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import numpy as np
_test_rad = np.random.randint(25, size=[6, 8])
#_test_thr = [15, 10, 5, 1, 1]
_test_thr = [5, 10, 15, 1, 1]
def test_11um(rad, threshold):
#if (~np.isnan(rad) or threshold[4] == 1.0):
confidence = conf_test(rad, threshold)
return confidence
def test_11_4diff(rad1, rad2, threshold):
raddiff = rad1 - rad2;
confidence = conf_test(raddiff, threshold)
return confidence
def simple_threshold_test(rad, threshold):
return conf_test(rad, threshold)
class CloudMaskTests(object):
def __init__(self, scene, radiance, coefficients):
self.scene = scene
self.coefficients = coefficients
def select_coefficients(self):
pass
def test_G1(self):
pass
def test_G2(self):
pass
def test_G3(self):
pass
def test_G4(self):
pass
def overall_confidence(self):
pass
def conf_test(rad=_test_rad, thr=_test_thr):
'''
Assuming a linear function between min and max confidence level, the plot below shows
how the confidence (y axis) is computed as function of radiance (x axis).
This case illustrates alpha < gamma, obviously in case alpha > gamma, the plot would be
flipped.
gamma
c 1 ________
o | /
n | /
f | /
i | beta /
d 1/2 |....../
e | /
n | /
c | /
e 0________/
| alpha
--------- radiance ---------->
'''
coeff = np.power(2, (thr[3] - 1))
hicut = thr[0]
beta = thr[1]
locut = thr[2]
power = thr[3]
radshape = rad.shape
rad = rad.reshape((rad.shape[0]*rad.shape[1]))
c = np.zeros(rad.shape)
if hicut > locut:
gamma = thr[0]
alpha = thr[2]
flipped = False
else:
gamma = thr[2]
alpha = thr[0]
flipped = True
# Rad between alpha and beta
range_ = 2. * (beta - alpha)
s1 = (rad[rad <= beta] - alpha) / range_
if flipped is False:
c[rad <= beta] = coeff * np.power(s1, power)
if flipped is True:
c[rad <= beta] = 1. - (coeff * np.power(s1, power))
# Rad between beta and gamma
range_ = 2. * (beta - gamma)
s1 = (rad[rad > beta] - gamma) / range_
if flipped is False:
c[rad > beta] = 1. - (coeff * np.power(s1, power))
if flipped is True:
c[rad > beta] = coeff * np.power(s1, power)
# Rad outside alpha-gamma interval
if flipped is False:
c[rad > gamma] = 1
c[rad < alpha] = 0
if flipped is True:
c[rad > gamma] = 0
c[rad < alpha] = 1
c[c > 1] = 1
c[c < 0] = 0
confidence = c.reshape(radshape)
return confidence
def conf_test_dble(rad, coeffs):
'''
gamma1 gamma2
c 1_______ ________
o | \ /
n | \ /
f | \ /
i | \ beta1 beta2 /
d 1/2 \....| |...../
e | \ /
n | \ /
c | \ /
e 0 \_____________/
| alpha1 alpha2
--------------------- radiance ------------------------->
'''
hicut = [coeffs[0], coeffs[1]]
locut = [coeffs[2], coeffs[3]]
midpt = [coeffs[4], coeffs[5]]
power = coeffs[6]
gamma1 = hicut[0]
gamma2 = hicut[1]
alpha1 = locut[0]
alpha2 = locut[1]
beta1 = midpt[0]
beta2 = midpt[1]
coeff = np.power(2, (power - 1))
radshape = rad.shape
rad = rad.reshape((rad.shape[0]*rad.shape[1]))
c = np.zeros(rad.shape)
# Find if interval between inner cutoffs passes or fails test
if (alpha1 - gamma1 > 0):
# Value is within range of lower set of limits
range_ = 2 * (beta1 - alpha1)
s1 = (rad[(rad <= alpha1) & (rad >= beta1)] - alpha1) / range_
c[(rad <= alpha1) & (rad >= beta1)] = coeff * np.power(s1, power)
range_ = 2 * (beta1 - gamma1)
s1 = (rad[(rad <= alpha1) & (rad < beta1)] - gamma1) / range_
c[(rad <= alpha1) & (rad < beta1)] = coeff * np.power(s1, power)
# Value is within range of upper set of limits
range_ = 2 * (beta2 - alpha2)
s1 = (rad[(rad > alpha1) & (rad <= beta2)] - alpha2) / range_
c[(rad > alpha1) & (rad <= beta2)] = coeff * np.power(s1, power)
range_ = 2 * (beta2 - gamma2)
s1 = (rad[(rad > alpha1) & (rad > beta2)] - gamma2) / range_
c[(rad > alpha1) & (rad > beta2)] = coeff * np.power(s1, power)
# Inner region fails test
# Check for value beyond function range
c[(rad > alpha1) & (rad < alpha2)] = 0
c[(rad < gamma1) | (rad > gamma2)] = 1
else:
# Value is withing range of lower set of limits
range_ = 2 * (beta1 - alpha1)
s1 = (rad[(rad <= gamma1) & (rad <= beta1)] - alpha1) / range_
c[(rad <= gamma1) & (rad <= beta1)] = coeff * np.power(s1, power)
range_ = 2 * (beta1 - gamma1)
s1 = (rad[(rad <= gamma1) & (rad > beta1)] - gamma1) / range_
c[(rad <= gamma1) & (rad > beta1)] = coeff * np.power(s1, power)
# Value is within range of upper set of limits
range_ = 2 * (beta2 - alpha2)
s1 = (rad[(rad > gamma1) & (rad >= beta2)] - alpha2) / range_
c[(rad > gamma1) & (rad >= beta2)] = coeff * np.power(s1, power)
range_ = 2 * (beta2 - gamma2)
s1 = (rad[(rad > gamma1) & (rad < beta2)] - gamma2) / range_
c[(rad > gamma1) & (rad < beta2)] = coeff * np.power(s1, power)
# Inner region passes test
# Check for value beyond function range
c[(rad > gamma1) & (rad < gamma2)] = 1
c[(rad < alpha1) | (rad > alpha2)] = 0
c[c>1] = 1
c[c<0] = 0
confidence = c.reshape(radshape)
return confidence
def test():
rad = np.random.randint(50, size=[4, 8])
#coeffs = [5, 42, 20, 28, 15, 35, 1]
#coeffs = [20, 28, 5, 42, 15, 35, 1]
coeffs = [35, 15, 20, 1, 1]
#confidence = conf_test_dble(rad, coeffs)
confidence = test_11um(rad, coeffs)
print(rad)
print('\n')
print(confidence)
if __name__ == "__main__":
test()