def build_multiband_image(rootDIR, outName, newfoldername, find, drivercode, ndvalue):
outdir = create_output_dir(rootDIR, newfoldername)
print "\nOutputting files to : {0}".format(outdir)
print "\nFinding HDF files in directory/subfolders: {0}".format(rootDIR)
hdfs = find_files(rootDIR, ".hdf")
print "\tFound {0} files.".format(len(hdfs))
print "\nGetting images to process of type {0}...".format(find)
toprocess = []
for hdf in hdfs:
sds = get_hdf_subdatasets(hdf)
for ds in sds:
if find.upper() in ds[1].upper():
toprocess.append(ds[0])
print "\t\t{0}".format(ds[0])
bands = len(toprocess)
print "\tFound {0} images of type {1}.".format(bands, find)
print "\nGetting output parameters..."
rows, cols, datatype, geotransform, projection = open_image(toprocess[0])
print "\tParameters: rows: {0}, cols: {1}, datatype: {2}, projection: {3}.".format(rows, cols, datatype, projection)
outfile = os.path.join(outdir, outName) + ".tif"
print "\nOutput file is: {0}".format(outfile)
outds = create_output_raster(outfile, cols, rows, bands, datatype, drivername=drivercode)
print "\tCreated output file."
print"\nAdding bands to output file..."
for i in range(0, bands):
print "\tProcessing band {0} of {1}...".format(i + 1, bands)
image = gdal.Open(toprocess[i])
band = image.GetRasterBand(1)
outband = outds.GetRasterBand(i + 1)
print "\t\tReading band data to array..."
data = band.ReadAsArray(0, 0, cols, rows)
print "\t\tWriting band data to output band..."
outband.WriteArray(data, 0, 0)
outband.SetNoDataValue(ndvalue)
outband.FlushCache()
del data, outband
image = ""
print "\tFinished adding bands to output file."
print "\nSetting transform and projection..."
outds.SetGeoTransform(geotransform)
outds.SetProjection(projection)
outDS = ""
print "\nProcess completed."
from create_rule_image_multiprocessed_bypx import phenological_classificaion, read_reference_file
import os
from pyhytemporal.utils import find_files
clip1refs = find_files("/Users/phoetrymaster/Documents/School/Geography/Thesis/Data/MODIS_KANSAS_2007-2012/reprojected/Refs/2012/clip1", "mean.ref")
clip2refs = find_files("/Users/phoetrymaster/Documents/School/Geography/Thesis/Data/MODIS_KANSAS_2007-2012/reprojected/Refs/2012/clip2", "mean.ref")
clip3refs = find_files("/Users/phoetrymaster/Documents/School/Geography/Thesis/Data/MODIS_KANSAS_2007-2012/reprojected/Refs/2012/clip3", "mean.ref")
clip4refs = find_files("/Users/phoetrymaster/Documents/School/Geography/Thesis/Data/MODIS_KANSAS_2007-2012/reprojected/Refs/2012/clip4", "mean.ref")
clip5refs = find_files("/Users/phoetrymaster/Documents/School/Geography/Thesis/Data/MODIS_KANSAS_2007-2012/reprojected/Refs/2012/clip5", "mean.ref")
clip6refs = find_files("/Users/phoetrymaster/Documents/School/Geography/Thesis/Data/MODIS_KANSAS_2007-2012/reprojected/Refs/2012/clip6", "mean.ref")
meanrefs = find_files("/Users/phoetrymaster/Documents/School/Geography/Thesis/Data/MODIS_KANSAS_2007-2012/reprojected/Refs/2012", ".ref", recursive=False)
reffiles = [clip1refs, clip2refs, clip3refs, clip4refs, clip5refs, clip6refs, meanrefs]
rootout = "/Users/phoetrymaster/Documents/School/Geography/Thesis/Data/MODIS_KANSAS_2007-2012/reprojected/Classified"
outfolders = [os.path.join(rootout, "clip1refs"), os.path.join(rootout, "clip2refs"), os.path.join(rootout, "clip3refs"), os.path.join(rootout, "clip4refs"), os.path.join(rootout, "clip5refs"), os.path.join(rootout, "clip6refs"), os.path.join(rootout, "meanrefs")]
clip1imgs = ["/Users/phoetrymaster/Documents/School/Geography/Thesis/Data/MODIS_KANSAS_2007-2012/reprojected/clips/KansasNDVI_2012_clip1.tif", "/Users/phoetrymaster/Documents/School/Geography/Thesis/Data/MODIS_KANSAS_2007-2012/reprojected/clips/KansasEVI_2012_clip1.tif"]
clip2imgs = ["/Users/phoetrymaster/Documents/School/Geography/Thesis/Data/MODIS_KANSAS_2007-2012/reprojected/clips/KansasNDVI_2012_clip2.tif", "/Users/phoetrymaster/Documents/School/Geography/Thesis/Data/MODIS_KANSAS_2007-2012/reprojected/clips/KansasEVI_2012_clip2.tif"]
clip3imgs = ["/Users/phoetrymaster/Documents/School/Geography/Thesis/Data/MODIS_KANSAS_2007-2012/reprojected/clips/KansasNDVI_2012_clip3.tif", "/Users/phoetrymaster/Documents/School/Geography/Thesis/Data/MODIS_KANSAS_2007-2012/reprojected/clips/KansasEVI_2012_clip3.tif"]
clip4imgs = ["/Users/phoetrymaster/Documents/School/Geography/Thesis/Data/MODIS_KANSAS_2007-2012/reprojected/clips/KansasNDVI_2012_clip4.tif", "/Users/phoetrymaster/Documents/School/Geography/Thesis/Data/MODIS_KANSAS_2007-2012/reprojected/clips/KansasEVI_2012_clip4.tif"]
clip5imgs = ["/Users/phoetrymaster/Documents/School/Geography/Thesis/Data/MODIS_KANSAS_2007-2012/reprojected/clips/KansasNDVI_2012_clip5.tif", "/Users/phoetrymaster/Documents/School/Geography/Thesis/Data/MODIS_KANSAS_2007-2012/reprojected/clips/KansasEVI_2012_clip5.tif"]
clip6imgs = ["/Users/phoetrymaster/Documents/School/Geography/Thesis/Data/MODIS_KANSAS_2007-2012/reprojected/clips/KansasNDVI_2012_clip6.tif", "/Users/phoetrymaster/Documents/School/Geography/Thesis/Data/MODIS_KANSAS_2007-2012/reprojected/clips/KansasEVI_2012_clip6.tif"]
imagelist = [clip1imgs, clip2imgs, clip3imgs, clip4imgs, clip5imgs, clip6imgs]
searchstrings = ["soy", "corn", "wwheat", "sorghum", "wwheatsoydbl"]
fitmethods = ["SLSQP"]#, "TNC"]
for method in fitmethods:
for images in imagelist:
from create_rule_image_multiprocessed_bypx import phenological_classificaion, read_reference_file
import os
from get_px_coords_from_point import get_px_coords_from_points
from pyhytemporal.utils import find_files
reffiles = []
clip1refs = find_files("/Users/phoetrymaster/Documents/School/Geography/Thesis/Data/MODIS_KANSAS_2007-2012/reprojected/Refs/2012clip1test2/clip1", "mean.ref")
reffiles.append(clip1refs)
#meanrefs = find_files("/Users/phoetrymaster/Documents/School/Geography/Thesis/Data/MODIS_KANSAS_2007-2012/reprojected/Refs/2012", ".ref", recursive=False)
#reffiles.append(meanrefs)
rootout = "/Users/phoetrymaster/Documents/School/Geography/Thesis/Data/MODIS_KANSAS_2007-2012/reprojected/Classified/test1_envicurves/fullpxonly/geometricmean"
#outfolders = [os.path.join(rootout, "clip1refs")]#, os.path.join(rootout, "clip2refs"), os.path.join(rootout, "clip3refs"), os.path.join(rootout, "clip4refs"), os.path.join(rootout, "clip5refs"), os.path.join(rootout, "clip6refs"),
outfolders = [os.path.join(rootout, "clip1refs")]
imagelist = []
clip1imgs = ["/Users/phoetrymaster/Documents/School/Geography/Thesis/Data/MODIS_KANSAS_2007-2012/reprojected/clips/KansasNDVI_2012_clip1.tif"]#, "/Users/phoetrymaster/Documents/School/Geography/Thesis/Data/MODIS_KANSAS_2007-2012/reprojected/clips/KansasEVI_2012_clip1.tif"]
imagelist.append(clip1imgs)
searchstrings = ["soy", "corn", "wwheat", "sorghum", "wwheatsoydbl"]
fitmethods = ["SLSQP"]#, "TNC"]
fullpixels = get_px_coords_from_points(clip1imgs[0], "/Users/phoetrymaster/Documents/School/Geography/Thesis/Data/MODIS_KANSAS_2007-2012/reprojected/clips/clip1_fullcells_points.shp")
for method in fitmethods:
for images in imagelist:
for img in images:
name = os.path.splitext(os.path.basename(img))[0]
if "NDVI" in name:
type = "NDVI"
#clip6imgs = ["/Users/phoetrymaster/Documents/School/Geography/Thesis/Data/MODIS_KANSAS_2007-2012/reprojected/clips/KansasNDVI_2012_clip6.tif", "/Users/phoetrymaster/Documents/School/Geography/Thesis/Data/MODIS_KANSAS_2007-2012/reprojected/clips/KansasEVI_2012_clip6.tif"]
#clip6out = os.path.join(outroot, "clip6")
#
#clips.append((clip6locs, clip6imgs, clip6out))
for locs, imgs, out in clips:
for img in imgs:
print "Processing {0}...".format(img)
if "NDVI" in img:
postfix = "_NDVI"
else:
postfix = "_EVI"
get_reference_curves(img, locs, 17, 16, outdir=out, filepostfix=postfix)
means = find_files(outroot, "mean.ref")
searchstrings = (["soy", "wwheat", "sorghum", "corn", "wwheatsoydbl"], ["_EVI", "_NDVI"])
for string in searchstrings[0]:
for vi in searchstrings[1]:
cropmeans = []
for mean in means:
if string + vi in mean:
cropmeans.append(mean)
cropname = string + vi
refvals = []
for cropmean in cropmeans:
error, vals = read_reference_file(cropmean)
if not error:
refvals.append(vals)
listedvals = []
