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EASE Grid 2.0 via Python, pyproj, and gdal_translate
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{ | |
"cell_type": "heading", | |
"level": 1, | |
"metadata": {}, | |
"source": [ | |
"EASE Grid 2.0 via Python, pyproj, and gdal_translate" | |
] | |
}, | |
{ | |
"cell_type": "heading", | |
"level": 2, | |
"metadata": {}, | |
"source": [ | |
"Prelude" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"This page is necessary because a snow data product I was trying to use is not georeferencing in standard off the shelf tools:\n", | |
"\n", | |
" [fedora@lugosi ~]$ gdalinfo NETCDF:nhtsw100e2_20030107_20030113.nc:merged_snow_cover_extent\n", | |
" Warning 1: dimension #2 (cols) is not a Longitude/X dimension.\n", | |
" Warning 1: dimension #1 (rows) is not a Latitude/Y dimension.\n", | |
" Driver: netCDF/Network Common Data Format\n", | |
" Files: nhtsw100e2_20030107_20030113.nc\n", | |
" Size is 180, 180\n", | |
" Coordinate System is `'\n", | |
"\n", | |
"Note that the coordinate system is empty and there are a couple of warnings. Turns out, the same thing fixes both problems. Long story short, the file is missing coordinate variables for the rows and cols dimensions. Now it seems obvious that this is what the warning messages were trying to tell me, but at the outset I was too dense to pick up on it. \n", | |
"\n", | |
"To experience the entire thread of discovery, and/or learn how to mess around with netCDF using Python, read on. If you just want to see how to fix it, skip ahead to the section \"So why isn't it working?\"" | |
] | |
}, | |
{ | |
"cell_type": "heading", | |
"level": 2, | |
"metadata": {}, | |
"source": [ | |
"Exploring the NetCDF file" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"This section starts with the basics. Open the file, sniff around to see what's in there, and print out some key bits of information. The very first thing to do is to tell Python what libraries we're going to need. " | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"import pyproj\n", | |
"import netCDF4 as nc\n", | |
"import os\n", | |
"import numpy as np\n", | |
"from mpl_toolkits.basemap import Basemap" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 1 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Before opening this notebook, I grabbed an example data file from the National Snow and Ice Data Center. (ftp://sidads.colorado.edu/pub/DATASETS/nsidc0531_MEASURES_nhsnow_weekly100/2003/nhtsw100e2_20030107_20030113.nc) This file is supposed to be in what's known as EASE Grid 2.0 format. Unfortunately, no known GIS tools (commercial or open source) read the file. Our objective here is to come up with a method of converting the file to something that the tools can read.\n", | |
"\n", | |
"So the very first step is to change to the directory containing the file using os.chdir. It works much as you should expect. Give it the full path to where you want to be." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"os.chdir(\"C:\\\\Users\\\\bnordgren\\\\Documents\\\\Orchidee\\\\Snow\")" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 2 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Now it's time to open the file. When we imported the NetCDF library above, we told python to associate it with the abbreviation \"nc\". To open the file, we need to use a function called \"Dataset\" in the netcdf library. All we need to do is tell it what filename we're interested in. This looks like the following:" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"d = nc.Dataset('nhtsw100e2_20030107_20030113.nc')" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 3 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Now we have a variable \"d\" that we can use to access the contents of the netCDF file. The NetCDF library is pretty smart. It doesn't try to load the whole file into memory at this point, as many NetCDF files can be pretty large. You can use this \"d\" variable to look at the descriptive metadata, get a sense for the size of the grids, or eventually, ask for the actual data values. For the moment, let's just do the blindingly simple thing and \"print\" the variable to the terminal." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"print d" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"<type 'netCDF4.Dataset'>\n", | |
"root group (NETCDF4_CLASSIC data model, file format HDF5):\n", | |
" Conventions: CF-1.6\n", | |
" Metadata_Conventions: CF-1.6, Unidata Dataset Discovery v1.0, GDS v2.0\n", | |
" standard_name_vocabulary: CF Standard Name Table (v22, 12 February 2013)\n", | |
" id: nhtsw100e2_20030107_20030113.nc\n", | |
" naming_authority: gov.nasa.eosdis\n", | |
" reference: http://dx.doi.org/10.5067/MEASURES/CRYOSPHERE/nsidc-0531.001\n", | |
" metadata_link: http://nsidc.org/api/metadata?id=nsidc-0531\n", | |
" title: MEaSUREs Northern Hemisphere Terrestrial Snow Cover Extent Weekly 100km EASE-Grid 2.0\n", | |
" product_version: v01r00\n", | |
" summary: This NASA MEaSUREs Earth System Data Record (ESDR) merges daily Northern Hemisphere snow cover extents over land derived from two independently produced sources. Variables include snow cover extent from the weekly NOAA/NCDC Northern Hemisphere Snow Cover Extent Climate Data Record (NH SCE CDR) and a gap-filled snow extent product derived from the Special Sensor Microwave/Imager (SSMI) and Special Sensor Microwave Imager/Sounder (SSMIS). The NSIDC Land-Ocean-Coast-Ice (LOCI) mask derived from BU-MODIS land cover data is consistently applied to each variable. Data are in a Northern Hemisphere equal area projection at 100 km resolution, and are contained in weekly netCDF files spanning from October 4, 1966 to December 31, 2012.\n", | |
" keywords: EARTH SCIENCE > CRYOSPHERE > SNOW/ICE > SNOW COVER, EARTH SCIENCE > TERRESTRIAL HYDROSPHERE > SNOW/ICE > SNOW COVER\n", | |
" keywords_vocabulary: NASA Global Change Master Directory (GCMD) Earth Science Keywords, Version 8.0\n", | |
" platform: NOAA POES (Polar Orbiting Environmental Satellites), DMSP (Defense Meteorological Satellite Program), GOES (Geostationary Operational Environmental Satellite), METEOSAT, GMS (Japan Geostationary Meteorological Satellite), METOP, TERRA > Earth Observing System TERRA (AM-1), AQUA > Earth Observing System AQUA\n", | |
" sensor: VISSR > Visible and Infrared Spin Scan Radiometer, VAS > VISSR Atmospheric Sounder, MODIS > Moderate-Resolution Imaging Spectroradiometer, AMSU-B > Advanced Microwave Sounding Unit-B, AMSR-E > Advanced Microwave Scanning Radiometer-EOS, SSMI > Special Sensor Microwave/Imager, SSMIS > Special Sensor Microwave Imager/Sounder, VIIRS > Visible-Infrared Imager-Radiometer Suite\n", | |
" cdm_data_type: Grid\n", | |
" source: ftp://data.ncdc.noaa.gov/cdr/snowcover/, ftp://sidads.colorado.edu/pub/DATASETS/nsidc0001_polar_stereo_tbs/\n", | |
" date_created: 2014-09-09T16:19:30Z\n", | |
" institution: Center for Environmental Prediction, Rutgers University\n", | |
" geospatial_lat_units: degrees_north\n", | |
" geospatial_lon_units: degrees_east\n", | |
" geospatial_lat_min: 0\n", | |
" geospatial_lat_max: 90\n", | |
" geospatial_lon_min: -180\n", | |
" geospatial_lon_max: 180\n", | |
" spatial_resolution: 100 km\n", | |
" time_coverage_start: 2003-01-07\n", | |
" time_coverage_end: 2003-01-13\n", | |
" license: No restrictions on access or use\n", | |
" dimensions(sizes): time(1), rows(180), cols(180)\n", | |
" variables(dimensions): int32 \u001b[4mtime\u001b[0m(time), float32 \u001b[4mlatitude\u001b[0m(rows,cols), float32 \u001b[4mlongitude\u001b[0m(rows,cols), int8 \u001b[4mmerged_snow_cover_extent\u001b[0m(time,rows,cols), int8 \u001b[4mweekly_climate_data_record_snow_cover_extent\u001b[0m(time,rows,cols), int8 \u001b[4mpassive_microwave_gap_filled_snow_cover_extent\u001b[0m(time,rows,cols), |S1 \u001b[4mcoord_system\u001b[0m()\n", | |
" groups: \n", | |
"\n" | |
] | |
} | |
], | |
"prompt_number": 4 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"This simple command gives us a lot of information. This particular file has a lot of metadata, which is all printed out. Some files will have more and some will have less. For the moment, just notice that the \"Conventions\" attribute specifies \"CF-1.6\". That means that the rest of the attributes should be interpreted using the CF v1.6 specification found on http://cfconventions.org/. The real meat and potatoes of a NetCDF file are the \"dimensions\" and \"variables\". All useful NetCDF files will have dimensions and variables.\n", | |
"\n", | |
"Dimensions have a name and a size. We can see from the above that this file has three dimensions (time, rows, cols). The time dimension has a size of 1, while rows and cols both have a size of 180. Dimensions in and of themselves are not very interesting. They exist mainly to specify the shape of gridded data stored in Variables.\n", | |
"\n", | |
"Variables have a name, a data type, and zero or more dimensions. If a variable has zero dimensions (like \"coord_system\", above), it is a scalar and can only store one value. Otherwise, the dimensions specify the shape of the grid. The time variable has one dimension, also named time. Latitude and longitude have two dimensions (rows and cols), and everything else except \"coord_system\" has three dimensions (time, rows, cols).\n", | |
"\n", | |
"Starting simple, let's look at the \"time\" variable." | |
] | |
}, | |
{ | |
"cell_type": "heading", | |
"level": 2, | |
"metadata": {}, | |
"source": [ | |
"Coordinate Variables" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"print d.variables['time']" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"<type 'netCDF4.Variable'>\n", | |
"int32 time(time)\n", | |
" calendar: gregorian\n", | |
" axis: T\n", | |
" units: days since 1966-10-03\n", | |
" long_name: time\n", | |
" standard_name: time\n", | |
"unlimited dimensions: time\n", | |
"current shape = (1,)\n", | |
"filling on, default _FillValue of -2147483647 used\n", | |
"\n" | |
] | |
} | |
], | |
"prompt_number": 5 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Printing an individual variable gives quite a bit more information about the variable than just printing out an overview of the file. This information tends to be directly related to the variable itself. We can see the variable is a 32 bit integer. Since it has a dimension of time, and the time dimension has a size of 1, we expect that the current_shape would be a single dimension of one value. The rest of the attributes are human friendly: you as a human can pretty much figure it out just by looking.\n", | |
"\n", | |
"Lets check if the contents of the time variable meet our expectations. From the filename, this file contains data for the week of Jan 7-13, 2003. Without being too careful about leap years and such, let's check to see if the time variable contains a number that works out to be about the number of days that would put us in Jan 2003 if we were to start counting on Oct 3, 1966." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"print d.variables['time'][0]" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"13251\n" | |
] | |
} | |
], | |
"prompt_number": 6 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Here for the first time, we've asked for actual data from the file. To get data, you index the variable of interest. In the above, we asked for the first value in the time array (index 0). \n", | |
"\n", | |
"Now we just have to see if 13251 days puts us in 2003." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"full_years = 365.25 * (2002-1966)\n", | |
"oct_to_jan = np.sum([(31-3),30,31]) # rest of oct, nov, dec\n", | |
"print full_years, full_years + oct_to_jan\n", | |
"print d.variables['time'][0] - (full_years + oct_to_jan)" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"13149.0 13238.0\n", | |
"13.0\n" | |
] | |
} | |
], | |
"prompt_number": 7 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Our faith in the ability of computers to count is affirmed. All hail our cyber overlords. The day specified is Jan 13, 2003 plus or minus 1 day for alignment of leap years with our time window.\n", | |
"\n", | |
"The attributes \"units\", \"standard_name\", and \"axis\" are part of the CF 1.6 convention that the file respects. And since this variable is in the file adhering to CF 1.6, the variable adheres to it too. These attributes have special meanings, and you have to read the convention specification to find out what that meaning is.\n", | |
"\n", | |
"One more term to remember is \"coordinate variable\". A one dimensional variable that has the same name as it's dimension is a coordinate variable. Hence, the time variable is the only coordinate variable in this file. Coordinate variables are how you translate from unitless grid indices to real values along the axis. In the above, index 0 for the time dimension stands for Jan 13, 2003. This relationship holds for any variable in this file which uses the time dimension." | |
] | |
}, | |
{ | |
"cell_type": "heading", | |
"level": 2, | |
"metadata": {}, | |
"source": [ | |
"Auxiliary Coordinate Variables" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Sometimes, the grid cells are not evenly spaced. The latitude and longitude variables for this EASE Grid 2.0 are examples of this. The data are spaced at even 100km intervals in the geospatial projection, which does not translate to nicely gridded lat/lon data. Auxiliary coordinate variables are 2D variables which can be used to lookup the latitude and longitude of any row/col pair." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"print d.variables['latitude']" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"<type 'netCDF4.Variable'>\n", | |
"float32 latitude(rows, cols)\n", | |
" _FillValue: -999.0\n", | |
" long_name: latitude of cell center in EASE-Grid-2.0\n", | |
" units: degrees_north\n", | |
" valid_range: [-90. 90.]\n", | |
" standard_name: latitude\n", | |
"unlimited dimensions: \n", | |
"current shape = (180, 180)\n", | |
"filling on\n" | |
] | |
} | |
], | |
"prompt_number": 8 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"This variable stores the latitude of each grid cell. The fact that \"units\" is \"degrees_north\" and/or the value \"latitude\" for the standard_name attribute may be used to determine that this is an auxiliary coordinate variable, and it contains the latitude values. The name of the variable is not special. It could be called 'i_do_not_contain_latitudes', but if one or more of the special attributes contained the magic latitude-indicating values, it would still be interpreted as latitudes.\n", | |
"\n", | |
"Let's see what's inside." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"print d.variables['latitude'][0,0]" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"--\n" | |
] | |
} | |
], | |
"prompt_number": 9 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Because this is a 2D variable, we had to provide a 2D index. The -- means that the latitude value at index [0,0] is nodata. The EASE Grid is a polar projection which looks more or less like a circle. The corners don't have data, so we really should have expected this. While we're here, let's load the whole latitude array into memory because it's small. " | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"lats = d.variables['latitude'][:]\n", | |
"print lats.shape" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"(180L, 180L)\n" | |
] | |
} | |
], | |
"prompt_number": 10 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"This gives us a new variable called \"lats\". It has a copy of the values that were in the file. All 180x180 of them. Changing the values in the \"lats\" variable does not change the values in the file itself. We used a technique called \"slicing\" (e.g., a colon instead of an index) to load in the values we wanted. Normally, you say [min_index:max_index] if you want to specify a subset. The default value of min_index is the first array value, and the default value of max_index is the last array value. In this case, we wanted everything, so we left out both the min and the max, allowing both to take on their default values. \n", | |
"\n", | |
"There's actually a little bit more than this going on since we're talking about a 2D array, but that is out of scope for this notebook. Slicing is a powerful python technique which you need to spend some brain cells on. Go forth and learn.\n" | |
] | |
}, | |
{ | |
"cell_type": "heading", | |
"level": 2, | |
"metadata": {}, | |
"source": [ | |
"Masked Arrays" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"In this case, the python variable \"lats\" is what's known as a masked array. The netcdf library automatically masked out the nodata values for us, which is why we just saw a '--' above when we printed out the latitude value of the corner. Masked arrays are a combination of a boolean (True/False) mask, and the values. If the mask flag for a cell is true, the cell is considered to contain \"nodata\". So let's revisit that masked out corner." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"print \"The mask is: \", lats.mask[0,0]\n", | |
"print \"The data value is: \", lats.data[0,0]" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"The mask is: True\n", | |
"The data value is: -999.0\n" | |
] | |
} | |
], | |
"prompt_number": 11 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"If you scroll back up, the fill value in the netCDF file was -999. So you can see that python is making things a little nicer for you in that you don't have to always check to see if the grid cell contains the fill value/nodata value.\n", | |
"\n", | |
"Let's set the corner value to something and then mask it back out." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"lats[0,0] = 42.\n", | |
"print lats[0,0]\n", | |
"print \"The mask is: \", lats.mask[0,0]\n", | |
"print \"The data value is: \", lats.data[0,0]" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"42.0\n", | |
"The mask is: False\n", | |
"The data value is: 42.0\n" | |
] | |
} | |
], | |
"prompt_number": 12 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"# ... and set it back\n", | |
"lats[0,0] = np.ma.masked\n", | |
"print lats[0,0]\n", | |
"print \"The mask is: \", lats.mask[0,0]\n", | |
"print \"The data value is: \", lats.data[0,0]" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"--\n", | |
"The mask is: True\n", | |
"The data value is: 42.0\n" | |
] | |
} | |
], | |
"prompt_number": 13 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Whoops! It masked the value out for us all right, but it didn't change the value back to -999. Keep this in mind. Depending on your situation, it might be important. You can explicitly set the value by using the \"data\" attribute. " | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"lats.data[0,0] = -999.0\n", | |
"print lats[0,0]\n", | |
"print \"The mask is: \", lats.mask[0,0]\n", | |
"print \"The data value is: \", lats.data[0,0]" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"--\n", | |
"The mask is: True\n", | |
"The data value is: -999.0\n" | |
] | |
} | |
], | |
"prompt_number": 14 | |
}, | |
{ | |
"cell_type": "heading", | |
"level": 2, | |
"metadata": {}, | |
"source": [ | |
"Variables...finally" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"So far we have explored a whole lot of nothing useful. We know where each grid cell is, but (in this case) not whether they are covered in snow. We want to know whether they are covered in snow, so we use the non-coordinate-variable, non-auxiliary-coordinate-variable called \"merged_snow_cover_extent\". We will create a python variable as a shorthand to the data in the file, but we're not going to give a slicing expression. Without the slicing expression, python won't copy the data into memory from the file." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"snow = d.variables['merged_snow_cover_extent']\n", | |
"print snow" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"<type 'netCDF4.Variable'>\n", | |
"int8 merged_snow_cover_extent(time, rows, cols)\n", | |
" flag_meanings: cdr_microwave_report_snow cdr_only_reports_snow microwave_only_reports_snow snow_free_land permanent_ice ocean\n", | |
" flag_values: [10 11 12 20 30 40]\n", | |
" _FillValue: -99\n", | |
" comment: 10: Snow cover reported by weekly_cdr, passive_microwave, 11: Snow cover reported by weekly_cdr only, 12: Snow cover reported by passive_microwave only, 20: Snow free land, 30: Permanent ice covered land, 40: Ocean\n", | |
" valid_range: [10 40]\n", | |
" coordinates: longitude latitude time\n", | |
" long_name: Merged Snow Cover Extent\n", | |
" grid_mapping: coord_system\n", | |
"unlimited dimensions: time\n", | |
"current shape = (1, 180, 180)\n", | |
"filling on\n" | |
] | |
} | |
], | |
"prompt_number": 15 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"See how the above behaved as if we typed \"print d.variables['merged_snow_cover_extent']\"? Now we can be lazy and just type \"print snow\" instead. You can still specify an index and get the data, but this time you are not working with a copy that resides in memory. Remember we did not explicitly ask python to copy all the data out of the file into a variable. Ergo, if you try and assign different values to cells in the \"snow\" variable, it will try to change the values in the file. In this case, we opened the file read-only (which is the default), so changing the value would fail." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"print snow[0,0,0]" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"--\n" | |
] | |
} | |
], | |
"prompt_number": 16 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"??! A 3D variable? Tricky. You can handle this by now, and you should be able to tell what order the coordinates go in by looking above.\n", | |
"\n", | |
"So what happens if we try to change the values in the snow variable? " | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"snow[0,0,0] = 42" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"ename": "RuntimeError", | |
"evalue": "NetCDF: HDF error", | |
"output_type": "pyerr", | |
"traceback": [ | |
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mRuntimeError\u001b[0m Traceback (most recent call last)", | |
"\u001b[1;32m<ipython-input-17-9e133be73ced>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0msnow\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m42\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", | |
"\u001b[1;32mC:\\Users\\bnordgren\\AppData\\Local\\Enthought\\Canopy\\User\\lib\\site-packages\\netCDF4.pyd\u001b[0m in \u001b[0;36mnetCDF4.Variable.__setitem__ (netCDF4.c:40585)\u001b[1;34m()\u001b[0m\n", | |
"\u001b[1;32mC:\\Users\\bnordgren\\AppData\\Local\\Enthought\\Canopy\\User\\lib\\site-packages\\netCDF4.pyd\u001b[0m in \u001b[0;36mnetCDF4.Variable._put (netCDF4.c:41927)\u001b[1;34m()\u001b[0m\n", | |
"\u001b[1;31mRuntimeError\u001b[0m: NetCDF: HDF error" | |
] | |
} | |
], | |
"prompt_number": 17 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"This translates roughly as \"Thou shalt not try to write to a file you opened as read only.\" Fear not the crappy error message. Just understand that when we created the \"snow\" variable in python, we essentially told python to treat the snow variable as a handle to the data on disk. Trying to set values in the snow variable attempts to write the new values to the file...which it can't do if the file has been opened for reading only.\n", | |
"\n", | |
"Now, one final thing: the coordinates attribute lists the (auxiliary) coordinate variables which contain the location information in each cell. The CF 1.6 standard requires that the lat/lon of each cell be provided if the lat/lons are not evenly spaced out. " | |
] | |
}, | |
{ | |
"cell_type": "heading", | |
"level": 2, | |
"metadata": {}, | |
"source": [ | |
"Projection information" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"If you look above where we printed out the snow variable, you should see a \"grid_mapping\" attribute. This is another one of those attributes specified by CF-1.6. It contains the name of a dummy variable which describes the projection in which the data are presented. It's a dummy variable because it contains no data values, it serves only as a container for a bunch of descriptive attributes....all of which are described by CF-1.6. The snow variable declares that its projection information is contained in the \"coord_system\" dummy variable, so let's take a look. " | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"coords = d.variables['coord_system']\n", | |
"print coords" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"<type 'netCDF4.Variable'>\n", | |
"|S1 coord_system()\n", | |
" comment: EASE-Grid-2.0 projection definition documention: http://nsidc.org/data/ease/ease_grid2.html, NSIDC mapx grid parameter definition file: EASE2_N100km.gpd, grid_id: EASE2_N100km\n", | |
" grid_mapping_name: lambert_azimuthal_equal_area\n", | |
" longitude_of_projection_origin: 0.0\n", | |
" latitude_of_projection_origin: 90.0\n", | |
" false_easting: 0.0\n", | |
" false_northing: 0.0\n", | |
" scale_factor_at_projection_origin: 25\n", | |
" semimajor_axis: 6.37814e+06\n", | |
" semiminor_axis: 6.35675e+06\n", | |
" inverse_flattening: 298.257\n", | |
"unlimited dimensions: \n", | |
"current shape = ()\n", | |
"filling on, default _FillValue of \u0000 used\n", | |
"\n" | |
] | |
} | |
], | |
"prompt_number": 18 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"The only parameter that a variable pointed to by a \"grid_mapping\" attribute must have is \"grid_mapping_name\". These names select the projection and are defined in http://cfconventions.org/Data/cf-conventions/cf-conventions-1.6/build/cf-conventions.html#appendix-grid-mappings. The other attributes define the parameters of the projection, which of course may vary from projection to projection. The parameters of the sphereoid may appear regardless of the projection selected. \n", | |
"\n", | |
"This information gives enough information to calculate projected coordinates given lat/lon, or vice-versa. You will want to use pre-written library code for that." | |
] | |
}, | |
{ | |
"cell_type": "heading", | |
"level": 2, | |
"metadata": {}, | |
"source": [ | |
"Summary" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Here is what we've discovered so far: \n", | |
"\n", | |
"* Grid cells are regularly spaced in projected coordinates.\n", | |
"* Latitude/Longitude do not comprise a regular grid.\n", | |
"* There are no coordinate variables (1d) to describe rows and cols.\n", | |
"* Auxiliary coordinate variables (2d) are identified in the \"coordinates\" attribute, and tie each grid cell to a set of lat/lon coordinates.\n", | |
"* The names of the auxiliary coordinate variables are not special: latitude and longitude are identified by matching the \"units\" to accepted values.\n", | |
"* Variables containing geolocated data refer to their spatial reference system by placing the name of a variable in the \"grid_mapping\" attribute. This variable has a series of attributes which describe the spatial reference system.\n" | |
] | |
}, | |
{ | |
"cell_type": "heading", | |
"level": 1, | |
"metadata": {}, | |
"source": [ | |
"So why isn't it working?" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"The big thing that is missing is a simple translation between rows/cols and locations in the projected coordinates. Presumably, once we provide this, other programs will start to grok the NetCDF file, or we can export to a different format like geotiff. Specifically, the most likely reason that the projection is not being read is that the file technically violates the CF 1.6 specification. When the projection is Lambert Azimuthal Equal Area, the spec says \"The x (abscissa) and y (ordinate) rectangular coordinates are identified by the standard_name attribute values projection_x_coordinate and projection_y_coordinate respectively.\" \n", | |
"\n", | |
"So, software designed to the specification is expecting coordinate variables for rows and cols, and will be looking for these variables to be tagged as \"projection_x_coordinate\" and \"projection_y_coordinate\". As noted above, these variables are entirely missing. It is possible that if they are provided, the software may start working." | |
] | |
}, | |
{ | |
"cell_type": "heading", | |
"level": 2, | |
"metadata": {}, | |
"source": [ | |
"Calculating projected coordinates" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"We are going to assume that the relationship between the grid cell indices and the projected coordinates is linear, that the axes are orthogonal, and the (i,j) grid cell axes are not rotated with respect to the axes of the projected coordinate system. We will only generalize to this more complex scenario if our assumptions turn out to be false.\n", | |
"\n", | |
"We need to find two points which have lat/lon data, these two points should be as far apart as possible, and the points should not be in the same column or row." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"unmasked_ij = np.where(np.logical_not(lats.mask))\n", | |
"indices = zip(*unmasked_ij)\n", | |
"first = indices[0]\n", | |
"last = indices[-1]\n", | |
"print \"First: \", first\n", | |
"print \"Last: \", last" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"First: (0, 80)\n", | |
"Last: (179, 99)\n" | |
] | |
} | |
], | |
"prompt_number": 19 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"lons = d.variables['longitude']" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 20 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"print \"First: \", lats[first], lons[first]\n", | |
"print \"Last: \", lats[last], lons[last]" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"First: 0.123689 -173.941\n", | |
"Last: 0.123689 6.05899\n" | |
] | |
} | |
], | |
"prompt_number": 21 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"If we were trying to get a simple linear fit from rows/cols to lat/lon, this would be a problem. However, we know this is not possible so let's take these lat/lons and calculate the projected coordinates. We will be using Proj.4. (http://trac.osgeo.org/proj/) The first step is to get the parameter values for the projection from the coord_system variable. \n", | |
"\n", | |
"We have not yet done this. We've printed the values and looked at them, but we are now moving into the realm of having the computer fetch specific values and use them automatically. Getting the value of an attribute in the netCDF file is accomplished exactly as if it were an attribute of a python variable: " | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"#remember that coords is the python variable referring to the \"coord_system\" netcdf variable.\n", | |
"lat_0 = coords.latitude_of_projection_origin \n", | |
"lon_0 = coords.longitude_of_projection_origin\n", | |
"false_e = coords.false_easting\n", | |
"false_n = coords.false_northing\n", | |
"s_major = coords.semimajor_axis \n", | |
"s_minor = coords.semiminor_axis" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 22 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Now we make a projection object. It's just a handy way to carry around a description of which projection equations to use, and what the parameters are." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"the_proj = pyproj.Proj(proj=\"laea\", lat_0=lat_0, lon_0=lon_0, x_0=false_e, y_0=false_n, a=s_major, b=s_minor)\n", | |
"geo = pyproj.Proj(proj=\"latlong\", a=s_major, b=s_minor)" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 23 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Now we can compute the projected coordinates for the lat/lon coordinates of the two points." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"x1,y1 = pyproj.transform(geo,the_proj, lons[first], lats[first])\n", | |
"x2,y2 = pyproj.transform(geo,the_proj, lons[last], lats[last])" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 24 | |
}, | |
{ | |
"cell_type": "heading", | |
"level": 2, | |
"metadata": {}, | |
"source": [ | |
"Construct the linear fits" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"So, it's now a matter of making a linear fit for x and a linear fit for y." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"# it's (rows,cols), so index[1]==column \n", | |
"m_x = (x2-x1)/(last[1]-first[1])\n", | |
"b_x = x1 - (m_x*first[1])\n", | |
"print x2, last[1]*m_x+b_x" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"950000.167584 950000.167584\n" | |
] | |
} | |
], | |
"prompt_number": 25 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Woo hoo the fit for x seems to work. Now on to y." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"# it's (rows,cols) so index[0]==rows\n", | |
"m_y = (y2-y1)/(last[0]-first[0])\n", | |
"b_y = y1 -(m_y*first[0])\n", | |
"print y2, last[0]*m_y+b_y" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"-8950001.73729 -8950001.73729\n" | |
] | |
} | |
], | |
"prompt_number": 26 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Just to doublecheck, we know that the data set is on a 100km grid, so m_x and m_y should both be 100000." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"print m_x, m_y" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"100000.041162 -100000.019146\n" | |
] | |
} | |
], | |
"prompt_number": 27 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Close enough for me." | |
] | |
}, | |
{ | |
"cell_type": "heading", | |
"level": 2, | |
"metadata": {}, | |
"source": [ | |
"Create the coordinate variables..." | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Python, specifically the \"numpy\" library, makes it easy to write equations which manipulate entire arrays of data. Usually, the equation is applied to each element of the input array and produces a corresponding output array. Numpy is well worth learning.\n", | |
"\n", | |
"In the following, the function \"np.arange\" generates an array containing the numbers 0, 1, 2, ... {n-1}, where n is either the number of rows or cols. This entire array is transformed with the linear fit we produced above, yielding the projected x/y coordinates for each row and column." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"cv_rows = m_x * np.arange(len(d.dimensions['rows'])) + b_x\n", | |
"cv_cols = m_y * np.arange(len(d.dimensions['cols'])) + b_y" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 28 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Now we need to store these new coordinate variables in the file and add the expected attributes. To do so, we need to close the netcdf file and open it again for writing. Make sure we open for read/write! Opening just for write will try to delete the existing file so you have a nice clean slate." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"# first, close the current file\n", | |
"d.close()\n", | |
"# now open again for writing\n", | |
"d = nc.Dataset('nhtsw100e2_20030107_20030113.nc', 'r+')" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 29 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Creating variables in netcdf is pretty simple. You just need to specify the variable name, the data type, and a list of dimensions as strings. Coordinate variables are super simple because the name of the variable is the same as the name of the only dimension they have." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"# make the rows coordinate variable\n", | |
"# note the variable name is the same as the dimension name\n", | |
"rows_var = d.createVariable('rows',np.float32, ('rows',))\n", | |
"\n", | |
"# give it the expected attributes\n", | |
"rows_var.standard_name = 'projection_y_coordinate'\n", | |
"rows_var.axis = 'Y'\n", | |
"rows_var.units = 'meters'\n", | |
"\n", | |
"# write the values to the variable\n", | |
"rows_var[:] = cv_rows" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 30 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"# make the cols coordinate variable\n", | |
"# note the variable name is the same as the dimension name\n", | |
"cols_var = d.createVariable('cols',np.float32, ('cols',))\n", | |
"\n", | |
"# give it the expected attributes\n", | |
"cols_var.standard_name = 'projection_x_coordinate'\n", | |
"cols_var.axis = 'X'\n", | |
"cols_var.units = 'meters'\n", | |
"\n", | |
"# write the values to the variable\n", | |
"cols_var[:] = cv_cols" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 31 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"#close the file to make sure everything is written\n", | |
"d.close()" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 32 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Poof. GDAL works now: \n", | |
"\n", | |
" [fedora@lugosi ~]$ gdalinfo NETCDF:nhtsw100e2_20030107_20030113.nc:merged_snow_cover_extent\n", | |
" Driver: netCDF/Network Common Data Format\n", | |
" Files: nhtsw100e2_20030107_20030113.nc\n", | |
" Size is 180, 180\n", | |
" Coordinate System is:\n", | |
" PROJCS[\"LAEA (WGS84) \",\n", | |
" GEOGCS[\"WGS 84\",\n", | |
" DATUM[\"WGS_1984\",\n", | |
" SPHEROID[\"WGS 84\",6378137,298.257223563,\n", | |
" AUTHORITY[\"EPSG\",\"7030\"]],\n", | |
" TOWGS84[0,0,0,0,0,0,0],\n", | |
" AUTHORITY[\"EPSG\",\"6326\"]],\n", | |
" PRIMEM[\"Greenwich\",0,\n", | |
" AUTHORITY[\"EPSG\",\"8901\"]],\n", | |
" UNIT[\"degree\",0.0174532925199433,\n", | |
" AUTHORITY[\"EPSG\",\"9108\"]],\n", | |
" AUTHORITY[\"EPSG\",\"4326\"]],\n", | |
" PROJECTION[\"Lambert_Azimuthal_Equal_Area\"],\n", | |
" PARAMETER[\"latitude_of_center\",90],\n", | |
" PARAMETER[\"longitude_of_center\",0],\n", | |
" PARAMETER[\"false_easting\",0],\n", | |
" PARAMETER[\"false_northing\",0]]\n", | |
" \n" | |
] | |
}, | |
{ | |
"cell_type": "heading", | |
"level": 2, | |
"metadata": {}, | |
"source": [ | |
"Geotiffs via gdal_translate" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Because gdal is now detecting the spatial reference system, one can use it to convert the variables to geotiff. But we are not quite done. Remember the slope of the line fit between \"cols\" and the projected y coordinate? It was negative, right? " | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"print m_y" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"-100000.019146\n" | |
] | |
} | |
], | |
"prompt_number": 33 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Yup. Negative. That means row zero translates to the biggest projected Y value and row 179 translates to the smallest projected Y value. So row 0 starts at the top and works its way down. The netcdf driver for gdal tries to detect whether the rows are top-down or bottom up, but sometimes it doesn't. Our data set is one of the lucky winners.\n", | |
"\n", | |
"Configuration options from http://www.gdal.org/frmt_netcdf.html: \n", | |
"\n", | |
"> **GDAL_NETCDF_BOTTOMUP**=[YES/NO] : Set the y-axis order for import, overriding the order detected by the driver. This option is usually not needed unless a specific dataset is causing problems (which should be reported in GDAL trac).\n", | |
"\n", | |
"In this case, gdal 1.11.1 autodetects a bottom-up dataset when it should be top down, so we must override. The working conversion command for gdal_translate is as follows: \n", | |
" \n", | |
" gdal_translate -of GTiff -b 1 --config GDAL_NETCDF_BOTTOMUP NO \\\n", | |
" NETCDF:nhtsw100e2_20030107_20030113:merged_snow_cover_extent snowcover.tif" | |
] | |
}, | |
{ | |
"cell_type": "heading", | |
"level": 1, | |
"metadata": {}, | |
"source": [ | |
"Looking at the hard won data" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Here we view the data in the file. First, we don't do anything special, just dump it to the screen exactly as it exists in the file." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"d = nc.Dataset('nhtsw100e2_20030107_20030113.nc')\n", | |
"snow = d.variables['merged_snow_cover_extent'][:]\n", | |
"lats = d.variables['latitude'][:]\n", | |
"lons = d.variables['longitude'][:]\n", | |
"imshow(snow[0,...])" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"metadata": {}, | |
"output_type": "pyout", | |
"prompt_number": 34, | |
"text": [ | |
"<matplotlib.image.AxesImage at 0xa089ac8>" | |
] | |
}, | |
{ | |
"metadata": {}, | |
"output_type": "display_data", | |
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zkZlN5M29nsNLH+x0v4IrMxiQ/4NDRaAPCefChOlseupfzG46ydiKChLinEcm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\nFBQUgP8PQuZKAFnA+UcAAAAASUVORK5CYII=\n", | |
"text": [ | |
"<matplotlib.figure.Figure at 0xa066710>" | |
] | |
} | |
], | |
"prompt_number": 34 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"That's great, but we don't really have any indication that the georeferencing worked. The axes are strictly (i,j) without any physical meaning. In fact, we could have done this right off the bat, without fixing anything. What we really want to do is draw a quick map with crude coastlines and some lat/lon lines." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"def init_basemap() : \n", | |
"\n", | |
" m = Basemap(width=100000*180, height=100000*180,\n", | |
" #llcrnrx=cv_cols[0], llcrnry=cv_rows[-1], urcrnrx=cv_cols[-1], urcrnry=cv_rows[0],\n", | |
" resolution='l', projection='laea',\n", | |
" lon_0=lon_0, lat_0=lat_0)\n", | |
" m.drawcoastlines()\n", | |
" m.drawcountries()\n", | |
" m.drawmeridians(np.arange(-180.,180.,20.),labels=[False,False,False,True])\n", | |
" m.drawparallels(np.arange(10.,80.,20.), labels=[True,False,False,False])\n", | |
" return m" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 35 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"The basic strategy is to use the map template above by calling \"init_basemap\", then draw the data on it. The data are drawn using \"filled contours\" between the data points. This may or may not be what we want." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"figsize(12,10)\n", | |
"m = init_basemap()\n", | |
"c = m.contourf(lons[:], lats[:],\n", | |
" snow[0,...], 20, latlon=True)\n", | |
"cb = m.colorbar(c)\n", | |
"title(\"Snow cover, 13 Jan 2003\")\n" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"metadata": {}, | |
"output_type": "pyout", | |
"prompt_number": 36, | |
"text": [ | |
"<matplotlib.text.Text at 0xa7809e8>" | |
] | |
}, | |
{ | |
"metadata": {}, | |
"output_type": "display_data", | |
"png": 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YHqFyMB2BLf9+s2bNGpuP16BBg0Q1EK5VqxaHDh2iSRPVabBy5cqxI89+/vln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xyJw3HIxfkqDu9eapwQA01D7vokRyHJ5P8gyfI5Vceai1vIqv3/SEl0L4yEsKwFS9+WnyIFEqeA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VUBhiOk20p0C1v+f8oMe0ccYdk7HWBacnnR+yRuY38fSd28RaXxcpNNz+91ud3/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9CiGWq7NoP9nIWvj9fDRs2JDly5dn+gHUq1eP8uXL07lzZ158UWwXOdwUXQcpz7Zjxw6I\nvgRx1zF/MBTtyhW0hx5GM/k/r9E0jY8mmqgRIGTFGwKRfLysJDR3Mx5Hwt9H47OIUoZwA0nSKMTd\nUdM3q5CMxMsF3+4D8YBXkWi2j4F1tlexb5DQskbAVQs8tWMAy688CSdPYvR5i8BOsT6tSb6xB/9c\nqBYkKKAdMmT56stm4J3wHUYyf08h9ppDiGRMenuK6ogsdiNSrInlI6ArlChn5ug+/zrprcDjjeDx\nxjayx16ckyy8XWc76fO2jvHLzxAcjHngIEwvdHJx4erVqmOdOQP188aUZerqVbRA745FTdfRa9ZC\ny+e9tpExbw7GlK8xT/gE6/ChcPkyWng4pgHved3uVsP4Zp5HKRaApReEV2d2mcoLSJJOc7LW8qYj\nk+6qiNf/5ZdfZvPmzVm4x2xkNvz2VJYtW5Y1a9ZkyUFs376d3Llzp1TYiIqKcvn9zJkzbNu2jYCA\nAHLkyIGph6MepVauPGrP7nTv02zWqBwAF5J9x+hVQeRUeyLz+Wy4ojBCio8gHVpmzTJPIt69t8l2\nIcQhKQZ3Oult+W5Znh0lrtKfp5/AFKATe+EGZ2o8za8l3pT6vnXqYqrhTqsuI4hHnkArEndXBrF1\nmGzLJyIh538hduIZeKbNCqk/sByxafyAPIXVAeeMy4WIpt9gxMl+DZn2/Gbbdg0wCCGZUxHFyKup\n9mUgVsGZyLTGJh9uI3uBkbEkfxTN2r89W/isiID2HiDuPtDHziEqnWTPDm/rqOvXMVatxDRilMd1\nTAMHoXbuxDJ6JKa+/VBHj6B5kMPyF0opjHlz0R9vBkphTJmCqWcv1LEotIjKacSfbydUUhLqyhWP\nUizRlxRfnsr8Kk4G0ie05Nb1kTltn9bvvENYWBj169dn6dKlhITcTZHF/034TfgKFy7M0aNHs+Qg\ncuXKlVKFA2DPnj2AlGCbOXMmTZo0ISoqinfesZVYd5ZoyZcPLvzj136U1ZpiCbx2TfFVvJA9X4Wa\ndIR0nEEibDKr/uC/BRpi6r+EI2j4ZjufWIRcV8qEtv4NWIakKFT2teJtxs9fHSWyzHUeeTw3MZ0H\nsSzGUT/Wfh/Vpl8w5s/FPGW6Hy1Wx7tL9DqSXJGIiMLYw0F2IXToXcQ9+wgUywMnPkPskanzGBXi\nVi2P5IHPRHKR70ecpeMREqnZ1imJWObyIHUs+uAQePoYiSm0J5gEIKqGFxHRmtO2/x9Chmr38rhb\nV0nAybfI+2W3lylE7mYFQhU/GAEje/7Mn93q2oLDItK4aJXVinXAu+i1a6M1b+FXgoQ6fQrr7FkQ\nE4OpX3+vKg2apkGpkujWx7BOn4re4uaCD5RScPky6vQpUAp14wbq1N9YJ32Kljcv+ptv3VT7mQ1j\n/jyIjcUyxokUK4UWHMyxwn/z1ioLzxSCBzJZ1f8YMs3p7mvFLECpUqUA+PTTTzl69CgTJky4qaIJ\n2ch6+E347Na15ORkAjzEx90McubMydGjRylVqhQnT55k1qxZNGrUiOPHjxMeHu4ge0BiqNNMIjQE\n08WLXtu2LlqAOnBAZp3lK7D0nXepEN6Xto1Hsn+tf8eXC9E/y0V2LJknhCPhQNsQq0SY99W9YoOt\nPU9RUf8lxCL1ILylBNxuTAL2BcMqi5VhL52xLe1I/S3vs6Wsq4h6+i18dtKX2nXbDmE2/0MSHez4\nFSFVw5HIwtxQfC3UbQWV3oflXyJEUUOsclURUtYKSY84hVj47EkAjWwfhYO2XkVMaG0RYngBB4l0\nzji5ilR5fd92rCUQoufdFq6sVkzjJ1Dv0EQOzTvOtGtyROcQ22IY0he1rwaHBgzgaEykvHx2Z0dk\nKqudpmFM+QpjyldoTZsTMDdNVkwKjAP7UWtWQ6FC6E2aolWt6pckl5YvP6pcOYwunTHt+MPn+i7n\nqxTExUFcHNahH0C5crBvL6YvvsJYuADjt23oTZujP97Uq0bf7YCKjkadPoV57HjX5UpBfDwFEuby\ncz/Frgfhp0wkfKcRW3Y3XytmItxl5j744INYLBb69+/Pxx9/TEhICF26dLmFR5UNf+GXWy3C3QAA\nIABJREFULIsdK1euJGfOnNSvXz/LDqhKlSrs3r2bMmXKsGTJEsqX953PFDB9Jugm9Jat3P5uGTUC\n0zvvgq6j/tiF2rcXLbwA9f+Zyrq+P7Eb/2swrkGGFn8Cqf+r2IQkGGREPsRAgv4bIdm/2dY9SYQ4\nwp2bsJEaMTh0+gCevDaFlUkO1UZj/TqPFr6kGTaZELdlvpxJ3z9Itu27pH1KfkPcrDuB3VD/Y+hT\nk8Bayo3QcHvEWV7aafvRiLiJvaRaAqLbl4AU9TuFzJV3I9SrPhK/97xtfWdiegkRbO6GXxGY5SOg\ni0L/uxf6Ey3Rihdn0j29uPjEUr7/1cHn7HisTQi/TvaPRSQ/2wa16RdM4ydieq6923WU1UpyQbE4\n6mM+Qn3/HeaFS9By+6fsZixdggovAJt/RX+6NZrNCuQJ6vp1jMULUVFHISAQkpMh8QbKUGgVKsCl\nS+iPNUSvcufm51uGDcH0xpseYxHLz3uDohtm80OcYoqf2Q7bESmUrz38rhDr3mW8quhkOnxJsURH\nR5OYmEifPn0YMGBAphZs8BfZsiyeka6pUpMmTViwYEGWHMilS5eYP38+Y8eOBeDw4cN+ZwXrTz6N\nOnsGy4B3UXFxaVcwDMko0zRRP9d1jLWr2Vh1MLnvzc2Rj6Br4bSbuUMJoACieZUN96iDWKXmZmDb\nOKQiQy6yyR5Ix34d13oPdzpCkYHKLolcoWdXWoYuS/ldq1MX04RP3W4b2MmWNVo+ws2vdiKlEJvi\nW7g+JYmI47sSQsisUOFJeK8xgbWkLwmMjCXws1gCo2Mxb4qDKjkgfzeYG4IIOk9ArIKV4YEYKPI3\n8CViW0tCCswNAN5B0ok0pNpGXds6qRGOBI1Ms62fCuUjXD9dwZRvJuQIQq9WDS08nNdvTOfYC1WJ\nsgVw2utlTNlSggvfr3N7Hd0h4NsFBJ69kIbslQk9xLTQl/go7jm6rXOSec6VGy2yit9kTxkGxu+/\nY6pVGy2iMuTJg4pNHbsoMP48gGXUCIzPP0N/pBbmD4Zh6tsPvVlz9BatMPV7F71SBOY+fe9osmds\n3YJWvoLXxJOzi3bwfCf/UzUOImTPWx3cVUiZw1tJ9vxBWFgYhQsXZvDgwRQuXJgGDRqQlJQ9Wt4p\nSBfhM5lM5MrljwSo/7BarUyePBmr1cqhQ4do2LBhygNSsaL/ksem7q9i6tIN4//snXdYFFcbxX+z\nuxRRUdbeOzaMvfcWjQZjiR0bGqMmlhgb9l6jMWqiRiXWWKLRiL1HLJ9GY++9IAoKiNKWnZnvj0tn\ngaWj4TzPPsrunZm7ZWbOfcs5G9fHJoper1CVyO45ba8+6BYvRVOlKlU3DmXULxBcwbzjlEZ0xJ0w\ne2b/TZRGFCi7IqRkzcFrRDVUDTLrJMMRjOj/zFhJLPMQ3s2nbIPtV5wiSJ/qfhJ5xNA4t4uf9IFQ\nZSyIiLeH4wUwGNrchFaHoW9RGDEKJgzEsmr0/vGVWfvQ+qucGBvWg0v/gq8vzf+qDDoXxKc9E8pW\ngnzrofoDRONFM0SNXokYc6mCUD+7h/gFm8IniLRzDPITRvCiPiz7+iNv2gAhBpTz5wCQZ8/EdZWO\nHqtqsLZ1pG/xD6WXm7RLSwzs9XdwxZmTzXYws+J+7s46z9ZvYf4IUL4djFQpru8gNpS/dqL5oj0A\nmlatUfbsRlm5AggjgxcvIv+wAOOMaajXrqEdMRLtmHFIZcqghoTAs2comzYg1auPRp8LTfXY1mQZ\nCWpwMMruv9AkIA2jhBoJCDRvn6GImk2IW2/zDaL9KCO7X5QvXx69Xs/SpUtxd3dn2LBhCW+UiVRH\novWELSwsMBqN6Mwo+k0Ie/fupVy5cnh4eGBhYcHUqVMjjrF48WJGjBhBUFAQWcIElROCVKoUnLRE\n+X0T2p6Rpueaz9qiHtiP1KZtrG3yNquITXmJ1Q9Vs82mWyCiLvcRxCYTpqEFmiMSWWF9g3HiPuJ2\nOIbMyF5UXEWozX2on0l7YN57CKgPw5Y4gfNGdjdslWANX4QI8+pPTKR37yPOwCDEr+YJ5H4IVZeB\n1QqkJ2ew+D12UX+4AG5ZGwMvDaK9oyLCl/notrB6t0614dBGGNgOrXcBKGiBbKOBzZ9g2mu2DKJH\nvSwi+R4XAXNFuA+HIbwbt2/07ljln3/QFCsGgYFIFSoKgfldfyKVLcfKPgdZCSzaU4w1n23lnl/y\n5HvDyV7Br6+z+bigzzY3gBuRCe4K/6zg9pedE+yIVRUF5c8/keztkXLkRLK3R9PXGeW0O6FfdoDK\nVdBWq45m8BAkE0EDedi3aLp1R7fk52S9p7SE/NOPaL/5NsH6xrIu7Rje4weKlol3GCqiNcgDEaOO\n69s9gLiWZmTCByK16uDgQJkyZShSpAhTpkyhUqVKfPllxtFO/K8hUTV8AFevXuXMmTMJiiPHh9u3\nb/P06VM8PDyoVKkSNWrEDkzLsoxOp6NPnz6sXbs23v1FtVoDkH9ciFS3Hpo6ogbHOGYU2pmzTRp6\nO+rdGHy6L12bGpkcar7O2V2EYEMLM8f/l/EccSusi+hpjIlgRM/iO8StMxOROIn4THKn90SSAT9g\nugTvVJikjmfOH9XN6tKNu57vLaLC8wSwH2gOJZpDnaowACxKeSBlj/1Lc9S74So7s6y8gQn3YCTi\nproHIcDS1RqGvK5A67KeGLz9GbyhBq8sC/PH3y3hgDPcNqVi7IuwfKsa9v9OQAMoH2OR6rsKlPci\nfazJFkH2lBvXUfftBQsLPgvZTO7iNmTLZcXxVY+wLFGQS6vCqvasrSFvXiz+PoVkm4OUQDgBXmBv\nYM9D+CbG6xKwvT4EfdqQM22XoL5+g1SjhkmCo+z+C/LmRapSFWXbFpT795BCDFC6FGi0aLt0M/md\nqM+fIa9aiXbkKLDNkWJ+7akN5chhCHiP5osOCY5dKjkxpoAbU0JMX//C8Q7xmyyAqIE1lUs7iHAc\nSi95sOTYqb18+RKtVouzszNLly6lePHiKTexKEh0DV/3NKzh2/wB1fABfPLJJ3h6euKdQGesKbx9\n+5Z169bh7++Pp6cn/fr1M0n2QKSP27Rpw7p165BlOd79huhtIx4AmmEjUD1fIP+4UAzIkcMk2QNw\n83Hk7/pjeBMqdPZMVACahD1iFbYGsTLLRNwojOgyXY6oO4mJ3xCxmkyyFx23EantD5nsgbg59Q7j\nP8ZgY7w1fOYhnPCUC/t/A3gU6ZttilgA7DjWkzxvvSl0Q9ykw8VePkdU7jUPhkb2Nzk2xxfZILOs\n6zlWvtpByW6WIE1GaPi9ibLHT8D+T6jjBJ0nQr3mkG0ItLodIYtCm6uQ93toYglNbKHIXKi5XZC9\nw4dQ/tiK1KMnaxw3sGf+XdYOucyVyeco9NIrguxpZ83B4vlLLC9dSzGyF463b1VmPhQRPS+id3yq\nQP/TcP1Hd4pN6YxyYB/Kwh9i7UNVVZTz59DUqYtkbY22d180pUqjGTwE3VeD0PZ1xuj4GeqL6MUd\nypXLYGmFpmZtERX8QMie+uoVyuVLZpE9V70zhi/dKJMA2QPR4z0C0c9tiuwZEdI8aa0+OEBVIx7J\nQf78+cmTJw9z5szBwsKCVq1aoSiZlgZpiSSVBo0YMYLFixebPV5VVVavXk1oaCi3b9+mRo0aEa4a\n8WHlypWAsHYLDTXf3EzSatF26ARZs6FcvIiUgCDkPJ+xdNwqak/ML4EWzRvNEOv6TMQPCbF69UdU\nYIHoyD2CsK9LINvxn0QOPnyyF44qYUWZp+acSbCGz3ycRsSHE0Z4d67hW1v6ntxBkfqFGRwlJxbe\nJLT6Bdj0FouQGoB+EOiH9YdbcxF+ud8hYoKVoKsM1bLALCcYlBXK3YXi+aH8eWj/Au7/CO8OwoR5\n8FUf+Lo/uMxEYg/GqZNR/fzQTZ1B/7+a0rfWQ9ojJI1+uAPNrkfOzcXFH54l5AmUeARfvUerRkaa\n60WmYoKJMX5ABz9op7/PyMkq6vPY81CPHkHTvHnk3y9eoD56iKa4qHeUtFp0+w6hXr2CcuumGBMY\niOK2G/XBfTSfO8baZ0aFqijIPy5EOzhmPDQ2XPXO9Ph+B3OPiUa2hCAhSgxMtXcEI0SG6pL2hC+l\n4eDgQMGCBVm4cCF//vlnRClXJlIfSSrEs7OzQ6vVRhNLjgvHjx8nd+7ceHl5YTAYmDMnbrX2mChc\nuDCnTp2iQYMGNG7cmIMHD5I9jtW7KWi6dMHYszvaUaMTHLunxTrG+E9nXZGFtHxrXmpXiwi/L0DU\nnsVvJJQJHULSJivC3KoUoq/Sig+3Ri21EADsJLLT9UOHXdjS0n36KXRPt5mnw1cl7HErZvVneIrX\nA9E5exmoAzXAodk/OIZsR2cVeWmb5zM2uhTLZnjWfCPO11vRu3AIls9F5V14EjY88vc1ogb1/nmV\n1msKMfPe9zC3N8LbYgL6p6UJuPGMnLfX8mb/dZScfkh9O6K+eoT6x3domjZF260Hwg1EQPXywhgY\nhHbeD6DRkL9uHlbeNTAVoe95LWzca8T5URBYnXcOoa/nYPnGdMdrQhiunYbnBU88L77E96EfhWoV\nxBhi5MLMo8yvpGK4AbvCxvY2sf1ToPwNeL3pGJpm38d6XTlxHO2MWeL9BQUJQjR9ZrQxko0Nakgw\nBAejXL+OPHYUuj37P5ioXjiUlcvROPUyWYcYFY56NxxlN353FfeJcsk4phF4glgwfyz3mPD6vhIl\nSlCtWjW+++472rRpQ8uWLdN7ah81El3DF44LFy7w+PHjOAswHz9+zNWrVwkKCqJAgQI0atQoyZOc\nO3cuLi4uAFy/ft3s7l0rH3/kjeuR3dyw3PpHguPt9Xe4Lgnph5mYL3SrIAQdGpJxvU4zGjYi9Nq6\nISzoMxEdoQjlt48l8vmPDVwsDhdvgmb2PPjnnFlOGxG6eVERoaF3HtEM0Q3KD4Nl8P2Ngiz5zpNQ\nGUrmhSpFofwPjZgl/y3YWwRZ/ARGrmJo/kncmvgKd4OQUG4cxzxqAe9DKrBtmQ8vHhl4dN6X1pVV\nZoYJpRWtkoNnuWqi6d0XTeXKKMePoalTD8leKHaqqoo8fy5S9uxIX3aBO7c52WYc9SxvRBxjNJH6\nnl6IutaoeRSb3FkIPXsTSR+fYEckVFnGeWU5ti3wwqE0VC8PxQrAuetgZQHdLcF9h/g4ZYSoTC5g\nGtEXYAcBu0rg26UCu8rNQNMssnJZOXMafH3RtP1cvMdpk9EOHIxU0LTOVWgfJwgKQrdmbZyp94wK\n1dcHZc0aswIIjno3lrztRy19KE5K8pr7vBCNGr1Iu4VxctO3icWTJ0/Inj07nTt3ZsuWLeTJkyfJ\n+8qs4YsbSVZ7qF69OhcvXoz1fGBgIKtXryY4OJgnT57QtWvXZJE9gHHjxkXk+h0cHKhfv36CdX3h\n0Dr1RlOxIsq1qygn/0Y1sZ0hVw4MuXLwasqvrL9Yipq2ka3x5kCDWHmFklnPZw7uIlJILRGRhcwq\njtj4lciI08eAXwMF2Zukjkfbz9nsGj7LKv6xpEvoDpE93wWAB8BYBuYewNrvPdkti+Tr917w4gKs\naH2SFrnnhG0XBZ5fcXy0GzsCLbmaV/wWveKYx3kgm9VNFi98ybY/fVhzXqXzKhHruwSMz/2Wgi/P\nI/dxwjhyBFKRoshbhZuF6ueLsmAemiZN0Q75FmXFLxjbO+JS8ka0Y0RN3+cltvtu4Osgaq/tiZpA\neYvhsihh6ftiNH/O9mKjPyz7F/ptgmY/gMsBGOkGT3aIqsTyQFcE6TMlY50PePYOnl55i1Q30vtG\nffUKZcd2pM/aAKD8uhLN51+YJHuqtzeK2240Q4ejXbgYZY+b2TqrGQXKtq1oupsWrDaFhXNkmlkl\nj+zdRMiH9+bjzoIUK1YMvV7P4sWL8fHxoVOnTuk9pY8SSdZW8fDw4Nat6F1rGzZsoFWrVty7d49e\nvXoxdGhK1OkISJKELMtMnDiROXPm0KZNGw4eNE+2XNu7D8Yxo5Dy50fj54fULrpngVSjJuqFf/Ce\nvhqLLqU5VRuKHRYXc3O9S5sgZFXLEt1mPRPRoSDK3mXEjaYcQqS3CZnuJeEwIiKfKVuen3h4IiLX\nDxGp5eRYu1XWwcrfYC+J89Idq58nCmWjYAYifcjmAVB+ALT+l7xHW7O1pjftQkVXOAhT+f7AwWB4\n02087S/mZxf9Iu0qasBdn7I4611xfeHMVL2B5f4wBNMOMeeB83GISmqPwKLP/el6A7RTpyOVLoPk\n7Y1x7Gh464d29DghGwUUN17nhTV8kVVEFP0RIrsx43ZVENIxCmIBAHBq1hmcHb9nY5kl0cY66t2o\nwE1uUoE7zewpy13mbF3DqiDhDRJz2iqRkb3cCBnrQZjOUJQE1nmAQ10Jef5c0GjAGIpkp0c7eQqS\nRoNy6CDo9WhMNOGpDx+AJKE+fIDWsZ2o37t1A4KCIIGSoIwCVZZRPT2RChU2a3zboO18t1ThjyCR\njk0K3iHS/CmrfJuxUalSJWRZZsaMGaxatYrAwECGDx+e3tP6aJDolO6jR4/47LPPqFixIiNHjqR+\n/fqcOycEQk+fPs0XX3xBqVKlTO0uxVC/fn3OnDmDLMto4vFVjCrXovzvLFK5cshzZqOdPhPJKrIa\nQlVVQu1Lgo8PLkcbMvuFO8t7iW6paZgvAvwOEeV7h3CLyER0KIh6x/5Ej2b4I0jOHsCJD1NkOCVx\nCFHbmB6l7O+A9YjvKjz5WSILPAoS/+9RAPJZQhYttMoFdXOCRQJfWLAM+pPws197BgWtQzUYwGBI\n0MFhrH4e06/MjvX85MrjmXFsliBuhgPg8hnDqkCRy9HlKlTE70pC6Jv16ADydhdmnpgNl6Nr4Dnq\n3Vhl6MecsqH85gFDikLHvOJ9HnwN195D1ueiqD4uqAix8Xo9C3P2ck50f5+C9++RF/2Absq0iHHK\n9WtYd23FpxUD6HY0diQvJnwRNcJR4fy/PhGkr6nvatbnH8eLPaF4eEHDIZZofjNQ8StRfzcfiOkD\nET5XXdhn1AFoE88cRlvBuEstGDerALqFP6KJ4acuL/pBfK/heP8e7cTJoNVi7NUD3fRZEentcIT2\ncUI7dIRJkpjRoBw/BhoNmsZNEhzrqnfmebcd7NoqrnVJxXaEwkFiXKdTCmmd0jWFt2/f8vbtW6ZN\nm8bAgQOpXbu2WdtlpnTjRqIJX1BQEF27dmXnzp14eXlx6tQpbGxs0Gg0fPbZZ6k9XwDu379PmTJl\ncHJyYsOGDXGOi6nPB6A+fYK85Ce0k6fG6t5VVRV53hwcrf6i6ePbjNgInyJqe8zFNURaqHlCA/9j\nMCLSE4UxbRlkRMiQWCLkCcx0uvvooCJIlyXp0403AfH7HVAIzuolfl2nxeETDe4nFB49VLGygmtX\nVB7eV9m7WyWnHbRqo6FQYTh7SuXsaZWFy7RIEjRtoaFkKYl9bgrjpmfD84iIdcTnpRuOsfp5TPhn\nNtObwbr3oj0DROfs2jmwZawLa7fY8LzHJNZUBOVG7H2sQBDn4Qh/5/VAi6LwzXF7uuaM7Z4drtN3\n+6bK4gUyRw4qeEfJ8fYsALU9xfcS1xX7KyCnFcx76ci3PktRr1wBiEUU1MBASnzTiLu772EPjEJE\n2xTEdx9tLKLJqTCCuEVtm8jX+hNeHYiuU1hZB/eMIkLYN565GhCuHcVI2PN6tBVUcSrOySLOoCjo\nhn8X73jjxPFINWqgnj2Ldu58k80Z6osXYAhBDQhAU9EhgRmkL+SN69E0bZZghM9V70yNBTuoO0ak\nx+OrtgxBfK+Nib3IPYLIFOUkfVK5GYHwhePevXvkzp2bDh06sHfv3gTdvjIJX9xINOE7cOAAz58/\nR5Zl2rRpw8aNGyMaKtIS33//PYsWLcLHxwc7O9M+hqYIH4Dq44Pxu2HoXNchaSOb4OWVy5Gq10RT\nowYLAroxvOh+QKRTEvMNvUIIuX6VyO0+VqiIKMV+RBlVfAGhc4iLnC2iOuu/Bm9Eint8Oh1/A0Ls\neepsLdtdziY4PvT5K/x3uyO/eUvocy/8NuxDDQqJeN2iREGMr/2QBg9FN1a8K3MifB3X1Gf3+OuU\nMQqh2Z2IzyYrYnEQfoSeBaBJDAvbcLmf8Dat7xAaj++AH4FngEPtLDTd0o+cxUVM8GYMFcg7RDZb\nSJLEi6ELsPnrD/xeQJAsJJnCVQCLIyJ/EvBt2Nw6lIaAtqU48q4p2tFjkfLGbk2Sf1mGPCm2GMpE\nBLGLi4R5AFNNPD8Bca49RzRflCPlouXnK4G7PjcP1v6LvH07mjp10FQwHe9UgoIwNm+Cxe69YGkZ\nryyWsscN1d8fTfceGbpjV/51JZovO8fbMOOod2P7FSe61gFjMMQX/vBD2Eh6IMh+1Do/I2KBUpv0\nawLMSIQvHNeuXSMoKIgVK1bg6uoa57hMwhc3EkX43r17R+fOnVm9ejW//PILLi4uiZJJSUmoqhqR\nzp00aRLTp0+Pd3xM8mecNQNtv/7RCoyNM6ahmzQFEB27Pwf0pXG2m4xDSCSYCwVxU7EmeXVPHwvO\nIur2PjdzfAiiO3EoIp3+X/LV9UAU7KfXe/YiUo8tqTIg4VCDg1Hv30MqXwHjlx2QatVG06AhxmsK\nHP0dJm2B928hm6hWtKwizlE1MJDQIgXY2Q68d8deNBkQROuzpvDn8djHPY6opx2OWKxNQRCg8G0V\nIMgeZnjAly1g6TjIGqVDxt/BEmetK24+0ZPq4c4Ulq0NjDwimo8CEUX1doh+koKAG6Jm7ipQpLIt\nr449M/n5GHLFXaVZh/jTgZeAX8L+b43QaUvswjQxKNQRvn1ux8N//JAWL0V6/AjdxMmxxiknjqMc\n3A9Fi6EdNMQsEqc+eIBxogu637dmWNInL1mMxnlAvIsUR70b0/90ollnmKTELaGiItLs5RD1kUsQ\nqhAngAaIBd8E0v4akBFJXkyEhoby4MED3NzcyJs3r0k930zCFzfMXgBevXqVixcvYmVlxfPnz5k9\ne3a6kT0QX2pwcDCtW7dmxowZSJKUKPcPzWdtkBcvQlVVVP+3qG/eQIy6lCxZNcysL8RQEwMN4sL/\nG1EVuP6beIyIftRLxDZWiJqla4g6lox/GUo57EKs/tML4bczu0LWKPv3JWtfkrU1GodKSFotFjt3\nox01BuNWH9h0CrzLwvAp8N338K0LfDsWw97o5285vWkCY4lwZjFF9iBSy64s4rcU9RwMT5Xb3QWP\n7BB4EErWh/o1oVVtaFALJpc0sOxdPxz1btH2e9enLPW1p7i1zILlwFFEGaENglBeCNt/A0STSy7g\n2RV/SncuTWjnjoQ2boAaEOnlo500hZy929Cuvx11ckD+sOerkPDiqCqwEGHqFi49nZrniZUWtDoJ\n7VgXpPt3RSr2ZaRvjmowoJw5jXJwP1KRougGf2M+eStZEt30Waj/O4uaQZ0XpIoOqFcuJzhu8wHo\nmSV+vTwZ0Ve+vQD8uA42DBJR3SOImsvR/LcWuYmBhYUF5cqVo1+/fnz66ad07tyZmzdvpve0Phgk\nGOF7/fo1Bw4coHDhwrx69YpGjRqxevVqJk6cmIbTjB+PHj2iZMmSACiKYvJCEzPCp6oqxq8HIBUp\nCoqClCcP5MuPtpPQFQw3Fs/z1XUqrYa5JF70UkXoJzkARRL9rj4ObEVomJVIwrYqIiLzG+IGaF5/\n3IcLD4RIa/6EBqYi3gBL7GCAW2Om932BZF8WTYsWqPfvI5Uti6aHE1I8jVLxwbDWVrCizQcQgiBb\nIl+0Gw7ZPKFucYpanyVkzyle+YhoR2LxAhHVq4/w4lhG/OeuN6ITOQRB3k4C4foDeVs4UN31K2yK\niBihm48jtguG0TVoAz/dUlh9SLylM4i63agS0QZE9K9ab/h2vXiuntv3BDz25srQ9bHmYY8gp9Ux\nPxoOolSiOKLrPbVg2RpcnmTH+8RjJJ0O5ZQ7qCqaho1ELZ5sRJ47B7VIESzGJb4gQZVl5EFfoZ0x\nGyl/ep4BpiGv+hVN69bifhEHxurnIXWYzYtdIh0bFx4AW7Xw4AJMqSI+qxnSbL6sBfnPm68MkdL4\nECJ8MXHz5k3y5MlD165dOXjwIBYWFpkRvniQoCyLLMvcuXOHHj0iayyyZs3KvXv3KFMmY8jClihR\nghYtWnDkyBGqVavGpUuXYo0J99kFQf4kSULT6jOkvHnRNIytE3jXpyx39WWpmeM65RDio+0SOS8J\nURCdHeGc8F9qrw9BFM1/TdKbDyTEjbo9InKyFejCx1sX+RwRHU7P250CWGjBvn5uLP73F/j5ot65\ng8bxC5T1a1EvXUKqnjThIcu+/hiwBbkZKA1ihDF+ggHQqdkmtl9ZwPydkSnLxKIgUA1B9gDeEz/h\nyxP2CIcDIh37Kg8EW9zgUInhDB+lwcICPp++jl2PQznzPi+PPn1JnUOCpJu6wSuIRqXa62HncOjt\nCmcchb93noI6fL5fQD6bl6hzF+H5JJS7iDKGmL4iCSG1W+X6F4Dmb0DNlhWePoGSpZDK2KNs3Yxa\nuw7G70egnTAJ7bJfkKdPTdIxJK0W3SpX5B8WIJUvj6ZtYihv6kHeuQP17l2kHDkSJHvTNsym5q74\nZbmMwAEdTJooyN48n7EASM3Pc/iGO3cHh7B7ecq+h7jwIRK8mKhQoQKqqrJkyRKOHTvGoUOH0ntK\nGRoJRvi8vb3JnTu6o2dISAguLi4sWrQotednNs6dO0edOqKBPSFBz/Bon6ooKK5rwBiKdtAQk2Nd\n9c5oP99B971Jr5E5haiN6piEbT9EhCKsoYJJWmTPFEIQURcrBE9IjphpRkQAoi6rQTrPIxhBOpZ5\ntmWk5e/RXlMuXEBeOB+LzduSdQzDr2fg4FpRwxcFnZptYvlxJ35sB0vfC4mepGpaBiMaNB6G/T2R\npEkltckHO4PhtAH+DYXu07XsKzGIdyu38eZfb/r4x68f+Rwhu1IR6J4far6GJzInuH1BAAAgAElE\nQVT4DocrPzrgjCt5v2zBsR3+5Aamk7HSeR3yQ58ycE0qwouN51BdV4NOh9T/K+RBA8HODt2ChUga\nDfLOHUg2WdG0ap3k46n37kGOHODnF0vGJa2huO1GDQlB+2X8Og1j9fOYvnE2Y3vBNmAscUdSPLPD\n2U+gursL833HieOcP4dUtBhNln9GrsMPaX4nRd9GnPgYCF9UBAcH8+zZM+zt7TMjfHEgwdyMl1ds\n7XkrKysaNWrE4cOHU2VSSUFUjZ6lS81T8Zc0GrQDvgKDAfXRQ5NjnH1cebdJxPaSWltVD6Ede5D/\nRj3aU0Q9SkqRPRBErwqi/kVGiGLH7zfwYSGYyFqs9IRv2L/Z9NHFQYyzZqD+exHdmrXJPoZF3xpY\nrFtEp2abIh7tqq+ixyInPmkBZ96LLuWkkr1vECndJg6w/6VwMXWLd4u4se8VWL2FZkHgbIRfJ8qM\ntF7JuuP56JJT6ErGhxAiawg3v4QyRghUodhisO5znV+N/dg31p8mluZ3ZCoIAevU/r1M0UDul3D4\nkiWeW/9FkzUr2qHDkWrVwVijKjg4gIOobTNOcEGytEL6tFWyjimVKYN67y7ymqQk81MOyvFjqC88\nzCN7V2az7FuR0fiKuMlet/ywQAH7Ld9GkD1VVVHWrYXgIKq0KcAj08ISmTAD1tbWGSbrmFGRIOFb\nvTqmkaXAF198wZ49ezAajSk+qcTgxo0bSJIUrW5v2LBh8ZLREL1txANAM3AQ8qwZqG9NU7pvQoRe\n2IkkMhgNIq1piajr+ZhxFJFC65VK+3dA+MteRdxIPeIf/sHgNPHX/aQVrlhD+ZLgZhnD2kiW0Xw1\nECkFnBFU95OUGdMCV9mZDjv64lG/F8cKDmTleGiviMhe0p00xTmWRwcL/7Vgdb6JrN2iwzIFwmZ6\noI8C33QysqvUdTY/FZZX8aEUQlg8vL3BEViOeH8110M9ixs0qg8nDGKhtCyO/QQhHBsmI8okdpF0\nB4e4kAfRtLMCQfZehPVPqHefI1mKBYBxplBD0G3bjnr+HOqokai3bqKdPhNN289TpMtWU78B2gkT\nMX49ADU47ZdB8tYtqE8eo/16sFnjQwww2V9E9uLyBa8FTPsUijpWYLmNcIpRvbww9nZC+9NSpOIl\nyGKrQ0nliMAAVY14ZOK/hwQJX5MmTUzmxSVJYsCAAXESwrRCqVKlGD16NDY2NnTp0gXbMM2n3r0T\nuhRHQrK2RqpVB+XAAdOv68SazT8ZbM0aIbA5H/BJ+m4yNLwRheOp3aCiQRDKd4immHeI2pgPFSpC\ncy29vXO75ocdwVBg6/RokiSq0QhvXqeYZMaYL85zbvgtDlY34PSlkWHvVbxyQOeQlLElnALcMcKz\np+LvHDnAN4WaP/2ANwrsfygizg3N2CYX0dO0OoQeZbiW4PlQEcEeQKS7ymuE7MsOhJ7gcIRbSLjk\n4BeIhU9KwBPwyS5qZbsjGk1eKNBtTxcs37xFsrJCuXIZedMGUfdcuhSacuXR1hJLFKlY8Wh6pimC\n7LZounUHr1cm/c9TA+pbP4zTpyJly4a2r7PZ2x08Aw4WcZM9EGU9G/ZCq59aimP5v4XAALTDRkTc\nX2xyWuKTEcL8mfhokSDhc3R0ZN++fSbr4ooWLYq7uzseHukXZ7G2tmb+/PkEBASwdetWXr9+DcDL\nly8TZc6tHfg1+PkhL1uC6umJ+uQxahSZl/ptsnHKI3kpWQ2i5f4FKb86T2+oCCcDS+JXl09JFEZo\nlR1HdEl+qBI4fyN03JJsbJ0CGFAQ5juCrUNh3ItEr2dVrlyG8vGZipkPe/0dqh9eiNMX0EWYUOB7\nFTZ5xr9dYhDezT25aygVuEmDR0a8UoAzhCAkX4ogxNXNXdiUQxC3qJwzD6ImeAVCc3ICIsJbGlGy\n4IJIFx9AWO1ZIppDmoaN/5yUEVVeiogaztZAt71daLvyM75Y5wgSbOl3HHnvHuSVK5Ds7JBy5UZT\nsyZSDiFWrenaDQB5+S+UyXGTEobT2OvvxPswF5IkoWnaHHnaFLPkUJIL1f8t8swZaIcNT1TDiKqq\nLP8DesSzWrsMTAJ2/ABZ84rWPfX0aeTfN6GpWTNiXMHy2fEJTthqLxOZSCoSvMdIkkSLFi1wd3en\nUaPo3azTp0+nTp06vH79mkKFCqXaJBMDCwsLdu3aRfv27Wnbti3Tpk2jZpSTKj5ovx6E+vQJyr49\nkMUG3vmjensjZctGpyF6Tu97z2OSV5tmjYhGGRGpp5g2Sh8i3iKkIb4jfXxwHRGEcybC6SA7Gavw\nPSGUIX1/B7WACTVg0WYd8tnDSGF6lKrBgPzjQvDxQdOtR4odr04DWBgqvFs7xDHmEcJb+VuS3pW9\n7yKsWTGbm0NEU4xq5r5qxfg7ALGYWRH2dxZEJ7C52pLWQF1iLxYlBInLSvQO/qjnUJ2w+bxBiPTG\n3SeaeIS7ggA0993An76ROgTa5VuRh32L3LsnUtfuaPr0RVM0etuLVKgw6HSo+/fyusQhXj0LxdZO\nS4uuOSjlYEWTDrbkyq9Do4n81N307SI6U82BdvVvqIcOIm/ZjLZb9+S83Tih+vsjz5iOdvwEpJym\nXZtMYax+HlV/nMP2c8SZYggFNiHEletXFXXcxkkT0LTviG58dGkzSRLffWayNROpBbOCCq1bt2bS\npEmxCJ9Op2PIkCFoUzqcn0x88cUXLFq0iJEjR7J//36++uorfv31V7O2lYoWQ9v/q2jPqf7+3N8s\neuV/RYidJgdVEDe0FcCwZO4rvWFA3LjKkj5kj7DjS4ioiBewBuE3ml7zSQweIiJ8/dJxDoHA7N3Q\n44fy/BHFAkyytIS3b4U1WIxO/eSgv5PM4yCReo2JYIRC38Wwv39GEJPEogQiUlZssKgpzYt5ZG9A\nQdjTDRQVCtnBoWuw/BjULwNN/yfO27IIApcYIiohRMS7mjl2AWIRkFrWWrcQZG/ej1rOj1gXPYXv\n4YFUxh6Ny0SUaZNR9+/FGGpAKlIUbfeeSFEK4ys0tOPmcW866EP5qRO8DJD5/boPR/fBb6M98TdA\n0yLQrhTYWUG/b66jtFJY8NY8O05JkqBESSgQhOrlZdKiLjlQg4KQZ0xD6zIByc783MRY/TxGn5lN\nxdHQRY77RnoYqAT0ugyTK49nzmlHNB2CkMqWizU20C+UN8Efr+xUJtIfZt0TdTod2bJlw88velND\nu3btcHNLav9b6uK7775DlmWmTZvGqlWrGDlyZJL3Jdna0tclL/f7i+LplEAJRDryKB92t+k/iI7c\nquk9EURUrxCiS/MgYl4ZfbVchNTXUUsIB8P+/X3UNdQYGpaaVq1R9uxOsWOF3HvKhXMq/9QxffF5\niiB7hRCp2aRaE+YI29d7RBlFbMfa2MhmB9ILcFwEAw7noenK7PxVuBZNd/SkzNnx2DaBPsCXYfNL\nDEoALYme1o0POUk9smcE3LTw8+roZE81GlHOnkG9dRP14gV0w4ajGTQE9LlQ795F9fcjtEM75DWr\nUA4dpNu1IWR57E1loPoVWL8YDq2C3Keg61OYEwSLZCj8GH45Cj32QbW2sDTnXOyH1YtoyFAVBTUk\nBPX2bZS/T0RzIwGQ7O2RbLJi7N83UWU6CUENCUGePxft0GHxeuTGxFj9PCZfnE2XZlBKFil7U7iK\nqN3bdECQvbmPByOPG4NUvgJS1tiqrFntLKlbILLBJxOZSGmYXTbUrVs3tmzZwqBBgyKeq1evHhMm\nTKB9+/apMrnkQqPRMHnyZKysrBg3bhx58+Zl3LhxSdrX03sh1FqTsvPLgiCQwYjUzocQkYqKowhT\n+jrpPZEYsAFaIKKPSxH6h4XIeCtnBZiKkCBJTzQNmweAsXWLaB666rOnkERnDZNQVGQZht0CU2pt\n9sA8hHn8KxIvdh6OIYjmKGvMFzz/xhe6loUqM4tQt3U2bLJpEfHPK8AVzj2CaYjfVGLFxLMiInyV\nEKLQ6QUVuJ8VSleBY/0jyZ5y7n9IxYqjrP4V7SrXCJ9y3aw5qGPGIo8ZhfLXX0j58iH/ugKCgjmU\nN5Qs72FEPMezQlwf6iAiipaA/XtosukG2m35qdezKLf/9sb7USD5SmfFNq81z669JaudJW9fBvPD\n3U8Zk30rUunS6HbsQp49E+23QyPqCJMK5cRxlBPH0HbriRQjVZ0QVFXlmzngHxK3GoE7oiTBbS2s\najWeOXsbopxYisVfe+Lddw6rj1/JIRPpB7MJX5EiRTh9+jR16tShSpUqgAi3v3//PtUml1IYO3Ys\nnp6euLi4cPbsWXbt2hXRcRjTgSMueDwQp2FKpmA1iOLrLQhrpIxGnOJDEKKzNCuCrGY0WIQ9eoX9\nOxshm6Al4xC/YEQaOr0dWHICpyZBgxnRn1dlGfX2bXQzZ6fYsazKFqN1W4nTf6jUCTt2TOgRXajJ\ngYTojk0MPgX23YGbg58x2x8aFoJSOSFEhhHVoNYT0UDhCxRIwpy+JH1+ez1bwIYicPExXHsGwToo\ntPMX3Hwcwwi9FmXdWrRjxprUWZRy5ES3cjXqy5eot2+hPnuKPGIYLz3glz5QaR3cNmMe4W4n94A/\ngYf1VI79/YRZpaFrW7C1CgACeFsNeu83stsAI4ofYMDqpqzvcBzJ0hKpeHEwhKIajRHdrUmBPHE8\nFqfOJmnbM/PO8uaakNe5YeL1EGA3cHoHbOk4nrln2yOVskNjnfAywSY3GB4naVpxIlOCJRPhMPuM\nOXToEA8fPmTBggUUKFCA+/fv06xZsw+C8AEsXryYYsWKMXLkSOrXr8+ZM2cStf3R7f4saAQ5T6b8\n3NojCsP/Rki3ZHSEILyFXUi6bVpaIbwEeyDi4nwLUUeVEUjfLkQhfnoT/fNAn3eg1UlobkUKkEta\nLYSEpGjBfME7J7h9U+VpsFg0JC9Ok7JoHvbgtYjrXX8kakIDgepXRUQ2N0kjeyAIzyRENDWxvtyJ\nQQCim9gInLGBYaehfMsCVOqUj0b22bhdry/7fNuiPryG+vcJpCJF0f2yIoG9gpQ/f4TPraZ9R5r8\n2Zf1vxzhEuL8Sowl3Bsgx99hTTuPYFsM2VTHsMdXwOoB/zK7ZiemFt6BtmcvjOPHIVWrHiGKrKoq\nPH8GefMhWZn3yUqftUFx243GMXExZPXZU87+cI57v8ObODSmPYHGTQTZm+czFnnNKDQdOqKpm3Cb\nTzZr8f1lIhOpgQSt1cJfr1SpElOmTMHX15cBAwbw8OFDbG1t+e233xgzZkxazTfZ2LhxI7169aJo\n0aLcuXMH6yirrrgifOpbP0JLFuN2P3D/LXXm5Yeo+ahB6tXupAR8EMSpDh9WJyyIG6AfQsalGOKz\nTq80ejCCSORMxzlExXtgmg38/qAsufOLb3bPWl9m+Y9D074jUrZsyT5GKzZztMggRuSD3I9EN3Va\n4SWCYF9EuN50QxDdwiS+Hi85CEEIMSdHWNoU/BDyH5cQjUBlgVJ5ocpILe7tZvN3PlGKoxqNqGdO\nQxYblD+3o5szL8nHDLcUm9lLNLLpEaSvKiJjkRK4ikihAwzfUYeqjgVQZBWPm/5sn3iDt69CePxv\nZG15uUa5yV3MhkbOxSlUPjs58lljCJJ5eMGXV/ffY18/F7Z5rdhn1R63gSfQ/RSX1LVpVN43lnyr\nVuB2H1a/iP16DaB7Cajp2pMtRWZiHDQQ3fpNZhFRV70zwQN2sHwNDEpwtPn4WCN8165d4+7du3Tq\nFF0kXpLMty/7r1mrJUj4zpw5w6RJkzh69CivXr0ibwp3SaU1AgICyBZ283JycmL+/Pnkz58/lqhs\nVPJnHD8OZeVylJGwJhXtgxVgFqIr0XxxgLRDKOLGcheon85zSQ7C9fqWAM6IG1Vaa+DdAc6RsFND\nWmEHIiq0rB04lgKtBoYcgU7Hy/PZ671IuRKbII0OR70bZaf24eovMqW8hW7iwJSYeALoXwDmNYRF\n+8H7XeTzDtng9ntBUMzzU0gZ3EKQTqdk7KPLt7DpFmgkGFAJZv4PFv8LbUpAxzLQugRktQD/eZY4\na10j6/QOH0KqVh159Ei0K1ZFuGckB+GkT/4XznnCnoew5Q7ktRFp8E7/wroU0FjcB5zICv5BkMUS\n3ocJFB91gRolQFEg0ADr3GHz/yA4FLz8IZ8tPPeBioXh2Rt4GVaeWqwgaNo1w2PWTrPn4Kh345+C\nTqwIAQ8T6vk1gd1DYNXVwnj+egzJ1xc1KBBN1dhVm/KuP9G2j+6u7qp35tNfdlDsGyGynZxr0sdK\n8sLx/PlzihQRSpgxOUyiCV/5NPysbmVwwvfNN98wdOhQ7O3tU0xpPz3h5uZGu3btKFOmDPfu3QNE\nc0ehQoWoW7cuVapUwcXFJRrhU/btJeeY3nj3MLJ6YerOLxRx47VAFHhnJOxAyFuY4y7wISD8G14I\nhCtipVXU8iaiQSE9xZaj4i+EU4o3IkKkI7q0mMXdh0kifWpoKGVWfo2V259cuqDyGaIRw0jKlQPc\nRTQjWAGuiOiWFWCXBS4WgUCL7Iw71BC7gkIdd+03l7j8x0OKhkAF/7Qvo/BANGzF7AtVEaLsp4Ds\nNeDZM3j6SixK3oe9ZucA255rKNe8AC/vvcPPM5j8ZbIzfEcdcuaP/onupANuPo4oFy8iZc2KvHkj\n2j79kEqWStH3M1Y/D0ciO7llWeXUnnds/vE1XrcD6fwePAJEZqAxyevor4lIeSqIsow+wGZMp8hD\ngQeIaH4WxOf7CNEZ/0YPA62gwM/D+bfx9ASP66h3o/I3Tkz+BX5EWNBFRTlgcQs49Tofb347DLdu\nod69g3bo8Fj7kn/fhLJ9GxZ//hXteVe9Mz037sCil1iMJuda9LETvkOHDtGqVSuGDx/O4sWLo72W\nSfjiRoKEz9vbmyVLljB9esInxYeAvn37sm7dOoxGI19++SW7du3C0tISg8EQbcym6bMjWue/fjee\nzVV/Zolv2tRXPEB0s2Ul7VwrEsJZoCIi3ZxRSEpKIQBxIziDsJayJnWJXyiwmviN1tMLKqInNRvi\nxhgK3KkMu3wteHPRCylKx+5Y/TzKEr97wg+fnyav8orR3iqnLgjB4pskLsJnAJ6F/WtARL8LExkl\nvYCoq3uMuLnrAQsLqD1Mw5/PauK1ZG+EmHQ4rJwdaZv9FBteKayN23Y7VXAIkUaO6l0SitD4fAgM\nbw9+PWpTpYg3V4Y8ZMklITMzsAYYuuXnQA9XHlnVxzh5IlL7DmiqVI32vYRDvXMH9e4d1OBgpNy5\n0DRtnhZvLxp6P/mOUmtd2fAL3DQK+7iU9IwORBDJ8sQmYQnBIztMD4CWP7akinNlLLOJiGdMYWhH\nvRvfrXWiQT8hrdMlxn6qAD2LQc4vanBp3D6MA/ujHTUGTaXYVY3K3j2oPm9QX7xANza6FqGr3pke\nG3ag7S00WpNT6vGxE74LFy5Qs2ZNfHx8sLOLng/LJHxxI8H7Te7cucmePTv379+ndOnSaTGnVMW6\ndesA0Gq17Nq1CyBCgqBmzZr88ccfTJ48mVy1qvHpp5/Spk0b6ub4Gb0vjEEUXad2zVUphJfmGmAU\n6d9gICMkMhzIeAQlJZAV8d7KAscQ368Dos4qNd7vHUTHZkb8LCXEDSwcVoD9FXhJKB0nlWLPrEcA\njMk5lwEH5jB1DvgFQjYrqBTmNWapg/MPIKcNvL4E/1SHtRciLc/KISzEoqIW8Mn3oKoQEAqHn8DO\n++AbDP+8hELZIKc1ZNHBVk/QacDGAgZ9AuvLQMmcQiw53NTBf54lzTf25ZVDAzQWsem7Ln8uvB+p\n7PEW0aK0rKNsgYjihR/3GsLJowzw+jfY1rcTzj6uALhfrIfLkBvks4ELCx1wxpVHPmUB0DRpgvrP\neaRq0R2I1VevkH9bg7Z9R9TAQLRhFmjpgfXFfmTsT/m50ns2rTrCG0toVgBstLD+BYQq4G2Aw0/F\nYit/Ivdvg+iaDgj7f2K+x3zvxHdwcPhhDg4/zNzhMKo3UC2S9Dnq3dhy0YlWX4tmrxYm9vNPeShe\nXCLL7EH8u20fuqnToVjxWOOUE8dRnz9DqlYdKa9phclQWbyHpPweP3aSFxU1atRAUZSPIuuYljCr\naSMkJISxY8fy448/fvAfsKurKzVr1qRChQroorT1Ozg4cP369Yi/ly5dyokTJ9ixY0e07ZOiwZVU\nKMA2oAGRN8u0xhtgFYLsZoTmgrSAgrgBN0ak0sqRshG/EwhSb64Xa0bAKCBEB7/fLsO8ZTn5d9k/\nZDEKe7ESiBuuB0L2xh/x3nwQn2HMrtbrREb43oRt97ouXDJY8+yuAUOISv5iFnT8Wk+uAjrsq1hT\nskLkWRccqHD1bCCf1LXB2sb0r3Ie4/jLvQQ8fGiyE7O0zVUajm7MthUKPYzRSW5aYAdCh/A+4rf2\nNTB/A2xyiiR74TilFxWzzrhyN4zshcM4awa6CZMAIaMjDx6Ids58lAP70PRwyjDX67H6ebQM3MX4\nrk+5dDIASZJo3N4WfT4dpe188V0hs+mJaPbwREgoJeY6+zfid+eY0MAYeIkgcRMQiz29BG3aQOFt\no7lnU5l1/3NiWHM4FwhDiS1BFQrMsYZV+7X8VGoRBxc9QTtxcrTPXTUYUH5eCnnzounhhLL0JzR9\n+sbSEnTVO9Nu1Q7yDITEtZII/JcIX3zIjPDFDbOCDFZWVrRs2ZIjR47QsmXL1J5TqsLZ2Tni/9ev\nX6d79+5cu3aNxo0bRyN8Q4cOBaBhw4ZUd3dnMaLQOi1lSDSIri9b4DlpT/q8ETfyQfx3yB6I99oX\nUWe2DkFojiPSOck9U58ifkMfEtkD0YV5oRx0LC3qXm/Og9CxotM1sSiHSLsuQ6RhnVpAjt41uJ9v\nHlKpkpDTjpeqyvKoacqYRfJVEa3OwXEfR8r1EuX4MZOvaaytmL9YR1Y3AzuepD3ha4gg/kcRzjBz\n4iB7AA18Tse5H1WrFY4RY0eh6dINTQ8nyJYNbc+4JIHTB/N8xrItuD6vy2ykhNt3ADwJewRyF9fR\nzrSubaB1mKfeY+J2sDCF2oh0/xWgciK2y49YgHQPe/ipcOYO7Gz9GxZWGnIcg6qKWJyY0hs9BVTK\nBvv2qHhVvI5u0g/RXldu3hAah4MGI5UoCQirzriEo0OMGTPyn4mPA2bfx9u0acPu3bsJDAxMePAH\ngooVK3Lt2jUAfv7554jnDx06xOHDh7G2tsbd3Z2DCJHN9NDIK42IgLghLo5pBRWhQfaUjKWVlpbQ\nIezvwlNvT4i0IUsqtIgC8g8N7wDX61CzuahrzZ3IgikFUSd5FzhZDuZbQOf64LEK6h7uhGvbo2hq\n1ECy0yNJksmatMRCyp8f9cF9VINp74I7WntuBwhf3LSGFUJCpTnxk724oBoMqB7PUa9cwThlItph\nI5Bq1kLTpKnZWnRpjXvj/8B/8Czu+pSN9nDzccRZ60rdc5aEtoRppWCtBfRKRGzBGrFAu03y7BRH\ntYJ5J3QodWoRHGDExVFEX01JZT1AdA8XcgDd2KFcbTs34jVVlpFXrkB1P4l29txIsmc0QjyC0UGG\nTMKXidSD2VdVSZJwcXFh9OjRBAfHs6z+wFCxYsVof4eEhNCyZUtatGjBzz//jK2tLbcQxeHphfKI\n1NkLhI5XWuA3RONIozQ6XkaGLSLtkxMR7TuCsE5K7HcRiiCMDik6u7TBIYTMxs9HSlB9UFWaziai\nXSMAkfpWEdGSdzG2DUZECNcC58pCSGM7hv9SkE9PObBrQOKITqKhzwUm0pqqLLNy8ivuBqbPbzwH\nIoLezClxZE/190c5+TfqwQPIK5ajW+2KlL8A6osXsRpT0hLq69fIG9ahynLcg/Llj7PTO5z0Be63\nZPhtC+p9LvFlVokvm5k/Bz3QGfgBIR9lDkIRUcH5QI7K4GgtUbWxnntLjqEvnIW5e0QNc8xtziAa\nK5Z8DR5dW7NUnRzh/KE+eog8fhxSw4Zovx4sBMzDoF65guaTuGOQl5+IxqZMZCI1YLbwcjgePHjA\n5s2bmThxYhxbfZi4e/cuDg4OFChQgMuXL2NnZ4eqqnTu3JnbO3YwBEGA0hNBCIeLCak8l5sI+RU7\nMqZtWnojAJE+2onwRdUj6tQSut2GIjoK0zp9mFxcR6S2bw2CZ8sd2K06UvLrOXy/SpC5EEQET09k\n5rUj4jf0GpH2qga4rYOpvcez/kA+/NbuociWWbFq0lISqtGIPGUSullzoj+vqmQZ1JEyb86xRw1g\nz4lUm0K8uP0Z/HY9C+/2XkAqFH/BhhoQgLx4Edp+/ZEXzkf7Q/R6auPUyWhHfo9kmyO1p20Sof16\no+7+CywtsfT0jvW6snsXqp8f2t59492PvV4sI5SAIG5mEzmVy3Xh77MievccoWOoQ0T1SiPkq6I2\nAXkhzk/bsEdUPEUs3sPvapcQdaQqUL6ihM3IAVzyLQ/377N06hOGF9rLEFmoFNgift93EYvwDp1h\nT/E6XBwn4v6qqqJs2gDe3mi+GWpS51D+eSmank5IOWMrrbrqnbnUZgf39wvLzcQis4ZPILOGL24k\nOm9SqlQpAgICCA0NTY35pBvs7e1xd3fHw8OD5s2b4+XlhSRJrFmzhhvAH02a0CeO1FBaIQvCzuwf\nxEUnNRBCmA4YmWQvLmRFkOG+iJvNfiIdPOI7K7YhIjsfEl4jor27a4HNMkuccWW+7zgsf+3E65Hw\npCkYPwX5U3CvD3vCNGaLFoNjNqDmhW21YM96Qfbm+YzlRZUeBM5fm7pkT1WRJ00wTTDevcN/+zHs\nlkwkeJsFX+SL/3sLx4BWkY/kohawYDYoOkuMc0x7FasGA6rRSGiPruDnh5QvP+TOjW7h4ljNGNqv\nBqJs3JD8iSUR6nVRGoPBEKtuUrn0L+orrwTJHhCR5r0fUgULLx8KrZ9K83sw1woeWoGfpch0fJkV\nvrGF6tngDy1stITKuWFAQRhfEGyyQwW9+LtHfvH6XC2s0EDdbNDKVjx+1/t8b6EAACAASURBVINv\nM3j6xoLcS8Zx+WF+uHULba8+zOjvgVGGndbQJCu0sYVxdnAzLxSvCZ7zhnBhzH7x/l++RJ7gglS2\nHNrvvo9T1Fr18zNJ9sJx4rZQCzAXA1Q14vGx49KlSx+MlWtGRZLKBfr06cPSpUsZOXJkSs8nXVG7\ndm1Gjx7N3LlzyZcvH6dOnaJ+/foYDAZat27Nzz//nGitp5SGNUIuJCfiwpeS4f8HpJ0DwseA8NXS\n1wjC4IuIwv6G0BtTiDzBVISlV0Z0UIkLwYi01ZSyUPmMcG0IJ2nOPq6wEOy5w+Mo2+QlspGji1Fl\nfJdnWP9ZlN8pGyF1obqfRNm8Cd3qVPIpBJRlS9B80R6pbOzbp2Rri/aHRRxpOJ0SfoLqWQH9EM4b\nUSO1bxDRnbLFYbC9hlcvoVJliRzBMgf/hnyIBoykLNklCfJVzod/kegtPOq9e2BthXHSBLT9+qMd\nPxHy5UM74Ku491WoMOrr10JzzzotW8sELP4+TWgR0Y8tr3VF0zRKLlZRIU/uRO9T0mrxbvsdJZrV\nYM2r9lSqLM64kBCVIf1lRv+qxcZGYnioypaNCm3Gy4wapaV+I4k+ZSUqlQrF+wVUqyFx6ZXK4uVa\nKlWWOHFU5XWISmgovPFSuFBBy5Z1XbiZrzLk8UbTqjVkz8bLvZcp/V1rOmQ5yPwuglD5vIWLNyFn\nzX6szD4LSaNB+Wsnys2baCdMitBuNQVVliGe2tTAt6Hc8RR1w5mIjlGjRrFw4UKWL1/OoEEpaTz3\n30KiU7rhmDp1Kt9//z3Zs6elI2bqYuPGjZw7d47Lly9z6tQpADZt2kSPHj24ffs2jRo1oq+3N/bp\nPE8Q9T+bETZsKdFB64W46b0nbb1FPzYYiGyu2Q/0QhCn+4hoWYd0mpe5eIvokLyAqG3qUwTmPrSg\nv+63CIuuqAhPwcUFj4GzKbjShXu+kT2XqsEABkOK+PPGBeOkCWgnT423rk0NDka9fIly137j+rit\ngCDoeRDE1Q/xnfUtAnZfadmdvTs+ucsQeO46LTXHqPLvG34+JWSTEitpXAuQt0C9wdaElK2Kbs9+\nVPeT4OeL6vkSqVgxpMZNkLKY3+Kj3rmDcu6sWZG0lIZy6V+MLZoiOVTC4u9TqKGhqLdvod6/h3rx\nItqxLkjJuFfY6+9QNiyvcWPydgq2r45dtRLRxry//5JL36zj7ZWnhLwSHmrZKxQk76eVKPh5VbyO\n3sAqjy3F+zfGwtYGVVUjIqXhv215+c9oevRE/fsExlEj0fTrT2GvM9RcNwhjYAgBD7x4sfMC95yX\noZ5yR/n7BJrmzdE0M6XQF+MzunYVHj1E0669ydeHnv2MS2PO0P2m+Z/LfyGyt2nTJpycnLCysuLZ\ns2fkyRO/E3VmSjduJIvwDRgwgMKF00shLmXx4sULChWKm+qcPXuW+/fv06tXLx4/fkyxYsUAWJ2O\nOlcqoraqHiSbhB5A1GDVSu6kMhEBA6Kx4QUiIishZHYymsRN4wYw6g6cfwsGFapmh3Z5oVt+sD4S\n3Y81sZB/WIBm8JBokQ/l6JFUj/CpDx+g7N+H9puhZo3/3G439X/pS8dtMn6h8DAIClhB5exgPBD7\nM7DX38EVZ7L3u06DTbCgLFS3BY9gcPeFmwHgEwpvDOJfWx2UyQplbMTjXW6Ye0vD22HT0RQugrJ/\nL9pvh8G7d2jq1kvy+zZOmYh28rRojQKpDXnnDuQBQu7K4tot1BcvULZvQ2raFKlAQaRKn6SYHqBy\n+BCEhKD5PO7foxoYCG9eo7i7Q5kyQpvQZTyaNo4JEmjjtCloJ08V72vaZDRduiHPn4vm83bIS39C\nsrdHChNV1nzaSogox9N1GxXyyhVoOnZCioOwtJxbk1oP7lLwz/j3818geVGRK1cufHx8OHnyJA0b\nJmzsmUn44kaSUrpBQUE8e/aMAgViSqp+mHj8+DElSojV4oMHD3j+/DkWFhb06NEDT09PQkJCqFtX\niDeUKlWKoKCg9JxuBCSgLSIFdZKkdRuqCP/R9kDinVIzER8sETV+xYBZwGBgI8JSKzcikmRK7iGt\nEAK8awB1b8PIsVpmddRQpCjRbs7JIXsAqiEEjNEr5KSGjdDWrpPkfZoDqWQpsLXFOHE8mm7d0TjE\n70y9x7cdz/VfI3VdSf+vtZQMe96I6c/grk9ZnPWu/2fvvMOjqLo4/N6ZTegEQkcJvYiFJkW6NJGu\nSBMEBAQFUfwEEemCdBSkCQioiIgoXZAmvfdOKKHXhBISQpLdmfv9cZOQkLa72U0C5n2ePEl279x7\nk92dOXPK7zB3XhcWdjnN+FEGE65CjpxQtZGg5SsaOXJAdm/Ill3wIFBy/hycO2Oy7aTk6Kl0BE2c\nDYv/QAY+QBQqlOge7UFr0gy5agWiefL4ks0jh6OMPcvWHRi//ITwKYg+eqxbRJ/NvXvQuyWcdCIy\nZoSMPpAxA+a0KXgsW4kxdQqyVGnEi4nUyGsa3L2L+defaI2bopV+Eb4ahDnle/SffkErVNjpv0sG\n+Mdr7AGc3uJPv3pw1qnZn13++OMPfHx8KF68eEpv5anHKYNv4sSJDB8+HD0Z7yLdSd68edmwYQPV\nq1cnXbp0FCmiTvd+fn5cuHCB8uXLExioQgTnz59n48aNlCrliCyo+8iFqhyzRnzPjv35RBLVMq0G\nT1du2dOGFRiJMszzo/7vS1Ge2f3A6yjDL7nV06Z6w3P5slJ+9iAOlsrPwTjGJMXYA1QLqcBAiCY0\nmxw5fAD6e52QpqkkMr4ZHa/XS5omxrgxHChajuc/+okdT1zQ4/sfRBp9JWucIeuaxxWh5yK+ouO/\n9TTejYqwceAgXt8zDN8pAWiNmqA3aYYMC4MAf6RhJNkzp1WshG3QV4hmLZKny0bGCM9t2XKYixeh\n9+yNyJ3bLUvZxo5Ge/kVRJ6425JFIg0DY/gQ5JUrWOb8hNB1tJatELnzIG/eROSNv4Gb3ud/GGNG\noVWvgVaxEubhQ4jnnkdr3AStcJF4j0sMKWWc8kCRFPU4yMUTgVR+Lc3ge5K6dZO/B/SzilMh3REj\nRtCjRw9yu+mDndpYtmwZb72l7phfe+01du3axVtvvcVff/0V46SakuFdUOLQDVFacfZwBxUS7kPq\nCzM+S0xDySwUjOO5PSiZlqGoPs37UDlh7hZfLd8Bqv4lKPZwd4z8OlcjL/hhLl2C/r++jx9Lhhy+\n6JinT2H+sQiRMSNa6zYIn5ivhG3USHVBL+M6wRwZFAQWC8aokehdu2F8MwJ9+EgQAhFHZMT6dnMs\nYycgXODFMNevAw8PtNqvJ3muxAivVR2OH0P/dSH6m43cupa5aiXkyYNWMeHEE+OXnxAlS6E94UU2\nfp4HDx6g9/7UrvVkWBjG2NFobzSMNZejyNOnMY8fQ3+nVaznSnj78sE/rVjywSU6XI37+P9aGDcp\npIV048ep6/wnn3zCuHHjSMhYfJZo0aJFlIdvw4YN9OrVi6VLl1KnjgOqoMlAj4jvv9gx9hhKE+4z\n0ow9d3IH1XTdJ57nK6M8eyNRRt4dVOHMeFTI9bQb9pS3BdRbq5P71zFuNfYAKFQYGRqqdNgizhdy\n21aMPvbl1rkCrdQLWIYMQ+vxEcaM6THOW8bE8WiVqyTZ2JNSIq1WzL9XYR49gtGnN3LvHrQGb4CX\nF5bZcxH588dp7AFYfluEjDjHJBVRrz7mxg0umSs+ZFgYxvSpcP0agNuNPQBRrRrmLz/HvZ97dzFm\nzcQ2eCBkyhyngaZ17IzW4i1sX32Z6LVL+p3H1rwJ+uChSTf2Hj3CmDcnZuVyBJG5oNeHXCJrPMZe\nGmm4ikSv9Rs3boz1mJeXFy1atODbb791y6ZSI1mzZkVKScaMGZk6dSqenp5s3ryZAQMGpPTWotBQ\nPVrroqQkHsQz7j5KKNiHpPeGTSNh/FDGdWL/ZwvK8GuJCg12RL1Oh1AtyRaiOljcTOJ+SraBzrst\nZF85m1u1XS9vIK1W5M2byPPnkf7+YBjo77bH/PtvsNmAiBy+SVNcvnZiiCxZ0OrURW7e9HivQQ/Q\n6iZeYRkX0jQx9+3D3LkDY9RIzDmzo8J2+o/z0GrVRqtRE5HdO/HJAgMxpn3vkptoIQTilTKYh+IK\n0icdc/06jK+HoTV8E+7eRdSq7ZZ1nkRk94Y4igTllcsY/fuhNWyIPuxr9JbvxH28EJA7D1qt2nAz\n/k+SMWsm8u49LAsXJTksLgPvYwwZhN7nf3F2GfmSsZT65Dgz9ilx8jTScCeJhnSnT5+Ot7c3bdq0\nifX8+vXruXTpEt26dXPnHlMlNpsNjwjJB39/f3LmjKkzldLh3XWoilATVSAQndmovsCpQV7mWcYK\nHER58ZJCKMrz9wA4BRRByeiUQ72+CWc0PaZbfpj0LiwMf5XDQ2PfyDmLNAxl6Jw/jzFjKuLlVyBT\nZnjwQMmMnD2DaNAw6kKcHFW68e41QpDZMlKJHRuTvkVr0y5ez1uMY8PC4OJFpN955IMHyIMH0Bo3\ngbAwRPUakD59kgwEefo0MuQhWvkKTs8RNZfNhjFyOJZhI5I8VyTm8WOY/6xBe/kVpVUHyLt3IVs2\nl/Q+tgdj4ni07h/GkHgxT52Es2filTt5EmkY2N5sgOXnX2O87tJqRe7dAzYbolhxRAKqDXatc/Mm\nxoRx6IMGxyu2PNe7C37V/mLVTnhSZTEtjOscaSHd+Ek0Vahly5b8+OOPrFixgmbNmsV4rn79+gwe\nPJiQkBAyZkzJesPkx2KxcOvWLfLkyUOuXLlSXXi7AcoztBYV6hUoA2Q5Shsu+aVZ/3sEA9dcME96\nlDbic6iWToEoI+8yyut3DFURXCBibB7id92v3QUZGtiv7WYPtvZtEWXLIYoWQx80FOGdsEcrOap0\n411bCLSKFTH37kGrVBmsVoijK4I0DLh9G0IfYf67EVGkCMa8uehffIkMClKyIG+9HW9HBWeQVy4j\ngx6ACww+YbEgfAoiz59HFC2atH2dPYuxYD6ieAnlqYomQ5LYa+1qRLFiyIsX1E0FYK5bi7luLdrb\nLe2fQ9exrFmH+ftCRPHiaJUqI4OD4e4dzN8Xon8/NemevQeBGBPHo389UlUNx0NQQBiTD8Kz1cIg\njdRKordlHTp0oFWrVty5c4cRI0Zw8uRJpk2bxrRp01i9ejVWq5URI1x3F/k0kTt3bt54Q/VZOnPG\nXc3OnKcwytibheqiEYoK5aZ0T+D/CkewX5A3FCXZsgLoS8Kt87xQr2MFoDZKO/EVVMX1HVT4dycq\nrH8x4rGwiGNfLQ1n/z7vwF9hB5cvo3/6GXqr1nYZAMmdw/ckokkzzBXLMVcsh0KFwdsbGRyMuX4d\n8splbJ/0Qp4+he3TjyF9BsjqhahRC8tP89Feehm9dRtEpkwuNfYAtPoN4P595K1brpmv7bsYv//m\n9PHyymVsI4Zjbt2MPmAgevsOdmvOuY3MWZAnTqj9XbyAuW0rlgnfolWt5tA0QtcRBQogcuRUYdf/\nfYo8fw7LlGkuqW42Jo5H//KrBI09gO1jdtGmpBL6TiMNd5NoSHfPnj2MGzeOIUOGYBgGBw4cwDRN\njh49yhtvvMGDBw94++23yeCAIvyzRuQJYs+ePVSqFLuCLKXDu3dQ3r5VwLAU3cl/i3Woilt7fN/h\nQK9ov/fFsZ6a0TEBA1XwkRPYjDIIg5+D4NcFy06UwJjzh9Icy50Hkc5xQRgpJXLvHsx/1sDDh8rz\nk9++Rn/JXaUbta5pgmFg/rMa4/eFCCnRp0zHVrc2Hpu3Ygwbgj5mvArVOmhAuApjwXy0mrUQBeIr\n83FwvimT0Vq1SVCK5EnkrVsY8+YgvLzQOnZOsF1YSmAs+RN58BDCOztaz4+T1ErO+OUnjO8n47Fl\nu8v+TnPFMsicOdHuG389rEinEmcYEKqE2SEtjOsK0kK68ZOoh69SpUosXryYX375haxZs9KtWze6\nd+9O9erV2bdvH+3bt/9PG3sAu3fvBlQv3tTIFVRBQD1UGDB1yEY/2xxA3bXbm+jgiWqTVxuYiArV\njgCezLS7hMr1OZTAXBpK8+9llCewHTCmPvhXESw9UgRr8w4AGF8PQ548gbV9O8wTxzEmjkdeuYy5\neRMy8D7yzh0V2oyGvOCHbdBXGGNHw61b6IOHYhk3wW5jD9zr4ZNSYp48oXL1Zs5AGgbWDu8iw8Ox\nFi8MNhvmot/RXquKKFYM4e2Nx7adiGzZsUyagkifPsWMPQDtzcYYP8xw3Xwd3sP8db5dY+XduxgT\nxmMuXID+UU/0j3qlOmMPQH/7HaQm0D7smSRjz1yxXOWWjhmHOeuHWO91Z4gs5EnM2NvuXY3VTc9Q\nOZqxl8Z/DyHEXCHELSHEsWiPjRBCHBFCHBZCbBRCFIjn2ItCiKNCiENCiL1xjXkSuzJthRCMHj2a\nPn36EBISAkDbtm0JDg4mICDAnimeaSpXrszbb78NQK9evRIZnbyEATqq8rM6yhC5QprR525y4Xjn\nkjJAe9RrtQ9lnO+LeC4UGIOSbwFl0NmDFShQASrfEWyStTH/3obe+xNEwUJYZs9FK1cey/iJiKLF\noGhRyJxZee2Cg7G1boncuBFrg7qYWzZjbfMOtmlTwKcg+ocfqdy3kBDME8eRYWHKSDTNRPNZ7a3S\nlYahPIlXLiNtNsxdO5Hh4Rh/LEKGhmIbPhT58CHWNu8gHzwg/MVSEHgf47NPITgYefMmhIejd34f\nNA2P46cRGTLg8etCLB9/gnjueeSBA0nq8epysmVDlCmrvJEuQGT3BmkiH8Qv+SIfPMCYMhlzzmy0\nzu8rb61X6jZD9OZvYa5dE+txY8GvGBPGqQKbeJA2G/LSReTlS4igIFW16+kJD4OTvC95/BhaxYoJ\njtnuXY3iHx/n5yOO92BO45ljHko+NzrjpJRlpJRlgWUomda4kEBtKWU5KaVdXVHtLq3y8PBg1qxZ\nfP7559y7dw+AN998k/3799s7xTPNX3/9BcD06dMJS+Bkk5xIYALKyxMpNfsWyhiZgAr9peF6bgP/\noooonKUL0BmIbCIlgIeovMy+qPZs0akU7asiytCfDvTVob8U3Ok3gkczl8TpsRH580PoIwgOxvxx\nNqRPj/HTPETZssigB4hmzTFvXEfUqYs+fCQi9BEIobTewsIwRn4N9+9ja9QQbtzAWuYl5LWrWGtU\nRd64gbVpI+TNm1jfeQt56xa2hvWxffgB1lZvI2/fxvpWM/W93uvImzcJr/yqOq7sy3DjBrau78Pd\nuxizfoCgIOSxoxD6CFGkCEiJ3rc/pE+Px5794JUNj7UbEFmyYBk6HJEhA1q9+qqI4YlIhNatO+bf\nK5PwKrkeoWkILy/MKZNdNqf2boc4vXzy0SOMmTMwpn6P1rIVer/+iJxP1vSnTkT+/KoKPBrG5O8w\nBg5QNwTbtsZ5nLTZkHt2Y0wYj/7xJ4jixREeHui9emPr3g0zCdcz+egR5q/zEbXj12ct4e0LwLwT\n8BIqHzeN/y5Sym3AvSceC4r2a2YgIa+aQ+Fhhztt3L17l8GDB9OvXz8WLFjAG2+8wauvvurIms8s\nmzZtihJjju//mlz5fGGoLg4VgbgC7uGocGHOiDFpuI4w4AZQyM3r+KKqrnPnh0KFIFM6uBkIfrfB\nMOHrllBtpAcNsu7gzN3YGYHSNJHbt2Fu3qQ06pq3UDl9Scytk4YR1ZOUbNng0kUo4IM8fw5RpCjy\n9Cl4vgDcvIEoVhwuXIDChcHfH/LkgZAQyJxZdaVw8+fFNvobLAMGunUNR5E3bsCjENUP2EXYRgxX\nBl369MjwcMzffkVevozesROikL29eVIPMiQEc+r36F98GfVYeJWKcFaVO1k2bEIrF1PZTkqJrWF9\nLLPnQAGfWO8tGRSE9DsPDx86HNaXNpvS2/v4kwTTG0p4+/Kj+T7tc5yg1X0Y/ZTk7F26dAlPT09y\n5syJxWJJnrZ9TvK05fAJIQoBK6WUL0d77BuUoEYIUEVKef/JqYQQfijRBgOYKaWcndjyDpdceXt7\nM3bsWAYOHEiVKlUomsSS/2eJ2rVrR/38559/8s47cQuAuhsTFQIMIn75FU+UN0gH/gbqk1a96wok\n8B2Pu564i93A78AbxSC0ajYuCS+y50tHwXLZKF8wE4UrZCPMotGA/nEaewDmvDmI/M+hDxyc5B6u\n0YmaK1JoNsJwEaVeAED6+2N+P+mxDl/JiP1F6p5ljexKmzxIKVPVBUzky4et10dobdqi1azlkjn1\n1m0xfv8NkT4D8vRJ9LbtEZ27uGTuFCEsFHnlSoyHLNN+QPqdR2vxFsIjZtKDuWE90s8Py68LEbly\nxTmlyJJFdTsJDkbabA5VJJvfTkDv+oFduay7/gkmWzqlp5lasVqtnDt3jrp166JpGteuPRaYeuON\nNxg/fjzXrl3j3r17tGvXLgV3+mwipRwIDBRCfIm6pLwfx7BqUsobQohcwHohxOkIj2G8ONVLN434\n6dOnD5Mnq3DM/v37qVAhpqZWcnj4tqPCim/bMdYKbEKJNIehwr9pOI8N1SEjB+7rYnIU1QN5ZycI\n+ekl3pdzOHM8M/LQIeSpk0irFQBRqDCiXDlEiZJxGnQp5d1KqSrduDC3bUVev47epm1KbyUG8tpV\n8M4RKwydFKwfvI/+0ccuEXZOSWRYGMaAL9CHDk8011BarZgLF6C9Xgd5/z5ahH5fgsecO4fts0+w\nrPjbrhsB89xZzN8XYhk0JNGxJbx9yfJmHbrLYGxrU2dVrpQSLZqQdocOHfjhhx8wDAOLxcKUKVOY\nMWMG+fLl4+DBg5w4cYJixYql4I5j4rCHD3e+BpsjviIZbpeHL9pzPsBqKeVLCa0ihBgKBEspJyY0\nLoVFlZ5NPv30UyZPnkz//v3ZsCFmT8voH3B3GH/HUbkh9ib1e6BEmo+iwpCZUEUDaTjHUpTwcU0X\nzmkFDgMeZeG6Bbb7wrr/Qciwl+jCXM7eK4l4DsRzz0OTpkBESsHFC5iHDmIuXwYZM0JwMCJXbkTJ\nkoiSpVJMLFxu25pinTaeRFR5DXPCuJTeRmyyemF9rRIe+w7G8lY5i969J/LyJZcIO6co164iKlVJ\n3Ni7egU8PJEXL4J3DjQ7pW5EsWJY5v6MuWwJWpNmCf7/zRPHMSaMR2vV2q65fY9lIsteT9pcDUm1\n6hZ37tyJ+jmuc0T//v3p378/UkpKlSrFgwfxNfFMQ+ku1I72+/BEjxBCFJdSno34tTlxiDIIITIC\nupQySAiRCXUZT3TyNIPPxWzcuJHjx49H/ZycSJRsR3ZUVwZHeAUl4zEe1cc1Nw5U9KQBKO9eQ5QE\njquwAlOAnM9BhY55KeBTiC/LenGuaGbGJBCuFUJA4SLohR8HjqSUEBCA9D2NuXIF3EpqZ17nSMlO\nG08iDx1EK1M2pbcRC5ElCx7bdsL9+xBPCNJRtIoVsa1Yhmz+VqoKYTuKvHo10bZn8sEDzN8WIF4o\njWXIMMcX8fZGHjgA1Wogc+WK8/9lHj6EuWolomIltCqv2TWtuXIFzZs3T7XGHsCYMWMAWL16dZzP\n37t3jzZt2nDlyhW8vLwoWzb1fX6eFoQQC1GdTnMKIa6gKnIbCSFKonLzzgMfRYzND8yWUjYG8gJL\nIt6XFmCBlHJdYuulGXwupEiRIly4cCHqdx8f14in2kMYqvL2c5xvmyYijr8KzCCmEHAaiXMZWI3S\n03MFJqpm/4Xi0O/Ui9QK3Pn4ybuOzyeEgFy5ELlyIcuWw/x3g2pTlcxJ+6nKw/fyKxiDvkLUb+Ay\nT5qrkP9uwNyxA8u4CS6bU6tVG7l5E+L1+CtJUzvm5k3o/frH+ZyUEs6eVSHZVWucNmyFrmMZOQrb\nsCGIMmXQ33rcus34bQHS7zyiYEH0gYMxxoyyu8WcuWI57wwZ7NSekotIXdnhw4fz5ptvxnq+f//+\nrF+/nlq1ajFz5swY4d80HENKGVcC5Nx4xl4HGkf87Ac4bGmnvVIuJLqxB7Bly5YEx3eTMuorKYSi\njI0eJL1Hrg74oMR61wCnkjjffwmdiFsxF7EJ8MgPfY6UjmnsxYG8fAl58YLdYVp5/hxaqzaYK5Yr\ngeKIvL/kwF4dvuRAZMiAKFQIooWxUguiWQv0j3oi7zlh3cc3Z916mP8mb+TB5ZgmxFNkZHu/I9Ia\njuXPpS7xYuo9PkKrVgNzn9K1NXdsByGwDBqC/l4nCA9X+0mEMO+s+D0KItsZX+rVS1iUOaXZvn07\n06dPj1GoEZ22bdtSp04ddu7cSalSpQgPD0/mHabhLIkafGlFG/YjpcRms7F06VIAChcuzIgRI9yu\ny3cbOIaSWHEFAvAGSqOKOP5GybikET8m8Beu+z89BPZ6Qde/ilDv0a5ExxtzZmPu2IExZhS20d9g\nGzMKY/EfSF/fODsIyHPn0F4ojf5JH7TadTAGD8Q8cMBFu0+YlO6lGx1j+lQlAZMnT0pvJRZCCIyf\n5iKPHUt8sJ1If3+whmPeeXoF84WPDzxRoWtu2Yzx42wsg4YiXijtsmIXkS8f8tIlzI0bkPfuYq5d\ng9b2sVPG3LYFLZo6Q0IsWbKEpk2b4uniHsyu5syZM/Ts2ZOxY8fG+XydOnXYuHEjTZo0AZRGbxpP\nB4lW6c6bN4/OnTsn346eEQ4cOMDmzZvp27cvACNGjGDQoEHxjne2gGMVKt/OLpltJ4hexRuIEv5N\nIzZ+KBFVR7trRCcYOILqhnLRE+p0zM7qMRcS9VRIKTFGf4Plq8fvL2kYcP485tEjSr4iLBQAkSkT\nokRJzC2bVZVjRB9daZqYC39DXrmM3vNjhBulUVJTla5t0FdorVqnyjw+UFWm8p81aE2bJW0eX1+M\nxYvAywu9XfunRmA5LuSjRxhjR2MZ9rXS4/t5HlrjJsiAAIcqkKW/P+bmTWjvtEr0M2YG3sdWuyaW\ntevQcj/uSywD72N8MxJ9xDcJ9qQO885KzZo16devH02bNrV7jylBtR2RmgAAIABJREFUpNKEaZoJ\n/l8aNmxI+fLlGTZsWKoyYlNXlW6sFVO0l26iBh8oi7948eLJtqlniTt37pAz4uS6efNmatVKXFfL\nXuPvAsrISIeqrnUnvqiw8SuoTh1puQAx2YTyhpZy8vgf88GeG+rnXxtB/oUv0NC2265j5dUrmP9u\nRO/YOfGxQUHIM2fA/zZaw9j5OfL2bYzpU9AqVkZr3MSRP8FuzI0bUk0OnzRNzNkzkZ6eaJ3eT3X5\nSNI0MT75GH3s+CT1tbUNHIA+aIhLZV5SAnPlCsy9e9Df64QMDET4+GAumI/Wq3eCBldcWBvWR+7b\ni0fA/cQNvv37kXt2I6pXR5R6IcZa8tpVjJkz0IeNQER7/4R5P75punHjBqVLl+bmzZukc3CfyUmF\nChU4ePAgo0aNYsCAAQmOHTt2LCNGjGDixIn06OFu5VH7STP4EljdHoMPYMeOHVStWjVZNvWsYRgG\nq1atomfPnhw4cIC8efMmON4eg08Cs4B3SJpXyVFmAW+gwsepr616yuAPXMS5jiUSCKgOX22H54t6\nsuhkMTw8Narf3WH3HObypVCsONqLCUo1OYS5cQPmpn/RP+qVaEWko6QmD18k5r8bISQErYlrvS8y\nOBjj+0mq84gQEP18G/33yM+8lAiLB6RPB57pIH065P1A1ZXkpZfBsIFhgM1QP9uifRmG6sErBHh4\nQLS8TOnri969O1qVp/scbvuiL9o3oxHXrmL++SeiRg00Jyu+w4sXhrt38bwTf5/hSIyZM9BavI0x\ndhTa+11j6fmZx48h//kHvW+/qMeiG3xff/01Z8+eZf782C3uUgvR9ffy5MnDzZuJV/F//PHHnDhx\ngk2bNrl7e3aTZvDFT6JVugUKFODKlStUq1aNdu3a8dtvvyXHvp4pdF2nefPm7Nq1i3z58hEeHp6k\nvIe7wJ/AByS/p+0DVGh3IvAVqlDh6RV4cA3hOJ+7d+ZVmLAdiu7/mQwVXqAHxCu1Eh/m6dPojV1r\nqGh16ymNuhnTwTs7Wqf3XdaNIzVV6UYiatbC6NkDUb4CmAby6lXImBGRMRM895xTnjHzyGHMhb+h\nf/Y5woEcQWmzQWgohIVBaCjy7BlMqxWtenVVrKBbwGIBS/SfLaDrMTxMMea0WjFGjXzqDT5RrRpy\n0kTMy1ewTJmWpLm0xk0QLyUsxCzDwzG/nwxeXog8ebB8Oxnjrz8xdu1C7/7Yq6W99DKmvz/Gz/PQ\nO8VuirB48WJmz06081WKIoRg+PDhDB06lO+++86uY5o0acK5c+fcvLM0XEWiHr6ff/6ZXr16ERwc\nTI0aNdiyZQs2my0tUdMJbt68Sb58+fjhhx/sdoE/6e0LQVXl3gVSUtvchuro8QBoyn/X6JPAH0AL\n7Nffk6g+x7wE/5hZuD3nX0SJEk7vwdUdM2RQENwJgDx5ERkyYB47ivnrfLT3OqK9FEsM3vH5U6GH\nD0A+fIgxZTLCO4d6PR4+RIaEIPfsRv/iS0Tu3PbNY5oYEydA1qzoH3SP1whzBOP3hYgiRdAqVXZ+\njh+mozVtpgS6nzKkzQZWK9Za1dE+6I7erbvbtQTlmTMYP85C/6gnIrqe5bVrYLNCaBiiZMybsx//\nXo6XlxfNmj3OuTxw4ACvvvoqAQEB5MiRnPEYx1mxQukEXr58mQIFCiQ6fuDAgYwaNSpVFXemefji\nJ1EPX8eOHSlbtix9+/Zl9+7dUS7f4cOHM2RI4q1k0nhM3rx5OXDgANWqVaNo0aJOlecfB24CSUvh\nTjoWoAZK/+8H1H5cG/h7OjCA/Njfh1gCPwGPcsNzjUtwu8Nyu/pvxprHMOBhsArvuTjvzFy/Fvwu\ngEVHhoWpsGPWrBgff4RZ/w30j3sn2uUgIVKjhw9UQYvly69iPS4bNcY2dDBavfoIKZVBHBwEQUHI\n0FCElxdagzcQRYqqHMjvJkLRomjPPe8SYw9APPccIlv2JM2htXsXc/Ys9L5fuGRPyYWUUmklVqqE\n5e81mLNnudXYk1JiTvpWFbjEUYwhnntOtV/7eqjqzRttL++99x6TJk1iz549VK6sjHPDMChVqlSq\nN/YMw6B58+YAPGdnGseLL76Y6vJe04gfh3rpBgQE8OKLL3L79m0AqlWrxpo1a8iSJYvbN/osMXPm\nTD788ENKlCiBr69vgmOje/iWAeVQOnmpyaN2E+XdWgJ0IXXtzd2sBF7Afm/rEeDfHPC/S83pGfaL\nw+uZO3dgbtwAFgsimzK6tLr1k+QhjLXGP2sgTx60cuVjPSdv3cL4cRYiXz609zo5JVacWj188WGe\nPoX54yxEnXrK8MqSBTJngSxZIH16CAjAXLtGtfASAv3TzxCZM6tCiSHDHC4miAtpmti6dMIydUaS\n/m+2MaPQP/7k6fnf79mNOW8u+thxkNVLSdUs+h2ROzeai8WjpZRw5gzGz/PQ2rRNtHJbGgbGZ5+g\nDx6GiOiGEuadFSklI0eOxGazAUqPtXTp0kyfPt2l+3U1a9eupWHDhhQpUoTz58/bdcyxY8do1KgR\n58+fTzWVumkevgRWd8Tgi+S9997j119/BSBHjhz8+eeflCxZkosXL/Laa/a1mPmvs3jxYlq3bs2+\nfft49dVXEx1/5coV/Pz8KFu2LF5eXoB7evE6iw04h5JxCQKe7kwh+4lsZWePiIkEugP5ckKR7lXZ\nXbALWq3aURcLe7ANG6LkVNz42ptbt4Cuo1WrHv+Y06dUmLdqNcSbjRzaT2qq0k0IaZqY8+ZAWBha\n9w8RFscaE5mnTyEPHEBv38El+zG3bUVUqpwkA1JevIC5YQN6tw9csid3IcPDsb3fEcvkqfAoBPFE\nH1zbJ72wfJ+0HD6IkL3ZvQtz5w51E1KiJNqbjRKVJYosyFizZg1ly5YlW7ZscbZLmzZtGh9//DFb\nt26lRo0aSd6vO3n33XdZuHBhohJi0QkJCSFTpkwcPXqUl19OerqHK0gz+BJY3RmDD+CXX36hU6dO\nsR5PTLsnjccsXbqUbt26sWjRogTDuw8fPqRWrVps3ryZzNHuzFOTwRfJLVSeoR/wEpD65Gxdx0HU\n3/mOneNvAZGn0f4P+zLx8DvILZuVGK6HB1rFiogqVREZM8Y7h23MqDjDjq7E3LcP7t9Dq98g8bGb\nN2Gu/QetdZs4PYJx8TR4+OT16xjfT0Jr2w6tbDmn57H1/R/6oMFJDsdCRO/WaVOwzI6z85L9exo2\nWHmlXFSE42qM7yYiKlUGT0/EqxVjXE/k/XuYP/8UZZg5i7xzB2PWDwBoVaogqlZ3yJCOXoH7+eef\nU6VKFVq1ahVjzNy5c+natataLxXluMXFxYsXKVxYqazeunWL3HbmqwJkypSJ69evRzkiUpo0gy+B\n1Z01+ADOnz9PsWIxg1njxo2jX79+8RyRxpNEilyuW7eO+vXrx3r+1KlTLFu2jP79+8fKlUiNBl8k\nu1Ghzt1AXZ7Nps2hKLFkRyRsCwG9CsKZS+Bx/DQiXz5AGUFy/z7krp3IR48QWbMiqtdAlCkbdWGW\nVivm5O/cnoNlnjwB586iNWth13hpGJiLfkee8UXv9gHi+YSTvVO7h89cvhTz5EkVmk3A+LYHefs2\nxrw5CJ+C6O3eTdpcjx6B/2147vkkGWvmnt3g7+9yCZqkYu7bhzx4QBl5BQvGEoc29+3FXL4MvWu3\nGEUUoD4b3LsH3t6xPLHyzBnIm1d1VAkOQh45grlhHfrn/RBZnTNSoht8pmly5MgRtmzZQp8+faIe\nL1GiBGfPnmXRokW0bt3aqXWSi0ijetq0afTs2dOhYzNnzszVq1fJls35vF5XkmbwJbB6Ugw+gODg\n4Fg5fFOnTqVXr14u2eB/gS1btlC7dm2qV6/O+vXrSZ9edcQNDAzk4cOHHDp0iMaNGyc4R2o0/h4B\nW4DyKO9W6nD4u4ZQYDgwAseM2UpAno2Qty7oM39Ef6dVnONk4H3k9u2YR4+AYajcsRdKI0+fQu/c\nJcn7Twh58QLmrl0OGyjy4UPMObPBakX7oEe8YbHU6uGTd+9izpuLKFsWra5r+50a30+CcCt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VuwllSeF23kN1g++tiVW091xOfhSwh58yYiS2bMUyeRhw8jz5+DwEBE3ryIMmXR2ndAZEsp32/i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\n/1z3AAAAAElFTkSuQmCC\n", | |
"text": [ | |
"<matplotlib.figure.Figure at 0xa085c18>" | |
] | |
} | |
], | |
"prompt_number": 36 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"As shown, the data values line up with the coastlines, so it appears that the georeferencing worked just fine. " | |
] | |
} | |
], | |
"metadata": {} | |
} | |
] | |
} |
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