{ "cells": [ { "cell_type": "markdown", "id": "0", "metadata": {}, "source": [ "# Demo: Using Google Earth engine\n", "\n", "\n", " This example serves as a demonstration on how to interact with Google Earth Engine (GEE) from within the MetObs-toolkit framework." ] }, { "cell_type": "code", "execution_count": 1, "id": "1", "metadata": { "execution": { "iopub.execute_input": "2025-05-14T11:41:27.147042Z", "iopub.status.busy": "2025-05-14T11:41:27.146462Z", "iopub.status.idle": "2025-05-14T11:41:32.540359Z", "shell.execute_reply": "2025-05-14T11:41:32.536127Z" } }, "outputs": [], "source": [ "import metobs_toolkit" ] }, { "cell_type": "markdown", "id": "2", "metadata": {}, "source": [ "## GEE authentication\n", "\n", "Before proceeding, make sure you have **set up a Google developers account and a GEE project**. See [Using Google Earth Engine](https://metobs-toolkit.readthedocs.io/en/latest/topics/gee_authentication.html#using-google-earth-engine) for a detailed description of this.\n", "\n", "To test the GEE authentication we can use the ``metobs_toolkit.connect_to_gee`` method. If you authenticate using new credentials, make sure **NOT** to check the *read-only-scopes*. (See Extracting ERA5 data)." ] }, { "cell_type": "code", "execution_count": 2, "id": "3", "metadata": { "execution": { "iopub.execute_input": "2025-05-14T11:41:32.551430Z", "iopub.status.busy": "2025-05-14T11:41:32.550373Z", "iopub.status.idle": "2025-05-14T11:41:32.579537Z", "shell.execute_reply": "2025-05-14T11:41:32.577268Z" } }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%script true \n", "\n", "metobs_toolkit.connect_to_gee() #Uses a credetial file that is stored on your computer" ] }, { "cell_type": "markdown", "id": "4", "metadata": {}, "source": [ "If something is wrong with your credential (or it is the first time/ expiered credetials) you can force the creation of a new credential (file). You only need to run this if the `metobs_toolkit.connect_to_gee()` returned an error." ] }, { "cell_type": "code", "execution_count": 3, "id": "5", "metadata": { "execution": { "iopub.execute_input": "2025-05-14T11:41:32.584018Z", "iopub.status.busy": "2025-05-14T11:41:32.583495Z", "iopub.status.idle": "2025-05-14T11:41:32.596757Z", "shell.execute_reply": "2025-05-14T11:41:32.595829Z" } }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%script true \n", "\n", "metobs_toolkit.connect_to_gee(\n", " force=True, #create new credentials\n", " auth_mode='notebook', # 'notebook', 'localhost', 'gcloud' (requires gcloud installed) or 'colab' (works only in colab)\n", ")" ] }, { "cell_type": "markdown", "id": "6", "metadata": {}, "source": [ "For more details on authentication we refer to the `Using Google Earth Engine <../Using_gee.html>`_ page and the [authentication info page](https://developers.google.com/earth-engine/guides/auth) of GEE." ] }, { "cell_type": "markdown", "id": "7", "metadata": {}, "source": [ "## GEE interaction from the MetObs-toolkit\n", "\n", "There are two classes that facilitate the interaction with GEE and the metobs_toolkit:\n", "\n", " * ``GeeStaticDatasetManager``: This class handles GEE Datasets that do not have a time dimension (static).\n", " * ``GeeDynamicDatasetManager``: This class handles GEE Datasets that have a time dimension.\n", "\n", " Both classes holds all the information to extract data, at the location of your stations, \n", " from the corresponding GEE dataset. For the ``GeeStaticDatasetManager`` the extracted data will update the metadata of the stations and the corresponding ``Station.site`` attribute will be update. Typical examples are the extraction of Local Climate Zones (LCZ), altitude and landcoverfractions. \n", "\n", " When data is extracted using a ``GeeDynamicDatasetManager``, timeseries is typically extracted. These timeseries are stored in ``ModelTimeSeries``. Typical examples are the extraction of ERA5 equivalent timeseries for a sensor.\n", "\n", "\n", "By default some a GEEDatasetManager is create for some popular datasets:" ] }, { "cell_type": "code", "execution_count": 4, "id": "8", "metadata": { "execution": { "iopub.execute_input": "2025-05-14T11:41:32.600490Z", "iopub.status.busy": "2025-05-14T11:41:32.600109Z", "iopub.status.idle": "2025-05-14T11:41:32.618060Z", "shell.execute_reply": "2025-05-14T11:41:32.616969Z" } }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "{'LCZ': GeeStaticDatasetManager representation of LCZ ,\n", " 'altitude': GeeStaticDatasetManager representation of altitude ,\n", " 'worldcover': GeeStaticDatasetManager representation of worldcover ,\n", " 'ERA5-land': GeeDynamicDatasetManager representation of ERA5-land }" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "default_managers=metobs_toolkit.default_GEE_datasets\n", "default_managers" ] }, { "cell_type": "markdown", "id": "9", "metadata": {}, "source": [ "We can see that by default there are 3 ``GeeStaticDatasetManagers`` (lcz, altitude and worldcover), and one ``GeeDynamicDatasetManager`` (ERA5-land).\n", "\n", "Let's look at the local climate zones (LCZ) dataset manager as example:" ] }, { "cell_type": "code", "execution_count": 5, "id": "10", "metadata": { "execution": { "iopub.execute_input": "2025-05-14T11:41:32.622108Z", "iopub.status.busy": "2025-05-14T11:41:32.621639Z", "iopub.status.idle": "2025-05-14T11:41:32.630892Z", "shell.execute_reply": "2025-05-14T11:41:32.629770Z" } }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "================================================================================\n", " General info of GEEStaticDataset \n", "================================================================================\n", "\n", "\n", "--- GEE Dataset details ---\n", "\n", " -name: LCZ\n", " -location: RUB/RUBCLIM/LCZ/global_lcz_map/latest\n", " -value_type: categorical\n", " -scale: 100\n", " -is_static: True\n", " -is_image: False\n", " -is_mosaic: True\n", " -credentials: Demuzere M.; Kittner J.; Martilli A.; Mills, G.; Moede, C.; S...\n", " -target band: LCZ_Filter\n", " -classification: \n", " -1: Compact highrise\n", " -2: Compact midrise\n", " -3: Compact lowrise\n", " -4: Open highrise\n", " -5: Open midrise\n", " -6: Open lowrise\n", " -7: Lightweight lowrise\n", " -8: Large lowrise\n", " -9: Sparsely built\n", " -10: Heavy industry\n", " -11: Dense Trees (LCZ A)\n", " -12: Scattered Trees (LCZ B)\n", " -13: Bush, scrub (LCZ C)\n", " -14: Low plants (LCZ D)\n", " -15: Bare rock or paved (LCZ E)\n", " -16: Bare soil or sand (LCZ F)\n", " -17: Water (LCZ G)\n", " -aggregation: \n", " -colors: \n", " -1: #8c0000\n", " -2: #d10000\n", " -3: #ff0000\n", " -4: #bf4d00\n", " -5: #ff6600\n", " -6: #ff9955\n", " -7: #faee05\n", " -8: #bcbcbc\n", " -9: #ffccaa\n", " -10: #555555\n", " -11: #006a00\n", " -12: #00aa00\n", " -13: #648525\n", " -14: #b9db79\n", " -15: #000000\n", " -16: #fbf7ae\n", " -17: #6a6aff\n", "\n" ] } ], "source": [ "default_managers['LCZ'].get_info()" ] }, { "cell_type": "markdown", "id": "11", "metadata": {}, "source": [ "As you can see, the DatasetManager contains info on where this dataset is stored on GEE, some details on the dataset and some defenitions (color schemes, aggregation, label defenitions)." ] }, { "cell_type": "markdown", "id": "12", "metadata": {}, "source": [ "# Extracting data from GEE\n", "\n", "As a demonstration we are going to extract data from GEE. When we use the MetObs-toolkit for it, it will **extract data from GEE only at the locations of the stations**. This extracted data is thus linked to a station (by location).\n", "\n", "Some common pracktices are to get the LCZ, altitude and some landcover information of all the stations. We start by importin the demo data and extract these details from corresponding datasets on GEE." ] }, { "cell_type": "code", "execution_count": 6, "id": "13", "metadata": { "execution": { "iopub.execute_input": "2025-05-14T11:41:32.634403Z", "iopub.status.busy": "2025-05-14T11:41:32.634006Z", "iopub.status.idle": "2025-05-14T11:41:38.721361Z", "shell.execute_reply": "2025-05-14T11:41:38.718915Z" } }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "Luchtdruk is present in the datafile, but not found in the template! This column will be ignored.\n", "Neerslagintensiteit is present in the datafile, but not found in the template! This column will be ignored.\n", "Neerslagsom is present in the datafile, but not found in the template! This column will be ignored.\n", "Rukwind is present in the datafile, but not found in the template! This column will be ignored.\n", "Luchtdruk_Zeeniveau is present in the datafile, but not found in the template! This column will be ignored.\n", "Globe Temperatuur is present in the datafile, but not found in the template! This column will be ignored.\n", "The following columns are present in the data file, but not in the template! They are skipped!\n", " ['Luchtdruk_Zeeniveau', 'Luchtdruk', 'Rukwind', 'Neerslagsom', 'Globe Temperatuur', 'Neerslagintensiteit']\n", "The following columns are found in the metadata, but not in the template and are therefore ignored: \n", "['Network', 'sponsor', 'stad', 'benaming']\n" ] } ], "source": [ "#Import the demo dataset\n", "dataset = metobs_toolkit.Dataset()\n", "dataset.import_data_from_file(\n", " template_file=metobs_toolkit.demo_template,\n", " input_data_file=metobs_toolkit.demo_datafile,\n", " input_metadata_file=metobs_toolkit.demo_metadatafile,\n", ")" ] }, { "cell_type": "markdown", "id": "14", "metadata": {}, "source": [ "The current metdata is rather limited:" ] }, { "cell_type": "code", "execution_count": 7, "id": "15", "metadata": { "execution": { "iopub.execute_input": "2025-05-14T11:41:38.733167Z", "iopub.status.busy": "2025-05-14T11:41:38.732111Z", "iopub.status.idle": "2025-05-14T11:41:39.853207Z", "shell.execute_reply": "2025-05-14T11:41:39.851421Z" } }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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latlonschoolgeometry
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vlinder0150.9804383.815763UGentPOINT (3.81576 50.98044)
vlinder0251.0223793.709695UGentPOINT (3.7097 51.02238)
vlinder0351.3245834.952109Heilig GrafPOINT (4.95211 51.32458)
vlinder0451.3355224.934732Heilig GrafPOINT (4.93473 51.33552)
vlinder0551.0526553.675183Sint-BarbaraPOINT (3.67518 51.05266)
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" ], "text/plain": [ " lat lon school geometry\n", "name \n", "vlinder01 50.980438 3.815763 UGent POINT (3.81576 50.98044)\n", "vlinder02 51.022379 3.709695 UGent POINT (3.7097 51.02238)\n", "vlinder03 51.324583 4.952109 Heilig Graf POINT (4.95211 51.32458)\n", "vlinder04 51.335522 4.934732 Heilig Graf POINT (4.93473 51.33552)\n", "vlinder05 51.052655 3.675183 Sint-Barbara POINT (3.67518 51.05266)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset.metadf.head(5)" ] }, { "cell_type": "markdown", "id": "16", "metadata": {}, "source": [ "To extract geospatial information for your stations, the **lat** and **lon** (latitude and longitude)\n", "of your stations must be present in the metadf. If so, then geospatial\n", "information will be extracted from GEE at these locations." ] }, { "cell_type": "markdown", "id": "17", "metadata": {}, "source": [ "## Extracting LCZ from GEE\n", "To extract the Local Climate Zones (LCZs) of your stations:" ] }, { "cell_type": "code", "execution_count": 8, "id": "18", "metadata": { "execution": { "iopub.execute_input": "2025-05-14T11:41:39.861682Z", "iopub.status.busy": "2025-05-14T11:41:39.860937Z", "iopub.status.idle": "2025-05-14T11:41:42.946663Z", "shell.execute_reply": "2025-05-14T11:41:42.941028Z" } }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " LCZ\n", "name \n", "vlinder01 Low plants (LCZ D)\n", "vlinder02 Large lowrise\n", "vlinder03 Open midrise\n", "vlinder04 Sparsely built\n", "vlinder05 Water (LCZ G)\n" ] } ], "source": [ "LCZ_values = dataset.get_LCZ()\n", "\n", "# The LCZs for all your stations are extracted\n", "print(LCZ_values.head(5))" ] }, { "cell_type": "markdown", "id": "19", "metadata": {}, "source": [ "The LCZ data is returned by the ``Dataset.get_lcz()`` method. In addition metadata (stored in the ``Station.site`` attribute) of your dataset is also updated." ] }, { "cell_type": "code", "execution_count": 9, "id": "20", "metadata": { "execution": { "iopub.execute_input": "2025-05-14T11:41:42.955639Z", "iopub.status.busy": "2025-05-14T11:41:42.954174Z", "iopub.status.idle": "2025-05-14T11:41:43.947197Z", "shell.execute_reply": "2025-05-14T11:41:43.945017Z" } }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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latlonLCZschoolgeometry
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vlinder0150.9804383.815763Low plants (LCZ D)UGentPOINT (3.81576 50.98044)
vlinder0251.0223793.709695Large lowriseUGentPOINT (3.7097 51.02238)
vlinder0351.3245834.952109Open midriseHeilig GrafPOINT (4.95211 51.32458)
vlinder0451.3355224.934732Sparsely builtHeilig GrafPOINT (4.93473 51.33552)
vlinder0551.0526553.675183Water (LCZ G)Sint-BarbaraPOINT (3.67518 51.05266)
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" ], "text/plain": [ " lat lon LCZ school \\\n", "name \n", "vlinder01 50.980438 3.815763 Low plants (LCZ D) UGent \n", "vlinder02 51.022379 3.709695 Large lowrise UGent \n", "vlinder03 51.324583 4.952109 Open midrise Heilig Graf \n", "vlinder04 51.335522 4.934732 Sparsely built Heilig Graf \n", "vlinder05 51.052655 3.675183 Water (LCZ G) Sint-Barbara \n", "\n", " geometry \n", "name \n", "vlinder01 POINT (3.81576 50.98044) \n", "vlinder02 POINT (3.7097 51.02238) \n", "vlinder03 POINT (4.95211 51.32458) \n", "vlinder04 POINT (4.93473 51.33552) \n", "vlinder05 POINT (3.67518 51.05266) " ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset.metadf.head(5)" ] }, { "cell_type": "markdown", "id": "21", "metadata": {}, "source": [ "*Note* All the methods in this demo that are applied on a ``Dataset`` can also be applied on a ``Station``" ] }, { "cell_type": "code", "execution_count": 10, "id": "22", "metadata": { "execution": { "iopub.execute_input": "2025-05-14T11:41:43.955795Z", "iopub.status.busy": "2025-05-14T11:41:43.953953Z", "iopub.status.idle": "2025-05-14T11:41:44.231936Z", "shell.execute_reply": "2025-05-14T11:41:44.230878Z" } }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "The LCZ of vlinder21 is: Sparsely built\n", "================================================================================\n", " General Info of Site \n", "================================================================================\n", "\n", "Site of vlinder21:\n", " -Coordinates (51.260389, 2.991917) (latitude, longitude)\n", " -Altitude is unknown\n", " -LCZ: Sparsely built (from GEE extraction)\n", " -Land cover fractions are unknown\n", " -Extra metadata from the metadata file:\n", " -school: Zeelyceum\n", "\n" ] } ], "source": [ "#demonstration an Station-level\n", "your_station = dataset.get_station('vlinder21')\n", "the_LCZ_of_your_statation = your_station.get_LCZ()\n", "\n", "print(\"The LCZ of vlinder21 is: \", the_LCZ_of_your_statation)\n", "\n", "#or instpect the site attribute of the station\n", "your_station.site.get_info()" ] }, { "cell_type": "markdown", "id": "23", "metadata": {}, "source": [ "## Extracting alitutde from GEE\n", "\n", "Similar as LCZ extraction we can get the altitude of the stations. " ] }, { "cell_type": "code", "execution_count": 11, "id": "24", "metadata": { "execution": { "iopub.execute_input": "2025-05-14T11:41:44.236333Z", "iopub.status.busy": "2025-05-14T11:41:44.235819Z", "iopub.status.idle": "2025-05-14T11:41:46.537127Z", "shell.execute_reply": "2025-05-14T11:41:46.536152Z" } }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "================================================================================\n", " General info of GEEStaticDataset \n", "================================================================================\n", "\n", "\n", "--- GEE Dataset details ---\n", "\n", " -name: altitude\n", " -location: CGIAR/SRTM90_V4\n", " -value_type: numeric\n", " -scale: 100\n", " -is_static: True\n", " -is_image: True\n", " -is_mosaic: False\n", " -credentials: SRTM Digital Elevation Data Version 4\n", " -target band: elevation\n", " -classification: \n", " -aggregation: \n", " -colors: \n", "\n" ] }, { "data": { "text/html": [ "
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" ], "text/plain": [ " lat lon LCZ altitude school \\\n", "name \n", "vlinder01 50.980438 3.815763 Low plants (LCZ D) 12 UGent \n", "vlinder02 51.022379 3.709695 Large lowrise 7 UGent \n", "vlinder03 51.324583 4.952109 Open midrise 30 Heilig Graf \n", "vlinder04 51.335522 4.934732 Sparsely built 25 Heilig Graf \n", "vlinder05 51.052655 3.675183 Water (LCZ G) 0 Sint-Barbara \n", "\n", " geometry \n", "name \n", "vlinder01 POINT (3.81576 50.98044) \n", "vlinder02 POINT (3.7097 51.02238) \n", "vlinder03 POINT (4.95211 51.32458) \n", "vlinder04 POINT (4.93473 51.33552) \n", "vlinder05 POINT (3.67518 51.05266) " ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Get altitude\n", "altitude_data = dataset.get_altitude()\n", "\n", "#FYI, the details of the DEM dataset:\n", "metobs_toolkit.default_GEE_datasets['altitude'].get_info()\n", "\n", "#The metadata is updated (altitude in meters)\n", "dataset.metadf.head(5)" ] }, { "cell_type": "markdown", "id": "25", "metadata": {}, "source": [ "## Extracting landcover buffer fractions\n", "\n", "A more detailed description of the landcover/land use in the microenvironment can be extracted in the form of landcover fractions in a circular buffer for each station.\n", "\n", "You can select to aggregate the landcover classes to water - pervious and impervious, or set aggregation to false to extract the landcover classes as present in the worldcover_10m dataset." ] }, { "cell_type": "code", "execution_count": 12, "id": "26", "metadata": { "execution": { "iopub.execute_input": "2025-05-14T11:41:46.547850Z", "iopub.status.busy": "2025-05-14T11:41:46.547016Z", "iopub.status.idle": "2025-05-14T11:41:49.204814Z", "shell.execute_reply": "2025-05-14T11:41:49.203737Z" } }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " water pervious impervious\n", "name buffer_radius \n", "vlinder01 100 0.000000 0.981781 0.018219\n", "vlinder02 100 0.000000 0.428769 0.571231\n", "vlinder03 100 0.000000 0.245454 0.754546\n", "vlinder04 100 0.000000 0.979569 0.020431\n", "vlinder05 100 0.446604 0.224871 0.328525\n" ] } ], "source": [ "aggregated_landcover = dataset.get_landcover_fractions(\n", " buffers=[100, 250], # a list of buffer radii in meters\n", " aggregate=True #if True, aggregate landcover classes to the water, pervious and impervious.\n", " )\n", "\n", "print(aggregated_landcover.head(5))" ] }, { "cell_type": "markdown", "id": "27", "metadata": {}, "source": [ "We have extracted the landcover fractions for multiple buffers. Similar as with LCZ and altitude extraction, the metadata of the stations is updated." ] }, { "cell_type": "code", "execution_count": 13, "id": "28", "metadata": { "execution": { "iopub.execute_input": "2025-05-14T11:41:49.217332Z", "iopub.status.busy": "2025-05-14T11:41:49.216093Z", "iopub.status.idle": "2025-05-14T11:41:50.374404Z", "shell.execute_reply": "2025-05-14T11:41:50.371229Z" } }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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vlinder0251.0223793.709695Large lowrise7UGent0.0000000.4287690.5712310.0000000.5359440.464056POINT (3.7097 51.02238)
vlinder0351.3245834.952109Open midrise30Heilig Graf0.0000000.2454540.7545460.0000000.1608310.839169POINT (4.95211 51.32458)
vlinder0451.3355224.934732Sparsely built25Heilig Graf0.0000000.9795690.0204310.0000000.8819480.118052POINT (4.93473 51.33552)
vlinder0551.0526553.675183Water (LCZ G)0Sint-Barbara0.4466040.2248710.3285250.2424060.5269770.230617POINT (3.67518 51.05266)
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" ], "text/plain": [ " lat lon LCZ altitude school \\\n", "name \n", "vlinder01 50.980438 3.815763 Low plants (LCZ D) 12 UGent \n", "vlinder02 51.022379 3.709695 Large lowrise 7 UGent \n", "vlinder03 51.324583 4.952109 Open midrise 30 Heilig Graf \n", "vlinder04 51.335522 4.934732 Sparsely built 25 Heilig Graf \n", "vlinder05 51.052655 3.675183 Water (LCZ G) 0 Sint-Barbara \n", "\n", " water_frac_100m pervious_frac_100m impervious_frac_100m \\\n", "name \n", "vlinder01 0.000000 0.981781 0.018219 \n", "vlinder02 0.000000 0.428769 0.571231 \n", "vlinder03 0.000000 0.245454 0.754546 \n", "vlinder04 0.000000 0.979569 0.020431 \n", "vlinder05 0.446604 0.224871 0.328525 \n", "\n", " water_frac_250m pervious_frac_250m impervious_frac_250m \\\n", "name \n", "vlinder01 0.000000 0.963635 0.036365 \n", "vlinder02 0.000000 0.535944 0.464056 \n", "vlinder03 0.000000 0.160831 0.839169 \n", "vlinder04 0.000000 0.881948 0.118052 \n", "vlinder05 0.242406 0.526977 0.230617 \n", "\n", " geometry \n", "name \n", "vlinder01 POINT (3.81576 50.98044) \n", "vlinder02 POINT (3.7097 51.02238) \n", "vlinder03 POINT (4.95211 51.32458) \n", "vlinder04 POINT (4.93473 51.33552) \n", "vlinder05 POINT (3.67518 51.05266) " ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset.metadf.head(5)\n" ] }, { "cell_type": "markdown", "id": "29", "metadata": {}, "source": [ "## Plotting a GeeStaticDatasetManager\n", "\n", "You can make an interactive plot of a static GEE dataset, by using the ``GeeStaticDatasetManager.make_gee_plot()`` method. Equivalent is the the ``Dataset.make_gee_plot()`` method, that will also show the locations of the stations on the map.\n", "\n", "*Note: Not all Python environments can visualize the interactive map. If that is the case for you, you can save the map\n", "as a (html) file, and open it with your browser.*" ] }, { "cell_type": "code", "execution_count": 14, "id": "30", "metadata": { "execution": { "iopub.execute_input": "2025-05-14T11:41:50.392870Z", "iopub.status.busy": "2025-05-14T11:41:50.392263Z", "iopub.status.idle": "2025-05-14T11:41:52.156327Z", "shell.execute_reply": "2025-05-14T11:41:52.154658Z" } }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Get a GeeStaticDatasetManager to plot\n", "geemanager_to_plot = metobs_toolkit.default_GEE_datasets['LCZ']\n", "\n", "dataset.make_gee_plot(geedatasetmanager=geemanager_to_plot)" ] }, { "cell_type": "markdown", "id": "31", "metadata": {}, "source": [ "## Extracting ERA5 timeseries \n", "\n", "Now we demonstrate how to use the `GeeDynamicDatasetManager`, and use the ERA5-land (GEE) dataset for it." ] }, { "cell_type": "code", "execution_count": 15, "id": "32", "metadata": { "execution": { "iopub.execute_input": "2025-05-14T11:41:52.164327Z", "iopub.status.busy": "2025-05-14T11:41:52.163276Z", "iopub.status.idle": "2025-05-14T11:41:52.194820Z", "shell.execute_reply": "2025-05-14T11:41:52.192612Z" } }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "================================================================================\n", " General info of GEEDynamicDataset \n", "================================================================================\n", "\n", "\n", "--- GEE Dataset details ---\n", "\n", " -name: ERA5-land\n", " -location: ECMWF/ERA5_LAND/HOURLY\n", " -value_type: numeric\n", " -scale: 2500\n", " -is_static: False\n", " -is_image: False\n", " -is_mosaic: False\n", " -credentials: \n", " -time res: 1h\n", "\n", "--- Known Modelobstypes ---\n", "\n", " -temp : ModelObstype instance of temp\n", " -conversion: kelvin --> degree_Celsius\n", " -pressure : ModelObstype instance of pressure\n", " -conversion: 1.000 pascal --> hectopascal\n", " -wind : ModelObstype_Vectorfield instance of wind\n", " -vectorfield that will be converted to: \n", " -wind_speed\n", " -wind_direction\n", " -conversion: meter / second --> meter / second\n", "\n" ] } ], "source": [ "era5_manager = metobs_toolkit.default_GEE_datasets['ERA5-land']\n", "era5_manager.get_info()" ] }, { "cell_type": "markdown", "id": "33", "metadata": {}, "source": [ "As you can see, a ``GeeDynamicDatasetManager`` holds the information on the location of the dataset on GEE and a collection of ``ModelObstype``s. These ``ModelObstypes`` are used to interpret the bands of the dataset to obstypes (unit conversions etc.)" ] }, { "cell_type": "markdown", "id": "34", "metadata": {}, "source": [ "As an example we donwload 2m temperature timeseries for one day, for all the stations (locations) in the dataset." ] }, { "cell_type": "code", "execution_count": 16, "id": "36", "metadata": { "execution": { "iopub.execute_input": "2025-05-14T11:41:52.202046Z", "iopub.status.busy": "2025-05-14T11:41:52.201096Z", "iopub.status.idle": "2025-05-14T11:41:54.399965Z", "shell.execute_reply": "2025-05-14T11:41:54.398987Z" } }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.microsoft.datawrangler.viewer.v0+json": { "columns": [ { "name": "('name', 'datetime')", "rawType": "object", "type": "unknown" }, { "name": "temp", "rawType": "float64", "type": "float" } ], "conversionMethod": "pd.DataFrame", "ref": "138ef466-8bd0-4232-b235-487a5c9b8cd5", "rows": [ [ "('vlinder01', Timestamp('2022-09-02 00:00:00+0000', tz='UTC'))", "17.70563659667971" ], [ "('vlinder01', Timestamp('2022-09-02 01:00:00+0000', tz='UTC'))", "17.230676269531273" ], [ "('vlinder01', Timestamp('2022-09-02 02:00:00+0000', tz='UTC'))", "16.739312744140648" ], [ "('vlinder01', Timestamp('2022-09-02 03:00:00+0000', tz='UTC'))", "16.493768310546898" ], [ "('vlinder01', Timestamp('2022-09-02 04:00:00+0000', tz='UTC'))", "16.269143676757835" ] ], "shape": { "columns": 1, "rows": 5 } }, "text/html": [ "
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" ], "text/plain": [ " temp\n", "name datetime \n", "vlinder01 2022-09-02 00:00:00+00:00 17.705637\n", " 2022-09-02 01:00:00+00:00 17.230676\n", " 2022-09-02 02:00:00+00:00 16.739313\n", " 2022-09-02 03:00:00+00:00 16.493768\n", " 2022-09-02 04:00:00+00:00 16.269144" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "#Specify a start and end timestamp\n", "tstart = pd.Timestamp('2022-09-02 00:00:00')\n", "tend = pd.Timestamp('2022-09-03 00:00:00')\n", "\n", "#Extract the timeseries \n", "era5_temp = dataset.get_gee_timeseries_data(\n", " geedynamicdatasetmanager=era5_manager, #The datasetmanager to use\n", " startdt_utc=tstart,\n", " enddt_utc=tend,\n", " target_obstypes=['temp'], #the observationtypes to extract, must be knonw modelobstypes\n", " get_all_bands=False, #If true, all bands are extracted (but not stored in the stations if the band is unknwown)\n", " )\n", "\n", "era5_temp.head()" ] }, { "cell_type": "markdown", "id": "37", "metadata": {}, "source": [ "As you can see, the temperature data is :\n", "* Extracted as timeseries at the location of the stations\n", "* The timeseries are formatted in a pandas Dataframe\n", "* If the requested bands have a corresponding ModelObstype: \n", " * Units are converted to the standard unit. So that the unit of the observations is the same as those of the extracted modeldata.\n", " * The timeseries are stored as ``ModelTimeSeries`` for each ``Station``. \n", "\n", "The temperature data is thus stored as timeseries in each station." ] }, { "cell_type": "code", "execution_count": 17, "id": "38", "metadata": { "execution": { "iopub.execute_input": "2025-05-14T11:41:54.402724Z", "iopub.status.busy": "2025-05-14T11:41:54.402399Z", "iopub.status.idle": "2025-05-14T11:41:54.410628Z", "shell.execute_reply": "2025-05-14T11:41:54.408804Z" } }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "{'temp': }" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\n", "dataset.get_station('vlinder12').modeldata" ] }, { "cell_type": "markdown", "id": "39", "metadata": {}, "source": [ "More convenient is to use the ``.modeldatadf`` attribute to get all the stored modeldata in a dataframe." ] }, { "cell_type": "code", "execution_count": 18, "id": "40", "metadata": { "execution": { "iopub.execute_input": "2025-05-14T11:41:54.414187Z", "iopub.status.busy": "2025-05-14T11:41:54.413871Z", "iopub.status.idle": "2025-05-14T11:41:54.681784Z", "shell.execute_reply": "2025-05-14T11:41:54.680812Z" } }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.microsoft.datawrangler.viewer.v0+json": { "columns": [ { "name": "('datetime', 'obstype', 'name')", "rawType": "object", "type": "unknown" }, { "name": "value", "rawType": "float32", "type": "float" }, { "name": "details", "rawType": "object", "type": "string" } ], "conversionMethod": "pd.DataFrame", "ref": "3f070b8a-7f53-4677-a610-8ef01b955552", "rows": [ [ "(Timestamp('2022-09-02 00:00:00+0000', tz='UTC'), 'temp', 'vlinder01')", "17.705637", "ERA5-land:temperature_2m converted from kelvin -> degree_Celsius" ], [ "(Timestamp('2022-09-02 00:00:00+0000', tz='UTC'), 'temp', 'vlinder02')", "17.631418", "ERA5-land:temperature_2m converted from kelvin -> degree_Celsius" ], [ "(Timestamp('2022-09-02 00:00:00+0000', tz='UTC'), 'temp', 'vlinder03')", "19.090403", "ERA5-land:temperature_2m converted from kelvin -> degree_Celsius" ], [ "(Timestamp('2022-09-02 00:00:00+0000', tz='UTC'), 'temp', 'vlinder04')", "19.090403", "ERA5-land:temperature_2m converted from kelvin -> degree_Celsius" ], [ "(Timestamp('2022-09-02 00:00:00+0000', tz='UTC'), 'temp', 'vlinder05')", "17.781809", "ERA5-land:temperature_2m converted from kelvin -> degree_Celsius" ], [ "(Timestamp('2022-09-02 00:00:00+0000', tz='UTC'), 'temp', 'vlinder06')", "19.166574", "ERA5-land:temperature_2m converted from kelvin -> degree_Celsius" ], [ "(Timestamp('2022-09-02 00:00:00+0000', tz='UTC'), 'temp', 'vlinder07')", "19.166574", "ERA5-land:temperature_2m converted from kelvin -> degree_Celsius" ], [ "(Timestamp('2022-09-02 00:00:00+0000', tz='UTC'), 'temp', 'vlinder08')", "19.166574", "ERA5-land:temperature_2m converted from kelvin -> degree_Celsius" ], [ "(Timestamp('2022-09-02 00:00:00+0000', tz='UTC'), 'temp', 'vlinder09')", "18.11384", "ERA5-land:temperature_2m converted from kelvin -> degree_Celsius" ], [ "(Timestamp('2022-09-02 00:00:00+0000', tz='UTC'), 'temp', 'vlinder10')", "17.908762", "ERA5-land:temperature_2m converted from kelvin -> degree_Celsius" ], [ "(Timestamp('2022-09-02 00:00:00+0000', tz='UTC'), 'temp', 'vlinder11')", "18.73884", "ERA5-land:temperature_2m converted from kelvin -> degree_Celsius" ], [ "(Timestamp('2022-09-02 00:00:00+0000', tz='UTC'), 'temp', 'vlinder12')", "18.73884", "ERA5-land:temperature_2m converted from kelvin -> degree_Celsius" ], [ "(Timestamp('2022-09-02 00:00:00+0000', tz='UTC'), 'temp', 'vlinder13')", "18.73884", "ERA5-land:temperature_2m converted from kelvin -> degree_Celsius" ], [ "(Timestamp('2022-09-02 00:00:00+0000', tz='UTC'), 'temp', 'vlinder14')", "18.434153", "ERA5-land:temperature_2m converted from kelvin -> degree_Celsius" ], [ "(Timestamp('2022-09-02 00:00:00+0000', tz='UTC'), 'temp', 'vlinder15')", "18.285715", "ERA5-land:temperature_2m converted from kelvin -> degree_Celsius" ], [ "(Timestamp('2022-09-02 00:00:00+0000', tz='UTC'), 'temp', 'vlinder16')", "18.434153", "ERA5-land:temperature_2m converted from kelvin -> degree_Celsius" ], [ "(Timestamp('2022-09-02 00:00:00+0000', tz='UTC'), 'temp', 'vlinder17')", "19.504465", "ERA5-land:temperature_2m converted from kelvin -> degree_Celsius" ], [ "(Timestamp('2022-09-02 00:00:00+0000', tz='UTC'), 'temp', 'vlinder18')", "19.592356", "ERA5-land:temperature_2m converted from kelvin -> degree_Celsius" ], [ "(Timestamp('2022-09-02 00:00:00+0000', tz='UTC'), 'temp', 'vlinder19')", "18.205637", "ERA5-land:temperature_2m converted from kelvin -> degree_Celsius" ], [ "(Timestamp('2022-09-02 00:00:00+0000', tz='UTC'), 'temp', 'vlinder20')", "18.205637", 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" ], "text/plain": [ " value \\\n", "datetime obstype name \n", "2022-09-02 00:00:00+00:00 temp vlinder01 17.705637 \n", " vlinder02 17.631418 \n", " vlinder03 19.090403 \n", " vlinder04 19.090403 \n", " vlinder05 17.781809 \n", "... ... \n", "2022-09-03 00:00:00+00:00 temp vlinder24 19.034998 \n", " vlinder25 18.999842 \n", " vlinder26 19.835779 \n", " vlinder27 18.843592 \n", " vlinder28 18.562342 \n", "\n", " details \n", "datetime obstype name \n", "2022-09-02 00:00:00+00:00 temp vlinder01 ERA5-land:temperature_2m converted from kelvin... \n", " vlinder02 ERA5-land:temperature_2m converted from kelvin... \n", " vlinder03 ERA5-land:temperature_2m converted from kelvin... \n", " vlinder04 ERA5-land:temperature_2m converted from kelvin... \n", " vlinder05 ERA5-land:temperature_2m converted from kelvin... \n", "... ... \n", "2022-09-03 00:00:00+00:00 temp vlinder24 ERA5-land:temperature_2m converted from kelvin... \n", " vlinder25 ERA5-land:temperature_2m converted from kelvin... \n", " vlinder26 ERA5-land:temperature_2m converted from kelvin... \n", " vlinder27 ERA5-land:temperature_2m converted from kelvin... \n", " vlinder28 ERA5-land:temperature_2m converted from kelvin... \n", "\n", "[700 rows x 2 columns]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset.modeldatadf" ] }, { "cell_type": "markdown", "id": "41", "metadata": {}, "source": [ "## Plotting with GeeDynamicDataset\n", "\n", "There are two implementation to plot a dynamic dataset.\n", "\n", "1. You can plot the extracted timeseries using the `.make_plot_of_modeldata()` or use the `.make_plot(..., show_modeldata=True,... )` method.\n", "\n", "2. You can make an interactive spatial plot of the GEE dataset using the `.make_gee_plot()` method." ] }, { "cell_type": "code", "execution_count": 19, "id": "42", "metadata": { "execution": { "iopub.execute_input": "2025-05-14T11:41:54.686185Z", "iopub.status.busy": "2025-05-14T11:41:54.685769Z", "iopub.status.idle": "2025-05-14T11:41:55.252581Z", "shell.execute_reply": "2025-05-14T11:41:55.251993Z" } }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Plot the modeldata and observational temperatures of a single station\n", "dataset.get_station('vlinder04').make_plot(obstype='temp', show_modeldata=True)" ] }, { "cell_type": "code", "execution_count": 20, "id": "43", "metadata": { "execution": { "iopub.execute_input": "2025-05-14T11:41:55.254686Z", "iopub.status.busy": "2025-05-14T11:41:55.254452Z", "iopub.status.idle": "2025-05-14T11:42:27.673529Z", "shell.execute_reply": "2025-05-14T11:42:27.672168Z" } }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Interactive spatial plot of the GEE dataset (with the stations as markers)\n", "\n", "dataset.make_gee_plot(\n", " geedatasetmanager=era5_manager,\n", " timeinstance=pd.Timestamp('2022-09-02 13:00:00'),\n", " modelobstype='temp',\n", " vmax=None, #if None, the colorscale is optimalized for the extend of all stations\n", " vmin=None, #if None, the colorscale is optimalized for the extend of all stations\n", " )" ] }, { "cell_type": "markdown", "id": "44", "metadata": {}, "source": [ "## Transfer of larger amounts of data \n", "\n", "There is a limit to the amount of data that can be transferred directly from GEE. When the data cannot be transferred directly, **it will be written to a file on your Google Drive**. The location of the file will be printed out. When the writing to the file is done, you must download the file and import it using the ``Dataset.import_gee_data_from_file()`` method.\n", "\n", "*Note*: The reason not to check the *read-only* option upon GEE authentication is to enable possibility to write files to your google drive.\n", "\n", "As a demonstration we download the temperature and pressure timeseries from ERA5-land, for all the stations and for the same period as the observations (15 days). This amount of data is to large to transfer directly. " ] }, { "cell_type": "code", "execution_count": 21, "id": "45", "metadata": { "execution": { "iopub.execute_input": "2025-05-14T11:42:27.677469Z", "iopub.status.busy": "2025-05-14T11:42:27.676631Z", "iopub.status.idle": "2025-05-14T11:43:55.520304Z", "shell.execute_reply": "2025-05-14T11:43:55.516704Z" } }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING::THE DATA AMOUNT IS TOO LARGE FOR INTERACTIVE SESSION, THE DATA WILL BE EXPORTED TO YOUR GOOGLE DRIVE!\n", "WARNING::The timeseries will be written to your Drive in gee_timeseries_data/ERA5-land_timeseries_data_of_full_dataset_28_stations \n", "WARNING::The data is transfered! Open the following link in your browser: \n", "\n", "\n", "WARNING::https://drive.google.com/#folders/1QE0DA-YqeHh5gSUG2L1EWh7ndyxa7xNj \n", "\n", "\n", "WARNING::To upload the data to the model, use the Dataset.import_gee_data_from_file() method.\n", "WARNING::No data is returned by the GEE api request.\n" ] } ], "source": [ "\n", "#Extract the timeseries \n", "_nothing_returned = dataset.get_gee_timeseries_data(\n", " geedynamicdatasetmanager=era5_manager, #The datasetmanager to use\n", " startdt_utc=None, #If None, the startdt of the dataset observations is used\n", " enddt_utc=None, #If None, the startdt of the dataset observations is used\n", " target_obstypes=['temp', 'pressure'], #the observationtypes to extract, must be knonw modelobstypes\n", " )\n" ] }, { "cell_type": "markdown", "id": "46", "metadata": {}, "source": [ "The data request was to big for direct transfer, a (CSV) file is written to your Google Drive (See the link in the printed warning).\n", "\n", "Download that file, and then import the modeldata from the file." ] }, { "cell_type": "code", "execution_count": 22, "id": "47", "metadata": { "execution": { "iopub.execute_input": "2025-05-14T11:43:55.531602Z", "iopub.status.busy": "2025-05-14T11:43:55.529317Z", "iopub.status.idle": "2025-05-14T11:43:55.558864Z", "shell.execute_reply": "2025-05-14T11:43:55.556304Z" } }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%script true \n", "\n", "#Import the ERA5 data\n", "dataset.import_gee_data_from_file(\n", " filepath= '... path to the downloaded file ...',\n", " geedynamicdatasetmanager=era5_manager\n", " )" ] }, { "cell_type": "markdown", "id": "48", "metadata": {}, "source": [ "## Extending the defaults\n", "\n", "The default GeeDatasetManager are illustrative and they may not be sufficient. Here are some demonstrations on how to extend upon the defaults." ] }, { "cell_type": "markdown", "id": "49", "metadata": {}, "source": [ "### Using a new ModelObstype\n", "\n", "In this demo we add the surface sensible heat flux as a new modelobstype and download the timeseries at the location of the stations.\n", "\n", "See the [details of the ERA5-land](https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_LAND_HOURLY) dataset." ] }, { "cell_type": "code", "execution_count": 23, "id": "50", "metadata": { "execution": { "iopub.execute_input": "2025-05-14T11:43:55.569079Z", "iopub.status.busy": "2025-05-14T11:43:55.567208Z", "iopub.status.idle": "2025-05-14T11:43:57.723089Z", "shell.execute_reply": "2025-05-14T11:43:57.722276Z" } }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.microsoft.datawrangler.viewer.v0+json": { "columns": [ { "name": 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" ], "text/plain": [ " value \\\n", "datetime obstype \n", "2022-09-01 00:00:00+00:00 sensible_heat_flux -2671.636963 \n", "2022-09-01 01:00:00+00:00 sensible_heat_flux 156.804245 \n", "2022-09-01 02:00:00+00:00 sensible_heat_flux 286.745514 \n", "2022-09-01 03:00:00+00:00 sensible_heat_flux 417.542999 \n", "2022-09-01 04:00:00+00:00 sensible_heat_flux 551.778992 \n", "... ... \n", "2022-09-15 20:00:00+00:00 sensible_heat_flux -287.355988 \n", "2022-09-15 21:00:00+00:00 sensible_heat_flux -172.500000 \n", "2022-09-15 22:00:00+00:00 sensible_heat_flux -58.152000 \n", "2022-09-15 23:00:00+00:00 sensible_heat_flux 30.048000 \n", "2022-09-16 00:00:00+00:00 sensible_heat_flux 99.676003 \n", "\n", " details \n", "datetime obstype \n", "2022-09-01 00:00:00+00:00 sensible_heat_flux ERA5-land:surface_sensible_heat_flux converted... \n", "2022-09-01 01:00:00+00:00 sensible_heat_flux ERA5-land:surface_sensible_heat_flux converted... \n", "2022-09-01 02:00:00+00:00 sensible_heat_flux ERA5-land:surface_sensible_heat_flux converted... \n", "2022-09-01 03:00:00+00:00 sensible_heat_flux ERA5-land:surface_sensible_heat_flux converted... \n", "2022-09-01 04:00:00+00:00 sensible_heat_flux ERA5-land:surface_sensible_heat_flux converted... \n", "... ... \n", "2022-09-15 20:00:00+00:00 sensible_heat_flux ERA5-land:surface_sensible_heat_flux converted... \n", "2022-09-15 21:00:00+00:00 sensible_heat_flux ERA5-land:surface_sensible_heat_flux converted... \n", "2022-09-15 22:00:00+00:00 sensible_heat_flux ERA5-land:surface_sensible_heat_flux converted... \n", "2022-09-15 23:00:00+00:00 sensible_heat_flux ERA5-land:surface_sensible_heat_flux converted... \n", "2022-09-16 00:00:00+00:00 sensible_heat_flux ERA5-land:surface_sensible_heat_flux converted... \n", "\n", "[386 rows x 2 columns]" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#1. Create a observation type\n", "sensible_heat_flux = metobs_toolkit.Obstype(\n", " obsname='sensible_heat_flux',\n", " std_unit='kJ/m^2',\n", " description='The 2m sensible heat flux.')\n", "\n", "#2. Make a ModelObstype\n", "sensible_heat_flux_era5 = metobs_toolkit.ModelObstype(\n", " obstype=sensible_heat_flux,\n", " model_unit='J/m^2', #see details on GEE\n", " model_band='surface_sensible_heat_flux') #see details on GEE\n", "\n", "#3. Add the ModelObstype to the dataset manager\n", "era5_manager = metobs_toolkit.default_GEE_datasets['ERA5-land']\n", "era5_manager.add_modelobstype(sensible_heat_flux_era5)\n", "\n", "\n", "#4. Extract some sensible heat flux data (for a single station as demo)\n", "sens_heat_flx_data = dataset.get_station('vlinder04').get_gee_timeseries_data(\n", " geedynamicdatasetmanager=era5_manager,\n", " startdt_utc=None,\n", " enddt_utc=None,\n", " target_obstypes=['sensible_heat_flux'] #refer by obsname\n", " )\n", "#The sensible heat flux data is stored as modeldata in the station (if the direct transfer is succescfull!)\n", "\n", "#5. make a plot\n", "dataset.get_station('vlinder04').make_plot_of_modeldata(obstype='sensible_heat_flux')\n", "\n", "#6. You can also get the data using the modeldatadf attribute\n", "dataset.get_station('vlinder04').modeldatadf\n" ] }, { "cell_type": "markdown", "id": "51", "metadata": {}, "source": [ "### Defining a new GEE dataset\n", "\n", "In this example we create a new ``GeeDyanamicDatasetManager`` that is a JAXA reanalysis product for global precipitation, see [JAXA_GPM_L3_GSMaP_v8_operational](https://developers.google.com/earth-engine/datasets/catalog/JAXA_GPM_L3_GSMaP_v8_operational) details.\n" ] }, { "cell_type": "code", "execution_count": 24, "id": "52", "metadata": { "execution": { "iopub.execute_input": "2025-05-14T11:43:57.725459Z", "iopub.status.busy": "2025-05-14T11:43:57.725202Z", "iopub.status.idle": "2025-05-14T11:44:03.202467Z", "shell.execute_reply": "2025-05-14T11:44:03.201725Z" } }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { 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datetimeobstype
2022-09-01 00:00:00+00:00precip_rate0.000000JAXA_GPM_reanalysis:hourlyPrecipRate converted...
sensible_heat_flux-2671.636963ERA5-land:surface_sensible_heat_flux converted...
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2022-09-15 22:00:00+00:00sensible_heat_flux-58.152000ERA5-land:surface_sensible_heat_flux converted...
2022-09-15 23:00:00+00:00precip_rate0.000000JAXA_GPM_reanalysis:hourlyPrecipRate converted...
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" ], "text/plain": [ " value \\\n", "datetime obstype \n", "2022-09-01 00:00:00+00:00 precip_rate 0.000000 \n", " sensible_heat_flux -2671.636963 \n", "2022-09-01 01:00:00+00:00 precip_rate 0.000000 \n", " sensible_heat_flux 156.804245 \n", "2022-09-01 02:00:00+00:00 precip_rate 0.000000 \n", "... ... \n", "2022-09-15 22:00:00+00:00 sensible_heat_flux -58.152000 \n", "2022-09-15 23:00:00+00:00 precip_rate 0.000000 \n", " sensible_heat_flux 30.048000 \n", "2022-09-16 00:00:00+00:00 precip_rate 0.000000 \n", " sensible_heat_flux 99.676003 \n", "\n", " details \n", "datetime obstype \n", "2022-09-01 00:00:00+00:00 precip_rate JAXA_GPM_reanalysis:hourlyPrecipRate converted... \n", " sensible_heat_flux ERA5-land:surface_sensible_heat_flux converted... \n", "2022-09-01 01:00:00+00:00 precip_rate JAXA_GPM_reanalysis:hourlyPrecipRate converted... \n", " sensible_heat_flux ERA5-land:surface_sensible_heat_flux converted... \n", "2022-09-01 02:00:00+00:00 precip_rate JAXA_GPM_reanalysis:hourlyPrecipRate converted... \n", "... ... \n", "2022-09-15 22:00:00+00:00 sensible_heat_flux ERA5-land:surface_sensible_heat_flux converted... \n", "2022-09-15 23:00:00+00:00 precip_rate JAXA_GPM_reanalysis:hourlyPrecipRate converted... \n", " sensible_heat_flux ERA5-land:surface_sensible_heat_flux converted... \n", "2022-09-16 00:00:00+00:00 precip_rate JAXA_GPM_reanalysis:hourlyPrecipRate converted... \n", " sensible_heat_flux ERA5-land:surface_sensible_heat_flux converted... \n", "\n", "[747 rows x 2 columns]" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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yy5YtrsvLy2w2a+DAgRo4cKBmzJihadOm6eGHH9by5ct17rnnlno/ffF7LSohIUHh4eHFfi9S/n01m80+7YS66qqrNHbsWG3dulXvv/++wsPDNWTIEJ8dv6jk5GQ5nU5XZ2iBovfXm7/9k+nYsaNef/11/fHHHx5DkX/++WfX5VJ+x2BaWpratGlT7BjTpk3TtGnTtGHDBtf+AABUZQzPBACgEnnppZc8zs+aNUuSNHjw4JNez2Kx6LLLLtOiRYv022+/Fbv84MGDrtOXXXaZDh06pBdffLHYfmXpcilvjWVR0H3jXodhGHr++eeL7RsRESFJxQKmK6+8Ug6HQ48//nix69jt9pMGUnXq1FHHjh01f/58jyGDy5Yt0+bNm4vVajKZ5HA4XNt27NihJUuWlFhrSbdrsViKPeazZs3yOKa3LrzwQu3fv99jRUi73a5Zs2YpMjLSNSTSW0eOHCm2rSAYKRjWV9rvxBe/16IsFovOP/98ffzxxx5Dgw8cOKB3331Xffr0KffQyZJcdtllslgsWrhwoT788ENdfPHFrlorQsHf0wsvvOCxfebMmR7nvfnbP5lLLrlEQUFBevnll13bDMPQK6+8orp167pWQR0/frwWL17s8fPqq69KkkaPHq3FixeXecgvAACVHZ1mAABUIikpKRo6dKguuOACrVq1Su+8846uvvpqdejQ4ZTXffLJJ7V8+XL16NFDN910k1q3bq0jR45o/fr1+uqrr1yhx3XXXae33npLEyZM0OrVq9W3b19lZmbqq6++0tixY085sfnp1HgqLVu2VJMmTXTvvfdqz549io6O1qJFi0qcc6pLly6S8j/EDxo0SBaLRcOHD1f//v11yy23aPr06dq4caPOP/98BQUF6a+//tKHH36o559/XpdffnmpNUyfPl0XXXSR+vTpozFjxujIkSOaNWuW2rRpo4yMDNd+F110kWbMmKELLrhAV199tVJTU/XSSy+padOm+vXXX4vV+tVXX2nGjBlKSkpSo0aN1KNHD1188cV6++23FRMTo9atW2vVqlX66quvFBcXV+7H8Oabb9arr76q0aNHa926dWrYsKE++ugj/fDDD5o5c6aioqLKddypU6fq+++/10UXXaTk5GSlpqbq5ZdfVr169dSnTx9J+V1+sbGxeuWVVxQVFaWIiAj16NHDJ7/XkjzxxBNatmyZ+vTpo7Fjx8pqterVV19Vbm6unn766XLdz9IkJibq7LPP1owZM3T8+HFdddVVPj1+UR07dtSIESP08ssv69ixY+rVq5e+/vprbdu2rdi+Zf3bP5l69erprrvu0jPPPCObzaZu3bppyZIlWrFihRYsWOAKPjt37qzOnTt7XLcgtGzTpo2GDRt22vcdAIBK44yv1wkAAIqZNGmSIcnYvHmzcfnllxtRUVFGjRo1jDvuuMPIzs722FeScfvtt5d4nAMHDhi33367Ub9+fSMoKMioXbu2MXDgQOO1117z2C8rK8t4+OGHjUaNGrn2u/zyy43t27d73M6kSZPKVeOpjBo1yoiIiCjxss2bNxvnnnuuERkZacTHxxs33XST8csvvxiSjLlz57r2s9vtxrhx44yEhATDZDIZRd/WvPbaa0aXLl2MsLAwIyoqymjXrp1x//33G3v37j1lfYsWLTJatWplhISEGK1btzb++9//GqNGjTKSk5M99nvjjTeMZs2aGSEhIUbLli2NuXPnuh4nd1u2bDH69etnhIWFGZKMUaNGGYZhGGlpacb1119vxMfHG5GRkcagQYOMLVu2GMnJya59TiY5Odm46KKLim0/cOCA67jBwcFGu3btPB47wzCMlJQUQ5LxzDPPnPJ2DMMwvv76a+OSSy4xkpKSjODgYCMpKckYMWKE8eeff3rs9/HHHxutW7c2rFarx+/MF7/Xos9JwzCM9evXG4MGDTIiIyON8PBw4+yzzzZ+/PFHj33mzp1rSDLWrFnjsX358uWGJGP58uVlegzmzJljSDKioqJKfM6X9Bwp7e/o4MGDJdaYkpLi2padnW2MHz/eiIuLMyIiIowhQ4YYu3fvLvFxKMvffsH9/fDDD0u8fw6Hw5g2bZqRnJxsBAcHG23atDHeeeedUz4u3j6XAACoKkyG4eVsowAAwOcmT56sKVOm6ODBg4qPj/d3OSWqCjUCAAAAvsKcZgAAAAAAAEARzGkGAAB85tixY8rOzj7pPrVr1z5D1QAAAADlR2gGAAB85s4779T8+fNPug8zQwAAAKAqYE4zAADgM5s3b9bevXtPus+55557hqoBAAAAyo/QDAAAAAAAACiChQAAAAAAAACAIgJ+TjOn06m9e/cqKipKJpPJ3+UAAAAAAADAjwzD0PHjx5WUlCSzufR+soAPzfbu3av69ev7uwwAAAAAAABUIrt371a9evVKvTzgQ7OoqChJ+Q9EdHS0n6sBAAAAAACAP6Wnp6t+/fquzKg0AR+aFQzJjI6OJjQDAAAAAACAJJ1yGi+/LgTw/fffa8iQIUpKSpLJZNKSJUtK3ffWW2+VyWTSzJkzz1h9AAAAAAAAqJ78GpplZmaqQ4cOeumll0663+LFi/XTTz8pKSnpDFUGAAAAAACA6syvwzMHDx6swYMHn3SfPXv2aNy4cfrf//6niy666AxVBgAAAAAAgOqsUs9p5nQ6de211+q+++5TmzZtynSd3Nxc5ebmus6np6dXVHkAAAAAzhDDMGS32+VwOPxdCgCgkrNYLLJaraecs+xUKnVo9tRTT8lqtWr8+PFlvs706dM1ZcqUCqwKAAAAwJmUl5enffv2KSsry9+lAACqiPDwcNWpU0fBwcHlPkalDc3WrVun559/XuvXr/cqGXzwwQc1YcIE1/mCZUQBAAAAVD1Op1MpKSmyWCxKSkpScHDwaXcOAAACl2EYysvL08GDB5WSkqJmzZrJbC7flP6VNjRbsWKFUlNT1aBBA9c2h8Ohe+65RzNnztSOHTtKvF5ISIhCQkLOUJUAAAAAKlJeXp6cTqfq16+v8PBwf5cDAKgCwsLCFBQUpJ07dyovL0+hoaHlOk6lDc2uvfZanXvuuR7bBg0apGuvvVbXX3+9n6oCAAAA4A/l7RIAAFRPvvh/w6+hWUZGhrZt2+Y6n5KSoo0bN6pmzZpq0KCB4uLiPPYPCgpS7dq11aJFizNdKgAAAAAAAKoRv4Zma9eu1dlnn+06XzAX2ahRozRv3jw/VQUAAAAAAIDqzq89zgMGDJBhGMV+SgvMduzYobvuuuuM1ggAAAAAqN7mzZun2NhYnxxrx44dMplM2rhxY7muP3nyZHXs2NEntZTHgAEDqsTn8oYNG2rmzJn+LqNSMJlMWrJkiaTTf/4V8Pfz8ExhYgAAAAAA8LHRo0dr2LBhHtumT58ui8WiZ555ptj+EydOVMOGDXX8+HGP7UOGDFG/fv3kdDrLfKxTycvL0zPPPKPOnTsrIiJCMTEx6tChgx555BHt3bvX4z6YTCaZTCYFBweradOmmjp1qux2uyTp22+/lclkUo0aNZSTk+NxG2vWrHFdF57q16+vffv2qW3btv4u5YyYPHmy67lgtVrVsGFD3X333crIyKjQ212zZo1uvvlmr65TUKfJZFJ0dLS6deumjz/+2Ktj+CKU2rVrl+6991516NBB8fHxaty4sS6//HJ98cUX5T5mgcr4/Pv111/Vt29fhYaGqn79+nr66adL3fe9996TyWQq9vpaUQjNAAAAcNqOZdvkdBr+LgOo1N58803df//9evPNN4tdNnXqVEVGRrqmrCnYf/ny5Zo7d26xCa1PdqyTyc3N1Xnnnadp06Zp9OjR+v7777Vp0ya98MILOnTokGbNmuWx/wUXXKB9+/bpr7/+0j333KPJkycXC+qioqK0ePFij21vvPGGGjRo4FVt7hwOR7GgMFBYLBbVrl1bVmulXZevRHl5eeW+bps2bbRv3z7t2LFDTz31lF577TXdc889Pr8ddwkJCeVacXfu3Lnat2+f1q5dq969e+vyyy/Xpk2bfFJTWbz99ttq27at9uzZo8mTJ+vrr7/WwoULddZZZ+nmm2/WddddJ4fDUe7jV6bnn81mU3p6us4//3wlJydr3bp1euaZZzR58mS99tprxfbfsWOH7r33XvXt2/eM1UhoBgAAgNOyfEuquj3xlYa8uJLgDCjFd999p+zsbE2dOlXp6en68ccfPS4PCQnR/PnzNX/+fH3xxRfatWuX7r77bj399NNq0qSJV8c6meeee04rV67UN998o/Hjx6tLly5q0KCB+vfvr1deeUXTpk0rVlft2rWVnJys2267Teeee64++eQTj31GjRrlEd5lZ2frvffe06hRo8pcV8Hwx08++UStW7dWSEiIdu3apdzcXN17772qW7euIiIi1KNHD3377beu6x0+fFgjRoxQ3bp1FR4ernbt2mnhwoUexx4wYIDGjx+v+++/XzVr1lTt2rU1efJkj31mzJihdu3aKSIiQvXr19fYsWNL7YTasWOHzGaz1q5d67F95syZSk5OltPpVFpamkaOHKmEhASFhYWpWbNmmjt3ruv67p1IJ9v3ZN5++201bNhQMTExGj58uEeXYm5ursaPH6/ExESFhoaqT58+WrNmTbHH292SJUs8OgMLht+9/vrratSokUJDQ4vVMHXq1BI7ljp27KhHH33Udd5qtap27dqqV6+errrqKo0cOdL1PCrtdo4ePaobb7xRCQkJio6O1jnnnKNffvnF43b+7//+T926dVNoaKji4+N16aWXui4rOjzTZDJp9uzZGjx4sMLCwtS4cWN99NFHxWqPjY1V7dq11bx5cz3++OOy2+1avny56/IvvvhCffr0UWxsrOLi4nTxxRdr+/btrssbNWokSerUqZNMJpMGDBjguuz1119Xq1atFBoaqpYtW+rll18udn/uu+8+ffnll1q4cKEuvfRSdejQQT169NC9996rP/74Q6mpqa7hsU6nU/Xq1dPs2bM9jrNhwwaZzWbt3Lmz2P0r+vwr6Bj9+uuv1bVrV4WHh6tXr17aunWrx/WefPJJ1apVS1FRUbrhhhuKdZee6v4V3O7777+v/v37KzQ0VAsWLNCCBQuUl5enN998U23atNHw4cM1fvx4zZgxw+PYDodDI0eO1JQpU9S4ceNit11R/B8tAgAAoEq7fl7+B7Hf96br+78OakCLRD9XhOrg9RV/6/UVKafcr23daL0+qpvHthvnr9Fve9JPed0b+zbSjX198+HsjTfe0IgRIxQUFKQRI0bojTfeUK9evTz26dKlix588EHdeOONatKkibp3767bbrutXMcqzcKFC3XeeeepU6dOJV5+quGUYWFhOnz4sMe2a6+9Vs8884x27dqlBg0aaNGiRWrYsKE6d+5cppoKZGVl6amnntLrr7+uuLg4JSYm6o477tDmzZv13nvvKSkpSYsXL9YFF1ygTZs2qVmzZsrJyVGXLl00ceJERUdH67PPPtO1117revwKzJ8/XxMmTNDPP/+sVatWafTo0erdu7fOO+88SZLZbNYLL7ygRo0a6e+//9bYsWN1//33Fws1pPww5txzz9XcuXPVtWtX1/a5c+dq9OjRMpvNevTRR7V582Z9/vnnio+P17Zt25SdnV3i/T7VvgMGDFDDhg095v7evn27lixZok8//VRpaWm68sor9eSTT+rf//63JOn+++/XokWLNH/+fCUnJ+vpp5/WoEGDtG3bNtWsWbPMv5Nt27Zp0aJF+u9//yuLxVLs8jFjxmjKlClas2aNunXL/zvbsGGDfv31V/33v/8t9bhhYWEeHWUl3c4VV1yhsLAwff7554qJidGrr76qgQMH6s8//1TNmjX12Wef6dJLL9XDDz+st956S3l5eVq6dOlJ78+jjz6qJ598Us8//7zefvttDR8+XJs2bVKrVq2K7Wu32/XGG29IkoKDg13bMzMzNWHCBLVv314ZGRl67LHHdOmll2rjxo0ym81avXq1unfvrq+++kpt2rRxXXfBggV67LHH9OKLL6pTp07asGGDbrrpJkVERGjUqFHKy8vTHXfcoXnz5umss87SypUrddddd2n37t269NJLlZWVpUGDBmnBggVq3ry57rrrLjVp0kQjRozQu+++6/FasWDBAvXu3VvJycknfTzcPfzww3r22WeVkJCgW2+9VWPGjNEPP/wgSfrggw80efJkvfTSS+rTp4/efvttvfDCCx7h1anuX4EHHnhAzz77rDp16qTQ0FBNnDhR/fr183iMBw0apKeeekppaWmqUaOGpPyANjExUTfccINWrFhR5vt12owAd+zYMUOScezYMX+XAgAAEJCSJ37q+lm+5YC/y0GAyc7ONjZv3mxkZ2d7bJ/x5VaP515pP8NeWlnsmMNeWlmm6874cmu56x41apRxySWXGIaR/5kkLCzM2Lhxo2EYhrFhwwYjMjLSOH78eLHr5eXlGfXr1zdCQkKMnTt3Frvcm2OVJDQ01Bg/frzHtmHDhhkRERFGRESE0bNnzxLvg9PpNJYtW2aEhIQY9957r2EYhrF8+XJDkpGWlmYMGzbMmDJlimEYhnH22Wcbzz//vLF48WKjrB85586da0hy3S/DMIydO3caFovF2LNnj8e+AwcONB588MFSj3XRRRcZ99xzj+t8//79jT59+njs061bN2PixImlHuPDDz804uLiPOqLiYlxnX///feNGjVqGDk5OYZhGMa6desMk8lkpKSkGIZhGEOGDDGuv/76Eo+dkpJiSDI2bNhwyn0NwzCuvfZa44EHHnCdnzRpkhEeHm6kp6e7tt13331Gjx49DMMwjIyMDCMoKMhYsGCB6/K8vDwjKSnJePrpp0u8P4ZhFPt9TZo0yQgKCjJSU1M99uvfv79x5513us4PHjzYuO2221znx40bZwwYMMDjOB06dHCdX7t2rREfH29cfvnlpd7OihUrjOjoaNfjW6BJkybGq6++ahiGYfTs2dMYOXJkCY9YvuTkZOO5555znZdk3HrrrR779OjRw6N2SUZoaKgRERFhmM1mQ5LRsGFD4/Dhw6XezsGDBw1JxqZNmwzDKP77da/93Xff9dj2+OOPu/7mvvzyS6NLly6GYRhGWlqaUbNmTeOxxx4zNmzYYDz88MOGxWIx5s6daxiGYVxzzTXG7NmzDcPIfw0wmUyu1wuHw2HUrVvXdXnB/Vq8eHGJ9RX8HX/11Veu/T/77DNDkut1t2fPnsbYsWOLPXbuv9dT3b+C2505c6bHPuedd55x8803e2z7/fffDUnG5s2bDcPIfz7UrVvXOHjwoGEYnq9NJ1Pa/x+GUfasiOGZAAAAOC0jexTOW5QQFeLHSlCdRIVaVTs69JQ/cRHBxa4bFxFcputGhfpmYM7ChQvVpEkTdejQQVL+0LXk5GS9//77xfZdtmyZ9u/fL6fT6TGcrjzHKquXX35ZGzdu1JgxY5SVleVx2aeffqrIyEiFhoZq8ODBuuqqq4oNbZTyO47mzZunv//+W6tWrdLIkSO9riM4OFjt27d3nd+0aZMcDoeaN2+uyMhI1893333nGg7ncDj0+OOPq127dqpZs6YiIyP1v//9T7t27fI4tvtxJalOnTpKTU11nf/qq680cOBA1a1bV1FRUbr22mt1+PDhYo9HgWHDhslisbjmcps3b57OPvtsNWzYUJJ022236b333lPHjh11//33n3QI7an2feuttzR9+nSPbQ0bNlRUVFSJ92f79u2y2Wzq3bu36/KgoCB1795df/zxR6l1lCQ5OVkJCQkn3eemm27SwoULlZOTo7y8PL377rsaM2aMxz6bNm1SZGSkwsLC1L17d/Xs2VMvvvhiqbfzyy+/KCMjQ3FxcR6/+5SUFNfvfuPGjRo4cKBX96dnz57Fzhd9TJ577jlt3LhRn3/+uVq3bq3XX3/dozvvr7/+0ogRI9S4cWNFR0e7fudFn3PuMjMztX37dt1www0e9+eJJ55w3Z9Nmza5OkZ//PFHxcXFacqUKerYsaOeeOIJ17BPKf/3nZaWJin/NaBVq1Z69913JeUP305NTdUVV1zh1WPj/jdSp04dSXI9p/744w/16NHDY3/3x7Is96+Ae3dmWRw/flzXXnut5syZo/j4eK+u6wsMzwQAAMBpMbsN5wrQebtRCd3Yt3G5h04WHa5Z0d544w39/vvvHhNvO51Ovfnmm7rhhhtc29LS0nTTTTfpkUcekWEYGjt2rPr37+/xQbGsxypNs2bNis1VVPABuaRhe2effbZmz56t4OBgJSUllTp5+ODBg3XzzTfrhhtu0JAhQxQXF3fKWooKCwvzGB6akZEhi8WidevWFRsaGBkZKUl65pln9Pzzz2vmzJmuOcnuuuuuYpPJBwUFeZw3mUyuhQZ27Nihiy++WLfddpv+/e9/q2bNmlq5cqVuuOEG5eXllTiZfHBwsK677jrNnTtX//rXv/Tuu+/q+eef93g8du7cqaVLl2rZsmUaOHCgbr/9dv3nP/8pdixv9i3L/SkLs9ksw/Ccg9JmsxXbLyIi4pTHGjJkiEJCQrR48WIFBwfLZrPp8ssv99inRYsW+uSTT2S1WpWUlOQxFK+k28nIyFCdOnU85q8rUDAXW1hY2ClrK4/atWuradOmatq0qebOnasLL7xQmzdvVmJi/tQDQ4YMUXJysubMmaOkpCQ5nU61bdv2pAsYFMyPN2fOnGLhU8Fz2263u+5TXl5escek4DkvSevXr9ctt9ziOj9y5Ei9++67euCBB/Tuu+/qggsu8Ppv0P05VfB3WNbnVFnuX4Gi96t27do6cOCAx7aC87Vr19b27du1Y8cODRkyxHV5QV1Wq1Vbt24tNu+jL9FpBgAAgNNidpsCyWmwEADgbtOmTVq7dq2+/fZbbdy40fXz7bffatWqVdqyZYtr33Hjxql27dp66KGH9PDDD6tu3bq6/fbby3Ws0owYMULLli3Thg0bylR/RESEmjZtqgYNGpx0tT2r1arrrrtO3377bbEuo/Lq1KmTHA6HUlNTXSFGwU/t2rUlST/88IMuueQSXXPNNerQoYMaN26sP//806vbWbdunZxOp5599lmdddZZat68ufbu3XvK691444366quv9PLLL8tut+tf//qXx+UJCQkaNWqU3nnnHc2cObPE1QDLs++pNGnSRMHBwa75qKT8QGzNmjVq3bq16/aOHz+uzMxM1z4FE8N7y2q1atSoUZo7d67mzp2r4cOHFwu0goOD1bRpUzVs2LBYYFaSzp07a//+/bJarcV+9wUhcvv27fX11197VetPP/1U7HxJ85kV6N69u7p06eKaK+7w4cPaunWrHnnkEQ0cOFCtWrVydXy531dJHitc1qpVS0lJSfr777+L3Z+CDrKmTZu6Vuns1q2btmzZoo8//lhOp1Mff/yxfvnlF2VnZ+uZZ57R7t27NXToUNfxr776av32229at26dPvroo3J1ep5Mq1at9PPPP3tsc38sy3L/StOzZ099//33HqHtsmXL1KJFC9WoUUMtW7bUpk2bPF7zhg4dqrPPPlsbN25U/fr1fXpfi6LTDAAAAKfF7JaaEZoBnt544w11795d/fr1K3ZZt27d9MYbb+iZZ57R4sWL9eGHH2rdunWucGr+/Pnq2rWrFi1apMsuu6zMxzqZu+++W5999pkGDhyoSZMmqW/fvqpRo4b+/PNPff755yVO9l5Wjz/+uO67775ydZmVpHnz5ho5cqSuu+4618ThBw8e1Ndff6327dvroosuUrNmzfTRRx/pxx9/VI0aNTRjxgwdOHDAFQ6VRdOmTWWz2TRr1iwNGTJEP/zwg1555ZVTXq9Vq1Y666yzNHHiRI0ZM8YjKHrsscfUpUsXtWnTRrm5ufr0009LDWdOte91112nunXrFhuiWZqIiAjddtttuu+++1SzZk01aNBATz/9tLKyslzdiD169FB4eLgeeughjR8/Xj///LPHQgPeuvHGG101u4d15XXuueeqZ8+eGjZsmJ5++mlXkFkw+X/Xrl01adIkDRw4UE2aNNHw4cNlt9u1dOlSTZw4sdTjfvjhh+ratav69OmjBQsWaPXq1a7J/ktz11136dJLL9X999+vOnXqKC4uTq+99prq1KmjXbt26YEHHvDYPzExUWFhYfriiy9Ur149hYaGKiYmRlOmTNH48eMVExOjCy64QLm5uVq7dq3S0tI0YcIEnXvuubrpppv0559/qnnz5nrppZc0YsQI5eXlqVu3bho0aJDuvPNODR48WF9//bVCQgqnQ2jYsKF69eqlG264QQ6HwyNQ84U777xTo0ePVteuXdW7d28tWLBAv//+u8dCAKe6f6W5+uqrNWXKFN1www2aOHGifvvtNz3//PN67rnnJEmhoaHFVmgt6DYsaeVWX6PTDAAAAKdl7g87XKfTc+z+KwSoRJxOp8xms9555x1ddtllJe5z2WWX6a233tLBgwd16623atKkSR4fAtu1a6dJkyZp7NixSk1NLdOxShpi5y40NFRff/21Jk6cqLlz56pPnz5q1aqV7rrrLvXu3VtLliwp930ODg5WfHz8KVfg9MbcuXN13XXX6Z577lGLFi00bNgwrVmzRg0a5M+l+Mgjj6hz584aNGiQBgwYoNq1a2vYsGFe3UaHDh00Y8YMPfXUU2rbtq0WLFhQ5oCqYAhn0e664OBgPfjgg2rfvr369esni8Wi9957r8RjnGrfXbt2ad++fV7dpyeffFKXXXaZrr32WnXu3Fnbtm3T//73P9dKhDVr1tQ777yjpUuXql27dlq4cGGJc9WVVbNmzdSrVy+1bNmy2PC88jCZTFq6dKn69eun66+/Xs2bN9fw4cO1c+dO1apVS1L+qqIffvihPvnkE3Xs2FHnnHOOVq9efdLjTpkyRe+9957at2+vt956SwsXLjxlwHrBBReoUaNG+ve//y2z2az33ntP69atU9u2bXX33XcXC6qtVqteeOEFvfrqq0pKStIll1wiKT9YfP311zV37ly1a9dO/fv317x581ydWNHR0Zo4caKuvPJKHT58WGPGjNHRo0e1e/durVq1Su+8846OHTumjz/+WPXq1StW58iRI/XLL7/o0ksv9fnQ1auuukqPPvqo7r//fnXp0kU7d+4strLvqe5faWJiYvTll18qJSVFXbp00T333KPHHntMN998s0/vQ3mZjKIDmQNMenq6YmJidOzYMUVHR/u7HAAAgIDT8IHPXKfnj+mu/s1PPmk04I2cnBylpKSoUaNGCg0N9Xc5ZXbBBReoadOmHpOdI/A8/vjj+vDDD/Xrr7/6uxS/MgxDzZo109ixY0/aVeRPJpNJixcv9jpUPZMK5jL89NNP9dhjj2nYsGFKSEhQZmamvvjiCz3++ON6/fXXvZ5Mv7o62f8fZc2KGJ4JAAAAn7H4sMMEqIrS0tL0ww8/6Ntvv9Wtt97q73JQQTIyMrRjxw69+OKLeuKJJ/xdjl8dPHhQ7733nvbv36/rr7/e3+VUaSaTSbNnz9bgwYP19NNP69Zbb5XVapXdblfXrl31yCOPEJidYYRmAAAAOC1t60brtz3pkqSzGhdffQ+oTsaMGaM1a9bonnvucQ3LOpPatGmjnTt3lnjZq6++6vMJwstq8ODBWrFiRYmXPfTQQ3rooYfOcEWn54477tDChQs1bNgwny18UFUlJiYqPj5er732mmv4J07P0KFDNXToUGVnZ+vQoUOKjY1VVFSUv8uqlgjNAAAAcFoKusvMJslqYcpcVG+LFy/26+0vXbq01HnNCuaC8ofXX39d2dnZJV5Ws2bVC9vnzZt3WhPnB5KqMuNTVanTXVhYWIWvDomTIzQDAADAabE58j+IWM0EZoC/JScn+7uEEtWtW9ffJQCA13hnAwAAgNPicJ4IzSzMZ4aKUxW7RAAA/uOL/zcIzQAAAHBath44LknKynNoW2qGn6tBoAkKCpIkZWVl+bkSAEBVUvD/RsH/I+XB8EwAAAD4zI5DmWqaGOnvMhBALBaLYmNjlZqaKkkKDw+XiVVaAQClMAxDWVlZSk1NVWxsrCwWS7mPRWgGAAAAn3EwhA4VoHbt2pLkCs4AADiV2NhY1/8f5UVoBgAAgNPSrm6MNu05Jol5p1AxTCaT6tSpo8TExFJXhgQAoEBQUNBpdZgVIDQDAADAaRnaIckVmjmcfi4GAc1isfjkQxAAAGXBQgAAAAA4Le7TSznpNAMAAAGC0AwAAACnxWIuTM0IzQAAQKAgNAMAAMBp+Ss1w3Wa0AwAAAQKQjMAAACclnd/3uU67WROMwAAECAIzQAAAFBuRVfLdNBpBgAAAgShGQAAAMrN4fQMyeIjg/1UCQAAgG8RmgEAAKDc7G6h2VmNa+qclrX8WA0AAIDvEJoBAACg3NxDM6uZt5YAACBw8M4GAAAA5eZwFIZmFrPJj5UAAAD4FqEZAAAAys3mtlxmkIXQDAAABA5CMwAAAJSb+0IAX/2Rqo837vFjNQAAAL5DaAYAAIByszmcHucPHs/1UyUAAAC+RWgGAACAcnPvNJMkwyhlRwAAgCqG0AwAAADlVjc2TFOGtnGdd5CaAQCAAEFoBgAAgHKzWsyqExPqOu8kNAMAAAGC0AwAAACnxWwqXDXT6SQ0AwAAgYHQDAAAAKfFYnYLzcjMAABAgCA0AwAAQLntP5ajOSv+dp1neCYAAAgUhGYAAAAot11HsvTj9sOu8wzPBAAAgYLQDAAAAOVmdzg9zpOZAQCAQOHX0Oz777/XkCFDlJSUJJPJpCVLlrgus9lsmjhxotq1a6eIiAglJSXpuuuu0969e/1XMAAAADzY3VIyk0nq1TTOj9UAAAD4jl9Ds8zMTHXo0EEvvfRSscuysrK0fv16Pfroo1q/fr3++9//auvWrRo6dKgfKgUAAEBJHG6h2d3nNlevJvF+rAYAAMB3rP688cGDB2vw4MElXhYTE6Nly5Z5bHvxxRfVvXt37dq1Sw0aNDgTJQIAAOAkbG7DM91X0QQAAKjq/BqaeevYsWMymUyKjY0tdZ/c3Fzl5ua6zqenp5+BygAAAKon906zIAuhGQAACBxVZiGAnJwcTZw4USNGjFB0dHSp+02fPl0xMTGun/r165/BKgEAAKoXm1todjzHrsxcux+rAQAA8J0qEZrZbDZdeeWVMgxDs2fPPum+Dz74oI4dO+b62b179xmqEgAAoPpxOAuHZ876Zpue/mKLH6sBAADwnUo/PLMgMNu5c6e++eabk3aZSVJISIhCQkLOUHUAAADVm81heJx3GEYpewIAAFQtlTo0KwjM/vrrLy1fvlxxcSxhDgAAUJlEh3q+nXSSmQEAgADh1+GZGRkZ2rhxozZu3ChJSklJ0caNG7Vr1y7ZbDZdfvnlWrt2rRYsWCCHw6H9+/dr//79ysvL82fZAAAAOOGCtnX0xV19XecNOs0AAECA8Gun2dq1a3X22We7zk+YMEGSNGrUKE2ePFmffPKJJKljx44e11u+fLkGDBhwpsoEAADASZhNhatmOmg1AwAAAcKvodmAAQNO+m0k31QCAABUfu6hGZkZAAAIFFVi9UwAAABUXubCzExOUjMAABAgKvVCAAAAAKjclmzYoxnL/nSddzJSAAAABAg6zQAAAFBuOw5nateRLNd5B5kZAAAIEIRmAAAAKDd7kZSMTjMAABAoGJ4JAACAcrO7zWH20IUtdUGbOn6sBgAAwHfoNAMAAEC52R1O1+nODWqoQVy4H6sBAADwHUIzAAAAlJt7p5nVwltLAAAQOHhnAwAAgHKzOws7zaxmkx8rAQAA8C1CMwAAAJSbw63T7LNN+/Tj9kN+rAYAAMB3WAgAAAAA5WZzWz1z9rfbtXlvuno1ifdjRQAAAL5BpxkAAADKzb3TTJKchlHKngAAAFULoRkAAADKbUCLBI3o3sB1ntAMAAAECkIzAAAAlNslHetq0pDWrvNu6wIAAABUaYRmAAAAOC0mt0UzHXSaAQCAAEFoBgAAgNNidkvNDEIzAAAQIAjNAAAAUG52h9MjNHOSmQEAgABh9XcBAAAAqLque3O1ftx+2HW+6GqaAAAAVRWdZgAAACg3u8MzJGN4JgAACBSEZgAAACg3u9tymVGhVkWEMJABAAAEBt7VAAAAoNzsJ4Zjmk3SpsmD/FwNAACA79BpBgAAgHIrGJ5pNfO2EgAABBbe3QAAAKDcCib+t1pMp9gTAACgaiE0AwAAQLnZTsxpZjETmgEAgMBCaAYAAIByK+g0O55j1z0f/KLpS//wc0UAAAC+4fVCACkpKVqxYoV27typrKwsJSQkqFOnTurZs6dCQ0MrokYAAABUUgVzmknSovX/qGFcuB68sJUfKwIAAPCNModmCxYs0PPPP6+1a9eqVq1aSkpKUlhYmI4cOaLt27crNDRUI0eO1MSJE5WcnFyRNQMAAKCSsJ8YnlnAYRil7AkAAFC1lCk069Spk4KDgzV69GgtWrRI9evX97g8NzdXq1at0nvvvaeuXbvq5Zdf1hVXXFEhBQMAAKDymDWis7JtDo16c7UkqUiGBgAAUGWVKTR78sknNWjQoFIvDwkJ0YABAzRgwAD9+9//1o4dO3xVHwAAACqx7o1qSpJqRYfoQHqunHSaAQCAAFGmhQAKAjO73a633npLBw4cKHXfuLg4denSxTfVAQAAoEqwmPJXzyQ0AwAAgcKr1TOtVqtuvfVW5eTkVFQ9AAAAqIJMJ0IzB8MzAQBAgPB69czu3btr48aNTPYPAAAAfbPlgMwmk/YczZYkGXSaAQCAAOF1aDZ27FhNmDBBu3fvVpcuXRQREeFxefv27X1WHAAAACovwzA0Zt5aj22sngkAAAKF16HZ8OHDJUnjx493bTOZTDIMQyaTSQ6Hw3fVAQAAoNJyOIsHZM4StgEAAFRFXodmKSkpFVEHAAAAqhh7kYBsSIckhQdZ/FQNAACAb3kdmjGXGQAAACTPTrM+TeM1a0QnP1YDAADgW16HZm+99dZJL7/uuuvKXQwAAACqDrujMDSzmE1+rAQAAMD3vA7N7rzzTo/zNptNWVlZCg4OVnh4OKEZAABANWF3Ol2ngyyEZgAAILCYvb1CWlqax09GRoa2bt2qPn36aOHChRVRIwAAACoh9+GZdJoBAIBA43VoVpJmzZrpySefLNaFBgAAgMBlcwvN/vf7AXX791fq+sRXfqwIAADAd7wenlnqgaxW7d2711eHAwAAQCXncHiunnnweK4kyTAMmUx0ngEAgKrN69Dsk08+8ThvGIb27dunF198Ub179/bqWN9//72eeeYZrVu3Tvv27dPixYs1bNgwj2NPmjRJc+bM0dGjR9W7d2/Nnj1bzZo187ZsAAAA+JjDMGQxmzyGaUqSYUhkZgAAoKrzOjRzD7UkyWQyKSEhQeecc46effZZr46VmZmpDh06aMyYMfrXv/5V7PKnn35aL7zwgubPn69GjRrp0Ucf1aBBg7R582aFhoZ6WzoAAAB8qFF8hLZPu1CGYeiy2T9q/a6jkvLDNLNIzQAAQNXmdWjmdFsl6XQNHjxYgwcPLvEywzA0c+ZMPfLII7rkkkskSW+99ZZq1aqlJUuWaPjw4T6rAwAAAOVnMplktRROles0jJPsDQAAUDWc1kIAhmHIqKA3RSkpKdq/f7/OPfdc17aYmBj16NFDq1atKvV6ubm5Sk9P9/gBAABAxXJfPJPMDAAABIJyhWZvvfWW2rVrp7CwMIWFhal9+/Z6++23fVrY/v37JUm1atXy2F6rVi3XZSWZPn26YmJiXD/169f3aV0AAAAozuKWmhWd4wwAAKAq8np45owZM/Too4/qjjvucE38v3LlSt166606dOiQ7r77bp8X6Y0HH3xQEyZMcJ1PT08nOAMAAKgAOw5lat6PO2Qxm/TDtsOu7QzPBAAAgcDr0GzWrFmaPXu2rrvuOte2oUOHqk2bNpo8ebLPQrPatWtLkg4cOKA6deq4th84cEAdO3Ys9XohISEKCQnxSQ0AAAAo3d5j2Zr3445i22k0AwAAgcDr4Zn79u1Tr169im3v1auX9u3b55OiJKlRo0aqXbu2vv76a9e29PR0/fzzz+rZs6fPbgcAAADlY3eUnI45Sc0AAEAA8LrTrGnTpvrggw/00EMPeWx///331axZM6+OlZGRoW3btrnOp6SkaOPGjapZs6YaNGigu+66S0888YSaNWumRo0a6dFHH1VSUpKGDRvmbdkAAADwMfe5y7o3rKkrutaT2WRSWLDFj1UBAAD4hteh2ZQpU3TVVVfp+++/d81p9sMPP+jrr7/WBx984NWx1q5dq7PPPtt1vmAuslGjRmnevHm6//77lZmZqZtvvllHjx5Vnz599MUXXyg0NNTbsgEAAOBjNofTdbp/iwRd0ZV5ZAEAQODwOjS77LLL9PPPP+u5557TkiVLJEmtWrXS6tWr1alTJ6+ONWDAABknmSjWZDJp6tSpmjp1qrdlAgAAoIK5d5pZ3VbPBAAACAReh2aS1KVLF73zzju+rgUAAABViN09NLN4PVUuAABApVau0MzpdGrbtm1KTU2V0+n0uKxfv34+KQwAAACVm93tfWDq8Rxt3psup2GoWa1IhViZ1wwAAFRtXodmP/30k66++mrt3Lmz2NBKk8kkh8Phs+IAAABQebmvnvnqd3/r1e/+liStuP9s1a8Z7q+yAAAAfMLr0OzWW29V165d9dlnn6lOnToymZi/AgAAoDpyn9OsLNsBAACqEq9Ds7/++ksfffSRmjZtWhH1AAAAoIqoHROqs1skyO409Me+4zqUkStJcp5koScAAICqwusZW3v06KFt27ZVRC0AAACoQga0SNTc67vr7Rt6aECLBNd2QjMAABAIytRp9uuvv7pOjxs3Tvfcc4/279+vdu3aKSgoyGPf9u3b+7ZCAAAAVHoWtyk7GJ0JAAACQZlCs44dO8pkMnlM/D9mzBjX6YLLWAgAAACgejK7jV9gTjMAABAIyhSapaSkVHQdAAAAqMLMHp1mhGYAAKDqK1NolpycXNF1AAAAoIqZ+0OK3l61UxazSRZzYWhGZgYAAAKB16tnAgAAAJJ0OCNPfx/KlCQ1SYhwbWd4JgAACARer54JAAAASJLdLRwLtlpcpxmeCQAAAgGdZgAAACgXh9PpOn3foOZqmxQjk8mk2PCgk1wLAACgaihzp9nff/9dkXUAAACgirE5CjvKaoQHKzE6VAlRIQqyMJgBAABUfWV+R9O+fXu1bdtWDz30kH7++eeKrAkAAABVgPvcZQRlAAAg0JT53c2hQ4c0ffp0paam6pJLLlGdOnV000036f/+7/+Uk5NTkTUCAACgErK7Dc90Xz0TAAAgEJQ5NAsNDdWQIUP0+uuva9++fVq0aJHi4uI0ceJExcfHa9iwYXrzzTd18ODBiqwXAAAAlYTdbXjmxt1H9ep32/Xyt9u0+0iWH6sCAADwjXL10ZtMJvXq1UtPPvmkNm/erA0bNqhv376aN2+e6tWrp5deesnXdQIAAKCScR+euWr7YU3/fIue/mKrUg5l+rEqAAAA3/DJ6pnNmjXTPffco3vuuUeHDx/WkSNHfHFYAAAAVGK2UuY0cxpGSbsDAABUKT4JzdzFxcUpLi7O14cFAABAJXN5l3rqVD9WDqehgxm5ru2EZgAAIBD4PDQDAABA9dC/eYL6N0+QJL20fJtru9v6AAAAAFUWa4MDAADgtJncFs+k0wwAAAQCr0Izh8Oh77//XkePHq2gcgAAAFAVmd1SM0IzAAAQCLwKzSwWi84//3ylpaVVVD0AAACoIvYfy9HuI1nadyxbZo9OM//VBAAA4Ctez2nWtm1b/f3332rUqFFF1AMAAIAqYvzCDVq9I3/V9IkXtHRtp9MMAAAEAq/nNHviiSd077336tNPP9W+ffuUnp7u8QMAAIDqwe4243+QpbDVzEGrGQAACABed5pdeOGFkqShQ4fK5DZ3hWEYMplMcjgcvqsOAAAAlVZBOGY2SQlRIWpZO0omk0nRoUF+rgwAAOD0eR2aLV++vCLqAAAAQBVjc+SHZlazWZd0rKtLOtb1c0UAAAC+43Vo1r9//4qoAwAAAFVMQaeZxX0VAAAAgADh9ZxmkrRixQpdc8016tWrl/bs2SNJevvtt7Vy5UqfFgcAAIDKq2BOM6uF0AwAAAQer0OzRYsWadCgQQoLC9P69euVm5srSTp27JimTZvm8wIBAABQOdmdBcMzCc0AAEDgKdfqma+88ormzJmjoKDCSV579+6t9evX+7Q4AAAAVF72gjnNLGZ9/+dBjXz9Jw1/bZWWbT7g58oAAABOn9dzmm3dulX9+vUrtj0mJkZHjx71RU0AAACoAlzDM80mHTyeqx+2HZYkXdSujj/LAgAA8AmvO81q166tbdu2Fdu+cuVKNW7c2CdFAQAAoPJzXwjAfTGAE5sBAACqNK87zW666SbdeeedevPNN2UymbR3716tWrVK9957rx599NGKqBEAAACV0OKxvZVrd8pskjbtOeba7iA1AwAAAcDr0OyBBx6Q0+nUwIEDlZWVpX79+ikkJET33nuvxo0bVxE1AgAAoBKqXzPcdXrzvnTXaadBaAYAAKo+r0Mzk8mkhx9+WPfdd5+2bdumjIwMtW7dWpGRkRVRHwAAAKoAs6lweCaZGQAACARez2k2ZswYHT9+XMHBwWrdurW6d++uyMhIZWZmasyYMRVRIwAAACo5tynN5CA1AwAAAcDr0Gz+/PnKzs4utj07O1tvvfWWT4oCAABA5WYYht5etUPv/rxLX20+4NFpxvBMAAAQCMo8PDM9PV2GYcgwDB0/flyhoaGuyxwOh5YuXarExMQKKRIAAACVi8Np6NGPf5ckdU2uoVv7N3Fd5mQhAAAAEADKHJrFxsbKZDLJZDKpefPmxS43mUyaMmWKT4sDAABA5WR3C8asFpMsZvdOM39UBAAA4FtlDs2WL18uwzB0zjnnaNGiRapZs6brsuDgYCUnJyspKcmnxTkcDk2ePFnvvPOO9u/fr6SkJI0ePVqPPPKITG5DAAAAAHBmOdxDM7NZ9WuG6+Z+jWUySZ0b1PBjZQAAAL5R5tCsf//+kqSUlBQ1aNDgjIRWTz31lGbPnq358+erTZs2Wrt2ra6//nrFxMRo/PjxFX77AAAAKJndURiaWcwmNU2M1EMXtvJjRQAAAL7l9UIAycnJWrlypa655hr16tVLe/bskSS9/fbbWrlypU+L+/HHH3XJJZfooosuUsOGDXX55Zfr/PPP1+rVq316OwAAAPCO3el0nQ6yMAIAAAAEHq9Ds0WLFmnQoEEKCwvT+vXrlZubK0k6duyYpk2b5tPievXqpa+//lp//vmnJOmXX37RypUrNXjw4FKvk5ubq/T0dI8fAAAA+Jb7nGbu85kBAAAECq9DsyeeeEKvvPKK5syZo6CgINf23r17a/369T4t7oEHHtDw4cPVsmVLBQUFqVOnTrrrrrs0cuTIUq8zffp0xcTEuH7q16/v05oAAABQZCEAs1mGYcjhNGRzOGV3OE9yTQAAgKrB69Bs69at6tevX7HtMTExOnr0qC9qcvnggw+0YMECvfvuu1q/fr3mz5+v//znP5o/f36p13nwwQd17Ngx18/u3bt9WhMAAAAkh8Nz9cx1O9PU5KGlavbw53rqiy1+rAwAAMA3yrwQQIHatWtr27Ztatiwocf2lStXqnHjxr6qS5J03333ubrNJKldu3bauXOnpk+frlGjRpV4nZCQEIWEhPi0DgAAAHiyuc1pZjGbPBaJcmtCAwAAqLK87jS76aabdOedd+rnn3+WyWTS3r17tWDBAt1777267bbbfFpcVlaWzGbPEi0Wi5xOWv4BAAD8yWwyqUHNcCXFhKpmeLDcpzVzkJoBAIAA4HWn2QMPPCCn06mBAwcqKytL/fr1U0hIiO69916NGzfOp8UNGTJE//73v9WgQQO1adNGGzZs0IwZMzRmzBif3g4AAAC80yg+Qt/ff7br/K//HHWdNgxCMwAAUPV5HZqZTCY9/PDDuu+++7Rt2zZlZGSodevWioyM9Hlxs2bN0qOPPqqxY8cqNTVVSUlJuuWWW/TYY4/5/LYAAABQfma34ZkOQjMAABAAvA7NCgQHB6t169a+rKWYqKgozZw5UzNnzqzQ2wEAAMDpccvMmNMMAAAEBK9Ds5ycHM2aNUvLly9XampqsfnF1q9f77PiAAAAUDVY3CY1c5KaAQCAAOB1aHbDDTfoyy+/1OWXX67u3bt7rJQEAACA6mHz3nTNWPanrGaTBrerrVZ1ol2XORmeCQAAAoDXodmnn36qpUuXqnfv3hVRDwAAAKqAQxm5+uqPA5Kk5rUi1SbJPTTzV1UAAAC+Y/b2CnXr1lVUVFRF1AIAAIAqwuGWjFktZo+FABieCQAAAoHXnWbPPvusJk6cqFdeeUXJyckVURMAAAAqOZujcF5bi9mkOjFheueGHjKbpMToED9WBgAA4Bteh2Zdu3ZVTk6OGjdurPDwcAUFBXlcfuTIEZ8VBwAAgMrJvdMsyGJSWLBFfZrF+7EiAAAA3/I6NBsxYoT27NmjadOmqVatWiwEAAAAUA3Z3UIzi9nrGT8AAAAqPa9Dsx9//FGrVq1Shw4dKqIeAAAAVAF2Z+HwTKuZL1EBAEDg8To0a9mypbKzsyuiFgAAAFQRdof7QgAm5dgcWrX9sBxOQ/FRIepYP9Z/xQEAAPiA1730Tz75pO655x59++23Onz4sNLT0z1+AAAAEPjch2dazSal59h0/bw1uvGttXpp+TY/VgYAAOAbXneaXXDBBZKkgQMHemw3DEMmk0kOh8M3lQEAAKDSKjqnmcVtnlvDMEq6CgAAQJXidWi2fPnyiqgDAAAAVUizxEiN7NFADqehRvERMruFZu4rawIAAFRVXodm/fv3r4g6AAAAUIWc1ThOZzWOc50/lmVznSYzAwAAgaBModmvv/6qtm3bymw269dffz3pvu3bt/dJYQAAAKg6zG4z5ToZngkAAAJAmUKzjh07av/+/UpMTFTHjh1lMplKnKuCOc0AAACqJ/fhmYRmAAAgEJQpNEtJSVFCQoLrNAAAAODOYnYLzZx+LAQAAMBHyhSaJScnl3gaAAAA1dOMZX/qle+2y2o26Y1R3dQ5OdZ1mYNOMwAAEADKFJp98sknZT7g0KFDy10MAAAAqoY8uzP/R5LJJFnchmeWNI0HAABAVVOm0GzYsGFlOhhzmgEAAFQPdkfhGEyr2SSzyeQKz9znNwMAAKiqyhSaOZmYAgAAAG7szsJuMqvFLLPZpJTpF/mxIgAAAN8yn3oXAAAAwJOtSKcZAABAoClTp9kLL7xQ5gOOHz++3MUAAACgarA7CjvNgix8DwsAAAJPmUKz5557rkwHM5lMhGYAAADVgM1t+g4LnWYAACAAlSk0S0lJqeg6AAAAUIU4nO6dZvmh2bSlfyjX5lBCVIjuOKeZv0oDAADwiTKFZgAAAIA79+GZ1hPDMxf8tFOZeQ41rxVJaAYAAKq8MoVmEyZM0OOPP66IiAhNmDDhpPvOmDHDJ4UBAACg8nJfCCDoxPBMsyn/X/cuNAAAgKqqTKHZhg0bZLPZXKdLYzIxnwUAAEB1cPvZTXVZl3qyOwxFhwVJkswnwjODzAwAAASAMoVmy5cvL/E0AAAAqqcO9WPVoci2gvUAHKRmAAAgALA+OAAAAHyiYBVNJ6EZAAAIAF4vBJCTk6NZs2Zp+fLlSk1NldNtuXFJWr9+vc+KAwAAQNVRMFVHkbeHAAAAVZLXodkNN9ygL7/8Updffrm6d+/OPGYAAADV0C+7jyrb5lCQxaQuyTUlFQ7PpNMMAAAEAq9Ds08//VRLly5V7969K6IeAAAAVAEPL9mk3/aky2o2adu0CyVJFhPDMwEAQODwek6zunXrKioqqiJqAQAAQBVhd+QHYwXzmEmFwzMdDM8EAAABwOvQ7Nlnn9XEiRO1c+fOiqgHAAAAVYDdmR+aBVkK3072ahKnc1omqm+zeH+VBQAA4DNeD8/s2rWrcnJy1LhxY4WHhysoKMjj8iNHjvisOAAAAFRO9hPtZFZLYafZM1d08Fc5AAAAPud1aDZixAjt2bNH06ZNU61atVgIAAAAoBqynRieaTV7PXABAACgSvA6NPvxxx+1atUqdejAN4kAAADVld2Z32kWZOELVAAAEJi8/mqwZcuWys7OrohaAAAAUEUULARgJTQDAAAByutOsyeffFL33HOP/v3vf6tdu3bF5jSLjo72WXEAAAConGwn5jQLchueeeP8tdp+MENBFpO+vLu/v0oDAADwCa9DswsuuECSNHDgQI/thmHIZDLJ4XD4pjIAAABUWgWrZ1rMhZ1me49mK+VQpoKtzHMGAACqPq9Ds+XLl1dEHaXas2ePJk6cqM8//1xZWVlq2rSp5s6dq65du57ROgAAAFCoIDSzWgoDsoKmM8Mw/FESAACAT3kdmvXvf+Za7dPS0tS7d2+dffbZ+vzzz5WQkKC//vpLNWrUOGM1AAAAoLjNUwbJ7jTkdAvIzCdWVXc4Cc0AAEDVV6bQbNeuXWrQoEGZD7pnzx7VrVu33EUVeOqpp1S/fn3NnTvXta1Ro0anfVwAAACcHqvFLKvFc1tBaEZmBgAAAkGZJpzo1q2bbrnlFq1Zs6bUfY4dO6Y5c+aobdu2WrRokU+K++STT9S1a1ddccUVSkxMVKdOnTRnzpyTXic3N1fp6ekePwAAAKh4btObyUlyBgAAqrgydZpt3rxZ//73v3XeeecpNDRUXbp0UVJSkkJDQ5WWlqbNmzfr999/V+fOnfX000/rwgsv9Elxf//9t2bPnq0JEybooYce0po1azR+/HgFBwdr1KhRJV5n+vTpmjJlik9uHwAAAGXnviiA0zBklukkewMAAFRuJsOLmVqzs7P12WefaeXKldq5c6eys7MVHx+vTp06adCgQWrbtq1PiwsODlbXrl31448/uraNHz9ea9as0apVq0q8Tm5urnJzc13n09PTVb9+fR07dkzR0dE+rQ8AAKA6ys5zaOZXf8pqMalZYpSGdcqfluPKV1dpdcoRSdLWJy5QSNHxmwAAAJVAenq6YmJiTpkVebUQQFhYmC6//HJdfvnlp11gWdSpU0etW7f22NaqVauTDv8MCQlRSEhIRZcGAABQbWXm2fXq939Lks5tlegKzSymws4yFtAEAABVXZnmNPOX3r17a+vWrR7b/vzzTyUnJ/upIgAAANgdhYmY1Vz4dtLtpMeqmgAAAFWRV51mZ9rdd9+tXr16adq0abryyiu1evVqvfbaa3rttdf8XRoAAEC1ZXM4XaetlsLusmvPaqjzWtWS2WzyCNMAAACqokodmnXr1k2LFy/Wgw8+qKlTp6pRo0aaOXOmRo4c6e/SAAAAqi27073TrDA0u6BtbX+UAwAAUCEqdWgmSRdffLEuvvhif5cBAACAExxO904zOsoAAEBg4l0OAAAAvGJzm9MsyG14JgAAQCApV2j29ttvq3fv3kpKStLOnTslSTNnztTHH3/s0+IAAABQ+ZS2EMChjFztOpylHYcyZXeb9wwAAKAq8jo0mz17tiZMmKALL7xQR48elcPhkCTFxsZq5syZvq4PAAAAlYzNWfJCAI8s/k39nlmuAf/5Vocz8/xRWoXbdThLr3y3XbuPZPm7FAAAUMG8Ds1mzZqlOXPm6OGHH5bFYnFt79q1qzZt2uTT4gAAAFD52D2GZxa+nXRfMNPhtlhAILny1VV68vMtGjHnJ3+XAgAAKpjXCwGkpKSoU6dOxbaHhIQoMzPTJ0UBAACg8ooIsahn4zjZnU7Vrxnu2m42FXadOY3ADM32p+dIkv5Jy/ZzJQAAoKJ5HZo1atRIGzduVHJyssf2L774Qq1atfJZYQAAAKic2iTFaOHNZxXb7hGaBeiUZg1qhmvXkSzVjAj2dykAAKCCeR2aTZgwQbfffrtycnJkGIZWr16thQsXavr06Xr99dcrokYAAABUARZz4HeaFawWamOhAwAAAp7XodmNN96osLAwPfLII8rKytLVV1+tpKQkPf/88xo+fHhF1AgAAIAqwK3RLIBDs/yJ2wjNAAAIfF6HZpI0cuRIjRw5UllZWcrIyFBiYqKv6wIAAEAVE+hzmhmGoWxb/srxOTZCMwAAAp3Xq2eec845Onr0qCQpPDzcFZilp6frnHPO8WlxAAAAqHy+2XJAF8z8XkNmrdRnv+5zbbd4hGb+qKxi2Z2Gdh7Ocp0P1BVCAQBAPq87zb799lvl5eUV256Tk6MVK1b4pCgAAABUXkcybdqy/3j+6azC94Vmt69jAzFQsjs875PN4ZTFbPFTNQAAoKKVOTT79ddfXac3b96s/fv3u847HA598cUXqlu3rm+rAwAAQKVjd5vPK8ht8v9AH55pK7IkqM3hVGgQoRkAAIGqzKFZx44dZTKZZDKZShyGGRYWplmzZvm0OAAAAFQ+NrcuMqulsL3sznOb6YY+jWQ2mVQnNtQfpVUom90zNCvaeQYAAAJLmUOzlJQUGYahxo0ba/Xq1UpISHBdFhwcrMTERFksfNMGAAAQ6Dw6zSyF3WWJUaFKjPJHRWeGvciQU7rMAAAIbGUOzZKTkyVJTicrBQEAAFRn7h1WVrPX60pVWTa3sPCi9nUUFkxoBgBAIPN6IYACmzdv1q5du4otCjB06NDTLgoAAACVl/vcXla3TrNA5x4Wus/lBgAAApPXodnff/+tSy+9VJs2bZLJZJJxYpJX04mJXx0Oh28rBAAAQKXi2WlWGB6t35WmzXvT5TQMDWpTW7WiA2teM5vHsNTq02EHAEB15fX/9nfeeacaNWqk1NRUhYeH6/fff9f333+vrl276ttvv62AEgEAAFCZ2EtZCOB/v+3XI0t+02Mf/64dhzL9UVqFsrmFhbuOZCnHxpfFAAAEMq9Ds1WrVmnq1KmKj4+X2WyW2WxWnz59NH36dI0fP74iagQAAEAl4rEQgFunmdnttDMAF5a0uw1L/TnliLbsP+7HagAAQEXzenimw+FQVFT+skjx8fHau3evWrRooeTkZG3dutXnBQIAAKBy6dc8QZGhVjkchhrEhbu2u0/z5TQCLzVrkhCpbg1raM2ONEmewzUBAEDg8To0a9u2rX755Rc1atRIPXr00NNPP63g4GC99tpraty4cUXUCAAAgErkrMZxOqtxXLHtFpN7p1nghWYRIVZ1b1SzMDSzE5oBABDIvA7NHnnkEWVm5s9RMXXqVF188cXq27ev4uLi9P777/u8QAAAAFQNJrfQzBGI4zMlWc2Fs5vk0WkGAEBA8zo0GzRokOt006ZNtWXLFh05ckQ1atTweKMEAACA6sXiNj4zABvNJEnB1sLQzH1hAAAAEHi8WgjAZrPJarXqt99+89hes2ZNAjMAAIBq4liWTQeP5+poVp5HR5n7nGaB2Gm271i2/vf7ftd55jQDACCwedVpFhQUpAYNGsjhYHltAACA6mrSJ79pyca9kqRv7x2ghvERkjyHZwbinGYbdx3Vr/8cc50nNAMAILB51WkmSQ8//LAeeughHTlypCLqAQAAQCVnc+sis1oKgzL34ZkB2GhWbA4zhmcCABDYvJ7T7MUXX9S2bduUlJSk5ORkRUREeFy+fv16nxUHAACAysfuFh4FWQq/g40ODVK9GmEym0wKDfL6u9lKz14kJKPTDACAwOZ1aDZs2LAKKAMAAABVhft8ZVa37rKrezTQ1T0a+KOkM8LuLNppRmgGAEAg8zo0mzRpUpn2W7hwoYYOHVqsEw0AAABVm/uwRKsl8DrKSuN+v+8c2Ez/6lzPj9UAAICKVmHvcm655RYdOHCgog4PAAAAP3HvuAqyVJ8V1N07yxonRCgyxOvvnwEAQBVSYaGZEYArJgEAAKBIp5m5+nSa2avp/QYAoLri6zEAAAB4xX0hAPc5zb7/86De+WmnnIah0b0aqU+zeH+UV2Fsbh121mrUYQcAQHVFaAYAAACv2E8sBGA2SWa30GzP0Wx9uTl/eo7zWtfyS20Vyb3TbMHPuxQbFqQejeP8WBEAAKhI9JUDAADAKwXDM4suAmAxFQZozgCcqcNSpKuuICAEAOR76ostuuiFFfr1n6P+LgXwCTrNAAAA4JWXR3ZWZq5djiLJmFtmJmcAzm97+9lN1bdZvIa++IMkz4UBAKC623k4U7O/3S5JGjnnZ22aMsjPFQGnr8JCs+TkZAUFBVXU4QEAAOAnjeIjStxudu80C8RWM0lBbt11hGYAUOjg8VzX6eO5dtfptMw8/XnguMKDraoTG6r4yBB/lAeUS7lDs7Vr1+qPP/6QJLVq1Updu3b1uPy33347vcoAAABQpbgPXwzQzMwjNMuzB+idBAAfWrczTTe+tVaSdO/5zXXHOc38XBFQdl6HZv/8849GjBihH374QbGxsZKko0ePqlevXnrvvfdUr149X9cIAACAKsB9eGbRoZuBItgtNLM76TQDgAId6se6TteKLuwmy7I5XKfDgpkhClWL1wsB3HjjjbLZbPrjjz905MgRHTlyRH/88YecTqduvPHGiqgRAAAAlcjHG/fov+v/0VdFJsL37DQLvNDs/TW79PCSTa7zDM8EgEJBFrMiQ/JDsejQwqmasvMKh2pmuQ3bBKoCr2Pe7777Tj/++KNatGjh2taiRQvNmjVLffv29WlxAAAAqHwmffK7jmbZ1DAuXOe2ruXa7jGnWQCGZj//fUQr/jrkOs/wTADwFBpkUUauXVl5hd1lqemFc509u+xPjRvI8ExUHV53mtWvX182m63YdofDoaSkJJ8UVZonn3xSJpNJd911V4XeDgAAAEpnd+SHRVaL51tJz9DsjJZ0RtiK3Ck6zQDAU3iwRZKU7TYkE6jKvA7NnnnmGY0bN05r1651bVu7dq3uvPNO/ec///Fpce7WrFmjV199Ve3bt6+w2wAAAMCpFYRFVrfhmJJUv2aYRnRvoGvOaqDWdaL9UVqFshcJyQjNAKDQr/8c1a4jWZKkI5l5ru15vFaiCvN6eObo0aOVlZWlHj16yGrNv7rdbpfVatWYMWM0ZswY175HjhzxSZEZGRkaOXKk5syZoyeeeMInxwQAAED52E90XAUV6TRrkxSj6f9q54+Szgj3kCwq1Kra0aF+rAYAKpdV2w+7Ts+8qqPrdHYeXWeourwOzWbOnFkBZZzc7bffrosuukjnnnvuKUOz3Nxc5eYWjplOT0+v6PIAAACqDcMwXCtjWi2mU+wdWGyOwuGZK+8/RzHhQSfZGwCqF/d5zNxfH7MYqokqzOvQbNSoURVRR6nee+89rV+/XmvWrCnT/tOnT9eUKVMquCoAAIDqyT04CjJ7PdNHlWZ3FnaaVbfAEABOJcctHAsPsrhO02mGqqxMoVl6erqio6Ndp0+mYD9f2L17t+68804tW7ZMoaFla39/8MEHNWHCBNf59PR01a9f32c1AQAAVGfVOThyDwyr230HgFNx7zQLDy6MGgjNUJWV6evBGjVqKDU1VZIUGxurGjVqFPsp2O5L69atU2pqqjp37iyr1Sqr1arvvvtOL7zwgqxWqxyO4n98ISEhio6O9vgBAACAb3gGR55vJVdtP6yWj36u5o98rhlfbj3TpVU49znNqluXHQCcintoNuubv5SaniNJemlkZ7VJKvxcnmdnYQBUHWXqNPvmm29Us2ZNSdLy5csrtCB3AwcO1KZNmzy2XX/99WrZsqUmTpwoi8VSyjUBAABQEewewZFnt5UhQzm2/MttTkOBxu4WGN741lrFhAXpObfJrgGgOsu22V2nv9x8QGPPbqrE6FBZzCbFR4YU7pfnULCVLx5QNZQpNOvfv3+JpytaVFSU2rZt67EtIiJCcXFxxbYDAACg4jkNKTEqRHanoegwz4nwzSaT236BF5r1aRav+jXDtHTTfn2zJVWJUSGnvhIAVBNZRYZhZuUVhmjhwYUNL1k2u2LEQiqoGrxeCGDu3LmKjIzUFVdc4bH9ww8/VFZW1hlfKAAAAABnTkJUiFY/fG6Jl1ncOs+cAdhpNvGClpKk/s8s187DWR7DNQGguisamrkvDHDvoBa6bUAThQdbPLrOgMrO657I6dOnKz4+vtj2xMRETZs2zSdFncy3336rmTNnVvjtAAAAwDvuozUDMDNzCToxl5v7cE0AqO6KTvhfEKLN+vovfbxxr37555iaJka5XkOBqsDrTrNdu3apUaNGxbYnJydr165dPikKAAAAVU+gD88sUPCBL49OMwBwqVcjTJv2HHOdLwjRXl+ZomPZNjWMC9e1ZyX7qzygXLyOeBMTE/Xrr78W2/7LL78oLi7OJ0UBAACg6vEIzQK41SzYkn8/GZ4JAIVmX9NFz17RwXU++8TwzILwLCzY654dwO+8ftaOGDFC48ePV1RUlPr16ydJ+u6773TnnXdq+PDhPi8QAAAAlceuw1l66n9bFGQ2qV/zBP2rcz3XZZ6dZv6ormKdN+M7Hc+xa396jqT8++hwGh5zuQFAdeYx4X+eQ3aH09WV+8e+dC1cvUtnNY5To/gIf5UIeMXr0Ozxxx/Xjh07NHDgQFmt+Vd3Op267rrrzsicZgAAAPCfw5m5+uzXfZKk2PBgz9DMbQxDIA7P3J+eo+M5do9tNodTFrOllGsAQPUS6haaZec5XN1mBR787yY9e0UHQjNUGV6HZsHBwXr//ff1+OOP65dfflFYWJjatWun5GTGJgMAAAQ6u1sLWZDFs8Mq0Oc0K2k4Zp7DqdAgQjMAkKRwt9fDbJuj2OIAkpRlK74NqKzKPai4YcOGMgxDTZo0cXWcAQAAILC5B0cWs+f0uHVrhOmVa7rIbMo/HWhKWi2TFTQBQMrKs2v03DXak5YtSeqaXENJMaGuFTTdZefZi20DKiuv066srCyNGzdO8+fPlyT9+eefaty4scaNG6e6devqgQce8HmRAAAAqBzcQ6KinWbRoUG6oG3tM13SGWEYhkeX3XU9kxVkMRd7DACgOsrItWt1yhFJ0nmta2nOdV0l5c9jVlRJQRpQWXm9euaDDz6oX375Rd9++61CQ0Nd288991y9//77Pi0OAAAAlYvdWdhpZjV7/VayynIPzLo1rKGpl7TVoxe3VlRokB+rAoDKwX0YZliQ52IAJ9sXqOy87jRbsmSJ3n//fZ111lkyuc1b0aZNG23fvt2nxQEAAKBysbl1mlmrUZeV+7DUIEv1CQsBoCzcw7HwIosBnGxfoLLz+n/8gwcPKjExsdj2zMxMjxANAAAAgedkwzNzbA6tTjmiVdsP688Dx890aRXKMywkNAMAd+5BWJhbaBYRYtFZjWsqMsRa4r5AZef1//hdu3bVZ5995jpfEJS9/vrr6tmzp+8qAwAAQKVzsuGZhzJydeWrqzRizk96/uu/znRpFcru3mlmzn//axiGjABcJRQAvJXjtiLm3B926JIXV+qOd9erU4Maeu/mnvrf3f1K3Beo7Lwenjlt2jQNHjxYmzdvlt1u1/PPP6/Nmzfrxx9/1HfffVcRNQIAAKCSOFmnmdlt1EGghUnuc5p9vSVVzR5eKpvD0H/H9lLnBjX8WBkA+F/R7rFf/jnmOWTTY54zVs9E1eF1p1mfPn30yy+/yG63q127dvryyy+VmJioVatWqUuXLhVRIwAAACqJBnHhurxLPQ3rmKTGCZEel1nMhaGZW0NaQIgKteo/V3TQ9H+1U6s60a7hmu4hIgBUVyUFYUWHbEaFWlUrOkQxYSyggqrDq04zm82mW265RY8++qjmzJlTUTUBAACgkurWsKa6NaxZ4mXu09s6AqzTLDzYqsu71JMk7UnL1h/70iV5LhAAANVVSRP+uw/DDA2yaNPkQWeyJMAnvOo0CwoK0qJFiyqqFgAAAFRhlgAenunOffXMPEIzAChxcv+sPIfeWJmiC59foctm/6jf9hzzQ2XA6fF6eOawYcO0ZMmSCigFAAAAVZn7nGYOZ+CGZla3udxsdkIzAGhXL0a39G+s63omu+a7zLY59E9aljbvS9e6nWnKtbMAAKoerxcCaNasmaZOnaoffvhBXbp0UUREhMfl48eP91lxAAAAqDrM7nOaBVhmlpVn145DWQqymJSeY3NttzGnGQB4DN3/60CGVv19WJJ0NKvw9TIsyOv4AfA7r5+1b7zxhmJjY7Vu3TqtW7fO4zKTyURoBgAAEMBe+367Xvxmm4IsZs0c3lF9myW4LnPLzOQMsOGZf+xL12WzV0nyXPCAOc0AwFN4cOFKmYcycl2nX/t+u2xOQ3aHU69e29UfpQFe8zo0S0lJqYg6AAAAUAVk5jqUnpO/SlrRIZgeq2cGWGjm3lEWHmzR8ROPAaEZAHgKcwvNjmTmuU6v25Wm3UeyJeX//+H+fwZQWXk9p5k7wzACepJXAEDgWfnXIY1dsE6rU474uxSgSrI7C0Mi9wnxJc85zZwBliXZi4RmBRieCQD5K2XaT3yJEBZUcmhWMyLEdTrbxvxmqBrKNaj4jTfe0HPPPae//vpLUv48Z3fddZduvPFGnxYHAICvXfPGz5KkpZv2a8eTF/m5GqDqcQ+PrEW6BEKsZv0x9QKZTAq4DgKbWwoYHmyVlD/kiE4zAJDGLdygZZsPKMhi0pShbZUUG6bwYIveW7PbtU9cRLDrdFaeXZEhzHGGys/rZ+ljjz2mGTNmaNy4cerZs6ckadWqVbr77ru1a9cuTZ061edFAgAAoHJw76yyFuk0M5lMHsNyAon7Kpl9msbroQtbKchiUrNaUX6sCgAqh+y8/M4xm8PQ0I5JrkBsyca9kqRgi1lRodZi+wOVndeh2ezZszVnzhyNGDHCtW3o0KFq3769xo0bR2gGAKi0is6/ZBiGTKbA6oYBKprn8Mzq8/djd3v9qF8zTOe1ruXHagCgcsnKs7tOuw/PzDkxDDMs2OIxtD2L0AxVhNdzmtlsNnXtWnyliy5dushut5dwDQAAKoei82cwnwbgPY9OM/NpTY9bpbgPw6xO9xsAyqIgBAu2mj2G5xeEaeHBFoUFWYvtD1R2XneaXXvttZo9e7ZmzJjhsf21117TyJEjfVYYAAC+lpXr+eVOnt2p8OBSdgZQIrvj5J1mM77cKpvTUK2oEI3u3ehMllah3Odyq04ddgBQFgVfRIYHW+RwGsrKsyvb5tANfRrJ5jAUYjUrLatwUQCGZ6KqKPdCAF9++aXOOussSdLPP/+sXbt26brrrtOECRNc+xUN1gAA8KcMt9DsX53qKpbEDPCa+zDFonOaSdLs77bL5jDUtm50QIVm7p1muXanVm0/LJvDqaTYUDVNZF4zANVbQedYeJBFSzbs0T0f/iJJevySNrq5X0NJ0kvLt7ntzyg1VA1eh2a//fabOnfuLEnavn27JCk+Pl7x8fH67bffXPsxRwwAoLIJtpo1qE0tZeU51KI2H3KB8vAcplj8/V7+e0BDzgBbVNLmFhamHMrUE5/9IUka07uRHhvS2l9lAUClUNA5FhZs8VgQxn0qDPe5zpgiA1WF16HZ8uXLK6IOAAAqXL0a4Xr12uLzcgIou+t7N9J5rWvJ7jAUF1m8W7MgR3MaRrHLqrIrutTToNa1ZHMa2n0kSwt+3iXJM0QEgOrIMAy34ZlWj9DMfe6yFrWj9K/OdRUWZFGDmuFnvE6gPMo1PBMAAADVU5fkGuqSXKPUyy0nRhsEWmgWGmRR6IkuieM5Ntd2QjMA1V2ew+laoTwsyKJwt46y7QczlZaZp/AQi3o3jVfvpvH+KhMoF5b+AQBUKz9uO6Sr5/yki2et0Mcb9/i7HCDgmF2hmZ8LqUBBbnO55RGaAajm3Cf1Lzo88/9+2atOjy/TA4s2+aM04LTRaQYAqFbSc+z6cfthSdKeo9l+rgYIPAXT2joDODULdgvNbI7AvZ8AUBbhwVZ9dGtPZeU5FBVqVbhbaFYgrIRtQFVAaAYAqDYWrt6lB/9b+E3n8RxWbgK8tXX/cWXbHLKaTWqTFF1s8SeLOTCHZ/7092Ft3H1UVrNJnd2Gp9rpNANQzQVbzerasKbr/D9pWcX2cV8EQMqfB43FA1EVEJoBAKqNwxm5HufTs22l7AmgNA8t3qR1O9MkSdunXShLkc88gTo8c/nWVL363d+SpJdHdnZtZ04zAPAUHlw8ZggPtmhbaoaueOVHZeU5dGmnunrysvZ+qA7wDqEZAKDayMzzXN6cTjPAewWdVSZTYVeZu4LOAUeApWZ2t2GY7sOM8hieCQAeShueGWwxKy0r/wvLzDyHHE5Dz/xvq2wOpyZe0FLBVqZcR+VDaAYAqDYycz1DsvQcOs0AbxXM4RVkLvnDTecGsTqabVNCVMiZLKvCuQ/DdF8Zzman0wxA9bb/WI427EpTWLBFTRMjVTc2TCaT5D5KPzzIc4GA7Dy7Plq3W698t12S1DghQiN7JJ/p0oFTIjQDAFQbmbl0mp0Ou8OpH7cfVvNaUaodE+rvcuAndmd+SGQtOi7zhNeu63omyzljbM6SO80CraMOALy1YVeabluwXpL04OCWuqV/E310a08t3rBH7/y0S1L+kE33DrSsPIe+/+uQ63xkCNEEKieemQCAaiMrr0inGXOaeeXNH1I0bekWxUeGaOXEsxUaxEpY1VHBMEVrCUMzA5l7R1lokEVbHr9AQRZziUNUfSHH5lCwxSxzNXucAVQ9WW7TXxQEY12Sa2rDrqOu7aHBFo/FALLyHIoIKXxd7dygcIEVoDJh0DAAoNpgTrPTszrliCTpUEau/j6Y6edq4C+2E51mQZbq9TbS7tZRFmQxKzTIUmGBmSQ9uuQ3NXvkc3V9YplSDvH3BqDyyrIVvr8Kc1sEIMdte3iQRWazSaFB+f93ZOc5tC01I/86QRbVjQ07Q9UC3qHTDABQbTCn2elJcntDy4qB1Zer06yU4ZmByv05fya67I5k5snhNHQoI08RIXR1Aqi8st06+Yt2kxUo6EALD7Yqx5ano9l5Ong8f1XzxgkRdNWi0iI0AwBUG+6h2V3nNlN0aJAMw3Ct9lcau8MpazXrqimJ+xLyRQNIVB821/DMkv8mbntnnXanZSk82KoPbul5JkurUO6rZ56JLrt1u9Jcp99YkaIHL2xV4bcJAOVRUji2avthxUWGaFTPZF3Sqa6aJkZKKgzVDqTnuq6z83CW5v2QohE9GijEypcEqFwIzQAA1UbBm7qaEcG669zmp9zf6TR07Zs/69d/jmnOdV11VuO4ii6xUot063bJIDSrthynWAjgzwPHtf1gpqICbFLnggUQpPz7/vK325SWmafwYKvuPu/UryfeOppV2Am771hOma9nGIYumLlCNSOC1alBrO6/oKXPawMAd9l57sMz898rPP/1n/rp7/xpHR4Y3Mq13X0xgAIZuXZN/r/NGtAiUQ3jI85AxUDZVfqvzadPn65u3bopKipKiYmJGjZsmLZu3ervsgAAVdD9F7TQ1EvalPkD7nd/HtQP2w7reI5d4xduqODqKr9lmw+4TmcVmR8O1cepFgIomOfLaQTWqpJ1Y8PUsnaUGidEKNhq1oKfdmnOihS9u3pXhd92WlaeF/vatPXAca36+7Be/na7dh/JqsDKAKDkTjP3YZrZtuKhWkn2HM2ugOqA01PpvwL87rvvdPvtt6tbt26y2+166KGHdP7552vz5s2KiCCFBgCU3cXtk7zaf/vBDNfp1OO5J9kz8BmGoV/+OeY6T6dZ9fXjg+fI7jBUWiRmPjHc2RFgodmUS9p6nA860WlXEfP75do9Q2n3rrNTST3u2ZX27dZUXduzoS/KAoASlTZ3WeHldtWMCJYk3XN+C2Xl2nX/R7/qeJH3Ev+kEfKj8qn0odkXX3zhcX7evHlKTEzUunXr1K9fPz9VBQCoygzDUGaeQ+nZNsWGB3m8sXPnzQfVQJdr9wwGmNOs+ooKDTrp5QWhmTOwMrNiCuY1s9l9H5qlZXq+9njTaZaa7hnw7zla9qGdAFAe2Ta3hQBOvKdy7yj7cO0/Gj+wmSxmk/o3T5AkPf/1X9qy/7jHcfak0WmGyqfSD88s6tix/G+5a9asWeLlubm5Sk9P9/gBAMDd7O+2q+2k/6nXk9/o+z8PlbrfXoYJuBzP8QzJCM1QmoL1AZwBnpq5QrMKuJ9HMj1DsrRML0KzIl2xvI4BqGhmk0kh1vzXxIJhme5zlz3/9V/FrrN4bG99Oq6P7hvUwrXtH0IzVEKVvtPMndPp1F133aXevXurbdu2Je4zffp0TZky5QxXBgCo7HLtDv19MFMRRbrKjueU3k2243Cm6/S/OtWtsNqqgqKPUyZzmqEUFlNgzmlWVNCJD4g2h7NMq/B6o2holpnnUJ7dqWDrqb/vLjo8k9AMQEV78erOkiSH01DBdJfuc5pJhfNdFggLtqht3Rg1io/QM//Ln7P8H16vUAlVqdDs9ttv12+//aaVK1eWus+DDz6oCRMmuM6np6erfv36Z6I8AEAltictW4OfXyGpcC4iSUrPKb1jav+JFetqR4dqxlUdK7S+yq5op1nROZdQPeTZnXpx+TYFmU1Kjo/Q0A7F5wk0uQ3P9HWY5E/3ffiL9h7LVkSwVa9d11XBJ15HDCP/g2Jpq4mWx+HM4nMoHs3KU2J06CmvW3R4JqEZgDPFPRgrbcL/vUeztfdotrLyHOpQL1Yx4UGqER6ktCwbwzNRKVWZ0OyOO+7Qp59+qu+//1716tUrdb+QkBCFhIScwcoAAFVBZm5hyFM7JlS7j+S/MSvooDIMQ9k2h8f8ZisnnqMDx3N0OKPsQ6MClXtodmv/JnpgcEs/VoMzLfV4joLMZlksJr1wYphN32bxJYZm7s0EhiEFSGam9bvStP1gpqJC8l8jCoZnSpLNYcha+oJwXuvXLEHv33yWbnxrretvLy3LVqbQ7GCR4Zn703NkdzhltVS5WVkAVGHhpYRmb63aqVe+2y5JWnjTWerZJE71aoQrLesYr1eolCr9s9EwDN1xxx1avHixvvnmGzVq1MjfJQEAqqDMvMLQp7bbB8/07PztYxesV7vJX+q91btcl5nNJtWJCVPbujFnrtBKyn14Zmz4ySeCR2DZuv+4ej/5jXpM/1opBwuHLAeV8qHGvdMgkIZo2hz596VgWKb7/c/z8QqaNSKC1aNxnEZ0b+DaVtbFAA6kew7PdBrSgWq++i9Q1ew8nKmPN+5RdiWeCsEwDB3OKP21pejwzJK2f/LLHuXaHaobGyYpv2t33zEWL0HlUuk7zW6//Xa9++67+vjjjxUVFaX9+/dLkmJiYhQWFubn6gAAVUWWe2gWEyYpTVJ+GHTweK4+/y3//5cH/rtJw90+qCKfe6dZVGilf/sAH/p4454TgZGhDbvSXNuLzk9TYHi3BhrQIlEmkwJmaKYk2U8EY9YT99uz08z3K2hK0oAWCaoRHqyaEUFqFB9RpusUXQhAyh8OVfChFEDlZnM4dcUrq5R6PFc39W2khy9q7e+SSjRu4QZ9+us+XdiutmLCgmU2SXVrhGnsgKaSSl9p2b0DbeHq3Zo0pI3q1QhTdKhV9WqEK9tWeYNCVE+V/l3v7NmzJUkDBgzw2D537lyNHj36zBcEAKiS3Idn1olx6zTLsalGeJDCgy3KOvGN7rFsm2LC8t/svf3TTi38eZeOZds06+pO6tygxpktvJJId+s0K+2NMAKT1S0cC3XrEAgqZQ6vy7qUPo1GVVawSmZBWNa6TpSybXYFWcyuxQ98rVeTePVqEu/Vde4c2Ex7jmZrzoq/XWE385oBVUdaVp4r/J6zIqVShmY5Noc+/XWfJGnppv2u7S1rR7lCs8Htauuu9/O3d29U07VPaJFhm6FBFj0wuKUeubjy3U9AqgKhmRFAbf0AAP/JzC3slKrlNjzzeI5dVotZV3atr3k/7pAkbdiVpl1HsrQnLVuf/rpPe0584KzOc5tZzCbFRwbrUEaeXvzmL/114LjuOb/Fqa+IKs99sQz3iZ2t5ko/y4dPuTrNToSFEyrw+b98S6ocTkM1I4PVqX6sVx17BaFl08RIjV2wXrHhQcq1VUwnHADfS4wKVcf6sdq4+6gkKS0zTzUigv1bVBH7SxlC6f5/hPvQUvfusmMlDDVnDjNUZjw7AQDVQqbbm7f4yGBX90xBB1WX5MIOsvU70/TpL/v06vd/uwIzKb8Drbq6vncjrX3kPNWNDdOfBzK0cPVuf5eEMyTd7XnvvlCGL1eLrApcc5qdgQ9305b+oRvfWquRc34u9xDXc1om6vcpg7TxsfN1ZTdWkgeqkk4NYl2nN/5z1G91lGbvscL3Ru4vUQVd+pJc3fuS5zxmIW6rpiTHhRc79qGMXL25MqVav+dC5UJoBgCoFrLcOs0iQ6yuebkKhi+5h2brdqVpx+FMFXW0jBNxB7KIkPw3u+6dewhs7p1mX/xWOAwnqJROs4xcuw5n5Org8Vw5nYEzYsBWZE6zinQkM/+1pmZEsA5l5Gpb6nGlHCr+mnQyoUEWRYRU+kElAErQsX6s6/TGXUf9Vkdp6tcI1wODW2pUz2TNvKqjLutcT/VqhGl0r4aufRxOQ/Vrhik+MsSjU+7KbvWVGBWi0CCzXrmmS7Fjv79mt6Z+ullnTftan2/adybuDnBS/E8KAKgWMtwWAggPturN0d0UYrUoJjxI835IUWx44Ru6H7cfVkmzA6TzrafrQ3i2zSGH0yh1MngEDvf57Bat/8d1urROs/ELN+ibLamSpA2PnlfphhWVl915ZjrNnE7DtVKm0zDU9YmvJEnntkrU66O6nfS6hzJylZ5tU63oUAIzoArrVL/wi7yCYZqVSf2a4bq1fxPX+Us61i1xnxX3n1Nse0xYkL6//2w5DcOje3na0j/0x750rfjrkKT89xlPfbFFx3PturIr3bLwH/43BQBUC1luCwFEhFjUvl6spPxvQqct3aI8t9Xv3AOzpomR2paaIal6D88sEOn2QTwzz65oFgUIeKWFxaWFR+45qjNA5qZ1Og05ToRmBWHh6yv+1kfr/pHN4dTzwzupbd0Yn9zW0WybChr0GidEaN+JuYPSsk79+vPf9f9o2tItkqSXru6si9rX8UlNAM6cJRv26N3Vu1znN+4+KsMwAmo1YvdFZQqs+OuQ/tiX7rFtx+Es/XXg+JkqCygRoRkAoFp4YHBLjT27iTJzHapXI8y1fe/RbI/ArKj29WJcodnRahyaPfHpZh3KyHV9AyzlD9EkNAt8x3OKD8VtWzdaSbGhJewtjw92jgAJzQxJt/ZvIrvDqaTY/NePQxl52rI//8Nchg+HKxcMzZSkWlGhigqx6niu3dV9djKp6bmu0/GRwVq84R+tTjmiPUdz9PLIzh6hN4DK6e9DmVqdcsR1/li2TSmHMtU4IdKPVVW8urFhxUIziS8s4X/8zwkAqBYiQqwlDlf6222eoN5N4/TDtsMel3esH6v/rt8jqXq/cftmS6rHYyVJmW7dewhcRTvNbujTSI9e3LrU/d3n/LI7AiM0s5hNemBwS49twW7DU20nCd695R6a1YwIVmxEkI7n2nW0DJ1mB44XhmaJ0aF6f+1u1+vX3qPZal4rymd1AqgYJXX3btx9tNTQLDPXrp/+PqxtqRlKjovQBW1rV3SJ2n4wQ3ERwYoJC/JZB1xpncnV+b0XKgcWAgAAVEu/7z2m/67/R1M++d217cJ2dTTunKaqHV3YQdPObchVWT60Bqr0ErqNWAzg9B3PsWnSx7/pte+3+7uUEjmcho4X+T3vLGGRDHfuq6cF8t+M+/BUX4aDRzILg68aEcGqcWK+xaNZeadcWCE1Pcd1OjEqRHVjC7tq3VcCBlB5uc8jWWD3kdL/fg8ez9UN89dq+udbtGTDnooszWXYSz+o49RlumDmCp8ds3/zBNfpa85q4DpNaAZ/o9MMAFAtLVq3R2/+kOKxrXmtKI3skay1O9K0/8SHz8bxka7hUdV5IYDjJbyJJzQ7fXNWpGj+qp2SpA71YtWjcZyfK/JkNkk/PHCOjmbl6aIXVkrKn2PmZNwX1QjkFWetbqHZyYZ4e+tIZuHfWlxEsOvxdBr5H6bdH9+iDp7oNIs80Vmb5Baa7TuaU9rVAFQi6dmF/7c+d1UHDWxV66RTIdSvGa5gq1l5dqe2Hcyo8Poycu2uYfux4b6bouGSjklavjVVEcFWPXxha32w5h/lOZw6ls17DfgXoRkAoFpY8PNOZec5FBMWpCu61ldUaPH/AhvFR0gq7KSpER6kmPAg3X1ec5lNUq3okudwCnR5dqdy7cVDAV/O41RdvfD1X67Tn/yyt9KFZiaTSXVjw1Q3NkzNa0XqzwMZ2nU466Qrp9aMKPwQVZbJ66sCp9OQzelUkNks84n7HVRhwzMLO81qRgSrRrjn43my0Cz1RGiWGBUiSaoTU/iatZdOM6BKcO80G9y2TomT5rszm6TG8RHasv+4dhzKlM3hrNBVfve5vZa4B/OnKzY8WPOu7+46Hx1m1aGMvGr9hSUqB0IzAEC18Mp327X7SLbiIoJ1Rdf6ig7z/HY0KtSquIhgGYahi9rXUcqhTIWdWAp9TJ9G/ii50ijaZdajUU1FhlhVI6L0D+/wXkkT7lcmyXER+vNAhvIcTvV7erkeGNxSQzokFdvPPdQ5EiCdZnuOZqvv08slSUM7JOmFEZ0UbC38UOrL0EySokOtSs+xnwjNCh/PtKw8NVJEidfJyrO7guyEE6GZ+/BMQjOgaigIiUKs5lMGZpI08vWfXYuS2J2Gdh7OVNPEipu/cO+xwq5V92De16LDggjNUCkQmgEAqoWsE5PWh4fkvwEt2mnWOD7CNZntwxeVPsl5deQe5hQEBjh9Tqehvs3iXSuSlrRqWGXSMC7cdXrP0WxXV1NRNd2HZ2YGRmjmHopZXZ1mbqGZ3Xdzmt1xTjPdcU4z2RxOmU0mrdxWuGLtyYa7uq+cmXiiK7YOc5oBVU5BSFT0y73S7E/3HHq9LTWjQkMz906zOj7sNCuqYH7M47n2k3Y3AxWN0AwAUC0UdGBEnOgeKzo/SMHQTBTnHpqVNKwV5WM2m/T2DT104fMrtHlfurYfzFB2nkNhwafuLDhTtqUe14q/DpU4n4778ER3NdyGZwZKp5ndbQJ+q6V4aObLOc0KFBzfo9Mss/SOC/cQs2B4ZmSIVTFhQTqWbdPeY4RmQFVQsPBOdKhVOTaHPlr3jzbuPqpa0SG6b1DLYvsfKvIFxrbUip3XzL3TLKkCO83cF5VJz7bR3Q6/4Z0vACDg2R2Fc3JFhJwIzcKKdJqVspS7JGXnOXQ4M1dHs2xqGB+hyJDq9d/n8dzCD+pRJ5mMGOXTJilam/ely2lIf+xPV+cGNfxdksv6XUc15f82S5IeuaiVHh/WVo8u+U2SZDWXPGdOi9rR+vDWnqoRHqzE6JAzVmtF8ug0OxFmVdScZkUN6ZCkvs3iVSM8+KSdJ6nHCz/I1nJ73GtHh+pYtk2p6bkyDMPVUQug8rE7nK4v+Qr+3h858ZrbsX6s7hvkuX+OzVFsdeu/Kjg08+g0i6m4TrOBLRPVoGa4YsKCZCnlSxrgTKhe7/oBANXG3qPZen/Nbg1slaiGbl1k4Se6eNw7Z0b2aKBx5zQt9VjPf/2XXvluuyTp3Rt7qFfT+AqqunKi06xita0bow/X/SNJ+n3PsUoVmrnPJZMYHeoxv521lA8xkSFWdWtYs8JrO5NsjsJOs+AToVmbpGg9MLilgizmCr2/NSOCVbMMHRYXtq2jdY/E6UB6ruIiC/ePjwrW1gNSrt2p47n2k67CB8C/HIahe85rrvQcm2pFhyo0yKI6MaHadyzHtUiRu8MlDIGv6E6zfe6dZrEV12l2bc+GFXZswBu88wUABKRJn/yuZZsP6N3Vu7Tk9t6u7SUNzzyeYz9p94X7EIFj1XBC2qSYMI3o3kDHc2yKCLboohdWKDPXrvNa12L+Nx/oWD9WvZvGqU1SjNrWjfF3OR7cOxiiQ61Kc/uAZq1G88vYS5jTrGlilM/nDTIMQ7e9s17RYVa1rB3t1SIkZrNJcZEhiov07O7r2yxBtaPDFB8VLMN3U68BqAAhVovGDWzmsa1BzXDtO5ajtCybjmXbPN6THCxhbsntBzPkdBqulX59rWBRkbAgi0ctQKAiNAMABKRlmw9Iyn9D+dueY67tJS0EkJ5z8iCsuodm7erFaHq9dpKk3UeyNPnEcD33eU3gvfOf+07hwVZ1blBDC248y9/llMi90yw6LKjI3F4lD88MRO6dZhV5v7PyHPri9/2SpJ6N43yycu+t/Zuc9jFKk2Nz6N4Pf5HDaWjGlR0r1Xx8QKBoGBehn1OOSJJ2Hc5Su3qFX64UDc3a14tR04RIZdkcFTKVhGEYrk6zpNhQhnujWiA0AwAEnOw8h8f51SfebEqFnWZRoVZFh1oVFRp0yqFPseGFodnRahiauYtwexOemWs/yZ44meM5Nv15IH8ITWXu2HIPlKNDg7R4wz+u80cySl49U5J+2HZI/6Rl6XiOXTf2bVyhNZ4Jdmdhp1lpCyD4whG3Tr6aJ4ZYOp2G3l+7W2lZeYoODdI1ZyVX2O17a873f+vTX/dJktrWTdHtZ5c+zB1A+TRwW7l455FMj9DskNvr8PR/tdOI7g0qtBaTyaQ1j5yrfUezlVXkvVZFybHl305oEKE8/IPQDAAQcEwmaXi3+npvzW5J0podhaFZQaeZ1WLWr5MHlXj9oqp7p5m7iJDCN61ZuWfmDXMgKgjMJKlZLd8O8fOl9Gy34ZlhVv25v7DuzJN8YHr2y61av+uoJGlUr4YeK01WRe4T/RfcF5vDqbSsPNkchsKCLGWad+xU3OcnijtxPJNJmvTx78pzONWydlSpodm0pX8oPjJYLWtHq1/zhNOupSwKAjNJ+uqPA4RmwGnKznMoz+5UZKhVlhNfqDSMK5yXdefhLI/93TvNEiLPzMIrkSHWM/L/1jdbDujWd9Yrz+7U/Re00NgBvL7AP6r2OxgAAEoQGmTRhPOau87/dSBDbetGq3F8hGpFeT9pbXUPzQy3iZBCrBZXp00GnWbl9ueB467TLWrlr9xqGIZ2H8nSHreVyfytaKfZ/DHdJUkhVrOu6Fqv1Ou5B0hHs6r+34zn8Mz85//ve9PV/d9fq/eT32jWN3/55HaOZBZ+AK4RXhCamVzdrqU9lll5dr32/d+atnSLZn71Z4n72B1OZeX59m/WUOHjcnH7JJ8eG6iOPt64Rx2mfqmmDy/VB2vzv/hLdu80K7IYgHtoFh918tAsx+bw+P+8sgsNsijvxMrn1fG9FyoPOs0AAAEpMTpU8ZEhOpSRq5Ags/7vjj7lnnvDIzQLgADAW3cs3KDlW1IVFWrVx7f3UUSIVUezbMr08Qfw6mTr/sLQrHntKP2255iunvOT0nPsGt2roSYPbePH6goVzGkWbDUrNMiink3i9M09/RUZYlXiSQLo2PDC0CwtK08Jp/gwV9l1a1hT797UQ3aH4er6cB+m6d6JdjoOZ7h1mrmtgFkzIlipx3OVllV8pTxJSjlU+EG6UXykx2XbUjN01aurdCQrT1d1ra8nL2vvk1olyXziNTXIYtKonpVn2ChQVRV8UWEYhdNJuIdmO4p0mo3qlaxeTeJ0MCNXDd32y7M7FWwt7I/5ZfdRXfP6z6oTG6qPb+9TJeYfdH/v5d71DJxphGYAgIDVJila3/15UEezbNp7LEd1Y8PKdZyY8OrdaXY8x66sPIey8hwKC7IoIvhEaEanWbn9lfr/7d13eBTV+gfw75ZsdtP7JiGNBEJvElpAQKSIqDQFLFywo9guF1EuoqI/C3pV7Hi9gOUiKMWuqCCoV1AEAiEBAoRQEtIIaZue7Pn9kexkZrObbEJC2vfzPDwkszNnZ3dPZmfeec975Jlm7tCoVdJMlYnn8+1tdtkV1uyTfLbZSH83e6tL5Jlm8hk32ysfVx1io/wUy+RDTisqmyd7IzW3NstQHpS0ZJqVVZpRUl5V54JXHjSL9HdVPOah10rDPi/UU4euscxmgdM1WS+hPi6damIIopZiPSQeANz1TvB11SGnqLxOppn1LL6PbY7H7lMXUFhaibjlE6SbhU9+lYjCskoUZprw6V9nMX9k0yYZ+TExA8czCxHkacDVvQIUN0iam/x7p6ATnntR28GgGRERdTi7ky/A3dkJYT61d10T0/KbHDRzd66uLVJlFp00aFb7mt30WqmuWRFrmjVZUk1tMD83HXxr6tB08TIgLa8ER84XwGwWULeBCQK6eBugVgM+ro3LFJNPnmEvO6q9UwTNminTLDm7tmZct4Da4KS37ML0YnE5uuiUx7JT2bKgmZ8yaObjqoNKVZ25km1qvs8iq7AMpRXVr1tec4moLauoMuPxLYdxNL0Aq+YMRHQbqykpP8eQB40m9DaitKIKEX6uEELYzZw/n1+Ccxerg+/ZpjIp+H7oXJ60zpH0gibv33eH0/HFwfMAgO2LxrRo0Kyz37CktoNBMyIi6nAe3RQv1YV6YUY/9A32RHRgw9kx9qhUKnjotcgtrkBeSccMANTHkm3kqtNAo1ZJM2iWVFShyiykYsXkmBxTmZTx012WIdAn2ANpeSUoKq/C7uQcjOruZ6+Jy+aze0c0aTsfxfDMjnmxIx+eWd5MQbOTWbUzqsqHZMkvTC+ayuvcAFBmmimPdVqNGj4u1VkqFwqbL9NM/pwRvq4oraiCTqNuE8FeInvW/Z6CLQeqZwH+v2+P4qOaOo1thbyOpHx4oqPDqrsFuOG3ExcAVB9PLEEzV51Gmrwl+xKOA+fzS6Wfg70aXyO2Mdx0WqhVgFkwaEati3nURETUoZRVViE9vzpg1j/EEzcPDcP+MxfxtzV7cccHf+GkbFhcY2xaEIvdj4/DtodHN+futguWTDP3mrvebs6199xY16zx5DNn9gisDZqN6xkg/fzo5kPIa8cZWoogTwcYnnk2pxg/HcnErqQsZBVUXzTqmjnTrMospEBUuK+LIpNNHkD78mBanW1P1WSoqVTKdS38arIZs01lzVYIXD5MbO3vKei5fBvOXCyuZwui1pVfUoFV22sn7fj1eDbOtbE+Kx+G6CELmtlSVlmFn45k4uC5PGQVVh+X5BmqybIMVHlm2tH0pp0HAZDOrzwNTnDRtWz+jVqtkt4DeTCR6HJj0IyIiDqU1NwSmGuuCS3DM5Ozi/BnykX8fCxLGk7UWN0C3BDsZZCyrDoTS6aZm776tc8ZEoYnpvTCizP6KQIH5Bj5zJnyoUGzYkIxItIXAJCeX4qlWw+3q5nO5Lxlw2rac/DPYvvRTNz90T7MX/cX9pzKAWA9PPPSP6eKKjMeHt8dM67oggm9AxWPzbwiBM41Rb0/2XtWUSdOCIFTNcG2Ll4G6J3qFvj2c68OYpZXmlHYTLUIuxvdcYdVXaQMWRYKUVvjaXDCuvlDFMs27U9tpb2xzVLbEgDc9fWfb2Tkl+Luj/Zh2tu/45mvjwAAusqGSp+tCWxXmQX+MbF2RvGMglLkNKG+YUWVGefzqv/Gm1ruorEsQ1SZaUatiWe6RETUocizHyx1duQF613awYxRbUmVWaC4ZkiH5QR+Sv8g3HVlJOYMDbN5gU71m9I/CP/5Wwz+MSEaQ7v6SMvVahVenT1AGpLzfUIGPtt3rrV285IoJgLoAMMzK821wXZLsMxJ27yZZnonDe4f2w2vzhqIxyf3VDzm7+6MOUNCAQDF5VX4ZO9Z6bGconIpsN3Vz3ZtMX+32pp0zTVEc3C4N568vjeemNJLWpZZwKAZtW3DIn3x25KrpN837TuHKnPbuTlhCQ656jSKwLxFWWWVFPCSD7O0zFAcLjsGWGba1KhVuH1kV0WQOymz8dlm5y4WS+9VV//LU8fQ8n1YUFIBcxv6nKhzYdCMiIg6lDOy6djDfV2QX1yB7xMypGWdMVPsUpgUd73rHypCjvFzc8b43kY8eHV3xVAaAAjyNGDlzH7S7898fQSmVpqlND41Dzf/+w/c+/E+fHc4vVHbervq4O3ihEg/V0UArb2SZ5JZLmS1stpd5ZXNU9OsPnePjkSknyuendYXd46qvfiVTwIQZWdmUz9Z0OxS6hnZEuhZW9cog0EzagdCfVwwvlf1cPj0/FL8ejy7lfeolmV4pvXQzMLSCox88Wf0Wr4Nj3x6EIByNlxL0CzIQw9dTUDfeqbNOUNDsf6uYYhbPqHObMCOOC1rr+tlmvzDEjQzC8DEchDUSnjlQEREHYo8aBbh54rtRzNRUlE7y2NTM80OnsvDgTO5yC+pwE0xIQjxrls3qCOS1xFpaKgINY9r+gZhUh8jfkjMRFF5FRLT8jGsZtjm5XQ+r1Qaijgg1KtR2/q5OSPuyYktsFetQ55Jpq2ZAKAlZs+sT4i3C3b8Y0ydWfNcdBpMGxiMUxeK0CvI9kyAfu6yTLNmnEETAAI9ZEEzDs+kdmL2kDBsP5oFANiw9yyuktWUbE2W71wPq5tUbs7a6mwrUXueIw+AWwLjarUKYT4uOJllwpmcYsVMzNFG90uaLTTlgvL86nL4+4RoLBgTBQ+DFgZmtlMr4dkvERF1KPI7oeE+LnUCPU0tXLv9SCbe2nkSQPWwpM4SNCuUZZp51LyXpRVVuFhUjuLySvi76RXTwlPzeOjq7pgzNAw9jO4I8mzZGcrskQdMrS/gOptKeaaZujpYplGr8O1Do6DTqOHSDBmsp7JNCPTU13uMsg6YAUDfLp5YNWdQvW0rhmc2oZaRtdKKKhSUVsDfzVmRacbhmdQW7UrKwlcHzyM60B3X9AlEhJ8rrurhjwB3Z2QVlmHHsSxkFZZKM022pi8XjkJBaQWs/9JVKhXCfF2QeL4AaXklqKgy2xyeCQARvtVBs7JKMzILS2EWgJNaBT83Z4dntz14Lg8f7TmN6wcE46oe1QHF07IZc7v6XZ5zoMHh3pfleYjqw6AZERF1KGdr7sAanDTwd3eGt9XQMI2DJ4zW5FO/d6aCtKE+BqybPwSFZZVS4d+Ne8/i6Zqiw6/NHoDpg0JacxfblYS0fCRlFKJ3sAe6BbjZrFkDAH2CPdHnMu+btcbM4tbRVZjrZpoB1Z9TcxBCYMa7u5FXXIErwryw9f6RDm0nzyKpz4goX6y+bTD83XXo6md7CGdjHDyXhzn//gOuOg0WjImSlqcz04zaoD9TLmJrXPWss9FGN0T4uUKrUeOmmBCs+/00bhgQrAiMtyb5jMrWInxdkXi+AFVmgbTcEmTLh2fKAuPhsqGTpy8U48Pdp7EtMQM6rRq7Fo9FcANF/M1mgX9uPYwj6QXYfiQT+5dPgJNGLc3ua9kXos6CQTMiIuowKqvMOJdbHTQL93WBSqWCk0aF8b0CsP1oFmIu4Y6lPJsqrxMFzdz1TnWGrcjrwpnKqqw3oXp8HX8e7/1yCgCwdn4MxvU0tvIe2VdgI8uws6qorFvTrDnlFJUjr2bCBEeyYRPP5+PD3aeRcqEIn907wmYGmlywl6HBC+XGsNRKKiqvgofBCX5uOlwwlTPTjNqk4xm2Zyy+58oo3De2G9zaSa3TcN/a7K4zF4uRXVg71FqeaXZd/yD0DHRHVz9X9AryQFpeCYDqc6Ti8ip8/McZHDmfj9uGh9sM/KvVKgR66nEkvQAFpZU4kWlC72APDAj1REWVGTlF5R2iViWRo9rHEYKIiMgBF4vL4e2iQ1ZhmeIu6Ms3DsCu41m4srt/k9uWZ5oVdKKgmS3yC4yiVipS314dOV8g/dwryKMV96Rhl5pp9p/fTmFPcg5yi8uxdv4QeLm034ss5eyZTctWrU9ylkn62XpyCFse33IYh9PyAQB7TuU0qaj3pbCubWT00OOCqRxZhWWoMosmZ/QStQTLTJFuzlopYxpAuystoAia5RRJmWYqlXLG4kFh3hgUVnuT0BI0C/TQ48+UHCz/IgEA0MPobjdbdkSkL34+Vl3z7XBaHnoHe+DRST1trtuSsgvLkHg+H/klFegT7IFuAU2vyUbUVAyaERFRhxHgrsfeZeNRVFaJ4vLaDChvV90lDyH0ll3wp+eXXFJb7Z0rg2ZNdjS9+uLN28VJUUDdllPZJuw7k4vjGYW4Z3QkAhpYv7kp69k1/uLyyPkC7Ki56MopKm/XQTP57JladW2mWX5JBX45no3EtHz0C/HEdf2Dm9T+yezaoFmUA0Gz+bER+MemQwCAW97/E6E+BsyOCcUD47o36fkb64zVLHqBHnpp2FiOqeyy91Uie0xllUjNrf7Ojja6NZiV2ZrOXSzGnlM58NA7oU+wB0J9lHXD5MMuv4lPR1ZNZqePi85uBmxxeSUuFlVnpIV4uyiCZAmymzjW+oXUrnc4LR+zhzT+9TSHP1Ny8MAncQCAZdf2YtCMWgWDZkRE1OG4OmsVgZ3m0DPIHWpV9bTnf6XkNmvbbVlytgkZ+aVwc9YiKsANblbvbRGHZzosq7BUKsLeK8ijwYu3z/alYvUvyQCAkd38LnsgQjERgKHxf0/yIFluUTnQ9ETPVqdSAVq1CpVmocg0y8gvxUMbqi/orusf1PSgmSzTLMq/4VpB1w0IwgvfH5Vmwjx3sUQxS7At8al5OJ9XiuLySsy44tJuIlhqG2nVKgR76XH36EjMHhKKQE99uw6OUsdzPLN2aGZ99cIqqswoqzS36lDNuHN5WLI5HgDwxJReuOvKSMXjg8K80MXLgLS8EuxNuSgt95PVM7OWllt7k6+LtwE9A92hUatQZRZIlAXNSsqrsDUuFcMjfRHp54o+wbWZ0IdT8y/5tTVVZ60nS20Lg2ZEREQO8NA7oV8XTxxKzUdSZiFyTGXwredEtaPYJAvcfHL3MMRG+XF4ZhNZsswAoLcDQzN7yi7wjmUU1qkt19IUwzObkGnmLRv6lFvcvi92np/eD89P7wchlMXCo/xdoXdSo7TCjIS0pl9YJmfXZm45MjzTWavBLUPD8MbPJ6VlDRX4f3RTPJIyC6HTqjF9UJcmZ9wIIXCmZsKVMB8XaDVqDI/0bVJbRC3NXj0zi4S0fCz7IgFH0wuwYEwUFk2Ivpy7p9DQkHhnrQavzBqAm9//A0IAOo0a3z08Cj6udc9Fzl0sRnK2CR/tOSMtC/E2QO+kQZS/K45nmnAisxBllVVw1mqw/0wuln1ePWzzzlFdsfy63oj0d8Wp7CIcTS9EaUUV9E6aFnjV9WPQjNqC5q9kSkRE1Eoqq8wNr3QJ5BeGf8ru8nZk8inmvQzVGSQuutoTZ1M5g2ZypRVVeOCTA5j57m6pjoyFvJ5Z7+CGg2byrIikDPvDaFqKZSIAnVbdpIsl+cy1uUXl9azZfqhUKkWwSatRS7XpTucUK7LzGsNS08xDr1XMglefW4eHK36PbCBDzVIovLzSjMJLCHZnFZZJWW3yGktEbVFSA5lmHnonHDqXh/JKMw6n5l3GPatLkd1r50bF8Ehf3HNlJAxOGjx9Qx9E+bvZLMq//MsEzF/3l1SXDIBUz61vzRDNSrPAwbN5AIA/TuVI6/WvGZrZv0v1/+VVZsx6bw9i/m87blq9G+cu1tY0bGny96Gpx1eiS8WgGRERdQj5xRUY9vwOLN50SDFsoTkNj6oNmslPMDuqorJK7DpefcLt66pDtLE6k4WZZvb9dCQT38SnY/+ZXLzyY5LisaPpjZsEIMrfDdqagurHZNkSzSnxfD7+PJVTJ4MKAGbFhODOUV1xy9CwJrUtrwOYW9wxgma29JXVCEpMa3xws6isUgqwRgU4XnPJ6KHHjEFdAACuOo3NLBo5P7faz+NCYVmj99Mi7mzt8PQIv4aHkhK1JsXwTBt/I6E+BnjVZMXGp+bbPBZeLgUlsjqS9QyJXzQxGtseuRK3DAuze7yQT4ZkEeJdHeS+Mrp24pBvD6cDUJ7TjKi5Qdi3S+2xLT41HxdMZfjrdG6TJoZpKmaaUVvA4ZlERNQhbEtMR05ROTbvT4WH3glDu/o0+3MMifBBFy8DhkR4Y2S3yztbXWv4+VgWSiuqs/eu6RsIbU2hYU4EYN+E3kbp560H0vDM1L5SkPFITdBMp1Ejyr/hIXg6rRpd/VxxIsuEU9lFqKgy2y323FRv/XwS3ydkoKufKz64fYii0PTtI7teUtverh1neGZ9+skuLBPS8jEiqnFDFU/Jh2Y60C/knpnWFz0C3RET4d1gLSZ53aPswjJENvK5gOoabku3HpZ+HxjqBaA6e+1wWh4y8svgptdiTHQ7LmBHHUpSzQ0HPzedzZIKKpUK/bp44rcTF5BTVI7z+aWKGTYvJ3kmlWc9gSlnrUZxrLbFVhZoF+/q1zW+lxE6rRrllWZ8dzgDj07qgUM1WXaR/q5S/cz+IV512vBx1dW7b83Ng0EzagOYaUZERB3CV4fOSz9PHdi0YtwNcXPW4vfHx2HVnEGY1CewRZ6jLfk2Pl36eUr/IOlnnVYNXU3wxsSJABT0ThrcNrw2M+vb+Op+WVpRhVM1MyR2C3CDTuvYKZhlOFF5lVkxVLY55JjKsP1oJoDqmTKDm/lC0dt6IoB2bO3/UvDklwl45usjKLUquN+nS23WYML5xtc1MwsBo0f1xbwj9czk3Jy1uHdMFAaHN3yTwDI8E4A0gUBj+bs7Y/aQ6v49JtpfmvigpKIKM9/dg4WfHMC/f01uUttEza3KLHDP6EjMvCIE1/S1/509QBYcij+X1/I7Zsel1pGUs5VpFuxVHQxz1zthbE1g+4KpDO/sSpZmCJaXoegT7AFXnUZRg7PrZc4u1ahVcK+5IcCgGbUWZpoREVG7l1VQit3J1UMLwn1dpHoc1HSmskrsTKoemunnpsOwrsrsmW8eGgWDkwbuep5KWLtpcCj++8dZAMDm/amYPSQM+SUVGNXdH0fOFzhUz8yiZ6A7vqkJXh7LKET3BobgNcbncWnShdLMwV3gpFE3azabvM5Oex+e+dORTOypGb60eJKyUHj3AHfoNGqUV5lxuAmTAQwI9cIvj16F//5xBlf3Mja8QRPJM80ss7g2lkatwuOTe2JAiCdiu/lBUzN82EOvhcFJg5KKKmTklzbL/hJdKo1ahXtGRzW4Xj/ZOUN8Wj4m9wuqZ+2Wk9/ARACNIc8069fFE09M6QVnbW1tyusGBOPHI9U3Td7dVRvolgfNXJ21OPz0JBzLKMS1b/wGwHYwrqV5GJxQWFapGL5KdDm1i0yzt99+GxEREdDr9Rg2bBj27t3b2rtERERtyNfx6bCUIZk6ILjJs8JRrR1HM1FWWT00c3LfIOni2CLa6I5QHxd4udQtQNzZVFaZsf1IJszm6k7YP8QT3Wsyhv46nYuUC0Uweujx0R1Dse+J8Xhuel+H2+4RWBtgS2rGumZCCHy275z0+xVh3nj6q0QMfW47TmaZUFllhqms8pLq+3gpZs9s30GzSnPtJCPWQUWdVo2eQdXBzJQLRTA1Yciy3kmDu66MbNEsDj935fDMSzG5X5BiiJZKpUKgZ3UWS2bBpbVNdLkpMs1acTIAy+QrKhWk7KqmCvF2gfxre5jVDLfjewUgNsoXT1/fGyHetVnGw61KW6jVKqTIspy7+l3+yT8sAcSCkgocTs3HXR/uw6s/HUdFC0/+RGTR5oNmn376KRYtWoSnnnoKBw4cwIABAzBp0iRkZWU1vDEREXUK8qGZN7TQ0ExrprJK7EnuuJMB2BuaSUpFZZW45+P9uOujfZj+7m4cOpcHlUqFm2JCpHU27z+n2EZ+t78h8sLVzTkZwKHUfBzPrB4uGhPujdTcEnyw+zRyiyuwad85JGUWou9TPyDqn99hxdeJTXoOZ231UNV7x0TipsGhzbbvjVFRZcbvJy9gb8pFKajZtHZqt9Wq6wbl+9RMBiCEcpbUtsS/iZlm38anK4r/22MZYmoqq0QhZ7mjdsTo4SwNX45Pzb+kY8WlKKzJNHN31kJt4zjTGDqtWqphdjqnqM4NEBedFp/cPRw3xYRK2aHyemZyp3Nqg2atMfmHp6E6k7W8yozp7/yO7Ucz8caOE7j7o30o5gzedBm0+TEVr776Ku6++27cfvvtAIDVq1fj22+/xdq1a/H444+38t61jvJKs+LgVZ9wXxfFyXl+cQUyCxtOm9eqVXUKxJ7PK3Ho7qmH3km622hxMqsQjnz/BHrqFWP4S8qrcC7XsWmNo/zdFJkQF0xluOhADRW9VoMwq2KZZ3OKUVrZcJ0eH1edYrhDlVkguaZmTUNCvV1g0NV+NgWlFQ4NaVCrgG4ByuE5GfmlDk3D7OasrVOzJjnbhCoHPhyjux6esqyB0ooqnHVwyumufq6KO/MXi8odOmHXadR1vpzPXSyWprqvj5eLEwLca/uhEAInshz7bLp4GRSFzk1llThfM7NZQ6xnL8sqKEWeAzUYXHQaaVYji5QLRQ7dRfN3c4a3bBhUZztGHDibi0M1NUh6B3nU+ftoCUu3HsZn+86hyiywecEIu8Mouvm7KU58swvLHMq4MThpEOrTtOOSr6uy2LHluCQEkFlQivjUPBxOy0eVWaB3sCf6d/FEqI8LVCog0s8VWo0ahaUV2HU8G0B1DaMhEbbrJV0wleH+9QeQnGVCUXkloo3uGN3dH6O6+ymG5jlr1XWKFjv6t+ztolPUYjKbBU46eJwN8TbARVf7t1xYWoF0B46zKqDOMMjMgtI69VTKKsz45+eHpSF5R87no7AmU2DaoC5YuS0JVWaBTftSsWhCjzrZeo6+hih/V4T5uGCYLANAPiNcfYK9DHVmO03LK8GHu09Ly2YNCcX4Xka8+P1RVFQJbDmQKtXWMgs4XH/Nlv+b1k/62dZ7aIurs7ZOIe5T2SZUOnCQCHB3VmQ/LvrsEL6uCaqH+7pg7vBwjOruB7VVNqq7XosgT+VznswywVxzoWmZ8EKrVtnMZB3a1RtncorQt4snfGtmqTyTUyRla9ry9aHzuGlwaJ1zkJbi5177vsgzzeo77p+7WIy/f3YQALByZj9MHxRicz0ACJRdbO9NuYhQHxeed/C8o47Led5xItOEYZE+inN1W1QqFQaEeGL70SwUllbizMViKevzUr63rY8BBSUV+PV4NnYcy0JSRiF6BLrj6p4BuH5AMLob3eFhcIK3i1OzFdqP8HXFuYslKCytRG5xheJ72WL/mVzp2Doi0vYkJspMs8sfNFt/13Bo1CqUlFdhzr/34FBq9XfurqRs3Pz+n3huWl/F91RLf/fb0pzfW2WVVTiT49ixLsLXVfHac4vKke3Asc5Jo67zWabmFqO43P6xztYxo7No00Gz8vJy7N+/H0uXLpWWqdVqjB8/Hnv27LG5TVlZGcrKajtKQUHbvNt3KbJNZZj42q8Orbt90WjFBeR3CemKWY/sCfbUY/fSqxXLnv3mCL5PyGhw2zlDQvHizP6KZVPf+h1F9fwRWrxz6xW4VlZH4Eh6Pma+a/uztpawYpLiwuCjPWfwxo4TDW53RZgXtt4/UrHsoY1xOOhAIdBHxnfHI+Nra5sUlVc6/NlsvT8WV4R5S7//kpSNBzfENbidu7MWh1dMUix75cckbNqf2uC2U/oH4e1brlAsu+X9PxwaSvHSzP6YNaQ2U+BMTjEmrXLstf6x9GpFkGTL/lQ8993RBreLNrrhx7+PUSz75+eH8duJCw1ue9eornjiut6KZY5+Nh/dMRSjZTN/7Tt9EfPX/eXQtqdfnKL4ffUvp7D295QGtxsd7Y+P7hiqWHbXh38hObvhk9AnpvTCXVdGSr93tmPErf/5s7ady5Rl5u3iJF303bja/jHqyDOTFCduH+05jTd/Ptlg+zHh3th8X6xi2YMbDkgnivVZNCEaD13dXfrdVGr/uLT9qDJr+69l4+Hv7gyNWoUVN/TBt/Hp6G50sxns+eNUDpIyChHl74q9KRcBVN+hj0/Nx1s7la+xT7AHvn3oSsWyxZsO4c+a7epz39goPHZNT+n3CrPZ4f694e7hipkM9yTn4J6P9ze4nU6jxvHnJiuWvfnzCalOmS3uei3emzsYsVHVs6oGuOsxNtofO45lIauwDIfT8qWZBhtDrVZhxz/G1lnu6Hvwwe1DMLZHgPT7/jO5+Nva2jIXrjoNpvQLgquzFhN6G/Hd4QxcMJXj0c3x0jqXWpDaYtX2E9iw1/57aDGpjxHvzY1RLJu7Zi/SHAgiPDe9L24dFi79PnVAsBQ0O5NTjP/71vZ3z4wruuDVWQMVy25cvRt5VrN+ajW2A5/TB4XUCSgt+O8BHE2v/zz03V3JuH1kBJZN6V3ves3Bx0UHjVoFT4MTXGTnS44e9/em5NYfNJMFHe/8cB8Anne0xnnHu78kY93vpxvc7lLOO5Zf1xt3jqqdXbetn3cM6+qDT+8dUe86/UO8pO/Eo+kFUkDh0r634xBfz/d24vkCJJ4vgKeLDt2N7vhiYfX1yKUMi5fLLKgNFO1JzrGZNW7QaTC+lxF/puQo6plZmM0Cm2XXGK1R08xyDmLQabDu9qGYt3YvkjILUV5pxqFzebjuzf8p1v/s3hGKGdR/P3kBC/57oMHn0TupcexZ5Xd/a3xvpeaWOPz39NuSqxTB2i8OpmHF10ca3C7SzxU/Lx6rWLb8iwTsTMq2u824ngFYO3+IQ/vV0bTpoNmFCxdQVVUFo1FZFNVoNOLYsWM2t3nhhRewYsWKy7F7RETUhjhpVLh+wOUJml3VMwDv7Oq4M8S56LS4eWgYbh4aZvfkvaLKjNW/JMNUWokQbwO0ahVOO3hntKMJ9tTjgzuG1sn4uGVYGHYcq74I+zExo0lBs5Y2dVAXKcPl5qFh+O5w3QvQxs7o2JZc1TMA1/QJRGZhKeLO5l1ye+E+zXvBWGkWzTbxQkO0GjWSnr0G2iY839CuPlhxQ59612nMBBdEl5N1drst1/UPQu8gD/QP9VRkDLaUEG8DUnOrAypX9wxQPNZcdVnnx3bFPz+vDkjaew+GRPhgSIQPqsxCyqyVU6tVGBDqhUPn8tAtwE2REdkafFx1+PrBUTiWUYB5a/eyhiJdFm06aNYUS5cuxaJFi6TfCwoKEBraOnU0WoqLkwazYxx7TdZ3hyP9XB3aVl681yK2m59Dd5tjbAzjmXFFCMrrGaJgIS9ECQC+rs4Ov1brGiN9gz0c2tbWsIgJvY2KOjL2WGqYWDip1Q7vr69VinSoj4tD2+qd6p7sDunqU2eoiS39Q+vOKHjDgGCHZqPp6q+8UPA0ODn8Wg1Oyvo90YHuDm1rqY8iNybaH8FWQ2hsGRjmVWeZo/trfWIR5GlweFtb+zG7rOFtuxvrXpRO7hvkULHmHoHKvtoZjxFqNTCxT2CdYUAtZUiED16+sT/2na6/zo91hlbfLp4Ovb/hNgrtju9lRM/Ahi9K5VPDA4CTViU9p5tei35dPNEvxBNOajUOp+UjPi0PeUXV2TTONo4v9k7er+zujz1Wd/2zCkrx64kLiDubi0pZDShbn8u4ngEO3bHu30V53FKrVA737wCrY0iwl2N/yxob2USDw71RUVn3gsLo4YzbRoTbvMi6upcRL93YHwUlFZg7IrzO45fC0ffAeshhoKde2tbPXaeYWe7K7v546cb+2C/r1z2D3DG+mWZ0jAn3dqhWUJ8udfv5dQOCpH5anyir4VsatQqr5w4GUD3ccuuBVOSY6g61uiLcq86yaQO7oESWAWvQaTB7iOPfBZP6GOv0X2tdvA1YMKbh2f2ai62AWUPHfW9XHe4bG9XgMN1r+wYia0ovnMisHZLI847Lf94xKMwbxWUNZ25fynlHtNW2bfm8w9tVh9tHRjTYfqS/W53hn8ClfW9P6GVEL9n3tlqtQv8QT4zrGQCjhx6ZBaX441ROi9UJuykmBEVllfByccLgcO9619WoVdDA9vf9q7MGYOuBVFzX//LcmHREz0APfLlwFP77x5k6fdbPTXmNFeLt2DWWk7bu62+N7y13vdbhvyfrIGb3AMeOdfLh+hZXdvevN2BsmfCmM1KJ5sr/bAHl5eVwcXHB5s2bMW3aNGn5vHnzkJeXhy+//LLBNgoKCuDp6Yn8/Hx4ePAOGBERERERERFRZ+ZorKhNz56p0+kwePBg7NixQ1pmNpuxY8cOjBhR/7h0IiIiIiIiIiKipmrzwzMXLVqEefPmISYmBkOHDsWqVatQVFQkzaZJRERERERERETU3Np80Gz27NnIzs7Gk08+iYyMDAwcOBDbtm2rMzkAERERERERERFRc2nTNc2aA2uaERERERERERGRRYeoaUZERERERERERNQaGDQjIiIiIiIiIiKywqAZERERERERERGRFQbNiIiIiIiIiIiIrDBoRkREREREREREZIVBMyIiIiIiIiIiIisMmhEREREREREREVlh0IyIiIiIiIiIiMgKg2ZERERERERERERWGDQjIiIiIiIiIiKyom3tHWhpQggAQEFBQSvvCRERERERERERtTZLjMgSM7KnwwfNCgsLAQChoaGtvCdERERERERERNRWFBYWwtPT0+7jKtFQWK2dM5vNOH/+PNzd3aFSqVp7d5pFQUEBQkNDce7cOXh4eLT27lA7wD5DjcU+Q43FPkONxT5DjcH+Qo3FPkONxT7TuQghUFhYiODgYKjV9iuXdfhMM7VajZCQkNbejRbh4eHBP2ZqFPYZaiz2GWos9hlqLPYZagz2F2os9hlqLPaZzqO+DDMLTgRARERERERERERkhUEzIiIiIiIiIiIiKwyatUPOzs546qmn4Ozs3Nq7Qu0E+ww1FvsMNRb7DDUW+ww1BvsLNRb7DDUW+wzZ0uEnAiAiIiIiIiIiImosZpoRERERERERERFZYdCMiIiIiIiIiIjICoNmREREREREREREVhg0IyIiIiIiIiIissKgWT1eeOEFDBkyBO7u7ggICMC0adOQlJSkWKe0tBQLFy6Er68v3NzcMHPmTGRmZkqPHzp0CDfffDNCQ0NhMBjQq1cvvP7664o2tm7digkTJsDf3x8eHh4YMWIEfvjhhwb3TwiBJ598EkFBQTAYDBg/fjxOnDihWOfAgQOYMGECvLy84Ovri3vuuQcmk6nBtuPj43HllVdCr9cjNDQUL730kuLxxMREzJw5ExEREVCpVFi1alWDbXYG7DP2+8zWrVsRExMDLy8vuLq6YuDAgfj4448bbLejY5+x32c++OADqFQqxT+9Xt9gux0d+4z9PjN27Ng6fUalUmHKlCkNtt2Rsc/Y7zMVFRV45plnEBUVBb1ejwEDBmDbtm0NttuRddb+Ulpaivnz56Nfv37QarWYNm1anXXS09Nxyy23IDo6Gmq1Go888kiD+9sZsM/Y7zP/+9//MHLkSPj6+sJgMKBnz5547bXXGtznjo59xn6f2bVrl81zmYyMjAb3m1qIILsmTZok1q1bJxISEsTBgwfFtddeK8LCwoTJZJLWWbBggQgNDRU7duwQ+/btE8OHDxexsbHS42vWrBEPPfSQ2LVrl0hOThYff/yxMBgM4s0335TWefjhh8XKlSvF3r17xfHjx8XSpUuFk5OTOHDgQL379+KLLwpPT0/xxRdfiEOHDokbbrhBdO3aVZSUlAghhEhLSxPe3t5iwYIF4tixY2Lv3r0iNjZWzJw5s9528/PzhdFoFLfeeqtISEgQGzZsEAaDQbz33nvSOnv37hWLFy8WGzZsEIGBgeK1115rzFvbYbHP2O8zO3fuFFu3bhVHjhwRJ0+eFKtWrRIajUZs27atUe9xR8M+Y7/PrFu3Tnh4eIj09HTpX0ZGRqPe346IfcZ+n8nJyVH0l4SEBKHRaMS6desa8xZ3OOwz9vvMkiVLRHBwsPj2229FcnKyeOedd4Rer29wnzuyztpfTCaTWLBggfj3v/8tJk2aJKZOnVpnnZSUFPHQQw+JDz/8UAwcOFA8/PDDDryjHR/7jP0+c+DAAfHJJ5+IhIQEkZKSIj7++GPh4uKiOA51Ruwz9vvMzp07BQCRlJSkOKepqqpy5K2lFsCgWSNkZWUJAOKXX34RQgiRl5cnnJycxKZNm6R1jh49KgCIPXv22G3n/vvvF1dddVW9z9W7d2+xYsUKu4+bzWYRGBgoXn75ZWlZXl6ecHZ2Fhs2bBBCCPHee++JgIAAxR9YfHy8ACBOnDhht+133nlHeHt7i7KyMmnZY489Jnr06GFz/fDwcAbN7GCfsd1nLAYNGiSeeOKJetfpbNhnavvMunXrhKenZ72vgdhn6jvOvPbaa8Ld3V1xEk7sM/I+ExQUJN566y3FdjNmzBC33nprva+rM+ks/UVu3rx5Ni9m5caMGcOgmR3sM/WbPn26uO222xxat7Ngn6llCZrl5uY61A61PA7PbIT8/HwAgI+PDwBg//79qKiowPjx46V1evbsibCwMOzZs6fedixt2GI2m1FYWFjvOikpKcjIyFA8t6enJ4YNGyY9d1lZGXQ6HdTq2o/ZYDAAqE4VtmfPnj0YPXo0dDqdtGzSpElISkpCbm6u3e2oLvYZ231GCIEdO3YgKSkJo0ePtttuZ8Q+o+wzJpMJ4eHhCA0NxdSpU5GYmGi3zc6Kfcb+d9OaNWswZ84cuLq62m23M2Kfqe0zZWVldYZ9GwyGetvtbDpLf6Hmwz5jX1xcHHbv3o0xY8Y0a7vtHftMXQMHDkRQUBAmTJiA33//vVnapKZh0MxBZrMZjzzyCEaOHIm+ffsCADIyMqDT6eDl5aVY12g02h1zvHv3bnz66ae455577D7Xv/71L5hMJsyaNcvuOpb2jUaj3eceN24cMjIy8PLLL6O8vBy5ubl4/PHHAVTXZKivbVvtyp+XGsY+U7fP5Ofnw83NDTqdDlOmTMGbb76JCRMm2G23s2GfUfaZHj16YO3atfjyyy/x3//+F2azGbGxsUhNTbXbbmfDPmP/u2nv3r1ISEjAXXfdZbfNzoh9RtlnJk2ahFdffRUnTpyA2WzGTz/9hK1bt9bbbmfSmfoLNQ/2GdtCQkLg7OyMmJgYLFy4kN9NMuwzSkFBQVi9ejW2bNmCLVu2IDQ0FGPHjsWBAwcuqV1qOgbNHLRw4UIkJCRg48aNTW4jISEBU6dOxVNPPYWJEyfaXOeTTz7BihUr8NlnnyEgIAAAsH79eri5uUn/fvvtN4eer0+fPvjwww/xyiuvwMXFBYGBgejatSuMRqMUFe/Tp4/U7uTJk5v82qgu9pm63N3dcfDgQfz111947rnnsGjRIuzatatRbXRk7DNKI0aMwN/+9jcMHDgQY8aMwdatW+Hv74/33nvP4TY6OvYZ+9asWYN+/fph6NChTdq+o2KfUXr99dfRvXt39OzZEzqdDg888ABuv/12RfZAZ8b+Qo3FPmPbb7/9hn379mH16tVYtWoVNmzY0Og2Oir2GaUePXrg3nvvxeDBgxEbG4u1a9ciNjaWE0i0ptYeH9oeLFy4UISEhIhTp04plu/YscPmeOOwsDDx6quvKpYlJiaKgIAA8c9//tPu81gK1H7zzTeK5QUFBeLEiRPSv+LiYpGcnCwAiLi4OMW6o0ePFg899FCdtjMyMkRhYaEwmUxCrVaLzz77TAghxOnTp6V2U1NThRBCzJ07t8746p9//lkAEBcvXqzTNmua1cU+U3+fsbjzzjvFxIkT7T7embDPONZnbrzxRjFnzhy7j3cm7DP2+4zJZBIeHh5i1apVdl9XZ8Q+Y7/PlJSUiNTUVGE2m8WSJUtE79697b6+zqKz9Rc51jRrGvaZqXb3We7ZZ58V0dHRDq3b0bHPTLW7z3KLFy8Ww4cPd2hdan4MmtXDbDaLhQsXiuDgYHH8+PE6j1sKFG7evFladuzYsToFChMSEkRAQIB49NFH7T7XJ598IvR6vfjiiy8c3rfAwEDxr3/9S1qWn5+vKFBoy5o1a4SLi0u9hQUthXPLy8ulZUuXLuVEAA5gn3Gsz1jcfvvtYsyYMQ7tf0fFPuN4n6msrBQ9evQQf//73x3a/46KfabhPrNu3Trh7OwsLly44NB+d3TsM44fZ8rLy0VUVJRYunSpQ/vfEXXW/iLHoFnjsM80LgCyYsUKER4e7tC6HRX7TOP6zPjx48X06dMdWpeaH4Nm9bjvvvuEp6en2LVrl2K61+LiYmmdBQsWiLCwMPHzzz+Lffv2iREjRogRI0ZIjx8+fFj4+/uL2267TdFGVlaWtM769euFVqsVb7/9tmKdvLy8evfvxRdfFF5eXuLLL78U8fHxYurUqYqpcIUQ4s033xT79+8XSUlJ4q233hIGg0G8/vrr9babl5cnjEajmDt3rkhISBAbN26sMzVyWVmZiIuLE3FxcSIoKEgsXrxYxMXFOTxbSEfFPmO/zzz//PPixx9/FMnJyeLIkSPiX//6l9BqteL99993+P3tiNhn7PeZFStWiB9++EEkJyeL/fv3izlz5gi9Xi8SExMdfn87IvYZ+33GYtSoUWL27NkNvpedBfuM/T7zxx9/iC1btojk5GTx66+/inHjxomuXbt26lnLOmt/EaI6YyUuLk5cf/31YuzYsdK5rpxl2eDBg8Utt9wi4uLi+L3EPmO3z7z11lviq6++EsePHxfHjx8X//nPf4S7u7tYtmyZI29th8U+Y7/PvPbaa+KLL74QJ06cEIcPHxYPP/ywUKvVYvv27Y68tdQCGDSrBwCb/9atWyetU1JSIu6//37h7e0tXFxcxPTp00V6err0+FNPPWWzDfndhTFjxthcZ968efXun9lsFsuXLxdGo1E4OzuLq6++WiQlJSnWmTt3rvDx8RE6nU70799ffPTRRw699kOHDolRo0YJZ2dn0aVLF/Hiiy8qHk9JSbG5z509a4h9xn6fWbZsmejWrZvQ6/XC29tbjBgxQmzcuNGhtjsy9hn7feaRRx4RYWFhQqfTCaPRKK699lpx4MABh9ruyNhn7PcZIWrvRP/4448OtdkZsM/Y7zO7du0SvXr1Es7OzsLX11fMnTtXpKWlOdR2R9WZ+0t4eLjNfWro/ensWUPsM/b7zBtvvCH69OkjXFxchIeHhxg0aJB45513RFVVlUPtd1TsM/b7zMqVK0VUVJTQ6/XCx8dHjB07Vvz8888OtU0tQyWEECAiIiIiIiIiIiIJpwYiIiIiIiIiIiKywqAZERERERERERGRFQbNiIiIiIiIiIiIrDBoRkREREREREREZIVBMyIiIiIiIiIiIisMmhEREREREREREVlh0IyIiIiIiIiIiMgKg2ZERERERERERERWGDQjIiKiTmX+/PmYNm1aa+9Gu1deXo5u3bph9+7dAIDTp09DpVLh4MGDrbtjjbB69Wpcf/31rb0bRERE1EYxaEZEREQdhkqlqvff008/jddffx0ffPBBq+5nRwjcrV69Gl27dkVsbCwAIDQ0FOnp6ejbt2+T26wv8DZ27Fg88sgj2LVrV4Of865duwAAW7ZswdixY+Hp6Qk3Nzf0798fzzzzDC5evAgAuOOOO3DgwAH89ttvTd5nIiIi6rgYNCMiIqIOIz09Xfq3atUqeHh4KJYtXrwYnp6e8PLyau1dbdeEEHjrrbdw5513Sss0Gg0CAwOh1Wpb9LljY2MVn+msWbNwzTXXKJbFxsZi2bJlmD17NoYMGYLvv/8eCQkJeOWVV3Do0CF8/PHHAACdTodbbrkFb7zxRovuMxEREbVPDJoRERFRhxEYGCj98/T0hEqlUixzc3Ork+U1duxYPPjgg3jkkUfg7e0No9GI999/H0VFRbj99tvh7u6Obt264fvvv1c8V0JCAiZPngw3NzcYjUbMnTsXFy5ckB7fvHkz+vXrB4PBAF9fX4wfPx5FRUV4+umn8eGHH+LLL7+skxn12GOPITo6Gi4uLoiMjMTy5ctRUVEhtfn0009j4MCBWLt2LcLCwuDm5ob7778fVVVVeOmllxAYGIiAgAA899xzin1VqVR49913MXnyZBgMBkRGRmLz5s3S4+Xl5XjggQcQFBQEvV6P8PBwvPDCC3bf5/379yM5ORlTpkyRlllniVkywnbs2IGYmBi4uLggNjYWSUlJDn+etuh0OsVnajAY4OzsrFh28OBBPP/883jllVfw8ssvIzY2FhEREZgwYQK2bNmCefPmSe1df/31+Oqrr1BSUnJJ+0VEREQdD4NmRERE1Ol9+OGH8PPzw969e/Hggw/ivvvuw0033YTY2FgcOHAAEydOxNy5c1FcXAwAyMvLw7hx4zBo0CDs27cP27ZtQ2ZmJmbNmgWgOuPt5ptvxh133IGjR49i165dmDFjBoQQWLx4cZ3sKMsQR3d3d3zwwQc4cuQIXn/9dbz//vt47bXXFPuanJyM77//Htu2bcOGDRuwZs0aTJkyBampqfjll1+wcuVKPPHEE/jzzz8V2y1fvhwzZ87EoUOHcOutt2LOnDk4evQoAOCNN97AV199hc8++wxJSUlYv349IiIi7L5fv/32G6Kjo+Hu7t7ge7ts2TK88sor2LdvH7RaLe644w6HP5emWr9+vRRQtEWeaRgTE4PKyso67xcRERFRy+bPExEREbUDAwYMwBNPPAEAWLp0KV588UX4+fnh7rvvBgA8+eSTePfddxEfH4/hw4fjrbfewqBBg/D8889LbaxduxahoaE4fvw4TCYTKisrMWPGDISHhwMA+vXrJ61rMBhQVlaGwMBAxX5Y9gEAIiIisHjxYmzcuBFLliyRlpvNZqxduxbu7u7o3bs3rrrqKiQlJeG7776DWq1Gjx49sHLlSuzcuRPDhg2Ttrvppptw1113AQCeffZZ/PTTT3jzzTfxzjvv4OzZs+jevTtGjRoFlUol7bM9Z86cQXBwsEPv7XPPPYcxY8YAAB5//HFMmTIFpaWl0Ov1Dm3fFCdOnEBkZCScnJwaXNfFxQWenp44c+ZMi+0PERERtU8MmhEREVGn179/f+lnjUYDX19fRZDLaDQCALKysgAAhw4dws6dO+Hm5lanreTkZEycOBFXX301+vXrh0mTJmHixIm48cYb4e3tXe9+fPrpp3jjjTeQnJwsBd48PDwU60RERCgyvIxGIzQaDdRqtWKZZV8tRowYUed3y1DK+fPnY8KECejRoweuueYaXHfddZg4caLd/SwpKXE46CV/b4OCggBUv49hYWEObd8UQohGrW8wGKQsQiIiIiILDs8kIiKiTs86I0mlUimWqVQqANVZXgBgMplw/fXX4+DBg4p/J06cwOjRo6HRaPDTTz/h+++/R+/evfHmm2+iR48eSElJsbsPe/bswa233oprr70W33zzDeLi4rBs2TKUl5c3al8tyyz76ogrrrgCKSkpePbZZ1FSUoJZs2bhxhtvtLu+n58fcnNzHWq7vvfRmiVAmJ+fX+exvLw8eHp6OvSc0dHROHXqlKIeXH0uXrwIf39/h9YlIiKizoNBMyIiIqJGuuKKK5CYmIiIiAh069ZN8c/V1RVAdYBo5MiRWLFiBeLi4qDT6fD5558DqC5mX1VVpWhz9+7dCA8Px7JlyxATE4Pu3bs365DBP/74o87vvXr1kn738PDA7Nmz8f777+PTTz/Fli1bcPHiRZttDRo0CMeOHWt0RldDfHx84Ofnh/379yuWFxQU4OTJk4iOjnaonVtuuQUmkwnvvPOOzcfz8vKkn5OTk1FaWopBgwY1eb+JiIioY+LwTCIiIqJGWrhwId5//33cfPPNWLJkCXx8fHDy5Els3LgR//nPf7Bv3z7s2LEDEydOREBAAP78809kZ2dLQaqIiAj88MMPSEpKgq+vLzw9PdG9e3ecPXsWGzduxJAhQ/Dtt99KQbbmsGnTJsTExGDUqFFYv3499u7dizVr1gAAXn31VQQFBWHQoEFQq9XYtGkTAgMDFQXz5a666iqYTCYkJiaib9++zbaPALBo0SI8//zzMBqNGD58OHJycvDss8/C398fM2bMcKiNYcOGYcmSJfjHP/6BtLQ0TJ8+HcHBwTh58iRWr16NUaNG4eGHHwZQPalBZGQkoqKimvV1EBERUfvHoBkRERFRIwUHB+P333/HY489hokTJ6KsrAzh4eG45pproFar4eHhgV9//RWrVq1CQUEBwsPD8corr2Dy5MkAgLvvvhu7du1CTEwMTCYTdu7ciRtuuAF///vf8cADD6CsrAxTpkzB8uXL8fTTTzfLPq9YsQIbN27E/fffj6CgIGzYsAG9e/cGUD1r50svvYQTJ05Ao9FgyJAh0sQCtvj6+mL69OlYv349XnjhhWbZP4slS5bAzc0NK1euRHJyMnx8fDBy5Ejs3LkTBoPB4XZWrlyJwYMH4+2338bq1athNpsRFRWFG2+8EfPmzZPW27BhgzThAxEREZGcSjR3Xj0RERERtSkqlQqff/45pk2b1mxtxsfHY8KECUhOTrY5IUJ7kJiYiHHjxuH48eMO10sjIiKizoM1zYiIiIio0fr374+VK1fWO7lBW5eeno6PPvqIATMiIiKyiZlmRERERB1cS2SaEREREXV0rGlGRERE1MHxHikRERFR43F4JhERERERERERkRUGzYiIiIiIiIiIiKwwaEZERERERERERGSFQTMiIiIiIiIiIiIrDJoRERERERERERFZYdCMiIiIiIiIiIjICoNmREREREREREREVhg0IyIiIiIiIiIisvL/S4WRMWgAIWQAAAAASUVORK5CYII=", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#1. Create an pricipitation observationtype\n", "\n", "precip_rate = metobs_toolkit.Obstype(obsname='precip_rate',\n", " std_unit='mm/hour',\n", " description='Rain precipitation rate at surface.')\n", "#2. Create a ModelObstype \n", "precip_GPM = metobs_toolkit.ModelObstype(\n", " obstype=precip_rate,\n", " model_unit='mm/hour', #See GEE dataset details\n", " model_band='hourlyPrecipRate',#See GEE dataset details\n", " ) \n", "\n", "#3. Create a ModelDataManager\n", "GPM_v8_manager = metobs_toolkit.GEEDynamicDatasetManager(\n", " name='JAXA_GPM_reanalysis', \n", " location=\"JAXA/GPM_L3/GSMaP/v8/operational\", #See GEE dataset details\n", " value_type='numeric',\n", " scale=50,\n", " time_res='1h',\n", " modelobstypes=[precip_GPM],\n", " is_image=False,\n", " is_mosaic=False,\n", ")\n", "\n", "#4. Extract precipitation timeseries data\n", "precip_data = dataset.get_station('vlinder04').get_gee_timeseries_data(\n", " geedynamicdatasetmanager=GPM_v8_manager,\n", " startdt_utc=None,\n", " enddt_utc=None,\n", " target_obstypes=['precip_rate'] #refer by obsname\n", " )\n", "\n", "# 5. make a plot\n", "dataset.get_station('vlinder04').make_plot_of_modeldata(obstype='precip_rate')\n", "\n", "# 6. You can also get the data using the modeldatadf attribute\n", "dataset.get_station('vlinder04').modeldatadf" ] }, { "cell_type": "code", "execution_count": 25, "id": "53", "metadata": { "execution": { "iopub.execute_input": "2025-05-14T11:44:03.204838Z", "iopub.status.busy": "2025-05-14T11:44:03.204598Z", "iopub.status.idle": "2025-05-14T11:44:26.285169Z", "shell.execute_reply": "2025-05-14T11:44:26.284318Z" } }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Or you can make an interactive spatial plot\n", "dataset.make_gee_plot(\n", " geedatasetmanager=GPM_v8_manager,\n", " modelobstype='precip_rate',\n", " timeinstance=pd.Timestamp('2022-09-08 16:00:00'))\n" ] } ], "metadata": { "kernelspec": { "display_name": "metobs_dev", "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": "3.12.10" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { "3130c5a424d64841862d0b57e31cc9d1": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": "500px" } } }, "version_major": 2, "version_minor": 0 } } }, "nbformat": 4, "nbformat_minor": 5 }