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."
Esempio n. 2
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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"
Esempio n. 4
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#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 = []