From 47fb72b0c47a6115e6d4f234d206c059cedb3d46 Mon Sep 17 00:00:00 2001 From: Mike Bauer Date: Tue, 26 Mar 2024 12:14:32 -0400 Subject: [PATCH 1/2] Added SNODAS sidecar creator Added SNODAS sidecar creator --- staremaster/create_sidecar_files.py | 5 + staremaster/products/__init__.py | 1 + staremaster/products/snodas.py | 1740 +++++++++++++++++++++++++++ 3 files changed, 1746 insertions(+) create mode 100644 staremaster/products/snodas.py diff --git a/staremaster/create_sidecar_files.py b/staremaster/create_sidecar_files.py index f82f236..564470c 100755 --- a/staremaster/create_sidecar_files.py +++ b/staremaster/create_sidecar_files.py @@ -17,6 +17,9 @@ def create_grid_sidecar(grid, out_path, n_workers): granule = staremaster.products.IMERG() elif grid[0] == 'h' and grid[3] == 'v': granule = staremaster.products.ModisTile(grid) + elif grid == 'snodas': + granule = staremaster.products.SNODAS() + granule.load() else: print('unknown grid') exit() @@ -46,6 +49,8 @@ def create_sidecar(file_path, n_workers, product, cover_res, out_path, archive): granule = staremaster.products.ATMS(file_path) elif product == 'GOES_ABI_FIXED_GRID': granule = staremaster.products.GOES_ABI_FIXED_GRID(file_path) + elif product == 'SNODAS': + granule = staremaster.products.SNODAS(file_path) else: print('product not supported') print('supported products are {}'.format(get_installed_products())) diff --git a/staremaster/products/__init__.py b/staremaster/products/__init__.py index 7abcf9c..0a6d766 100644 --- a/staremaster/products/__init__.py +++ b/staremaster/products/__init__.py @@ -8,3 +8,4 @@ from staremaster.products.satcorps import satCORPS from staremaster.products.modis_tilegrid import ModisTile from staremaster.products.goes_abi_fixed_grid import GOES_ABI_FIXED_GRID +from staremaster.products.snodas import SNODAS diff --git a/staremaster/products/snodas.py b/staremaster/products/snodas.py new file mode 100644 index 0000000..9e668fd --- /dev/null +++ b/staremaster/products/snodas.py @@ -0,0 +1,1740 @@ +#! /usr/bin/env python -tt +# -*- coding: utf-8; mode: python -*- +r"""Sidecar creation utility for Snow Data Assimilation System (SNODAS) Data Products + +snodas.py +~~~~~~~~~ + +Edit: STAREMaster_py/staremaster/products/__init__.py + Add: + from staremaster.products.snodas import SNODAS + +Edit: STAREMaster_py/staremaster/create_sidecar_file.py + Add: + def create_grid_sidecar(): + elif grid == 'snodas': + granule = staremaster.products.SNODAS() + granule.load() + def create_sidecar(): + elif product == 'SNODAS': + granule = staremaster.products.SNODAS(file_path) + +Create sidecar file: + + $ cd /Users/mbauer/SpatioTemporal/STAREMaster_py/staremaster + + $ python create_sidecar_files.py --workers 1 --product snodas --grid SNODAS --out_path /Users/mbauer/tmp/data/snodas/ + +""" +# Standard Imports +import os +import pickle + +# Third-Party Imports +# import netCDF4 +import numpy as np + +# STARE Imports +from staremaster.sidecar import Sidecar +import staremaster.conversions +import pystare + +## +# List of Public objects from this module. +__all__ = ['SNODAS'] + +## +# Markup Language Specification (see Google Python Style Guide https://google.github.io/styleguide/pyguide.html) +__docformat__ = "Google en" +# ------------------------------------------------------------------------------ + + +############################################################################### +# PUBLIC Class: SNODAS +# -------------------- +class SNODAS: + """Specification for the data-grid of the Snow Data Assimilation System (SNODAS). + ================================================================================= + SNODAS NATIVE RESOLUTION + Extent + Southernmost Latitude 24.9500 N + Northernmost Latitude 52.8750 N + Westernmost Longitude 124.7333 W + Easternmost Longitude 66.9417 W + Dimensions: + nlon = 6935 + nlat = 3351 + """ + + ########################################################################### + # PRIVATE Instance-Constructor: __init__() + # ---------------------------------------- + def __init__(self, ): + """Initialize SNODAS""" + ## + # Declare Public-Attributes + # print("\t__init__():") + + # Array of grid/point mid-point/center latitudes (units: deg, format: +/-90) + self.lats = [] + + # Array of grid/point mid-point/center longitudes (units: deg, format: +/-180) + self.lons = [] + + # Array of STARE Spatial Indices (SIDs) + self.sids = [] + + # Array of STARE cover SIDs + self.cover_sids = [] + + # String identifier for data-grids with different resolutions (N/A to SNODAS) + self.nom_res = '' + + ########################################################################### + # PUBLIC Instance-Method: load() + # ------------------------------ + def load(self): + # print("\tload():") + self.get_latlon() + + ########################################################################### + # PUBLIC Instance-Method: get_latlon() + # ------------------------------------ + def get_latlon(self): + """Data Grid defined by SNODAS.""" + # print("\tget_latlon():") + + # Number of columns/longitudes of the data-grid. + nlon = 6935 + + # Number of rows/latitudes of the data-grid. + nlat = 3351 + + # Number of enumerated data-points in the data-grid (nlon * nlat). + maxid = 23239185 + + # Mid-point longitudes of data-grid (-124.7296 to -66.9463) + snodas_lons = [-124.7296, -124.7213, -124.7129, -124.7046, -124.6963, -124.6879, -124.6796, -124.6713, + -124.6629, -124.6546, -124.6463, -124.6379, -124.6296, -124.6213, -124.6129, -124.6046, + -124.5963, -124.5879, -124.5796, -124.5713, -124.5629, -124.5546, -124.5463, -124.5379, + -124.5296, -124.5213, -124.5129, -124.5046, -124.4963, -124.4879, -124.4796, -124.4713, + -124.4629, -124.4546, -124.4463, -124.4379, -124.4296, -124.4213, -124.4129, -124.4046, + -124.3963, -124.3879, -124.3796, -124.3713, -124.3629, -124.3546, -124.3463, -124.3379, + -124.3296, -124.3213, -124.3129, -124.3046, -124.2963, -124.2879, -124.2796, -124.2713, + -124.2629, -124.2546, -124.2463, -124.2379, -124.2296, -124.2213, -124.2129, -124.2046, + -124.1963, -124.1879, -124.1796, -124.1713, -124.1629, -124.1546, -124.1463, -124.1379, + -124.1296, -124.1213, -124.1129, -124.1046, -124.0963, -124.0879, -124.0796, -124.0713, + -124.0629, -124.0546, -124.0463, -124.0379, -124.0296, -124.0213, -124.0129, -124.0046, + -123.9963, -123.9879, -123.9796, -123.9713, -123.9629, -123.9546, -123.9463, -123.9379, + -123.9296, -123.9213, -123.9129, -123.9046, -123.8963, -123.8879, -123.8796, -123.8713, + -123.8629, -123.8546, -123.8463, -123.8379, -123.8296, -123.8213, -123.8129, -123.8046, + -123.7963, -123.7879, -123.7796, -123.7713, -123.7629, -123.7546, -123.7463, -123.7379, + -123.7296, -123.7213, -123.7129, -123.7046, -123.6963, -123.6879, -123.6796, -123.6713, + -123.6629, -123.6546, -123.6463, -123.6379, -123.6296, -123.6213, -123.6129, -123.6046, + -123.5963, -123.5879, -123.5796, -123.5713, -123.5629, -123.5546, -123.5463, -123.5379, + -123.5296, -123.5213, -123.5129, -123.5046, -123.4963, -123.4879, -123.4796, -123.4713, + -123.4629, -123.4546, -123.4463, -123.4379, -123.4296, -123.4213, -123.4129, -123.4046, + -123.3963, -123.3879, -123.3796, -123.3713, -123.3629, -123.3546, -123.3463, -123.3379, + -123.3296, -123.3213, -123.3129, -123.3046, -123.2963, -123.2879, -123.2796, -123.2713, + -123.2629, -123.2546, -123.2463, -123.2379, -123.2296, -123.2213, -123.2129, -123.2046, + -123.1963, -123.1879, -123.1796, -123.1713, -123.1629, -123.1546, -123.1463, -123.1379, + -123.1296, -123.1213, -123.1129, -123.1046, -123.0963, -123.0879, -123.0796, -123.0713, + -123.0629, -123.0546, -123.0463, -123.0379, -123.0296, -123.0213, -123.0129, -123.0046, + -122.9963, -122.9879, -122.9796, -122.9713, -122.9629, -122.9546, -122.9463, -122.9379, + -122.9296, -122.9213, -122.9129, -122.9046, -122.8963, -122.8879, -122.8796, -122.8713, + -122.8629, -122.8546, -122.8463, -122.8379, -122.8296, -122.8213, -122.8129, -122.8046, + -122.7963, -122.7879, -122.7796, -122.7713, -122.7629, -122.7546, -122.7463, -122.7379, + -122.7296, -122.7213, -122.7129, -122.7046, -122.6963, -122.6879, -122.6796, -122.6713, + -122.6629, -122.6546, -122.6463, -122.6379, -122.6296, -122.6213, -122.6129, -122.6046, + -122.5963, -122.5879, -122.5796, -122.5713, -122.5629, -122.5546, -122.5463, -122.5379, + -122.5296, -122.5213, -122.5129, -122.5046, -122.4963, -122.4879, -122.4796, -122.4713, + -122.4629, -122.4546, -122.4463, -122.4379, -122.4296, -122.4213, -122.4129, -122.4046, + -122.3963, -122.3879, -122.3796, -122.3713, -122.3629, -122.3546, -122.3463, -122.3379, + -122.3296, -122.3213, -122.3129, -122.3046, -122.2963, -122.2879, -122.2796, -122.2713, + -122.2629, -122.2546, -122.2463, -122.2379, -122.2296, -122.2213, -122.2129, -122.2046, + -122.1963, -122.1879, -122.1796, -122.1713, -122.1629, -122.1546, -122.1463, -122.1379, + -122.1296, -122.1213, -122.1129, -122.1046, -122.0963, -122.0879, -122.0796, -122.0713, + -122.0629, -122.0546, -122.0463, -122.0379, -122.0296, -122.0213, -122.0129, -122.0046, + -121.9963, -121.9879, -121.9796, -121.9713, -121.9629, -121.9546, -121.9463, -121.9379, + -121.9296, -121.9213, -121.9129, -121.9046, -121.8963, -121.8879, -121.8796, -121.8713, + -121.8629, -121.8546, -121.8463, -121.8379, -121.8296, -121.8213, -121.8129, -121.8046, + -121.7963, -121.7879, -121.7796, -121.7713, -121.7629, -121.7546, -121.7463, -121.7379, + -121.7296, -121.7213, -121.7129, -121.7046, -121.6963, -121.6879, -121.6796, -121.6713, + -121.6629, -121.6546, -121.6463, -121.6379, -121.6296, -121.6213, -121.6129, -121.6046, + -121.5963, -121.5879, -121.5796, -121.5713, -121.5629, -121.5546, -121.5463, -121.5379, + -121.5296, -121.5213, -121.5129, -121.5046, -121.4963, -121.4879, -121.4796, -121.4713, + -121.4629, -121.4546, -121.4463, -121.4379, -121.4296, -121.4213, -121.4129, -121.4046, + -121.3963, -121.3879, -121.3796, -121.3713, -121.3629, -121.3546, -121.3463, -121.3379, + -121.3296, -121.3213, -121.3129, -121.3046, -121.2963, -121.2879, -121.2796, -121.2713, + -121.2629, -121.2546, -121.2463, -121.2379, -121.2296, -121.2213, -121.2129, -121.2046, + -121.1963, -121.1879, -121.1796, -121.1713, -121.1629, -121.1546, -121.1463, -121.1379, + -121.1296, -121.1213, -121.1129, -121.1046, -121.0963, -121.0879, -121.0796, -121.0713, + -121.0629, -121.0546, -121.0463, -121.0379, -121.0296, -121.0213, -121.0129, -121.0046, + -120.9963, -120.9879, -120.9796, -120.9713, -120.9629, -120.9546, -120.9463, -120.9379, + -120.9296, -120.9213, -120.9129, -120.9046, -120.8963, -120.8879, -120.8796, -120.8713, + -120.8629, -120.8546, -120.8463, -120.8379, -120.8296, -120.8213, -120.8129, -120.8046, + -120.7963, -120.7879, -120.7796, -120.7713, -120.7629, -120.7546, -120.7463, -120.7379, + -120.7296, -120.7213, -120.7129, -120.7046, -120.6963, -120.6879, -120.6796, -120.6713, + -120.6629, -120.6546, -120.6463, -120.6379, -120.6296, -120.6213, -120.6129, -120.6046, + -120.5963, -120.5879, -120.5796, -120.5713, -120.5629, -120.5546, -120.5463, -120.5379, + -120.5296, -120.5213, -120.5129, -120.5046, -120.4963, -120.4879, -120.4796, -120.4713, + -120.4629, -120.4546, -120.4463, -120.4379, -120.4296, -120.4213, -120.4129, -120.4046, + -120.3963, -120.3879, -120.3796, -120.3713, -120.3629, -120.3546, -120.3463, -120.3379, + -120.3296, -120.3213, -120.3129, -120.3046, -120.2963, -120.2879, -120.2796, -120.2713, + -120.2629, -120.2546, -120.2463, -120.2379, -120.2296, -120.2213, -120.2129, -120.2046, + -120.1963, -120.1879, -120.1796, -120.1713, -120.1629, -120.1546, -120.1463, -120.1379, + -120.1296, -120.1213, -120.1129, -120.1046, -120.0963, -120.0879, -120.0796, -120.0713, + -120.0629, -120.0546, -120.0463, -120.0379, -120.0296, -120.0213, -120.0129, -120.0046, + -119.9963, -119.9879, -119.9796, -119.9713, -119.9629, -119.9546, -119.9463, -119.9379, + -119.9296, -119.9213, -119.9129, -119.9046, -119.8963, -119.8879, -119.8796, -119.8713, + -119.8629, -119.8546, -119.8463, -119.8379, -119.8296, -119.8213, -119.8129, -119.8046, + -119.7963, -119.7879, -119.7796, -119.7713, -119.7629, -119.7546, -119.7463, -119.7379, + -119.7296, -119.7213, -119.7129, -119.7046, -119.6963, -119.6879, -119.6796, -119.6713, + -119.6629, -119.6546, -119.6463, -119.6379, -119.6296, -119.6213, -119.6129, -119.6046, + -119.5963, -119.5879, -119.5796, -119.5713, -119.5629, -119.5546, -119.5463, -119.5379, + -119.5296, -119.5213, -119.5129, -119.5046, -119.4963, -119.4879, -119.4796, -119.4713, + -119.4629, -119.4546, -119.4463, -119.4379, -119.4296, -119.4213, -119.4129, -119.4046, + -119.3963, -119.3879, -119.3796, -119.3713, -119.3629, -119.3546, -119.3463, -119.3379, + -119.3296, -119.3213, -119.3129, -119.3046, -119.2963, -119.2879, -119.2796, -119.2713, + -119.2629, -119.2546, -119.2463, -119.2379, -119.2296, -119.2213, -119.2129, -119.2046, + -119.1963, -119.1879, -119.1796, -119.1713, -119.1629, -119.1546, -119.1463, -119.1379, + -119.1296, -119.1213, -119.1129, -119.1046, -119.0963, -119.0879, -119.0796, -119.0713, + -119.0629, -119.0546, -119.0463, -119.0379, -119.0296, -119.0213, -119.0129, -119.0046, + -118.9963, -118.9879, -118.9796, -118.9713, -118.9629, -118.9546, -118.9463, -118.9379, + -118.9296, -118.9213, -118.9129, -118.9046, -118.8963, -118.8879, -118.8796, -118.8713, + -118.8629, -118.8546, -118.8463, -118.8379, -118.8296, -118.8213, -118.8129, -118.8046, + -118.7963, -118.7879, -118.7796, -118.7713, -118.7629, -118.7546, -118.7463, -118.7379, + -118.7296, -118.7213, -118.7129, -118.7046, -118.6963, -118.6879, -118.6796, -118.6713, + -118.6629, -118.6546, -118.6463, -118.6379, -118.6296, -118.6213, -118.6129, -118.6046, + -118.5963, -118.5879, -118.5796, -118.5713, -118.5629, -118.5546, -118.5463, -118.5379, + -118.5296, -118.5213, -118.5129, -118.5046, -118.4963, -118.4879, -118.4796, -118.4713, + -118.4629, -118.4546, -118.4463, -118.4379, -118.4296, -118.4213, -118.4129, -118.4046, + -118.3963, -118.3879, -118.3796, -118.3713, -118.3629, -118.3546, -118.3463, -118.3379, + -118.3296, -118.3213, -118.3129, -118.3046, -118.2963, -118.2879, -118.2796, -118.2713, + -118.2629, -118.2546, -118.2463, -118.2379, -118.2296, -118.2213, -118.2129, -118.2046, + -118.1963, -118.1879, -118.1796, -118.1713, -118.1629, -118.1546, -118.1463, -118.1379, + -118.1296, -118.1213, -118.1129, -118.1046, -118.0963, -118.0879, -118.0796, -118.0713, + -118.0629, -118.0546, -118.0463, -118.0379, -118.0296, -118.0213, -118.0129, -118.0046, + -117.9963, -117.9879, -117.9796, -117.9713, -117.9629, -117.9546, -117.9463, -117.9379, + -117.9296, -117.9213, -117.9129, -117.9046, -117.8963, -117.8879, -117.8796, -117.8713, + -117.8629, -117.8546, -117.8463, -117.8379, -117.8296, -117.8213, -117.8129, -117.8046, + -117.7963, -117.7879, -117.7796, -117.7713, -117.7629, -117.7546, -117.7463, -117.7379, + -117.7296, -117.7213, -117.7129, -117.7046, -117.6963, -117.6879, -117.6796, -117.6713, + -117.6629, -117.6546, -117.6463, -117.6379, -117.6296, -117.6213, -117.6129, -117.6046, + -117.5963, -117.5879, -117.5796, -117.5713, -117.5629, -117.5546, -117.5463, -117.5379, + -117.5296, -117.5213, -117.5129, -117.5046, -117.4963, -117.4879, -117.4796, -117.4713, + -117.4629, -117.4546, -117.4463, -117.4379, -117.4296, -117.4213, -117.4129, -117.4046, + -117.3963, -117.3879, -117.3796, -117.3713, -117.3629, -117.3546, -117.3463, -117.3379, + -117.3296, -117.3213, -117.3129, -117.3046, -117.2963, -117.2879, -117.2796, -117.2713, + -117.2629, -117.2546, -117.2463, -117.2379, -117.2296, -117.2213, -117.2129, -117.2046, + -117.1963, -117.1879, -117.1796, -117.1713, -117.1629, -117.1546, -117.1463, -117.1379, + -117.1296, -117.1213, -117.1129, -117.1046, -117.0963, -117.0879, -117.0796, -117.0713, + -117.0629, -117.0546, -117.0463, -117.0379, -117.0296, -117.0213, -117.0129, -117.0046, + -116.9963, -116.9879, -116.9796, -116.9713, -116.9629, -116.9546, -116.9463, -116.9379, + -116.9296, -116.9213, -116.9129, -116.9046, -116.8963, -116.8879, -116.8796, -116.8713, + -116.8629, -116.8546, -116.8463, -116.8379, -116.8296, -116.8213, -116.8129, -116.8046, + -116.7963, -116.7879, -116.7796, -116.7713, -116.7629, -116.7546, -116.7463, -116.7379, + -116.7296, -116.7213, -116.7129, -116.7046, -116.6963, -116.6879, -116.6796, -116.6713, + -116.6629, -116.6546, -116.6463, -116.6379, -116.6296, -116.6213, -116.6129, -116.6046, + -116.5963, -116.5879, -116.5796, -116.5713, -116.5629, -116.5546, -116.5463, -116.5379, + -116.5296, -116.5213, -116.5129, -116.5046, -116.4963, -116.4879, -116.4796, -116.4713, + -116.4629, -116.4546, -116.4463, -116.4379, -116.4296, -116.4213, -116.4129, -116.4046, + -116.3963, -116.3879, -116.3796, -116.3713, -116.3629, -116.3546, -116.3463, -116.3379, + -116.3296, -116.3213, -116.3129, -116.3046, -116.2963, -116.2879, -116.2796, -116.2713, + -116.2629, -116.2546, -116.2463, -116.2379, -116.2296, -116.2213, -116.2129, -116.2046, + -116.1963, -116.1879, -116.1796, -116.1713, -116.1629, -116.1546, -116.1463, -116.1379, + -116.1296, -116.1213, -116.1129, -116.1046, -116.0963, -116.0879, -116.0796, -116.0713, + -116.0629, -116.0546, -116.0463, -116.0379, -116.0296, -116.0213, -116.0129, -116.0046, + -115.9963, -115.9879, -115.9796, -115.9713, -115.9629, -115.9546, -115.9463, -115.9379, + -115.9296, -115.9213, -115.9129, -115.9046, -115.8963, -115.8879, -115.8796, -115.8713, + -115.8629, -115.8546, -115.8463, -115.8379, -115.8296, -115.8213, -115.8129, -115.8046, + -115.7963, -115.7879, -115.7796, -115.7713, -115.7629, -115.7546, -115.7463, -115.7379, + -115.7296, -115.7213, -115.7129, -115.7046, -115.6963, -115.6879, -115.6796, -115.6713, + -115.6629, -115.6546, -115.6463, -115.6379, -115.6296, -115.6213, -115.6129, -115.6046, + -115.5963, -115.5879, -115.5796, -115.5713, -115.5629, -115.5546, -115.5463, -115.5379, + -115.5296, -115.5213, -115.5129, -115.5046, -115.4963, -115.4879, -115.4796, -115.4713, + -115.4629, -115.4546, -115.4463, -115.4379, -115.4296, -115.4213, -115.4129, -115.4046, + -115.3963, -115.3879, -115.3796, -115.3713, -115.3629, -115.3546, -115.3463, -115.3379, + -115.3296, -115.3213, -115.3129, -115.3046, -115.2963, -115.2879, -115.2796, -115.2713, + -115.2629, -115.2546, -115.2463, -115.2379, -115.2296, -115.2213, -115.2129, -115.2046, + -115.1963, -115.1879, -115.1796, -115.1713, -115.1629, -115.1546, -115.1463, -115.1379, + -115.1296, -115.1213, -115.1129, -115.1046, -115.0963, -115.0879, -115.0796, -115.0713, + -115.0629, -115.0546, -115.0463, -115.0379, -115.0296, -115.0213, -115.0129, -115.0046, + -114.9963, -114.9879, -114.9796, -114.9713, -114.9629, -114.9546, -114.9463, -114.9379, + -114.9296, -114.9213, -114.9129, -114.9046, -114.8963, -114.8879, -114.8796, -114.8713, + -114.8629, -114.8546, -114.8463, -114.8379, -114.8296, -114.8213, -114.8129, -114.8046, + -114.7963, -114.7879, -114.7796, -114.7713, -114.7629, -114.7546, -114.7463, -114.7379, + -114.7296, -114.7213, -114.7129, -114.7046, -114.6963, -114.6879, -114.6796, -114.6713, + -114.6629, -114.6546, -114.6463, -114.6379, -114.6296, -114.6213, -114.6129, -114.6046, + -114.5963, -114.5879, -114.5796, -114.5713, -114.5629, -114.5546, -114.5463, -114.5379, + -114.5296, -114.5213, -114.5129, -114.5046, -114.4963, -114.4879, -114.4796, -114.4713, + -114.4629, -114.4546, -114.4463, -114.4379, -114.4296, -114.4213, -114.4129, -114.4046, + -114.3963, -114.3879, -114.3796, -114.3713, -114.3629, -114.3546, -114.3463, -114.3379, + -114.3296, -114.3213, -114.3129, -114.3046, -114.2963, -114.2879, -114.2796, -114.2713, + -114.2629, -114.2546, -114.2463, -114.2379, -114.2296, -114.2213, -114.2129, -114.2046, + -114.1963, -114.1879, -114.1796, -114.1713, -114.1629, -114.1546, -114.1463, -114.1379, + -114.1296, -114.1213, -114.1129, -114.1046, -114.0963, -114.0879, -114.0796, -114.0713, + -114.0629, -114.0546, -114.0463, -114.0379, -114.0296, -114.0213, -114.0129, -114.0046, + -113.9963, -113.9879, -113.9796, -113.9713, -113.9629, -113.9546, -113.9463, -113.9379, + -113.9296, -113.9213, -113.9129, -113.9046, -113.8963, -113.8879, -113.8796, -113.8713, + -113.8629, -113.8546, -113.8463, -113.8379, -113.8296, -113.8213, -113.8129, -113.8046, + -113.7963, -113.7879, -113.7796, -113.7713, -113.7629, -113.7546, -113.7463, -113.7379, + -113.7296, -113.7213, -113.7129, -113.7046, -113.6963, -113.6879, -113.6796, -113.6713, + -113.6629, -113.6546, -113.6463, -113.6379, -113.6296, -113.6213, -113.6129, -113.6046, + -113.5963, -113.5879, -113.5796, -113.5713, -113.5629, -113.5546, -113.5463, -113.5379, + -113.5296, -113.5213, -113.5129, -113.5046, -113.4963, -113.4879, -113.4796, -113.4713, + -113.4629, -113.4546, -113.4463, -113.4379, -113.4296, -113.4213, -113.4129, -113.4046, + -113.3963, -113.3879, -113.3796, -113.3713, -113.3629, -113.3546, -113.3463, -113.3379, + -113.3296, -113.3213, -113.3129, -113.3046, -113.2963, -113.2879, -113.2796, -113.2713, + -113.2629, -113.2546, -113.2463, -113.2379, -113.2296, -113.2213, -113.2129, -113.2046, + -113.1963, -113.1879, -113.1796, -113.1713, -113.1629, -113.1546, -113.1463, -113.1379, + -113.1296, -113.1213, -113.1129, -113.1046, -113.0963, -113.0879, -113.0796, -113.0713, + -113.0629, -113.0546, -113.0463, -113.0379, -113.0296, -113.0213, -113.0129, -113.0046, + -112.9963, -112.9879, -112.9796, -112.9713, -112.9629, -112.9546, -112.9463, -112.9379, + -112.9296, -112.9213, -112.9129, -112.9046, -112.8963, -112.8879, -112.8796, -112.8713, + -112.8629, -112.8546, -112.8463, -112.8379, -112.8296, -112.8213, -112.8129, -112.8046, + -112.7963, -112.7879, -112.7796, -112.7713, -112.7629, -112.7546, -112.7463, -112.7379, + -112.7296, -112.7213, -112.7129, -112.7046, -112.6963, -112.6879, -112.6796, -112.6713, + -112.6629, -112.6546, -112.6463, -112.6379, -112.6296, -112.6213, -112.6129, -112.6046, + -112.5963, -112.5879, -112.5796, -112.5713, -112.5629, -112.5546, -112.5463, -112.5379, + -112.5296, -112.5213, -112.5129, -112.5046, -112.4963, -112.4879, -112.4796, -112.4713, + -112.4629, -112.4546, -112.4463, -112.4379, -112.4296, -112.4213, -112.4129, -112.4046, + -112.3963, -112.3879, -112.3796, -112.3713, -112.3629, -112.3546, -112.3463, -112.3379, + -112.3296, -112.3213, -112.3129, -112.3046, -112.2963, -112.2879, -112.2796, -112.2713, + -112.2629, -112.2546, -112.2463, -112.2379, -112.2296, -112.2213, -112.2129, -112.2046, + -112.1963, -112.1879, -112.1796, -112.1713, -112.1629, -112.1546, -112.1463, -112.1379, + -112.1296, -112.1213, -112.1129, -112.1046, -112.0963, -112.0879, -112.0796, -112.0713, + -112.0629, -112.0546, -112.0463, -112.0379, -112.0296, -112.0213, -112.0129, -112.0046, + -111.9963, -111.9879, -111.9796, -111.9713, -111.9629, -111.9546, -111.9463, -111.9379, + -111.9296, -111.9213, -111.9129, -111.9046, -111.8963, -111.8879, -111.8796, -111.8713, + -111.8629, -111.8546, -111.8463, -111.8379, -111.8296, -111.8213, -111.8129, -111.8046, + -111.7963, -111.7879, -111.7796, -111.7713, -111.7629, -111.7546, -111.7463, -111.7379, + -111.7296, -111.7213, -111.7129, -111.7046, -111.6963, -111.6879, -111.6796, -111.6713, + -111.6629, -111.6546, -111.6463, -111.6379, -111.6296, -111.6213, -111.6129, -111.6046, + -111.5963, -111.5879, -111.5796, -111.5713, -111.5629, -111.5546, -111.5463, -111.5379, + -111.5296, -111.5213, -111.5129, -111.5046, -111.4963, -111.4879, -111.4796, -111.4713, + -111.4629, -111.4546, -111.4463, -111.4379, -111.4296, -111.4213, -111.4129, -111.4046, + -111.3963, -111.3879, -111.3796, -111.3713, -111.3629, -111.3546, -111.3463, -111.3379, + -111.3296, -111.3213, -111.3129, -111.3046, -111.2963, -111.2879, -111.2796, -111.2713, + -111.2629, -111.2546, -111.2463, -111.2379, -111.2296, -111.2213, -111.2129, -111.2046, + -111.1963, -111.1879, -111.1796, -111.1713, -111.1629, -111.1546, -111.1463, -111.1379, + -111.1296, -111.1213, -111.1129, -111.1046, -111.0963, -111.0879, -111.0796, -111.0713, + -111.0629, -111.0546, -111.0463, -111.0379, -111.0296, -111.0213, -111.0129, -111.0046, + -110.9963, -110.9879, -110.9796, -110.9713, -110.9629, -110.9546, -110.9463, -110.9379, + -110.9296, -110.9213, -110.9129, -110.9046, -110.8963, -110.8879, -110.8796, -110.8713, + -110.8629, -110.8546, -110.8463, -110.8379, -110.8296, -110.8213, -110.8129, -110.8046, + -110.7963, -110.7879, -110.7796, -110.7713, -110.7629, -110.7546, -110.7463, -110.7379, + -110.7296, -110.7213, -110.7129, -110.7046, -110.6963, -110.6879, -110.6796, -110.6713, + -110.6629, -110.6546, -110.6463, -110.6379, -110.6296, -110.6213, -110.6129, -110.6046, + -110.5963, -110.5879, -110.5796, -110.5713, -110.5629, -110.5546, -110.5463, -110.5379, + -110.5296, -110.5213, -110.5129, -110.5046, -110.4963, -110.4879, -110.4796, -110.4713, + -110.4629, -110.4546, -110.4463, -110.4379, -110.4296, -110.4213, -110.4129, -110.4046, + -110.3963, -110.3879, -110.3796, -110.3713, -110.3629, -110.3546, -110.3463, -110.3379, + -110.3296, -110.3213, -110.3129, -110.3046, -110.2963, -110.2879, -110.2796, -110.2713, + -110.2629, -110.2546, -110.2463, -110.2379, -110.2296, -110.2213, -110.2129, -110.2046, + -110.1963, -110.1879, -110.1796, -110.1713, -110.1629, -110.1546, -110.1463, -110.1379, + -110.1296, -110.1213, -110.1129, -110.1046, -110.0963, -110.0879, -110.0796, -110.0713, + -110.0629, -110.0546, -110.0463, -110.0379, -110.0296, -110.0213, -110.0129, -110.0046, + -109.9963, -109.9879, -109.9796, -109.9713, -109.9629, -109.9546, -109.9463, -109.9379, + -109.9296, -109.9213, -109.9129, -109.9046, -109.8963, -109.8879, -109.8796, -109.8713, + -109.8629, -109.8546, -109.8463, -109.8379, -109.8296, -109.8213, -109.8129, -109.8046, + -109.7963, -109.7879, -109.7796, -109.7713, -109.7629, -109.7546, -109.7463, -109.7379, + -109.7296, -109.7213, -109.7129, -109.7046, -109.6963, -109.6879, -109.6796, -109.6713, + -109.6629, -109.6546, -109.6463, -109.6379, -109.6296, -109.6213, -109.6129, -109.6046, + -109.5963, -109.5879, -109.5796, -109.5713, -109.5629, -109.5546, -109.5463, -109.5379, + -109.5296, -109.5213, -109.5129, -109.5046, -109.4963, -109.4879, -109.4796, -109.4713, + -109.4629, -109.4546, -109.4463, -109.4379, -109.4296, -109.4213, -109.4129, -109.4046, + -109.3963, -109.3879, -109.3796, -109.3713, -109.3629, -109.3546, -109.3463, -109.3379, + -109.3296, -109.3213, -109.3129, -109.3046, -109.2963, -109.2879, -109.2796, -109.2713, + -109.2629, -109.2546, -109.2463, -109.2379, -109.2296, -109.2213, -109.2129, -109.2046, + -109.1963, -109.1879, -109.1796, -109.1713, -109.1629, -109.1546, -109.1463, -109.1379, + -109.1296, -109.1213, -109.1129, -109.1046, -109.0963, -109.0879, -109.0796, -109.0713, + -109.0629, -109.0546, -109.0463, -109.0379, -109.0296, -109.0213, -109.0129, -109.0046, + -108.9963, -108.9879, -108.9796, -108.9713, -108.9629, -108.9546, -108.9463, -108.9379, + -108.9296, -108.9213, -108.9129, -108.9046, -108.8963, -108.8879, -108.8796, -108.8713, + -108.8629, -108.8546, -108.8463, -108.8379, -108.8296, -108.8213, -108.8129, -108.8046, + -108.7963, -108.7879, -108.7796, -108.7713, -108.7629, -108.7546, -108.7463, -108.7379, + -108.7296, -108.7213, -108.7129, -108.7046, -108.6963, -108.6879, -108.6796, -108.6713, + -108.6629, -108.6546, -108.6463, -108.6379, -108.6296, -108.6213, -108.6129, -108.6046, + -108.5963, -108.5879, -108.5796, -108.5713, -108.5629, -108.5546, -108.5463, -108.5379, + -108.5296, -108.5213, -108.5129, -108.5046, -108.4963, -108.4879, -108.4796, -108.4713, + -108.4629, -108.4546, -108.4463, -108.4379, -108.4296, -108.4213, -108.4129, -108.4046, + -108.3963, -108.3879, -108.3796, -108.3713, -108.3629, -108.3546, -108.3463, -108.3379, + -108.3296, -108.3213, -108.3129, -108.3046, -108.2963, -108.2879, -108.2796, -108.2713, + -108.2629, -108.2546, -108.2463, -108.2379, -108.2296, -108.2213, -108.2129, -108.2046, + -108.1963, -108.1879, -108.1796, -108.1713, -108.1629, -108.1546, -108.1463, -108.1379, + -108.1296, -108.1213, -108.1129, -108.1046, -108.0963, -108.0879, -108.0796, -108.0713, + -108.0629, -108.0546, -108.0463, -108.0379, -108.0296, -108.0213, -108.0129, -108.0046, + -107.9963, -107.9879, -107.9796, -107.9713, -107.9629, -107.9546, -107.9463, -107.9379, + -107.9296, -107.9213, -107.9129, -107.9046, -107.8963, -107.8879, -107.8796, -107.8713, + -107.8629, -107.8546, -107.8463, -107.8379, -107.8296, -107.8213, -107.8129, -107.8046, + -107.7963, -107.7879, -107.7796, -107.7713, -107.7629, -107.7546, -107.7463, -107.7379, + -107.7296, -107.7213, -107.7129, -107.7046, -107.6963, -107.6879, -107.6796, -107.6713, + -107.6629, -107.6546, -107.6463, -107.6379, -107.6296, -107.6213, -107.6129, -107.6046, + -107.5963, -107.5879, -107.5796, -107.5713, -107.5629, -107.5546, -107.5463, -107.5379, + -107.5296, -107.5213, -107.5129, -107.5046, -107.4963, -107.4879, -107.4796, -107.4713, + -107.4629, -107.4546, -107.4463, -107.4379, -107.4296, -107.4213, -107.4129, -107.4046, + -107.3963, -107.3879, -107.3796, -107.3713, -107.3629, -107.3546, -107.3463, -107.3379, + -107.3296, -107.3213, -107.3129, -107.3046, -107.2963, -107.2879, -107.2796, -107.2713, + -107.2629, -107.2546, -107.2463, -107.2379, -107.2296, -107.2213, -107.2129, -107.2046, + -107.1963, -107.1879, -107.1796, -107.1713, -107.1629, -107.1546, -107.1463, -107.1379, + -107.1296, -107.1213, -107.1129, -107.1046, -107.0963, -107.0879, -107.0796, -107.0713, + -107.0629, -107.0546, -107.0463, -107.0379, -107.0296, -107.0213, -107.0129, -107.0046, + -106.9963, -106.9879, -106.9796, -106.9713, -106.9629, -106.9546, -106.9463, -106.9379, + -106.9296, -106.9213, -106.9129, -106.9046, -106.8963, -106.8879, -106.8796, -106.8713, + -106.8629, -106.8546, -106.8463, -106.8379, -106.8296, -106.8213, -106.8129, -106.8046, + -106.7963, -106.7879, -106.7796, -106.7713, -106.7629, -106.7546, -106.7463, -106.7379, + -106.7296, -106.7213, -106.7129, -106.7046, -106.6963, -106.6879, -106.6796, -106.6713, + -106.6629, -106.6546, -106.6463, -106.6379, -106.6296, -106.6213, -106.6129, -106.6046, + -106.5963, -106.5879, -106.5796, -106.5713, -106.5629, -106.5546, -106.5463, -106.5379, + -106.5296, -106.5213, -106.5129, -106.5046, -106.4963, -106.4879, -106.4796, -106.4713, + -106.4629, -106.4546, -106.4463, -106.4379, -106.4296, -106.4213, -106.4129, -106.4046, + -106.3963, -106.3879, -106.3796, -106.3713, -106.3629, -106.3546, -106.3463, -106.3379, + -106.3296, -106.3213, -106.3129, -106.3046, -106.2963, -106.2879, -106.2796, -106.2713, + -106.2629, -106.2546, -106.2463, -106.2379, -106.2296, -106.2213, -106.2129, -106.2046, + -106.1963, -106.1879, -106.1796, -106.1713, -106.1629, -106.1546, -106.1463, -106.1379, + -106.1296, -106.1213, -106.1129, -106.1046, -106.0963, -106.0879, -106.0796, -106.0713, + -106.0629, -106.0546, -106.0463, -106.0379, -106.0296, -106.0213, -106.0129, -106.0046, + -105.9963, -105.9879, -105.9796, -105.9713, -105.9629, -105.9546, -105.9463, -105.9379, + -105.9296, -105.9213, -105.9129, -105.9046, -105.8963, -105.8879, -105.8796, -105.8713, + -105.8629, -105.8546, -105.8463, -105.8379, -105.8296, -105.8213, -105.8129, -105.8046, + -105.7963, -105.7879, -105.7796, -105.7713, -105.7629, -105.7546, -105.7463, -105.7379, + -105.7296, -105.7213, -105.7129, -105.7046, -105.6963, -105.6879, -105.6796, -105.6713, + -105.6629, -105.6546, -105.6463, -105.6379, -105.6296, -105.6213, -105.6129, -105.6046, + -105.5963, -105.5879, -105.5796, -105.5713, -105.5629, -105.5546, -105.5463, -105.5379, + -105.5296, -105.5213, -105.5129, -105.5046, -105.4963, -105.4879, -105.4796, -105.4713, + -105.4629, -105.4546, -105.4463, -105.4379, -105.4296, -105.4213, -105.4129, -105.4046, + -105.3963, -105.3879, -105.3796, -105.3713, -105.3629, -105.3546, -105.3463, -105.3379, + -105.3296, -105.3213, -105.3129, -105.3046, -105.2963, -105.2879, -105.2796, -105.2713, + -105.2629, -105.2546, -105.2463, -105.2379, -105.2296, -105.2213, -105.2129, -105.2046, + -105.1963, -105.1879, -105.1796, -105.1713, -105.1629, -105.1546, -105.1463, -105.1379, + -105.1296, -105.1213, -105.1129, -105.1046, -105.0963, -105.0879, -105.0796, -105.0713, + -105.0629, -105.0546, -105.0463, -105.0379, -105.0296, -105.0213, -105.0129, -105.0046, + -104.9963, -104.9879, -104.9796, -104.9713, -104.9629, -104.9546, -104.9463, -104.9379, + -104.9296, -104.9213, -104.9129, -104.9046, -104.8963, -104.8879, -104.8796, -104.8713, + -104.8629, -104.8546, -104.8463, -104.8379, -104.8296, -104.8213, -104.8129, -104.8046, + -104.7963, -104.7879, -104.7796, -104.7713, -104.7629, -104.7546, -104.7463, -104.7379, + -104.7296, -104.7213, -104.7129, -104.7046, -104.6963, -104.6879, -104.6796, -104.6713, + -104.6629, -104.6546, -104.6463, -104.6379, -104.6296, -104.6213, -104.6129, -104.6046, + -104.5963, -104.5879, -104.5796, -104.5713, -104.5629, -104.5546, -104.5463, -104.5379, + -104.5296, -104.5213, -104.5129, -104.5046, -104.4963, -104.4879, -104.4796, -104.4713, + -104.4629, -104.4546, -104.4463, -104.4379, -104.4296, -104.4213, -104.4129, -104.4046, + -104.3963, -104.3879, -104.3796, -104.3713, -104.3629, -104.3546, -104.3463, -104.3379, + -104.3296, -104.3213, -104.3129, -104.3046, -104.2963, -104.2879, -104.2796, -104.2713, + -104.2629, -104.2546, -104.2463, -104.2379, -104.2296, -104.2213, -104.2129, -104.2046, + -104.1963, -104.1879, -104.1796, -104.1713, -104.1629, -104.1546, -104.1463, -104.1379, + -104.1296, -104.1213, -104.1129, -104.1046, -104.0963, -104.0879, -104.0796, -104.0713, + -104.0629, -104.0546, -104.0463, -104.0379, -104.0296, -104.0213, -104.0129, -104.0046, + -103.9963, -103.9879, -103.9796, -103.9713, -103.9629, -103.9546, -103.9463, -103.9379, + -103.9296, -103.9213, -103.9129, -103.9046, -103.8963, -103.8879, -103.8796, -103.8713, + -103.8629, -103.8546, -103.8463, -103.8379, -103.8296, -103.8213, -103.8129, -103.8046, + -103.7963, -103.7879, -103.7796, -103.7713, -103.7629, -103.7546, -103.7463, -103.7379, + -103.7296, -103.7213, -103.7129, -103.7046, -103.6963, -103.6879, -103.6796, -103.6713, + -103.6629, -103.6546, -103.6463, -103.6379, -103.6296, -103.6213, -103.6129, -103.6046, + -103.5963, -103.5879, -103.5796, -103.5713, -103.5629, -103.5546, -103.5463, -103.5379, + -103.5296, -103.5213, -103.5129, -103.5046, -103.4963, -103.4879, -103.4796, -103.4713, + -103.4629, -103.4546, -103.4463, -103.4379, -103.4296, -103.4213, -103.4129, -103.4046, + -103.3963, -103.3879, -103.3796, -103.3713, -103.3629, -103.3546, -103.3463, -103.3379, + -103.3296, -103.3213, -103.3129, -103.3046, -103.2963, -103.2879, -103.2796, -103.2713, + -103.2629, -103.2546, -103.2463, -103.2379, -103.2296, -103.2213, -103.2129, -103.2046, + -103.1963, -103.1879, -103.1796, -103.1713, -103.1629, -103.1546, -103.1463, -103.1379, + -103.1296, -103.1213, -103.1129, -103.1046, -103.0963, -103.0879, -103.0796, -103.0713, + -103.0629, -103.0546, -103.0463, -103.0379, -103.0296, -103.0213, -103.0129, -103.0046, + -102.9963, -102.9879, -102.9796, -102.9713, -102.9629, -102.9546, -102.9463, -102.9379, + -102.9296, -102.9213, -102.9129, -102.9046, -102.8963, -102.8879, -102.8796, -102.8713, + -102.8629, -102.8546, -102.8463, -102.8379, -102.8296, -102.8213, -102.8129, -102.8046, + -102.7963, -102.7879, -102.7796, -102.7713, -102.7629, -102.7546, -102.7463, -102.7379, + -102.7296, -102.7213, -102.7129, -102.7046, -102.6963, -102.6879, -102.6796, -102.6713, + -102.6629, -102.6546, -102.6463, -102.6379, -102.6296, -102.6213, -102.6129, -102.6046, + -102.5963, -102.5879, -102.5796, -102.5713, -102.5629, -102.5546, -102.5463, -102.5379, + -102.5296, -102.5213, -102.5129, -102.5046, -102.4963, -102.4879, -102.4796, -102.4713, + -102.4629, -102.4546, -102.4463, -102.4379, -102.4296, -102.4213, -102.4129, -102.4046, + -102.3963, -102.3879, -102.3796, -102.3713, -102.3629, -102.3546, -102.3463, -102.3379, + -102.3296, -102.3213, -102.3129, -102.3046, -102.2963, -102.2879, -102.2796, -102.2713, + -102.2629, -102.2546, -102.2463, -102.2379, -102.2296, -102.2213, -102.2129, -102.2046, + -102.1963, -102.1879, -102.1796, -102.1713, -102.1629, -102.1546, -102.1463, -102.1379, + -102.1296, -102.1213, -102.1129, -102.1046, -102.0963, -102.0879, -102.0796, -102.0713, + -102.0629, -102.0546, -102.0463, -102.0379, -102.0296, -102.0213, -102.0129, -102.0046, + -101.9963, -101.9879, -101.9796, -101.9713, -101.9629, -101.9546, -101.9463, -101.9379, + -101.9296, -101.9213, -101.9129, -101.9046, -101.8963, -101.8879, -101.8796, -101.8713, + -101.8629, -101.8546, -101.8463, -101.8379, -101.8296, -101.8213, -101.8129, -101.8046, + -101.7963, -101.7879, -101.7796, -101.7713, -101.7629, -101.7546, -101.7463, -101.7379, + -101.7296, -101.7213, -101.7129, -101.7046, -101.6963, -101.6879, -101.6796, -101.6713, + -101.6629, -101.6546, -101.6463, -101.6379, -101.6296, -101.6213, -101.6129, -101.6046, + -101.5963, -101.5879, -101.5796, -101.5713, -101.5629, -101.5546, -101.5463, -101.5379, + -101.5296, -101.5213, -101.5129, -101.5046, -101.4963, -101.4879, -101.4796, -101.4713, + -101.4629, -101.4546, -101.4463, -101.4379, -101.4296, -101.4213, -101.4129, -101.4046, + -101.3963, -101.3879, -101.3796, -101.3713, -101.3629, -101.3546, -101.3463, -101.3379, + -101.3296, -101.3213, -101.3129, -101.3046, -101.2963, -101.2879, -101.2796, -101.2713, + -101.2629, -101.2546, -101.2463, -101.2379, -101.2296, -101.2213, -101.2129, -101.2046, + -101.1963, -101.1879, -101.1796, -101.1713, -101.1629, -101.1546, -101.1463, -101.1379, + -101.1296, -101.1213, -101.1129, -101.1046, -101.0963, -101.0879, -101.0796, -101.0713, + -101.0629, -101.0546, -101.0463, -101.0379, -101.0296, -101.0213, -101.0129, -101.0046, + -100.9963, -100.9879, -100.9796, -100.9713, -100.9629, -100.9546, -100.9463, -100.9379, + -100.9296, -100.9213, -100.9129, -100.9046, -100.8963, -100.8879, -100.8796, -100.8713, + -100.8629, -100.8546, -100.8463, -100.8379, -100.8296, -100.8213, -100.8129, -100.8046, + -100.7963, -100.7879, -100.7796, -100.7713, -100.7629, -100.7546, -100.7463, -100.7379, + -100.7296, -100.7213, -100.7129, -100.7046, -100.6963, -100.6879, -100.6796, -100.6713, + -100.6629, -100.6546, -100.6463, -100.6379, -100.6296, -100.6213, -100.6129, -100.6046, + -100.5963, -100.5879, -100.5796, -100.5713, -100.5629, -100.5546, -100.5463, -100.5379, + -100.5296, -100.5213, -100.5129, -100.5046, -100.4963, -100.4879, -100.4796, -100.4713, + -100.4629, -100.4546, -100.4463, -100.4379, -100.4296, -100.4213, -100.4129, -100.4046, + -100.3963, -100.3879, -100.3796, -100.3713, -100.3629, -100.3546, -100.3463, -100.3379, + -100.3296, -100.3213, -100.3129, -100.3046, -100.2963, -100.2879, -100.2796, -100.2713, + -100.2629, -100.2546, -100.2463, -100.2379, -100.2296, -100.2213, -100.2129, -100.2046, + -100.1963, -100.1879, -100.1796, -100.1713, -100.1629, -100.1546, -100.1463, -100.1379, + -100.1296, -100.1213, -100.1129, -100.1046, -100.0963, -100.0879, -100.0796, -100.0713, + -100.0629, -100.0546, -100.0463, -100.0379, -100.0296, -100.0213, -100.0129, -100.0046, + -99.9963, -99.9879, -99.9796, -99.9713, -99.9629, -99.9546, -99.9463, -99.9379, + -99.9296, -99.9213, -99.9129, -99.9046, -99.8963, -99.8879, -99.8796, -99.8713, + -99.8629, -99.8546, -99.8463, -99.8379, -99.8296, -99.8213, -99.8129, -99.8046, + -99.7963, -99.7879, -99.7796, -99.7713, -99.7629, -99.7546, -99.7463, -99.7379, + -99.7296, -99.7213, -99.7129, -99.7046, -99.6963, -99.6879, -99.6796, -99.6713, + -99.6629, -99.6546, -99.6463, -99.6379, -99.6296, -99.6213, -99.6129, -99.6046, + -99.5963, -99.5879, -99.5796, -99.5713, -99.5629, -99.5546, -99.5463, -99.5379, + -99.5296, -99.5213, -99.5129, -99.5046, -99.4963, -99.4879, -99.4796, -99.4713, + -99.4629, -99.4546, -99.4463, -99.4379, -99.4296, -99.4213, -99.4129, -99.4046, + -99.3963, -99.3879, -99.3796, -99.3713, -99.3629, -99.3546, -99.3463, -99.3379, + -99.3296, -99.3213, -99.3129, -99.3046, -99.2963, -99.2879, -99.2796, -99.2713, + -99.2629, -99.2546, -99.2463, -99.2379, -99.2296, -99.2213, -99.2129, -99.2046, + -99.1963, -99.1879, -99.1796, -99.1713, -99.1629, -99.1546, -99.1463, -99.1379, + -99.1296, -99.1213, -99.1129, -99.1046, -99.0963, -99.0879, -99.0796, -99.0713, + -99.0629, -99.0546, -99.0463, -99.0379, -99.0296, -99.0213, -99.0129, -99.0046, + -98.9963, -98.9879, -98.9796, -98.9713, -98.9629, -98.9546, -98.9463, -98.9379, + -98.9296, -98.9213, -98.9129, -98.9046, -98.8963, -98.8879, -98.8796, -98.8713, + -98.8629, -98.8546, -98.8463, -98.8379, -98.8296, -98.8213, -98.8129, -98.8046, + -98.7963, -98.7879, -98.7796, -98.7713, -98.7629, -98.7546, -98.7463, -98.7379, + -98.7296, -98.7213, -98.7129, -98.7046, -98.6963, -98.6879, -98.6796, -98.6713, + -98.6629, -98.6546, -98.6463, -98.6379, -98.6296, -98.6213, -98.6129, -98.6046, + -98.5963, -98.5879, -98.5796, -98.5713, -98.5629, -98.5546, -98.5463, -98.5379, + -98.5296, -98.5213, -98.5129, -98.5046, -98.4963, -98.4879, -98.4796, -98.4713, + -98.4629, -98.4546, -98.4463, -98.4379, -98.4296, -98.4213, -98.4129, -98.4046, + -98.3963, -98.3879, -98.3796, -98.3713, -98.3629, -98.3546, -98.3463, -98.3379, + -98.3296, -98.3213, -98.3129, -98.3046, -98.2963, -98.2879, -98.2796, -98.2713, + -98.2629, -98.2546, -98.2463, -98.2379, -98.2296, -98.2213, -98.2129, -98.2046, + -98.1963, -98.1879, -98.1796, -98.1713, -98.1629, -98.1546, -98.1463, -98.1379, + -98.1296, -98.1213, -98.1129, -98.1046, -98.0963, -98.0879, -98.0796, -98.0713, + -98.0629, -98.0546, -98.0463, -98.0379, -98.0296, -98.0213, -98.0129, -98.0046, + -97.9963, -97.9879, -97.9796, -97.9713, -97.9629, -97.9546, -97.9463, -97.9379, + -97.9296, -97.9213, -97.9129, -97.9046, -97.8963, -97.8879, -97.8796, -97.8713, + -97.8629, -97.8546, -97.8463, -97.8379, -97.8296, -97.8213, -97.8129, -97.8046, + -97.7963, -97.7879, -97.7796, -97.7713, -97.7629, -97.7546, -97.7463, -97.7379, + -97.7296, -97.7213, -97.7129, -97.7046, -97.6963, -97.6879, -97.6796, -97.6713, + -97.6629, -97.6546, -97.6463, -97.6379, -97.6296, -97.6213, -97.6129, -97.6046, + -97.5963, -97.5879, -97.5796, -97.5713, -97.5629, -97.5546, -97.5463, -97.5379, + -97.5296, -97.5213, -97.5129, -97.5046, -97.4963, -97.4879, -97.4796, -97.4713, + -97.4629, -97.4546, -97.4463, -97.4379, -97.4296, -97.4213, -97.4129, -97.4046, + -97.3963, -97.3879, -97.3796, -97.3713, -97.3629, -97.3546, -97.3463, -97.3379, + -97.3296, -97.3213, -97.3129, -97.3046, -97.2963, -97.2879, -97.2796, -97.2713, + -97.2629, -97.2546, -97.2463, -97.2379, -97.2296, -97.2213, -97.2129, -97.2046, + -97.1963, -97.1879, -97.1796, -97.1713, -97.1629, -97.1546, -97.1463, -97.1379, + -97.1296, -97.1213, -97.1129, -97.1046, -97.0963, -97.0879, -97.0796, -97.0713, + -97.0629, -97.0546, -97.0463, -97.0379, -97.0296, -97.0213, -97.0129, -97.0046, + -96.9963, -96.9879, -96.9796, -96.9713, -96.9629, -96.9546, -96.9463, -96.9379, + -96.9296, -96.9213, -96.9129, -96.9046, -96.8963, -96.8879, -96.8796, -96.8713, + -96.8629, -96.8546, -96.8463, -96.8379, -96.8296, -96.8213, -96.8129, -96.8046, + -96.7963, -96.7879, -96.7796, -96.7713, -96.7629, -96.7546, -96.7463, -96.7379, + -96.7296, -96.7213, -96.7129, -96.7046, -96.6963, -96.6879, -96.6796, -96.6713, + -96.6629, -96.6546, -96.6463, -96.6379, -96.6296, -96.6213, -96.6129, -96.6046, + -96.5963, -96.5879, -96.5796, -96.5713, -96.5629, -96.5546, -96.5463, -96.5379, + -96.5296, -96.5213, -96.5129, -96.5046, -96.4963, -96.4879, -96.4796, -96.4713, + -96.4629, -96.4546, -96.4463, -96.4379, -96.4296, -96.4213, -96.4129, -96.4046, + -96.3963, -96.3879, -96.3796, -96.3713, -96.3629, -96.3546, -96.3463, -96.3379, + -96.3296, -96.3213, -96.3129, -96.3046, -96.2963, -96.2879, -96.2796, -96.2713, + -96.2629, -96.2546, -96.2463, -96.2379, -96.2296, -96.2213, -96.2129, -96.2046, + -96.1963, -96.1879, -96.1796, -96.1713, -96.1629, -96.1546, -96.1463, -96.1379, + -96.1296, -96.1213, -96.1129, -96.1046, -96.0963, -96.0879, -96.0796, -96.0713, + -96.0629, -96.0546, -96.0463, -96.0379, -96.0296, -96.0213, -96.0129, -96.0046, + -95.9963, -95.9879, -95.9796, -95.9713, -95.9629, -95.9546, -95.9463, -95.9379, + -95.9296, -95.9213, -95.9129, -95.9046, -95.8963, -95.8879, -95.8796, -95.8713, + -95.8629, -95.8546, -95.8463, -95.8379, -95.8296, -95.8213, -95.8129, -95.8046, + -95.7963, -95.7879, -95.7796, -95.7713, -95.7629, -95.7546, -95.7463, -95.7379, + -95.7296, -95.7213, -95.7129, -95.7046, -95.6963, -95.6879, -95.6796, -95.6713, + -95.6629, -95.6546, -95.6463, -95.6379, -95.6296, -95.6213, -95.6129, -95.6046, + -95.5963, -95.5879, -95.5796, -95.5713, -95.5629, -95.5546, -95.5463, -95.5379, + -95.5296, -95.5213, -95.5129, -95.5046, -95.4963, -95.4879, -95.4796, -95.4713, + -95.4629, -95.4546, -95.4463, -95.4379, -95.4296, -95.4213, -95.4129, -95.4046, + -95.3963, -95.3879, -95.3796, -95.3713, -95.3629, -95.3546, -95.3463, -95.3379, + -95.3296, -95.3213, -95.3129, -95.3046, -95.2963, -95.2879, -95.2796, -95.2713, + -95.2629, -95.2546, -95.2463, -95.2379, -95.2296, -95.2213, -95.2129, -95.2046, + -95.1963, -95.1879, -95.1796, -95.1713, -95.1629, -95.1546, -95.1463, -95.1379, + -95.1296, -95.1213, -95.1129, -95.1046, -95.0963, -95.0879, -95.0796, -95.0713, + -95.0629, -95.0546, -95.0463, -95.0379, -95.0296, -95.0213, -95.0129, -95.0046, + -94.9963, -94.9879, -94.9796, -94.9713, -94.9629, -94.9546, -94.9463, -94.9379, + -94.9296, -94.9213, -94.9129, -94.9046, -94.8963, -94.8879, -94.8796, -94.8713, + -94.8629, -94.8546, -94.8463, -94.8379, -94.8296, -94.8213, -94.8129, -94.8046, + -94.7963, -94.7879, -94.7796, -94.7713, -94.7629, -94.7546, -94.7463, -94.7379, + -94.7296, -94.7213, -94.7129, -94.7046, -94.6963, -94.6879, -94.6796, -94.6713, + -94.6629, -94.6546, -94.6463, -94.6379, -94.6296, -94.6213, -94.6129, -94.6046, + -94.5963, -94.5879, -94.5796, -94.5713, -94.5629, -94.5546, -94.5463, -94.5379, + -94.5296, -94.5213, -94.5129, -94.5046, -94.4963, -94.4879, -94.4796, -94.4713, + -94.4629, -94.4546, -94.4463, -94.4379, -94.4296, -94.4213, -94.4129, -94.4046, + -94.3963, -94.3879, -94.3796, -94.3713, -94.3629, -94.3546, -94.3463, -94.3379, + -94.3296, -94.3213, -94.3129, -94.3046, -94.2963, -94.2879, -94.2796, -94.2713, + -94.2629, -94.2546, -94.2463, -94.2379, -94.2296, -94.2213, -94.2129, -94.2046, + -94.1963, -94.1879, -94.1796, -94.1713, -94.1629, -94.1546, -94.1463, -94.1379, + -94.1296, -94.1213, -94.1129, -94.1046, -94.0963, -94.0879, -94.0796, -94.0713, + -94.0629, -94.0546, -94.0463, -94.0379, -94.0296, -94.0213, -94.0129, -94.0046, + -93.9963, -93.9879, -93.9796, -93.9713, -93.9629, -93.9546, -93.9463, -93.9379, + -93.9296, -93.9213, -93.9129, -93.9046, -93.8963, -93.8879, -93.8796, -93.8713, + -93.8629, -93.8546, -93.8463, -93.8379, -93.8296, -93.8213, -93.8129, -93.8046, + -93.7963, -93.7879, -93.7796, -93.7713, -93.7629, -93.7546, -93.7463, -93.7379, + -93.7296, -93.7213, -93.7129, -93.7046, -93.6963, -93.6879, -93.6796, -93.6713, + -93.6629, -93.6546, -93.6463, -93.6379, -93.6296, -93.6213, -93.6129, -93.6046, + -93.5963, -93.5879, -93.5796, -93.5713, -93.5629, -93.5546, -93.5463, -93.5379, + -93.5296, -93.5213, -93.5129, -93.5046, -93.4963, -93.4879, -93.4796, -93.4713, + -93.4629, -93.4546, -93.4463, -93.4379, -93.4296, -93.4213, -93.4129, -93.4046, + -93.3963, -93.3879, -93.3796, -93.3713, -93.3629, -93.3546, -93.3463, -93.3379, + -93.3296, -93.3213, -93.3129, -93.3046, -93.2963, -93.2879, -93.2796, -93.2713, + -93.2629, -93.2546, -93.2463, -93.2379, -93.2296, -93.2213, -93.2129, -93.2046, + -93.1963, -93.1879, -93.1796, -93.1713, -93.1629, -93.1546, -93.1463, -93.1379, + -93.1296, -93.1213, -93.1129, -93.1046, -93.0963, -93.0879, -93.0796, -93.0713, + -93.0629, -93.0546, -93.0463, -93.0379, -93.0296, -93.0213, -93.0129, -93.0046, + -92.9963, -92.9879, -92.9796, -92.9713, -92.9629, -92.9546, -92.9463, -92.9379, + -92.9296, -92.9213, -92.9129, -92.9046, -92.8963, -92.8879, -92.8796, -92.8713, + -92.8629, -92.8546, -92.8463, -92.8379, -92.8296, -92.8213, -92.8129, -92.8046, + -92.7963, -92.7879, -92.7796, -92.7713, -92.7629, -92.7546, -92.7463, -92.7379, + -92.7296, -92.7213, -92.7129, -92.7046, -92.6963, -92.6879, -92.6796, -92.6713, + -92.6629, -92.6546, -92.6463, -92.6379, -92.6296, -92.6213, -92.6129, -92.6046, + -92.5963, -92.5879, -92.5796, -92.5713, -92.5629, -92.5546, -92.5463, -92.5379, + -92.5296, -92.5213, -92.5129, -92.5046, -92.4963, -92.4879, -92.4796, -92.4713, + -92.4629, -92.4546, -92.4463, -92.4379, -92.4296, -92.4213, -92.4129, -92.4046, + -92.3963, -92.3879, -92.3796, -92.3713, -92.3629, -92.3546, -92.3463, -92.3379, + -92.3296, -92.3213, -92.3129, -92.3046, -92.2963, -92.2879, -92.2796, -92.2713, + -92.2629, -92.2546, -92.2463, -92.2379, -92.2296, -92.2213, -92.2129, -92.2046, + -92.1963, -92.1879, -92.1796, -92.1713, -92.1629, -92.1546, -92.1463, -92.1379, + -92.1296, -92.1213, -92.1129, -92.1046, -92.0963, -92.0879, -92.0796, -92.0713, + -92.0629, -92.0546, -92.0463, -92.0379, -92.0296, -92.0213, -92.0129, -92.0046, + -91.9963, -91.9879, -91.9796, -91.9713, -91.9629, -91.9546, -91.9463, -91.9379, + -91.9296, -91.9213, -91.9129, -91.9046, -91.8963, -91.8879, -91.8796, -91.8713, + -91.8629, -91.8546, -91.8463, -91.8379, -91.8296, -91.8213, -91.8129, -91.8046, + -91.7963, -91.7879, -91.7796, -91.7713, -91.7629, -91.7546, -91.7463, -91.7379, + -91.7296, -91.7213, -91.7129, -91.7046, -91.6963, -91.6879, -91.6796, -91.6713, + -91.6629, -91.6546, -91.6463, -91.6379, -91.6296, -91.6213, -91.6129, -91.6046, + -91.5963, -91.5879, -91.5796, -91.5713, -91.5629, -91.5546, -91.5463, -91.5379, + -91.5296, -91.5213, -91.5129, -91.5046, -91.4963, -91.4879, -91.4796, -91.4713, + -91.4629, -91.4546, -91.4463, -91.4379, -91.4296, -91.4213, -91.4129, -91.4046, + -91.3963, -91.3879, -91.3796, -91.3713, -91.3629, -91.3546, -91.3463, -91.3379, + -91.3296, -91.3213, -91.3129, -91.3046, -91.2963, -91.2879, -91.2796, -91.2713, + -91.2629, -91.2546, -91.2463, -91.2379, -91.2296, -91.2213, -91.2129, -91.2046, + -91.1963, -91.1879, -91.1796, -91.1713, -91.1629, -91.1546, -91.1463, -91.1379, + -91.1296, -91.1213, -91.1129, -91.1046, -91.0963, -91.0879, -91.0796, -91.0713, + -91.0629, -91.0546, -91.0463, -91.0379, -91.0296, -91.0213, -91.0129, -91.0046, + -90.9963, -90.9879, -90.9796, -90.9713, -90.9629, -90.9546, -90.9463, -90.9379, + -90.9296, -90.9213, -90.9129, -90.9046, -90.8963, -90.8879, -90.8796, -90.8713, + -90.8629, -90.8546, -90.8463, -90.8379, -90.8296, -90.8213, -90.8129, -90.8046, + -90.7963, -90.7879, -90.7796, -90.7713, -90.7629, -90.7546, -90.7463, -90.7379, + -90.7296, -90.7213, -90.7129, -90.7046, -90.6963, -90.6879, -90.6796, -90.6713, + -90.6629, -90.6546, -90.6463, -90.6379, -90.6296, -90.6213, -90.6129, -90.6046, + -90.5963, -90.5879, -90.5796, -90.5713, -90.5629, -90.5546, -90.5463, -90.5379, + -90.5296, -90.5213, -90.5129, -90.5046, -90.4963, -90.4879, -90.4796, -90.4713, + -90.4629, -90.4546, -90.4463, -90.4379, -90.4296, -90.4213, -90.4129, -90.4046, + -90.3963, -90.3879, -90.3796, -90.3713, -90.3629, -90.3546, -90.3463, -90.3379, + -90.3296, -90.3213, -90.3129, -90.3046, -90.2963, -90.2879, -90.2796, -90.2713, + -90.2629, -90.2546, -90.2463, -90.2379, -90.2296, -90.2213, -90.2129, -90.2046, + -90.1963, -90.1879, -90.1796, -90.1713, -90.1629, -90.1546, -90.1463, -90.1379, + -90.1296, -90.1213, -90.1129, -90.1046, -90.0963, -90.0879, -90.0796, -90.0713, + -90.0629, -90.0546, -90.0463, -90.0379, -90.0296, -90.0213, -90.0129, -90.0046, + -89.9963, -89.9879, -89.9796, -89.9713, -89.9629, -89.9546, -89.9463, -89.9379, + -89.9296, -89.9213, -89.9129, -89.9046, -89.8963, -89.8879, -89.8796, -89.8713, + -89.8629, -89.8546, -89.8463, -89.8379, -89.8296, -89.8213, -89.8129, -89.8046, + -89.7963, -89.7879, -89.7796, -89.7713, -89.7629, -89.7546, -89.7463, -89.7379, + -89.7296, -89.7213, -89.7129, -89.7046, -89.6963, -89.6879, -89.6796, -89.6713, + -89.6629, -89.6546, -89.6463, -89.6379, -89.6296, -89.6213, -89.6129, -89.6046, + -89.5963, -89.5879, -89.5796, -89.5713, -89.5629, -89.5546, -89.5463, -89.5379, + -89.5296, -89.5213, -89.5129, -89.5046, -89.4963, -89.4879, -89.4796, -89.4713, + -89.4629, -89.4546, -89.4463, -89.4379, -89.4296, -89.4213, -89.4129, -89.4046, + -89.3963, -89.3879, -89.3796, -89.3713, -89.3629, -89.3546, -89.3463, -89.3379, + -89.3296, -89.3213, -89.3129, -89.3046, -89.2963, -89.2879, -89.2796, -89.2713, + -89.2629, -89.2546, -89.2463, -89.2379, -89.2296, -89.2213, -89.2129, -89.2046, + -89.1963, -89.1879, -89.1796, -89.1713, -89.1629, -89.1546, -89.1463, -89.1379, + -89.1296, -89.1213, -89.1129, -89.1046, -89.0963, -89.0879, -89.0796, -89.0713, + -89.0629, -89.0546, -89.0463, -89.0379, -89.0296, -89.0213, -89.0129, -89.0046, + -88.9963, -88.9879, -88.9796, -88.9713, -88.9629, -88.9546, -88.9463, -88.9379, + -88.9296, -88.9213, -88.9129, -88.9046, -88.8963, -88.8879, -88.8796, -88.8713, + -88.8629, -88.8546, -88.8463, -88.8379, -88.8296, -88.8213, -88.8129, -88.8046, + -88.7963, -88.7879, -88.7796, -88.7713, -88.7629, -88.7546, -88.7463, -88.7379, + -88.7296, -88.7213, -88.7129, -88.7046, -88.6963, -88.6879, -88.6796, -88.6713, + -88.6629, -88.6546, -88.6463, -88.6379, -88.6296, -88.6213, -88.6129, -88.6046, + -88.5963, -88.5879, -88.5796, -88.5713, -88.5629, -88.5546, -88.5463, -88.5379, + -88.5296, -88.5213, -88.5129, -88.5046, -88.4963, -88.4879, -88.4796, -88.4713, + -88.4629, -88.4546, -88.4463, -88.4379, -88.4296, -88.4213, -88.4129, -88.4046, + -88.3963, -88.3879, -88.3796, -88.3713, -88.3629, -88.3546, -88.3463, -88.3379, + -88.3296, -88.3213, -88.3129, -88.3046, -88.2963, -88.2879, -88.2796, -88.2713, + -88.2629, -88.2546, -88.2463, -88.2379, -88.2296, -88.2213, -88.2129, -88.2046, + -88.1963, -88.1879, -88.1796, -88.1713, -88.1629, -88.1546, -88.1463, -88.1379, + -88.1296, -88.1213, -88.1129, -88.1046, -88.0963, -88.0879, -88.0796, -88.0713, + -88.0629, -88.0546, -88.0463, -88.0379, -88.0296, -88.0213, -88.0129, -88.0046, + -87.9963, -87.9879, -87.9796, -87.9713, -87.9629, -87.9546, -87.9463, -87.9379, + -87.9296, -87.9213, -87.9129, -87.9046, -87.8963, -87.8879, -87.8796, -87.8713, + -87.8629, -87.8546, -87.8463, -87.8379, -87.8296, -87.8213, -87.8129, -87.8046, + -87.7963, -87.7879, -87.7796, -87.7713, -87.7629, -87.7546, -87.7463, -87.7379, + -87.7296, -87.7213, -87.7129, -87.7046, -87.6963, -87.6879, -87.6796, -87.6713, + -87.6629, -87.6546, -87.6463, -87.6379, -87.6296, -87.6213, -87.6129, -87.6046, + -87.5963, -87.5879, -87.5796, -87.5713, -87.5629, -87.5546, -87.5463, -87.5379, + -87.5296, -87.5213, -87.5129, -87.5046, -87.4963, -87.4879, -87.4796, -87.4713, + -87.4629, -87.4546, -87.4463, -87.4379, -87.4296, -87.4213, -87.4129, -87.4046, + -87.3963, -87.3879, -87.3796, -87.3713, -87.3629, -87.3546, -87.3463, -87.3379, + -87.3296, -87.3213, -87.3129, -87.3046, -87.2963, -87.2879, -87.2796, -87.2713, + -87.2629, -87.2546, -87.2463, -87.2379, -87.2296, -87.2213, -87.2129, -87.2046, + -87.1963, -87.1879, -87.1796, -87.1713, -87.1629, -87.1546, -87.1463, -87.1379, + -87.1296, -87.1213, -87.1129, -87.1046, -87.0963, -87.0879, -87.0796, -87.0713, + -87.0629, -87.0546, -87.0463, -87.0379, -87.0296, -87.0213, -87.0129, -87.0046, + -86.9963, -86.9879, -86.9796, -86.9713, -86.9629, -86.9546, -86.9463, -86.9379, + -86.9296, -86.9213, -86.9129, -86.9046, -86.8963, -86.8879, -86.8796, -86.8713, + -86.8629, -86.8546, -86.8463, -86.8379, -86.8296, -86.8213, -86.8129, -86.8046, + -86.7963, -86.7879, -86.7796, -86.7713, -86.7629, -86.7546, -86.7463, -86.7379, + -86.7296, -86.7213, -86.7129, -86.7046, -86.6963, -86.6879, -86.6796, -86.6713, + -86.6629, -86.6546, -86.6463, -86.6379, -86.6296, -86.6213, -86.6129, -86.6046, + -86.5963, -86.5879, -86.5796, -86.5713, -86.5629, -86.5546, -86.5463, -86.5379, + -86.5296, -86.5213, -86.5129, -86.5046, -86.4963, -86.4879, -86.4796, -86.4713, + -86.4629, -86.4546, -86.4463, -86.4379, -86.4296, -86.4213, -86.4129, -86.4046, + -86.3963, -86.3879, -86.3796, -86.3713, -86.3629, -86.3546, -86.3463, -86.3379, + -86.3296, -86.3213, -86.3129, -86.3046, -86.2963, -86.2879, -86.2796, -86.2713, + -86.2629, -86.2546, -86.2463, -86.2379, -86.2296, -86.2213, -86.2129, -86.2046, + -86.1963, -86.1879, -86.1796, -86.1713, -86.1629, -86.1546, -86.1463, -86.1379, + -86.1296, -86.1213, -86.1129, -86.1046, -86.0963, -86.0879, -86.0796, -86.0713, + -86.0629, -86.0546, -86.0463, -86.0379, -86.0296, -86.0213, -86.0129, -86.0046, + -85.9963, -85.9879, -85.9796, -85.9713, -85.9629, -85.9546, -85.9463, -85.9379, + -85.9296, -85.9213, -85.9129, -85.9046, -85.8963, -85.8879, -85.8796, -85.8713, + -85.8629, -85.8546, -85.8463, -85.8379, -85.8296, -85.8213, -85.8129, -85.8046, + -85.7963, -85.7879, -85.7796, -85.7713, -85.7629, -85.7546, -85.7463, -85.7379, + -85.7296, -85.7213, -85.7129, -85.7046, -85.6963, -85.6879, -85.6796, -85.6713, + -85.6629, -85.6546, -85.6463, -85.6379, -85.6296, -85.6213, -85.6129, -85.6046, + -85.5963, -85.5879, -85.5796, -85.5713, -85.5629, -85.5546, -85.5463, -85.5379, + -85.5296, -85.5213, -85.5129, -85.5046, -85.4963, -85.4879, -85.4796, -85.4713, + -85.4629, -85.4546, -85.4463, -85.4379, -85.4296, -85.4213, -85.4129, -85.4046, + -85.3963, -85.3879, -85.3796, -85.3713, -85.3629, -85.3546, -85.3463, -85.3379, + -85.3296, -85.3213, -85.3129, -85.3046, -85.2963, -85.2879, -85.2796, -85.2713, + -85.2629, -85.2546, -85.2463, -85.2379, -85.2296, -85.2213, -85.2129, -85.2046, + -85.1963, -85.1879, -85.1796, -85.1713, -85.1629, -85.1546, -85.1463, -85.1379, + -85.1296, -85.1213, -85.1129, -85.1046, -85.0963, -85.0879, -85.0796, -85.0713, + -85.0629, -85.0546, -85.0463, -85.0379, -85.0296, -85.0213, -85.0129, -85.0046, + -84.9963, -84.9879, -84.9796, -84.9713, -84.9629, -84.9546, -84.9463, -84.9379, + -84.9296, -84.9213, -84.9129, -84.9046, -84.8963, -84.8879, -84.8796, -84.8713, + -84.8629, -84.8546, -84.8463, -84.8379, -84.8296, -84.8213, -84.8129, -84.8046, + -84.7963, -84.7879, -84.7796, -84.7713, -84.7629, -84.7546, -84.7463, -84.7379, + -84.7296, -84.7213, -84.7129, -84.7046, -84.6963, -84.6879, -84.6796, -84.6713, + -84.6629, -84.6546, -84.6463, -84.6379, -84.6296, -84.6213, -84.6129, -84.6046, + -84.5963, -84.5879, -84.5796, -84.5713, -84.5629, -84.5546, -84.5463, -84.5379, + -84.5296, -84.5213, -84.5129, -84.5046, -84.4963, -84.4879, -84.4796, -84.4713, + -84.4629, -84.4546, -84.4463, -84.4379, -84.4296, -84.4213, -84.4129, -84.4046, + -84.3963, -84.3879, -84.3796, -84.3713, -84.3629, -84.3546, -84.3463, -84.3379, + -84.3296, -84.3213, -84.3129, -84.3046, -84.2963, -84.2879, -84.2796, -84.2713, + -84.2629, -84.2546, -84.2463, -84.2379, -84.2296, -84.2213, -84.2129, -84.2046, + -84.1963, -84.1879, -84.1796, -84.1713, -84.1629, -84.1546, -84.1463, -84.1379, + -84.1296, -84.1213, -84.1129, -84.1046, -84.0963, -84.0879, -84.0796, -84.0713, + -84.0629, -84.0546, -84.0463, -84.0379, -84.0296, -84.0213, -84.0129, -84.0046, + -83.9963, -83.9879, -83.9796, -83.9713, -83.9629, -83.9546, -83.9463, -83.9379, + -83.9296, -83.9213, -83.9129, -83.9046, -83.8963, -83.8879, -83.8796, -83.8713, + -83.8629, -83.8546, -83.8463, -83.8379, -83.8296, -83.8213, -83.8129, -83.8046, + -83.7963, -83.7879, -83.7796, -83.7713, -83.7629, -83.7546, -83.7463, -83.7379, + -83.7296, -83.7213, -83.7129, -83.7046, -83.6963, -83.6879, -83.6796, -83.6713, + -83.6629, -83.6546, -83.6463, -83.6379, -83.6296, -83.6213, -83.6129, -83.6046, + -83.5963, -83.5879, -83.5796, -83.5713, -83.5629, -83.5546, -83.5463, -83.5379, + -83.5296, -83.5213, -83.5129, -83.5046, -83.4963, -83.4879, -83.4796, -83.4713, + -83.4629, -83.4546, -83.4463, -83.4379, -83.4296, -83.4213, -83.4129, -83.4046, + -83.3963, -83.3879, -83.3796, -83.3713, -83.3629, -83.3546, -83.3463, -83.3379, + -83.3296, -83.3213, -83.3129, -83.3046, -83.2963, -83.2879, -83.2796, -83.2713, + -83.2629, -83.2546, -83.2463, -83.2379, -83.2296, -83.2213, -83.2129, -83.2046, + -83.1963, -83.1879, -83.1796, -83.1713, -83.1629, -83.1546, -83.1463, -83.1379, + -83.1296, -83.1213, -83.1129, -83.1046, -83.0963, -83.0879, -83.0796, -83.0713, + -83.0629, -83.0546, -83.0463, -83.0379, -83.0296, -83.0213, -83.0129, -83.0046, + -82.9963, -82.9879, -82.9796, -82.9713, -82.9629, -82.9546, -82.9463, -82.9379, + -82.9296, -82.9213, -82.9129, -82.9046, -82.8963, -82.8879, -82.8796, -82.8713, + -82.8629, -82.8546, -82.8463, -82.8379, -82.8296, -82.8213, -82.8129, -82.8046, + -82.7963, -82.7879, -82.7796, -82.7713, -82.7629, -82.7546, -82.7463, -82.7379, + -82.7296, -82.7213, -82.7129, -82.7046, -82.6963, -82.6879, -82.6796, -82.6713, + -82.6629, -82.6546, -82.6463, -82.6379, -82.6296, -82.6213, -82.6129, -82.6046, + -82.5963, -82.5879, -82.5796, -82.5713, -82.5629, -82.5546, -82.5463, -82.5379, + -82.5296, -82.5213, -82.5129, -82.5046, -82.4963, -82.4879, -82.4796, -82.4713, + -82.4629, -82.4546, -82.4463, -82.4379, -82.4296, -82.4213, -82.4129, -82.4046, + -82.3963, -82.3879, -82.3796, -82.3713, -82.3629, -82.3546, -82.3463, -82.3379, + -82.3296, -82.3213, -82.3129, -82.3046, -82.2963, -82.2879, -82.2796, -82.2713, + -82.2629, -82.2546, -82.2463, -82.2379, -82.2296, -82.2213, -82.2129, -82.2046, + -82.1963, -82.1879, -82.1796, -82.1713, -82.1629, -82.1546, -82.1463, -82.1379, + -82.1296, -82.1213, -82.1129, -82.1046, -82.0963, -82.0879, -82.0796, -82.0713, + -82.0629, -82.0546, -82.0463, -82.0379, -82.0296, -82.0213, -82.0129, -82.0046, + -81.9963, -81.9879, -81.9796, -81.9713, -81.9629, -81.9546, -81.9463, -81.9379, + -81.9296, -81.9213, -81.9129, -81.9046, -81.8963, -81.8879, -81.8796, -81.8713, + -81.8629, -81.8546, -81.8463, -81.8379, -81.8296, -81.8213, -81.8129, -81.8046, + -81.7963, -81.7879, -81.7796, -81.7713, -81.7629, -81.7546, -81.7463, -81.7379, + -81.7296, -81.7213, -81.7129, -81.7046, -81.6963, -81.6879, -81.6796, -81.6713, + -81.6629, -81.6546, -81.6463, -81.6379, -81.6296, -81.6213, -81.6129, -81.6046, + -81.5963, -81.5879, -81.5796, -81.5713, -81.5629, -81.5546, -81.5463, -81.5379, + -81.5296, -81.5213, -81.5129, -81.5046, -81.4963, -81.4879, -81.4796, -81.4713, + -81.4629, -81.4546, -81.4463, -81.4379, -81.4296, -81.4213, -81.4129, -81.4046, + -81.3963, -81.3879, -81.3796, -81.3713, -81.3629, -81.3546, -81.3463, -81.3379, + -81.3296, -81.3213, -81.3129, -81.3046, -81.2963, -81.2879, -81.2796, -81.2713, + -81.2629, -81.2546, -81.2463, -81.2379, -81.2296, -81.2213, -81.2129, -81.2046, + -81.1963, -81.1879, -81.1796, -81.1713, -81.1629, -81.1546, -81.1463, -81.1379, + -81.1296, -81.1213, -81.1129, -81.1046, -81.0963, -81.0879, -81.0796, -81.0713, + -81.0629, -81.0546, -81.0463, -81.0379, -81.0296, -81.0213, -81.0129, -81.0046, + -80.9963, -80.9879, -80.9796, -80.9713, -80.9629, -80.9546, -80.9463, -80.9379, + -80.9296, -80.9213, -80.9129, -80.9046, -80.8963, -80.8879, -80.8796, -80.8713, + -80.8629, -80.8546, -80.8463, -80.8379, -80.8296, -80.8213, -80.8129, -80.8046, + -80.7963, -80.7879, -80.7796, -80.7713, -80.7629, -80.7546, -80.7463, -80.7379, + -80.7296, -80.7213, -80.7129, -80.7046, -80.6963, -80.6879, -80.6796, -80.6713, + -80.6629, -80.6546, -80.6463, -80.6379, -80.6296, -80.6213, -80.6129, -80.6046, + -80.5963, -80.5879, -80.5796, -80.5713, -80.5629, -80.5546, -80.5463, -80.5379, + -80.5296, -80.5213, -80.5129, -80.5046, -80.4963, -80.4879, -80.4796, -80.4713, + -80.4629, -80.4546, -80.4463, -80.4379, -80.4296, -80.4213, -80.4129, -80.4046, + -80.3963, -80.3879, -80.3796, -80.3713, -80.3629, -80.3546, -80.3463, -80.3379, + -80.3296, -80.3213, -80.3129, -80.3046, -80.2963, -80.2879, -80.2796, -80.2713, + -80.2629, -80.2546, -80.2463, -80.2379, -80.2296, -80.2213, -80.2129, -80.2046, + -80.1963, -80.1879, -80.1796, -80.1713, -80.1629, -80.1546, -80.1463, -80.1379, + -80.1296, -80.1213, -80.1129, -80.1046, -80.0963, -80.0879, -80.0796, -80.0713, + -80.0629, -80.0546, -80.0463, -80.0379, -80.0296, -80.0213, -80.0129, -80.0046, + -79.9963, -79.9879, -79.9796, -79.9713, -79.9629, -79.9546, -79.9463, -79.9379, + -79.9296, -79.9213, -79.9129, -79.9046, -79.8963, -79.8879, -79.8796, -79.8713, + -79.8629, -79.8546, -79.8463, -79.8379, -79.8296, -79.8213, -79.8129, -79.8046, + -79.7963, -79.7879, -79.7796, -79.7713, -79.7629, -79.7546, -79.7463, -79.7379, + -79.7296, -79.7213, -79.7129, -79.7046, -79.6963, -79.6879, -79.6796, -79.6713, + -79.6629, -79.6546, -79.6463, -79.6379, -79.6296, -79.6213, -79.6129, -79.6046, + -79.5963, -79.5879, -79.5796, -79.5713, -79.5629, -79.5546, -79.5463, -79.5379, + -79.5296, -79.5213, -79.5129, -79.5046, -79.4963, -79.4879, -79.4796, -79.4713, + -79.4629, -79.4546, -79.4463, -79.4379, -79.4296, -79.4213, -79.4129, -79.4046, + -79.3963, -79.3879, -79.3796, -79.3713, -79.3629, -79.3546, -79.3463, -79.3379, + -79.3296, -79.3213, -79.3129, -79.3046, -79.2963, -79.2879, -79.2796, -79.2713, + -79.2629, -79.2546, -79.2463, -79.2379, -79.2296, -79.2213, -79.2129, -79.2046, + -79.1963, -79.1879, -79.1796, -79.1713, -79.1629, -79.1546, -79.1463, -79.1379, + -79.1296, -79.1213, -79.1129, -79.1046, -79.0963, -79.0879, -79.0796, -79.0713, + -79.0629, -79.0546, -79.0463, -79.0379, -79.0296, -79.0213, -79.0129, -79.0046, + -78.9963, -78.9879, -78.9796, -78.9713, -78.9629, -78.9546, -78.9463, -78.9379, + -78.9296, -78.9213, -78.9129, -78.9046, -78.8963, -78.8879, -78.8796, -78.8713, + -78.8629, -78.8546, -78.8463, -78.8379, -78.8296, -78.8213, -78.8129, -78.8046, + -78.7963, -78.7879, -78.7796, -78.7713, -78.7629, -78.7546, -78.7463, -78.7379, + -78.7296, -78.7213, -78.7129, -78.7046, -78.6963, -78.6879, -78.6796, -78.6713, + -78.6629, -78.6546, -78.6463, -78.6379, -78.6296, -78.6213, -78.6129, -78.6046, + -78.5963, -78.5879, -78.5796, -78.5713, -78.5629, -78.5546, -78.5463, -78.5379, + -78.5296, -78.5213, -78.5129, -78.5046, -78.4963, -78.4879, -78.4796, -78.4713, + -78.4629, -78.4546, -78.4463, -78.4379, -78.4296, -78.4213, -78.4129, -78.4046, + -78.3963, -78.3879, -78.3796, -78.3713, -78.3629, -78.3546, -78.3463, -78.3379, + -78.3296, -78.3213, -78.3129, -78.3046, -78.2963, -78.2879, -78.2796, -78.2713, + -78.2629, -78.2546, -78.2463, -78.2379, -78.2296, -78.2213, -78.2129, -78.2046, + -78.1963, -78.1879, -78.1796, -78.1713, -78.1629, -78.1546, -78.1463, -78.1379, + -78.1296, -78.1213, -78.1129, -78.1046, -78.0963, -78.0879, -78.0796, -78.0713, + -78.0629, -78.0546, -78.0463, -78.0379, -78.0296, -78.0213, -78.0129, -78.0046, + -77.9963, -77.9879, -77.9796, -77.9713, -77.9629, -77.9546, -77.9463, -77.9379, + -77.9296, -77.9213, -77.9129, -77.9046, -77.8963, -77.8879, -77.8796, -77.8713, + -77.8629, -77.8546, -77.8463, -77.8379, -77.8296, -77.8213, -77.8129, -77.8046, + -77.7963, -77.7879, -77.7796, -77.7713, -77.7629, -77.7546, -77.7463, -77.7379, + -77.7296, -77.7213, -77.7129, -77.7046, -77.6963, -77.6879, -77.6796, -77.6713, + -77.6629, -77.6546, -77.6463, -77.6379, -77.6296, -77.6213, -77.6129, -77.6046, + -77.5963, -77.5879, -77.5796, -77.5713, -77.5629, -77.5546, -77.5463, -77.5379, + -77.5296, -77.5213, -77.5129, -77.5046, -77.4963, -77.4879, -77.4796, -77.4713, + -77.4629, -77.4546, -77.4463, -77.4379, -77.4296, -77.4213, -77.4129, -77.4046, + -77.3963, -77.3879, -77.3796, -77.3713, -77.3629, -77.3546, -77.3463, -77.3379, + -77.3296, -77.3213, -77.3129, -77.3046, -77.2963, -77.2879, -77.2796, -77.2713, + -77.2629, -77.2546, -77.2463, -77.2379, -77.2296, -77.2213, -77.2129, -77.2046, + -77.1963, -77.1879, -77.1796, -77.1713, -77.1629, -77.1546, -77.1463, -77.1379, + -77.1296, -77.1213, -77.1129, -77.1046, -77.0963, -77.0879, -77.0796, -77.0713, + -77.0629, -77.0546, -77.0463, -77.0379, -77.0296, -77.0213, -77.0129, -77.0046, + -76.9963, -76.9879, -76.9796, -76.9713, -76.9629, -76.9546, -76.9463, -76.9379, + -76.9296, -76.9213, -76.9129, -76.9046, -76.8963, -76.8879, -76.8796, -76.8713, + -76.8629, -76.8546, -76.8463, -76.8379, -76.8296, -76.8213, -76.8129, -76.8046, + -76.7963, -76.7879, -76.7796, -76.7713, -76.7629, -76.7546, -76.7463, -76.7379, + -76.7296, -76.7213, -76.7129, -76.7046, -76.6963, -76.6879, -76.6796, -76.6713, + -76.6629, -76.6546, -76.6463, -76.6379, -76.6296, -76.6213, -76.6129, -76.6046, + -76.5963, -76.5879, -76.5796, -76.5713, -76.5629, -76.5546, -76.5463, -76.5379, + -76.5296, -76.5213, -76.5129, -76.5046, -76.4963, -76.4879, -76.4796, -76.4713, + -76.4629, -76.4546, -76.4463, -76.4379, -76.4296, -76.4213, -76.4129, -76.4046, + -76.3963, -76.3879, -76.3796, -76.3713, -76.3629, -76.3546, -76.3463, -76.3379, + -76.3296, -76.3213, -76.3129, -76.3046, -76.2963, -76.2879, -76.2796, -76.2713, + -76.2629, -76.2546, -76.2463, -76.2379, -76.2296, -76.2213, -76.2129, -76.2046, + -76.1963, -76.1879, -76.1796, -76.1713, -76.1629, -76.1546, -76.1463, -76.1379, + -76.1296, -76.1213, -76.1129, -76.1046, -76.0963, -76.0879, -76.0796, -76.0713, + -76.0629, -76.0546, -76.0463, -76.0379, -76.0296, -76.0213, -76.0129, -76.0046, + -75.9963, -75.9879, -75.9796, -75.9713, -75.9629, -75.9546, -75.9463, -75.9379, + -75.9296, -75.9213, -75.9129, -75.9046, -75.8963, -75.8879, -75.8796, -75.8713, + -75.8629, -75.8546, -75.8463, -75.8379, -75.8296, -75.8213, -75.8129, -75.8046, + -75.7963, -75.7879, -75.7796, -75.7713, -75.7629, -75.7546, -75.7463, -75.7379, + -75.7296, -75.7213, -75.7129, -75.7046, -75.6963, -75.6879, -75.6796, -75.6713, + -75.6629, -75.6546, -75.6463, -75.6379, -75.6296, -75.6213, -75.6129, -75.6046, + -75.5963, -75.5879, -75.5796, -75.5713, -75.5629, -75.5546, -75.5463, -75.5379, + -75.5296, -75.5213, -75.5129, -75.5046, -75.4963, -75.4879, -75.4796, -75.4713, + -75.4629, -75.4546, -75.4463, -75.4379, -75.4296, -75.4213, -75.4129, -75.4046, + -75.3963, -75.3879, -75.3796, -75.3713, -75.3629, -75.3546, -75.3463, -75.3379, + -75.3296, -75.3213, -75.3129, -75.3046, -75.2963, -75.2879, -75.2796, -75.2713, + -75.2629, -75.2546, -75.2463, -75.2379, -75.2296, -75.2213, -75.2129, -75.2046, + -75.1963, -75.1879, -75.1796, -75.1713, -75.1629, -75.1546, -75.1463, -75.1379, + -75.1296, -75.1213, -75.1129, -75.1046, -75.0963, -75.0879, -75.0796, -75.0713, + -75.0629, -75.0546, -75.0463, -75.0379, -75.0296, -75.0213, -75.0129, -75.0046, + -74.9963, -74.9879, -74.9796, -74.9713, -74.9629, -74.9546, -74.9463, -74.9379, + -74.9296, -74.9213, -74.9129, -74.9046, -74.8963, -74.8879, -74.8796, -74.8713, + -74.8629, -74.8546, -74.8463, -74.8379, -74.8296, -74.8213, -74.8129, -74.8046, + -74.7963, -74.7879, -74.7796, -74.7713, -74.7629, -74.7546, -74.7463, -74.7379, + -74.7296, -74.7213, -74.7129, -74.7046, -74.6963, -74.6879, -74.6796, -74.6713, + -74.6629, -74.6546, -74.6463, -74.6379, -74.6296, -74.6213, -74.6129, -74.6046, + -74.5963, -74.5879, -74.5796, -74.5713, -74.5629, -74.5546, -74.5463, -74.5379, + -74.5296, -74.5213, -74.5129, -74.5046, -74.4963, -74.4879, -74.4796, -74.4713, + -74.4629, -74.4546, -74.4463, -74.4379, -74.4296, -74.4213, -74.4129, -74.4046, + -74.3963, -74.3879, -74.3796, -74.3713, -74.3629, -74.3546, -74.3463, -74.3379, + -74.3296, -74.3213, -74.3129, -74.3046, -74.2963, -74.2879, -74.2796, -74.2713, + -74.2629, -74.2546, -74.2463, -74.2379, -74.2296, -74.2213, -74.2129, -74.2046, + -74.1963, -74.1879, -74.1796, -74.1713, -74.1629, -74.1546, -74.1463, -74.1379, + -74.1296, -74.1213, -74.1129, -74.1046, -74.0963, -74.0879, -74.0796, -74.0713, + -74.0629, -74.0546, -74.0463, -74.0379, -74.0296, -74.0213, -74.0129, -74.0046, + -73.9963, -73.9879, -73.9796, -73.9713, -73.9629, -73.9546, -73.9463, -73.9379, + -73.9296, -73.9213, -73.9129, -73.9046, -73.8963, -73.8879, -73.8796, -73.8713, + -73.8629, -73.8546, -73.8463, -73.8379, -73.8296, -73.8213, -73.8129, -73.8046, + -73.7963, -73.7879, -73.7796, -73.7713, -73.7629, -73.7546, -73.7463, -73.7379, + -73.7296, -73.7213, -73.7129, -73.7046, -73.6963, -73.6879, -73.6796, -73.6713, + -73.6629, -73.6546, -73.6463, -73.6379, -73.6296, -73.6213, -73.6129, -73.6046, + -73.5963, -73.5879, -73.5796, -73.5713, -73.5629, -73.5546, -73.5463, -73.5379, + -73.5296, -73.5213, -73.5129, -73.5046, -73.4963, -73.4879, -73.4796, -73.4713, + -73.4629, -73.4546, -73.4463, -73.4379, -73.4296, -73.4213, -73.4129, -73.4046, + -73.3963, -73.3879, -73.3796, -73.3713, -73.3629, -73.3546, -73.3463, -73.3379, + -73.3296, -73.3213, -73.3129, -73.3046, -73.2963, -73.2879, -73.2796, -73.2713, + -73.2629, -73.2546, -73.2463, -73.2379, -73.2296, -73.2213, -73.2129, -73.2046, + -73.1963, -73.1879, -73.1796, -73.1713, -73.1629, -73.1546, -73.1463, -73.1379, + -73.1296, -73.1213, -73.1129, -73.1046, -73.0963, -73.0879, -73.0796, -73.0713, + -73.0629, -73.0546, -73.0463, -73.0379, -73.0296, -73.0213, -73.0129, -73.0046, + -72.9963, -72.9879, -72.9796, -72.9713, -72.9629, -72.9546, -72.9463, -72.9379, + -72.9296, -72.9213, -72.9129, -72.9046, -72.8963, -72.8879, -72.8796, -72.8713, + -72.8629, -72.8546, -72.8463, -72.8379, -72.8296, -72.8213, -72.8129, -72.8046, + -72.7963, -72.7879, -72.7796, -72.7713, -72.7629, -72.7546, -72.7463, -72.7379, + -72.7296, -72.7213, -72.7129, -72.7046, -72.6963, -72.6879, -72.6796, -72.6713, + -72.6629, -72.6546, -72.6463, -72.6379, -72.6296, -72.6213, -72.6129, -72.6046, + -72.5963, -72.5879, -72.5796, -72.5713, -72.5629, -72.5546, -72.5463, -72.5379, + -72.5296, -72.5213, -72.5129, -72.5046, -72.4963, -72.4879, -72.4796, -72.4713, + -72.4629, -72.4546, -72.4463, -72.4379, -72.4296, -72.4213, -72.4129, -72.4046, + -72.3963, -72.3879, -72.3796, -72.3713, -72.3629, -72.3546, -72.3463, -72.3379, + -72.3296, -72.3213, -72.3129, -72.3046, -72.2963, -72.2879, -72.2796, -72.2713, + -72.2629, -72.2546, -72.2463, -72.2379, -72.2296, -72.2213, -72.2129, -72.2046, + -72.1963, -72.1879, -72.1796, -72.1713, -72.1629, -72.1546, -72.1463, -72.1379, + -72.1296, -72.1213, -72.1129, -72.1046, -72.0963, -72.0879, -72.0796, -72.0713, + -72.0629, -72.0546, -72.0463, -72.0379, -72.0296, -72.0213, -72.0129, -72.0046, + -71.9963, -71.9879, -71.9796, -71.9713, -71.9629, -71.9546, -71.9463, -71.9379, + -71.9296, -71.9213, -71.9129, -71.9046, -71.8963, -71.8879, -71.8796, -71.8713, + -71.8629, -71.8546, -71.8463, -71.8379, -71.8296, -71.8213, -71.8129, -71.8046, + -71.7963, -71.7879, -71.7796, -71.7713, -71.7629, -71.7546, -71.7463, -71.7379, + -71.7296, -71.7213, -71.7129, -71.7046, -71.6963, -71.6879, -71.6796, -71.6713, + -71.6629, -71.6546, -71.6463, -71.6379, -71.6296, -71.6213, -71.6129, -71.6046, + -71.5963, -71.5879, -71.5796, -71.5713, -71.5629, -71.5546, -71.5463, -71.5379, + -71.5296, -71.5213, -71.5129, -71.5046, -71.4963, -71.4879, -71.4796, -71.4713, + -71.4629, -71.4546, -71.4463, -71.4379, -71.4296, -71.4213, -71.4129, -71.4046, + -71.3963, -71.3879, -71.3796, -71.3713, -71.3629, -71.3546, -71.3463, -71.3379, + -71.3296, -71.3213, -71.3129, -71.3046, -71.2963, -71.2879, -71.2796, -71.2713, + -71.2629, -71.2546, -71.2463, -71.2379, -71.2296, -71.2213, -71.2129, -71.2046, + -71.1963, -71.1879, -71.1796, -71.1713, -71.1629, -71.1546, -71.1463, -71.1379, + -71.1296, -71.1213, -71.1129, -71.1046, -71.0963, -71.0879, -71.0796, -71.0713, + -71.0629, -71.0546, -71.0463, -71.0379, -71.0296, -71.0213, -71.0129, -71.0046, + -70.9963, -70.9879, -70.9796, -70.9713, -70.9629, -70.9546, -70.9463, -70.9379, + -70.9296, -70.9213, -70.9129, -70.9046, -70.8963, -70.8879, -70.8796, -70.8713, + -70.8629, -70.8546, -70.8463, -70.8379, -70.8296, -70.8213, -70.8129, -70.8046, + -70.7963, -70.7879, -70.7796, -70.7713, -70.7629, -70.7546, -70.7463, -70.7379, + -70.7296, -70.7213, -70.7129, -70.7046, -70.6963, -70.6879, -70.6796, -70.6713, + -70.6629, -70.6546, -70.6463, -70.6379, -70.6296, -70.6213, -70.6129, -70.6046, + -70.5963, -70.5879, -70.5796, -70.5713, -70.5629, -70.5546, -70.5463, -70.5379, + -70.5296, -70.5213, -70.5129, -70.5046, -70.4963, -70.4879, -70.4796, -70.4713, + -70.4629, -70.4546, -70.4463, -70.4379, -70.4296, -70.4213, -70.4129, -70.4046, + -70.3963, -70.3879, -70.3796, -70.3713, -70.3629, -70.3546, -70.3463, -70.3379, + -70.3296, -70.3213, -70.3129, -70.3046, -70.2963, -70.2879, -70.2796, -70.2713, + -70.2629, -70.2546, -70.2463, -70.2379, -70.2296, -70.2213, -70.2129, -70.2046, + -70.1963, -70.1879, -70.1796, -70.1713, -70.1629, -70.1546, -70.1463, -70.1379, + -70.1296, -70.1213, -70.1129, -70.1046, -70.0963, -70.0879, -70.0796, -70.0713, + -70.0629, -70.0546, -70.0463, -70.0379, -70.0296, -70.0213, -70.0129, -70.0046, + -69.9963, -69.9879, -69.9796, -69.9713, -69.9629, -69.9546, -69.9463, -69.9379, + -69.9296, -69.9213, -69.9129, -69.9046, -69.8963, -69.8879, -69.8796, -69.8713, + -69.8629, -69.8546, -69.8463, -69.8379, -69.8296, -69.8213, -69.8129, -69.8046, + -69.7963, -69.7879, -69.7796, -69.7713, -69.7629, -69.7546, -69.7463, -69.7379, + -69.7296, -69.7213, -69.7129, -69.7046, -69.6963, -69.6879, -69.6796, -69.6713, + -69.6629, -69.6546, -69.6463, -69.6379, -69.6296, -69.6213, -69.6129, -69.6046, + -69.5963, -69.5879, -69.5796, -69.5713, -69.5629, -69.5546, -69.5463, -69.5379, + -69.5296, -69.5213, -69.5129, -69.5046, -69.4963, -69.4879, -69.4796, -69.4713, + -69.4629, -69.4546, -69.4463, -69.4379, -69.4296, -69.4213, -69.4129, -69.4046, + -69.3963, -69.3879, -69.3796, -69.3713, -69.3629, -69.3546, -69.3463, -69.3379, + -69.3296, -69.3213, -69.3129, -69.3046, -69.2963, -69.2879, -69.2796, -69.2713, + -69.2629, -69.2546, -69.2463, -69.2379, -69.2296, -69.2213, -69.2129, -69.2046, + -69.1963, -69.1879, -69.1796, -69.1713, -69.1629, -69.1546, -69.1463, -69.1379, + -69.1296, -69.1213, -69.1129, -69.1046, -69.0963, -69.0879, -69.0796, -69.0713, + -69.0629, -69.0546, -69.0463, -69.0379, -69.0296, -69.0213, -69.0129, -69.0046, + -68.9963, -68.9879, -68.9796, -68.9713, -68.9629, -68.9546, -68.9463, -68.9379, + -68.9296, -68.9213, -68.9129, -68.9046, -68.8963, -68.8879, -68.8796, -68.8713, + -68.8629, -68.8546, -68.8463, -68.8379, -68.8296, -68.8213, -68.8129, -68.8046, + -68.7963, -68.7879, -68.7796, -68.7713, -68.7629, -68.7546, -68.7463, -68.7379, + -68.7296, -68.7213, -68.7129, -68.7046, -68.6963, -68.6879, -68.6796, -68.6713, + -68.6629, -68.6546, -68.6463, -68.6379, -68.6296, -68.6213, -68.6129, -68.6046, + -68.5963, -68.5879, -68.5796, -68.5713, -68.5629, -68.5546, -68.5463, -68.5379, + -68.5296, -68.5213, -68.5129, -68.5046, -68.4963, -68.4879, -68.4796, -68.4713, + -68.4629, -68.4546, -68.4463, -68.4379, -68.4296, -68.4213, -68.4129, -68.4046, + -68.3963, -68.3879, -68.3796, -68.3713, -68.3629, -68.3546, -68.3463, -68.3379, + -68.3296, -68.3213, -68.3129, -68.3046, -68.2963, -68.2879, -68.2796, -68.2713, + -68.2629, -68.2546, -68.2463, -68.2379, -68.2296, -68.2213, -68.2129, -68.2046, + -68.1963, -68.1879, -68.1796, -68.1713, -68.1629, -68.1546, -68.1463, -68.1379, + -68.1296, -68.1213, -68.1129, -68.1046, -68.0963, -68.0879, -68.0796, -68.0713, + -68.0629, -68.0546, -68.0463, -68.0379, -68.0296, -68.0213, -68.0129, -68.0046, + -67.9963, -67.9879, -67.9796, -67.9713, -67.9629, -67.9546, -67.9463, -67.9379, + -67.9296, -67.9213, -67.9129, -67.9046, -67.8963, -67.8879, -67.8796, -67.8713, + -67.8629, -67.8546, -67.8463, -67.8379, -67.8296, -67.8213, -67.8129, -67.8046, + -67.7963, -67.7879, -67.7796, -67.7713, -67.7629, -67.7546, -67.7463, -67.7379, + -67.7296, -67.7213, -67.7129, -67.7046, -67.6963, -67.6879, -67.6796, -67.6713, + -67.6629, -67.6546, -67.6463, -67.6379, -67.6296, -67.6213, -67.6129, -67.6046, + -67.5963, -67.5879, -67.5796, -67.5713, -67.5629, -67.5546, -67.5463, -67.5379, + -67.5296, -67.5213, -67.5129, -67.5046, -67.4963, -67.4879, -67.4796, -67.4713, + -67.4629, -67.4546, -67.4463, -67.4379, -67.4296, -67.4213, -67.4129, -67.4046, + -67.3963, -67.3879, -67.3796, -67.3713, -67.3629, -67.3546, -67.3463, -67.3379, + -67.3296, -67.3213, -67.3129, -67.3046, -67.2963, -67.2879, -67.2796, -67.2713, + -67.2629, -67.2546, -67.2463, -67.2379, -67.2296, -67.2213, -67.2129, -67.2046, + -67.1963, -67.1879, -67.1796, -67.1713, -67.1629, -67.1546, -67.1463, -67.1379, + -67.1296, -67.1213, -67.1129, -67.1046, -67.0963, -67.0879, -67.0796, -67.0713, + -67.0629, -67.0546, -67.0463, -67.0379, -67.0296, -67.0213, -67.0129, -67.0046, + -66.9963, -66.9879, -66.9796, -66.9713, -66.9629, -66.9546, -66.9463] + + # Mid-point latitudes of data-grid (24.9537 to 52.8704) + snodas_lats = [24.9537, 24.9621, 24.9704, 24.9787, 24.9871, 24.9954, 25.0037, + 25.0121, 25.0204, 25.0287, 25.0371, 25.0454, 25.0537, 25.0621, 25.0704, + 25.0787, 25.0871, 25.0954, 25.1037, 25.1121, 25.1204, 25.1287, 25.1371, + 25.1454, 25.1537, 25.1621, 25.1704, 25.1787, 25.1871, 25.1954, 25.2037, + 25.2121, 25.2204, 25.2287, 25.2371, 25.2454, 25.2537, 25.2621, 25.2704, + 25.2787, 25.2871, 25.2954, 25.3037, 25.3121, 25.3204, 25.3287, 25.3371, + 25.3454, 25.3537, 25.3621, 25.3704, 25.3787, 25.3871, 25.3954, 25.4037, + 25.4121, 25.4204, 25.4287, 25.4371, 25.4454, 25.4537, 25.4621, 25.4704, + 25.4787, 25.4871, 25.4954, 25.5037, 25.5121, 25.5204, 25.5287, 25.5371, + 25.5454, 25.5537, 25.5621, 25.5704, 25.5787, 25.5871, 25.5954, 25.6037, + 25.6121, 25.6204, 25.6287, 25.6371, 25.6454, 25.6537, 25.6621, 25.6704, + 25.6787, 25.6871, 25.6954, 25.7037, 25.7121, 25.7204, 25.7287, 25.7371, + 25.7454, 25.7537, 25.7621, 25.7704, 25.7787, 25.7871, 25.7954, 25.8037, + 25.8121, 25.8204, 25.8287, 25.8371, 25.8454, 25.8537, 25.8621, 25.8704, + 25.8787, 25.8871, 25.8954, 25.9037, 25.9121, 25.9204, 25.9287, 25.9371, + 25.9454, 25.9537, 25.9621, 25.9704, 25.9787, 25.9871, 25.9954, 26.0037, + 26.0121, 26.0204, 26.0287, 26.0371, 26.0454, 26.0537, 26.0621, 26.0704, + 26.0787, 26.0871, 26.0954, 26.1037, 26.1121, 26.1204, 26.1287, 26.1371, + 26.1454, 26.1537, 26.1621, 26.1704, 26.1787, 26.1871, 26.1954, 26.2037, + 26.2121, 26.2204, 26.2287, 26.2371, 26.2454, 26.2537, 26.2621, 26.2704, + 26.2787, 26.2871, 26.2954, 26.3037, 26.3121, 26.3204, 26.3287, 26.3371, + 26.3454, 26.3537, 26.3621, 26.3704, 26.3787, 26.3871, 26.3954, 26.4037, + 26.4121, 26.4204, 26.4287, 26.4371, 26.4454, 26.4537, 26.4621, 26.4704, + 26.4787, 26.4871, 26.4954, 26.5037, 26.5121, 26.5204, 26.5287, 26.5371, + 26.5454, 26.5537, 26.5621, 26.5704, 26.5787, 26.5871, 26.5954, 26.6037, + 26.6121, 26.6204, 26.6287, 26.6371, 26.6454, 26.6537, 26.6621, 26.6704, + 26.6787, 26.6871, 26.6954, 26.7037, 26.7121, 26.7204, 26.7287, 26.7371, + 26.7454, 26.7537, 26.7621, 26.7704, 26.7787, 26.7871, 26.7954, 26.8037, + 26.8121, 26.8204, 26.8287, 26.8371, 26.8454, 26.8537, 26.8621, 26.8704, + 26.8787, 26.8871, 26.8954, 26.9037, 26.9121, 26.9204, 26.9287, 26.9371, + 26.9454, 26.9537, 26.9621, 26.9704, 26.9787, 26.9871, 26.9954, 27.0037, + 27.0121, 27.0204, 27.0287, 27.0371, 27.0454, 27.0537, 27.0621, 27.0704, + 27.0787, 27.0871, 27.0954, 27.1037, 27.1121, 27.1204, 27.1287, 27.1371, + 27.1454, 27.1537, 27.1621, 27.1704, 27.1787, 27.1871, 27.1954, 27.2037, + 27.2121, 27.2204, 27.2287, 27.2371, 27.2454, 27.2537, 27.2621, 27.2704, + 27.2787, 27.2871, 27.2954, 27.3037, 27.3121, 27.3204, 27.3287, 27.3371, + 27.3454, 27.3537, 27.3621, 27.3704, 27.3787, 27.3871, 27.3954, 27.4037, + 27.4121, 27.4204, 27.4287, 27.4371, 27.4454, 27.4537, 27.4621, 27.4704, + 27.4787, 27.4871, 27.4954, 27.5037, 27.5121, 27.5204, 27.5287, 27.5371, + 27.5454, 27.5537, 27.5621, 27.5704, 27.5787, 27.5871, 27.5954, 27.6037, + 27.6121, 27.6204, 27.6287, 27.6371, 27.6454, 27.6537, 27.6621, 27.6704, + 27.6787, 27.6871, 27.6954, 27.7037, 27.7121, 27.7204, 27.7287, 27.7371, + 27.7454, 27.7537, 27.7621, 27.7704, 27.7787, 27.7871, 27.7954, 27.8037, + 27.8121, 27.8204, 27.8287, 27.8371, 27.8454, 27.8537, 27.8621, 27.8704, + 27.8787, 27.8871, 27.8954, 27.9037, 27.9121, 27.9204, 27.9287, 27.9371, + 27.9454, 27.9537, 27.9621, 27.9704, 27.9787, 27.9871, 27.9954, 28.0037, + 28.0121, 28.0204, 28.0287, 28.0371, 28.0454, 28.0537, 28.0621, 28.0704, + 28.0787, 28.0871, 28.0954, 28.1037, 28.1121, 28.1204, 28.1287, 28.1371, + 28.1454, 28.1537, 28.1621, 28.1704, 28.1787, 28.1871, 28.1954, 28.2037, + 28.2121, 28.2204, 28.2287, 28.2371, 28.2454, 28.2537, 28.2621, 28.2704, + 28.2787, 28.2871, 28.2954, 28.3037, 28.3121, 28.3204, 28.3287, 28.3371, + 28.3454, 28.3537, 28.3621, 28.3704, 28.3787, 28.3871, 28.3954, 28.4037, + 28.4121, 28.4204, 28.4287, 28.4371, 28.4454, 28.4537, 28.4621, 28.4704, + 28.4787, 28.4871, 28.4954, 28.5037, 28.5121, 28.5204, 28.5287, 28.5371, + 28.5454, 28.5537, 28.5621, 28.5704, 28.5787, 28.5871, 28.5954, 28.6037, + 28.6121, 28.6204, 28.6287, 28.6371, 28.6454, 28.6537, 28.6621, 28.6704, + 28.6787, 28.6871, 28.6954, 28.7037, 28.7121, 28.7204, 28.7287, 28.7371, + 28.7454, 28.7537, 28.7621, 28.7704, 28.7787, 28.7871, 28.7954, 28.8037, + 28.8121, 28.8204, 28.8287, 28.8371, 28.8454, 28.8537, 28.8621, 28.8704, + 28.8787, 28.8871, 28.8954, 28.9037, 28.9121, 28.9204, 28.9287, 28.9371, + 28.9454, 28.9537, 28.9621, 28.9704, 28.9787, 28.9871, 28.9954, 29.0037, + 29.0121, 29.0204, 29.0287, 29.0371, 29.0454, 29.0537, 29.0621, 29.0704, + 29.0787, 29.0871, 29.0954, 29.1037, 29.1121, 29.1204, 29.1287, 29.1371, + 29.1454, 29.1537, 29.1621, 29.1704, 29.1787, 29.1871, 29.1954, 29.2037, + 29.2121, 29.2204, 29.2287, 29.2371, 29.2454, 29.2537, 29.2621, 29.2704, + 29.2787, 29.2871, 29.2954, 29.3037, 29.3121, 29.3204, 29.3287, 29.3371, + 29.3454, 29.3537, 29.3621, 29.3704, 29.3787, 29.3871, 29.3954, 29.4037, + 29.4121, 29.4204, 29.4287, 29.4371, 29.4454, 29.4537, 29.4621, 29.4704, + 29.4787, 29.4871, 29.4954, 29.5037, 29.5121, 29.5204, 29.5287, 29.5371, + 29.5454, 29.5537, 29.5621, 29.5704, 29.5787, 29.5871, 29.5954, 29.6037, + 29.6121, 29.6204, 29.6287, 29.6371, 29.6454, 29.6537, 29.6621, 29.6704, + 29.6787, 29.6871, 29.6954, 29.7037, 29.7121, 29.7204, 29.7287, 29.7371, + 29.7454, 29.7537, 29.7621, 29.7704, 29.7787, 29.7871, 29.7954, 29.8037, + 29.8121, 29.8204, 29.8287, 29.8371, 29.8454, 29.8537, 29.8621, 29.8704, + 29.8787, 29.8871, 29.8954, 29.9037, 29.9121, 29.9204, 29.9287, 29.9371, + 29.9454, 29.9537, 29.9621, 29.9704, 29.9787, 29.9871, 29.9954, 30.0037, + 30.0121, 30.0204, 30.0287, 30.0371, 30.0454, 30.0537, 30.0621, 30.0704, + 30.0787, 30.0871, 30.0954, 30.1037, 30.1121, 30.1204, 30.1287, 30.1371, + 30.1454, 30.1537, 30.1621, 30.1704, 30.1787, 30.1871, 30.1954, 30.2037, + 30.2121, 30.2204, 30.2287, 30.2371, 30.2454, 30.2537, 30.2621, 30.2704, + 30.2787, 30.2871, 30.2954, 30.3037, 30.3121, 30.3204, 30.3287, 30.3371, + 30.3454, 30.3537, 30.3621, 30.3704, 30.3787, 30.3871, 30.3954, 30.4037, + 30.4121, 30.4204, 30.4287, 30.4371, 30.4454, 30.4537, 30.4621, 30.4704, + 30.4787, 30.4871, 30.4954, 30.5037, 30.5121, 30.5204, 30.5287, 30.5371, + 30.5454, 30.5537, 30.5621, 30.5704, 30.5787, 30.5871, 30.5954, 30.6037, + 30.6121, 30.6204, 30.6287, 30.6371, 30.6454, 30.6537, 30.6621, 30.6704, + 30.6787, 30.6871, 30.6954, 30.7037, 30.7121, 30.7204, 30.7287, 30.7371, + 30.7454, 30.7537, 30.7621, 30.7704, 30.7787, 30.7871, 30.7954, 30.8037, + 30.8121, 30.8204, 30.8287, 30.8371, 30.8454, 30.8537, 30.8621, 30.8704, + 30.8787, 30.8871, 30.8954, 30.9037, 30.9121, 30.9204, 30.9287, 30.9371, + 30.9454, 30.9537, 30.9621, 30.9704, 30.9787, 30.9871, 30.9954, 31.0037, + 31.0121, 31.0204, 31.0287, 31.0371, 31.0454, 31.0537, 31.0621, 31.0704, + 31.0787, 31.0871, 31.0954, 31.1037, 31.1121, 31.1204, 31.1287, 31.1371, + 31.1454, 31.1537, 31.1621, 31.1704, 31.1787, 31.1871, 31.1954, 31.2037, + 31.2121, 31.2204, 31.2287, 31.2371, 31.2454, 31.2537, 31.2621, 31.2704, + 31.2787, 31.2871, 31.2954, 31.3037, 31.3121, 31.3204, 31.3287, 31.3371, + 31.3454, 31.3537, 31.3621, 31.3704, 31.3787, 31.3871, 31.3954, 31.4037, + 31.4121, 31.4204, 31.4287, 31.4371, 31.4454, 31.4537, 31.4621, 31.4704, + 31.4787, 31.4871, 31.4954, 31.5037, 31.5121, 31.5204, 31.5287, 31.5371, + 31.5454, 31.5537, 31.5621, 31.5704, 31.5787, 31.5871, 31.5954, 31.6037, + 31.6121, 31.6204, 31.6287, 31.6371, 31.6454, 31.6537, 31.6621, 31.6704, + 31.6787, 31.6871, 31.6954, 31.7037, 31.7121, 31.7204, 31.7287, 31.7371, + 31.7454, 31.7537, 31.7621, 31.7704, 31.7787, 31.7871, 31.7954, 31.8037, + 31.8121, 31.8204, 31.8287, 31.8371, 31.8454, 31.8537, 31.8621, 31.8704, + 31.8787, 31.8871, 31.8954, 31.9037, 31.9121, 31.9204, 31.9287, 31.9371, + 31.9454, 31.9537, 31.9621, 31.9704, 31.9787, 31.9871, 31.9954, 32.0037, + 32.0121, 32.0204, 32.0287, 32.0371, 32.0454, 32.0537, 32.0621, 32.0704, + 32.0787, 32.0871, 32.0954, 32.1037, 32.1121, 32.1204, 32.1287, 32.1371, + 32.1454, 32.1537, 32.1621, 32.1704, 32.1787, 32.1871, 32.1954, 32.2037, + 32.2121, 32.2204, 32.2287, 32.2371, 32.2454, 32.2537, 32.2621, 32.2704, + 32.2787, 32.2871, 32.2954, 32.3037, 32.3121, 32.3204, 32.3287, 32.3371, + 32.3454, 32.3537, 32.3621, 32.3704, 32.3787, 32.3871, 32.3954, 32.4037, + 32.4121, 32.4204, 32.4287, 32.4371, 32.4454, 32.4537, 32.4621, 32.4704, + 32.4787, 32.4871, 32.4954, 32.5037, 32.5121, 32.5204, 32.5287, 32.5371, + 32.5454, 32.5537, 32.5621, 32.5704, 32.5787, 32.5871, 32.5954, 32.6037, + 32.6121, 32.6204, 32.6287, 32.6371, 32.6454, 32.6537, 32.6621, 32.6704, + 32.6787, 32.6871, 32.6954, 32.7037, 32.7121, 32.7204, 32.7287, 32.7371, + 32.7454, 32.7537, 32.7621, 32.7704, 32.7787, 32.7871, 32.7954, 32.8037, + 32.8121, 32.8204, 32.8287, 32.8371, 32.8454, 32.8537, 32.8621, 32.8704, + 32.8787, 32.8871, 32.8954, 32.9037, 32.9121, 32.9204, 32.9287, 32.9371, + 32.9454, 32.9537, 32.9621, 32.9704, 32.9787, 32.9871, 32.9954, 33.0037, + 33.0121, 33.0204, 33.0287, 33.0371, 33.0454, 33.0537, 33.0621, 33.0704, + 33.0787, 33.0871, 33.0954, 33.1037, 33.1121, 33.1204, 33.1287, 33.1371, + 33.1454, 33.1537, 33.1621, 33.1704, 33.1787, 33.1871, 33.1954, 33.2037, + 33.2121, 33.2204, 33.2287, 33.2371, 33.2454, 33.2537, 33.2621, 33.2704, + 33.2787, 33.2871, 33.2954, 33.3037, 33.3121, 33.3204, 33.3287, 33.3371, + 33.3454, 33.3537, 33.3621, 33.3704, 33.3787, 33.3871, 33.3954, 33.4037, + 33.4121, 33.4204, 33.4287, 33.4371, 33.4454, 33.4537, 33.4621, 33.4704, + 33.4787, 33.4871, 33.4954, 33.5037, 33.5121, 33.5204, 33.5287, 33.5371, + 33.5454, 33.5537, 33.5621, 33.5704, 33.5787, 33.5871, 33.5954, 33.6037, + 33.6121, 33.6204, 33.6287, 33.6371, 33.6454, 33.6537, 33.6621, 33.6704, + 33.6787, 33.6871, 33.6954, 33.7037, 33.7121, 33.7204, 33.7287, 33.7371, + 33.7454, 33.7537, 33.7621, 33.7704, 33.7787, 33.7871, 33.7954, 33.8037, + 33.8121, 33.8204, 33.8287, 33.8371, 33.8454, 33.8537, 33.8621, 33.8704, + 33.8787, 33.8871, 33.8954, 33.9037, 33.9121, 33.9204, 33.9287, 33.9371, + 33.9454, 33.9537, 33.9621, 33.9704, 33.9787, 33.9871, 33.9954, 34.0037, + 34.0121, 34.0204, 34.0287, 34.0371, 34.0454, 34.0537, 34.0621, 34.0704, + 34.0787, 34.0871, 34.0954, 34.1037, 34.1121, 34.1204, 34.1287, 34.1371, + 34.1454, 34.1537, 34.1621, 34.1704, 34.1787, 34.1871, 34.1954, 34.2037, + 34.2121, 34.2204, 34.2287, 34.2371, 34.2454, 34.2537, 34.2621, 34.2704, + 34.2787, 34.2871, 34.2954, 34.3037, 34.3121, 34.3204, 34.3287, 34.3371, + 34.3454, 34.3537, 34.3621, 34.3704, 34.3787, 34.3871, 34.3954, 34.4037, + 34.4121, 34.4204, 34.4287, 34.4371, 34.4454, 34.4537, 34.4621, 34.4704, + 34.4787, 34.4871, 34.4954, 34.5037, 34.5121, 34.5204, 34.5287, 34.5371, + 34.5454, 34.5537, 34.5621, 34.5704, 34.5787, 34.5871, 34.5954, 34.6037, + 34.6121, 34.6204, 34.6287, 34.6371, 34.6454, 34.6537, 34.6621, 34.6704, + 34.6787, 34.6871, 34.6954, 34.7037, 34.7121, 34.7204, 34.7287, 34.7371, + 34.7454, 34.7537, 34.7621, 34.7704, 34.7787, 34.7871, 34.7954, 34.8037, + 34.8121, 34.8204, 34.8287, 34.8371, 34.8454, 34.8537, 34.8621, 34.8704, + 34.8787, 34.8871, 34.8954, 34.9037, 34.9121, 34.9204, 34.9287, 34.9371, + 34.9454, 34.9537, 34.9621, 34.9704, 34.9787, 34.9871, 34.9954, 35.0037, + 35.0121, 35.0204, 35.0287, 35.0371, 35.0454, 35.0537, 35.0621, 35.0704, + 35.0787, 35.0871, 35.0954, 35.1037, 35.1121, 35.1204, 35.1287, 35.1371, + 35.1454, 35.1537, 35.1621, 35.1704, 35.1787, 35.1871, 35.1954, 35.2037, + 35.2121, 35.2204, 35.2287, 35.2371, 35.2454, 35.2537, 35.2621, 35.2704, + 35.2787, 35.2871, 35.2954, 35.3037, 35.3121, 35.3204, 35.3287, 35.3371, + 35.3454, 35.3537, 35.3621, 35.3704, 35.3787, 35.3871, 35.3954, 35.4037, + 35.4121, 35.4204, 35.4287, 35.4371, 35.4454, 35.4537, 35.4621, 35.4704, + 35.4787, 35.4871, 35.4954, 35.5037, 35.5121, 35.5204, 35.5287, 35.5371, + 35.5454, 35.5537, 35.5621, 35.5704, 35.5787, 35.5871, 35.5954, 35.6037, + 35.6121, 35.6204, 35.6287, 35.6371, 35.6454, 35.6537, 35.6621, 35.6704, + 35.6787, 35.6871, 35.6954, 35.7037, 35.7121, 35.7204, 35.7287, 35.7371, + 35.7454, 35.7537, 35.7621, 35.7704, 35.7787, 35.7871, 35.7954, 35.8037, + 35.8121, 35.8204, 35.8287, 35.8371, 35.8454, 35.8537, 35.8621, 35.8704, + 35.8787, 35.8871, 35.8954, 35.9037, 35.9121, 35.9204, 35.9287, 35.9371, + 35.9454, 35.9537, 35.9621, 35.9704, 35.9787, 35.9871, 35.9954, 36.0037, + 36.0121, 36.0204, 36.0287, 36.0371, 36.0454, 36.0537, 36.0621, 36.0704, + 36.0787, 36.0871, 36.0954, 36.1037, 36.1121, 36.1204, 36.1287, 36.1371, + 36.1454, 36.1537, 36.1621, 36.1704, 36.1787, 36.1871, 36.1954, 36.2037, + 36.2121, 36.2204, 36.2287, 36.2371, 36.2454, 36.2537, 36.2621, 36.2704, + 36.2787, 36.2871, 36.2954, 36.3037, 36.3121, 36.3204, 36.3287, 36.3371, + 36.3454, 36.3537, 36.3621, 36.3704, 36.3787, 36.3871, 36.3954, 36.4037, + 36.4121, 36.4204, 36.4287, 36.4371, 36.4454, 36.4537, 36.4621, 36.4704, + 36.4787, 36.4871, 36.4954, 36.5037, 36.5121, 36.5204, 36.5287, 36.5371, + 36.5454, 36.5537, 36.5621, 36.5704, 36.5787, 36.5871, 36.5954, 36.6037, + 36.6121, 36.6204, 36.6287, 36.6371, 36.6454, 36.6537, 36.6621, 36.6704, + 36.6787, 36.6871, 36.6954, 36.7037, 36.7121, 36.7204, 36.7287, 36.7371, + 36.7454, 36.7537, 36.7621, 36.7704, 36.7787, 36.7871, 36.7954, 36.8037, + 36.8121, 36.8204, 36.8287, 36.8371, 36.8454, 36.8537, 36.8621, 36.8704, + 36.8787, 36.8871, 36.8954, 36.9037, 36.9121, 36.9204, 36.9287, 36.9371, + 36.9454, 36.9537, 36.9621, 36.9704, 36.9787, 36.9871, 36.9954, 37.0037, + 37.0121, 37.0204, 37.0287, 37.0371, 37.0454, 37.0537, 37.0621, 37.0704, + 37.0787, 37.0871, 37.0954, 37.1037, 37.1121, 37.1204, 37.1287, 37.1371, + 37.1454, 37.1537, 37.1621, 37.1704, 37.1787, 37.1871, 37.1954, 37.2037, + 37.2121, 37.2204, 37.2287, 37.2371, 37.2454, 37.2537, 37.2621, 37.2704, + 37.2787, 37.2871, 37.2954, 37.3037, 37.3121, 37.3204, 37.3287, 37.3371, + 37.3454, 37.3537, 37.3621, 37.3704, 37.3787, 37.3871, 37.3954, 37.4037, + 37.4121, 37.4204, 37.4287, 37.4371, 37.4454, 37.4537, 37.4621, 37.4704, + 37.4787, 37.4871, 37.4954, 37.5037, 37.5121, 37.5204, 37.5287, 37.5371, + 37.5454, 37.5537, 37.5621, 37.5704, 37.5787, 37.5871, 37.5954, 37.6037, + 37.6121, 37.6204, 37.6287, 37.6371, 37.6454, 37.6537, 37.6621, 37.6704, + 37.6787, 37.6871, 37.6954, 37.7037, 37.7121, 37.7204, 37.7287, 37.7371, + 37.7454, 37.7537, 37.7621, 37.7704, 37.7787, 37.7871, 37.7954, 37.8037, + 37.8121, 37.8204, 37.8287, 37.8371, 37.8454, 37.8537, 37.8621, 37.8704, + 37.8787, 37.8871, 37.8954, 37.9037, 37.9121, 37.9204, 37.9287, 37.9371, + 37.9454, 37.9537, 37.9621, 37.9704, 37.9787, 37.9871, 37.9954, 38.0037, + 38.0121, 38.0204, 38.0287, 38.0371, 38.0454, 38.0537, 38.0621, 38.0704, + 38.0787, 38.0871, 38.0954, 38.1037, 38.1121, 38.1204, 38.1287, 38.1371, + 38.1454, 38.1537, 38.1621, 38.1704, 38.1787, 38.1871, 38.1954, 38.2037, + 38.2121, 38.2204, 38.2287, 38.2371, 38.2454, 38.2537, 38.2621, 38.2704, + 38.2787, 38.2871, 38.2954, 38.3037, 38.3121, 38.3204, 38.3287, 38.3371, + 38.3454, 38.3537, 38.3621, 38.3704, 38.3787, 38.3871, 38.3954, 38.4037, + 38.4121, 38.4204, 38.4287, 38.4371, 38.4454, 38.4537, 38.4621, 38.4704, + 38.4787, 38.4871, 38.4954, 38.5037, 38.5121, 38.5204, 38.5287, 38.5371, + 38.5454, 38.5537, 38.5621, 38.5704, 38.5787, 38.5871, 38.5954, 38.6037, + 38.6121, 38.6204, 38.6287, 38.6371, 38.6454, 38.6537, 38.6621, 38.6704, + 38.6787, 38.6871, 38.6954, 38.7037, 38.7121, 38.7204, 38.7287, 38.7371, + 38.7454, 38.7537, 38.7621, 38.7704, 38.7787, 38.7871, 38.7954, 38.8037, + 38.8121, 38.8204, 38.8287, 38.8371, 38.8454, 38.8537, 38.8621, 38.8704, + 38.8787, 38.8871, 38.8954, 38.9037, 38.9121, 38.9204, 38.9287, 38.9371, + 38.9454, 38.9537, 38.9621, 38.9704, 38.9787, 38.9871, 38.9954, 39.0037, + 39.0121, 39.0204, 39.0287, 39.0371, 39.0454, 39.0537, 39.0621, 39.0704, + 39.0787, 39.0871, 39.0954, 39.1037, 39.1121, 39.1204, 39.1287, 39.1371, + 39.1454, 39.1537, 39.1621, 39.1704, 39.1787, 39.1871, 39.1954, 39.2037, + 39.2121, 39.2204, 39.2287, 39.2371, 39.2454, 39.2537, 39.2621, 39.2704, + 39.2787, 39.2871, 39.2954, 39.3037, 39.3121, 39.3204, 39.3287, 39.3371, + 39.3454, 39.3537, 39.3621, 39.3704, 39.3787, 39.3871, 39.3954, 39.4037, + 39.4121, 39.4204, 39.4287, 39.4371, 39.4454, 39.4537, 39.4621, 39.4704, + 39.4787, 39.4871, 39.4954, 39.5037, 39.5121, 39.5204, 39.5287, 39.5371, + 39.5454, 39.5537, 39.5621, 39.5704, 39.5787, 39.5871, 39.5954, 39.6037, + 39.6121, 39.6204, 39.6287, 39.6371, 39.6454, 39.6537, 39.6621, 39.6704, + 39.6787, 39.6871, 39.6954, 39.7037, 39.7121, 39.7204, 39.7287, 39.7371, + 39.7454, 39.7537, 39.7621, 39.7704, 39.7787, 39.7871, 39.7954, 39.8037, + 39.8121, 39.8204, 39.8287, 39.8371, 39.8454, 39.8537, 39.8621, 39.8704, + 39.8787, 39.8871, 39.8954, 39.9037, 39.9121, 39.9204, 39.9287, 39.9371, + 39.9454, 39.9537, 39.9621, 39.9704, 39.9787, 39.9871, 39.9954, 40.0037, + 40.0121, 40.0204, 40.0287, 40.0371, 40.0454, 40.0537, 40.0621, 40.0704, + 40.0787, 40.0871, 40.0954, 40.1037, 40.1121, 40.1204, 40.1287, 40.1371, + 40.1454, 40.1537, 40.1621, 40.1704, 40.1787, 40.1871, 40.1954, 40.2037, + 40.2121, 40.2204, 40.2287, 40.2371, 40.2454, 40.2537, 40.2621, 40.2704, + 40.2787, 40.2871, 40.2954, 40.3037, 40.3121, 40.3204, 40.3287, 40.3371, + 40.3454, 40.3537, 40.3621, 40.3704, 40.3787, 40.3871, 40.3954, 40.4037, + 40.4121, 40.4204, 40.4287, 40.4371, 40.4454, 40.4537, 40.4621, 40.4704, + 40.4787, 40.4871, 40.4954, 40.5037, 40.5121, 40.5204, 40.5287, 40.5371, + 40.5454, 40.5537, 40.5621, 40.5704, 40.5787, 40.5871, 40.5954, 40.6037, + 40.6121, 40.6204, 40.6287, 40.6371, 40.6454, 40.6537, 40.6621, 40.6704, + 40.6787, 40.6871, 40.6954, 40.7037, 40.7121, 40.7204, 40.7287, 40.7371, + 40.7454, 40.7537, 40.7621, 40.7704, 40.7787, 40.7871, 40.7954, 40.8037, + 40.8121, 40.8204, 40.8287, 40.8371, 40.8454, 40.8537, 40.8621, 40.8704, + 40.8787, 40.8871, 40.8954, 40.9037, 40.9121, 40.9204, 40.9287, 40.9371, + 40.9454, 40.9537, 40.9621, 40.9704, 40.9787, 40.9871, 40.9954, 41.0037, + 41.0121, 41.0204, 41.0287, 41.0371, 41.0454, 41.0537, 41.0621, 41.0704, + 41.0787, 41.0871, 41.0954, 41.1037, 41.1121, 41.1204, 41.1287, 41.1371, + 41.1454, 41.1537, 41.1621, 41.1704, 41.1787, 41.1871, 41.1954, 41.2037, + 41.2121, 41.2204, 41.2287, 41.2371, 41.2454, 41.2537, 41.2621, 41.2704, + 41.2787, 41.2871, 41.2954, 41.3037, 41.3121, 41.3204, 41.3287, 41.3371, + 41.3454, 41.3537, 41.3621, 41.3704, 41.3787, 41.3871, 41.3954, 41.4037, + 41.4121, 41.4204, 41.4287, 41.4371, 41.4454, 41.4537, 41.4621, 41.4704, + 41.4787, 41.4871, 41.4954, 41.5037, 41.5121, 41.5204, 41.5287, 41.5371, + 41.5454, 41.5537, 41.5621, 41.5704, 41.5787, 41.5871, 41.5954, 41.6037, + 41.6121, 41.6204, 41.6287, 41.6371, 41.6454, 41.6537, 41.6621, 41.6704, + 41.6787, 41.6871, 41.6954, 41.7037, 41.7121, 41.7204, 41.7287, 41.7371, + 41.7454, 41.7537, 41.7621, 41.7704, 41.7787, 41.7871, 41.7954, 41.8037, + 41.8121, 41.8204, 41.8287, 41.8371, 41.8454, 41.8537, 41.8621, 41.8704, + 41.8787, 41.8871, 41.8954, 41.9037, 41.9121, 41.9204, 41.9287, 41.9371, + 41.9454, 41.9537, 41.9621, 41.9704, 41.9787, 41.9871, 41.9954, 42.0037, + 42.0121, 42.0204, 42.0287, 42.0371, 42.0454, 42.0537, 42.0621, 42.0704, + 42.0787, 42.0871, 42.0954, 42.1037, 42.1121, 42.1204, 42.1287, 42.1371, + 42.1454, 42.1537, 42.1621, 42.1704, 42.1787, 42.1871, 42.1954, 42.2037, + 42.2121, 42.2204, 42.2287, 42.2371, 42.2454, 42.2537, 42.2621, 42.2704, + 42.2787, 42.2871, 42.2954, 42.3037, 42.3121, 42.3204, 42.3287, 42.3371, + 42.3454, 42.3537, 42.3621, 42.3704, 42.3787, 42.3871, 42.3954, 42.4037, + 42.4121, 42.4204, 42.4287, 42.4371, 42.4454, 42.4537, 42.4621, 42.4704, + 42.4787, 42.4871, 42.4954, 42.5037, 42.5121, 42.5204, 42.5287, 42.5371, + 42.5454, 42.5537, 42.5621, 42.5704, 42.5787, 42.5871, 42.5954, 42.6037, + 42.6121, 42.6204, 42.6287, 42.6371, 42.6454, 42.6537, 42.6621, 42.6704, + 42.6787, 42.6871, 42.6954, 42.7037, 42.7121, 42.7204, 42.7287, 42.7371, + 42.7454, 42.7537, 42.7621, 42.7704, 42.7787, 42.7871, 42.7954, 42.8037, + 42.8121, 42.8204, 42.8287, 42.8371, 42.8454, 42.8537, 42.8621, 42.8704, + 42.8787, 42.8871, 42.8954, 42.9037, 42.9121, 42.9204, 42.9287, 42.9371, + 42.9454, 42.9537, 42.9621, 42.9704, 42.9787, 42.9871, 42.9954, 43.0037, + 43.0121, 43.0204, 43.0287, 43.0371, 43.0454, 43.0537, 43.0621, 43.0704, + 43.0787, 43.0871, 43.0954, 43.1037, 43.1121, 43.1204, 43.1287, 43.1371, + 43.1454, 43.1537, 43.1621, 43.1704, 43.1787, 43.1871, 43.1954, 43.2037, + 43.2121, 43.2204, 43.2287, 43.2371, 43.2454, 43.2537, 43.2621, 43.2704, + 43.2787, 43.2871, 43.2954, 43.3037, 43.3121, 43.3204, 43.3287, 43.3371, + 43.3454, 43.3537, 43.3621, 43.3704, 43.3787, 43.3871, 43.3954, 43.4037, + 43.4121, 43.4204, 43.4287, 43.4371, 43.4454, 43.4537, 43.4621, 43.4704, + 43.4787, 43.4871, 43.4954, 43.5037, 43.5121, 43.5204, 43.5287, 43.5371, + 43.5454, 43.5537, 43.5621, 43.5704, 43.5787, 43.5871, 43.5954, 43.6037, + 43.6121, 43.6204, 43.6287, 43.6371, 43.6454, 43.6537, 43.6621, 43.6704, + 43.6787, 43.6871, 43.6954, 43.7037, 43.7121, 43.7204, 43.7287, 43.7371, + 43.7454, 43.7537, 43.7621, 43.7704, 43.7787, 43.7871, 43.7954, 43.8037, + 43.8121, 43.8204, 43.8287, 43.8371, 43.8454, 43.8537, 43.8621, 43.8704, + 43.8787, 43.8871, 43.8954, 43.9037, 43.9121, 43.9204, 43.9287, 43.9371, + 43.9454, 43.9537, 43.9621, 43.9704, 43.9787, 43.9871, 43.9954, 44.0037, + 44.0121, 44.0204, 44.0287, 44.0371, 44.0454, 44.0537, 44.0621, 44.0704, + 44.0787, 44.0871, 44.0954, 44.1037, 44.1121, 44.1204, 44.1287, 44.1371, + 44.1454, 44.1537, 44.1621, 44.1704, 44.1787, 44.1871, 44.1954, 44.2037, + 44.2121, 44.2204, 44.2287, 44.2371, 44.2454, 44.2537, 44.2621, 44.2704, + 44.2787, 44.2871, 44.2954, 44.3037, 44.3121, 44.3204, 44.3287, 44.3371, + 44.3454, 44.3537, 44.3621, 44.3704, 44.3787, 44.3871, 44.3954, 44.4037, + 44.4121, 44.4204, 44.4287, 44.4371, 44.4454, 44.4537, 44.4621, 44.4704, + 44.4787, 44.4871, 44.4954, 44.5037, 44.5121, 44.5204, 44.5287, 44.5371, + 44.5454, 44.5537, 44.5621, 44.5704, 44.5787, 44.5871, 44.5954, 44.6037, + 44.6121, 44.6204, 44.6287, 44.6371, 44.6454, 44.6537, 44.6621, 44.6704, + 44.6787, 44.6871, 44.6954, 44.7037, 44.7121, 44.7204, 44.7287, 44.7371, + 44.7454, 44.7537, 44.7621, 44.7704, 44.7787, 44.7871, 44.7954, 44.8037, + 44.8121, 44.8204, 44.8287, 44.8371, 44.8454, 44.8537, 44.8621, 44.8704, + 44.8787, 44.8871, 44.8954, 44.9037, 44.9121, 44.9204, 44.9287, 44.9371, + 44.9454, 44.9537, 44.9621, 44.9704, 44.9787, 44.9871, 44.9954, 45.0037, + 45.0121, 45.0204, 45.0287, 45.0371, 45.0454, 45.0537, 45.0621, 45.0704, + 45.0787, 45.0871, 45.0954, 45.1037, 45.1121, 45.1204, 45.1287, 45.1371, + 45.1454, 45.1537, 45.1621, 45.1704, 45.1787, 45.1871, 45.1954, 45.2037, + 45.2121, 45.2204, 45.2287, 45.2371, 45.2454, 45.2537, 45.2621, 45.2704, + 45.2787, 45.2871, 45.2954, 45.3037, 45.3121, 45.3204, 45.3287, 45.3371, + 45.3454, 45.3537, 45.3621, 45.3704, 45.3787, 45.3871, 45.3954, 45.4037, + 45.4121, 45.4204, 45.4287, 45.4371, 45.4454, 45.4537, 45.4621, 45.4704, + 45.4787, 45.4871, 45.4954, 45.5037, 45.5121, 45.5204, 45.5287, 45.5371, + 45.5454, 45.5537, 45.5621, 45.5704, 45.5787, 45.5871, 45.5954, 45.6037, + 45.6121, 45.6204, 45.6287, 45.6371, 45.6454, 45.6537, 45.6621, 45.6704, + 45.6787, 45.6871, 45.6954, 45.7037, 45.7121, 45.7204, 45.7287, 45.7371, + 45.7454, 45.7537, 45.7621, 45.7704, 45.7787, 45.7871, 45.7954, 45.8037, + 45.8121, 45.8204, 45.8287, 45.8371, 45.8454, 45.8537, 45.8621, 45.8704, + 45.8787, 45.8871, 45.8954, 45.9037, 45.9121, 45.9204, 45.9287, 45.9371, + 45.9454, 45.9537, 45.9621, 45.9704, 45.9787, 45.9871, 45.9954, 46.0037, + 46.0121, 46.0204, 46.0287, 46.0371, 46.0454, 46.0537, 46.0621, 46.0704, + 46.0787, 46.0871, 46.0954, 46.1037, 46.1121, 46.1204, 46.1287, 46.1371, + 46.1454, 46.1537, 46.1621, 46.1704, 46.1787, 46.1871, 46.1954, 46.2037, + 46.2121, 46.2204, 46.2287, 46.2371, 46.2454, 46.2537, 46.2621, 46.2704, + 46.2787, 46.2871, 46.2954, 46.3037, 46.3121, 46.3204, 46.3287, 46.3371, + 46.3454, 46.3537, 46.3621, 46.3704, 46.3787, 46.3871, 46.3954, 46.4037, + 46.4121, 46.4204, 46.4287, 46.4371, 46.4454, 46.4537, 46.4621, 46.4704, + 46.4787, 46.4871, 46.4954, 46.5037, 46.5121, 46.5204, 46.5287, 46.5371, + 46.5454, 46.5537, 46.5621, 46.5704, 46.5787, 46.5871, 46.5954, 46.6037, + 46.6121, 46.6204, 46.6287, 46.6371, 46.6454, 46.6537, 46.6621, 46.6704, + 46.6787, 46.6871, 46.6954, 46.7037, 46.7121, 46.7204, 46.7287, 46.7371, + 46.7454, 46.7537, 46.7621, 46.7704, 46.7787, 46.7871, 46.7954, 46.8037, + 46.8121, 46.8204, 46.8287, 46.8371, 46.8454, 46.8537, 46.8621, 46.8704, + 46.8787, 46.8871, 46.8954, 46.9037, 46.9121, 46.9204, 46.9287, 46.9371, + 46.9454, 46.9537, 46.9621, 46.9704, 46.9787, 46.9871, 46.9954, 47.0037, + 47.0121, 47.0204, 47.0287, 47.0371, 47.0454, 47.0537, 47.0621, 47.0704, + 47.0787, 47.0871, 47.0954, 47.1037, 47.1121, 47.1204, 47.1287, 47.1371, + 47.1454, 47.1537, 47.1621, 47.1704, 47.1787, 47.1871, 47.1954, 47.2037, + 47.2121, 47.2204, 47.2287, 47.2371, 47.2454, 47.2537, 47.2621, 47.2704, + 47.2787, 47.2871, 47.2954, 47.3037, 47.3121, 47.3204, 47.3287, 47.3371, + 47.3454, 47.3537, 47.3621, 47.3704, 47.3787, 47.3871, 47.3954, 47.4037, + 47.4121, 47.4204, 47.4287, 47.4371, 47.4454, 47.4537, 47.4621, 47.4704, + 47.4787, 47.4871, 47.4954, 47.5037, 47.5121, 47.5204, 47.5287, 47.5371, + 47.5454, 47.5537, 47.5621, 47.5704, 47.5787, 47.5871, 47.5954, 47.6037, + 47.6121, 47.6204, 47.6287, 47.6371, 47.6454, 47.6537, 47.6621, 47.6704, + 47.6787, 47.6871, 47.6954, 47.7037, 47.7121, 47.7204, 47.7287, 47.7371, + 47.7454, 47.7537, 47.7621, 47.7704, 47.7787, 47.7871, 47.7954, 47.8037, + 47.8121, 47.8204, 47.8287, 47.8371, 47.8454, 47.8537, 47.8621, 47.8704, + 47.8787, 47.8871, 47.8954, 47.9037, 47.9121, 47.9204, 47.9287, 47.9371, + 47.9454, 47.9537, 47.9621, 47.9704, 47.9787, 47.9871, 47.9954, 48.0037, + 48.0121, 48.0204, 48.0287, 48.0371, 48.0454, 48.0537, 48.0621, 48.0704, + 48.0787, 48.0871, 48.0954, 48.1037, 48.1121, 48.1204, 48.1287, 48.1371, + 48.1454, 48.1537, 48.1621, 48.1704, 48.1787, 48.1871, 48.1954, 48.2037, + 48.2121, 48.2204, 48.2287, 48.2371, 48.2454, 48.2537, 48.2621, 48.2704, + 48.2787, 48.2871, 48.2954, 48.3037, 48.3121, 48.3204, 48.3287, 48.3371, + 48.3454, 48.3537, 48.3621, 48.3704, 48.3787, 48.3871, 48.3954, 48.4037, + 48.4121, 48.4204, 48.4287, 48.4371, 48.4454, 48.4537, 48.4621, 48.4704, + 48.4787, 48.4871, 48.4954, 48.5037, 48.5121, 48.5204, 48.5287, 48.5371, + 48.5454, 48.5537, 48.5621, 48.5704, 48.5787, 48.5871, 48.5954, 48.6037, + 48.6121, 48.6204, 48.6287, 48.6371, 48.6454, 48.6537, 48.6621, 48.6704, + 48.6787, 48.6871, 48.6954, 48.7037, 48.7121, 48.7204, 48.7287, 48.7371, + 48.7454, 48.7537, 48.7621, 48.7704, 48.7787, 48.7871, 48.7954, 48.8037, + 48.8121, 48.8204, 48.8287, 48.8371, 48.8454, 48.8537, 48.8621, 48.8704, + 48.8787, 48.8871, 48.8954, 48.9037, 48.9121, 48.9204, 48.9287, 48.9371, + 48.9454, 48.9537, 48.9621, 48.9704, 48.9787, 48.9871, 48.9954, 49.0037, + 49.0121, 49.0204, 49.0287, 49.0371, 49.0454, 49.0537, 49.0621, 49.0704, + 49.0787, 49.0871, 49.0954, 49.1037, 49.1121, 49.1204, 49.1287, 49.1371, + 49.1454, 49.1537, 49.1621, 49.1704, 49.1787, 49.1871, 49.1954, 49.2037, + 49.2121, 49.2204, 49.2287, 49.2371, 49.2454, 49.2537, 49.2621, 49.2704, + 49.2787, 49.2871, 49.2954, 49.3037, 49.3121, 49.3204, 49.3287, 49.3371, + 49.3454, 49.3537, 49.3621, 49.3704, 49.3787, 49.3871, 49.3954, 49.4037, + 49.4121, 49.4204, 49.4287, 49.4371, 49.4454, 49.4537, 49.4621, 49.4704, + 49.4787, 49.4871, 49.4954, 49.5037, 49.5121, 49.5204, 49.5287, 49.5371, + 49.5454, 49.5537, 49.5621, 49.5704, 49.5787, 49.5871, 49.5954, 49.6037, + 49.6121, 49.6204, 49.6287, 49.6371, 49.6454, 49.6537, 49.6621, 49.6704, + 49.6787, 49.6871, 49.6954, 49.7037, 49.7121, 49.7204, 49.7287, 49.7371, + 49.7454, 49.7537, 49.7621, 49.7704, 49.7787, 49.7871, 49.7954, 49.8037, + 49.8121, 49.8204, 49.8287, 49.8371, 49.8454, 49.8537, 49.8621, 49.8704, + 49.8787, 49.8871, 49.8954, 49.9037, 49.9121, 49.9204, 49.9287, 49.9371, + 49.9454, 49.9537, 49.9621, 49.9704, 49.9787, 49.9871, 49.9954, 50.0037, + 50.0121, 50.0204, 50.0287, 50.0371, 50.0454, 50.0537, 50.0621, 50.0704, + 50.0787, 50.0871, 50.0954, 50.1037, 50.1121, 50.1204, 50.1287, 50.1371, + 50.1454, 50.1537, 50.1621, 50.1704, 50.1787, 50.1871, 50.1954, 50.2037, + 50.2121, 50.2204, 50.2287, 50.2371, 50.2454, 50.2537, 50.2621, 50.2704, + 50.2787, 50.2871, 50.2954, 50.3037, 50.3121, 50.3204, 50.3287, 50.3371, + 50.3454, 50.3537, 50.3621, 50.3704, 50.3787, 50.3871, 50.3954, 50.4037, + 50.4121, 50.4204, 50.4287, 50.4371, 50.4454, 50.4537, 50.4621, 50.4704, + 50.4787, 50.4871, 50.4954, 50.5037, 50.5121, 50.5204, 50.5287, 50.5371, + 50.5454, 50.5537, 50.5621, 50.5704, 50.5787, 50.5871, 50.5954, 50.6037, + 50.6121, 50.6204, 50.6287, 50.6371, 50.6454, 50.6537, 50.6621, 50.6704, + 50.6787, 50.6871, 50.6954, 50.7037, 50.7121, 50.7204, 50.7287, 50.7371, + 50.7454, 50.7537, 50.7621, 50.7704, 50.7787, 50.7871, 50.7954, 50.8037, + 50.8121, 50.8204, 50.8287, 50.8371, 50.8454, 50.8537, 50.8621, 50.8704, + 50.8787, 50.8871, 50.8954, 50.9037, 50.9121, 50.9204, 50.9287, 50.9371, + 50.9454, 50.9537, 50.9621, 50.9704, 50.9787, 50.9871, 50.9954, 51.0037, + 51.0121, 51.0204, 51.0287, 51.0371, 51.0454, 51.0537, 51.0621, 51.0704, + 51.0787, 51.0871, 51.0954, 51.1037, 51.1121, 51.1204, 51.1287, 51.1371, + 51.1454, 51.1537, 51.1621, 51.1704, 51.1787, 51.1871, 51.1954, 51.2037, + 51.2121, 51.2204, 51.2287, 51.2371, 51.2454, 51.2537, 51.2621, 51.2704, + 51.2787, 51.2871, 51.2954, 51.3037, 51.3121, 51.3204, 51.3287, 51.3371, + 51.3454, 51.3537, 51.3621, 51.3704, 51.3787, 51.3871, 51.3954, 51.4037, + 51.4121, 51.4204, 51.4287, 51.4371, 51.4454, 51.4537, 51.4621, 51.4704, + 51.4787, 51.4871, 51.4954, 51.5037, 51.5121, 51.5204, 51.5287, 51.5371, + 51.5454, 51.5537, 51.5621, 51.5704, 51.5787, 51.5871, 51.5954, 51.6037, + 51.6121, 51.6204, 51.6287, 51.6371, 51.6454, 51.6537, 51.6621, 51.6704, + 51.6787, 51.6871, 51.6954, 51.7037, 51.7121, 51.7204, 51.7287, 51.7371, + 51.7454, 51.7537, 51.7621, 51.7704, 51.7787, 51.7871, 51.7954, 51.8037, + 51.8121, 51.8204, 51.8287, 51.8371, 51.8454, 51.8537, 51.8621, 51.8704, + 51.8787, 51.8871, 51.8954, 51.9037, 51.9121, 51.9204, 51.9287, 51.9371, + 51.9454, 51.9537, 51.9621, 51.9704, 51.9787, 51.9871, 51.9954, 52.0037, + 52.0121, 52.0204, 52.0287, 52.0371, 52.0454, 52.0537, 52.0621, 52.0704, + 52.0787, 52.0871, 52.0954, 52.1037, 52.1121, 52.1204, 52.1287, 52.1371, + 52.1454, 52.1537, 52.1621, 52.1704, 52.1787, 52.1871, 52.1954, 52.2037, + 52.2121, 52.2204, 52.2287, 52.2371, 52.2454, 52.2537, 52.2621, 52.2704, + 52.2787, 52.2871, 52.2954, 52.3037, 52.3121, 52.3204, 52.3287, 52.3371, + 52.3454, 52.3537, 52.3621, 52.3704, 52.3787, 52.3871, 52.3954, 52.4037, + 52.4121, 52.4204, 52.4287, 52.4371, 52.4454, 52.4537, 52.4621, 52.4704, + 52.4787, 52.4871, 52.4954, 52.5037, 52.5121, 52.5204, 52.5287, 52.5371, + 52.5454, 52.5537, 52.5621, 52.5704, 52.5787, 52.5871, 52.5954, 52.6037, + 52.6121, 52.6204, 52.6287, 52.6371, 52.6454, 52.6537, 52.6621, 52.6704, + 52.6787, 52.6871, 52.6954, 52.7037, 52.7121, 52.7204, 52.7287, 52.7371, + 52.7454, 52.7537, 52.7621, 52.7704, 52.7787, 52.7871, 52.7954, 52.8037, + 52.8121, 52.8204, 52.8287, 52.8371, 52.8454, 52.8537, 52.8621, 52.8704] + + self.lats = np.ascontiguousarray(np.tile(snodas_lats, (nlon, 1)).transpose()) + self.lons = np.tile(snodas_lons, (nlat, 1)) + + # print("\nget_latlon():") + # print(f"\tlats {self.lats.shape}:") + # print(f"\t\tMin: {np.amin(self.lats):+.1f}") + # print(f"\t\tMax: {np.amax(self.lats):+.1f}") + # print(f"\t\t[{self.lats[0, 0]:+.1f}, {self.lats[1, 0]:+.1f} ... {self.lats[-2, 0]:+.1f}, {self.lats[-1, 0]:+.1f}]") + # print("\t\t[(0, 0), (1, 0) ... (-2, 0), (-1, 0)]") + # print(f"\tlons {self.lons.shape}:") + # print(f"\t\tMin: {np.amin(self.lons):+.3f}") + # print(f"\t\tMax: {np.amax(self.lons):+.3f}") + # print(f"\t\t[{self.lons[0, 0]:+.3f}, {self.lons[0, 1]:+.3f} ... {self.lons[0, -2]:+.3f}, {self.lons[0, -1]:+.3f}]") + # print("\t\t[(0, 0), (0, 1) ... (0, -2), (0, -1)]") + + ########################################################################### + # PUBLIC Instance-Method: make_sids() + # ----------------------------------- + def make_sids(self): + """ + def from_latlon_2d(lat, lon, level=None, adapt_level=False, fill_value_in=None, fill_value_out=None): + Coverts latitudes and longitudes to SIDs. The STARE Level can be automatically adapted match the resolution of the geolocations. + + level: int (0<=level<=27) + Level of the SIDs. + If unset, level will me automatically adapted. + If set, adapt_level will be set to false. + adapt_level: bool + if True, level will adapted to match resolution of lat/lon. + Overwrites level. + fill_value_in: STARE indices are not calculated for lat/lon of this value + fill_value_out: set indices to this value where lat/lon is fill_value_in + + Gives same result as staremaster.conversions.latlon2stare() + """ + # print("\tmake_sids():") + self.sids = pystare.from_latlon_2d(self.lats, self.lons, adapt_level=True) + r""" + make_sids(): + sids_adapted.shape = (3351, 6935) + type(self.sids) = type(self.sids[0, 0]) = + sids_adapted[0, 0] = 3370343140356223660 + sids_res = 7 + + STARE Q-Level to form indices. + | Q-Level | R | L | + |---------|---------:|-----------:| + | 27 | | ~0.1m | + | ... | | | + | 23 |~1 m | ~1.2 m | + | 22 |~2 m | ~2.4 m | + | 21 |~4 m | ~5 m | + | 20 |~8 m | ~10 m | + | 19 |~15 m | ~19 m | + | 18 |~31 m | ~38 m | + | 17 |~61 m | ~77 m | + | 16 |~122 m | ~153 m | + | 15 |~245 m | ~307 m | + | 14 |~490 m | ~615 m | + | 13 |~1 km | ~1.2 km | + | 12 |~2 km | ~2 km | + | 11 | ~4 km | ~5 km | + | 10 | ~8 km | ~10 km | + | 09 | ~16 km | ~20 km | + | 08 | ~31 km | ~39 km | + | 07 | ~63 km | ~78 km | <= sids_res + | 06 | ~125 km | ~157 km | + | 05 | ~251 km | ~314 km | + | 04 | ~501 km | ~628 km | + | 03 | ~1003 km | ~1,256 km | + | 02 | ~2005 km | ~2,500 km | + | 01 | ~4011 km | ~5,000 km | + | 00 | ~8021 km | ~10,000 km | + [Table 1. Approximate uncertainties in terms of the area + (radius (R)) and the edge length (L) of the trixel by Q-level.] + """ + # print("\tmake_sids(): min_resolution") + sids_res = staremaster.conversions.min_resolution(self.sids) + # print(f"{self.sids.shape = }") + # print(f"{type(self.sids) = } {type(self.sids[0, 0]) = }") + # print(f"{self.sids[0, 0] = }") + # print(f"{sids_res = }") + + ########################################################################### + # PUBLIC Instance-Method: load_sids_pickle() + # ------------------------------------------ + def load_sids_pickle(self, pickle_name): + # print(f"\tload_sids_pickle({pickle_name}):") + with open(pickle_name, 'rb') as pickel_file: + self.sids = pickle.load(pickel_file) + + ########################################################################### + # PUBLIC Instance-Method: save_sids_pickle() + # ------------------------------------------ + def save_sids_pickle(self, pickle_name): + # print(f"\tsave_sids_pickle({pickle_name}):") + with open(pickle_name, 'wb') as pickel_file: + pickle.dump(self.sids, pickel_file) + + ########################################################################### + # PUBLIC Instance-Method: load_cover_pickle() + # ------------------------------------------ + def load_cover_pickle(self, pickle_name): + # print(f"\tload_cover_pickle({pickle_name}):") + with open(pickle_name, 'rb') as pickel_file: + self.cover_sids = pickle.load(pickel_file) + + ########################################################################### + # PUBLIC Instance-Method: save_cover_pickle() + # ------------------------------------------ + def save_cover_pickle(self, pickle_name): + # print(f"\tsave_cover_pickle({pickle_name}):") + with open(pickle_name, 'wb') as pickel_file: + pickle.dump(self.cover_sids, pickel_file) + + ########################################################################### + # PUBLIC Instance-Method: get_sids() + # ---------------------------------- + def get_sids(self, out_path): + print(f"\tget_sids({out_path = }):") + pickle_name = f"{out_path}sondas_sids.pkl" + if os.path.exists(pickle_name): + ## + # Read SIDs from file + self.load_sids_pickle(pickle_name) + else: + ## + # Determine SIDs + self.make_sids() + ## + # Save SIDs to pickle + self.save_sids_pickle(pickle_name) + + ########################################################################### + # PUBLIC Instance-Method: get_cover() + # ---------------------------------- + def get_cover(self, out_path): + """ + cover_res = 5 + + STARE Q-Level to form indices. + | Q-Level | R | L | + |---------|---------:|-----------:| + | 27 | | ~0.1m | + | ... | | | + | 23 |~1 m | ~1.2 m | + | 22 |~2 m | ~2.4 m | + | 21 |~4 m | ~5 m | + | 20 |~8 m | ~10 m | + | 19 |~15 m | ~19 m | + | 18 |~31 m | ~38 m | + | 17 |~61 m | ~77 m | + | 16 |~122 m | ~153 m | + | 15 |~245 m | ~307 m | + | 14 |~490 m | ~615 m | + | 13 |~1 km | ~1.2 km | + | 12 |~2 km | ~2 km | + | 11 | ~4 km | ~5 km | + | 10 | ~8 km | ~10 km | + | 09 | ~16 km | ~20 km | + | 08 | ~31 km | ~39 km | + | 07 | ~63 km | ~78 km | <= sids_res + | 06 | ~125 km | ~157 km | + | 05 | ~251 km | ~314 km | <= cover_res + | 04 | ~501 km | ~628 km | + | 03 | ~1003 km | ~1,256 km | + | 02 | ~2005 km | ~2,500 km | + | 01 | ~4011 km | ~5,000 km | + | 00 | ~8021 km | ~10,000 km | + [Table 1. Approximate uncertainties in terms of the area + (radius (R)) and the edge length (L) of the trixel by Q-level.] + + sids_adapted.shape = (361, 576) + type(sids_adapted) = type(sids_adapted[0, 0]) = + sids_adapted[0, 0] = 2287822013634445285 + cf. self.sids[0, 0] = 2287822013634445311 + + self.cover_sids.shape = (8,) + type(self.cover_sids) = type(self.cover_sids[0]) = + self.cover_sids = [0 576460752303423488 1152921504606846976 1729382256910270464 2305843009213693952 2882303761517117440 3458764513820540928 4035225266123964416] + """ + # print(f"get_cover({out_path = }):") + pickle_name = f"{out_path}snodas_cover_sids.pkl" + if os.path.exists(pickle_name): + ## + # Read cover from file + self.load_cover_pickle(pickle_name) + return + + ## + # Find a Q-Level for cover encoding + cover_res = staremaster.conversions.min_resolution(self.sids) + # print(f"\t{cover_res = }") + + # Drop the resolution to make the cover less sparse + cover_res -= 2 + if cover_res < 0: + cover_res = 0 + # print(f"\t{cover_res = }") + + ## + # Clear the SID location bits up to the encoded spatial resolution + sids_adapted = pystare.spatial_coerce_resolution(self.sids, cover_res) + # print(f"\t{sids_adapted.shape = }") + # print(f"\t{type(sids_adapted) = } {type(sids_adapted[0, 0]) = }") + # print(f"\t{sids_adapted[0, 0] = }") + # print(f"\t{sids_adapted = }") + + ## + # Find the cover + self.cover_sids = staremaster.conversions.merge_stare(sids_adapted, n_workers=1) + # print(f"{self.cover_sids.shape = }") + # print(f"{type(self.cover_sids) = } {type(self.cover_sids[0]) = }") + # print(f"{self.cover_sids[0] = }") + # print(self.cover_sids) + + ## + # Save cover to pickle + self.save_cover_pickle(pickle_name) + + ########################################################################### + # PUBLIC Instance-Method: create_sidecar() + # ---------------------------------------- + def create_sidecar(self, out_path=None, n_workers=1): + # print(f"\ncreate_sidecar({out_path = }):") + + ## + # Find SIDs for each data-point + self.get_sids(out_path) + + ## + # Find the STARE cover for self.sids + self.get_cover(out_path) + + # ## ---------------------------------------------------------------------------- + # # Plot START + # # Third-Party Imports + # import matplotlib as mpl + # import matplotlib.pyplot as plt + # import matplotlib.tri as tri + # import cartopy.crs as ccrs + # import cartopy.feature as cf + # import shapely + # from PIL import Image + # import geopandas + + # ## + # # Set up the projection and transformation + # proj = ccrs.PlateCarree() + # # proj = ccrs.AzimuthalEquidistant(central_longitude=0.0, central_latitude=-90) + # # proj = ccrs.AzimuthalEquidistant(central_longitude=0.0, central_latitude=90) + # transf = ccrs.Geodetic() + + # ## + # # Plot Options + # plot_options = {'projection':proj, 'transform':transf} + # default_dpi = mpl.rcParamsDefault['figure.dpi'] + # mpl.rcParams['figure.dpi'] = 1.5 * default_dpi + + # class figax_container(object): + # def __init__(self, figax): + # self.fig = figax[0] + # self.ax = figax[1] + # return + + # def add_coastlines(figax, set_global=False): + # "Add coastlines to the plot." + # ax = figax.ax + # if set_global: + # ax.set_global() + # ax.coastlines() + # return figax + + # def hello_plot(spatial_index_values=None, figax=None, plot_options={'projection':ccrs.PlateCarree(), 'transform':ccrs.Geodetic()}, set_global=False, set_coastlines=True, show=True, color=None, lw=1): + # if figax is None: + # figax = figax_container(plt.subplots(1, subplot_kw=plot_options)) + # if set_global: + # figax.ax.set_global() + # if set_coastlines: + # figax.ax.coastlines() + # else: + # ax = figax.ax + + # if spatial_index_values is not None: + # # Calculate vertices and interconnection matrix + # lons, lats, intmat = pystare.triangulate_indices(spatial_index_values) + + # # Make triangulation object & plot + # siv_triang = tri.Triangulation(lons, lats, intmat) + # figax.ax.triplot(siv_triang, c=color, transform=plot_options['transform'], lw=lw) + + # if show: + # plt.show() + # return figax + + # def plot_segment(i0, i1, figax): + # lat = lat0[i0:i1] + # lon = lon0[i0:i1] + # spatial_id = spatial_id0[i0:i1] + # figax = hello_plot(spatial_id, figax=figax, show=False) + # figax.ax.scatter([lon], [lat], s=1, c='r') + # return figax + + # ## + # # Plot cover + # hello_plot(self.cover_sids, plot_options=plot_options, set_global=False, set_coastlines=True) + # # Plot END + # ## ---------------------------------------------------------------------------- + + ## + # Make Sidecar + r"""i = 361, j = 576, l = 8""" + i = self.lats.shape[0] + j = self.lats.shape[1] + l = self.cover_sids.size + # print(f"{i = }, {j = }, {l = }") + + sidecar_name = f"{out_path}snodas_sidecar.hdf" + sidecar = Sidecar(granule_path=sidecar_name, out_path=out_path) + + ## + # Save Sidecar to file + sidecar.write_dimensions(i, j, l) + sidecar.write_sids(self.sids) + sidecar.write_lons(self.lons) + sidecar.write_lats(self.lats) + sidecar.write_cover(self.cover_sids) + + +# >>>> ::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: <<<< +# >>>> END OF FILE | END OF FILE | END OF FILE | END OF FILE | END OF FILE | END OF FILE <<<< +# >>>> ::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: <<<< + + + From f8eb1c7de4e43a62c73c8df548e2665cc3622762 Mon Sep 17 00:00:00 2001 From: Mike Bauer Date: Mon, 14 Apr 2025 12:11:08 -0400 Subject: [PATCH 2/2] Tweaks to pass pytest Tweaks to pass pytest --- examples/MOD05.ipynb | 24 +- examples/MOD09.ipynb | 820 +++++---------------------- staremaster/create_sidecar_files.py | 13 +- staremaster/find_missing_sidecars.py | 4 +- staremaster/products/__init__.py | 1 + staremaster/products/merra2.py | 622 ++++++++++++++++++++ staremaster/products/snodas.py | 38 +- staremaster/sidecar.py | 6 +- 8 files changed, 823 insertions(+), 705 deletions(-) create mode 100644 staremaster/products/merra2.py diff --git a/examples/MOD05.ipynb b/examples/MOD05.ipynb index ba8e694..88ad690 100644 --- a/examples/MOD05.ipynb +++ b/examples/MOD05.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -17,7 +17,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -28,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -57,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -72,13 +72,13 @@ } ], "source": [ - "staremaster.conversions.gring2cover(granule.gring_lats, \n", + "staremaster.conversions.gring2cover(granule.gring_lats,\n", " granule.gring_lons, 8).dtype" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -98,7 +98,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -107,7 +107,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -119,10 +119,10 @@ " ...,\n", " [12, 12, 12, ..., 11, 11, 11],\n", " [12, 12, 12, ..., 11, 11, 11],\n", - " [12, 12, 12, ..., 11, 11, 11]])" + " [12, 12, 12, ..., 11, 11, 11]], shape=(406, 270))" ] }, - "execution_count": 9, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -141,7 +141,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "stare2", "language": "python", "name": "python3" }, @@ -155,7 +155,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.4" + "version": "3.13.2" } }, "nbformat": 4, diff --git a/examples/MOD09.ipynb b/examples/MOD09.ipynb index 0e3a75b..6463bde 100644 --- a/examples/MOD09.ipynb +++ b/examples/MOD09.ipynb @@ -46,8 +46,9 @@ "metadata": {}, "outputs": [], "source": [ - "file_path = '../tests/data/MOD09.A2002299.0710.006.2015151173939.hdf'\n", - "file_path = '/home/griessbaum/Dropbox/UCSB/spires/colocation/MOD09.A2020032.1940.006.2020034015024.hdf'\n", + "# file_path = '../tests/data/MOD09.A2002299.0710.006.2015151173939.hdf'\n", + "file_path = '/Users/mbauer/SpatioTemporal/STAREMaster_py/tests/data/mod09/MOD09.A2002299.0710.006.2015151173939.hdf'\n", + "# file_path = '/home/griessbaum/Dropbox/UCSB/spires/colocation/MOD09.A2020032.1940.006.2020034015024.hdf'\n", "granule = staremaster.products.MOD09(file_path)" ] }, @@ -62,25 +63,44 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mbauer/miniconda3/envs/stare2/lib/python3.13/site-packages/distributed/client.py:3383: UserWarning: Sending large graph of size 41.94 MiB.\n", + "This may cause some slowdown.\n", + "Consider loading the data with Dask directly\n", + " or using futures or delayed objects to embed the data into the graph without repetition.\n", + "See also https://docs.dask.org/en/stable/best-practices.html#load-data-with-dask for more information.\n", + " warnings.warn(\n", + "/Users/mbauer/miniconda3/envs/stare2/lib/python3.13/site-packages/distributed/client.py:3383: UserWarning: Sending large graph of size 167.77 MiB.\n", + "This may cause some slowdown.\n", + "Consider loading the data with Dask directly\n", + " or using futures or delayed objects to embed the data into the graph without repetition.\n", + "See also https://docs.dask.org/en/stable/best-practices.html#load-data-with-dask for more information.\n", + " warnings.warn(\n" + ] + } + ], "source": [ "granule.make_sids(n_workers=4)" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "3377442121606107563" + "np.int64(3520522815746171596)" ] }, - "execution_count": 7, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -91,7 +111,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -101,22 +121,22 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[12, 12, 12, ..., 12, 12, 12],\n", - " [12, 12, 12, ..., 12, 12, 12],\n", - " [12, 12, 12, ..., 12, 12, 12],\n", + "array([[31, 15, 31, ..., 13, 13, 13],\n", + " [31, 15, 31, ..., 13, 13, 13],\n", + " [31, 15, 31, ..., 13, 13, 13],\n", " ...,\n", - " [12, 12, 12, ..., 12, 12, 12],\n", - " [12, 12, 12, ..., 12, 12, 12],\n", - " [12, 12, 12, ..., 12, 12, 12]])" + " [31, 15, 31, ..., 13, 13, 13],\n", + " [31, 15, 31, ..., 13, 13, 13],\n", + " [31, 15, 31, ..., 13, 13, 13]], shape=(4060, 2708))" ] }, - "execution_count": 9, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -127,16 +147,41 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mbauer/miniconda3/envs/stare2/lib/python3.13/site-packages/distributed/client.py:3383: UserWarning: Sending large graph of size 41.94 MiB.\n", + "This may cause some slowdown.\n", + "Consider loading the data with Dask directly\n", + " or using futures or delayed objects to embed the data into the graph without repetition.\n", + "See also https://docs.dask.org/en/stable/best-practices.html#load-data-with-dask for more information.\n", + " warnings.warn(\n", + "/Users/mbauer/miniconda3/envs/stare2/lib/python3.13/site-packages/distributed/client.py:3383: UserWarning: Sending large graph of size 167.77 MiB.\n", + "This may cause some slowdown.\n", + "Consider loading the data with Dask directly\n", + " or using futures or delayed objects to embed the data into the graph without repetition.\n", + "See also https://docs.dask.org/en/stable/best-practices.html#load-data-with-dask for more information.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "hdfeos 2882303761517117440 3458764513820540928\n" + ] + }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 10, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -147,12 +192,25 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mbauer/miniconda3/envs/stare2/lib/python3.13/site-packages/distributed/client.py:3383: UserWarning: Sending large graph of size 41.94 MiB.\n", + "This may cause some slowdown.\n", + "Consider loading the data with Dask directly\n", + " or using futures or delayed objects to embed the data into the graph without repetition.\n", + "See also https://docs.dask.org/en/stable/best-practices.html#load-data-with-dask for more information.\n", + " warnings.warn(\n" + ] + } + ], "source": [ - "sids = staremaster.conversions.latlon2stare(granule.lats['1km'], \n", - " granule.lons['1km'], \n", + "sids = staremaster.conversions.latlon2stare(granule.lats['1km'],\n", + " granule.lons['1km'],\n", " n_workers=4)" ] }, @@ -167,7 +225,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -176,24 +234,24 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b60eaf7d788b47c7a27af44a488ac920", + "model_id": "c4a0785f46c24ffe9e17310d4f41c22a", "version_major": 2, "version_minor": 0 }, - "image/png": "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", + "image/png": "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", "text/html": [ "\n", "
\n", "
\n", " Figure\n", "
\n", - " \n", + " \n", "
\n", " " ], @@ -208,10 +266,10 @@ "source": [ "fig = plt.figure(figsize=(12, 7), dpi=150)\n", "ax = fig.add_subplot()\n", - "df = geopandas.GeoDataFrame(geometry=geopandas.points_from_xy(granule.lons['1km'][-15:, -10:].flatten(), \n", + "df = geopandas.GeoDataFrame(geometry=geopandas.points_from_xy(granule.lons['1km'][-15:, -10:].flatten(),\n", " granule.lats['1km'][-15:, -10:].flatten()))\n", "\n", - "df2 = geopandas.GeoDataFrame(geometry=geopandas.points_from_xy(granule.lons['500m'][-30:, -20:].flatten(), \n", + "df2 = geopandas.GeoDataFrame(geometry=geopandas.points_from_xy(granule.lons['500m'][-30:, -20:].flatten(),\n", " granule.lats['500m'][-30:, -20:].flatten()))\n", "\n", "df.plot(ax=ax, color='red', markersize=10)\n", @@ -223,24 +281,24 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b0b6cb1196fd4cc19d3ad7cc1f343628", + "model_id": "52a983746e954ecba91be455700eeb7b", "version_major": 2, "version_minor": 0 }, - "image/png": "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", + "image/png": "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", "text/html": [ "\n", "
\n", "
\n", " Figure\n", "
\n", - " \n", + " \n", "
\n", " " ], @@ -256,10 +314,10 @@ "fig = plt.figure(figsize=(12, 7), dpi=150)\n", "ax = fig.add_subplot()\n", "\n", - "df = geopandas.GeoDataFrame(geometry=geopandas.points_from_xy(granule.lons['1km'][0:10, -10:].flatten(), \n", + "df = geopandas.GeoDataFrame(geometry=geopandas.points_from_xy(granule.lons['1km'][0:10, -10:].flatten(),\n", " granule.lats['1km'][0:10, -10:].flatten()))\n", "\n", - "df2 = geopandas.GeoDataFrame(geometry=geopandas.points_from_xy(granule.lons['500m'][0:30, -20:].flatten(), \n", + "df2 = geopandas.GeoDataFrame(geometry=geopandas.points_from_xy(granule.lons['500m'][0:30, -20:].flatten(),\n", " granule.lats['500m'][0:30, -20:].flatten()))\n", "\n", "df.plot(ax=ax, color='red', markersize=10)\n", @@ -269,24 +327,24 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "717cffcb24cc458386c4accd6d6234c0", + "model_id": "8fcfb54fcae947eb9e9a7d48e17fb21b", "version_major": 2, "version_minor": 0 }, - "image/png": "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", + "image/png": "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", "text/html": [ "\n", "
\n", "
\n", " Figure\n", "
\n", - " \n", + " \n", "
\n", " " ], @@ -302,10 +360,10 @@ "fig = plt.figure(figsize=(12, 7), dpi=150)\n", "ax = fig.add_subplot()\n", "\n", - "df = geopandas.GeoDataFrame(geometry=geopandas.points_from_xy(granule.lons['1km'][0:15, 670:680].flatten(), \n", + "df = geopandas.GeoDataFrame(geometry=geopandas.points_from_xy(granule.lons['1km'][0:15, 670:680].flatten(),\n", " granule.lats['1km'][0:15, 670:680].flatten()))\n", "\n", - "df2 = geopandas.GeoDataFrame(geometry=geopandas.points_from_xy(granule.lons['500m'][0:30, 1340:1360].flatten(), \n", + "df2 = geopandas.GeoDataFrame(geometry=geopandas.points_from_xy(granule.lons['500m'][0:30, 1340:1360].flatten(),\n", " granule.lats['500m'][0:30, 1340:1360].flatten()))\n", "\n", "\n", @@ -326,570 +384,81 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "import starepandas\n", "import netCDF4\n", "import numpy\n", - "import pandas" + "import pandas\n", + "import pystare" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "read_granule sidecar_path = None\n", + "read_granule granule = \n" + ] + } + ], "source": [ - "file_path = '../tests/data/MOD09.A2002299.0710.006.2015151173939.hdf'\n", - "sdf = starepandas.read_granule(file_path, sidecar=True)" + "# file_path = '../tests/data/MOD09.A2002299.0710.006.2015151173939.hdf'\n", + "file_path = '/Users/mbauer/SpatioTemporal/STAREMaster_py/tests/data/mod09/MOD09.A2002299.0710.006.2015151173939.hdf'\n", + "sdf = starepandas.read_granule(file_path, sidecar=True)\n" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 17, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([31, 31, 31, ..., 12, 12, 12], dtype=object)" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "sdf.spatial_resolution()" + "sdf['level'] = pystare.spatial_resolution(sdf['sids'])\n", + "# sdf.spatial_resolution()" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 18, "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", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
sids1km Atmospheric Optical Depth Band 11km Atmospheric Optical Depth Band 31km Atmospheric Optical Depth Band 81km Atmospheric Optical Depth Model1km water_vapor1km Atmospheric Optical Depth Band QA1km Atmospheric Optical Depth Band CM1km Surface Reflectance Band 11km Surface Reflectance Band 2...1km Surface Reflectance Band 131km Surface Reflectance Band 141km Surface Reflectance Band 151km Surface Reflectance Band 161km Surface Reflectance Band 261km Reflectance Band Quality1km b8-15 Reflectance Band Quality1km b16 Reflectance Band Quality1km Reflectance Data State QA1km Band 3 Path Radiance
0-21NaNNaNNaNNaN2.931638621NaNNaN...-0.01-0.01-0.01-0.010.07874106713722304989208560.0
1-21NaNNaNNaNNaN2.931638621NaNNaN...-0.01-0.01-0.01-0.010.07874106713722304989208560.0
2-21NaNNaNNaNNaN2.931638621NaNNaN...-0.01-0.01-0.01-0.010.07874106713722304989208560.0
3-21NaNNaNNaNNaN2.931638621NaNNaN...-0.01-0.01-0.01-0.010.07874106713722304989208560.0
4-21NaNNaNNaNNaN2.931638621NaNNaN...-0.01-0.01-0.01-0.010.07874106713722304989208560.0
..................................................................
27486153520425476210836779NaNNaNNaNNaN0.971638621NaNNaN...-0.01-0.01-0.01-0.010.010021590353722304989208560.0
27486163520427033364044971NaNNaNNaNNaN0.971638621NaNNaN...-0.01-0.01-0.01-0.010.010021590353722304989208560.0
27486173520426917205266315NaNNaNNaNNaN0.971638621NaNNaN...-0.01-0.01-0.01-0.010.010021590353722304989208560.0
27486183520522723574710187NaNNaNNaNNaN0.971638621NaNNaN...-0.01-0.01-0.01-0.010.010021590353722304989208560.0
27486193520522815746171595NaNNaNNaNNaN0.971638621NaNNaN...-0.01-0.01-0.01-0.010.010021590353722304989208560.0
\n", - "

2748620 rows × 30 columns

\n", - "
" - ], - "text/plain": [ - " sids 1km Atmospheric Optical Depth Band 1 \\\n", - "0 -21 NaN \n", - "1 -21 NaN \n", - "2 -21 NaN \n", - "3 -21 NaN \n", - "4 -21 NaN \n", - "... ... ... \n", - "2748615 3520425476210836779 NaN \n", - "2748616 3520427033364044971 NaN \n", - "2748617 3520426917205266315 NaN \n", - "2748618 3520522723574710187 NaN \n", - "2748619 3520522815746171595 NaN \n", - "\n", - " 1km Atmospheric Optical Depth Band 3 \\\n", - "0 NaN \n", - "1 NaN \n", - "2 NaN \n", - "3 NaN \n", - "4 NaN \n", - "... ... \n", - "2748615 NaN \n", - "2748616 NaN \n", - "2748617 NaN \n", - "2748618 NaN \n", - "2748619 NaN \n", - "\n", - " 1km Atmospheric Optical Depth Band 8 \\\n", - "0 NaN \n", - "1 NaN \n", - "2 NaN \n", - "3 NaN \n", - "4 NaN \n", - "... ... \n", - "2748615 NaN \n", - "2748616 NaN \n", - "2748617 NaN \n", - "2748618 NaN \n", - "2748619 NaN \n", - "\n", - " 1km Atmospheric Optical Depth Model 1km water_vapor \\\n", - "0 NaN 2.93 \n", - "1 NaN 2.93 \n", - "2 NaN 2.93 \n", - "3 NaN 2.93 \n", - "4 NaN 2.93 \n", - "... ... ... \n", - "2748615 NaN 0.97 \n", - "2748616 NaN 0.97 \n", - "2748617 NaN 0.97 \n", - "2748618 NaN 0.97 \n", - "2748619 NaN 0.97 \n", - "\n", - " 1km Atmospheric Optical Depth Band QA \\\n", - "0 16386 \n", - "1 16386 \n", - "2 16386 \n", - "3 16386 \n", - "4 16386 \n", - "... ... \n", - "2748615 16386 \n", - "2748616 16386 \n", - "2748617 16386 \n", - "2748618 16386 \n", - "2748619 16386 \n", - "\n", - " 1km Atmospheric Optical Depth Band CM \\\n", - "0 21 \n", - "1 21 \n", - "2 21 \n", - "3 21 \n", - "4 21 \n", - "... ... \n", - "2748615 21 \n", - "2748616 21 \n", - "2748617 21 \n", - "2748618 21 \n", - "2748619 21 \n", - "\n", - " 1km Surface Reflectance Band 1 1km Surface Reflectance Band 2 ... \\\n", - "0 NaN NaN ... \n", - "1 NaN NaN ... \n", - "2 NaN NaN ... \n", - "3 NaN NaN ... \n", - "4 NaN NaN ... \n", - "... ... ... ... \n", - "2748615 NaN NaN ... \n", - "2748616 NaN NaN ... \n", - "2748617 NaN NaN ... \n", - "2748618 NaN NaN ... \n", - "2748619 NaN NaN ... \n", - "\n", - " 1km Surface Reflectance Band 13 1km Surface Reflectance Band 14 \\\n", - "0 -0.01 -0.01 \n", - "1 -0.01 -0.01 \n", - "2 -0.01 -0.01 \n", - "3 -0.01 -0.01 \n", - "4 -0.01 -0.01 \n", - "... ... ... \n", - "2748615 -0.01 -0.01 \n", - "2748616 -0.01 -0.01 \n", - "2748617 -0.01 -0.01 \n", - "2748618 -0.01 -0.01 \n", - "2748619 -0.01 -0.01 \n", - "\n", - " 1km Surface Reflectance Band 15 1km Surface Reflectance Band 16 \\\n", - "0 -0.01 -0.01 \n", - "1 -0.01 -0.01 \n", - "2 -0.01 -0.01 \n", - "3 -0.01 -0.01 \n", - "4 -0.01 -0.01 \n", - "... ... ... \n", - "2748615 -0.01 -0.01 \n", - "2748616 -0.01 -0.01 \n", - "2748617 -0.01 -0.01 \n", - "2748618 -0.01 -0.01 \n", - "2748619 -0.01 -0.01 \n", - "\n", - " 1km Surface Reflectance Band 26 1km Reflectance Band Quality \\\n", - "0 0.0 787410671 \n", - "1 0.0 787410671 \n", - "2 0.0 787410671 \n", - "3 0.0 787410671 \n", - "4 0.0 787410671 \n", - "... ... ... \n", - "2748615 0.0 1002159035 \n", - "2748616 0.0 1002159035 \n", - "2748617 0.0 1002159035 \n", - "2748618 0.0 1002159035 \n", - "2748619 0.0 1002159035 \n", - "\n", - " 1km b8-15 Reflectance Band Quality 1km b16 Reflectance Band Quality \\\n", - "0 3722304989 208 \n", - "1 3722304989 208 \n", - "2 3722304989 208 \n", - "3 3722304989 208 \n", - "4 3722304989 208 \n", - "... ... ... \n", - "2748615 3722304989 208 \n", - "2748616 3722304989 208 \n", - "2748617 3722304989 208 \n", - "2748618 3722304989 208 \n", - "2748619 3722304989 208 \n", - "\n", - " 1km Reflectance Data State QA 1km Band 3 Path Radiance \n", - "0 56 0.0 \n", - "1 56 0.0 \n", - "2 56 0.0 \n", - "3 56 0.0 \n", - "4 56 0.0 \n", - "... ... ... \n", - "2748615 56 0.0 \n", - "2748616 56 0.0 \n", - "2748617 56 0.0 \n", - "2748618 56 0.0 \n", - "2748619 56 0.0 \n", - "\n", - "[2748620 rows x 30 columns]" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "sdf.to_stare_resolution(11)" + "# sdf.to_stare_resolution(11)" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 19, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/griessbaum/.virtualenvs/staremaster_py/lib/python3.8/site-packages/geopandas/geodataframe.py:1472: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " super().__setitem__(key, value)\n" - ] - } - ], + "outputs": [], "source": [ - "s['sids'] = s['sids'].astype(numpy.int64)" + "sdf['sids'] = sdf['sids'].astype(numpy.int64)" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 20, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "array([ 0, 0, 11])" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" + "ename": "AttributeError", + "evalue": "'STAREDataFrame' object has no attribute 'spatial_resolution'", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mAttributeError\u001b[39m Traceback (most recent call last)", + "\u001b[32m/var/folders/zw/hjvgtpxd2_74tgkdh4jltpkh0000gn/T/ipykernel_46092/1263138818.py\u001b[39m in \u001b[36m?\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m starepandas.STAREDataFrame(sids=[\u001b[32m3520425476210836992\u001b[39m, \u001b[32m3520426917205266432\u001b[39m, \u001b[32m3520522815746171403\u001b[39m]).spatial_resolution()\n", + "\u001b[32m~/miniconda3/envs/stare2/lib/python3.13/site-packages/pandas/core/generic.py\u001b[39m in \u001b[36m?\u001b[39m\u001b[34m(self, name)\u001b[39m\n\u001b[32m 6295\u001b[39m \u001b[38;5;28;01mand\u001b[39;00m name \u001b[38;5;28;01mnot\u001b[39;00m \u001b[38;5;28;01min\u001b[39;00m self._accessors\n\u001b[32m 6296\u001b[39m \u001b[38;5;28;01mand\u001b[39;00m self._info_axis._can_hold_identifiers_and_holds_name(name)\n\u001b[32m 6297\u001b[39m ):\n\u001b[32m 6298\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m self[name]\n\u001b[32m-> \u001b[39m\u001b[32m6299\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m object.__getattribute__(self, name)\n", + "\u001b[31mAttributeError\u001b[39m: 'STAREDataFrame' object has no attribute 'spatial_resolution'" + ] } ], "source": [ @@ -898,7 +467,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -907,42 +476,9 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "masked_array(\n", - " data=[[--, --, --, ..., 3311031317780292908, 3311031377246612044,\n", - " 3311031153792310316],\n", - " [--, --, --, ..., 3311031063151311500, 3311031256519239020,\n", - " 3311031242577335372],\n", - " [--, --, --, ..., 3311031079302192428, 3311031251813509420,\n", - " 3311031185903903788],\n", - " ...,\n", - " [--, --, --, ..., 3520523832472108108, 3520523863776842348,\n", - " 3520523841722790540],\n", - " [--, --, --, ..., 3520523808312187020, 3520522731892836108,\n", - " 3520522805301576524],\n", - " [--, --, --, ..., 3520426917205266316, 3520522723574710188,\n", - " 3520522815746171596]],\n", - " mask=[[ True, True, True, ..., False, False, False],\n", - " [ True, True, True, ..., False, False, False],\n", - " [ True, True, True, ..., False, False, False],\n", - " ...,\n", - " [ True, True, True, ..., False, False, False],\n", - " [ True, True, True, ..., False, False, False],\n", - " [ True, True, True, ..., False, False, False]],\n", - " fill_value=18446744073709551615,\n", - " dtype=uint64)" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "sids = ds['STARE_index_1km'][:, :]\n", "sids" @@ -950,24 +486,9 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "masked_array(data=[--, --, --, ..., 3520426917205266316,\n", - " 3520522723574710188, 3520522815746171596],\n", - " mask=[ True, True, True, ..., False, False, False],\n", - " fill_value=18446744073709551615,\n", - " dtype=uint64)" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "#sids = sids.astype(numpy.int64)\n", "sids.flatten()" @@ -975,61 +496,18 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\n", - "[18446744073709551615, 18446744073709551615, 18446744073709551615,\n", - " 18446744073709551615, 18446744073709551615, 18446744073709551615,\n", - " 18446744073709551615, 18446744073709551615, 18446744073709551615,\n", - " 18446744073709551615,\n", - " ...\n", - " 3521544298562029900, 3521542587346850892, 3521542630509968524,\n", - " 3520425380908115436, 3520425312089826540, 3520425476210836780,\n", - " 3520427033364044972, 3520426917205266316, 3520522723574710188,\n", - " 3520522815746171596]\n", - "Length: 2748620, dtype: UInt64" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "pandas.array(sids.flatten(), dtype='UInt64')" ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 \n", - "1 \n", - "2 \n", - "3 \n", - "4 \n", - " ... \n", - "2748615 3520425476210836992\n", - "2748616 3520427033364044800\n", - "2748617 3520426917205266432\n", - "2748618 3520522723574710272\n", - "2748619 3520522815746171392\n", - "Length: 2748620, dtype: UInt64" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "pandas.Series(ds['STARE_index_1km'][:, :].flatten(), dtype='UInt64')" ] @@ -1044,7 +522,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "stare2", "language": "python", "name": "python3" }, @@ -1058,7 +536,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.6" + "version": "3.13.2" } }, "nbformat": 4, diff --git a/staremaster/create_sidecar_files.py b/staremaster/create_sidecar_files.py index 564470c..e8f119d 100755 --- a/staremaster/create_sidecar_files.py +++ b/staremaster/create_sidecar_files.py @@ -11,7 +11,7 @@ import re -def create_grid_sidecar(grid, out_path, n_workers): +def create_grid_sidecar(grid, out_path, n_workers, for_mcms): grid = grid.lower() if grid == 'imerg': granule = staremaster.products.IMERG() @@ -20,6 +20,9 @@ def create_grid_sidecar(grid, out_path, n_workers): elif grid == 'snodas': granule = staremaster.products.SNODAS() granule.load() + elif grid == 'merra2': + granule = staremaster.products.MERRA2(for_mcms) + granule.load() else: print('unknown grid') exit() @@ -51,6 +54,8 @@ def create_sidecar(file_path, n_workers, product, cover_res, out_path, archive): granule = staremaster.products.GOES_ABI_FIXED_GRID(file_path) elif product == 'SNODAS': granule = staremaster.products.SNODAS(file_path) + elif product == 'MERRA2': + granule = staremaster.products.MERRA2(file_path) else: print('product not supported') print('supported products are {}'.format(get_installed_products())) @@ -69,7 +74,7 @@ def list_granules(folder, product): if not product: product = '' files = glob.glob(folder + '/*') - pattern = '.*{product}.*[^_stare]\.(nc|hdf|HDF5)'.format(product=product.upper()) + pattern = '.*{product}.*[^_stare]\.(nc4|nc|hdf|HDF5)'.format(product=product.upper()) granules = list(filter(re.compile(pattern).match, files)) return granules @@ -127,6 +132,8 @@ def main(): Record all create sidecars and their corresponding granules in it.''') parser.add_argument('--parallel_files', dest='parallel_files', action='store_true', help='Process files in parallel rather than looking up SIDs in parallel') + parser.add_argument('--as_mcms', metavar='as_mcms', type=int, + help='MCMS specific layout for MERRA2 grid', default=0) parser.set_defaults(archive=False) parser.set_defaults(parallel_files=False) @@ -138,7 +145,7 @@ def main(): elif args.folder: file_paths = list_granules(args.folder, product=args.product) elif args.grid: - create_grid_sidecar(args.grid, args.out_path, n_workers=args.workers) + create_grid_sidecar(args.grid, args.out_path, n_workers=args.workers, for_mcms=args.as_mcms) quit() else: print('Wrong usage; need to specify a folder, file, or grid\n') diff --git a/staremaster/find_missing_sidecars.py b/staremaster/find_missing_sidecars.py index d5028c8..43f76fd 100755 --- a/staremaster/find_missing_sidecars.py +++ b/staremaster/find_missing_sidecars.py @@ -10,14 +10,14 @@ def get_granule_paths(folder, granule_pattern): granule_paths = sorted(glob.glob(os.path.expanduser(folder) + '/' + '*' )) - pattern = '.*/{}.*[^_stare]\.(nc|hdf|HDF5)'.format(granule_pattern) + pattern = '.*/{}.*[^_stare]\.(nc4|nc|hdf|HDF5)'.format(granule_pattern) granule_paths = list(filter(re.compile(pattern).match, granule_paths)) return granule_paths def get_sidecar_paths(folder, granule_pattern): sidecar_paths = sorted(glob.glob(os.path.expanduser(folder) + '/' + '*' )) - pattern = '.*/{}.*_stare\.(nc|hdf|HDF5)'.format(granule_pattern) + pattern = '.*/{}.*_stare\.(nc4|nc|hdf|HDF5)'.format(granule_pattern) sidecar_paths = list(filter(re.compile(pattern).match, sidecar_paths)) return sidecar_paths diff --git a/staremaster/products/__init__.py b/staremaster/products/__init__.py index 0a6d766..289ebb3 100644 --- a/staremaster/products/__init__.py +++ b/staremaster/products/__init__.py @@ -9,3 +9,4 @@ from staremaster.products.modis_tilegrid import ModisTile from staremaster.products.goes_abi_fixed_grid import GOES_ABI_FIXED_GRID from staremaster.products.snodas import SNODAS +from staremaster.products.merra2 import MERRA2 diff --git a/staremaster/products/merra2.py b/staremaster/products/merra2.py new file mode 100644 index 0000000..d19028b --- /dev/null +++ b/staremaster/products/merra2.py @@ -0,0 +1,622 @@ +#! /usr/bin/env python -tt +# -*- coding: utf-8; mode: python -*- +r"""Sidecar creation utility for NASA's MERRA-2 Reanalysis (also MCMS) + +merra2.py +~~~~~~~~~ + +Edit: STAREMaster_py/staremaster/products/__init__.py + Add: + from staremaster.products.merra2 import MERRA2 + +Edit: STAREMaster_py/staremaster/create_sidecar_file.py + Add: + def create_grid_sidecar(): + elif grid == 'merra2': + granule = staremaster.products.MERRA2(for_mcms) + granule.load() + def create_sidecar(): + elif product == 'MERRA2': + granule = staremaster.products.MERRA2(file_path) + def guess_product(): + elif 'MERRA2' in file_path and '.nc4' in file_name: + product = 'MERRA2' + def main(): + parser.add_argument('--as_mcms', metavar='as_mcms', type=int, + help='MCMS specific layout for MERRA2 grid', default=0) + +Create sidecar file: + + $ cd /Users/mbauer/SpatioTemporal/STAREMaster_py/staremaster + + $ python create_sidecar_files.py --workers 1 --product merra2 --grid MERRA2 --out_path /Users/mbauer/tmp/output/merra2_files/ +or + $ python create_sidecar_files.py --workers 1 --product merra2 --grid MERRA2 --out_path /Users/mbauer/tmp/output/merra2_files/ --as_mcms 1 + + +$ cd ~/.ssh; ssh-add bayesics_mbauer_id_rsa mbauer288-GitHub_id_ed25519 id_ed25519; cd; source bash_profile_CONDA.sh; conda activate stare; cd /Users/mbauer/SpatioTemporal/STAREMaster_py/staremaster + +See + +""" +# Standard Imports +import os +import pickle + +# Third-Party Imports +# import netCDF4 +import numpy as np + +# STARE Imports +from staremaster.sidecar import Sidecar +import staremaster.conversions +import pystare + +## +# List of Public objects from this module. +__all__ = ['MERRA2'] + +## +# Markup Language Specification (see Google Python Style Guide https://google.github.io/styleguide/pyguide.html) +__docformat__ = "Google en" +# ------------------------------------------------------------------------------ + +############################################################################### +# PUBLIC Class: MERRA2 +# -------------------- +class MERRA2: + """Specification for the data-grid of the NASA MERRA-2 Reanalysis (ASM, 3 hourly). + + ====================================================================== + MERRA-2 NATIVE RESOLUTION + 0.5 x 0.625 centered on (-180, -90) + + * The horizontal discretization of the MERRA-2 model output is placed on a latitude-longitude grid. + * The sea-level pressure field used by MCMS represents instantaneous point measurements at the mid-points of this latitude-longitude grid. + * That is, the data is Point-Registered: + * The data grids represent a 2D set of unconnected coordinate pairs, which may or may not be regularly spaced, with no areal extent (i.e., no cells). + * It is also sometimes referred to as an Uniform/Equi-Angular latitude grid. + * The poles are at corners/edges of first and last grid-cells. + * The dateline is at west edge of first longitude grid-cell. + * From the MERRA-2 documentation we get. + The horizontal native grid origin, associated with variables indexed (i=0, j=0) represents a grid-point located at (180W, 90S). + Latitude and longitude of grid-points as a function of their indices (i, j) or (col, row) and can be determined by: + + nlon = 576 + nlat = 361 + dlon = 0.625 deg + dlat = 0.5 deg + + lon_i = -180.0 + dlon * i, where i = 0, nlon - 1 + lat_j = -90.0 + dlat * j, where j = 0, nlat - 1 + + For example, + (i = 0 , j = 0) corresponds to a grid point at (lon = -180, lat = -90) + (i = nlon - 1, j = 0) corresponds to a grid point at (lon = +179.375, lat = -90) + ... + (i = 288 , j = 180) corresponds to a grid point at (lon = 0, lat = 0) + ... + (i = 0 , j = nlat - 1) corresponds to a grid point at (lon = -180, lat = +90) + (i = nlon - 1, j = nlat - 1) corresponds to a grid point at (lon = +179.375, lat = +90) + + Giving a domain range of grid-points: + + (+179.375, +90)|(-180, +90) ... (+179.375, +90)|(+180, +90) + |. ... .| + |. ... .| + (+179.375, -90)|(-180, -90) ... (+179.375, -90)|(+180, -90) + + * The cyclic nature of longitude works here in terms of thinking of these as regularly dlon spaced points. + +179.375 + dlon = +180 + -180 - dlon = -180.625 == +179.375 + + Indeed, we can see how these can be taken as grid-cells centered on the mid-points with edges offset by half dlon (0.3125 deg). + Wrap Around<>Wrap Around + |(+179.6875)-------(-180.0000)-------(-179.6875)| ... |(+179.0625)-------(+179.3750)-------(+179.6875)||(+179.6875)-------(-180.0000)-------(-179.6875) + i = 0 ... i = nlon - 1 i = 0 + * The polar singularities in latitude are fine in the sense the first and last rows are at the poles, which means + each col has the same value in these rows (i.e., the poles are a single point). + + It is more difficult to portray the latitude grid as a series of grid-cells centered on the mid-points with edges offset by half dlat (0.025 deg). + + Here lat_m are the latitude mid-points and lat_e are the latitude edges. + ------------------- -90.25 = lat_e[0] + lat_m[0] = -90.00 + ------------------- -89.75 = lat_e[1] + lat_m[1] = -89.50 + ------------------- -89.25 = lat_e[2] + ... + ------------------- +89.25 = lat_e[359] + lat_m[359] = +89.50 + ------------------- +89.75 = lat_e[360] + lat_m[360] = +90.00 + ------------------- +90.25 = lat_e[361] + + The issue here is that the polar grids extend past the poles or are half-grid in height. + * One way to handle this is to consider the polar row as being half-height triangles rather than rectangular (i.e., ignore the past-pole extent). + * Basically, in the polar-most rows the x-edges and x-centers are the same (given the polar point contains all longitudes). + + Half-Height Triangular Cell: + ------------------[-180.0, +90.0]----------------- + | | + [+179.6875, +89.75] -------------------------------------------------- [-179.6875, +89.75] + | | + | [-180.0, +89.5] | + | | + [+179.6875, +75.0] -------------------------------------------------- [-179.6875, +75.0] + . . + . . + . . + [+179.6875, -75.0] -------------------------------------------------- [-179.6875, -75.0] + | | + | [-180.0, -89.5] | + | | + [+179.6875, -89.75] -------------------------------------------------- [-179.6875, -89.75] + | | + ------------------[-180.0, -90.0]----------------- + * Other software either ignore the polar overshoot, or don't treat the data-points as a data-grid, to avoid this problem. + * Thus, the MERRA-2 is a sort of regular, but asymmetrical data-grid. + * The coordinates are regular, but their implied boundaries are irregular (at the poles in this case). + + MCMS and MERRA-2 + * For MCMS, MERRA-2 is one of the many data-grids it must deal with. As a result, MCMS reorganizes each input dataset to a common framework. + 1) For most among these is that MCMS works with data-grids rather than data-points. + * That is, MCMS treats the MERRA-2 data-points is being a Grid- or Gridline-Registered data-grid. + * The given coordinates represent the mid-points of the data-cells. + * The edges/gridlines are one-half the grid height and width to the left and down from the mid-points. + (grid_edge_x[col], grid_edge_y[row + 1])---------------------------------------(grid_edge_x[col + 1], grid_edge_y[row + 1]) + | | + | grid[col, row] | + | | + (grid_edge_x[col], grid_edge_y[row])---------------------------------------(grid_edge_x[col + 1], grid_edge_y[row]) + * The first and last columns and rows of data-grids thus straddle the edges of the grid domain. + 2) For some operations, such as storage, it is easier for MCMS to have longitudes as positive values (i.e., 0-360 rather than +/-180). + * For MERRA-2 then lon_m[0] = -180.0 is converted to 180.0 + 3) Do to point 2), MCMS shifts the origin of the data-grid so that its first column/longitude is near zero degrees. + * For MERRA-2 then lon_m[0] = 180.0 is shifted so that to lon_m_360[0] = 0.0, lon_m_260[288] = 180.0 and lon_m_260[575] = 359.375 + ====================================================================== + """ + + ########################################################################### + # PRIVATE Instance-Constructor: __init__() + # ---------------------------------------- + def __init__(self, as_mcms): + """Initialize MERRA2""" + ## + # Declare Public-Attributes + + # String path to where sidecar file is to be stored + # self.file_path = file_path + + # Array of grid/point mid-point/center latitudes (units: deg, format: +/-90) + self.lats = [] + + # Array of grid/point mid-point/center longitudes (units: deg, format: +/-180) + self.lons = [] + + # Array of STARE Spatial Indices (SIDs) + self.sids = [] + + # Array of STARE cover SIDs + self.cover_sids = [] + + # String identifier for data-grids with different resolutions (N/A to MERRA-2) + self.nom_res = '' + + # Flag to use the MCMS specific layout + self.as_mcms = as_mcms + + + ########################################################################### + # PUBLIC Instance-Method: load() + # ------------------------------ + def load(self): + self.get_latlon() + + ########################################################################### + # PUBLIC Instance-Method: get_latlon() + # ------------------------------------ + def get_latlon(self): + """Data Grid defined by the NASA MERRA-2 Reanalysis.""" + # print("\tget_latlon():") + + # Number of columns/longitudes of the data-grid. + nlon = 576 + + # Number of rows/latitudes of the data-grid. + nlat = 361 + + # Number of enumerated data-points in the data-grid (nlon * nlat). + maxid = 207936 + + # Column holding the middle data-grid longitude. + halfway = 288 + + # Row holding the equator latitude + eq_grid = 180 + + # Grid Spacing X/Lon degrees (5/8 degrees) + dx = 0.625 + dx_half = 0.3125 + + # Grid Spacing Y/Lat degrees (1/2 degrees) + dy = 0.5 + dy_half = 0.25 + + # Set starting point + first_lat_mpnt = -90.0 + first_lon_mpnt = 0.0 if self.as_mcms else -180.0 + + # Mid-point latitudes of data-grid (-90 to +90) + r""" + get_latlon(as_mcms = False): + lats (361, 576): + Min: -90.0000 + Max: +90.0000 + [ -90.0, -89.5 ... +89.5, +90.0] + [(0, 0), (1, 0) ... (-2, 0), (-1, 0)] + lons (361, 576): + Min: -180.000 + Max: +179.375 + [-180.000, -179.375 ... +178.750, +179.375] + [(0, 0), (0, 1) ... (0, -2), (0, -1)] + + get_latlon(as_mcms = 1): + lats (361, 576): + Min: -90.0 + Max: +90.0 + [-90.0, -89.5 ... +89.5, +90.0] + [(0, 0), (1, 0) ... (-2, 0), (-1, 0)] + lons (361, 576): + Min: +0.000 + Max: +359.375 + [+0.000, +0.625 ... +358.750, +359.375] + [(0, 0), (0, 1) ... (0, -2), (0, -1)] + """ + self.lats = np.ascontiguousarray(np.tile(np.linspace(first_lat_mpnt, -1.0 * first_lat_mpnt, num=nlat, endpoint=True), (nlon, 1)).transpose()) + + # Mid-point longitudes of data-grid (-180 to +179.375) + if self.as_mcms: + self.lons = np.tile(np.linspace(first_lon_mpnt, 359.375, num=nlon, endpoint=True), (nlat, 1)) + else: + self.lons = np.tile(np.linspace(first_lon_mpnt, 179.375, num=nlon, endpoint=True), (nlat, 1)) + # print(f"\nget_latlon(as_mcms = {self.as_mcms}):") + # print(f"\tlats {self.lats.shape}:") + # print(f"\t\tMin: {np.amin(self.lats):+.1f}") + # print(f"\t\tMax: {np.amax(self.lats):+.1f}") + # print(f"\t\t[{self.lats[0, 0]:+.1f}, {self.lats[1, 0]:+.1f} ... {self.lats[-2, 0]:+.1f}, {self.lats[-1, 0]:+.1f}]") + # print("\t\t[(0, 0), (1, 0) ... (-2, 0), (-1, 0)]") + # print(f"\tlons {self.lons.shape}:") + # print(f"\t\tMin: {np.amin(self.lons):+.3f}") + # print(f"\t\tMax: {np.amax(self.lons):+.3f}") + # print(f"\t\t[{self.lons[0, 0]:+.3f}, {self.lons[0, 1]:+.3f} ... {self.lons[0, -2]:+.3f}, {self.lons[0, -1]:+.3f}]") + # print("\t\t[(0, 0), (0, 1) ... (0, -2), (0, -1)]") + + ########################################################################### + # PUBLIC Instance-Method: make_sids() + # ----------------------------------- + def make_sids(self): + """ + def from_latlon_2d(lat, lon, level=None, adapt_level=False, fill_value_in=None, fill_value_out=None): + Coverts latitudes and longitudes to SIDs. The STARE Level can be automatically adapted match the resolution of the geolocations. + + level: int (0<=level<=27) + Level of the SIDs. + If unset, level will me automatically adapted. + If set, adapt_level will be set to false. + adapt_level: bool + if True, level will adapted to match resolution of lat/lon. + Overwrites level. + fill_value_in: STARE indices are not calculated for lat/lon of this value + fill_value_out: set indices to this value where lat/lon is fill_value_in + + Gives same result as staremaster.conversions.latlon2stare() + """ + # Note: + # in pystare -> if adapt_level: level = 27 + # self.sids = pystare.from_latlon_2d(self.lats, self.lons, adapt_level=True) + self.sids = pystare.from_latlon_2d(self.lats, self.lons, level=10, adapt_level=False) + r""" + make_sids(as_mcms = 1): + self.sids.shape = (361, 576) + type(self.sids) = type(self.sids[0, 0]) = + self.sids[0, 0] = 2287822013634445311 + sids_res = 7 + + make_sids(as_mcms = 0): + self.sids.shape = (361, 576) + type(self.sids) = type(self.sids[0, 0]) = + self.sids[0, 0] = 2287822013634445311 + sids_res = 7 + + STARE Q-Level to form indices. + | Q-Level | R | L | + |---------|---------:|-----------:| + | 27 | | ~0.1m | + | ... | | | + | 23 |~1 m | ~1.2 m | + | 22 |~2 m | ~2.4 m | + | 21 |~4 m | ~5 m | + | 20 |~8 m | ~10 m | + | 19 |~15 m | ~19 m | + | 18 |~31 m | ~38 m | + | 17 |~61 m | ~77 m | + | 16 |~122 m | ~153 m | + | 15 |~245 m | ~307 m | + | 14 |~490 m | ~615 m | + | 13 |~1 km | ~1.2 km | + | 12 |~2 km | ~2 km | + | 11 | ~4 km | ~5 km | + | 10 | ~8 km | ~10 km | + | 09 | ~16 km | ~20 km | + | 08 | ~31 km | ~39 km | + | 07 | ~63 km | ~78 km | <= sids_res + | 06 | ~125 km | ~157 km | + | 05 | ~251 km | ~314 km | + | 04 | ~501 km | ~628 km | + | 03 | ~1003 km | ~1,256 km | + | 02 | ~2005 km | ~2,500 km | + | 01 | ~4011 km | ~5,000 km | + | 00 | ~8021 km | ~10,000 km | + [Table 1. Approximate uncertainties in terms of the area + (radius (R)) and the edge length (L) of the trixel by Q-level.] + """ + # print(f"\tmake_sids(as_mcms = {self.as_mcms}):") + sids_res = staremaster.conversions.min_resolution(self.sids) + # print(f"{self.sids.shape = }") + # print(f"{type(self.sids) = } {type(self.sids[0, 0]) = }") + # print(f"{self.sids[0, 0] = }") + # print(f"{sids_res = }") + + ########################################################################### + # PUBLIC Instance-Method: load_sids_pickle() + # ------------------------------------------ + def load_sids_pickle(self, pickle_name): + with open(pickle_name, 'rb') as pickel_file: + self.sids = pickle.load(pickel_file) + + ########################################################################### + # PUBLIC Instance-Method: save_sids_pickle() + # ------------------------------------------ + def save_sids_pickle(self, pickle_name): + with open(pickle_name, 'wb') as pickel_file: + pickle.dump(self.sids, pickel_file) + + ########################################################################### + # PUBLIC Instance-Method: load_cover_pickle() + # ------------------------------------------ + def load_cover_pickle(self, pickle_name): + with open(pickle_name, 'rb') as pickel_file: + self.cover_sids = pickle.load(pickel_file) + + ########################################################################### + # PUBLIC Instance-Method: save_cover_pickle() + # ------------------------------------------ + def save_cover_pickle(self, pickle_name): + with open(pickle_name, 'wb') as pickel_file: + pickle.dump(self.cover_sids, pickel_file) + + ########################################################################### + # PUBLIC Instance-Method: get_sids() + # ---------------------------------- + def get_sids(self, out_path): + # print(f"\tget_sids({out_path = }):") + if self.as_mcms: + pickle_name = f"{out_path}merra2_mcms_sids.pkl" + else: + pickle_name = f"{out_path}merra2_sids.pkl" + if os.path.exists(pickle_name): + ## + # Read SIDs from file + self.load_sids_pickle(pickle_name) + else: + ## + # Determine SIDs + self.make_sids() + ## + # Save SIDs to pickle + self.save_sids_pickle(pickle_name) + + ########################################################################### + # PUBLIC Instance-Method: get_cover() + # ---------------------------------- + def get_cover(self, out_path): + """ + cover_res = 5 + + STARE Q-Level to form indices. + | Q-Level | R | L | + |---------|---------:|-----------:| + | 27 | | ~0.1m | + | ... | | | + | 23 |~1 m | ~1.2 m | + | 22 |~2 m | ~2.4 m | + | 21 |~4 m | ~5 m | + | 20 |~8 m | ~10 m | + | 19 |~15 m | ~19 m | + | 18 |~31 m | ~38 m | + | 17 |~61 m | ~77 m | + | 16 |~122 m | ~153 m | + | 15 |~245 m | ~307 m | + | 14 |~490 m | ~615 m | + | 13 |~1 km | ~1.2 km | + | 12 |~2 km | ~2 km | + | 11 | ~4 km | ~5 km | + | 10 | ~8 km | ~10 km | + | 09 | ~16 km | ~20 km | + | 08 | ~31 km | ~39 km | + | 07 | ~63 km | ~78 km | <= sids_res + | 06 | ~125 km | ~157 km | + | 05 | ~251 km | ~314 km | <= cover_res + | 04 | ~501 km | ~628 km | + | 03 | ~1003 km | ~1,256 km | + | 02 | ~2005 km | ~2,500 km | + | 01 | ~4011 km | ~5,000 km | + | 00 | ~8021 km | ~10,000 km | + [Table 1. Approximate uncertainties in terms of the area + (radius (R)) and the edge length (L) of the trixel by Q-level.] + + sids_adapted.shape = (361, 576) + type(sids_adapted) = type(sids_adapted[0, 0]) = + sids_adapted[0, 0] = 2287822013634445285 + cf. self.sids[0, 0] = 2287822013634445311 + + self.cover_sids.shape = (8,) + type(self.cover_sids) = type(self.cover_sids[0]) = + self.cover_sids = [0 576460752303423488 1152921504606846976 1729382256910270464 2305843009213693952 2882303761517117440 3458764513820540928 4035225266123964416] + """ + # print(f"get_cover({out_path = }):") + if self.as_mcms: + pickle_name = f"{out_path}merra2_mcms_cover_sids.pkl" + else: + pickle_name = f"{out_path}merra2_cover_sids.pkl" + if os.path.exists(pickle_name): + ## + # Read cover from file + self.load_cover_pickle(pickle_name) + return + + ## + # Find a Q-Level for cover encoding + cover_res = staremaster.conversions.min_resolution(self.sids) + # print(f"\t{cover_res = }") + + # Drop the resolution to make the cover less sparse + cover_res -= 2 + if cover_res < 0: + cover_res = 0 + # print(f"\t{cover_res = }") + + ## + # Clear the SID location bits up to the encoded spatial resolution + sids_adapted = pystare.spatial_coerce_resolution(self.sids, cover_res) + # print(f"\t{sids_adapted.shape = }") + # print(f"\t{type(sids_adapted) = } {type(sids_adapted[0, 0]) = }") + # print(f"\t{sids_adapted[0, 0] = }") + # print(f"\t{sids_adapted = }") + + ## + # Find the cover + self.cover_sids = staremaster.conversions.merge_stare(sids_adapted, n_workers=1) + # print(f"{self.cover_sids.shape = }") + # print(f"{type(self.cover_sids) = } {type(self.cover_sids[0]) = }") + # print(f"{self.cover_sids[0] = }") + # print(self.cover_sids) + + ## + # Save cover to pickle + self.save_cover_pickle(pickle_name) + + + ########################################################################### + # PUBLIC Instance-Method: create_sidecar() + # ---------------------------------------- + def create_sidecar(self, out_path=None, n_workers=1): + # print(f"\ncreate_sidecar({out_path = }):") + + ## + # Find SIDs for each data-point + self.get_sids(out_path) + + ## + # Find the STARE cover for self.sids + self.get_cover(out_path) + + # # Third-Party Imports + # import matplotlib as mpl + # import matplotlib.pyplot as plt + # import matplotlib.tri as tri + # import cartopy.crs as ccrs + # import cartopy.feature as cf + # import shapely + # from PIL import Image + # import geopandas + + # ## + # # Set up the projection and transformation + # proj = ccrs.PlateCarree() + # # proj = ccrs.AzimuthalEquidistant(central_longitude=0.0, central_latitude=-90) + # # proj = ccrs.AzimuthalEquidistant(central_longitude=0.0, central_latitude=90) + # transf = ccrs.Geodetic() + + # ## + # # Plot Options + # plot_options = {'projection':proj, 'transform':transf} + # default_dpi = mpl.rcParamsDefault['figure.dpi'] + # mpl.rcParams['figure.dpi'] = 1.5 * default_dpi + + # class figax_container(object): + # def __init__(self, figax): + # self.fig = figax[0] + # self.ax = figax[1] + # return + + # def add_coastlines(figax, set_global=False): + # "Add coastlines to the plot." + # ax = figax.ax + # if set_global: + # ax.set_global() + # ax.coastlines() + # return figax + + # def hello_plot(spatial_index_values=None, figax=None, plot_options={'projection':ccrs.PlateCarree(), 'transform':ccrs.Geodetic()}, set_global=False, set_coastlines=True, show=True, color=None, lw=1): + # if figax is None: + # figax = figax_container(plt.subplots(1, subplot_kw=plot_options)) + # if set_global: + # figax.ax.set_global() + # if set_coastlines: + # figax.ax.coastlines() + # else: + # ax = figax.ax + + # if spatial_index_values is not None: + # # Calculate vertices and interconnection matrix + # lons, lats, intmat = pystare.triangulate_indices(spatial_index_values) + + # # Make triangulation object & plot + # siv_triang = tri.Triangulation(lons, lats, intmat) + # figax.ax.triplot(siv_triang, c=color, transform=plot_options['transform'], lw=lw) + + # if show: + # plt.show() + # return figax + + # def plot_segment(i0, i1, figax): + # lat = lat0[i0:i1] + # lon = lon0[i0:i1] + # spatial_id = spatial_id0[i0:i1] + # figax = hello_plot(spatial_id, figax=figax, show=False) + # figax.ax.scatter([lon], [lat], s=1, c='r') + # return figax + + # ## + # # Plot cover + # hello_plot(self.cover_sids, plot_options=plot_options, set_global=False, set_coastlines=True) + + ## + # Make Sidecar + r"""i = 361, j = 576, l = 8""" + i = self.lats.shape[0] + j = self.lats.shape[1] + l = self.cover_sids.size + # print(f"{i = }, {j = }, {l = }") + + if self.as_mcms: + sidecar_name = f"{out_path}merra2_mcms_sidecar.hdf" + else: + sidecar_name = f"{out_path}merra2_sidecar.hdf" + sidecar = Sidecar(granule_path=sidecar_name, out_path=out_path) + + ## + # Save Sidecar to file + sidecar.write_dimensions(i, j, l) + # print(f"{self.sids = }") + sidecar.write_sids(self.sids, fill_value=0) + sidecar.write_lons(self.lons) + sidecar.write_lats(self.lats) + sidecar.write_cover(self.cover_sids) + +# >>>> ::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: <<<< +# >>>> END OF FILE | END OF FILE | END OF FILE | END OF FILE | END OF FILE | END OF FILE <<<< +# >>>> ::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: <<<< + diff --git a/staremaster/products/snodas.py b/staremaster/products/snodas.py index 9e668fd..3af2451 100644 --- a/staremaster/products/snodas.py +++ b/staremaster/products/snodas.py @@ -1406,6 +1406,23 @@ def get_latlon(self): self.lats = np.ascontiguousarray(np.tile(snodas_lats, (nlon, 1)).transpose()) self.lons = np.tile(snodas_lons, (nlat, 1)) + ## + # Trim for SNODAS west, which unlike full SNODAS works. + snodas_lons_1d = self.lons[0, :] + snodas_lats_1d = self.lats[:, 0] + + snodas_w_cutoff = min((lidx for lidx, lat in enumerate(snodas_lats_1d) if lat >= 30.0)) + snodas_w_lats = snodas_lats_1d[snodas_w_cutoff:] + + snodas_w_cutoff = max((lidx for lidx, lon in enumerate(snodas_lons_1d) if lon <= -110)) + snodas_w_lons = snodas_lons_1d[:snodas_w_cutoff] + + snodas_lons = self.lons[snodas_w_cutoff:, :snodas_w_cutoff] + snodas_lats = self.lats[snodas_w_cutoff:, :snodas_w_cutoff] + + self.lons = snodas_lons + self.lats = snodas_lats + # print("\nget_latlon():") # print(f"\tlats {self.lats.shape}:") # print(f"\t\tMin: {np.amin(self.lats):+.1f}") @@ -1439,7 +1456,9 @@ def from_latlon_2d(lat, lon, level=None, adapt_level=False, fill_value_in=None, Gives same result as staremaster.conversions.latlon2stare() """ # print("\tmake_sids():") - self.sids = pystare.from_latlon_2d(self.lats, self.lons, adapt_level=True) + # self.sids = pystare.from_latlon_2d(self.lats, self.lons, adapt_level=True) + # 1-km spatial resolution + self.sids = pystare.from_latlon_2d(self.lats, self.lons, level=15, adapt_level=False) r""" make_sids(): sids_adapted.shape = (3351, 6935) @@ -1460,7 +1479,7 @@ def from_latlon_2d(lat, lon, level=None, adapt_level=False, fill_value_in=None, | 18 |~31 m | ~38 m | | 17 |~61 m | ~77 m | | 16 |~122 m | ~153 m | - | 15 |~245 m | ~307 m | + | 15 |~245 m | ~307 m | <= sids_res | 14 |~490 m | ~615 m | | 13 |~1 km | ~1.2 km | | 12 |~2 km | ~2 km | @@ -1468,7 +1487,7 @@ def from_latlon_2d(lat, lon, level=None, adapt_level=False, fill_value_in=None, | 10 | ~8 km | ~10 km | | 09 | ~16 km | ~20 km | | 08 | ~31 km | ~39 km | - | 07 | ~63 km | ~78 km | <= sids_res + | 07 | ~63 km | ~78 km | | 06 | ~125 km | ~157 km | | 05 | ~251 km | ~314 km | | 04 | ~501 km | ~628 km | @@ -1556,7 +1575,7 @@ def get_cover(self, out_path): | 18 |~31 m | ~38 m | | 17 |~61 m | ~77 m | | 16 |~122 m | ~153 m | - | 15 |~245 m | ~307 m | + | 15 |~245 m | ~307 m | <= sids_res | 14 |~490 m | ~615 m | | 13 |~1 km | ~1.2 km | | 12 |~2 km | ~2 km | @@ -1564,7 +1583,7 @@ def get_cover(self, out_path): | 10 | ~8 km | ~10 km | | 09 | ~16 km | ~20 km | | 08 | ~31 km | ~39 km | - | 07 | ~63 km | ~78 km | <= sids_res + | 07 | ~63 km | ~78 km | | 06 | ~125 km | ~157 km | | 05 | ~251 km | ~314 km | <= cover_res | 04 | ~501 km | ~628 km | @@ -1574,15 +1593,6 @@ def get_cover(self, out_path): | 00 | ~8021 km | ~10,000 km | [Table 1. Approximate uncertainties in terms of the area (radius (R)) and the edge length (L) of the trixel by Q-level.] - - sids_adapted.shape = (361, 576) - type(sids_adapted) = type(sids_adapted[0, 0]) = - sids_adapted[0, 0] = 2287822013634445285 - cf. self.sids[0, 0] = 2287822013634445311 - - self.cover_sids.shape = (8,) - type(self.cover_sids) = type(self.cover_sids[0]) = - self.cover_sids = [0 576460752303423488 1152921504606846976 1729382256910270464 2305843009213693952 2882303761517117440 3458764513820540928 4035225266123964416] """ # print(f"get_cover({out_path = }):") pickle_name = f"{out_path}snodas_cover_sids.pkl" diff --git a/staremaster/sidecar.py b/staremaster/sidecar.py index 747439e..4c275f1 100644 --- a/staremaster/sidecar.py +++ b/staremaster/sidecar.py @@ -93,7 +93,7 @@ def write_lats(self, lats, nom_res=None, group=None, fill_value=None): lats_netcdf.units = 'degrees_north' lats_netcdf[:, :] = lats - def write_sids(self, sids, nom_res=None, group=None, fill_value=-1): + def write_sids(self, sids, nom_res=None, group=None, fill_value=None): i = sids.shape[0] j = sids.shape[1] varname = 'STARE_index'.format(nom_res=nom_res) @@ -107,8 +107,8 @@ def write_sids(self, sids, nom_res=None, group=None, fill_value=-1): if group: grp = rootgrp.createGroup(group) else: - grp = rootgrp - sids_netcdf = grp.createVariable(varname=varname, + grp = rootgrp + sids_netcdf = grp.createVariable(varname=varname, datatype='u8', dimensions=(i_name, j_name), chunksizes=[i, j],