From 3be6c0945ce0b81f37cfe72cb38058bf43467070 Mon Sep 17 00:00:00 2001 From: Andy Barrett Date: Wed, 2 Jul 2025 14:24:00 -0600 Subject: [PATCH 01/35] updated access, download and reading --- .../Snow-tutorial_rendered.ipynb | 4951 ++++++++++++++--- 1 file changed, 4064 insertions(+), 887 deletions(-) diff --git a/notebooks/SnowEx_ASO_MODIS_Snow/Snow-tutorial_rendered.ipynb b/notebooks/SnowEx_ASO_MODIS_Snow/Snow-tutorial_rendered.ipynb index 0adf60f..d4dc498 100644 --- a/notebooks/SnowEx_ASO_MODIS_Snow/Snow-tutorial_rendered.ipynb +++ b/notebooks/SnowEx_ASO_MODIS_Snow/Snow-tutorial_rendered.ipynb @@ -6,17 +6,22 @@ "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", + "## Overview\n", "\n", - "### Here are the steps you will learn in this snow data notebook:\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, or NSIDC DAAC. \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", + "## 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 buffer [??];\n", + "5. how to extract and visualize raster values at point locations;\n", + "6. how to save output as shapefile.\n", "\n", "\n", "---\n", @@ -29,14 +34,11 @@ "source": [ "___\n", "\n", - "## Explore snow products and resources\n", - "\n", - "\n", - "### NSIDC introduction\n", + "## Snow data and resources at NSIDC DAAC\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", + "[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. NSIDC creates supports data users, creates tools for data access, performs scientific research, and educates the public about the cryosphere. \n", "\n", - "#### Select Data Resources\n", + "#### Selected NSIDC DAAC and NASA Data Resources\n", "\n", "* [NSIDC Data Search](https://nsidc.org/data/search/#keywords=snow)\n", " * Search NSIDC snow data\n", @@ -52,25 +54,42 @@ "\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", + "### Snow-related missions and data sets featured in this tutorial:\n", "\n", - "* [SnowEx](https://nsidc.org/data/snowex)\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 | Landing Page URL |\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", + " \n", "#### Other relevant snow products:\n", "\n", + "In addition to the three datasets featured in this tutorial, NSIDC hosts many other snow datasets. A selection is listed below.\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" + "* [Near-Real-Time SSM/I-SSMIS EASE-Grid Daily Global Ice Concentration and Snow Extent (NISE), Version 5](https://doi.org/10.5067/3KB2JPLFPK3R)" ] }, { @@ -80,7 +99,7 @@ "___\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." + "We will start by importing the packages we use in this tutorial." ] }, { @@ -91,25 +110,38 @@ }, "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 os\n", + "# import datetime as dt\n", + "import dateutil\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", + "# from shapely.geometry.polygon import orient # Probably don't need this\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 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", + "# 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" + "# import tutorial_helper_functions as fn" ] }, { @@ -121,7 +153,7 @@ "\n", "## Data Discovery\n", "\n", - "Start by identifying your study area and exploring coincident data over the same time and area. \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", "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" @@ -131,154 +163,128 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Identify area and time of interest\n", + "### Get study area and time-range for SnowEx GPR\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", + "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", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Create polygon coordinate string\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.\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. " + "\n" ] }, { - "cell_type": "code", - "execution_count": 2, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Polygon coordinates to be used in search: -108.2352445938561,38.98556907427165,-107.85284607930835,38.978765032966244,-107.85494925720668,39.10596902171742,-108.22772795408136,39.11294532581687,-108.2352445938561,38.98556907427165\n" - ] - }, - { - "data": { - "image/svg+xml": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], "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", + "#### Method 1. Use Spatial Coverage from dataset landing page\n", "\n", - "gdf = gpd.read_file(polygon_filepath) #Return a GeoDataFrame object\n", + "The Overview section of the SnowEx17 GPR dataset landing page gives the **Spatial Coverage** of the data collection.\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", "\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" + "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": [ - "#### Set time range\n", + "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", - "We are interested in accessing files within each data set over the same time range, so we'll start by searching all of 2017." + "For the values above this is" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "temporal = '2017-01-01T00:00:00Z,2017-12-31T23:59:59Z' # Set temporal range" + "roi_bounding_box = (-108.22367, 39.11115, -107.85785, 38.9935)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Create data dictionary \n", + "We will use this with `earthaccess.search_data`.\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": 4, - "metadata": {}, - "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", - " }" + "_do we do this now, or below once I have defined all methods_" ] }, { "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", + "#### Method 2. Draw and export a region of interest using the Data Access Tool map\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": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "There are 3 files of SNEX17_GPR version 2 over my area and time of interest.\n", - "The average size of each file is 69.73 MB and the total size of all 3 granules is 209.20 MB\n", - "There are 5 files of ASO_3M_SD version 1 over my area and time of interest.\n", - "The average size of each file is 1689.92 MB and the total size of all 5 granules is 8449.60 MB\n", - "There are 364 files of MOD10A1 version 6 over my area and time of interest.\n", - "The average size of each file is 8.23 MB and the total size of all 364 granules is 2995.34 MB\n" - ] - } - ], - "source": [ - "for k, v in data_dict.items(): fn.granule_info(data_dict[k])" + "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 \"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": [ - "### 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. " + "We can use Geopandas to read the GeoJSON file. This returns a Geopandas GeoSeries." ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 3, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "19 total files returned\n" - ] - }, { "data": { "text/html": [ @@ -300,796 +306,3975 @@ " \n", " \n", " \n", - " dataset_id\n", - " short_name\n", - " version\n", - " producer_granule_id\n", - " start_date\n", - " end_date\n", + " geometry\n", " \n", " \n", " \n", " \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", - " \n", - " \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", - " \n", - " \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", - " \n", - " \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", - " \n", - " \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", - " \n", - " \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", - " \n", - " \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", - " \n", - " \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", - " \n", - " \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", - " \n", - " \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", - " \n", - " \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", - " \n", - " \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", - " \n", - " \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", - " \n", - " \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", - " \n", - " \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", - " \n", - " \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", - " \n", - " \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", - " \n", - " \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", - " \n", - " \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", + " POLYGON ((-108.23524 38.98557, -107.85285 38.9...\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 " + " geometry\n", + "0 POLYGON ((-108.23524 38.98557, -107.85285 38.9..." ] }, - "execution_count": 6, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "search_df = fn.time_overlap(data_dict)\n", - "print(len(search_df), ' total files returned')\n", - "search_df" + "roi_polygon_gdf = gpd.read_file('Data/nsidc-polygon.json')\n", + "roi_polygon_gdf" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "___\n", + "To define a polygon for `earthaccess`, we create a list of tuples from the GeoSeries.\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." + "_check that earthaccess checks orientation_" ] }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "scrolled": true - }, + "execution_count": 4, + "metadata": {}, "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']" + "[(-108.2352445938561, 38.98556907427165),\n", + " (-107.85284607930835, 38.978765032966244),\n", + " (-107.85494925720668, 39.10596902171742),\n", + " (-108.22772795408136, 39.11294532581687),\n", + " (-108.2352445938561, 38.98556907427165)]" ] }, - "execution_count": 7, + "execution_count": 4, "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" + "roi_polygon = [tuple(xy.values) for _, xy in roi_polygon_gdf.get_coordinates().iterrows()]\n", + "roi_polygon" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Additional data access and subsetting services\n", + "#### Method 3. Retrieve Spatial Coverage from metdata using `earthaccess`\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. " + "`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": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Datasets found: 1\n" + ] + } + ], + "source": [ + "r = earthaccess.search_datasets(\n", + " short_name = \"SNEX17_GPR\",\n", + " version = '2',\n", + " # cloud_hosted=True\n", + ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "#### Add service request options for MODIS data\n", + "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", + "_Add a description of UMM-C and UMM-G to CookBook_\n", + "\n", + "For the SnowEx17 GPR dataset, spatial extent is described as a bounding box. It can be found at:\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." + "```\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": 8, + "execution_count": 6, "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" - ] + "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": [ - "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'])" + "spatial_coverage = r[0]['umm']['SpatialExtent']['HorizontalSpatialDomain']['Geometry']['BoundingRectangles'][0]\n", + "spatial_coverage" ] }, { "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." + "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": 9, + "execution_count": 7, "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" - ] + "data": { + "text/plain": [ + "(-108.22367, 38.9935, -107.85785, 39.11115)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" } ], "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" + "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": [ - "### Download options\n", + "### Search for Data\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.***" + "Now that we have a bounding box for the SnowEx17 GPR we can start 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": 2, - "metadata": { - "tags": [] - }, + "execution_count": 8, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "zsh:1: command not found: aws\n" + "Granules found: 3\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" + "snowex_result = earthaccess.search_data(\n", + " short_name = \"SNEX17_GPR\",\n", + " version = '2',\n", + ")" ] }, { "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" + "There are three files found. We can get some basic information about these files." ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
collectiontracelonglatelevtwttThicknessSWExyUTM_Zone
0GPR_0042_0208172581-108.06685639.0431463240.25.890.692225753854.8800924.325659e+0612 S
1GPR_0042_0208172582-108.06685639.0431463240.25.890.692225753854.8993854.325660e+0612 S
2GPR_0042_0208172583-108.06685639.0431463240.25.870.690224753854.9186864.325660e+0612 S
3GPR_0042_0208172584-108.06685539.0431463240.25.860.689224753854.9379874.325660e+0612 S
4GPR_0042_0208172585-108.06685539.0431473240.25.840.686223753854.9572804.325660e+0612 S
\n", - "
" + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + "

Data: SnowEx17_GPR_Version2_Week1.csv

\n", + "

Size: 57.32 MB

\n", + "

Cloud Hosted: True

\n", + "
\n", + "
\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", + "Collection: {'EntryTitle': 'SnowEx17 Ground Penetrating Radar V002'}\n", + "Spatial coverage: {'HorizontalSpatialDomain': {'Geometry': {'GPolygons': [{'Boundary': {'Points': [{'Longitude': -108.06789, 'Latitude': 39.05189}, {'Longitude': -108.07092, 'Latitude': 39.04958}, {'Longitude': -108.13422, 'Latitude': 39.02644}, {'Longitude': -108.18504, 'Latitude': 39.04032}, {'Longitude': -108.2211, 'Latitude': 39.0357}, {'Longitude': -108.21534, 'Latitude': 39.01719}, {'Longitude': -108.18261, 'Latitude': 38.99637}, {'Longitude': -108.11049, 'Latitude': 39.00562}, {'Longitude': -108.06225, 'Latitude': 39.02413}, {'Longitude': -108.06213, 'Latitude': 39.03338}, {'Longitude': -108.08619, 'Latitude': 39.02876}, {'Longitude': -108.05301, 'Latitude': 39.04264}, {'Longitude': -108.05289, 'Latitude': 39.05189}, {'Longitude': -108.06786, 'Latitude': 39.0542}, {'Longitude': -108.06789, 'Latitude': 39.05189}]}}]}}}\n", + "Temporal coverage: {'RangeDateTime': {'BeginningDateTime': '2017-02-08T00:00:00.000Z', 'EndingDateTime': '2017-02-10T23:59:59.000Z'}}\n", + "Size(MB): 57.3195\n", + "Data: ['https://data.nsidc.earthdatacloud.nasa.gov/nsidc-cumulus-prod-protected/SNOWEX/SNEX17_GPR/2/2017/02/08/SnowEx17_GPR_Version2_Week1.csv']" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ "\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 " + "
\n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "

Data: SnowEx17_GPR_Version2_Week2.csv

\n", + "

Size: 85.52 MB

\n", + "

Cloud Hosted: True

\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " " + ], + "text/plain": [ + "Collection: {'EntryTitle': 'SnowEx17 Ground Penetrating Radar V002'}\n", + "Spatial coverage: {'HorizontalSpatialDomain': {'Geometry': {'GPolygons': [{'Boundary': {'Points': [{'Longitude': -107.88943, 'Latitude': 39.10738}, {'Longitude': -107.89539, 'Latitude': 39.10738}, {'Longitude': -107.95508, 'Latitude': 39.0912}, {'Longitude': -108.02372, 'Latitude': 39.07271}, {'Longitude': -108.09234, 'Latitude': 39.0542}, {'Longitude': -108.16078, 'Latitude': 39.04264}, {'Longitude': -108.2113, 'Latitude': 39.0357}, {'Longitude': -108.2113, 'Latitude': 39.03338}, {'Longitude': -108.20533, 'Latitude': 39.0195}, {'Longitude': -108.18454, 'Latitude': 39.00099}, {'Longitude': -108.12811, 'Latitude': 39.00099}, {'Longitude': -108.08653, 'Latitude': 39.00099}, {'Longitude': -108.02094, 'Latitude': 39.02644}, {'Longitude': -107.94938, 'Latitude': 39.0357}, {'Longitude': -107.93155, 'Latitude': 39.02413}, {'Longitude': -107.89867, 'Latitude': 39.04726}, {'Longitude': -107.85677, 'Latitude': 39.08195}, {'Longitude': -107.86257, 'Latitude': 39.10507}, {'Longitude': -107.88644, 'Latitude': 39.10969}, {'Longitude': -107.88943, 'Latitude': 39.10738}]}}]}}}\n", + "Temporal coverage: {'RangeDateTime': {'BeginningDateTime': '2017-02-14T00:00:00.000Z', 'EndingDateTime': '2017-02-17T23:59:59.000Z'}}\n", + "Size(MB): 85.516\n", + "Data: ['https://data.nsidc.earthdatacloud.nasa.gov/nsidc-cumulus-prod-protected/SNOWEX/SNEX17_GPR/2/2017/02/14/SnowEx17_GPR_Version2_Week2.csv']" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
\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", + " " + ], + "text/plain": [ + "Collection: {'EntryTitle': 'SnowEx17 Ground Penetrating Radar V002'}\n", + "Spatial coverage: {'HorizontalSpatialDomain': {'Geometry': {'GPolygons': [{'Boundary': {'Points': [{'Longitude': -108.06789, 'Latitude': 39.05189}, {'Longitude': -108.06792, 'Latitude': 39.04958}, {'Longitude': -108.08616, 'Latitude': 39.03107}, {'Longitude': -108.15531, 'Latitude': 39.0195}, {'Longitude': -108.14352, 'Latitude': 39.00331}, {'Longitude': -108.11049, 'Latitude': 39.00562}, {'Longitude': -108.05349, 'Latitude': 39.00562}, {'Longitude': -108.05334, 'Latitude': 39.01719}, {'Longitude': -108.02919, 'Latitude': 39.02876}, {'Longitude': -108.05586, 'Latitude': 39.0542}, {'Longitude': -108.06786, 'Latitude': 39.0542}, {'Longitude': -108.06789, 'Latitude': 39.05189}]}}]}}}\n", + "Temporal coverage: {'RangeDateTime': {'BeginningDateTime': '2017-02-21T00:00:00.000Z', 'EndingDateTime': '2017-02-25T23:59:59.000Z'}}\n", + "Size(MB): 66.3598\n", + "Data: ['https://data.nsidc.earthdatacloud.nasa.gov/nsidc-cumulus-prod-protected/SNOWEX/SNEX17_GPR/2/2017/02/21/SnowEx17_GPR_Version2_Week3.csv']" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "[None, None, None]" ] }, - "execution_count": 11, + "execution_count": 9, "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()" + "[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": [ - "### Convert to time values and extract a single day" + "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", + "In this vein, we will set a `survey_id` variable to `0` so that this is all we have to change to explore other GPR surveys." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "survey_id = 0" ] }, { "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:" + "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": [ + "#### 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 table [_add ref_] we now that the `short_name` for the ASO data is `ASO_3M_SD` and we want version 1." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Granules found: 1\n" + ] + } + ], + "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": 15, + "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \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", + " \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": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Granules found: 3\n" + ] + } + ], + "source": [ + "modis_result = earthaccess.search_data(\n", + " short_name = \"MOD10A1\",\n", + " version = \"61\",\n", + " bounding_box = roi_bbox,\n", + " temporal = temporal_range,\n", + " cloud_hosted=True\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are three MODIS scenes. We can use display again to see an overview of the results." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
\n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "

Data: MOD10A1.A2017039.h09v05.061.2021265053227.hdf

\n", + "

Size: 9.53 MB

\n", + "

Cloud Hosted: True

\n", + "
\n", + "
\n", + " \"Data\n", + "
\n", + "
\n", + "
\n", + "
\n", + " " + ], + "text/plain": [ + "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']" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
\n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "

Data: MOD10A1.A2017040.h09v05.061.2021265174122.hdf

\n", + "

Size: 8.04 MB

\n", + "

Cloud Hosted: True

\n", + "
\n", + "
\n", + " \"Data\n", + "
\n", + "
\n", + "
\n", + "
\n", + " " + ], + "text/plain": [ + "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']" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
\n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "

Data: MOD10A1.A2017041.h09v05.061.2021266011855.hdf

\n", + "

Size: 9.26 MB

\n", + "

Cloud Hosted: True

\n", + "
\n", + "
\n", + " \"Data\n", + "
\n", + "
\n", + "
\n", + "
\n", + " " + ], + "text/plain": [ + "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": 17, + "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. The 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": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Opening 3 granules, approx size: 0.2 GB\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "a34ba7887aad41979eefbd04fc182733", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "QUEUEING TASKS | : 0%| | 0/3 [00:00\n", + "\n", + "
collectiontracelonglatelevtwttThicknessSWExyUTM_Zonedate
0GPR_0042_0208172581-108.06685639.0431463240.205.890.692225753854.8800924.325659e+0612 S2017-02-08
109172GPR_0043_0208176360-108.06320939.0492023248.4911.491.350439754148.8537004.326342e+0612 S2017-02-08
109173GPR_0043_0208176361-108.06320939.0492023248.5011.561.358441754148.8825494.326342e+0612 S2017-02-08
\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
collectiontracelonglatelevtwttThicknessSWExyUTM_Zone
109174GPR_0043_0208176362-108.06320839.0492023248.5011.621.365444754148.9114074.326342e+060GPR_0042_0208172581-108.06685639.0431463240.25.890.692225753854.8800924.325659e+0612 S2017-02-08
109175GPR_0043_0208176363-108.06320839.0492023248.5011.641.368445754148.9474664.326342e+0612 S2017-02-081GPR_0042_0208172582-108.06685639.0431463240.25.890.692225753854.8993854.325660e+0612 S
2GPR_0042_0208172583-108.06685639.0431463240.25.870.690224753854.9186864.325660e+0612 S
3GPR_0042_0208172584-108.06685539.0431463240.25.860.689224753854.9379874.325660e+0612 S
4GPR_0042_0208172585-108.06685539.0431473240.25.840.686223753854.9572804.325660e+0612 S
\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": 19, + "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": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
collectiontracelonglatelevtwttThicknessSWExyUTM_Zone
collection
2017-02-08GPR_0042_0208172581-108.06685639.0431463240.25.890.692225753854.8800924.325659e+0612 S
2017-02-08GPR_0042_0208172582-108.06685639.0431463240.25.890.692225753854.8993854.325660e+0612 S
2017-02-08GPR_0042_0208172583-108.06685639.0431463240.25.870.690224753854.9186864.325660e+0612 S
2017-02-08GPR_0042_0208172584-108.06685539.0431463240.25.860.689224753854.9379874.325660e+0612 S
2017-02-08GPR_0042_0208172585-108.06685539.0431473240.25.840.686223753854.9572804.325660e+0612 S
\n", + "
" + ], + "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": 20, + "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": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
collectiontracelonglatelevtwttThicknessSWExyUTM_Zone
collection
2017-02-08GPR_0042_0208172581-108.06685639.0431463240.205.890.692225753854.8800924.325659e+0612 S
2017-02-08GPR_0042_0208172582-108.06685639.0431463240.205.890.692225753854.8993854.325660e+0612 S
2017-02-08GPR_0042_0208172583-108.06685639.0431463240.205.870.690224753854.9186864.325660e+0612 S
2017-02-08GPR_0042_0208172584-108.06685539.0431463240.205.860.689224753854.9379874.325660e+0612 S
2017-02-08GPR_0042_0208172585-108.06685539.0431473240.205.840.686223753854.9572804.325660e+0612 S
....................................
2017-02-08GPR_0043_02081798131-108.06682639.0431533242.825.580.656213753857.4282304.325660e+0612 S
2017-02-08GPR_0043_02081798132-108.06682639.0431523242.825.560.653212753857.4215814.325660e+0612 S
2017-02-08GPR_0043_02081798133-108.06682639.0431523242.815.470.643209753857.4149324.325660e+0612 S
2017-02-08GPR_0043_02081798134-108.06682739.0431523242.815.330.626203753857.4082754.325660e+0612 S
2017-02-08GPR_0043_02081798135-108.06682739.0431523242.805.310.624203753857.4016264.325660e+0612 S
\n", + "

163764 rows × 11 columns

\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": 21, + "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": 22, + "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 does have projected x and y coordinates. However, 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` fo the northern hemisphere UTM zone 12.\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
datecollectiontracelonglatelevtwttThicknessSWExyUTM_Zonegeometry
02017-02-08GPR_0042_0208172581-108.06685639.0431463240.25.890.692225753854.8800924.325659e+0612 SPOINT (-108.06686 39.04315)
12017-02-08GPR_0042_0208172582-108.06685639.0431463240.25.890.692225753854.8993854.325660e+0612 SPOINT (-108.06686 39.04315)
22017-02-08GPR_0042_0208172583-108.06685639.0431463240.25.870.690224753854.9186864.325660e+0612 SPOINT (-108.06686 39.04315)
32017-02-08GPR_0042_0208172584-108.06685539.0431463240.25.860.689224753854.9379874.325660e+0612 SPOINT (-108.06686 39.04315)
42017-02-08GPR_0042_0208172585-108.06685539.0431473240.25.840.686223753854.9572804.325660e+0612 SPOINT (-108.06686 39.04315)
\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": 23, + "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": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAbIAAAG+CAYAAAD2sYFnAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8ekN5oAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOydebwkVXm/n/ecquru23eZGWZjvTMDKC5oFBIEF+ISjMYt8hOjxjVK0KgBjCKKRkVkFQiiotEQ0LhEEZfERElU3HAN7hvLzGWZjdnu1ktVnfP+/jjVd4EBZoaZuTPMefgUt293ddWp6jvn2+973kVUVYlEIpFIZC/FzPUAIpFIJBJ5IEQhi0QikcheTRSySCQSiezVRCGLRCKRyF5NFLJIJBKJ7NVEIYtEIpHIXk0UskgkEons1UQhi0QikcheTRSySCQSiezVRCGLRCKRyF5NFLL74W//9m859NBDaTQaLFq0iOc+97n87ne/u8/3jI+Pc+qppzI8PEyj0eC4447jxz/+8ax9VJV3vetdHHDAATQaDf70T/+UX//611s9nqryjGc8AxHhi1/84naN/9e//jUnnngiy5YtQ0S49NJLt+v9kUgksqcThQz40z/9U/71X/91q68dddRRXHnllfz2t7/la1/7GqrKCSecgHPuXo/36le/muuuu45PfOIT/PKXv+SEE07gaU97GnfeeefUPhdccAEXX3wxl19+OT/+8Y9ZunQpf/Znf8b4+Pg9jnfppZciIjt0ba1WixUrVnDeeeexdOnSHTpGJBKJ7NFoRI8//ni98sort2nfn//85wrozTffvNXXW62WWmv1P/7jP2Y9/+hHP1rf/va3q6qq916XLl2q55133tTrnU5Hh4aG9Iorrpj1vp/97Gd60EEH6Zo1axTQa6+9dtbrd9xxh5500kk6b948XbBggT7nOc/RlStXbnVsw8PDeskll2zTdUYikcjeQrTItoPJyUmuvPJKli9fzsEHH7zVfcqyxDlHvV6f9Xyj0eC73/0uACtXrmTt2rWccMIJU6/XajWOP/54vv/9708912q1eNGLXsTll1++VWuq1Wrx5Cc/mf7+fr797W/z3e9+l/7+fv78z/+cPM93xiVHIpHIHk8Usm3gQx/6EP39/fT39/Pf//3fXHfddWRZttV9BwYGOPbYYzn77LNZvXo1zjk++clP8sMf/pA1a9YAsHbtWgCWLFky671LliyZeg3gtNNO47jjjuO5z33uVs/1mc98BmMMH/vYxzjyyCN52MMexpVXXsltt93Gt771rZ1w5ZFIJLLns08K2fve974pYerv7+c73/kOp5xyyj2e6/GSl7yEG2+8keuvv57DDz+ck046iU6nc6/H/8QnPoGqcuCBB1Kr1bjssst48YtfjLV21n53X/dS1annvvzlL/ONb3zjPoMzfvrTn3LzzTczMDAwNe4FCxbQ6XS45ZZbduDORCKRyN5HMtcDmAtOOeUUTjrppKnfX/KSl3DiiSfy/Oc/f+q5Aw88cOrx0NAQQ0NDHH744TzucY9j/vz5XHvttbzoRS/a6vEPPfRQrr/+eiYnJxkbG2P//ffnhS98IcuXLweYchOuXbuW/ffff+p969evn7LSvvGNb3DLLbcwb968Wcc+8cQTeeITn8i3vvUtvPccddRR/Nu//ds9xrBo0aLtvCuRSCSyd7JPCtmCBQtYsGDB1O+NRoPFixdz2GGHbdP7VZVut3u/+zWbTZrNJps3b+ZrX/saF1xwAQDLly9n6dKlXHfddTzmMY8BIM9zrr/+es4//3wA3vrWt/LqV7961vGOPPJILrnkEp797GcD8NjHPpbPfvazLF68mMHBwW0aeyQSiTzY2Cddi9vKrbfeyrnnnstPf/pTbrvtNm644QZOOukkGo0Gz3zmM6f2e+pTn8rll18+9fvXvvY1/vu//5uVK1dy3XXX8eQnP5mHPvShvPKVrwSCS/HUU0/lfe97H9deey2/+tWveMUrXkFfXx8vfvGLgWC1PfKRj5y1ARxyyCFTlt1LXvISFi5cyHOf+1y+853vsHLlSq6//nr+/u//njvuuAMIAvmzn/2Mn/3sZ+R5zp133snPfvYzbr755t1yDyORSGRXs09aZNtKvV7nO9/5DpdeeimbN29myZIlPOlJT+L73/8+ixcvntrvlltuYcOGDVO/j46OcuaZZ3LHHXewYMECTjzxRM455xzSNJ3a5y1veQvtdpvXve51bN68mWOOOYavf/3rDAwMbPP4+vr6+Pa3v80ZZ5zB85//fMbHxznwwAN56lOfOmWhrV69esrqA7jooou46KKLOP7442NASCQSeVAgqqpzPYhIJBKJRHaU6FqMRCKRyF5NFLJIJBKJ7NXsU2tk3ntWr17NwMDADtcujEQikcj2oaqMj49zwAEHYMzOt5/2KSFbvXr1vZaWikQikciu5fbbb+eggw7a6cfdp4SsFxF4++23x7yrSCQS2U2MjY1x8MEHb1dU9vawTwlZz504ODgYhSwSiUR2M7tqSScGe0QikUhkryYKWSQSiUT2aqKQRSKRSGSvJgpZJBKJRPZqopBFIpFIZK8mClkkEolE9mqikEUikUhkryYKWSQSiUT2avaphOhIJPLg5OM/+i5nf/0HMKho4kGAHEb+/oy5HlpkNxCFLBKJ7HEMf/A8yKd///bzX8QtbpJH9i+camp766238uQvfgbpZNCn6CIPHUXKBE1LGIThK88Dgq6teuVbp473ks9dyne3TPLypUfwnmefuDsvLbIL2Kcaa46NjTE0NMTo6GgsURWJ3AubNm3iTbe8li25AP0YgVZhaJLTbAhr28JEPkBqlEw8AnTV8K2nXgbA0R+7gLtKBRSY5N+f8dccM/xQnvCRC7jdKXhAhN//zRt56FUXIjXQXKAQEIFuCv0ldARyA3UBE8RIbi7xhyv0ARsSRAwUoPMctIHUQMMBQiiGFP6vKOARW6J3ZJhags9K6ACJgUYJdQ27rwEWz151mYfw81dF625H2dVzbxSySORBxp984lzWUyJpTpoASDWfC64wuK4BlKSvxQlLvgl9Cb8ffzjWb+DAwYwtuQWaGFE2dSCzhiVZjljDzaM1UpOS2hb9RkgTUARQfvSbISgXQa3L4sXjTBaWskzp3lUDl4VxGGByDAbrmH6P35IACYx5mKfQsUHEJixYIBFCeb4usqaBX9qFGjBqEGORCYOf76D0SGLRPjclYTCzrp+DpERvr2MaBu9KjEnwfQU0PKKg6wTmS/BT+WoL8lkdw7MEw49Ofsuu/PgelOzquTe6FiORPZwNGzZw9GkfZQEpY/tZaIKZhLIfXL+DhQWSgHpBaiVJPScTAEuSeEongKHsGnxhsHVHltzOyYf/gl92FzIyvoy6X83imSJG+H47lBi62hOxhNRkpLbLYCa0yozbRhM23NlAkgEoE6jnLF4yzmS3ErGxBrgkWFotqK1YR3fDfOyAx21OARtEbFDAK4gDFaRl0HkaREwc2U01ukd2SW7KcIflQAIadNGnlTvRuOqO3b0wrUJaohsMpmbwfSVmPMHXyyBigG4WmFeJWBtIZwtYkGthHTD80fMZOTlaZ3sSUcgikd2EqnLop8/GtQES6ELwmQkYDZ64dhCdA5f/Cp8a1lz/UBauyajV64wdAqYjMAnlfoprOlhQhInYC1JzpFlZTf6WxE6LmCsEXwikDpu0ecGhNzFJysjEgRTesnxIcSpAE7Agjk0doZHA4gZs6QjQR81M0EyF1RM1Noz2U7TrSJqghYVE6RscI3cWa1Ja4yl0BUHQNtRWjJFP9gMe1yGcx3qksKjx0ElgfgEbLTrkwQQxUdOhu6gBo1DOK6EtSKZIUbn/rIaZLNXKGlNmiZl4bAquVQsuyTHQFMg8YoFCkG6Kzi9hkmkRc11QC2KgJqgC3RLEMTY5yWCzuUv/XiLbTgy/j0R2Ays+9W6WXXk2rp1BN4HCglhIAakm3raFVPijR/8KU7es+d4RLFyT0a6BOwSkI5CD9gGJQt8MEUs9Ip408UCCEcLEi0F92AcMaQYH9d/JIVmbW7v7A3UabCKjRuENicycFFKcGsBQkpKJ4sXQdhndsh/v62hpwmRfCqQF/Q0oSujm4f04i1rAtsiSNmhCMh9op5WbEVgwY3XDAbkELe09N5rAQg8TJqxj+XC7FA3evx5TA99KqxANq2ZqFMGi4iANGqWToP3VkeozRExSqDukWSBJgaQFMuCgTzjyqkt36O8gsmuIFlkksgsZPu8C7CFtXEdAM3AejA1GQ6ph4haBlkESYdGKlfQ3DT/73YHU16RAWHYCQRKwk9Cdp7imD8ZTNf9KoiTB2waAMR5frfF4D+oBE1x1ywbGIDF0XY1EYP++glRAMWxVBADwGIF5ibJq3DA6LrhuGBM+vMfWqN5fuQx7h1LIDnKkiQMMIiEYAwEKA42tnG7KqCrD8a0CJlix/VpZrhqsto6E/R3VjHY3i2xrB54plGrCkKmOQfgyQKNAfAJu9rHEOBiaJaGROSZaZJHILmD47HNZdsFFpIe2w2QrGYiH0oQgBgmTKSrgDQaDmoLDBibZ0jUw1k+jBE2AuiAlU7EShFiNKUOuF9FHzwqbmnerB1MGT3iQmtkTvVNTWW86c2egwMpU1ANlZQl5GoSIC+HeBWMGComd/mX6XDMQwnmE4GZ1vT0s9BcwXrlFN2dIQ8O114AGyGaLOgelVNGJ9xyTmCpy0Zvws5w5gvD79A0poFEiPkVLQXNBu9WWA95Cbhm++uz7v/bIbiFaZJHITmL4n94FRR3aiqQpOtjCF+ALpr7kYwyUOsMaI7gLUcg61GuwuW3BmaBzNmjd/RoZ9/XCVMxCWOfZ3E3AO1JTUCqMtOvUshZN42hVE7pBWVBXXGnY0oYFtZItXcEVMw844xyJhnWvIQWKGS95EItzldEjjnKzhH28hZoPkYp1IHMwKjDkkI0pOi+sk5kFID/J8Ed2saOKa4NSIJ0UNR5pGnRckaEE9SVqtLrsnpAbyhZoXxtT9qGpx2iCL134omAVGTNos7Ky1CJq0A7gTfgC0rMaPWhLIDXBMovsEUSLLBJ5AKxfv55l//qPDF9yPlI2oakIKWKFZEG1k87MaOo9mqE+PZGqrBYRBVOt/7jK8qq8fj1twFePKyPKu7DZylrz3mBM2MEmiqnC8L2HX25YyuYCltXuROgCAwyaDgUK5BjxaDXm0RI6ZBjxNNMWm8oalf8NRFEHiA8uxk7K5vEQkp+lADn0efDQvcswkfeT1HPAYuZVY2+AmaAKUDFTX60Vh+Rhfc9PpLj6OJpDXu+S3l6HfkXJgzjXPNK0waqatL3FwRn3WEATZDH4vAAVfOmgY6AE6atW27oyZWQGEQOaHrogowkymSDtBHINn5WfMjMjc0wUskhkB1l21ft4/PUfRMf7MIVF+x1sMjDIVDAiUCnR3Q2nGVZNJT60E4oCUnHQl9O2BJdiW0PARB4i2e2kYFsWxsP7RBT1Bl8KeQlQ4nvGWG89Cg/G4dpCqYv59eZFHJQVLGlsJDWOn2xeiJUO87IuXh1KcDcuqHtyB+s6GUvSHK85mW0h0sFmVKH1LgwkgWJsAOMcXguygRIRD7aAySalFxb0T2KaOX7CAh1wJqQQbBFEJCixM6FSR6tAxgTyFFYAq3JYAkW9QzpSQzZm6ECOdgpUHaoFnz768Yy86q3QMagLAqV4MpPwqGQJckABnW64OS3QSQnfM+Y52GKCSNWCC5IGyJYEqSXBjWnDJrUE6Sboljh97ilE12IksgMMf+R92KEu+WaB0lAF9zG9YBWwKSEIrheM4H0I7y4lTJilQFYJlja4daLO8nldBpauZ2LxITTXGJINkPdpSDzug2QMXAOYTNFGgRgJMREIZTclqRWIOkrXC8EvsakFPK4wFJOG7647GtEf8eRFt/NNuqxt788fRg/iyOYd2JqyJRecDqAqzKsZoGSChP2bCf/8yPN48vfOZsuYIrUC7aZI5pDEACmb1gySzB+jVlMYhByL1qB7a8aWFf0smj/KpqxO2amj7Q6QoAbMXRLyyWoSXI11YFIZ+buQgPzO//4CV2/6Beqg6Liw8DYKqcm4+fR/mPX5jLz2zdv8WS6/5P34WheWFLBOIBNIPIzaIGAbIWmBqb73e5SyT5H94vS5pxAre0Qi28FPfvITTvzpdWA82bycfHMdmUwhBa2VsN4iTcG0DW7hBLX5SpkLrmNC5EYZ1oxwAqmvcm2rqEUEzTZw7JFryK3lp/+3FPu7BcwfM0zWoTwY6ArGghmDfEBxdQfzC6SvikwkWBTGeNK0oBcNYoyfstJcEaw3dSGCcLj5Q55xyGayVHlK/zk8+rCnbdO9eOxX38zoeBN1gi8NmiuSSYgCBLTwjLz6Hfd43/AH3kntACUxbfpSZaIr/O7Ei3bCp7NzGP7oecHlSAK3Qp8afBIsN1EwZVjT7GYet79j5I0xOfr+iJU9IpE9hA//6H8571c/Cr/0ATPD1XsBf9Yh4yk+9TBZR+d3SLJQ58/1rIiug9SG0PPMQ6HQ59GWQfKF3PBL5bFHrOUJf3Inv9p/Axt+cQi1lXUGbzJMzPP4/cA3BeOFZCzhhHlLuPwVL9vt9+P/nnkhAA/9wlspixRqweq7/vEnc9BBB93r+0be8J5dPrY/+uBFbCmCpfqBJz6JZx/zx9v83nli2dJ1cCcYa2gt9CE4xwIFmNIiW6DeNbTW77JLiGwH0SKLRLaRZVe/D+0YEEWaweLJN6cwmSGFoEMKUpKsy3ADimkLrtkhO8AhKGUOPhfUVd8fuwTrDA0urJypUk4giBXUF7xw8UFc8OLdL1R7G8MfvnD2E8EjingJlmwpfO/lL2L//fe//2Ndcj52Q4Lf3099HkBIRE8JgrbJkrbh9+8/fSdfyYOPaJFFInsAy64+hykLTKZzoabWiEoDXlErlFlOMl7DDXikVaO4VdGhNukCsBmolhxdW8RnnvPaub2oBwnLPnRhCNiogg571nFIAge1IRHcd+HxV30aX5asOuv+Cv8qftBjRqvIRAtk1XHboeeZn++QToxc3BOIQhaJbANiemtQhDWtHHwKaR/k7QJfCjIuyIBF5znKVhfGk5Do3ARp91HeBicdvJwLTor9r3YWyz50Yche6Ll2e95eCelfUzl4prJ6FUyZMHzOBVBzgIWGTle876lhasAKvgiWtSlA81CcRTJBSoNPPfnWqpJEdjtRyCKR++Hff/WD6V+MhvDwElyZYtKCbL+CfCMoKTJukCwk+Gqfx+OghJWvfQvGxHDtncmyiy8ElZC90IsUrVFFZBACSCsxUxseewMmgaRMKBMH6qCdwEBVwzGtijdbg6YeMQafKb4rJG1B8krvMoFC8IMxKXpPIApZJHI/vOXH30R6tfgMVSw9aNdSClhbkO2XU+YFflygSKEsQOqMvP6t93rcyI6z7P0XgQ3qNbXI70E6oIkEQeqlRMzIi57KjrBAbmGJw240uEkPTQ3NPVMFE1RPUx+q7NcUp2Crws1kYBC83WdCDPZoopBFIveHma4UEfp+UVllHt9J8CKhlKJRZBA0V9A6I6+MYdm7gmXvv2i61ONMFLQMOXmaVIUnq5D5rcmNqAF1OCkQlzAV96aE9bBO9bgqaqlpqPhhJKQDhtbYu+oqI9tD9HVEIvfDw2v9+DwIFSiSaUiY9UBuoLBoN8F3UrSTQJlHEdtFHHrZRUGcckG6gumGn3QJCedpcCPSZUq9ZtZMFjf9e68mI81gXeGqQs6uqgVmQh0wrSqzYKsizlWhFBRWnbntideRXUcUskjkfvjqX70RvIQ2WD0xS32owt5wSM0jtgTnkESxTc/wpW9j2XkX8IqPXDzXw39Q4UpCJGKiaKLBtWeCEImTUK94pjuxR0lwPbqQWyaOEEoPU61wgNlWXu+x2cpzAn52N7TIHBJdi5HINiAZaEfwiWJqVeX6arb0JVAKUgNUg+sxqaP98M3CMXz5hWHfqrzg917w4vtMGI5snWUXX4jWq1+qaEQhuHrVgfES7n2V5wXVJyRBwCSXXjF+Su9D5X0B2tP7zRLA3uOZeuWq3w1c/pRtq4AS2fVEiywS2Qa+8OSTglA5wU8afMdWmyCmJ2KCtj1+ogHNJFROr7sQPJBWrqsMHv+VTzH8ntjLansY/uCFaIPZLdCkikg04UuCNxpat/S6sUAQrpZgJg3SkRC56BXtc6HqvQdLFqrfWw0WW2Xl4aVaY6umybKKASGkYzzrMY/ZzXchcm9EIYtEtoHHHnwYqA9BHXVF6j5sNXo9SMgaLeoLu9QXtqkNhi0bakO9A7ZqyNWLE++rMfzBKGbbwurVq8NMtcFjbwdzOzDup4NwemLWyyErAZQFBthUYtpAqXjxlI0SP69A5vvwUWwxeOfRWogI0Z6Z12u0Wa2FoSAt8Ar/9JjDueW0WM1jTyKWqIpEtoNlV7+X0MtjxpPlBLq6GZ63CvO6pAtyGnXoFgYjhva4hW4WIhEKIFNYq5hDOwz2d/n5c983Nxe0FzD89vOh38B+M6aqjpCsNZTzy/AalfuwFFSVTx1zHMcddxwAD7/qPCZVp7toA+Qg4ynqq7YtKVWwSCWIRdhRvAk/RwXrhZvfGQVsR4glqiKRPYgVYrhV3fSiytoMkgVIz+1VghnPKDYWFEtbDC4q6RaQNSH3hByk6q0JCR5Du7A84ktvwwj88jlR0GZyxHsvgYOrxStXfXsQIFPKZQ7uqAovZ2YqTJ6UKRED+M3LZ+fyveATH+FHE6NoveeDrOLz64TPJxcEAx5SI9z0hjft+guNPCCiRRaJbCfLrnpH+Na+ah6JmTbN1IeuztoX1lJc7jDDYzQHhMIZOuMJdGqhwHBNSdZY/KEtbKo0+zpV/7CcMq/x6+fv+grxewPDV1wATkIcRrUQIj39MYBR0hGhOLh6viusultvssjcs6vn3rhGFolsJ6tefjb2tnmYDFxDcX2KayiaCNYIphVKIdkswY/XEfEhN9fM7hSt1SOtrDtrPKWzZGmXwz8XK4LcddddU9GhmmtIUO4oajQUAvaAh2LpdMLYy1YcNncDjswZOyRkH/rQh1i+fDn1ep2jjjqK73znO/e5//XXX89RRx1FvV5nxYoVXHHFFbNe//Wvf82JJ57IsmXLEBEuvfTSnXLeSGRXsOKci/FN8I2QIKtVoqw2g6BhJAQGoLApnXrftO9DYRRcEpKepDLqrAGwIVhOUh79H2/l4dfsu4J29L9fGdx8latPev8VEkLmEw2RhVn1hkJ4z/OeN5dDjswR2y1kn/3sZzn11FN5+9vfzo033sgTn/hEnvGMZ3Dbbbdtdf+VK1fyzGc+kyc+8YnceOONvO1tb+ONb3wj11xzzdQ+rVaLFStWcN5557F06dKdct5IZFehTQ3ljJyGyDkbfqqrrIW6hn9YaQjdnhIwZ6aqR0hLYGnI0jX36ARiqWUFrbaQZMKRX3ory67cB9fOcgkdp2uEgIyaQi1EiAq9vm29ttew6rToUtxX2e41smOOOYbHPvaxfPjDH5567mEPexjPe97zOPfcc++x/xlnnMGXv/xlfvvb3049d8opp/Dzn/+cG2644R77L1u2jFNPPZVTTz31AZ13a8Q1ssgDZfk570f7FDJFbRkCBHqJtC0Qn4amiy2D71O05Rg8cpRONyHf2AhdoTcDS7qYfoNJHDbJ6at7vBecNyTW4b2n063R18gpSgNqaG2pc+0fPZPH7AP5S8suuTB0aO4zaF+onyhbBI9HD6gELQ9fGhAYOeX++otF5pI9ao0sz3N++tOfcsIJJ8x6/oQTTuD73//+Vt9zww033GP/pz/96fzkJz+hKIpddl6AbrfL2NjYrC0SeSBoEzRzaKMM1dcLE6qoFwZSQfsKNPVoVn0/rHXpFoZ8bQ02Kdgu5tAuph+MddjE06gpRsB5Q0hcml0zyYjBmAJMyV/+8j9Z8el3z8GV71605dGHFriDc/x+OW5pl/KIDn5/R321gW4VodhhOucrss+yXUK2YcMGnHMsWbJk1vNLlixh7dq1W33P2rVrt7p/WZZs2LBhl50X4Nxzz2VoaGhqO/jgg7fpfJHIvaIawr2dww4UJIM5yWCXZDBHmgU4D30urN8UYA/s0N3UJFs6iV1eYOcrJlHSusemOY1ajjVKWYWWJ1axRimK6TpLIlBPwGQenEWM4bB//0fWrVs3hzdi1zF84ftgPwGXIi5BXAJlCqWFIUf7kJJktQm5eMDI66M1tq+zQ8EeIjLrd1W9x3P3t//Wnt/Z5z3zzDMZHR2d2m6//fbtOl8kcnc0K4CSZEDxheByweUGl4cSVMmAB1eitkQHtjB/qENjvzFULWK6DAx06G92aNS69Dc8gpAXwaVoxAGOsgTnLX2NUFbC+VBRQqp/rS4H9YbHX385h3/uzDm8Gzuf93z581XkDICiXYUxQbqE3K4ygT5HmWlsoRKZYrsSohcuXIi19h5W0Pr16+9hLfVYunTpVvdPkoT99ttvl50XoFarUavVtukckcg2kYFtgivAZlVZI9VelSpcDskAlCgjrwklqI75rzdRapfeYlqnAKhTul6ZdiW1DsUhHlrdXjMsi2pwNXYduBkTt6ogWBILh3/uTB7Snsd/vmzvbh3zvR/8gI+v/kNIbu5WC49Njw4A4xZpGegTKA16YImMWf7v5L+Z62FH9gC2yyLLsoyjjjqK6667btbz11133axM+pkce+yx99j/61//OkcffTRpmm71PTvjvJHILqFW4j0YC65jcN0Enye4boLrmPB8ATMXbq5ecTKKRzAkYulLE2ppSZ5P0JcWNLMSvKPdyZjsNhAp6GskCJ7SJSTWIaRA/R7NJMNqWp3fpuMsu/ptrFmzZrfdip3NX3/7O5h2DaMpJk9I1mWYVRmMGpjvUOORAsBAI1z5tn4Zjjy42e4SVaeffjovfelLOfroozn22GP56Ec/ym233cYpp5wCBHfenXfeydVXXw2ECMXLL7+c008/nde85jXccMMNfPzjH+fTn/701DHzPOc3v/nN1OM777yTn/3sZ/T393PYYYdt03kjkd2BSNVXsZugJYBhKhFMBVcaJCkxjen3PPShD+XQjeNs6BhUe2GOsLCvJDUT3DG2MMzN9appFoKqo3AJIgXOGyZHJSQHW8VmTCWl9WKOBwccW+5q8tQfXoo1HX75nH/aHbdjpzB83rmwBFhcPVEYmBTYz0IhZOtTcilg0MEGG+oiCtP5Y5F9nu0Wshe+8IVs3LiR97znPaxZs4ZHPvKRfPWrX2V4eBiANWvWzMrtWr58OV/96lc57bTT+OAHP8gBBxzAZZddxoknnji1z+rVq2eFFF900UVcdNFFHH/88XzrW9/apvNGIrsDScC3klC6o9urQNtzEZrQodgD9dmhdJ877mM899uvJktaoRsxPWtKeciCuyi98PsNC+mVcBcpyUxBoWklYo3Kf+JQBVPpp8hUcXbIlKKo0z/geOhn3sLv/+qC3XJPdpTLrv9vLr7jJ9hDes9IKHtoFE0F31bMZILrE2Rjgvbnod2KhIToVW+K3ZkjgVhrMRLZDpZddTaaZ9ARRLbumVf1yGCXVS9/xz1ee+71J5OkOvUN0gGKofTQdcrttxsmiyWhCScGfBZUygKU2BqhnYwBEOr1gjxP6Gt0GB3tw9iSwf4OfbbD6s0L8RsSvvZnf8kRRxyx82/GA+Cwz5wNxk0lg6sXVA3eAdVPdRbGBVMkZHcJnYO60LbQD4vWpPzk3afN6TVEtp09Ko8sEtnX0VymRCxYDg6tlWi9DPllSYik1bGtOzu+dPxHmSgs7ULoeEPhhXahdFyGiOGIZfCwQzdiUgcYMCXYElsrMSkzREyBgm43BJuoBy1DZK9XIcvAmglkyPH0677M8Affxze/+c3ddp/ui4d87l2IdZjKkFUfAjt6eXWYUO1ExEMf4KuSi0WvaWYZRSwyiyhkkch2YYKIJQ4aBZI5JFHEKpKG5zQtqUyorfK146/Aa68LpKGWGPqTggRo+QTnhYcccBdZ350kdUfa8BjjMdZXhYcVcCQWIEGky2TbgkswVjESnCwDDUVSQpdqzXjFTTcwfMW5jIyM7OJ7dO8c/rl3IQasra7J+DBmEwTNGMXaqpulDZ2fe8WVMQqJMnLK2+Zs/JE9k9iPLBLZHroSJtaaA7Uh4GOmcz4FUgfq7vMwB2Z1bs1Lkl70CKENSQlYyWmmjuGFGXW7mt+u2Y/wTzVYLlkGRQHOJ0CXWiaMj9eAkFydmPAN1VdraT4D6YAWCTRKnvQ/n0ISWPXy3ZuDtn79eqx1iFRjEyoxdhSlYKzBOwlibRRRmXVraSgjr9m7Uwwiu4ZokUUi24WFWgHeVgtcVQJZb3NAYaeqTtwbHznuUqAgs22qbo6IyZmXtWkmjrE8ITGCKAwvGuXA+eOIFIhYisICjmajg7WG8fEG5EI26DEmR6TL+gnDRCfhHivgDihAu7Dsqm2rUbqzePz1H8C5BOcSvE8oy4RON6FbQJoogseYXrEEpgUeoaBk5DXREotsnShkkch28K/HPyN4DXuR8qkPJatqLjzWqgZgYRkfH7/PY133px8h9wmlF1IVjBe25AkTZZ3E2BC1SIIRz13jgiv7yDfnFIXFFQ22bOqjM9qE3FEfKkiTLs2aYyBVRFKgEQzDmaUb6bk0A8uu3j1idvjn/hExNlhbopULMfxUn06JGVJFfVIZtV1gVBk5N5ahitw7Ucgike3gyUceGSwugJpHpGrnUvb8ZQpZcCs+63Mfvo8jBZ7L0WTGo0bBGupWyEyOxZP7hNwJ6sFpPwDpvDq6rka9bwN9gx365k/SN7/DQKNDs9amaXPWT9aYbIVkK3UmjO3uGKbz33Yxh372XRRtS3dzSndzSj6eUnQsRR7cqsZ41IfiCNJLSnCgTpDVlpVnx/YskfsmClkksiPUwoKWaiVcSRmCPYwLOWaJZ1Vx/5ktpzzp1YxOGsrSUqpQqqXUBOeE3Ckpjju29OG9rdyYQN3T3rg/j/RDWDPOQNamKLq08xobWzVa7T6ggStAS4GOQQ3Vinhl7syw0pZdfc5Ovjkzjv2JsynX19FNDZjIYDJDJ1LcWErZSijz3hqh0i3AVq5Flxv+/WFPZdW7o4hF7p8Y7BGJbC+JBtdi4apICg9Z5VXMBSihse3WznefcQUP/fypLFvQoV5Vqyg9eFdj1WiG8w1cadGekFnwE8q//01wty27+r0hv6zCl6DOoIVAy1bVhj2IhmoY3UpgKy/orkTvrGO9wRACN9QoKoqvhSajTsDYIrgYMah6XCGMvOLtu3ZgkQcVUcgike3FKrQd+ASaoYJHTw80rX6bNMFq20Yergfzs3UTpGkZ8qsIHWFUU3wpwRpTh/YCStSw7OozWPWy81n1srMAGP7AuaGbMkDXEvLQFPCQKlgHbQmdq6dy0XYdy8+5COZ3cRm4wsBYSlLaUNlEFY9Bc4MrBZOFtIKiFG598T/u0nFFHnxE12Iksr2UVCLmEeuQrMTWHbbuSOolYkpoeujedwj+TK59wZvACUW3Rl5trqjhC4N6i3qPzaDswHSOWp3Hfe30qWOMvOFMRk4+EwgdrKn5KhilDO7PIogYnhD7PoPjPvO+B3ZP7sbyT74LPaSFDHhMw5EtzLHDLcrFLUqrWCeYTgiKUdebhpSbXvCenTqOyL5BFLJIZHspXBAxU4YSS2XoR+ac4JwBTBCz+vb98/qPx5yITT3qfBXsAOoVY8sgYqVAkSIdpnwphYfH/dfps44zcvLbGPnbM8IBvAcfeqVhQCxIVrVIqdamxMJa3Xbr8b54ylUXMHzF+/ATDXSyhk5m+HZG0UlRB+m8El3YxguIVuNC8B7OTv94p4whsu8RhSwS2V48iHEgUrn9ADWzc8rUIGl5PweazSMe8QjKjRZJBZtptYFYoegI2k2hY8Np6orNQhkrsQWv/p97RkiO/O2ZSJ9D6qHYsdR6w9MZa2M6o+rwA2PZpRdw62ZBJjOkZZGuRToWOgk6aVCf4gvB9ntcvcT4XtHlkkdgeOFznvPABxHZJ4lCFolsN6ZqqhkmYrESxMEbqCyyENp+72Wq7o1Vr307bnWNYlNK0U7CNpHCZBqaS+YS3IWmpK+vp0A1fl3cfI9jffSaa8JamPhp8ZoSMA/iw9hRljzAqWDZBRdhSovxFiMGg8HkguQGcQJ5gk4K3tlQT3GgJ/LKfnXDF1/wzgd0/si+TRSySGQ7uf4vXozLK2vCCOrNtEXme5utAjK2P7R95LS3QLcNmyxmU4bZkiKdBLyidQd1R9LsYG1JI61MKQuHf352pN85624Ommo0bNYjiQ8/bSgwjCpi4IYX73jVjOUXvB8suNThkhKHw6viE5BEkK4gvifsJty3pDfuDj/5f1HEIg+MGLUYiWwny5Ytg29DcCEGQVMnIfTeV88DoFDfsXOM/P27WXb12/B5k6lqHOKRJCdrlvQ1HNY4SgciOZBiRTjiC2fitYrhN018B2wdEA1jnOFCFOtB71b4Yzu55ppr8PPKuxmfiqqHMcGrxVhBCw1Waj30HMMLBY6RN0QRizxwokUWiewoBlAJAtGREAlYkxACXyM8dpbhD+1YRODfHbCM+fuPMrh4gv5FEwwuajFvYU5fo8Cagj7rETHURLES4jqct+QdIR8JlUAQiwtNzyqrTKeq9fcSrLW149PA6XfdHLo5j5mwTQgU4XwMKSoOXxUA7q3FiQBbDCNvj40xIzuHKGSRyA7RC5KoRCyTquQ8Ic+rtymQJaxdu3a7z/Dmp51MPWmTpW3qWZcs7WJNTn+toGY8JQbo0vEprQKUOiBk9QLEIAiUBkqL61SV5Ku0MgDnFNex4LZ/LQ9g+B/PJ7krIWknJFsstS0Ws9nCpA33xAODweSbqoZlPEUBL126bIfOGYlsjShkkcgOsOplwSWmjumcLE/IMcsFaYWNtkAOx3z56h06zw+e/gEyU5AarTZw3qASREzVkhjBuTq9PmXd3wwG56YnhLe7EHfvOxbX29oWSgu5CaH628nwRRfA/lAuKSkXFJT7OfIBjxEhHQtBHhTT5acCCqbg+Z0DeO9L/mqH7kcksjXiGlnkQc/wZefAPCA3VU4VUCtpzGsTZlrPwrTJd5/9dj5743c487fXkUqbMxY9gVc97bn3fmDV0IElY6o9iuRVaGDVxkUU6Bq0qzz5o5fwzZO3r7PxsqveCxzI4gV30chyshRQGC8FqJGIMtFNaXXCYlyWKkVvwcoJIqFzNF5ClY+eZVSJnLS3vUbVhd//HJ/f8iO8wvwjoHQJk60E361BLUFzwVnFjlnMeBXAmYayVN4B9RarXvqu7br+SGRbENV7dCx60DI2NsbQ0BCjo6MMDg7O9XAiu5hlH7gQHcyhmCFgzZJGXxuMw1pPUdbIWwlqIE0nsEkNAYo8VI/3peA7VQJxTbEJ7Ndcg08N45P9dNfPh8wEEeuY6T5kPStNodcUTHMNEYnbOv6r3wva+64ppM1x+mpt+tJQvqpQy2QrQ3t1qaRDlhgmfzWENCtXYqazBWwmBax6w32vU61fv57jv3cxQa0VyOmvQ+GFzDpyJ5RlysR4hhYZ5AYZs2QThnzIowMOGTNgYdWpcU1sX2VXz73RIos8KPnTy/8JHcgRb9AZIpbWgoh5FbrjTXxHkKZSSycQU0OwFLnBlwbfsaEuYa3EJCB2khWLxrhzS5PJDQMwVodaCZhqnayqwhs8fNNUXaAl3fbxr/jXd4Us5hkKVEwO0MLS6kyLW4ieUKBLlnomNoIMApMgdSAX1FL9S59RLHgqwvI+xvDpd6I+AfqwU2M3TKAY6QKGzIZFwWZ/weS4RdWgNcVNghTVGZ1h1ZvetO0XH4lsJ1HIIvfJGd/5FNeu/x0iBtWCviw0Q0QKAAYl5VvP2L2dhreFlaaLGNC8qi/oHKKQpGCtJ2838bmASTBaYK1FldkiloGIBhEzBSsWjrGx3aDTHoC8HqpXbEnwB1WqpYLiw78qw5SgiTdTleaXXXIRq06779Yky64+K4Sz+xp3L1FfTJb0DeYUDsAi4kgTQBOKEshTVEJ1fvFVWSpXCfndzLKV9yIuf3fNlfy3uwObTitvmQMebGYpCiVJaljfJSehbko0EWyWU7pw36juJ8Cqt0QRi+xaopBF7pUjvvBOvIZKFfUkxyaKlQJLSZZ52nlKkXT40/99Iw3rQi1dFWpS4kTIXajePp4L7W4dAxTe4J2tqq8nIbKu4m0PeQovP/KJD3jcrVYLEg9ULsVSkaaCDW2di7KGzw0UCTJYkGYdvCYIlTsxN8GCAsQ6QGjUWpQKE60absKAD5UrdB7QViQVNHWhyrwEI0yrqEYtHVJUCdOJ5yHXvBMjEywedKwfUx6xeDO3jfezcXQJxrTBN6uq9VvzBw7SGp8ga1bXh6UoS2qJD4nZpGA8DFl0VEPljsbdDtEGnxVbvXeHf+49gAueUYVeDluagapQdoWkBmVpEEmoZSVeIDVKliquraH3mQNtekZes/2BJJHI9hKjFiNb5ehr31WJGNTTaRHLTEmSKL5M6Us8fWlBX+ooEbwqmQ0i1i1TnKZsavXR6dbxPqNb1LFYxBiMpBgJwQgiYAyce9M3eOgX3v2Ax/6lP/x+doJur4hEotRTBZIgOAhpAmlSTv1DCMm6ZsoNaCuX3FCjxJEANdAU0elTiLdo5qGjsMqS3Jxgbk7gDgMTComG1wGMcMDQOP2JY77dxEDDceNth7N5fH+S1OPyFNsH02GHWxEz7ae7JUW9qbaMbpkiYsP+NY8aD0OgdUUnFCbCT+0ovt/BvHsujR/+uX8kTUpqmU5tSeKw1qEarFObQdmFnojWLfT6OpupmwhGlWfM69++Dy4S2UGikEW2yoT6KvfHkSaKwQNKlni6ZYoaT2YdiVEKL3iF/tTj1dAtw96j7T5KpygZ3husdZRqMRIkoCdiMmOuFuCIByhmTzj4EOjC3Svhei9T7q4Zc+50w8pZo5ApDRGjGFGmKmxUr00FldsO/AbMLSkyBG6x4ptKtimhcXMGawSsolIlmYmnmWxmdWeAjZuXVK5LwVTh9ADGOqju+dYq+krSIB8rKNqWom3JJxPyVhqsLwPUHZo6NFV0nuLnhZ866KDPQTn7mA+75h3Ua440LbG2wNoSYzyJ1bAllZiZcH/M3WYOodc/DRgTjBGueNXr7vuDikR2EtG1GNkqXg0ikBgXSuOJI00cDkstURIDxngMIRcYSnJvqRtPl4TJ3FKUFvAUhWAtGCP4qveUbMXQmHLHPUAO3m8/QoSDCy46C5oDaunWZMb5hbIDvkhJ61uxfXqxEV7IS6Fmq4WiYH+EfGdR+IMgKwza9sHdmIOiFIc6ii70j6RMJDksUMg3s35UeNgS5caRQWwahNRQUM+g0wLvIKt5uiWgPRfi3V2NiiQD1HScTllZilS75go1QWxoCUNlWSM+mJFdZpXOuuOOO6jXcuppiTW9ixYKL3RKi/cpRoK/ULCzepmVDoxVSg/drgFn+cbzT+TQQw/d0Y8vEtluopBF7hNbzYEKIJAXQpYA+Go+MxSqNC20vDDheiLRC9dOKpdXifey1VWfmfQE7rXf+xQffvyLd3jcpjD4Dkjq0dJAmYYGk96AdHB5A9AQlWcVIxOV+yxjZjifK0Nx3bFuHyv6ctbSqQIZEhgKrj/ZP8OUdkpLhKAZjCs+9UwOO+xIgltYcsgRHdaN9rOx0waagAartLrPWcOTT6RommCzLi6vV1nXlmkx66n9JL9/+XRR4t/cdBPP+O41SNOjk4Ru0nWtBkMI+Oia0Cm6O21SPe/nl9Jfd7QLofC9AA9Hah39mWMiB8hIEqEsmaGnJWXVsbooEnwrZeTVcU0ssvuJQha5T0oH6f2EjU8bUcF1pz3VQ0Mwh/TcY2arltjW+N3o9pd0msnKU9/E8CUXos08BD/UDDqpdLRJfWAsjMnk0EnxInTbNdI6oTBvrUS7CZIZ1FkkcXS7GRtbKfOaY2wsG5SjJVpLkNFg/bhaZf1BuOauBHdh1+D7PWllwQU8fiv/9AonwSIySlEY0swjpk3ZqbKgp260YtIut774vbPe/6wvfRn6K7lrKtrVkBbQs+jEI30OdfDJY0Pvryu+fg0Yx2ReIzUzzWFLt0wwktNMc8a71RgAHHjrqNcclhD00xqtM/I3Zz2gz2xX8LBrz6IvWc/y+aEeZenhNxssv/3LK+Z6aJGdSBSyyFYx4lEMThOEIkxhHhKr4Zs+VDOmYgBXFfIzU2tHQbh6U3tP2FS37laciSqcdMhjH/A1jJz2Zn6wciUv/OLnoZZDImjL0Pb9iHFIzaMdj7ZTXD1BKcjqE6B1yi4oCaIGX3pMYrlrfIhFA1uYN7SOzX4Jbgy0YYLxOS5V9QzQAQ0BFz705WJC6O7vIG9VI6vcnjOu1/sEr+BdTtYn5BN1coUs82TNElOlO6hCN0+49UXvvcf1OhxSWjQjRGqmIYl71rlKoDQ88YiHA/Ax912EUC1/rN3LWwtfPBo1h5CS+xxjCsTVpkpOpWmbolDOPPjZvOiPn/SAP6tdwSOvfQuPWryOLaMpN21ejFaCnqWTPP/bL+fmLQfzi+fc8z5G9j6ikEW2yk9OeAuP/dpFgCEvhDRN6TroT3PahcUmhtJ6MjyK0HUJxnrqFkYLIdRu6gNcCAu3BmN8aDsC9ypovTWy1z7yT3fKdTxu+XJGTrv3ihIjIyM86X+uCpZZp0ZnPPgHX3TgMp6czufk23+CZBZfRauv94tAchqNTUzcuRi5y2L6xsiOLBCB7iao/34Jk8bBsEdTMLnBN5T6kgKDoc4E89NJ7qBEJORbVbYraSIUpSMbaIcAjjwDcWAVSgHTZdXL3rX1i+kn1HZsG7RRuT1nNKnuhcXLxHRIp9eMdjese2apn7r/ItDtWlQ89SwjFUe7hLLrWfnX93L+PYiHXfsWHrVkHb+660C63amMcACMsfyu7OfA9HaO/PLb+OVzdqw7QWTPIQpZZKv09/fznSe8lid+90N0ywwhhxRahUGkoCABZ0hQFMUh9OPpqAFymllK6Rx4pRSLV0E0WAeqFmPuKWa9SfSA2sBuu87h4WFG/ubee2KN8PR7fW3ZW84iO6YTAiPKBiIGMwj5H09gxyfR3y/EL/NoYkIxYVJc2WFoSLltg2FgcJTx8QXYREP1EZPiAGM9fbUC7yBNgxKpB0vBr088/17H8/GjnszfXP8tJBcUC4mfrnClQEvCa04ZGRlheHiYdldIE8Wj1I2fCufsFmCTFIPgfYjaVBJW/vXe0T9sfnMzv1x/AHmeVRGW025TxZDnlhF3CI3abRz6sTO55dV7XlJ/ZNuJ4feRe2XJkiX84cR385M/O51OmTHRbtDOGzgveF9QlJa2s1gEI8qES6jjqSUhCm+o0cKpkCRd1CvOW1LjURzubuWReiK2IGnwjb84ffdf7HbinCM7RnBVVJ/LLd6XhGZcHulvYI8eg+4kimLWrMc7y/rJAdQZBobmMc9vBB3DuZCjpV7xPkXV0OrWMNbifNh+d+J771PEAJ72x38MfRqKAecGaRmkY5CuQcYNklvUKfTDUz7zeSBYgDVTMFAvSFNPasPWX/c00w4eR7cwdEvhphfsHSIGsLSe40shy3KSpCRJCuq1nEYtRyixVvEuwblF9C/xDH/sXJZdduFcDzuyg0SLLHK/DA4O8ocT33Wf+/zZ//494GhV1ldqSozJWdhfMJ6HaJHSQV7WsdZjTEHppPJ3KUuTfr793G0vqDvXPOyLb0N9hoil7EJazylaKb63FthOqir7Cs1R/P4NakloULZ2dAAxyuKmYaivYKK4CVcchtgQ7KGaUOZw64t3QDiMoE0Pk4T1uaq6veJR62AAUMFVgR2p5GQZCErddEmqGUE8TCIMmIxxLL949kU747btNm7efABmanbzJDZ8WShKoV7zdLolxiZ08zqNuoOshUo/w5ddyMgbY3HjvY0oZJGdwnVP/ae5HsJuRchwPkUEas2C7nhIN9CJJHSGnpGnpdqENvz2JW/d9QMbBYYEBhUvftqjFrrVhDJZ40AdTvzKudTrYOliraAIqiE3sASsTbB0qZf3cq49lId/8UxUwxeqmcErAIl1lE5o1D3tDqgmiDiSWhnqSaae4Q+eDwk71KctMjdEIYtEdgBjwvpe2Z1OWNaJFBpVoV6qXOa2BPEAhi8/j2a/8JtX7JoJcviDZyOmgXYU6lXid/XaVEZEpyom7OFmNtJPF2sNuSsxZJRV9RNHQmJapEZoZt1dMt5dwcO/+DbyPLh6dcYCoUlK0poDEhJbBm8AJZBijUd6qywZ0DLglOF/Ph8MjPxNFLQ9nbhGFok8AEzmKdtJUIr6DBGbFGjZ8C+sL0eGcmSoZLJdsOzyC3b6OFauXAlkoWJ/V5BRgSJoqAqhX9oWQboCTdjfCjWrZBZKp9QTy0Ba0p96BjLPQJKTSEq7NBiz95hk7YmEslPD5ylaCFoIOIMvM7qTGc5D6QRVUyX7K0UHfE/0ZnQtoAyCP/zx84OoRfZYopBFIjtCrxBxL6VuMg0iJpWIqUCji/QXSB1sBjZTkgUKC3KWfWTn5i89/adXMVUAsg+og7QEu8VgtxikJSF9rQl4uOH1b+K4oUMpnNBMFOc942XKaFFjS15j0mU49dQToevgtttu26nj3RWs+NQ78RN1tJVAkVZbgnZsEDS1dCdTwrTXy61rk2PwZVWWZSq4seeWJBhuBob/+bzdeDWR7SEKWSSyAzhVVJViyusmM5ZjBBo5kgUBS5IOadqpivHmZFmXbL8OR3/uHTtlLOde8+nqUdUJ2gXLkCb4ZihgTJOwbqeAD7P1pY9/HfVEaXvDWLfBWKfGeKfGRLfGWCdjrNukVaQYEl5zy56fa+U2ZtBNgyVVKJITKpsUBnITxAw7FTHrPNjEUHQTtKhM6ZzZCy6zKloLw/8cw/T3RKKQRSI7wB9OfC/WliS1hPDtfkb3ZWFKxEQ6GCMk1uO9Q4BaBrVMaCVw2L+/7QGP5ePupvDA9vLGQgg+XqZ9iyrhOQeShsn5yf/7RkqvbGnXaXVqtDt12p1atdWZaDXYPFGjUEO6h88Uy/7pIqgnYFzlS+01OXVAVVuyCGJWdKsuBuIougmu2wBXJYnnppoV7616tWH4X6KY7Wns4X+ekcieyx/+39moFmHynFFCijS4rUQcxkCaODpdS1/DsaB/nMUDYdt/3jhLh1occc0DSzuQXmeZegm40BFbFLwiLhQGwYUimJp6Vv5d6FCtKGPdGu1OhvcZecvQHTN0xyz5eOg35n2dzRN9NBJ77wPYA1BTQmEBG1yBOVAa8FWPNuNCwWgNZnO7ZehO9uNaDShtsMZaQFp9lCmE+pZ3F7TwJeB3v/vd7rq0yDYQhSwSeQDc8sKzWfXSs5FmEQwzAaoGlImtylbllr7GBIv6OxgLrSKd2tTAgQtyHvWVU3fo/MMfPRcFihxsw4c+ZDg0BVJCP7JU0RQ0dXzn2X819V4RmOxk5O2U7sYMnahDpw6dGtqu011fo7vZ4H2ddy36u51wt3YNwx8+D6yFLWDbBilDwWYRQToC49U0VwCF4Dcovj0ARQLYIHqTBhKDlKCJzjbI7jFLCk+/4drdcGWRbSUKWSSyE1j18rN5V99w+KXodeQEazz1GgzUlVYh1fJU5e6iIEVoFQnz+nbwxEloNaOlwSZK0l8ifQWYIggaoZIKtosMtDnkkEOm3vpvR5yJK2voWA3yBPEGUQmbM+AMFHW6m4UTf3XVjt+cXU0JbAkuU9+nobloL1YjAxkQZHK6UPOqN7w3hNS3JLgcpVofKzWIWI9Ee63npnuqziiDPfyRPX/dcF8hClkkspN45YtexMgr3wpecFVQXF6AMeN4BVVPPVHqiaOeOFSVjk+BksJZ/vg/T92u8w3/8/uC9ys3pHXIW4Yk9WSDjmR+gZnXxczrkCxsIwPFPYoNH3DAARR3WfAJYmVWA2wsSFKtqxV1vN+TXYsWSQ00QJ1HvUfFow2P9nk08WifwiShpU/FyBveMi1OiUIyI7CjJ2K9wI9ew26ZIXSaMPyxs+Oa2R5AFLJIZCcz8vq3ouV0F+q+DPoST5Z4UI+qx6nSSJVG2kHVYMSRZdt+jrGxMaCymrAUhZDWoTtpyFuKiCethUnd5Qba9yJEvh6CRKxHMxe2NAgB2hMzQz6+HYPb3YwDNQ3dsAVoaKg5mVVb7/d5hpGTZwfXjLz2zYy89i186dgTgsjZ0A8OIYhYL6+salOEAbHaiycJ90dLhv8lWmdzSRSySGQXMPKys2i3MkSUxDg2tlN8GQIw8kIZ69QZbStOoZGWeE3wuu1Wz6O+eHn1SEIFkSKh6Aq2JqR1g4gJApkY1BtWvfpeQv0tUPOQOcSGjcSHfmqpmyoJouUePlX0rMmGhoCNlKmMCKT6vakMf2Dric1/9Ed/xMjfnsF/HvcXUw1SUYLbsiditvrpmdFEXKrkQR/FbA7Zw/86I5G9l1UveSci44x1DCngpEZHa6jU8D4UsR3rZFVAYaiav83IjK0wQcxcgmsnFO2EopNQtFJcnkJ365XojvrwBdAokbrH1DympkhNMTWHJB4SRROHWHjtoQ+80ekuxQE1goj1outl9k8ABu/7MI985CMRq8E6MxosNBtETIwGzTKEMH2oXI0JiEFq2/H5RXYqUcgikV1IvZbRaidsnuznri393LVlgLtGLc2aI5EaaIeJTnAzbiuXfOITU/lqwf8Xkn61DNaZqqBOQjforuG9h/3JVo+zIS2DgFlHknps6rGJR/BIokgaxAzgrU951gO+F7uKJzVsyBnruQTv5Vb2cpuX/ct9l5ta9aozoQxfNCSpNqOgEkSsW6mj9cHSAySZvXwW2b1EIYtEdhHHfOVNdLs1QhhdleMEGEm4a0uNLZOGRGp4+kiAT6zYthD3S4s7AJBEp2dPIyFPqrDQtZDbkEMljpc+5SlbPY70OYwNVob3YVMFsaG5pxgF8UEk9mA+cVrVv87ezfraClMW1f0wcvKZaFfQHNSF+6KOsNaoBowitsR3YbrcVWSuiEIWiewiNrZrOJ9iTIIxTG1KCiQ4n5KmoDqJAocddti2Hbg3UVcWA8ZPJ+5OhYqHyrcjr9l65ZBzP3cB1oJITprkJDZHpJhdkcl4JNN7tXD2JMSY+xSwKbbjWr587DNCOa+8+mLgqnNYhyRFqJTvUiTxQeSALVu2bP/gIw+YKGSRyC7gH/79IxiTYkzPEuuFvllELMYE0+GuLRbVDFds5wl6RpKpXF9pJWgSNslciNi7Fz7mt9DXaJOmHjHBvdjfLKnX2khVpUQqD9oLDjrkXo+zp7DqDf8QAjPuhbQ2QdacIOufpDYwwUOu+cf7PeajH/3oEKXYlyO1HKkVSD3HNByagLZTMNUH58Ej/M03r9lJVxTZHqKQRSK7gC/LaspSyFuQtx35hIat68hbniBoCSHGW/jm0y/b9oMXVamlXpCjaLVeM70hVd+xrfDYL76NWuax1lPLHLXM0Ug9RQnOC331EpGeu8xz0XP/aqvH2dP4xlP+X3ABztLvLllzAowFDN0NQndtH921NYY/+I+c9rn7bqkz8uq3oYWipQ1rkIXBt0NVfUyOJIoWNtzzrnBA/7xdeIWReyMKWSSyCyhywbcBD2nDkw2EzSSAs+Qtj4ghSwG2s3GlM8FDlhvwYU1rlsvMgzrhowcetdW3m3SS/QcnWdiYZH6tRX+WkyQFaeKwxlE4SJMc2Cu8ilMceuihoQwV02KWNQvUW/JNSndtP/h+hATRFLSfL6wTHvWhC+/zuCOvegdIgWQO2+ewzRxJCySxQcQSj5bBvfuBPztxF19lZGtEIYtEdgGaQ9Lv6Z9fUs+gnkEthWbD0zevAC/kLSUv4BfPvvz+DzgDEaBj0CpiUfMqMdoZcIKqQMdw8rrvzXrff/3oe/zld17DwfNaDNY6lNrFGs9gltOfFDSSkiRRjFFqVf5zcyfdj93FyKvPmJrUVCeDiG1RKPuDq7QXuMF0aP2oKMP31+zUG9QrPhdcS9C8qq4vihoPPoHtiDyN7Fy2nmASiUR2mFarhW16GrXZz4uESdRa6J/vmNhk2G5rDELVkNSgHdAaVWBHFY/vTSiC68BYw19+7xUk4rGqdKljghFH6WFBXVG6bO7UaKYepcApJJIy2Q1uyV+/eOf0TNudrHz5GQA87Np3UJZA3gxWa1lZmDZEY065ZiuLNjTOFEZec8ZWjppBmaO9FuC4qto+0E2RmkM7e3D1kwc50SKLRHYyD//shVMi1guY6NF7LAK26bn5pPfvwBny0FfMGKQwSMcinST8LE0Qs9QjFsa6KYkpKKRBIjmpdABIrYRqTMD8epfJEmqJklko79G6ZO8lpBBIEDFDELGkEjFPKPDctcFNWxooheEPze4EPfzPZwMONAP1iO1VEq5y7dRBp8Z2JbRHdipRyCKRnY6bJV53p/fa3S22bUUGFPWOqWIgM3KntACkROoOaxWvKYYGTdmMp4bDkEgHBZwIuQNISOlijWLETUWx3/LCvc8am0mCCcWbe7OchynL1RHy7BIN/eOqSiYkHjLuJmYmRIWiQIYWGeLDpt0MoRZclSYK2VwRhSwS2clk2T0tsa1xf6/fG6tefhYy0EWaZaj2Xlab90izRJoeW/MgJX2pAxHSJCXM3hn9CRgUESW1YCgZbIRjJ6IoMJju4OD2IH75l+/GTOXcESp/pFVRYG8gc1N1Ekl9WEATH8QsheGP9sTMg6nKdlVWl1b/hccuvJ7GxOi5IgpZJLKTecr+B+7yc2TNCUytQAYKZMCFrb9EshJbd9jEUUs7JJWV4DFkODyQV9X2Q9CDoZhhSLiQR80Pn3XOLr+G3YNOF97oVbIvqDpoE8SrbWAsCS7Grg2P2wRRA2iDSTzYEsk0CJapxCtx1XMl6qNFNldEIYtEdjJXPOVvt5LPNJteWagd5Q//70KSWou0z5H2FaR9ZXhc89jEkSQdhvramMri6BX8AFONS0PgAjI9ToVumfCz59x3LcK9iZte8B6gFa6xlzDtqSwzDy0L/R4WtEkWdkj26yCL2tBfQtuGnm/zLW7SBjEzJZgSScOGDb+bxMPYntyz7cFNFLJIZBfQa6y5NTHridx3n/IPD+gcT18kNPu2kCQdrC2wtiBJOgz2TbBf/wSLGjmpBCtMxFNWFXV7fdIkrJSRWBhvQ7cU/u3w1z2gMe2JjPzdP4KUzKphpcCkgYEOuAJadcrNdcotdXS0DmUCfR1oS7DgJhU3maDiMYnHJFptwa3r1hkY2PvdsXsrUcgikV3Ab/7yPVMi1hOumaL2J4PLmT94Pz1F7ocPPOlcHjFvghX7beDAeZs4YN4mDp4/xpLmGAsbOYl4UuNDAIcvKMkwKJnxqFpEFFv1G8up8T9P/UBIKn4QMvJ3ZwYZK3vh8x6aRaiS4upV25aqgn6vfUtZg3oO3mAWmdBhGoObtLjJDDeZ4iYNbEkxi4Wai9lMc4WoPohibe+HsbExhoaGGB0dZfABTiKRyLbwoV/9Lx+65frpsHvgR39+FvXtaQd9P7zoBy+n9NPOQ4DMKl5DFGLTdhjPa+T0YeiQ4OloH6CUThkvDN966kd22nj2dIY/fB4kHskKtF2JWGmZXXW4ilI0PlR6ThymBuDxmwBnwXroV0wm+K4w8qqtF2iO7Pq5NwpZJPIA+bPPXcBKnU5sFoHHDR3EJ0545W4bw9k/OYPfuTvxGhpkGXE0rMdol/GiXolYTkZJS+uhH6eDrzzpn3fbGPckhj90bohOxAQRywXwoTmnAB3Ca5mHpAPUqlB9xczwY/leLlqpjJwchezeiEK2E4lCFtnZHPbvb6fZYDqPy0M7F9RniAg3vWD35mL9buUvOW/NeSCGloKjATjqktP2KWD5wuP3TfG6O8NXnAdeQiWUGiFSsTSIghqFPg3rY5mHpCr/YXW24VYCCOQlI69/++6/iL2EXT33RqduJLKDHPmlt9PXKEhQBMhVQDL66kpZdunmNR7z+fdy4/87a7eN6YjlR/Kvy/9tt51vr8YqdAyoYjYlaFNDD9Re54AtglICBtIStOpJNquUsob+ZHtbUcoHGTsU7PGhD32I5cuXU6/XOeqoo/jOd75zn/tff/31HHXUUdTrdVasWMEVV1xxj32uueYaHv7wh1Or1Xj4wx/OtddeO+v1siw566yzWL58OY1GgxUrVvCe97wHH3M3InPAo77yZjp5zmSrwZbWAJsnm0y2EkS6qOYkCVjTZWLfcXjsfSihOWnHogNa1V6sonIEGFCk34YgDxGk5kK4vbiwWRfC+NsSHkfmjO0Wss9+9rOceuqpvP3tb+fGG2/kiU98Is94xjO47bbbtrr/ypUreeYzn8kTn/hEbrzxRt72trfxxje+kWuumW5Ad8MNN/DCF76Ql770pfz85z/npS99KSeddBI//OEPp/Y5//zzueKKK7j88sv57W9/ywUXXMCFF17IBz7wgR247Ehkx9myZQuTrX5cOYC6FHUG9QaoMzE5QF6Aak69DvuQ537vZEKgCZo6tO7Quq82h/Y51Dikz0Jh0fEk9IEzVdmWtoWOIE2PRCGbU7Z7jeyYY47hsY99LB/+8IennnvYwx7G8573PM4999x77H/GGWfw5S9/md/+9rdTz51yyin8/Oc/54YbbgDghS98IWNjY/zXf/3X1D5//ud/zvz58/n0pz8NwLOe9SyWLFnCxz/+8al9TjzxRPr6+vjEJz6xTWOPa2SRncGyT7wbypTZiyWEyLbEIwb6m+NAxmQr4+aT7r8bcWRuWHbZRWjDBYHaWve1KvBD+h1aCNPf/UNUo6Q+uBZtycqXvGe3jXtvY1fPvdtlkeV5zk9/+lNOOOGEWc+fcMIJfP/739/qe2644YZ77P/0pz+dn/zkJxRFcZ/7zDzmE57wBP73f/+XP/zhDwD8/Oc/57vf/S7PfOYz73W83W6XsbGxWVsk8kBYduX7Qui1+GkXk7hQt6+0+K5BPUxMpqEvWGSPRsVV+uVD0Ecu1VaF3ytQB1aXSN0jtbLaHFLviZgjiR1c5pTtCvbYsGEDzjmWLFky6/klS5awdu3arb5n7dq1W92/LEs2bNjA/vvvf6/7zDzmGWecwejoKEcccQTWWpxznHPOObzoRS+61/Gee+65vPvd796eS4xE7hvxgGIbOUbAK7h2FkKwxYFafAk2qxNmxlhzYI+mRhAtEShnfPGoVdGMopAKmgnGeozVsIQm4FXAB5dinse4ublkh/6Vyd3KdqvqPZ67v/3v/vz9HfOzn/0sn/zkJ/nUpz7F//3f/3HVVVdx0UUXcdVVV93rec8880xGR0entttvv/3+Ly4SuReW/et7sY0uOq6U65vk6/op1zdQX1AbqPLIEhdCuA2AcvNJe3crlAc9uYbPyxlEBUGQRJDcBmHruRxrgklKktSFLXGhDxmQZAqthOF/efDUqNzb2K6vEQsXLsRaew/ra/369fewqHosXbp0q/snScJ+++13n/vMPOab3/xm3vrWt/JXf/VXABx55JGMjIxw7rnn8vKXv3yr567VatRqO9j0KRK5GzrepVw3yNRqSgqSWZjM6Ex2qe3fIZ8IGbWqcP0THlgtxchuYMJiJwyJkbAe5kKxfNdwyJCgvULDkpOk4F34bI2BWt1TllB00hCav7U1tshuYbuELMsyjjrqKK677jr+8i//cur56667juc+97lbfc+xxx7LV77ylVnPff3rX+foo48mTdOpfa677jpOO+20Wfscd9xxU7+3Wi2MmW1AWmtj+H1kp7Li7IvBBA+iIdSalRKcgG3MB6+oBWlUX9YnFe1TRGp01ygy4AALOs78+fPn+Goi98Wysy4krVt0cLrTC1QtySYtZe6QBYImCibFldDb03vwPqHsgHZ7Ve/jmuhcsd2O3dNPP52XvvSlHH300Rx77LF89KMf5bbbbuOUU04Bgjvvzjvv5OqrrwZChOLll1/O6aefzmte8xpuuOEGPv7xj09FIwL8/d//PU960pM4//zzee5zn8uXvvQl/ud//ofvfve7U/s8+9nP5pxzzuGQQw7hEY94BDfeeCMXX3wxr3rVqx7oPYjso6xcuZInf3E6DYQMWACUoTa8L0FyISkF01GkDa4hwSJrKdIHWhOkJWjTAzWSvjHKyYRbXnjh3FxUZJsx/Zay6gg9ZWaXgnEgA0IyZik7VTBI01K2CYtjBnChlxs5wRqzVYmryJyw3UL2whe+kI0bN/Ke97yHNWvW8MhHPpKvfvWrDA8PA7BmzZpZOWXLly/nq1/9Kqeddhof/OAHOeCAA7jssss48cQTp/Y57rjj+MxnPsNZZ53FO97xDg499FA++9nPcswxx0zt84EPfIB3vOMdvO51r2P9+vUccMAB/O3f/i3vfOc7H8j1R/ZBzr72C3xs9S3hl57nuQCTm9D1twbqFEkEFaUUSBBMB7St+AYYL3gNLVG0BkyCNIViVHhqfWiuLi2yjaw4+/34+T5YW2mvTQFQCj43SA6mXzDjgqsBatEuIDN6jlWdA7CEoBCK3XwVkR6x1mLkQc+nfvoDzvzJ9VO/CybMOWPBbSTNyk0ISLcqQVQjWGYqwb3YEqwTTKmUDfApUID2VVUguqBNRZMJRk55126/xsj2MfzBC6BWhddPffjVVCggLYPpWOwY5AtzGCyBdFq8ekw9VEhyRl4Wg3u2Rqy1GInsIMMfmhFF1svzEVB8mKz2M0hX0HFFBsJ8pqnHFAb1VckiB2pAUqYnPdF7xPtOfRuM0fZ7PO/4j2sgq0pL+RCpCKFnNlahpmjD4wsTOmynIeUifLO5+5RZJUZLwbFlTCabK6KQRR5UXHLVVVy6eS3hq7KECcsCuWGqTYcS4jGcR7JKzEQRFdRoEKWC4GKEEEo/s4v9ffgwRk5+1y65rsjO4+rbbw5JzwaoezQhfKZdkNKiLQ99iiY+JLU3BRGLqkPIUW8Jbw7iZhrgJyzft/d11siuJApZ5EHBn3/gn/h9u0CMYDUJ3ZgFUMWLhlYcCHR12jrrRUw3FMaAgUqhjN5z3b4q9DClYnr3l/2UZyqyh5MD9RntWHohiylo3UFLoCOQeJ63fD6fn9wYxMxatAOkiqQhLl9bFj8B5MJrHvGI3X8tESA6QiIPAlZccDF/yB1iTRVVViW1GoFCMGoqi0wBM0OUCJOZAYNs9V+DQBC1LlCF4XtAOqANrVyVQB+sesObd/3FRh44NWZ81pXlXv3AEUTOCyTw/r85GVTRSVAnSFb9XXkTEqnTqqSVCmf96bPm6IIi0SKL7JV8+cc/5tRvV+2Dtr5sEeYnK2ihSCZV1zBfNUuc3jXMYYL21M1Xx3RhB3GC9YLxSpEBFjzTIqZdz8gZUcT2Ku5eB9NVX2SkCgCp+SlLTZqgkwKTBsVBX/XeFgQfNdAoicwdUcgiexUvvORifjxzzqjWu3rGFjCdE9QTs0pxFAnt6u8+53hw+FBGsTe/GUKJohJkEsiVPAMSDZWJ+kLbqlWnvWkXXWlkVzB82fkheqf3t9Lr+GwJX2DMdDDPTa+qCjTcZZClHh2r/tiqamRTxYb7PLLJcP1Nv+H4wx++ey8oAkQhi+wlrDjv4tlRzwLqmZ6Aeq7CrYnZzEX4u30RFyW4kfoqe6wLpEApSAve9KgjeP1f/MXOvpzIbmbZJReBVURM5UIMnaEpq8od6GwxQ8myYLYLFl0LssSDltN/azXCezYZjE+5+oavRSGbI6KQRfZoXvmxj3P9ptFpgar+YrWae6qC89PC5bmnmM2kmPGaC7/rhMIQkMPIadFF+GBj+GPvxSwJfwiqgnYMFBYpQrFgymCkTYkZzPrC43EkZMg6obRd6FUeGwfbDWJXUvKttL27LilyN6KQRfY4ll35vuk+h1agv/opCpsNVtLwl1slJeMIa14l1ToH9xIiX/kOMwUHkht0DGQIVv1dFLAHG8d//mxu64DUJARqGMUmCv0OV3p0MqlC7k2ooTkz2Kf6+/mvlT+FhQXlBovFYF2GbJiRd4ZS4qHf8coDHrbbrzESiEIW2WNYduX70OKeSVvS8GFeaQvMV9xkji0ySII1Jho6aohsXb/UKd5W8fRJCK0fOfmMXX49kblj+GPvhXbK9N+SokbxjRJT8ySpUvSVqA8iRlmVmqr+gv7tj0Oj3zN/83lsI8EttLi2hUkzZawpQFNBPNIsOev4E4nMDVHIInsEwx89j96fYy+CsPdTC0FSh/YRcnyaCpuZDvAomIqqn0UvCFE8ZJ6R10Xx2hdYdtGFCDVkRreMUKlFUZ/ifYkYh00UZ0owaVgnnd6bJzzmMVO/1erQ8S5EqjaqmosQhC/xSOIwaZfI3BGFLDLnXPmtb9Fb2OqVC5JZURmCFjaIGYARXF+Bbadb/wvW6QfeeUbeEt2G+wrL3//+4IY2levPC0apzHWBrkdJ8NZj+6qamnnv3cFa/81LXz/jiJ5apjiXU5oM9VUlEAABkwAUDA7GyvdzSRSyyJzzrj/8AElBt0zncsk8haKKGJvK9gL6qjdlHu6+tj4j5B6FW98UQ+P3JZZdehFaVbLvfR0iUXyVC4instI8WliqqtAhNSMJYfj/9sjjaDabU8d0roW1Q/Q1lCLN6XbBm+CuFFGSzNGogXfu7sOJ7EaikEXmlL+56nKYqMpr1KdDoHVzmHBkngv17yoX41StxB5aRTDCVHWGUwTecvrpu/lKInPJ8D+fOx1NqKBewrpXxyBSdTkoZbpKS8/t6Kr9Es/Ia99yj+NmNsFRAClpoqRJL9y1OhEAJWJau/LyIvdDFLLInPI/GydDcnHmp+sbWkKPKBesNOl305aZr8oJtauJqGQqIfrWt0Tx2tdYdtX7AJCsMsN7paYMqKm6FLRC6bLeuut0XTHC31GijLzhniIGUBQ11ExibR/T0+XMsNgC56Bmm1t9f2T3EIUsMmc89iPnB1ehEiISZ62LaXD79BMKtdY1uB/bwV3Uy9+RBG55UxSwfY3hfz6vWlY1Vf8dDTU2LSA+JD8joYJ9TZA81E6UvJIg4yk7cNOrT59KfN4a7aLGvH7oFJNAH2kSghxNZZgV6qinXdb/PgrZXBKFLDJnbJykWmw3oTxUnekvux1BbVW+owzWl/YK+44FqyxaYPsewx89L6RQWIKAuV4xaAhBQYqkBpwHo4ipKnd0CYZY1RGBRsnIy++/CWZ+u6W7Auq1JimTGAOFF6xR+muwOc8ofBNTLtp1Fx25X6KQReaOOtA2U70KJZ8RLm08YglVGAZ9SBRrGxg3jJyxdTdQ5MHLaZ+6jC+Zu6jNDxaRdymqBl8KFDYImvNgBHWKJDK9eGpmRAEpaK3LyCu3rZPz/CUJmydr9NOlkTZJTOUBV9iSQ1HC5pvrSDdGLc4lUcgic4eGppXi79k/RYypKnV4NIf/ffZfc9ghh+z+MUbmnD/60unU94PFAAidMkQcttuGghpeQHOCmIkP66gKsxvEhQLQqp6RN5y1zecetx5uaTCxUCgXKtbkIIqqUJYZ+R0W02ri6zGPbC6JQhaZOzqEHmJKKBU0Y41Ma70w6lAbL4rYvsdffOP1YHMOng8eg/cJk4XBZorzHhoJIl3ybh2XVG5Gxz0KQ091QfDCyn/4h+0ag8uB/T2s6SMft6h41CiiBvGC6YKngPnRIptLopBF5gwxBrpgVGZV5hBAuhIqKdRCjbzIvsNvf/tb3rPh7QwkGbUkZ1ETfAl3dlOMGSIvM1pk1BMHdaF0Dq8GLXSqnGYPhdDtGVi5A3mFfYmhhUf3L/FbHNJOSKqyVyUO5pdQlyn3eGRuiLc/Mnd4wYjgEg0L+D0PowcpBOMF362qL0Qe9BRFwWHvvYBQDfpxsKBEBrvUhiZ45PzVLF/Q5a7WJjawgFIh1wwQElvgyxTXcyXq1P+g4AGVJvv1i85k+SfOQRIPCyzqS8oZX7nECBiHSeKXrbkkCllkzpBCcHUf5q27LZNporiuYgsh1kx48HPo6e+nPikM0AAE31RcK6Hbl9FZmPFLnwK3s3x+m7bPQTNyD41EKexMAaseiIITRv7mzAc0LhHBJIovBVNz+AKmTT4fylRZwbWU4feez8hZsZ7nXBCFLDJnaDrD2rrbujwGaIBzitxd5SIPKh710ouZl4NRRQV8Cq4EkwjiDB2t0bWeW7LFHDQ0wvxkjEnXwKBTX3K8B7wB45HEs+rlb9tp43MtCXUZteoRVEVDSmIQUZx6IMNG/+KcEWeIyNxRJ6xp3H2dfOZzDVhi7r56H3mw8Mf/7yJqXY8xCkZJnFLreLIxxXaUZFKotQ1M1pjoNLmrXWcgBcO0FdbpGrQjgOdrT3geq16x80QMgDzFtQwej0k8Jg0F80UcLvfQzqAT9G34g+fv3HNHton4FSIytzhCmP1Mem2kfPj5g9fHxOcHI3/04osoFhm0qp9pCki6im0raa5kLcgT8E6QCUPZTej6Okhn6rtOxwllu8YnHvIEnvSkJ+2Sccomiy4CbYGbqkwNU9NnCTJh7xbuH9mdRCGLzB1tMO5upaksaDc0QbxHgeB9mNd98jP856bbZv+LLeBYO8hnXn/KnI1rRzni5PdTLDdok/DxF5DkQpkLyZjCmCfLFdcV3CQk/UKpAJ5uCc5ZnBfGRmuMvGLbkpt3lFVn/QPLzrsIHfChE/nMv9dckHGDLYWydreQychuIwpZZE5Ydv5FmKmaQTNwIXdMAJ9rNXHs2wyfd2HoDLDJUF8npC7cn1w8N+w/xrJzL2TVmXtPz7XDz7yE/KguNEuMDVXINDe4doLdkKJiEG+wmz22q9CUqj6iYygZY3WnyXhu+cHTL95tY9bSkYynOHxwiQN0wGLAKb6mUPOQRiGbC6KQReYEs7FKHOsHSkg6oaMGCyptc1KVFopQU2q/t/R1BGuAlkKuZAL9ZcLme/hm92zyh29k3rxRFvZ5BOiUhvG8znirhqs14I46tiOUk5DkAEopJQPNMfoF1uVDfO/pl+3eQdeEkgLTTTAtQqNOp1Wuo+JrLrjDx3fvsCKBKGSR3c6KN18MFjIFaYWKQkUGDEK6Acr5oJaQR7aP+xaHz7uA7E5LsyOYzY76ppxsSxvbzqfqK9fShOFXXsDIlXt+DcrhT7yRp6xYT1qb3elgvG25I5vHalHKxYayU0ezUHoqzxQWjXPMktu57HFfmJuBZ0Ai+FqJ75rgM0hA01BpHwOkgrSiRTYXRCGL7FYOfdfFJBnkDU+nSUj5aQtJYTCblGIQ7GZw+831SPcQLNQmgog1V7epbZ7Ai5IfOIhvZqiEGINDfrdlrke6TTz9YSMcnLZJq5nHK4wXFmk0eVi2Ea/CHZ0aLgt5GblAd9EGRl5/7hyOGqiBjBt0KAiXzgq1FUhBRg0qsVTVXBCFLLJbcepwB8x+TmtK4T2y2ZBNCkVzqgM9q/5u+2rjPegYDzEv9bu61DZPkA/UKPfvp9swdJtCvlDQukCxH494wfn8+nN7bkLuG398Agc0EhRLWfXALIFaKgynY9zeabKsbzMb+oYopB8Zh59/fvvLSu0S8tD81YxavPWhj15oYg4dMC2LFxdzHueIKGSR3cayd1+ILtUQXt9DgYLQN2qBJ1cTWtJ3waf7tltxiq5SG+/gRSn372ei3zKx3FAumaTvkDUctfhXPGVhG4znX37/WWCIVz30m3M96nswVLf06QYOTAukaiO2thQ2+wXkknJQbZIRajTqkxTlAv7v83vQl5hJoAk+c0hhkHEzFZ+oKN46SMBsET75H//BXz/rWXM52n2OKGSR3cKNN94YRIyqKWKPHLBVDykEHfIkay3ewDee//w5Gu2eQ0ICuce2c7oHDNJtGCZWGMqHbORRy27hL5feQt0Wob0IoxyKJzVr+MbN+7PWL+HFD/nZXF8CAO/4xdN4WO129ksK6saTED76/ZKUja7NTd2FFDJAwiR9RvHtPcuyGTnzLQxffgF40H6PFjNeDF1lkNzg2463r/wNf00Ust1JFLLIbuF5P/r6VCj9rGX+DEAhF7RUJBOMgEtgxYoVczDSPYtb3nM6j33B+wHQ/pROUygPanH4gat53tJbqCcdrLR4bNpiibVkpoYlAZTcb+anN+/PUYetmduLAB5eG2E4a+FEmdQaIGR0WJTkNIxFpOQ3nX4Wmy7jBfzy/afN9ZDvgdwJuojpekhVexgIImbHDG64Cy69lyNEdhVRyCK7nHXr1kEGgg85Q4BYAEGqPDLNNIQ0FyEA4NZ3xGoePTb1KwsARegsEZJFExw8uAZrC6zAn2QtliY1MkkREdq+E2oQGji8L2XdrfuzZMXcidm//eZcDmlM8rt8iLXlUtouQxGseBbYuxiurePApMMdrKNjF/LL5/7TnI31vrCPzuHnNdyQzJ45S7ATghvsYOcp5V1zNsR9lihkkV3On3z+X6BrgHTWugI1hzR9VYyVqhirYOy9HGgfZfggS54moB6fCQv6Co4Z2ExmSpaxkSU2iFhJybjPmfSedQ5GvcVjMQIr7tifQw+aGzEbMh/gh+2l/GH8IFa2FjGe99GrQTaYLeGIoYUcP/RrltXG+eGmhXMyxm2hbEHy6BxuAzZmpBg8issc7tAS24CyTVjjjexWopBFdin/+IVPB5eielR9+ItrELpDt1KUEml6RA2aACX8/pxojc3k+rNP46FbLmbJ9evhjxZhrZIayPDsn0Fmwj/jcc25s/TcVtbZ4vrZ5BqMln1Axg04muOP5E0P+9VuH/+dWuOGTcu5c/JgBAuMsX8jmOZrOnV+dNeRdArlRQt/z0WP+/puH9+2MvKqsxj+t/eSHCJwSI6vvpbZKtexdAqtLJZinwPiLY/sUv515DZkQ4qMp8hEirRTZHMKbUHnFUg3QdvVzgKPS4fmdLx7Kq6Rs+74xaR/KGjnhqJy0c4TiyBMapeNznNb2eCOYj9WdhexPl+M9xmDrOaP0z/wOLuJT/7iYH7zm9/s1rH/pn0od04eDEzyx/NH8Hcp//eHFfzfTYey5vYDOKJ/Fb/Ysgwre37nueSuBuUWDR6FqnSxopTjClsyyIWb/27PW997sBOFLLLLGD73QoxPkIaErR7WxKQDYhIYFbRRQLuqHO7hMyefPNfD3iNZ8JSV+EYJh2aMbmpww+h+FDodOJPjWOtgkxvkrmKQrm8waG7jxf2/4lnzNrKgXpLUDA8dKhlvPpUf3rT/bhv7zzcfBEwyf3IjX/rF0awrDiEZmKRvwQR2oOCbv30Ma24/hLfcdOBuG9OOcsupb6LZaeDX1Cg31ig3ZvgNNWSiDjl87a//miSJjq7dTbzjkV3CsnMvQgYkVEDo1VKqpl0RwbQUX0/QehGsM5Q/vDR+k7039uvL2XLMajq/WED620F+vXB/jp+/mlHn6EvCnR33DTaVNbq+j7pZx0n9dzFqoOsMfepZrRmFKkoCVvjKrct5VHodwwcftkvH3inncfTQbXzl9qPp328thwx1aGYOkd7rhpHNCWtu3ztC1n9zakjSfuLVH+L2sUkAzjz+WP72sY+fy2Ht00Qhi+x0fnfbbehgCQ2dtvm9QClQCGRVp92OwkR4WbxQq9Xu7ZD7PG9pvIwzmp9F/2QTW1p93LXhYD4iOY1D7+IFWVijURImXRPI+aNsHbmFrjeI99xOHaeh0PBNG5t8dc2jWV3OI+MiXrbua7z+6JW7cPSWr/zqkQwtvJOHLMyxVkmkE5ZOgdQID1tc43Y792kC28N3Xva6uR5CpEJUdZ8pnzA2NsbQ0BCjo6MMDg7O9XAetAx//FzEOKTSJe0CJKhoELI8tOkwbcGlOahh5PQ9t7TSnsIzvv1qRjsppa/TrArVqodrl30W1zTc0OnjxtYw1hv+euiXrAUWALf4OlbhzrbhIzc9jdXrDwCCK5e7ahifoHgy0+YP73jXTh/34772Jtbc3sfRD11LPWmxX9omS2DKJFOlW8L6oo8f/PogRl593k4fQ2Ru2dVzb7TIIjuVZVe+F+4StAy5QtQcDDhMfwkFaFo1oMoN2Goiq+0z36UeEAdmo8AQ4EjE4FUQlJeveTZXHvR1EpkAPF4MNQP4hDt9SaF1Jn2Hj970NFavPxipl+hIA+MT/Pw2AwfdyqMWb2EwhVO+/wLmr3om5774lTtt3BaYv2gjWdJm/3objKNPCpLq43cKSZpwsGnRmsN8t8jeSxSyyE5jxT+eTzbWQPLQGtOnwDyLn1CKZoEsdYhzaGrR3Aeh64eR17x1roe+d5A4hmWUjS7DaQNfBXskJueMTY/jJHs9zfooY8V+U81v2tRp0uGXmw5i9ebFmIESf3Md8Ra/YiV/dtg6+muOmilD/UMm6H/kv/Lun/8rOUdyzqMvf8DD/t7T389zvvUaFqUtMtNhohBuGl3MmnwIp5aadDm8uY5D5rU4bOABny6yDxKFLLJTeOun/53m7xL8IkKjsZbSvwGKddBeZKiR0V3fxSx1qAMM+Nwz8poz53roew0mN5SZI1HLQhmlZ9JMFspkmfHvPJ4jJr9HmS1mUze0FkkIu31/0+Fo0UDzktRnFAdv4GmHrWOg5qglJYZxlqRtPDDuamzK+8l1Ha/+8UlYmeAjR3/1AY09M0qWFNy5pcaNY4cTkgvD+Aua3Dg6nxtHx3n2ITc9oPNE9k2ikEV2Cl/4we10/8SH+cmBrDdoXRCnDKzzjIvBkKL7Vf2aFEbOfPOcjnlv46bOAg7PNrE4mWAydHUEIEtK5ps2bW/4v/x47poYpI/bedrQRnz1T3zM9QEeNqSUqWPFgWup1Rz9tsQxzpKsjdcad3SarO4MsikfpNWtcdvKBdCtM/y9c4EOI3//7h0ae1lCUTpuHHsE7U6KasLsqpseYwb5ym0P4ZxHP6DbFNkHiXlkkQfM8AfeR37UJLKojQy1sYvb6GFtJh9S0E6FziKorfekGLRVvcnFMj7bS9E23DneTyEGg5+xKR5Dq0hY01kAGN7+qF9x16iwn+lQKCRShsUqteig4+CBSWomxN4ckBR4tazLG9zZGWRNaz6/+/0SbvvVwZg7Bqmvq9FYWSMbGeKwt13MoadeuN1j/7dj/onvrz2QVjtFNQUEXzK1gcH7lHZngLVr1+7cGxd50BOFLPKAGP7IeyAzaLtRbXXceFXmfl4Xd3iBs0LdE7ooVp1cRt4YrbHt5faRpeSS8fvRBYx2G7hSoVQmC8Nt4/O4s72QelJy1+ZghT3rUasZTr5DCzh24HeESsIeDDSsYiiBAmdKPAmTrp8t+SArR+bBxADZZktt3OCsUBwAfj9QB0nD8tAzLuahb7qYh7zpkm0ae7PZpKuLgDSIlzp8qfhS8KXgcocvQTXlL3589q65gZEHLdG1GNlhhq84H2jcrS+LABYKA66EZpfOIoPtmDCRivKVo/9ibga8t+OadLoZQ42c9d1B1mOmcs0T4+hPcjouY/P66QoZiw48jGezEmE5n/xdC+ZnMNmg1NDLOMXRB6xxli15QqfMYHIAs9lixw3lPNCmQgfsRMj3s0X1kUuo8/zwf6jEzMNvLr73pPZW2+JLcJMKeQMwlZUI6mtAAfNzRtuxTFlk+4gWWWSHWPbhC6E0oaFgIVMbnqqSh4C34eF+DuqAUaQPHvWoR83t4PdSxAm3rTqILe2MxEC/7TKUdBmwXUDoFBl/WNmA6xMOO/n8We991oqVvPu4/4QUTMuwaqxGqUwVvgVDqQllmUKZkCF4QPsUcjATICokbrpQi5ZgHJgS8MHgfshpl/DIv9+6lebLFDdRiVjXIGsM6UpLusoiqw10U9zmOlON6yKRbSRaZJHtZnR0FHVU4dpAL9jbC+IFVQ1/WWrQbgJJSL7tpm1GXnnWHI1670fWCHqQ5fZVB0H/Rg5amGONx6uweTJlYv0g5DWWqDDh7/lP+xWP+DmveAQMf/Rd3PyLg3nokyeoWWgBQmh57L2Ez7EEN5+gWh5MJthQjSl82i4YU7mFcr7iaj7UzWxCV2D40nO54aSXc8ABB8wYgYOiD1YLfWst9RK0atljnNC5wzC5BAr2nrxCVeXQSy6e1fV8MMn42d+9Ye4GtQ8SLbLIdvOoT34UbFgvmdoE1GqwulSq9Rimeo259SUj744i9kBYef6bkK4guYXx/bjjtv0ZufVAbl95ABObFkFeZ+kNKdKB4j6MmpGT38XIP5zDf35jMd0SXJHQb6FmCqytPjglfBkRMB2C5Q1hxqg8goWFYqFSDnXRpQViQNoGaSVImnDctVdx7FWXTZ3XTQDrhME7LNoHow9VRo8M25YjlHI+zFuXwEhzF93Bncs/f+MbHHrZxfcwB8Zczop/ej8bxsbmZmD7IFHIItvF8IcuhC6YjsF4g1GDcQZpC3SqJbKqkn1AYVT43WdjCaqdweLvlcgdBukkYcur7a6EpTckqFccQWDuj5E3X8Elj/0yf9iwAOO7LMjuIklC5Q2dHRk/ZXTjQYrwORdDiuvLkfkg4wI+ga5F1hnkToOsTlmzscuyD1RRjoWlPmKZGFbaR04gh61hweGrmX/YanT5OroPbTG+XJm3bu/orHruL2+c/kVmbBV/cuU/7+4h7bNE12Jk++iAJOBNUX0tF4xNECNIKfiOh1pYT+n1bBr5h7fP9agfFKxatYpDz/glnP8YuF1mx9gA6sJ6VmvIoPPybT7uFU/+MgDv+8XjWN4/wq3NIYpkCWbSQlYZ1TNahQnhOV9TGFIYIwjYnYZGpYDaKz92h+LwnHnZJWA9+QDw8NtZvgjGc4v3FjHKknkl9YX/n73zjrOsKPP3U1Un3dC5e/JMTyAMiAQZQVAwg7q7ZsVdxdVVd/WHqwIqgqIEBZUg66q4uO6uYVVMrOiacBVMGAgikmFmepjYufumk6rq90fd7p6ByUwC7vP53Ome2+eeU+fcc+qteut9v+8wq4ohlcasx3CV9g1LLrsCMZPT/WiaC4nX3/UXXvqUI/Zhy56ctAxZi53muZ++Cjp0syJL8wkOLIYMGiA9H5lLzGZD+PZCa9K/p/hS5S1Eso1N/Qm9AyE+uKraFoQG7QsaHYLaQQY6kl3e/3lH/s79chz0f+o8vIEedIfAFoG65ZG9tvUtwgOMh1grKSAwkcDWwGtMLa8JRCS5brXmu298Oa9s/y88FKvu7YBGyHSPX0hRXTWWLIhZ1Rh9LJdpnyBcKtwONoL3/PQnLUO2D2gZshY7zUoVQwwUzXQQAHHTDVSwmEaG9HxIAB8QOXe+sSVBtaeQQtDrpxz8sgd54NZDkA8FlGM3M8slxL0W3auhmPGFY5/3mI41cOYlABz0nk9i53vkkyCwqGY04/R62SSQQRFBrgT+GKgcRMNO9/OmCnlZ8vov/wJxjCJ/aDb+oCJIXNszCabbIxsPWRWPsbC/xtq1a1mwYMFjOocDgcdP2Mrjm5Yha7FT9H/249CR4gUZSroHVGceNjKYREJdufpjtZnPDPxTy4jtSTwxyaZ0EYvLo9hjH+LBjgVM1qKm78+66NBig6VL7uWUo8/bI8d88Kr3A7Ds0ivJxkFkFqFcKgAZLlJ+g0D7EEyCqljC0Rx/NMbLtDOySpH0RtRnK+wts+lYryhoi2x28xpIRwS1To+ULsZ7kgPfiDX1QndIvrcb0gJahqzFTqJ6aggBJpPOXSQNUUGDzYmF55ZQ6tK5GVEMvLOl3LGnWUjKSplRQnBI+0a6nxIznhbJtNNc9FWDecURhB3Y48d+6Nyzpn8/9CyXJ6YmJXnJ5ZLZWCArluIDVYrrh1GNGOE3tSDTlOhhn03nFui7cS5h04DNZLCBn1m8Ics4HpUDvFbgUa/4GPbECDE13dqai7H5t/94zsn7qllPalqGrMUOWfK187FJgG1stjAgLfVEI/yEQimnYQWmHoJvWaZb62J7g69u7OfZc0a5vzoLaTwWF8cISqNoC1JAPfWYMCkPTc7Zq+24r6neceS7P8VEJ1gsfg7RQzXKK9dCvYHNNLa9zdWeq9QQvqL9xn6maoBv3vdPJViHQNuQYXSyuFfb/1gxPaGbaQmcC/2Ry4dNI2ZTeO7Tn77P2/dkpGXIWuwQM1IAfPesKmA6f0xiA0mDBsrPMKEHdcnPzzx7/zb4CcpwfSlz7QCULY20yLqsk9Q0Z2MiZnFxkmPlEL8eeO4+ac+f/8UZtP6LL0WNK8oPPgwTVURXB6K7A3zPaVhlOXZskrZm1vDWJjBT70WAN3jgDoT+6SNfBiGQ4wLTaxEpzr06Zcw2M2IiaQlj7ytahqzFdum/8hOILECa5kMpwCiLUBZ8iU3BEFLoi8kUfPSZjy3I4PHO0i9dghbNYJhEslyV+Mmb371H9j2RlPiPTU/jjbNvIyjX6MomyIlQZJRVAz+zfGHDCozZt1qFA+efy1FvugJVqSGXLMAoD+s1jZEQEPjQ0zVjwGqahge6CCKHQh1kSWFpLjvtfObAPuf2VYPIQCCHLbYssN5WwjkEiFxgV7VCPfYVLUPWYpss/uRlqHGPwM5kemoBMrIY32KsgUBApkhip+Zx+rHH7d9G7yc+fO3n+W/WUew0SOkmInkueSBN6P/ix3jwje/Hb64Z7S4mDxirdvBlTqZXbWRF13raVIOq9vjlpoMYNXMZniwjqe94Z3uYXAjE/NnkYUC8oETuKxpKgIVybpF1lw4wKTT1QzyY3VR/8WFyAsJxTfdqgy0phDhwZ2QIAZkh75KEqy15j8B04XrSpgiAqIDaYDHl1oxsX9EyZC22STgu0YFT4RNYJE4xnYZAxECbxAQGayVGwxWdh+zfBu8nbrnlFq4vP0QbzehBLFKA7yl8H+rScNCXLmPgrY8xklBY6kk7hozYm8+wns+UP6saQ5wWSJIQ9PhjPqdd5a7/PIvnH/lBNixrIz7aQ7dppqIhJjOFWhMRbtLEi3zCHORgM3Eai/Yg6VUMBoK+dfDg587a3qH2O8HqmLJfotpmUaml8KCYljlOgEaHhTZ46KoD+zyeSOzW0Odzn/scS5YsIYoijj32WH71q19td/ubbrqJY489liiKWLp0KZ///Ocftc13vvMdDj/8cMIw5PDDD+e666571Dbr1q3jDW94Az09PRSLRY4++mhuvfXW3TmFFjtg6fmXkxUERjlhV6MgL1my0GJDkNYpeZAACIS0vPrVr97fzd7nHPTN9/N3q79PvVGkWouo1nwnmmwtntB4ylIqZohizOJ/2/WClFtgYzCSuBEwHrcxUikzUikxXGmj0mgnSSLn+mX/KGOsPbSN2kkCHWjUsMLfoPA2KWxiyeZbkvk+xQkXph9ULUHV4lcgqAiiCuh2yXhN7/hA+xmZWmQlpVwVRAjiLhhfYJmYb0k6oZTBMzYc+OfxRGKXDdm1117Le97zHj74wQ9y++23c9JJJ/HiF7+YNWvWbHX7VatW8ZKXvISTTjqJ22+/nfPOO493vetdfOc735ne5uabb+a0007j9NNP54477uD000/nta99Lb///e+ntxkbG+OZz3wmvu/zox/9iLvvvpsrrriCzs7OXT/rFttl6QVXYtsFtmzdq2RBgGqKx+aFpjFrALkbWf/hhXtmHejxwtKvXcSSr16MTtvJYp+8KbekM59avUS1oTDGEiiDklAsGCyPrXMz1unUCynQmUccB8SxTxL7mFwilMW3I5h4/2gVxs+QyIckHfd7ROsEnhb4uaDwoMT7paU4Ab4BmgMjo0BK8LTFq0BYgfjpB66TqNFoQG6xh5ZpG0gJH26gxgzlcUv7OkHbeiivTyneUuO/r33//m7ukwphrd2lFcnjjz+epz3taVx99dXT7x122GG8/OUv59JLL33U9ueccw7XX38999xzz/R7b3/727njjju4+eabATjttNOYnJzkRz/60fQ2L3rRi+jq6uLrX/86AB/4wAf4zW9+s8PZ3/aYnJyko6ODiYkJ2g/wXJX9xZIrrgCmVsRm3D9TK/WqJjACyMFPBGmHxrRlDJzx5Eh+fuCBB3jBL77JtDR8Ux9KeBbhW4QyWGNRvqFccoZHCoHWivFNbbz80Fn8y0lv2a1j93/1Avo6BhmrzXbalrLZAgtGQ8QIdd2F1Smr33jJnjvpneCgM64kN4ZiXWIWuvfMlN6itXgPGzwlyYpgi6Kp0wkqtqgEvAwyC/V58NDHDkyXXP/nLiH6RUBHQbh8B2sp3Febng2kQHZoiVrNcte337s/m3rAsbf73l2akaVpyq233sopp5yyxfunnHIKv/3tb7f6mZtvvvlR25966qnccsstZFm23W023+f111/PihUreM1rXsOsWbM45phj+MIXtq8unSQJk5OTW7xabJull1+JyFyhTLKmcoMFIYQrzQKYgnVLHyXAsxgsv/zrv9ufzd5nXP5/1/OC/7sOagHUJNSE+6k9bOJhYoHVEqkE1kK15uNjCJRBCAta8IN1W/dc7BSJz8jkLISss6h9Az51rE2YVdhEwR+mrnswWhKV972cRDY3nzZimQdxmyGdrUnnaNK5BuFJ4jlORiuZZUi6DUmvIemAtEeQBeALEI193vQdcuT3z6H/Ux+EzCNekjPRsKBdZEdjeZna8jK1Q0tkhxRJa5bq0pacx75ml+bxw8PDaK2ZPXv2Fu/Pnj2bjRs3bvUzGzdu3Or2eZ4zPDzM3Llzt7nN5vtcuXIlV199NWeddRbnnXcef/jDH3jXu95FGIa88Y1v3OqxL730Ui688MJdOcUnLYs/frm7GzzcIr0EclB5U0gocG9btVkekAXRJujv799Prd63/Ouf70VoiaC5PgiAxSZgPANlD2tzrBFYK1F+iK/SzSSUp1UKdwvh1zBJEakKrBopgJIIAesnZgOuwCaZJQz2Q9j3MJi5zojpPo3XVydPAK2wawX1OREUXTVpURXT5X50yaKVS8JSm+BAUCe86Mv/zRc33UvX8hiDpBF7eJUudKfFLtdoPCasJFplplVKNFBdLNHSwPLW+ti+Zrcc0kJs+TBaax/13o62f+T7O9qnMYYVK1ZwySXOZXLMMcdw1113cfXVV2/TkJ177rmcddaMm2JycpKFCxdu79SelPRffhm0uRGmQEIikFpghEUXDbIunZKBcu6UZq1MUm1YfcaTw4XS//nLkFohcjHtxhDNAlQWi8oV2bh1FY9LTdfTdHYsNBoCEbmgmN2lvcsyOZyT1yLwcyTWFTQFbBKANQTtMTSrPe9LRB1MGXSvQXbXyeseNpYwoSgMSJK5oBqCPLTgG0QmUKnESy06sCSdFjUmsAiWfvQKTBE3mJq+jDPXzSoDoQU/RRU1gdegWHB/q8eWMIAk9ZACslxhtCAMM5yMsUJrTdqQ2EkJRJB6MAZhTZLOsti54/QenoKUmBzSiRAZCEgt5IJseUL2kIKlwXTKmwFiP4VlGuIDd53vicouXfHe3l6UUo+afQ0ODj5qRjXFnDlztrq953n09PRsd5vN9zl37lwOP/zwLbY57LDDtggaeSRhGBKG4Tb//mTnvC9fw3/rMehgWpnA+gYKoBsS1VCY1GAKBtmQM3eLctuvuvTJYcQAaFhEPuOL11jQFpmAbOYPSalcCZvSVDllQ12DFJJkrISIcjxv90frQiSUu6FWtdjUw2TNL0QYhJfhFzKKBUNxP8zIInxSz6L6aujYw8YKJhTKKJK5FtOdYNqZThS31l0rMQl+TZFjyTtAz7LggTBT9stOq8kgm/dnaMBLCYs5hTBBKQkYstzie5Ik9THGkOU+UZRgPIGUHmmmyWIPM1EEpdyilgdiTBLUJFmfxfbFdPVopIQkd4aQWDr1Dg0i97EyQxyuiZMGMzNs6+SqagqRHcB5cE9QdumKB0HAscceyw033LDF+zfccAMnnnjiVj9zwgknPGr7n/70p6xYsWI6QXRb22y+z2c+85ncd999W2xz//33P2ncWnuaH//mN/x3ZQw2SuQmhRxUMCqhItwDXjKYSCNdPPeWZG4E/mRCaoWbgwk0FlEDvyEQnsQKic0lKpX4cYC5B9AGKesEUjCy1kNEoAJN4D/yYu48xUCjlKa9I6XQGRN11Qg7axQ6G7R3JRQLGkho7HopssfMvdeciSkYTAY2lRArFBI9a5iOFRvpPmiM9p4JovYGqiOBUuIqUbdD1qaRKaT+I4wYjzBingXfgtIERUsYZCglEMJQjyVSQpaHWDQ6D5wRswIllTNiSYSZDJ0RawA+iFgSphJTBBNpVLGBkmCQgMLaZgRoTjP5H0TqYyc916gprQCtoOIjjGLL8tot9gW7PAc+66yzOP3001mxYgUnnHAC11xzDWvWrOHtb3874Nx569at48tf/jLgIhQ/85nPcNZZZ/G2t72Nm2++mS9+8YvT0YgA7373uzn55JP5xCc+wcte9jK+973v8bOf/Yxf//rX09uceeaZnHjiiVxyySW89rWv5Q9/+APXXHMN11xzzWO9Bk9K/umGXxPGPip23bMFbGDJJyDr0IDBBgaTOENmhWXzmsQPXXBgRpbtTVy6s8XWwPcFpiqwyqLLBtuWQhmmCmzJQUvxEMvgUDci0qgwIwhyJkbgLytXcsTSpbt8/EYGxSCnnhoC3ydQzYm0hdSAkgmhZ9gwVNjDZ76TBGCNAi0RyRDeQQEeHvVMEniGMNBEQYPUl9STiFQAdbBlhagIKNotNQubNkQgsMKCci/pawQ5oWqWcLXQFmqqqY+UmiwL8Hw3qzJaIYQmzwJMQ7pFXmsRQmIzg6gIRCaw7RYKlkK05SkJYaBgEeMCwuaySAYikJDILQWDDU76Zt97dp/07LIhO+200xgZGeGiiy5iw4YNHHHEEfzwhz+cnhlt2LBhi5yyJUuW8MMf/pAzzzyTz372s8ybN49Pf/rTvOpVr5re5sQTT+Qb3/gGH/rQhzj//PNZtmwZ1157Lccff/z0Nk9/+tO57rrrOPfcc7noootYsmQJV111Fa9//esfy/k/KTnsk1dSHHJK9lnJgrSIqqCQufUfaRWJtNDnRsRiyhtmLdRh5flPPiM2hcYSWGfE8naD7U4Q7SClxvM1QmhkCUw7jI2VCQoJSrnEaJ1bqJT565uuZfXSXU9XqNULdHY2IBAIl4mOsaCkK0UmhSHPLDbZP+rx333hFcwqwBBwX1bg3vpsfjW8mI21+aR5iTRXREFO6GdAjDGSXLvZW17UUJOIAk6rkM2WxuRmL0B5To9YSGfEM+3es1YQeIYsEyjpAmuaUfKAgsx3xjDBXbBMMF3Pwd34jzgjjRDglQ3aaISRzbB7ZvQgN/MsuuJrltXva5Uw2tfsch7Z45lWHplj+RlX0OjXLoR+CgHUBN5qRYgg7tToRRqRurUEmQmsD6vOfnIq2/dffhl+rtC5RRmBNZDPSpDtBhXkeF5KIdBYAca4zlNKyDLwfUm1BvlgG6JNAx6r3/SBXW/Df15E16w6bX4DoWAmGsIgsTQSmIwL1Ec7GXjrru9/dxlfs5DQ85yXTQisdWkZY0nCDUmJm8eWcMvI4VQStxhbinKKXsZIrUBcD7E1D2KnsSimAjstWOVmaAI387WhAWXwyymhn1EMU6yFVEPoWSqNkMDX1OoRhShxKQ8ojNU06kX0mO8ClhoC4QOpIBgTqJok7bbkPRle1yRdZY1UGt2UXqs2AtKxAG9jEdMGVloX+OE/woWYtYzYttjbfW8rvOZJSOPw5hTLx/WFBucOKVryQ3K43yNoSBpG0xRaRArBg2c/eWdiFx59DBfdckfzWgl0WUPJuo7VSwhDQyNRWDwEDTxpyTRoG2LSnHywGwIXCfeoNcedJQ+oVDPnwsw0BWWchm3ukeGRplCvlaC+79Zoqmv7iTzXjUxHGQsnYdYTRpwqatjOAcbzdu4ZC4mzEmmu8SR4KkeoYGbmta1mG2buU+WSv42a+dNMcLNbXJPSYq1oGrJtYJ2bOGvqh4pUQCzI04icOqr5UW2hEKXQJUhVDdaFSOm5yNSpo1brDHzo/F2+di32HC1D9iSj/zOfhMjOLFLDjNumqXKfL84JVzdvDQO9Adxy1pPXiAG86QUv4MJb7nDXKbVQskgFnpdTCA21xBmwhR0xTrxd4DauYzWsbkBuypBPCQvvOqKqyL0Ck9ZHqYQ80M0Qc0GaheSpgsRH6L0nUXXbmg8xpL+EIURiCYWhJFM6BPR7bm1ONH2D1lo6g4hFusLy8jBravNIssiFxAcKbzNXnhgFupp5ipZHGDfbXH8CpMBkAu1JMg1SSpQ0NBLwPUtu3PXNckEYWLR2sR2gwVcu0c2zYKSLKimCzS1SC2SsMHXFpC/paFN4UuN7kiyHYpTg+wlZe44xU2tjDR467RN77Vq32HlahuzJRmRdbXrfrTFgwebgwsJgSsZbAuRw7tOexj8978ldY2yKVR84m6Ufv9L9Z0qyS2XEGUCDpT0xqbHE2pDpIhKNwEMD8+eMM/Bw5xbFF3eZREBDoZFoIcnSZjuaRU7REhpTQs57nu8+tJSGKWHoQQKBzFEUGNcJNVUnp8EybybQRAi3QHWoB3cHMW1BxljdYpuJiMaAyQQYhfQV1totU8aaMzArQBgBWmAl2NwjyyyJ9ChETkVDW0Egc7LcR6qUPA3Bz5v6lgrPT9FKQSohENiadcEp1pJ6ENbBF5KUEA2MmRqdHaCkIfIt2ko8DwphzF8RcdGLL9o7F7nFbtEyZE8i+v/9ExBmSC+hELneIk4EVoZgLSYVbkiMIMcy8M/n7N8GH4Cs/MBZHPShT027B30P0jygv6NCajTVtICnDJ5yofoeFo1A24g5cwfYuH7RdHmTXcVrKPIQrNEQ+VjN1BKZowEiV3h7IY/pSw8cRrf08VWOi0U3aK2YIKAoYVQLAlGlZhqU5JbGrE1KIgFquqFuetVII2dYNoLwcJJfJYudDsEXzrenwGrr1FQMLgfNWOrGGcNSIacQQiMRhEFGkvp4QUK9EVIsJuRaEwaQFxKnLJUEULDQEFjfYLsECZIggaAu0PUIXfAZ68jB16Atq//pQ9sVfWixf2kZsicTwSBBJDCmRJKCUjE9HTnomJFqhAwjTGzBE8jdq/DzpODBj57J4osvx7RPqdTkWAnVNHRGjJyOICUDMiObD5nBF7BRbAS2Lh6wUzRAoNza5ZScmBXOtgA0KxTsSX7wwELmehZLAFh8keEbGFcBnVYzbkLaZEDVBGw0CcsecesYDHUD9VxirECKDGUhrQSwxseTLo9AaTA1lwpiCrbZOzULcNJUTTECEQuIA2zdwy9GHEnCb0nwZYaSgq4oJzaKBjFxIjFa4BUNYWTQfp1kModGAKE3PXs1PZrYuP1jLP969DG89IUv3LMXssVeo2XIniQs/tJ5MNhDWvedALCy5MUig+WEsKfK7M46m8YAWQAluPuzZ+7vJh/QrD7/vfR/5lLqocAPMkATKA9JTltoaOgMX4InQ2hKI1mZcPDijAcGdi/aQ1rwMoXGYIu22cHPBFiIukBpuZUw8t3nJysXkMoi1jh3c4ICfErS0i4aTBroFJLY+lRNSGpn/JpTAdErU3i40cZE6hLtilFGZaiNgX/48B5rZ4snNy1D9iTgyl9dj//7bkIpMWFTITC2GKOodyiSVDIyd5xiMaZqImy6w122AAbeeS79n7kEf944wrog+I7QoHWGkiG5FTRygTYeLrs3ADIOnr96t46XS+v0JlI5HXU3XUlmSoEl323P5aP47kOH0NA+Q7oLg0Bh6VINerwGNROALNIr6gxLn1iDL7wtlv+EEORG86e4xG2jc6knBaKgxm0vuXzPNLBFiyYtQ/Yk4Jqv3U8215L16Zlgg3EIxj3KE5KqjUhKZfoWVKjVLBcX5u3nFj9+GHjnebzrfz7HPfbPSDJiLfBkQJxLUu0jhFtPEsKgjYtkrFPk/vvv55BDDtmlY3nGCRRbhcvtQ0yHpVsAr1m0YA8FLf5yfBE12zdtnDxhKYkKncEohwUbqeGD9IGMELe26omZmZixlp9MpHx1zQmsry/m/lddvGca1qLFI2gZsic4B73nU+QHN7uixpQbykIbpF052VpJeURSGQvJ5lWwFk4//fT91+DHIZ9++f/j+b94O76xFERKRkSqfSAnywXGRoxOgskLTIkHvmTsCzx4yGW7dqA66HbwcguqKSvWjPKXAFkziXgP8IbfvxbLfAJyp/OLADyqKGo2wiI4trCe1Aa0E+OkNxsskBJjLZNJwjWjZ/LhFe/nlYdv91AtWjxmWobsCU4+N0fmUwoQTazFWrC5wS4w5A0Fo56T8mm5FXeL4fFOutsqZEJQy1wJ7VwLJhsRcb2MabgZC4EFoTD1gP5/v4iBt+78OpGogywLcs/iWYvQU6VkAGkxCkwA3mMsTvn63/8t4FPPFXV8wBJKgy9TLFDNC6ynm0VBlXaVgYAYxXwV0zV/PQBF4MO7LifZosVu0QpNewKz5JOXI73mcN1riq5KQDihVGFcnbG4XSNjgZKw+s0f2t/NflwShQkN7WTatXFGy9gZIyYikIWEsJgQFhKCUkLQoen/0kd2+hiBdSVjpBHkAeQ+aM+iPUvuN41YIh7zYGQ0iRiOu6jnJep5kXpeYDwtMZyUsCgsltS0sTYtAYJJK2mTFZ61eP1jO3CLFrtJy5A9QVl6wRV4dYlXEciqe5ExnZeDAJHh3utzIdLVydbtsDss/urFeApKysMld3kYC8OjTBsxoRKUb8lTRTLpk1ZC0okI8oDF/3nBTh1HAqLhjJnXLH4qrEAYgcoEXuwUNYLJ3T+XU2/6e1JdwkcTyZxIZvhCkxkwNmA0cTliqfFpmDYgZ9QUeOWy1bt/0BYtHiMt1+ITjFdccSV/shY6IG9K+wgroAoylhBaTHONxekICReu3bGJv7zmiv3d/MclXujGBwl+U8fDNNUryq6+lkhRCvKGh419l6s0FaghJDb16L/6Egbecd52j2PrlkBAKgW26ARatkCAHH9s0ffCFJHCUNOeUwrBokRO0TNobdH41POQoqdxOWV1/v7gtbt/wBYt9gAtQ/YEYvGVl8PkZpUlJE6/ToNoA1MFmYim1qIFLRBSYI1l4MMtI7Y7LP3olchDITUeHjm1LABqQARIhAdhwZI0VNOIKfASVFuNWR0JoZ8RCsgMLPna2az6u21/DyZ03uGgAjoFHUBzCQtRB9+4WfbuFj19/i/egbEeWe6z2dIbGuskoFSONZpYBxS9OpF8kL9Z2jJiLfY/LUP2BGHxRy7HSyVTdYwtoIWFMciFwbYBZRAVgTQCY6Zq8FpO0P7+bPrjGgFkCVBQWJMjpESIIkq6GYsQwmkKJsoJ1XoJHbPHmFVKyRGM1DyyvIixHn4Ih373HO575TaEaMcssugKUIpEoBKcm9g6SScA8RiqMiW5Ry0N0CZwwriAUjmBkoSuhCXtQU6sXbdx/hGrdvtYLQ58sizjqB98bIvcQGvhnQc9i3ce+YL91q6t0VoUeQJw0LmfIhASQoEOBSYEQld6RcYCDwl1QIL2LaRis1Iilq//83v2W9sfzyy95Ar3AE0oKrGkkYfT11UIBUy6EHkrmqr3UOieYFYpJTaSjRMlkrQDYyRS5ER+jZKf85Tvns0h3350WZCCsOQKZN0ZLGktMncuRmEtIrV4ddDhriuHWGsZrZVIMqcaLaVBKUOWK2pJSCVRZNpnMhUYC6d6LSHpJzKT1SpH/uBjM8Ixm/387EO/ZsX/fGz/NW4rtAzZ45xlF16J6bDkZTBtFlO26MiSh9a5nRSIhkClTSWICKbkxa01rD6rVQhwt5mq5lEPSGsFqnVJPQ+JUw9rBAtm5U2VlKYeopfTW86wEoYrEcaESKnpKgyypHeI+Z0xnaWEWZ0Ji7rHeNFP/3GLw/3xm+/FFixagVcHVQcVW1TDGTCVOT1cb3jXDdlTvncOxgjiRkBlstB8ReS5h7WGLA+pZ05qC3LeeOybHvv1a3HActxPL9/CeE39nPq9ZjPWj4/un8ZthZYhexxz8PlXIirgDYPaBGIClxTrg/VAR054VQBiKgx/CgWrznnvfmr545/+Cy8BnH1SuYSxkGSywGQtohb71NOAahKA3iyEUGkCT1NLFZYIITRdhU10t3lU0gLjcUg9K9HISlSzEhO08ayfvX2L40YbckzJkoQC47njWwmpD2kZSA2FTbsefx83fOJaCZMHWKPcy3qkjdBVcbaaJAvQGm54zucfy6VrcYCzcdMmmhV4tsqUQXvu/3163zZsO7TWyB4H9P/7ZS4sbgqNywHrBonENBOZ/WGBrVvydlzBQAVaWTwlnGuxA8jBRJZPP+fZ++dkngD0/9fF+F7BrRpJp9quGh7GSrKGIQtC6sK66MS6It2UwKwErCSQMJK5GZuUGZ0lwXjsY21InAu0luip9SkpUcrjhJ++i5tPaXYamzIKCNI2yNvFdLAHdUswbgknEwZ9yerVq1m8ePFOnc+yb5yLyctgBDbfLHFeWvAsFp+4bimUMix7r2hniwODE39+NVFh+9tY64qdHyi0DNkBSL1e57D//NdmiDxu2G3A1XF3nZc1xpV9z0BogQgE2WyLv0ngTVpyAZRwHZ2eeYmG5aELzt5PZ/b453/u/gMMS8gFRK7khzECpXEVhhMJstnZG4HM4RPPOZEFvs9rb/u5U1Sx7nvtLo5SSQOsDZlsSJIkQDc8ZqbOBlXIScOZ44+dUqDvpw2KNTCVECuFK4FiDWJTSg3glIjnfPvbrH7vzs24dVbAJsKJNNoZQ2aFBW0RvsFKH2tTbEv65YnPAWSgdpaWITvA6P/8J91IOMQNe4xwofIS0AqMdp1o85uzvgErXQVdBVmvJRoWmNRiIjZTprKIhmgZscfIu397A9gADYhmhWYbgEkBY3FSh+6ia5zyxque9SwAbl64kFf96VKkcAXDegswlIRUGoL6eAGrN8sxA8CSZz7aVzz1+nO486WfQEvD0KlFvNsNPZtid5vgJtyjiwvYQwRaGiw7v05mYw9yD5QBz7j7zTQDVDKJtSAii9aCX7/wc3vgKrY4oDEzbsVt1RIVAvQj8xj3Iy1DdgDRf+UncJrmcqZER+jcO24xxjhjNl0aGLf27oFNrdPd810HKnMwzY9ZbckDy+rzWmtie4xC8xo3FxOsApRg5tl29cKO9GZ6gnnz5vGWW0/hq8WfUo+bhQispDYRYLMAfivoTrzpAXEqYbI7xz41oDLa3HMn6HGDOFqyyS+6L3sqvzqHXBrotJDsQjXjXEEhZ9pX2pyViVBjDZB42NS4aNgWTwIU1uptug6njFx6AE3OH4eTyCcmiy+/HGU8lFVIK1FGoYx01XBzOVNkSjZrduRM5xBN/21qMB/hZmi4/KI8N6y+sGXE9ggCECkWi86sU9HQAjxXrHT6lQtsAt9771lbfPytf/M3/Neys5FynAdHQBuwaUj3LzzahUdltmD0YBhZDrUu6Br18H8tsVkEgE0sdFryksFo68q6WIsxlqzUNGK62ZadoP/qT0BBOxUP0wzDtM5daXXTqAU5ZJI3eUfu2WvZ4oBE55BmMwZr86CPqd/zHMzkgbNe2pqRHQAsufhylN1sTOEk8zBCICOJiQ02anZOUrgOZ/M0xUcOvm1zfUMAFVh5acuI7TEUiFkGNgEFi45BTa8rNX+mTg2DbSyYL168mHsXX8Uh334voOn8RUClV5IdMklhnqbgudmXtVBPFdmPQritmbTua6cWXLDY0GI3c0MCYAQiF9hoZ12LejrHze3qETeTbbq2reBDr3zdTu6zxeOVxV+5BOoKPEhTjee5oI7NjVquweSKX7/kTfu1rZvTmpHtZ5ZedCXCl9iAmVfk3ILCAg2BNNK5iqZmYI/EbvbTAkmzuGIOD33srK18oMVuYwQ2BSsaWA9sZNFlp0CvVfNnwWIKMYesuG+7u7r/1ZcTxilxhyQ7dpz2RSlCGCoTHpWJItXJEmns47+oDpNOd0q0O1exSKVbx5I0n2IBuatqYHzjZMh2hqD50wrIhLvPEuH8mkY07ym5FWHHFk9ELECs0LkCq8hSV0E+yyVpJkkThckVOlYsXLhwfzd3mpYh248s/eiVLqijuc41pUqPAQI3+mk6CLc0YFPDo6lBtxEzdammEnBLsPLclhHb86QuJ68TLA2ssFiv6eITFisNRtTxFuVU9Y5dL703HkS8pEF7V05lQtKolLD1CDvmYwd97KaIbG07PNvta/Wbz4WixkjjSvE0BCIWiEQgNM6I+Qaxs74W2zRYsYBEIRLpXqmEhnQGzdLy3TxZsDhX8oQgTxUmB2sEVguMFphcoBMFj6HCwt6gZcj2E+//8lcAEElToaEOsgHT0QICpkPSki2CD90/XvN3jSvF0vzdH4HnHNnHyvNaRmxv8PR2H2sEwlOIHgldMbajge1rYLsa0JNBj0TInJ15vFYNafzFCfW6BFvETkjEcIA37BNOeESTHuF4gFpbov/SiwEQBQuhwUQaExiMbzChwUQGQoMomBnxxR1hgbpApgqVOx1OaQQyF8jMRS2Siq17Alo88UhBtmkwFjEWYCc8TKqcUUsUdkLBhISufH+3dAta46z9xLfvGiKogfKaC2JZc729CLposU0jZsGFw0Iznntqncz9XyQSaSUkEIwK7r38zP13Uk8Cvv3qD7LkqxdiUh88gQhmZl02AJtbvFJOGBp8uRPGpCSJQk11MsJOCOSoh68loi7w6jSrQIOPIC+WOOgjV8LSHBF5MwEZUwiLkC5VQ3g5L77hHH70wm0IEE9RB6mVs3sGNg/9Fx5ILTBCuqqeLZ4ECKxVyO4cE1uoKRifusctFHJkUbu/HUC0DNl+4NB3foqwALpdYFSzs/KcZ1FVASPQwmIDnDZi0pQ3z3FGDPiXY57Dy487br+dw5OZYludOC6iE4HNZ/zBwjd4ZYvycpRMaQt2XE/lpx/9O174h39xmmJ1ha8lcsxpY1KeDiAEwLMCtdFillcwaQcIiVCbGcup2bo0+GGDcbvj+GiRe4gcTNgsuMqM21pmomlIhcv4aPHER+KS40OFKGpENJXfMVUtA0zs4TqjA4eWIdsP2B5LLsV0ByU8EAWXVOsBfh1MhJuVAUYZzu0/mLe/8pX7q8ktNuPDwdO4iFtIRAE3jZHNn4YwtCiZ0lms0RfWdriv2bNnA2BzUHWFqAgXOd/mcpSz0KLbMzegqYIf+MzqNgyO1rGmgNWbRRkKkJ5BejHdbXpqzLN9cjBFOxMoNBWlKJrGLRNIK9B6Z3bW4nGPseAJZ8x8sYV33AIkgBSoHUhY7Wtahmwfc9AFnyIvGJjqPASQg0wUygjyglOVkrFwHUxqWf3hlkL9gcTHGreyYv5D/Hl4PtqUsVY2FRAsUsZ0RDHzSuNEO/l0TdwCLHJh/F4dKEMSQrZolPmLNjG/bZJOL0UAQ4nPxmQes7pTJuqaNPWdqCYgpCYIMtoijRAWIXZi1Ny0xVN5hw7XgVlrwbdQF3S2NBaf8Bz63fchbI41c5z4Qi6m5dSAGYUhY5wL+wCiZcj2IR/59vXkIserS2QyM9TR2qILGhMJ/FihccEfeTd8fPmBE+LaoomEJLMcP2cD6+pF6nkJayVKGiLVoDuIKfk5BZ3t1O4GLryExf91IRBBoTkTW7KJFYeu4gWd9/D04hBLA0EkfBKT8tf3vQ5DTmfJYosa0+xspLDTBtUX2qnDbIfFn7wc4U8p6NstY1Om18zABJY7zmjlIj7x8Sl2xdTGE1xBQ5o5QFMjbglGo0raDXIOIFqGbB/ypT/fR6CUEyqbWtoQoJRApYJUaLRnEJFENdyN8trXvnb/NbjFVolzwUgym15/AwtLNTzq0ysGPi6ItEzG3bXund7nhw4+jktX3gVKEUcZxxw8wBv6fsmLyiGBKgMghCC0AULkSDyM1Qih8OVUfTmwaEIBWgi8HU3IgqYalaBZUQGm1kMsuDUzTauXeBLw1O+dBwgWdI3xkO5Bpwk2kbgvv+k6Fyl+O1hryMcOrJuiFX6/DwlQ5BrSkibtyElLmtxYhAasINAKU3Kq9jmw8gOtEPoDEWsLVPMCqxp9VBJFQEaXzOhSGZHIqGeGe6vdDAx37vQ+3/rMF6M9DVhYMsQJ7Q/w4nJEoJyih2iqtwohKEiLEBIpBEpoAuVeoXTrYkYILBnD9eIOToRm/ySQDYHIJSJv/mwIJ4o8tfzX4gmNkACabpmTr0/xI4PXZvDbU/z2BL89wyu7yuF5Are++u072uU+pWXI9hEHffhKkr4MvTjDztLYboOdpdH9GXFX6joLAYwC1vKVN5y4n1vcYlsIOcFYLWA0bWMw7uP+ah8PVHt5sNLLfdVeNtXnMFjrIChkfPb2q3Z6v6s/fA4Y6Our8qK2dfjKjXrFIyTIB+sRBS9troNJtBXuhUAIiSWn4Fka1R2MmpvVw4VpakVuXu5HClfzrkYrh+xJQinS3D05l6ces4p0rSavAxikci+jDcm4B5P30dvbu7+buwUH1vzwCUw2L4MoQwZ2ulyVzp3f2XYqEpESjgYoBEwKnvGMZ+zX9rbYNve/6lMc/K2PMFYFW/QQMsRvJiBnVtKIFbUk4KDZG7mfG4H37PS+7/rUWZz0w7dzaBQ+yoBN0WgEVGRMWzHDWkFqprQSLQXl1jQGq208NevY/sGaaR8YsI/MExNAAtKTGLv9KdlFH/5PvrlyI/UuhemRHNQl+Nm7W+WCHi8c8q3zCUOJsYJEh2ysdnHc8Q/w0HA3IwO9kEc4EYYxjjpsLde/4Bv7u8mPomXI9gH//F//Am0JQZDhe80lMgO5lFjfI08Mtt0nGzUo4P5PtZKaD3RKhXXU43mMVCRSapQ0GCvQWiFEzqzOcboC55v7zp+/w6uOfNVO73vNeok66tFRgolJWJ2kJBMR1cCS1RJ6C+m0yLAAKjFUszbqDckdd/rbPY5oOIUqAotVzWCPqZQhCwQCUguNrX/+uXPPYPjF/VQWC+wRlvBuSfv9GWlV85xvXEwOvOCfFnLBm9600+feYt+jPNDGIIRHe5QxGbdz58Y2uopjHHfk/UQKNlQFD9cXHpBGDFqGbJ9wvRyhvS3Hl3a6UF1uBZ6x5LkBfDKdYbrBjra8vY8Hjpk9yspJwXC1jSwvkhsJaApRlZ5SQk9Up+RlQEoSfQTYeUM28NbPka1btsV7D8cV7rLtZLYIoyHVosYUBRvtlkXCrIE49UjHQ7wd5EMLJTA+LsQeZkQ9pgJAfBf0IdNHzwxfOPcMRl7UT2WpwOqM7t/6dGyo4QFGgS14+EJw0xcf5rmfvZBf/PEjO33+LfY1At8zaO2R4lNoqtLU807unehCCIMnQckD11wcuC17AtHTkzFRcUJDoCkWDb5n8aXLmrcWdBhgSrD6Pa2csccD7V7M0vYKswop9byCQbpScCqj4OUUVE5BprR7FRCC391zD8847LCd3v+dgylHzYkQQjCRV7jTtDOmfeqmhEg97GiRWp4QFDS+B2DRRpCmAlPzEWMR0Q6ERVyeGNNrYGK6uvWMigP+lvWoAJ678N3UD51No8vD9sZ0/iygY0MNKSBtC6iWJOkshY0EIrd4G3NOftaF/PLXLWN2oLH4Kx8jLLnC81JqMq3wcO5qJUFJyI0iztys7UClNfzfyxzyrffhq5yezoxZ3Q26O1KMsUxM+mgLoWfxfYvyWhpAjyf+5Zgf0u6l9IR15hZrzC3UmFOo0R0mtHkpJRUjhKbDMyhheccD/7pL+3/aMQ9P5+rckvmMaJ+7Kt3cOjmP4gYQiQfDEelEkdpYgdpYgXi8gBkuIEYj5LiYnmhtCxs4/SuBS4i2xuWTWelmdsLg9BwfUaQzyHOSuRFZt4F1isJIigIa7QEbD/EZeQZUTh5GPH8j+QsGGXtenbXHl3naiRft0jVosW8wWpKbAIHF93Jybck0xJkkzgFhkRjue+XH93dTt0lrRrYXOfnH/8gRs5w7sWYEuSlQs4JSBKFqMFEp0NGeoaRGoGhrDSseV3T5GxjL5hEST5cEkxZyPASaHn+cSBgiC9uI29guunwbonYsdVvg/ko7a9K51NMAf9zgrVTofoUdZ4vweCFBTAoKFbj1P969/QNM5Z8ZIDBuvWyqnVPVFZItZYqOe+ZH6RZAUUII0QaPqJagC5Khg33sSQMcM3+C2dE4HUqTWRiaV2bd8jZWdize9YvQYq+x9GsXAh7GKIw1pHmAFJrA0/gKwJDmkOaKLDuwlV1ahmwvcPn3/42r6+uA+aybAMjoK0/SUa7TJQW1rEgDn0Ihpd6QlCK3uv6Xt5yzfxveYpe44Iib+eTdJzGWR2S2gJOdN7TLCu0qpSANvV6FtbGk2ijwil++g+tOvnqn9x+2t0P7A9QePIK1+WxWTUb0+T7hYIppizADgiQA2+0OTQPCMYFQFn9Uo9QOOp9m7TsbNau4TgV5NLESiCwinrFkgWlul9rpwpvCWiqRjz1pNS9ddgfP7FjF8lBTlJAYGMjhnvJsbjy5xtLTrmDlta2IxgMDtxhqctBCgWfAKpJMkWRTlRAteQ462X7g0P6mZcj2MP1fuBBXdrfgQps9jRCSoWovI/Uqi/vGKPh1UlOkEMBoI0QWU7L88S3KumnTKK98878jzUxPaIXAeIL/vPylHHroofuxdXuPjsYYxVJIbIs4KQxDICySjF6vjm8Mf6nMI9dFambHavhbo6IFE0mZRUVXqbl91Ti2p5e05CGlwIwACIS1SGuQo5bSg2M73rEA6zWtl3VK95tjBSAtNpyZ8uWedAGNG1PkkgJxwclwjfUrXr7kj7x39ho6/BApZjq+/sByZLCJXjXOylfvIEm7xT7hkG+fz9QCqbUSrcFaiZQG2ZypGwvWSHQmWXX6+fu1vTui5czaQyz+4iX0f+ljiMBDBNbJ/yCwsYfNPHQqMKaNTcMlIs9S8i1KwlSxsdV/e+H+PoXdZt2GYV79919AGItVAqZe0iIzwz+853t885v/s7+buVd4WnQtKk5YGoyx0B9loT/ObG+ChX4VtOHWyR5uXHc4USF5DHnFmk2NACTcv64NHQaUbhkm3JQRjhqiMUs0ZghHLdGoprxygnB8xwvz/UKAcjMroXFCwapZ8VpZ52XUYtrdeNzXLmBiXoAJfEqrxwiGDCzT1DxJvnwNH5y7hq4gQgr3mak8OCUl3X7EqeWEUxbctdtXocVewgqslpgcjJHkuSLPFSZX6EyygzTCA4KWIXuMHHzt+Rz63fMJu2KK7THFjhgVJQiVO4Vo32JzCbmHNVDTndRyhZApU5OXWrx/z+Gxctqbv4hVgkYoGJ0tGZmnGJmnqPdKEt/NFK766gP7u5l7hac/5Slcdvcr+frAQtbVQyZTj5HE585KFz8YWsp3Vj2NNG+jLaoBkv4LdlDociv806F3s6BsuGtNO6Pr5zBwXBee79F17wil2wYprYsprU8oPTRO1y1DRCMZq49u2+F+b/pn5+ITNIM8piM/3MsK69bRtGBychK/a4S0XxB3tRNUG3TePIL3QMCmgxUnHv4LagpuTmJujAW/alhuiRts0jGmacLb/IhTewZ2+fxb7FkOu+48QKL8GZFprMAaZ7h03nxlEmsk7xDBfm7xjmm5FneTO++7j7/+5bW4OhggAoNQFhVofF8TBCmNuitcaD2LzcHkAhUocuMTyCll9Jy7X3npfjuPx8Jzn/9xcmugoBjtE9CpXOHP5n0fJxK6LHpDTnHC8G9f+wb/9Hev27+N3gv8/oX/zPE3XMWtGyOggedBngNESC+jXK4wq2DICKBj96JTpYDKpvlIUUcf7rMq66D/wZRgogEPV6a3yztLrFnioVfsZOejN1O+94x7TWGkE/3MJUd+9fMsOkigu8cZfFoXs2+1tG8apf0bkwwcO5tXLh3l6g2HsDGdi0HS7tWYVx5jnj/EIeE4Tw0DlJUcG26rIVvniGvPp7djIzUrOaitzp8H2qj+ZT4hIWkEtjfG60nQSYCdBMKNnPG035CanDv1CkASawEoJlNFJSkyNuFjcuf6NLUM4XWAEBwaRPzkDe/ZtQbuIxZffjnWz6HNabEKwGYAiltO+3/0te144DKF74HWxgUGSQNGzqjZb17JFVcK6Jw3fHDPnsxeoGXIdpEXX/EZ7qvVEUgkRReq3A42NthQY42ASOB5OUGQkeYSISVWSNyNobEWjBUYDX885fGZN3bEKy6nJ9PYQDLa3TRiXvMhmErEFYASxHM9qGZ86Vtr+ae/24+N3kv09fWxbN4oGycDkrQESLzQEvpVikGdvqImNrIZTKFY8fdXcsuXdk0Q+t5VEWJAwDE5jOSYowJWL/bx14S0j7hBUbXLI1nou+CPINm5HQsB0kCkXecYw0zoonEFPQGbSjIjOfgZo9xv2tjQ3kXx/hIdmxqcdvbXeO+v3ggbupGJxEQGZtUp9tZ4zqwHSXpXEoghnhIUiNTOj+4Xf+V8lvZtpGIt87w6v731YOTGEoESJN0pYm4NkSosAXYy4PlH/5jnzxni+uGjcOs/ilQ7g7Wp5jM0VkLHZWwOaN+dXyqwxES9sEbGHHbdBVgLjU0w8I4Ldrqtu0r/RZdAj4eQEqquCvcUD571Ht78ha9yzKJZfP3eexj0DLYvbW4hsZnFSjFdLX7Ftz7X/KRl4B8+sM1j3nvvvZz6/W/RsRyUcupCyjfovBnrY1wfNbUvIQ1e+DjwK9IyZDvN4k9cDkXcbENKpJGIBFQOdtyiQ+nWxBDkWIgkQWDIfONci80AMmvBkxm1JOTTXa+gu3vnS30cKBzxD1fQNpkjAe0BPR5IgchAarYIB7c+GCWID1ZET2Cv0thkwJz2HJ+xaYe9JyDWkBiJEh4bJkHuSMh3G4wPzaNsFGkIWZfFjGWIDo/sqR4jmz3GBov1M0TXTnZAwkKoIQZrvS3VPaApT5WDlIzXfTqjjAUrBlh7y0JMX0C1GnDtN99Cp1YznbG0ZOs6qEdlfniMjxKWWXMqHBpsORv9l3uPYQWDFCJDLuB7g4v46pefyYL7ZpORsfA9G6nYTpZ3b+SXf1qG3FjCF4KkU8PsGqQSESj0kM/SpbdxypwhfjR2OBIfgyRuGqtNNZ/hiRI6bsPmdjMjJvFmNQgCZ8+nJiVSQnEOHPLtC7j/1Rfs1ve1LZZcdAWmO0XMCqDi1hGnJkGiCrY9ZemXPgkFwU13b0CEHrZ9SyOGlO5z1k6vQ7oZlaD/Pz6+VWM2NDTEqb+8DrVAT695WWvxPFck0xqxRY0xJ0htuP9Vj4+1+5Yh2wE/v+02/uHnP0f4TXVwAA0G47yKsURmEi+x5NJgkVipsKHFWuMekOm9WSCnLDU/e9G/7I/Tecwcc9plYCS+dk+DzQEFMoVcQ7W3BgfVUT7oUYV/XxeFWKAjiVCPj9Hd7vD9p3+A1/zp48giCJsjBWQWpPCwwEgVxjf1Eg3tXnSqQFBbOAKpRXgausAmGbohXRg8uPWsokGEFqF2rqin69CBKSMmDfgJQSmjHNaJAhe9tnEA6pNtVMop7VHOEc96mKGqoP4/i4nbFZPtFpRF1CGMJbICx7b9L+866T7+kvZxY+UQfjIBc4OY9sozONZ/mJOKkFlDPYH/d+sLCb/0FJYY14x1p+cskYrZ3hAPbOqAOCIUkEVAW0ahAHE1wGYGIsMrFt7HeBoS6xIgyK1wJW2sB5TIG0UX2DIVRq6BsjNi09f4EV+NUrDs2gt46LQLdus7eyRLLrkC050gvADqTSOmcIE2VbB9KbYkENIZVWE8rMxcu6zAZlvm9G0uKj1l2EBw3+AGDp01d4tjr7j+ahgKoc0pdbjIRIHW7qfnGZqPtLtuBtTj6HltGbId8JZf/AIhmmPNKYskmv9oiwkNWInKBSIRELkFUqunVs2bRQpzC56hzVvP/zz7C/vlXHaXo152GV4gUNai+yTR0Gam2QA5VPyUJX91J73tlmpSwFqJNy+l++iV/PKebsq3LeE3337iVhmeO3cuD/9HL51HTNBVAL/ZXzYSqKQR48Nl5NoiUXVmPLQrXPbcU/nYmm8wNjCO17cAjXDZz5FuxsnjZlcChEzxo508im8gby66KAOFOr3tVTqLdXrDGqmAkThi4UEFYAxPwPCIpatTUPmfWdSPMNBRwy+4x8O/6Udc/PZ1HNWmuT1T/O/kIcS6g3YSZgcpBS/nad46ioEbDHYAZ254Pvrap1DarN9UgaZBG/OLNe4e7ERoDxBYD4g0xgiEMpgxCW0V5kY5f67MATwkltyAEoZEO5co1sOmU1pcBrSi0LHtRPWpGVqwh+Ic4jhG5ID0XeK8Fq5yU7MmHCHYomjWBWu2KwJKzQAcK5pSK5tnrT+yze79U37wpUfPyrQPRQsorNUoYZpG0WmEGjv1eRcAopTmnldcsmdOfh/QMmTbYfHllyOkwCqDCZpfvAUSiTDS1XEyzVD7HCQCnVvwRdNNYTF5cyancvxghD+96vFjxA772yso1w1em0RVDKYomk+YM85GCEIgz+DEV/2Z1XEPaysR1lqMdSO7EQHzFw2zjof289nsfcqrSkzWiowvqKOaHmPdAMYC1KhHcaPANxArOP4jn+L3F+58lYPXHPl0vj72efLFNVZpTSwEeSrY8hF2owo/zEmTnZz5TQ3KhAUvpqetSk95kp4oYXWlA2sj4tzVehFYLIqoqJlYn1BvjMJwL16a0PP5nPGlZT551ToOK2oGLNwXLyS13WQkqADavYSQMcq+weJqqP1+uIO771tAf33r3bMSLr9pxm0JKIuZmoVa3ExUQMyWVmdKNXLGY6aa59r8e9NY7ciY7QkOu/qzzUGGmAoSdF9dDqIOZk4KUmzZHsFMn9P8/7ZK+0wxNSvbytkAEr0R6FdkGjylkcpirEAy5aq0KGnI4gNbyeORtAzZ9pBgiikUDNLPmuUOwKQSm/hQ9RBaYqRBCtUUXXUIMSX/oyDPGXjbgZ1QuDmv/vB/ctfQGB11Q9YpEHUnHmqai8u5LzCRQuYaUoN+5q2sasyhnkrixEc3R89gUSqjEPUyb9HQfj2nfcEvLnkDL3zfV8k3lcg7wQbg5eDVIWiAp92YJi8LhrJd6yFP/vHZHNRVo7utwR33eHS058RbuAnc71JqjAE7Gu3UfoXXdA97lrCsKRcz5hUS7pvsoJGVKIo6Y/cEiFU9FLQiHLGM9kJykIYXTuLdZZn7RTDlgL53raW7aDAK1jR6GNW9aKOZ4xlCUjIEh3g1rACJpgzcaI6Cwfbp/nqqC9apQlGlrkEIgxHNv2ZALpCRdTNbCeQeWChTp+n0nxItQYqtGKotntNd+hp2Hyea0TwoMy5CD5QRNGNStmzPlNLK5sbsMWEReUSaNAhCtxQw1ThfGXIDxiqyzOf+1zw+1samaBmybXD0VVdiixmyPUF5Ynok5KmmpE+UkCmNGCs8+sNSIz1NfUJy03P+jsWLF+/bxj8GjjjjMvLQdVjWA4Rba0Ay/fDlJYGuSVSsMaFiVp9ivCZJ0xJY2RzFuqdPW0W1FpDlcPjF7+Du83deomlXWfyJy5qHFciaZOWFW0YG9v/7R12wTh3EUIgtNFDzDCv/7gIajQZn/Pk1xInPRNzBQ5U2GnE3jBYQ1oM7IfAkRkGIxQCNwGDn1Ckv3UT14S684U78LkU0BtHwo8fFWkAcCufisZql/3UxeihE/UURVWuU1uW0b6rgpzlaCEwUEocBI7PKHHTeCN1+Ay0trCxQWRYThDlhkE9NktEGklSQ1kK8ewt89rofc8YrXgTAm3/xN+Smyq3jvdTGD0GMdmHJoaMKdIKEwE9o8+oMpYpGFtLu1Rm4s49wZQllBNGIpT4bkiUaemN8X1O8O6BUG+Whp83lkwd9nV4ssQVDCYVPhg8yIZIAHmHzoiim4p8kJI9OZy1c56H/IWd1pcySzo08OFRGWx+VS3TdQ7an2Fyiugx6vMyquse84hj3Zgtc/IoSpNonkBZfpu4CTZk4KZmKSNrejGxPzcamEHnTHm0uBbY9QxrjLlJb80NNUWfY9sxsZq3skX9IwQtBgV7rky7IUZ5bBwRFmrvN0gT02p0bBB1ItAzZNhjXGtGWIKUgb0hsoiCfCkcziCjDL+dk4ynC+ggLWlqsZxBRwtsLT+W9r331/j2JXeBt/+/f+F29Ar0KmUFoLEbinnJhm14ri2gARUGjJBG5h1fPyfVs0rSENRIhR3neggc4uM1JJN0+1s0fNhxGEpfoO279Hm/3kk9f7h5cgYsqba6zmEiz+IrLUA/n5IcCnkVEEqxGthtMIXFG96Ei/f96McGcmCg8jP7ONbxu0a+4ozafjXE/fyn3MFbpgqcWSA203yVJQ0HeC8VJD7GmjepQEZaP07dkFaO/Poi4B1RN4Gs77d7SQpCFOJsRQxCnqK4GaSklnetRGwmIsxJjazroeiCh4+FJ/LEafpxQSjN6ijFaWhITUX7Ao6oi4rk5aaSnO2JjBLYmEQMR7eOGy75/J1fcdzeiYckLT2f5U//E547+NffHf+Tnm5Zz85oVyKFuTLu7aIGEdj9nTd3lJA2sbUetLxEKgUKgi5asT0CbISzG5L/rpmv1OLbgky0VzAqMi9QErBXNocyWU4y82cdmTK32pG6d7xFd0Zyqz6qx+cztW0dvOeGh9hrZeAdhYhGjPo1291lrPdCaL9xzFBevuJV2Oca47sHNzDKkUAReDGoSIduwVrlpsec0BP3tSAhOFcDdE3zxr/+at1z7A2xuEL5096kCctdviMTF22xhWGWOMAGWFIFtGmCLEI82/MC0AfvL6/75UX8LOgXpmMXWLCLyMGs9dCFG9cxsozeA0BErOh4/QR5TtAzZNrCFDE8J8gnPLZTKZr4NgFHYqkdWS6AnQa4LsVhMYMFLWP3Gx9e0/NmnfoxKILHLQrQQSNPMK5nqhZv/l5mLDbCRRfdKahLCgmS86gJcnjX3V1zUfxfdgXIyRYDtW8tk/6384z1Hc//kztfj2hbWWpZ+9MqZ4JtAICLhwsSb8kr4To0C36IXevh/sWSzBbZPIwpgcgUyQxiFWRQjHo7I2gEEA+OL+LoN+Zu+25xbqg/+IiSjRsB4gcmDDZ0PKJIxS9ptCSYFbQ1FZVUHSds9qHgZtijQfaBzMT0JmHrSRGyJBSx66a9RYh4bmY0QgrQ3RY8BiwLGVAiig04E3lgV2YjpjhoYG3DPyFyCyZTyAyFZxSdp126GZ0BMSKKKJBoEMZwijxCYAkgUfqPMvXccz8cI+eChNxLMuRchBb/lOKiHkEtSA1JYjPXpClPGxnqJMkHuCfxBQzoHTEmDbyhGECcK3wCBD8otF+tmRyyEdq49YrCChoGCzFmfB3T5DYxQVDH8TXQbP1+wmJw+HmlTlnylwKrT5sP8dRy/ZDW/qy4l2dhGUJckq9pgWQWb5chuj3T4YC6+fZwPHfMQPxkTNHQ3kdLE2tITQjZ7kg3rC00r4YEvSTeGeAuSra6VTU1qrjn2tMd8zwI879BDofADqOXQEbj7NHPrdbYIcjRERylCMeNjnQUM5jChoEMjlMFmAivtFuH3m8/AQiloKz5az/KB15xP/xcvglLgBju+QNgIs655PNVcE2zTfOcft52LdqDSkqjaFqWUfFI1FzoMFFI3Q+tIkO0JlFIQPtQFModM5fzhTW9k4B2Pn7UwgGc/+yI3LO1znRHN2Y2ettt2esHcjQotwSjIzGA7BI35CmMintb5G6466B56Q2/aiE1Fe3aEPv995J3ofPcDPoZGRlh+xpUcc/qn6LlP0HWfoPCAwBsSqCrOZWgFIpGug/CbltgHM9+j53cWhhW2MZXQ56MijUBg0dghhc49jPV4eKKXmyuHcWJpPSW/Rm9xAj/KsJ5LEG5IUIlAVJw7D08QTnp4QyXqUU6aAbFFWItQLrhQ5BaZWBIBhYbliHbNeNJOwWsQ+Dl+ALRbjLWYPoj7AtL2AjYKEEAoLYlRrHxQkNUEQSOl7WHofFDRfo9Hx70eHWskxU0gGwlhCsbX2KLBhBZbAE/73H/fMr43cSTHFBKO7pmgo2sMGi4Ct1YPqWdu1J8bEPguLzBp3hqhwIY8Otcsy5HDktuqfQwBBQHtYgRNSiQNmzJJTIC0kmH6iLU7RiQsR7Q3OHrZWh5eYKa9bZs7xpZcWyC88iA2XXIE74+eyREn3kMyexzqoO5sxz5YQHkpoj2llq3gAz8+jZH1q3lB5x+I1BiRyglUwqK2jGMOXo9QA+BlzuKGkvraR8+6puzCZUe+lJP7H/vga4pV7zsbqUNsLXXLE9imMDPY3CJG7fSxp23TLKDdYjdJ7AROx9VorNXN7Wau2t/0L+P+N71/m8cfeMuHwWZQMlhpsLnFKpzykGewJc3A2x9/RgxaM7Jt4kWQ1zwINaIQU4hc4iCYZn4K5EVFMhLy4Id3TaXhQODjn/k+X/rWncwqBs2nxvVKCjAeZKEgqFpyYzGhRCPxU1dp0UgIKgJyi1aWulFceeidBM1S6HWbc3fDMmrb8EXGYV7MnMDnNyf+cbfaOjg4yKlnfZWCsagcKDTzeGMINgnqFYFYBHmHhRoIK1ziazMiz4SGuAj+ekXWjhPELRh04v7O/BxGQrA5aeoRhTljSZkB3UWHN86cYjubagkTUQhVRTbLEm0UeBayTouqCPwYButPgSWTqPu7sZkg7XQGTFgwzZF2sQ6VjnFO7FrPmsZ81tV6yXWOlAHSyzAFBZ5H0i1I2gPCiQAVp1gD44mCuJP6MwNm/SbF6AQzz0NOjUcTQ7ghRwGDS0NkfxXtS/AMNlHIHETawV2b5hB3/Zk5/hDL2jdxW88s5FCEsQUGO8v4QRWrC9Oyi1MIg3PLGcgN5MqSKChWUrpWZXxl1TM44ejrkEYz12/Q5w0ylC9A4iFNjQl8ujzDrWk7K8Q4KMkohgvn38Q7Xy1Y9ZWjWDwiHuGMdBxz7DzOeNspnMEpO3HHuMHk27b2p2fO/Hrmj64l6LB84sTX8e3bf8/nN/yW2GZc+bRXc9zcpTtxnF1n1Xln87ZvfYuf3bsGU05cBe6GQJR9hAlgVYpdBHhiyzW6bsvAW857zMcf+Cd3bfqv+TibDxkEsPpxOBObomXItoXFuROjBsWiQTTlwa2V02K/fmDx5uxeaY79yfGvuYKNJ1RYmkhMOWI61b85uzAS8k5JWjN4k5a8w6JDN3RUqUFJizbO+CntjHtX5G6lH0wIbph4Og9U+tBNHcqAGsd2PcxZc/6yW+095ayv4qWWrAxJUWAjXOmvFNSkpTgOtTbpRtkdNBfJm7OyZhKp7RF0DuQMLxKuMjK4nWAQ0/p/LqLF9yDRASntdErNRgXBVCAmQEizkxcuyVY2XRtWcMJB93KzOQo1UCSckHgWaAaup8pS6a7zrud9HVRER5QxnFoyK5wxUz6mYCF17iZbEhC4GeQdpwvav+A7r1gMg8cEdN2bU9qkkdqt1FsliAuK0XkeehHQDuQzo373fUmGTQ/jNqRL5vRGGW4lSyAKGWvXa45fmrAxK2DQGCnBd5KL1C2iLrBFSb2hEIsmqCxpp/CXhPa1VVb9YiHfnL2cV8y9l3lS88zyw/xoEmABQ3mRLmAIRZcQ/C7pYC5r6Q/d7P/yp9xI48M38rY/nkz2kyNZMOQhgAVLynz9387YrftmR3zqxTNuw1cfczyvPub4vXKcR/KF17xmm38bGhrivb/4KRc84ySWLFq019ow8Dg2WlujZci2wUcPfg4fuOO300bMWkmeS9L65rk7OUERDv3WB7nvNR/bn83daY4/+/1I1QnjBSSJi42QEmLjpg29bnnehILGLI9oMCcYN6Ql0CWJDiQydR25Bm791ntZ+vULEMAPxgWfXfc8Ruu95HpzH1QHg/Uebhnr5cYFu95mL7U0ugSmy60p5OCMWRkoCPxOgz9osV2CfCqqa2qNr4lIIITmm2IL/9VMmYpmONlmf5satJipoDdwhnJ6xzOfRBheuvQOUgJu61hCfawEDa/p4swRvRVevPB2eueXQbtoQzfLV/gqJZXAw6EzQAZnmHWzAZP9CIYQHTF6fRtSScae5TG2CcJG8/tQkPUAHoiq3m7EtsBimgoYrv0WPMtBi3IuW/ozXvaXF0C5k0bQSTkWmB5JNGlIa5KsS6IbIe3L6lQeUBTWh7RvqrHol4Jv61O446/n8sFlv8D3NW/peJgB8zD/O7GYCrPJU8MgZaDCSr2YH07OxpOKPu9BLj36Fh44DHjjdhr+BKevr48vvfb1+7sZjztahmwb/O2xz+EDf/nltBGrV5w6gAoNUmZI6fzZWiv0ziaf7meO+/G7EGsWcshr/sL6O49ohgK7aKhgOIMFijTDyUtJiy4LGqFHWrOocYNXMyTAnd/bUuh45d9eQHX9f3D1upMYrMyjK9zIU2bV8IOZRfThGtw5PJfX3vAcvvnCG3e6zce88Up0CUy3aOZgWWxz1uJkhkC3AR0WbzOnlIWm4XEVjgujW+ZbuXBCA/gwaEBlIDRCZmQaApUhiBkxZaoJTDYkNFw4QjAs0AKnqVlx7ciw0BWjJyNOW/pHnt/3AD8fW85I3oHAMs8b5q9m3UYWRfgCBjMP2zQivnCK5JmGbEmKGfTwJ0BVNSLOpqIn6MlqHLSwwQMDKWEtxB8TpIEl6cHNuDLw6gJiS9IziZK+qzVmZ66FVoZeOUqnTBlIunhgsg3GPQyGMGxwZPQgs/2I3x3za54tO9k4XiRbE4IShBbCtRbtKcz8gMkxS9upo2yik/hun561VZZfX6X228WcsfgdpO0StWaQUtRHjZQLLvgS3d2WW8ZLPGgOp07Afx333zt9L7RosS1ahmx7eE4rsVGTID2CMGvmXaQEzRCrNAP8gMVfPp/Vb7x4PzZ2+zzrx++iO4QJJKJkwDfUgTA3IAVh4JEOpwQEpEVQIU4xobnAn3cJ/vaYwznnDS/Z6v5/N5kxWJ3D8s6HKJdgOCtCw5sOP0dqVszfwC3rnrprDY8t2dzNjJhnkVXIhYb5Q3T1rGZ8cjkIg180KKCxyiB7fUw9crO2XhheBOq3YFU4HX2qQtCxQdRC6E9QHhQj0FrTHdboV5u4O5/PunqJrFFEGAUVFy2eFcAUIKgJpLbUOlI6u4f48P0v5eIV11Nu07yu7Y8Ezei91EbEdOCLDDBszGbRyJ2/MrcQNNfzvIIljzTpAo+R9pCOexpYISAKufefn0p2+SbE4irJakmQ+ngZiMmZWZX2DFmhhpovXHRmJiCXzph5YP1JjpyzFmVhY9rHurE5yMzHFKp0lsd4Ud+9QESS5/z1vF9zg+hjgJMJHy6QdbkozLa7LfWqIpsTUkl9OK7BxLNTJtf66GEfG5YQB41jv1fkJ194J8uWLWt+mRcAcOqu3QEtWuyQliHbHqmTxzG5R1DMUCol8CXGeNMSOZ6ygKbQtnt1pvY2Sz98BeExG2kruDLmAGt+Mh8ONVQWlQjX1KBTgidoM5JkIIZ5HvjKKWJjkDXDbd/afrmZ+5M5GDtOEAmqWZmiygimfJCA1dDIiiyftRsKH0UwEVjfImNB3jfGUQffy583LGW8tpyolNN42Kc+3ImaMwxt7RhrEQWarkSD56WYE0HopDkb89Cxwq5R0J0QFHKUStA6Z0H7MPOjAW6cPJjVY0U2DHcjxgtQhY7VisyzJCFQA4ylIhIKRz9MJenEbOjgmuuK/PMr62QUiJuzLiUsRRIyBA832lmfdDCYhCS5q5gwlZAqJRAZrLbYWLL6bxfT/531ZEqS9pbZ9PVOii9fSWMJpGMFmAwQxgV7WJlDTx2vK8PoAjYViLpCJgIRQxZmHHToQ7yq6y5+U+3i2ocOxaztgEKFWcvWc86yH/DsYkScZ3xurJM7hss8PHIMcqElnVsnlSk9vYMc2TPEwOR8BkZK5Ld10z5WQg0XGHtm7iT/RUKhDPd8+8O7/l23aLEbtAzZdhh48wUc/j/nIwODUhm+p8i1RGuB1pvrzGh8X3DYdR/gnld8fH82eQv6P/wx6J8gQDNlUQyWxmAf4phNVBf3Em7yaBtPoD0AXxIWPMIxAA3a8I6/X8zrXrfjYpgemmfPrbJJl+n0MlI0iQm2EDBQKqVgIu6//34OOeSQnTqHIHBLUsazkEDeFbN82UPcNTIXZEAU5TTW+gjbhjdvmGy8HYouUhArkEGMrghyW3Jx83lTVw6wpKhlCcrPKBUztLYs7HiYFZ0PsrK6iFvXzae+YR6i6hOtliig3m6xJfAqljiLsSetQxmfxvr5eINOoX1Av5pzfvwz3ve89XQEzUfMGkYyGMp6ebDRzWCtA23KWOMhRE6mPWAm/Brl1rykJ9jwgj70SqisGOXlr7yJF/RtYnxTzoUPvxY5L3CZtMJgtMLkBp0XsHUPUfGRsUIkliyq89Qj/8D5B9/CtzfM4Zu3vwDqAR3L7+bMJT/j5e2KSgIfGxLctHYRa8afRVMqHWSVjp4hlveMM1idxc//vBgmN/B3L7ibrqca3n/4L3bqu2zRYm/RMmQ7xCCVJvAtWS7JMolSzRHw9LKLIs/VHpe0eSz0/+tFKMrMmj823S4XuOBUFA5/qMZdy4qMUCS5z6drU8NJBoUuOzRN4ebf7fyI+h+P+jP/++s30KZyEgtjaYlG7jfLaLhZkS8z2r067xu8kO8dsnNrI7//6tkcdu6VWGlRWkBvle72DL2pl3J7g0z7YNqgMIk1BUCiPI1JJdKP0RUJWQkaAiUlVtIMwbMIGWDWBVhP8Kczz97pc905XD7Pc254JUd0OPWNCe1TTdsYSgpoUyTRPpkx+BIaRiIF5ImYiVIRgBLoIOAv//eIFI+lcPoJu9ey4w+By0589PtdwMUAR8FFf3ounUFOxWRUTA8TWYCxHl3dQxzdl9DhTdKjDFX9yDTmFi32PbuVEP25z32OJUuWEEURxx57LL/61a+2u/1NN93EscceSxRFLF26lM9//vOP2uY73/kOhx9+OGEYcvjhh3Pddddtc3+XXnopQgje85737E7zd4m7X/4xPAnGeGSZQAhJlgmMbpZrse6lc4kxPtde99293qbtcfWPvsayay+EaoRpMygBaSYo+JY0h+Kb12G0ZezBpSxfNYb31EGqL8x5+JSQgecWWH1iyOq/auySEZuhQI5mY73ERFxiaLLAprEym8bKDI6XGawU2dRox5hdvO2aljiXhu7yJKNxGbDkGuJNLu+oa06Kjj2ILCYDpMULLFCcMWI5WGGxwiK0QNYFMhbYqmXxZZfvxvnumBtf+F0+c9z3uGnjIdw1toSBWi/1rINq6qOtRkpLnDvV9iQTWONB1pQwmkqO3Q+yBWOUqeHTJn2WBGM8tbyBI8oP89TSOpZHw/QoS81GjOV7qM5JixaPgV1+RK699lre85738MEPfpDbb7+dk046iRe/+MWsWbNmq9uvWrWKl7zkJZx00kncfvvtnHfeebzrXe/iO9/5zvQ2N998M6eddhqnn346d9xxB6effjqvfe1r+f3vf/+o/f3xj3/kmmuu4cgjj9zVpu82xjgFByGVkx2ygqlyULL5Es3aPuclf95n7XokB3/rbD5Vu4d8Ukyvm2gLsQ4RwlDNFUEBgtetwWjD5MpFzPrfucz7tceStQlzV2nmr1YMnHPpbh1fYRhJAkbrBYYnyjSqZbI4JItD0kZEvdrG8EQ7G2pdu7Tfez5+tptFSSh6BmM8F4iCBzoA35V5wgon8QNIaUgmJMQCSdOI+RaRClRDomKBsk7tT/oSGUuWXrB3jBnAn//mMl6YLCJOBLnWCDRZHpBmIdZClguyug+xQOQStMDTArSrun3vvffutbZtjTCvoTOfGj4VE5HpAtiI3BSomAJVG4HRZGxFNLtFi33MLhuyK6+8kre85S289a1v5bDDDuOqq65i4cKFXH311lXNP//5z7No0SKuuuoqDjvsMN761rfyD//wD1x++UyncdVVV/HCF76Qc889l+XLl3Puuefy/Oc/n6uuumqLfVWrVV7/+tfzhS98ga6uXesMHwuvBXItMBpATBuwKVFRY5pR7Aqme9J9zCFfu4hiYSrHzRlamUg2jUvCQNNIFUVfM5Yowi7oeefDyJevwZBgbEb8lGGOe9MEv/367rvYNIZ6VqIyWSZPAoTKCQsNCqU6KmhgEeRJyHilY5f37dUlIhfUc4GnsmZVZNtMGAOdAsJulhOGkygxbpBhsYhMIBPhihoWQJetq/I9Va/Qlyz95JW7ff474uMv/2fuevknGR8OiZOAPIMsh3o1IK0XsLGPaLiSJLLalPnCBVkeeuihe61dW2OkETFiI/LMx2CpA1WrmoVSAC0ZzdsI5eQ+bVeLFltjlwxZmqbceuutnHLKljIxp5xyCr/97W+3+pmbb775Udufeuqp3HLLLWRZtt1tHrnPM844g7/6q7/iBS94wU61N0kSJicnt3jtDh99zUVgLdY0Z2KbGTBgOjLPNAMXl31j34bh9//7x/H9CawNpoVPrWdRKZiJDiJPM9EokOSKUqDJrWK4obDdAvWmQeybN/HWFc/jX160ezOxKf790E8yOB5gjaSjc4K5PWMs6hpnUec4C7rH6e4cx4/q5EnIU753zi7t+4FLzwQBI5UO+qIazYxhCFPIYXxTiIoyJx7MZjnNU7/4ILR76aIFH2iA2gThKkH7bYa+n8bM/1aFE1dcwI037p4Kyc4w8OaLSdZ1EA+VSUdL2FoI4x6i4fK+ZEXi5wKRAlUnDbWjgop7mkHdQyQrjNqI4axMkgVo7ZHlAcNZiUEdYYQmzR5/JT9aPPHYpWCP4eFhtNbMnj17i/dnz57Nxo0bt/qZjRs3bnX7PM8ZHh5m7ty529xm831+4xvf4LbbbuOPf9x5vb5LL72UCy+8cKe33y6iKUo0ZcQyoClRJKWbkdnczRLkPlz/7v/iR8Dz6O6AiWZittcGeWzRVYsYCtmgCsxd2KCaBIzXDZ3FnIJnsdYyVFXc84o9Mwvp6+sji8v0dA1jhGCkVmZkWtPc4KsGczom2GihUdv1OKNV559N/0WXUJujiEpryfNZFLqhUTUI04byh8nxmyKsEuHHWOFcdxgQmXDFDT2cERuBwnpLaWNMcaiG9CAphdRmRfz9D6+He74FEt6SKT78zj0bSr763e9j8Yc+gbwbON5z91cMfj7jTqQGYW4R9WyPHntniLMSSR7R7leomTJVimjdnCEKCGVM0cKf6rs+u27RYk+zW8vIjxwdTpUT2JXtH/n+9vb58MMP8+53v5uvfvWrRNHOjwDPPfdcJiYmpl8PP/zwTn/2kdz/qoub7WoaMWWQvkaKpkCEBelppG8wGTz7249tdrMjtNYs/vIHwRYQ04bTOCUNaSBI0W0GlQrEhk42rOymWrN0FC1SKDLtMfCAv8eM2DSyQjX1qSftbD5OEgi0LjJY7WB2xwRG716QwMCHz+OW3x/GsbMmsWbQqXl31LAJpEO9eB0VbA3ncvSA0EzPygQ49fYcVBXCQShtTCgN1aDgMTa/jYHneSSnV2h/1iRzFo/Tf9A43182wtN/9G6efv27H+PF2RJbgPZM0vGHnOghCIdBToKagGAcV3urkaMH9+hhd4rhJGCg0UM1j2iXOR1qglneBH3eBO2qimcEaxpF4mzfufhbtNgWuzQs7u3tRSn1qNnX4ODgo2ZUU8yZM2er23ueR09Pz3a3mdrnrbfeyuDgIMcee+z037XW/PKXv+Qzn/kMSZKg1KPXpsIwJAzDR72/+zQXU6RxHXOyWdlkwGnHa1RgWNsUct0bHPwff0/Q0wumh6Z0LbUYpNJAjvI8bKchJyWXAaIm8DdE2A1zGPSA3CADwcCH9nTIOUSFGEs3RiseXf7WIhUM1cpAstvHGDj3IwAc/KV/5vjFD3JTvYSau4B8Q4Te0IuIxlF+Tj7ZhijWMLUyQipnzxQQg4oFUV1TqOcIBbXekKFjBMVj63gqpquUUU9DxmoCUC7xXcFR15+LQnDbSy/Z7fZPowyNeT5tayxdAxn1XgGl5n2cGNSQgQwmDt/3WTI5RWKdMVD36PKqtAURXvPrzDQMpQFjWQfr6nvy+WrRYvfYpSckCAKOPfZYbrjhBl7xildMv3/DDTfwspe9bKufOeGEE/j+97+/xXs//elPWbFiBX6zPOsJJ5zADTfcwJlnnrnFNiee6JJdnv/853PnnXdusY83v/nNLF++nHPOOWerRmxv8NDrPsKSr16MUGAyD5sCtc2NmZN00hiE3POG7MEHH+T5P7uaIw+Hu9YWZ8KzraSSlOjtqGGMJk4kypPQmaMDgy0rsrxZR0Qm/OjZr+Hwww/f4+0DsHSjc4982siL6b9IlQMeSkFbW/UxH+uBv/9X98szdrxt/2WXYAmnc7RUDP5oiqolmJJPvdNDHF7DU5qeUkYlDUkzD6XcRZZC4ysnrGuN4KnXf4A7X/rYkt8LC9aTxkuoWY+8ZvFHNN5gjsApS6W9HvVugZ7z2K/VrqJkg6FaSF8Jch0yqjN8FzJDbgSx8ZioOyPfosX+ZpeHemeddRann346K1as4IQTTuCaa65hzZo1vP3tbwecO2/dunV8+ctfBuDtb387n/nMZzjrrLN429vexs0338wXv/hFvv71r0/v893vfjcnn3wyn/jEJ3jZy17G9773PX72s5/x61//GoC2tjaOOOKILdpRKpXo6el51Pt7H4PJFHZCIEyzGOXmk7JMYcdy6NqzklVPfe9ZxGI+R71ohJGkzZV4n5Z/Eujco16DMHIznThRKOXhtRusNbji8in3v3rvqvTnmUSnCulnhEGdyHeJ2PXUJ2uEGC0hkoQi3avteCQD7zuPxZdd7qIBfcDOdMG5L6nOUxSKmoIXU0sVaaZQyiLQRF5G2dN4Xo5CI7FkVnLCD9/FzS/59G6157oH/40XH7GS765dSDLfI68K/A4PqQVWOCWTtAC6x/DXz/s/YN/KPXVEOaP1doZqkjY/piwC8ub9lmQwmvgkeZFyWNmn7WrRYmvssiE77bTTGBkZ4aKLLmLDhg0cccQR/PCHP6S/vx+ADRs2bJFTtmTJEn74wx9y5pln8tnPfpZ58+bx6U9/mle96lXT25x44ol84xvf4EMf+hDnn38+y5Yt49prr+X44/dNfaBdQoKtSmfEpq6e3fLvQnrYMbO1T+8yB519BRGCSrkDZEZn0TJUac6uplSyMoFRimraQWAm6GxL8JQg14opd6eX59z2iiv2SJu2h049ouIYs9sTCsHMpRG2QSWB9aOdZLHPc3qW7PW2PJLV73svB3/0SrRwidHT35AQTlwYQ8E3VPMQT4JFE3kpnWFG0asTeTmGBp3N7308ghf97I38+AVf3uW2NPg8C3zLvEMfYv19yyBU6E4xUzcmAG0NonOQ13W/avs728Mc9M0LmdeliLw6cV4k1QGVzEwPnFIN1noEKm5WDG3RYv8irD2QhJX2LpOTk3R0dDAxMUF7e/tu7WPpVy9Ej4QIb7MxwBB4DVeQUhiLLgPdsPqM9+52W1/ylQu4/442IisQ7YPUTS95d8yzj3+AldUyG4a70GkRmwkn/SAMIrQImaM8EMIVTNRaoBshA2/+0G63ZVc49Ntn09+boK2ilk65npywcimwiNzw4MZeVp9+wT5pz9b44x//yN9e+ys67snpXD2BKQeseW6Z4PgR5rXVGE0iLJJIaTrDOm1+Bc+rEUhBUUE2HfpowaYMx5b/Oenbu9SGrz34NJTN+WN9Ln9ZN497HloGJnBVJgXgZcxbuIaTltzLJ4+6cS9cha2z5L8vQvmG2V2TBCqhkSkyE5LrKakxixDOwBf8jDgLufnUvZd71+KJwZ7oe7dHS2txF7nx5Ddz8nXfdP+pAXUBPZBXcd47mq6rqmXxZy/fLWN21A/OYOL+fopWIBD0PKNG/Ns+yCSZFnSHGRPFjEqqEZ7C5hasxMYWqzxMDgjPySrmMPDWx14ifWc46CsfY8mChMlEUU8LaOOz+RpZPc0oBg2624f3SXu2xdOf/nRu6O3lJR+5jraREL+eEK3KMcdLMrPZLFKkKJngeTU6pCCXPnUtgIReVcWXhtxCFhb55e2/5ORjTt6p4z+w9ltARsMqnlrYRLhAc9i8UeI4IDMSXxqiKKEoJzgyGttbl+FRHP4/5yBVCXCyZqAo+JZ2WaeWTQXvWEq+JjOQao8bn7N3o3NbtNgZ9oOK2+ObRYsWzZj/1BmtcC34scCXEl9J/EzgjwrYjT7oade/m/H13QTGm1ZpRzjVerkpZGPNw1cJpbCBCutuJuabZtYsYBXkyrVN7zsjBpAzRiWVVOIyaeajtXBCy7mrGJBmPpW4THAA5NAuWbKEe758FhvmF8k0dK6sU1+rGIs9VLPKtC8MZS8mkh659MmMoM8bZXlxnMjPyKUCFdEX5vyvvGjnD67fRUE0EMLDB44sDnJENMCS9kEO6hzkoPb1HFNYw5HRGPE+Uoo5+n/PIq7PjGtrSYDEI9USaxSRB4GyRB4YrUi1T5Z7ezgquEWL3aM1I9td1gEl8KsC3d50B2mYtjxlgWctv/rVrzjppJN2uLs1a9Zw0k/+g0VLckZqpekRhgAqD4bkUUyUF1k52MGc0jCzS5OAYLRiyeLIlRie/lTORzqP5s0vfekeP+3t0VaMqcRlci1JJjys8Tdrk0HIlLADKnFpn7Zre9z3HTdjPv5pH6H3PzXj7w+JOmpoWwSc7FhJaSpa0eeN0hVmDGZtDNQChhpdGOsU98HyzBtez29euBOq/hLmqBoNWyQjIDWastJ0eG7koy00rCQWPn9/8K176cxnOPv244DnYq1iShjLWJ9qYigGgsxasmbskhDOWZxpwWSjZcRaHBi0DNluYNGIkkcwCabdVc11XZnT9LM4AyQQvPkXf+DBHRiy4w95F0Mv66ftmTHabB6y7n5NV85HHrMBfX+B4I4F/NHPOXb+OPPbxukKG4wlEeA3k8hH+N2pX9pr5749FnfB/eM+8VjoXJtTFwVASKyIiMcyCgdgDu3vb5tRgDnk7PMInx2TNqNBYiuBlJ7AGbG7JzqZiLswVlMONBaNO9leTvjxu7j5RduOZPz3P72XZ/XAHA8qdoJh3QEiJLbeZnWBBIHM6VXr99bpTvOOm57Ouvwp+IAQOeAjpSXPJQ0CjJWEnkEK7bQsraChPWqJ5N5X7oFcuhYt9gAt1+Ju8Pel2VAGW5gxYrlnyNo0eadBd2vyLk1W1pgdSOSdNOf/Mfmsfuws8L1mR+YZDE1xVmcd6R7NyOZUsEWNuqWf3920lAc3tYHRzA1rFBnn9y+6cr8ZMYDvPOtK4jEFsjk+yjd7ZbgTEj6NsQM70u3+Ky7hzpdeSZwWIAXw6FE1qsZjVTVkIu4CqykFTpEmM4JaKqmlPhrFyf+3bQUQX/wvFigB/V7KHDVEUUwQiga+yPBFQpucYJ7ayIK9PMw89abX85I5I1QpkgGlssVoXIUHBHmuSLKQyUZAJY6oxhETdffznlfs3TSOFi12hZYh2w0uetObXMCa7+ZOWWgwZQORS5idDosPLabHsPjirZcHOa799VQP7cNvqtUbIwiUgWJM7husmPFUpqsX0V2rIw5bS9xbQ6VFan9azH03Lee2X3bw81O/uM/Of1sEQQBEzmDFAqoSMeEhJjyYUDAhnVETB8Ai2U7wp5d+grFGG5ATSUtiA0aTTozVLrXAQiXxqSYRtSRivBYxVimzcbzEUT9431b3WUXRAUxY6BawPBAcFlRYHoxwWDDIU4Jhjg4r9Htw+MINe+3cXvmjV+NTxFNQEg1AUggMkGCMwBqBEII8dwYtzTzi1CfPfe571QX7XMS4RYvt0TJku8tgs3wIFlswLlJRN19GbPYCuh+d4dD/r2fSWexALys5N6SGaiyxxiPsrKFLGXnonFbWHYp841yiW/ppGyxTrCqiqmLlhe9j4MMHWPhzVSAqCq+q8CoSvyIJKgpvTMGIakZ3Pj748amfI0tdlJ6xPtoUKAfu+6xnAY3UZ3IypDZZJK37WOthTcjEeJnFXzn3UftbmfQwpCEQMGYhtZZuIZivJAuUZI50a2Rmz+bTb8Gy/34nbT0hBWUQFvrbRwA3G+vuzsAmGD1VPNYV3jNWolPJg6/9yN5rWIsWu0nLkO0mqy9xQQJmqiSIpllw0yniW2WwnsE2JY4Wf+ay6c/2X3wh7df77uLL/9/encdJVd35/3+du9Stqu7qnaZpwGYTN9yAqLgAMQ7RjBMdMy4JPxIzCRMncTeLGozGxOBuJmpiMkaimUQdt69O4hKMewDRCAoYRZAdmqX3ru1un98ft7uxBRSaboqG83w86pH0rVv3nk9Z1LvuveeeE/VMVI0KP52gLR+jtiyLMagZtyyPmwhw40KAEEj0yOOz+LbLWXzbZR9vVuH5oDImVtrA8g2UocCJHoZhEM+asG7vPrX4cfcf/yibgiKiQZkNBMh6kPMM0mmb0DeJJdsZMWgLw6u3MGxAAwcOaeDAoe0M+5+LurZTd9NMPvSGsdK1CAJIGJAR2ChCSxiyOQxpkOjA/tp1fXPUOuz3P+LgwfXRjyMxaBSbSqcF22jHx0CFMKDSoziVIQyyhEFI4HoYRppl5/2wT9qkabtLB9nuyBPNVBwShZghiBkgsRDiHiSihyQ8xAzJZDLUXf0jKo5soWJ99JPbSAd4ReD4wOY4jc1x2nJxhte2U3JQPcGBzeQHZckMzpMekiFz1Eo+uG37p632Cq0GVjYKsNAguoaYBSMDhgchiphhMuu1vxa6pbvk/mMexVBtKBV15QnFJu9ZhL5NLNlGXWUaMRR+KBhdVziFA2uFiU9fxMKFC8GCCtXGay0jede1aPKgVKBCKZKGoozoSOz5Fpv/OWFFr9cw4o/X4BQFDHai31uZENJeCYc4azi+ZhlmR5i5gYFthlRXBFSVZyktc7kiOXyPjWmqabtK91rcDct+cjnD/usWOsc6EiMEx++YJDq6YB5dLxOwfQ65/+colSIRayNwoi+FouUZ8jUxrCaIGyY5StgS2LQkHUqTeWpr2jBq2tjSCu+d/V8FqnTnKdeIpm6LxifG8Dv6YHacXTUVBEpx/V/e4usnfq6ALd11tx75PCfMPh8oQcTEy1uYsZC6yjx+qIgZIW5o4oYKL4TOG4gtBy75xiNwchnrclU0Spyw2eTg5EYGO62kVPTmbPINlmSqWJCp4+u93PZR/3tNx9Q5afICloIAxYsttfxzVY5ji5YRr/XY3FLK4vRgfLHxRBigNjHxgCVcMGZhL7dI03qPDrLd5QIJolOIsY4QE6NrlPVIx9A+RSFUhPiiaBldRmJdG8XrmmjdXIRXYeM0gOWb5LcUE5YV0WCHEChiaxVL7+r9KVf6gpEHbIWRBrMjxHyg47YsyIEVQizbP3/d/+2ffsdxz14MgAQWVqwpCjEV4oYWXgiOCjCUQeewxCufSlGXVBSvFD7cXMwhg9JsdEv50D2AKqOBElvI+Ir6oJxio423Xq6DE3qvzaMfnhGd4u0Y4brBTzDA9im18zR6Nfxli8cplRs5JrkCN2FyXPghICSMNhZuKOeHOsS0vZw+tbibVn6v4zSfEaIsIDQRD4Q0ZrwJK9GEqHbE73hugE9bxiY31oXWNggCBr64FqvBxSuF0IdEMxQvUxS/b5JYJ/0mxLp4UYj5QLosIH9AQH6gT36gjzskIFMeIALjvndHoVvaI/NO/QVK5QCoTXmAwpVoeCtDBaT9GDnPIe2apF2LeGMRtHmUNATkl9TwYUM1jW41pmSIWxahsgmx8H2TOR8ewKqre+99qbvjekLVOXh0dG+jHxSTMF2KbJ8iK0+9V8f/23Qkf9kymk35CnzfYUlDBf+zYQK3Ter7G7I1bXfpI7LeYgqEBuKnOXLESoYVt5CwoznJWr04G9JFLFhRi6KUTC5JalQzy88+kpH/+zZmWSmD/7qaXCpOy0EVSLkF2TzFr6xjzqbfFLiwXWNioFwhQJGvdaHMxSoKMIyOoYN9RZg0cZNx2vpZp4+Pmn/qfzH8D1djGgABIRaGCnD9GF5oYisf2wpZ+2w1CSAWRD1QyxZDM7UsGZqlsqaIeif6jLSkLVo3lFP0fyZM7Z02DrvjVmJDPES2jq4SBCbNWZvVZjEHFGcgBgnLI+3HWO0NZWXjUAyVp8bJ8NwJOzFKiabtBXSQ9YKVF36PuntnIr7LP4+Zy2llS5iQJBqjTwI+dIUFRSkqEmP46+LRkCnDdRxiX9zA0vLDGfr8ehJLN2EHJVQtcGFzA0vPqGFhPwsxgA+uv5yDvnc7uUoXszaL4wRYH8mrwIYgJuSsDK6X3PGG+oHbS4/lmqbnqamk42jMxgtNYoZLIDY518WWGMQVoVKYnkuqOUbsbYP0uiRNA4toTIbggrMRqlf7vPHMtl32e6Luhlsg5ROzIe+CMqLOJ17OoslMYZmCYDMs1ozEDGJGgKAwlc/qLcKsibs2mr+mFZIOst6SU0w+4hVurn0fx46+oJVSiAiHW3CIk2eQNZfcQfD60yeSV8XEqgwGTNnEphNN8s0HRl34nXZI17Hqwt2bfbiQQhSqLk1RMsA0QuyO2bJDDLzAxDRAlYRk89kCt3T3/OvpZ/Dd3y8kYDMArq8wCAnERiTE81PRiimDfHGceHMWyhVOOobT5iH1gKEgJ6gA1ObemQl62DW3QhVQ1DnqlcJQIZYT4udN8hmbzZSwOR2jPRUnaYUohKxvsGJTgpVTt38Dv6btrXSQ9ZJVF15JetXdvBYY0ZxSQCl5xiccRATLtDi5WPFhbjF/Kz0KZ3URbpvD5pIcFIeQBFqApQlWzezfY9jFDZ94sUexk6XYzhM3o9OKuUCR9mO4ng04eGV9eNfvHrJ82rUc8/hFxEogFJOY6ZL2oNjxyLgOXkKIp4Utoxyql4HdlMUmj1cWA9OGMITNGZTAS4t2f9inurt+DHayq7ORHygsKwAUphX9oPDzJvl0DJRJvUQDOOfzQGCycpq+4Vnrf3SQ9ZKvzfssm90pCCYmHuVOjmq7hVXZ5RyRzDLKsTENk88Xt3FLdQ5vaA5ZnYD1DgYQqlbIhqy6uX+HGEDbqFZGJpoxbIUXWOR9hQFYRkBFPE9GebS4YNv7xjBH88+6k8l/vRCXGIFY2EYeiGEogWof/0MTs8pik4LK5YpYKFhZHzJ5xDTJFCf4/LeG7nY7xj3xPWgbAGVEvxxche9bxOMurmtiWQGW5aOUhYgQ+jZuDpQRYFkBy76iQ0zrn3SQ7aafvvMvzHprBH7bFLqGe0+lGTSghU3FxTQGCcJwCaVkqI7HOCAeJ76wCrdCIBlAGYQNisp8JW/duheO1NEDwwZtwTNsslmDQBw6bz8wlYdjB6QcF1eyuH6s0E3tNS997i6OefYyQrEoi4dkg46ZdZJZsimLojYTq9KiodKKbqT3w+iu5CRkgyw//trXdmv/Jzz9LRpaylChgYQCpqDyJoGr8C0D2/bxvGhWa8vyMRQQiw4IfV9Yds5Pe+Fd0LTC0EG2G66cdyYPPn0isWaLhEQ3PwdxwUuUsqGlmPaajVADc9WBVBS9y8kiKKXIDW2G1iS0gRFbyYob7il0Kb0qTNi05xMYKLzobBZKKUwzRsaFvKeoTuRpzYWFbWgv+9PYqzj9rZs67gCPJqcrrvZo9zOkNyWxmw2cUEW3aVgKHyFr5Vl1++4N/XTp306iMX8SEjrRbfhtCqkQ8BW02+QNwYn7ODGfoOMtD0WBCNkWg1Vf/8nuFa5pBaaDrIfa29t55HcnUL5REcv60VQugF9p4acMcnlFmwykpdinMV5Cc9ZGJXxEhHe+MgtByOUDakb03QjnhXDDi3fTnk+QyRmEYQzfi7p+KwVuzMc0PRw7TlvgYuAXuLW9q7q6mkACEEXcDMngEbdjqNoc7cU+XquDF9jRYNJGAMk2jqrddkDpXXHCn79IIjkUI7oTHxIQazLIqwCxBJW3kGZFPmnixgMMM/pv4XvAJotVl+25GcQ1ra/oIOuhcRfcQe2WGB1jAnecPAPZ4pNuADXUJlAmG0rKqEql2VBUgchGGjNZVhqKd3Mx/v3QtYUsoU887b9HOldCvt0hGk1563WwwLUAkzCVAxwcu0CN7EN/P+3nfObPF1FWFJKMeWRciNkxKip9gnIfEYVSglJgqCxPfuHnPd7XyFnf5z/GNfHM5loAlO0jsRAPhWpQSKUghqBcM5pGp1UIFUgIKFh12V48Zqem7QI9skcP1P1oBjWbu4dYJwUUCzhrPGItBrQnCbC61pq55Ui+sehf+fdDV+7hVu8ZzZk4+fYkEKNrvMmud8gC4mTb4oDD/NNu2cFW+rc3/vlOAIqsKMySMRdwMY0AywwwDZdip523diPE6n75E2pq0zgdP0VDgaKSPOAj5YLhKswGE3wQR5CYIHY0hx6NsOpSHWLavkMHWQ9UzY91fTV/PMQ6/y4ScDKAHw0BX6M2cer8z3BG/vssOePuPdncPSrdEqdzOKRt353O/3XI5PZos/a4653/jxAhYQUU2XlKHJeUk6XESVOZzPHaKXf2eNvDbr8Fu8ojYYUoBY7lAwGmAUZ5Gol5hBVCSIi9xcDZYOLUmxj1ClqzrPqJDjFt36JPLe6i8f92K8XiAN2/pj9OAWa9B0cISbON9aHHX894aI+0sbCSfPI7E02Dkkvv2x+9CRMmMJsJHP/MRRTFQ+KmEApIzmD2aXf1eLt1P58JiRDbjjpshCGUxdrZFEuRc4spK8nRarbht8TBjuF1/VYNwGlk1fR98yhY27/t298mfUAs+cSvadj6NW4Cyt7Cv5Ss5fzDV/Ztw/YanVcLd/QudSyXT3sX9w1zTuv5kddHDbv/J0jaQoUxxI5GRMmLzZZ8ggPijTQXF7OmySLnxykq8rFL2sh50eSZhgoJQoN//KsOMW3fpINsF6ld6DHuA/874RyOOVwP+bMt/dHbGcPum4lyPCSwIei8IBZ12Mi5Nkvayjmpaj3tRfUYStjUniSTL8Lz43ROohOLbeatL9xXyDI0rU/pb5NdZIWKUNHV0WN7Op/6n1v/mTFjxuyRdu09oqMxkyynHzafPNEQSDGV5anFE+iao+urehSJT7Jy5Uom/fVBzKRHmDe7X80OLTzfJ+PG2OzWsKDR5YjyLdTGWlgWryLjF0ej8ZNjtL2WGz7zasHq0LQ9QQfZLvrbw5dx9L/eSkU++vvjJ8g6QyxU7IchBlcefgIL/Ftp9CpZmR3M1o+Yx2dGLaXcT/OXleMK2cS92u9+9zuuzdVDPJpVXEKQUHULMuWZ+J6JaVo0p5OskOGsz1cxrGgDw+NpbKedpoxi9oZRPHLWg4UrRtP2EB1ku8g0TYaXxdiwySXZkVofPzgT4JVnf7Cnm7ZXaA9/R32+mrjKUeF4QKbjmQRtrmKjUcYfjj+wkE3cK4386e0E8RBJhVGIBQorDmHHANRdXWINAU9BewzXiEa3b0yb2GaMlnwpi4FcAG3NCVZ8VY/Yoe0fdJD1wJMPXMZv/vAC9/7PG8Ql+m6BKMBGja7kt3d+s6DtK6RXs4qKWJpRyTW0B6Xk/eiuZ9toZkSihQ8zA7m5/imeHPVvBW7p3mHU9+5ASoTAFiQh0UzjquPm+o/+QgpU9EFTCpRCZUxEHFxT4acE14j+Kfs+0Oyw6ju9M6+ZpvUHOsh66D+mnsx/TD250M3Y65SrPNV2IytzQ8gFFls/YgFOkKLcbCbYz29fPOz7dxCI4BeBlBBdNkwClmy9BY8os1CCsgTxAQLEVNHpbFGorAnEkVw0+zQ+DDYM5lx6xR6uSNMKSweZ1qtqU6tZk62l3Y+TCxL4AQgKyxASlkfOMqlO1Be6mXvcT+77L+7dnMbe4mCjCGwFHbdyiAGobYeJCVwwnOgp8UOIG5DzEdMCPxqAGgFChaiQu0cfyumnn16A6jStsHSQab1qTbaWLfkS1rcWIZLo9pxSHuUJGynu/xNq7qxhD/wEyZnQZGG3O9jKiMInAWJ2HnV95AWdeWaC+BbYPsoIMByTMN8RZvgg0esjIau+vXsj6Gtaf6aDTOtVLW6cdS0V5FwD342x9VyZoAyTILAR2bemb/m4unt+gooDhoHkLGgyMH0TI6+6B1BngEnHH6IgUNFAv0pAFIELZkxhmAE4gJjRacYEoEL+JV7KneddvOeL1LS9iA4yrVetbC4mnbEI/TgQYtpbeywEnk263SKUisI1sA/U/eYm6Mxmy0XFFdHMlQAGBiYGYHbMWdctwAKirvUBKANEdYSZ1XltzCbwo674pg0QoEwIc4qV51+zJ8vUtL2WDjKtV+XcEkI/jmn72FaA6wkSRKfKioohl1dk006hm9ljz7zxDFese45sOkWYiYNYUYgpBWaAsokCCCMa2sUAZaroxkIA2To6jIrm3gQFKg9iGkQzXxogYVeYARBaBPnoetnKr+seiZr2UTrItF7luzFMO4+3XvCkFKU6eigKtBGA1YY9MGDNmjUMHTq0sI3dCStXruTW5WfSShHv5I4kmy/HzVYirhmdIwyJHgZg+x09N8KOESe3hhdhdOZQdfz/KMAUogQVgCGKMCOQiMJMLAMCkM57O3Kw6qL9895ETfs0Osi0XiWhgVdvoMwEYoFYHYcfIeAplF+Gt76Nia/cy4qpPy5oW3fkgWee4brmORyUeodkAhJGJcvcUbheOb4bQwID8To6bdh0HW0ZNoj78d4bIKEQAoGpsILoaZUBiojCTAQVgqEUtENoGlv/ZeZh5dW6O72mfRIdZFqvUnhglSBOHiOVIxkHw4BsHrw2B3EdVK4YO95c6KZu47inv8eW9hhhaGCaW5gyZDVPNEzktJLXWbShBN83EDEgNOjqnIFsZ6D/zq6HIYSKUIWYMRNJCGGbwui4XSxoB4rp6Loo0fUyASNQ4MLyay7fY7VrWn+mg0zrVcGmFJTmIO0SfliK5xsoIDBCGOhh1LYSNpaQX2MXuqndjH36e+Q9mzBwAI9vj57HXxqOZKBZz/z20USHXh3dDTs7bXTqvIzlg+rsjOF2dKE3BMqFoDEA04RikHYwUZgh0AoBgsRAGYrvHXco//HPn9+jtWtaf6eDTOtd8TwsM0m0l5OtzJMf5EO+4zqZGRLOLYVjWmBzqrDt7PDZFy6kNZ8gCCGfNzEsj9AzOCAp5BpKOCDRxppsCdscdn10yrXOa16eiVkcEOQUGAEiJioWIjmgwojCLGYSWhD6AvnotKNpmnxww2V7smxN26foINN6V30ep72C7Jg0NDmYjTEUqmu+zaA2RDY67A2jVB33p0tQMRMRELEAG8f2yHodQxoCQaiIKZeO0Q+jF6rOroYCHhAKmApCCz8XdNwDBtG5QhOjKESCEClXBB3nDwNg1X9cXYCqNW3fo4NM61XxpjJyR6RRW+KYyoy+/k0653iM7qjykvhmU0HbefDjVzKoLCDtJrAV+B3JKh03J/shmHiszCQZV7qele1VKEOhlAlGGCWddHTscATy0Q3MuA5i5rGKIMgC5tZRTFQcVn5V3/ulab1NB5nWq0LDgEYLA5PQkq6u5509/ARQnsIMygvWxgMfuYbqVI6YAWkU0nW0FZL3AIQXm2r5TNFSXksfxiHxBbwWG4GIQ9r3MWxFGBJ1wTeIplUxBYKO0ekDB98jCrw8vPT58xg2bFjB6tW0fZ0OMq1XeRUBRmhHwywtgYREF5L8OASlEBYJkhSUp8hmsyQSiU/ZYu+qu+WnJEaCbW3tbegLKOWjlEno29hxj+dWHMiPx77MG+k8920cz79Xvcp/b5qEJMrJZAUVj0UdWAIz6opPx+SXEt1Utupb39+jdWna/kwHmdarxBZYoJBjc3CEIp2xiS8xKMtAvhmy5QpvgCClwphf/IrlP9gzXcyXLl3KF375J9Qx/taFIZiGhx9aJGwhDEMyYYiXB8Ms4fq3j2PGEfN4cN1Y7m+cxMSSReTDOK80j8YPUuQtgzAUDqKcP5+jb1bWtELRQab1mqOe+nemnrKCqn8JEFGsy5i82zqU90fUUL+qivK3DIqahLShcGPykdHb+9ZFF93Oq3kIh0f3tAHkPRNlCI4ZEIQ2biA4tosIZHMWYSA48QH8+K0vkoit4bKD5rIpC82hzTPjfkRNTc2eabymaZ9KB5nWK3447xhuPHgtdTHBBNIC6x2LUakWFqXW8VfzCJrCQVQvNHDS4GcVUiyfut3dddDXbicR6+hEaELY0U+jJR+jyAkwjYC84QI2fgjFjothuPiBjesqDMsgHw7gm4cv7PO2aprWMzrItN1228KhHBy3uGbxv9DoVqKUUJVs4tyBb3LigHWg0rSEMf7WVkImniKeFcyMwu/j/h51l19PFcV09htUAUjagoQQhjGaMi6lSUhZPh4+Wc8mFIu4DdgexLPMP/Wuvm2kpmm7TQeZttt+t/AUWt3REGw9V7ihpYyfb6jlqcErufnQZ8iVtPFWaTOZoSmKPoj6WXx4Rd+MIdjQ0MC4p26muKE66iGPQiGodgVODLfEJxYLSbvFeGE75QkwTEjYPgYeoSiK81X83xdu6ZP2aZrWu3SQabvlhN+cQWtwHJS2UZL0UAqynombTkImxofrRvAzeyL/dsB8BiQPYU1R9LqAvjuteNHi6UjDeGKd3RINQMBqg7DUwt8SgyqXWCzA9YvY2KYwjGhw4zBUvHfWT/qsbZqm9T4dZNpuWRscziEHzmXygDYcMxrkYn3GZEHbcNY0l+JuLuetlaO5YuQrmB33VXmAUdw3Q3u8+cEZLGmpgyD6aCtAhYIIOFmBBoVLHD9v4pe4mMUBlgVBYOBugVX/eV2ftEvTtL6jg0zrsXfeeYcLTvgbLdjEaMWWkAaxsRMV/FPxMhbZKebLQXgNlbT5ig1tNkXLIVOkWHp9744t2Ny8nMb28/h7GODLaFBbpwlTRDMvKyU4jQJZhZeyEc+Gxmj+S9UAq27So81rWn+kg0zrsT9yCWc477E+m+J/M+MJSQABR6n3GVC0kkMrD6beXcPyLSm8UMhtGESRC+//vvcC47WljzIieRGWUqQMhTAIS/ngBLhJCysDygPDFkJRKBHiOcHJApuibSiBhffqENO0/koHmdZjXyl6jxs2nsL6zUWsaRoEEk3N8gajSJr1/ODwxzi+ElY0jOK/F47ksHeKefrh3guMuu9/hdcveQkDhVLR9bAilaY62UpzqpRssU0sY2BJR5iZAkZ0pKZMIADDg7d+p0NM0/ozHWRaj9zx9gjmpU/h9WVDIbA5dOg/+GLl+7yePZCsOJSpdv7ccBQjqnMYRsj/nfMafLn39n/Y2dfz0m0vYaru06t8JtbMa6ktrCoZQH5ojDYvRnGTQUyIBqMPumYVIy+w+H4dYprW3+0Fk2lo/dFnY1neX18JgcVFRz+HHU/wfPpwquw2DinZxMDiLIdWwj9ah+Lne2+/M//4KCcccz3t49MkTBDp3vuxMmZwdMkKDh2wltigNoJROVpG+jQXC7kY5G1ojQWkjhAdYpq2j9BHZFqP3NJ0DM3tlXz+wHm80n4YtfZ6zMBiXnMdGzan8JpTEJooK8e4Ee/0yj7rLp3JgA8t3CEmlQd5UY/Ejx2RAZyZ2ozB21TYOd5NVrCluhJ/mBV16jBbWDH1F73SHk3T9g46yLQe8SgHCakxM+TYhO8neGn9cDIbBwAKEm04JR6Ggr+vOJRhd/2ElRf2fC6uuntnkko7ODkPd5hD54GYiGwTZqZh8K+ljUwp+htvVcVplFKSuHzloHd3o2JN0/ZW+tSi1iMGWbBcFgYjGFbk8mZjXRRi8TTVw9YAJvnWMrItZWCVIAmXursu6tG+6u65EVaB0XmKUqBxfRxvOyH2UUlTMaEozzcPWqhDTNP2YTrItB65a/TdUfc/Qta0pmisLwE7y8CqJjZtHAxmHGWCMlXHIwnFtXztf2/a6X2k02nq/usmiAmGb3V9WI3NLtaqKt5qKyEU2eY6GWy9drY+P3T3i9U0ba+mg0zrkaqqKghtQNiSL4V8ksraRjY2DI66trcpZL2JWmdhrLWQdRZsUbyUCXdq++d+dSaH3nd3dPLb+MhE0zFFxXqfeBYu+suXWJ8zuga7ko+FWoOvGDfi9d4tXNO0vY4OMq3HVn79OgzSSNTtgvbWBJggGxR2s4292aJ8iaJ8saLsPQN7cwzWmgz73cxP3O7RX7qV10tMiAvYgAKJh5CAwFS4jkHsgzTFH1Zx5uNf5+drR9PmCb5Ej3QgXPrmOA6pW7dH3gdN0wpLd/bQdssVXMJvzdsAIZ8rh1aF3Ryj/N0Ap93DzAcooqOp4g02mWKTRnZ8Xevw6bdT2uTRNNkGI4gGZgyBgSH59aCKDAxxKN6UJb2wHZUt5ckNX+QhW5CYoDKK4r+nWfTSj/bMG6BpWsH16Ijsl7/8JcOHDycejzNu3DheffXVT1z/5ZdfZty4ccTjcUaMGME999yzzTqPPfYYhx56KI7jcOihh/LEE090e37mzJl85jOfIZVKUV1dzZlnnsn777/fk+ZrvejEE0+k0qoE3GhB2qTybY94Qw6KDIISG7/YIiyxUXEhtSlHxevbnxr64KvvQKmPfChDIK8gUBCH3GAPL6XIFBl41QkSjkPFOxmqX2un7sU0tc9lWHHbFTrENG0/s8tB9vDDD3PppZfywx/+kAULFnDSSSdx2mmnsXr16u2uv2LFCr7whS9w0kknsWDBAq6++mouvvhiHnvssa515s6dy7nnnsu0adN4++23mTZtGueccw6vv771+sbLL7/Md77zHebNm8fs2bPxfZ8pU6aQTqd7ULbWm2498V4OO+BtQLA/VNitLmKb2O0hVi7E9AUzF2K3C6FtUtSQ44u3391tGyf/6A5oi2ZvBqKRfPNA5xWwUMGQkPRQl3QFpAeZtNXYtA0romlEESuOsJkzXweYpu2PlGyvy9cnOPbYYxk7diy/+tWvupYdcsghnHnmmcycue21jx/84Ac89dRT/OMf/+hadsEFF/D2228zd+5cAM4991xaW1t55plnutY59dRTKS8v58EHH9xuOzZv3kx1dTUvv/wyEydO3Km2t7a2UlpaSktLCyUlJTv1Gm3n1f32Qmp/OxDTNjEVEAo+ECYUhIKdj25gDoGWhMk7z/4AgMPPvhV3tIHdqjBdofyDPFsOj9E+SpBEAGkFRR2dRGyJQs4jyjgDiuKKd792ZUFq1jTt0/X1d+8uHZG5rsvf//53pkyZ0m35lClTmDNnznZfM3fu3G3W//znP8+bb76J53mfuM6OtgnQ0tICQEVFxQ7XyefztLa2dntofWfVN+7CTICpwA2FzKAYuTqHfK1Dfkic9CAHd6CFIUJJm8ukz87ksPNvIXdI52QrICoaElGt9aONZhUUSRRmWQWeilayFPiKVd+6SoeYpu3ndinItmzZQhAEDBw4sNvygQMHUl9fv93X1NfXb3d93/fZsmXLJ66zo22KCJdffjknnngiY8aM2WF7Z86cSWlpaddj6FB9T1Gf803cUGg/wKFlqEm61qC9WtFercgMM1g/FJpMhRmzaKmz8IaYqDAKsQAgA22OQXlTSPEiQWGgsmbULSkJHVNlQlax6iIdYJqm9bCzx8dHU9jeMEGftv7Hl+/KNi+88ELeeeedHZ527HTVVVfR0tLS9VizZs0nrq/tPlNB+5AYucEG+Rqf7PAc+VKPfJVPw7zpRAAAEZ1JREFUtjWg7EODChSuDV7KRKyt/43FATEgrLNpNxWVy3xSs32KPwCVNVGZ6JH6fyGrLvpBAavUNG1vskvd76uqqjBNc5sjpU2bNm1zRNWppqZmu+tblkVlZeUnrrO9bV500UU89dRTvPLKKwwZMuQT2+s4Do7jfGpdWi8KhfRQg+CAHAgYjTHCRIhqV5QGJkVNOVCKfIlBmCA6wLKIOj3GwE8prBbBHxmjpVko2uTiLPaoWhwdsbXYBu88d1VBS9Q0be+yS0dksViMcePGMXv27G7LZ8+ezfHHH7/d10yYMGGb9f/yl78wfvx4bNv+xHU+uk0R4cILL+Txxx/nhRdeYPjw4bvSdG0PaQSCyo5Jv9ptcKKLXuZ6A9UiOEbHEZilQEWzMwsgQXTpS1LglSrEUgQxReYgh6aDHBpHOmwa4fDOc/pITNO07nb5hujLL7+cadOmMX78eCZMmMBvfvMbVq9ezQUXXABEp/PWrVvHAw88AEQ9FO+66y4uv/xypk+fzty5c/ntb3/b7bTgJZdcwsSJE7nppps444wzePLJJ3n++ed57bXXutb5zne+wx//+EeefPJJUqlU1xFcaWkpiURit94Erff4hyWgPAc5CzM0CKwQJYqkKEy2dpBVrqACoh6INpAAsh2TTKeiTok4Kko5BWEMls68rAAVaZq2t9vlIDv33HNpaGjg+uuvZ8OGDYwZM4ann36auro6ADZs2NDtnrLhw4fz9NNPc9lll3H33XdTW1vLL37xC770pS91rXP88cfz0EMPMWPGDK655hpGjhzJww8/zLHHHtu1Tmd3/8mTJ3drz6xZszj//PN3tQytj0iFQsVA2lR0wcyhI5XYGmMKEhkhtwWMIUKAip7rCDMDEAuIA350C9kHOsQ0TduBXb6PrD/T95H1vflLl3LO3EeRBhvTMwlKApQLyfdM7HZIrclhKYUY0DbAIDvYRhKKIDrLjOr2aRQExQfX6RDTtP5sr7qPTNM+zTGjR2898goAF8QTsiUhFCvclI2KLo+R2hyS2OCh0oLpCqrzyM0QRAnkdIhpmvbp9KDBWu9rBBIBkjcwfIPQCpAqwW0RqDBRbR5JI0qzkg0hxZvztJcr8kkTHIHVIUue0feIaZq2c/QRmdbrVl12NcRCQjtA5QFRiBKywwJyCSE3Kk5TZYxcICglGH50dPZvdbDk19/XIaZp2i7RR2Ran1j1jaupu/dnBM2CkTWjm52LhdyogLyroqO2WhtsWHnr9wrdXE3T+jEdZFqfWfXNqwGou/dn0Aq0K6pVnDeu1te9NE3rPTrItD7XGWiapml9QV8j0zRN0/o1HWSapmlav6aDTNM0TevXdJBpmqZp/ZoOMk3TNK1f00GmaZqm9Ws6yDRN07R+TQeZpmma1q/pINM0TdP6tf1qZI/OqddaW1sL3BJN07T9R+d3bl9Nf7lfBVlbWxsAQ4cOLXBLNE3T9j9tbW2Ulpb2+nb3qxmiwzBk/fr1pFIplFKFbs5Oa21tZejQoaxZs2afn9l6f6l1f6kT9p9adZ07JiK0tbVRW1uLYfT+Fa396ojMMAyGDBlS6Gb0WElJyT79D+Sj9pda95c6Yf+pVde5fX1xJNZJd/bQNE3T+jUdZJqmaVq/poOsH3Ach2uvvRbHcQrdlD63v9S6v9QJ+0+tus7C2a86e2iapmn7Hn1EpmmapvVrOsg0TdO0fk0HmaZpmtav6SDTNE3T+jUdZAXQ1NTEtGnTKC0tpbS0lGnTptHc3PyJrxERrrvuOmpra0kkEkyePJklS5Zss97cuXM5+eSTKSoqoqysjMmTJ5PNZrut8+c//5ljjz2WRCJBVVUVZ511Vm+W102hawXI5/McddRRKKVYuHBhL1XWXaHqXLlyJd/4xjcYPnw4iUSCkSNHcu211+K6bl+UWdD/nj3Z9+7oq1onT56MUqrb47zzzuu2ztKlSznjjDOoqqqipKSEE044gRdffLG3SwQKWyf00veRaHvcqaeeKmPGjJE5c+bInDlzZMyYMXL66ad/4mtuvPFGSaVS8thjj8miRYvk3HPPlUGDBklra2vXOnPmzJGSkhKZOXOmLF68WJYuXSqPPPKI5HK5rnUeffRRKS8vl1/96lfy/vvvy3vvvSePPPLIPllrp4svvlhOO+00AWTBggW9XaKIFK7OZ555Rs4//3x57rnnZPny5fLkk09KdXW1XHHFFftUnT3d995Y66RJk2T69OmyYcOGrkdzc3O37YwaNUq+8IUvyNtvvy1Lly6Vb3/725JMJmXDhg37VJ299X2kg2wPe/fddwWQefPmdS2bO3euAPLee+9t9zVhGEpNTY3ceOONXctyuZyUlpbKPffc07Xs2GOPlRkzZuxw357nyeDBg+Xee+/thUo+XSFr7fT000/LwQcfLEuWLOmzINsb6vyom2++WYYPH76LVXy6QtbZk33vjr6sddKkSXLJJZfscN+bN28WQF555ZWuZa2trQLI888/vxtVbauQdfbm95E+tbiHzZ07l9LSUo499tiuZccddxylpaXMmTNnu69ZsWIF9fX1TJkypWuZ4zhMmjSp6zWbNm3i9ddfp7q6muOPP56BAwcyadIkXnvtta7XvPXWW6xbtw7DMDj66KMZNGgQp5122nZP8/T3WgE2btzI9OnT+f3vf08ymeyDCiOFrvPjWlpaqKio6IXKuitknT3Z9+7oq1o7/eEPf6CqqorDDjuM7373u10zcwBUVlZyyCGH8MADD5BOp/F9n1//+tcMHDiQcePG7TN19ub3kQ6yPay+vp7q6uptlldXV1NfX7/D1wAMHDiw2/KBAwd2Pffhhx8CcN111zF9+nSeffZZxo4dy+c+9zk++OCDbdaZMWMGf/rTnygvL2fSpEk0Njb2ToEfa3ehahURzj//fC644ALGjx/fazXtqM2FqvPjli9fzp133skFF1zQ43p2pJB19mTfu6OvagWYOnUqDz74IC+99BLXXHMNjz32WLfrQkopZs+ezYIFC0ilUsTjce644w6effZZysrKeqG67m0uVJ29+X2kg6yXXHfdddtc2Pz448033wTY7hQyIvKpU8t8/PmPviYMQwC+9a1v8fWvf52jjz6aO+64g4MOOoj77ruv2zo//OEP+dKXvsS4ceOYNWsWSikeeeSRfarWO++8k9bWVq666qqdrqs/1vlR69ev59RTT+Xss8/mm9/85j5XZ0/3vTfVCjB9+nROOeUUxowZw3nnncejjz7K888/z1tvvdW1/re//W2qq6t59dVXmT9/PmeccQann346GzZs2Gfq7K3vI9jPpnHpSxdeeOF2e+R81LBhw3jnnXfYuHHjNs9t3rx5m184nWpqaoDol9CgQYO6lm/atKnrNZ3LDz300G6vPeSQQ1i9evUO13EchxEjRnStszP6Q60vvPAC8+bN22Y8uPHjxzN16lTuv//+T2w/9I86O61fv57PfvazTJgwgd/85jefUll3/aHOmpqaXd739hS61u0ZO3Ystm3zwQcfMHbsWF544QX+9Kc/0dTU1DVNyi9/+Utmz57N/fffz5VXXrlP1Nlb30eA7rW4p3VeXH399de7ls2bN2+nLq7edNNNXcvy+Xy3i6thGEptbe02F8yPOuooueqqq0REpKWlRRzH6XZx1XVdqa6ull//+te9VmOnQta6atUqWbRoUdfjueeeE0AeffRRWbNmzT5Tp4jI2rVr5cADD5TzzjtPfN/vzdK6KWSdPdn37uirWrdn0aJFAsjLL78sIiJPPfWUGIYhbW1t3dYbPXq03HDDDbtT1jYKWWdvfh/pICuAU089VY444giZO3euzJ07Vw4//PBtursedNBB8vjjj3f9feONN0ppaak8/vjjsmjRIvnyl7+8TXfXO+64Q0pKSuSRRx6RDz74QGbMmCHxeFyWLVvWtc4ll1wigwcPlueee07ee+89+cY3viHV1dXS2Ni4z9X6UStWrOjz7veFqHPdunUyatQoOfnkk2Xt2rXdujrvS3Xu7L739lqXLVsmP/7xj+WNN96QFStWyJ///Gc5+OCD5eijj+76EbJ582aprKyUs846SxYuXCjvv/++fPe73xXbtmXhwoX7TJ0ivfd9pIOsABoaGmTq1KmSSqUklUrJ1KlTpampqds6gMyaNavr7zAM5dprr5WamhpxHEcmTpwoixYt2mbbM2fOlCFDhkgymZQJEybIq6++2u1513XliiuukOrqakmlUnLKKafI4sWL+6JMESlsrR/V10FWqDpnzZolwHYf+1KdO7vv3tQXta5evVomTpwoFRUVEovFZOTIkXLxxRdLQ0NDt+2+8cYbMmXKFKmoqJBUKiXHHXecPP300/tcnb31faSncdE0TdP6Nd1rUdM0TevXdJBpmqZp/ZoOMk3TNK1f00GmaZqm9Ws6yDRN07R+TQeZpmma1q/pINM0TdP6NR1kmqZpWr+mg0zTNK0fuOGGGzj++ONJJpM7PZ2LiHDddddRW1tLIpFg8uTJ28z3VV9fz7Rp06ipqaGoqIixY8fy6KOP7lLb/vu//5uTTjqJ8vJyysvLOeWUU5g/f/4ubWN36CDTNE3rB1zX5eyzz+Y///M/d/o1N998M7fffjt33XUXb7zxBjU1NfzTP/1Ttwkup02bxvvvv89TTz3FokWLOOusszj33HNZsGDBTu/npZde4stf/jIvvvgic+fO5YADDmDKlCmsW7dul2rssV0e1ErTNE0rmFmzZklpaemnrtc5Sv2NN97YtSyXy20zSn1RUZE88MAD3V5bUVHRbVT6tWvXyjnnnCNlZWVSUVEhX/ziF2XFihU73Lfv+5JKpeT+++/f+cJ2gz4i0zRN2wetWLGC+vp6pkyZ0rXMcRwmTZrEnDlzupadeOKJPPzwwzQ2NhKGIQ899BD5fJ7JkycDkMlk+OxnP0txcTGvvPIKr732GsXFxZx66qm4rrvdfWcyGTzPo6Kiok9r7KQn1tQ0TdsH1dfXA2wz2eXAgQNZtWpV198PP/ww5557LpWVlViWRTKZ5IknnmDkyJEAPPTQQxiGwb333ts1A/SsWbMoKyvjpZde6haUna688koGDx7MKaec0lfldaOPyDRN0wrkuuuuQyn1iY8333xzt/bRGT6dRKTbshkzZtDU1MTzzz/Pm2++yeWXX87ZZ5/NokWLAPj73//OsmXLSKVSFBcXU1xcTEVFBblcjuXLl2+zv5tvvpkHH3yQxx9/nHg8vltt31n6iEzTNK1ALrzwQs4777xPXGfYsGE92nZNTQ0QHZkNGjSoa/mmTZu6jtKWL1/OXXfdxeLFiznssMMAOPLII3n11Ve5++67ueeeewjDkHHjxvGHP/xhm30MGDCg29+33norP/vZz3j++ec54ogjetTuntBBpmmaViBVVVVUVVX1ybaHDx9OTU0Ns2fP5uijjwaino8vv/wyN910ExBdywIwjO4n50zTJAxDAMaOHcvDDz9MdXU1JSUlO9zfLbfcwk9/+lOee+45xo8f3xcl7ZA+tahpmtYPrF69moULF7J69WqCIGDhwoUsXLiQ9vb2rnUOPvhgnnjiCSA6pXjppZfys5/9jCeeeILFixdz/vnnk0wm+cpXvtK1/qhRo/jWt77F/PnzWb58ObfddhuzZ8/mzDPPBGDq1KlUVVVxxhln8Oqrr7JixQpefvllLrnkEtauXQtEpxNnzJjBfffdx7Bhw6ivr6e+vr5b2/rUHukbqWmapu2Wr33tawJs83jxxRe71gFk1qxZXX+HYSjXXnut1NTUiOM4MnHiRFm0aFG37S5dulTOOussqa6ulmQyKUccccQ23fE3bNggX/3qV6Wqqkocx5ERI0bI9OnTpaWlRURE6urqttu2a6+9tq/ejm6UiMieiUxN0zRN63361KKmaZrWr+kg0zRN0/o1HWSapmlav6aDTNM0TevXdJBpmqZp/ZoOMk3TNK1f00GmaZqm9Ws6yDRN07R+TQeZpmma1q/pINM0TdP6NR1kmqZpWr/2/wOAP7sG1Xw01wAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "snowex_gpr.plot(\"Thickness\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Open and Read ASO Snow Depth Data\n", + "\n", + "As with the SnowEx GPR data, we will _stream_ the ASO data and open it directly into memory.\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": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024.7.0\n", + "0.9.0\n", + "0.15.0\n" + ] + } + ], + "source": [ + "print(xr.__version__)\n", + "print(earthaccess.__version__)\n", + "print(rioxarray.__version__)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Getting 1 granules, approx download size: 1.65 GB\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "576763575d9f4776afef023b6eb6a21f", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "QUEUEING TASKS | : 0%| | 0/1 [00:00\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<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>
" + ], + "text/plain": [ + " 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": 45, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Getting 3 granules, approx download size: 0.03 GB\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "99a02d031c1d43998d25f20352456bd4", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "QUEUEING TASKS | : 0%| | 0/3 [00:00\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": 53, + "metadata": {}, + "outputs": [], + "source": [ + "# !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": 59, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 40.9 ms, sys: 722 µs, total: 41.6 ms\n", + "Wall time: 41.2 ms\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<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
" + ], + "text/plain": [ + " 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": 59, + "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": [ + "### Plot MODIS Snow\n", + "\n", + "- add blue earth as background image\n", + "- grey out missing data\n", + "- indicate clouds\n", + "- plot on Albers of a US continental projection" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "modis_snow_fraction = modis.NDSI_Snow_Cover\n", + "modis_snow_fraction.where((modis_snow_fraction > 0) & (modis_snow_fraction <= 100)).plot(vmin=1, vmax=100, cmap=\"Blues\")\n", + "# modis.where(.NDSI_Snow_Cover.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Clip ASO Data to 500 m Buffer Around Mesa Lakes SNOTEL site\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 a 500 m region around the Mesa Lakes SNOTEL site.\n", + "\n", + "_We could also clip to the extent of the SnowEx GPR survey points_\n", + "\n", + "```\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The first step is to define the clip region. There are several ways to do this. Here, we will use GeoPandas to create a `GeoSeries` containing a single point for the snotel.\n", + "\n", + "The [Mesa Lakes SNOTEL](https://wcc.sc.egov.usda.gov/nwcc/site?sitenum=622) site is located at 39.05 N and -108.05 E. _Where did this come from?_" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "snotel = gpd.GeoSeries([Point(-108.067,39.05)], index=['Mesa Lakes'], crs=\"EPSG:4326\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To create the _clip region_, the `snotel` `GeoSeries` is reprojected to the same CRS as the ASO data. This allows us the define the radius of the buffer in meters. We then use the `buffer` method to create a 500 m circle around the SNOTEL site." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "# clip_region = snotel.to_crs(aso.rio.crs).buffer(1000) # Original tutorial clips to 500 m buffer around Snotel\n", + "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": 35, + "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": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array(0., dtype=float32), array(4.0321507, dtype=float32))" + ] + }, + "execution_count": 36, + "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": [ + "Here, we plot the ASO raster and GPR snow depths, along with the location of the Mesa Lakes SNOTEL site.\n", + "\n", + "We create a `matplotlib` figure and axis. We then use the plot methods for the cropped ASO `xarray.DataArray`, and SNOTEL and SnowEx `geopandas.GeoDataFrame`. Both the SNOTEL and 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 ASO snow depth points, the ASO data is lightened by setting `alpha=0.9` which reduces the saturation of the colors while retaining the hue so that the two datasets can be compared.\n", + "\n", + "_DOES THIS WORK? Can anyone think of a better way?_" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Airborne lidar and GPR snow depths')" + ] + }, + "execution_count": 40, + "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", + "aso_cropped.band_data.plot(ax=ax, vmin=vmin, vmax=vmax, alpha=0.9)\n", + "snotel.to_crs(aso_cropped.rio.crs).plot(ax=ax, c='red')\n", + "snowex_gpr.to_crs(aso_cropped.rio.crs).plot('Thickness', ax=ax, s=5, vmin=vmin, vmax=vmax); #, 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": 42, + "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": [ + "We then use the `xarray.Dataset.interp` method to interpolate ASO raster snow depths to the locations of GPR survey points. `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. \n", + "\n", + "This produces a 1D dataset of ASO snow depths for the GPR survey points." + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<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>
    • dim_0
      PandasIndex
      PandasIndex(RangeIndex(start=0, stop=163764, step=1, name='dim_0'))
    • " ], "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 " + " 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": 12, + "execution_count": 43, "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()" + "aso_transect = aso.interp(x=x, y=y, method='linear')\n", + "aso_transect" ] }, { "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:" + "We can now add the ASO snow depth data to the `snowex_gpr` `GeoDataFrame`. " ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 44, "metadata": {}, "outputs": [ { @@ -1113,224 +4298,216 @@ " \n", " \n", " \n", - " collection\n", - " trace\n", + " date\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\n", " \n", " \n", " \n", " \n", " 0\n", - " GPR_0042_020817\n", - " 2581\n", + " 2017-02-08\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", + " 0.725680\n", " \n", " \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", + " 1\n", " 2017-02-08\n", - " POINT (754148.854 4326341.915)\n", + " -108.066856\n", + " 39.043146\n", + " 0.692\n", + " 225\n", + " 0.726302\n", " \n", " \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", + " 2\n", " 2017-02-08\n", - " POINT (754148.883 4326341.916)\n", + " -108.066856\n", + " 39.043146\n", + " 0.690\n", + " 224\n", + " 0.726953\n", " \n", " \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", + " 3\n", " 2017-02-08\n", - " POINT (754148.911 4326341.917)\n", + " -108.066855\n", + " 39.043146\n", + " 0.689\n", + " 224\n", + " 0.727630\n", " \n", " \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", + " 4\n", " 2017-02-08\n", - " POINT (754148.947 4326341.918)\n", + " -108.066855\n", + " 39.043147\n", + " 0.686\n", + " 223\n", + " 0.728338\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) " + " 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": 13, + "execution_count": 44, "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()" + "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": "markdown", + "cell_type": "code", + "execution_count": 97, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 97, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "### Buffer data around SNOTEL site" + "snowex_gpr[[\"Thickness\", \"ASO\"]].plot()" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 69, "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "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:" + "fig, ax = plt.subplots()\n", + "snowex_gpr.plot.scatter(x=\"Thickness\", y=\"ASO\", s=2, c=\"0.25\", ax=ax)\n", + "ax.set_xlim(0,3)\n", + "ax.set_ylim(0,3)\n", + "ax.set_aspect(\"equal\")" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 89, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "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')" + "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", + "cell_type": "code", + "execution_count": null, "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. " - ] + "outputs": [], + "source": [] }, { - "cell_type": "code", - "execution_count": 15, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "aso_path = './ASO_3M_SD_USCOGM_20170208.tif' # Define local filepath\n", - "\n", - "aso = rasterio.open(aso_path)" + "### Buffer data around SNOTEL site" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Clip data to SNOTEL buffer\n", + "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", - "In order to reduce the data volume to the buffered region of interest, we can subset this GeoTIFF to the same SNOTEL buffer:" + "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": 16, + "execution_count": 14, "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", + "# 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", - "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)" + "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')" ] }, { @@ -1804,7 +4981,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.9.18" } }, "nbformat": 4, From 40ca8afab0635890a3d4fd2358771e407e3ca0ec Mon Sep 17 00:00:00 2001 From: Andy Barrett Date: Thu, 10 Jul 2025 14:30:09 -0600 Subject: [PATCH 02/35] update and refactor tutorial --- .../Snow-tutorial_rendered.ipynb | 8666 +++++++++++++++-- 1 file changed, 8058 insertions(+), 608 deletions(-) diff --git a/notebooks/SnowEx_ASO_MODIS_Snow/Snow-tutorial_rendered.ipynb b/notebooks/SnowEx_ASO_MODIS_Snow/Snow-tutorial_rendered.ipynb index d4dc498..80033ab 100644 --- a/notebooks/SnowEx_ASO_MODIS_Snow/Snow-tutorial_rendered.ipynb +++ b/notebooks/SnowEx_ASO_MODIS_Snow/Snow-tutorial_rendered.ipynb @@ -8,7 +8,7 @@ "\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, or NSIDC DAAC. \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", @@ -19,23 +19,17 @@ "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 buffer [??];\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.\n", - "\n", - "\n", - "---\n", - "\n" + "6. how to save output as shapefile." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "___\n", - "\n", "## Snow data and resources at NSIDC DAAC\n", - "\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", @@ -78,7 +72,7 @@ " 'modis': {'short_name': 'MOD10A1','version': '6','polygon': polygon,'temporal':temporal}\n", " }\n", " -->\n", - " \n", + " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "___\n", - "### Import Packages\n", + "## Import Packages\n", "\n", "We will start by importing the packages we use in this tutorial." ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 71, "metadata": { "tags": [] }, "outputs": [], "source": [ - "# import os\n", - "# import datetime as dt\n", - "import dateutil\n", - "\n", "# For search and access\n", "import earthaccess\n", "\n", @@ -121,7 +111,6 @@ "import pandas as pd \n", "import geopandas as gpd\n", "from shapely.geometry import Polygon, Point, box #, mapping\n", - "# from shapely.geometry.polygon import orient # Probably don't need this\n", "\n", "# For reading ASO and MODIS\n", "import xarray as xr\n", @@ -129,34 +118,27 @@ "\n", "# For Plotting\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", + "import matplotlib as mpl\n", + "from matplotlib.colors import Normalize\n", + "import cartopy.crs as ccrs\n", + "import cartopy.feature as cfeature\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" + "# Miscellaneous imports\n", + "import dateutil\n", + "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "___\n", - "\n", - "\n", "## 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", - "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" + "" ] }, { @@ -171,9 +153,7 @@ "\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.\n", - "\n", - "\n" + "3. Retrieve the bounding polygon from the collection metadata using the `earthaccess` package." ] }, { @@ -380,7 +360,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 68, "metadata": {}, "outputs": [ { @@ -395,7 +375,6 @@ "r = earthaccess.search_datasets(\n", " short_name = \"SNEX17_GPR\",\n", " version = '2',\n", - " # cloud_hosted=True\n", ")" ] }, @@ -418,7 +397,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 69, "metadata": {}, "outputs": [ { @@ -430,7 +409,7 @@ " 'SouthBoundingCoordinate': 38.9935}" ] }, - "execution_count": 6, + "execution_count": 69, "metadata": {}, "output_type": "execute_result" } @@ -520,7 +499,7 @@ "data": { "text/html": [ "\n", - "
    \n", + "
    \n", " 
    <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
    " + ], + "text/plain": [ + " 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": 74, + "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": 42, + "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": 42, + "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": 43, + "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": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2025-07-09T18:09:07.743780\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.9.4, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "
    <cartopy.crs.Sinusoidal object at 0x76cfa9b86520>
    " + ], + "text/plain": [ + "\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": 44, + "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": 73, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "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", + "# p.cmap.colorbar_extend = False\n", + "\n", + "# Add SNOTEL location\n", + "snotel.to_crs(map_proj).plot(ax=p.axes, c='red')\n", + "\n", + "# p.axes.set_extent(roi_bounding_box, ccrs.PlateCarree())\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 this case, we transform the SnowEx data to the MODIS coordinate system before clipping. We could skip this step and pass the SnowEx CRS to `clip` but this creates some missing values in the resulting clipped dataset because the clip region in WGS84 is a parallelagram when viewed in the MODIS Sinusoidal CRS." + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "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": [ + "_Text explaining MODIS Sinusoidal to be added here_" + ] + }, + { + "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", + " )\n", + "\n", + "map_proj = modis_projection" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# 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", + "snotel.to_crs(map_proj).plot(ax=p.axes, marker=\"*\", c='red')\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": 49, + "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": 50, + "metadata": {}, + "outputs": [], + "source": [ + "modis_snow_cover_point = modis.NDSI_Snow_Cover.sel(x=x, y=y, method=\"nearest\")" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray 'NDSI_Snow_Cover' (point: 163764)> Size: 655kB\n",
+       "dask.array<vindex-merge, shape=(163764,), dtype=float32, chunksize=(163764,), chunktype=numpy.ndarray>\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
    " + ], + "text/plain": [ + " 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": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "modis_snow_cover_point" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "snowex_gpr[\"modis_snow_cover\"] = modis_snow_cover_point.to_pandas()" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", - "
    \n", - "
    \n", - "
    \n", - "
    \n", - "
    \n", - "

    Data: SnowEx17_GPR_Version2_Week1.csv

    \n", - "

    Size: 57.32 MB

    \n", - "

    Cloud Hosted: True

    \n", - "
    \n", - "
    \n", - " \n", - "
    \n", - "
    \n", - "
    \n", - "
    \n", - " " - ], - "text/plain": [ - "Collection: {'EntryTitle': 'SnowEx17 Ground Penetrating Radar V002'}\n", - "Spatial coverage: {'HorizontalSpatialDomain': {'Geometry': {'GPolygons': [{'Boundary': {'Points': [{'Longitude': -108.06789, 'Latitude': 39.05189}, {'Longitude': -108.07092, 'Latitude': 39.04958}, {'Longitude': -108.13422, 'Latitude': 39.02644}, {'Longitude': -108.18504, 'Latitude': 39.04032}, {'Longitude': -108.2211, 'Latitude': 39.0357}, {'Longitude': -108.21534, 'Latitude': 39.01719}, {'Longitude': -108.18261, 'Latitude': 38.99637}, {'Longitude': -108.11049, 'Latitude': 39.00562}, {'Longitude': -108.06225, 'Latitude': 39.02413}, {'Longitude': -108.06213, 'Latitude': 39.03338}, {'Longitude': -108.08619, 'Latitude': 39.02876}, {'Longitude': -108.05301, 'Latitude': 39.04264}, {'Longitude': -108.05289, 'Latitude': 39.05189}, {'Longitude': -108.06786, 'Latitude': 39.0542}, {'Longitude': -108.06789, 'Latitude': 39.05189}]}}]}}}\n", - "Temporal coverage: {'RangeDateTime': {'BeginningDateTime': '2017-02-08T00:00:00.000Z', 'EndingDateTime': '2017-02-10T23:59:59.000Z'}}\n", - "Size(MB): 57.3195\n", - "Data: ['https://data.nsidc.earthdatacloud.nasa.gov/nsidc-cumulus-prod-protected/SNOWEX/SNEX17_GPR/2/2017/02/08/SnowEx17_GPR_Version2_Week1.csv']" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "
    \n", - " \n", - "
    \n", - "
    \n", - "
    \n", - "
    \n", - "
    \n", - "

    Data: SnowEx17_GPR_Version2_Week2.csv

    \n", - "

    Size: 85.52 MB

    \n", - "

    Cloud Hosted: True

    \n", - "
    \n", - "
    \n", - " \n", - "
    \n", - "
    \n", - "
    \n", - "
    \n", - " " - ], - "text/plain": [ - "Collection: {'EntryTitle': 'SnowEx17 Ground Penetrating Radar V002'}\n", - "Spatial coverage: {'HorizontalSpatialDomain': {'Geometry': {'GPolygons': [{'Boundary': {'Points': [{'Longitude': -107.88943, 'Latitude': 39.10738}, {'Longitude': -107.89539, 'Latitude': 39.10738}, {'Longitude': -107.95508, 'Latitude': 39.0912}, {'Longitude': -108.02372, 'Latitude': 39.07271}, {'Longitude': -108.09234, 'Latitude': 39.0542}, {'Longitude': -108.16078, 'Latitude': 39.04264}, {'Longitude': -108.2113, 'Latitude': 39.0357}, {'Longitude': -108.2113, 'Latitude': 39.03338}, {'Longitude': -108.20533, 'Latitude': 39.0195}, {'Longitude': -108.18454, 'Latitude': 39.00099}, {'Longitude': -108.12811, 'Latitude': 39.00099}, {'Longitude': -108.08653, 'Latitude': 39.00099}, {'Longitude': -108.02094, 'Latitude': 39.02644}, {'Longitude': -107.94938, 'Latitude': 39.0357}, {'Longitude': -107.93155, 'Latitude': 39.02413}, {'Longitude': -107.89867, 'Latitude': 39.04726}, {'Longitude': -107.85677, 'Latitude': 39.08195}, {'Longitude': -107.86257, 'Latitude': 39.10507}, {'Longitude': -107.88644, 'Latitude': 39.10969}, {'Longitude': -107.88943, 'Latitude': 39.10738}]}}]}}}\n", - "Temporal coverage: {'RangeDateTime': {'BeginningDateTime': '2017-02-14T00:00:00.000Z', 'EndingDateTime': '2017-02-17T23:59:59.000Z'}}\n", - "Size(MB): 85.516\n", - "Data: ['https://data.nsidc.earthdatacloud.nasa.gov/nsidc-cumulus-prod-protected/SNOWEX/SNEX17_GPR/2/2017/02/14/SnowEx17_GPR_Version2_Week2.csv']" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "
    \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", - " " - ], - "text/plain": [ - "Collection: {'EntryTitle': 'SnowEx17 Ground Penetrating Radar V002'}\n", - "Spatial coverage: {'HorizontalSpatialDomain': {'Geometry': {'GPolygons': [{'Boundary': {'Points': [{'Longitude': -108.06789, 'Latitude': 39.05189}, {'Longitude': -108.06792, 'Latitude': 39.04958}, {'Longitude': -108.08616, 'Latitude': 39.03107}, {'Longitude': -108.15531, 'Latitude': 39.0195}, {'Longitude': -108.14352, 'Latitude': 39.00331}, {'Longitude': -108.11049, 'Latitude': 39.00562}, {'Longitude': -108.05349, 'Latitude': 39.00562}, {'Longitude': -108.05334, 'Latitude': 39.01719}, {'Longitude': -108.02919, 'Latitude': 39.02876}, {'Longitude': -108.05586, 'Latitude': 39.0542}, {'Longitude': -108.06786, 'Latitude': 39.0542}, {'Longitude': -108.06789, 'Latitude': 39.05189}]}}]}}}\n", - "Temporal coverage: {'RangeDateTime': {'BeginningDateTime': '2017-02-21T00:00:00.000Z', 'EndingDateTime': '2017-02-25T23:59:59.000Z'}}\n", - "Size(MB): 66.3598\n", - "Data: ['https://data.nsidc.earthdatacloud.nasa.gov/nsidc-cumulus-prod-protected/SNOWEX/SNEX17_GPR/2/2017/02/21/SnowEx17_GPR_Version2_Week3.csv']" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "[None, None, None]" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "[display(result) for result in snowex_result]" ] @@ -660,28 +469,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "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" - ] - } - ], + "outputs": [], "source": [ "for r in snowex_result:\n", " print(\n", @@ -705,7 +495,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -721,7 +511,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -740,21 +530,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "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" - } - ], + "outputs": [], "source": [ "temporal_range = (\n", " dateutil.parser.isoparse(begin_datetime), \n", @@ -765,23 +543,9 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "The two datetime objects represent the date range 2017-02-08 00:00:00+00:00 to 2017-02-10 23:59:59+00:00" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# This is a temporary solution. I am trying jupyter_contrib_nbextensions but running into an issue\n", "# In a markdown cell I should be able to...\n", @@ -802,17 +566,9 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Granules found: 1\n" - ] - } - ], + "outputs": [], "source": [ "aso_result = earthaccess.search_data(\n", " short_name = \"ASO_3M_SD\",\n", @@ -831,59 +587,9 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
    \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", - " \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" - } - ], + "outputs": [], "source": [ "display(aso_result[0])" ] @@ -898,17 +604,9 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Granules found: 3\n" - ] - } - ], + "outputs": [], "source": [ "modis_result = earthaccess.search_data(\n", " short_name = \"MOD10A1\",\n", @@ -928,167 +626,9 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
    \n", - " \n", - "
    \n", - "
    \n", - "
    \n", - "
    \n", - "
    \n", - "

    Data: MOD10A1.A2017039.h09v05.061.2021265053227.hdf

    \n", - "

    Size: 9.53 MB

    \n", - "

    Cloud Hosted: True

    \n", - "
    \n", - "
    \n", - " \"Data\n", - "
    \n", - "
    \n", - "
    \n", - "
    \n", - " " - ], - "text/plain": [ - "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']" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "
    \n", - " \n", - "
    \n", - "
    \n", - "
    \n", - "
    \n", - "
    \n", - "

    Data: MOD10A1.A2017040.h09v05.061.2021265174122.hdf

    \n", - "

    Size: 8.04 MB

    \n", - "

    Cloud Hosted: True

    \n", - "
    \n", - "
    \n", - " \"Data\n", - "
    \n", - "
    \n", - "
    \n", - "
    \n", - " " - ], - "text/plain": [ - "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']" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "
    \n", - " \n", - "
    \n", - "
    \n", - "
    \n", - "
    \n", - "
    \n", - "

    Data: MOD10A1.A2017041.h09v05.061.2021266011855.hdf

    \n", - "

    Size: 9.26 MB

    \n", - "

    Cloud Hosted: True

    \n", - "
    \n", - "
    \n", - " \"Data\n", - "
    \n", - "
    \n", - "
    \n", - "
    \n", - " " - ], - "text/plain": [ - "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": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "[display(r) for r in modis_result]" ] @@ -1140,2594 +680,330 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Opening 3 granules, approx size: 0.2 GB\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "fb70723134794f768fd2b184e4aad09e", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "QUEUEING TASKS | : 0%| | 0/3 [00:00\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    collectiontracelonglatelevtwttThicknessSWExyUTM_Zone
    0GPR_0042_0208172581-108.06685639.0431463240.25.890.692225753854.8800924.325659e+0612 S
    1GPR_0042_0208172582-108.06685639.0431463240.25.890.692225753854.8993854.325660e+0612 S
    2GPR_0042_0208172583-108.06685639.0431463240.25.870.690224753854.9186864.325660e+0612 S
    3GPR_0042_0208172584-108.06685539.0431463240.25.860.689224753854.9379874.325660e+0612 S
    4GPR_0042_0208172585-108.06685539.0431473240.25.840.686223753854.9572804.325660e+0612 S
    \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": 21, - "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": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    collectiontracelonglatelevtwttThicknessSWExyUTM_Zone
    collection
    2017-02-08GPR_0042_0208172581-108.06685639.0431463240.25.890.692225753854.8800924.325659e+0612 S
    2017-02-08GPR_0042_0208172582-108.06685639.0431463240.25.890.692225753854.8993854.325660e+0612 S
    2017-02-08GPR_0042_0208172583-108.06685639.0431463240.25.870.690224753854.9186864.325660e+0612 S
    2017-02-08GPR_0042_0208172584-108.06685539.0431463240.25.860.689224753854.9379874.325660e+0612 S
    2017-02-08GPR_0042_0208172585-108.06685539.0431473240.25.840.686223753854.9572804.325660e+0612 S
    \n", - "
    " - ], - "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": 22, - "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": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    collectiontracelonglatelevtwttThicknessSWExyUTM_Zone
    collection
    2017-02-08GPR_0042_0208172581-108.06685639.0431463240.205.890.692225753854.8800924.325659e+0612 S
    2017-02-08GPR_0042_0208172582-108.06685639.0431463240.205.890.692225753854.8993854.325660e+0612 S
    2017-02-08GPR_0042_0208172583-108.06685639.0431463240.205.870.690224753854.9186864.325660e+0612 S
    2017-02-08GPR_0042_0208172584-108.06685539.0431463240.205.860.689224753854.9379874.325660e+0612 S
    2017-02-08GPR_0042_0208172585-108.06685539.0431473240.205.840.686223753854.9572804.325660e+0612 S
    ....................................
    2017-02-08GPR_0043_02081798131-108.06682639.0431533242.825.580.656213753857.4282304.325660e+0612 S
    2017-02-08GPR_0043_02081798132-108.06682639.0431523242.825.560.653212753857.4215814.325660e+0612 S
    2017-02-08GPR_0043_02081798133-108.06682639.0431523242.815.470.643209753857.4149324.325660e+0612 S
    2017-02-08GPR_0043_02081798134-108.06682739.0431523242.815.330.626203753857.4082754.325660e+0612 S
    2017-02-08GPR_0043_02081798135-108.06682739.0431523242.805.310.624203753857.4016264.325660e+0612 S
    \n", - "

    163764 rows × 11 columns

    \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": 23, - "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": 24, - "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 does have projected x and y coordinates. However, 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": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    datecollectiontracelonglatelevtwttThicknessSWExyUTM_Zonegeometry
    02017-02-08GPR_0042_0208172581-108.06685639.0431463240.25.890.692225753854.8800924.325659e+0612 SPOINT (-108.06686 39.04315)
    12017-02-08GPR_0042_0208172582-108.06685639.0431463240.25.890.692225753854.8993854.325660e+0612 SPOINT (-108.06686 39.04315)
    22017-02-08GPR_0042_0208172583-108.06685639.0431463240.25.870.690224753854.9186864.325660e+0612 SPOINT (-108.06686 39.04315)
    32017-02-08GPR_0042_0208172584-108.06685539.0431463240.25.860.689224753854.9379874.325660e+0612 SPOINT (-108.06686 39.04315)
    42017-02-08GPR_0042_0208172585-108.06685539.0431473240.25.840.686223753854.9572804.325660e+0612 SPOINT (-108.06686 39.04315)
    \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": 25, - "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": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
    " - ] - }, - "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": "stdout", - "output_type": "stream", - "text": [ - " Getting 1 granules, approx download size: 1.65 GB\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "c40a48fecef945f783a97b6d1ce1b377", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "QUEUEING TASKS | : 0%| | 0/1 [00:00\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <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>
    " - ], - "text/plain": [ - " 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": "stdout", - "output_type": "stream", - "text": [ - " Getting 3 granules, approx download size: 0.03 GB\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "519aac51491446518f5a82ce16a5b599", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "QUEUEING TASKS | : 0%| | 0/3 [00:00\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": [ - "# Your gdal may not have \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 58 ms, sys: 4.23 ms, total: 62.3 ms\n", - "Wall time: 62 ms\n" - ] - }, - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <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
    " - ], - "text/plain": [ - " 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" + "outputs": [], + "source": [ + "f_snex = earthaccess.open(snowex_result) # Open all the results" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We now have an `xarray.Dataset` containing the MODIS data." + "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": [ - "\n", - "## Clip ASO Data to the bounding-box of the SnowEx GPR data\n", + "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", - "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." + "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": "markdown", + "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "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", + "import re\n", + "import datetime as dt\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", + "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", - "```\n", - "aso.rio.crs\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 `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", + "The SnowEx data does have projected x and y coordinates. However, 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": 30, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "# The original tutorial created a buffer around the Mesa Lakes SNOTEL site. \n", - "# Clipping to the GPR data makes more sense to me. But I am leaving the code here for review.\n", - "# snotel = gpd.GeoSeries([Point(-108.067,39.05)], index=['Mesa Lakes'], crs=\"EPSG:4326\")\n", - "# clip_region = snotel.to_crs(aso.rio.crs).buffer(1000) # Original tutorial clips to 500 m buffer around Snotel" + "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": 31, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "clip_region = [box(*snowex_gpr.to_crs(aso.rio.crs).total_bounds)] # Clip for extent of survey data" + "snowex_gpr.plot(\"Thickness\", legend=\"True\", legend_kwds={\"label\": \"Thickness (m)\"});" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We then use the `rioxarray` `clip` method to crop the ASO data." + "### 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": 32, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "aso_cropped = aso.rio.clip(clip_region)" + "%%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": [ - "#### Plot ASO and SnowEx GPR snow depth, and SNOTEL location\n", + "### Open and Read MODIS Snow Cover\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." + "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": [ - "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. " + "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": 33, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array(0., dtype=float32), array(4.0321507, dtype=float32))" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "vmin, vmax = (aso_cropped.band_data.min().values, aso_cropped.band_data.max().values)\n", - "vmin, vmax" + "# 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": [ - "Here, we plot the ASO raster and GPR snow depths, along with the location of the Mesa Lakes SNOTEL site.\n", + "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", - "We create a `matplotlib` figure and axis. We then use the plot methods for the cropped ASO `xarray.DataArray`, and SNOTEL and SnowEx `geopandas.GeoDataFrame`. Both the SNOTEL and 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", + "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", - "To distinguish the ASO snow depth raster from the ASO snow depth points, the ASO data is lightened by setting `alpha=0.9` which reduces the saturation of the colors while retaining the hue so that the two datasets can be compared.\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", - "_DOES THIS WORK? Can anyone think of a better way?_" + "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": [ - "The Mesa Lakes SNOTEL site is near to the GPR transect. It is helpful to plot the location of this site for orientation. To facilitate this, we create a `Geopandas.GeoSeries` containing the data point for that station." + "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": 34, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "snotel = gpd.GeoSeries([Point(-108.067,39.05)], index=['Mesa Lakes'], crs=\"EPSG:4326\")" + "vmin, vmax = (aso_cropped.band_data.min().values, aso_cropped.band_data.max().values)\n", + "vmin, vmax" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We can now create the plot." + "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": 35, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Airborne lidar and GPR snow depths')" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig, ax = plt.subplots()\n", - "aso_cropped.band_data.plot(ax=ax, vmin=vmin, vmax=vmax, alpha=0.9)\n", - "snotel.to_crs(aso_cropped.rio.crs).plot(ax=ax, c='red')\n", - "snowex_gpr.to_crs(aso_cropped.rio.crs).plot('Thickness', ax=ax, s=5, vmin=vmin, vmax=vmax); #, edgecolor='0.25')\n", - "ax.set_title(\"Airborne lidar and GPR snow depths\")" + "aso_cropped.band_data.plot(ax=ax, vmin=vmin, vmax=vmax, \n", + " cmap=\"viridis\",\n", + " cbar_kwargs={\"label\": \"ASO [m]\"})\n", + "# snotel.to_crs(aso_cropped.rio.crs).plot(ax=ax, c='red')\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\");" ] }, { From 8121dfad45c1324d5ae3e86a812bc5c77f687ebc Mon Sep 17 00:00:00 2001 From: Andy Barrett Date: Thu, 17 Jul 2025 17:34:34 -0600 Subject: [PATCH 13/35] add justification for bilinear interpolation method --- .../snow_tutorial_rendered.ipynb | 3078 +++++++++++++++-- 1 file changed, 2880 insertions(+), 198 deletions(-) diff --git a/notebooks/SnowEx_ASO_MODIS_Snow/snow_tutorial_rendered.ipynb b/notebooks/SnowEx_ASO_MODIS_Snow/snow_tutorial_rendered.ipynb index dc48334..4e089e6 100644 --- a/notebooks/SnowEx_ASO_MODIS_Snow/snow_tutorial_rendered.ipynb +++ b/notebooks/SnowEx_ASO_MODIS_Snow/snow_tutorial_rendered.ipynb @@ -354,7 +354,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -389,9 +389,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "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" @@ -406,9 +420,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "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", @@ -432,9 +457,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Granules found: 3\n" + ] + } + ], "source": [ "snowex_result = earthaccess.search_data(\n", " short_name = \"SNEX17_GPR\",\n", @@ -451,9 +484,167 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
    \n", + " \n", + "
    \n", + "
    \n", + "
    \n", + "
    \n", + "
    \n", + "

    Data: SnowEx17_GPR_Version2_Week1.csv

    \n", + "

    Size: 57.32 MB

    \n", + "

    Cloud Hosted: True

    \n", + "
    \n", + "
    \n", + " \n", + "
    \n", + "
    \n", + "
    \n", + "
    \n", + " " + ], + "text/plain": [ + "Collection: {'EntryTitle': 'SnowEx17 Ground Penetrating Radar V002'}\n", + "Spatial coverage: {'HorizontalSpatialDomain': {'Geometry': {'GPolygons': [{'Boundary': {'Points': [{'Longitude': -108.06789, 'Latitude': 39.05189}, {'Longitude': -108.07092, 'Latitude': 39.04958}, {'Longitude': -108.13422, 'Latitude': 39.02644}, {'Longitude': -108.18504, 'Latitude': 39.04032}, {'Longitude': -108.2211, 'Latitude': 39.0357}, {'Longitude': -108.21534, 'Latitude': 39.01719}, {'Longitude': -108.18261, 'Latitude': 38.99637}, {'Longitude': -108.11049, 'Latitude': 39.00562}, {'Longitude': -108.06225, 'Latitude': 39.02413}, {'Longitude': -108.06213, 'Latitude': 39.03338}, {'Longitude': -108.08619, 'Latitude': 39.02876}, {'Longitude': -108.05301, 'Latitude': 39.04264}, {'Longitude': -108.05289, 'Latitude': 39.05189}, {'Longitude': -108.06786, 'Latitude': 39.0542}, {'Longitude': -108.06789, 'Latitude': 39.05189}]}}]}}}\n", + "Temporal coverage: {'RangeDateTime': {'BeginningDateTime': '2017-02-08T00:00:00.000Z', 'EndingDateTime': '2017-02-10T23:59:59.000Z'}}\n", + "Size(MB): 57.3195\n", + "Data: ['https://data.nsidc.earthdatacloud.nasa.gov/nsidc-cumulus-prod-protected/SNOWEX/SNEX17_GPR/2/2017/02/08/SnowEx17_GPR_Version2_Week1.csv']" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
    \n", + " \n", + "
    \n", + "
    \n", + "
    \n", + "
    \n", + "
    \n", + "

    Data: SnowEx17_GPR_Version2_Week2.csv

    \n", + "

    Size: 85.52 MB

    \n", + "

    Cloud Hosted: True

    \n", + "
    \n", + "
    \n", + " \n", + "
    \n", + "
    \n", + "
    \n", + "
    \n", + " " + ], + "text/plain": [ + "Collection: {'EntryTitle': 'SnowEx17 Ground Penetrating Radar V002'}\n", + "Spatial coverage: {'HorizontalSpatialDomain': {'Geometry': {'GPolygons': [{'Boundary': {'Points': [{'Longitude': -107.88943, 'Latitude': 39.10738}, {'Longitude': -107.89539, 'Latitude': 39.10738}, {'Longitude': -107.95508, 'Latitude': 39.0912}, {'Longitude': -108.02372, 'Latitude': 39.07271}, {'Longitude': -108.09234, 'Latitude': 39.0542}, {'Longitude': -108.16078, 'Latitude': 39.04264}, {'Longitude': -108.2113, 'Latitude': 39.0357}, {'Longitude': -108.2113, 'Latitude': 39.03338}, {'Longitude': -108.20533, 'Latitude': 39.0195}, {'Longitude': -108.18454, 'Latitude': 39.00099}, {'Longitude': -108.12811, 'Latitude': 39.00099}, {'Longitude': -108.08653, 'Latitude': 39.00099}, {'Longitude': -108.02094, 'Latitude': 39.02644}, {'Longitude': -107.94938, 'Latitude': 39.0357}, {'Longitude': -107.93155, 'Latitude': 39.02413}, {'Longitude': -107.89867, 'Latitude': 39.04726}, {'Longitude': -107.85677, 'Latitude': 39.08195}, {'Longitude': -107.86257, 'Latitude': 39.10507}, {'Longitude': -107.88644, 'Latitude': 39.10969}, {'Longitude': -107.88943, 'Latitude': 39.10738}]}}]}}}\n", + "Temporal coverage: {'RangeDateTime': {'BeginningDateTime': '2017-02-14T00:00:00.000Z', 'EndingDateTime': '2017-02-17T23:59:59.000Z'}}\n", + "Size(MB): 85.516\n", + "Data: ['https://data.nsidc.earthdatacloud.nasa.gov/nsidc-cumulus-prod-protected/SNOWEX/SNEX17_GPR/2/2017/02/14/SnowEx17_GPR_Version2_Week2.csv']" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
    \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", + " " + ], + "text/plain": [ + "Collection: {'EntryTitle': 'SnowEx17 Ground Penetrating Radar V002'}\n", + "Spatial coverage: {'HorizontalSpatialDomain': {'Geometry': {'GPolygons': [{'Boundary': {'Points': [{'Longitude': -108.06789, 'Latitude': 39.05189}, {'Longitude': -108.06792, 'Latitude': 39.04958}, {'Longitude': -108.08616, 'Latitude': 39.03107}, {'Longitude': -108.15531, 'Latitude': 39.0195}, {'Longitude': -108.14352, 'Latitude': 39.00331}, {'Longitude': -108.11049, 'Latitude': 39.00562}, {'Longitude': -108.05349, 'Latitude': 39.00562}, {'Longitude': -108.05334, 'Latitude': 39.01719}, {'Longitude': -108.02919, 'Latitude': 39.02876}, {'Longitude': -108.05586, 'Latitude': 39.0542}, {'Longitude': -108.06786, 'Latitude': 39.0542}, {'Longitude': -108.06789, 'Latitude': 39.05189}]}}]}}}\n", + "Temporal coverage: {'RangeDateTime': {'BeginningDateTime': '2017-02-21T00:00:00.000Z', 'EndingDateTime': '2017-02-25T23:59:59.000Z'}}\n", + "Size(MB): 66.3598\n", + "Data: ['https://data.nsidc.earthdatacloud.nasa.gov/nsidc-cumulus-prod-protected/SNOWEX/SNEX17_GPR/2/2017/02/21/SnowEx17_GPR_Version2_Week3.csv']" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "[None, None, None]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "[display(result) for result in snowex_result]" ] @@ -469,9 +660,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "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", @@ -495,7 +705,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -511,7 +721,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -530,9 +740,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "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", @@ -543,9 +765,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/markdown": [ + "The two datetime objects represent the date range 2017-02-08 00:00:00+00:00 to 2017-02-10 23:59:59+00:00" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# This is a temporary solution. I am trying jupyter_contrib_nbextensions but running into an issue\n", "# In a markdown cell I should be able to...\n", @@ -566,9 +802,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Granules found: 1\n" + ] + } + ], "source": [ "aso_result = earthaccess.search_data(\n", " short_name = \"ASO_3M_SD\",\n", @@ -587,9 +831,59 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
    \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", + " \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])" ] @@ -604,9 +898,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Granules found: 3\n" + ] + } + ], "source": [ "modis_result = earthaccess.search_data(\n", " short_name = \"MOD10A1\",\n", @@ -626,9 +928,167 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
    \n", + " \n", + "
    \n", + "
    \n", + "
    \n", + "
    \n", + "
    \n", + "

    Data: MOD10A1.A2017039.h09v05.061.2021265053227.hdf

    \n", + "

    Size: 9.53 MB

    \n", + "

    Cloud Hosted: True

    \n", + "
    \n", + "
    \n", + " \"Data\n", + "
    \n", + "
    \n", + "
    \n", + "
    \n", + " " + ], + "text/plain": [ + "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']" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
    \n", + " \n", + "
    \n", + "
    \n", + "
    \n", + "
    \n", + "
    \n", + "

    Data: MOD10A1.A2017040.h09v05.061.2021265174122.hdf

    \n", + "

    Size: 8.04 MB

    \n", + "

    Cloud Hosted: True

    \n", + "
    \n", + "
    \n", + " \"Data\n", + "
    \n", + "
    \n", + "
    \n", + "
    \n", + " " + ], + "text/plain": [ + "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']" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
    \n", + " \n", + "
    \n", + "
    \n", + "
    \n", + "
    \n", + "
    \n", + "

    Data: MOD10A1.A2017041.h09v05.061.2021266011855.hdf

    \n", + "

    Size: 9.26 MB

    \n", + "

    Cloud Hosted: True

    \n", + "
    \n", + "
    \n", + " \"Data\n", + "
    \n", + "
    \n", + "
    \n", + "
    \n", + " " + ], + "text/plain": [ + "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]" ] @@ -680,57 +1140,216 @@ }, { "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, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Opening 3 granules, approx size: 0.2 GB\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "d02d2923579f4258ac665874a6ab2133", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "QUEUEING TASKS | : 0%| | 0/3 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    collectiontracelonglatelevtwttThicknessSWExyUTM_Zone
    0GPR_0042_0208172581-108.06685639.0431463240.25.890.692225753854.8800924.325659e+0612 S
    1GPR_0042_0208172582-108.06685639.0431463240.25.890.692225753854.8993854.325660e+0612 S
    2GPR_0042_0208172583-108.06685639.0431463240.25.870.690224753854.9186864.325660e+0612 S
    3GPR_0042_0208172584-108.06685539.0431463240.25.860.689224753854.9379874.325660e+0612 S
    4GPR_0042_0208172585-108.06685539.0431473240.25.840.686223753854.9572804.325660e+0612 S
    \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": [ - "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", + "%%time\n", + "df = pd.read_csv(f_snex[survey_id], sep='\\t') # f_snex[0] is week1 and tab-delimited\n", "df.head()" ] }, @@ -738,154 +1357,2194 @@ "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", + "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", - "The SnowEx data does have projected x and y coordinates. However, 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", + "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", - "```{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. " + "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": [ - "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", + "execution_count": 21, "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    collectiontracelonglatelevtwttThicknessSWExyUTM_Zone
    collection
    2017-02-08GPR_0042_0208172581-108.06685639.0431463240.25.890.692225753854.8800924.325659e+0612 S
    2017-02-08GPR_0042_0208172582-108.06685639.0431463240.25.890.692225753854.8993854.325660e+0612 S
    2017-02-08GPR_0042_0208172583-108.06685639.0431463240.25.870.690224753854.9186864.325660e+0612 S
    2017-02-08GPR_0042_0208172584-108.06685539.0431463240.25.860.689224753854.9379874.325660e+0612 S
    2017-02-08GPR_0042_0208172585-108.06685539.0431473240.25.840.686223753854.9572804.325660e+0612 S
    \n", + "
    " + ], + "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": [ - "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", + "import re\n", + "import datetime as dt\n", "\n", - "Looking at this figure, we can see that the data are in the \"MOD_Grid_Snow_500m\" group.\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", - "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" + "df.index = df.collection.apply(collection_to_date)\n", + "df.head()" ] }, { "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." + "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, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    collectiontracelonglatelevtwttThicknessSWExyUTM_Zone
    collection
    2017-02-08GPR_0042_0208172581-108.06685639.0431463240.205.890.692225753854.8800924.325659e+0612 S
    2017-02-08GPR_0042_0208172582-108.06685639.0431463240.205.890.692225753854.8993854.325660e+0612 S
    2017-02-08GPR_0042_0208172583-108.06685639.0431463240.205.870.690224753854.9186864.325660e+0612 S
    2017-02-08GPR_0042_0208172584-108.06685539.0431463240.205.860.689224753854.9379874.325660e+0612 S
    2017-02-08GPR_0042_0208172585-108.06685539.0431473240.205.840.686223753854.9572804.325660e+0612 S
    ....................................
    2017-02-08GPR_0043_02081798131-108.06682639.0431533242.825.580.656213753857.4282304.325660e+0612 S
    2017-02-08GPR_0043_02081798132-108.06682639.0431523242.825.560.653212753857.4215814.325660e+0612 S
    2017-02-08GPR_0043_02081798133-108.06682639.0431523242.815.470.643209753857.4149324.325660e+0612 S
    2017-02-08GPR_0043_02081798134-108.06682739.0431523242.815.330.626203753857.4082754.325660e+0612 S
    2017-02-08GPR_0043_02081798135-108.06682739.0431523242.805.310.624203753857.4016264.325660e+0612 S
    \n", + "

    163764 rows × 11 columns

    \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 does have projected x and y coordinates. However, 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", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    datecollectiontracelonglatelevtwttThicknessSWExyUTM_Zonegeometry
    02017-02-08GPR_0042_0208172581-108.06685639.0431463240.25.890.692225753854.8800924.325659e+0612 SPOINT (-108.06686 39.04315)
    12017-02-08GPR_0042_0208172582-108.06685639.0431463240.25.890.692225753854.8993854.325660e+0612 SPOINT (-108.06686 39.04315)
    22017-02-08GPR_0042_0208172583-108.06685639.0431463240.25.870.690224753854.9186864.325660e+0612 SPOINT (-108.06686 39.04315)
    32017-02-08GPR_0042_0208172584-108.06685539.0431463240.25.860.689224753854.9379874.325660e+0612 SPOINT (-108.06686 39.04315)
    42017-02-08GPR_0042_0208172585-108.06685539.0431473240.25.840.686223753854.9572804.325660e+0612 SPOINT (-108.06686 39.04315)
    \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": [ + "
    " + ] + }, + "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": "stdout", + "output_type": "stream", + "text": [ + " Getting 1 granules, approx download size: 1.65 GB\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "78ef103083f446969cfae4211fb227de", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "QUEUEING TASKS | : 0%| | 0/1 [00:00\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <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>
    " + ], + "text/plain": [ + " 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": "stdout", + "output_type": "stream", + "text": [ + " Getting 3 granules, approx download size: 0.03 GB\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "f9bf78cf6379486d8f5fddaee4c6ef27", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "QUEUEING TASKS | : 0%| | 0/3 [00:00\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 40.8 ms, sys: 2.93 ms, total: 43.7 ms\n", + "Wall time: 43.4 ms\n" + ] + }, + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <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
    " + ], + "text/plain": [ + " 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", @@ -929,7 +3588,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -945,7 +3604,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -970,9 +3629,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": {}, - "outputs": [], + "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" @@ -989,15 +3659,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
    " + ] + }, + "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", - "# snotel.to_crs(aso_cropped.rio.crs).plot(ax=ax, c='red')\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", @@ -1038,9 +3720,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We then use the `xarray.Dataset.interp` method to interpolate ASO raster snow depths to the locations of GPR survey points. `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. \n", + "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 \n", "\n", - "This produces a 1D dataset of ASO snow depths for the GPR survey points." + "The interpolation produces a 1D dataset of ASO snow depths for the GPR survey points." ] }, { From b32877f042cc6fda63f6e4f4680cb6e2b633172c Mon Sep 17 00:00:00 2001 From: Andy Barrett Date: Thu, 17 Jul 2025 17:48:36 -0600 Subject: [PATCH 14/35] remove confusing text --- .../snow_tutorial_rendered.ipynb | 464 +++++++++--------- 1 file changed, 229 insertions(+), 235 deletions(-) diff --git a/notebooks/SnowEx_ASO_MODIS_Snow/snow_tutorial_rendered.ipynb b/notebooks/SnowEx_ASO_MODIS_Snow/snow_tutorial_rendered.ipynb index 4e089e6..6e75654 100644 --- a/notebooks/SnowEx_ASO_MODIS_Snow/snow_tutorial_rendered.ipynb +++ b/notebooks/SnowEx_ASO_MODIS_Snow/snow_tutorial_rendered.ipynb @@ -491,7 +491,7 @@ "data": { "text/html": [ "\n", - "
    \n", + "
    \n", " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    collectiontracelonglatelevtwttThicknessSWExyUTM_Zone
    0GPR_0042_0208172581-108.06685639.0431463240.25.890.692225753854.8800924.325659e+0612 S
    1GPR_0042_0208172582-108.06685639.0431463240.25.890.692225753854.8993854.325660e+0612 S
    2GPR_0042_0208172583-108.06685639.0431463240.25.870.690224753854.9186864.325660e+0612 S
    3GPR_0042_0208172584-108.06685539.0431463240.25.860.689224753854.9379874.325660e+0612 S
    4GPR_0042_0208172585-108.06685539.0431473240.25.840.686223753854.9572804.325660e+0612 S
    \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" - } - ], + "outputs": [], "source": [ "%%time\n", "df = pd.read_csv(f_snex[survey_id], sep='\\t') # f_snex[0] is week1 and tab-delimited\n", @@ -1366,155 +1228,9 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    collectiontracelonglatelevtwttThicknessSWExyUTM_Zone
    collection
    2017-02-08GPR_0042_0208172581-108.06685639.0431463240.25.890.692225753854.8800924.325659e+0612 S
    2017-02-08GPR_0042_0208172582-108.06685639.0431463240.25.890.692225753854.8993854.325660e+0612 S
    2017-02-08GPR_0042_0208172583-108.06685639.0431463240.25.870.690224753854.9186864.325660e+0612 S
    2017-02-08GPR_0042_0208172584-108.06685539.0431463240.25.860.689224753854.9379874.325660e+0612 S
    2017-02-08GPR_0042_0208172585-108.06685539.0431473240.25.840.686223753854.9572804.325660e+0612 S
    \n", - "
    " - ], - "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" - } - ], + "outputs": [], "source": [ "import re\n", "import datetime as dt\n", @@ -1537,254 +1253,9 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    collectiontracelonglatelevtwttThicknessSWExyUTM_Zone
    collection
    2017-02-08GPR_0042_0208172581-108.06685639.0431463240.205.890.692225753854.8800924.325659e+0612 S
    2017-02-08GPR_0042_0208172582-108.06685639.0431463240.205.890.692225753854.8993854.325660e+0612 S
    2017-02-08GPR_0042_0208172583-108.06685639.0431463240.205.870.690224753854.9186864.325660e+0612 S
    2017-02-08GPR_0042_0208172584-108.06685539.0431463240.205.860.689224753854.9379874.325660e+0612 S
    2017-02-08GPR_0042_0208172585-108.06685539.0431473240.205.840.686223753854.9572804.325660e+0612 S
    ....................................
    2017-02-08GPR_0043_02081798131-108.06682639.0431533242.825.580.656213753857.4282304.325660e+0612 S
    2017-02-08GPR_0043_02081798132-108.06682639.0431523242.825.560.653212753857.4215814.325660e+0612 S
    2017-02-08GPR_0043_02081798133-108.06682639.0431523242.815.470.643209753857.4149324.325660e+0612 S
    2017-02-08GPR_0043_02081798134-108.06682739.0431523242.815.330.626203753857.4082754.325660e+0612 S
    2017-02-08GPR_0043_02081798135-108.06682739.0431523242.805.310.624203753857.4016264.325660e+0612 S
    \n", - "

    163764 rows × 11 columns

    \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" - } - ], + "outputs": [], "source": [ "df = df.loc[\"2017-02-08\"]\n", "df" @@ -1799,7 +1270,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1822,158 +1293,9 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    datecollectiontracelonglatelevtwttThicknessSWExyUTM_Zonegeometry
    02017-02-08GPR_0042_0208172581-108.06685639.0431463240.25.890.692225753854.8800924.325659e+0612 SPOINT (-108.06686 39.04315)
    12017-02-08GPR_0042_0208172582-108.06685639.0431463240.25.890.692225753854.8993854.325660e+0612 SPOINT (-108.06686 39.04315)
    22017-02-08GPR_0042_0208172583-108.06685639.0431463240.25.870.690224753854.9186864.325660e+0612 SPOINT (-108.06686 39.04315)
    32017-02-08GPR_0042_0208172584-108.06685539.0431463240.25.860.689224753854.9379874.325660e+0612 SPOINT (-108.06686 39.04315)
    42017-02-08GPR_0042_0208172585-108.06685539.0431473240.25.840.686223753854.9572804.325660e+0612 SPOINT (-108.06686 39.04315)
    \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" - } - ], + "outputs": [], "source": [ "snowex_gpr = gpd.GeoDataFrame(df, geometry=gpd.points_from_xy(df.long, df.lat, crs=4326))\n", "snowex_gpr.head()" @@ -1988,20 +1310,9 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "snowex_gpr.plot(\"Thickness\", legend=\"True\", legend_kwds={\"label\": \"Thickness (m)\"});" ] @@ -2019,542 +1330,9 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Getting 1 granules, approx download size: 1.65 GB\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "00d8a2ea47bf4296b57e3cabd5d7dd5c", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "QUEUEING TASKS | : 0%| | 0/1 [00:00\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <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>
    " - ], - "text/plain": [ - " 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" - } - ], + "outputs": [], "source": [ "%%time\n", "# f_aso = earthaccess.open(aso_result)\n", @@ -2575,76 +1353,9 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Getting 3 granules, approx download size: 0.03 GB\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "15c9cb290a3d41a9b405c6087d740e9d", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "QUEUEING TASKS | : 0%| | 0/3 [00:00\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <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
    " - ], - "text/plain": [ - " 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" - } - ], + "outputs": [], "source": [ "%%time\n", "modis = xr.open_dataset(f_modis[0], group=\"MOD_Grid_Snow_500m\", engine=\"rasterio\", chunks=\"auto\").squeeze()\n", @@ -3588,7 +1439,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -3604,7 +1455,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -3629,20 +1480,9 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array(0., dtype=float32), array(4.0321507, dtype=float32))" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "vmin, vmax = (aso_cropped.band_data.min().values, aso_cropped.band_data.max().values)\n", "vmin, vmax" @@ -3659,20 +1499,9 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig, ax = plt.subplots()\n", "\n", @@ -3708,7 +1537,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -3727,460 +1556,9 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <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>
    " - ], - "text/plain": [ - " 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" - } - ], + "outputs": [], "source": [ "aso_transect = aso.interp(x=x, y=y, method='linear')\n", "aso_transect" @@ -4195,102 +1573,9 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    datelonglatThicknessSWEASO
    02017-02-08-108.06685639.0431460.6922250.725680
    12017-02-08-108.06685639.0431460.6922250.726302
    22017-02-08-108.06685639.0431460.6902240.726953
    32017-02-08-108.06685539.0431460.6892240.727630
    42017-02-08-108.06685539.0431470.6862230.728338
    \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" - } - ], + "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" @@ -4298,30 +1583,9 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "snowex_gpr[[\"Thickness\", \"ASO\"]].plot()" ] @@ -4335,30 +1599,9 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "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" - } - ], + "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", @@ -4380,862 +1623,10 @@ ] }, { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <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
    " - ], - "text/plain": [ - " 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" - } - ], + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "modis" ] @@ -5251,20 +1642,9 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": null, "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" - } - ], + "outputs": [], "source": [ "modis.rio.crs.to_proj4()" ] @@ -5280,7 +1660,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -5291,6184 +1671,9 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2025-07-17T17:44:02.429238\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.9.4, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - "
    <cartopy.crs.Sinusoidal object at 0x793172983c10>
    " - ], - "text/plain": [ - "\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" - } - ], + "outputs": [], "source": [ "modis_projection" ] @@ -11495,20 +1700,9 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Get state boundaries\n", "states = cfeature.NaturalEarthFeature(\n", @@ -11570,7 +1764,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -11587,20 +1781,9 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "map_proj = modis_projection\n", "\n", @@ -11646,7 +1829,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -11663,7 +1846,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -11672,647 +1855,34 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray 'NDSI_Snow_Cover' (point: 163764)> Size: 655kB\n",
-       "dask.array<vindex-merge, shape=(163764,), dtype=float32, chunksize=(163764,), chunktype=numpy.ndarray>\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
    " - ], - "text/plain": [ - " 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" - } - ], + "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": 52, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "snowex_gpr[\"modis_snow_cover\"] = modis_snow_cover_point.to_pandas()" + "snowex_gpr[\"modis_scf\"] = modis_snow_cover_point.to_pandas()" ] }, { "cell_type": "code", - "execution_count": 54, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    datecollectiontracelonglatelevtwttThicknessSWExyUTM_ZonegeometryASOmodis_snow_cover
    02017-02-08GPR_0042_0208172581-108.06685639.0431463240.25.890.692225753854.8800924.325659e+0612 SPOINT (-108.06686 39.04315)0.72568077.0
    12017-02-08GPR_0042_0208172582-108.06685639.0431463240.25.890.692225753854.8993854.325660e+0612 SPOINT (-108.06686 39.04315)0.72630277.0
    22017-02-08GPR_0042_0208172583-108.06685639.0431463240.25.870.690224753854.9186864.325660e+0612 SPOINT (-108.06686 39.04315)0.72695377.0
    32017-02-08GPR_0042_0208172584-108.06685539.0431463240.25.860.689224753854.9379874.325660e+0612 SPOINT (-108.06686 39.04315)0.72763077.0
    42017-02-08GPR_0042_0208172585-108.06685539.0431473240.25.840.686223753854.9572804.325660e+0612 SPOINT (-108.06686 39.04315)0.72833877.0
    \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 ASO modis_snow_cover \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": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "snowex_gpr.head()" ] @@ -12328,17 +1898,17 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Finally, the dataframe can be exported as a shapefile for further analysis in GIS:" + "Finally, the `snowex_gpr` dataframe can be exported as a shapefile for further analysis in GIS:" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "gdf_buffer = gdf_buffer.drop(columns=['date'])\n", - "gdf_buffer.to_file('snow-data-20170208.shp')" + "snowex_gpr['date'] = snowex_gpr['date'].apply(lambda x: x.strftime(\"%Y-%m-%d\"))\n", + "snowex_gpr.to_file('snow-data-20170208.shp')" ] }, { From 8b58167d87e7f0345f8995c31e181bcf1a7f6120 Mon Sep 17 00:00:00 2001 From: Andy Barrett Date: Thu, 17 Jul 2025 18:06:04 -0600 Subject: [PATCH 18/35] update non-rendered notebook and final save --- .../SnowEx_ASO_MODIS_Snow/Snow-tutorial.ipynb | 791 -- .../SnowEx_ASO_MODIS_Snow/snow_tutorial.ipynb | 1382 ++ .../snow_tutorial_rendered.ipynb | 10559 +++++++++++++++- 3 files changed, 11880 insertions(+), 852 deletions(-) delete mode 100644 notebooks/SnowEx_ASO_MODIS_Snow/Snow-tutorial.ipynb create mode 100644 notebooks/SnowEx_ASO_MODIS_Snow/snow_tutorial.ipynb diff --git a/notebooks/SnowEx_ASO_MODIS_Snow/Snow-tutorial.ipynb b/notebooks/SnowEx_ASO_MODIS_Snow/Snow-tutorial.ipynb deleted file mode 100644 index 37a48a0..0000000 --- a/notebooks/SnowEx_ASO_MODIS_Snow/Snow-tutorial.ipynb +++ /dev/null @@ -1,791 +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" - ] - }, - { - "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": null, - "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" - ] - }, - { - "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.ipynb b/notebooks/SnowEx_ASO_MODIS_Snow/snow_tutorial.ipynb new file mode 100644 index 0000000..7bea00d --- /dev/null +++ b/notebooks/SnowEx_ASO_MODIS_Snow/snow_tutorial.ipynb @@ -0,0 +1,1382 @@ +{ + "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 | Landing Page URL |\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", + "# 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": 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": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# This is a temporary solution. I am trying jupyter_contrib_nbextensions but running into an issue\n", + "# In a markdown cell I should be able to...\n", + "# The two datetime objects represent the date range {{temporal_range[0]}} to {{temporal_range[1]}}\n", + "Markdown(f\"The two datetime objects represent the date range {temporal_range[0]} to {temporal_range[1]}\")" + ] + }, + { + "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", + " cloud_hosted=True\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 does have projected x and y coordinates. However, 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": "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 index 399f9f0..27c8c95 100644 --- a/notebooks/SnowEx_ASO_MODIS_Snow/snow_tutorial_rendered.ipynb +++ b/notebooks/SnowEx_ASO_MODIS_Snow/snow_tutorial_rendered.ipynb @@ -1206,9 +1206,147 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 930 ms, sys: 220 ms, total: 1.15 s\n", + "Wall time: 15.7 s\n" + ] + }, + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    collectiontracelonglatelevtwttThicknessSWExyUTM_Zone
    0GPR_0042_0208172581-108.06685639.0431463240.25.890.692225753854.8800924.325659e+0612 S
    1GPR_0042_0208172582-108.06685639.0431463240.25.890.692225753854.8993854.325660e+0612 S
    2GPR_0042_0208172583-108.06685639.0431463240.25.870.690224753854.9186864.325660e+0612 S
    3GPR_0042_0208172584-108.06685539.0431463240.25.860.689224753854.9379874.325660e+0612 S
    4GPR_0042_0208172585-108.06685539.0431473240.25.840.686223753854.9572804.325660e+0612 S
    \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", @@ -1228,9 +1366,155 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    collectiontracelonglatelevtwttThicknessSWExyUTM_Zone
    collection
    2017-02-08GPR_0042_0208172581-108.06685639.0431463240.25.890.692225753854.8800924.325659e+0612 S
    2017-02-08GPR_0042_0208172582-108.06685639.0431463240.25.890.692225753854.8993854.325660e+0612 S
    2017-02-08GPR_0042_0208172583-108.06685639.0431463240.25.870.690224753854.9186864.325660e+0612 S
    2017-02-08GPR_0042_0208172584-108.06685539.0431463240.25.860.689224753854.9379874.325660e+0612 S
    2017-02-08GPR_0042_0208172585-108.06685539.0431473240.25.840.686223753854.9572804.325660e+0612 S
    \n", + "
    " + ], + "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", @@ -1253,9 +1537,254 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    collectiontracelonglatelevtwttThicknessSWExyUTM_Zone
    collection
    2017-02-08GPR_0042_0208172581-108.06685639.0431463240.205.890.692225753854.8800924.325659e+0612 S
    2017-02-08GPR_0042_0208172582-108.06685639.0431463240.205.890.692225753854.8993854.325660e+0612 S
    2017-02-08GPR_0042_0208172583-108.06685639.0431463240.205.870.690224753854.9186864.325660e+0612 S
    2017-02-08GPR_0042_0208172584-108.06685539.0431463240.205.860.689224753854.9379874.325660e+0612 S
    2017-02-08GPR_0042_0208172585-108.06685539.0431473240.205.840.686223753854.9572804.325660e+0612 S
    ....................................
    2017-02-08GPR_0043_02081798131-108.06682639.0431533242.825.580.656213753857.4282304.325660e+0612 S
    2017-02-08GPR_0043_02081798132-108.06682639.0431523242.825.560.653212753857.4215814.325660e+0612 S
    2017-02-08GPR_0043_02081798133-108.06682639.0431523242.815.470.643209753857.4149324.325660e+0612 S
    2017-02-08GPR_0043_02081798134-108.06682739.0431523242.815.330.626203753857.4082754.325660e+0612 S
    2017-02-08GPR_0043_02081798135-108.06682739.0431523242.805.310.624203753857.4016264.325660e+0612 S
    \n", + "

    163764 rows × 11 columns

    \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" @@ -1270,7 +1799,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -1293,9 +1822,158 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    datecollectiontracelonglatelevtwttThicknessSWExyUTM_Zonegeometry
    02017-02-08GPR_0042_0208172581-108.06685639.0431463240.25.890.692225753854.8800924.325659e+0612 SPOINT (-108.06686 39.04315)
    12017-02-08GPR_0042_0208172582-108.06685639.0431463240.25.890.692225753854.8993854.325660e+0612 SPOINT (-108.06686 39.04315)
    22017-02-08GPR_0042_0208172583-108.06685639.0431463240.25.870.690224753854.9186864.325660e+0612 SPOINT (-108.06686 39.04315)
    32017-02-08GPR_0042_0208172584-108.06685539.0431463240.25.860.689224753854.9379874.325660e+0612 SPOINT (-108.06686 39.04315)
    42017-02-08GPR_0042_0208172585-108.06685539.0431473240.25.840.686223753854.9572804.325660e+0612 SPOINT (-108.06686 39.04315)
    \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()" @@ -1310,17 +1988,28 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "snowex_gpr.plot(\"Thickness\", legend=\"True\", legend_kwds={\"label\": \"Thickness (m)\"});" - ] - }, - { - "cell_type": "markdown", + "execution_count": 25, "metadata": {}, - "source": [ + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
    " + ] + }, + "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", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + "\n", + " \n", + " 23765\n", + " 17534\n", + "\n", + " \n", + " \n", + "
    • x
      PandasIndex
      PandasIndex(Index([208864.5, 208867.5, 208870.5, 208873.5, 208876.5, 208879.5, 208882.5,\n",
+       "       208885.5, 208888.5, 208891.5,\n",
+       "       ...\n",
+       "       280129.5, 280132.5, 280135.5, 280138.5, 280141.5, 280144.5, 280147.5,\n",
+       "       280150.5, 280153.5, 280156.5],\n",
+       "      dtype='float64', name='x', length=23765))
    • y
      PandasIndex
      PandasIndex(Index([4350070.5, 4350067.5, 4350064.5, 4350061.5, 4350058.5, 4350055.5,\n",
+       "       4350052.5, 4350049.5, 4350046.5, 4350043.5,\n",
+       "       ...\n",
+       "       4297498.5, 4297495.5, 4297492.5, 4297489.5, 4297486.5, 4297483.5,\n",
+       "       4297480.5, 4297477.5, 4297474.5, 4297471.5],\n",
+       "      dtype='float64', name='y', length=17534))
    • " + ], + "text/plain": [ + " 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", @@ -1353,9 +2575,76 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Getting 3 granules, approx download size: 0.03 GB\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "3adabf49721a41799667f3c91a73c4cb", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "QUEUEING TASKS | : 0%| | 0/3 [00:00\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <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
    " + ], + "text/plain": [ + " 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", @@ -1439,7 +3588,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -1455,7 +3604,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -1480,9 +3629,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": {}, - "outputs": [], + "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" @@ -1499,9 +3659,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = plt.subplots()\n", "\n", @@ -1537,7 +3708,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -1556,9 +3727,460 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <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>
    " + ], + "text/plain": [ + " 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" @@ -1573,9 +4195,102 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    datelonglatThicknessSWEASO
    02017-02-08-108.06685639.0431460.6922250.725680
    12017-02-08-108.06685639.0431460.6922250.726302
    22017-02-08-108.06685639.0431460.6902240.726953
    32017-02-08-108.06685539.0431460.6892240.727630
    42017-02-08-108.06685539.0431470.6862230.728338
    \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" @@ -1583,9 +4298,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "snowex_gpr[[\"Thickness\", \"ASO\"]].plot()" ] @@ -1599,9 +4335,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "metadata": {}, - "outputs": [], + "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", @@ -1624,9 +4381,861 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <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
    " + ], + "text/plain": [ + " 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" ] @@ -1642,9 +5251,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "metadata": {}, - "outputs": [], + "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()" ] @@ -1660,7 +5280,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, "metadata": {}, "outputs": [], "source": [ @@ -1671,9 +5291,6184 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2025-07-17T18:00:45.129910\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.9.4, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "
    <cartopy.crs.Sinusoidal object at 0x7e1afb176910>
    " + ], + "text/plain": [ + "\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" ] @@ -1700,9 +11495,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Get state boundaries\n", "states = cfeature.NaturalEarthFeature(\n", @@ -1764,7 +11570,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ @@ -1781,9 +11587,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 45, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "map_proj = modis_projection\n", "\n", @@ -1829,7 +11646,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 46, "metadata": {}, "outputs": [], "source": [ @@ -1846,7 +11663,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 47, "metadata": {}, "outputs": [], "source": [ @@ -1855,9 +11672,468 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 48, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray 'NDSI_Snow_Cover' (point: 163764)> Size: 655kB\n",
+       "dask.array<vindex-merge, shape=(163764,), dtype=float32, chunksize=(163764,), chunktype=numpy.ndarray>\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
    " + ], + "text/plain": [ + " 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" ] @@ -1871,7 +12147,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 49, "metadata": {}, "outputs": [], "source": [ @@ -1880,9 +12156,170 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 50, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    datecollectiontracelonglatelevtwttThicknessSWExyUTM_ZonegeometryASOmodis_scf
    02017-02-08GPR_0042_0208172581-108.06685639.0431463240.25.890.692225753854.8800924.325659e+0612 SPOINT (-108.06686 39.04315)0.72568077.0
    12017-02-08GPR_0042_0208172582-108.06685639.0431463240.25.890.692225753854.8993854.325660e+0612 SPOINT (-108.06686 39.04315)0.72630277.0
    22017-02-08GPR_0042_0208172583-108.06685639.0431463240.25.870.690224753854.9186864.325660e+0612 SPOINT (-108.06686 39.04315)0.72695377.0
    32017-02-08GPR_0042_0208172584-108.06685539.0431463240.25.860.689224753854.9379874.325660e+0612 SPOINT (-108.06686 39.04315)0.72763077.0
    42017-02-08GPR_0042_0208172585-108.06685539.0431473240.25.840.686223753854.9572804.325660e+0612 SPOINT (-108.06686 39.04315)0.72833877.0
    \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 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()" ] @@ -1903,7 +12340,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 51, "metadata": {}, "outputs": [], "source": [ From 689742df112ed9b14105f2d7552d2d1c8b3e69bd Mon Sep 17 00:00:00 2001 From: Andy Barrett Date: Thu, 17 Jul 2025 18:08:05 -0600 Subject: [PATCH 19/35] more cleanup --- .../tutorial_helper_functions.py | 659 ------------------ 1 file changed, 659 deletions(-) delete mode 100644 notebooks/SnowEx_ASO_MODIS_Snow/tutorial_helper_functions.py 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. -# ''' -# -# 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 From 8ccf3edebb93a9e31c34823307864ce85579488d Mon Sep 17 00:00:00 2001 From: Andy Barrett Date: Thu, 17 Jul 2025 18:10:20 -0600 Subject: [PATCH 20/35] update README --- notebooks/SnowEx_ASO_MODIS_Snow/README.md | 23 ++++++++++++----------- 1 file changed, 12 insertions(+), 11 deletions(-) diff --git a/notebooks/SnowEx_ASO_MODIS_Snow/README.md b/notebooks/SnowEx_ASO_MODIS_Snow/README.md index 6dee2bf..4242cca 100644 --- a/notebooks/SnowEx_ASO_MODIS_Snow/README.md +++ b/notebooks/SnowEx_ASO_MODIS_Snow/README.md @@ -1,17 +1,18 @@ # Snow Depth and Snow Cover Data Exploration -## Summary +## Overview -This tutorial demonstrates how to access and compare coincident snow data from the National Snow and Ice Data Center Distributed Active Archive Center (NSIDC DAAC) across in-situ, airborne, and satellite platforms from NASA's SnowEx, ASO, and MODIS data sets, respectively. +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). -## Key Learning Objectives +## What you will learn in this tutorial -1. Learn about the coverage, resolution, and structure of snow data sets from NASA's SnowEx, ASO, and MODIS data sets. +In this tutorial you will learn: -2. Learn how to find and download spatiotemporally coincident data across in-situ, airborne, and satellite observations. - -3. Learn how to read data into Python from CSV and GeoTIFF formats. - -4. Learn how to subset data based on a buffered area. - -5. Learn how to extract and visualize raster values at point locations. +1. what snow data and information is available from NSIDC and the resources available to search and access this data; +2. how to search and access spatiotemporally coincident data across in-situ, airborne, and satellite observations. +3. how to read SnowEx GPR data into a Geopandas GeoDataFrame; +4. how to read ASO snow depth data from GeoTIFF files using xarray; +5. how to read MODIS Snow Cover data from HDF-EOS files using xarray; +6. how to subset gridded data using a bounding box; +5. how to extract and visualize raster values at point locations; +6. how to save output as shapefile. \ No newline at end of file From 4d8c0e66e9bdc43c98a2b149409f9cfecae07ac2 Mon Sep 17 00:00:00 2001 From: Andy Barrett Date: Mon, 21 Jul 2025 14:55:03 -0600 Subject: [PATCH 21/35] change table heading to doi --- notebooks/SnowEx_ASO_MODIS_Snow/snow_tutorial_rendered.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/notebooks/SnowEx_ASO_MODIS_Snow/snow_tutorial_rendered.ipynb b/notebooks/SnowEx_ASO_MODIS_Snow/snow_tutorial_rendered.ipynb index 27c8c95..77954dc 100644 --- a/notebooks/SnowEx_ASO_MODIS_Snow/snow_tutorial_rendered.ipynb +++ b/notebooks/SnowEx_ASO_MODIS_Snow/snow_tutorial_rendered.ipynb @@ -54,7 +54,7 @@ "\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 | Landing Page URL |\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", From 9a9e36d933fc15af36fa294fc0ad46700dbef079 Mon Sep 17 00:00:00 2001 From: Andy Barrett Date: Tue, 22 Jul 2025 12:40:55 -0600 Subject: [PATCH 22/35] add warnings filter to suppress warnings --- .../SnowEx_ASO_MODIS_Snow/snow_tutorial_rendered.ipynb | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/notebooks/SnowEx_ASO_MODIS_Snow/snow_tutorial_rendered.ipynb b/notebooks/SnowEx_ASO_MODIS_Snow/snow_tutorial_rendered.ipynb index 77954dc..d3dbdee 100644 --- a/notebooks/SnowEx_ASO_MODIS_Snow/snow_tutorial_rendered.ipynb +++ b/notebooks/SnowEx_ASO_MODIS_Snow/snow_tutorial_rendered.ipynb @@ -98,7 +98,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 5, "metadata": { "tags": [] }, @@ -107,6 +107,10 @@ "# 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", From 8beb0943e0ade9780eb02013d6b9c2939827d13b Mon Sep 17 00:00:00 2001 From: Andy Barrett Date: Tue, 22 Jul 2025 15:36:48 -0600 Subject: [PATCH 23/35] add cell to check versions --- .../snow_tutorial_rendered.ipynb | 44 +++++++++++++++++++ 1 file changed, 44 insertions(+) diff --git a/notebooks/SnowEx_ASO_MODIS_Snow/snow_tutorial_rendered.ipynb b/notebooks/SnowEx_ASO_MODIS_Snow/snow_tutorial_rendered.ipynb index d3dbdee..b883aee 100644 --- a/notebooks/SnowEx_ASO_MODIS_Snow/snow_tutorial_rendered.ipynb +++ b/notebooks/SnowEx_ASO_MODIS_Snow/snow_tutorial_rendered.ipynb @@ -135,6 +135,50 @@ "import numpy as np" ] }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Platform: linux\n", + "Python (sys): 3.9.18 | packaged by conda-forge | (main, Aug 30 2023, 03:49:32) \n", + "[GCC 12.3.0]\n", + "earthaccess: 0.9.0\n", + "pandas: 2.2.0\n", + "geopandas: 0.14.4\n", + "xarray: 2024.7.0\n", + "rioxarray: 0.15.0\n", + "matplotlib: 3.9.4\n", + "cartopy: 0.23.0\n", + "numpy: 1.23.5\n" + ] + } + ], + "source": [ + "# For checking versions # Can be deleted before publication\n", + "# Andy's environment has:\n", + "# earthaccess: 0.9.0\n", + "# pandas: 2.2.0\n", + "# geopandas: 0.14.4\n", + "# xarray: 2024.7.0\n", + "# rioxarray: 0.15.0\n", + "# matplotlib: 3.9.4\n", + "# cartopy: 0.23.0\n", + "# numpy: 1.23.5\n", + "\n", + "import sys\n", + "import cartopy\n", + "\n", + "print(f\"Platform: {sys.platform}\")\n", + "print(f\"Python (sys): {sys.version}\")\n", + "for pkg in [earthaccess, pd, gpd, xr, rioxarray, mpl, cartopy, np]:\n", + " print(f\"{pkg.__name__}: {pkg.__version__}\")" + ] + }, { "cell_type": "markdown", "metadata": {}, From 4b172713386c141dcc5c47da7738fb8713da38bc Mon Sep 17 00:00:00 2001 From: Andy Barrett Date: Tue, 29 Jul 2025 14:08:00 -0600 Subject: [PATCH 24/35] add shapefile and snowex download --- .gitignore | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/.gitignore b/.gitignore index 9ed8e86..dec9aca 100644 --- a/.gitignore +++ b/.gitignore @@ -49,3 +49,13 @@ package-lock.json geckodriver.log *.iml +# For SnowEx_ASO_MODIS_Snow +notebooks/SnowEx_ASO_MODIS_Snow/download + +# Shape files +*.cpg +*.dbf +*.prj +*.shp +*.shx + From 6afcc18096faeacba8c64bc88902c813882e477a Mon Sep 17 00:00:00 2001 From: Andy Barrett Date: Tue, 29 Jul 2025 17:30:11 -0600 Subject: [PATCH 25/35] modify date rendering --- .../snow_tutorial_rendered.ipynb | 1529 ++++++++++------- 1 file changed, 888 insertions(+), 641 deletions(-) diff --git a/notebooks/SnowEx_ASO_MODIS_Snow/snow_tutorial_rendered.ipynb b/notebooks/SnowEx_ASO_MODIS_Snow/snow_tutorial_rendered.ipynb index b883aee..384faae 100644 --- a/notebooks/SnowEx_ASO_MODIS_Snow/snow_tutorial_rendered.ipynb +++ b/notebooks/SnowEx_ASO_MODIS_Snow/snow_tutorial_rendered.ipynb @@ -98,7 +98,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 2, "metadata": { "tags": [] }, @@ -137,7 +137,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -145,16 +145,15 @@ "output_type": "stream", "text": [ "Platform: linux\n", - "Python (sys): 3.9.18 | packaged by conda-forge | (main, Aug 30 2023, 03:49:32) \n", - "[GCC 12.3.0]\n", - "earthaccess: 0.9.0\n", - "pandas: 2.2.0\n", - "geopandas: 0.14.4\n", - "xarray: 2024.7.0\n", - "rioxarray: 0.15.0\n", - "matplotlib: 3.9.4\n", - "cartopy: 0.23.0\n", - "numpy: 1.23.5\n" + "Python (sys): 3.12.11 | packaged by conda-forge | (main, Jun 4 2025, 14:45:31) [GCC 13.3.0]\n", + "earthaccess: 0.14.0\n", + "pandas: 2.3.1\n", + "geopandas: 1.1.1\n", + "xarray: 2025.7.1\n", + "rioxarray: 0.19.0\n", + "matplotlib: 3.10.3\n", + "cartopy: 0.24.0\n", + "numpy: 2.3.2\n" ] } ], @@ -237,7 +236,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -304,7 +303,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -345,7 +344,7 @@ "0 POLYGON ((-108.23524 38.98557, -107.85285 38.9..." ] }, - "execution_count": 3, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -366,20 +365,20 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[(-108.2352445938561, 38.98556907427165),\n", - " (-107.85284607930835, 38.978765032966244),\n", - " (-107.85494925720668, 39.10596902171742),\n", - " (-108.22772795408136, 39.11294532581687),\n", - " (-108.2352445938561, 38.98556907427165)]" + "[(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, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -402,17 +401,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Datasets found: 1\n" - ] - } - ], + "outputs": [], "source": [ "r = earthaccess.search_datasets(\n", " short_name = \"SNEX17_GPR\",\n", @@ -437,7 +428,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -449,7 +440,7 @@ " 'SouthBoundingCoordinate': 38.9935}" ] }, - "execution_count": 6, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -468,7 +459,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -477,7 +468,7 @@ "(-108.22367, 38.9935, -107.85785, 39.11115)" ] }, - "execution_count": 7, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -505,17 +496,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Granules found: 3\n" - ] - } - ], + "outputs": [], "source": [ "snowex_result = earthaccess.search_data(\n", " short_name = \"SNEX17_GPR\",\n", @@ -532,14 +515,14 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", - "
    \n", + "
    \n", " 
    <xarray.Dataset> Size: 2GB\n",
        "Dimensions:      (x: 23765, y: 17534)\n",
        "Coordinates:\n",
@@ -2508,7 +2472,9 @@
        "  * 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>
    " + " dtype='float64', name='y', length=17534))
    • " ], "text/plain": [ " Size: 2GB\n", @@ -2598,7 +2564,7 @@ " band_data (y, x) float32 2GB dask.array" ] }, - "execution_count": 26, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -2623,73 +2589,31 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 28, "metadata": {}, "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - " Getting 3 granules, approx download size: 0.03 GB\n" + "QUEUEING TASKS | : 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 7341.26it/s]\n", + "PROCESSING TASKS | : 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 81180.08it/s]\n", + "COLLECTING RESULTS | : 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 110376.42it/s]" ] }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "3adabf49721a41799667f3c91a73c4cb", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "QUEUEING TASKS | : 0%| | 0/3 [00:00 span {\n", @@ -2950,15 +2929,15 @@ "}\n", "\n", ".xr-dim-list:before {\n", - " content: '(';\n", + " content: \"(\";\n", "}\n", "\n", ".xr-dim-list:after {\n", - " content: ')';\n", + " content: \")\";\n", "}\n", "\n", ".xr-dim-list li:not(:last-child):after {\n", - " content: ',';\n", + " content: \",\";\n", " padding-right: 5px;\n", "}\n", "\n", @@ -2975,7 +2954,9 @@ ".xr-var-item label,\n", ".xr-var-item > .xr-var-name span {\n", " background-color: var(--xr-background-color-row-even);\n", + " border-color: var(--xr-background-color-row-odd);\n", " margin-bottom: 0;\n", + " padding-top: 2px;\n", "}\n", "\n", ".xr-var-item > .xr-var-name:hover span {\n", @@ -2986,6 +2967,7 @@ ".xr-var-list > li:nth-child(odd) > label,\n", ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n", " background-color: var(--xr-background-color-row-odd);\n", + " border-color: var(--xr-background-color-row-even);\n", "}\n", "\n", ".xr-var-name {\n", @@ -3035,8 +3017,15 @@ ".xr-var-data,\n", ".xr-index-data {\n", " display: none;\n", - " background-color: var(--xr-background-color) !important;\n", - " padding-bottom: 5px !important;\n", + " border-top: 2px dotted var(--xr-background-color);\n", + " padding-bottom: 20px !important;\n", + " padding-top: 10px !important;\n", + "}\n", + "\n", + ".xr-var-attrs-in + label,\n", + ".xr-var-data-in + label,\n", + ".xr-index-data-in + label {\n", + " padding: 0 1px;\n", "}\n", "\n", ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n", @@ -3049,6 +3038,12 @@ " float: right;\n", "}\n", "\n", + ".xr-var-data > pre,\n", + ".xr-index-data > pre,\n", + ".xr-var-data > table > tbody > tr {\n", + " background-color: transparent !important;\n", + "}\n", + "\n", ".xr-var-name span,\n", ".xr-var-data,\n", ".xr-index-name div,\n", @@ -3108,6 +3103,14 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", + "\n", + ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n", + ".xr-var-data-in:checked + label > .xr-icon-database,\n", + ".xr-index-data-in:checked + label > .xr-icon-database {\n", + " color: var(--xr-font-color0);\n", + " filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n", + " stroke-width: 0.8px;\n", + "}\n", "
    <xarray.Dataset> Size: 161MB\n",
        "Dimensions:                             (x: 2400, y: 2400)\n",
        "Coordinates:\n",
@@ -3136,9 +3139,9 @@
        "    TileID:                             51009005\n",
        "    VERSIONID:                          61\n",
        "    VERTICALTILENUMBER:                 5\n",
-       "    WESTBOUNDINGCOORDINATE:             -117.486656023174