def plot_pixel(pixelproperties, dims=(11, 8), color=None):
x, y = get_sort_dates_values(pixelproperties[4])
figure = plt.figure()
axes = figure.add_subplot(1,1,1)
axes.plot(x, y, '{0}-'.format(color))
axes.set_xlabel("Pixel col {0} row {1}: {2} as {3} in classified".format(pixelproperties[0], pixelproperties[1], pixelproperties[3], pixelproperties[2]))
return figure
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)
def get_mean_values(referencevalues, starydoy, doyinterval):
for i in range(0, len(referencevalues)):
if not i:
vals1 = referencevalues[i]
valdict = vallist_to_dict(vals1, starydoy, doyinterval)
doys = []
for k, v in valdict.items():
doys.append(k)
else:
curve1 = interpolate_ref(valdict)
vals2 = referencevalues[i]
valdict2 = vallist_to_dict(vals2, starydoy, doyinterval)
res = find_fit(valdict2, curve1, 0, -3000, "SLSQP")
transforms = list(res[1])
xscale = (float(transforms[1]) - 1) / 2.0 + 1
yscale = (float(transforms[0]) - 1) / 2.0 + 1
tshift = float(transforms[2]) / 2.0
valdict = get_transformed_values(curve1, doys, xscale, yscale, tshift)
x, y = get_sort_dates_values(valdict)
return y
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")
