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Merged
kriboe90 merged 5 commits into from
Mar 9, 2026
Merged

edit config #124

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1a47334
Write layer heights to results files
kriboe90 Mar 9, 2026
7944240
Extent results output
kriboe90 Mar 9, 2026
9ce5599
Adapt postprocessing to new output files structure and allow to chose…
kriboe90 Mar 9, 2026
d133ced
minor changes
kriboe90 Mar 9, 2026
414a84f
Merge pull request #122 from FireDynamics/develop_improve_analysis_ou…
kriboe90 Mar 9, 2026
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13 changes: 10 additions & 3 deletions ledsa/analysis/ExtinctionCoefficients.py
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Original file line number Diff line number Diff line change
Expand Up @@ -63,10 +63,11 @@ def __init__(self, experiment, reference_property='sum_col_val', num_ref_imgs=10
self.cc_matrix = None
self.average_images = average_images
self.solver = None
self.lambda_reg = None

def __str__(self):
out = str(self.experiment) + \
f'reference_property: {self.reference_property}, num_ref_imgs: {self.num_ref_imgs}, LEDSA {version("ledsa")}\n'
f'Reference_property: {self.reference_property}, Ref_img_indices: {self.ref_img_indices}, Solver: {self.solver}, Lambda_reg: {self.lambda_reg}, LEDSA {version("ledsa")}\n'
return out

def calc_and_set_coefficients(self) -> None:
Expand Down Expand Up @@ -141,10 +142,16 @@ 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 += 'Layer_bottom_height[m],'
header += ','.join(map(str, self.experiment.layers.borders[:-1])) + '\n'
header += 'Layer_top_height[m],'
header += ','.join(map(str, self.experiment.layers.borders[1:])) + '\n'
header += 'Layer_center_height[m],'
header += ','.join(map(lambda x: f"{x:.2f}", (np.array(self.experiment.layers.borders[:-1]) + np.array(
self.experiment.layers.borders[1:])) / 2)) + '\n'
header += 'Experiment_Time[s],'
header += 'layer0'
for i in range(self.experiment.layers.amount - 1):
header += f',layer{i + 1}'
header += ''.join(f',layer{i + 1}' for i in range(self.experiment.layers.amount - 1))

experiment_times = _get_experiment_times_from_image_infos_file(self.average_images)
coefficients_per_time_and_layer = np.column_stack(
Expand Down
47 changes: 36 additions & 11 deletions ledsa/postprocessing/simulation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -41,6 +41,11 @@ def __init__(self, path_simulation: str, read_all=True, remove_duplicates=False,
self.n_layers = None
self.search_area_radius = None
self.path_images = None
self.lower_domain_bound = None
self.upper_domain_bound = None
self.layer_bottom_heights = None
self.layer_top_heights = None
self.layer_center_heights = None

if load_config_params:
os.chdir(path_simulation)
Expand All @@ -59,8 +64,13 @@ def __init__(self, path_simulation: str, read_all=True, remove_duplicates=False,
self.num_ref_images = int(self.config_analysis['DEFAULT']['num_ref_images'])
self.n_layers = int(self.config_analysis['model_parameters']['num_layers'])
domain_bounds = self.config_analysis.get_list_of_values('model_parameters', 'domain_bounds', dtype=float)
self.bottom_layer_height = domain_bounds[0]
self.top_layer_height = domain_bounds[1]
self.lower_domain_bound = domain_bounds[0]
self.upper_domain_bound = domain_bounds[1]
layer_heights = [(l / self.n_layers) * (self.upper_domain_bound - self.lower_domain_bound) + self.lower_domain_bound for l in
range(self.n_layers + 1)]
self.layer_bottom_heights = np.round(np.array(layer_heights[:-1]), 2)
self.layer_top_heights = np.round(np.array(layer_heights[1:]), 2)
self.layer_center_heights = np.round((self.layer_bottom_heights + self.layer_top_heights) / 2, 2)

self.path_simulation = path_simulation
self.led_info_path = os.path.join(self.path_simulation, 'analysis', 'led_search_areas_with_coordinates.csv')
Expand All @@ -76,10 +86,8 @@ def __init__(self, path_simulation: str, read_all=True, remove_duplicates=False,
if remove_duplicates == True:
self.remove_duplicate_heights()

height_from_layer = lambda self, layer: (
layer / self.n_layers * (self.top_layer_height - self.bottom_layer_height) + self.bottom_layer_height)
layer_from_height = lambda self, height: int(
(height - self.bottom_layer_height) / (self.top_layer_height - self.bottom_layer_height) * self.n_layers)
(height - self.lower_domain_bound) / (self.upper_domain_bound - self.lower_domain_bound) * self.n_layers)

def _apply_moving_average(self, df: pd.DataFrame, window: int, smooth: str = 'ma') -> pd.DataFrame:
"""
Expand Down Expand Up @@ -160,7 +168,7 @@ def _get_extco_df_from_path(self):
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)
file_df = pd.read_csv(file, skiprows=7)
channel = int(file.split('channel_')[1].split('_')[0])
led_array = int(file.split('array_')[1].split('.')[0])
# For backwards compatibility, check if Experiment_Time[s] is already in the dataframe
Expand Down Expand Up @@ -197,7 +205,7 @@ def remove_duplicate_heights(self):
self.ch1_ledparams_df = self.ch1_ledparams_df.groupby(['Experiment_Time[s]', 'height']).last()
self.ch2_ledparams_df = self.ch2_ledparams_df.groupby(['Experiment_Time[s]', 'height']).last()

def get_extco_at_time(self, channel: int, time: int, yaxis='layer', window=1, smooth='ma') -> pd.DataFrame:
def get_extco_at_time(self, channel: int, time: int, yaxis='layer', height_reference='center', window=1, smooth='ma') -> pd.DataFrame:
"""
Retrieves a DataFrame containing smoothed extinction coefficients at a specific time.

Expand All @@ -214,6 +222,8 @@ def get_extco_at_time(self, channel: int, time: int, yaxis='layer', window=1, sm
:type time: int
:param yaxis: Determines whether the y-axis of the returned DataFrame should represent 'layer' or 'height'. Defaults to 'layer'.
:type yaxis: str, optional
:param height_reference: Specifies the reference point for height calculation. Acceptable values are 'center', 'top', and 'bottom'.
:type height_reference: str
:param window: The window size for the smoothing. A value of 1 returns raw data without any smoothing applied.
Defaults to 1.
:type window: int, optional
Expand All @@ -231,11 +241,18 @@ def get_extco_at_time(self, channel: int, time: int, yaxis='layer', window=1, sm
if yaxis == 'layer':
ma_ch_extco_df.index.names = ["Layer"]
elif yaxis == 'height':
ma_ch_extco_df.index = [round(self.height_from_layer(layer), 2) for layer in ma_ch_extco_df.index]
if height_reference == 'center':
ma_ch_extco_df.index = self.layer_center_heights
elif height_reference == 'top':
ma_ch_extco_df.index = self.layer_top_heights
elif height_reference == 'bottom':
ma_ch_extco_df.index = self.layer_bottom_heights
else:
raise ValueError("height_reference must be 'center', 'top', or 'bottom'")
ma_ch_extco_df.index.names = ["Height / m"]
return ma_ch_extco_df

def get_extco_at_led_array(self, channel: int, led_array_id: int, yaxis='layer', window=1,
def get_extco_at_led_array(self, channel: int, led_array_id: int, yaxis='layer', height_reference='center', window=1,
smooth='ma') -> pd.DataFrame:
"""
Retrieves a DataFrame containing smoothed extinction coefficients for a specific LED array.
Expand All @@ -250,6 +267,8 @@ def get_extco_at_led_array(self, channel: int, led_array_id: int, yaxis='layer',
:type led_array_id: int
:param yaxis: Determines whether the DataFrame's columns should represent 'layer' or 'height'. Defaults to 'layer'.
:type yaxis: str, optional
:param height_reference: Specifies the reference point for height calculation. Acceptable values are 'center', 'top', and 'bottom'.
:type height_reference: str
:param window: The window size for the smoothing. A value of 1 returns raw data without any smoothing applied.
Defaults to 1.
:type window: int, optional
Expand All @@ -265,8 +284,14 @@ def get_extco_at_led_array(self, channel: int, led_array_id: int, yaxis='layer',
if yaxis == 'layer':
ma_ch_extco_df.columns.names = ["Layer"]
elif yaxis == 'height':
ma_ch_extco_df.columns = [self.height_from_layer(layer) for layer in ma_ch_extco_df.columns]
ma_ch_extco_df.columns.names = ["Height / m"]
if height_reference == 'center':
ma_ch_extco_df.index = self.layer_center_heights
elif height_reference == 'top':
ma_ch_extco_df.index = self.layer_top_heights
elif height_reference == 'bottom':
ma_ch_extco_df.index = self.layer_bottom_heights
else:
raise ValueError("height_reference must be 'center', 'top', or 'bottom'")
return ma_ch_extco_df

def get_extco_at_layer(self, channel: int, layer: int, window=1, smooth='ma') -> pd.DataFrame:
Expand Down
5 changes: 1 addition & 4 deletions ledsa/tests/AcceptanceTests/LedsaATestLibrary.py
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Original file line number Diff line number Diff line change
Expand Up @@ -152,10 +152,7 @@ def create_cc_matrix_file(self):
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=','
)
os.path.join('analysis', 'extinction_coefficients', solver, filename),delimiter=',')
time = data[:, 0]
extinction_coefficients_computed = data[:, 1:]
return time, extinction_coefficients_computed
Expand Down
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