Following @Dango's idea I created and tested (on small rasters with the same extent and cell size) the following code:
import arcpy, numpy
inRaster = r"C:\tmp\RastersArray.gdb\InRaster"
inRaster2 = r"C:\tmp\RastersArray.gdb\InRaster2"
##Get properties of the input raster
inRasterDesc = arcpy.Describe(inRaster)
#coordinates of the lower left corner
rasXmin = inRasterDesc.Extent.Xmin
rasYmin = inRasterDesc.Extent.Ymin
# Cell size, raster size
rasMeanCellHeight = inRasterDesc.MeanCellHeight
rasMeanCellWidth = inRasterDesc.MeanCellWidth
rasHeight = inRasterDesc.Height
rasWidth = inRasterDesc.Width
##Calculate coordinates basing on raster properties
#create numpy array of coordinates of cell centroids
def rasCentrX(rasHeight, rasWidth):
coordX = rasXmin + (0.5*rasMeanCellWidth + rasWidth)
return coordX
inRasterCoordX = numpy.fromfunction(rasCentrX, (rasHeight,rasWidth)) #numpy array of X coord
def rasCentrY(rasHeight, rasWidth):
coordY = rasYmin + (0.5*rasMeanCellHeight + rasHeight)
return coordY
inRasterCoordY = numpy.fromfunction(rasCentrY, (rasHeight,rasWidth)) #numpy array of Y coord
#combine arrays of coordinates (although array for Y is before X, dstack produces [X, Y] pairs)
inRasterCoordinates = numpy.dstack((inRasterCoordY,inRasterCoordX))
##Raster conversion to NumPy Array
#create NumPy array from input rasters
inRasterArrayTopLeft = arcpy.RasterToNumPyArray(inRaster)
inRasterArrayTopLeft2 = arcpy.RasterToNumPyArray(inRaster2)
#flip array upside down - then lower left corner cells has the same index as cells in coordinates array
inRasterArray = numpy.flipud(inRasterArrayTopLeft)
inRasterArray2 = numpy.flipud(inRasterArrayTopLeft2)
# combine coordinates and value
inRasterFullArray = numpy.dstack((inRasterCoordinates, inRasterArray.T))
#add values from second raster
rasterValuesArray = numpy.dstack((inRasterFullArray, inRasterArray2.T))
Based on @hmfly code, you can have access to desired values:
(height, width, dim )=rasterValuesArray.shape
for row in range(0,height):
for col in range(0,width):
#now you have access to single array of values for one cell location
Unfortunately there's one 'but' - the code is right for NumPy arrays which can be handled by system memory. For my system (8GB), the largest array was about 9000,9000.
As my experience doesn't let me provide more help, you can consider some suggestions about dealing wiht large arrays:
https://stackoverflow.com/questions/1053928/python-numpy-very-large-matrices
arcpy.RasterToNumPyArray method allows to specify the subset of raster converted to NumPy array (ArcGIS10 help page) what can be useful when chunking large dataset into submatrices.
With Python, you can access raster statistics using the Python GDAL/OGR API.
from osgeo import gdal
# open raster and choose band to find min, max
raster = r'C:\path\to\your\geotiff.tif'
gtif = gdal.Open(raster)
srcband = gtif.GetRasterBand(1)
# Get raster statistics
stats = srcband.GetStatistics(True, True)
# Print the min, max, mean, stdev based on stats index
print "[ STATS ] = Minimum=%.3f, Maximum=%.3f, Mean=%.3f, StdDev=%.3f" % (
stats[0], stats[1], stats[2], stats[3])
>>> print "[ STATS ] = Minimum=%.3f, Maximum=%.3f, Mean=%.3f, StdDev=%.3f" % (
... stats[0], stats[1], stats[2], stats[3])
[ STATS ] = Minimum=1.000, Maximum=4.000, Mean=1.886, StdDev=0.797
>>>
Best Answer
Is Python an option?
Use RasterIO (a Python GDAL/ numpy bridge) to load the raster to NumPy array, then use numpy.amax() to find the maximum value, followed by numpy.where() to find the row/column indices, then calculate the lat and lon from the raster extents.