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import numpy as np
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def test(flipped=False):
bt = np.arange(265, 275)
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if flipped is False:
thr = np.array([267, 270, 273, 1, 1])
else:
thr = np.array([273, 270, 267, 1, 1])
c = conf_test(bt, thr)
print(c)
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def test_dble(flipped=False):
bt = np.arange(260, 282)
if flipped is False:
thr = np.array([264, 267, 270, 273, 276, 279, 1, 1])
else:
thr = np.array([279, 276, 273, 270, 267, 264, 1, 1])
c = conf_test_dble(bt, thr)
print(c)
def conf_test_new(rad: np.ndarray,
thr: np.ndarray) -> np.ndarray:
"""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 ---------->
radshape = rad.shape
rad = rad.ravel()
thr = np.array(thr)
if thr.ndim == 1:
thr = np.full((rad.shape[0], 4), thr[:4]).T
coeff = np.power(2, (thr[3, :] - 1))
locut = thr[0, :]
beta = thr[1, :]
hicut = thr[2, :]
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power = thr[3, :]
confidence = np.zeros(rad.shape)
alpha, gamma = np.empty(rad.shape), np.empty(rad.shape)
flipped = np.zeros(rad.shape)
gamma[hicut > locut] = thr[2, hicut > locut]
alpha[hicut > locut] = thr[0, hicut > locut]
flipped[hicut > locut] = 0
gamma[hicut < locut] = thr[0, hicut < locut]
alpha[hicut < locut] = thr[2, hicut < locut]
flipped[hicut < locut] = 1
# Rad between alpha and beta
range_ = 2. * (beta - alpha)
s1 = (rad - alpha) / range_
idx = np.nonzero((rad <= beta) & (flipped == 0))
confidence[idx] = coeff[idx] * np.power(s1[idx], power[idx])
idx = np.nonzero((rad <= beta) & (flipped == 1))
confidence[idx] = 1.0 - coeff[idx] * np.power(s1[idx], power[idx])
# Rad between beta and gamma
range_ = 2. * (beta - gamma)
s1 = (rad - gamma) / range_
idx = np.nonzero((rad > beta) & (flipped == 0))
confidence[idx] = 1.0 - coeff[idx] * np.power(s1[idx], power[idx])
idx = np.nonzero((rad > beta) & (flipped == 1))
confidence[idx] = coeff[idx] * np.power(s1[idx], power[idx])
# Rad outside alpha-gamma interval
confidence[(rad > gamma) & (flipped is False)] = 1
confidence[(rad < alpha) & (flipped is False)] = 0
confidence[(rad > gamma) & (flipped is True)] = 0
confidence[(rad < alpha) & (flipped is True)] = 1
confidence = np.clip(confidence, 0, 1)
return confidence.reshape(radshape)
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def conf_test(rad, 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 ---------->
'''
radshape = rad.shape
rad = rad.reshape(np.prod(radshape))
if thr.ndim == 1:
thr = np.full((rad.shape[0], 4), thr[:4]).T
coeff = np.power(2, (thr[3] - 1))
hicut = thr[2, :]
locut = thr[0, :]
confidence = np.zeros(rad.shape)
alpha, gamma = np.empty(rad.shape), np.empty(rad.shape)
flipped = np.zeros(rad.shape)
gamma[hicut > locut] = thr[2, hicut > locut]
alpha[hicut > locut] = thr[0, hicut > locut]
flipped[hicut > locut] = 0
gamma[hicut < locut] = thr[0, hicut < locut]
alpha[hicut < locut] = thr[2, hicut < locut]
flipped[hicut < locut] = 1
# Rad between alpha and beta
range_ = 2. * (beta - alpha)
s1 = (rad - alpha) / range_
idx = np.nonzero((rad <= beta) & (flipped == 0))
confidence[idx] = coeff[idx] * np.power(s1[idx], power[idx])
idx = np.nonzero((rad <= beta) & (flipped == 1))
confidence[idx] = 1.0 - coeff[idx] * np.power(s1[idx], power[idx])
# Rad between beta and gamma
range_ = 2. * (beta - gamma)
s1 = (rad - gamma) / range_
idx = np.nonzero((rad > beta) & (flipped == 0))
confidence[idx] = 1.0 - coeff[idx] * np.power(s1[idx], power[idx])
idx = np.nonzero((rad > beta) & (flipped == 1))
confidence[idx] = coeff[idx] * np.power(s1[idx], power[idx])
# Rad outside alpha-gamma interval
confidence[(rad > gamma) & (flipped is False)] = 1
confidence[(rad < alpha) & (flipped is False)] = 0
confidence[(rad > gamma) & (flipped is True)] = 0
confidence[(rad < alpha) & (flipped is True)] = 1
confidence[confidence > 1] = 1
confidence[confidence < 0] = 0
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 ------------------------->
# '''
coeffs = np.array(coeffs)
radshape = rad.shape
confidence = np.zeros(rad.shape)
alpha1, gamma1 = np.empty(rad.shape), np.empty(rad.shape)
alpha2, gamma2 = np.empty(rad.shape), np.empty(rad.shape)
if coeffs.ndim == 1:
coeffs = np.full((rad.shape[0], 7), coeffs[:7]).T
gamma1 = coeffs[0, :]
beta1 = coeffs[1, :]
alpha1 = coeffs[2, :]
alpha2 = coeffs[3, :]
beta2 = coeffs[4, :]
gamma2 = coeffs[5, :]
power = coeffs[6, :]
coeff = np.power(2, (power - 1))
# radshape = rad.shape
# rad = rad.reshape((rad.shape[0]*rad.shape[1]))
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# ## Find if interval between inner cutoffs passes or fails test
# Inner region fails test
# Value is within range of lower set of limits
range_ = 2. * (beta1 - alpha1)
s1 = (rad - alpha1) / range_
idx = np.nonzero((rad <= alpha1) & (rad >= beta1) & (alpha1 - gamma1 > 0))
confidence[idx] = coeff[idx] * np.power(s1[idx], power[idx])
range_ = 2. * (beta1 - gamma1)
s1 = (rad - gamma1) / range_
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idx = np.nonzero((rad >= gamma1) & (rad < beta1) & (alpha1 - gamma1 > 0))
confidence[idx] = 1.0 - coeff[idx] * np.power(s1[idx], power[idx])
# Value is within range of upper set of limits
range_ = 2. * (beta2 - alpha2)
s1 = (rad - alpha2) / range_
idx = np.nonzero((rad > alpha1) & (rad <= beta2) & (alpha1 - gamma1 > 0))
confidence[idx] = coeff[idx] * np.power(s1[idx], power[idx])
range_ = 2. * (beta2 - gamma2)
s1 = (rad - gamma2) / range_
idx = np.nonzero((rad > alpha1) & (rad > beta2) & (alpha1 - gamma1 > 0))
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confidence[idx] = 1.0 - coeff[idx] * np.power(s1[idx], power[idx])
# Check for value beyond function range
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confidence[(alpha1 - gamma1 > 0) & (rad > alpha1) & (rad < alpha2)] = 0
confidence[(alpha1 - gamma1 > 0) & ((rad < gamma1) | (rad > gamma2))] = 1
###
# Inner region passes test
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print("I NEED TO REVIEW THIS TO WRITE IT MORE CLEARLY")
# FOR NOW ALPHA AND GAMMA ARE SWITCHED BECAUSE OF HOW THE ARRAYS ARE DEFINED.
# THINK ON HOW THIS COULD BE WRITTEN SO THAT IT'S EASIER TO UNDERSTAND (AND DEBUG)
# Value is within range of lower set of limits
range_ = 2 * (beta1 - alpha1)
s1 = (rad - alpha1) / range_
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idx = np.nonzero((rad > alpha1) & (rad <= gamma1) & (rad <= beta1) & (alpha1 - gamma1 <= 0))
confidence[idx] = 1.0 - coeff[idx] * np.power(s1[idx], power[idx])
range_ = 2 * (beta1 - gamma1)
s1 = (rad - gamma1) / range_
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idx = np.nonzero((rad > alpha1) & (rad <= gamma1) & (rad > beta1) & (alpha1 - gamma1 <= 0))
confidence[idx] = coeff[idx] * np.power(s1[idx], power[idx])
# Values is within range of upper set of limits
range_ = 2 * (beta2 - alpha2)
s1 = (rad - alpha2) / range_
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idx = np.nonzero((rad > gamma2) & (rad < alpha2) & (rad >= beta2) & (alpha1 - gamma1 <= 0))
confidence[idx] = 1.0 - coeff[idx] * np.power(s1[idx], power[idx])
range_ = 2 * (beta2 - gamma2)
s1 = (rad - gamma2) / range_
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idx = np.nonzero((rad > gamma2) & (rad < alpha2) & (rad < beta2) & (alpha1 - gamma1 <= 0))
confidence[idx] = coeff[idx] * np.power(s1[idx], power[idx])
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confidence[(alpha1 - gamma1 <= 0) & ((rad > gamma1) | (rad < gamma2))] = 0
confidence[(alpha1 - gamma1 <= 0) & (rad <= alpha1) & (rad >= alpha2)] = 1
# confidence = np.clip(confidence, 0, 1)
return confidence.reshape(radshape)
if __name__ == "__main__":
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test_dble()