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LandWaterMask.py
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LandWaterMask.py
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#!/usr/bin/env python
# encoding: utf-8
"""
LandWaterMask.py
* DESCRIPTION: Class to granulate the DEM Land Water Mask data product
Created by Geoff Cureton on 2013-03-05.
Copyright (c) 2013 University of Wisconsin SSEC. All rights reserved.
"""
file_Date = '$Date: 2014-09-08 17:01:37 -0500 (Mon, 08 Sep 2014) $'
file_Revision = '$Revision: 2224 $'
file_Author = '$Author: geoffc $'
file_HeadURL = '$HeadURL: https://svn.ssec.wisc.edu/repos/jpss_adl/trunk/scripts/edr/GridIP/LandWaterMask.py $'
file_Id = '$Id: LandWaterMask.py 2224 2014-09-08 22:01:37Z geoffc $'
__author__ = 'G.P. Cureton <geoff.cureton@ssec.wisc.edu>'
__version__ = '$Id: LandWaterMask.py 2224 2014-09-08 22:01:37Z geoffc $'
__docformat__ = 'Epytext'
import os, sys, logging, traceback
from os import path,uname,environ
import string
import re
import uuid
from glob import glob
from time import time
from datetime import datetime,timedelta
from scipy import round_
import numpy as np
from numpy import ma
import copy
from bisect import bisect_left,bisect_right
import ctypes
from numpy.ctypeslib import ndpointer
import tables as pytables
from tables import exceptions as pyEx
import ViirsData
# skim and convert routines for reading .asc metadata fields of interest
import adl_blob2 as adl_blob
import adl_asc
from adl_asc import skim_dir, contiguous_granule_groups, granule_groups_contain, effective_anc_contains,eliminate_duplicates,_is_contiguous, RDR_REQUIRED_KEYS, POLARWANDER_REQUIRED_KEYS
from adl_common import ADL_HOME, CSPP_RT_HOME, CSPP_RT_ANC_HOME
# every module should have a LOG object
try :
sourcename= file_Id.split(" ")
LOG = logging.getLogger(sourcename[1])
except :
LOG = logging.getLogger('LandWaterMask')
from Utils import getURID, getAscLine, getAscStructs, findDatelineCrossings, shipOutToFile
from Utils import index, find_lt, find_le, find_gt, find_ge
from Utils import plotArr
class LandWaterMask() :
def __init__(self,inDir=None, sdrEndian=None, ancEndian=None):
self.collectionShortName = 'VIIRS-GridIP-VIIRS-Lwm-Mod-Gran'
self.xmlName = 'VIIRS_GRIDIP_VIIRS_LWM_MOD_GRAN.xml'
self.blobDatasetName = 'landWaterMask'
self.dataType = 'uint8'
self.sourceType = 'DEM'
self.sourceList = ['']
self.trimObj = ViirsData.ViirsTrimTable()
if inDir is None :
self.inDir = path.abspath(path.curdir)
else :
self.inDir = inDir
if sdrEndian is None :
self.sdrEndian = adl_blob.LITTLE_ENDIAN
else :
self.sdrEndian = sdrEndian
if ancEndian is None :
self.ancEndian = adl_blob.LITTLE_ENDIAN
else :
self.ancEndian = ancEndian
# Digital Elevation Model (DEM) land sea mask types
self.DEM_list = ['DEM_SHALLOW_OCEAN','DEM_LAND','DEM_COASTLINE',
'DEM_SHALLOW_INLAND_WATER','DEM_EPHEMERAL_WATER',
'DEM_DEEP_INLAND_WATER','DEM_MOD_CONT_OCEAN','DEM_DEEP_OCEAN']
self.DEM_dict = {
'DEM_SHALLOW_OCEAN' : 0,
'DEM_LAND' : 1,
'DEM_COASTLINE' : 2,
'DEM_SHALLOW_INLAND_WATER' : 3,
'DEM_EPHEMERAL_WATER' : 4,
'DEM_DEEP_INLAND_WATER' : 5,
'DEM_MOD_CONT_OCEAN' : 6,
'DEM_DEEP_OCEAN' : 7
}
def setGeolocationInfo(self,dicts):
'''
Populate this class instance with the geolocation data for a single granule
'''
# Set some environment variables and paths
ANC_SCRIPTS_PATH = path.join(CSPP_RT_HOME,'viirs')
ADL_ASC_TEMPLATES = path.join(ANC_SCRIPTS_PATH,'asc_templates')
# Collect some data from the geolocation dictionary
self.geoDict = dicts
URID = dicts['URID']
geo_Collection_ShortName = dicts['N_Collection_Short_Name']
N_Granule_ID = dicts['N_Granule_ID']
ObservedStartTimeObj = dicts['ObservedStartTime']
geoFiles = glob('%s/%s*' % (self.inDir,URID))
geoFiles.sort()
LOG.debug("###########################")
LOG.debug(" Geolocation Information ")
LOG.debug("###########################")
LOG.debug("N_Granule_ID : %r" % (N_Granule_ID))
LOG.debug("ObservedStartTime : %s" % (ObservedStartTimeObj.__str__()))
LOG.debug("N_Collection_Short_Name : %s" %(geo_Collection_ShortName))
LOG.debug("URID : %r" % (URID))
LOG.debug("geoFiles : %r" % (geoFiles))
LOG.debug("###########################")
# Do we have terrain corrected geolocation?
terrainCorrectedGeo = True if 'GEO-TC' in geo_Collection_ShortName else False
# Do we have long or short style geolocation field names?
if (geo_Collection_ShortName=='VIIRS-MOD-GEO-TC' or geo_Collection_ShortName=='VIIRS-MOD-RGEO') :
longFormGeoNames = True
LOG.debug("We have long form geolocation names")
elif (geo_Collection_ShortName=='VIIRS-MOD-GEO' or geo_Collection_ShortName=='VIIRS-MOD-RGEO-TC') :
LOG.debug("We have short form geolocation names")
longFormGeoNames = False
else :
LOG.error("Invalid geolocation shortname: %s" %(geo_Collection_ShortName))
return -1
# Get the geolocation xml file
geoXmlFile = "%s.xml" % (string.replace(geo_Collection_ShortName,'-','_'))
geoXmlFile = path.join(ADL_HOME,'xml/VIIRS',geoXmlFile)
if path.exists(geoXmlFile):
LOG.debug("We are using for %s: %s,%s" %(geo_Collection_ShortName,geoXmlFile,geoFiles[0]))
# Open the geolocation blob and get the latitude and longitude
endian = self.sdrEndian
geoBlobObj = adl_blob.map(geoXmlFile,geoFiles[0], endian=endian)
# Get scan_mode to find any bad scans
scanMode = geoBlobObj.scan_mode[:]
badScanIdx = np.where(scanMode==254)[0]
LOG.debug("Bad Scans: %r" % (badScanIdx))
# Detemine the min, max and range of the latitude and longitude,
# taking care to exclude any fill values.
if longFormGeoNames :
if endian==adl_blob.BIG_ENDIAN:
latitude = getattr(geoBlobObj,'latitude').byteswap()
longitude = getattr(geoBlobObj,'longitude').byteswap()
latitude = latitude.astype('float')
longitude = longitude.astype('float')
else:
latitude = getattr(geoBlobObj,'latitude').astype('float')
longitude = getattr(geoBlobObj,'longitude').astype('float')
else :
latitude = getattr(geoBlobObj,'lat').astype('float')
longitude = getattr(geoBlobObj,'lon').astype('float')
latitude = ma.masked_less(latitude,-800.)
latMin,latMax = np.min(latitude),np.max(latitude)
latRange = latMax-latMin
longitude = ma.masked_less(longitude,-800.)
lonMin,lonMax = np.min(longitude),np.max(longitude)
lonRange = lonMax-lonMin
LOG.debug("min,max,range of latitide: %f %f %f" % (latMin,latMax,latRange))
LOG.debug("min,max,range of longitude: %f %f %f" % (lonMin,lonMax,lonRange))
# Determine the latitude and longitude fill masks, so we can restore the
# fill values after we have scaled...
latMask = latitude.mask
lonMask = longitude.mask
# Check if the geolocation is in radians, convert to degrees
if 'RGEO' in geo_Collection_ShortName :
LOG.debug("Geolocation is in radians, convert to degrees...")
latitude = np.degrees(latitude)
longitude = np.degrees(longitude)
latMin,latMax = np.min(latitude),np.max(latitude)
latRange = latMax-latMin
lonMin,lonMax = np.min(longitude),np.max(longitude)
lonRange = lonMax-lonMin
LOG.debug("New min,max,range of latitude: %f %f %f" % (latMin,latMax,latRange))
LOG.debug("New min,max,range of longitude: %f %f %f" % (lonMin,lonMax,lonRange))
# Restore fill values to masked pixels in geolocation
geoFillValue = self.trimObj.sdrTypeFill['VDNE_FLOAT64_FILL'][latitude.dtype.name]
latitude = ma.array(latitude,mask=latMask,fill_value=geoFillValue)
self.latitude = latitude.filled()
geoFillValue = self.trimObj.sdrTypeFill['VDNE_FLOAT64_FILL'][longitude.dtype.name]
longitude = ma.array(longitude,mask=lonMask,fill_value=geoFillValue)
self.longitude = longitude.filled()
# Shift the longitudes to be between -180 and 180 degrees
if lonMax > 180. :
LOG.debug("\nFinal min,max,range of longitude: %f %f %f" % (lonMin,lonMax,lonRange))
dateLineIdx = np.where(longitude>180.)
LOG.debug("dateLineIdx = %r" % (dateLineIdx))
longitude[dateLineIdx] -= 360.
lonMax = np.max(ma.array(longitude,mask=lonMask))
lonMin = np.min(ma.array(longitude,mask=lonMask))
lonRange = lonMax-lonMin
LOG.debug("\nFinal min,max,range of longitude: %f %f %f" % (lonMin,lonMax,lonRange))
# Record the corners, taking care to exclude any bad scans...
nDetectors = 16
firstGoodScan = np.where(scanMode<=2)[0][0]
lastGoodScan = np.where(scanMode<=2)[0][-1]
firstGoodRow = firstGoodScan * nDetectors
lastGoodRow = lastGoodScan * nDetectors + nDetectors - 1
latCrnList = [latitude[firstGoodRow,0],latitude[firstGoodRow,-1],latitude[lastGoodRow,0],latitude[lastGoodRow,-1]]
lonCrnList = [longitude[firstGoodRow,0],longitude[firstGoodRow,-1],longitude[lastGoodRow,0],longitude[lastGoodRow,-1]]
# Check for dateline/pole crossings
num180Crossings = findDatelineCrossings(latCrnList,lonCrnList)
LOG.debug("We have %d dateline crossings."%(num180Crossings))
# Copy the geolocation information to the class object
self.latMin = latMin
self.latMax = latMax
self.latRange = latRange
self.lonMin = lonMin
self.lonMax = lonMax
self.lonRange = lonRange
self.scanMode = scanMode
self.latitude = latitude
self.longitude = longitude
self.latCrnList = latCrnList
self.lonCrnList = lonCrnList
self.num180Crossings = num180Crossings
# Parse the geolocation asc file to get struct information which will be
# written to the ancillary asc files
geoAscFileName = path.join(self.inDir,URID+".asc")
LOG.debug("\nOpening %s..." % (geoAscFileName))
geoAscFile = open(geoAscFileName,'rt')
self.ObservedDateTimeStr = getAscLine(geoAscFile,"ObservedDateTime")
#self.RangeDateTimeStr = self.ObservedDateTimeStr
self.RangeDateTimeStr = getAscLine(geoAscFile,"ObservedDateTime")
self.RangeDateTimeStr = string.replace(self.RangeDateTimeStr,"ObservedDateTime","RangeDateTime")
self.GRingLatitudeStr = getAscStructs(geoAscFile,"GRingLatitude",12)
self.GRingLongitudeStr = getAscStructs(geoAscFile,"GRingLongitude",12)
self.North_Bounding_Coordinate_Str = getAscLine(geoAscFile,"North_Bounding_Coordinate")
self.South_Bounding_Coordinate_Str = getAscLine(geoAscFile,"South_Bounding_Coordinate")
self.East_Bounding_Coordinate_Str = getAscLine(geoAscFile,"East_Bounding_Coordinate")
self.West_Bounding_Coordinate_Str = getAscLine(geoAscFile,"West_Bounding_Coordinate")
geoAscFile.close()
def subset(self):
'''Subsets the LSM dataset to cover the required geolocation range.'''
# Get the subset of DEM global dataset.
DEM_dLat = 30.*(1./3600.)
DEM_dLon = 30.*(1./3600.)
DEM_fileName = path.join(CSPP_RT_ANC_HOME,'LSM/dem30ARC_Global_LandWater_uncompressed.h5')
self.sourceList.append(path.basename(DEM_fileName))
try :
# TODO : Use original HDF4 file which contains elevation and LWM.
DEMobj = pytables.openFile(DEM_fileName,'r')
DEM_node = DEMobj.getNode('/demGRID/Data Fields/LandWater')
except Exception, err :
LOG.exception("%s"%(err))
LOG.exception("Problem opening DEM file (%s), aborting."%(DEM_fileName))
sys.exit(1)
try :
DEM_gridLats = -1. * (np.arange(21600.) * DEM_dLat - 90.)
DEM_gridLons = np.arange(43200.) * DEM_dLon - 180.
LOG.debug("min,max DEM Grid Latitude values : %f,%f"%(DEM_gridLats[0],DEM_gridLats[-1]))
LOG.debug("min,max DEM Grid Longitude values : %f,%f"%(DEM_gridLons[0],DEM_gridLons[-1]))
latMin = self.latMin
latMax = self.latMax
lonMin = self.lonMin
lonMax = self.lonMax
DEM_latMask = np.equal((DEM_gridLats<(latMax+DEM_dLat)),(DEM_gridLats>(latMin-DEM_dLat)))
DEM_lonMask = np.equal((DEM_gridLons<(lonMax+DEM_dLon)),(DEM_gridLons>(lonMin-DEM_dLon)))
DEM_latIdx = np.where(DEM_latMask==True)[0]
DEM_lonIdx = np.where(DEM_lonMask==True)[0]
DEM_latMinIdx = DEM_latIdx[0]
DEM_latMaxIdx = DEM_latIdx[-1]
DEM_lonMinIdx = DEM_lonIdx[0]
DEM_lonMaxIdx = DEM_lonIdx[-1]
LOG.debug("DEM_latMinIdx = %d" % (DEM_latMinIdx))
LOG.debug("DEM_latMaxIdx = %d" % (DEM_latMaxIdx))
LOG.debug("DEM_lonMinIdx = %d" % (DEM_lonMinIdx))
LOG.debug("DEM_lonMaxIdx = %d" % (DEM_lonMaxIdx))
lat_subset = DEM_gridLats[DEM_latMinIdx:DEM_latMaxIdx+1]
self.gridLat = lat_subset
if self.num180Crossings == 2 :
# We have a dateline crossing, so subset the positude and negative
# longitude grids and sandwich them together.
posLonCrn = np.min(ma.masked_less_equal(np.array(self.lonCrnList),0.))
negLonCrn = np.max(ma.masked_outside(np.array(self.lonCrnList),-800.,0.))
posIdx = index(DEM_gridLons,find_lt(DEM_gridLons,posLonCrn))
negIdx = index(DEM_gridLons,find_gt(DEM_gridLons,negLonCrn))
posLons_subset = DEM_gridLons[posIdx:]
negLons_subset = DEM_gridLons[:negIdx]
lon_subset = np.concatenate((posLons_subset,negLons_subset))
# Do the same with the DEM data
posBlock = DEM_node[DEM_latMinIdx:DEM_latMaxIdx+1,posIdx:]
negBlock = DEM_node[DEM_latMinIdx:DEM_latMaxIdx+1,:negIdx]
DEM_subset = np.concatenate((posBlock,negBlock),axis=1)
else :
DEM_subset = DEM_node[DEM_latMinIdx:DEM_latMaxIdx+1,DEM_lonMinIdx:DEM_lonMaxIdx+1]
lon_subset = DEM_gridLons[DEM_lonMinIdx:DEM_lonMaxIdx+1]
self.gridLon = lon_subset
# Copy DEM data to the GridIP object
self.gridData = DEM_subset.astype(self.dataType)
DEM_node.close()
DEMobj.close()
except Exception, err :
LOG.debug("EXCEPTION: %s" % (err))
DEM_node.close()
DEMobj.close()
def _grid2Gran(self, dataLat, dataLon, gridData, gridLat, gridLon):
'''Granulates a gridded dataset using an input geolocation'''
nData = np.int64(dataLat.size)
gridRows = np.int32(gridLat.shape[0])
gridCols = np.int32(gridLat.shape[1])
data = np.ones(np.shape(dataLat),dtype=np.float64)* -999.9
dataIdx = np.ones(np.shape(dataLat),dtype=np.int64) * -99999
ANC_SCRIPTS_PATH = path.join(CSPP_RT_HOME,'viirs')
libFile = path.join(ANC_SCRIPTS_PATH,'libgriddingAndGranulation.so.1.0.1')
LOG.debug("Gridding and granulation library file: %s" % (libFile))
lib = ctypes.cdll.LoadLibrary(libFile)
grid2gran = lib.grid2gran_nearest
grid2gran.restype = None
grid2gran.argtypes = [
ndpointer(ctypes.c_double,ndim=1,shape=(nData),flags='C_CONTIGUOUS'),
ndpointer(ctypes.c_double,ndim=1,shape=(nData),flags='C_CONTIGUOUS'),
ndpointer(ctypes.c_double,ndim=1,shape=(nData),flags='C_CONTIGUOUS'),
ctypes.c_int64,
ndpointer(ctypes.c_double,ndim=2,shape=(gridRows,gridCols),flags='C_CONTIGUOUS'),
ndpointer(ctypes.c_double,ndim=2,shape=(gridRows,gridCols),flags='C_CONTIGUOUS'),
ndpointer(ctypes.c_double,ndim=2,shape=(gridRows,gridCols),flags='C_CONTIGUOUS'),
ndpointer(ctypes.c_int64,ndim=1,shape=(nData),flags='C_CONTIGUOUS'),
ctypes.c_int32,
ctypes.c_int32
]
'''
int snapGrid_ctypes(double *lat,
double *lon,
double *data,
long nData,
double *gridLat,
double *gridLon,
double *gridData,
long *gridDataIdx,
int nGridRows,
int nGridCols
)
'''
LOG.debug("Calling C routine grid2gran()...")
retVal = grid2gran(dataLat,
dataLon,
data,
nData,
gridLat,
gridLon,
gridData,
dataIdx,
gridRows,
gridCols)
LOG.debug("Returning from C routine grid2gran()")
return data,dataIdx
def granulate(self,GridIP_objects):
'''
Granulates the GridIP DEM files.
'''
# Generate the lat and lon grids, and flip them and the data over latitude
gridLon,gridLat = np.meshgrid(self.gridLon,self.gridLat[::-1])
gridData = self.gridData[::-1,:]
latitude = self.latitude
longitude = self.longitude
# If we have a dateline crossing, remove the longitude discontinuity
# by adding 360 degrees to the negative longitudes.
if self.num180Crossings == 2 :
gridLonNegIdx = np.where(gridLon < 0.)
gridLon[gridLonNegIdx] += 360.
longitudeNegIdx = np.where(longitude < 0.)
longitude[longitudeNegIdx] += 360.
LOG.info("Granulating %s ..." % (self.collectionShortName))
LOG.debug("latitide,longitude shapes: %s, %s"%(str(latitude.shape) , str(longitude.shape)))
LOG.debug("gridData.shape = %s" % (str(gridData.shape)))
LOG.debug("gridLat.shape = %s" % (str(gridLat.shape)))
LOG.debug("gridLon.shape = %s" % (str(gridLon.shape)))
LOG.debug("min of gridData = %r"%(np.min(gridData)))
LOG.debug("max of gridData = %r"%(np.max(gridData)))
t1 = time()
data,dataIdx = self._grid2Gran(np.ravel(latitude),
np.ravel(longitude),
gridData.astype(np.float64),
gridLat.astype(np.float64),
gridLon.astype(np.float64))
t2 = time()
elapsedTime = t2-t1
LOG.info("Granulation took %f seconds for %d points" % (elapsedTime,latitude.size))
data = data.reshape(latitude.shape)
dataIdx = dataIdx.reshape(latitude.shape)
# Convert granulated data back to original type...
data = data.astype(self.dataType)
# Convert any "inland water" to "sea water"
shallowInlandWaterValue = self.DEM_dict['DEM_SHALLOW_INLAND_WATER']
shallowOceanValue = self.DEM_dict['DEM_SHALLOW_OCEAN']
deepInlandWaterValue = self.DEM_dict['DEM_DEEP_INLAND_WATER']
deepOceanValue = self.DEM_dict['DEM_DEEP_OCEAN']
shallowInlandWaterMask = ma.masked_equal(data,shallowInlandWaterValue).mask
shallowOceanMask = ma.masked_equal(data,shallowOceanValue).mask
deepInlandWaterMask = ma.masked_equal(data,deepInlandWaterValue).mask
totalWaterMask = shallowInlandWaterMask #+ shallowOceanMask + deepInlandWaterMask
data = ma.array(data,mask=totalWaterMask,fill_value=deepOceanValue)
data = data.filled()
LOG.debug("Shape of granulated %s data is %s" % (self.collectionShortName,np.shape(data)))
LOG.debug("Shape of granulated %s dataIdx is %s" % (self.collectionShortName,np.shape(dataIdx)))
# Explicitly restore geolocation fill to the granulated data...
fillMask = ma.masked_less(self.latitude,-800.).mask
fillValue = self.trimObj.sdrTypeFill['MISS_FILL'][self.dataType]
data = ma.array(data,mask=fillMask,fill_value=fillValue)
self.data = data.filled()
# Moderate resolution trim table arrays. These are
# bool arrays, and the trim pixels are set to True.
#modTrimMask = self.trimObj.createModTrimArray(nscans=48,trimType=bool)
# Fill the required pixel trim rows in the granulated GridIP data with
# the ONBOARD_PT_FILL value for the correct data type
#fillValue = self.trimObj.sdrTypeFill['ONBOARD_PT_FILL'][self.dataType]
#data = ma.array(data,mask=modTrimMask,fill_value=fillValue)
#self.data = data.filled()
def shipOutToFile(self):
''' Pass the current class instance to this Utils method to generate
a blob/asc file pair from the input ancillary data object.'''
#shipOutToFile(self)
pass