def NDVI():
import time
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from astropy.nddata import NDDataArray
print("This programm will lead you through creating a NDVI map from images")
print("It works under assumption that you input two images into it: A Red band image and Near infrared image")
print("The images must be monochrome, cloudless and it would be best if they had a ocean mask applied.")
print("If they contain clouds/waterbodies, those will also be falsely ranged on scale.")
print("NIR is Band 4, R is Band 3 or broadband VIS")
# pathNIR=input("Please input the path to near infrared image: ")
# pathR=input("Please input the path to red image: ")
print("Please select a color pallete you want to use. Available palletes:")
print("Greens Greys hot seismic")
cmapN = input()
r = mpimg.imread('C:/Users/Karol/Desktop/NIR.png')
nir = mpimg.imread('C:/Users/Karol/Desktop/R.png')
numerator = NDDataArray.subtract(nir, r)
denominator = NDDataArray.add(nir, r)
ndvi = NDDataArray.divide(numerator, denominator)
plt.imshow(ndvi, cmap=cmapN)
plt.colorbar()
plt.show()
def NOAADUST(ch4,ch5):
numerator = NDDataArray.subtract(ch5, ch4)
denominator = NDDataArray.add(ch4, ch5)
dust = NDDataArray.divide(numerator, denominator)
plt.imshow(dust,cmap="hot")
plt.show()
return dust
from astropy.nddata import NDDataArray, StdDevUncertainty
from copy import deepcopy
n1 = NDDataArray(data=3., uncertainty=StdDevUncertainty(4.))
n2 = NDDataArray(data=4., uncertainty=StdDevUncertainty(3.))
n3 = NDDataArray(data=5., uncertainty=StdDevUncertainty(2.))
n4 = deepcopy(n2)
#ok
print("n1.multiply(n2)...OK")
n1.multiply(n2)
#ok
print("n3.multiply(n1)...OK")
n3.multiply(n1)
# will raise AttributeError
try:
print("Reuse operand as operator : n2.divide(n1)...")
n2.divide(n1)
except AttributeError as ex:
print("## Caught AttributeError")
print(''.join(
traceback.format_exception(etype=type(ex),
value=ex,
tb=ex.__traceback__)))
#
print("Use deepcopy of n2 instead...OK")
n4.divide(n1)
