Add experiment time to results csv

ledsa/analysis/ExtinctionCoefficients.py

@@ -131,10 +131,15 @@ def save(self) -> None:
             path.mkdir(parents=True)
         path = path / f'extinction_coefficients_{self.solver}_channel_{self.experiment.channel}_{self.reference_property}_led_array_{self.experiment.led_array}.csv'
         header = str(self)
+        header += 'Experiment_Time[s],'
         header += 'layer0'
         for i in range(self.experiment.layers.amount - 1):
             header += f',layer{i + 1}'
-        np.savetxt(path, self.coefficients_per_image_and_layer, delimiter=',', header=header)
+
+        experiment_times = _get_experiment_times_from_image_infos_file(self.average_images)
+        coefficients_per_time_and_layer = np.column_stack(
+            (experiment_times, self.coefficients_per_image_and_layer))
+        np.savetxt(path, coefficients_per_time_and_layer, delimiter=',', header=header)
 
     def calc_distance_array(self) -> np.ndarray:
         """
@@ -214,3 +219,11 @@ def multiindex_series_to_nparray(multi_series: pd.Series) -> np.ndarray:
     for i in range(num_imgs):
         array[i] = multi_series.loc[i + 1]
     return array
+
+def _get_experiment_times_from_image_infos_file(average_images):
+    if average_images == True:
+        image_infos_file = 'analysis/image_infos_analysis_avg.csv'
+    else:
+        image_infos_file = 'analysis/image_infos_analysis.csv'
+    image_info_df = pd.read_csv(image_infos_file)
+    return image_info_df['Experiment_Time[s]'].to_numpy()

ledsa/postprocessing/simulation.py

@@ -116,14 +116,17 @@ def _get_extco_df_from_path(self):
         """
         extco_list = []
         files_list = glob.glob(
-            os.path.join(self.path_simulation, 'analysis', 'extinction_coefficients', self.solver, f'extinction_coefficients*.csv'))
+            os.path.join(self.path_simulation, 'analysis', 'extinction_coefficients', self.solver,
+                         f'extinction_coefficients*.csv'))
         for file in files_list:
             file_df = pd.read_csv(file, skiprows=4)
             channel = int(file.split('channel_')[1].split('_')[0])
             led_array = int(file.split('array_')[1].split('.')[0])
             n_layers = len(file_df.columns)
-            time = self.image_info_df['Experiment_Time[s]'].astype(int)
-            file_df = file_df.merge(time, left_index=True, right_index=True)
+            # For backwards compatibility, check if Experiment_Time[s] is already in the dataframe
+            if 'Experiment_Time[s]' not in file_df.columns:
+                time = self.image_info_df['Experiment_Time[s]'].astype(int)
+                file_df = file_df.merge(time, left_index=True, right_index=True)
             file_df.set_index('Experiment_Time[s]', inplace=True)
             iterables = [[channel], [led_array], [i for i in range(0, n_layers)]]
             file_df.columns = pd.MultiIndex.from_product(iterables, names=["Channel", "LED Array", "Layer"])

ledsa/tests/AcceptanceTests/LedsaATestLibrary.py

@@ -68,17 +68,16 @@ def extco_quad(z):
 
     @keyword
     def plot_input_vs_computed_extinction_coefficients(self, solver, first=1, last=4, led_array=0, channel=0):
-        filename = f'extinction_coefficients_{solver}_channel_{channel}_sum_col_val_led_array_{led_array}.csv'
-        extinction_coefficients_computed = (
-            np.loadtxt(os.path.join('analysis', 'extinction_coefficients', solver, filename), skiprows=5, delimiter=','))
+        time, extinction_coefficients_computed = load_extinction_coefficients_computed(solver, channel, led_array)
+
         for image_id in range(first, last + 1):
             extinction_coefficients_input = np.loadtxt(os.path.join('test_data', f'test_extinction_coefficients_input_{image_id}.csv'), delimiter=',')
             num_of_layers = extinction_coefficients_input.shape[0]
             plt.plot(extinction_coefficients_input, range(num_of_layers, 0, -1), '.-', label='Input')
             plt.plot(extinction_coefficients_computed[image_id - 1, :], range(num_of_layers, 0, -1), '.-', label='Computed')
             plt.xlabel('Extinction coefficient / $\mathrm{m}^{-1}$')
             plt.ylabel('Layer / -')
-            plt.title(f'Input vs Computed {solver} Extinction Coefficients - Image {image_id}')
+            plt.title(f'Input vs Computed {solver} Extinction Coefficients - Image {image_id}, t = {time[image_id - 1]} s')
             plt.xlim(-0.1, 0.6)
             plt.ylim(num_of_layers, 0)
             plt.grid(linestyle='--', alpha=0.5)
@@ -90,9 +89,7 @@ def plot_input_vs_computed_extinction_coefficients(self, solver, first=1, last=4
 
     @keyword
     def check_input_vs_computed_extinction_coefficients(self, image_id, solver, led_array=0, channel=0):
-        filename = f'extinction_coefficients_{solver}_channel_{channel}_sum_col_val_led_array_{led_array}.csv'
-        extinction_coefficients_computed = (
-            np.loadtxt(os.path.join('analysis', 'extinction_coefficients',solver, filename), skiprows=5, delimiter=','))
+        _, extinction_coefficients_computed = load_extinction_coefficients_computed(solver, channel, led_array)
         extinction_coefficients_input = np.loadtxt(os.path.join('test_data', f'test_extinction_coefficients_input_{image_id}.csv'), delimiter=',')
         rmse = np.sqrt(
             np.mean((extinction_coefficients_input - extinction_coefficients_computed[int(image_id) - 1, :]) ** 2))
@@ -152,6 +149,16 @@ def create_cc_matrix_file(self):
         file.write("2,3,4\n1,2,7\n3,4,5")
         file.close()
 
+def load_extinction_coefficients_computed(solver, channel, led_array):
+    filename = f'extinction_coefficients_{solver}_channel_{channel}_sum_col_val_led_array_{led_array}.csv'
+    data = np.loadtxt(
+        os.path.join('analysis', 'extinction_coefficients', solver, filename),
+        skiprows=5,
+        delimiter=','
+    )
+    time = data[:, 0]
+    extinction_coefficients_computed = data[:, 1:]
+    return time, extinction_coefficients_computed
 
 def create_test_image(image_id, experiment):
     """ Creates three test images with black and gray pixels representing 3 leds and sets the exif data needed