![](\"Data-download-polygon-export.png\")
\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Create polygon coordinate string\n",
- "\n",
- "Read in the geojson file as a GeoDataFrame object and simplify and reorder using the shapely package. This will be converted back to a dictionary to be applied as our polygon search parameter. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "polygon_filepath = str(os.getcwd() + '/Data/nsidc-polygon.json') # Note: A shapefile or other vector-based spatial data format could be substituted here.\n",
- "\n",
- "gdf = gpd.read_file(polygon_filepath) #Return a GeoDataFrame object\n",
- "\n",
- "# Simplify polygon for complex shapes in order to pass a reasonable request length to CMR. The larger the tolerance value, the more simplified the polygon.\n",
- "# Orient counter-clockwise: CMR polygon points need to be provided in counter-clockwise order. The last point should match the first point to close the polygon.\n",
- "poly = orient(gdf.simplify(0.05, preserve_topology=False).loc[0],sign=1.0)\n",
- "\n",
- "#Format dictionary to polygon coordinate pairs for CMR polygon filtering\n",
- "polygon = ','.join([str(c) for xy in zip(*poly.exterior.coords.xy) for c in xy])\n",
- "print('Polygon coordinates to be used in search:', polygon)\n",
- "poly"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Set time range\n",
- "\n",
- "We are interested in accessing files within each data set over the same time range, so we'll start by searching all of 2017."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "temporal = '2017-01-01T00:00:00Z,2017-12-31T23:59:59Z' # Set temporal range"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Create data dictionary \n",
- "\n",
- "Create a nested dictionary with each data set shortname and version, as well as shared temporal range and polygonal area of interest. Data set shortnames, or IDs, as well as version numbers, are located at the top of every NSIDC landing page."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "data_dict = { 'snowex': {'short_name': 'SNEX17_GPR','version': '2','polygon': polygon,'temporal':temporal},\n",
- " 'aso': {'short_name': 'ASO_3M_SD','version': '1','polygon': polygon,'temporal':temporal},\n",
- " 'modis': {'short_name': 'MOD10A1','version': '6','polygon': polygon,'temporal':temporal}\n",
- " }"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Determine how many files exist over this time and area of interest, as well as the average size and total volume of those files\n",
- "\n",
- "We will use the `granule_info` function to query metadata about each data set and associated files using the [Common Metadata Repository (CMR)](https://cmr.earthdata.nasa.gov/search/site/docs/search/api.html), which is a high-performance, high-quality, continuously evolving metadata system that catalogs Earth Science data and associated service metadata records. Note that not all NSIDC data can be searched at the file level using CMR, particularly those outside of the NASA DAAC program. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "for k, v in data_dict.items(): fn.granule_info(data_dict[k])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Find coincident data\n",
- "\n",
- "The function above tells us the size of data available for each data set over our time and area of interest, but we want to go a step further and determine what time ranges are coincident based on our bounding box. This `time_overlap` helper function returns a dataframe with file names, dataset_id, start date, and end date for all files that overlap in temporal range across all data sets of interest. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "search_df = fn.time_overlap(data_dict)\n",
- "print(len(search_df), ' total files returned')\n",
- "search_df"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "___\n",
- "\n",
- "## Data Access\n",
- "\n",
- "The number of files has been greatly reduced to only those needed to compare data across these data sets. This CMR query will collect the data file URLs, including the associated quality and metadata files if available."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "scrolled": true,
- "tags": []
- },
- "outputs": [],
- "source": [
- "# Create new dictionary with fields needed for CMR url search\n",
- "\n",
- "url_df = search_df.drop(columns=['start_date', 'end_date','version','dataset_id'])\n",
- "url_dict = url_df.to_dict('records')\n",
- "\n",
- "# CMR search variables\n",
- "granule_search_url = 'https://cmr.earthdata.nasa.gov/search/granules'\n",
- "headers= {'Accept': 'application/json'}\n",
- "\n",
- "# Create URL list from each df row\n",
- "urls = []\n",
- "for i in range(len(url_dict)):\n",
- " response = requests.get(granule_search_url, params=url_dict[i], headers=headers)\n",
- " results = json.loads(response.content)\n",
- " urls.append(fn.cmr_filter_urls(results))\n",
- "# flatten url list\n",
- "urls = list(np.concatenate(urls))\n",
- "urls"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Additional data access and subsetting services\n",
- "\n",
- "#### API Access\n",
- "Data can be accessed directly from our HTTPS file system through the URLs collected above, or through our Application Programming Interface (API). Our API offers you the ability to order data using specific temporal and spatial filters, as well as subset, reformat, and reproject select data sets. The same subsetting, reformatting, and reprojection services available on select data sets through NASA Earthdata Search can also be applied using this API. These options can be requested in a single access command without the need to script against our data directory structure. See our [programmatic access guide](https://nsidc.org/support/how/how-do-i-programmatically-request-data-services) for more information on API options. "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Add service request options for MODIS data\n",
- "\n",
- "According to https://nsidc.org/support/faq/what-data-subsetting-reformatting-and-reprojection-services-are-available-for-MODIS-data, we can see that spatial subsetting and GeoTIFF reformatting are available for MOD10A1 so those options are requested below. The area subset must be described as a bounding box, which can be created based on the polygon bounds above. We will also add GeoTIFF reformatting to the MOD10A1 data dictionary and the temporal range will be set based on the range of MOD10A1 files in the dataframe above. These new parameters will be added to the API request below."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "bounds = poly.bounds # Get polygon bounds to be used as bounding box input\n",
- "data_dict['modis']['bbox'] = ','.join(map(str, list(bounds))) # Add bounding box subsetting to MODIS dictionary\n",
- "data_dict['modis']['format'] = 'GeoTIFF' # Add geotiff reformatting to MODIS dictionary\n",
- "\n",
- "# Set new temporal range based on dataframe above. Note that this will request all MOD10A1 data falling within this time range.\n",
- "modis_start = min(search_df.loc[search_df['short_name'] == 'MOD10A1', 'start_date'])\n",
- "modis_end = max(search_df.loc[search_df['short_name'] == 'MOD10A1', 'end_date'])\n",
- "data_dict['modis']['temporal'] = ','.join([modis_start,modis_end])\n",
- "print(data_dict['modis'])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Create the data request API endpoint\n",
- "Programmatic API requests are formatted as HTTPS URLs that contain key-value-pairs specifying the service operations that we specified above. We will first create a string of key-value-pairs from our data dictionary and we'll feed those into our API endpoint. This API endpoint can be executed via command line, a web browser, or in Python below."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "base_url = 'https://n5eil02u.ecs.nsidc.org/egi/request' # Set NSIDC data access base URL\n",
- "#data_dict['modis']['request_mode'] = 'stream' # Set the request mode to asynchronous\n",
- "\n",
- "param_string = '&'.join(\"{!s}={!r}\".format(k,v) for (k,v) in data_dict['modis'].items()) # Convert param_dict to string\n",
- "param_string = param_string.replace(\"'\",\"\") # Remove quotes\n",
- "\n",
- "api_request = [f'{base_url}?{param_string}']\n",
- "print(api_request[0]) # Print API base URL + request parameters"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Download options\n",
- "\n",
- "The following functions will return the file URLs and the MOD10A1 API request. For demonstration purposes, these functions have been commented out, and instead the data utilized in the following steps will be accessed from a staged directory. ***Note that if you are running this notebook in Binder, the memory may not be sufficient to download these files. Please use the Docker or local Conda options provided in the README if you are interested in downloading all files.***"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "from pathlib import Path\n",
- "\n",
- "path = Path(\".\") / \"Data\"\n",
- "\n",
- "if not os.path.exists(path):\n",
- " print(f\"creating data directory: {path}\")\n",
- " os.mkdir(path)\n",
- "\n",
- "print(f\"Downloading data from S3 to {path}\")\n",
- "os.chdir(path)\n",
- "# pull data from staged bucket for demonstration\n",
- "!awscliv2 --no-sign-request s3 cp s3://snowex-aso-modis-tutorial-data/ ./ --recursive #access data in staged directory"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "___\n",
- "## Read in SnowEx data and buffer points around Snotel location\n",
- "\n",
- "This SnowEx data set is provided in CSV. A [Pandas DataFrame](https://pandas.pydata.org/pandas-docs/stable/getting_started/overview.html) is used to easily read in data. For these next steps, just one day's worth of data will be selected from this file and the coincident ASO and MODIS data will be selected.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "snowex_path = 'SnowEx17_GPR_Version2_Week1.csv' # Define local filepath\n",
- "print(snowex_path, os.getcwd())\n",
- "df = pd.read_csv(snowex_path, sep='\\t')\n",
- "df.head()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Convert to time values and extract a single day"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The collection date needs to be extracted from the `collection` value and a new dataframe will be generated as a subset of the original based on a single day:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "df['date'] = df.collection.str.rsplit('_').str[-1].astype(str)\n",
- "df.date = pd.to_datetime(df.date, format=\"%m%d%y\")\n",
- "df = df.sort_values(['date'])\n",
- "df_subset = df[df['date'] == '2017-02-08'] # subset original dataframe and only select this date\n",
- "df.head()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Convert to Geopandas dataframe to provide point geometry\n",
- "\n",
- "According to the SnowEx documentation, the data are available in UTM Zone 12N so we'll set to this projection so that we can buffer in meters in the next step:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "gdf_utm= gpd.GeoDataFrame(df_subset, geometry=gpd.points_from_xy(df_subset.x, df_subset.y), crs='EPSG:32612')\n",
- "gdf_utm.head()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Buffer data around SNOTEL site"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We can further subset the SnowEx snow depth data to get within a 500 m radius of the [SNOTEL Mesa Lakes](https://wcc.sc.egov.usda.gov/nwcc/site?sitenum=622&state=co) site.\n",
- "\n",
- "First we'll create a new geodataframe with the SNOTEL site location, set to our SnowEx UTM coordinate reference system, and create a 500 meter buffer around this point. Then we'll subset the SnowEx points to the buffer and convert back to the WGS84 CRS:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Create another geodataframe (gdfsel) with the center point for the selection\n",
- "df_snotel = pd.DataFrame(\n",
- " {'SNOTEL Site': ['Mesa Lakes'],\n",
- " 'Latitude': [39.05],\n",
- " 'Longitude': [-108.067]})\n",
- "gdf_snotel = gpd.GeoDataFrame(df_snotel, geometry=gpd.points_from_xy(df_snotel.Longitude, df_snotel.Latitude), crs='EPSG:4326')\n",
- "\n",
- "gdf_snotel.to_crs('EPSG:32612', inplace=True) # set CRS to UTM 12 N\n",
- "\n",
- "buffer = gdf_snotel.buffer(500) #create 500 m buffer\n",
- "\n",
- "gdf_buffer = gdf_utm.loc[gdf_utm.geometry.within(buffer.unary_union)] # subset dataframe to buffer region\n",
- "gdf_buffer = gdf_buffer.to_crs('EPSG:4326')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "___\n",
- "## Read in Airborne Snow Observatory data and clip to SNOTEL buffer\n",
- "\n",
- "Snow depth data from the ASO L4 Lidar Snow Depth 3m UTM Grid data set were calculated from surface elevation measured by the Riegl LMS-Q1560 airborne laser scanner (ALS). The data are provided in GeoTIFF format, so we'll use the [Rasterio](https://rasterio.readthedocs.io/en/latest/) library to read in the data. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "aso_path = './ASO_3M_SD_USCOGM_20170208.tif' # Define local filepath\n",
- "\n",
- "aso = rasterio.open(aso_path)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Clip data to SNOTEL buffer\n",
- "\n",
- "In order to reduce the data volume to the buffered region of interest, we can subset this GeoTIFF to the same SNOTEL buffer:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "buffer = buffer.to_crs(crs=aso.crs) # convert buffer to CRS of ASO rasterio object\n",
- "out_img, out_transform = mask(aso, buffer, crop=True)\n",
- "out_meta = aso.meta.copy()\n",
- "epsg_code = int(aso.crs.data['init'][5:])\n",
- "out_meta.update({\"driver\": \"GTiff\", \"height\": out_img.shape[1], \"width\": out_img.shape[2], \"transform\": out_transform, \"crs\": '+proj=utm +zone=13 +datum=WGS84 +units=m +no_defs'})\n",
- "out_tif = 'clipped_ASO_3M_SD_USCOGM_20170208.tif'\n",
- "\n",
- "with rasterio.open(out_tif, 'w', **out_meta) as dest:\n",
- " dest.write(out_img)\n",
- " \n",
- "clipped_aso = rasterio.open(out_tif)\n",
- "aso_array = clipped_aso.read(1, masked=True)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "___ \n",
- "## Read in MODIS Snow Cover data \n",
- "\n",
- "We are interested in the Normalized Difference Snow Index (NDSI) snow cover value from the MOD10A1 data set, which is an index that is related to the presence of snow in a pixel. According to the [MOD10A1 FAQ](https://nsidc.org/support/faq/what-ndsi-snow-cover-and-how-does-it-compare-fsc), snow cover is detected using the NDSI ratio of the difference in visible reflectance (VIS) and shortwave infrared reflectance (SWIR), where NDSI = ((band 4-band 6) / (band 4 + band 6)).\n",
- "\n",
- "Note that you may need to change this filename output below if you download the data outside of the staged bucket, as the output names may vary per request. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "modis_path = './MOD10A1_A2017039_h09v05_006_2017041102600_MOD_Grid_Snow_500m_NDSI_Snow_Cover_99f6ee91_subsetted.tif' # Define local filepath\n",
- "modis = rasterio.open(modis_path)\n",
- "modis_array = modis.read(1, masked=True)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "___\n",
- "## Add ASO and MODIS data to GeoPandas dataframe"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "In order to add data from these ASO and MODIS gridded data sets, we need to define the geometry parameters for theses, as well as the SnowEx data. The SnowEx geometry is set using the longitude and latitude values of the geodataframe:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "snowex_geometry = prs.geometry.SwathDefinition(lons=gdf_buffer['long'], lats=gdf_buffer['lat'])\n",
- "print('snowex geometry: ', snowex_geometry)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "With ASO and MODIS data on regular grids, we can create area definitions for these using projection and extent metadata:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "pprint.pprint(clipped_aso.profile)\n",
- "print('')\n",
- "print(clipped_aso.bounds)\n",
- "\n",
- "\n",
- "pprint.pprint(modis.profile)\n",
- "print('')\n",
- "print(modis.bounds)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Create area definition for ASO\n",
- "area_id = 'UTM_13N' # area_id: ID of area\n",
- "description = 'WGS 84 / UTM zone 13N' # description: Description\n",
- "proj_id = 'UTM_13N' # proj_id: ID of projection (being deprecated)\n",
- "projection = 'EPSG:32613' # projection: Proj4 parameters as a dict or string\n",
- "width = clipped_aso.width # width: Number of grid columns\n",
- "height = clipped_aso.height # height: Number of grid rows\n",
- "area_extent = (234081.0, 4326303.0, 235086.0, 4327305.0)\n",
- "aso_geometry = prs.geometry.AreaDefinition(area_id, description, proj_id, projection, width, height, area_extent)\n",
- "\n",
- "# Create area definition for MODIS\n",
- "area_id = 'Sinusoidal' # area_id: ID of area\n",
- "description = 'Sinusoidal Modis Spheroid' # description: Description\n",
- "proj_id = 'Sinusoidal' # proj_id: ID of projection (being deprecated)\n",
- "projection = 'PROJCS[\"Sinusoidal Modis Spheroid\",GEOGCS[\"Unknown datum based upon the Authalic Sphere\",DATUM[\"Not_specified_based_on_Authalic_Sphere\",SPHEROID[\"Sphere\",6371007.181,887203.3395236016,AUTHORITY[\"EPSG\",\"7035\"]],AUTHORITY[\"EPSG\",\"6035\"]],PRIMEM[\"Greenwich\",0],UNIT[\"degree\",0.0174532925199433],AUTHORITY[\"EPSG\",\"4035\"]],PROJECTION[\"Sinusoidal\"],PARAMETER[\"longitude_of_center\",0],PARAMETER[\"false_easting\",0],PARAMETER[\"false_northing\",0],UNIT[\"metre\",1,AUTHORITY[\"EPSG\",\"9001\"]]]' # projection: Proj4 parameters as a dict or string\n",
- "width = modis.width # width: Number of grid columns\n",
- "height = modis.height # height: Number of grid rows\n",
- "area_extent = (-9332971.361735353, 4341240.1538655795, -9331118.110869242, 4343093.404731691)\n",
- "modis_geometry = prs.geometry.AreaDefinition(area_id, description, proj_id, projection, width, height, area_extent)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Interpolate ASO and MODIS values onto SnowEx points\n",
- "\n",
- "To interpolate ASO snow depth and MODIS snow cover data to SnowEx snow depth points, we can use the `pyresample` library. The `radius_of_influence` parameter determines maximum radius to look for nearest neighbor interpolation."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# add ASO values to geodataframe\n",
- "import warnings\n",
- "warnings.filterwarnings('ignore') # ignore warning when resampling to a different projection\n",
- "gdf_buffer['aso_snow_depth'] = prs.kd_tree.resample_nearest(aso_geometry, aso_array, snowex_geometry, radius_of_influence=3)\n",
- "\n",
- "# add MODIS values to geodataframe\n",
- "gdf_buffer['modis_ndsi'] = prs.kd_tree.resample_nearest(modis_geometry, modis_array, snowex_geometry, radius_of_influence=500)\n",
- "\n",
- "gdf_buffer.head()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "___ \n",
- "## Visualize data and export for further GIS analysis\n",
- "\n",
- "The rasterio plot module allows you to directly plot GeoTIFFs objects. The SnowEx `Thickness` values are plotted against the clipped ASO snow depth raster."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "gdf_buffer_aso_crs = gdf_buffer.to_crs('EPSG:32613') \n",
- "\n",
- "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
- "fig, ax = plt.subplots(figsize=(10, 10))\n",
- "show(clipped_aso, ax=ax)\n",
- "divider = make_axes_locatable(ax)\n",
- "cax = divider.append_axes(\"right\", size=\"5%\", pad=0.1) # fit legend to height of plot\n",
- "gdf_buffer_aso_crs.plot(column='Thickness', ax=ax, cmap='OrRd', legend=True, cax=cax, legend_kwds=\n",
- " {'label': \"Snow Depth (m)\",});"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We can do the same for MOD10A1: This was subsetted to the entire Grand Mesa region defined by the SnowEx data set coverage. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Set dataframe to MOD10A1 Sinusoidal projection\n",
- "gdf_buffer_modis_crs = gdf_buffer.to_crs('PROJCS[\"Sinusoidal Modis Spheroid\",GEOGCS[\"Unknown datum based upon the Authalic Sphere\",DATUM[\"Not_specified_based_on_Authalic_Sphere\",SPHEROID[\"Sphere\",6371007.181,887203.3395236016,AUTHORITY[\"EPSG\",\"7035\"]],AUTHORITY[\"EPSG\",\"6035\"]],PRIMEM[\"Greenwich\",0],UNIT[\"degree\",0.0174532925199433],AUTHORITY[\"EPSG\",\"4035\"]],PROJECTION[\"Sinusoidal\"],PARAMETER[\"longitude_of_center\",0],PARAMETER[\"false_easting\",0],PARAMETER[\"false_northing\",0],UNIT[\"metre\",1,AUTHORITY[\"EPSG\",\"9001\"]]]')\n",
- "\n",
- "fig, ax = plt.subplots(figsize=(10, 10))\n",
- "show(modis, ax=ax)\n",
- "divider = make_axes_locatable(ax)\n",
- "cax = divider.append_axes(\"right\", size=\"5%\", pad=0.1) # fit legend to height of plot\n",
- "gdf_buffer_modis_crs.plot(column='Thickness', ax=ax, cmap='OrRd', legend=True, cax=cax, legend_kwds=\n",
- " {'label': \"Snow Depth (m)\",});"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Additional data imagery services\n",
- "\n",
- "#### NASA Worldview and the Global Browse Imagery Service\n",
- "\n",
- "NASA’s EOSDIS Worldview mapping application provides the capability to interactively browse over 900 global, full-resolution satellite imagery layers and then download the underlying data. Many of the available imagery layers are updated within three hours of observation, essentially showing the entire Earth as it looks “right now.\"\n",
- "\n",
- "According to the [MOD10A1 landing page](https://nsidc.org/data/mod10a1), snow cover imagery layers from this data set are available through NASA Worldview. This layer can be downloaded as various image files including GeoTIFF using the snapshot feature at the top right of the page. This link presents the MOD10A1 NDSI layer over our time and area of interest: https://go.nasa.gov/35CgYMd. \n",
- "\n",
- "Additionally, the NASA Global Browse Imagery Service provides up to date, full resolution imagery for select NSIDC DAAC data sets as web services including WMTS, WMS, KML, and more. These layers can be accessed in GIS applications following guidance on the [GIBS documentation pages](https://wiki.earthdata.nasa.gov/display/GIBS/Geographic+Information+System+%28GIS%29+Usage). "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Export dataframe to Shapefile"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Finally, the dataframe can be exported as an Esri shapefile for further analysis in GIS:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "gdf_buffer = gdf_buffer.drop(columns=['date'])\n",
- "gdf_buffer.to_file('snow-data-20170208.shp')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.16"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/notebooks/SnowEx_ASO_MODIS_Snow/Snow-tutorial_rendered.ipynb b/notebooks/SnowEx_ASO_MODIS_Snow/Snow-tutorial_rendered.ipynb
deleted file mode 100644
index 0adf60f..0000000
--- a/notebooks/SnowEx_ASO_MODIS_Snow/Snow-tutorial_rendered.ipynb
+++ /dev/null
@@ -1,1812 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Snow Depth and Snow Cover Data Exploration \n",
- "\n",
- "This tutorial demonstrates how to access and compare coincident snow data across in-situ, airborne, and satellite platforms from NASA's SnowEx, ASO, and MODIS data sets, respectively. All data are available from the NASA National Snow and Ice Data Center Distributed Active Archive Center, or NSIDC DAAC. \n",
- "\n",
- "### Here are the steps you will learn in this snow data notebook:\n",
- "\n",
- "1. Explore the coverage and structure of select NSIDC DAAC snow data products, as well as available resources to search and access data.\n",
- "2. Search and download spatiotemporally coincident data across in-situ, airborne, and satellite observations.\n",
- "3. Subset and reformat MODIS data using the NSIDC DAAC API.\n",
- "4. Read CSV and GeoTIFF formatted data using geopandas and rasterio libraries.\n",
- "5. Subset data based on buffered area.\n",
- "5. Extract and visualize raster values at point locations.\n",
- "6. Save output as shapefile for further GIS analysis.\n",
- "\n",
- "\n",
- "---\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "___\n",
- "\n",
- "## Explore snow products and resources\n",
- "\n",
- "\n",
- "### NSIDC introduction\n",
- "\n",
- "[The National Snow and Ice Data Center](https://nsidc.org) provides over 1100 data sets covering the Earth's cryosphere and more, all of which are available to the public free of charge. Beyond providing these data, NSIDC creates tools for data access, supports data users, performs scientific research, and educates the public about the cryosphere. \n",
- "\n",
- "#### Select Data Resources\n",
- "\n",
- "* [NSIDC Data Search](https://nsidc.org/data/search/#keywords=snow)\n",
- " * Search NSIDC snow data\n",
- "* [NSIDC Data Update Announcements](https://nsidc.org/the-drift/data-update/) \n",
- " * News and tips for data users\n",
- "* [NASA Earthdata Search](http://search.earthdata.nasa.gov/)\n",
- " * Search and access data across the NASA Earthdata\n",
- "* [NASA Worldview](https://worldview.earthdata.nasa.gov/)\n",
- " * Interactive interface for browsing full-resolution, global, daily satellite images\n",
- " \n",
- " \n",
- "#### Snow Today\n",
- "\n",
- "[Snow Today](https://nsidc.org/snow-today), a collaboration with the University of Colorado's Institute of Alpine and Arctic Research (INSTAAR), provides near-real-time snow analysis for the western United States and regular reports on conditions during the winter season. Snow Today is funded by NASA Hydrological Sciences Program and utilizes data from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument and snow station data from the Snow Telemetry (SNOTEL) network by the Natural Resources Conservation Service (NRCS), United States Department of Agriculture (USDA) and the California Department of Water Resources: www.wcc.nrcs.usda.gov/snow.\n",
- "\n",
- "### Snow-related missions and data sets used in the following steps:\n",
- "\n",
- "* [SnowEx](https://nsidc.org/data/snowex)\n",
- " * SnowEx17 Ground Penetrating Radar, Version 2: https://doi.org/10.5067/G21LGCNLFSC5\n",
- "* [ASO](https://nsidc.org/data/aso)\n",
- " * ASO L4 Lidar Snow Depth 3m UTM Grid, Version 1: https://doi.org/10.5067/KIE9QNVG7HP0\n",
- "* [MODIS](https://nsidc.org/data/modis)\n",
- " * MODIS/Terra Snow Cover Daily L3 Global 500m SIN Grid, Version 6: https://doi.org/10.5067/MODIS/MOD10A1.006\n",
- "\n",
- "\n",
- "#### Other relevant snow products:\n",
- "\n",
- "* [VIIRS Snow Data](http://nsidc.org/data/search/#sortKeys=score,,desc/facetFilters=%257B%2522facet_sensor%2522%253A%255B%2522Visible-Infrared%2520Imager-Radiometer%2520Suite%2520%257C%2520VIIRS%2522%255D%252C%2522facet_parameter%2522%253A%255B%2522SNOW%2520COVER%2522%252C%2522Snow%2520Cover%2522%255D%257D/pageNumber=1/itemsPerPage=25)\n",
- "\n",
- "* [AMSR-E and AMSR-E/AMSR2 Unified Snow Data](http://nsidc.org/data/search/#sortKeys=score,,desc/facetFilters=%257B%2522facet_sensor%2522%253A%255B%2522Advanced%2520Microwave%2520Scanning%2520Radiometer-EOS%2520%257C%2520AMSR-E%2522%252C%2522Advanced%2520Microwave%2520Scanning%2520Radiometer%25202%2520%257C%2520AMSR2%2522%255D%252C%2522facet_parameter%2522%253A%255B%2522SNOW%2520WATER%2520EQUIVALENT%2522%252C%2522Snow%2520Water%2520Equivalent%2522%255D%257D/pageNumber=1/itemsPerPage=25)\n",
- "\n",
- "* [MEaSUREs Snow Data](http://nsidc.org/data/search/#keywords=measures/sortKeys=score,,desc/facetFilters=%257B%2522facet_parameter%2522%253A%255B%2522SNOW%2520COVER%2522%255D%252C%2522facet_sponsored_program%2522%253A%255B%2522NASA%2520National%2520Snow%2520and%2520Ice%2520Data%2520Center%2520Distributed%2520Active%2520Archive%2520Center%2520%257C%2520NASA%2520NSIDC%2520DAAC%2522%255D%252C%2522facet_format%2522%253A%255B%2522NetCDF%2522%255D%252C%2522facet_temporal_duration%2522%253A%255B%252210%252B%2520years%2522%255D%257D/pageNumber=1/itemsPerPage=25)\n",
- " \n",
- "* Near-Real-Time SSM/I-SSMIS EASE-Grid Daily Global Ice Concentration and Snow Extent (NISE), Version 5: https://doi.org/10.5067/3KB2JPLFPK3R"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "___\n",
- "### Import Packages\n",
- "\n",
- "Get started by importing packages needed to run the following code blocks, including the `tutorial_helper_functions` module provided within this repository."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "import os\n",
- "import geopandas as gpd\n",
- "from shapely.geometry import Polygon, mapping\n",
- "from shapely.geometry.polygon import orient\n",
- "import pandas as pd \n",
- "import matplotlib.pyplot as plt\n",
- "import rasterio\n",
- "from rasterio.plot import show\n",
- "import numpy as np\n",
- "import pyresample as prs\n",
- "import requests\n",
- "import json\n",
- "import pprint\n",
- "from rasterio.mask import mask\n",
- "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
- "\n",
- "\n",
- "# This is our functions module. We created several helper functions to discover, access, and harmonize the data below.\n",
- "import tutorial_helper_functions as fn"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "___\n",
- "\n",
- "\n",
- "## Data Discovery\n",
- "\n",
- "Start by identifying your study area and exploring coincident data over the same time and area. \n",
- "\n",
- "NASA Earthdata Search can be used to visualize file coverage over mulitple data sets and to access the same data you will be working with below: \n",
- "https://search.earthdata.nasa.gov/projects?projectId=5366449248\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Identify area and time of interest\n",
- "\n",
- "Since our focus is on the Grand Mesa study site of the NASA SnowEx campaign, we'll use that area to search for coincident data across other data products. From the [SnowEx17 Ground Penetrating Radar Version 2](https://doi.org/10.5067/G21LGCNLFSC5) landing page, you can find the rectangular spatial coverage under the Overview tab, or you can draw a polygon over your area of interest in the map under the Download Data tab and export the shape as a geojson file using the Export Polygon icon shown below. An example polygon geojson file is provided in the /Data folder of this repository. \n",
- "\n",
- "\n",
- "\n",
- "\n",
- " \n",
- "
\n",
- "
"
- ],
- "text/plain": [
- " dataset_id short_name version \\\n",
- "0 SnowEx17 Ground Penetrating Radar V002 SNEX17_GPR 002 \n",
- "1 SnowEx17 Ground Penetrating Radar V002 SNEX17_GPR 002 \n",
- "2 SnowEx17 Ground Penetrating Radar V002 SNEX17_GPR 002 \n",
- "3 ASO L4 Lidar Snow Depth 3m UTM Grid V001 ASO_3M_SD 001 \n",
- "4 ASO L4 Lidar Snow Depth 3m UTM Grid V001 ASO_3M_SD 001 \n",
- "6 ASO L4 Lidar Snow Depth 3m UTM Grid V001 ASO_3M_SD 001 \n",
- "7 ASO L4 Lidar Snow Depth 3m UTM Grid V001 ASO_3M_SD 001 \n",
- "46 MODIS/Terra Snow Cover Daily L3 Global 500m SI... MOD10A1 006 \n",
- "47 MODIS/Terra Snow Cover Daily L3 Global 500m SI... MOD10A1 006 \n",
- "48 MODIS/Terra Snow Cover Daily L3 Global 500m SI... MOD10A1 006 \n",
- "52 MODIS/Terra Snow Cover Daily L3 Global 500m SI... MOD10A1 006 \n",
- "53 MODIS/Terra Snow Cover Daily L3 Global 500m SI... MOD10A1 006 \n",
- "54 MODIS/Terra Snow Cover Daily L3 Global 500m SI... MOD10A1 006 \n",
- "55 MODIS/Terra Snow Cover Daily L3 Global 500m SI... MOD10A1 006 \n",
- "59 MODIS/Terra Snow Cover Daily L3 Global 500m SI... MOD10A1 006 \n",
- "60 MODIS/Terra Snow Cover Daily L3 Global 500m SI... MOD10A1 006 \n",
- "61 MODIS/Terra Snow Cover Daily L3 Global 500m SI... MOD10A1 006 \n",
- "62 MODIS/Terra Snow Cover Daily L3 Global 500m SI... MOD10A1 006 \n",
- "63 MODIS/Terra Snow Cover Daily L3 Global 500m SI... MOD10A1 006 \n",
- "\n",
- " producer_granule_id start_date \\\n",
- "0 SnowEx17_GPR_Version2_Week1.csv 2017-02-08T00:00:00.000Z \n",
- "1 SnowEx17_GPR_Version2_Week2.csv 2017-02-14T00:00:00.000Z \n",
- "2 SnowEx17_GPR_Version2_Week3.csv 2017-02-21T00:00:00.000Z \n",
- "3 ASO_3M_SD_USCOGM_20170208 2017-02-08T00:00:00.000Z \n",
- "4 ASO_3M_SD_USCOGM_20170216 2017-02-16T00:00:00.000Z \n",
- "6 ASO_3M_SD_USCOGM_20170221 2017-02-21T00:00:00.000Z \n",
- "7 ASO_3M_SD_USCOGM_20170225 2017-02-25T00:00:00.000Z \n",
- "46 MOD10A1.A2017039.h09v05.006.2017041102600.hdf 2017-02-08T16:20:00.000Z \n",
- "47 MOD10A1.A2017040.h09v05.006.2017042102640.hdf 2017-02-09T17:05:00.000Z \n",
- "48 MOD10A1.A2017041.h09v05.006.2017043095629.hdf 2017-02-10T16:10:00.000Z \n",
- "52 MOD10A1.A2017045.h09v05.006.2017047103323.hdf 2017-02-14T17:20:00.000Z \n",
- "53 MOD10A1.A2017046.h09v05.006.2017052213130.hdf 2017-02-15T16:30:00.000Z \n",
- "54 MOD10A1.A2017047.h09v05.006.2017053103120.hdf 2017-02-16T17:10:00.000Z \n",
- "55 MOD10A1.A2017048.h09v05.006.2017050103600.hdf 2017-02-17T16:15:00.000Z \n",
- "59 MOD10A1.A2017052.h09v05.006.2017054100801.hdf 2017-02-21T17:30:00.000Z \n",
- "60 MOD10A1.A2017053.h09v05.006.2017055094801.hdf 2017-02-22T16:35:00.000Z \n",
- "61 MOD10A1.A2017054.h09v05.006.2017059063600.hdf 2017-02-23T17:15:00.000Z \n",
- "62 MOD10A1.A2017055.h09v05.006.2017057092149.hdf 2017-02-24T16:20:00.000Z \n",
- "63 MOD10A1.A2017056.h09v05.006.2017058092815.hdf 2017-02-25T17:05:00.000Z \n",
- "\n",
- " end_date \n",
- "0 2017-02-10T23:59:59.000Z \n",
- "1 2017-02-17T23:59:59.000Z \n",
- "2 2017-02-25T23:59:59.000Z \n",
- "3 2017-02-08T23:59:59.000Z \n",
- "4 2017-02-16T23:59:59.000Z \n",
- "6 2017-02-21T23:59:59.000Z \n",
- "7 2017-02-25T23:59:59.000Z \n",
- "46 2017-02-08T19:40:00.000Z \n",
- "47 2017-02-09T18:50:00.000Z \n",
- "48 2017-02-10T19:30:00.000Z \n",
- "52 2017-02-14T19:05:00.000Z \n",
- "53 2017-02-15T18:10:00.000Z \n",
- "54 2017-02-16T18:55:00.000Z \n",
- "55 2017-02-17T19:35:00.000Z \n",
- "59 2017-02-21T19:10:00.000Z \n",
- "60 2017-02-22T18:20:00.000Z \n",
- "61 2017-02-23T19:00:00.000Z \n",
- "62 2017-02-24T19:40:00.000Z \n",
- "63 2017-02-25T18:50:00.000Z "
- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "search_df = fn.time_overlap(data_dict)\n",
- "print(len(search_df), ' total files returned')\n",
- "search_df"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "___\n",
- "\n",
- "## Data Access\n",
- "\n",
- "The number of files has been greatly reduced to only those needed to compare data across these data sets. This CMR query will collect the data file URLs, including the associated quality and metadata files if available."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "['https://n5eil01u.ecs.nsidc.org/DP1/SNOWEX/SNEX17_GPR.002/2017.02.08/SnowEx17_GPR_Version2_Week1.csv',\n",
- " 'https://n5eil01u.ecs.nsidc.org/DP1/SNOWEX/SNEX17_GPR.002/2017.02.08/SnowEx17_GPR_Version2_Week1.csv.xml',\n",
- " 'https://n5eil01u.ecs.nsidc.org/DP1/SNOWEX/SNEX17_GPR.002/2017.02.14/SnowEx17_GPR_Version2_Week2.csv',\n",
- " 'https://n5eil01u.ecs.nsidc.org/DP1/SNOWEX/SNEX17_GPR.002/2017.02.14/SnowEx17_GPR_Version2_Week2.csv.xml',\n",
- " 'https://n5eil01u.ecs.nsidc.org/DP1/SNOWEX/SNEX17_GPR.002/2017.02.21/SnowEx17_GPR_Version2_Week3.csv',\n",
- " 'https://n5eil01u.ecs.nsidc.org/DP1/SNOWEX/SNEX17_GPR.002/2017.02.21/SnowEx17_GPR_Version2_Week3.csv.xml',\n",
- " 'https://n5eil01u.ecs.nsidc.org/DP1/ASO/ASO_3M_SD.001/2017.02.08/ASO_3M_QF_USCOGM_20170208.tif',\n",
- " 'https://n5eil01u.ecs.nsidc.org/DP1/ASO/ASO_3M_SD.001/2017.02.08/ASO_3M_SD_USCOGM_20170208.tif',\n",
- " 'https://n5eil01u.ecs.nsidc.org/DP1/ASO/ASO_3M_SD.001/2017.02.08/ASO_3M_SD_USCOGM_20170208.tif.xml',\n",
- " 'https://n5eil01u.ecs.nsidc.org/DP1/ASO/ASO_3M_SD.001/2017.02.16/ASO_3M_QF_USCOGM_20170216.tif',\n",
- " 'https://n5eil01u.ecs.nsidc.org/DP1/ASO/ASO_3M_SD.001/2017.02.16/ASO_3M_SD_USCOGM_20170216.tif',\n",
- " 'https://n5eil01u.ecs.nsidc.org/DP1/ASO/ASO_3M_SD.001/2017.02.16/ASO_3M_SD_USCOGM_20170216.tif.xml',\n",
- " 'https://n5eil01u.ecs.nsidc.org/DP1/ASO/ASO_3M_SD.001/2017.02.21/ASO_3M_QF_USCOGM_20170221.tif',\n",
- " 'https://n5eil01u.ecs.nsidc.org/DP1/ASO/ASO_3M_SD.001/2017.02.21/ASO_3M_SD_USCOGM_20170221.tif',\n",
- " 'https://n5eil01u.ecs.nsidc.org/DP1/ASO/ASO_3M_SD.001/2017.02.21/ASO_3M_SD_USCOGM_20170221.tif.xml',\n",
- " 'https://n5eil01u.ecs.nsidc.org/DP1/ASO/ASO_3M_SD.001/2017.02.25/ASO_3M_QF_USCOGM_20170225.tif',\n",
- " 'https://n5eil01u.ecs.nsidc.org/DP1/ASO/ASO_3M_SD.001/2017.02.25/ASO_3M_SD_USCOGM_20170225.tif',\n",
- " 'https://n5eil01u.ecs.nsidc.org/DP1/ASO/ASO_3M_SD.001/2017.02.25/ASO_3M_SD_USCOGM_20170225.tif.xml',\n",
- " 'https://n5eil01u.ecs.nsidc.org/DP4/MOST/MOD10A1.006/2017.02.08/MOD10A1.A2017039.h09v05.006.2017041102600.hdf',\n",
- " 'https://n5eil01u.ecs.nsidc.org/DP4/MOST/MOD10A1.006/2017.02.08/MOD10A1.A2017039.h09v05.006.2017041102600.hdf.xml',\n",
- " 'https://n5eil01u.ecs.nsidc.org/DP4/MOST/MOD10A1.006/2017.02.09/MOD10A1.A2017040.h09v05.006.2017042102640.hdf',\n",
- " 'https://n5eil01u.ecs.nsidc.org/DP4/MOST/MOD10A1.006/2017.02.09/MOD10A1.A2017040.h09v05.006.2017042102640.hdf.xml',\n",
- " 'https://n5eil01u.ecs.nsidc.org/DP4/MOST/MOD10A1.006/2017.02.10/MOD10A1.A2017041.h09v05.006.2017043095629.hdf',\n",
- " 'https://n5eil01u.ecs.nsidc.org/DP4/MOST/MOD10A1.006/2017.02.10/MOD10A1.A2017041.h09v05.006.2017043095629.hdf.xml',\n",
- " 'https://n5eil01u.ecs.nsidc.org/DP4/MOST/MOD10A1.006/2017.02.14/MOD10A1.A2017045.h09v05.006.2017047103323.hdf',\n",
- " 'https://n5eil01u.ecs.nsidc.org/DP4/MOST/MOD10A1.006/2017.02.14/MOD10A1.A2017045.h09v05.006.2017047103323.hdf.xml',\n",
- " 'https://n5eil01u.ecs.nsidc.org/DP4/MOST/MOD10A1.006/2017.02.15/MOD10A1.A2017046.h09v05.006.2017052213130.hdf',\n",
- " 'https://n5eil01u.ecs.nsidc.org/DP4/MOST/MOD10A1.006/2017.02.15/MOD10A1.A2017046.h09v05.006.2017052213130.hdf.xml',\n",
- " 'https://n5eil01u.ecs.nsidc.org/DP4/MOST/MOD10A1.006/2017.02.16/MOD10A1.A2017047.h09v05.006.2017053103120.hdf',\n",
- " 'https://n5eil01u.ecs.nsidc.org/DP4/MOST/MOD10A1.006/2017.02.16/MOD10A1.A2017047.h09v05.006.2017053103120.hdf.xml',\n",
- " 'https://n5eil01u.ecs.nsidc.org/DP4/MOST/MOD10A1.006/2017.02.17/MOD10A1.A2017048.h09v05.006.2017050103600.hdf',\n",
- " 'https://n5eil01u.ecs.nsidc.org/DP4/MOST/MOD10A1.006/2017.02.17/MOD10A1.A2017048.h09v05.006.2017050103600.hdf.xml',\n",
- " 'https://n5eil01u.ecs.nsidc.org/DP4/MOST/MOD10A1.006/2017.02.21/MOD10A1.A2017052.h09v05.006.2017054100801.hdf',\n",
- " 'https://n5eil01u.ecs.nsidc.org/DP4/MOST/MOD10A1.006/2017.02.21/MOD10A1.A2017052.h09v05.006.2017054100801.hdf.xml',\n",
- " 'https://n5eil01u.ecs.nsidc.org/DP4/MOST/MOD10A1.006/2017.02.22/MOD10A1.A2017053.h09v05.006.2017055094801.hdf',\n",
- " 'https://n5eil01u.ecs.nsidc.org/DP4/MOST/MOD10A1.006/2017.02.22/MOD10A1.A2017053.h09v05.006.2017055094801.hdf.xml',\n",
- " 'https://n5eil01u.ecs.nsidc.org/DP4/MOST/MOD10A1.006/2017.02.23/MOD10A1.A2017054.h09v05.006.2017059063600.hdf',\n",
- " 'https://n5eil01u.ecs.nsidc.org/DP4/MOST/MOD10A1.006/2017.02.23/MOD10A1.A2017054.h09v05.006.2017059063600.hdf.xml',\n",
- " 'https://n5eil01u.ecs.nsidc.org/DP4/MOST/MOD10A1.006/2017.02.24/MOD10A1.A2017055.h09v05.006.2017057092149.hdf',\n",
- " 'https://n5eil01u.ecs.nsidc.org/DP4/MOST/MOD10A1.006/2017.02.24/MOD10A1.A2017055.h09v05.006.2017057092149.hdf.xml',\n",
- " 'https://n5eil01u.ecs.nsidc.org/DP4/MOST/MOD10A1.006/2017.02.25/MOD10A1.A2017056.h09v05.006.2017058092815.hdf',\n",
- " 'https://n5eil01u.ecs.nsidc.org/DP4/MOST/MOD10A1.006/2017.02.25/MOD10A1.A2017056.h09v05.006.2017058092815.hdf.xml']"
- ]
- },
- "execution_count": 7,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Create new dictionary with fields needed for CMR url search\n",
- "\n",
- "url_df = search_df.drop(columns=['start_date', 'end_date','version','dataset_id'])\n",
- "url_dict = url_df.to_dict('records')\n",
- "\n",
- "# CMR search variables\n",
- "granule_search_url = 'https://cmr.earthdata.nasa.gov/search/granules'\n",
- "headers= {'Accept': 'application/json'}\n",
- "\n",
- "# Create URL list from each df row\n",
- "urls = []\n",
- "for i in range(len(url_dict)):\n",
- " response = requests.get(granule_search_url, params=url_dict[i], headers=headers)\n",
- " results = json.loads(response.content)\n",
- " urls.append(fn.cmr_filter_urls(results))\n",
- "# flatten url list\n",
- "urls = list(np.concatenate(urls))\n",
- "urls"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Additional data access and subsetting services\n",
- "\n",
- "#### API Access\n",
- "Data can be accessed directly from our HTTPS file system through the URLs collected above, or through our Application Programming Interface (API). Our API offers you the ability to order data using specific temporal and spatial filters, as well as subset, reformat, and reproject select data sets. The same subsetting, reformatting, and reprojection services available on select data sets through NASA Earthdata Search can also be applied using this API. These options can be requested in a single access command without the need to script against our data directory structure. See our [programmatic access guide](https://nsidc.org/support/how/how-do-i-programmatically-request-data-services) for more information on API options. "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Add service request options for MODIS data\n",
- "\n",
- "According to https://nsidc.org/support/faq/what-data-subsetting-reformatting-and-reprojection-services-are-available-for-MODIS-data, we can see that spatial subsetting and GeoTIFF reformatting are available for MOD10A1 so those options are requested below. The area subset must be described as a bounding box, which can be created based on the polygon bounds above. We will also add GeoTIFF reformatting to the MOD10A1 data dictionary and the temporal range will be set based on the range of MOD10A1 files in the dataframe above. These new parameters will be added to the API request below."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "{'short_name': 'MOD10A1', 'version': '6', 'polygon': '-108.2352445938561,38.98556907427165,-107.85284607930835,38.978765032966244,-107.85494925720668,39.10596902171742,-108.22772795408136,39.11294532581687,-108.2352445938561,38.98556907427165', 'temporal': '2017-02-08T16:20:00.000Z,2017-02-25T18:50:00.000Z', 'page_size': 2000, 'page_num': 1, 'bbox': '-108.2352445938561,38.978765032966244,-107.85284607930835,39.11294532581687', 'format': 'GeoTIFF'}\n"
- ]
- }
- ],
- "source": [
- "bounds = poly.bounds # Get polygon bounds to be used as bounding box input\n",
- "data_dict['modis']['bbox'] = ','.join(map(str, list(bounds))) # Add bounding box subsetting to MODIS dictionary\n",
- "data_dict['modis']['format'] = 'GeoTIFF' # Add geotiff reformatting to MODIS dictionary\n",
- "\n",
- "# Set new temporal range based on dataframe above. Note that this will request all MOD10A1 data falling within this time range.\n",
- "modis_start = min(search_df.loc[search_df['short_name'] == 'MOD10A1', 'start_date'])\n",
- "modis_end = max(search_df.loc[search_df['short_name'] == 'MOD10A1', 'end_date'])\n",
- "data_dict['modis']['temporal'] = ','.join([modis_start,modis_end])\n",
- "print(data_dict['modis'])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Create the data request API endpoint\n",
- "Programmatic API requests are formatted as HTTPS URLs that contain key-value-pairs specifying the service operations that we specified above. We will first create a string of key-value-pairs from our data dictionary and we'll feed those into our API endpoint. This API endpoint can be executed via command line, a web browser, or in Python below."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "https://n5eil02u.ecs.nsidc.org/egi/request?short_name=MOD10A1&version=6&polygon=-108.2352445938561,38.98556907427165,-107.85284607930835,38.978765032966244,-107.85494925720668,39.10596902171742,-108.22772795408136,39.11294532581687,-108.2352445938561,38.98556907427165&temporal=2017-02-08T16:20:00.000Z,2017-02-25T18:50:00.000Z&page_size=2000&page_num=1&bbox=-108.2352445938561,38.978765032966244,-107.85284607930835,39.11294532581687&format=GeoTIFF\n"
- ]
- }
- ],
- "source": [
- "base_url = 'https://n5eil02u.ecs.nsidc.org/egi/request' # Set NSIDC data access base URL\n",
- "#data_dict['modis']['request_mode'] = 'stream' # Set the request mode to asynchronous\n",
- "\n",
- "param_string = '&'.join(\"{!s}={!r}\".format(k,v) for (k,v) in data_dict['modis'].items()) # Convert param_dict to string\n",
- "param_string = param_string.replace(\"'\",\"\") # Remove quotes\n",
- "\n",
- "api_request = [f'{base_url}?{param_string}']\n",
- "print(api_request[0]) # Print API base URL + request parameters"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Download options\n",
- "\n",
- "The following functions will return the file URLs and the MOD10A1 API request. For demonstration purposes, these functions have been commented out, and instead the data utilized in the following steps will be accessed from a staged directory. ***Note that if you are running this notebook in Binder, the memory may not be sufficient to download these files. Please use the Docker or local Conda options provided in the README if you are interested in downloading all files.***"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "zsh:1: command not found: aws\n"
- ]
- }
- ],
- "source": [
- "path = str(os.getcwd() + '/Data')\n",
- "if not os.path.exists(path):\n",
- " os.mkdir(path)\n",
- "os.chdir(path)\n",
- "#fn.cmr_download(urls)\n",
- "#fn.cmr_download(api_request)\n",
- "\n",
- "\n",
- "# pull data from staged bucket for demonstration\n",
- "!awscliv2 --no-sign-request s3 cp s3://snowex-aso-modis-tutorial-data/ ./ --recursive #access data in staged directory"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "___\n",
- "## Read in SnowEx data and buffer points around Snotel location\n",
- "\n",
- "This SnowEx data set is provided in CSV. A [Pandas DataFrame](https://pandas.pydata.org/pandas-docs/stable/getting_started/overview.html) is used to easily read in data. For these next steps, just one day's worth of data will be selected from this file and the coincident ASO and MODIS data will be selected.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "| \n", - " | dataset_id | \n", - "short_name | \n", - "version | \n", - "producer_granule_id | \n", - "start_date | \n", - "end_date | \n", - "
|---|---|---|---|---|---|---|
| 0 | \n", - "SnowEx17 Ground Penetrating Radar V002 | \n", - "SNEX17_GPR | \n", - "002 | \n", - "SnowEx17_GPR_Version2_Week1.csv | \n", - "2017-02-08T00:00:00.000Z | \n", - "2017-02-10T23:59:59.000Z | \n", - "
| 1 | \n", - "SnowEx17 Ground Penetrating Radar V002 | \n", - "SNEX17_GPR | \n", - "002 | \n", - "SnowEx17_GPR_Version2_Week2.csv | \n", - "2017-02-14T00:00:00.000Z | \n", - "2017-02-17T23:59:59.000Z | \n", - "
| 2 | \n", - "SnowEx17 Ground Penetrating Radar V002 | \n", - "SNEX17_GPR | \n", - "002 | \n", - "SnowEx17_GPR_Version2_Week3.csv | \n", - "2017-02-21T00:00:00.000Z | \n", - "2017-02-25T23:59:59.000Z | \n", - "
| 3 | \n", - "ASO L4 Lidar Snow Depth 3m UTM Grid V001 | \n", - "ASO_3M_SD | \n", - "001 | \n", - "ASO_3M_SD_USCOGM_20170208 | \n", - "2017-02-08T00:00:00.000Z | \n", - "2017-02-08T23:59:59.000Z | \n", - "
| 4 | \n", - "ASO L4 Lidar Snow Depth 3m UTM Grid V001 | \n", - "ASO_3M_SD | \n", - "001 | \n", - "ASO_3M_SD_USCOGM_20170216 | \n", - "2017-02-16T00:00:00.000Z | \n", - "2017-02-16T23:59:59.000Z | \n", - "
| 6 | \n", - "ASO L4 Lidar Snow Depth 3m UTM Grid V001 | \n", - "ASO_3M_SD | \n", - "001 | \n", - "ASO_3M_SD_USCOGM_20170221 | \n", - "2017-02-21T00:00:00.000Z | \n", - "2017-02-21T23:59:59.000Z | \n", - "
| 7 | \n", - "ASO L4 Lidar Snow Depth 3m UTM Grid V001 | \n", - "ASO_3M_SD | \n", - "001 | \n", - "ASO_3M_SD_USCOGM_20170225 | \n", - "2017-02-25T00:00:00.000Z | \n", - "2017-02-25T23:59:59.000Z | \n", - "
| 46 | \n", - "MODIS/Terra Snow Cover Daily L3 Global 500m SI... | \n", - "MOD10A1 | \n", - "006 | \n", - "MOD10A1.A2017039.h09v05.006.2017041102600.hdf | \n", - "2017-02-08T16:20:00.000Z | \n", - "2017-02-08T19:40:00.000Z | \n", - "
| 47 | \n", - "MODIS/Terra Snow Cover Daily L3 Global 500m SI... | \n", - "MOD10A1 | \n", - "006 | \n", - "MOD10A1.A2017040.h09v05.006.2017042102640.hdf | \n", - "2017-02-09T17:05:00.000Z | \n", - "2017-02-09T18:50:00.000Z | \n", - "
| 48 | \n", - "MODIS/Terra Snow Cover Daily L3 Global 500m SI... | \n", - "MOD10A1 | \n", - "006 | \n", - "MOD10A1.A2017041.h09v05.006.2017043095629.hdf | \n", - "2017-02-10T16:10:00.000Z | \n", - "2017-02-10T19:30:00.000Z | \n", - "
| 52 | \n", - "MODIS/Terra Snow Cover Daily L3 Global 500m SI... | \n", - "MOD10A1 | \n", - "006 | \n", - "MOD10A1.A2017045.h09v05.006.2017047103323.hdf | \n", - "2017-02-14T17:20:00.000Z | \n", - "2017-02-14T19:05:00.000Z | \n", - "
| 53 | \n", - "MODIS/Terra Snow Cover Daily L3 Global 500m SI... | \n", - "MOD10A1 | \n", - "006 | \n", - "MOD10A1.A2017046.h09v05.006.2017052213130.hdf | \n", - "2017-02-15T16:30:00.000Z | \n", - "2017-02-15T18:10:00.000Z | \n", - "
| 54 | \n", - "MODIS/Terra Snow Cover Daily L3 Global 500m SI... | \n", - "MOD10A1 | \n", - "006 | \n", - "MOD10A1.A2017047.h09v05.006.2017053103120.hdf | \n", - "2017-02-16T17:10:00.000Z | \n", - "2017-02-16T18:55:00.000Z | \n", - "
| 55 | \n", - "MODIS/Terra Snow Cover Daily L3 Global 500m SI... | \n", - "MOD10A1 | \n", - "006 | \n", - "MOD10A1.A2017048.h09v05.006.2017050103600.hdf | \n", - "2017-02-17T16:15:00.000Z | \n", - "2017-02-17T19:35:00.000Z | \n", - "
| 59 | \n", - "MODIS/Terra Snow Cover Daily L3 Global 500m SI... | \n", - "MOD10A1 | \n", - "006 | \n", - "MOD10A1.A2017052.h09v05.006.2017054100801.hdf | \n", - "2017-02-21T17:30:00.000Z | \n", - "2017-02-21T19:10:00.000Z | \n", - "
| 60 | \n", - "MODIS/Terra Snow Cover Daily L3 Global 500m SI... | \n", - "MOD10A1 | \n", - "006 | \n", - "MOD10A1.A2017053.h09v05.006.2017055094801.hdf | \n", - "2017-02-22T16:35:00.000Z | \n", - "2017-02-22T18:20:00.000Z | \n", - "
| 61 | \n", - "MODIS/Terra Snow Cover Daily L3 Global 500m SI... | \n", - "MOD10A1 | \n", - "006 | \n", - "MOD10A1.A2017054.h09v05.006.2017059063600.hdf | \n", - "2017-02-23T17:15:00.000Z | \n", - "2017-02-23T19:00:00.000Z | \n", - "
| 62 | \n", - "MODIS/Terra Snow Cover Daily L3 Global 500m SI... | \n", - "MOD10A1 | \n", - "006 | \n", - "MOD10A1.A2017055.h09v05.006.2017057092149.hdf | \n", - "2017-02-24T16:20:00.000Z | \n", - "2017-02-24T19:40:00.000Z | \n", - "
| 63 | \n", - "MODIS/Terra Snow Cover Daily L3 Global 500m SI... | \n", - "MOD10A1 | \n", - "006 | \n", - "MOD10A1.A2017056.h09v05.006.2017058092815.hdf | \n", - "2017-02-25T17:05:00.000Z | \n", - "2017-02-25T18:50:00.000Z | \n", - "
\n",
- "\n",
- "\n",
- " \n",
- "
\n",
- "
"
- ],
- "text/plain": [
- " collection trace long lat elev twtt Thickness \\\n",
- "0 GPR_0042_020817 2581 -108.066856 39.043146 3240.2 5.89 0.692 \n",
- "1 GPR_0042_020817 2582 -108.066856 39.043146 3240.2 5.89 0.692 \n",
- "2 GPR_0042_020817 2583 -108.066856 39.043146 3240.2 5.87 0.690 \n",
- "3 GPR_0042_020817 2584 -108.066855 39.043146 3240.2 5.86 0.689 \n",
- "4 GPR_0042_020817 2585 -108.066855 39.043147 3240.2 5.84 0.686 \n",
- "\n",
- " SWE x y UTM_Zone \n",
- "0 225 753854.880092 4.325659e+06 12 S \n",
- "1 225 753854.899385 4.325660e+06 12 S \n",
- "2 224 753854.918686 4.325660e+06 12 S \n",
- "3 224 753854.937987 4.325660e+06 12 S \n",
- "4 223 753854.957280 4.325660e+06 12 S "
- ]
- },
- "execution_count": 11,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "snowex_path = './SnowEx17_GPR_Version2_Week1.csv' # Define local filepath\n",
- "df = pd.read_csv(snowex_path, sep='\\t') \n",
- "df.head()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Convert to time values and extract a single day"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The collection date needs to be extracted from the `collection` value and a new dataframe will be generated as a subset of the original based on a single day:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "| \n", - " | collection | \n", - "trace | \n", - "long | \n", - "lat | \n", - "elev | \n", - "twtt | \n", - "Thickness | \n", - "SWE | \n", - "x | \n", - "y | \n", - "UTM_Zone | \n", - "
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", - "GPR_0042_020817 | \n", - "2581 | \n", - "-108.066856 | \n", - "39.043146 | \n", - "3240.2 | \n", - "5.89 | \n", - "0.692 | \n", - "225 | \n", - "753854.880092 | \n", - "4.325659e+06 | \n", - "12 S | \n", - "
| 1 | \n", - "GPR_0042_020817 | \n", - "2582 | \n", - "-108.066856 | \n", - "39.043146 | \n", - "3240.2 | \n", - "5.89 | \n", - "0.692 | \n", - "225 | \n", - "753854.899385 | \n", - "4.325660e+06 | \n", - "12 S | \n", - "
| 2 | \n", - "GPR_0042_020817 | \n", - "2583 | \n", - "-108.066856 | \n", - "39.043146 | \n", - "3240.2 | \n", - "5.87 | \n", - "0.690 | \n", - "224 | \n", - "753854.918686 | \n", - "4.325660e+06 | \n", - "12 S | \n", - "
| 3 | \n", - "GPR_0042_020817 | \n", - "2584 | \n", - "-108.066855 | \n", - "39.043146 | \n", - "3240.2 | \n", - "5.86 | \n", - "0.689 | \n", - "224 | \n", - "753854.937987 | \n", - "4.325660e+06 | \n", - "12 S | \n", - "
| 4 | \n", - "GPR_0042_020817 | \n", - "2585 | \n", - "-108.066855 | \n", - "39.043147 | \n", - "3240.2 | \n", - "5.84 | \n", - "0.686 | \n", - "223 | \n", - "753854.957280 | \n", - "4.325660e+06 | \n", - "12 S | \n", - "
\n",
- "\n",
- "\n",
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- " collection trace long lat elev twtt \\\n",
- "0 GPR_0042_020817 2581 -108.066856 39.043146 3240.20 5.89 \n",
- "109172 GPR_0043_020817 6360 -108.063209 39.049202 3248.49 11.49 \n",
- "109173 GPR_0043_020817 6361 -108.063209 39.049202 3248.50 11.56 \n",
- "109174 GPR_0043_020817 6362 -108.063208 39.049202 3248.50 11.62 \n",
- "109175 GPR_0043_020817 6363 -108.063208 39.049202 3248.50 11.64 \n",
- "\n",
- " Thickness SWE x y UTM_Zone date \n",
- "0 0.692 225 753854.880092 4.325659e+06 12 S 2017-02-08 \n",
- "109172 1.350 439 754148.853700 4.326342e+06 12 S 2017-02-08 \n",
- "109173 1.358 441 754148.882549 4.326342e+06 12 S 2017-02-08 \n",
- "109174 1.365 444 754148.911407 4.326342e+06 12 S 2017-02-08 \n",
- "109175 1.368 445 754148.947466 4.326342e+06 12 S 2017-02-08 "
- ]
- },
- "execution_count": 12,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df['date'] = df.collection.str.rsplit('_').str[-1].astype(str)\n",
- "df.date = pd.to_datetime(df.date, format=\"%m%d%y\")\n",
- "df = df.sort_values(['date'])\n",
- "df_subset = df[df['date'] == '2017-02-08'] # subset original dataframe and only select this date\n",
- "df.head()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Convert to Geopandas dataframe to provide point geometry\n",
- "\n",
- "According to the SnowEx documentation, the data are available in UTM Zone 12N so we'll set to this projection so that we can buffer in meters in the next step:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "| \n", - " | collection | \n", - "trace | \n", - "long | \n", - "lat | \n", - "elev | \n", - "twtt | \n", - "Thickness | \n", - "SWE | \n", - "x | \n", - "y | \n", - "UTM_Zone | \n", - "date | \n", - "
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", - "GPR_0042_020817 | \n", - "2581 | \n", - "-108.066856 | \n", - "39.043146 | \n", - "3240.20 | \n", - "5.89 | \n", - "0.692 | \n", - "225 | \n", - "753854.880092 | \n", - "4.325659e+06 | \n", - "12 S | \n", - "2017-02-08 | \n", - "
| 109172 | \n", - "GPR_0043_020817 | \n", - "6360 | \n", - "-108.063209 | \n", - "39.049202 | \n", - "3248.49 | \n", - "11.49 | \n", - "1.350 | \n", - "439 | \n", - "754148.853700 | \n", - "4.326342e+06 | \n", - "12 S | \n", - "2017-02-08 | \n", - "
| 109173 | \n", - "GPR_0043_020817 | \n", - "6361 | \n", - "-108.063209 | \n", - "39.049202 | \n", - "3248.50 | \n", - "11.56 | \n", - "1.358 | \n", - "441 | \n", - "754148.882549 | \n", - "4.326342e+06 | \n", - "12 S | \n", - "2017-02-08 | \n", - "
| 109174 | \n", - "GPR_0043_020817 | \n", - "6362 | \n", - "-108.063208 | \n", - "39.049202 | \n", - "3248.50 | \n", - "11.62 | \n", - "1.365 | \n", - "444 | \n", - "754148.911407 | \n", - "4.326342e+06 | \n", - "12 S | \n", - "2017-02-08 | \n", - "
| 109175 | \n", - "GPR_0043_020817 | \n", - "6363 | \n", - "-108.063208 | \n", - "39.049202 | \n", - "3248.50 | \n", - "11.64 | \n", - "1.368 | \n", - "445 | \n", - "754148.947466 | \n", - "4.326342e+06 | \n", - "12 S | \n", - "2017-02-08 | \n", - "
\n",
- "\n",
- "\n",
- " \n",
- "
\n",
- "
"
- ],
- "text/plain": [
- " collection trace long lat elev twtt \\\n",
- "0 GPR_0042_020817 2581 -108.066856 39.043146 3240.20 5.89 \n",
- "109172 GPR_0043_020817 6360 -108.063209 39.049202 3248.49 11.49 \n",
- "109173 GPR_0043_020817 6361 -108.063209 39.049202 3248.50 11.56 \n",
- "109174 GPR_0043_020817 6362 -108.063208 39.049202 3248.50 11.62 \n",
- "109175 GPR_0043_020817 6363 -108.063208 39.049202 3248.50 11.64 \n",
- "\n",
- " Thickness SWE x y UTM_Zone date \\\n",
- "0 0.692 225 753854.880092 4.325659e+06 12 S 2017-02-08 \n",
- "109172 1.350 439 754148.853700 4.326342e+06 12 S 2017-02-08 \n",
- "109173 1.358 441 754148.882549 4.326342e+06 12 S 2017-02-08 \n",
- "109174 1.365 444 754148.911407 4.326342e+06 12 S 2017-02-08 \n",
- "109175 1.368 445 754148.947466 4.326342e+06 12 S 2017-02-08 \n",
- "\n",
- " geometry \n",
- "0 POINT (753854.880 4325659.484) \n",
- "109172 POINT (754148.854 4326341.915) \n",
- "109173 POINT (754148.883 4326341.916) \n",
- "109174 POINT (754148.911 4326341.917) \n",
- "109175 POINT (754148.947 4326341.918) "
- ]
- },
- "execution_count": 13,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "gdf_utm= gpd.GeoDataFrame(df_subset, geometry=gpd.points_from_xy(df_subset.x, df_subset.y), crs='EPSG:32612')\n",
- "gdf_utm.head()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Buffer data around SNOTEL site"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We can further subset the SnowEx snow depth data to get within a 500 m radius of the [SNOTEL Mesa Lakes](https://wcc.sc.egov.usda.gov/nwcc/site?sitenum=622&state=co) site.\n",
- "\n",
- "First we'll create a new geodataframe with the SNOTEL site location, set to our SnowEx UTM coordinate reference system, and create a 500 meter buffer around this point. Then we'll subset the SnowEx points to the buffer and convert back to the WGS84 CRS:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Create another geodataframe (gdfsel) with the center point for the selection\n",
- "df_snotel = pd.DataFrame(\n",
- " {'SNOTEL Site': ['Mesa Lakes'],\n",
- " 'Latitude': [39.05],\n",
- " 'Longitude': [-108.067]})\n",
- "gdf_snotel = gpd.GeoDataFrame(df_snotel, geometry=gpd.points_from_xy(df_snotel.Longitude, df_snotel.Latitude), crs='EPSG:4326')\n",
- "\n",
- "gdf_snotel.to_crs('EPSG:32612', inplace=True) # set CRS to UTM 12 N\n",
- "\n",
- "buffer = gdf_snotel.buffer(500) #create 500 m buffer\n",
- "\n",
- "gdf_buffer = gdf_utm.loc[gdf_utm.geometry.within(buffer.unary_union)] # subset dataframe to buffer region\n",
- "gdf_buffer = gdf_buffer.to_crs('EPSG:4326')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "___\n",
- "## Read in Airborne Snow Observatory data and clip to SNOTEL buffer\n",
- "\n",
- "Snow depth data from the ASO L4 Lidar Snow Depth 3m UTM Grid data set were calculated from surface elevation measured by the Riegl LMS-Q1560 airborne laser scanner (ALS). The data are provided in GeoTIFF format, so we'll use the [Rasterio](https://rasterio.readthedocs.io/en/latest/) library to read in the data. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [],
- "source": [
- "aso_path = './ASO_3M_SD_USCOGM_20170208.tif' # Define local filepath\n",
- "\n",
- "aso = rasterio.open(aso_path)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Clip data to SNOTEL buffer\n",
- "\n",
- "In order to reduce the data volume to the buffered region of interest, we can subset this GeoTIFF to the same SNOTEL buffer:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {},
- "outputs": [],
- "source": [
- "buffer = buffer.to_crs(crs=aso.crs) # convert buffer to CRS of ASO rasterio object\n",
- "out_img, out_transform = mask(aso, buffer, crop=True)\n",
- "out_meta = aso.meta.copy()\n",
- "epsg_code = int(aso.crs.data['init'][5:])\n",
- "out_meta.update({\"driver\": \"GTiff\", \"height\": out_img.shape[1], \"width\": out_img.shape[2], \"transform\": out_transform, \"crs\": '+proj=utm +zone=13 +datum=WGS84 +units=m +no_defs'})\n",
- "out_tif = 'clipped_ASO_3M_SD_USCOGM_20170208.tif'\n",
- "\n",
- "with rasterio.open(out_tif, 'w', **out_meta) as dest:\n",
- " dest.write(out_img)\n",
- " \n",
- "clipped_aso = rasterio.open(out_tif)\n",
- "aso_array = clipped_aso.read(1, masked=True)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "___ \n",
- "## Read in MODIS Snow Cover data \n",
- "\n",
- "We are interested in the Normalized Difference Snow Index (NDSI) snow cover value from the MOD10A1 data set, which is an index that is related to the presence of snow in a pixel. According to the [MOD10A1 FAQ](https://nsidc.org/support/faq/what-ndsi-snow-cover-and-how-does-it-compare-fsc), snow cover is detected using the NDSI ratio of the difference in visible reflectance (VIS) and shortwave infrared reflectance (SWIR), where NDSI = ((band 4-band 6) / (band 4 + band 6)).\n",
- "\n",
- "Note that you may need to change this filename output below if you download the data outside of the staged bucket, as the output names may vary per request. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {},
- "outputs": [],
- "source": [
- "modis_path = './MOD10A1_A2017039_h09v05_006_2017041102600_MOD_Grid_Snow_500m_NDSI_Snow_Cover_99f6ee91_subsetted.tif' # Define local filepath\n",
- "modis = rasterio.open(modis_path)\n",
- "modis_array = modis.read(1, masked=True)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "___\n",
- "## Add ASO and MODIS data to GeoPandas dataframe"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "In order to add data from these ASO and MODIS gridded data sets, we need to define the geometry parameters for theses, as well as the SnowEx data. The SnowEx geometry is set using the longitude and latitude values of the geodataframe:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "snowex geometry: Shape: (26516,)\n",
- "Lons: [-108.063209 -108.06320867 -108.06320833 ... -108.06127167 -108.061267\n",
- " -108.06214585]\n",
- "Lats: [39.04920167 39.04920167 39.04920167 ... 39.04973833 39.04973658\n",
- " 39.05015724]\n"
- ]
- }
- ],
- "source": [
- "snowex_geometry = prs.geometry.SwathDefinition(lons=gdf_buffer['long'], lats=gdf_buffer['lat'])\n",
- "print('snowex geometry: ', snowex_geometry)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "With ASO and MODIS data on regular grids, we can create area definitions for these using projection and extent metadata:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "{'count': 1,\n",
- " 'crs': CRS.from_epsg(32613),\n",
- " 'driver': 'GTiff',\n",
- " 'dtype': 'float32',\n",
- " 'height': 334,\n",
- " 'interleave': 'band',\n",
- " 'nodata': -9999.0,\n",
- " 'tiled': False,\n",
- " 'transform': Affine(3.0, 0.0, 234081.0,\n",
- " 0.0, -3.0, 4327305.0),\n",
- " 'width': 335}\n",
- "\n",
- "BoundingBox(left=234081.0, bottom=4326303.0, right=235086.0, top=4327305.0)\n",
- "{'compress': 'deflate',\n",
- " 'count': 1,\n",
- " 'crs': CRS.from_wkt('PROJCS[\"Sinusoidal Modis Spheroid\",GEOGCS[\"Unknown datum based upon the Authalic Sphere\",DATUM[\"Not_specified_based_on_Authalic_Sphere\",SPHEROID[\"Sphere\",6371007.181,0,AUTHORITY[\"EPSG\",\"7035\"]],AUTHORITY[\"EPSG\",\"6035\"]],PRIMEM[\"Greenwich\",0],UNIT[\"degree\",0.0174532925199433],AUTHORITY[\"EPSG\",\"4035\"]],PROJECTION[\"Sinusoidal\"],PARAMETER[\"longitude_of_center\",0],PARAMETER[\"false_easting\",0],PARAMETER[\"false_northing\",0],UNIT[\"metre\",1,AUTHORITY[\"EPSG\",\"9001\"]]]'),\n",
- " 'driver': 'GTiff',\n",
- " 'dtype': 'uint8',\n",
- " 'height': 34,\n",
- " 'interleave': 'band',\n",
- " 'nodata': None,\n",
- " 'tiled': False,\n",
- " 'transform': Affine(463.3127165279165, 0.0, -9356136.99756175,\n",
- " 0.0, -463.3127165279165, 4349579.782763082),\n",
- " 'width': 110}\n",
- "\n",
- "BoundingBox(left=-9356136.99756175, bottom=4333827.150401132, right=-9305172.598743679, top=4349579.782763082)\n"
- ]
- }
- ],
- "source": [
- "pprint.pprint(clipped_aso.profile)\n",
- "print('')\n",
- "print(clipped_aso.bounds)\n",
- "\n",
- "\n",
- "pprint.pprint(modis.profile)\n",
- "print('')\n",
- "print(modis.bounds)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Create area definition for ASO\n",
- "area_id = 'UTM_13N' # area_id: ID of area\n",
- "description = 'WGS 84 / UTM zone 13N' # description: Description\n",
- "proj_id = 'UTM_13N' # proj_id: ID of projection (being deprecated)\n",
- "projection = 'EPSG:32613' # projection: Proj4 parameters as a dict or string\n",
- "width = clipped_aso.width # width: Number of grid columns\n",
- "height = clipped_aso.height # height: Number of grid rows\n",
- "area_extent = (234081.0, 4326303.0, 235086.0, 4327305.0)\n",
- "aso_geometry = prs.geometry.AreaDefinition(area_id, description, proj_id, projection, width, height, area_extent)\n",
- "\n",
- "# Create area definition for MODIS\n",
- "area_id = 'Sinusoidal' # area_id: ID of area\n",
- "description = 'Sinusoidal Modis Spheroid' # description: Description\n",
- "proj_id = 'Sinusoidal' # proj_id: ID of projection (being deprecated)\n",
- "projection = 'PROJCS[\"Sinusoidal Modis Spheroid\",GEOGCS[\"Unknown datum based upon the Authalic Sphere\",DATUM[\"Not_specified_based_on_Authalic_Sphere\",SPHEROID[\"Sphere\",6371007.181,887203.3395236016,AUTHORITY[\"EPSG\",\"7035\"]],AUTHORITY[\"EPSG\",\"6035\"]],PRIMEM[\"Greenwich\",0],UNIT[\"degree\",0.0174532925199433],AUTHORITY[\"EPSG\",\"4035\"]],PROJECTION[\"Sinusoidal\"],PARAMETER[\"longitude_of_center\",0],PARAMETER[\"false_easting\",0],PARAMETER[\"false_northing\",0],UNIT[\"metre\",1,AUTHORITY[\"EPSG\",\"9001\"]]]' # projection: Proj4 parameters as a dict or string\n",
- "width = modis.width # width: Number of grid columns\n",
- "height = modis.height # height: Number of grid rows\n",
- "area_extent = (-9332971.361735353, 4341240.1538655795, -9331118.110869242, 4343093.404731691)\n",
- "modis_geometry = prs.geometry.AreaDefinition(area_id, description, proj_id, projection, width, height, area_extent)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Interpolate ASO and MODIS values onto SnowEx points\n",
- "\n",
- "To interpolate ASO snow depth and MODIS snow cover data to SnowEx snow depth points, we can use the `pyresample` library. The `radius_of_influence` parameter determines maximum radius to look for nearest neighbor interpolation."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "| \n", - " | collection | \n", - "trace | \n", - "long | \n", - "lat | \n", - "elev | \n", - "twtt | \n", - "Thickness | \n", - "SWE | \n", - "x | \n", - "y | \n", - "UTM_Zone | \n", - "date | \n", - "geometry | \n", - "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", - "GPR_0042_020817 | \n", - "2581 | \n", - "-108.066856 | \n", - "39.043146 | \n", - "3240.20 | \n", - "5.89 | \n", - "0.692 | \n", - "225 | \n", - "753854.880092 | \n", - "4.325659e+06 | \n", - "12 S | \n", - "2017-02-08 | \n", - "POINT (753854.880 4325659.484) | \n", - "
| 109172 | \n", - "GPR_0043_020817 | \n", - "6360 | \n", - "-108.063209 | \n", - "39.049202 | \n", - "3248.49 | \n", - "11.49 | \n", - "1.350 | \n", - "439 | \n", - "754148.853700 | \n", - "4.326342e+06 | \n", - "12 S | \n", - "2017-02-08 | \n", - "POINT (754148.854 4326341.915) | \n", - "
| 109173 | \n", - "GPR_0043_020817 | \n", - "6361 | \n", - "-108.063209 | \n", - "39.049202 | \n", - "3248.50 | \n", - "11.56 | \n", - "1.358 | \n", - "441 | \n", - "754148.882549 | \n", - "4.326342e+06 | \n", - "12 S | \n", - "2017-02-08 | \n", - "POINT (754148.883 4326341.916) | \n", - "
| 109174 | \n", - "GPR_0043_020817 | \n", - "6362 | \n", - "-108.063208 | \n", - "39.049202 | \n", - "3248.50 | \n", - "11.62 | \n", - "1.365 | \n", - "444 | \n", - "754148.911407 | \n", - "4.326342e+06 | \n", - "12 S | \n", - "2017-02-08 | \n", - "POINT (754148.911 4326341.917) | \n", - "
| 109175 | \n", - "GPR_0043_020817 | \n", - "6363 | \n", - "-108.063208 | \n", - "39.049202 | \n", - "3248.50 | \n", - "11.64 | \n", - "1.368 | \n", - "445 | \n", - "754148.947466 | \n", - "4.326342e+06 | \n", - "12 S | \n", - "2017-02-08 | \n", - "POINT (754148.947 4326341.918) | \n", - "
\n",
- "\n",
- "\n",
- " \n",
- "
\n",
- "
"
- ],
- "text/plain": [
- " collection trace long lat elev twtt \\\n",
- "109172 GPR_0043_020817 6360 -108.063209 39.049202 3248.49 11.49 \n",
- "109173 GPR_0043_020817 6361 -108.063209 39.049202 3248.50 11.56 \n",
- "109174 GPR_0043_020817 6362 -108.063208 39.049202 3248.50 11.62 \n",
- "109175 GPR_0043_020817 6363 -108.063208 39.049202 3248.50 11.64 \n",
- "109176 GPR_0043_020817 6364 -108.063207 39.049202 3248.50 11.68 \n",
- "\n",
- " Thickness SWE x y UTM_Zone date \\\n",
- "109172 1.350 439 754148.853700 4.326342e+06 12 S 2017-02-08 \n",
- "109173 1.358 441 754148.882549 4.326342e+06 12 S 2017-02-08 \n",
- "109174 1.365 444 754148.911407 4.326342e+06 12 S 2017-02-08 \n",
- "109175 1.368 445 754148.947466 4.326342e+06 12 S 2017-02-08 \n",
- "109176 1.372 446 754148.983533 4.326342e+06 12 S 2017-02-08 \n",
- "\n",
- " geometry aso_snow_depth modis_ndsi \n",
- "109172 POINT (-108.06321 39.04920) 1.302658 71 \n",
- "109173 POINT (-108.06321 39.04920) 1.302658 71 \n",
- "109174 POINT (-108.06321 39.04920) 1.302658 71 \n",
- "109175 POINT (-108.06321 39.04920) 1.302658 71 \n",
- "109176 POINT (-108.06321 39.04920) 1.302658 71 "
- ]
- },
- "execution_count": 21,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# add ASO values to geodataframe\n",
- "import warnings\n",
- "warnings.filterwarnings('ignore') # ignore warning when resampling to a different projection\n",
- "gdf_buffer['aso_snow_depth'] = prs.kd_tree.resample_nearest(aso_geometry, aso_array, snowex_geometry, radius_of_influence=3)\n",
- "\n",
- "# add MODIS values to geodataframe\n",
- "gdf_buffer['modis_ndsi'] = prs.kd_tree.resample_nearest(modis_geometry, modis_array, snowex_geometry, radius_of_influence=500)\n",
- "\n",
- "gdf_buffer.head()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "___ \n",
- "## Visualize data and export for further GIS analysis\n",
- "\n",
- "The rasterio plot module allows you to directly plot GeoTIFFs objects. The SnowEx `Thickness` values are plotted against the clipped ASO snow depth raster."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "| \n", - " | collection | \n", - "trace | \n", - "long | \n", - "lat | \n", - "elev | \n", - "twtt | \n", - "Thickness | \n", - "SWE | \n", - "x | \n", - "y | \n", - "UTM_Zone | \n", - "date | \n", - "geometry | \n", - "aso_snow_depth | \n", - "modis_ndsi | \n", - "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 109172 | \n", - "GPR_0043_020817 | \n", - "6360 | \n", - "-108.063209 | \n", - "39.049202 | \n", - "3248.49 | \n", - "11.49 | \n", - "1.350 | \n", - "439 | \n", - "754148.853700 | \n", - "4.326342e+06 | \n", - "12 S | \n", - "2017-02-08 | \n", - "POINT (-108.06321 39.04920) | \n", - "1.302658 | \n", - "71 | \n", - "
| 109173 | \n", - "GPR_0043_020817 | \n", - "6361 | \n", - "-108.063209 | \n", - "39.049202 | \n", - "3248.50 | \n", - "11.56 | \n", - "1.358 | \n", - "441 | \n", - "754148.882549 | \n", - "4.326342e+06 | \n", - "12 S | \n", - "2017-02-08 | \n", - "POINT (-108.06321 39.04920) | \n", - "1.302658 | \n", - "71 | \n", - "
| 109174 | \n", - "GPR_0043_020817 | \n", - "6362 | \n", - "-108.063208 | \n", - "39.049202 | \n", - "3248.50 | \n", - "11.62 | \n", - "1.365 | \n", - "444 | \n", - "754148.911407 | \n", - "4.326342e+06 | \n", - "12 S | \n", - "2017-02-08 | \n", - "POINT (-108.06321 39.04920) | \n", - "1.302658 | \n", - "71 | \n", - "
| 109175 | \n", - "GPR_0043_020817 | \n", - "6363 | \n", - "-108.063208 | \n", - "39.049202 | \n", - "3248.50 | \n", - "11.64 | \n", - "1.368 | \n", - "445 | \n", - "754148.947466 | \n", - "4.326342e+06 | \n", - "12 S | \n", - "2017-02-08 | \n", - "POINT (-108.06321 39.04920) | \n", - "1.302658 | \n", - "71 | \n", - "
| 109176 | \n", - "GPR_0043_020817 | \n", - "6364 | \n", - "-108.063207 | \n", - "39.049202 | \n", - "3248.50 | \n", - "11.68 | \n", - "1.372 | \n", - "446 | \n", - "754148.983533 | \n", - "4.326342e+06 | \n", - "12 S | \n", - "2017-02-08 | \n", - "POINT (-108.06321 39.04920) | \n", - "1.302658 | \n", - "71 | \n", - "
"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "gdf_buffer_aso_crs = gdf_buffer.to_crs('EPSG:32613') \n",
- "\n",
- "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
- "fig, ax = plt.subplots(figsize=(10, 10))\n",
- "show(clipped_aso, ax=ax)\n",
- "divider = make_axes_locatable(ax)\n",
- "cax = divider.append_axes(\"right\", size=\"5%\", pad=0.1) # fit legend to height of plot\n",
- "gdf_buffer_aso_crs.plot(column='Thickness', ax=ax, cmap='OrRd', legend=True, cax=cax, legend_kwds=\n",
- " {'label': \"Snow Depth (m)\",});"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We can do the same for MOD10A1: This was subsetted to the entire Grand Mesa region defined by the SnowEx data set coverage. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "
"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Set dataframe to MOD10A1 Sinusoidal projection\n",
- "gdf_buffer_modis_crs = gdf_buffer.to_crs('PROJCS[\"Sinusoidal Modis Spheroid\",GEOGCS[\"Unknown datum based upon the Authalic Sphere\",DATUM[\"Not_specified_based_on_Authalic_Sphere\",SPHEROID[\"Sphere\",6371007.181,887203.3395236016,AUTHORITY[\"EPSG\",\"7035\"]],AUTHORITY[\"EPSG\",\"6035\"]],PRIMEM[\"Greenwich\",0],UNIT[\"degree\",0.0174532925199433],AUTHORITY[\"EPSG\",\"4035\"]],PROJECTION[\"Sinusoidal\"],PARAMETER[\"longitude_of_center\",0],PARAMETER[\"false_easting\",0],PARAMETER[\"false_northing\",0],UNIT[\"metre\",1,AUTHORITY[\"EPSG\",\"9001\"]]]')\n",
- "\n",
- "fig, ax = plt.subplots(figsize=(10, 10))\n",
- "show(modis, ax=ax)\n",
- "divider = make_axes_locatable(ax)\n",
- "cax = divider.append_axes(\"right\", size=\"5%\", pad=0.1) # fit legend to height of plot\n",
- "gdf_buffer_modis_crs.plot(column='Thickness', ax=ax, cmap='OrRd', legend=True, cax=cax, legend_kwds=\n",
- " {'label': \"Snow Depth (m)\",});"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Additional data imagery services\n",
- "\n",
- "#### NASA Worldview and the Global Browse Imagery Service\n",
- "\n",
- "NASA’s EOSDIS Worldview mapping application provides the capability to interactively browse over 900 global, full-resolution satellite imagery layers and then download the underlying data. Many of the available imagery layers are updated within three hours of observation, essentially showing the entire Earth as it looks “right now.\"\n",
- "\n",
- "According to the [MOD10A1 landing page](https://nsidc.org/data/mod10a1), snow cover imagery layers from this data set are available through NASA Worldview. This layer can be downloaded as various image files including GeoTIFF using the snapshot feature at the top right of the page. This link presents the MOD10A1 NDSI layer over our time and area of interest: https://go.nasa.gov/35CgYMd. \n",
- "\n",
- "Additionally, the NASA Global Browse Imagery Service provides up to date, full resolution imagery for select NSIDC DAAC data sets as web services including WMTS, WMS, KML, and more. These layers can be accessed in GIS applications following guidance on the [GIBS documentation pages](https://wiki.earthdata.nasa.gov/display/GIBS/Geographic+Information+System+%28GIS%29+Usage). "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Export dataframe to Shapefile"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Finally, the dataframe can be exported as an Esri shapefile for further analysis in GIS:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "metadata": {},
- "outputs": [],
- "source": [
- "gdf_buffer = gdf_buffer.drop(columns=['date'])\n",
- "gdf_buffer.to_file('snow-data-20170208.shp')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.16"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/notebooks/SnowEx_ASO_MODIS_Snow/Data/nsidc-polygon.json b/notebooks/SnowEx_ASO_MODIS_Snow/data/nsidc-polygon.json
similarity index 100%
rename from notebooks/SnowEx_ASO_MODIS_Snow/Data/nsidc-polygon.json
rename to notebooks/SnowEx_ASO_MODIS_Snow/data/nsidc-polygon.json
diff --git a/notebooks/SnowEx_ASO_MODIS_Snow/environment/environment.yml b/notebooks/SnowEx_ASO_MODIS_Snow/environment/environment.yml
index 3a50a14..6cfe6ae 100644
--- a/notebooks/SnowEx_ASO_MODIS_Snow/environment/environment.yml
+++ b/notebooks/SnowEx_ASO_MODIS_Snow/environment/environment.yml
@@ -1,19 +1,24 @@
-name: nsidc-tutorials
+name: nsidc-tutorials-snowex
channels:
- conda-forge
dependencies:
-- python=3.9
-- pangeo-notebook
-- xarray
+- python=3.12
+
+- jupyterlab
+- jupyter_contrib_nbextensions
+
- earthaccess
-- matplotlib-base
-- shapely
-- geopandas
+
+- xarray
+- rioxarray
+- dask
+- bottleneck
- h5py
-- pyresample
-- fiona
-- descartes
-- rasterio
+- libgdal-hdf4
+
+- geopandas
+
+- matplotlib
- cartopy
platforms:
- linux-64
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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Snow Depth and Snow Cover Data Exploration \n",
+ "\n",
+ "## Overview\n",
+ "\n",
+ "This tutorial demonstrates how to access and compare coincident snow data from in-situ Ground Pentrating Radar (GPR) measurements, and airborne and satellite platforms from NASA's SnowEx, ASO, and MODIS data sets. All data are available from the NASA National Snow and Ice Data Center Distributed Active Archive Center (NSIDC DAAC). \n",
+ "\n",
+ "## What you will learn in this tutorial\n",
+ "\n",
+ "In this tutorial you will learn:\n",
+ "\n",
+ "1. what snow data and information is available from NSIDC and the resources available to search and access this data;\n",
+ "2. how to search and access spatiotemporally coincident data across in-situ, airborne, and satellite observations.\n",
+ "3. how to read SnowEx GPR data into a Geopandas GeoDataFrame;\n",
+ "4. how to read ASO snow depth data from GeoTIFF files using xarray;\n",
+ "5. how to read MODIS Snow Cover data from HDF-EOS files using xarray;\n",
+ "6. how to subset gridded data using a bounding box;\n",
+ "5. how to extract and visualize raster values at point locations;\n",
+ "6. how to save output as shapefile."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Snow data and resources at NSIDC DAAC\n",
+ "\n",
+ "\n",
+ "In this tutorial we use snow depth and snow cover data collected on the Grand Mesa, Colorado, during NASA's SnowEx 2017 campaign. [SnowEx]() was a multi-year field experiment to collect an extensive set of measurements of snow cover characteristics and conditions, in conjunction with airborne and satellite data, to assess the ability of different remote sensing techniques to measure snow pack characteristics in a variety of snow environments.\n",
+ "\n",
+ "We use snow depths estimated from surface-based ground penetrating radar (GPR) and the Airborne Snow Observatory (ASO) airborne lidar, and fractional snow cover area retrieved from the MODIS/Terra satellite. The links to the dataset landing pages are below.\n",
+ "\n",
+ "| Dataset | Short Name | Version | DOI |\n",
+ "|---------|------------|---------|------------------|\n",
+ "| SnowEx17 Ground Penetrating Radar | SNEX17_GPR | 2 | https://doi.org/10.5067/G21LGCNLFSC5 |\n",
+ "| ASO L4 Lidar Snow Depth 3m UTM Grid | ASO_3M_SD | 1 | https://doi.org/10.5067/KIE9QNVG7HP0 |\n",
+ "| MODIS/Terra Snow Cover Daily L3 Global 500m SIN Grid | MOD10A1 | 6 | https://doi.org/10.5067/MODIS/MOD10A1.006 |\n",
+ "\n",
+ "\n",
+ "\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Import Packages\n",
+ "\n",
+ "We will start by importing the packages we use in this tutorial."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# For notebook rendering\n",
+ "from IPython.display import Markdown\n",
+ "\n",
+ "# Adding this to suppress runtime and deprecations warnings \n",
+ "import warnings\n",
+ "warnings.simplefilter(\"ignore\")\n",
+ "\n",
+ "# For search and access\n",
+ "import earthaccess\n",
+ "\n",
+ "# For reading SnowEx GPR data\n",
+ "import pandas as pd \n",
+ "import geopandas as gpd\n",
+ "from shapely.geometry import Polygon, Point, box #, mapping\n",
+ "\n",
+ "# For reading ASO and MODIS\n",
+ "import xarray as xr\n",
+ "import rioxarray\n",
+ "\n",
+ "# For Plotting\n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib as mpl\n",
+ "from matplotlib.colors import Normalize\n",
+ "import cartopy.crs as ccrs\n",
+ "import cartopy.feature as cfeature\n",
+ "\n",
+ "# Miscellaneous imports\n",
+ "import dateutil\n",
+ "import numpy as np"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Data Discovery\n",
+ "\n",
+ "We start by identifying the study area and time-range using the spatial and temporal coverage of the SnowEx GPR surveys and then searching for ASO and MODIS data collected for the same time and area. \n",
+ "\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Get study area and time-range for SnowEx GPR\n",
+ "\n",
+ "The NASA SnowEx 2017 field experiment was conducted on the Grand Mesa, Colorado. Observations were collected between September 2016 and July 2017, with an intensive observing period from 6 February to 25 February, 2017. \n",
+ "\n",
+ "There are a number of ways to get the spatial coverage of this dataset.\n",
+ "\n",
+ "1. Use the Spatial Coverage of the dataset from the [Overview](https://nsidc.org/data/snex17_gpr/versions/2#anchor-overview) section of the dataset landing page.\n",
+ "2. Draw a polygon for your area of interest on the map in the [Data Access Tool](https://nsidc.org/data/data-access-tool/SNEX17_GPR/versions/2) for the data.\n",
+ "3. Retrieve the bounding polygon from the collection metadata using the `earthaccess` package."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Method 1. Use Spatial Coverage from dataset landing page\n",
+ "\n",
+ "The Overview section of the SnowEx17 GPR dataset landing page gives the **Spatial Coverage** of the data collection.\n",
+ "\n",
+ "![](\"images/data_landing_page_overview.png\")
\n",
+ "\n",
+ "We can see that the latitude and longitude ranges for the collection are:\n",
+ "- 39.11115 N to 38.9935 N \n",
+ "- -108.22367 E to -107.85785 E "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To define create a bounding box that can be passed to `earthaccess`, we simply copy these values into a Python tuple in the order \n",
+ "\n",
+ "```\n",
+ "(lower_left_longitude, lower_left_latitude, upper_right_longitude, upper_right_latitude)\n",
+ "```\n",
+ "\n",
+ "For the values above this is"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "roi_bbox = (-108.22367, 39.11115, -107.85785, 38.9935)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Method 2. Draw and export a region of interest using the Data Access Tool map\n",
+ "\n",
+ "NSIDC's Data Access Tool allows you to draw and export a polygon to define your region of interest. To go to the Data Access Tool, click on \"Data Access and Tools\" in the menu on the right side of the dataset landing page. Then select the \"Data Access Tool\" card by clicking \"Data Access Tool\".\n",
+ "\n",
+ "![](\"images/data_download_polygon_export.png\")
\n",
+ "\n",
+ "Click on the Polygon Drawing button and create a polygon by clicking on the map to add points. Finish drawing the polygon by clicking on the first point you added. The shape of the polygon can be edited by dragging the points.\n",
+ "\n",
+ "To export the polygon, click on the \"Floppy Disk\" icon. The polygon is exported as a GeoJSON file. An example is shown below.\n",
+ "\n",
+ "```\n",
+ "{\n",
+ " \"type\": \"Feature\",\n",
+ " \"geometry\": {\n",
+ " \"type\": \"Polygon\",\n",
+ " \"coordinates\": [\n",
+ " [\n",
+ " [\n",
+ " -108.2352445938561,\n",
+ " 38.98556907427165\n",
+ " ],\n",
+ " [\n",
+ " -107.85284607930835,\n",
+ " 38.978765032966244\n",
+ " ],\n",
+ " [\n",
+ " -107.85494925720668,\n",
+ " 39.10596902171742\n",
+ " ],\n",
+ " [\n",
+ " -108.22772795408136,\n",
+ " 39.11294532581687\n",
+ " ],\n",
+ " [\n",
+ " -108.2352445938561,\n",
+ " 38.98556907427165\n",
+ " ]\n",
+ " ]\n",
+ " ]\n",
+ " },\n",
+ " \"properties\": {}\n",
+ "}\n",
+ "```\n",
+ "\n",
+ "An example polygon geojson file is provided in the /Data folder of this repository."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can use Geopandas to read the GeoJSON file. This returns a Geopandas GeoSeries."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "roi_polygon_gdf = gpd.read_file('Data/nsidc-polygon.json')\n",
+ "roi_polygon_gdf"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To define a polygon for `earthaccess`, we create a list of tuples from the GeoSeries.\n",
+ "\n",
+ "_check that earthaccess checks orientation_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "roi_polygon = [tuple(xy.values) for _, xy in roi_polygon_gdf.get_coordinates().iterrows()]\n",
+ "roi_polygon"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Method 3. Retrieve Spatial Coverage from metdata using `earthaccess`\n",
+ "\n",
+ "`earthaccess.search_datasets` returns a list of objects containing metadata for datasets found. This metadata contains the spatial extent of the dataset.\n",
+ "\n",
+ "We search for the SnowEx17 GPR dataset using `earthaccess`. This has the shortname \"SNEX17_GPR\". We want version 2."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "r = earthaccess.search_datasets(\n",
+ " short_name = \"SNEX17_GPR\",\n",
+ " version = '2',\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This returns a single dataset as a Python list with length 1. The metadata object contained in the list is a mixture of nested Python dictionaries and lists. You can inspect the structure by typing `print(r[0])`.\n",
+ "\n",
+ "For the SnowEx17 GPR dataset, spatial extent is described as a bounding box. It can be found at:\n",
+ "\n",
+ "```\n",
+ "umm/SpatialExtent/HorizontalSpatialDomain/Geometry/BoundingRectangles\n",
+ "```\n",
+ "\n",
+ "We translate this path into the keys of a nested Python dictionary, as we do in the code cell below. The value of `BoundingRectangles` is a list because there can be more than one bounding rectangle. However, in this case, we know there is only one bounding rectangle, so we get the first element of that list. Also note that we have to get the first element of the results `r` from `search_datasets`. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "spatial_coverage = r[0]['umm']['SpatialExtent']['HorizontalSpatialDomain']['Geometry']['BoundingRectangles'][0]\n",
+ "spatial_coverage"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The `BoundingRectangle` is returned as a dictionary. We have to transform this into a tuple `(xmin, ymin, xmax, ymax)` that is expected by `earthaccess`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "roi_bbox = (\n",
+ " spatial_coverage['WestBoundingCoordinate'],\n",
+ " spatial_coverage['SouthBoundingCoordinate'],\n",
+ " spatial_coverage['EastBoundingCoordinate'],\n",
+ " spatial_coverage['NorthBoundingCoordinate']\n",
+ ")\n",
+ "roi_bbox"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Search for Data\n",
+ "\n",
+ "Now that we have a bounding box saved as `roi_bbox` for the SnowEx17 GPR we can use it to look for ASO and MODIS data. First, we will see what GPR data are available. We do this using `earthaccess.search_data`. This is similar to `earthaccess.search_datasets` but looks for data files (also called granules) instead of datasets.\n",
+ "\n",
+ "We could use our region of interest bounding box or polygons but we don't need these for the SnowEx17 GPR data because we know this data is in pretty much the same location. So we just supply the dataset short name and version."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "snowex_result = earthaccess.search_data(\n",
+ " short_name = \"SNEX17_GPR\",\n",
+ " version = '2',\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "There are three files found. We can get some basic information about these files."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "[display(result) for result in snowex_result]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "But to refine our search for coincident ASO and MODIS data, we need the beginning and end time and date of each GPR survey. This is contained in the file metadata and we can access this in a similar way to how we got the spatial extent for the SnowEx data collection.\n",
+ "\n",
+ "Below, we get the file name, beginning date and time, and ending date and time for each SnowEx17 GPR file found. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "for r in snowex_result:\n",
+ " print(\n",
+ " f\"Granule-ID: {r['umm']['GranuleUR']}\\n\",\n",
+ " f\" Begin: {r['umm']['TemporalExtent']['RangeDateTime']['BeginningDateTime']}\\n\"\n",
+ " f\" End: {r['umm']['TemporalExtent']['RangeDateTime']['EndingDateTime']}\\n\"\n",
+ ")"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "For the rest of the tutorial, we are going to focus on the GPR survey collected in week 1 and compare snow depths retrieved from this survey with snow depth from ASO and snow cover fraction from MODIS.\n",
+ "\n",
+ "We'll set a temporal range for the ASO and MODIS data searches using the beginning and ending datetimes for the week 1 survey. We could do this by copying the dates by hand but this means that if you want to change the date range of the search you have to find the cell with the dates and manually change them. It is better to automate the process. This also avoids cut-and-paste mistakes. \n",
+ "\n",
+ "To facilitate this, we will create a `survey_id` variable with a value `0`. Then if we want to use a different survey, we can just change the `survey_id` value."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "survey_id = 0"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We extract the beginning and ending datetimes for the first survey."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "begin_datetime = snowex_result[survey_id]['umm']['TemporalExtent']['RangeDateTime']['BeginningDateTime']\n",
+ "end_datetime = snowex_result[survey_id]['umm']['TemporalExtent']['RangeDateTime']['EndingDateTime']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we can create a `temporal_range` that we can use in searches for ASO and MODIS. \n",
+ "\n",
+ "We'll parse the `begin_datetime` and `end_datetime` into `datetime` objects using the `dateutil` package. This avoids inputting incorrect formats to the `earthaccess` and CMR search."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "temporal_range = (\n",
+ " dateutil.parser.isoparse(begin_datetime), \n",
+ " dateutil.parser.isoparse(end_datetime)\n",
+ ")\n",
+ "temporal_range"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The two datetime objects represent the date range. They are `datetime` objects. To see the times in ISO format."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(f\"Temporal Range: {temporal_range[0].isoformat()} to {temporal_range[1].isoformat()}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Search for ASO Flightlines\n",
+ "\n",
+ "Now that we have a region of interest and a date range defined, we can search for coincident ASO and MODIS data. \n",
+ "\n",
+ "From the table of datasets we know that the `short_name` for the ASO data is `ASO_3M_SD` and we want version 1."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "aso_result = earthaccess.search_data(\n",
+ " short_name = \"ASO_3M_SD\",\n",
+ " version = '1',\n",
+ " bounding_box = roi_bbox,\n",
+ " temporal = temporal_range,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This returns one granule."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "display(aso_result[0])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Search for MODIS Snow Cover Data\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "modis_result = earthaccess.search_data(\n",
+ " short_name = \"MOD10A1\",\n",
+ " version = \"61\",\n",
+ " bounding_box = roi_bbox,\n",
+ " temporal = temporal_range,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "There are three MODIS scenes. We can use display again to see an overview of the results. You can click on the thumbnails to download a larger version. The green region is snow free land, the blue is cloud cover and the orange hues are snow cover fraction."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "[display(r) for r in modis_result]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Access and Read the Data\n",
+ "\n",
+ "In this section we are going to access the data granules and read granules into data objects for visualization and analysis. A data object, in this context, is a Python data structure that contains the data values and associated metadata, and has a set of methods associated with it. \n",
+ "\n",
+ "We have three datasets. The SnowEx GPR has three surveys but we are going to use the survey from the first week. There is one temporal and spatially coincident ASO snow depth data granule, and three MODIS scenes. From the results summaries we can see that the data is in three different file formats. SnowEx GPR is a CSV file. ASO snow depth is a GeoTIFF. The MODIS snow cover data are in HDF files. In fact this is HDF-EOS. We will use the Pandas, Geopandas and xarray Python packages to read these data granules.\n",
+ "\n",
+ "All the datasets we are working with are in the cloud. For the SnowEx ~and ASO~ datasets, rather than downloading the data, we will _stream_ the data loading it directly into memory. Unfortunately, we cannot use this method for the MODIS snow cover data because the nested group structure of HDF-EOS does not allow this kind of access. \n",
+ "\n",
+ "If you are working on a local machine and would rather download the data, use the following command, specifying the list of results returned by `earthaccess.search_data` and the local download path:\n",
+ "```\n",
+ "earthaccess.download(, local_path=)\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### SnowEx GPR\n",
+ "\n",
+ "SnowEx GPR data have the `.csv` file extension, indicating that they are comma-delimited. This is not entirely true. Unfortunately, files in this data collection have inconsistent formatting. `SnowEx17_GPR_Version2_Week1.csv` and `SnowEx17_GPR_Version2_Week2.csv` are tab-delimted. `SnowEx17_GPR_Version2_Week3.csv` is comma-delimted.\n",
+ "\n",
+ "We demonstrate reading week 1 but show the code below to read weeks 2 and 3.\n",
+ "\n",
+ "To read `SnowEx17_GPR_Version2_Week1.csv` and `SnowEx17_GPR_Version2_Week2.csv` use \n",
+ "```\n",
+ "pd.read_csv(, sep='\\t')\n",
+ "```\n",
+ "To read `SnowEx17_GPR_Version2_Week3.csv` use\n",
+ "```\n",
+ "pd.read_csv()\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To _stream_ the data, we first have to open a link to the remote file system."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "f_snex = earthaccess.open(snowex_result) # Open all the results"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This returns a _list_ of _file-like objects_, that we can read using `pandas.read_csv`. In this example, we have opened all three SnowEx granules but we only read the granule for week into a `pandas.DataFrame`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%%time\n",
+ "df = pd.read_csv(f_snex[survey_id], sep='\\t') # f_snex[0] is week1 and tab-delimited\n",
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Data for the week 1 survey were collected over multiple days between 2017-02-08 and 2017-02-10. Because we want to find temporally coincident data, we need to subset by day. \n",
+ "\n",
+ "There is no timestamp in the data but the day that data were collected is encoded in the _collection_ name column. We will create new index containing the day of collection so that we can subset the data.\n",
+ "\n",
+ "We use the `re` package to perform a regular expression search and to extract the date portion of a collection name. This date-string is then converted to a DateTime object using the `datetime` package. This is written as the function `collection_to_date`. We then apply this function to the _collection_ column and assign the result as the index of the DataFrame."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import re\n",
+ "import datetime as dt\n",
+ "\n",
+ "def collection_to_date(x):\n",
+ " date_str = re.search(r'GPR_\\d{4}_(\\d{6})', x)\n",
+ " if date_str:\n",
+ " return dt.datetime.strptime(date_str.groups(0)[0], \"%m%d%y\")\n",
+ "\n",
+ "df.index = df.collection.apply(collection_to_date)\n",
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "pandas recognizes a DataFrame with a datetime index as a time series and allows a simple subsetting based on a date string."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df = df.loc[\"2017-02-08\"]\n",
+ "df"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As you can see in the table above, the index is the same for every row. For future analysis, we want a unique index. So we reset the index to a unique numeric index. We set the name of the index first to preserve the date information for future work."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df.index.name = \"date\"\n",
+ "df = df.reset_index()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "For plotting and analysis, we create a `geopandas.GeoDataFrame`. A `GeoFataFrame` is similar to and supports all the functionality of a `pandas.DataFrame` but it has a `geometry` column and methods that allow GIS-like operations, such as spatial joins, intersections, etc. We create the `geometry` for the `GeoDataFrame` from the latitude and longitude columns.\n",
+ "\n",
+ "The SnowEx data files have columns containing projected x and y coordinates. However, there are some issues with these values. In some files, these coordinates are for different UTM zones, which makes plotting and reprojection for the whole DataFrame difficult. Latitude and longitude are in a consistent CRS, WGS-84 (EPSG:4326).\n",
+ "\n",
+ "```{note}\n",
+ "The `UTM_Zone` column gives the UTM zone as `12 S`. This is wrong and should be `12 N` for the northern hemisphere UTM zone 12.\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "snowex_gpr = gpd.GeoDataFrame(df, geometry=gpd.points_from_xy(df.long, df.lat, crs=4326))\n",
+ "snowex_gpr.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We now have a georeferenced set of survey points that we can plot. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "snowex_gpr.plot(\"Thickness\", legend=\"True\", legend_kwds={\"label\": \"Thickness (m)\"});"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Open and Read ASO Snow Depth Data\n",
+ "\n",
+ "\n",
+ "ASO data are GeoTIFFs. We can use `xarray.open_dataset` with `engine=rasterio` to open a GeoTIFF. We set `chunks='auto'` to allow _out-of-memory_ operations. The `squeeze` method removes dimensions of length 1."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%%time\n",
+ "# f_aso = earthaccess.open(aso_result)\n",
+ "f_aso = earthaccess.download(aso_result, local_path=\"download\")\n",
+ "\n",
+ "aso = xr.open_dataset(f_aso[0], engine='rasterio', masked=True, chunks='auto').squeeze(drop=True)\n",
+ "aso"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Open and Read MODIS Snow Cover\n",
+ "\n",
+ "At this time, `xarray` cannot read HDF-EOS file-like objects. _Don't ask me why_. So we need to download the MODIS file. The downloaded files are written to the `download` directory."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%%time\n",
+ "f_modis = earthaccess.download(modis_result, local_path='download')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "HDF-EOS is a hierachical data format. Data variables are organized into groups that mimic a directory structure. To find the data we want, we need to know something about the groups in the files. This can be found in the MOD10A1 User Guide section 1.2.2.\n",
+ "\n",
+ "\n",
+ "\n",
+ "Looking at this figure, we can see that the data are in the \"MOD_Grid_Snow_500m\" group.\n",
+ "\n",
+ "Another way to discover this information is to use `gdalinfo`. Uncomment (Ctrl/) and run the cell below. If we scroll to the Subdataset section, there is a list of SUBDATASETs. You can interpret these as `::`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Warning! Your gdal may not have the driver for hdf-eos\n",
+ "# !gdalinfo {f_modis[0]} # The {var} syntax is used to pass a variable in a jupyter notebook to a shell command"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We set `group=\"MOD_Grid_Snow_500m\"` to tell xarray to get the data in this group. `engine=\"rasterio\"` tells `xarray` to use `rasterio`, actually GDAL, to read the file."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%%time\n",
+ "modis = xr.open_dataset(f_modis[0], group=\"MOD_Grid_Snow_500m\", engine=\"rasterio\", chunks=\"auto\").squeeze()\n",
+ "modis"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We now have an `xarray.Dataset` containing the MODIS data."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "## Clip ASO Data to the bounding-box of the SnowEx GPR data\n",
+ "\n",
+ "The ASO data are large. The data can be clipped to a smaller region of interest using the `clip` method for `rioxarray.DataSets`. As an example, we will _clip_ the ASO data from 8 February to the bounding box of the SnowEx GPR survey, using the `rioxarray` `clip` method."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The first step is to define the clip region. There are several ways to do this. Here, we use the `total_bounds` attribute for the `snowex_gpr` `GeoDataFrame`.\n",
+ "\n",
+ "Before we define the bounding box, we need to make sure that the ASO data and SnowEx GPR data are in the same CRS. We use the `to_crs` method to reproject the GPR data to the CRS for ASO. We can use the `rio` accessor to get the ASO crs\n",
+ "\n",
+ "```\n",
+ "aso.rio.crs\n",
+ "```\n",
+ "\n",
+ "The `rioxarray` `clip` method expects a list of geometry objects, in this case a bounding box. We use a `shapely.geometry.box` to create a bounding box geometry object. `box` expects for values defining _minimum-x_, _minimum-y_, _maximum-x_, and _maximum-y_. `total_bounds` returns a tuple. We use the `*` operator to unpack the tuple returned by `total_bounds` into four values. The `[]` are used to create a list with one element.\n",
+ "\n",
+ "\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "clip_region = [box(*snowex_gpr.to_crs(aso.rio.crs).total_bounds)] # Clip for extent of survey data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We then use the `rioxarray` `clip` method to crop the ASO data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "aso_cropped = aso.rio.clip(clip_region)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Plot ASO and SnowEx GPR snow depth, and SNOTEL location\n",
+ "\n",
+ "We can plot the ASO Lidar snow depth and the GPR snow depth to compare the two datasets. We plot this as a map showing the raster ASO snow depth overlaid with the GPR snow depth."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "For any comparison plot, we want to make sure that our two datasets have the same range for the color bar. Here, we do this by getting the minimum and maximum values of the ASO data. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "vmin, vmax = (aso_cropped.band_data.min().values, aso_cropped.band_data.max().values)\n",
+ "vmin, vmax"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We create a `matplotlib` figure and axis. We then use the plot methods for the cropped ASO `xarray.DataArray` and SnowEx `geopandas.GeoDataFrame`. The SnowEx data are in WGS-84 but the ASO data are in UTM Zone 12 N. We use the Geopandas `to_crs` with the CRS for the ASO data accessed using the `rioxarray` accessor for the crs attribute. This avoids having to hard-code information and, hopefully, avoids mistakes.\n",
+ "\n",
+ "To distinguish the ASO snow depth raster from the GPR snow depth points we use the Viridis colormap but reverse it for the GPR data. The idea here is that similar snow depths have high contrast, whereas dissimilar snow depths have low contrast."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fig, ax = plt.subplots()\n",
+ "\n",
+ "aso_cropped.band_data.plot(ax=ax, vmin=vmin, vmax=vmax, \n",
+ " cmap=\"viridis\",\n",
+ " cbar_kwargs={\"label\": \"ASO [m]\"})\n",
+ "\n",
+ "snowex_gpr.to_crs(aso_cropped.rio.crs).plot('Thickness', ax=ax, s=5, \n",
+ " vmin=vmin, vmax=vmax,\n",
+ " cmap=\"viridis_r\",\n",
+ " legend=True,\n",
+ " legend_kwds={\"label\":\"Snowex GPR [m]\"}); #, edgecolor='0.25')\n",
+ "ax.set_title(\"Airborne lidar and GPR snow depths\");"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Compare ASO and GPR snow depths along the survey transect\n",
+ "\n",
+ "We can also compare ASO Lidar and SnowEx GPR measurements along the GPR transect in two ways. First as a plot of snow depths along a transect. Second with a scatter plot."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "First, we extract the ASO data that corresponds to the GPR measurement points. The GPR points and ASO grid do not match exactly, so we interpolate from the ASO grid points to the GPR measurement points.\n",
+ "\n",
+ "We use _vectorized_ indexing to select data that correspond to the SnowEx GPR points by passing `x` and `y` coordinates as `xarray.DataArray` objects. `xarray.interp` interprets this input as selecting only the `(x,y)` points. If we passed `x` and `y` as lists or `numpy.arrays`, interp would return a 2D surface."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x = xr.DataArray(snowex_gpr.to_crs(aso_cropped.rio.crs).geometry.x)\n",
+ "y = xr.DataArray(snowex_gpr.to_crs(aso_cropped.rio.crs).geometry.y)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The GPR coordinates do not exactly match the grid coordinates of 3 m resolution ASO data. With such high resolution gridded data, it seems reasonable to interpolate the ASO snow depths to the GPR coordinates. We use the `xarray.Dataset.interp` method to do this. `xarray.Dataset.interp` is a wrapper for [`scipy.interpolate.interpn`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.interpn.html#scipy.interpolate.interpn). We could use any one of several interpolation methods but choose the `linear` (bilinear in this case) method. An alternative approach would be to extract snow depth for the nearest ASO grid point. We use this \"nearest-neighbor\" approach to extract MODIS data below.\n",
+ "\n",
+ "The interpolation produces a 1D dataset of ASO snow depths for the GPR survey points."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "aso_transect = aso.interp(x=x, y=y, method='linear')\n",
+ "aso_transect"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can now add the ASO snow depth data to the `snowex_gpr` `GeoDataFrame`. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "snowex_gpr[\"ASO\"] = aso_transect.band_data.to_pandas()\n",
+ "snowex_gpr[[\"date\",\"long\",\"lat\",\"Thickness\",\"SWE\",\"ASO\"]].head() # Just show coordinates and snow data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "snowex_gpr[[\"Thickness\", \"ASO\"]].plot()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can also compare the snow depths on a scatterplot"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fig, ax = plt.subplots()\n",
+ "ax.scatter(snowex_gpr.Thickness, aso_transect.band_data, c='0.25', s=2, alpha=0.5)\n",
+ "ax.set_xlabel('GPR (m)')\n",
+ "ax.set_ylabel('ASO (m)')\n",
+ "ax.set_xlim(0,3)\n",
+ "ax.set_ylim(0,3)\n",
+ "ax.set_aspect('equal')\n",
+ "ax.axline((0.,0.), slope=1., c='k')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Plot MODIS Snow\n",
+ "\n",
+ "Now, let's take a look at the MODIS data. We want to explore snow cover fraction. In the MOD10A1 dataset, snow cover fraction as a percentage is calculated from NDSI and stored in the `NDSI_Snow_Cover` variable. By clicking on the file icon on the row for this variable in the dataset view below, we can see that the data variable doesn't just contain snow cover fraction but also has coded data values for missing data and other quality flags."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "modis"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We will plot snow cover fraction for the MODIS image over the western USA. We use a combination of `matplotlib` and `cartopy`. I use the Albers Equal Area projection with projection parameters for the contiguous USA.\n",
+ "\n",
+ "MODIS data are in the [MODIS Sinusoidal Grid](https://modis-land.gsfc.nasa.gov/GCTP.html). This uses a Sinusoidal projection, which a pseudocylindrical equal area projection. To plot the data correctly using `cartopy`, we need to define the CRS for the MODIS Sinusoidal projection. We can access the CRS for the data using the `rioxarray` accessor. Here, we print this as proj4 string."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "modis.rio.crs.to_proj4()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can learn a few things about the MODIS Sinusoidal projection from this. The `+lon_0=0` tells us that the central longitude is $0\\ ^{\\circ}E$. `+x_0` and `+y_0` are the false Easting and false Northing, which are both zero. The `+R=6371007.181` is the semimajor axis of the Spheroid. You can see a list of Proj4 parameters [here](https://proj.org/en/stable/usage/index.html) \n",
+ "\n",
+ "`cartopy.crs` has a Sinusoidal projection. Looking at the Docstring for `cartopy.crs.Sinusoidal`, we can see that the projection uses a default Globe. The `Globe` object defines the datum and ellipsoid used for the CRS and projection. Looking at the [cartopy documentation for [`cartopy.crs.Globe`](https://scitools.org.uk/cartopy/docs/latest/reference/generated/cartopy.crs.Globe.html) the default ellipse is WGS84. So we can't use the `cartopy.crs.Sinusoidal` projection _out-of-the-box_, we have to create a projection using the projection parameters for the MODIS Sinusoidal projection."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "modis_projection = ccrs.Sinusoidal(\n",
+ " globe=ccrs.Globe(semimajor_axis=modis.rio.crs['R'], ellipse=\"sphere\"),\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "modis_projection"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "To show snow cover fraction and missing data, we use color normalization to map only the values between 0.001 and 100 to the Blues colormap. We then use the Colormap object to set values less than 0.001% to transparent.\n",
+ "\n",
+ "```\n",
+ "p.axes.cmap.set_under(\"none\")\n",
+ "```\n",
+ "\n",
+ "Values greater than 100 are set to a dark grey to indicate where clouds were detected or where QA was not passed.\n",
+ "\n",
+ "```\n",
+ "p.axes.cmap.set_over(\"0.25\")\n",
+ "```\n",
+ "\n",
+ "To add orientation we add state and country boundaries, along with the coastline."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Get state boundaries\n",
+ "states = cfeature.NaturalEarthFeature(\n",
+ " category='cultural',\n",
+ " name='admin_1_states_provinces_lines',\n",
+ " scale='50m',\n",
+ " facecolor='none')\n",
+ "\n",
+ "# Set map projection to Albers Equal Area with\n",
+ "# projection parameters for contiguous US\n",
+ "# From Snyder (https://pubs.usgs.gov/pp/1395/report.pdf)\n",
+ "map_proj = ccrs.AlbersEqualArea(\n",
+ " central_longitude=-100., \n",
+ " central_latitude=40., \n",
+ " standard_parallels=(29.5, 45.5)) \n",
+ "\n",
+ "# Set colormap and normalization\n",
+ "norm = Normalize(vmin=0.001, vmax=100)\n",
+ "cmap = mpl.colormaps['Blues']\n",
+ "# cmap='Blues'\n",
+ "\n",
+ "p = modis.NDSI_Snow_Cover.plot(\n",
+ " subplot_kws=dict(projection=map_proj),\n",
+ " transform=modis_projection,\n",
+ " norm=norm,\n",
+ " cmap=cmap,\n",
+ " cbar_kwargs={\"extend\": \"neither\", \"orientation\": \"horizontal\", \"label\": \"%\", \"pad\": 0.01},\n",
+ ")\n",
+ "p.cmap.set_over(\"0.25\")\n",
+ "p.cmap.set_under(\"none\")\n",
+ "\n",
+ "# Add state boundaries\n",
+ "p.axes.add_feature(states, edgecolor=\"0.75\")\n",
+ "p.axes.add_feature(cfeature.COASTLINE)\n",
+ "p.axes.add_feature(cfeature.BORDERS)\n",
+ "p.axes.add_feature(cfeature.OCEAN)\n",
+ "p.axes.add_feature(cfeature.LAND)\n",
+ "\n",
+ "p.axes.set_title(\"MODIS Snow Cover Fraction\");"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Plot MODIS Snow Cover for GPR Survey Region\n",
+ "\n",
+ "_I am not sure if we use just use this section and delete the preceding section. If we use just this section, then I will copy some of the text from above here._\n",
+ "\n",
+ "We want to be able to match MODIS snow cover fraction with the GPR Survey points. A good first step is to visualize the MODIS data and GPR survey transect. To do this, we'll clip the MODIS data to the bounding box of the survey data, using a similar approach to clipping the ASO data."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We define the bounding box of the SnowEx GPR data in the MODIS coordinate system. Then we use this `clip_region` to clip the MODIS snow cover."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "clip_region = [box(*snowex_gpr.to_crs(modis.rio.crs).total_bounds)]\n",
+ "snow_cover_clipped = modis.NDSI_Snow_Cover.rio.clip(clip_region, all_touched=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Unlike the plot of MODIS data above for the western US, we will use the MODIS Sinusoidal projection for our plot over the GPR region."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "map_proj = modis_projection\n",
+ "\n",
+ "# Define based on search polygon\n",
+ "# coords = roi_polygon_gdf.to_crs(map_proj.to_wkt()).geometry.get_coordinates()\n",
+ "# roi_bbox_map = [coords.x.min(), coords.y.min(), coords.x.max(), coords.y.max()]\n",
+ "\n",
+ "norm = Normalize(vmin=0.001, vmax=100)\n",
+ "# cmap = Colormap('Blues')\n",
+ "cmap='Blues'\n",
+ "\n",
+ "# p = modis.NDSI_Snow_Cover.rio.clip(box(*roi_bbox_map)).plot(\n",
+ "p = snow_cover_clipped.plot(\n",
+ " subplot_kws=dict(projection=map_proj),\n",
+ " transform=modis_projection,\n",
+ " norm=norm,\n",
+ " cmap=cmap,\n",
+ " cbar_kwargs={\n",
+ " \"extend\": \"neither\", \n",
+ " \"orientation\": \"horizontal\", \n",
+ " \"label\": \"%\", \n",
+ " \"pad\": 0.01},\n",
+ ")\n",
+ "p.cmap.set_over(\"0.25\")\n",
+ "p.cmap.set_under(\"none\")\n",
+ "\n",
+ "# Add SNOTEL location\n",
+ "snowex_gpr.to_crs(map_proj).plot(ax=p.axes, c=\"k\")\n",
+ "\n",
+ "p.axes.set_title(\"MODIS Snow Cover Fraction\");"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Extract Snow Cover From Modis for GPR Survey\n",
+ "\n",
+ "We can use a similar approach to the one we used to extract the ASO snow thickness to extract snow cover fraction. However, in this case we are going to select the values for MODIS pixels nearest to the survey points.\n",
+ "\n",
+ "We first convert the x and y coordinates of the survey points to the MODIS CRS. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x = xr.DataArray(snowex_gpr.to_crs(modis.rio.crs).geometry.x, dims=[\"point\"])\n",
+ "y = xr.DataArray(snowex_gpr.to_crs(modis.rio.crs).geometry.y, dims=[\"point\"])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The we use the `sel` method to extract the nearest data points."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "modis_snow_cover_point = modis.NDSI_Snow_Cover.sel(x=x, y=y, method=\"nearest\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "modis_snow_cover_point"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As with the ASO data, we add the MODIS snow cover as a column to the SnowEx GPR `geopandas.GeoDataFrame`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "snowex_gpr[\"modis_scf\"] = modis_snow_cover_point.to_pandas()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "snowex_gpr.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Export SnowEx GeoDataFrame with ASO and MODIS snow cover to Shapefile"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Finally, the `snowex_gpr` dataframe can be exported as a shapefile for further analysis in GIS:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "snowex_gpr['date'] = snowex_gpr['date'].apply(lambda x: x.strftime(\"%Y-%m-%d\"))\n",
+ "snowex_gpr.to_file('snow-data-20170208.shp')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Additional data imagery services\n",
+ "\n",
+ "#### NASA Worldview and the Global Browse Imagery Service\n",
+ "\n",
+ "NASA’s EOSDIS Worldview mapping application provides the capability to interactively browse over 900 global, full-resolution satellite imagery layers and then download the underlying data. Many of the available imagery layers are updated within three hours of observation, essentially showing the entire Earth as it looks “right now.\"\n",
+ "\n",
+ "According to the [MOD10A1 landing page](https://nsidc.org/data/mod10a1), snow cover imagery layers from this data set are available through NASA Worldview. This layer can be downloaded as various image files including GeoTIFF using the snapshot feature at the top right of the page. This link presents the MOD10A1 NDSI layer over our time and area of interest: https://go.nasa.gov/35CgYMd. \n",
+ "\n",
+ "Additionally, the NASA Global Browse Imagery Service provides up to date, full resolution imagery for select NSIDC DAAC data sets as web services including WMTS, WMS, KML, and more. These layers can be accessed in GIS applications following guidance on the [GIBS documentation pages](https://wiki.earthdata.nasa.gov/display/GIBS/Geographic+Information+System+%28GIS%29+Usage). "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Cleanup\n",
+ "\n",
+ "To cleanup your directory, uncomment and run the cell below. This will remove the files you have downloaded to the download directory and the shapefile you have saved."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# !rm -rf download\n",
+ "# !rm snow-data-20170208.*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.18"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/SnowEx_ASO_MODIS_Snow/snow_tutorial_rendered.ipynb b/notebooks/SnowEx_ASO_MODIS_Snow/snow_tutorial_rendered.ipynb
new file mode 100644
index 0000000..5540a76
--- /dev/null
+++ b/notebooks/SnowEx_ASO_MODIS_Snow/snow_tutorial_rendered.ipynb
@@ -0,0 +1,12662 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Snow Depth and Snow Cover Data Exploration \n",
+ "\n",
+ "## Overview\n",
+ "\n",
+ "This tutorial demonstrates how to access and compare coincident snow data from in-situ Ground Pentrating Radar (GPR) measurements, and airborne and satellite platforms from NASA's SnowEx, ASO, and MODIS data sets. All data are available from the NASA National Snow and Ice Data Center Distributed Active Archive Center (NSIDC DAAC). \n",
+ "\n",
+ "## What you will learn in this tutorial\n",
+ "\n",
+ "In this tutorial you will learn:\n",
+ "\n",
+ "1. what snow data and information is available from NSIDC and the resources available to search and access this data;\n",
+ "2. how to search and access spatiotemporally coincident data across in-situ, airborne, and satellite observations.\n",
+ "3. how to read SnowEx GPR data into a Geopandas GeoDataFrame;\n",
+ "4. how to read ASO snow depth data from GeoTIFF files using xarray;\n",
+ "5. how to read MODIS Snow Cover data from HDF-EOS files using xarray;\n",
+ "6. how to subset gridded data using a bounding box;\n",
+ "5. how to extract and visualize raster values at point locations;\n",
+ "6. how to save output as shapefile."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Snow data and resources at NSIDC DAAC\n",
+ "\n",
+ "\n",
+ "In this tutorial we use snow depth and snow cover data collected on the Grand Mesa, Colorado, during NASA's SnowEx 2017 campaign. [SnowEx]() was a multi-year field experiment to collect an extensive set of measurements of snow cover characteristics and conditions, in conjunction with airborne and satellite data, to assess the ability of different remote sensing techniques to measure snow pack characteristics in a variety of snow environments.\n",
+ "\n",
+ "We use snow depths estimated from surface-based ground penetrating radar (GPR) and the Airborne Snow Observatory (ASO) airborne lidar, and fractional snow cover area retrieved from the MODIS/Terra satellite. The links to the dataset landing pages are below.\n",
+ "\n",
+ "| Dataset | Short Name | Version | DOI |\n",
+ "|---------|------------|---------|------------------|\n",
+ "| SnowEx17 Ground Penetrating Radar | SNEX17_GPR | 2 | https://doi.org/10.5067/G21LGCNLFSC5 |\n",
+ "| ASO L4 Lidar Snow Depth 3m UTM Grid | ASO_3M_SD | 1 | https://doi.org/10.5067/KIE9QNVG7HP0 |\n",
+ "| MODIS/Terra Snow Cover Daily L3 Global 500m SIN Grid | MOD10A1 | 6 | https://doi.org/10.5067/MODIS/MOD10A1.006 |\n",
+ "\n",
+ "\n",
+ "\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Import Packages\n",
+ "\n",
+ "We will start by importing the packages we use in this tutorial."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# Adding this to suppress runtime and deprecations warnings \n",
+ "import warnings\n",
+ "warnings.simplefilter(\"ignore\")\n",
+ "\n",
+ "# For search and access\n",
+ "import earthaccess\n",
+ "\n",
+ "# For reading SnowEx GPR data\n",
+ "import pandas as pd \n",
+ "import geopandas as gpd\n",
+ "from shapely.geometry import Polygon, Point, box #, mapping\n",
+ "\n",
+ "# For reading ASO and MODIS\n",
+ "import xarray as xr\n",
+ "import rioxarray\n",
+ "\n",
+ "# For Plotting\n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib as mpl\n",
+ "from matplotlib.colors import Normalize\n",
+ "import cartopy.crs as ccrs\n",
+ "import cartopy.feature as cfeature\n",
+ "\n",
+ "# Miscellaneous imports\n",
+ "import dateutil\n",
+ "import numpy as np"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Data Discovery\n",
+ "\n",
+ "We start by identifying the study area and time-range using the spatial and temporal coverage of the SnowEx GPR surveys and then searching for ASO and MODIS data collected for the same time and area. \n",
+ "\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Get study area and time-range for SnowEx GPR\n",
+ "\n",
+ "The NASA SnowEx 2017 field experiment was conducted on the Grand Mesa, Colorado. Observations were collected between September 2016 and July 2017, with an intensive observing period from 6 February to 25 February, 2017. \n",
+ "\n",
+ "There are a number of ways to get the spatial coverage of this dataset.\n",
+ "\n",
+ "1. Use the Spatial Coverage of the dataset from the [Overview](https://nsidc.org/data/snex17_gpr/versions/2#anchor-overview) section of the dataset landing page.\n",
+ "2. Draw a polygon for your area of interest on the map in the [Data Access Tool](https://nsidc.org/data/data-access-tool/SNEX17_GPR/versions/2) for the data.\n",
+ "3. Retrieve the bounding polygon from the collection metadata using the `earthaccess` package."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Method 1. Use Spatial Coverage from dataset landing page\n",
+ "\n",
+ "The Overview section of the SnowEx17 GPR dataset landing page gives the **Spatial Coverage** of the data collection.\n",
+ "\n",
+ "\n",
+ "\n",
+ "We can see that the latitude and longitude ranges for the collection are:\n",
+ "- 39.11115 N to 38.9935 N \n",
+ "- -108.22367 E to -107.85785 E "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To define create a bounding box that can be passed to `earthaccess`, we simply copy these values into a Python tuple in the order \n",
+ "\n",
+ "```\n",
+ "(lower_left_longitude, lower_left_latitude, upper_right_longitude, upper_right_latitude)\n",
+ "```\n",
+ "\n",
+ "For the values above this is"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "roi_bbox = (-108.22367, 39.11115, -107.85785, 38.9935)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Method 2. Draw and export a region of interest using the Data Access Tool map\n",
+ "\n",
+ "NSIDC's Data Access Tool allows you to draw and export a polygon to define your region of interest. To go to the Data Access Tool, click on \"Data Access and Tools\" in the menu on the right side of the dataset landing page. Then select the \"Data Access Tool\" card by clicking \"Data Access Tool\".\n",
+ "\n",
+ "\n",
+ "\n",
+ "Click on the Polygon Drawing button and create a polygon by clicking on the map to add points. Finish drawing the polygon by clicking on the first point you added. The shape of the polygon can be edited by dragging the points.\n",
+ "\n",
+ "To export the polygon, click on the \"Floppy Disk\" icon. The polygon is exported as a GeoJSON file. An example is shown below.\n",
+ "\n",
+ "```\n",
+ "{\n",
+ " \"type\": \"Feature\",\n",
+ " \"geometry\": {\n",
+ " \"type\": \"Polygon\",\n",
+ " \"coordinates\": [\n",
+ " [\n",
+ " [\n",
+ " -108.2352445938561,\n",
+ " 38.98556907427165\n",
+ " ],\n",
+ " [\n",
+ " -107.85284607930835,\n",
+ " 38.978765032966244\n",
+ " ],\n",
+ " [\n",
+ " -107.85494925720668,\n",
+ " 39.10596902171742\n",
+ " ],\n",
+ " [\n",
+ " -108.22772795408136,\n",
+ " 39.11294532581687\n",
+ " ],\n",
+ " [\n",
+ " -108.2352445938561,\n",
+ " 38.98556907427165\n",
+ " ]\n",
+ " ]\n",
+ " ]\n",
+ " },\n",
+ " \"properties\": {}\n",
+ "}\n",
+ "```\n",
+ "\n",
+ "An example polygon geojson file is provided in the /Data folder of this repository."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can use Geopandas to read the GeoJSON file. This returns a Geopandas GeoSeries."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ ", local_path=)\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### SnowEx GPR\n",
+ "\n",
+ "SnowEx GPR data have the `.csv` file extension, indicating that they are comma-delimited. This is not entirely true. Unfortunately, files in this data collection have inconsistent formatting. `SnowEx17_GPR_Version2_Week1.csv` and `SnowEx17_GPR_Version2_Week2.csv` are tab-delimted. `SnowEx17_GPR_Version2_Week3.csv` is comma-delimted.\n",
+ "\n",
+ "We demonstrate reading week 1 but show the code below to read weeks 2 and 3.\n",
+ "\n",
+ "To read `SnowEx17_GPR_Version2_Week1.csv` and `SnowEx17_GPR_Version2_Week2.csv` use \n",
+ "```\n",
+ "pd.read_csv(, sep='\\t')\n",
+ "```\n",
+ "To read `SnowEx17_GPR_Version2_Week3.csv` use\n",
+ "```\n",
+ "pd.read_csv()\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To _stream_ the data, we first have to open a link to the remote file system."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "QUEUEING TASKS | : 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 2003.97it/s]\n",
+ "PROCESSING TASKS | : 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:01<00:00, 2.44it/s]\n",
+ "COLLECTING RESULTS | : 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 39321.60it/s]\n"
+ ]
+ }
+ ],
+ "source": [
+ "f_snex = earthaccess.open(snowex_result) # Open all the results"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This returns a _list_ of _file-like objects_, that we can read using `pandas.read_csv`. In this example, we have opened all three SnowEx granules but we only read the granule for week into a `pandas.DataFrame`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 617 ms, sys: 200 ms, total: 817 ms\n",
+ "Wall time: 14 s\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "We can see that the latitude and longitude ranges for the collection are:\n",
+ "- 39.11115 N to 38.9935 N \n",
+ "- -108.22367 E to -107.85785 E "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To define create a bounding box that can be passed to `earthaccess`, we simply copy these values into a Python tuple in the order \n",
+ "\n",
+ "```\n",
+ "(lower_left_longitude, lower_left_latitude, upper_right_longitude, upper_right_latitude)\n",
+ "```\n",
+ "\n",
+ "For the values above this is"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "roi_bbox = (-108.22367, 39.11115, -107.85785, 38.9935)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Method 2. Draw and export a region of interest using the Data Access Tool map\n",
+ "\n",
+ "NSIDC's Data Access Tool allows you to draw and export a polygon to define your region of interest. To go to the Data Access Tool, click on \"Data Access and Tools\" in the menu on the right side of the dataset landing page. Then select the \"Data Access Tool\" card by clicking \"Data Access Tool\".\n",
+ "\n",
+ "\n",
+ "\n",
+ "Click on the Polygon Drawing button and create a polygon by clicking on the map to add points. Finish drawing the polygon by clicking on the first point you added. The shape of the polygon can be edited by dragging the points.\n",
+ "\n",
+ "To export the polygon, click on the \"Floppy Disk\" icon. The polygon is exported as a GeoJSON file. An example is shown below.\n",
+ "\n",
+ "```\n",
+ "{\n",
+ " \"type\": \"Feature\",\n",
+ " \"geometry\": {\n",
+ " \"type\": \"Polygon\",\n",
+ " \"coordinates\": [\n",
+ " [\n",
+ " [\n",
+ " -108.2352445938561,\n",
+ " 38.98556907427165\n",
+ " ],\n",
+ " [\n",
+ " -107.85284607930835,\n",
+ " 38.978765032966244\n",
+ " ],\n",
+ " [\n",
+ " -107.85494925720668,\n",
+ " 39.10596902171742\n",
+ " ],\n",
+ " [\n",
+ " -108.22772795408136,\n",
+ " 39.11294532581687\n",
+ " ],\n",
+ " [\n",
+ " -108.2352445938561,\n",
+ " 38.98556907427165\n",
+ " ]\n",
+ " ]\n",
+ " ]\n",
+ " },\n",
+ " \"properties\": {}\n",
+ "}\n",
+ "```\n",
+ "\n",
+ "An example polygon geojson file is provided in the /Data folder of this repository."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can use Geopandas to read the GeoJSON file. This returns a Geopandas GeoSeries."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "roi_polygon_gdf = gpd.read_file('Data/nsidc-polygon.json')\n",
+ "roi_polygon_gdf"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To define a polygon for `earthaccess`, we create a list of tuples from the GeoSeries.\n",
+ "\n",
+ "_check that earthaccess checks orientation_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "roi_polygon = [tuple(xy.values) for _, xy in roi_polygon_gdf.get_coordinates().iterrows()]\n",
+ "roi_polygon"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Method 3. Retrieve Spatial Coverage from metdata using `earthaccess`\n",
+ "\n",
+ "`earthaccess.search_datasets` returns a list of objects containing metadata for datasets found. This metadata contains the spatial extent of the dataset.\n",
+ "\n",
+ "We search for the SnowEx17 GPR dataset using `earthaccess`. This has the shortname \"SNEX17_GPR\". We want version 2."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "r = earthaccess.search_datasets(\n",
+ " short_name = \"SNEX17_GPR\",\n",
+ " version = '2',\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This returns a single dataset as a Python list with length 1. The metadata object contained in the list is a mixture of nested Python dictionaries and lists. You can inspect the structure by typing `print(r[0])`.\n",
+ "\n",
+ "For the SnowEx17 GPR dataset, spatial extent is described as a bounding box. It can be found at:\n",
+ "\n",
+ "```\n",
+ "umm/SpatialExtent/HorizontalSpatialDomain/Geometry/BoundingRectangles\n",
+ "```\n",
+ "\n",
+ "We translate this path into the keys of a nested Python dictionary, as we do in the code cell below. The value of `BoundingRectangles` is a list because there can be more than one bounding rectangle. However, in this case, we know there is only one bounding rectangle, so we get the first element of that list. Also note that we have to get the first element of the results `r` from `search_datasets`. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "spatial_coverage = r[0]['umm']['SpatialExtent']['HorizontalSpatialDomain']['Geometry']['BoundingRectangles'][0]\n",
+ "spatial_coverage"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The `BoundingRectangle` is returned as a dictionary. We have to transform this into a tuple `(xmin, ymin, xmax, ymax)` that is expected by `earthaccess`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "roi_bbox = (\n",
+ " spatial_coverage['WestBoundingCoordinate'],\n",
+ " spatial_coverage['SouthBoundingCoordinate'],\n",
+ " spatial_coverage['EastBoundingCoordinate'],\n",
+ " spatial_coverage['NorthBoundingCoordinate']\n",
+ ")\n",
+ "roi_bbox"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Search for Data\n",
+ "\n",
+ "Now that we have a bounding box saved as `roi_bbox` for the SnowEx17 GPR we can use it to look for ASO and MODIS data. First, we will see what GPR data are available. We do this using `earthaccess.search_data`. This is similar to `earthaccess.search_datasets` but looks for data files (also called granules) instead of datasets.\n",
+ "\n",
+ "We could use our region of interest bounding box or polygons but we don't need these for the SnowEx17 GPR data because we know this data is in pretty much the same location. So we just supply the dataset short name and version."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "snowex_result = earthaccess.search_data(\n",
+ " short_name = \"SNEX17_GPR\",\n",
+ " version = '2',\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "There are three files found. We can get some basic information about these files."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "[display(result) for result in snowex_result]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "But to refine our search for coincident ASO and MODIS data, we need the beginning and end time and date of each GPR survey. This is contained in the file metadata and we can access this in a similar way to how we got the spatial extent for the SnowEx data collection.\n",
+ "\n",
+ "Below, we get the file name, beginning date and time, and ending date and time for each SnowEx17 GPR file found. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "for r in snowex_result:\n",
+ " print(\n",
+ " f\"Granule-ID: {r['umm']['GranuleUR']}\\n\",\n",
+ " f\" Begin: {r['umm']['TemporalExtent']['RangeDateTime']['BeginningDateTime']}\\n\"\n",
+ " f\" End: {r['umm']['TemporalExtent']['RangeDateTime']['EndingDateTime']}\\n\"\n",
+ ")"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "For the rest of the tutorial, we are going to focus on the GPR survey collected in week 1 and compare snow depths retrieved from this survey with snow depth from ASO and snow cover fraction from MODIS.\n",
+ "\n",
+ "We'll set a temporal range for the ASO and MODIS data searches using the beginning and ending datetimes for the week 1 survey. We could do this by copying the dates by hand but this means that if you want to change the date range of the search you have to find the cell with the dates and manually change them. It is better to automate the process. This also avoids cut-and-paste mistakes. \n",
+ "\n",
+ "To facilitate this, we will create a `survey_id` variable with a value `0`. Then if we want to use a different survey, we can just change the `survey_id` value."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "survey_id = 0"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We extract the beginning and ending datetimes for the first survey."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "begin_datetime = snowex_result[survey_id]['umm']['TemporalExtent']['RangeDateTime']['BeginningDateTime']\n",
+ "end_datetime = snowex_result[survey_id]['umm']['TemporalExtent']['RangeDateTime']['EndingDateTime']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we can create a `temporal_range` that we can use in searches for ASO and MODIS. \n",
+ "\n",
+ "We'll parse the `begin_datetime` and `end_datetime` into `datetime` objects using the `dateutil` package. This avoids inputting incorrect formats to the `earthaccess` and CMR search."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "temporal_range = (\n",
+ " dateutil.parser.isoparse(begin_datetime), \n",
+ " dateutil.parser.isoparse(end_datetime)\n",
+ ")\n",
+ "temporal_range"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The two datetime objects represent the date range. They are `datetime` objects. To see the times in ISO format."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(f\"Temporal Range: {temporal_range[0].isoformat()} to {temporal_range[1].isoformat()}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Search for ASO Flightlines\n",
+ "\n",
+ "Now that we have a region of interest and a date range defined, we can search for coincident ASO and MODIS data. \n",
+ "\n",
+ "From the table of datasets we know that the `short_name` for the ASO data is `ASO_3M_SD` and we want version 1."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "aso_result = earthaccess.search_data(\n",
+ " short_name = \"ASO_3M_SD\",\n",
+ " version = '1',\n",
+ " bounding_box = roi_bbox,\n",
+ " temporal = temporal_range,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This returns one granule."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "display(aso_result[0])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Search for MODIS Snow Cover Data\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "modis_result = earthaccess.search_data(\n",
+ " short_name = \"MOD10A1\",\n",
+ " version = \"61\",\n",
+ " bounding_box = roi_bbox,\n",
+ " temporal = temporal_range,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "There are three MODIS scenes. We can use display again to see an overview of the results. You can click on the thumbnails to download a larger version. The green region is snow free land, the blue is cloud cover and the orange hues are snow cover fraction."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "[display(r) for r in modis_result]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Access and Read the Data\n",
+ "\n",
+ "In this section we are going to access the data granules and read granules into data objects for visualization and analysis. A data object, in this context, is a Python data structure that contains the data values and associated metadata, and has a set of methods associated with it. \n",
+ "\n",
+ "We have three datasets. The SnowEx GPR has three surveys but we are going to use the survey from the first week. There is one temporal and spatially coincident ASO snow depth data granule, and three MODIS scenes. From the results summaries we can see that the data is in three different file formats. SnowEx GPR is a CSV file. ASO snow depth is a GeoTIFF. The MODIS snow cover data are in HDF files. In fact this is HDF-EOS. We will use the Pandas, Geopandas and xarray Python packages to read these data granules.\n",
+ "\n",
+ "All the datasets we are working with are in the cloud. For the SnowEx ~and ASO~ datasets, rather than downloading the data, we will _stream_ the data loading it directly into memory. Unfortunately, we cannot use this method for the MODIS snow cover data because the nested group structure of HDF-EOS does not allow this kind of access. \n",
+ "\n",
+ "If you are working on a local machine and would rather download the data, use the following command, specifying the list of results returned by `earthaccess.search_data` and the local download path:\n",
+ "```\n",
+ "earthaccess.download(\n",
+ "\n",
+ "We can see that the latitude and longitude ranges for the collection are:\n",
+ "- 39.11115 N to 38.9935 N \n",
+ "- -108.22367 E to -107.85785 E "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To define create a bounding box that can be passed to `earthaccess`, we simply copy these values into a Python tuple in the order \n",
+ "\n",
+ "```\n",
+ "(lower_left_longitude, lower_left_latitude, upper_right_longitude, upper_right_latitude)\n",
+ "```\n",
+ "\n",
+ "For the values above this is"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "roi_bbox = (-108.22367, 39.11115, -107.85785, 38.9935)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Method 2. Draw and export a region of interest using the Data Access Tool map\n",
+ "\n",
+ "NSIDC's Data Access Tool allows you to draw and export a polygon to define your region of interest. To go to the Data Access Tool, click on \"Data Access and Tools\" in the menu on the right side of the dataset landing page. Then select the \"Data Access Tool\" card by clicking \"Data Access Tool\".\n",
+ "\n",
+ "\n",
+ "\n",
+ "Click on the Polygon Drawing button and create a polygon by clicking on the map to add points. Finish drawing the polygon by clicking on the first point you added. The shape of the polygon can be edited by dragging the points.\n",
+ "\n",
+ "To export the polygon, click on the \"Floppy Disk\" icon. The polygon is exported as a GeoJSON file. An example is shown below.\n",
+ "\n",
+ "```\n",
+ "{\n",
+ " \"type\": \"Feature\",\n",
+ " \"geometry\": {\n",
+ " \"type\": \"Polygon\",\n",
+ " \"coordinates\": [\n",
+ " [\n",
+ " [\n",
+ " -108.2352445938561,\n",
+ " 38.98556907427165\n",
+ " ],\n",
+ " [\n",
+ " -107.85284607930835,\n",
+ " 38.978765032966244\n",
+ " ],\n",
+ " [\n",
+ " -107.85494925720668,\n",
+ " 39.10596902171742\n",
+ " ],\n",
+ " [\n",
+ " -108.22772795408136,\n",
+ " 39.11294532581687\n",
+ " ],\n",
+ " [\n",
+ " -108.2352445938561,\n",
+ " 38.98556907427165\n",
+ " ]\n",
+ " ]\n",
+ " ]\n",
+ " },\n",
+ " \"properties\": {}\n",
+ "}\n",
+ "```\n",
+ "\n",
+ "An example polygon geojson file is provided in the /Data folder of this repository."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can use Geopandas to read the GeoJSON file. This returns a Geopandas GeoSeries."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " geometry\n",
+ "0 POLYGON ((-108.23524 38.98557, -107.85285 38.9..."
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "roi_polygon_gdf = gpd.read_file('data/nsidc-polygon.json')\n",
+ "roi_polygon_gdf"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To define a polygon for `earthaccess`, we create a list of tuples from the GeoSeries.\n",
+ "\n",
+ "_check that earthaccess checks orientation_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[(np.float64(-108.2352445938561), np.float64(38.98556907427165)),\n",
+ " (np.float64(-107.85284607930835), np.float64(38.978765032966244)),\n",
+ " (np.float64(-107.85494925720668), np.float64(39.10596902171742)),\n",
+ " (np.float64(-108.22772795408136), np.float64(39.11294532581687)),\n",
+ " (np.float64(-108.2352445938561), np.float64(38.98556907427165))]"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "roi_polygon = [tuple(xy.values) for _, xy in roi_polygon_gdf.get_coordinates().iterrows()]\n",
+ "roi_polygon"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Method 3. Retrieve Spatial Coverage from metdata using `earthaccess`\n",
+ "\n",
+ "`earthaccess.search_datasets` returns a list of objects containing metadata for datasets found. This metadata contains the spatial extent of the dataset.\n",
+ "\n",
+ "We search for the SnowEx17 GPR dataset using `earthaccess`. This has the shortname \"SNEX17_GPR\". We want version 2."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "r = earthaccess.search_datasets(\n",
+ " short_name = \"SNEX17_GPR\",\n",
+ " version = '2',\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This returns a single dataset as a Python list with length 1. The metadata object contained in the list is a mixture of nested Python dictionaries and lists. You can inspect the structure by typing `print(r[0])`.\n",
+ "\n",
+ "For the SnowEx17 GPR dataset, spatial extent is described as a bounding box. It can be found at:\n",
+ "\n",
+ "```\n",
+ "umm/SpatialExtent/HorizontalSpatialDomain/Geometry/BoundingRectangles\n",
+ "```\n",
+ "\n",
+ "We translate this path into the keys of a nested Python dictionary, as we do in the code cell below. The value of `BoundingRectangles` is a list because there can be more than one bounding rectangle. However, in this case, we know there is only one bounding rectangle, so we get the first element of that list. Also note that we have to get the first element of the results `r` from `search_datasets`. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'WestBoundingCoordinate': -108.22367,\n",
+ " 'NorthBoundingCoordinate': 39.11115,\n",
+ " 'EastBoundingCoordinate': -107.85785,\n",
+ " 'SouthBoundingCoordinate': 38.9935}"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "spatial_coverage = r[0]['umm']['SpatialExtent']['HorizontalSpatialDomain']['Geometry']['BoundingRectangles'][0]\n",
+ "spatial_coverage"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The `BoundingRectangle` is returned as a dictionary. We have to transform this into a tuple `(xmin, ymin, xmax, ymax)` that is expected by `earthaccess`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(-108.22367, 38.9935, -107.85785, 39.11115)"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "roi_bbox = (\n",
+ " spatial_coverage['WestBoundingCoordinate'],\n",
+ " spatial_coverage['SouthBoundingCoordinate'],\n",
+ " spatial_coverage['EastBoundingCoordinate'],\n",
+ " spatial_coverage['NorthBoundingCoordinate']\n",
+ ")\n",
+ "roi_bbox"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Search for Data\n",
+ "\n",
+ "Now that we have a bounding box saved as `roi_bbox` for the SnowEx17 GPR we can use it to look for ASO and MODIS data. First, we will see what GPR data are available. We do this using `earthaccess.search_data`. This is similar to `earthaccess.search_datasets` but looks for data files (also called granules) instead of datasets.\n",
+ "\n",
+ "We could use our region of interest bounding box or polygons but we don't need these for the SnowEx17 GPR data because we know this data is in pretty much the same location. So we just supply the dataset short name and version."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "snowex_result = earthaccess.search_data(\n",
+ " short_name = \"SNEX17_GPR\",\n",
+ " version = '2',\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "There are three files found. We can get some basic information about these files."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "[display(result) for result in snowex_result]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "But to refine our search for coincident ASO and MODIS data, we need the beginning and end time and date of each GPR survey. This is contained in the file metadata and we can access this in a similar way to how we got the spatial extent for the SnowEx data collection.\n",
+ "\n",
+ "Below, we get the file name, beginning date and time, and ending date and time for each SnowEx17 GPR file found. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Granule-ID: SnowEx17_GPR_Version2_Week1.csv\n",
+ " Begin: 2017-02-08T00:00:00.000Z\n",
+ " End: 2017-02-10T23:59:59.000Z\n",
+ "\n",
+ "Granule-ID: SnowEx17_GPR_Version2_Week2.csv\n",
+ " Begin: 2017-02-14T00:00:00.000Z\n",
+ " End: 2017-02-17T23:59:59.000Z\n",
+ "\n",
+ "Granule-ID: SnowEx17_GPR_Version2_Week3.csv\n",
+ " Begin: 2017-02-21T00:00:00.000Z\n",
+ " End: 2017-02-25T23:59:59.000Z\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "for r in snowex_result:\n",
+ " print(\n",
+ " f\"Granule-ID: {r['umm']['GranuleUR']}\\n\",\n",
+ " f\" Begin: {r['umm']['TemporalExtent']['RangeDateTime']['BeginningDateTime']}\\n\"\n",
+ " f\" End: {r['umm']['TemporalExtent']['RangeDateTime']['EndingDateTime']}\\n\"\n",
+ ")"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "For the rest of the tutorial, we are going to focus on the GPR survey collected in week 1 and compare snow depths retrieved from this survey with snow depth from ASO and snow cover fraction from MODIS.\n",
+ "\n",
+ "We'll set a temporal range for the ASO and MODIS data searches using the beginning and ending datetimes for the week 1 survey. We could do this by copying the dates by hand but this means that if you want to change the date range of the search you have to find the cell with the dates and manually change them. It is better to automate the process. This also avoids cut-and-paste mistakes. \n",
+ "\n",
+ "To facilitate this, we will create a `survey_id` variable with a value `0`. Then if we want to use a different survey, we can just change the `survey_id` value."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "survey_id = 0"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We extract the beginning and ending datetimes for the first survey."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "begin_datetime = snowex_result[survey_id]['umm']['TemporalExtent']['RangeDateTime']['BeginningDateTime']\n",
+ "end_datetime = snowex_result[survey_id]['umm']['TemporalExtent']['RangeDateTime']['EndingDateTime']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we can create a `temporal_range` that we can use in searches for ASO and MODIS. \n",
+ "\n",
+ "We'll parse the `begin_datetime` and `end_datetime` into `datetime` objects using the `dateutil` package. This avoids inputting incorrect formats to the `earthaccess` and CMR search."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(datetime.datetime(2017, 2, 8, 0, 0, tzinfo=tzutc()),\n",
+ " datetime.datetime(2017, 2, 10, 23, 59, 59, tzinfo=tzutc()))"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "temporal_range = (\n",
+ " dateutil.parser.isoparse(begin_datetime), \n",
+ " dateutil.parser.isoparse(end_datetime)\n",
+ ")\n",
+ "temporal_range"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The two datetime objects represent the date range. They are `datetime` objects. To see the times in ISO format."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Temporal Range: 2017-02-08T00:00:00+00:00 to 2017-02-10T23:59:59+00:00\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(f\"Temporal Range: {temporal_range[0].isoformat()} to {temporal_range[1].isoformat()}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Search for ASO Flightlines\n",
+ "\n",
+ "Now that we have a region of interest and a date range defined, we can search for coincident ASO and MODIS data. \n",
+ "\n",
+ "From the table of datasets we know that the `short_name` for the ASO data is `ASO_3M_SD` and we want version 1."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "aso_result = earthaccess.search_data(\n",
+ " short_name = \"ASO_3M_SD\",\n",
+ " version = '1',\n",
+ " bounding_box = roi_bbox,\n",
+ " temporal = temporal_range,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This returns one granule."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
\n",
+ " \n",
+ "
\n",
+ " \n",
+ "
\n",
+ " Data: SnowEx17_GPR_Version2_Week3.csv
\n", + "Size: 66.36 MB
\n", + "Cloud Hosted: True
\n", + "\n",
+ " \n",
+ "
\n",
+ " \n",
+ " \n",
+ "
\n",
+ " \n",
+ "
\n",
+ " "
+ ],
+ "text/plain": [
+ "Collection: {'EntryTitle': 'ASO L4 Lidar Snow Depth 3m UTM Grid V001'}\n",
+ "Spatial coverage: {'HorizontalSpatialDomain': {'Geometry': {'GPolygons': [{'Boundary': {'Points': [{'Longitude': -108.35131, 'Latitude': 38.77793}, {'Longitude': -107.53149, 'Latitude': 38.79858}, {'Longitude': -107.54845, 'Latitude': 39.27213}, {'Longitude': -108.37374, 'Latitude': 39.25112}, {'Longitude': -108.35131, 'Latitude': 38.77793}]}}]}}}\n",
+ "Temporal coverage: {'RangeDateTime': {'BeginningDateTime': '2017-02-08T00:00:00.010Z', 'EndingDateTime': '2017-02-08T23:59:59.590Z'}}\n",
+ "Size(MB): 1689.92\n",
+ "Data: ['https://data.nsidc.earthdatacloud.nasa.gov/nsidc-cumulus-prod-protected/ASO/ASO_3M_SD/1/2017/02/08/ASO_3M_SD_USCOGM_20170208.tif']"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "display(aso_result[0])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Search for MODIS Snow Cover Data\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "modis_result = earthaccess.search_data(\n",
+ " short_name = \"MOD10A1\",\n",
+ " version = \"61\",\n",
+ " bounding_box = roi_bbox,\n",
+ " temporal = temporal_range,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "There are three MODIS scenes. We can use display again to see an overview of the results. You can click on the thumbnails to download a larger version. The green region is snow free land, the blue is cloud cover and the orange hues are snow cover fraction."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
\n",
+ " \n",
+ "
\n",
+ " \n",
+ "
\n",
+ " Data: ASO_3M_SD_USCOGM_20170208.tif
\n", + "Size: 1689.92 MB
\n", + "Cloud Hosted: True
\n", + "\n",
+ " \n",
+ "
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+ " \n",
+ " \n",
+ "
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+ " \n",
+ "
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+ " "
+ ],
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+ "Collection: {'EntryTitle': 'MODIS/Terra Snow Cover Daily L3 Global 500m SIN Grid V061'}\n",
+ "Spatial coverage: {'HorizontalSpatialDomain': {'Geometry': {'GPolygons': [{'Boundary': {'Points': [{'Longitude': -92.131858571552, 'Latitude': 29.9009502428382}, {'Longitude': -104.256722414513, 'Latitude': 40.0742066197196}, {'Longitude': -117.486656023174, 'Latitude': 39.9999999964079}, {'Longitude': -103.835851753394, 'Latitude': 29.8360532722546}, {'Longitude': -92.131858571552, 'Latitude': 29.9009502428382}]}}]}}}\n",
+ "Temporal coverage: {'RangeDateTime': {'BeginningDateTime': '2017-02-08T00:00:00.000Z', 'EndingDateTime': '2017-02-08T23:59:59.000Z'}}\n",
+ "Size(MB): 9.52992\n",
+ "Data: ['https://data.nsidc.earthdatacloud.nasa.gov/nsidc-cumulus-prod-protected/MODIS/MOD10A1/61/2017/02/08/MOD10A1.A2017039.h09v05.061.2021265053227.hdf']"
+ ]
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+ "\n",
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\n", + "Size: 9.53 MB
\n", + "Cloud Hosted: True
\n", + "\n",
+ " ![\"Data](\"https://data.nsidc.earthdatacloud.nasa.gov/nsidc-cumulus-prod-public/MODIS/MOD10A1/61/2017/02/08/BROWSE.MOD10A1.A2017039.h09v05.061.2021265053227.1.jpg\")
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+ "Collection: {'EntryTitle': 'MODIS/Terra Snow Cover Daily L3 Global 500m SIN Grid V061'}\n",
+ "Spatial coverage: {'HorizontalSpatialDomain': {'Geometry': {'GPolygons': [{'Boundary': {'Points': [{'Longitude': -92.131858571552, 'Latitude': 29.9009502428382}, {'Longitude': -104.256722414513, 'Latitude': 40.0742066197196}, {'Longitude': -117.486656023174, 'Latitude': 39.9999999964079}, {'Longitude': -103.835851753394, 'Latitude': 29.8360532722546}, {'Longitude': -92.131858571552, 'Latitude': 29.9009502428382}]}}]}}}\n",
+ "Temporal coverage: {'RangeDateTime': {'BeginningDateTime': '2017-02-09T00:00:00.000Z', 'EndingDateTime': '2017-02-09T23:59:59.000Z'}}\n",
+ "Size(MB): 8.04295\n",
+ "Data: ['https://data.nsidc.earthdatacloud.nasa.gov/nsidc-cumulus-prod-protected/MODIS/MOD10A1/61/2017/02/09/MOD10A1.A2017040.h09v05.061.2021265174122.hdf']"
+ ]
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+ },
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+ "text/html": [
+ "\n",
+ " \n",
+ "
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+ " \n",
+ "
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+ " \n",
+ "
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+ " Data: MOD10A1.A2017040.h09v05.061.2021265174122.hdf
\n", + "Size: 8.04 MB
\n", + "Cloud Hosted: True
\n", + "\n",
+ " ![\"Data](\"https://data.nsidc.earthdatacloud.nasa.gov/nsidc-cumulus-prod-public/MODIS/MOD10A1/61/2017/02/09/BROWSE.MOD10A1.A2017040.h09v05.061.2021265174122.1.jpg\")
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+ "Collection: {'EntryTitle': 'MODIS/Terra Snow Cover Daily L3 Global 500m SIN Grid V061'}\n",
+ "Spatial coverage: {'HorizontalSpatialDomain': {'Geometry': {'GPolygons': [{'Boundary': {'Points': [{'Longitude': -92.131858571552, 'Latitude': 29.9009502428382}, {'Longitude': -104.256722414513, 'Latitude': 40.0742066197196}, {'Longitude': -117.486656023174, 'Latitude': 39.9999999964079}, {'Longitude': -103.835851753394, 'Latitude': 29.8360532722546}, {'Longitude': -92.131858571552, 'Latitude': 29.9009502428382}]}}]}}}\n",
+ "Temporal coverage: {'RangeDateTime': {'BeginningDateTime': '2017-02-10T00:00:00.000Z', 'EndingDateTime': '2017-02-10T23:59:59.000Z'}}\n",
+ "Size(MB): 9.25648\n",
+ "Data: ['https://data.nsidc.earthdatacloud.nasa.gov/nsidc-cumulus-prod-protected/MODIS/MOD10A1/61/2017/02/10/MOD10A1.A2017041.h09v05.061.2021266011855.hdf']"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[None, None, None]"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "[display(r) for r in modis_result]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Access and Read the Data\n",
+ "\n",
+ "In this section we are going to access the data granules and read granules into data objects for visualization and analysis. A data object, in this context, is a Python data structure that contains the data values and associated metadata, and has a set of methods associated with it. \n",
+ "\n",
+ "We have three datasets. The SnowEx GPR has three surveys but we are going to use the survey from the first week. There is one temporal and spatially coincident ASO snow depth data granule, and three MODIS scenes. From the results summaries we can see that the data is in three different file formats. SnowEx GPR is a CSV file. ASO snow depth is a GeoTIFF. The MODIS snow cover data are in HDF files. In fact this is HDF-EOS. We will use the Pandas, Geopandas and xarray Python packages to read these data granules.\n",
+ "\n",
+ "All the datasets we are working with are in the cloud. For the SnowEx ~and ASO~ datasets, rather than downloading the data, we will _stream_ the data loading it directly into memory. Unfortunately, we cannot use this method for the MODIS snow cover data because the nested group structure of HDF-EOS does not allow this kind of access. \n",
+ "\n",
+ "If you are working on a local machine and would rather download the data, use the following command, specifying the list of results returned by `earthaccess.search_data` and the local download path:\n",
+ "```\n",
+ "earthaccess.download(\n",
+ "
\n",
+ " \n",
+ "
\n",
+ " \n",
+ "
\n",
+ " Data: MOD10A1.A2017041.h09v05.061.2021266011855.hdf
\n", + "Size: 9.26 MB
\n", + "Cloud Hosted: True
\n", + "\n",
+ " ![\"Data](\"https://data.nsidc.earthdatacloud.nasa.gov/nsidc-cumulus-prod-public/MODIS/MOD10A1/61/2017/02/10/BROWSE.MOD10A1.A2017041.h09v05.061.2021266011855.1.jpg\")
\n",
+ "
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+ " \n",
+ " \n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " collection trace long lat elev twtt Thickness \\\n",
+ "0 GPR_0042_020817 2581 -108.066856 39.043146 3240.2 5.89 0.692 \n",
+ "1 GPR_0042_020817 2582 -108.066856 39.043146 3240.2 5.89 0.692 \n",
+ "2 GPR_0042_020817 2583 -108.066856 39.043146 3240.2 5.87 0.690 \n",
+ "3 GPR_0042_020817 2584 -108.066855 39.043146 3240.2 5.86 0.689 \n",
+ "4 GPR_0042_020817 2585 -108.066855 39.043147 3240.2 5.84 0.686 \n",
+ "\n",
+ " SWE x y UTM_Zone \n",
+ "0 225 753854.880092 4.325659e+06 12 S \n",
+ "1 225 753854.899385 4.325660e+06 12 S \n",
+ "2 224 753854.918686 4.325660e+06 12 S \n",
+ "3 224 753854.937987 4.325660e+06 12 S \n",
+ "4 223 753854.957280 4.325660e+06 12 S "
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%time\n",
+ "df = pd.read_csv(f_snex[survey_id], sep='\\t') # f_snex[0] is week1 and tab-delimited\n",
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Data for the week 1 survey were collected over multiple days between 2017-02-08 and 2017-02-10. Because we want to find temporally coincident data, we need to subset by day. \n",
+ "\n",
+ "There is no timestamp in the data but the day that data were collected is encoded in the _collection_ name column. We will create new index containing the day of collection so that we can subset the data.\n",
+ "\n",
+ "We use the `re` package to perform a regular expression search and to extract the date portion of a collection name. This date-string is then converted to a DateTime object using the `datetime` package. This is written as the function `collection_to_date`. We then apply this function to the _collection_ column and assign the result as the index of the DataFrame."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | collection | \n", + "trace | \n", + "long | \n", + "lat | \n", + "elev | \n", + "twtt | \n", + "Thickness | \n", + "SWE | \n", + "x | \n", + "y | \n", + "UTM_Zone | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "GPR_0042_020817 | \n", + "2581 | \n", + "-108.066856 | \n", + "39.043146 | \n", + "3240.2 | \n", + "5.89 | \n", + "0.692 | \n", + "225 | \n", + "753854.880092 | \n", + "4.325659e+06 | \n", + "12 S | \n", + "
| 1 | \n", + "GPR_0042_020817 | \n", + "2582 | \n", + "-108.066856 | \n", + "39.043146 | \n", + "3240.2 | \n", + "5.89 | \n", + "0.692 | \n", + "225 | \n", + "753854.899385 | \n", + "4.325660e+06 | \n", + "12 S | \n", + "
| 2 | \n", + "GPR_0042_020817 | \n", + "2583 | \n", + "-108.066856 | \n", + "39.043146 | \n", + "3240.2 | \n", + "5.87 | \n", + "0.690 | \n", + "224 | \n", + "753854.918686 | \n", + "4.325660e+06 | \n", + "12 S | \n", + "
| 3 | \n", + "GPR_0042_020817 | \n", + "2584 | \n", + "-108.066855 | \n", + "39.043146 | \n", + "3240.2 | \n", + "5.86 | \n", + "0.689 | \n", + "224 | \n", + "753854.937987 | \n", + "4.325660e+06 | \n", + "12 S | \n", + "
| 4 | \n", + "GPR_0042_020817 | \n", + "2585 | \n", + "-108.066855 | \n", + "39.043147 | \n", + "3240.2 | \n", + "5.84 | \n", + "0.686 | \n", + "223 | \n", + "753854.957280 | \n", + "4.325660e+06 | \n", + "12 S | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
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+ "
"
+ ],
+ "text/plain": [
+ " collection trace long lat elev twtt \\\n",
+ "collection \n",
+ "2017-02-08 GPR_0042_020817 2581 -108.066856 39.043146 3240.2 5.89 \n",
+ "2017-02-08 GPR_0042_020817 2582 -108.066856 39.043146 3240.2 5.89 \n",
+ "2017-02-08 GPR_0042_020817 2583 -108.066856 39.043146 3240.2 5.87 \n",
+ "2017-02-08 GPR_0042_020817 2584 -108.066855 39.043146 3240.2 5.86 \n",
+ "2017-02-08 GPR_0042_020817 2585 -108.066855 39.043147 3240.2 5.84 \n",
+ "\n",
+ " Thickness SWE x y UTM_Zone \n",
+ "collection \n",
+ "2017-02-08 0.692 225 753854.880092 4.325659e+06 12 S \n",
+ "2017-02-08 0.692 225 753854.899385 4.325660e+06 12 S \n",
+ "2017-02-08 0.690 224 753854.918686 4.325660e+06 12 S \n",
+ "2017-02-08 0.689 224 753854.937987 4.325660e+06 12 S \n",
+ "2017-02-08 0.686 223 753854.957280 4.325660e+06 12 S "
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import re\n",
+ "import datetime as dt\n",
+ "\n",
+ "def collection_to_date(x):\n",
+ " date_str = re.search(r'GPR_\\d{4}_(\\d{6})', x)\n",
+ " if date_str:\n",
+ " return dt.datetime.strptime(date_str.groups(0)[0], \"%m%d%y\")\n",
+ "\n",
+ "df.index = df.collection.apply(collection_to_date)\n",
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "pandas recognizes a DataFrame with a datetime index as a time series and allows a simple subsetting based on a date string."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | collection | \n", + "trace | \n", + "long | \n", + "lat | \n", + "elev | \n", + "twtt | \n", + "Thickness | \n", + "SWE | \n", + "x | \n", + "y | \n", + "UTM_Zone | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|
| collection | \n", + "\n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " |
| 2017-02-08 | \n", + "GPR_0042_020817 | \n", + "2581 | \n", + "-108.066856 | \n", + "39.043146 | \n", + "3240.2 | \n", + "5.89 | \n", + "0.692 | \n", + "225 | \n", + "753854.880092 | \n", + "4.325659e+06 | \n", + "12 S | \n", + "
| 2017-02-08 | \n", + "GPR_0042_020817 | \n", + "2582 | \n", + "-108.066856 | \n", + "39.043146 | \n", + "3240.2 | \n", + "5.89 | \n", + "0.692 | \n", + "225 | \n", + "753854.899385 | \n", + "4.325660e+06 | \n", + "12 S | \n", + "
| 2017-02-08 | \n", + "GPR_0042_020817 | \n", + "2583 | \n", + "-108.066856 | \n", + "39.043146 | \n", + "3240.2 | \n", + "5.87 | \n", + "0.690 | \n", + "224 | \n", + "753854.918686 | \n", + "4.325660e+06 | \n", + "12 S | \n", + "
| 2017-02-08 | \n", + "GPR_0042_020817 | \n", + "2584 | \n", + "-108.066855 | \n", + "39.043146 | \n", + "3240.2 | \n", + "5.86 | \n", + "0.689 | \n", + "224 | \n", + "753854.937987 | \n", + "4.325660e+06 | \n", + "12 S | \n", + "
| 2017-02-08 | \n", + "GPR_0042_020817 | \n", + "2585 | \n", + "-108.066855 | \n", + "39.043147 | \n", + "3240.2 | \n", + "5.84 | \n", + "0.686 | \n", + "223 | \n", + "753854.957280 | \n", + "4.325660e+06 | \n", + "12 S | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " collection trace long lat elev twtt \\\n",
+ "collection \n",
+ "2017-02-08 GPR_0042_020817 2581 -108.066856 39.043146 3240.20 5.89 \n",
+ "2017-02-08 GPR_0042_020817 2582 -108.066856 39.043146 3240.20 5.89 \n",
+ "2017-02-08 GPR_0042_020817 2583 -108.066856 39.043146 3240.20 5.87 \n",
+ "2017-02-08 GPR_0042_020817 2584 -108.066855 39.043146 3240.20 5.86 \n",
+ "2017-02-08 GPR_0042_020817 2585 -108.066855 39.043147 3240.20 5.84 \n",
+ "... ... ... ... ... ... ... \n",
+ "2017-02-08 GPR_0043_020817 98131 -108.066826 39.043153 3242.82 5.58 \n",
+ "2017-02-08 GPR_0043_020817 98132 -108.066826 39.043152 3242.82 5.56 \n",
+ "2017-02-08 GPR_0043_020817 98133 -108.066826 39.043152 3242.81 5.47 \n",
+ "2017-02-08 GPR_0043_020817 98134 -108.066827 39.043152 3242.81 5.33 \n",
+ "2017-02-08 GPR_0043_020817 98135 -108.066827 39.043152 3242.80 5.31 \n",
+ "\n",
+ " Thickness SWE x y UTM_Zone \n",
+ "collection \n",
+ "2017-02-08 0.692 225 753854.880092 4.325659e+06 12 S \n",
+ "2017-02-08 0.692 225 753854.899385 4.325660e+06 12 S \n",
+ "2017-02-08 0.690 224 753854.918686 4.325660e+06 12 S \n",
+ "2017-02-08 0.689 224 753854.937987 4.325660e+06 12 S \n",
+ "2017-02-08 0.686 223 753854.957280 4.325660e+06 12 S \n",
+ "... ... ... ... ... ... \n",
+ "2017-02-08 0.656 213 753857.428230 4.325660e+06 12 S \n",
+ "2017-02-08 0.653 212 753857.421581 4.325660e+06 12 S \n",
+ "2017-02-08 0.643 209 753857.414932 4.325660e+06 12 S \n",
+ "2017-02-08 0.626 203 753857.408275 4.325660e+06 12 S \n",
+ "2017-02-08 0.624 203 753857.401626 4.325660e+06 12 S \n",
+ "\n",
+ "[163764 rows x 11 columns]"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df = df.loc[\"2017-02-08\"]\n",
+ "df"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As you can see in the table above, the index is the same for every row. For future analysis, we want a unique index. So we reset the index to a unique numeric index. We set the name of the index first to preserve the date information for future work."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df.index.name = \"date\"\n",
+ "df = df.reset_index()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "For plotting and analysis, we create a `geopandas.GeoDataFrame`. A `GeoFataFrame` is similar to and supports all the functionality of a `pandas.DataFrame` but it has a `geometry` column and methods that allow GIS-like operations, such as spatial joins, intersections, etc. We create the `geometry` for the `GeoDataFrame` from the latitude and longitude columns.\n",
+ "\n",
+ "The SnowEx data files have columns containing projected x and y coordinates. However, there are some issues with these values. In some files, these coordinates are for different UTM zones, which makes plotting and reprojection for the whole DataFrame difficult. Latitude and longitude are in a consistent CRS, WGS-84 (EPSG:4326).\n",
+ "\n",
+ "```{note}\n",
+ "The `UTM_Zone` column gives the UTM zone as `12 S`. This is wrong and should be `12 N` for the northern hemisphere UTM zone 12.\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | collection | \n", + "trace | \n", + "long | \n", + "lat | \n", + "elev | \n", + "twtt | \n", + "Thickness | \n", + "SWE | \n", + "x | \n", + "y | \n", + "UTM_Zone | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|
| collection | \n", + "\n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " |
| 2017-02-08 | \n", + "GPR_0042_020817 | \n", + "2581 | \n", + "-108.066856 | \n", + "39.043146 | \n", + "3240.20 | \n", + "5.89 | \n", + "0.692 | \n", + "225 | \n", + "753854.880092 | \n", + "4.325659e+06 | \n", + "12 S | \n", + "
| 2017-02-08 | \n", + "GPR_0042_020817 | \n", + "2582 | \n", + "-108.066856 | \n", + "39.043146 | \n", + "3240.20 | \n", + "5.89 | \n", + "0.692 | \n", + "225 | \n", + "753854.899385 | \n", + "4.325660e+06 | \n", + "12 S | \n", + "
| 2017-02-08 | \n", + "GPR_0042_020817 | \n", + "2583 | \n", + "-108.066856 | \n", + "39.043146 | \n", + "3240.20 | \n", + "5.87 | \n", + "0.690 | \n", + "224 | \n", + "753854.918686 | \n", + "4.325660e+06 | \n", + "12 S | \n", + "
| 2017-02-08 | \n", + "GPR_0042_020817 | \n", + "2584 | \n", + "-108.066855 | \n", + "39.043146 | \n", + "3240.20 | \n", + "5.86 | \n", + "0.689 | \n", + "224 | \n", + "753854.937987 | \n", + "4.325660e+06 | \n", + "12 S | \n", + "
| 2017-02-08 | \n", + "GPR_0042_020817 | \n", + "2585 | \n", + "-108.066855 | \n", + "39.043147 | \n", + "3240.20 | \n", + "5.84 | \n", + "0.686 | \n", + "223 | \n", + "753854.957280 | \n", + "4.325660e+06 | \n", + "12 S | \n", + "
| ... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "
| 2017-02-08 | \n", + "GPR_0043_020817 | \n", + "98131 | \n", + "-108.066826 | \n", + "39.043153 | \n", + "3242.82 | \n", + "5.58 | \n", + "0.656 | \n", + "213 | \n", + "753857.428230 | \n", + "4.325660e+06 | \n", + "12 S | \n", + "
| 2017-02-08 | \n", + "GPR_0043_020817 | \n", + "98132 | \n", + "-108.066826 | \n", + "39.043152 | \n", + "3242.82 | \n", + "5.56 | \n", + "0.653 | \n", + "212 | \n", + "753857.421581 | \n", + "4.325660e+06 | \n", + "12 S | \n", + "
| 2017-02-08 | \n", + "GPR_0043_020817 | \n", + "98133 | \n", + "-108.066826 | \n", + "39.043152 | \n", + "3242.81 | \n", + "5.47 | \n", + "0.643 | \n", + "209 | \n", + "753857.414932 | \n", + "4.325660e+06 | \n", + "12 S | \n", + "
| 2017-02-08 | \n", + "GPR_0043_020817 | \n", + "98134 | \n", + "-108.066827 | \n", + "39.043152 | \n", + "3242.81 | \n", + "5.33 | \n", + "0.626 | \n", + "203 | \n", + "753857.408275 | \n", + "4.325660e+06 | \n", + "12 S | \n", + "
| 2017-02-08 | \n", + "GPR_0043_020817 | \n", + "98135 | \n", + "-108.066827 | \n", + "39.043152 | \n", + "3242.80 | \n", + "5.31 | \n", + "0.624 | \n", + "203 | \n", + "753857.401626 | \n", + "4.325660e+06 | \n", + "12 S | \n", + "
163764 rows × 11 columns
\n", + "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " date collection trace long lat elev twtt \\\n",
+ "0 2017-02-08 GPR_0042_020817 2581 -108.066856 39.043146 3240.2 5.89 \n",
+ "1 2017-02-08 GPR_0042_020817 2582 -108.066856 39.043146 3240.2 5.89 \n",
+ "2 2017-02-08 GPR_0042_020817 2583 -108.066856 39.043146 3240.2 5.87 \n",
+ "3 2017-02-08 GPR_0042_020817 2584 -108.066855 39.043146 3240.2 5.86 \n",
+ "4 2017-02-08 GPR_0042_020817 2585 -108.066855 39.043147 3240.2 5.84 \n",
+ "\n",
+ " Thickness SWE x y UTM_Zone \\\n",
+ "0 0.692 225 753854.880092 4.325659e+06 12 S \n",
+ "1 0.692 225 753854.899385 4.325660e+06 12 S \n",
+ "2 0.690 224 753854.918686 4.325660e+06 12 S \n",
+ "3 0.689 224 753854.937987 4.325660e+06 12 S \n",
+ "4 0.686 223 753854.957280 4.325660e+06 12 S \n",
+ "\n",
+ " geometry \n",
+ "0 POINT (-108.06686 39.04315) \n",
+ "1 POINT (-108.06686 39.04315) \n",
+ "2 POINT (-108.06686 39.04315) \n",
+ "3 POINT (-108.06686 39.04315) \n",
+ "4 POINT (-108.06686 39.04315) "
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "snowex_gpr = gpd.GeoDataFrame(df, geometry=gpd.points_from_xy(df.long, df.lat, crs=4326))\n",
+ "snowex_gpr.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We now have a georeferenced set of survey points that we can plot. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "| \n", + " | date | \n", + "collection | \n", + "trace | \n", + "long | \n", + "lat | \n", + "elev | \n", + "twtt | \n", + "Thickness | \n", + "SWE | \n", + "x | \n", + "y | \n", + "UTM_Zone | \n", + "geometry | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "2017-02-08 | \n", + "GPR_0042_020817 | \n", + "2581 | \n", + "-108.066856 | \n", + "39.043146 | \n", + "3240.2 | \n", + "5.89 | \n", + "0.692 | \n", + "225 | \n", + "753854.880092 | \n", + "4.325659e+06 | \n", + "12 S | \n", + "POINT (-108.06686 39.04315) | \n", + "
| 1 | \n", + "2017-02-08 | \n", + "GPR_0042_020817 | \n", + "2582 | \n", + "-108.066856 | \n", + "39.043146 | \n", + "3240.2 | \n", + "5.89 | \n", + "0.692 | \n", + "225 | \n", + "753854.899385 | \n", + "4.325660e+06 | \n", + "12 S | \n", + "POINT (-108.06686 39.04315) | \n", + "
| 2 | \n", + "2017-02-08 | \n", + "GPR_0042_020817 | \n", + "2583 | \n", + "-108.066856 | \n", + "39.043146 | \n", + "3240.2 | \n", + "5.87 | \n", + "0.690 | \n", + "224 | \n", + "753854.918686 | \n", + "4.325660e+06 | \n", + "12 S | \n", + "POINT (-108.06686 39.04315) | \n", + "
| 3 | \n", + "2017-02-08 | \n", + "GPR_0042_020817 | \n", + "2584 | \n", + "-108.066855 | \n", + "39.043146 | \n", + "3240.2 | \n", + "5.86 | \n", + "0.689 | \n", + "224 | \n", + "753854.937987 | \n", + "4.325660e+06 | \n", + "12 S | \n", + "POINT (-108.06686 39.04315) | \n", + "
| 4 | \n", + "2017-02-08 | \n", + "GPR_0042_020817 | \n", + "2585 | \n", + "-108.066855 | \n", + "39.043147 | \n", + "3240.2 | \n", + "5.84 | \n", + "0.686 | \n", + "223 | \n", + "753854.957280 | \n", + "4.325660e+06 | \n", + "12 S | \n", + "POINT (-108.06686 39.04315) | \n", + "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "snowex_gpr.plot(\"Thickness\", legend=\"True\", legend_kwds={\"label\": \"Thickness (m)\"});"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Open and Read ASO Snow Depth Data\n",
+ "\n",
+ "\n",
+ "ASO data are GeoTIFFs. We can use `xarray.open_dataset` with `engine=rasterio` to open a GeoTIFF. We set `chunks='auto'` to allow _out-of-memory_ operations. The `squeeze` method removes dimensions of length 1."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "QUEUEING TASKS | : 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 6462.72it/s]\n",
+ "PROCESSING TASKS | : 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 31068.92it/s]\n",
+ "COLLECTING RESULTS | : 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 62601.55it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 34.2 ms, sys: 3.01 ms, total: 37.2 ms\n",
+ "Wall time: 36.1 ms\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ " Size: 2GB\n",
+ "Dimensions: (x: 23765, y: 17534)\n",
+ "Coordinates:\n",
+ " * x (x) float64 190kB 2.089e+05 2.089e+05 ... 2.802e+05 2.802e+05\n",
+ " * y (y) float64 140kB 4.35e+06 4.35e+06 ... 4.297e+06 4.297e+06\n",
+ " spatial_ref int64 8B ...\n",
+ "Data variables:\n",
+ " band_data (y, x) float32 2GB dask.array"
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%time\n",
+ "# f_aso = earthaccess.open(aso_result)\n",
+ "f_aso = earthaccess.download(aso_result, local_path=\"download\")\n",
+ "\n",
+ "aso = xr.open_dataset(f_aso[0], engine='rasterio', masked=True, chunks='auto').squeeze(drop=True)\n",
+ "aso"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Open and Read MODIS Snow Cover\n",
+ "\n",
+ "At this time, `xarray` cannot read HDF-EOS file-like objects. _Don't ask me why_. So we need to download the MODIS file. The downloaded files are written to the `download` directory."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "QUEUEING TASKS | : 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 6183.25it/s]\n",
+ "PROCESSING TASKS | : 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 46951.16it/s]\n",
+ "COLLECTING RESULTS | : 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 21147.75it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 3.83 ms, sys: 903 µs, total: 4.73 ms\n",
+ "Wall time: 4.32 ms\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "%%time\n",
+ "f_modis = earthaccess.download(modis_result, local_path='download')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "HDF-EOS is a hierachical data format. Data variables are organized into groups that mimic a directory structure. To find the data we want, we need to know something about the groups in the files. This can be found in the MOD10A1 User Guide section 1.2.2.\n",
+ "\n",
+ "\n",
+ "\n",
+ "Looking at this figure, we can see that the data are in the \"MOD_Grid_Snow_500m\" group.\n",
+ "\n",
+ "Another way to discover this information is to use `gdalinfo`. Uncomment (Ctrl/) and run the cell below. If we scroll to the Subdataset section, there is a list of SUBDATASETs. You can interpret these as `::`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Warning! Your gdal may not have the driver for hdf-eos\n",
+ "# !gdalinfo {f_modis[0]} # The {var} syntax is used to pass a variable in a jupyter notebook to a shell command"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We set `group=\"MOD_Grid_Snow_500m\"` to tell xarray to get the data in this group. `engine=\"rasterio\"` tells `xarray` to use `rasterio`, actually GDAL, to read the file."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 33.4 ms, sys: 20 µs, total: 33.4 ms\n",
+ "Wall time: 33.2 ms\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ " Size: 161MB\n",
+ "Dimensions: (x: 2400, y: 2400)\n",
+ "Coordinates:\n",
+ " band int64 8B 1\n",
+ " * x (x) float64 19kB -1.001e+07 ... -8.89...\n",
+ " * y (y) float64 19kB 4.448e+06 ... 3.336e+06\n",
+ " spatial_ref int64 8B ...\n",
+ "Data variables:\n",
+ " NDSI_Snow_Cover (y, x) float32 23MB dask.array\n",
+ " NDSI_Snow_Cover_Basic_QA (y, x) float32 23MB dask.array\n",
+ " NDSI_Snow_Cover_Algorithm_Flags_QA (y, x) float32 23MB dask.array\n",
+ " NDSI (y, x) float32 23MB dask.array\n",
+ " Snow_Albedo_Daily_Tile (y, x) float32 23MB dask.array\n",
+ " orbit_pnt (y, x) float32 23MB dask.array\n",
+ " granule_pnt (y, x) float32 23MB dask.array\n",
+ "Attributes: (12/94)\n",
+ " ALGORITHMPACKAGEACCEPTANCEDATE: 12-2005\n",
+ " ALGORITHMPACKAGEMATURITYCODE: Normal\n",
+ " ALGORITHMPACKAGENAME: MOD_PR10A1\n",
+ " ALGORITHMPACKAGEVERSION: 5\n",
+ " ASSOCIATEDINSTRUMENTSHORTNAME.1: MODIS\n",
+ " ASSOCIATEDPLATFORMSHORTNAME.1: Terra\n",
+ " ... ...\n",
+ " SOUTHBOUNDINGCOORDINATE: 29.9999999973059\n",
+ " SPSOPARAMETERS: none\n",
+ " TileID: 51009005\n",
+ " VERSIONID: 61\n",
+ " VERTICALTILENUMBER: 5\n",
+ " WESTBOUNDINGCOORDINATE: -117.486656023174"
+ ]
+ },
+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%time\n",
+ "modis = xr.open_dataset(f_modis[0], group=\"MOD_Grid_Snow_500m\", engine=\"rasterio\", chunks=\"auto\").squeeze()\n",
+ "modis"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We now have an `xarray.Dataset` containing the MODIS data."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "## Clip ASO Data to the bounding-box of the SnowEx GPR data\n",
+ "\n",
+ "The ASO data are large. The data can be clipped to a smaller region of interest using the `clip` method for `rioxarray.DataSets`. As an example, we will _clip_ the ASO data from 8 February to the bounding box of the SnowEx GPR survey, using the `rioxarray` `clip` method."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The first step is to define the clip region. There are several ways to do this. Here, we use the `total_bounds` attribute for the `snowex_gpr` `GeoDataFrame`.\n",
+ "\n",
+ "Before we define the bounding box, we need to make sure that the ASO data and SnowEx GPR data are in the same CRS. We use the `to_crs` method to reproject the GPR data to the CRS for ASO. We can use the `rio` accessor to get the ASO crs\n",
+ "\n",
+ "```\n",
+ "aso.rio.crs\n",
+ "```\n",
+ "\n",
+ "The `rioxarray` `clip` method expects a list of geometry objects, in this case a bounding box. We use a `shapely.geometry.box` to create a bounding box geometry object. `box` expects for values defining _minimum-x_, _minimum-y_, _maximum-x_, and _maximum-y_. `total_bounds` returns a tuple. We use the `*` operator to unpack the tuple returned by `total_bounds` into four values. The `[]` are used to create a list with one element.\n",
+ "\n",
+ "\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "clip_region = [box(*snowex_gpr.to_crs(aso.rio.crs).total_bounds)] # Clip for extent of survey data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We then use the `rioxarray` `clip` method to crop the ASO data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "aso_cropped = aso.rio.clip(clip_region)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Plot ASO and SnowEx GPR snow depth, and SNOTEL location\n",
+ "\n",
+ "We can plot the ASO Lidar snow depth and the GPR snow depth to compare the two datasets. We plot this as a map showing the raster ASO snow depth overlaid with the GPR snow depth."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "For any comparison plot, we want to make sure that our two datasets have the same range for the color bar. Here, we do this by getting the minimum and maximum values of the ASO data. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(array(0., dtype=float32), array(4.0321507, dtype=float32))"
+ ]
+ },
+ "execution_count": 32,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "vmin, vmax = (aso_cropped.band_data.min().values, aso_cropped.band_data.max().values)\n",
+ "vmin, vmax"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We create a `matplotlib` figure and axis. We then use the plot methods for the cropped ASO `xarray.DataArray` and SnowEx `geopandas.GeoDataFrame`. The SnowEx data are in WGS-84 but the ASO data are in UTM Zone 12 N. We use the Geopandas `to_crs` with the CRS for the ASO data accessed using the `rioxarray` accessor for the crs attribute. This avoids having to hard-code information and, hopefully, avoids mistakes.\n",
+ "\n",
+ "To distinguish the ASO snow depth raster from the GPR snow depth points we use the Viridis colormap but reverse it for the GPR data. The idea here is that similar snow depths have high contrast, whereas dissimilar snow depths have low contrast."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ "<xarray.Dataset> Size: 2GB\n", + "Dimensions: (x: 23765, y: 17534)\n", + "Coordinates:\n", + " * x (x) float64 190kB 2.089e+05 2.089e+05 ... 2.802e+05 2.802e+05\n", + " * y (y) float64 140kB 4.35e+06 4.35e+06 ... 4.297e+06 4.297e+06\n", + " spatial_ref int64 8B ...\n", + "Data variables:\n", + " band_data (y, x) float32 2GB dask.array<chunksize=(1411, 23765), meta=np.ndarray>
\n",
+ "
"
+ ],
+ "text/plain": [
+ "<xarray.Dataset> Size: 161MB\n", + "Dimensions: (x: 2400, y: 2400)\n", + "Coordinates:\n", + " band int64 8B 1\n", + " * x (x) float64 19kB -1.001e+07 ... -8.89...\n", + " * y (y) float64 19kB 4.448e+06 ... 3.336e+06\n", + " spatial_ref int64 8B ...\n", + "Data variables:\n", + " NDSI_Snow_Cover (y, x) float32 23MB dask.array<chunksize=(2400, 2400), meta=np.ndarray>\n", + " NDSI_Snow_Cover_Basic_QA (y, x) float32 23MB dask.array<chunksize=(2400, 2400), meta=np.ndarray>\n", + " NDSI_Snow_Cover_Algorithm_Flags_QA (y, x) float32 23MB dask.array<chunksize=(2400, 2400), meta=np.ndarray>\n", + " NDSI (y, x) float32 23MB dask.array<chunksize=(2400, 2400), meta=np.ndarray>\n", + " Snow_Albedo_Daily_Tile (y, x) float32 23MB dask.array<chunksize=(2400, 2400), meta=np.ndarray>\n", + " orbit_pnt (y, x) float32 23MB dask.array<chunksize=(2400, 2400), meta=np.ndarray>\n", + " granule_pnt (y, x) float32 23MB dask.array<chunksize=(2400, 2400), meta=np.ndarray>\n", + "Attributes: (12/94)\n", + " ALGORITHMPACKAGEACCEPTANCEDATE: 12-2005\n", + " ALGORITHMPACKAGEMATURITYCODE: Normal\n", + " ALGORITHMPACKAGENAME: MOD_PR10A1\n", + " ALGORITHMPACKAGEVERSION: 5\n", + " ASSOCIATEDINSTRUMENTSHORTNAME.1: MODIS\n", + " ASSOCIATEDPLATFORMSHORTNAME.1: Terra\n", + " ... ...\n", + " SOUTHBOUNDINGCOORDINATE: 29.9999999973059\n", + " SPSOPARAMETERS: none\n", + " TileID: 51009005\n", + " VERSIONID: 61\n", + " VERTICALTILENUMBER: 5\n", + " WESTBOUNDINGCOORDINATE: -117.486656023174
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, ax = plt.subplots()\n",
+ "\n",
+ "aso_cropped.band_data.plot(ax=ax, vmin=vmin, vmax=vmax, \n",
+ " cmap=\"viridis\",\n",
+ " cbar_kwargs={\"label\": \"ASO [m]\"})\n",
+ "\n",
+ "snowex_gpr.to_crs(aso_cropped.rio.crs).plot('Thickness', ax=ax, s=5, \n",
+ " vmin=vmin, vmax=vmax,\n",
+ " cmap=\"viridis_r\",\n",
+ " legend=True,\n",
+ " legend_kwds={\"label\":\"Snowex GPR [m]\"}); #, edgecolor='0.25')\n",
+ "ax.set_title(\"Airborne lidar and GPR snow depths\");"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Compare ASO and GPR snow depths along the survey transect\n",
+ "\n",
+ "We can also compare ASO Lidar and SnowEx GPR measurements along the GPR transect in two ways. First as a plot of snow depths along a transect. Second with a scatter plot."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "First, we extract the ASO data that corresponds to the GPR measurement points. The GPR points and ASO grid do not match exactly, so we interpolate from the ASO grid points to the GPR measurement points.\n",
+ "\n",
+ "We use _vectorized_ indexing to select data that correspond to the SnowEx GPR points by passing `x` and `y` coordinates as `xarray.DataArray` objects. `xarray.interp` interprets this input as selecting only the `(x,y)` points. If we passed `x` and `y` as lists or `numpy.arrays`, interp would return a 2D surface."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x = xr.DataArray(snowex_gpr.to_crs(aso_cropped.rio.crs).geometry.x)\n",
+ "y = xr.DataArray(snowex_gpr.to_crs(aso_cropped.rio.crs).geometry.y)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The GPR coordinates do not exactly match the grid coordinates of 3 m resolution ASO data. With such high resolution gridded data, it seems reasonable to interpolate the ASO snow depths to the GPR coordinates. We use the `xarray.Dataset.interp` method to do this. `xarray.Dataset.interp` is a wrapper for [`scipy.interpolate.interpn`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.interpn.html#scipy.interpolate.interpn). We could use any one of several interpolation methods but choose the `linear` (bilinear in this case) method. An alternative approach would be to extract snow depth for the nearest ASO grid point. We use this \"nearest-neighbor\" approach to extract MODIS data below.\n",
+ "\n",
+ "The interpolation produces a 1D dataset of ASO snow depths for the GPR survey points."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ " Size: 5MB\n",
+ "Dimensions: (dim_0: 163764)\n",
+ "Coordinates:\n",
+ " spatial_ref int64 8B ...\n",
+ " x (dim_0) float64 1MB 2.346e+05 2.346e+05 ... 2.346e+05 2.346e+05\n",
+ " y (dim_0) float64 1MB 4.326e+06 4.326e+06 ... 4.326e+06 4.326e+06\n",
+ " * dim_0 (dim_0) int64 1MB 0 1 2 3 4 ... 163760 163761 163762 163763\n",
+ "Data variables:\n",
+ " band_data (dim_0) float32 655kB dask.array"
+ ]
+ },
+ "execution_count": 35,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "aso_transect = aso.interp(x=x, y=y, method='linear')\n",
+ "aso_transect"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can now add the ASO snow depth data to the `snowex_gpr` `GeoDataFrame`. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ ""
+ ]
+ },
+ "execution_count": 37,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ "<xarray.Dataset> Size: 5MB\n", + "Dimensions: (dim_0: 163764)\n", + "Coordinates:\n", + " spatial_ref int64 8B ...\n", + " x (dim_0) float64 1MB 2.346e+05 2.346e+05 ... 2.346e+05 2.346e+05\n", + " y (dim_0) float64 1MB 4.326e+06 4.326e+06 ... 4.326e+06 4.326e+06\n", + " * dim_0 (dim_0) int64 1MB 0 1 2 3 4 ... 163760 163761 163762 163763\n", + "Data variables:\n", + " band_data (dim_0) float32 655kB dask.array<chunksize=(163764,), meta=np.ndarray>
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " date long lat Thickness SWE ASO\n",
+ "0 2017-02-08 -108.066856 39.043146 0.692 225 0.725680\n",
+ "1 2017-02-08 -108.066856 39.043146 0.692 225 0.726302\n",
+ "2 2017-02-08 -108.066856 39.043146 0.690 224 0.726953\n",
+ "3 2017-02-08 -108.066855 39.043146 0.689 224 0.727630\n",
+ "4 2017-02-08 -108.066855 39.043147 0.686 223 0.728338"
+ ]
+ },
+ "execution_count": 36,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "snowex_gpr[\"ASO\"] = aso_transect.band_data.to_pandas()\n",
+ "snowex_gpr[[\"date\",\"long\",\"lat\",\"Thickness\",\"SWE\",\"ASO\"]].head() # Just show coordinates and snow data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "| \n", + " | date | \n", + "long | \n", + "lat | \n", + "Thickness | \n", + "SWE | \n", + "ASO | \n", + "
|---|---|---|---|---|---|---|
| 0 | \n", + "2017-02-08 | \n", + "-108.066856 | \n", + "39.043146 | \n", + "0.692 | \n", + "225 | \n", + "0.725680 | \n", + "
| 1 | \n", + "2017-02-08 | \n", + "-108.066856 | \n", + "39.043146 | \n", + "0.692 | \n", + "225 | \n", + "0.726302 | \n", + "
| 2 | \n", + "2017-02-08 | \n", + "-108.066856 | \n", + "39.043146 | \n", + "0.690 | \n", + "224 | \n", + "0.726953 | \n", + "
| 3 | \n", + "2017-02-08 | \n", + "-108.066855 | \n", + "39.043146 | \n", + "0.689 | \n", + "224 | \n", + "0.727630 | \n", + "
| 4 | \n", + "2017-02-08 | \n", + "-108.066855 | \n", + "39.043147 | \n", + "0.686 | \n", + "223 | \n", + "0.728338 | \n", + "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "snowex_gpr[[\"Thickness\", \"ASO\"]].plot()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can also compare the snow depths on a scatterplot"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 38,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, ax = plt.subplots()\n",
+ "ax.scatter(snowex_gpr.Thickness, aso_transect.band_data, c='0.25', s=2, alpha=0.5)\n",
+ "ax.set_xlabel('GPR (m)')\n",
+ "ax.set_ylabel('ASO (m)')\n",
+ "ax.set_xlim(0,3)\n",
+ "ax.set_ylim(0,3)\n",
+ "ax.set_aspect('equal')\n",
+ "ax.axline((0.,0.), slope=1., c='k')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Plot MODIS Snow\n",
+ "\n",
+ "Now, let's take a look at the MODIS data. We want to explore snow cover fraction. In the MOD10A1 dataset, snow cover fraction as a percentage is calculated from NDSI and stored in the `NDSI_Snow_Cover` variable. By clicking on the file icon on the row for this variable in the dataset view below, we can see that the data variable doesn't just contain snow cover fraction but also has coded data values for missing data and other quality flags."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ " Size: 161MB\n",
+ "Dimensions: (x: 2400, y: 2400)\n",
+ "Coordinates:\n",
+ " band int64 8B 1\n",
+ " * x (x) float64 19kB -1.001e+07 ... -8.89...\n",
+ " * y (y) float64 19kB 4.448e+06 ... 3.336e+06\n",
+ " spatial_ref int64 8B ...\n",
+ "Data variables:\n",
+ " NDSI_Snow_Cover (y, x) float32 23MB dask.array\n",
+ " NDSI_Snow_Cover_Basic_QA (y, x) float32 23MB dask.array\n",
+ " NDSI_Snow_Cover_Algorithm_Flags_QA (y, x) float32 23MB dask.array\n",
+ " NDSI (y, x) float32 23MB dask.array\n",
+ " Snow_Albedo_Daily_Tile (y, x) float32 23MB dask.array\n",
+ " orbit_pnt (y, x) float32 23MB dask.array\n",
+ " granule_pnt (y, x) float32 23MB dask.array\n",
+ "Attributes: (12/94)\n",
+ " ALGORITHMPACKAGEACCEPTANCEDATE: 12-2005\n",
+ " ALGORITHMPACKAGEMATURITYCODE: Normal\n",
+ " ALGORITHMPACKAGENAME: MOD_PR10A1\n",
+ " ALGORITHMPACKAGEVERSION: 5\n",
+ " ASSOCIATEDINSTRUMENTSHORTNAME.1: MODIS\n",
+ " ASSOCIATEDPLATFORMSHORTNAME.1: Terra\n",
+ " ... ...\n",
+ " SOUTHBOUNDINGCOORDINATE: 29.9999999973059\n",
+ " SPSOPARAMETERS: none\n",
+ " TileID: 51009005\n",
+ " VERSIONID: 61\n",
+ " VERTICALTILENUMBER: 5\n",
+ " WESTBOUNDINGCOORDINATE: -117.486656023174"
+ ]
+ },
+ "execution_count": 39,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "modis"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We will plot snow cover fraction for the MODIS image over the western USA. We use a combination of `matplotlib` and `cartopy`. I use the Albers Equal Area projection with projection parameters for the contiguous USA.\n",
+ "\n",
+ "MODIS data are in the [MODIS Sinusoidal Grid](https://modis-land.gsfc.nasa.gov/GCTP.html). This uses a Sinusoidal projection, which a pseudocylindrical equal area projection. To plot the data correctly using `cartopy`, we need to define the CRS for the MODIS Sinusoidal projection. We can access the CRS for the data using the `rioxarray` accessor. Here, we print this as proj4 string."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'+proj=sinu +lon_0=0 +x_0=0 +y_0=0 +R=6371007.181 +units=m +no_defs=True'"
+ ]
+ },
+ "execution_count": 40,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "modis.rio.crs.to_proj4()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can learn a few things about the MODIS Sinusoidal projection from this. The `+lon_0=0` tells us that the central longitude is $0\\ ^{\\circ}E$. `+x_0` and `+y_0` are the false Easting and false Northing, which are both zero. The `+R=6371007.181` is the semimajor axis of the Spheroid. You can see a list of Proj4 parameters [here](https://proj.org/en/stable/usage/index.html) \n",
+ "\n",
+ "`cartopy.crs` has a Sinusoidal projection. Looking at the Docstring for `cartopy.crs.Sinusoidal`, we can see that the projection uses a default Globe. The `Globe` object defines the datum and ellipsoid used for the CRS and projection. Looking at the [cartopy documentation for [`cartopy.crs.Globe`](https://scitools.org.uk/cartopy/docs/latest/reference/generated/cartopy.crs.Globe.html) the default ellipse is WGS84. So we can't use the `cartopy.crs.Sinusoidal` projection _out-of-the-box_, we have to create a projection using the projection parameters for the MODIS Sinusoidal projection."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "modis_projection = ccrs.Sinusoidal(\n",
+ " globe=ccrs.Globe(semimajor_axis=modis.rio.crs['R'], ellipse=\"sphere\"),\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "Name: unknown\n",
+ "Axis Info [cartesian]:\n",
+ "- E[east]: Easting (metre)\n",
+ "- N[north]: Northing (metre)\n",
+ "Area of Use:\n",
+ "- undefined\n",
+ "Coordinate Operation:\n",
+ "- name: unknown\n",
+ "- method: Sinusoidal\n",
+ "Datum: unknown\n",
+ "- Ellipsoid: unknown\n",
+ "- Prime Meridian: Greenwich"
+ ]
+ },
+ "execution_count": 42,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "modis_projection"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "To show snow cover fraction and missing data, we use color normalization to map only the values between 0.001 and 100 to the Blues colormap. We then use the Colormap object to set values less than 0.001% to transparent.\n",
+ "\n",
+ "```\n",
+ "p.axes.cmap.set_under(\"none\")\n",
+ "```\n",
+ "\n",
+ "Values greater than 100 are set to a dark grey to indicate where clouds were detected or where QA was not passed.\n",
+ "\n",
+ "```\n",
+ "p.axes.cmap.set_over(\"0.25\")\n",
+ "```\n",
+ "\n",
+ "To add orientation we add state and country boundaries, along with the coastline."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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xRQICAvj8889Zu3Ytc+bM8ZqCefXVVyORSPj11195+eWXPeVDhgzxCLeGiIj4+HhWrlzJHXfcQc+ePT3BpgAOHDjARx99hCAIJCYmYjAYeOGFF3juuee49957GTVqFIWFhbz88suo1WqPFcZNx44dGThwILNmzSIjI4PFixd7bff19WXBggXcd999FBUVceuttxISEkJ+fj67d+8mPz//jB30uWDEiBF88803PPLII9x6661kZGQwY8YMwsPDPTNQAC6//HLuueceZs6cSV5eHiNGjEClUrFr1y40Gg0TJkwAoFu3bqxcuZIvv/zSEx21W7dutZ571qxZXHPNNVx11VU89dRTKJVK3n//ffbt28eKFSua3RIjItJstKBTp8gFTtXZGfVRl5d6QUGB8OyzzwodO3YU1Gq14OvrK/Tr10949913a/Wcp4qnvVQqFfz8/IRu3boJ48aNEzZv3lyjfm2zM3755RfhgQce8AS7ksvlQnh4uHDzzTfXeozqfPnll8Jdd90ltGvXTvD19RUUCoUQExMj3HPPPcKBAwe86sbGxgrDhw+vcYwrr7yyxv3Yu3evcMMNNwh6vV5QKpVCjx49vLz3q9KrVy8BEP73v/95yrKysgRACAwMPGPArKocO3ZMeOSRR4SEhARBpVIJPj4+QufOnYUnn3xSSE1N9ar74YcfCt27dxeUSqWg1+uFG2+8Udi/f3+tx128eLEA1BqEzM2GDRuE4cOHCwEBAYJCoRAiIyOF4cOHewWKcs/OyM/Pb9D1nCnYVG2/iaq8/vrrQlxcnKBSqYROnToJS5Ys8bShKg6HQ3jrrbeErl27eu7HwIEDPTNQBEEQ0tLShKFDhwo6nU4AhNjYWK82VP9+//rrL+Hqq68WtFqt4OPjIwwYMMDreIJQ9/+c+7rXr1/fgLskItJ8SAShAS7tIiIiIiIiIiLVEH0iRERERERERJqEKCJEREREREREmoQoIkRERERERESahCgiRERERERERJqEKCJEREREREREmkSD40SYzWasVuu5bIuIiIiIiIhIM6NUKlGr1efk2A0SEWazGb1eL4oIERERERGRC4ywsDBSU1PPiZBokIiwWq1YrVYGDBiAXO69y+efv9PsjWqtZGaWYDbbSEgIbumm/Oc4caIYu91JmzY1E1qdDwQBnE4Bh8OJ3e7AbnficJz+uNYF7HYnTqfTs93prD0Mi0QCMpkUuVyKVCpFJpMik0lOfVzrrvKq6xJPufQ8DURarQ4OH84jPj4QX9/683GIiDQFh0PgwIEcoqIM+PtrWro5Fx1GYzldu16B1WptORHh5ssv3+e++570Kqu+7iY5eUnTW9VKUSgq0Wg0+PnpWrop/zlUqkp0OvkFd+8FQfASHN7iQ6ghRhwOAavVLUAcdR5XIqFOkSGTSTzbXIJDUqPM9de1LJHUneDKbLah1Wrx89OJIkLknKHXl6NS+Vxw/98iTcidkZy8hMTE+vMTALXWudCFhcViFx+kLYTd7kSjufD8gCUSCQqFDIVC1uh9BUHA4RA8AsNtCXGLDafT6dnuXrZa7VXqueo0JCZtVcFx+iP1WFLy88spK6tEIpF4BItEIqlWv2aZe13M/SBSHwqFDJutbtEs0nppUgKuhgqJ6tS1z4UgLtxvkSqVoqWb8p/EZnM0qSO+kJFIJMjlEuTysxNPbjHiFhpOp1uQnF52Cw/3suvjRBAEDAYfzGY7ZrPNq35jA+ZXFRYucYGXyKhadrr89Lp72V3u5+eDSiXmELwYkMtl2O2iiLgQafJ/YFOFRG1cCOLCYrEDoFaLD63zjbtz+6+JiObCLUaae0a3IAheoqO+dfey91+81k+XOautu7efXnY4BMxmO9HR/s16TSItg0IhxWy2t3QzRJpAq+4RW5O4cIsI8c3n/GO3OwHO+o1cpHlxDW1IkLWAtjt8OO/8n1TknOGyRFhauhkXHYIgUFhYfk7PcVY9YnNaIxpDS4gLi8WOXO5yYBM5v7jHSkVLhIgbQajbGVTkwkMqldQ5k0nEG6dTwGZzYLc7cDgEfH1VSKU1/xfMZhvp6UUUFJSd0/ac9Wt1SwmJ2jiX4sJisYlWiBbCPVYql4siQsSFIAiIGuLiQSKh0T42FxNunzv39HGXSHCt22ze5dXFVkCAhpiYAK8yQRA4caIYp1Ogbdugc9r2ZukVW5OQqI362tZQgWE22/HxEZ0qWwKbzXkqroLYa4i4cIkI8fdwseBwXFyisOqU7qriwHv5dFltVhi5XIpcLkMul6JUytFopCgUrnXXXxnl5WayskrRaJQEBGg9Fonycgsmk5U2bYIA2zm91mZ7tW7tQqIuGmK9EAQBi8WOweBzvpolUgW73YFcLhM7DREPruGMlm6FSHNhMllRq1vfS5rb8bdqjBd3x1/VeuAtGFyOwdWRSiVewkCtVniW3eLAvS6XSxv0vFOr5VRUWMnMLMFotBAbG4BUKvH4kWk0SkymC0REwIUrJGqj6nVotVqeeuop5s9fwqFDh1rVrJH/Av/F6Z0i9SNaIi4u9HofsrJKsFrtKJXNP2zsmtHj7virxlvxDgBX23JtwyxVI87K5TJkMikajdwjANxlVddr81s4WyQSCXFxgRQUlJOZWcKhQ3kkJAR7rOYm07lPVdHs39bFJCTcBAa6Qi0XFhYCLoHx+off8uyDN9WoKwqM5sdmc4ozM0S8EC0RFxcBARpyc8vIyiohLi7QSyBWtQacjndyOtBa7WXe6/U5bbod5t2dvlIp9yzX9be1BVALCvLF11fF0aMnycoq9kx9dr2AndtznxNPwYtNSAQGBiIIAkVFRZ6y2gQEuARG1esXRcXZY7c70GiULd0MkVaEaIm4uJDJpERHG0hLK+LAgVykUolX8LP693WHcj8dBt4tBKqGhXevVxUNrU0MnA1qtYKoKH8yMoo5dCgXcM9oc57T856z6QYXk5AIDAykpKQEh6NmRLXarrOqkHAvV91WfV9RaNSPOJwhUh1RRFx8GAwaDIZKSksrCQjQolDIquV98c79crGJgObA31+DSiXjyJF8goN90elUGI0XkE/ExUpAQIBnKKM6dQml2oRFXXXcQqL3K0coLyknpk0wut9fwTb8FRRrX/xPiwx3AitxOEOkKuJwxsVJZKSB0tJKVCo5ISFiMq6mYLe7LDdBQb7nRWCd0ydza+/8Jr2zokH1AgMD6xQRZ6I2S0Vt9yUxMYlBfaMwFhRyYFcqAEcO5JDR5xluW17A6387ufqdE/R+5QjXfpBFYmIS87Zc/BOr3V7GoiVCxI3b8118A734UChkGAwa8vPLa53hIHJmKittSKUSlMrz88w85693rVlIzJ846ox1JBJJvZaIM1GbgKjLevHO9RoeGnsVgeEuR84uqR8QvWMOm9btYevch9Dr1eSkpLNv53EADD4yimxRdZ536W4pfWcebVK7WwtitEqR6rj7FlFDXJwEBfliszmoqDj3MwsuRiorrfj4KM6byD4vNuLWLCTOhF6vRy6X1yoiznRddVkc6ttv+pVydH6ueBQJ4xfATa8yoHQF24LuhW+fx0evx1lagObO2ST/k8XY24fVOEaZMxqAXRlGso6kEfvIZlYdUrAz15+FOyWE3/sb3abtp/+sY/SdeZTH1lTUex0tyeloleJwhogL0RJxcaPRKJBKJedleuLFiNMpnNf0DOfNJ+JCdbR0T++sOjOjKtWdI6teY23XW5+AcNf/O3kJiYlgsTsxmWxogB6XtMVofRFDUSUOhwOVXMa0YR2Zvbb2Y3DTq6zbcJQu/ToyqFs4S9enMWFoW/Zll9OlX0eG9I5kV1oxVpuD4goLkUk7EZwCHXvEcexQFu06R5GbXYpEKiEg0JcNT8Q29JY1KzabO/mWaIkQcSGKiIsbiUSCj48Co9Ei+kU0Abn8/GZEPa+OlReikAgMDMRut1NaWlpjW12C4Wyvs8gWxdaAe2DJ42hOlal+eokjsQ9x2w3d2bBDzo09Qnjs4+1sr3Yu97mdToGAYD9OHM9nl1bJ7ZdGM2PVfoYOiGFQt3CWJe/BbrPTsWskVpkUZ24qob37UVZmQXAK7N92iPiubZHJJKQezuKB1Qb+2ZWJzWLjhms6olXJ0alkHMo18vhl8Uz+eg/xEX4svLF5/+ntdofHC1tEBMThjP8CgYFaTpwoprS0Er1ejBTcGORyqceCe17Od97OdIoLTUgEBgZSVFTUICef2qwR1WnIlM6xtw+jfy3l3dIXcehd0A2dzkd/neCqftFM/d3KrTM/5u4udq9zHz2UR1ikgV59YzGWW1gzfSy2Xk9zR9cwXhh3M1GDXyQ3uxSz2U56WhH4h6M3+GCzOejVvy3HjxVQUliOzqAF4J9dmSiUCq4Z1Ibf/pdG3pGjPPTwMG7sFsK4j/+hb9cwMpY8zuvBi9ibUYJMIuG3FT+jDI/DWlpMQu9OpOw8CEDO59ef8V66cU3vFIcyRE4jWiIufvz9NZSUVJKaWohe70NEhF5MgNhAlEo5NpsrFPf5GNaQCA3oHcvKytDr9aSn78TPr3neNC8UITF69GisViurVq1qUP2GDFdUr9PQe1FVpHSd+D7tgtWs3XMS61fP1thm8JGzafY4AIyDX2T/+s0McP7mdbzD7SdQfOBf9B27YzVbqTy+HwKj6DWoCw6HQF6ukYBADeGhvpQaLezcfJSQqBD0Bh/MZju5GSe5/aZepJ0sJ1CnQuejoNBo4bIEf179cAsBof6YTRa0Oh/UajlHtu0BhRp9aDClKQdBH8qgId3ZtT0NuUKO0+EkKMxAZIQf9tVTSU5eQmpqIQ6Hk4SE4AbdI5GLH4vFzsGDubRtG4ROp27p5oicI5xOJ4WFJk6eNCKVSujQIQSpVHyhOBNms41Dh/Jo0yYIPz81ZWVGYmN7U1paip+fX7Ofr8W+kQvF2dJtiWgo7gBTDanTWCFVVYRc1cZA8gsPeARE1WNtO3TSIyAAjh7IZIDzN1K6TAKg9KoX2KK7nfLScgB89VpXIC1DKNjM7PphHXs27iRv5zZSDmSSml5CUVEl113fA73Bh9hIP3Q6Jf0ubYex0oZ99VSOpBez60g+SrmUNdPHMnlMP0zGShY+MpChA2IwGFwP+059O7geBAo12Mzs35vNgscvp1PKe3RJ/YDQzbOwr57KJ6t/BlzDGf/73zbP9RdaIykXYii0RlLiiGJDhi+dp+xp1H0UubARLRH/DaRSKcHBvrRtG4TV6iAnp6ylm3RBoFLJUSiklJebz8v5WlTWtXYhIZfL0ev1FBQUnLNz1OV8eaZ7c3Wsa0bFZ9/8UmOIpKr5f2vAPfhofej42HueWR/69TO44rreRLUJAx8dMpmULj1jienaAaV/ECjV4LCDXIEtK4XUA2mUFJaz5+BJ8rKL2flvFqHBWgJ1KjqG++I/+g3062cwpHcklye4Yravm5lEhyMLmPHDIY6fNFJSYgaHHf36GXQ4soCE/j3RR0TQpVsEi58cXeP67rvlOuDUcEbCAM99cZzqQAKVWRhkmXyyLYuIaH86T9nD6387ef1vpyu3yd9OXt3kIDExiTXH1Ny7qphDBUFklIc1+LsRaZ2cFhEt3BCR84JarSAwUEtJSaUYO6IBSCQSfH3VGI2W83K+Fh9kaikfiRHTl7Jm+tga5dFJb5Ox5HEA/P39kUgkjY4Rcba5MxrjU3HPzdfW8MVQrH3Rs10ilVCamcHqtU5mPTyA5Zthi/QaxoTqKCwyEdm9K8HBGtLTihCcAgqlgsj+fYmPNVBusrFtzQYwFlCqUGC32akwVjD4mq7IT421/ZNajGnlFM81b63S1uikt+kf68fX0+4nAJg94yOSX/gRgKBNr5E04yO2pZWRRe3fR5EtCqstm64R/hwuDEYuk9DWkO1Vx1hpw2i00uHIArbOhaD75hGd9DZtAlSsmDqGyLHz+ebfPIwrnmHqCsju9yyrHr2U5346SF5+BbrfXyG919MoFDIitr1e531v7YL3v8Rpx0pRRfxX0OlUFBSUY7XaUalaX8rw1oafn5riYhMWy7mfpdFiPhFVaa3+ER9//A5paYXMnTsXk8nU0s3xUF14JScvIWLsRvoXfVaj7u6oJAzBBgICfPA3+GBfPdWzLXLsfDZsScNmsVGa7xqyad+jLVqtgooKG1np+TgdTvwC/MhLzwabme6DepCRVsiz9/Ri+qLNBEcEMmNUNz544u4626sbNYdxl8Ywb8KdNbaNmrWMFVPHAND/6UVsnfuQp8N2OJzs3ZtNbGwAgm977E4nIarTIqLIFoUgCFz2/Fr6XRLLqEsi2J1TQVLfaB6843pe+OBrRr+1AYDhg9sTF6jmrc+2s37mCCbec0OtbS0Y9BxBm14DYP4n3zPpvpF1XlfewKmUFJbTrmMoAOEBGvKWPenZLgqPc0NFhYWjR/Pp0CHUk/JY5OLG4XCyf38O/v4aT4ZKkbpxOgX27csmONgXrVZ6cfpEVKW1Pmw//PBLzGZzswiIhgxRnGl/N25rRNVjZi+9osY+E+Z/QY/MJZSXlLN/8146Ruq9tmctnYRCqaA0OxtK88BUypHNOzl+9CSlJZXYrDYqjeXExvlzxdAexHTtwMHd6QzoF4vJ6qRd50hGXh7Hb0dcAiRh/AJPW99c9r3nPMYVz9QQEEOmudpttDgYMX0pAFvnPgRAnjmCxMQkciuCANeUpQBFJg6Ht94NUGQSqMxCo9NgXPEMi58cTWG5lWOFLl+P3mHFHJjdnWWTLuf3LSdYtTGdbumLePCzHYBLYFXnki6nhzvqExBuwiINpKcVE+inZn+Ka9jLf/QbgOt7WnVIQdfn9nHpnOOEj/rByx+mvo9I3YhTPP97yGRSIiL0FBZWUFLSel7oWitSqQSDwYeiIhPnegSoVVgi3LS2h+fIkSMJCQnhww8/bOmmAGfO+pmYmMTh9hPocGSBp0x+yyyOHi1ArpDRt1uY15sywNFOj1OYfoLwDglYzVbkCjkmowmVRkXBseOg0iKRyfHV+5LQMYyYUB0HjxdhMKgpKjJx7EAGAaEBWM1WZo7rzz8njKR8MIFPVv/s8WuoLYvp0lU/1RptsyqfffYuKSn5dOwYilrteuMc8WEOax4M99TJM0ew5nAuu7PKPcNQAD0mfcCkQXGs3J3FNW2C+TO9gH/SSjl4vIiAjTMbcrsbxMGER7nnxm5sn/ew51pr+x33nbyQxV9so1vvOK/hpsZy5dTFHMopZ1z/GGaMv7XODLFuWqtAPxuMRjPHjhXQqVOYOO2vFSIIAoKAJ423IAg4nafTetf81LbNVeZwnF63211lSqWMoCBfgoPPT4KpC5XKSiuHD59Er5fRu/cV58wS0ar+A1tbDImzSbx1LsgoD6tXQCSMX0BXhYx9R06X/73hIP0KPqVg0HOcyDOiqrafn0GDRBpLzr796GPjsZqtGA/vJvTq/6NUH4gtLx15aCzG9BR2FRSSFh7KZQPj2LI9A4O/hslJl7Nk9W7adw7H5hBI+WCC67jSDM9QRW3faYAi02v9s29+wVdywnMtI6YvpcyqAvI90SozysN47/ZQII9XNzl4oHcU4Zps7ugew6sffs32VT9xIK+UeRPuZPf88ZT2+p5co41Km4M108eivO11wkJ98R/zJla7k6NpRRQXVtDu4NtN+j7ANbOlxORKtdtj0ge0mbCNHrXU2z7vYVTx43E6z+61QKeSkbfsSWYsqykqaxMUDfl/utCEhvu9p7LSit3uRCJx+UfU9hdOrwNeyxci7mt3v/q5O2xw/XWve/890zYBp9O7rrvzr77ttCjwXnbXcy83FInENQtDKpVU+7hSfysUEs92mcyV9ruy0kp2diklJZXExPh7XjBEvPHxURIYqCUzM/+cnqdVWSLctBYh8dRTT7Ft2zY2btzY0k3xUJ+IqM7++PEYc7JI6NuVdnEBFC9/CgDnyJnEhurIWPI4BYOeAyA/1xWRM6FDKDu++w38glzTMI0FSAPCcVrMUFGMLjYBY2Y6gQntaNchhIRwP/73bzapB9JI6BbPczd3Zlh8JYmJSby0cDX7C8r4etr9tbZ/W46eWY/c7inPM0cgkcBDdw7npYWr+fnjOVw3bBg9ukeQWRHOq78fJW/Zk1z7wof8MuNB5n/yPR/uyOSqNgbGzPoNiVTC+NH92ZtRQkyQL70itWSUWJFJ4USRmYyCCuyrpxI65k227MxCb/DB6XQyfGCsZyilobjTtA+ZtoRVG9MbbN0oumLaWVtCRs/+hOVT7uPDL38kUJlVZ73G/B9dSEKitLSSjIxiT4bXplJVZLjWJVXKT2/3ru9Za/Rwiruz916vul3wqndaKHivnwtOCy8JUunp5arrUqmkjr+nt1fdVlUQ1Cw7XbcplJdbOHGiGJvNTni4nuBgX0wmK0ajhYAADUplq3o/bjHsdid79qQxePC158wS0SpFBLS8kFCpVDz77LOsXr2affv2tWhbAF744Osa5ms387cJbJjligvhP/oNj1hI7T6ZoGAtSqUM1U8veeqbhk5H8+t0wNWppR11OSvGtYvgyOad4LCBLghK8wjq0h2VWknW/kMo/YOIjAtFp1Oh+XU6CeMXUGKy4nAIVFjsfHlXINM32Jl+pZwr30onLNSXYD81ZSYrxhXPeLX5jY+/o63hJAt3SvhlxoOAqyObv03ggb4x3HfLdfS+Ywoju/phNLSl0Gxl+ZYsDL5KRveJYMZ3BwkM0LB3dxZmk5nK0lKQygiMCAHgmbt6sOSXFPR6NSH+GpRyqfdQzk2vwrfPk9p9MvF75jX6+xgybQn39Y7hnpuvbfS+Z8PYNz5j6VP3ALUHLWtqILPajtdaKSkxkZZWREJCMDKZpM63bKhtvb7OuuZbflVOr9YtBOrjdH8pqbbu3l5dxFRdd1tUqi5LPPXc22qWSajLSlN924WG0+kkJ6eM/Pxyr3KpVEL79iGiheIUJSVlxMf3+e+JCGhZIREREUFSUhKLFi0iNze3xdpRlfiH3iF10cQzdhTJyUsIH/UD4Z064HQ4a3SSBxMepVPKe4DLKqFUyNj00zZwOsBcDnYbPnEdsdvs2PLSkQREIpS47kGnKwdwcP3/GMAfxD/0Dr9uPEZImB99OoaQV1KJRiVnXP8Yhj+6iMAOnYmJCyAsUEvx8qc4mPAoQ65sx7sjtEz80cSIzkF88MTdBN03j/EDYpkx/lbGv/U57fx1KOQS8k6kcOJkMbbQNqzemUthkQml0jW0cWhvJmqNK4CV0+l0RcDUBbnar9Gj1LhiYshkMtQaNWaTmci4YBwOAWNZJQn75zfpO7AMe9kjyAZNWewV1Kuh1DW9uDG8tHA1PUMbFgTtYhMSxcUm0tOL6NYt4rxmKxRpnZhMVkwmKyqVHB8fJYcP5+Ln5yPO4jjFRRuxsiG05APtTNk7W4LURRNrLa9+n97bAUhl5GXk0aFDMJo7Z3u22Ya/Qr9LXBk5/Ue/gfT7afgoZWiDQ5D6GkDtC77+VB7fj81YAgo1IVEhIFOA0oeD6/+Hf/suALQP0dCjRwRSqYRt+/Pw0yiZelUCsQEaclbfzh8vDcVsthMTpGXWkmT+fm0Et/UIZeluKZVWO9/vy+f1D7/l2f9LYMb4W9HcOZsPnribvzMLuemtv8gsNmLOPsRzCzdzVccg1Go5hYWVlJSYadMhHKfTSXFOHqER/rwx0/V2/tjEkWByDc1YTZVUFuRRXlpOj8wlBG16jdDNs7BZbJ7rbygpXSZhGjqdjBPFGAe/SHqvp/nsp8MN3r8qZysgAF5++JYG1WtqVNTWjBixUqQqGo2SoCBfdDq1ayZXgJbiYpMYmOo80apFBLSckAgMDMRoNGK1ts6c9hN/9J7mlJy8hAnzvwBAJpGgj4igX8GnWL96FtPKKYDL6pCdbeTvv48BUFhmJqXLJCIDtZiMJpzlJWCtdB3QEIpUo6P7Vf246eoEeg3pT3yvLoT36oP01NtfrL+admF+6H5/hT6dQrgiwcDW7CIClVmEj/qBR7/ag16vxmJ3MjUpkSlrD7B670lu6RqBxepAKZfy7IM3YdAo0Nw5G9PKKQyaspiXl24jLMwXrVzghCyObumL+GXGg4QHaUlo40+X9kEM6xuF1Wxl0NBeyGQSvp52PwOMq9g+72HWv5+E1VSJUuODNjScPnkfk93vWcA1G6VTynt0fOw9ipc/1eDf12WXxNAlPgCNVo3JZCN219wmDYM0B/VNF24OEdDap5mKUzxF6sJkspKXZzxrB2aRhtPqRURLcb5nZjRWLD3SL6aGB/6CSXcBEKVXcWhuT6/6yttex+FwknM4hZCIAOZ89C0VFVbuvK4jP/x+xDU0IJXhExGHNjQcqUaHPkBPaKCG3fPHo/rpJUI3zyJ211zaHXyb5OQlbDxeyu7543lp4WoeuiSGpevT6B8RAMCg4QPZsfU4W37fTUpOGaNnf8KN3UJ4oE8U0346zJUdAkldNJGiK6bx+p/HOHy0kC36USxN3ot/sB6nU0ArE0iId/k4jJi+lBu7hXBJvD8Fn0xm0+xxdOsdy97dmejXz/BcZ49JH/DtkZMMKF2B3WbHZDSRMH4BDofLCc89E2PWYKXXvTsTecuepKDMzNIH+9G3axjbgu5FfsusRn1nzUVmRd2hu6v/js5GhLdeISF2ECI1sdkcHD9egI+PgpgYf9FSdZ64IERES1gjzreIqB6BsrblqnQMKqizzoi2Zv5I17qcB09RYbJiNFrRBoeg06l45oGbiI/ScyyvHKfDSWVBHlKNjsrsNCrSj+I0GdHofCgqM3sCQ0WOne91vkPvPgpAz9Aiis1WfhkfSaxfHgAzrusIgESh4n/rD/DEm3+SWWrh5YdvIdygxmhxUDDoOWIjXbM7QkK0jLpzIB2OLECplDK8TzhqmcD+nz4HYNm6Y2SWWtgwaxz+o98g/qF3aBvuR2VFJVvUI9kRej89Jn3A7vnjebh/DHtjHyIgNABfvS8pH0wgesecOu93Q5i1JJnIQC0f7cjk751Z9BvUgbgQ30Ydo7mI0jbOR+diExJi2GuR2nAn6GrTJoiAAG0Lt+a/wwUhIuD8C4mWjBFRm6NkQ0lOXkKhNZLXfjhE1xiXY5Fx8ItoNUrCQn2RK+QEBfiwRXE9KrmMTf875srm6bDjLC1w+URo9Nx4++UADOsVzrqZrvas23C0zvbOfux2rzKFXEqbDuEEhAYQ1yGKhM5RrNiQxsGER1n58yGWfn+AYwcy6N/Wn9DNs1D99BKpiyZy7Qsf0iUhiC3zn0QC3PfIZNc0yvGX8nBvgeTkJfzy8x5SF03kuo6BqNQqJBo9AaEBrP7tMOPeXE5xuY2tc27kzxeH0iX1gwbdv8+++aXe7Y98sZO9qYVkFFSg1SopKqrk6nb/Dcet1iYkBEEQBYSIFxaLnaKiCkJCdCgUspZuzn+KC0ZEwPkTEr6+viiVyhYTEdVDWtdXr7b1/3vlVwA278/FNHQ68VF6/LVKOkXq6dA5HKvNyQDbj+h8FJSXltNnYAKBCe1Aoye8QwL9hvQhb9mT5KTnsvVYMR0fc83keOfRS8/YpjJnNABvbUpFq1XSpWs4ZSUm2sYYeD6xE4fm9qSitIKCY8fpX/QZBrWccW8uZ9CUxQD8MuNB8pY9yaJFLmfQYE0pj/aBcE2O5xxZSy5nzkffsjO7HLlCzpBruxEb509omB9f7czlyTEjefCO61m20xXQqmoytB6TPqjR5iunLvaaqnm4/YQadfTrZ5CfbyI1rRiz2Y5MJqk1++j5oCmd+tmGXW9NQkIQRH8IEW+KiiqQSiUEBooWiPPNBSUi4PwIiZacmdGQ6zvTAz1h/3y0WiW6319B8+t0cgoqOJJezOJP/8bhcLJp7WZsw1/h05VbEWwWjEYLfgYNWoMfJqMJ6ffTAOhX8CmVVgc/bTwOwNC4ms6c1aMk+kkzAHh3hJaSEjMHDuQSEuaHwyHwT6aRjk//y6N3X0K/of1I7/U0OzKMhGt8uKNbOJ+s/tlzTJvNAeCJVlmddv753N09gg5HFuCnUfLoVfEsuacvU69O8LSt+vTL71JU7J4/niunLvY4WgJ8vyWDHaH303fyQgBP2HDNnbNxjpyJaeh0bp35MfnZheSk52KzOTx+GG6Bdb4Y+8ZnjJn7KcdKQs7reaH1CAnREiFSHYVChtMpiOKyBWjVcSLq41w+0Hr37s3w4cN59dVXcTrPLipeUzlTToTqdarWHf/W5yz9O4M2oTpUcinfbzhOQIAPudmlHsfCoiumoVBI2b9+M8rQaMaP7k+wVs6a6WMJum8ePko55ZU25t7QmQfvuL7O89V2/tGzP+GW9lYSE5OQ3zILnY+C3MIKTqQVERahx2SyoVDICNg4kynvrmL2Y7cz/q3PvURKYWEFGRnF9OgRWW+HcawkhKRPtzO0bxSllXaGdwj0JPs60z082ulx5Ao5gy6JxmSxe4J0VaVqJs9FK9fS9/Gv6ZP38Rnvw7mkerjr+vKp1MaZ/nfOFH6+pWNJ5OaWUVBQTteuES3aDpHWg8lk5ciRk8TE+Iv+ENX4T8eJqI9z+SALDAykpKSkxQREVRojINws/TuDwiITf+/J4ds/UshNz+X44Rx69Iggu9+zFAx6jiPb93P0QCbK0GgUSgXLkvegU8kY9+ZydGoFGUse56Nb9Dx4x/VEjp1fZ8dSPfPkguU/0D8ygH35gXR87D0Ki0wUlpnx16sJDNaRmV5IZIQfARtnkpy8hNmP3c6HX/5Yw8phszmQyaRnfON86v4beXhoW4a2CeS2LqEMijLWsJC4ZzNMeXeV177tDr7NJb0iyFo6qVYBAaczeXad+D4P3Tmcm0b2xDi46Qm0moLbsRVcIa+bIzJlfb+fMx2rpS0SoiVCpDo+PgoMBh9OnCimqKiipZvzn+KCFRFw7oRESzlV1nY9n33zi6e86Ippde67IcOXp3810//pRajkUvJyXZ7KdpsduUJOZWkpppVT0OmUtIsL4PPXbqNb7zgCwwLplr6ICXf25MWl/7D4ydGkLprIO5/94Dl21tJJ9b7tujulDRm+TBh9AxNG34CPQsahdx+lbYyBfzYdZv+Bk8hkEnz1WipMrtgb7oRiteV/sNsdDXKQSk5egs0hYLTZ6B1WXGsd2akOp7rzJ0DBJ5NxjpzJ0U6P19hWlX3vPOJq85LH0f3+yhnbVRW3v0dd62fC7dianLyEq+JDzmoaZ12zgOqituRe1Y9zvhF9IkSqI5FIiI0NIDBQy4kTxRQXi+nCzxcX7HBGVZr7gfboo49y7Ngxfv7552Y97pmo7aG++oiSnw8UcLK4EofDyZoHw+t9E7UMexmtRomPUsa+Q/mYK8zIFDIK9u+hy+BB+Bt8SE0rJmvrZvQdu9Mp5T1GTF/K7qxy0vOMSL+fRnTS27xzvabG8RvaWR0tDuaZB24CIHTMmxjNNnrH+rNp9jgSZ3zEXynFXNMxkNs72uo8RmpqIU6nk7Ztgxt0zvoocURhkLmcLF//20lhudWTbbQqkWPns+Xf7BrTQc83PSZ9gMFHhkImYd3MJK6cuhidSka0Xl3DYlMfDfnOGpPl0328xgqR5iYrq4SyMjOdOtUdL0Pkv4kgCJw4UUxpaSUdOoSKqeIRhzMaRHM+yCQSCf7+/hQUFJy58jkmMTGJ5VPuo+CTyXSO8fcIiNpwWwNmjuyC9ZRTosHgyhlRUVrBoFuuQff7Kxw6lEdWSgb46HhhTF8OJjzKmuljGdE5yONQmbGk5lt5Y+7xqv2uOAZT3l1F3rInMa2cQodgH97/fA33dnWw5Ca/egUEuCwRdTlV1kWhNbLWcoMsk1/TNITfsoq3F/7Olr05ZPd7lh2h93vVy1o6iQE9vcfZs/s9i3Pk2WXddHPl1IZZIHbPH09WiYXcMtc9Usgk7M4q5+/0Mrbl6M+6HY0d+nAPV9Xlg9MSiJYIkdqQSCRERRmQy6WcONF6UhZczFwUIqI5MRgMyGSyFpveWZXRsz+Bm15l1KxlPH91gtdUxarkmMIpskXxwnob2RWVhAdoyMw1kp1RhFwhp0fmEuyrp5I3cCoFx44TFBVKv2GX4quS0SnlPRLGL+CDJ+72OmZ1X4fG8PwgGR9++SMDIktJTl7C6Nmf0C8qwGua5pmw2U4PZ+zLDySjPOyMHWjVYZGFOyX8mqbxXMsLK/aii00AqYybLosjMNCHu2/rWyN/RtbSSZRe9YJn3WK2ecTV2TBq1jJPplU3CeMX1Fm/Y6jGM4SybmYSKrnrX/WHQwXsyw884/mqO16ejfWguoNqbbNyzicu46moIkRqRyaTEhFhoKLCSmVl/S8rImfPRSMimssa4Z7e2RIionrH/erKffDt86jkUnwlJ2o8vPPMrrfml387wszfj2LwkfPH0WLCDD68f1dvEjqE0iX1AxauWAvAyewi2vfrTkSkAen301j29L0AtZr2Aa594UNPuxpLoDLLcy0llXaU8ob/1ARBwG53olBISS8L5aTJzOd7slm5O5fExCSKbFFnPEZ6sYWlf2eQmJiEbtQcIra9TpfUD3j72WvZNHscqp9e4tC7j9bqUNk2xuBZjo3zrzVuRGM5WmCuMR3UoFFw68zaZ3qsmT6WIdOWMGL6UgDiAtVEG5TIpBJeGHdzjfr1BSir73+jocMdtdWr+ns8n0JC9IkQORN+fmpkMin5+UYxEdc55qLwiajK2T7M+vfvz+DBg3nttdeaqUVNxzLsZRKiDJ7hhdr8IN757Adm/3mM/DIzWpWcwjIzGRmljLyyDduP5qNY65pJsEV6Dah9eeO5kXw97X6v4606pGDF1DGe4wbdN48NG1Po1C0SX42CUqOFNQ+Gn4crduFwONm7N5vY2AD8/V3WhMOFwSQfzmNU13BPaO26WHVIwdPvbGTAoPZYbQ6ys40sfbAfi/9xxbCw2hw4BIGCTybX2Nc0dDqaX6c3+zUNmbaEpWuPcO917Wq1SKR8MIG+kxeyfd7DnvKOj73nCS0OrvwnT1zZhtmP3V7n0EJDE3M11pLQ0tM6q3LiRBFms5327c9/rAyRC4eCgnIyM0sID/cjJET3n53RI/pENJKzfdgFBga2ivTfXSe+j1ajpPyUOa76df2U6kPC+AU8u/YAB44XcuBALvuPFHDLJREYDGr2Z5R4zW6QBoQz5oEhXgLCbSmoKiAOt59A2xBfRgztRHSwL6aVUzxC5HzhDjRV5nB1EjmmcDoE5vPspVJi/fJITEzyWGFq4/aONhRKBcZyC3z7PKYKM5O+/Jetu3PYdTCPnCITqjr8LdwCYndUEv6j32DUrGVndS3umRg70kpRq+XoVLIa23ccOgnA9nkP0//pRYBLWFRY7ISOedNT1/rVsxRbrSxaubbO85U4Tltpqlq1zmSNaE0i4UyIlgiRhhAU5EtoqI6cnDJSUvIxGs0t3aSLkotORMDZCYmWzJlRld//yWDqkHZ8dIve63pu/DiPe1cV8/xnuyk0WpBJJAzvF41M5uqcVv+TDYDd4SRIr2bWkmQAevVP8GSurD5tb8bibzzLV1/elpW/HOG3TcfpFK5tkQ7GbnfF5/hol2v4IqPU5OkQl+6WMmbup9gcdcfwSExMYu0Lw1D99BL+o98gINAXpVJGRISOoE2vIf1+GllLJ9W6b+TY+aR2n0yPzCXkFlZ4Caym4I6aabE7kMkkfL4hzbPNf/Qb7D5RQm5WCXkDpwKwde5D9H96ESkfTCCnyITRbPNyyPRXKnnozuG1XjPgmYVSG2cKp36mIYvWghgnQqShhIX50aZNIE6nwLFjBWRnl7Z0ky46LkoRAU0XEq3FEvH3M23oHVbsZWrOM0cg/X4aHSL8KMx1CR2Dr4qNB09ySa8IuncKxmi00KdjCEajBZPFzuNf7QZAsfZF0stCazVjdw0u5GhxMLbhr5C1dBKzxvQiYf98Hu1zHi+4Cm5LhFSuwDLsZSYs28mO0PvZHZXEqr8zePOHI7z5Vyo5ptqHWJKTl5BurEB+yyxOFpsoLalEKpXAt8/Xe17T0On88dcxPho/kIJBz3Fob90dckNxWyJMK6egUsmJCjs9HFi8/Cl6xxn4aOLlBAe7ouyNf+tzSivtJIxfQJ82AZhWTvEa/hgQWfMhWJuzI5wWAC2Ra+NcIloiRBqKRCLBz8+H9u1DCA/34+RJI5WV1pZu1kXFRSsioPFCQi6Xo9frW3x6Z13Oaj5KGVdOXcz6f7PpnfMRuYUVZCx5HOn304gL0tInRk9mWgHdIrRYLHZMZjvD+kR4OoTqvgRVO4qb5/1Jp1NZPw/nm1q0A7HZHEilEr767SjZWWWYKsyoNWoCQv3RaBRotQrG9Y2ud7ZHW4Mve3dnYrM5uaJ/jMenY+r7q2pErnSj+XU6MoWMGeNvpbLSjtlkZmf4Aw1qc22JveC0JSJ0zJt0bhOIwVfltT2rxMLiLSc4fsz1m/vgibvR+8jZsjeHn7ZmUHTFNDR3zvbUL7JFMWPxNzVmXNQ3k6axWWBbq3hwI1oiRBqLRCJBp1MDp1PJizQPF7WIgMY9QAMCAoCWmZlRlarz8qsKij3ZJUQbVMhkUoLum4e/Xs3o2Z+Q0ecZZFIJ17QJplffWLallVFRYSP1eBGT+jXsYbt3ZhdSPphAcvKSBu9zrrDbHTgkcgb1jyUgUENleSUqjYoRl8dTWmrm5MkK5v8vFTgttKp3oG0NJ7l+cAcmXZfgFThr1iO31xq50k3C/vkARO+YQ/+izwiOCMI0dPoZ27x7/vg6Z1r0nbyQblE69D4KTwwPNyUmK74+ClRqpads3c4svnhoIAEbZ2I0Wti9O9uzLUCRSdfg07/PHFO4Z/bNfwlRQ4g0FpnM1d056hkKFWk8F72IgIYLiZac3lmVyLHzeXPZ9zXKP9qayXs/p2A22yn4ZDLlJht7c0wEBmooLLeyJasIp1Pg53UHUShkxO+Z16jztpY3UJvNia9awpadWXRuE0jK/L5seOlafvo7nYoKG9E75rBlZxb/5gWQnLzEa8pnVTHx5nU+DIuvrHVmQvUpkDMWf1OjHXkDpxIf59+g2Rr9n16Eo45XnO3zHmbdzCT2vfMIccFa+j+9iBHTl9J38kJUchmD2/tjMVvJ6PMMALrfX2HiPTcAELtrLsYSo6e9bsdJ9zU8cvcIHh413FNW/VpbOs/FuUCMEyHSFGQy12/G4RBNEc3Jf0JENJTAwEAqKyuprKxs0XZ0Ctfy5JiRnnV3R5B1shyz2Y5MJiFy7Hxyc8tZtHQ9CoWU//2bzdxV+6g023A6nBgM6gZHSGwNuJNkwelolf++3JF3R2i5fF4qb21K5e17eyOTSTiY8ChyhYwjxUbA9XZetQOtazpjbeIBXG/z7/6dVqNNoZtnYV89tUHt3zr3IZJfOD30EXRf7QLuSI6RA5ml/HGogO3zHiavpJLfjxSTsH9+neG2098fSGJiEjMWf1PDcdJ9DVVzndS2/VzQUgJF9IkQaQpuS0RrSKx4MfGfERENeZi2lpkZfcL0niBDcLrtNpuDuwe3Qb9+BllLJ5GTnodEocJkspGVlkd8mwAUa1+kT/82DOwcSlaJpaUuoVGUOaOJ0rpCZf+apsFmcwWacrM8aQC//5PB7Mdux+EQKCsqIyslgy1pZTz9q7lB0xjrI1yTQ3FF45yt+k5eWKOs6ndW8MlkEmd8VKNO8fKnMK54hlF9I/Af/QZSqQStSo78lll1nss9tOWjkJGYmMTR4mCKbFGeSJ6jZi1j4j031Jrb4lzTkHM1d3tEnwiRpiCRSJBIJOJwRjPznxERcOZOprWIiNmP3U6fMEONcv36Gayb6eoodoTej1whZ+LYQejXzyD9/YEk3xeCbfgrvDGyC7vnj2fPsZa/loawJ7uEzIowLp1znKFxJiosp/NmHCsJwepwotEoMA5+0RUOW6UAm5nL2ui5r1ckyclLKBdivI5Z3emwtm1VgzNZv3KlSD8TbutOYbnVKwJldNLbmE9NTXWn7k5+4QHP7Ay3wHCLj6VP3cN1XYIYmBDAocwS1+yRauhGuSwTo2Yt4490LUaLHYB2/vmMvX0YUQE+TLznBnqFGpi8YCVT33c5jJ6vYamGRqts7vaIlgiRpiKTScThjGbmPyUioP4HWmsREQCZ5aYaHSM3vQq4Hto2UwVmk5ldaafTXycmJqFY+yKT7nMNhQQF+Jy39p4Ng6KMDJn+M/cOactrm+zIBCdTvzgGuBwk2/nns+6xaEwmG1qtgsCwQJR6f15atpNJX/5LYmIS99x8red41Tu36t/55AUrPfe26qyG6/q7yqrmzqiOe7rln9sy2Hoq0dioWcuIMKg9Kbv/zShjyLQlxD/0jmd2hkImYcT0pZSYbOyPHw9AkI+Swgo7QXo1Tqfg+X7dDOkcBMCKqWNYMOku4oI0nmv57JtfMMhcwzhKmZR5E+5k1iO3U2SLOq+WiJbwoxEtESJNRSaTipaIZuY/JyKg9gefWq1Go9G0ihgR4HpTrdoxOkfO5LN7+3rW/cNDEWwW9u87Pc2x+pi/aeWUVu1Yl5iYxJM/VzJ/m0C/S2J575t9/P2vKzR1r74JNerLZBKeurEjERE6dAYdv01zZSZNTl7CmLmfetWtK/kUgFwi5Z6br63hvOoOOa1fP6PW9nad+D4A++PHo1bLPW80K6aOYevchzz1Cj6ZzLqZSaQumugpKzLZSSkwk/LBBC7pHwc3vcrVsRXse+cR9h8pwO5wkpdX7n0Np3ws3NdikGV6HCt9JSc81zfpvpEob3ud/k8vYm9uCe9/vqbW9p8rzreQEC0RIk3FZYkQRURz8p8UEVDzweeemXG+YkQ09MGbmJjEkHcz2P73Eb7ZfzpLpd1mp88VXWl38O1aj32m6IStAefImfy68RgLv9rNvkP52Cw25DZXmO8H+6pq1J97Z08ySy0o1r5ITFwAY5fvILvfsyQmJvH8RztYfURZb+4It5hwB2y6e9FmAOIfeqdB7XVn1XQ6nZjNdgwGdYOvtbDCzqF3Hz3tN/Ht86SWhgIQHaXHZLKREOfviVzZdeL7nqGTqr4OtUWkHDP3U67oEMhN7UOwOJyNypZ6YSJaIkSahssSIQ5nNCf/WREB3h25W0ScK0tE9VkBdVoIqpm0k5OXsPTevvQr+NTzZroj9H7KS8vJzHB1hlWDEV1IfHd/KL17RnLb9V2IiNAhlUqJCnZ1zFXzfrgZEFnKuplJRCe9TUmJGYdD4JJuYbz/+Ro6dQ5j+ZT7WH3kdLyF2iI4ViV0s8uZMXXRxBr3vTbiH3oHzZ2z6Za+iJx/trDt+z88QxPViU46Le6unLqY3FLXjJ9/0kqRSSRo7pyNv68CgAqTlQ6x/ljsTkwVVtJ7PU1+mZn92UacI2cyecHKWtsPsGjlWm5MsNApSIu/r5KhcaYzXgdc2FM/RUuESFORSqXi7Ixm5j8tIuD0gzkwMJCysjJstnOTf9790D7a6fF6H+DRwb41OovqkSbVGjUSqcQTB+LPdfs92z7fL79gOogiWxTGFc+w751H0OtUBIboqDhZiFOAO+6ovXNOTl5CXkkloZtnIZNJ8NcqGbt8B1KpBOfImV51a3OorPo9uDna6XFSjubXej6382R00tsE+ioxrZwCwAD5XwzgD7qkuiJVVk2UBRCgVXqSaW2YNY6CTyYjv2UWDkHAT+M6zv23DmPygpXEhfmhVcnJzimjQ4dgYnfNxWp30jPaD+n305g34c4a7SpzRgPwV3oBiYlJLJh0F1klDZ+a3JotVGdC9IkQaSricEbz858XEeB6oJ4vp8p9r3Wt9wHuTvvtJjExicfWVHjWF61ci0anwVmQyd7Yh9gb+xCCU6DvzKMkJiaxI8PISwtX8+k+15t8jim81YoKu9NJ3sCpjHtzOaaVU4iJ0hPm74PZKUEQBK+MlFX58i6X1UilkvPDn8cwm+2UlJpxOJzM/+Ewa46pPddf171ud/Btuk58H8uwl/EzaHDYHLUOa7hTcadklrBxb66nPLW7dxrxvGVPUnTFtNPHD1JTYTkdnTJy7Hx0Pgp6Rvtx/FQSoOTkJQyKMjK8SxCHMkuIidLjp1EwYf4XDO8WTJReVec1+EldviO3tLd6Eqj1DiuuUa/qTJTW+jtoLGLYYpGmIg5nND+iiDhFnz49GyUiagtaVN9ybet1Uf1hf12nQM/+IapsrGYr6IKoKCnDZrXhq/elQztXnR9/P8wDi7YQ4KNg+gY7Oh8FyclLvII5tRZCVNlERvix+MnRAATq1AT6KVAoXG2uLyMlAN8+T/cuoeh0KjQaBW0i9KhUchb8eoylvx2vczf3/TXbHKh+egmZTEKbdsEUlJnJ6PMMu6OS2BF6P9uC7gVgi+J6dm34lz2bD7BFcT0A8XvmsUU/ynPMo50e58iuI571r6fd7/GhAMhaOonrugSx9VgxkSG+Xu0ZGBNIZKCWz+8IoOCTySyYdBfbM8pZ9vS9TH1/1Rk7/6phsOu73osF0RIh0lTE2RnNjygicD2UrFb7GUVE9RTaDVluiHAYPfsTz/Inq3/2eiNO6TKJVTu8HeU2zbyeAcZVjLlnEF16xhIeHcjl7QIZMX0pCpUCg0HNmn35qBVSzwyPCaNvOGM7WgL76qmMfeMzCgY9h9Xm4JbeegK0Z+4g3E6jxhXPIP1+GgUFrmRkvdoHExOuI+XACeIfeoe+M4/W+R2kfDDB9Xf7PoZ0DyM9q4yTmfmYTWZspgqcRa77ntC/J5jLoTgHZHLP/gNKVwAuH5V2B9/GR693Ha/LpFrPd7zIQptQHQ6H4NUmgyyTd0e4sniOmfspo2d/whVt9PSY9AGzHrn9nAw9XMjCQvSJEGkq4nBG8yOKCFyRIJ1OgYcfvqtR+zXGAlG1zO2VD66YBMun3OcxSd93y3VoVa6OquvE90nYPx/jime89p3+2xGMg1/kt83p+OvVGAxq2gT4sOFwITo/H3Q6FSfyjOhUrkRd36WoWLrqp3qvpWr+ifPNiLZm/pocz7AuQWw5VML3u0oavK9t+CuEjnmTykpXIKa1vx8h0l+DzqDj5/VH0WqVDHk3o95jDLiuP29+tJmKCivd+8bT/4qOYCwAq5ktXE3K9n1gNYNSDdKaDp+20kK2cDWV2WlsUY/0JPGqzta5D9EuSM2JnDK+S1GRmJhUw0J0Y4KF5VPuY1h8Jbvnj+fjr+v/3s6GC3WIQ7REiDQVmUyKIOCKyyLSLIgiArCcigSoUsnrfOtzz6hojrfCJ8eM5NoXPiSlyyQevS6BQVMWe0zSyclLKK10OXd+/s0ulLe9TuTY+QyastgVV2HMSDKWPI5MJiV6xxzahfnxwOUxzFlzmB4xBjq2DcRotKD66SUO55k4klqMQaUgQFH30MBjayr4eEeGx1nvfPLmsu85aYkAXB1oj1gtd1wa0uD91zwYzsIbdbx7f1+46VUCQ3RkFZsIjfCnTbtgstLzCQ3WYhn2ct0H+fZ5AEzGSnZtTYFvnyd+YH/w0YHOFfBJEhIHSh+QKWrur1AT3quPa7nc5ZeQ0ecZlLe9Tna/Z72qlpjtxEfp+W73SUbNWsaE0Tcwf5v3A23+J674FQ0a0jlLLkQHS5eIaOlWiFyISKVi/ozmRhQReIsIOLcP1hxTOO989gOx/mpGXNWOowVm7uoe4dleaI30OFf6BfgxtEsw6zYcZdPscSQnL2HWkmSufeFDOsT6k93vWS6N9SNcq6Zzm0BsToGTxSZuHxTL0U6Ps3HrCfJzS3ltzeF63ziLKyy89/k/5Jed/1wb8fo8QlSuVNeCILjCWisa/7NUyqRYLHaCNr1GwSeTCdg4E5PJhiFIz47tJygpMSO/ZRYJ4xfU2Hd3VBJ98j5GrVVz75390Y2ag1otRx8bD1IZ2tBw4jpEoY+KRhvgT3qvp732D42PpryknPAePel+/WBSu08mJESLn8Y1tGQb/oqn7rqZSRxJLaa43MKKqWNqvZa3a0kG1ty4rWMXpiUC0RIh0iTETJ7NjygicIkIlUru9WCqLiSawwpRaI1kR04xu/NKeO/34xx691Gujvf3Cg4UqHQFlNoRej9Ws5XFPx7l/wa15bNvfuHz/XKyKyqJ9VfTIVTDuOvbUWl3cLTI5Q+Qll9BaamZj39JQalWojf4ENc2yJPKuq4Ow7RyCj0yl9DWcPKsru9scTgEBAFP3ozG0DusGNVPL3nWtwbcg379DAwGNcFhekoKyzH4KtlxqOY19sh0fa+xu+ZisTlQKWW0jTHQqWsEEpkcp8PJyewiLr+sDV16RKPVnrZGFF0xjfg983jg1p6EhfnSJT6AqwfGovrpJcL0PnRvG0iIv4bIsfM9+1zVO5LoYF8OJjyK5s7Z/PpvDveuKvZ8P1UjXZ4Ndf1m3cMYzWVZO58Ip6ZmiBpCpCm4M3mKfhHNhygiOC0iqlObkGgK7jwNgcosUgor+flgITs2HcQ4+EVmPXJ7redQa9SYTWYyj2ezaWs699x8LV9Pu58Fk+7yzGbYcLiQnDIrX0+7H4C4YC1Bm17DVGEmdtdcAjbORPXTS2eMXvnOZz94Qjqfb3bm+nuW7XbXlMjaAk01FLdTY/+izzAOftFjnQgO07P+zyMcO5RFeq+nMQ5+sdb9u4RrifTXcE3HQA7uyyYwPBCtXktF/knW/3kEX42CWXd099RXKKTkDZzKyl+OcEXXMFIXTaRLuMtJct87j3Bj52BkMgkJIRqUt71OwvgFFBgtdAjVIJVKWf/rPhQKGcYVz/D6h982+bpro/oQ3IUmGOpHVBEijUcUEc2PKCKoW0RA04XEsZIQDhcGc7Q4mD+Pn2RTpo6Tlgje/WIHf/x1jH5XdiEm3A/wdrR0c/WV7bDb7PS9tD3jb+nqtU03ag7rZiZhXPEMv8x40NNGd/6GhP3zGxX2WqWQ8tEXW1rEtN07rJhtOXoeW1OBzeYWEU3/WbqdGvfGPkRBfgVBm14DXGOgXXpE0613HLG75hIfpSejzzM19l8zfSztgtR8tT0HtUbNozd3JWH/fK4eOYDbbuhOTl45U7/cA4DyttdxOgXKSkzE75nH5sP5nmO4mTfhTkb1CmdnWgltQnW0DfJB56Ng3cwkOhxZQEhUCNt++puOj73Hsn+zPPEtmouLTUC4Y0SIwxkiTUEczmh+/vMiQhCEekUE0KgO2U2wn4qtOUV8+m82DkGga7ieh+4czqgbe1BhrCA8QENuYQWDpizmyTEjvfbdorudH3/aiz5Iz/7dGazamO4xh/edvBDjimcYNWuZp371zr+xHUaIKhudQVfrsc4lvV9xxVXoF16Kv1aJ/VQq7cYOZxRaIzlpiSC919PkDZxKRp9n6Ja+CLvN7qkTtOk1cnPLGdE7AuPgF5FJJMhkUk9+iqqs2pZFaloxgy6JRiGTMGTaEkxmOycKytGvn0HEKfFXWGSipMRMSJhrXfr9NEbNWuax6hzt9DjXvvAhG9NKySs0UVxu4chJE1GG09EsIyL9QO3LF6t3cFuXUHqHGoDav4eqjq9n8z3Vl1/kfNKU2SHicIbI2SBaIpqf/7yIqO5UeSYa0kEnJibhJ83g72MlXNsugCmLtjL6438YNWsZHy7+Gf9gf/73zwlu6R/F0uS9NfYPionAZqqgINMV7row38jeowWMmfspNodA/EPvcH2H8BqJts4m4dahuT0xDn6ROR9926T9G0NiYhLho34gdtdcCq2RFFojmXGVgsWLV2BxCNx660NnPkiVYz14x/U88NkOIiJ0rH7sMrZPa8fuqCRPVEo3EdteZ/2hAo4eyCRv2ZNc0SeS5b+m1Mg9MrJ3OCEhWu7sGcb8r/ayN9PI5MFtKTVaKL3qBaxfuWZcjLo8ln49wjmZW+bZd8XUMdidApZhL9Pu4Nv8MuNBlizfTGGhiZ7RfljtTjoFaT3RLI8cyEaiUHHjiB7MGH8rL4y7uc5r9ZNmsCHDt87tdd2fhm5vCWtFY895WkSIKkKk8UgkEiQSiTg7oxkRRUQjRURVbvw4jyd/rpmvoPSqF3j4OyOXxOmRS6RcP7gDRqOV40UW2vfrjs5PhUwmY8Vf6TX27TrxfRw2B9jMUJjJ6MSetDv4NgqFlN+PFHOioAK7U/BKE94YCq2RdW5b91g07fxr5pBo7NtimTO6xhBNYmIShdZIRnyYQ3qvpxlg/p7k5CXsyC72OJMOGHEHFaXFZ5w1sHDn6Q7ENHQ6W/SjWPNgODs2HaTUZKPvzKMcX9CPgwmPsnl/Ltz0qsdyY7bY6XlJGwoGPUfKBxMwGNSefBgA6b2epluIjphQHc9/tY+E/fMJ9lOz9rBrCu60Gzt56q6ZPhalQlYjk2pxuYW0YwXsjkoivdfTCE4BHx85O06U8vO6gyyYdJcnmqWPrw8/vnYTncO09H96EYOmnLaM1HYPekX6e3xs3FNj66Ou4bhzKRga+ntpShtOD2c0elcREcAdcEoczmguRBFhsSOVShrlzOc2w0q/n0bqooleD83ExCQO7kzhx5/2opZLmfXrETZuz6T0VHKke6+OZ3DfaG67ph23XhqDVCb1BIK6+8sith06iSHQF5+gUB6fOprd88eTnLwE3e+vEGFQk3WyvEZ+jcbg7rAbc61netg/tqaC1/92cqwkhG05esZ+sYtAnZIr30rn8/1yUktDeePj73jp1yPIZFJK8ktwjpxJYmISe3MrSExMovcrRygyVmJUBvL6306cI2dy+bzUWs/3cG/Bc89lMglShcI1NFKYyeCpyWQdSSMxMYlOKe+h1Srh2+c90yml309D+v00gja9hm34KxTkV3gSd6V0mURYmC8/HCogM7+cgI2ucq1KRlZhBUVFlSyY5B2QrLagNRqVHKvFSo/MJVjNVnz1vgwfGIvJYqdPv3je/3yNx/qRsH8+EQY1D/cWOFlmYdPscfXea7PN4RGQIarsBnfYtYmHpgqJM53zXAoUcThD5GwRQ183L6KIsNhRKuWNMo9m93sW/9FveNbdD83pG+zsDHel6/YL8GPp78fx1ShwOgWK8ooorbQz6+NtbNydzeo/jhHrr6Z3z0hW7MkkMTEJf62KyBBfUrfvpkfmErbOfYgrpy72dOQ3tQ9h3OD4FptJURfvjtDy7KVS2hpO0i+8lJP5FTzzwwH6dArh6+3ZPDlmJAu3neCfvbkoFTI0Og35+RWEjnkTg4+MrhPfp11CIM6sA5Sn7yNaryTE34egIC2JiUkcKgiqcc5rX/iQp381c/zoSWQyGeUl5bS/+koGXdUZgP3x4wkd8ybS76fV2NeNYu2LlJeWk5VVhnHwiyS0C8ZotJCeX45i7enZG51CfDCueIbBl3gH44p/6B1MFjuWYS97ZoUkjF9AiJ+K0Yk9sQ1/hVuv7cjQqzvwf7H+ZGYbyc0r55G7R3isHwuW/+CJk/HmdT41jl+9w3bXBbj6nRMN7rDrC6Lm5mwsCOfLn0J0rBQ5W0QR0byIIsJiR61u3FDG1qlta5QlJiZRVmmjd85H+IcGYTaZOXYoC5PZTs8uodhtdorLLcQlhNI2xkBeRh4PTFvN9h0ZmG0CCeMXkJFfTm5hBQNsPwKuGAQbZo0jYfwCShxRdAwqQCWTkl9m9pwTTk8hbQncbeg2bb9nvD5g40xGdA1mT0oB8aE6nCNnsj+tiPzsQqRSCXKFnMzjufyw5l+mvbqaXccK2PjtRoLjOlBeXs7X0+6n4JPJ5OWV03fyQh5buatG6OqHewukfDABX72W+A6R9Ooby1W9I7HaHMR3bcvyp68ip8jE7qgkDiY8Wmf7ffW+mE0W0lLyMBpdwbb8NEpP/pLIsfPZnlGO/JZZqKvMGtHcOZusIhNZJ8vp1yEEh81B38kLsdid/LQ1g2iDkqu6hmGxOxkYr2fpPxm8cmsXNjwRC5zuiN05Tdz3ceGKtZ5z7Ekp8BrecJOcvITRsz9Bo1FQLsSwJUvfiG+sfpoqBs6fP4XoEyFydojDGc2LKCLOMDOjLj66Re8xSVcNErQj9H50Bi2jE3sy6MoO7NicQvHyp+hf9Bmp2WUU5FdwsriS8NgwlHp/ilOPAVBpdbBt/R5eu7ErpqHT4aZXGXpJFMnJS5g7VI1BlkmJI4ogtYq8ZU8CrrdYoMn+EWeiIUMZ177wIQt3StBo1cyfOIr52wS6TnyfpU/dw62XxpC6aCIL7+iJ0WjhkoFt6RXnT4cOwdjy0tn+9u2gUJF2vBB9bDwSu5Xy8nJ2hN4PN72KTCbh+UEydDoVOp2Kp38113AEjN4xB4VCivWrZ/l16wm6xvgTunkWs349ysvXd2Lz3JuQK+ToRs2p0XbdqDkEBGrwD9QSEhGATCYlLFTH8exSBsS5ZlxkLZ3ED38eo7DIxP8O5Hn2zcuvoLDIRL+OIeyeP55ZSZewfd7DZCx5nIAAH2wOgfSiSvpG+6JWSCn4ZDLzJ47ytLu2+3y0OBiJBN74+Dtsw19B9/srdAj2qdGx3/hxHkE+SpxOgfwyCwMiS2v97poqCBqz3/me0SGmARc5W0RLRPPynxYRDocTm83RJBEB8PkdATXKbMYSOrQNYNfxQnRqBX3yPvZsKyw0YbfZ2f63yzegbcdIEvr3JL2o0mVdsJnZklXMsD4R8O3zbJ/3sNexDbJMrw4jSpsLtGwMgL9TilizM5tenUMwDZ3Ohlnj2HHU5Zz5y4wHWbD8B2QyCb06hRLsp2bjvlw2/rwTgEfuHsHjj15HWVEZFYVF6FRSysvLiW0XjkYtZ95dPen9yhGcToGCggp2Hcn35BABVwdmGfYyJpONvIFT0WgUbDt0EtvwV8jLK2fKN3sZ+dZftDv4NnKZ90+968T3qbQ6aBtjQKNRugI+GS08cFk0MaE6jhe5rBJB983DarbSuU2gV0RMpVKGUilDq5IxZNoSlj19r2fbtd1C+WXGg2QseZzlU+4jrcjC5AUr+fDLH9mSpeffvIBaO992/vl8tCOTtzalMvOGLgB8t/ek13RegCeHtCXprY3469VM+OrfWtO8N2WmTmOGNhrjoFk1QubZIs7OEDlbpFKpODujGZEIwpm1fVlZGXq9nvT0nfj56c5Hu84LJpOVI0dO0q5dMFqtqsnHqf5wjBw7n6ylk2rUy+jzDBazle7dwsnJK+fghi0EdepCaJgfRUWVBAT4EBPux3VdglgxdUyDHtBFtqh6k2s1hcaEQ24zYRs9MpfQd/JCDmSVciKzlJS9qZ402e6ZFkVXTCNg40x2RyUxblRfxl0Sw6gPt/LF2H7c9+l2nhkSh1/JMbabA/jteCVf3hXIbcsLKCwycctlMezNNHIkvZiTuWU8cUd31kwfS3qvp+nZJZTi5U+R2n0yUdF6TCYbGo2CAD81xhXPUHrVC9xyWQz55XbW/JVK9I45DJm2BJtDYE9GGWlZpUilrg7J6RRwOAQCAnywWOzofFUcTSmkU4cgCooqMejVWL96liHTlpBWaCavpJLSCitKhYyecf5sn/cwoWPe5JI4PX8cKsC0cgoff/0TS//J4I5u4URpc2vtSPs/vYhnL5VyuDCYDoH5NeqMmL6UNdPHeu7ljtD7+fqlYTyw4H/EtQnk/itiuaW91fPdVeViCTLlxv0/2759CBqNsqWbI3IBkpVVQlmZmU6daorvi5GyMiOxsb0pLS3Fz8+v2Y//n7ZEnJ7eWUtmxkZQ/UFdm4AASPy/NgQE+mIy2zl2KIvADp2xWWzofn+FdgmB5OWWcXPPUBb+eoy+kxeeHiY5NV2ytg6oOQRE9dklZ+p48synpxYeX9CP5OQlPD9Ixh9rd7D2iSuY8shVjH/rc6/jhARr0Y2aw9g7+rB17kPIZRJ+GR9J70lfo1j7Ii/M/RqA7cteY8IVcSQmJvHV6CD062ewbmYSJ/KMfDq2H3KFnPe+O0RGn2eIiNBxIqeMvbEPERCoITvbyC2XxaBY+yIqpQzb8FeQSiXsSCtl9/zxRO+Yg234K6ybmcS6PbkUL38KhcI1XBIYoAEgNtKPCf8Xj0olp8JkZVDfKFLTS7DZHMQEucJZr5uZRMoHE+gRY8BXo0Dno+BYXjkdH3uPuGAtuUYbEwbFA7BsRwaTB0g8VqNFK9e6hi46uWbY7Ax/gO83pXH3l0U8++BNXt9Fx8feAyClwEzomDdJTEzCOXImDoeDQB8V+17rimLti3yzK5dNmbWL+6pWgOa0CLQUoiVC5GwRhzOal/+8iJDJpMjlZ38bqnaYpVe94LVNfsss/Ee/wfZ5D7umDX77PL1zPqI4v5j3H7sMAOtXz5Kwfz6H8ytRq+VeQxnVI1qeKxpqgQhVu2YIXL/Y9bfIFkWRLYoBxlUcyCljUj8JQ+NM5JkjKBdiSE5egn31VIL81Lz73o9EJ71NcbmVxMQk0t8fSHLyEr5/bQgAGyz9GfPGesDlMCq/ZRYAWo2SD7dnkpeaQVlRGVlbN5OeVsT+jdtRqpRIpRKuvSyOPw8WMGH+Fxw65orroFTKsNgd5A2cSt7AqfRtF4xx8It0inHl7NBoFIwdFINKLuWWy2IwVtpYtjUTp1NAq1FyPLuUshIT+vUzKDFZ6Tt5IQBb1CP5/NejaFRyNCo5FrsDX5WMzKJKDmSV8uB7/3M5lGYbSUxM4o90lwDRKOWumBnpJziY8CjWglwyj+eydfNxr3tsG/4KTwyKo+/khWzYkYlSIaPjY+/x3f2hvDD+Ck+ytOTkJXx+RwAvfrOf1NLQBgcdu1CFhBgnQuRsEUVE8/KfFxFN9YeoDfeDW79+BgBXTl2Mc+RM4kJ8KV7+lKee/JZZhI55k34Fn9aIOxDsK/dKmlX1uK0hQJCbLVl6fhznskiMvX0YY28fVuNYoeps/s0qZuFOCQcTHiV10UQGmL8nNsA1lbHq9dhsDpyCgFGmp1v6Isa9uZwO4z7nREYp495cTqjBh93zx6MLCsQvwI8u115FaJgf7Qf2psORBZhMNnxVrmGFD/5KJ2jTa/RqG0RFhY0If5eVwWBQczzPSEK0gZQPJjBm7qfw7fN8tjWLKzsE8m9GGSN6hqFSyogO9vVEpuzRI4L98eM5kWPkcLYrOmVC365c2juSPjF6gnQqTCun0DvSl03/ZHB1xyCGXtEWs9nO45e5LBIf/JVO5yl76DDuc7YormfYzZchlUpRBISi1qipyD/pEZ8dH3sPk8nG8z8dIkynYPrtXckpMnHoXdcsk4d71xyB/GNiDPH6PK+yMwmKC1FIiJYIkbNFJpMgCLXHeBFpPP95EdHY6Z0Nwf3Q3jBrHMePFZBXUukJJ53afTL21VPJW/akJ39CVe7t6gqHXNsDvi5TdF2dQX3RKWtrb2NESlUHT/d+i1auZfZj3llJNXI517YNoVPKe54OrVe4L8pq1p/8ch9kciWzn7ia0DFv8sBTn+AsL+HEweM8MG01yd/txDLsZfwCdLRtE0BosJYhvSMpLalkb+xDhG6exbajBfy89QSBOhWlV73Ar1tOIJNJ2J9WRJf2rlgTFruTvGVPorztdT766wSWYS9TYbLy/c4cZBIJS39JwUfpsiyA60FjPzUdbM/Wwxh8VehGzXHNGPFRcOSkid3zxzPuzeWs+CebvPRs5n3xL3tSCsjLNfLd4ZN0nfg+D1wWjdlkxmkxg0LFzz/uxmwyYystZOjVHdBHRHDiuMu6cEUbPfr1M/i/dgGM7eHkeFElOh/XkNvZTsFsihBtTWJDtESInC1i/ozm5T8rIlyJt2zNaokA7/TLyclL2DuzC7f3CeeZB24iOXkJ5aXlnrpb5z7EGx9/55UCuuqQQkOd5OoqD1Rm1RqWuzqNcaSsa//3P1/DyTJLjfb0DismXp/nVXZ1bAWlJpvXMXxlZZgcEu7uYidv2ZNcffsQMJejDfAHUynO0gIyThTjdAr4KGWUGi1kFlWiN/jQsWskg6Ysxmi0ELTpNTZtTWdo3ygS2vijUMg4ebKCEzlGzGY7uXnlFF0xDWO5BaVChkolp32UAaPRwqat6VzWM4KcIhMFn0wGQPXTS5gtdnwNvnTv34FdB11v+2aznUPvPorRbKPjY+8x/+cU9uzPI6ZDLMWZmeTlljHokmjMNidXtTEw46v9WMwWBhhXIVH7IhgLMZvMoNKSkV/OpQPi8fF1WWja+Pny4Zc/cncXl8/OoOgAjyWrOaxRjTlG1ZkwrUFMiJYIkbPltCO1KCKag/+siHBnjCwuNpGVVUJBQTlGoxmr1UEDJqzUi1tIuB+6S5+6hw+//JHExCS6pS/yeog/df+NPPvgTV77V3d0PJskSamLJvLCetuZK54lVruTkyZXEKzaphxWp2NQgde6zeYgRCdh6u9WPvzyR0wrpzCAP+iWvgikMm65dygF+/eg1Sqw2J2kpxVxNKOEgI0zKSqqJC5AxcgB0ehGzWFgvxj2ZpRQbrKRnVWCUukKaZ6bVeJJM15SYqbM6BISf2xOx2ZzENcmkEqrA/vqqYArL4dl2MsUFZmIjtYTGqghItyP9KwyysosGAe/SFyQlhMF5aQezUNv8HGF4dboiI7xJ/m7nfzw5zGe/GgHAQE+OC1mtgbcg+CwwykhAS5B8uqwjghOgeTkJQT7qTzhyR9bU8GsP44CMOXdVZ77VeKIatT3U10oNjXFvYjIhc5pS4Q4nNEc/GdFhMVix+EQkMmklJaaycws4dixAg4cyGHv3mwOHcojNbWQ7OxSCgsrKC+3YLM1XGBUtyY8eMf1nvIziYKqpufaxrQbOoff/XffO49wtDj4jG09GybdN5KrYyt4//M1npkIjcFud6JQyJg1WElZpZ1FK9eydNVP7I19CGlAOLsPn+SNmfeQ8vc//PX7flRqJZpfp7M14B4iI/xYvjmTDQfz8dMouaZjIKpT6cQFp0BoqC8GvZrAEB1arRKtVkF0lB6Hw0luXjmDLokmLFRHaalr2qYbk8mKwyEQE6XHbLZz+FgRBw7kYjbb6dstjJISM7/vzGLH9hOoNCoSL4th5KA4/m9IF7KzypDJZKjUCoKCteTnm8BmRrBZXMnVHDbefsU19BMU4MMNb25EIpVw+bxUpFX+K4N0KvLyKwD4OaWQp381k1oayv23nvZBaQj1/c7q2l5beUuLDdESIXK2iMMZzUuj4kQcPryNkBD/89Guc05hYQUZGcV07x6JVCrB6RSwWu1YLDU/NpvDs59UKkGlkqNSyVEq5Z5llUqOXC6t8XBLTExi3JvLOZBnqpFcqbZhi6pl9W1vDImJSSSMX8DcoepG79sYyoUYfCUnmrTv/v05BARoCA/3DuFcW6eVN3AqmcdzCYkKJiRE60qgtvZFdKPm0CZUx+/bMwja9Bq6UXPYdyifiAgdQ7qHseFgPl1j/Nn4bzZOpxOHQ0CtlhMXqSfrZDlGo4WObQO9nGDB5QhbYbKi83XFEsnILMXhELisVwRZRSbMFjtDu4ey8s9UuiS4fC/2pxRgtTp5+97eLN+VTfJ3O/HR+nDdkE5s+TebrCNpKH19EZwCSrWSuIRQ0lLyiEsIxd/gw1ejg7h8XioqlZxnRnTAaLXz6f8ySE8rJiraJWrm39GTrsGFTbrftd3jxjpgtkQMiuJiE+npRXTrFuHpDEREGoPd7mDfvhzi4gIxGHzOvMMFzrmOE9Eoh4CMjCKCggyeMaULGYvFjkIh81yLVCpBrVagVteMGeF0Ch5BUVVoVFSYaggMVyTD08Lik08WMGnSBEpLXY6IVX0moG5/hOplVYdImhqJsKH7NvYc7vpNFRCCIGC3O5DLvTOp1tV5Tb+9K/N+UpGVUYzZrKJn+2D+1+9ZAksqsdidBG16DYCUtGLuHtqOX3dlc7ygklnDOzNnwzEUCilqtStstEIhI7ewAqPRwmU9I0j5YEKN8xWXVGKzOT0RK4NHzkSvVVJQZkYll2K2gNHipLLSTmZ+OVHBvkSE+7F7VwaPLP2HsqIyAkIDKDhyhA1bfIiNC+D+Ydfw6vsbmf/0YGYs/xenU0CukJOdUUxJiZnExKncMnUx76/cxcT3Cnnuvj74ahSYjCYO7C6jR+YSXvjd1b5z3Zm3poBVoiVC5GwRLRHNS6OkvMViJzu7Zpz+CxGLxdbgmRlSqQQfHwUGgw8hITqio/1JSAimS5dwunePpEOHUOLjAwkL88PXV4UgCJ4hkuPHC5g4cSLPP/88EydO5JtvNvLOO3PIyyujuNjE55+/h91e/zBJ9SGQhtDU+mcjUpqKwyEgCHj8FWo77rUvfOhZHhpnol20Af9ALRUVNsb1jcZUYUbnqyLYT+113PX7crHZHDx9RRte+PkQFruD7IxiAvzUnDxZQf6poYLgYG2tAgJA9/sryGQSbMNfAVzpxI0rngHAWGmjc4w/J8ssBAZqaB9lIMzgQ1p6CYnXdWbciA6oNWoKUtPRRkSj8/MhN7ec91btoffAdixed5yCE9lkphWg0WmIiPanKK+YLbrb2Z9txOl0EhymZ+GPR8nMNjLosrb0yHTdlynvrjprh9im/E5ackijttkZLT3EInJhIZFIkEoloohoJhpliQgP11NQUI6fnwo/vwvbDGSx2PH1bXqoazdugeHjU9OCIQgCVqvDY71YufJvhg+/BpPJSnGx3WuessuKIUelclkyXB8ZTz89gw8+mNnkzqKqKKjvGOcjFkVd2E9Np6xuiShxuBKQnbRE8NCdwz3l7k5DP/wVjhzIYeraAyiUCu7pH8nUj3cQO/wVfNQKtEUmQvw1nMTEqn05nMgoRatVEBymJz2rjPLSCkrz8ulzRddaU4YfTHiUTinvYRn2MvoqeTMmvbOC+RNHYVzxDAWDniNFJiUruwyFQkakQc3ve3KIiHBFkPw3wxVXApsFqVSK1eog8f/a8O6HG3C2DaIgvwKJQsWN13Xmu58PUJBdgM3sclBd89nP6Nu0d+X1KDOhUis4WWzC/Uub/djtzfp9NSZk9tnO6GkqtVkiWpOlROTCwJXJUxQRzUGjfCLS0nZSUGCmstJGhw6hKBSyM+3aKhEEgT17soiIMBAc7Nti7bDbnVit7iESh2fZLTyqfjNyudQzVFL9r0Ih8xofPlsfivON0Wjm2LECOnUK85pyO+LDHBRrX/Squ2jlWo+g2BF6P2qNGr8AHTKZlBPbd6AMjcY/2B+Hw0HHjqHs3Z3J188M5pNdWRzJKiU3t5xLuoXx87qDSKVSuqR+wOQFK5k34U5e+OBrZoy/lR6TPmB3WhF8+7zXuQdNWcym2eMoGPQcer2aEb0j+DejDJ2PgrST5SgVUnSnhsP2HM4nMsKPTWs3E9O9MycOHgdjAah9QaUFm5nwDgmYK8wYAn0xllUik8nIz87HWV7iOqHDNcUzvm8PUnftR+kfRO+cjwBXfpY7e4YxKMrYqHvdmLf21igi8vPLyc4uoUePxs1OERGpysGDufj5qYmMNLR0U845rconQiKBmJgADh/OIyOjmPj4wAtybNI1jZNmjxHRUNwPYLlcilyurDWRkCAI2GzOGsLCanVgMlmxWh1e9U0mE4GBepRKGddeey0lJSU8/PDdmExWlEqXyGit35XN5nojqBp+PDExidJBzxGEt09H38e/pg+Q3utp+uyaC8D4iZ/zwRN3M3LyQjIKTZgsdvbszyM3z0hEdABxQVqubufPT3+m0K5DCH/vzCKmTQhFhSYsw15m3oQ7AZgx/lYAds8f72nH7qgkemQuwTj4RUZ2CGETsGr8pTz1w35+2p2Lr0ZBXkklCeF+WOxOMvLLSQj3IzhYy+gBkezdHY1MJsFHr6eyotglDIwFIFOQs/tfkCmQytpRVlSGrSgPFCpXHYcNZAr6DbuU/bszGHXfEHYcPImt9ysM6hxKhcVBnslCiSMKg6x5E7Cdieqzf6r7+ZxLBEFotb9jkQsHMfR189GkLJ6lpZWkphYSGdmyb/JNpayskuPHC2u8+bYE7odvYx/CLpHh8IiLZctWc9ttN9RpyZBIJJ701VZ0/JlWyu09fVEqZSgUslqtGefrTfPkSSO5uWV07+4dYbO+wFsAKV0msXdmFz7fL6dfhD/zNh7HaLZRUFTJns0HwGZmgPl7Jsz/ggWT7iJ0zJtY7U5yCyvYtzONrr3jXMNRaoUnNsT4tz7nq39zPcGmAI52epxB/WPJW/ak1/ktw17GbLaj06lcTo9mOwnhfgT6Kukd4cucNYcJDdZSbrIh/X4aWxTXg0zuEgkKFTgdYK0EmQKf0EgsZlewruCIYPL27QGZwiUmrGbiLx9E+tEs+hV8CkDBoOc8DqSfrP6ZXVnFzJ84CmhcSu/6qO37r23IozFpwc+Wun4rIiKN4dixfKRSCfHxQS3dlHPOubZENDkVeGZmMYWFFbRvH1qrP0BrJj/fSHZ2Kd27R7b4W01Dhh6a0qELgoDD4TwlKFyiwi06bDaX0HAH3HIjk0lRKGRewqLqcnWh0Vw0NDVvYmISg6YsZtvxIqxfPcuHX/5IRnEld735Jx06h7Hl5630uqoPFRU2Ug9n0b1vPEVFlXz7+CDuWrKVoqJKbh+SQGG5lUiDiuW/pnAyMx+lWkn/gW0oNVpqDJ+4Ud72OiWlZg7tPUFcuwjCQk/l1rjpVZxOgYoKK06nwO2DYjHbnWyYNY6ULpOIiDSwZ+thl/XB6QC7DZRql0BQnPLJcTrAYSeofXs0WjUntu9wbbeZQSoDqxlJSByde8XjdArodCqSrohFJpVwIM9Eh2Affj5YSNbSSbzx8Xe0NZxsdmfDM8U3OV+iMy+vjJMny+nWLeLMlUVE6iAtrRC73UlCQt3xcy4WWtVwRlUiIgyUl1tITy+kffvQC2rap9nsSrzV0gICasaKSC0NrTWRUmORSCTI5TLkchkaTe11nE6XNcMlLqqKjNNDJtVNfjKZxEtU1PapLV5GfdjtzjNmUs0odwmMy2MNbJo97lSndj3c9CpKtRK+fZ5ho9/gaFoRWWn5dO8bT5sIPYq1L/LQZsjrMgmDvwaHU2DHoZOsOpRFv0vb0b1TCMXlFmQSCYM6h7J1bZWT3vQqB/dl0ynlPY6mFCKVSuid8xHpYU+7srECfPs85qHTsVjs9OoUSpRexSebM+k68X1S/zxGfIQfGaFBtBnUiQO7M5DKpFRkZ7gsDDazy0fCWklQpy7IZDJO7DvsEhXOKlOHw1xJvGLC/SgutxAZqGXu94dJvCyG9ftyORSk5Zkr29JuxBLgZIPve0Op6nRb1yye82W1EgQxb4bI2SOTSbFY7C3djIuCJosIqVRCbGwgR47kkZ1dSlSUoRmbdW5xZe9sPdaTxMQkJsz/gsTEu/jsm1/O23mrBs6C2mequISG/ZS4cGKz2T1/zWYbRqPZ49NQldOiQlpFXHiXuf00bDbHGZ10o31zT3VUrinGGeVhJCcv4YHVpaQ4HB6nyLWrf6as0saB/DL+Ti9jzKnyhP3z6Tt5IQeySikpMdM75yMqza8wICGQo3nl5C17kjuWfc+OW2aRmlZMWZGRLt8+z5Cx8/nZ9hDdds1lW9C9yPo9y6Ae4WTsgv5PL2Lr3IcoKTETFuZLeaWN7ZnlVJisZBWZcDgc/LruIEq1kh0b97msCqf8IVD6gMOOQh+IUq2kMKeQgNAApBodToUaH50vlZnHwOlApVbhcDhcScG+fZ4soLT7ZPZklNGrbRCJnULILq/EYg9sluBTDaGlHHZFnwiR5sA1xVMMe90cnJVDgI+PgogIA1lZJeh0KvT6C2Pap8ViJyCgjtfzFqKg0grQ5IBNTaXqG2VtTnouoaGoV3S5gkU5vSwZdrvDY+UoL7dis9W0akgkrmmdDocr0FRGRjFyubSK4JB6hEdVS1dy8hKKbHLKhRhgL3df34n73t3E3uQlnLQ4iNLmEqWFGN8wpiYlAi7/hZ+3nkCnU/HGfb1YsAsUa1/ksmGrWLEuhb5j3uTJb/fSv10Q9tVTMQ2dzkHZo7B0Et1OnbdfwadQABnbYML8L/jgr3QKBj1HxKbXyBs4lX7tgjicXYZUKqF7tB9H0zSUFpRSWXQqT0hFMVjNIHdApQ3kCuw2OzaTa5qn3WZ3zcwoL6ay9NT9lrrE1e039SIlp4yMPs/QuX0Qjj3ZVFjsZBWZPA6h55qq/jsthWiJEGkOZDKpmICrmWiyT4QbQRBITS2kosJKx46tf9qnw+Fk795sYmL8CQjQtnRzPNy7qphKq4OvRtfu6FPmjMZPmtHs5z1TyO3mxD184hIYTs9yXp4RlUqGVCr1iJHqSKUS5HIpTqkarcolOlavXkvwpTeyL3kxTz43A4lMTmaplfgQP/SyTKb+buXQu4+ycMVaJn6zl2PHiwgM1KD5dToT5n/BN3tP8uxVbRk5byOxu+biHDnTK15E3kCXs2Xo5llYhr1Mbm45sbvmohs1h8xcIyUlZgpzCxl2bVf2pxTQo0MI4QY1GYUmOob7kme0ceBEMft3Z1CRftTlD3EKfcfuREQHkJ1RRGl29ukLNZW66skVIJWhCI5CIpXgF+CHxWTBP1hPxLbXARgz91MySy2sm9kynfr5sEZU/y021H9GRKQ+/ktThVutT4QbiURCTIw/hw7lceJEEW3aBLVqc6N7HKylZ2VU59Pb/Zm/rW49dy4ERHXcgiJy7Pxzcnzv4RMXTqdAXp6R0FA/j6hzO4XabE6PRcNud3rEh90uYDJZuXrINditxXS54w6yju3zHDPtpGu66N0RanzeeA1z3l7mXSFjpV6DQq0iftC7aJ0Wpl0ZzZNJt7Pj87eRSJbw5M+VPLn8B2auO0resicJCPBBsfZFUrtPhqwyQsNcAjo1o4SKChvBwVpyUtLJW/Ykw59exI7jRRxKKyI+Sk+wVkFWiQWz2Y5UKgWN3iUQdEFQnENpeiql+UVgLODq24cgk0j47cvfXBegVHt8IqLahJF+NIuC1HQGDeuH3eGk42PvcejdRzl4shKd6uLLH1FfgDTREiHSHMhkEgTB9fy5kPz5WiNnbYlw4w4YFBGhJySk9jqtAXcCn65dI87ozPdfwC0c+k5eyPZ5D3vKz9eYt9Vq58CBXNq0CcLPr2EJwjIrwjyZQl2Cw5V7wy003NaMqkKk0irFbreBw+bq1Ku3Q5DiQEqQr5Riq5ISq8CRk5UE+2vZm2HEsf1Lttt7EV+6jsrKSkwmE8e6PkH8nnmAK214TLgOi9WBccUzRCe9ze7D+ajVckpKzGSkZCGU5ILVjDK6HdactNOWCbkCFGqXo2VV9KFoDX44HU4qM48hCYik/xUdiQ/V8b9d2dx+VZsaSd3ON+fbNyIzs5jycpfVU0SkqbjDFHTpEt7qrednS6u3RLjR6dSEhPiSnV2Kr6+q1gBKrQGz2XYqyFPrFBAtFQkwTKfwnHdDhi9Qfs7OlZiYxJVTF5PYMYwgmcsHpHrejPqommpcIpFw223j6r1n5UIM+7JL8Vcr0allPJV0Ox99NA+73cln/1opWL+ccePuxm538vvRSlSSClQ46WyQEqg20aEt0PYO7gCgvee4FrtA6eDncJoKqaw8TqWpErPZTOWQIVQeWIUm6irKKq2YgiSoc80YfcMpz8+lIjfNM9Uzvn9fUrfv5vFJN/L2rOWuA8sVBHXpjuAUMBlNGIIN2G2xdO0dR3SwL79tOk5sXAC9wn3Z1PDbfk6o7ffa3L/hqscTLREizUHVJFwXu4g41zSbJQJcpqGjR0/icDjp0CG0VabqTUsrxGZz0K5dSEs3pVXQEiGyExOTONx+Ah06h9Hh4KfceeedZ/1GcKaO69N9Mu7t6uBwYTDBfipKTbYaU2ndx3HTd/JCAjVyfp2ZhFqtJqdjEpGhvkQe/5qSuBFEheuwlJYTaz2IWq3Gx8cHRUhbNHLQKMBHDtI6ejyTxU6F2U652UGFxUFFpY0Kh5RKpxxjSSkmqQaUapxyBScOHoXACDp0iiCzsJKcfDMKHyVPDm/P4idHN/menUvOVRTLEyeKMJvttG8v/v+KNA73MKnDIWAyWUhPL6Z9+5BW+8LbXLTaYFN1YbHYOXw4D73eh9jYgGZraHNx+HAePj5KYmL8W7oprZLzFUa59ytHiN01lyVL3iIzs4QePc5d4K9/8wLoGVrkVZZjCidck0N6WSixfnmeOpkVYSzalsG+dx5h9OxPWH+kmKylkwBXQq6AYD80GgW6310ZPbcG3EP/os9qnDOlyyTCwvzw08rAasdeacGcm42f3heNuQitWo6vWo5Wq0arUaGVCxgC9PSK8SE9txyNWo5GKUVW33itRIqxrBSLxYLVasVisXiW3Z+q6+5lm83mVcdd5nDUdGhtKudKkKanF2G12sWXgIscQRBO+Sw4cToFnE7XsKX3es3lmn9d+zgcTqr3dHK5lNjYAHS6hg2jXqhcMMMZblQqOVFRBk6cKEanU53zGRDuTJkSictEVZ/1QxAELBY7/v6ta3pna8ItIKZvsHNTxxBefviWZu8QEhOTeGP+F1z94hJyckpRKM5tXo82wVrAW0Q8cvcIkpOXcKCwjEBdDP4+FhITkxj/1ufsSStCP2oOq7bnYCy3oALkt8yi0+qpODvPxGZzsC3oXpxFOQyoRUCk93qahF1zSbhiAb/9L42AQA1l5ZCVUQGHs0/PvnCj9oXyYt6d9yAzduXyx8ptANyWNJJ1vx1g4MB4ArUy0lILEcxmlBInN1/VlqO/rkClUnk+SqUSlUqFn5+fZ1mpVHqWz4TT6fQSGTabrcay3W73LFdft9vt2O12pkwZzxNPvOpZt1qtOBwOHA6Hp2zVqveb9J03V5yIlho2vJARBFeHLQhVl73/Vl8+0zbvj9NrvSFIpa603jKZ1OuvUuma7SWTSar9dS0DHDtWIObPaAaa3RLhJj29iNLSSjp0CD0nMyHsdifZ2SWUlpq9fggajQKDQUNwsG+Nh43ZbOPQoTzi4wMvmJgWFzsZGcWYTFY6dDi/jnKZFWEo5VLUChmVVgcKmRT5qYfL1a/9wbDL4jh+0ohp5RTPPlsD7kGukKPWqIlvF8qeXzciCYikZ/8EVFVShVfFNvwV4sL8+PGX/VQay8FS4criaSxwxYxwExjF289ey/Eii8s3Qq7gtqQbcToFNv2TgUqtICJCx9aNh4jrEMUnSf155oGbGnXNSqUShULhERZ1rbvL3Mvudblc7lVWfb0uiouL8fevafmrLiyqrjscDtq3j0cikSCRwPbte+jXrycmkxVBEPDzU3u2Vf0L1CiH6turlrmW3eXV16tSm3hpqJ6p7Slb/dHrWhU8detfd3fmp4/jfnuvbb3m3+piwLvc3eFXrdMUpFIJEonE09m7PzXLpDXquMtdAsC7nkwmOfX9Nk1QCoLA7t1ZREf7ExjYeqb6nwsuOEuEm6goAxUVVtLSCmnXLqRZp9EIgsv3wmZzEBzsi1arBCTYbHaMRgvZ2aWUlZmJjw/0WCYEQSA93fU2eqHl+mhptmTpGRBZek6O3ZBolVVprjfIKG0uT/9qZkCcH7e0dwX6+jcvgJcfvoWTofeTXxbGwcMF3D1lMfuzyihe/hQhUSHE75nHFq4mW6NCn9CJ0swMdv19iAG1nCNv4FQMToE//06lTYdw9m87BEB4myhydp3yx1CqXWLCUkGRyU6FxcEA+V8kjF+AwymQUWSiXbsgdD4KLHYHcoWcLu2DeGzlvzTWnuYeuqioqGj6jasHmUzmERfuT5s2bRg2bBhfffUVNpsNuVyOTCbzquNel8lkXsv5+fmeMqnUFRxIpZJz4MBRpFIp7drFIwiQkZFNZGR4tc6zaud7evlioz7xVJuIOv1X4rHeyuU1y90dvbu8asdfdXv9f2sXXq0BdxtFS8TZc85EhEwmJS4ugKNHT5KTU9qseduLi01YLPZanWICA30xGs0cP15AQUE5oaEu5ZWfX05lpY2EhGCUytYVI6I1UF/nfK4EBIDd7kCtbrioay4T9KGCIOYOLQCsnLREEKLKRquU8cnqnxkwdS0dw30prgjE4COjuNzCFk0ij13Tju/VzxJqdvkPdEp5D4DSgS+wZfNIBpi/9zpHSWE5CoWMyGh/zGa7Kx14gZmcXTuQBkXhzE0Fp4Pu1w8m/Xg+wxJCOFxkpHjsfE4UVJCWXoJWqyAuUk9MkJaPv/yHW2/qRdtgH8L0Puz7tVluRbPhtiRUJTjYleDo+PHjmM3m2nZrMF9++eUZ69T3+zgtMmpbr/nGX7XuqS11WBTqb1Nt/WhVi0f1OrVZTGqWt94O+kJBKpU02cIicppzOn1Co1ESHq4nP7+csrLKZjtuSUklWq2yTq9anU5NQICWvDwjFRVWCgsryMkpJTjYF1/fM48N/xep7+Fb4jh3Ud1stpaZYtUxqIAyZzQAISpXxMhgPxU/HMzhsoFx7M82MrJHKJ9vSCMyUEun/l3ZPu9hLGYbXTqHoNGquXXmxxxMeJSDW12BrvbHj/c6h86gRa2WI5NJcDqdaHQaV+pvwJmbiiQkDmV4HAqFlH6XxBLrl4fV4WR0rwhCDT5UGCtRKGQE6lScKHBZD/63K5t/M8q4oeOFkcJYLncJdrv9/CQ7qi8kt/tNu+o4uutNXFolr4src61SKUeplHuCo7k+CtTqmh8fn/o/te3jCiV/+tju8ymVcq9cM3K51NPOqkMBooA4e2QyqWiJaAbO+RzM4GBfdDo1J04U1xrOuCkolTKsVkeNMcWqhIfrUankHD16koyMYgwGDeHh+mY5/38NY6WdzIrmCTP8R7qW1UeUdJu2/1TODVcI65agehTQ7w/m8PuhQnyUchwOgffWHkEqlXAorQj9+hkA6PxU/LM1jSv6RPL1tPtRa9Qo9f5INHpi2ninFY7Y9jpms2uIrfCkkU6dwwjq1MXlWOnrT1hsGMERQdw7KMZrv+dW72VYp/9v77zDoyi7Nn7Plmz6JpveQyAh9A4KKCiKAoLEhkgTEURBwMrHa6+IBRELIPoKCCJYooD4giiiqAjSQVogpPdet873x7LLZrNlts9uzu+6ciWzU54zM5t57jnPec6JwPhRGbihTxxuzpDhwuVqKJpbkP/PYZw4U4HOEcGuvTgc4OIVcqeIMGWPYdVRgjBEKKQiXM7A5X59XVrsc+fKkJdXjc6dHU+LHRYWiMrKJjQ1Kcx6FkQiAdLTo9DQIIdQKCAPhJ0UNsXiUk0jRiQ5J/nUjSlNGLL0IiaOysDv+YEIZYEmNhKaVhli/IutH8CFDEuMQPZzD6DH4o+xf9lCZE59GwcPF8A/UIJB8z/EvsOFqC1vwOIZA7H7dAUSZq1A5blydDrxX/Rc8BFOrXwEAFB9/bOIjgpCU7MC1fk1uGZgEs4CUCjVEPuJ9aW9w8L8IRAwuCdTid6R6ShpFuPTJ29D6tS3MSQxAn/nN6BJrsaqx6agz6wVUKtZhIVlok/nSDQrnDcd0164dMwikcilAsJYOJhbppkYhDG6OBvCMdySDUosFiIlRYbGRjnKyxscPp5utodKZfkLIBAIIJUGkICwk7OVkahtVqJXbBiqFAlOe5v78ckRCAsQYcs/2myV77yyBGIeJCbrHFaO+9/agP3L5mDqsvWo2fgkZk7ogYiIQBRVNeHyuUKkZ8Zgx4uzoPjq/xAZIkFyohSVw/+Dv06XIub+5cic/yFksgCovlkCyY8vQK1UY99vOeiUFIajf19EVFQQRGIRJP4SlBbVorlZiV7PnsbPeZXYeaEMc5ZvRExYAD4+lI8XR4iQKpMg8N5luFRch9zjZyGRiJASLkFJg/OGB13Jgw9Odkn+iezstU4TBuSl6Jj4wnCGrqhhS4sCDQ2tqKlpRnl5A0pK6pCfX4NLlyqRk1PpUhvcFmEYEuKPmJgQlJTUIyhI4lDHrlDoimhRulJXoosbCBUUoFadaPKhrQtKtIVZ94zBwcjpmBTyO9B5Ot5++xlIxK5PcMWFzPBQZM7/EMeKmhBz/3JEBYsQJvVHWLAEsSmxbap8Hl/xMORjXsKj4zKwecn9KBz3MgorGiH+4XkA2tkZXWOC8fCIVLzz4wXcNbEfjp2vQEJqzBWvgj+amxV46t7emN5TDUADoAUfPz4PBQOeRlVjEoanSREedOV/RaNGXV0rxEIGSx+5x/0Xxw4+//xbDBkyxO79TaXUdjbkpeiYCIUMlEr+DGfo6gBppzlr9D9qtaZNXaCrP2qTgaEMw+hLO4jFQvj7u7afdOs0hdjYUDQ2ypGXV42uXWPsrl+huOLKpVkWrkUrELRxA7okVGWt8QCgH3qwVUAAwJovf8B9a/9Gk1ibdZBPueujQiVYOkobsJuV9QiyAQxf/DH2L5uDqNEvoueCjzA+MxKZMaGYceetCAr0w9GiJvRc8BG++d8ZdD3/vv5YgYFiDEmPxKrHpiAQwB+D/w/xB99Ade8noGhVIGtEJ+w9XnJFQGjJrYvB+xu3Y9/lSvyeU4NvjpRi6uAEbDtVgcjOaYiLCUaIF4lne4czdGmzzc0acmcJe8I3cdVwhi6vhk4A6H4MBYHxb93fpu1k9MG/IpHgSrD21eWrP1cDcQ2pr3fc+28Jt/bCDMMgJUWGc+fKUVBQjdTUCLviI5RKtT5SmXAd5gTC3Mnj7H5YVysTcbm6AWMGJkLz+zq0tl6HO++0XEDLncjEhcjKmo3nVn2NqcvW48DlekztmwBcERJ9R61HSVMrSi61otNDK5G7ZgHOArjp2bVIz4xByIA30bD5aZxMeQj9wgLw91sPIWTym6isbUFyoB+m3r0BfaPDMGvDP2hVavDxlP6Y+30OVt+uTeLWSVqGWnUi4oIlCA+WIKegFmqWRUiAGJWnT+C0UIj+Kd6Tsl0kEtk1nKETCYYCgcv2xvsRhDksBVbqUmfram1o02jrOnu2jTi4KhKufm6+TYG+oxeJBPDzEyEwsO1nhgJBNyuHz7gsY6UldGVYExLCEBVle5R5SUkdqqub0KNHvMO2EO7jYm00/iiswhufH8WN13XGEGkz4uRF+K41HU9dn4aU0PYFsTxBrToRM+8a0+azNV/+ALWahVDI4Keccvx6oRqvjcnEwIVfY+yYXjidU4lx16Yge18u4g++AQBImLUCibIAFFa3IDzID48N74RZ94xBn0Wr8OT1aThVXGcyB0cjm4yKejnOVtfjuU0n0DU9AoESEXZ88TMGjxnaZkiF79x6661ITU3F6tWrnXZMLsLCVQXACN+hvLxBX3XasOaGqTobhgiFV6cHX/1h2ogAU3/rpue6G6/NWGkJqTQAkZHBKC6utZjvwRz+/mIolRooFGr4+XmPa7ejc76mARnhwbjz1m5okqvQOVyM4soIVNS3Yk9uBWb18bSFV/lo0w7EBZagWpmI3/MqIFdqkBSsLUE+LjMRG37Lw0P3jsPY+5cjKSIQ/5xUYWJGNP5+6yEAQG7vJ3BLfDB2vfIgTqY8hPcXXge5Uo3FH2y9IhzykR6VAKC9iAhm8hEsBbac1mDBxEwAwO85NQDgVQICcM3sDOOhDHNigoQEYQld3yEUMpBI/AxyhxiLBF1NDoE+3TZxFY8FFcTHS9HUJMfly9Xo2jXaprLhujF07XgWiQhvYF9BMAA1ls2/B30WrYJCpUFDixxxkSGIafbDgdxazOrjfJVsD2HCQoQFaj0CfiLg9i5yvHOgBE9cw6CRTcayX3Ow6+EEZO0GCisaceZSNTqnyZAZqY2Czpz/IfDBPBzu9w5Wfr4dJQ0tGBxXB6AFcVdyVX9z3g93ZhRZtOPvtx7C8nXbcKGmAfXNSjAhEcZ1xHhNREQEUlJSIBaLMWrUqDY1Moz/NvzN5UdbrEljVkDQjAvCGrpMuZGRwT5fydOVeGQ4Q4eubHhoqD9SUmScFV5lZSMKC2vRq1e8TeKD8CznqqKw53IFfj1TCYGAwTN9NNiXJ0eXbunYeLAImybxr3R8rToR874+geYvF+Pzb3ehSa7Cs/87CyHD6OMYFuxsxsqxWnWw4iCLRYMZfHgY2POq/ePz+wqCsT+vDko1i8oGOQQCBgVrFzrvxNzAiBEjMHLkSJSVlbWpqWFYH8NRdIJCIvFDY2OTXlxoyz9fFRvaYDft3xkZaW1SR5tKI22uEJdxumpjGAaIiAhCUJD52WfkHeEHLMvixIkixMVJER3tvH6Nb/jkcIYOiUSEpKRw5OVVIySkCREcs/DV1bUgOFhCAsLL6BpRgYqWEOx5dTYGPrEackUpoiND8NTaQ0hICuflwzVMWIjXx3RDyqS1APJRIY/BH39dxvVDO+mnvxasXQiM1dq9aLC2g5k3AJhndC667bkgEQiRGi7BqdJmfLN5H8bcMcyp5+UOxGIxqqqqzMZDMAxjtviWpR+tW7nt37rf2rTWVz8zXNb9XVFRgRtvHAZLFTO1Qfsag3W67Sy/czU3KyEQCCyKCL59xzsqDMMgIECMlhalp03xajw+RzI8PBCNjXIUFtYiMFDCqcJmU5MCMTG+qxx9lZLmOPxTXIzl67YhJaQUJ06wUEGE9DPvQZP+KkImv+lpE02yaMYE/YO/k7QMXc+/j7LzwBPsO6hc/wTnsXdLAqKkOQ5B/iIcyK/C6NRmXJNQh//lqFBW24JNr9+NdX/zI4+GLYjFYovxENq05yq31dXQ4cpO/OxZfgQHE9wICPBDU5PC02Z4Nbx4lU9ICINEIsbly1WcM4jxfdoL0Z6L1Y0QCxm8/9dlzPmqAgDwT349Fq3cjLkjU9Gw+WnejWVnZc3GR5t2ICtrNk5VRGDEu3kAtB3R2omhTkurHBdYgrK6Vvxb3oRqZSIe/18LqpsUuFxUh/cX3YeGzU87fC7uRiQSQamktzyCv/j7iyGXK6mapwPwQkQIBAxSU2VQKtUoLKy1ur1QKHBaMS/CPewrCMbwxAb8eLQEoQFi9ErQDl35BfhjT04NPvz5EqqvfxZDnlrjYUvb8unWH/HIlNuQ9cp/UdzUgskjUhEy+U2ni52srNnIrWtCVJAY7+7PRe6aBShYuxDSva9g+OKPndqWu+CriOCbUCU8R0CAGCwLtLby73vqLXh8OEOHv78YiYlhyM+vQXCwBBERQWa3DQwUo7mZbrq3UK9JwlfHz+Kxz4ogkwXgr3/LMLqr9v5O6peMV+bdCzGA6h6LUFjdAsD8vXcnVYoERPgVYtHKzVixYDLqbngOSqUaPbpEovCG51CrTkSYsBDfnPfD4HgZFkwbD0DrlShojNVPCbVGQWPsFU9GM57a3Ype8UEY+NYG1LWq8M2hYuxfNseFZ+k6rA1neAqKSSB06IbPW1uVNqcaILTwwhOhQyYLgkwWiMLCWovBLoGBfmhuVlgNciI8TyObjPyqZoQEiFGaV4qGBgWUSjX6xwVAwwIpkaH6WIh+veMhV/HHwxThp52CqatgWni5EiEhElQ1yCHd+wp2XyjD/sIQPLb8V2w4VoTwqW/rOyiuAgIA6lqUqFUn4qYPCnB79ygU1ysRJhHjQkUrbugR7fwTcxNisZi3nghXeSPy8rwvdqUjIxRqs0ZScKX98EpEAEBiYhgkEqHF+Ag/P9GV7GIkIvhOSW0LnptzB5rkKiR1ScCNg5OgVrPY83cuGKEIapZFw+anMWf5RlTWtuDoKX4FptVrkgAAqzf/gMjYMFRVtSD3cg2SZr+HTX8V4pXtZyGNlCLIT5uIZsVB1uYOqmdUFXZfKMO1PWKhUGvgL2JwtKQB/mIB7uwe54rTcgvuGM7QVfO0x7ugExPOFBUpKYkA6LnkTdAMDcfgzXCGDoFAgNTUCJw/X47CwlqkpLTPHdDSooRYLLS7gBfhfloUKsTHh+DQmTLcc30Kbo1SQN7chHf3X0bZtUswplMLxszhXxpz3YyKGP9iJCVKIREJIFdpcPxcBaoqGpCcKkO/3vEoa1BCrWYRFSRG8+gXUa9JgoBhEMzkmz12uTwev+dVIlwixohUKZJDmhAVJMH7i+7DLc99gqggsX6IxBtxx3CGtWyV9h6Phjw6DgEBYlRUNIBlWcpGaQe87IV18RE1Nc2oqmpqt765WcFpKijheWpaFch65b9Qq7V170N+fhkTusaCUdXD308NlVqD/z05wtNmWqVamQiNhsWpsxUoKKzDgG7RGDwgCcf+zkFooB+2fHcMx06XQeovwrXdY/BvSb1FAQFoC5zdmaHAXwX1+OCvPCybfw++O1uGsmuXINBPgJs6e+9QBuDZmAhTAsKcMDD83F6vBuG9BASI9c8nwnZ4KSIA8/ERLMuipUVBQTBewuC4OtyQGoWq+lZIQ7QJeJKCS6FUqrW17sVCzgmYPEmzQoUucaHo1jUSmZ0jkLtmAZIjgzBhQj9EhkgQmxQJmSwAK368gMk940wW1jJFI5sMpZpFr7hAVF//LC6UNSIpUYoGuRpzJ49z8Vm5Fr7NzrCWIptmbXRMdC+kNKRhH7wbzjAkMTEMzc1KXL5chYwMbX0NhUINtZpFYCB5IryBek0SFky7FXH3L0dhRSPE0Ka/VqlKIBIJ8MiQZAAlnjbTKp/8U4hLZQ3onRyGsno5bl26Dh/vuYT46GB889M5VB4/jJD0nohJCEdRYzO6RnA77vmyBgxJDsGPZ6rQ0CDHoFQpjhY06FNmezN8nZ1hCWMhYc0rYVwMjPA+xGIhhEIBWlqUkEoDPG2O18FbTwSgi4/Q5Y+o0XshAG2mMYL/aK7MoPETC9EtORwAkCEr13si4gL5LyAAYELXKPRLDcc/78xFwdqFqG5WYetD1wIAwsK1dTMaLpzCqIFJuDGl/RCcKQqbYlHZKsfGg0X480gRunWNxIHcWgxODcXUZetddi7ugq+zM2zBXPClqWDMrKzZuHy5ADt37nW3mYQDXE1/TZkr7YHXnghAGx+hq68RFNQEhULb+egqeRL8paw1HjH+2ilvuuJR2dlroboyjVMk8p57GCgS4mJ5I7Kz1+L2z8qQEu4PuVKNlXf2wpfHi7BVLETfjCiobch8lxhUipd+qkf/FK24GtszGseKGhHkJ8TQxAjcaWdwIF/g23CGo3D1NDAMQx4KLyMgQIy6uhZPm+GV8F5EANr6Gk1NchQV1SIgQExBlV5CjH8xcutiAAB9Fq1CfqX2DV0XwCQW89oR1gahkMG8a1Ox42IzBNuexZiZa/HpcTmUahaf7cpB/ME3sHyBbZ1FVtZshEx+E7EhYgztIkNtqwo5qx5FDoCNrjkNtyISibxuOMMSXASdLncNCQfvIiDADxUVjVCpNDTrz0a8QkQAQHx8GBob5WhuViImhmq/ewMlzXF4/69L6LNoFe7qHoPy5lYATVAqtfk/vMkTkR5egbz6GPSNacVtVzqI8Zmx8BcLcXNaFDovsb3TePO/3yHrrb24pUcU5g3wrcA+sVgr9H3VE2HtXpEnwrswzFwZHGy+AivRHq+RXAIBg4SEMIjFQotldgn+EBdYgr6JweiXEISeUVX6WAHdcIa3DUmlhLZNhBUtKUaLQo0nZ95u1/GefmAilkzpi3RZkE8JCMA3RYS9ial87d76Iv7+IjAMKC7CDrzGEwEALKt1hUskXmV2h2Z6TzUu1kYAKNd/plSqIRQyPlGJNca/2O43zezstSiXR0PogwluRCLt/6gvDWfYA3khvAOGYeDvT5kr7cGremNdGmyh0GscKASAzmHlbZaVSo1XDWXYSlbWbM6dR7Sk2MXWeAZf9ETYAw1reA8BAdqaTIRteFVvfDUpCN1ob0alUjs0lNHIJjvNRXykNBxZ68utb2gDtnYWXM/Fm7Ip6jwRHVFEmEudTMMa/CYgQAy5XAmNDTOsCC8TERKJCAIBQ2rRy9HmiLD+1fslz3SsQDCT75TOdMqWamSfKUf2DM+ll7alY3Fl9Ulno/NEdLThDGuVhb3l/nVEAgPFYFltcCXBHa8SEQzDIDhYgrq6Vk+bQjiAdhqVZU9EVtZsrPkjH5nzP8TqIwwOlkidasOKgyzOnKvEkOQQlMs9V/jLWzwLtkLDGeZxduVQwjn4+1P6a3vwqpgIAIiICEJubhWamuQ0S8NL0WWrNKZWnQiRQIDfL1cAAB69PhVbT5Rh2z+V2CEoxk29Y5Ff3Yrlt9qfmjZ+1m8IDAmEQCBAcloUPn1ymn7KprsxjJ1wZnplPnROHXk4wxaoaih/EAoFkEhEJCJsxKs8EQAQGuoPPz8hKioaPW0KYQcajQYaDWsyoUuYsBDBTD7SQoORnb0Wkf4SPDI4GQ0Nchz++R90jQpEbZMC+wtD7Go7rz4GfYd0gVKhRENZKfz8hLj/rQ2OnpLdGJarttbx6+IhdJ2Npe350CF11OEMe6EiYPyA0l/bjteJCIZhEBUVjNraFigU9IDyNnSJpow9EQeKpPphha4RWk9EZmQllszOwuxbuqDbtX3w07lqyFVqbDxcrB/e4PrQbWST8eovF1Bb2wpFZSm69O+GjTMH4/Yucmedml0YiwNb9gP42+nQcIbtGAoJvt5XX0crIpRWY1uIq3jdcAagLRNeUlKPysomxMc7d6yccC26lNfGMRHXJNShoDEAMBqhGrHkY3xzqBgDMqPx84F8xMeHoKSsEYNvD8Fze5VWO98hSy+ia2cZ0mJUSI0MQmpkEC5nRKJy/ROQPeH5N3Z7seYGN/RyeAKRSHTF66Sx+xjmzo3vHay52Rlc4YMnqaMSEOAHjYaFQkH5iLjilVdJKBQgIiIIVVVNiI0N9YmkRR0FXbbKRiQiAFczQDayyUgKzgcAXKyNRozUHwqVBvuWjsGKddsQHizGzwfy0dCgQGxMMKZvrYFcpcGGU9GY3lNttr2/l3TG3O8bsOP3XPj5CTFlVBoiQ/yx1kse1OZyTuhEAl87VFdW8OSzF4beYL0b3awxXU4iwjpeKSIAQCoNQEVFI+RyFRXk8iKUSg0YBoj0LwVwVfwFM/n6vzuHlaNWnQiZuPBKZzkBS9dm48enR2LHuVL872Q50mJC8M1P5xAe5IfpPS3HSJStexxJV/6+a8EPV+aBe0cJck97FOxFLBY7HA9hLWmXt14baxifE3km3IfuhZRyRXDH62IidOjG1JVKiovwJlQqNUQioVWXb5hQW0K8sCkW2dlr8UdRNeZOHocdL86CSq2BWMggrXMkYsMCkFcfY/FYhnEHMf7FiAvkl4Aw1REaz8bwto7E0TLgps7X1HXytuviDHxROPEF3XPJkWG4jobXigjdzZbLzbuyCf5hKdFUQWNsm+VqZSISg0oBALP6aPSd6af39ddvc3zFw1g0Y0K7fd3N7O/qHdrf1Nunqc+8pdN0dDjDOMjQ3OwFX+9QzQ1lEa5BINA+m0hDcMdrRYSfnxDh4YEoKamjDGNehLm6GbXqRARJRDhSGo5zVVGoVmqHM4zJypqNY8U1mNo7HklRwYi5fzkSZq1ARaNnZ1msnRhq1372TNX0BjHhjOEMcxiKCsPrwIfrkp6eitGjr+eFLYTt0HCG7XitiACgLw1+4UIFmpo824kQ3DBVNyMrazbChIWQiQvRP7YGeQ1NmHXPGJP7v/3Z9zhR1ojpnx7E5dJ69E0KxRPXpaF/bI07zDeLvW/E9k7x1O3LVxwdzjCE69u4pwNNdTbpYisd+U7o8PQ5dTR0o6w0nMEdrxYRIpEA6enR8PcXISenArW1zZ42ibCCqWyV2dlr8cA3dZiypRpZWbPx6Z8FALTDG7l1MThbGal/mD4583bsOFiIoCAxxGIhDufXoZO0zFRTbsUVHbqlzoPvnYtIJHK6J8JUdk8+XQNDWxy1y/j7xKfz9GUYhgHDMDTLxga8dnaGDpFIgM6do5CfX43Ll6uRmSnW50An+AXLslfqZrTXrjUbnwQAfPb1j6hrUiLlvrUASpFXH4Mls7MAAMWD/w+Fl4oRkleFwYNS0LD5adz97iYAvikeuQYX8hFnTvH0lnMGgPPnc1Fe7lhVWMOhGlvKyhPOQSBgaDjDBrzaE6FDIGCQnCwDwzCorvbNDsUXUKlMZ6sEgI827UB29lqECQuRElqGWnUisrJmIyW0DA+/uwkAEH/wDWx/ZQKWPTQEt/eNwaMrvsDoVNP3+9Pj3vPVtjY7wxBv6VBckSfCm8SErRgOaRn/bQu+fI3cBYkI2/B6TwSgHWcvKqoDy7KUM4LHmMtWCaDdtMswYaH+ATo6tRmrALzxyXf4vwcn4qZn12LeAAAwn+N+x4uzMItHHa6lN0pbI/C9IT+CteEMe7JRGibY8hYxZQ5bvgvOOC7BHRIRtuHVIoJlWVRVNaGkpA4sCyQlhSM8PNDTZhFmuOqJsN1LoH04VnB6SO4rCLb5+K7Gkt32dop8FhP2zs6wFO/gzJgDT0GdPP8hEWEbXisimpsVKCioQUuLEjJZIOLipCbd5AR/sOSJcBY9/3MKyaky3L90HQDvmfpra0lovndGloYzuJQyN65YahgfwHcBYZxIje/3imiLQECBlbbgPQPHBjQ0tCInpwIsC6SnRyE5WUYCwgtQqdQQCgUurXWy8qEhqK1txeYl97usDXM0ssmctrP0Rm2ugzS1HZ87U0tTPLmUPTfc1paS6Z5CJ3oyMtIwatQwh6buEp6FYcgTYQte54morW1GXl41goP90amTTJ9hjOA/SqXGrqEMW8itbcHoIcl4cN52AKUubcsYw/of5rC3MzTujPj+Rm5qOMORXBh8nqniDKFg75CWL8SH8A0azrANrxIRra1K5OVVQyoNQEqKzOGSu4R70dXNcCWz+mig/Vq7V0A4E64dg7G7X/c3HzDlibB1yMYapoY73IWzO257j0cCwvkIBIw+fouwjte8xms0LC5froafnwjJyeEkILwQU4mmCNMY14vgmh6bLy50SzERzh6KsXc6pKPtORu+CMCODsVE2IbXiIiSkjrI5UqkptIQhreirZvhe/fOloe/PfP++SIMuCIQCCAQCOya4ukorr5W3nJsEiT2Q8MZtuEVwxlyuQoVFY2Ij5ciIMDP0+YQdmKqbkZHwpQ731tmHNiCWKzN1WIp2ZSrx/LtyUNhz/H4yuff7gJgPUaHaA8FVtqGV7wW6qYGhob6e9gSwl7Uag00GtakiNh92Tdye1irdWHqb1e54j3pvRCJtO8mxiKCD/Ug7LkutsSbuGKUlet1MtyOS5AvYRryRNiGV4gItVob5CIUeoW5hAl0gUqmhjPMpa7WUa9JcolNzsIeIWCqY/CVaYE6T4RuOMPUOXn6PG1p3zg+xd3YO2vD1DLfpwbzARIRtuEVvbJORLgyvwDhWnTeJHuGM0IFBc42x+04K/re0amh7sDScAZfK3DagjW7jWPyzOX4MO7Yjb1V1nKGGO5nLZuncfCttTY6MhRYaRsMy+Fq1dfXQyqVIi/vCEJDQ9xhVxuKi2tRU9OCHj3i3N424Rxqa5tx+XI1evaM98ngSnMYx0GYGspwpx3uIC4uDnPmzMHq1atRVqYt025KDPHB4+LIdTFl/4UL5fDzEyElRWazDY5U7TQ+D+M4G3NxN3y4B3yjsrIRhYW16NMnwSdmAdbXNyAlpT/q6uoQGhrq9ON7RWBlS4uSCmt5OUqlGgwDCIXe/0/JFWtvh+6s1OnOPBKWPBHmZpt4Sly54rqoVOo205mtnZthB28q94c1zG3rC14fT6DzeLOsa2JcfA3eiwiVSo3GRjni4qSeNoVwAG22SqFPKHuu2DsU4coO1dFO09TbsvGxjGMiTLXP9XN34azZHBKJGNXVTTh2LB8HDhzAb7/9ZrUNw3Vc0p4b3wNL2TwNt7H3e+XsJGF8RyciNBqWhtA5wHsRUVXVBACQyXwjgr+j4o5slXzHWVM5PTkUwMV+c7MzvA1r52ouU+jWratRWdmI66+/DqNH34i0tEhOsUCWOmtTgalcj+PId87Sd40vQ1LOxlBEENbhtYhQKtUoK2uATBbU4Tsgb0fr3rUtFuJcVRS6RlS4yCL3Y0tOCN12jooOa65ua14FLhjvr/NEbNy4Anfe+ZDNx+OKq9+QjYcZDO+fuQ5a93lcnBQvv/w2Jk+ejJKSCgwZ0tXskKx2qI9xyrlwTYHOxSvBtVCar4kJ3b2g4Epu8DawkmW1aa4bG1vRrVssiQgv5+zZMgQF+SEpKdym/YwfUGcrI5EZWels89yKpY6bS6EpR8fK7T0uV3f4yy9/hAkTJuCll17ifGxvg2s8i0KhwqVLVVAoVEhNjWiX60ahUOHMmVIwDIPExDCEhwfaPeRnLHgsweV+dLRhDB1NTQpcuFCOrl2jfSK5oasDK3kpIliWRUlJPcrLG5CaGoGwsACXt0m4llOnihEZGYzYWOd/iTsKjr7xca2/weUYlrZ//fWPMW7cWLz44ks+3QFxFVVqtQZ5edWor29FeHgg1GoN5HIVJBIRlEo1WlqUCA31R319KyIjg5CYaJvQtmSbKUHhy/fEGbS0KHHuXBnS06MQFCTxtDkO06FmZ7Asi7KyBtTWtqC1VYn4eCkJCB+AZVmoVNbLgB8pDUeKLBAqDYs5m4+ic1wolt/qm/ffHkHgiKfAGQKC67azZk1GeXl9h+isuJyjUChAp04RqKhoRGVlIyQSEUJCJJDL1VCrWURGBiMxMQwVFQ0oKqqDVBqAkBD7s/NamxVEWIZiImyDVyKitVWJ0tJ6hIb6IyFB6tA/EsEfrmartDwk1T+2BkANAODxmzrjaEkDyuXxEAkEkIkLXW2mW3FWB8vlLdMeAWHJ42AqNsAQlvXtqHZz01QtwTAMoqNDEB1t3pMbGRmM2toWFBTUoFu3WJuHNTwpGDw1RdcVkIiwDV5l/amtbYFQyCA1NYIEhA9hT7bKEUmNWDSYQXFtK2TiQp99o7IW/MYlOM6eh7a9aaetTS0EtA9fb5rKa2sAq6NDSubaYxgGMTEhUCjUUCjUNh/XGfbZ2qbxuXi7gAAosNJWeBUTcfFiBQQCBp06RbqsDcL91Ne34NKlKnTvHgs/P145v7weLkLC3m3sFSmFhbVobJQjMzPGpv08hbtmF3BpRy7XBlpGRwfj+effwKpVr4FhGAiFjElhZqu4ticrprHnyVlDY3xFo2Fx4kQRkpPDIZMFedoch+lQgZXnzpUhMND2CH6C31RVNaGgoMZn0si6GlMPbS4JhYz3t6U9YxxJk5yfX4PWVgUyMrxDRPAJ7ay0KtTVtbb5XCgUIDw8ADJZEAICxG3+jxyZlst1Sqitx/VmWJbF8eNFSEwMQ2RksKfNcZgOJSJOny6BTBZI2Sl9jNLSelRUNKJXr3hPm2KSi7XR6BxW7mkzbIKrl8DW9MnW4HKsvLxqKBQqpKdHc2qXaI9SqUZrqxIaDQuWZdHcrEB1dTNUKg38/UUIDPQDywJyuRIlJZXYv38//vnnH/3+/fr1g1QqhUqlgkqlQnBwMKRSKXJzc3HkyBGX2W3L1GU+c+JEEWJjQy3GsHgLrhYRvIqJUKs1VO7bB1Gp1HZV73QXviogdNtwfcPUxUlYipfgciyNRgOBgP6PHUEsFiIkxB9SaQDCwgIRHx+GHj3ikJYWiYAAP7S2qiCXK/Hnn4eQl5eHcePG4c4774S/vz9uvPFGTJgwAf369cO1116LkSNHonv37khOTsaQIUMA2Fe+3hLG3gzj74m3xTQxDFXy5ApvPBEsy+pzCZAnwrfIza2CRqNB585RLmvD17LmmcNT52nLW6U2tkk7rZFwHYb3pFu3bpgwYQL8/bUB6bt27cKBAwfabD9o0CCMHj0ab7/9NuRyudPSsNuCu767umnlIpHAriFUX/KKdxhPBMMwCAnRJlwhfAuqm2EeewLjPIE1D4UhrihcZMsMCm9763UGZ86cwYcffogdO3Zg3bp17QSEbhuGYfDKK88jLi7O7HXSpS13Bc68N42Ncsjl7Yu8AUBRUR1Ony7BmTOlKC2th1qtsenYAgFDUzw5whtPBABUVzcjP78aycnhDqV/JfjFv/+WICwsAPHxYZ42xSfgu9fl/Pky+Pv7ITmZPwHSvpTC2ZGOeMOG9/HXX/8iKioKO3fubBNHIZFIMHHiRGRmZuLUqVP45ptvbDp2ly5dEBsbi6NHj6KpqcnitvbeB7Vag9raFlRVNaG5WQEA6Nw5Up8SQK3WoKKiEaWl9ZDJgsAwQHV1E4RCAWJjQxEREcSpX/GlIP8OFVip0bDIz69GbW0LQkL80alThE8nrekIsCyLkyeLnRakdK4qCiIh43VxDPbirBkY7uTs2VIEB/sjMTHM06bo8eYgP2vYKioYhsHEiRORmpqKd999FwAQHByMKVOmQCqV4vjx47jmmmuwZcsWnD17ltMx+/XrhwkTJoBlWVRUVOCTTz7RV3FNTEzEwIEDcfr0aVy4cEG/j7X7oFSqUVPTDLFYiMBAMVpaVMjPr4ZGwyIkRIKIiCCUlzeiuVkBPz8hRCIB5HIVNBptFtD4eCkYhoFCoUJJST1qapohkYiQmBhmNQ/RhQvl8PMTISVFxun8+UyHGc4AtC6k1NQIdOoUgcbGVpSW1nvaJMJBNBoWGg3rtMDKrhEVvBcQznTZmhtC4HNH6IrhDGfA52vGBa6zZ6ydJ8uyKCwsRGhoKIRCIRISEvDAAw8gICAAn332GXbt2oWcnBzcfffduO+++5CQkKDfNzw8HIMHD8a8efOQkZEBQOvBGDBgAHJycvDRRx8hLCwMU6dORf/+/ZGWloYpU6agT58+mDBhgtXzYVkWjY1yXL5chdOnS1BSUoe8vGqcOVOGy5er4O8vRvfusejcOQphYYFIS4tEYmIYwsIC4O8vRmRkMLp1i0VCQpje46ATAxkZ0RCJBLh4sRJVVZY9JRRYyR1eeSIMKS2tR2lpPXr1iqcZG15Ma6sSZ8+WoUuXKAQHe38xG8I6VGzNfXBJT268LQDExcVh1qxZYBgGAoEARUVF2Lp1K+rrtS9uYrEYvXr1wqBBgyCTyfDDDz+gc+fO6N27t/4YZ86cwffff4/HHnsMEokEGzduxMWLF9G5c2cMHz4cycnJEAgEKC0txcGDBzFhwgQcOHAAe/fuhUKh0B9HZ6NCocbly1VoblZAIhEhMjIIMlkQNBoWra1KCAQCBASIHRKoWgFVi6qqJshkgYiICEZgoLjdEMelS9pKwWlp3p/4sEMNZxjS2ChHTk4FunaNQUCA6wJ9CNeiu4+ZmTHw96f72BE4ebIIMTG+Mcfe27AW+2EoJKKiopCamor6+nqcP3/e5Ju3WCzGfffdh9TUVLS0tOCnn35Cfn4+Jk+ejAsXLuDw4cOYN28etm/f3i7/REBAAGQyGUpKSsCyLK699lqMGDECcrkc58+fx+nTp5GbmwuxWIw1a95GcXEdACApSTvc4KqYOJZlUVnZiPLyBiiVGvj7i5GQIEVwsETf5uXLVVCpNOjSxXUzytxFh6riaYju+0MuJe/GnroZ3oy9sQp8jnGwFW+rneENcP1+cEltrqOiogIVFRUWj6dUKrF+/XpERESgtrYWarX2/7mlpQVxcXHo3bs35HI5Tpw40W7flpYWFBUV6Zf//PNPnD59GsOGDUNqaioGDBiA2NhQ1NY2Iy+vGoGBfkhNlbk8NT7DMIiKCkFkZDAaGuQoLa3DxYuVEAgYBAb6ITDQDyqVBhqNbTM6Oiq8HSdobJRDIGDo7dXLUSrVV1ymvtup2Bu0Z2raoq3FoPgGy7JgWXjN/ebTtTZV0MqeWSXG+zvjO1VVVaUXEADwyy+/IDExEddddx1OnjwJlcr0VEtj6urqsHPnTtx0U1/ExISgtLQera3a7KYZGdFura3DMAxCQ/2Rnh6Nzp0jERMTAqGQQXV1Exob5VCptMMoxtCLbVtsGs7o3783oqIiIJOFQSYLR3h42JW/tcthYVLIZGEIDw9DQID97iilUo2zZ0sRGhrgE9GxHZni4jrU1jaje/c4T5tiFke9AI5MHzQWIFxKe/MZtVqDkyeLkZIiQ3h4oKfN8QoM7/mK9duQElqm/5yrZ8FVtTAsERcXh/j4eJw4cUI/E4MLhufU0NAKjYaFVBrgChPtgmW14uHy5SoolZor00KBurpWsCwLhUINoVAAf38R4uKkvI/14lVMxN13j0d9fSOqq2tRU1OL6upa1NbWmXT7SCR+CA8PQ1iYFGFhUoSHSxEWFnrls1BIpdrlsLBQREbGITExAUKhACzLory8ERqNBpmZMZSkyMuhOgptMfdg5xIY5w2oVGqcOlWC1NQIhIXxp2PwRfjkRdHhS1U+VSo1SkvrUVmpnckhkwVCKBRAIhHp81UIhQw6d47i9fAdr2Ii3n77pXaBlRqNBvX1DW2ERU2NVlzU1NRd+bseNTW1yMm5jLq6OtTW1qO2tg4KxVX1+u233yA8XOt1CA31R1yclASED8D3bJWe6KiN3xptERZ8R5flz1uGM7wRPosHXxEQACASCZGYGI6IiGAIhUy7oRaJRIzLl6ugVmt4/YxzNR6bnaGtlXEW48dPRVBQIE6e3AeG0aYapSmdvsPZs2UIDvZDYqL3Z35zBc4UMa4SRLYM19CUXvdgbiiD7xiKZm8TFcY0NLTi4sVKdOsWA4mEv7F7Pptsqr6+EZMmzYZMFobs7HUQCLSFUkhA+BZ890S4k1p1oskHvbMe/nx4KJMnwv14SzCusYBwVtCnp9A911Sqjj2Lw2M99pkz51FSUob3338dGRmdPWUG4UJ0lfT4Or3T3Q+vMGGh2XV8fpByLbwFXI1cJxHhfLw5dbfxsJ2l77uhuODz/4XuhdfW4l6+hsdExJ492uGLnj27e8oEwsUoldp/LpGIn94lT5RCNmWDN7mjraHzRPA50Mwb0X03vKFzNQXXWAlvmp2ke651dE+ER5JNsSyLjRu/Bsuy2LNnH+64Yxw9dHwQlYrfiaaqFAm8eUg5Y3qouW3ceY40nOFa+CB87YHr9GVrU1r58v8KaL/jDMOQJ8ITjTIMg23bPsett96IBx98DDfffBf++usf6zsSXgXfs1U+OGms29vkEsVuy7GsPVTd/dD1pIjwxs6VK7YMKfEFSx4G48+4eFr4KKBEIgF5IjzVcEZGZ2zevAb79/+N5557A2PHTsb48aPxwgtPoXPnVE+ZRTgRnSeCr8MZnsLZnYGpiHdPRcB7MibClpTP1rbnK3zrRO3FmsDQfW5qOz55JIRCQYf3RPCiAJdGo8HXX2/Hyy+/g7KyCjzwwGQ8/fR8RERQtkpvRpuopRE9e8Z72hTew/XBaE4s6DD3cLY1bbK9D+nKykYUFtaib99Eu/Z3FGsZQI3hS2fEhY4iILwpd0pOTgVEIgFSUyM8bYpZfHaKpyECgQD33HM7Dh3ajWeeWYTNm7PRv/9NeO+9j9HaKve0eYSdKJU0vZMr1h6Ohi5ew211f5sK0OTytmeqHVu2N0ajYZ3mhbDXBl8KVPU1DO+NJQ+Et+BpT4RGw0KpVKOlRYGGhlbU1DSjoqIBJSV1KCioQW5uJS5erHSpDbyq4hkQ4I9Fix7C1Kl34c03P8Srr76LTz7ZhGeffRx33z0eAgEvNA/BEZVKDbGY7pkz4JoW2zgLpqW3Olc8rDduzMbgwYOdMnxgz1sn395UnYkviCNHbTeuDeLp+y0SCdDczK34mDU0GhZqtQYqlQYqlVr/99XPNG3WayuNth9IYBhtDguRSACRSAA/P9e+yPFiOMMcOTm5ePnlt7F9+2707t0dL730NEaOHOa29gnHOH++HP7+IiQn07CUvdgS28B1ehwXgcG1TePj3njjjejZsydWrlxpcVtd2/a0wcVrwxVPd0K24s0Cwpnw5b4ZFxhkWVYvBrQ/rL7z1/0YigHD36YEAaD1dugEwdW/hUafXV3WzRrRwasCXO4WEToOHDiM559fhkOHjmLUqOvx4otPoWfPTLfbQdjGv/+WICwsEPHxUk+b4lJs6QzL5fGoaVQiJkxiMfmUqTZ0mPI+mINrnQ5L+9pi4+jRo9GlSxd89NFHLmvHsD1z+3M9T750RvbSUUUFX+5bWVk9ysoaIBYL9YLAHAIBoxcBtvx2NP0BrwpweYprrhmAXbu2YMeO3Xjppbdx/fUTcN99d2DJkoVISOBviemODMtqx+o6wnCGLQ+0aEkxDhcHoGtEhd3tWRIUxi5vrlkCjbE3EFMsFttUFtp4f3swVf7aWoCpr3S+hh4dXzkna1gSje4WFyKREBoNi9BQ/zYdv/aHcaoY4CteISIAbW6J8eNvwa233oh167bgzTffxzff7MDcufdj0aI5kEqdr7AI+1GrWbAsKLDSBB8/PhVjXPSwc9c4sXGHpVsWiUQ2iwhH2ucSPGprUiNvwzC41teFhDUR6O44CaFQKwxiYkI77FR2rztrsViM2bOn4vDhPZg37wGsWbMe/fqNwkcffQa5nGZy8AW+Z6v0FAeKpMjOXouyVudNe7V1eMOVCarEYjFUKtsCzbh2fsaJiBwRB77a2TqalEq3vzeLLHfeW6qf4SUxEZYoKSnDsmXvY+PGr5GQEIdnnlmEu+6imRyexlvK5LqTgyVSxIUEICm41Kb9XFHl05rQ4Jp/wpjJkydDo9Fgy5YtDtnnbDzl7vY0jgxhObMztme6saP7u+NeNzcrcP58OTIyohEY6Ofy9uzBq2MifvjhJ2zYsBUbN34Esdg1HUlcXAxWrHgVjzwyEy+//A4eeuhJfPDBp3jxxadw443XuaRNwjq6ACMazriKRgOPCghHjsV13F0kEqGpqcnudpyNzu6OIh6MxZK9ycdc8TbvjGPacgx3DG3ohjPMzazoCLj0dX3Nmg3YvftXrFixxuXjpBkZnbFx40f48ccvERAQgDvvfABZWTNw7Ngpl7ZLmEapVOujkQkt52sacaQ0HLl1MZy2d+aD3NaHr73HsGc4g3AeljpNrkMVfBzqsbfejKvPhYYzXDycsW/fn5g4cQYAIDo6EocP70FwcJD91nKEZVn8+OPPeOmlt3D+/CVkZY3Ds88+hrS0FJe3TWgpLq5FbW2Lfv40AVyoicKLu88hLToEr9xg2TPnaLZGZx+XKw899BAKCgqwc+dOh47TUTwHzsbeVOem9ncWzgj4XPPlD3jo3nEO2eAKNBoWJ04UITk5HDKZ6/s2e/D6PBHHj5/GyJETAQCBgQH4448dSE1NtstYW1GpVNi8ORtvvPEeysurMH36PXj66fmIiYlyS/sdmby8aigUKqSnR3vaFN5xoEiKaxLqTK5z5GFrT0Iqrsflut+8efNw/vx5/PTTTza3Y6l9oj2uig1wdgyOqfgaT3g7XPU9On68CPHxoYiK4le8oA6vr53Rp08P7N+/AwDQ3NyCxYtfQUNDo6ubBaAdn5027W78888ePPfc4/j22x/Qv/8ovPrqctTVNbjFho4K1c0wjzkBAdj/oOO6nyOpp7m4w+3JE2GpbRIQ9rvyAc8JMGv1XTyBq4SLUMhAraaYCJfSo0dXbNyozWC3e/evSEsbhN2797qjaQDamhwLFszG0aM/Y86c6fjww/+if/9R+PDD/1KBLxehrZtBIoIr5mpNWHvwuvLBbKlGgbk2nSki+Dg27wnM3WNr3w9HBKmzv1d8EYOu+E55ugiXp3FbsqmxY2+CUCiEWq2GSqXCtGnz8OSTjyAzMx1jxoyCSOR6U8LCpHjhhScxe/ZUvPnmB3jhhTexatU6LFmyAJMmTXSLDR0FpVLTIbJVOgPjrJJcpto5mgvA+PjWhApXRCKRxcBKS67sjpAsyRFMzZBxtoDQtaM7hj0ZPvkiGHQYXzNnz9ro6CLCbU95hmHg56cNJouNjYZCocTK9z/D9Onz8fzzy9xlBgAgPj4WK1a8ir/+2olBg/pi/vwlGDZsHLZv3wUOISKEFXQFaGg4wzrmxoudWRjLGs48np+fXxtPhOFbrTWRQALCMubSPXMtvGYNc1lIzdnBp0ygxt4TdwYY03CGG+nSJQ1RcckoLS0HAHQfOAoT71+MNWs24NCho+40BQCQnp6Gzz5bib17s5GYGI/p0+fjppvuwr59f7rdFl/iarZK8kTYgqk3QFPLfEUo1IrGefOmW3TBGy+74m3al7HUYXLpPJ3RgRq37Uisjb1tW1tvfI1clUyro3si3Oa/V6vViIgIQ0V1E+Y+txZ7t69D1z7XYsiNd+Dk37tx//0LsW7dexg0qJ+7TNLTt29PfPPNZ/j99wN4+eW3MXHiDIwYMRTPP/8E+vfv7XZ7vB1KNGUbrgqKdBXG7nSde1il0uDUqWIIBJYLDRm6yE0lg3JVkiBfzlrJ9c3b3HbWOlRL+/FlKqk9bTnjuyYUCiCXd9zcKG57VVyy5DX89tsB3HrvIvS5djQWvf4Fho6eBKFIjAcWfwjGLwS33z4d+/f/7S6T2nHddddg9+6vsGnTKpSVlWPUqDsxffo8nD17wWM2eSNKJdXN4Io3dmjm3oR1WfsMRYQ9b76uDhT1FK7KwWDrtlxibEwJO+N76axhFK7DIpaGxBzxsDh6X7TDGR3XE+EWEXHhwiWsXfs57njwWQwZdUe79bLoBDz66iakZPTDHXfMxCefbDJ7rK1bv8e8eYtdFrvAMAzGjr0J+/fvwKpVb+L48X8xbNhtePjhp5GXV+CSNn0NnYjoqFXtOipz5y4BgDYlj7nMLnEnnhQSfBAxxmXTTcVBuMNOW2IYjMWPpX0MxY4twsIRIaEdzqCYCJeSnf0DgkOkuH7cVLPbSGXRmP/K5xg44nY89dSL2L59l8ntHnlkMb744lts3boNZWUVrjIZQqEQ996bhUOHdmHZsuexd+9+DBp0C5588kV9TAdhGpVKA5FI0KYzIZwLH4MQP/zwNQCwOJzhaEZFwnHMda7WvA+mBKEt99CUYNF9Zup74e54GXv/p3QxER01KN8tVTwfeGAR/s2pxKJlW61uy7Is3n92Csrzz+C9917FuHE3t1kfHp6u/7tfv174+utPIZOF22yTrTQ3t2Dt2s+xYsXHkMvlmD17GhYunO2Wtr2NgoIaNDcr0LUrtxoRhG/Q1CTHhQsV6No1BgEB9hXcc9ebcEfC3LRhrtfZWbEkXOIuzlVFoWtE25dDLsMvrqo4yoWammbk5VWjd+94XlaP9vqMlQCQk5OLqMTOnLZlGAYPLP4QcWm9MXXqI3jnnVWoqqoGgDYzOCbPew05lwowePCtWLFijctVYGBgABYunINjx/Zi/vxZ+PTTTejb90a8+eb7qK+n7JeGULZKbvDRm+AIpmIibMHwrdRZswgIx6diumN2kO74XSMqcKRU+2Jm7Tvgqu+JrcfTVfJUqTqmJ8LlIqKlpRU5OZcQk5DGeZ/g0HA88uI6jL57LpYufQ9dugxBRsa1GD36HoRHxuC1dX/i+nFTseT9/yGt13V46aW3MWXKw2hpaXXhmWiRSkPwn/8swtGjv2DatLuxfPlq9Os3CitXrkVzc4vL2/cGVCpKNMUFPrxxO/MBrBPyumEsc+5qV9tB8Acu99X4/+CVh+8yGf9gPNTiSmw5vs77oNF0zOBKlz/pN2zYAoVCid7X3Gx9YwMYhkHWzCV4+b/7MWvxBxh08xTcfOdDeG71z5BFJwAAwiPj8MDTK7VTRvf+iZkzF7htXCoqKgKvvfYfHD68B7fffiteeWU5+vcfhY8//hxyecdOpa1UUsprYzz9tmQOZwoZnSdixoxFZl3g5GHgL+ZmPTgCl++XpVkezpoBYg9cz72jlwN3eUzEuHH3oYkNx9znXHvzj+zfibWvP4w9e77GgAF9XNqWKfLyCrBs2fvYsuV7xMXF4Omn52Py5CyIxfaNDXsrLKstjRsfH4aoqGBPm+Nz8Dkwsbq6Cfn5NejdOwF33jnH6vZcUyrz7TwJbhgKSS5iwFqWVk+JT2vfP4VChX//LUVaWgRCQwPcZBV3vDomgmVZ5F4uQGxSuvWNHUQ3XKJUeibpR0pKEj766E389ddODB7cHwsXPoNrrhmDrVu/h1qt9ohNnkCtZsGylK3SFViKkucDOk8Ew5jOO6D7zSUFtvF+zsQXPSHu9vDYmzfC2owLw/WfbNlp9tjuxFrbVz0RFBPhdHJz81FSXIrO3Qa4shkAQFBIGACgttZ8mWV3kJHRGf/97wr89ts2dO3aBQ899CSGD78N3333Y4cYM9OlvPaFwMqWFiUqKhpQXd2EuroWNDbK0dqqhFKp1neYjmJrYSM+igcdLMtCIGD0MRHGgoHL26c7g/h8BXd3sNZqapibtmlrMGeEX5EDVjoXS9dYF0jcUYczXJr2+ueff4dQKER6ryGubAYA8NfPXwMAjh8/jVtvvdHl7VmjV69u+OKL1Th8+DiWLl2JmTMXoEePTCxZsgBjx97kszkUfClbZVVVIyorm8yuZxgGIhEDoVBg4ocx+fejjz6H1tZWyOVyfP31ap/q0DQa1uz32pKQMLc9V2ha6FWMk0m5W5A5Q9Dw1VNk7noyDNOh62e4NCbigQcW4tSFCjz+5tcOGcmFf/Ztx6fL5gMAli17Hg88MJlXpb0PHDiMpUvfw2+//YW+fXtiyZKFuPnmET4nJqqrm5GfX41eveL1bj5vpaGhFRcvViItLRIBAWKo1RqoVBqo1Rqjv1n9Z8bLljwWDAO9uBAIGL3gEAiuCg/d55Z+G779e5KSkjpUVzejR484i9sZd3SGkBiwD2vxBK46ti24c3aFKzF1PS9cKIdcrkLnzpEICPDzgFXmcXVMhEtFxMyZC3DiTDGeenebQ0ZypbmxDqtevB85/x5BdHQkJk++A3fdNR49enRFQUERiovLcM01rh9ascT+/X/jtddW4MCBfzBoUF8sWbIQI0cO40Un4AzKyxtQWlqP3r0THDqOQqHA5s3ZiIqKQHR0JKKiIhAZGYGgoEAnWWodlmVx5kwZgoL8kJIis/sYxiJDVypd97lGYyg8WP2y4efW0IkJnQAxXNYKjrbLbX9MfaYVJrYIlKKiWtTXt6Jbt1i7rhVhH66KLfHmjt7VGF9PpVKNS5cqIZerkJYWieBgiYcsa49Xi4idO3/GlClzMW3RWxg6+h6HDOWKRqNB/oUT+PuXb3H4t+/RUFeLmJgofYrsX3/9Dn369HCLLeZgWRa//voHXn99Bf755ziGDx+CZ599HEOG9PeoXc7AWR3J+PFTTRZjCwoKRGSkTC8qIiNlJn7LEBGh/e3v79g/c1lZPUpL69GjR7zHaoGwrDZY1VCAGP5u+7du3dW/ry63/YwrDIN2wsJYZAgEDFpalGAYBpmZMT5dMZNvuHJ2CwkJ8xhfV7Vag9zcKjQ1ydGpUyRCQ/09ZFlbvFpEsCwLmSwDXbr3xxNvZztkqD2olAqcPbYfh37dhoN7te2PGDEUixc/isGD+0Eo9Oy4Pcuy2LVrL1599V2cPn0WN988As899zh69eruUbscIS+vCgqFGunp0Q4dJzNzKKZNuxuzZ09DeXklKioqUVFRhYqKKlRWVqOyUve3brkaTU3N7Y4TGBiAyMgIRESEIyJCduV3OGQy3e+wK79lkMnCEB4uhZ/fVXekUqnG6dMl8PcXIyMj2u5sjHxDJ0w0GhazZj2FNWve0IsLjYYFy7Jtlk19pls2/B0S4o/YWOc/qAjLuHJYiISEaUxdY42GRW5uFZqbFcjMjOFFbJhXi4jWVjkSE/tgyKg7MXXhmx512Tc11OGHTcuxd9s6ANqy39u2fe4xewzRaDT47rsfsXTpe8jJycXEiWOwZMlCZGRwSxXOJ3JyKiASCZCaGmH3MTQaDWJiemDp0mfx4INTOO/X0tKKiooqVFVpRcXV3zWorq5p83dVVQ1qa+tMzpgJCQlCeHi4XlQEBARBLA7ADTeMxLhx1yMykjpJc5AHwjO4I7aExIRpjK+1SqXG2bNlCAz0Q6dOER4fqvZqEQEAb7yxEsuWvY+eA0fg/qfeR1CI1G5jncHP2Z/g67WvAAB27NiEYcMGe9QeQ1QqFTZvzsayZe+jpKQM9947EU8/PR8pKUmeNo0zZ86UIiTEH4mJYXYfo6amFmlpg7Bu3UrcfvsY5xlnhEajQW1t3RVhUYvq6hrU1NSiuroWVVXav3XLeXmFKCgoQliYFEOG9MeaNe9AKrW9GB1BuAJzRaqcKS7y6mOwaMYEu/f3ZYyva21tCy5frkKXLlEej49wtYhw+fSF//u/BRgwoA+mTZuHfTvWY+zkBa5u0iKjsh7EyAn3Y/74zvj55995JSJEIhGmTbsb99wzAevWfYnly1fjq6+247777sDo0TegR48MxMXFtHG38w2VSu1woqmKiioAQGSk/d4MLggEAshk4ZwqsSqVShw8eAS///43Vq9ej0ceeRqbNq1yqX0EP/DWKaTO9hxwFRC+MgvDEaRSf4hEAtTXt3pcRLgat8yBvPnmEZBKQ6DhQebGM0f3Q6nQFurq2TPTw9aYRiKR4KGHZmDq1LvxyScbsWbNBqxfvwWAdk5yTEwUEhJikZAQZ/QTi/j4WMTGRnsk3kMXwOfoOKBORERHRzrDLKcgFosxbNgQDBs2BN26peP++xfgjz8O8kqEEq7B0wKCLyLGUppyQ6+Hqcyqvi4mjO8RwzDw8xPp8+b4Mm4REXK5HJWV1QgI8rz7d+UzV8fY09O5Vxb1BEFBgVi4cA4WLJiNgoJi5ORcQlFRKQoLi1FUVIKiohKcOXMBRUUlbSqICoVCxMZGIz4+FvHxMUhIiEN8fCzi4mL0n8XGRjvdo6H7h3E0W2VlpVZEREW51hNhLxMm3Ir09DR88cU3JCIIl8NFQHBJI26vEDGVedK4Lcr30V5IiMVCfQZfX8YtIkIgEIBlWQh5lPxJIvFDRga/RYQOhmGQnJyA5GTTuRdYlkVtbR0KC7XCori41OCnDP/+ex7FxaXtZi9ERUUgNjYacXEx+p+ry9rfEREyzoFBun8Yxz0R1RCLxZBK+RnAyDAMRo8eie+//x9Y1nyWRoJwJ+aEhKMZLM15GQyXjfF1z4M5DK+xWCxAQ4PSwxa5Hrf06oWFxWBZFpGxye5oziJDR0/Cn7u34NNPV0Ai8Y2xKoZhEB4ehvDwMPTq1c3kNizLor6+US8uSkpKUVJShuLiMpSUlOHYsVP48cefUVFR1aaculgsRnR0JOLiohEbG42YGO3v2NioK39rf0dEhEOp1FzZx7GYiMrKKkRGchcvnuCGG4bjww//i19+2Y9Ro67ztDkEAcB8enFLAkK3zpI3wdH01p6swuludNczOFiCysomVFU1ISIiyNNmuQy3iIgzZ3IAAH4Sz5dJFYrE6NYtA+PG3expU9wKwzCQSkMglYagWzfzVVWVSiXKyipRWlqG0tJylJSUo7S0XL/811//oKysHFVVNW32EwqFiIyUISwsHMnJcYiJiUJMTBSio6MQHR2J6OjIK8uRCA4OsigQKiq0IoLP3HDDMIwYMRTz5i3GwYO7bJ61RBCuwlz1TFOYKpZlaXvD9baIgo4iIHRkZc3Gt99+DJksEAUFNVCp1IiJ4adn1VHcIiIuXLgIkUiM9F7XuKM5i1w+cwjXD+3laTN4i1gsRmJiHBITLdc/UCgUKCurRHl5BcrKKlBaWo7y8kr973//PY+9e/9ARUUlFIq2Lr2AAH9ERkYgJiYSUVHalNZXfyKxc+cedO+e4crTdBiBQID331+K3r1H4L331uLZZx/jteeEIGyBy9CH8ZCGK4pxeTN33DEH3377MViWRVVVM6KjQ3zyGeEWEVFRUYXQcM+7p4vzzqMg9xxuen6eR+3wBfz8/JCUFI+kpHiL27Esi7q6epSVVaC8vFKffbK8vAoVFZUoK6vAyZNn9J/rBAcfKrFaIykpHvPnz8Ly5atw6dJlLFw4B3379vS0WQThFKwJCd16c9vQVE+tkNiw4X3U1FSiqqoJkZHBnjbJ6bhFRDAMAwaARq2GwIOppvft2ICoqAjccssNHrOho8EwDMLCpAgLk6Jr1y4Wt9XFbVRVVSM+3juKOL3yyv8hLS0F7733MUaNuhNZWWMxc+ZkmrVBtIEv0zQN6agdu7uZPv1RrFz5JoqL6xAS4g+JhD8TDJyByzNWAsChQ0cxevQ9ePTVz9G9//V2GeoM9nz7MX7Y+DaKi0963CtC+BYqlQqrV6/Hpk1f4/z5S3jjjecwe/ZUT5tF8Ay+iAlbBARXe0mUmEcsFuP5559FeHggEhLC3Nq2qzNWuqUs4cCBfeHvL0Hx5XPuaM4skbEpaG2V65MZEYSzEIlEmD9/Fvbv34GZMyfjP/95DTk5uZ42i+ARfBAQWVmzXSIgCMsolUrk5uZBLld52hSn4xYRwTAMoqOjUFVW4I7mzBIRo82zUFBQ5FE7CN9FKBTiP/9ZiOjoCAwZcqvJcuZEx8TTHbI90zJt3d74x114+tpy4dixY6ivb0Vjo9zTpjgVt4iIhoZGFBQUISq+kzuaM0tUfCf4SST444+DHrWD8G1ksnD88ks2hg0bjBkzHkVhYYmnTSIIt2Krx8MZ7fGdI0eOoLCwEH/8cQoajdUoAq/BLSKirq4BLMsiLCLGHc2ZxT8gCL2H3IzNm7PBIRSEIOwmJiYK69atRECAP2bNWgQ1D+rGEB0brumzrXkRdALBndkqzeW+8ITXwxF++OEHREZGYtu2P3xGSLhFRPzyy+8AgIze17qjOYsMHzMFZ89ewL59f3raFMLHkcnC8cEHS3Hw4BFs3vytp80hOjhcOndT4sBQNFiqmeFq74NOKJir3+ENlJaWYuvWrUhNTcWCBS952hyn4BYRceTICcQmpCA41HrJZVeT0ftaJHfujrfe+pDeDgmXM2LEUNx552149NH/4MknX0R9fYOnTXIr3vigN4W3n4et9rt6e1swlcTKMNOmt92by5cvAwAiIiK8znZTuFxEqNVqbN36PXoPHevqpjjBMAwmPvAMDhw4jFdeWe5pcwgfh2EYrF27HMuWPY8vv8zGtGnz2lRc9XW8xc1sDV85D3NYS4tt636uwl47+YRCoUBpaSm6du0KwLtsN4XLs140NjahpaUVoeFRrm6KM936DUfWA0vw3nuvYeTIoRg5cpinTSJ8GIZhMGfONHTrlo5Jk2Zj9Oh7sGfP1/D3940CcIRvYarwlieqdZoSDOZqfHhbR1xZWYn4+KvZfvkw/ddeXO6JWLt2IwBgwHW3ubopmxiVNRtde1+DxYtfoSBLwi1cd9012LlzM06fPosff/zZ0+YQHQSusRCAZc+CI52cswIgzVUW9bYOODMzE6dOnWrzmbcJIR0uFxEKhQJ+EgkCg/hVwYxhGIy6Yw7On7+I/PxCT5tDdBD69u2J7t274vXXV6CoiKZ+Eq7tPAyPzaWjtTbzwpQQMCcOzAkHw8/MzbCwVcx4WwdcVlaGsLCwdp9723kAbhARN988Agq5HP/8ts3VTdlMYLAUANDU1HHGqAnP8/HH76CpqRkzZy5EXV3HCrQk2uPqt2hrsxm4Tv00NZRg+LmxMOByTHO2ct3XGwMrAW0abKVSaXKdt52Py2tnqFQqXHfdBDQrRHj2w51Qs26ZEMKJw7/twCdvzMPFiwchk3l+5gjRcfjnn2O4665ZiI+PxVdffYKEBMul1wnCWdjTSfFpuMBQtHhbhwtoveDPP/88tm/fjiNHjpjdzlnX3OtrZ4hEItx//yTkXzqDluZmVzdnE79uXwcA+Ptv8zeSIFzBwIF9sWvXFjQ0NGLq1EfMvpUQhDNxpNPlW4fNN3u4IpFoA6pbWix7wL3l/NziFrj++qEAgNNHfodIwJ8gxlvueQQJqZmYPecJlJSUedocooPRtWsXbNjwAU6dOouRI7OwY8duCvIlXIajAoIv3ghzwyDeQlyc1utYXV3dbp2pnBh8xy0iolu3dHTt2gW//bAR9Q31+CX7YxzZ/4M7mrZIz0E34vE3twIQYsOGrZ42h+iA9OvXC9nZ6xATE4lp0+bhuusm4K23PoBGo/G0aYQPYBgoae8MCVenlbalo/TWGAgd2dlr0aVLFzQ0NKCsrP2Lqzeem8tjInR8+WU2HnlkMboPGIHT//yKPtfegrnPfWzXsZzN6/Nuxi2jBuOtt17wtClEB+bTTzfh5ZffQX19AxIT43Hs2C8QCoWeNovwUlxdtdPZcPF2eGMnqyM7ey1YlsXZs2U4cOAQtm3b1mYdYPn87L0/ro6JcHmyKR333puF06fP4YMPPsWQG7Jw55zn3NW0RdQqJcpL8pGaeqenTSE6OLNmTcGsWVPwxx8HcdttUzB27H348cfNEAj4E4xM8BdHO1g+DVl4s1iwxMsvf4Tbb78d99wzGjNnjtd/zjWXB1/ujyFufTo988xjGDlyKI7/9T+ESCPc2bRZCi79C3lrKwYN6udpUwgCADBs2GB8/fV/cfDgEQwbdht+/fUPT5tEdBCs5Ylw9HimjuvttTC4wrIsJk26E1KpP4KCtMGV9uTx4BtuFRH+/hLU1zeitbUFahU/otE1Gm0RrmaezRwhOjajRl2H77/fgKioCEyaNJsSohEmcXanb+r4xu1wbctSxU9D+Ph27Wyys9eiuVmB1lYVIiKCHToW34SE24YzdPTv3xtHjpxAbVUZwiPjIPDwmG9Kem+IRGIcPnyCamgQvOL6669Fjx5d0aXLECxc+AzeffcVpKYme9oswsO4oxMxTBxlzQZTCadsEQx86xSdhfG51tQ0QywWIiRE0m4bW4UUn4Y23D7Y+uCDUyCR+OHZmcMwb3waHh6bgrLCS+42Q8/5439BpVLiuuuu8ZgNBGGOiAgZnn/+SRw9egpz5jxB+SQ6MJ509VvKRsml7obhtq72nngSc6IgK2s2WBYQiQRgGMbkvuaKi1lqhw+4bXaGIX/+eQjjxt2nXw6TReO1DX97JIAs+7M3sPurVZg7dwaef/5JBAT4u90GgrDGoUNHccstk/Dhh29g8uQ7PG0O4WY8KR68PUOkOzH0xBh39KWl9aioaESvXu2rdxpub8s15iImvD5jpSmGDh2E6urzqK4+jy1b1qK2uhwfvTjTI4l2eg+5CfEpXbB69XosW/a+29snCC4MGtQPiYnxyM7e6WlTCDdhKRjR3TZ42g4ueOLt3JyHxpQtKpUGQmFbL4Th/vZ4Z/hwT9weE6FD59IZPXok5s+fhQ8++BQ7N6/EuPsWutWOzt0H4rlVP+O/by7AX3/949a2CcIWBg7sgz//PORpMwgXYK4z4EMn4Q2400tii1gx9DA0N8v1szJMrbcXT8dH2OSJUCgULjHi5ZcX47777sCOjctx6NfvXdKGNVLSe+PEidOU/prgLf369UJFRRVUKpWnTSGcCAkF57B6s+1ZkB3pfA0zgeqWjdcb0tqqQkCA2GRQqjNyfHgKm0TESy+9g1OnzjrdCIZh8MEHb2DQoH7Y/MF/kJ9z0ultWOPam++G2M8fa9ZscHvbBMGFTp2SodFosGXLd542hXAAbxoi8BaysmYjxr+Y07aGQw/myptzxdQQhqEoMFwvEgnQ3Kxo85kz77+nvks2BVYGBAahpbkJN9wwDC+++BR69+7hVGPOncvBbbdNRX1DI15a+xvCImOdenxrfPDcNKTE+GHTplVubZcguMCyLCZOnIGCgiL8/vt2BAUFetokwgqGgXPuxlsCId1hp2HworVAUXOBjsadv61ejMrKRhQW1qJHjziIxUKXnbOxXa4OrLRJRLy5+QgunPwbP2x8G8X5FzF9+j14991XnDqrorq6Bv3734TIhHQ8tmwrhML2YRsajQbNDbXQaNQIDY9yWts7v3gP2zcux6pVb+Lee7OcdlyCcBZ//HEQEyfOwHPPPY4FC/jfQXQkPCkYiPYY3wtLnb6pIQZjMeEocrkSZ86UoXPnSEyf/qhTjmkOQ5t5NTtDJPZD/+Fj8Z8Pd+OeuS9iw4atWL9+i1MNksnCsW7dSuSePYofNq0wuc1Xa17EU5P7YfGUgdjzrfOKeN1810NI6JSJzz770mnHJAhnMmzYYNx++61Yt+5LVFRUedoc4gp8mEnRkTGeJWGrgDBe74pARbFY+0KsUKhdHgjpzu+hXS4EoVCEGybMxLDRk/DCC2+hsLDEqUaNHDkMixfPx49fvo+v1rSvrFlRfBkAEBoagm8/fR1njvzulHbFfv4YevMkHD9+GpWV9IAm+MnChXNQU1OHmTMXeNqUDo0vJ03yFgxjGMx1zMbDEMa1Okxl3NStcyYCAQORSAClUu3U45rDXd9Lm4Yzln99CgGBV5NNNTfW4fV5ozGwfzd8+aVzy3qzLIu5c5/C1q3fY/zUxzH2ytTPi//+g7efvBPvvfcapk69C2PH3oeLeWVY/O52BEtlDrdbW1WGZQvGIr1LErKz19G4M8FLvv/+R9x//wL88MMXGDp0kKfN8QlMvbmaekvlo2iwJ1GRt2HqHC2l0eZyD509ZGGNc+fK8Mcff2P79u1uaQ8A1q9fzp/hDGMCg6W4Y/YL2LVrL375xTneAB0Mw2DlytfwyCMzsX3jchz7cxcA6Gdu3HrrjRAIBHjhhSdRWVqIfT987pR2wyJi8NAL/8WhQ0fx+edbnXJMgnA248ffApFIhBMnTnvaFK/H2JtgOO2Oa4yDpbTQzoTruL4ncdX5c/X6mJt9YeiFsCQwXGWXRqOBXK5CQ0ODQ+3ZypQprvVYOpxsqt+wMejaawjmz1+C9957DTffPMIZdgEAJBIJXn11CXJycvH1mufRte9Q9Bs6Bjs2voNXX12OlStfx7XXDkTv3t3x567NTktUlZrRB4FBIfj++12YNu0e8kYQvKO2tg4qlQpRUZGeNsVnsPRWy6U+hKVxdXMFq5wFHwSEs3IeOIItSbvsEQ/mCo1xCcZ8663PcNNNN2Hu3En49ddfbW7bEFts1wVWugqHRQTDMJj+5AqsWHw37rnnQRw8uAvp6WnOsE1//LfffgnXDh2LLz98FjOeeBcjx8/E5s0f4YknHkZSUgJOnPgXCanp0Gg0Tpspcs/cl7Huncfwww8/4Z57bnfKMQnCWWzfvhtCoZCGMkxgb4dh/PA3flvlUpmSy9i6vZ0s32d+eMI2LveGi122eiR021tLdZ2VNRvNzc247bYbUFtbi99++w2vvvqbxX28Daf0uLKoeCxcqp3R4IrU0UlJ8Xhvxas4uPc7HNjzFW6c+CBYlsWyZe+jrq4eAJDeayhYjfMCVlpbGgEA3bplOO2YBOEszp69gM6dUxAXF+NpU3iF8Ru/teEFc52HuTF0Sx2SOde5sT3msNah8FlAOBN7ymJbOo4jMy9MeVZsuY/Z2WsRFSXF888vw+rVa7B370+cvpfehNNqZ4RHJQAAFi58Br//fgDvvfcaAgMDnHV49OmjTWxVcPE0ho6ehP7Dx2Lz5mzU1TVg4sQx+O679fh1+3rEJXVCdGI6Jt6/GLFJXexqq7G+BtvWL8OMGZPQq1c3p50DQTiL5OQE5OUVQi6XQyKRWN+hA2Dujd9S8iBzHY2zCyFZ6zD47mVwF87sWG2Zusn1c1vty88vxK5de3HX7OeQd+EEVHJuWTW9CaeJCIFAgPkvr8fh33bg66+/wk03jcCkSc4bBvjkk40AgBsmzAQAzHz6fQRLI7F75+fIzf0HM2dOxrZtu9Dc3IzNm7OR2Kk7bpv6mF1tVZbmo7mpEddffy3y8goQFhYGqdTxEugE4Sy6d+8KuVyBvLxCZGR09rQ5vMDSuLS5YQpbjq3DluEJSwF8vvIm6ixs7eCdcWxXs3z5agSHhmH4mPtw4q//oU+3OI/Y4UpsEhGtzY0W16d1H4C07gNw6d+/8ddfhzBmzI0OGWfIwIH9sGbNBmz7/B1MWbAUADDs1nvx5+4vcfvt0/Hllx+jb9+eKC0tx+bN2YiMS0ZLs31RsBExiUjL7ItZsxbpP3vyyUfw6KMPOuNUCMJhpNJQCAQCfPfdj5g7d4anzXE7U6YswKZNK9tEnm/atBIqlQr19Q1Yv3456usb2u1jCuPjmGL8+Jk227hp08o2+40fP1Nv46ZNK9vY5+oIemexadNKlx3b8HqsX7/c5OfehEajwZdfZmP4mOk4sOcb5J47jtvHDOZ0PiwL1NTUAADCw8PBMFZ2sEBDg+V+21E45YlobW1Fp06dUFpa6lJjCIIgCIJwLrGxscjNzYW/v7/Tj81JRABaIeGqUuAEQRAEQbgGPz8/lwgIwAYRQRAEQRAEYQjnmAjyRBAEQRCE9+FKTwQnEUExEQRBEAThnbgyJoKTiFAoFCgtLcWF3AKEhGgLeLC4MgrS9hdY/XL79cbrjAdSri63XW/0cbv9uay3duyrv20/L1NtWzpvW4/tyHpH7AYAje56mWlLY8EWjdG2+nVm71PbZU279br9r7RttGy4vt06o++axsQ+bY9p2gbdfhrW9HZgr67TsMb7GrVl/LnxNTVuw0ybV5e5t8kaf250HYzP13j7dutNttH2optbr/+ucW3LxDm1e6aYOX9Tdptab365/efG28DqMcwst/s/Md+mblmjMb3uqg3Gv1kz2+muh3Gb5s6JtbiO6zGMLoAZW9qfm+NtaWxc1n9LHTjGleV2/4S2HIfrtri6rFag9N/1UCgUnhMROkJDQ90iIuwRCdbWs0Ybte+YzNvt6HpXHtvVbbd/2Lbd3qxQ4CAi2osE1sp63f5X2jZaNlxvrlM0f17GxzTazmjZUofubBFhtk3j49vTprlraaYNe0SE8b7uEBHWbLHWliMiwvnL5rdjzIgIrsd0RAA4S0QYr2/3oGj3m+M2Ftdz7YxNfG7zMYyWNRzaMLdsR5u6S+EqnFNogiAIgiCIDgeJCIIgCIIg7IJEBEEQBEEQdkEigiAIgiAIuyARQRAEQRCEXZCIIAiCIAjCLkhEEARBEARhFyQiCIIgCIKwCxIRBEEQBEHYBYkIgiAIgiDsgkQEQRAEQRB2QSKCIAiCIAi7IBFBEARBEIRdkIggCIIgCMIuSEQQBEEQBGEXJCIIgiAIgrALEhEEQRAEQdgFiQiCIAiCIOyCRARBEARBEHZBIoIgCIIgCLsgEUEQBEEQhF2IbNm4vr4eLKv9m4X+D8NfFtcbr9Mtw2hfGK03+rjd/lzWWzv21d+2n5epti2dt63HduY1t8VuANDorpeZtjQWbNEYbatfZ/Y+tV3WtFuv2/9K20bLhuvbrTP6rmlM7NP2mKZt0O2nYU1vB/bqOg1rvK9RW8afG19T4zbMtHl1mXubrPHnRtfB+HyNt2+33mQbbS+6ufX67xrXtkycU7tnipnzN2W3qfXml9t/brwNrB7DzHK7/xPzbeqWNRrT667aYPybNbOd7noYt2nunFiL67gew+gCmLGl/bk53pbGxmX9t9SBY1xZbvdPaMtxuG6Lq8tqBVwJJxHh5+eH2NhYpHdKcqkxBEEQBEE4l+Dg4Hai3lkwLMcjt7a2QqFwraIhrFNfX4+kpCQUFBQgNDTU0+Z0aOhe8Au6H/yB7gV/0N2Luro6l9wLzsMZ/v7+8Pf3d7oBhH2EhobSPydPoHvBL+h+8Ae6F74PBVYSBEEQBGEXJCIIgiAIgrALEhFehkQiwQsvvACJROJpUzo8dC/4Bd0P/kD3gj+4+l5wDqwkCIIgCIIwhDwRBEEQBEHYBYkIgiAIgiDsgkQEQRAEQRB2QSKCIAiCIAi7IBHBQ5YuXYpBgwYhJCQE0dHRmDhxIs6dO9dmG5Zl8eKLLyI+Ph4BAQEYOXIkTp8+7SGLOw5Lly4FwzBYtGiR/jO6F+6lqKgIU6dORUREBAIDA9G3b18cPnxYv57uh3tQqVR49tln0alTJwQEBCAtLQ0vv/wyNBqNfhu6F67ht99+w/jx4xEfHw+GYfDdd9+1Wc/lusvlcjz66KOIjIxEUFAQJkyYgMLCQtuNYQneccstt7CfffYZe+rUKfbYsWPsuHHj2OTkZLaxsVG/zRtvvMGGhISw33zzDXvy5El20qRJbFxcHFtfX+9By32bgwcPsqmpqWzv3r3ZhQsX6j+ne+E+qqur2ZSUFPb+++9n//77bzY3N5fds2cPm5OTo9+G7od7ePXVV9mIiAh2x44dbG5uLvvVV1+xwcHB7IoVK/Tb0L1wDTt37mSfeeYZ9ptvvmEBsNnZ2W3Wc7nuc+fOZRMSEtiffvqJPXLkCHvDDTewffr0YVUqlU22kIjwAsrLy1kA7L59+1iWZVmNRsPGxsayb7zxhn6b1tZWViqVsqtXr/aUmT5NQ0MDm56ezv7000/siBEj9CKC7oV7Wbx4MTt8+HCz6+l+uI9x48axDzzwQJvP7rjjDnbq1Kksy9K9cBfGIoLLda+trWXFYjH75Zdf6rcpKipiBQIB+7///c+m9mk4wwuoq6sDAMhkMgBAbm4uSktLMXr0aP02EokEI0aMwJ9//ukRG32defPmYdy4cbjpppvafE73wr1s27YNAwcOxN13343o6Gj069cPa9eu1a+n++E+hg8fjp9//hnnz58HABw/fhz79+/H2LFjAdC98BRcrvvhw4ehVCrbbBMfH4+ePXvafG84F+AiPAPLsnj88ccxfPhw9OzZEwBQWloKAIiJiWmzbUxMDPLy8txuo6/z5Zdf4siRIzh06FC7dXQv3MulS5ewatUqPP744/jPf/6DgwcPYsGCBZBIJJg+fTrdDzeyePFi1NXVITMzE0KhEGq1Gq+99homT54MgP43PAWX615aWgo/Pz+Eh4e320a3P1dIRPCc+fPn48SJE9i/f3+7dQzDtFlmWbbdZ4RjFBQUYOHChdi9e7fFKrZ0L9yDRqPBwIED8frrrwMA+vXrh9OnT2PVqlWYPn26fju6H65ny5Yt2LhxI7744gv06NEDx44dw6JFixAfH48ZM2bot6N74Rnsue723BsazuAxjz76KLZt24a9e/ciMTFR/3lsbCwAtFOM5eXl7dQn4RiHDx9GeXk5BgwYAJFIBJFIhH379mHlypUQiUT66033wj3ExcWhe/fubT7r1q0b8vPzAdD/hjt56qmn8H//93+499570atXL0ybNg2PPfYYli5dCoDuhafgct1jY2OhUChQU1NjdhuukIjgISzLYv78+fj222/xyy+/oFOnTm3Wd+rUCbGxsfjpp5/0nykUCuzbtw9Dhw51t7k+zahRo3Dy5EkcO3ZM/zNw4EBMmTIFx44dQ1paGt0LNzJs2LB2053Pnz+PlJQUAPS/4U6am5shELTtQoRCoX6KJ90Lz8Dlug8YMABisbjNNiUlJTh16pTt98a+eFDClTz88MOsVCplf/31V7akpET/09zcrN/mjTfeYKVSKfvtt9+yJ0+eZCdPnkxTp9yE4ewMlqV74U4OHjzIikQi9rXXXmMvXLjAbtq0iQ0MDGQ3btyo34buh3uYMWMGm5CQoJ/i+e2337KRkZHs008/rd+G7oVraGhoYI8ePcoePXqUBcAuX76cPXr0KJuXl8eyLLfrPnfuXDYxMZHds2cPe+TIEfbGG2+kKZ6+AgCTP5999pl+G41Gw77wwgtsbGwsK5FI2Ouvv549efKk54zuQBiLCLoX7mX79u1sz549WYlEwmZmZrIff/xxm/V0P9xDfX09u3DhQjY5OZn19/dn09LS2GeeeYaVy+X6beheuIa9e/ea7CNmzJjBsiy3697S0sLOnz+flclkbEBAAHvbbbex+fn5NttCpcAJgiAIgrALiokgCIIgCMIuSEQQBEEQBGEXJCIIgiAIgrALEhEEQRAEQdgFiQiCIAiCIOyCRARBEARBEHZBIoIgCIIgCLsgEUEQBEEQhF2QiCAIgiAIwi5IRBBEB2PTpk1ISkqCTCbDU0891Wbd5cuXkZGRgfr6eg9ZRxCEN0FprwmiA1FZWYmkpCSsW7cOaWlpGDduHD777DOMGzcOADBmzBjMnj0bd9xxh4ctJQjCGyBPBEF0IC5dugSpVIpJkyZh0KBBuOGGG/Dvv/8CAL744gv4+fmRgCAIgjMkIgiiA5Geno7m5mYcPXoU1dXVOHToEHr37o3q6mo8//zz+OCDDzxtIkEQXgSJCILoQISHh2P9+vWYPn06Bg8ejOnTp+OWW27Bk08+iUcffRS5ubno168fevbsia+//trT5hIEwXMoJoIgOji//vornnrqKezbtw9dunTB5s2bERsbi8GDB+PChQuIjo72tIkEQfAU8kQQRAdGLpfjkUcewZo1a5CTkwOVSoURI0aga9euyMjIwN9//+1pEwmC4DEkIgiiA/PKK69gzJgx6N+/P9RqNVQqlX6dUqmEWq32oHUEQfAdkacNIAjCM5w+fRpbtmzBsWPHAACZmZkQCAT49NNPERsbi7Nnz2LQoEGeNZIgCF5DIoIgOiAsy2LOnDl49913ERQUBAAICAjAunXrMG/ePMjlcnzwwQdISEjwsKUEQfAZCqwkCIIgCMIuKCaCIAiCIAi7IBFBEARBEIRdkIggCIIgCMIuSEQQBEEQBGEXJCIIgiAIgrALEhEEQRAEQdgFiQiCIAiCIOyCRARBEARBEHZBIoIgCIIgCLsgEUEQBEEQhF2QiCAIgiAIwi7+H+u6HPyAqGXTAAAAAElFTkSuQmCC",
+ "text/plain": [
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ "<xarray.Dataset> Size: 161MB\n", + "Dimensions: (x: 2400, y: 2400)\n", + "Coordinates:\n", + " band int64 8B 1\n", + " * x (x) float64 19kB -1.001e+07 ... -8.89...\n", + " * y (y) float64 19kB 4.448e+06 ... 3.336e+06\n", + " spatial_ref int64 8B ...\n", + "Data variables:\n", + " NDSI_Snow_Cover (y, x) float32 23MB dask.array<chunksize=(2400, 2400), meta=np.ndarray>\n", + " NDSI_Snow_Cover_Basic_QA (y, x) float32 23MB dask.array<chunksize=(2400, 2400), meta=np.ndarray>\n", + " NDSI_Snow_Cover_Algorithm_Flags_QA (y, x) float32 23MB dask.array<chunksize=(2400, 2400), meta=np.ndarray>\n", + " NDSI (y, x) float32 23MB dask.array<chunksize=(2400, 2400), meta=np.ndarray>\n", + " Snow_Albedo_Daily_Tile (y, x) float32 23MB dask.array<chunksize=(2400, 2400), meta=np.ndarray>\n", + " orbit_pnt (y, x) float32 23MB dask.array<chunksize=(2400, 2400), meta=np.ndarray>\n", + " granule_pnt (y, x) float32 23MB dask.array<chunksize=(2400, 2400), meta=np.ndarray>\n", + "Attributes: (12/94)\n", + " ALGORITHMPACKAGEACCEPTANCEDATE: 12-2005\n", + " ALGORITHMPACKAGEMATURITYCODE: Normal\n", + " ALGORITHMPACKAGENAME: MOD_PR10A1\n", + " ALGORITHMPACKAGEVERSION: 5\n", + " ASSOCIATEDINSTRUMENTSHORTNAME.1: MODIS\n", + " ASSOCIATEDPLATFORMSHORTNAME.1: Terra\n", + " ... ...\n", + " SOUTHBOUNDINGCOORDINATE: 29.9999999973059\n", + " SPSOPARAMETERS: none\n", + " TileID: 51009005\n", + " VERSIONID: 61\n", + " VERTICALTILENUMBER: 5\n", + " WESTBOUNDINGCOORDINATE: -117.486656023174
<cartopy.crs.Sinusoidal object at 0x725fa04b0c20>" + ], + "text/plain": [ + "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Get state boundaries\n",
+ "states = cfeature.NaturalEarthFeature(\n",
+ " category='cultural',\n",
+ " name='admin_1_states_provinces_lines',\n",
+ " scale='50m',\n",
+ " facecolor='none')\n",
+ "\n",
+ "# Set map projection to Albers Equal Area with\n",
+ "# projection parameters for contiguous US\n",
+ "# From Snyder (https://pubs.usgs.gov/pp/1395/report.pdf)\n",
+ "map_proj = ccrs.AlbersEqualArea(\n",
+ " central_longitude=-100., \n",
+ " central_latitude=40., \n",
+ " standard_parallels=(29.5, 45.5)) \n",
+ "\n",
+ "# Set colormap and normalization\n",
+ "norm = Normalize(vmin=0.001, vmax=100)\n",
+ "cmap = mpl.colormaps['Blues']\n",
+ "# cmap='Blues'\n",
+ "\n",
+ "p = modis.NDSI_Snow_Cover.plot(\n",
+ " subplot_kws=dict(projection=map_proj),\n",
+ " transform=modis_projection,\n",
+ " norm=norm,\n",
+ " cmap=cmap,\n",
+ " cbar_kwargs={\"extend\": \"neither\", \"orientation\": \"horizontal\", \"label\": \"%\", \"pad\": 0.01},\n",
+ ")\n",
+ "p.cmap.set_over(\"0.25\")\n",
+ "p.cmap.set_under(\"none\")\n",
+ "\n",
+ "# Add state boundaries\n",
+ "p.axes.add_feature(states, edgecolor=\"0.75\")\n",
+ "p.axes.add_feature(cfeature.COASTLINE)\n",
+ "p.axes.add_feature(cfeature.BORDERS)\n",
+ "p.axes.add_feature(cfeature.OCEAN)\n",
+ "p.axes.add_feature(cfeature.LAND)\n",
+ "\n",
+ "p.axes.set_title(\"MODIS Snow Cover Fraction\");"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Plot MODIS Snow Cover for GPR Survey Region\n",
+ "\n",
+ "_I am not sure if we use just use this section and delete the preceding section. If we use just this section, then I will copy some of the text from above here._\n",
+ "\n",
+ "We want to be able to match MODIS snow cover fraction with the GPR Survey points. A good first step is to visualize the MODIS data and GPR survey transect. To do this, we'll clip the MODIS data to the bounding box of the survey data, using a similar approach to clipping the ASO data."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We define the bounding box of the SnowEx GPR data in the MODIS coordinate system. Then we use this `clip_region` to clip the MODIS snow cover."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "clip_region = [box(*snowex_gpr.to_crs(modis.rio.crs).total_bounds)]\n",
+ "snow_cover_clipped = modis.NDSI_Snow_Cover.rio.clip(clip_region, all_touched=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Unlike the plot of MODIS data above for the western US, we will use the MODIS Sinusoidal projection for our plot over the GPR region."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "map_proj = modis_projection\n",
+ "\n",
+ "# Define based on search polygon\n",
+ "# coords = roi_polygon_gdf.to_crs(map_proj.to_wkt()).geometry.get_coordinates()\n",
+ "# roi_bbox_map = [coords.x.min(), coords.y.min(), coords.x.max(), coords.y.max()]\n",
+ "\n",
+ "norm = Normalize(vmin=0.001, vmax=100)\n",
+ "# cmap = Colormap('Blues')\n",
+ "cmap='Blues'\n",
+ "\n",
+ "# p = modis.NDSI_Snow_Cover.rio.clip(box(*roi_bbox_map)).plot(\n",
+ "p = snow_cover_clipped.plot(\n",
+ " subplot_kws=dict(projection=map_proj),\n",
+ " transform=modis_projection,\n",
+ " norm=norm,\n",
+ " cmap=cmap,\n",
+ " cbar_kwargs={\n",
+ " \"extend\": \"neither\", \n",
+ " \"orientation\": \"horizontal\", \n",
+ " \"label\": \"%\", \n",
+ " \"pad\": 0.01},\n",
+ ")\n",
+ "p.cmap.set_over(\"0.25\")\n",
+ "p.cmap.set_under(\"none\")\n",
+ "\n",
+ "# Add SNOTEL location\n",
+ "snowex_gpr.to_crs(map_proj).plot(ax=p.axes, c=\"k\")\n",
+ "\n",
+ "p.axes.set_title(\"MODIS Snow Cover Fraction\");"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Extract Snow Cover From Modis for GPR Survey\n",
+ "\n",
+ "We can use a similar approach to the one we used to extract the ASO snow thickness to extract snow cover fraction. However, in this case we are going to select the values for MODIS pixels nearest to the survey points.\n",
+ "\n",
+ "We first convert the x and y coordinates of the survey points to the MODIS CRS. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x = xr.DataArray(snowex_gpr.to_crs(modis.rio.crs).geometry.x, dims=[\"point\"])\n",
+ "y = xr.DataArray(snowex_gpr.to_crs(modis.rio.crs).geometry.y, dims=[\"point\"])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The we use the `sel` method to extract the nearest data points."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "modis_snow_cover_point = modis.NDSI_Snow_Cover.sel(x=x, y=y, method=\"nearest\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 48,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ " Size: 655kB\n",
+ "dask.array\n",
+ "Coordinates:\n",
+ " band int64 8B 1\n",
+ " x (point) float64 1MB -9.333e+06 -9.333e+06 ... -9.333e+06\n",
+ " y (point) float64 1MB 4.341e+06 4.341e+06 ... 4.341e+06 4.341e+06\n",
+ " spatial_ref int64 8B ...\n",
+ " * point (point) int64 1MB 0 1 2 3 4 ... 163760 163761 163762 163763\n",
+ "Attributes:\n",
+ " Key: 0-100=NDSI snow, 200=missing data, 201=no decision, 211=nig...\n",
+ " long_name: NDSI snow cover from best observation of the day\n",
+ " units: none\n",
+ " valid_range: 0, 100"
+ ]
+ },
+ "execution_count": 48,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "modis_snow_cover_point"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As with the ASO data, we add the MODIS snow cover as a column to the SnowEx GPR `geopandas.GeoDataFrame`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 49,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "snowex_gpr[\"modis_scf\"] = modis_snow_cover_point.to_pandas()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 50,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
-# return some_value # Not required unless you need to return a value
-#
-#----------------------------------------------------------------------
-
-import h5py
-from pathlib import Path
-import pandas as pd
-import numpy as np
-import geopandas as gpd
-from datetime import datetime, timedelta
-import pyproj
-import requests
-import json
-from statistics import mean
-from xml.etree import ElementTree as ET
-import os
-import pprint
-import shutil
-import zipfile
-import io
-import time
-import itertools
-from urllib.parse import urlparse
-import netrc
-import base64
-from urllib.error import HTTPError, URLError
-from urllib.request import urlopen, Request, build_opener, HTTPCookieProcessor
-from getpass import getpass
-
-
-def granule_info(data_dict):
- '''
- Prints number of granules based on inputted data set short name, version, bounding box, and temporal range. Queries the CMR and pages over results.
-
- data_dict - a dictionary with the following CMR keywords:
- 'short_name',
- 'version',
- 'bounding_box',
- 'temporal'
- '''
- # set CMR API endpoint for granule search
- granule_search_url = 'https://cmr.earthdata.nasa.gov/search/granules'
-
- # add page size and page num to dictionary
- data_dict['page_size'] = 2000
- data_dict['page_num'] = 1
-
- granules = []
- headers={'Accept': 'application/json'}
- while True:
- response = requests.get(granule_search_url, params=data_dict, headers=headers)
- results = json.loads(response.content)
-
- if len(results['feed']['entry']) == 0:
- # Out of results, so break out of loop
- data_dict['page_num'] -= 1
- break
-
- # Collect results and increment page_num
- granules.extend(results['feed']['entry'])
- data_dict['page_num'] += 1
-
- # calculate granule size
- granule_sizes = [float(granule['granule_size']) for granule in granules]
- print('There are', len(granules), 'files of', data_dict['short_name'], 'version', data_dict['version'], 'over my area and time of interest.')
- print(f'The average size of each file is {mean(granule_sizes):.2f} MB and the total size of all {len(granules)} granules is {sum(granule_sizes):.2f} MB')
- return len(granules)
-
-def merge_intervals(intervals):
- sorted_by_lower_bound = sorted(intervals, key=lambda tup: tup[0])
- merged = []
- for higher in sorted_by_lower_bound:
- if not merged:
- merged.append(higher)
- else:
- lower = merged[-1]
- # test for intersection between lower and higher:
- # we know via sorting that lower[0] <= higher[0]
- if higher[0] <= lower[1]:
- upper_bound = max(lower[1], higher[1])
- merged[-1] = (lower[0], upper_bound) # replace by merged interval
- else:
- merged.append(higher)
- return merged
-
-
-def time_overlap(data_dict):
- '''
- Prints dataframe with file names, dataset_id, start date, and end date for all files that overlap in temporal range across all data sets in dictionary
-
- data_dict - a dictionary with the following CMR keywords:
- 'short_name',
- 'version',
- 'bounding_box',
- 'temporal'
- '''
- # set CMR API endpoint for granule search
- granule_search_url = 'https://cmr.earthdata.nasa.gov/search/granules'
- headers= {'Accept': 'application/json'}
-
- # Create dataframe with identifiers and temporal ranges
- granules = []
- column_names = ['dataset_id', 'short_name','version', 'producer_granule_id', 'start_date', 'end_date']
- df = pd.DataFrame(columns = column_names)
- for k, v in data_dict.items():
- # add page size and page num to dictionary
- data_dict[k]['page_size'] = 2000
- data_dict[k]['page_num'] = 1
-
- while True:
- response = requests.get(granule_search_url, params=data_dict[k], headers=headers)
- results = json.loads(response.content)
- if len(results['feed']['entry']) == 0:
- # Out of results, so break out of loop
- data_dict[k]['page_num'] -= 1
- break
- # Collect results and increment page_num
- granules.extend(results['feed']['entry'])
- data_dict[k]['page_num'] += 1
- # compile lists from granule metadata
- dataset_id = [granule['dataset_id'] for granule in granules]
- title = [granule['title'] for granule in granules]
- producer_granule_id = [granule['producer_granule_id'] for granule in granules]
- start_date = [granule['time_start'] for granule in granules]
- end_date = [granule['time_end'] for granule in granules]
-
- # split title to feed short_name and version lists
- title_split = [i.split(':') for i in title]
- name = [i[1] for i in title_split]
- name_split = [i.split('.') for i in name]
-
- df['dataset_id'] = dataset_id
- df['short_name'] = [i[0] for i in name_split]
- df['version'] = [i[1] for i in name_split]
- df['producer_granule_id'] = producer_granule_id
- df['start_date'] = start_date
- df['end_date'] = end_date
-
- # Convert state time to integers
- df['start_int'] = pd.DatetimeIndex(df['start_date']).astype(np.int64)
- df['end_int'] = pd.DatetimeIndex(df['end_date']).astype(np.int64)
-
- merged = merge_intervals(zip(df['start_int'], df['end_int']))
- df['overlap_group'] = df['start_int'].apply(lambda x: next(i for i, m in enumerate(merged) if m[0] <= x <= m[1]))
-
- # Find each unique value in overlap_group
- len_datasets = len(df.dataset_id.unique())
- len_groups = len(df.overlap_group.unique())
- unique_group = list(df.overlap_group.unique())
-
- # Loop over each overlap group
- tempdf = df.copy()
-
- for i in range(len_groups):
- tempdf = df.copy()
- # Filter rows corresponding to unique_group value
- filter_df = tempdf.loc[tempdf['overlap_group'] == unique_group[i]]
- # If not all datasets exist, remove this group from our main tempdf
- filter_len_datasets = len(filter_df.dataset_id.unique())
- if filter_len_datasets < len_datasets: df = df.loc[df.overlap_group != unique_group[i]]
-
- df = df.drop(columns=['start_int', 'end_int', 'overlap_group'])
- return df
-
-def get_username():
- username = ''
-
- # For Python 2/3 compatibility:
- try:
- do_input = raw_input # noqa
- except NameError:
- do_input = input
-
- while not username:
- try:
- username = do_input('Earthdata username: ')
- except KeyboardInterrupt:
- quit()
- return username
-
-
-def get_password():
- password = ''
- while not password:
- try:
- password = getpass('password: ')
- except KeyboardInterrupt:
- quit()
- return password
-
-def get_credentials(url):
- URS_URL = 'https://urs.earthdata.nasa.gov'
- """Get user credentials from .netrc or prompt for input."""
- credentials = None
- errprefix = ''
- try:
- info = netrc.netrc()
- username, account, password = info.authenticators(urlparse(URS_URL).hostname)
- errprefix = 'netrc error: '
- except Exception as e:
- if (not ('No such file' in str(e))):
- print('netrc error: {0}'.format(str(e)))
- username = None
- password = None
-
- while not credentials:
- if not username:
- username = get_username()
- password = get_password()
- credentials = '{0}:{1}'.format(username, password)
- credentials = base64.b64encode(credentials.encode('ascii')).decode('ascii')
-
- if url:
- try:
- req = Request(url)
- req.add_header('Authorization', 'Basic {0}'.format(credentials))
- opener = build_opener(HTTPCookieProcessor())
- opener.open(req)
- except HTTPError:
- print(errprefix + 'Incorrect username or password')
- errprefix = ''
- credentials = None
- username = None
- password = None
-
- return credentials
-
-def cmr_filter_urls(search_results):
- """Select only the desired data files from CMR response."""
- if 'feed' not in search_results or 'entry' not in search_results['feed']:
- return []
-
- entries = [e['links']
- for e in search_results['feed']['entry']
- if 'links' in e]
- # Flatten "entries" to a simple list of links
- links = list(itertools.chain(*entries))
-
- urls = []
- unique_filenames = set()
- for link in links:
- if 'href' not in link:
- # Exclude links with nothing to download
- continue
- if 'inherited' in link and link['inherited'] is True:
- # Why are we excluding these links?
- continue
- if 'rel' in link and 'data#' not in link['rel']:
- # Exclude links which are not classified by CMR as "data" or "metadata"
- continue
- if 'title' in link and 'opendap' in link['title'].lower():
- # Exclude OPeNDAP links--they are responsible for many duplicates
- # This is a hack; when the metadata is updated to properly identify
- # non-datapool links, we should be able to do this in a non-hack way
- continue
-
- filename = link['href'].split('/')[-1]
- if filename in unique_filenames:
- # Exclude links with duplicate filenames (they would overwrite)
- continue
- unique_filenames.add(filename)
-
- urls.append(link['href'])
-
- return urls
-
-def build_cmr_query_url(short_name, version, time_start, time_end,
- bounding_box=None, polygon=None,
- filename_filter=None):
- params = '&short_name={0}'.format(short_name)
- params += version
- params += '&temporal[]={0},{1}'.format(time_start, time_end)
- if polygon:
- params += '&polygon={0}'.format(polygon)
- elif bounding_box:
- params += '&bounding_box={0}'.format(bounding_box)
- if filename_filter:
- option = '&options[producer_granule_id][pattern]=true'
- params += '&producer_granule_id[]={0}{1}'.format(filename_filter, option)
- return CMR_FILE_URL + params
-
-def cmr_download(urls):
- """Download files from list of urls."""
- URS_URL = 'https://urs.earthdata.nasa.gov'
- if not urls:
- return
-
- url_count = len(urls)
- print('Downloading {0} files...'.format(url_count))
- credentials = None
-
- for index, url in enumerate(urls, start=1):
- if not credentials and urlparse(url).scheme == 'https':
- credentials = get_credentials(url)
-
- filename = url.split('/')[-1]
- filename = 'nsidc_api_output.zip' if filename.startswith('request') else filename
- print('{0}/{1}: {2}'.format(str(index).zfill(len(str(url_count))),
- url_count,
- filename))
-
- try:
- # In Python 3 we could eliminate the opener and just do 2 lines:
- # resp = requests.get(url, auth=(username, password))
- # open(filename, 'wb').write(resp.content)
- req = Request(url)
- if credentials:
- req.add_header('Authorization', 'Basic {0}'.format(credentials))
- opener = build_opener(HTTPCookieProcessor())
- data = opener.open(req).read()
- open(filename, 'wb').write(data)
- except HTTPError as e:
- print('HTTP error {0}, {1}'.format(e.code, e.reason))
- except URLError as e:
- print('URL error: {0}'.format(e.reason))
- except IOError:
- raise
- except KeyboardInterrupt:
- quit()
-
-
-def print_service_options(data_dict, response):
- '''
- Prints the available subsetting, reformatting, and reprojection services available based on inputted data set name, version, and Earthdata Login username and password.
-
- data_dict - a dictionary with the following keywords:
- 'short_name',
- 'version',
- 'uid',
- 'pswd'
- '''
-
- root = ET.fromstring(response.content)
-
- #collect lists with each service option
- subagent = [subset_agent.attrib for subset_agent in root.iter('SubsetAgent')]
-
- # variable subsetting
- variables = [SubsetVariable.attrib for SubsetVariable in root.iter('SubsetVariable')]
- variables_raw = [variables[i]['value'] for i in range(len(variables))]
- variables_join = [''.join(('/',v)) if v.startswith('/') == False else v for v in variables_raw]
- variable_vals = [v.replace(':', '/') for v in variables_join]
-
- # reformatting
- formats = [Format.attrib for Format in root.iter('Format')]
- format_vals = [formats[i]['value'] for i in range(len(formats))]
- if format_vals : format_vals.remove('')
-
- # reprojection options
- projections = [Projection.attrib for Projection in root.iter('Projection')]
- proj_vals = []
- for i in range(len(projections)):
- if (projections[i]['value']) != 'NO_CHANGE' :
- proj_vals.append(projections[i]['value'])
-
- #print service information depending on service availability and select service options
- print('Services available for', data_dict['short_name'],':')
- print()
- if len(subagent) < 1 :
- print('No customization services available.')
- else:
- subdict = subagent[0]
- if subdict['spatialSubsetting'] == 'true':
- print('Bounding box subsetting')
- if subdict['spatialSubsettingShapefile'] == 'true':
- print('Shapefile subsetting')
- if subdict['temporalSubsetting'] == 'true':
- print('Temporal subsetting')
- if len(variable_vals) > 0:
- print('Variable subsetting')
- if len(format_vals) > 0 :
- print('Reformatting to the following options:', format_vals)
- if len(proj_vals) > 0 :
- print('Reprojection to the following options:', proj_vals)
-
-
-
-
-def request_data(param_dict,session):
- '''
- Request data from NSIDC's API based on inputted key-value-pairs from param_dict.
- Different request methods depending on 'async' or 'sync' options.
-
- In addition to param_dict, input Earthdata login `uid` and `pswd`.
- '''
-
- # Create an output folder if the folder does not already exist.
- path = str(os.getcwd() + '/Outputs')
- if not os.path.exists(path):
- os.mkdir(path)
-
- # Define base URL
- base_url = 'https://n5eil02u.ecs.nsidc.org/egi/request'
-
- # Different access methods depending on request mode:
-
- if param_dict['request_mode'] == 'async':
- request = session.get(base_url, params=param_dict)
- print('Request HTTP response: ', request.status_code)
-
- # Raise bad request: Loop will stop for bad response code.
- request.raise_for_status()
- print()
- print('Order request URL: ', request.url)
- print()
- esir_root = ET.fromstring(request.content)
- #print('Order request response XML content: ', request.content)
-
- #Look up order ID
- orderlist = []
- for order in esir_root.findall("./order/"):
- orderlist.append(order.text)
- orderID = orderlist[0]
- print('order ID: ', orderID)
-
- #Create status URL
- statusURL = base_url + '/' + orderID
- print('status URL: ', statusURL)
-
- #Find order status
- request_response = session.get(statusURL)
- print('HTTP response from order response URL: ', request_response.status_code)
-
- # Raise bad request: Loop will stop for bad response code.
- request_response.raise_for_status()
- request_root = ET.fromstring(request_response.content)
- statuslist = []
- for status in request_root.findall("./requestStatus/"):
- statuslist.append(status.text)
- status = statuslist[0]
- #print('Data request is submitting...')
- print()
- print('Initial request status is ', status)
- print()
-
- #Continue loop while request is still processing
- loop_response = session.get(statusURL)
- loop_root = ET.fromstring(loop_response.content)
- while status == 'pending' or status == 'processing':
- print('Status is not complete. Trying again.')
- time.sleep(10)
- loop_response = session.get(statusURL)
-
- # Raise bad request: Loop will stop for bad response code.
- loop_response.raise_for_status()
- loop_root = ET.fromstring(loop_response.content)
-
- #find status
- statuslist = []
- for status in loop_root.findall("./requestStatus/"):
- statuslist.append(status.text)
- status = statuslist[0]
- print('Retry request status is: ', status)
- if status == 'pending' or status == 'processing':
- continue
-
- #Order can either complete, complete_with_errors, or fail:
- # Provide complete_with_errors error message:
- if status == 'failed':
- messagelist = []
- for message in loop_root.findall("./processInfo/"):
- messagelist.append(message.text)
- print('error messages:')
- pprint.pprint(messagelist)
- print()
-
- # Download zipped order if status is complete or complete_with_errors
- if status == 'complete' or status == 'complete_with_errors':
- downloadURL = 'https://n5eil02u.ecs.nsidc.org/esir/' + orderID + '.zip'
- print('Zip download URL: ', downloadURL)
- print('Beginning download of zipped output...')
- zip_response = session.get(downloadURL)
- # Raise bad request: Loop will stop for bad response code.
- zip_response.raise_for_status()
- with zipfile.ZipFile(io.BytesIO(zip_response.content)) as z:
- z.extractall(path)
- print('Data request is complete.')
- else: print('Request failed.')
-
- else:
- print('Requesting...')
- request = session.get(s.url,auth=(uid,pswd))
- print('HTTP response from order response URL: ', request.status_code)
- request.raise_for_status()
- d = request.headers['content-disposition']
- fname = re.findall('filename=(.+)', d)
- dirname = os.path.join(path,fname[0].strip('\"'))
- print('Downloading...')
- open(dirname, 'wb').write(request.content)
- print('Data request is complete.')
-
- # Unzip outputs
- for z in os.listdir(path):
- if z.endswith('.zip'):
- zip_name = path + "/" + z
- zip_ref = zipfile.ZipFile(zip_name)
- zip_ref.extractall(path)
- zip_ref.close()
- os.remove(zip_name)
-
-
-def clean_folder():
- '''
- Cleans up output folder by removing individual granule folders.
-
- '''
- path = str(os.getcwd() + '/Outputs')
-
- for root, dirs, files in os.walk(path, topdown=False):
- for file in files:
- try:
- shutil.move(os.path.join(root, file), path)
- except OSError:
- pass
- for name in dirs:
- os.rmdir(os.path.join(root, name))
-
-
-def load_icesat2_as_dataframe(filepath, VARIABLES):
- '''
- Load points from an ICESat-2 granule 'gt' groups as DataFrame of points. Uses VARIABLES mapping
- to select subset of '/gt/...' variables (Assumes these variables share dimensions)
- Arguments:
- filepath to ATL0# granule
- '''
-
- ds = h5py.File(filepath, 'r')
-
- # Get dataproduct name
- dataproduct = ds.attrs['identifier_product_type'].decode()
- # Convert variable paths to 'Path' objects for easy manipulation
- variables = [Path(v) for v in VARIABLES[dataproduct]]
- # Get set of beams to extract individially as dataframes combining in the end
- beams = {list(v.parents)[-2].name for v in variables}
-
- dfs = []
- for beam in beams:
- data_dict = {}
- beam_variables = [v for v in variables if beam in str(v)]
- for variable in beam_variables:
- # Use variable 'name' as column name. Beam will be specified in 'beam' column
- column = variable.name
- variable = str(variable)
- try:
- values = ds[variable][:]
- # Convert invalid data to np.nan (only for float columns)
- if 'float' in str(values.dtype):
- if 'valid_min' in ds[variable].attrs:
- values[values < ds[variable].attrs['valid_min']] = np.nan
- if 'valid_max' in ds[variable].attrs:
- values[values > ds[variable].attrs['valid_max']] = np.nan
- if '_FillValue' in ds[variable].attrs:
- values[values == ds[variable].attrs['_FillValue']] = np.nan
-
- data_dict[column] = values
- except KeyError:
- print(f'Variable {variable} not found in {filepath}. Likely an empty granule.')
- raise
-
- df = pd.DataFrame.from_dict(data_dict)
- df['beam'] = beam
- dfs.append(df)
-
- df = pd.concat(dfs, sort=True)
- # Add filename column for book-keeping and reset index
- df['filename'] = Path(filepath).name
- df = df.reset_index(drop=True)
-
- return df
-
-
-
-def convert_to_gdf(df):
- '''
- Converts a DataFrame of points with 'longitude' and 'latitude' columns to a
- GeoDataFrame
- '''
- gdf = gpd.GeoDataFrame(
- df,
- geometry=gpd.points_from_xy(df.longitude, df.latitude),
- crs={'init': 'epsg:4326'},
- )
-
- return gdf
-
-
-def convert_delta_time(delta_time):
- '''
- Convert ICESat-2 'delta_time' parameter to UTC datetime
- '''
- EPOCH = datetime(2018, 1, 1, 0, 0, 0)
-
- utc_datetime = EPOCH + timedelta(seconds=delta_time)
-
- return utc_datetime
-
-
-# def compute_distance(df):
-# '''
-# Calculates along track distance for each point within the 'gt1l', 'gt2l', and 'gt3l' beams, beginning with first beam index.
-
-# Arguments:
-# df: DataFrame with icesat-2 data
-
-# Returns:
-# add_dist added as new column to initial df
-# '''
-
-# beam_1 = df[df['beam'] == 'gt1l']
-# beam_2 = df[df['beam'] == 'gt2l']
-# beam_3 = df[df['beam'] == 'gt3l']
-
-# add_dist = []
-# add_dist.append(beam_1.height_segment_length_seg.values[0])
-
-# for i in range(1, len(beam_1)):
-# add_dist.append(add_dist[i-1] + beam_1.height_segment_length_seg.values[i])
-
-# add_dist_se = pd.Series(add_dist)
-# beam_1.insert(loc=0, column='add_dist', value=add_dist_se.values)
-# beam_1
-
-# add_dist = []
-# add_dist.append(beam_2.height_segment_length_seg.values[0])
-
-# for i in range(1, len(beam_2)):
-# add_dist.append(add_dist[i-1] + beam_2.height_segment_length_seg.values[i])
-
-# add_dist_se = pd.Series(add_dist)
-# beam_2.insert(loc=0, column='add_dist', value=add_dist_se.values)
-# beam_2
-
-# add_dist = []
-# add_dist.append(beam_3.height_segment_length_seg.values[0])
-
-# for i in range(1, len(beam_3)):
-# add_dist.append(add_dist[i-1] + beam_3.height_segment_length_seg.values[i])
-
-# add_dist_se = pd.Series(add_dist)
-# beam_3.insert(loc=0, column='add_dist', value=add_dist_se.values)
-# beam_3
-
-# beams = [beam_1,beam_2,beam_3]
-# df = pd.concat(beams,ignore_index=True)
-
-# return df
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+ "0 0.692 225 753854.880092 4.325659e+06 12 S \n",
+ "1 0.692 225 753854.899385 4.325660e+06 12 S \n",
+ "2 0.690 224 753854.918686 4.325660e+06 12 S \n",
+ "3 0.689 224 753854.937987 4.325660e+06 12 S \n",
+ "4 0.686 223 753854.957280 4.325660e+06 12 S \n",
+ "\n",
+ " geometry ASO modis_scf \n",
+ "0 POINT (-108.06686 39.04315) 0.725680 77.0 \n",
+ "1 POINT (-108.06686 39.04315) 0.726302 77.0 \n",
+ "2 POINT (-108.06686 39.04315) 0.726953 77.0 \n",
+ "3 POINT (-108.06686 39.04315) 0.727630 77.0 \n",
+ "4 POINT (-108.06686 39.04315) 0.728338 77.0 "
+ ]
+ },
+ "execution_count": 50,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "snowex_gpr.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Export SnowEx GeoDataFrame with ASO and MODIS snow cover to Shapefile"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Finally, the `snowex_gpr` dataframe can be exported as a shapefile for further analysis in GIS:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 51,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "snowex_gpr['date'] = snowex_gpr['date'].apply(lambda x: x.strftime(\"%Y-%m-%d\"))\n",
+ "snowex_gpr.to_file('snow-data-20170208.shp')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Additional data imagery services\n",
+ "\n",
+ "#### NASA Worldview and the Global Browse Imagery Service\n",
+ "\n",
+ "NASA’s EOSDIS Worldview mapping application provides the capability to interactively browse over 900 global, full-resolution satellite imagery layers and then download the underlying data. Many of the available imagery layers are updated within three hours of observation, essentially showing the entire Earth as it looks “right now.\"\n",
+ "\n",
+ "According to the [MOD10A1 landing page](https://nsidc.org/data/mod10a1), snow cover imagery layers from this data set are available through NASA Worldview. This layer can be downloaded as various image files including GeoTIFF using the snapshot feature at the top right of the page. This link presents the MOD10A1 NDSI layer over our time and area of interest: https://go.nasa.gov/35CgYMd. \n",
+ "\n",
+ "Additionally, the NASA Global Browse Imagery Service provides up to date, full resolution imagery for select NSIDC DAAC data sets as web services including WMTS, WMS, KML, and more. These layers can be accessed in GIS applications following guidance on the [GIBS documentation pages](https://wiki.earthdata.nasa.gov/display/GIBS/Geographic+Information+System+%28GIS%29+Usage). "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Cleanup\n",
+ "\n",
+ "To cleanup your directory, uncomment and run the cell below. This will remove the files you have downloaded to the download directory and the shapefile you have saved."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 53,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# !rm -rf download\n",
+ "# !rm snow-data-20170208.*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.18"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/SnowEx_ASO_MODIS_Snow/tutorial_helper_functions.py b/notebooks/SnowEx_ASO_MODIS_Snow/tutorial_helper_functions.py
deleted file mode 100644
index e51a273..0000000
--- a/notebooks/SnowEx_ASO_MODIS_Snow/tutorial_helper_functions.py
+++ /dev/null
@@ -1,659 +0,0 @@
-#----------------------------------------------------------------------
-# Functions for snow tutorial notebooks
-#
-# In Python a module is just a collection of functions in a file with
-# a .py extension.
-#
-# Functions are defined using:
-#
-# def function_name(argument1, arguments2,... keyword_arg1=some_variable)
-# '''A docstring explaining what the function does and what
-# arguments it expectes.
-# '''
-# | \n", + " | date | \n", + "collection | \n", + "trace | \n", + "long | \n", + "lat | \n", + "elev | \n", + "twtt | \n", + "Thickness | \n", + "SWE | \n", + "x | \n", + "y | \n", + "UTM_Zone | \n", + "geometry | \n", + "ASO | \n", + "modis_scf | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "2017-02-08 | \n", + "GPR_0042_020817 | \n", + "2581 | \n", + "-108.066856 | \n", + "39.043146 | \n", + "3240.2 | \n", + "5.89 | \n", + "0.692 | \n", + "225 | \n", + "753854.880092 | \n", + "4.325659e+06 | \n", + "12 S | \n", + "POINT (-108.06686 39.04315) | \n", + "0.725680 | \n", + "77.0 | \n", + "
| 1 | \n", + "2017-02-08 | \n", + "GPR_0042_020817 | \n", + "2582 | \n", + "-108.066856 | \n", + "39.043146 | \n", + "3240.2 | \n", + "5.89 | \n", + "0.692 | \n", + "225 | \n", + "753854.899385 | \n", + "4.325660e+06 | \n", + "12 S | \n", + "POINT (-108.06686 39.04315) | \n", + "0.726302 | \n", + "77.0 | \n", + "
| 2 | \n", + "2017-02-08 | \n", + "GPR_0042_020817 | \n", + "2583 | \n", + "-108.066856 | \n", + "39.043146 | \n", + "3240.2 | \n", + "5.87 | \n", + "0.690 | \n", + "224 | \n", + "753854.918686 | \n", + "4.325660e+06 | \n", + "12 S | \n", + "POINT (-108.06686 39.04315) | \n", + "0.726953 | \n", + "77.0 | \n", + "
| 3 | \n", + "2017-02-08 | \n", + "GPR_0042_020817 | \n", + "2584 | \n", + "-108.066855 | \n", + "39.043146 | \n", + "3240.2 | \n", + "5.86 | \n", + "0.689 | \n", + "224 | \n", + "753854.937987 | \n", + "4.325660e+06 | \n", + "12 S | \n", + "POINT (-108.06686 39.04315) | \n", + "0.727630 | \n", + "77.0 | \n", + "
| 4 | \n", + "2017-02-08 | \n", + "GPR_0042_020817 | \n", + "2585 | \n", + "-108.066855 | \n", + "39.043147 | \n", + "3240.2 | \n", + "5.84 | \n", + "0.686 | \n", + "223 | \n", + "753854.957280 | \n", + "4.325660e+06 | \n", + "12 S | \n", + "POINT (-108.06686 39.04315) | \n", + "0.728338 | \n", + "77.0 | \n", + "