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Scalar assignment issue #38

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Merged
davidfee5 merged 2 commits into from
Jul 3, 2026
Merged

Scalar assignment issue #38

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85b94fd
sigma_tau scalar assignment fix
davidfee5 Jul 3, 2026
badb50b
conf_int scalar assignment fix
davidfee5 Jul 3, 2026
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25 changes: 16 additions & 9 deletions lts_array/classes/lts_classes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -722,10 +722,12 @@ def post_process(dimension_number, co_array_num, alpha, h, nits, tau, xij, coeff
# Calculate the sigma_tau value (Szuberla et al. 2006).
residuals = tau[weights, jj, :] - (xij[weights, :] @ z_final)
m_w, _ = np.shape(xij[weights, :])
with np.errstate(invalid='raise'):

with np.errstate(invalid="raise"):
try:
sigma_tau[jj] = np.sqrt(tau[weights, jj, :].T @ residuals / (
m_w - dimension_number))[0]
sigma_tau_value = tau[weights, jj, :].T @ residuals
sigma_tau_value = sigma_tau_value / (m_w - dimension_number)
sigma_tau[jj] = np.sqrt(float(np.asarray(sigma_tau_value).squeeze()))
Comment on lines +728 to +730
except FloatingPointError:
pass

Expand Down Expand Up @@ -755,8 +757,8 @@ def post_process(dimension_number, co_array_num, alpha, h, nits, tau, xij, coeff
# Halving here s.t. +/- value expresses uncertainty bounds.
# Remove the 1/2's to get full values to express
# coverage ellipse area.
conf_int_baz[jj] = 0.5 * sig_theta
conf_int_vel[jj] = 0.5 * np.abs(np.diff(1 / eExtrm[:2]))
conf_int_baz[jj] = 0.5 * float(np.asarray(sig_theta).squeeze())
conf_int_vel[jj] = 0.5 * float(np.asarray(np.abs(np.diff(1 / eExtrm[:2]))).squeeze())
Comment on lines +760 to +761

# Cast weights to int for output
element_weights[:, jj] = weights * 1
Expand Down Expand Up @@ -932,8 +934,13 @@ def solve(self, data):

# Calculate the sigma_tau value (Szuberla et al. 2006).
residuals = self.tau[:, jj, :] - (self.xij @ z_final)
self.sigma_tau[jj] = np.sqrt(self.tau[:, jj, :].T @ residuals / (
self.co_array_num - self.dimension_number))[0]

sigma_tau_value = self.tau[:, jj, :].T @ residuals
sigma_tau_value = sigma_tau_value / (
self.co_array_num - self.dimension_number
)

self.sigma_tau[jj] = np.sqrt(float(np.asarray(sigma_tau_value).squeeze()))
Comment on lines +938 to +943

# Calculate uncertainties from Szuberla & Olson, 2004
# Equation 16
Expand All @@ -960,8 +967,8 @@ def solve(self, data):
# Halving here s.t. +/- value expresses uncertainty bounds.
# Remove the 1/2's to get full values to express
# coverage ellipse area.
self.conf_int_baz[jj] = 0.5 * sig_theta
self.conf_int_vel[jj] = 0.5 * np.abs(np.diff(1 / eExtrm[:2]))
self.conf_int_baz[jj] = 0.5 * float(np.asarray(sig_theta).squeeze())
self.conf_int_vel[jj] = 0.5 * float(np.asarray(np.abs(np.diff(1 / eExtrm[:2]))).squeeze())
Comment on lines +970 to +971

except ValueError:
self.conf_int_baz[jj] = np.nan
Expand Down