29K/MnQliSrd8ohdIS33b1GSHlXvbIietvvet41yvGB9nGX/pQMWZwPnN8DJHRwrFlOCXFdqMs9HlwJxCQASXcbo0kPLY92cKTVG59/lZih7cSjHcDGlcfIvyo/ivKfAF0vsVaMtrgWqGnVIuc88Pv3QO3mst7Euw2f5yo1uh44cACdOnVCQUEBmjRpgoMHD+L2229HdnY2zp8/j4YNG6J+/fpVEvbTTz9h5MiRaNu2rVIvW5IkhISEYPz48Thy5AheeuklrFmzpkq/56aiVM/QJhwbCHCMtjcRTYsEIar21WUl1jAjCtgnQwooxwf6WwKymrQRwJFvgY6vAPe/arQafezLogHA/z0nPz7zE1C3je816aG0X1d5A00m2cDKuyR7NmOqNjd6FKcNawgvcPWg5jwQiDj3MLWhTTUZUqNEpT95tEUpQsFf1wIJ7Y3RUk6lGta88soriIyMxO+//47169dDkiR89NFHOHv2LJYtW4asrCy88847VRKWn5+PpKQkAEBUVBRMJhNycqyJDh06dMCWLVuq9DtuOuw92qEx8mNhjqPBZTTqGzasOtD5dSC+pfyaYmMHf8V+EA+Ntj6n1h1SLxmSqucH0CjvRzwBWc2Rb+XHHbON1eEK8f8uzrGa48S6B2sZKYA1fIRaYqFFI1xLIO5DirkRelDdSRKhI5oebWKhI1qLxdDyuPJCosmmzsg5Z7SCyhnaW7duxbPPPouEhAQElMd2idCRvn37YtCgQRg/fnyVhCUkJODcOfkEBQYGom7duti+fbvy+eHDhxEaGqr3439O7JMhxU1tKQWKco3RpIc6wSYgCLj3ZaDfIvk1V2zwHPYeCLWhXUosyUkrGRKg7U1xSIYknoCshb1RSA2LKkbbHq1mTEaiFToCWD2Z1Erl6d1zgCpcgOB9pwfV+04ssMSCC7Du3FFzIGgtFpWa6ld9r6eqEJg3KmVoWywWxMXJtY9jYmJgNptt2qE3b94ce/bsqZKwBx54AN98843yeujQofjggw/wzDPPYPjw4fj000/x6KOPVul33HRoxWiLpBxynhTV4CIMKXFjlxYAFsJNKQB5MZB5FLh0mLZW+0kyONI6wFPzaGslQwK0J3yHZEg/8mgLrl8A8jKNVqGP8KiaNQztojzfanGFVjIkAIQTLZXnVugIMUNQIEnyGFysugao3nfX/pAfRclEgO5OnXINqxyZ/lBTXZKAjAOO7xOY5yoVo52cnKzU1g4ICEBycjLWr1+Pfv36AQC2bduGmJiYKgl75ZVXsGvXLhQVFSEkJAQTJ07EhQsXsHLlSpjNZgwcOBDvvfdelX7HTYe9oQ3IN0juufIbJNkQWZoIrQGB1pg6tReotJBW0pA9S/oA6Zvl53ePBrq8aawePey3JYPCZKOwJJ9eLLxuMiTRLVZAIxnSDz3aAPCvu4GxR/VrmBuJ4tHWyCcoJmpo23u0yYaO+HEy5JH/AMvtmuJRve/2LZYfRTgnoIrRJnZ+tTza4X7g0d41D/h+nOP7BK7fSnm0H3zwQaxYsUJ5/fzzz2PevHlITU1F586dsXDhQgwcOLBKwhISEtC7d2+EhMgTWGhoKObNm4esrCxcuXIFCxYsQHR0tItv+ZOhVZYnOKL8M2IDkIj1EjVFAUdDmzLCyAaArR8Zp8MV9oNMULh1IUatPq6Y9P0pGdL+nlOqjhC73+yxz9m4kUk3/lIp7xcKRNW1/YxaO3atGsSAtawmtcR0px5toh5Xwc7PHd+jmoQsxtzEDtb3xNxXSCysU2uxqDgQCM/LP8/Ufp+AQ6lSHu1//OMfGDBgAEpKShAUFISXXnoJN27cwKpVq2A2mzFp0iRMnDixQt955swZALKBrX7tCnE8A+2yPMILROBisyFfKznELA9IZcXlf0us5o8yFcDeWxIUavW+Ursm9OJFKYeO2BtW/pIMqeUJzr9qez9SQZ2w98Aka8URAACxChNaLdgBuqVWlcWths+N8gIXcNw1AGg6aCxlVqdGLVVFDKrhGKUahjZV54w7ENBcKUO7evXqaNPGWlLJZDLhtddew2uvvVZpIUlJSTCZTCgoKEBwcLDy2hVlZRXfTs7Ly8OsWbOwY8cO7Ny5E1lZWfjyyy8xdOhQh2OPHDmCMWPGYMuWLQgODkb37t3x/vvvo2bNmhX+vV5Hq3Wq8LRRM6rEFlSY3cQeFFZuaBObkPwVTY92uaFNYACyQa8Cgj8kQwrDSl3ej2qpMUA7hIFaWINALLDMQdZrQUDt/Go5O9SvqXlcnVYdIbzABWx3bgXUzi9gO5fZxD2LuP0s3+pxhZZHWzG0idkR7kBgnqtyl4LMzEykp6cDkI3lWrVqOf8BHb744guYTCYEBQXZvPYGV65cwZtvvomEhAS0bNkSmzZt0jzu3LlzuO+++xAdHY1p06YhLy8P7777Lg4ePIidO3ciOJhQDWJAx6MtDG1iK32tckdAeRJRDm1D+/Svtq/FtjBF7Ld9g8Lo7nJIeh5twlvYDh7t8ntPstCrU65Gy4tGzbMmEAusgCANg5Caoa2TDEl1610vARlQLXAJ3neAtkeb4ryhZ2hTrUSjdQ2LcYyis8MV/mxob9iwAX//+9+xb98+m/dbt26Nd955B6mpqRX6PntvspZ32VPEx8cjIyMDtWvXxu7du3HXXXdpHjdt2jTcuHEDe/bsUUJU2rZtiy5dumDBggUYMWKE1zRWCs1C80SNquJyA8XeSKW6xarm0iHb1+omMNRQG6d17gAS71F5tIldE646Q1Ic5O29P+rJqSSfrqFdrBErTN2jHWB29GhTQ4yz/hY6ounRFgtcgvcdoOPRJnZ+AetiPDDMttW9iNEuyZd3FqgkItuXCQY4dKSKVCoZMi0tDV27dkVGRgYmTJiAefPmYd68eRg/fjwyMjLw0EMPIS0tzdNaPUZISAhq167t8rhVq1bhkUcesYkDT01NRePGjbF8+XJvSqwcWsmQ4jk1o6pUQyvgH5317A0Syh5tYZxGxgEjNgIRt6g82sYPQDboJkMS3sJW7rlyQ8ocZNVPbRdJjTBI4lsCzeVqUeQ8awIR3mDW8GhT2+XQG9cCqYaO6DSJAuiX94NGEzaShrZGAxiAbvK/egdJIJwz1OYMdyCguVIe7ddeew3NmjXD5s2bUa2arTdv4sSJuOeee/Daa6+hZ8+eHhFpBOfPn0dmZibuvPNOh8/atm2L77//3gBVLhBVA9QebaqJb0KPvccviGioixp7g0RUdqGIxcmgSW3xJeqR64aOECzvl39FfhSTpskk33/F1+kZVWrUXjbqpbuchY5QW3yJcc2+5rdSjYbYuOZWjHYpzXwDrTAcivdcqcZOM2C7GCspoDOPiIWVevdI2VU03mjVRc/GIaC5Uh7tkydPYtiwYQ5GNiC3Sx8+fLhSZ9ttIQEBMJvNFf7nLTIyMgDIYSb2xMfH49q1aygq0v6PLSoqQm5urs0/r1NaDJwub0mvXjkrhjaxAV6pP+yPHm07g0RrC5MKSrMP1URK3aOtV0ebWuhI9llVjLbqGvCHyiPCSAkKo1vjWeAsGZLaNaE4EOwN7fJxTStkx0icebTV71H0amvNEdTmOUC7AQwgh5EoDa4IzXfKwlY9ZxB1zgg2vwcU6ZQnJWBoV8qj3bRpU2Rm6ncSu3TpEho3blyh75w8ebJD8mNaWhp+++03dO3aFU2aNAEAHD16FD/++COaNWuGxx9/vMLa3aWgQL45RB1vNaL1e0FBgebn06dPx5QpU7ymTZPc89bn9dtbn5P3aOt4fqglDamx70ZHzaumxq882jqTPtXQEXWsfkyi9bk/XMPqJE6RY0Ct+YugqLzNenCE7XUMEDS0dUJHxDkuIlYzWZw/s0YugXpRU1ZCLz5eq+47xcWt0Bka5fhZUJh8zVAaKywahjblPBkA2OCkYZy/GtozZ87EE088gbZt2+Kxxx6z+SwtLQ1z5szBsmXLKvSdb7zxhs3ruXPnIjMzE4cOHVKMbMGRI0fwwAMPoE6dOpWR7xZhYfJkqeW1LiwstDnGnldffRUvv/yy8jo3Nxf169f3gkoVwhsVVQ+oFmd9n2p5P91YRoIrfHvEVmDLAcD+f9PzDKspU3kDBVQXX3oVEKhWPxCTeuI9tosDf9iVUZclpL4wUGru36Lh0SZ27ymJZHYOBCU8h1gpN7HY1jKi1YsaaotcACjQOJcU7zn19WtPYBiALDq6JUkndITonKFHoy5A465yp0gCmt0ytHv06OHwXs2aNdGrVy/UqVMHjRo1AgCcOHECFy5cQOPGjfHPf/6zwpVH1MyaNQsvvPCCg5ENALfddhteeOEFzJw5E88880ylf4czRMiICCFRk5GRgdjYWE1vNiB7wfU+8xpKubzqtu9TDR1xtcVK0TMhENpCyzuTUpvs1WjFYFLNINdrnkG1+oFeu+1AfwgdUZUCpb4wUNfct9/toBbSoDgQ7MY1UcqtKIeWd1hZiGvMVzYebWLnGQBuXHZ8j+JiUa9nBECvGo06D0ZrzrCU0IzXt6ekQDXPGT9vuGVoHzhwQLOmtajGIepoBwYGIiEhAYWFhTh48GCVhJ07d06pqa1FUFAQzp07V6Xf4Yy6deuiZs2a2L17t8NnO3fuRKtWrbz2uyuF3qqZ6krUVdIQlYFHC2GQKIY2sXOrRmsbkKpHWy8xi2qHOiXJyQ/zDJQY7XD6CwPhuQyP1QgdIbZY1HMghMVArvktyX9PZOX6TXgcZRzWCB0JMEPRTG1Bk30GyLvk+D7Fe06vZwRAb6xQOzO0kiEB2v0BBCYTKYeSW4a2MKR9SbNmzfDZZ59h4MCBqFu3rs1n586dw2effYbmzZt7VUPv3r2xcOFCnD17Vgn92LBhA44dO4YxY8Z49XdXGL1VM1mPto7nh2osoxphpESUdwe1j9mmhFboCKEByAY9Q1sYgpYSuTJJQKVyuD2PVt16AAgpL/coYospIu6v4Eir/lKihrbQGhLlR8mQ9olvZnlsK8qVrwsqhrY4f3qGU0Bg+X1H7Dyf2WF9HhgGVE8ELh+lN88B1gRYrX4L1MYK9f0UoBFuCMiOJeqGdttnrItDAvNclTtDeosPPvgAXbt2RePGjdGzZ08lPOX48eP4v//7P0iShMWLF1f6+z/55BNkZ2fjwoULAIBvv/1W8ZCPGjUK0dHRmDhxIlasWIH7778fo0ePVlq3N2/eHMOGDav6H+lJdDstijraxl9sNuhNSEosI9EKCIDVwIoqXwAWXKNlAKrRSoYk69HWSYa0rzcbbGfYGoW6cocapYoH0XJ5gFVb+C30d5HUITr2izBKhnZZqTXPQMtDHBgqG9pUvJeAdV7Q0gvICxtLCa3zDFjnu9t6AH0XAnu+ANaMpXVuBXq7twC9sUK9c6HlnAHoXQv2jNoL3NIQOPKt/JqA7VMlQ/vnn3/GmjVrcPr0aQBAYmIiunfvjo4dO1ZZ2D333IMdO3Zg0qRJSEtLU6qAhIWFoWvXrpgyZUqVPNrvvvuuohsAVq9ejdWrVwMABg8ejOjoaNSvXx8///wzXn75ZbzyyisIDg5G9+7d8d577/k+BtsVeqEjVD3aZXpbrETb0qoRg3lUeTKuZJFjL8Oq6/+MUWiV91M82sQMbb1kSLUhW1JAyNBW1aJW4w/XcL5qYU69HKE6zEWy2H5GYBJVUI+xWiU/KSadOkuGBGxraVNCXL8RNWQHB+XcHq1dRYGYM6iMFWojWp0rE2CWG3FJZfQcNPaExsiPhMJmK2VoFxcXY8CAAYpnOSYmBgCQnZ2N9957Dz179sS///1vpzHW7tCsWTOkpaXBYrHg8mU58aFmzZoI8IDn0N1wmJSUFKxdu7bKv8/ruAwdMf5is0EvlpHaCl8LMZiHxQBBEUDJDXngp2hoK15iLY82ISMFUIWO2Jf3M8uLg7JiWuENesmQIiF560dAFydlp4zExqNNLE5UzYphQM4Z+XlQmKM3jVJIg3qMtR/XAJrn2VkyJEC3hv0vs+RHMd+Jhc2J9fR2F53tGlCrRqOeL+zz8szB8vhLaXGrhXAcECpJWKmrccqUKUhLS8PYsWORkZGBa9eu4dq1a7h48SLGjRuH1atX4803PTfBBAQEIC4uDnFxcR4xsm9KXIWOUPNo65X3EwmGhURjtMtKrMZecDXrwkCr1BQFLFox2sTraNu3YAdohjeIuEoRZymoUV4pyRRg7XZJDUV7lBynLd6TNNpaG0VpMfDbauvroFB5S1i9oCUwiSqI+ykgULsBDMWdA2fJkADdGvZiIVM9SX6s3cL6mbqnBAWU3VuNcywWClQcS1rzhSCQThUPG9QOo4ha1gUtoVykSnm0ly5diiFDhmDmzJk279eqVQszZszApUuXsGjRIrz11ltVEldYWIhVq1Zh7969yMnJgcVu0jKZTJg/f36VfsdNQ74qM18NVe+lq4Y1lDyXahSD2iR7tMOryx43KgOlPUqXL9XET7YzpJN20EHhcuMHSt5Asbi130Vq3E1+lCxAYbZ2tQGjUXvjhb6yYjlxy37hYBT2E2RQuDxejP0duPw7MOdeWpO+nvNAQDHpVKn77SQZEqAVOlJSYB0Hbi8vPVyjkXV3seAaEOPlvhUVwVlTIMVRQyV0RGMHVEA15FB97sYcsnriCXWzrJShnZGRgXbt2ul+3q5dO3z99deVFgUAp0+fxv3334/09HTExMQgJycHsbGxyM7ORllZGWrUqIHISCITAgX0QkfMRGO09ZIhKXou1YjYwLAY2XgNI568qRU6Qt2jrekNJBjfqoRf2C9ug+XdjuLr8sKMpKGt6gwZFC5fE2VF8t9E1tAuvwYCQ6xGKylDW8d5IKA4tjkzAgFV6AghQ1uMtQGB8o6MoHoikHmY3lishI5oJUMSmz8Uj7aGaUjIQ2yDOgxOfe/5e+hIvXr1sGnTJt3Pf/75Z9SrV6+ymgAA48ePR05ODrZv345jx45BkiQsW7YMeXl5mDFjBsLCwvwjdtpX6IaOEI/Rth/glZq+hAwqNfYLmnDiiW+anSHLz/mhVb7X4wzF0NYY5EXCISWPdr6ORxuwxmlT3elQd4Y0mayeteM/GqfJHvsJXZ10KgwBSiENes4DAcWut0oypM7igGLoyIn18mNYrG0cMdUkZLFzqJkMSSx0pEyjSpXATHQnVG8cJpQMWSlDe8iQIVi+fDmee+45/P777ygrK4PFYsHvv/+O559/HitWrMDQoUOrJOynn37CyJEj0bZtWyUuW5IkhISEYPz48ejcuTNeeumlKv2Om4bifOvE6VB1RMRoE/KiAPrbrBQThtTYL2iU5E1ig7tAy6MdrdpWJbDaVxAVJbQ82tTqzQLW+s4ir0CNyHwvzPGZHLexWFSGdvn9Js5r5hFjNGlhb2hTrwXvKt5Z6RFA6BouFjsbOosDQl5BhUu/yY/2vRbCYuRHavecEp7jxKNNZXHgbIeD4j0H6I/DYgep+IbhuSeVCh2ZOHEi/vjjD8ydOxeff/65YghbLBZIkoQhQ4Zg4sSJVRKWn5+PpKQkAEBUVBRMJhNycqw3UIcOHTBu3Lgq/Y6bBrEaDgiyJjYJKG65A65jtKUyWq2KBfZlFKl5JOyxaJT3S1aV3ywpoHOOnSVDUvRWiRCA4AjHzyiGCQjUi26hs+N4YN1kWkm99p4ztfdSXXaOSktoVzHaFMPMnLUHB2jGaIsxoOME2/epznVlThZgYh4pzJHDc7RCNnyJElIW5vhZINEYbWUctiv7quSeFMl/l9Y47SMq9b9qNpuxYMECvPzyy/j+++9t6mg//PDDaNGihYtvcE1CQoLSQCYwMBB169bF9u3b0atXLwDA4cOHERqqM6D92VB7We0nHIoJOIAqE1snRhuQbw6zhrfQSPw1dMShvF95a+WSAiA0SusnfY+z0BFqsYyAqo62s5rJxO47wNYQEeEYkbXlR0rXsTPPGcWW0M48l4A1nIjSOS6wcxzYQ9GjLcaAavG271MMzQGce4nFzhcgJ05H1PCFIn3UIWX2KB5tQtcCoN/PIDjSWhY2/5r/GdqCFi1aeMSo1uKBBx7AN998g9dffx0AMHToUEyfPh1ZWVmwWCxYtGgRnnrqKa/8br9DnQxgD8WSUmWlVqPKflIyB8tl0SRLuRFIzNDeNU9+FIafMLgPf0PHs6ZG6QyputVNJtkQLMmntQDTq6MN0GzsIK5hLe8P1QUuABxcLj+aQ6z1hinWr3fX0LaUACBgaCsebR1DW/FoE9k1sJQBBdnyc72EXcoebXsvvHLPUfNoO6mjbQ6U57jCHPneM9rQVidJ26Mk0RMLHdHr0GsyyddI3kXg3wOAZ38xrL462Rbsr7zyCnbt2oWioiKEhIRg4sSJuHDhAlauXAmz2YyBAwfi/fffN1omDQqdxIqqBx8qhfxtOqjZTUomk7wyLblBa3EgKMqTH8XEVKup9bPcC0B0Xd9rckaZTha5MLQpnWNxXTht7EDE0FafN61JKZDgAldwcKX8qN4Cphjfqp7QGz9k+5lNS+hiAMZ5qxRKXBja1BYzRdcBlMeu6jk0KMbl6lX7obqL5CoOPiy23NAmMLbpNeECrItbasmQzhYHtZrKhvalg8CV34Fat/lWWzluGdoBAQEwVdBTZzKZUFpa+VVwQkICEhISlNehoaGYN28e5s2bV+nvvGlRbg6NC019w5QW0mhfXaqxda3GHASUgN4WlaXMmnjRapD8WLu59XNqW5aAdjIkUH6tXKWjWZIc49/VUEs6VSZzk4sugMQmfcBqYPf63PoexfhWtXHXd4HtZ+odGiql50qdjMMAvTAzcW2azPoJnBSNV7Ej4ODRJliZCND3wAvCbwGyTtG4LsT/s+a8THDRBThfHPT7Cnin3I40cKfDLUN78uTJFTa0q0J+fj7uvfdePPPMM3juued89nv9FqcJDOqY5wIahrY6tlXLw06o0LwNBdlQPEBqYzCytrxqpjbAA6pkSDtDm1oZxZJ86/+31jY2taTTUtXgrjU2UgzZEghNUardF4oGlUiYjmvu6A00meTFo6WEzsTvbMIH6CVDqj2BevM7teuitFiuTw/4h0fbWUUwQTihsc3ZNUx1Xi51ojk0GqieLC9kDHTcuWVov/HGG16WYUt4eDhOnTrlU+PerynViVECZENWNKMoyQegc7P7EmWLVa+kFNGVs/A4hETZGq4UvYECvbqo1CYlpQmFRuUcgK43ULcLYPn5PbQSeOAfvtHkLloxjRTrlItrVy/R0VxuaFOp8ayXlCVQugBm0QjjU4wqJ0UFxN9CJddAVMUxBdgmEgI0d5GE8WwO1k/Go7QAK3ES5kIxMRYADpTnnOgtcJX638YtEAgE7GrTrVs3bkjjLs482ur3qSSJOIupAuje0Pmq6i5qqG5ZAqoyinbGCrVW9+qEXq0FtphURfKW0bi6hoWBQsFLZY+WdvuymhRwVhYNoBcHr5eUJRDjhlQGFBGIhXfmoBFQW5AXZsuPIVGOCxVxPRfn+VSSU9RhI3qOQyU/ItsXipxjX19fDaEGMDaIvBLdsdh4x12FkiHPnj2LgIAA1K0rbzkWFhbis88+cziuXr166NevX5WETZo0CX379sWTTz6JZ599FsnJyQgLcxwQYmMJtjf2Nc5itAF5sCzMpmMIuvKkUL2h9WrOUlvIqNGLDySwyrdBr7OpIJiYt8qVUdXyCeCXmfLWMbVqNFolvNRjB5Wymq7ag4dVl68bKrW/XTk8AkOAoAg50Tv/mrWSjlG4WiyqPyMzd5Tr0PIOU0uYBvSdM2ooeeLdSYaktNNsKbNqbvmE9jEEdsjdNrQPHjyI1q1b48MPP8QLL7wAALhx4wbGjRsHk8kESdV5x2w247bbbkPz5s31vs4lKSkpAOR62UuXLtU9rqysrNK/46bBVSgGNa+Eq6Qhqh5tPWOQ2mSkRi9Dn1pNVGftzAHbcnkUDFdXi8Vq5XWpLSWyh010BTQaSdI2sMxB9MpqOiuLBsjX9LU/6OwauIrRBuQdm5wbNBYHrsKfAFWuAREngrMFLsWmVs5K7wpIGdpOwp+UGG0icwZgmzcVUUv7GH8ytOfMmYPExESMHDnS4bPFixfjL3/5CwC5O2SnTp0wZ84cfPLJJ5UW5usETL/GlWeC0o0MuJ6QqCZd6FXFoLaFLcg5B5zfIz+3N2CVc0zEO+HK8yPOsWSRNeuVUPMV7txzgaGy9zj/Kg1D++j3wO4vrK/VBpbJJGsuzqNzHbtqaU6tEo2zpCxBeHUg5wyNxYGrnVCAXlicM0NQ8WgTWMQIxILK2e4FpcRpZwsZxWAlNC+L+ygkWr+rprJ76weG9saNG9GrVy+l3bqauLg4JCYmKq8HDhyI//znP1US5usETL9G2U7Ti1EidCMDrj0pBFagmuiFjojzXnzDt3pcsUtVCjOmvu1n1LYBRXyiiFe0xya0ocB4Q9tVfKvJJE+u1zNkr4vBUQIAgO/GyNVxBPYGljC0qcS4ukqGFMYLhdhWwHr/Bzmp6U0p8U3odVaJiloTGGdOGnFui3Lka8e+0pIRuBU6Qmgx4yz8ieJOs6t5AyBhT7idDJmeno6mTZvavBcYGIiWLVuiWjVbb01ycrLSlr0y5Ofno02bNpg9e3alv+NPhbLtrjObU0t8c+VJIbAC1UQvdETpWkjIkwIA1y/Jj4n3ANH1bD8jMPjYoAzwOkaKOUiu9wvQWDC6qjAB0ArZspQBNzJt37P3AFG7jl0lQ1I6v4DrcRhQVR4hYGgXuKGXmpPG2QI3LAZA+S44lWu4IqEjFBYzzs4vxdwpZzH7AgI75BVKhrRYLDavo6OjsW/fPofj7GO2KwqX96sgrgrikw0d8TePts7ERG0LWyCuixYaicnUBk1XyYWibXxxHhHPjzsVGwi1YS/MkcNuBHEa+TPUkslcxWhTG9cKdELL1FCqmewqLwKgl3/iLGQrwCwb2wVZ8t8WqROz60tczc0ArQWj0xbsBOdld8ZhAp54tz3a9erVw/79+906dv/+/ahXr57rA53A5f0qgKtVM6UYMMB1MmQgwVgwwOolsT/P1JqpCI79ID9qbVsSGHxscKsCAqHqLhXRS+G+c8d4ptYivNSFoU3N26qECTjxEFMJHbFYgC3vy8+deluJ9Qhw5aShlhDpTugIpRr2zsI6xbx8YoPv9LjCnZ1FAk4ltw3tLl26YMmSJcjMzHR6XGZmJpYsWYIuXbpUSdikSZNw7NgxPPnkk9iyZQvOnz+Pa9euOfxjYE3+0LuZxXY8ldhLV8mQ4sagMrgL9Cp4UGumAgBZ6dbn6g6AAgLbaTZolZyzJ5CQ4epWsw9ChqD9tXmrxvishI4QuY6F50wvHp9SbCvgnoeYSuhI+mbr82iN8UFA7RwrcfAu2txTWSy6EzpCKcen2I0u07nnfKfHFe5U+hELBAMdNG4b2uPGjUNJSQk6d+6M3bt3ax6ze/dupKamoqSkBGPHjq2SsJSUFBw+fBhLlixBx44dkZCQgJo1azr8+9NTVmJtfqA3wFOLvXS1CqWW5CTQm0gpho7kZlif12nt+Dm1ZEhXNYjVn1EwXN2pMEEptEE94T80C+g4wfEYalUblNARnaQ2StdDcb71mnArdMTgsSL3gvV5Si/94yidY8B1XDm1sdid0BFK87OzfgYpj1ufFxNZeDnrZClQzm+21+Xo4XaMdlJSEr7++msMGDAA7dq1Q6NGjdCsWTNERkYiLy8Phw4dwokTJxAWFoalS5ciOTm5SsK4vJ+bKDenST/zVmxlUhl83O2gRsUrAcj1h/ViMKlsB6sRWuveqV1zWtlOo2JoV6TUGIFJ39/0imszvhXQboT2MdRCoBRD24VHm0IokbjfAgKdl3IMIzIWC73N+rhZdYTANQy43r0lFzriQi+g0pxlbI8ASXK+KxNREwgIknsDFFxzft34CmedLAUExrUKJUM+8sgj2L9/P2bMmIE1a9YgLS1N+Sw+Ph7Dhw/HhAkT0KhRoyoL4/J+bnLtlPwYFiMng2ihvpEp4GoVStFwLcoFLKXyc38IHRH1s/UGeGqJLa6aLgG0trHdavZBqNqPq86bAJ2wBkCe9A8sk5+7TIYkcD2oDRRnhhKVscKd2GGAVvgTABz9Vn7U8xCLv+ePjcA9Y3yjSY/SYqD4uvzcaSWacs2WUnmeMapZVEm+NZRQ67owmeQxIu+ibLTaV7IyAreaRBl/z1XI0AaABg0aYM6cOQCA69evIzc3F9WqVUNUVFSVhJw5cwYAkJCQYPPaFeL4Py0HV8iPzpIBqG2nuUokozThC8S5CwxzvKnFjVycJydcGF3jGQCOfCc/umoKRMEbCMhtqQE/SoZ0x6MtYi8JGYL+ENYAACc3ypVSAP062uJ6oHB+3ak4ov48/5qx3ku9ngD2BBO6hgtzrdeE6LxqjzBST/3sG03OENeEKQAIjdE/LihMHitK8uXrwihDW9z35mAgOFL7mPDYckObwBgBWOPaids/FTa01VSrVs2hhnZlSUpKgslkQkFBAYKDg5XXrvjTt2AXRmttJ+3uqYViuOqWRS3UBXDuEQyJtravzr8GRMX7VpsWopRbo1Ttz8VgbmDcmg1udVAj5MF0JxlShHJR2Elyx7AisMWqcE6VB6RnpIj3KZ1fVx5icY7LimQjIUTHoPE27uxwANZzXHJD9tDqLXp8QZ6qEEPDztrH3NYD2Pg2AJNcO15vl9cXKPdcdUCj0Z8NYbFWQzu2amG3lUY9RujZXtRCc9yZNwiMa1UytD3JF198AZPJhKCgIJvXjAvExdP0Yf1jqN0crrLzKYaOOIu1CwiQb/T8q/I5pmBoq2O0taC2a+BOLKOyjU3Bo+1GeT8CA7yCW6EjxMYJgavENwp63WlWA8iNNczBcshWwTXjDG13djgA2dAWToSCa/qeZF8g7qOYRH2D/5aG5U8k2fvtaiHhTdypQiMIry5X8zDyWnZrjCDmBPOTcY2MoT106FCnr42gqKgIkydPxqJFi5CVlYUWLVpg6tSpVS5d6FHc2hIu/+zGZaCs1LEjnC8pvgFk/E9+rqdZvH/9glzv1ZU3wNtYLMC+r+TnurGBt8gTwZntQFyK77RpceOKaw8bpTABdeUcd2r6Xj3ufU2ucNWCHbD+LX/85H09rhC1b92J0S7MMT4EKlsVOhigM16Jv4XCuHbhf/KjK8NVxLlez5DvvRiDQh/d8QQCtk6EfIMN7dNb5Edn59gcJO8wFuUAOWeNNbTFOOXqmlAfY+R4XBFb4pdZcmJ1/bu8LsspfjKuGWzB0Gbo0KF4//33MWjQIHz00Ucwm814+OGHsWXLFqOlWXGnfJD6Ijz6nXf1uGLrx9bnkTrlGdU3+vEfvavHHY6vBQ5/Iz/XqyggtljXvOwTSU754RXrc72JlJS3VVU5x1l8oijzdmiV1yW5pNhFiUrAen0XXDN2As3LtHrgI5x0y1OHaPyWpnuYTxD3GwBE1NA+Rj1OGD2uHVopPzqrOCKgsMPobqgLQGOssFiADW+W63GxOBC7BP/7t3c1uWLfEvnRnV0LCufYnV0ZMX7kXQLmpwLXL3pflx5+NK6xoa3Dzp078fXXX2P69OmYNWsWRowYgZ9++gmJiYmYMEGjBq1RuDNgBoZYvUI5Bhebv3ZSfgyLBaonaR+jHpjUni2jEJVdnHH3i9bnRndbFP/HDTrpr97F9VKYLU9iRqLEBsY4j6lMukd+dBau4Svc8Qgm3Wd9nnveu3qckX3W+jyhg/5x5kBrEpS6zrIRiOsgvhVQT8drRmlcCyhfBOrlRKgxejfJVRk3ewhsvSs7XgBwr4seHTGJ8iOVBm3xrVwfQ+EcuxOGccdTtq/dmRu9hR+Na2xo67By5UqYzWaMGGGtORsaGorhw4fj119/xdmzZ538tI+wlFmT2VxtT905XH40Op5R/P4Hpzo/rs0w+ZGUx9UJTR4GYHL/eG8iJlFnE5KYYCWL8Y2B3I0XFQaXqNhgJMqk5MTQDgwGajSRnxvp0RZaa7dwHV7RZqjtzxiBelwbtMJ5ZQ4K45q6jFt8S9fHG21o25Rx87OwhuBIIOlu58c27yM/Gj0Oi2vSrcUXoXPs7JqIqW8b7mRkeVg/GtfIxGhTY9++fWjcuLFD2cK2bdsCAP73v/+hfv36FfvSo98DkR70xpXkAyg3OFxtp4nB/cL/gMP/8ZyGiiJag7vaslT07jNWL2CNKXdGgFn2yBZkydtT1QxMiMy7JD8681YFBgPB1WQD4dAqIDLON9q0uLBXfnTlXVNXbDi0Sr++srexlFq3LF2WcyvXfGK9tTSZrznzq60WZ4hjMg4Yd9/527hWlCs/uirjJhDX8dntxsQ8C2PDHCwnZ7pC6D3zq3uGuTdQ74S6QlwTV/8wdu7Iu2yrxxni78o8bJzmS7/ZanGHm3Fcy/N8VSs2tHXIyMhAfLyjsSTeu3BBfwuiqKgIRUVFyuvc3PKBOG0EEOKFSiqh0fptigVKYtYG+Z/RhOvEXdp/fnyt/I8KeuEugKy5IAv4L5HQIr3YVuXzW2RD+/txvtHjCld6gyPkmOjSAmDVcN9ocoY5GAhx0T9A3HfbPnZ+nC9wdc+pjzn1s/G1iP1tXAuLdS9xW1znh1YZm28QXsO9Ot4R5ef4wDJrEyGjiHDHA19+fi8fAZY/6V097uDOfSeuiTO/Wg1Io3A1Dsc2tIZ03ozjWpHnd0vZ0NahoKAAISGO8a2hoaHK53pMnz4dU6ZMcfyg7l1AmBdOudgqc8btj8ndsiiEYtzSCKjT2vkxKT2B9M00qmIAwPndQP32wH3j9Y/p9Aqwa761hrWRJLRz7S3r9CqwZ4HxYRiAbFC1H+n8GJMJSH0d+O3/fCLJJbc96tpQaT9S9nga3eo+MARoq9N6XU3T7nIC8o0r3tfkCn8a10wmoEV/945tOUD2wBdd96okp5hMQKtB7h3barDs7SwyOOY5wAy0f971cQnt5XNsZPywIOlu9xYHt3aRr+Xrl7yvyRmRtYDGXZ0fk/oG8OVOubZ6/fY+kaWLN8a1glIAnq0UZZIkCrMsPZo1a4a4uDhs2GDrJTl8+DBSUlIwe/ZsPPvss5o/q+XRrl+/PnJycqrcQZNhGIZhGIbxPLm5uYiOjvaovcYebR3i4+Nx/rxjpYCMjAwAQJ06dXR/NiQkRNMbzjAMwzAMw/x54KojOrRq1QrHjh2zxleXs2PHDuVzhmEYhmEYhtGDDW0d+vTpg7KyMsydO1d5r6ioCF9++SXatWtX8YojDMMwDMMwzJ8KDh3RoV27dujbty9effVVZGZmolGjRli4cCHS09Mxf/58o+UxDMMwDMMwxGFD2wlfffUVJk2ahEWLFiErKwstWrTAd999h/vuu8/1DzMMwzAMwzB/arjqiA/IyclBTEwMzp49y1VHGIZhGIZhCCKqxGVnZyM6Otoj38kebR9w9apc45XjuhmGYRiGYWhz9epVNrT9idhYuf3nmTNnPPYf523uuusu7Nq1y2gZbsN6vY+/aWa93sffNLNe7+JvegH/08x6vUtOTg4SEhIUu80TsKHtAwLK2/JGR0f7TeiI2Wz2G60A6/UF/qaZ9Xoff9PMer2Lv+kF/E8z6/UNwm7zyHd57JuYm4q//e1vRkuoEKzX+/ibZtbrffxNM+v1Lv6mF/A/zazX/+BkSB/gjZaeDMMwDMMwjOfwhr3GHm0fEBISgtdff53bsjMMwzAMwxDFG/Yae7QZhmEYhmEYxguwR5thGIZhGIZhvAAb2gzDMAzDMAzjBdjQ/hNSVFSEv//976hTpw7CwsLQrl07rFu3Tvk8Pz8fn376KR588EHEx8ejWrVqaN26Nf71r3+hrKyMpGYAmDZtGtq3b4+aNWsiNDQUt956K1566SVcvnyZpF412dnZqFWrFkwmE1auXOlDpTLu6O3UqRNMJpPDv27duvlcr7uaAaC4uBjTpk1D06ZNERoairi4OHTv3h3nzp0jpTc9PV3z/Ip/zzzzDCm9AGCxWDB79my0atUKkZGRiIuLw0MPPYRt27b5VKu7ektKSjBlyhQ0aNAAISEhaNCgAaZOnYrS0lKf683Ly8Prr7+Obt26ITY2FiaTCQsWLNA89siRI+jWrRsiIyMRGxuLJ5980pBxzV3NO3fuxMiRI9GmTRsEBQXBZDL5XCvgnl6LxYIFCxagR48eqF+/PiIiItCsWTNMnToVhYWF5PQCwOeff46OHTsiLi4OISEhSE5OxrBhw5Cenu5TvRXRrKakpAS33347TCYT3n33Xd8INRKJ+dPxxBNPSIGBgdK4ceOkOXPmSB06dJACAwOlzZs3S5IkSQcPHpRMJpOUmpoqzZw5U5o9e7bUs2dPCYD01FNPkdQsSZLUq1cv6dlnn5U++OADad68edLYsWOlqKgoqVGjRlJeXh45vWpGjRolRURESACkFStW+FSrJLmnt2PHjlK9evWkRYsW2fzbsGGDz/W6q7m4uFhKTU2VwsPDpdGjR0vz58+X3n33Xalv377SoUOHSOnNy8tzOLeLFi2SBg0aJAGQli9fTkqvJEnSyy+/LAGQBg8eLM2ZM0eaMWOG1KBBAykwMFDasWMHOb39+vWTTCaTNHz4cOlf//qXNGTIEAmA9Mwzz/hUqyRJ0qlTpyQAUkJCgtSpUycJgPTll186HHf27FmpRo0aUsOGDaWPPvpIevvtt6Xq1atLLVu2lIqKikhqfv3116WgoCCpTZs2UuPGjSWjTA139F6/fl0CILVv316aOnWqNHfuXGnYsGFSQECA1KlTJ8lisZDSK0mS9Pzzz0tDhgyR3n33XWn+/PnSa6+9JsXFxUk1atSQzp8/7zO9FdGs5r333lPmu1mzZvlGqIGwoe0BCgsLpQkTJkjx8fFSaGio1LZtW+nHH3+0OWbt2rXSX//6VyklJUUKCAiQEhMTDdG6Y8cOh4u7oKBAatiwodShQwdJkiTp8uXLmkbIsGHDJADS8ePHfaZXktzTrMfKlSslANK///1vb8tUqKjegwcPSoGBgdKbb75piKHtrt6OHTtKKSkpPtWmh7uaZ8yYIQUFBfnc6LOnKtdw586dpaioKKmgoMDbMhXc0VtSUiKFhYVJffr0sfnZkydPSgCkF198kZTenTt3SgCkSZMm2fzs2LFjJZPJJO3fv99neiVJnjcyMjIkSZKkXbt2OTWqwsLCpNOnTyvvrVu3TgIgzZkzx1dyJUlyX/PFixel/Px8SZIk6W9/+5thhrY7eouKiqStW7c6/OyUKVMkANK6det8IVWSJPfPrxa7d++WAEjTp0/3okJHKqr50qVLUnR0tDLf+drQvn79ujR58mSpa9euUvXq1XX1ikW4/b8mTZpU+Hdy6IgHGDp0KN5//30MGjQIH330EcxmMx5++GFs2bJFOWbp0qVYunQpoqOjUadOHcO0rly5EmazGSNGjFDeCw0NxfDhw/Hrr7/i7NmzqFGjBlJSUhx+tmfPngDkbUxf4o5mPZKSkgDIoRm+oqJ6R48ejZ49e+Lee+/1mUY1FdVbWlqKvLw8X8u0wR3NFosFH330EXr27Im2bduitLQU+fn5ZPVqkZGRgY0bN6JXr14IDQ31lVy39JaUlKCgoABxcXE2P1urVi0EBAQgLCyMlN7NmzcDAJ544gmbn33iiScgSRKWLVvmM72AXEasdu3aLo9btWoVHnnkESQkJCjvpaamonHjxli+fLk3JTrgrua4uDif/v/r4Y7e4OBg/OUvf3F434j5zt3zq4URcx1Qcc2vvPIKmjRpgsGDB3tRlT5XrlzBm2++iSNHjqBly5ZOjw0JCcGiRYts/s2aNavCv5MN7Sqyc+dOfP3115g+fTpmzZqFESNG4KeffkJiYiImTJigHDdt2jTk5uZi69atLv9zvcm+ffvQuHFjh0Lsbdu2BQD873//0/3ZixcvAgBq1KjhNX1aVESzJEm4cuUKLl68iM2bN+PFF1+E2WxGp06dSOpdsWIFtm3bhpkzZ/pMnz0V0Xvs2DFERESgWrVqqF27NiZNmoSSkhJfygXgnubDhw/jwoULaNGiBUaMGIGIiAhERESgRYsW2LhxIzm9Wnz99dewWCwYNGiQtyXa4I5eEQe9YMECLFmyBGfOnMGBAwcwdOhQVK9e3cbopaC3qKgIABwMwPDwcADAnj17fKC0Ypw/fx6ZmZm48847HT5r27Yt9u3bZ4CqPwdGzXcV4erVq8jMzMTu3bsxbNgwAEDnzp0NVqXPzp07sXDhQnz44YeGxe3Hx8cjIyMDp0+fdmk0BwYGYvDgwTb/Hn300Qr/zsDKimVknHlSJk6ciLNnz6J+/fqGerHVZGRkID4+3uF98d6FCxc0f664uBgffvghkpOTcdddd3lVoz0V0Xzp0iWbY+vVq4elS5eiadOm3hdajrt6CwoKMG7cOIwZMwZJSUmGJLIA7utt2LAh7r//fjRv3hw3btzAypUrMXXqVBw7dszn3kB3NIsEtw8++ACxsbGYM2cOAHnR261bN+zatQstWrQgo1eLJUuWID4+Hg888IBX9dnjrt7Fixejf//+Nt6pBg0aYOvWrWjQoIFvxMI9vU2aNAEAbN26FcnJycoxwtN9/vx5HyitGBkZGQCg+7ddu3YNRUVF3AzNC8ycORNRUVF46KGHjJaiS926dZUF5C233IKPP/4YXbp0MViVNpIkYdSoUejfvz86dOhg2HxXUQ98WVkZbty4UaUukWxoVxF3PCn169c3QpomBQUFmoOy2JYuKCjQ/LkXXngBhw8fxpo1axAY6NvLpiKaY2NjsW7dOhQWFmLfvn1YvXq1z8Mc3NX7zjvvoKSkBBMnTvSpPnvc1Tt//nybz5988kmMGDECn3/+OcaMGYP27dt7X2w57mgW/+/Xr1/Hvn37lPvwgQceQKNGjTBz5kwsXryYjF57jh07hj179mDMmDEICPDt5qO7eqtVq4aUlBR06NABnTt3xsWLF/HOO+/g8ccfx+bNm33mDXRH78MPP4zExESMGzcO4eHhaNOmDXbs2IF//OMfCAwM1B37jERocvW3saHtWaZNm4b169fjs88+Q0xMjNFydPnvf/+LwsJCHDlyBIsXL8aNGzeMlqTLggULcPDgQUOqalWW/Px8REVFIT8/H9WrV8eAAQMwY8YMREZGVuh72NCuIpX1VBlFWFiYsgJWI8oYacXVzZo1C59//jneeustPPzww17XaE9FNAcHByM1NRUA8Mgjj6Bz5864++67UatWLTzyyCNk9Kanp2PWrFn49NNPK3zTeprKXBOCsWPH4vPPP8f69et9ami7o1novvvuu20WuwkJCbjnnnt8WoKuMud4yZIlAODzsBHAPb2lpaVITU1Fp06d8M9//lM5JjU1FSkpKZg1axZmzJhBRm9oaCjWrFmDfv36oXfv3gBkA3bmzJl4++23Db8PtRDXRWXvT6biLFu2DK+99hqGDx+O559/3mg5Trn//vsBAA899BAee+wxNGvWDJGRkXjhhRcMVmZLbm4uXn31VYwfP56U49EZ8fHxmDBhAu644w5YLBb88MMP+Oyzz7B//35s2rSpQg5HjtGuIpX1EBuFiE+yR7xnH+KyYMEC/P3vf8dzzz2H1157zSca7amoZjV/+ctfEB8frxgtvsAdvZMnT0bdunXRqVMnpKenIz09XYkJvHz5MtLT02GxWMjo1UMMmteuXfOOOB3c0Sx02yfrAXLCXlZWlndFqqjMOV66dCmaNGmCNm3aeF2fPe7o/eWXX3Do0CH06NHD5phbb70Vt912G7Zu3eoTrYD75zclJQWHDh3CoUOHsHnzZly4cAHPPPMMrly5gsaNG/tMr7sIh43e3xYbG8vebA+ybt06PPXUU+jevTtmz55ttJwK0bBhQ7Ru3dqnc527vPvuuyguLkb//v2V+U70McjKykJ6ejqKi4sNVmnL9OnT8c4776Bfv3544oknsGDBArz99tvYunVrhb3ybGhXkap4A42gVatWOHbsGHJzc23e37Fjh/K54JtvvsHTTz+NXr164dNPP/WlTBsqolmLwsJC5OTkeEueA+7oPXPmDE6cOIEGDRogOTkZycnJGDBgAABg5MiRSE5Odvh5I/XqcfLkSQBAzZo1vaZPC3c0N2/eHEFBQZqxtxcuXPCp5oqe4x07duDEiROGeLOFHld6L126BACaTaxKSkp82gSmIufXZDIhJSUF99xzD2JjY7Fx40ZYLBZlJ4wSdevWRc2aNbF7926Hz3bu3Oly7GPcZ8eOHejZsyfuvPNOLF++3Ochkp6goKDAp3Odu5w5cwZZWVlISUlR5jtRZWvatGlITk7G4cOHDVbpGhHGt379+gr9HBvaVaQq3kAj6NOnD8rKyjB37lzlvaKiInz55Zdo166d4qH85Zdf8MQTT+C+++7DkiVLfB4jqsYdzTdu3NAs3bZq1SpkZWVpZu0bqXfq1KlIS0uz+ffWW28BACZMmIC0tDRERESQ0Zubm+uwoJQkCVOnTgUAdO3a1SdaK6K5WrVqePjhh7Ft2zYcPXpUOe7IkSPYtm2bT5OG3L3vBEuXLgUADBw40Gca1bijV3iAv/76a5uf3bt3L37//Xe0bt2alF4tCgoKMGnSJMTHxysLXWr07t0b3333nU0JyA0bNuDYsWPo27evgcpuHo4cOYLu3bsjKSkJ3333HTkHmZrS0lLN3bidO3fi4MGDPp3r3OXFF190mO9EcvrQoUORlpZmk6BMlbCwMNxyyy0V3sH1vyUbMVq1aoWNGzciNzfXJiHSXW+rr2nXrh369u2LV199FZmZmWjUqBEWLlyI9PR0Jdnt9OnT6NGjB0wmE/r06YMVK1bYfEeLFi18Vq3BXc3Hjx9Hamoq+vfvj6ZNmyIgIAC7d+/G4sWLkZSUhNGjR5PSe8899zj8nEi6ueuuu/D444+T0rt3714MGDAAAwYMQKNGjVBQUIC0tDRs3boVI0aMwB133OEzve5qBmRvyYYNG/DAAw/gxRdfBAB8/PHHiI2N9WkSqrt6AdlDvGzZMrRv3x4NGzb0mcaK6m3Tpg26dOmChQsXIjc3Fw8++CAyMjLwz3/+E2FhYXjppZdI6QWAfv36oU6dOrj99tuRm5uLL774AidPnsSaNWtQrVo1n+kVfPLJJ8jOzlZyeb799ltlS33UqFGIjo7GxIkTsWLFCtx///0YPXo08vLyMGvWLDRv3lwp6UZN8+nTp7Fo0SIAULzxYlGemJiIJ598kozegIAAdO3aFVlZWRg/fjzWrFlj8/MNGzZEhw4dyOiVJAn169dH//79kZKSgoiICBw8eBBffvkloqOjMWnSJJ9pdVfzHXfc4TBHiKojKSkpPp3vqsL169dx5cqViu+GVq3HDrN9+3aH7kaFhYVSo0aNpHbt2mn+TPfu3Q3rDClJcse0cePGSbVr15ZCQkKku+66S/rhhx+Uzzdu3KjZEUn8e/3118lpvnz5sjRixAipadOmUkREhBQcHCzdeuut0ksvvSRdvnyZnF4txHk3ogW7K70nT56U+vbtKyUlJUmhoaFSeHi41KZNG2n27Nk+bVFcEc2CPXv2SKmpqVJERIRUrVo16bHHHpOOHTtGVu8PP/wgAZA+/vhjn2tU447e/Px86c0335Ruv/12KSwsTIqOjpYeeeQRad++fST1zpgxQ2ratKkUGhoqVa9eXerRo4chWgWJiYm64+ypU6eU4w4dOiQ9+OCDUnh4uBQTEyMNGjRIunjxIlnNzuaQjh07ktIrWojr/RsyZAgpvUVFRdLo0aOlFi1aSFFRUVJQUJCUmJgoDR8+3OaaoaRZC3HejWzBrtfJsqCgQMrNzXU4fvz48RIAafXq1RX6PSZJkqSKmeaMPf369UNaWhrGjBmjeFJ27tyJDRs24L777gMAHDhwAP/5z38AyLVnL126hLFjxwIAWrZsWaki6AzDMAzDMIz7qD3w//rXv9CrVy8l1G3UqFHIyspC69atMWDAAKUHx9q1a/H999+jW7duWLNmTYXCadnQ9gCFhYWYNGkSFi9ejKysLLRo0QJvvfWWTdzqggULdLf5hgwZggULFvhILcMwDMMwzJ+TpKQknD59WvOzU6dOISYmBqNGjcL27dtx4cIFlJWVoVGjRhg0aBDGjRuHoKCgCv0+NrQZhmEYhmEYxgtw1RGGYRiGYRiG8QJsaDMMwzAMwzCMF2BDm2EYhmEYhmG8ABvaDMMwDMMwDOMF2NBmGIZhGIZhGC/AhjbDMAzDMAzDeAE2tBmGYRiGYRjGC7ChXUkWLFgAk8mE3bt3Gy2FYRiGYRiGIQgb2gzDMAzDMAzjBdjQZhiGYRiGYRgvwIa2hzhw4ACGDh2KBg0aIDQ0FLVr18Zf//pXXL161ea4N954AyaTCSdOnMDQoUMRExOD6OhoDBs2DPn5+QapZxiGYRiGYTxNoNECbhbWrVuHkydPYtiwYahduzZ+++03zJ07F7/99hu2b98Ok8lkc3y/fv2QnJyM6dOnY+/evZg3bx5q1aqFGTNmGPQXMAzDMAzDMJ6EDW0PMXLkSIwdO9bmvfbt22PAgAHYsmUL7r33XpvPWrdujfnz5yuvr169ivnz57OhzTAMwzAMc5PAoSMeIiwsTHleWFiIK1euoH379gCAvXv3Ohz/3HPP2by+9957cfXqVeTm5npXKMMwDMMwDOMT2ND2ENeuXcPo0aMRFxeHsLAw1KxZE8nJyQCAnJwch+MTEhJsXlevXh0AkJWV5X2xDMMwDMMwjNfh0BEP0a9fP2zbtg3jx49Hq1atEBkZCYvFgm7dusFisTgcbzabNb9HkiRvS2UYhmEYhmF8ABvaHiArKwsbNmzAlClTMHnyZOX948ePG6iKYRiGYRiGMRIOHfEAwjtt743+8MMPDVDDMAzDMAzDUIA92h4gKioK9913H2bOnImSkhLUrVsXP/74I06dOmW0NIZhGIZhGMYg2KNdSYT3Wnizly5diq5du+LTTz/Fq6++iqCgIPz3v/81UiLDMAzDMAxjICaJs+8qxccff4zRo0fjxIkTaNiwodFyGIZhGIZhGGKwR7uS7Nq1CxEREUhMTDRaCsMwDMMwDEMQjtGuIKtWrcKmTZuwZMkSPP300wgM5FPIMAzDMAzDOMKhIxUkOTkZ169fR8+ePfHhhx8iIiLCaEkMwzAMwzAMQdjQZhiGYRiGYRgvwDHaDMMwDMMwDOMF2NBmGIZhGIZhGC/AhjbDMAzDMAzDeAE2tHXYtWsXXnjhBaSkpCAiIgIJCQno168fjh075nDskSNH0K1bN0RGRiI2NhZPPvkkLl++bHPM0aNHMWHCBLRq1QrVqlVDfHw8unfvjt27dzt83+rVq9G/f380aNAA4eHhaNKkCcaOHYvs7Gxv/bkMwzAMwzCMh+FkSB369OmDrVu3om/fvmjRogUuXryITz75BHl5edi+fTuaNWsGADh37hxat26N6OhovPjii8jLy8O7776LhIQE7Ny5E8HBwQCAcePGYf78+ejduzfatm2LnJwczJkzB+np6fjhhx+QmpqqA1HnCQAABJ1JREFU/O4aNWqgTp06ePzxx5GQkICDBw9i9uzZaNCgAfbu3YuwsDBDzgnDMAzDMAzjPmxo67Bt2zbceeediqEMAMePH0fz5s3Rp08fLF68GAAwcuRILFiwAEePHkVCQgIAYP369ejSpQvmzJmDESNGAAD27NmDJk2aIDIyUvm+q1ev4rbbbkPjxo2xZcsW5f1NmzahU6dONnq++uorDBkyBJ9//jmefvppb/3ZDMMwDMMwjIdgQ7uCtGnTBoBsOANAXFwcOnbsiOXLl9sc16RJE9SvXx/r1693+n29e/fGpk2bcPXqVafHXb9+HVFRUXj55Zfx3nvvVeEvYBiGYRiGYXwBx2hXAEmScOnSJdSoUQMAcP78eWRmZuLOO+90OLZt27bYt2+fy++8ePGi8n2ujgPg1rEMwzAMwzCM8bChXQGWLFmC8+fPo3///gCAjIwMAEB8fLzDsfHx8bh27RqKiop0v2/z5s349ddfle9zxowZM2A2m9GnT59KqmcYhmEYhmF8CRvabnL06FH87W9/Q4cOHTBkyBAAQEFBAQAgJCTE4fjQ0FCbY+zJzMzEwIEDkZycjAkTJjj93UuXLsX8+fMxduxY3HrrrVX5MxiGYRiGYRgfEWi0AH/g4sWL6N69O6Kjo7Fy5UqYzWYAUKp/aHmtCwsLbY5Rc+PGDTzyyCO4fv06tmzZYpMgac/mzZsxfPhwdO3aFW+//bYn/hyGYRiGYRjGB7Ch7YKcnBw89NBDyM7OxubNm1GnTh3lMxEyIkJI1GRkZCA2NtbB211cXIxevXrhwIEDWLt2rVImUIv9+/ejR48eaNasGVauXInAQP7vYhiGYRiG8RfYcnNCYWEhHn30URw7dgzr16/H7bffbvN53bp1UbNmTc2mMzt37kSrVq1s3rNYLHjqqaewYcMGLF++HB07dtT93X/88Qe6deuGWrVq4fvvv3fq9WYYhmEYhmHowTHaOpSVlaF///749ddfsWLFCnTo0EHzuN69e+O7777D2bNnlfc2bNiAY8eOoW/fvjbHjho1CsuWLcNnn32GXr166f7uixcv4sEHH0RAQADWrl2LmjVreuaPYhiGYRiGYXwG19HW4aWXXsJHH32ERx99FP369XP4fPDgwQCAs2fPonXr1oiJicHo0aORl5eHWbNmoV69eti1a5cSOvLhhx9izJgx6NChA0aOHOnwfT179kRERAQAoFWrVti/fz8mTJiA5s2b2xwXFxeHLl26ePrPZRiGYRiGYTwMG9o6dOrUCT///LPu5+rT9ttvv+Hll1/Gli1bEBwcjO7du+O9995DXFyccszQoUOxcOFC3e87deoUkpKSAAAmk0n3uI4dO2LTpk3u/yEMwzAMwzCMIbChzTAMwzAMwzBegGO0GYZhGIZhGMYLsKHNMAzDMAzDMF6ADW2GYRiGYRiG8QJsaDMMwzAMwzCMF2BDm2EYhmEYhmG8ABvaDMMwDMMwDOMF2NBmGIZhGIZhGC/AhjbDMAzDMAzDeAE2tBmGYRiGYRjGC7ChzTAMwzAMwzBegA1thmEYhmEYhvECbGgzDMMwDMMwjBf4f59W7yckXSNtAAAAAElFTkSuQmCC", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "station = '04250'\n", "params = ['wind_speed', 'radia_glob'] # parameters to plot\n", "\n", "# Generate plot of data\n", "ax = df[station][params].plot(figsize=(8,5), legend=False, fontsize=12, rot=0, subplots=True)\n", "ax[0].set_ylabel('Air temperature [$^\\circ$C]', size=12)\n", "ax[1].set_ylabel('Global horizontal\\nirradiance [W/m$^2$]', size=12)\n", "ax[1].set_xlabel('', size=12)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.11.6" } }, "nbformat": 4, "nbformat_minor": 4 }