def get_bandDOYs(self, numberofbands, startDOY, imageryinterval, force=None):
if self.bandDOYs is None or force == True:
self.bandDOYs = []
for bandnumber in range(1, numberofbands + 1):
self.bandDOYs.append(band_number_to_doy(bandnumber, startDOY, imageryinterval))
else:
warnings.warn("Band DOYs already exist. Use force=True to overwrite.")
def write_mean_ref_to_txt(cropname, referencevalues, startdoy, doyinterval, outdir, comment="", postfix=""):
#TODO docstring
print "Writing mean reference curve to output file:"
print referencevalues
#output mean pixel values to file
with open(os.path.join(outdir, cropname + postfix + "_mean.ref"), "w") as f:
if comment:
f.write("//" + comment + "\n\n")
meanvals = get_mean_values(referencevalues)
imgnumber = 1
for val in meanvals:
doy = band_number_to_doy(imgnumber, startdoy, doyinterval)
f.write("{0} {1}\n".format(doy, val))
imgnumber += 1
def write_refs_to_txt(cropname, referencevalues, startdoy, doyinterval, outdir, comment="", postfix=""):
print "Writing pixel curves to output file:"
print referencevalues
#TODO: Rewrite this and next function with append on open, and integrate together to reduce redundant code
#output individual pixel curves to file
with open(os.path.join(outdir, cropname + postfix + "_points.ref"), "w") as f:
if comment:
f.write("//" + comment + "\n\n")
point = 1
for points in referencevalues:
f.write("\nPoint {0}:\n".format(point))
point += 1
imgnumber = 1
for val in points:
doy = band_number_to_doy(imgnumber, startdoy, doyinterval)
f.write("{0} {1}\n".format(doy, val))
imgnumber += 1
def process_pixel(bestguess, col, cropname, doyinterval, fitmthd, array, interpolatedCurve, outarray, row,
startDOY, ndvalue, bounds, meantype=None, thresh=None, logging=None):
#TODO docstrings
logf = print
if logging:
try:
logf = logging.log
except:
pass
valsf = {}
hasdata = True
pixel = array[row, col]
for i in range(pixel.size):
measured = pixel[i]
if measured == ndvalue:
hasdata = False
doy = band_number_to_doy(i+1, startDOY, doyinterval)
valsf[doy] = measured
if hasdata:
res, transforms, message = find_fit(valsf, interpolatedCurve, bestguess, fitmthd, bounds=bounds, mean=meantype,
threshold=thresh)
logf("\tPixel r{0}, c{1}: {2}: {3}, {4}, {5}".format(row, col, cropname, res, transforms, message))
else:
logf("\tPixel r{0}, c{1}: {2}: NO DATA.".format(row, col, cropname))
res = ndvalue
outarray[row, col] = res
return outarray
