Develop bugfixes

doc/conf.py

@@ -12,6 +12,7 @@
 #
 import os
 import sys
+from importlib.metadata import version as get_version
 sys.path.insert(0, os.path.abspath('..'))
 
 # -- Project information -----------------------------------------------------
@@ -20,7 +21,7 @@
 project = 'LEDSmokeAnalysis'
 copyright = '2026, CCE'
 author = 'CCE'
-release = '0.9.6'
+release = get_version('ledsa')
 
 # -- General configuration ---------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration

examples/postprocessing.ipynb

@@ -293,7 +293,7 @@
     "closest_time = sim.get_closest_time(time)\n",
     "\n",
     "extco = sim.get_extco_at_time(channel=channel, time=closest_time, window=window, yaxis='height')\n",
-    "sns.heatmap(extco, cmap='jet', vmax=0.4, cbar_kws={'label': 'Extinction Coefficient / $\\mathrm{m^{-1}}$'})\n",
+    "sns.heatmap(extco.iloc[::-1], cmap='jet', vmax=0.4, cbar_kws={'label': 'Extinction Coefficient / $\\mathrm{m^{-1}}$'})\n",
     "plt.tight_layout()"
    ],
    "outputs": [],

ledsa/analysis/ExtinctionCoefficients.py

@@ -7,6 +7,7 @@
 from ledsa.analysis.Experiment import Experiment, Layers, Camera
 from ledsa.core.file_handling import read_hdf, read_hdf_avg, extend_hdf, create_analysis_infos_avg
 
+from importlib.metadata import version
 
 class ExtinctionCoefficients(ABC):
     """
@@ -57,7 +58,7 @@ def __init__(self, experiment, reference_property='sum_col_val', num_ref_imgs=10
 
     def __str__(self):
         out = str(self.experiment) + \
-              f'reference_property: {self.reference_property}, num_ref_imgs: {self.num_ref_imgs}\n'
+              f'reference_property: {self.reference_property}, num_ref_imgs: {self.num_ref_imgs}, LEDSA {version("ledsa")}\n'
         return out
 
     def calc_and_set_coefficients(self) -> None:
@@ -151,7 +152,8 @@ def calc_distance_array(self) -> np.ndarray:
         """
         distances = np.zeros((self.experiment.num_leds, self.experiment.layers.amount))
         count = 0
-        for led in self.experiment.leds:
+        # self.experiment.leds need to be reversed to build the distance array the right way
+        for led in reversed(self.experiment.leds):
             d = self.experiment.calc_traversed_dist_per_layer(led)
             distances[count] = d
             count += 1

ledsa/analysis/ExtinctionCoefficientsLinear.py

@@ -51,11 +51,11 @@ def calc_coefficients_of_img(self, rel_intensities: np.ndarray) -> np.ndarray:
         """
         # Avoid log(0) by clipping intensities to a small positive value
         eps = 1e-10
-        target = np.clip(rel_intensities, eps, 1.0)
+        rel_intensities_clipped = np.clip(rel_intensities, eps, 1.0)
 
         # Compute the optical depth vector: b = -ln(I_e/I_0)
         # This linearizes the Beer-Lambert law: I_e/I_0 = exp(-sum(sigma_i * distance_i))
-        optical_depths = -np.log(target)
+        optical_depths = -np.log(rel_intensities_clipped)
 
         # Get dimensions of the problem
         n_leds, n_layers = self.distances_per_led_and_layer.shape
@@ -85,6 +85,7 @@ def calc_coefficients_of_img(self, rel_intensities: np.ndarray) -> np.ndarray:
 
         # Solve the non-negative least squares problem: min ||A_aug*x - b_aug||^2 subject to x >= 0
         sigmas, residuals = nnls(A_aug, b_aug)
+        # sigmas, residuals, rank, s = np.linalg.lstsq(A_aug, b_aug, rcond=None)
 
         return sigmas