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S weighting patch #1185

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Merged
merged 5 commits into from
Oct 3, 2024
Merged

S weighting patch #1185

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8ca54f1
typo in B0 formula
garrettwrong Oct 1, 2024
5837e4a
log diagnotics
garrettwrong Oct 1, 2024
b8d4b89
sanity check curve_fit start values
garrettwrong Oct 1, 2024
b10cfce
For now use default initial soln for Swt curve fitting
garrettwrong Oct 2, 2024
1b556a0
leave matlab based code as comment for now
garrettwrong Oct 2, 2024
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27 changes: 23 additions & 4 deletions src/aspire/abinitio/commonline_sync3n.py
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Original file line number Diff line number Diff line change
Expand Up @@ -714,13 +714,26 @@ def _triangle_scores(
* (a + 1)
)
# normalization of 2nd component: B = P*N_delta/sum(f), where f is the component formula
B0 = P ** (self.n_img * (self.n_img - 1) * (self.n_img - 2) / 2) / np.sum(
((1 - hist_x) ** b) * np.exp(-b / (1 - x0) * (1 - hist_x))
)
start_values = np.array([B0, P, b, x0], dtype=np.float64)
# B0 = (
# P
# * (self.n_img * (self.n_img - 1) * (self.n_img - 2) / 2)
# / np.sum(((1 - hist_x) ** b) * np.exp(-b / (1 - x0) * (1 - hist_x)))
# )
# P must be in lower and upper bounds or `curve_fit` will error
# This was not the case for MATLAB...
# P0 = np.clip(P, Pmin**3, Pmax**3)
# Note, MATLAB suggests the following, but I feel it is a bug.
# Will discuss with Yoel about the original code's intent.
# np.array([B0, P0, b, x0], dtype=np.float64)
start_values = None
lower_bounds = np.array([0, Pmin**3, 2, 0], dtype=np.float64)
upper_bounds = np.array([np.inf, Pmax**3, np.inf, 1], dtype=np.float64)

with np.printoptions(precision=2):
logger.info(f"curve_fit lower_bounds:{lower_bounds}")
logger.info(f"curve_fit start_values:{start_values}")
logger.info(f"curve_fit upper_bounds:{upper_bounds}")

# Fit distribution
def fun(x, B, P, b, x0, A=A, a=a):
"""Function to fit. x is data vector."""
Expand All @@ -742,6 +755,8 @@ def fun(x, B, P, b, x0, A=A, a=a):
P = P ** (1 / 3)
sigma = (1 - x0) / peak2sigma

logger.info(f"Estimated CL Errors P,STD:\t{100*P:.2f}%\t{sigma:.2f}")

# Initialize probability computations
# Local histograms analysis
A = a + 1 # distribution 1st component normalization factor
Expand All @@ -768,6 +783,10 @@ def fun(x, B, P, b, x0, A=A, a=a):
)
Pij = np.nan_to_num(Pij)

logger.info(
f"Common lines probabilities to be indicative Pij={100*np.mean(Pij):.2f}%"
)

return P, sigma, Pij, scores_hist

###########################################
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