cleaned conf.py

mvcm/conf.py

+"""Module defining confidence computations."""
 import numpy as np
 
 
 def conf_test_new(rad: np.ndarray, thr: np.ndarray) -> np.ndarray:
+    """Compute confidence based on thresholds."""
     """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
@@ -50,13 +52,13 @@ def conf_test_new(rad: np.ndarray, thr: np.ndarray) -> np.ndarray:
     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])
+    confidence[idx] = 1.0 - (coeff[idx] * np.power(s1[idx], power[idx]))
 
     # Rad between beta and gamma
     range_ = 2.0 * (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])
+    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])
 
@@ -72,6 +74,7 @@ def conf_test_new(rad: np.ndarray, thr: np.ndarray) -> np.ndarray:
 
 
 def conf_test(rad, thr):
+    """Compute confidence based on thresholds."""
     """
     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).
@@ -144,6 +147,7 @@ def conf_test(rad, thr):
 
 
 def conf_test_dble(rad, coeffs):
+    """Compute confidence based on thresholds."""
     # '''
     #            gamma1                         gamma2
     #    c  1_______                               ________
@@ -222,31 +226,23 @@ def conf_test_dble(rad, coeffs):
     # Value is within range of lower set of limits
     range_ = 2 * (beta1 - alpha1)
     s1 = (rad - alpha1) / range_
-    idx = np.nonzero(
-        (rad > alpha1) & (rad <= gamma1) & (rad <= beta1) & (alpha1 - gamma1 <= 0)
-    )
+    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_
-    idx = np.nonzero(
-        (rad > alpha1) & (rad <= gamma1) & (rad > beta1) & (alpha1 - gamma1 <= 0)
-    )
+    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_
-    idx = np.nonzero(
-        (rad > gamma2) & (rad < alpha2) & (rad >= beta2) & (alpha1 - gamma1 <= 0)
-    )
+    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_
-    idx = np.nonzero(
-        (rad > gamma2) & (rad < alpha2) & (rad < beta2) & (alpha1 - gamma1 <= 0)
-    )
+    idx = np.nonzero((rad > gamma2) & (rad < alpha2) & (rad < beta2) & (alpha1 - gamma1 <= 0))
     confidence[idx] = coeff[idx] * np.power(s1[idx], power[idx])
 
     confidence[(alpha1 - gamma1 <= 0) & ((rad > gamma1) | (rad < gamma2))] = 0