def main():
outpath = r"/Users/phoetrymaster/Documents/School/Geography/Thesis/GIS In Action/Images/example_fit_process_1.pdf"
wheat = r"/Users/phoetrymaster/Documents/School/Geography/Thesis/Data/MODIS_KANSAS_2007-2012/reprojected/Refs/2012clip1test2/clip1/wwheat_NDVI_mean.ref"
corn = r"/Users/phoetrymaster/Documents/School/Geography/Thesis/Data/MODIS_KANSAS_2007-2012/reprojected/Refs/2012clip1test2/clip1/corn_NDVI_mean.ref"
soy = r"/Users/phoetrymaster/Documents/School/Geography/Thesis/Data/MODIS_KANSAS_2007-2012/reprojected/Refs/2012clip1test2/clip1/soy_NDVI_mean.ref"
pixel = r"/Users/phoetrymaster/Documents/School/Geography/Thesis/Scripting/Testing/corn_example_point.ref"
wheatmean = create_rule_image_multiprocessed_bypx.read_reference_file(wheat)[1]
cornmean = create_rule_image_multiprocessed_bypx.read_reference_file(corn)[1]
soymean = create_rule_image_multiprocessed_bypx.read_reference_file(soy)[1]
refs = [('wheat', wheatmean), ('corn', cornmean), ('soy', soymean)]
print refs
pixelvalues = create_rule_image_multiprocessed_bypx.read_reference_file(pixel)[1]
toplot = [(wheatmean, 'b', 'Wheat Signature'), (cornmean, 'y', "Corn Signature"), (soymean, 'g', 'Soy Signature'), (pixelvalues, 'r', 'Unknown Pixel')]
figure = plt.figure()
axes = figure.add_subplot(1, 1, 1)
pdf = PdfPages(outpath)
for item, color, label in toplot:
print item
x, y = create_rule_image_multiprocessed_bypx.get_sort_dates_values(item)
newy = []
for value in y:
newy.append(value / 10000.0)
axes.plot(x, newy, '{0}-'.format(color), label=label)
handles, labels = axes.get_legend_handles_labels()
axes.set_xlabel("Day of Year")
axes.set_ylabel("NDVI")
axes.legend(handles, labels, fontsize='small')
pdf.savefig()
plt.close(figure)
pdf.close()
for crop, ref in refs:
interp = create_rule_image_multiprocessed_bypx.interpolate_ref(ref)
result = create_rule_image_multiprocessed_bypx.find_fit(pixelvalues, interp, 0, 0, 'SLSQP', bnds=((0.6, 1.4), (0.6, 1.4), (-10, 10)))
print(crop + ":")
print(result)
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:
for img in images:
name = os.path.splitext(os.path.basename(img))[0]
if "NDVI" in name:
type = "NDVI"
else:
type = "EVI"
for reffile, outfolder in zip(reffiles, outfolders):
refs = {}
for f in reffile:
if type in f:
for string in searchstrings:
if string + "_" + type in f:
error, refs[string] = read_reference_file(f)
if not error:
print refs
phenological_classificaion(img, outfolder, name + "_" + method, refs, "ENVI", 17, 16, -500, 0, method, toprint=False)
else:
print "ERROR +++++++++++++++++++++++++++++++++++++++++++++++++++ ERROR"
__author__ = 'phoetrymaster'
import create_rule_image_multiprocessed_bypx as d
print d.read_reference_file("/Users/phoetrymaster/Desktop/corn_mean.ref")
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 = []
for ref in refvals:
x, y = get_sort_dates_values(ref)
listedvals.append(y)
print cropname
write_mean_ref_to_txt(cropname, listedvals, 17, 16, outroot, comment="Generated by clipref.py version 0.1")
