T378 2D vs 3D example

docs/source/simpa_examples.rst

@@ -15,3 +15,4 @@ simpa\_examples
    perform_image_reconstruction
    perform_iterative_qPAI_reconstruction
    segmentation_loader
+   three_vs_two_dimensional_simulation_example

pyproject.toml

@@ -38,14 +38,16 @@ dependencies = [
 
 [project.optional-dependencies]
 docs = [
-    "sphinx-rtd-theme>=2.0.0,<3.0.0",
-    "Sphinx>=5.1.1,<6.0.0",
-    "myst-parser>=0.18.0,<1.1"
+    "sphinx-rtd-theme>=2.0.0,<3.0.0",   # Uses MIT-License (MIT compatible)
+    "Sphinx>=5.1.1,<6.0.0",             # Uses BSD-License (MIT compatible)
+    "myst-parser>=0.18.0,<1.1"          # Uses MIT-License (MIT compatible)
 ]
 profile = [
-    "pytorch_memlab>=0.3.0,<0.4.0",
-    "line_profiler>=4.0.0,<5.0.0",
-    "memory_profiler>=0.61.0,<0.62.0"
+    "pytorch_memlab>=0.3.0",     # Uses MIT-License (MIT compatible)
+    "line_profiler>=4.0.0",      # Uses BSD-License (MIT compatible)
+    "memory_profiler>=0.61.0,<0.62.0",   # Uses BSD-License (MIT compatible)
+    "tabulate>=0.9.0"   # Uses BSD-License (MIT compatible)
+
 ]
 testing = [
     "mdutils>=1.4.0",          # Uses MIT-License (MIT compatible)

simpa_examples/__init__.py

@@ -1,12 +1,3 @@
 # SPDX-FileCopyrightText: 2021 Division of Intelligent Medical Systems, DKFZ
 # SPDX-FileCopyrightText: 2021 Janek Groehl
 # SPDX-License-Identifier: MIT
-
-from simpa_examples.linear_unmixing import run_linear_unmixing
-from simpa_examples.minimal_optical_simulation import run_minimal_optical_simulation
-from simpa_examples.minimal_optical_simulation_uniform_cube import run_minimal_optical_simulation_uniform_cube
-from simpa_examples.msot_invision_simulation import run_msot_invision_simulation
-from simpa_examples.optical_and_acoustic_simulation import run_optical_and_acoustic_simulation
-from simpa_examples.perform_image_reconstruction import run_perform_image_reconstruction
-from simpa_examples.perform_iterative_qPAI_reconstruction import run_perform_iterative_qPAI_reconstruction
-from simpa_examples.segmentation_loader import run_segmentation_loader

simpa_examples/benchmarking/extract_benchmarking_data.py

@@ -1,6 +1,7 @@
 # SPDX-FileCopyrightText: 2021 Division of Intelligent Medical Systems, DKFZ
 # SPDX-FileCopyrightText: 2021 Janek Groehl
 # SPDX-License-Identifier: MIT
+import os
 
 import numpy as np
 import pandas as pd
@@ -67,17 +68,21 @@ def read_out_benchmarking_data(profiles: list = None, start: float = .2, stop: f
                 else:
                     break
 
-            benchmarking_file = open(file_name, 'r')
-            lines_with_sp_simulate = lines_that_contain("sp.simulate", benchmarking_file)
+            with open(file_name, 'r') as benchmarking_file:
+                lines_with_sp_simulate = lines_that_contain("sp.simulate", benchmarking_file)
 
-            examples_counter = 0
-            for lwss in lines_with_sp_simulate:
+            for e_idx, example in enumerate(current_examples):
                 try:
-                    value = float(lwss[info_starts[profile]:info_ends[profile]])
+                    value = float(lines_with_sp_simulate[e_idx][info_starts[profile]:info_ends[profile]])
+                    unit = str(lines_with_sp_simulate[e_idx][info_ends[profile]])
                 except ValueError:
-                    continue
+                    with open(file_name, 'r') as benchmarking_file:
+                        lines_with_run_sim = lines_that_contain("= run_sim", benchmarking_file)
+                    value = 0
+                    for line in lines_with_run_sim:
+                        value += float(line[info_starts[profile]:info_ends[profile]])
+                    unit = str(line[info_ends[profile]])
 
-                unit = str(lwss[info_ends[profile]])
                 if profile == "TIME":
                     value_with_unit = value
                 else:
@@ -90,41 +95,36 @@ def read_out_benchmarking_data(profiles: list = None, start: float = .2, stop: f
                     else:
                         raise ImportError(f'Unit {unit} not supported')
 
-                example = current_examples[examples_counter]
                 benchmarking_lists.append([example, spacing, profile, value_with_unit])
 
-                # lets you know which example you are on
-                examples_counter += 1
-                if examples_counter == len(current_examples):
-                    examples_counter = 0
-
     # creating data frame
     new_df = pd.DataFrame(benchmarking_lists, columns=['Example', 'Spacing', 'Profile', 'Value'])
     new_df.astype(dtype={"Example": "str", "Spacing": "float64", "Profile": "str", "Value": "float64"})
 
-    # if exists: load old dataframe and append OR just save df
+    # if exists: remove old file
     df_file = savefolder / 'benchmarking_data_frame.csv'
     if df_file.is_file():
-        old_df = pd.read_csv(df_file)
-        new_df = pd.concat([old_df, new_df])
+        os.remove(df_file)
     new_df.to_csv(df_file, index=False)
 
 
 if __name__ == "__main__":
     parser = ArgumentParser(description='Run benchmarking tests')
-    parser.add_argument("--start", default=.2,
+    parser.add_argument("--start", default=.15,
                         help='start spacing default .2mm')
-    parser.add_argument("--stop", default=.4,
+    parser.add_argument("--stop", default=.25,
                         help='stop spacing default .4mm')
-    parser.add_argument("--step", default=.1,
+    parser.add_argument("--step", default=.05,
                         help='step size mm')
     parser.add_argument("--profiles", default=None, type=str,
                         help='the profile to run')
     parser.add_argument("--savefolder", default=None, type=str, help='where to save the results')
     config = parser.parse_args()
 
     profiles = config.profiles
-    if profiles:
+    if profiles and "%" in profiles:
         profiles = profiles.split('%')[:-1]
+    elif profiles:
+        profiles = [profiles]
     read_out_benchmarking_data(start=float(config.start), stop=float(config.stop), step=float(config.step),
                                profiles=profiles, savefolder=config.savefolder)

simpa_examples/benchmarking/get_final_table.py

@@ -56,6 +56,9 @@ def get_final_table(savefolder: str = None):
     # save to csv at input location
     mean_df.to_csv(str(df_file).replace('.csv', '_mean.csv'), index=False)
 
+    # save to markdown for nice visualization
+    mean_df.to_markdown(str(df_file).replace('data_frame.csv', '_mean.md'), index=False)
+
 
 if __name__ == "__main__":
     parser = ArgumentParser(description='Run benchmarking tests')

simpa_examples/benchmarking/performance_check.py

@@ -26,12 +26,19 @@ def run_benchmarking_tests(spacing=0.4, profile: str = "TIME", savefolder: str =
     elif len(savefolder) > 0:
         os.environ["SIMPA_PROFILE_SAVE_FILE"] = savefolder+"/benchmarking_data_"+profile+"_"+str(spacing)+".txt"
 
-    import simpa_examples
-
-    examples = [simpa_examples.run_minimal_optical_simulation,
-                simpa_examples.run_minimal_optical_simulation_uniform_cube,
-                simpa_examples.run_optical_and_acoustic_simulation,
-                simpa_examples.run_segmentation_loader]
+    from simpa_examples.minimal_optical_simulation import run_minimal_optical_simulation
+    from simpa_examples.minimal_optical_simulation_uniform_cube import run_minimal_optical_simulation_uniform_cube
+    from simpa_examples.optical_and_acoustic_simulation import run_optical_and_acoustic_simulation
+    from simpa_examples.segmentation_loader import run_segmentation_loader
+    from simpa_examples.three_vs_two_dimensional_simulation_example import (
+        run_three_vs_two_dimensional_simulation_example)
+
+    examples = [run_minimal_optical_simulation,
+                run_minimal_optical_simulation_uniform_cube,
+                run_optical_and_acoustic_simulation,
+                run_segmentation_loader,
+                run_three_vs_two_dimensional_simulation_example
+                ]
 
     for example in examples:
         example(spacing=spacing, path_manager=None, visualise=False)

simpa_examples/linear_unmixing.py

@@ -15,7 +15,6 @@
 os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
 
 
-@profile
 def run_linear_unmixing(spacing: float | int = 0.25, path_manager=None, visualise: bool = True):
     """
 

simpa_examples/msot_invision_simulation.py

@@ -11,7 +11,6 @@
 path_manager = sp.PathManager()
 
 
-@profile
 def run_msot_invision_simulation(spacing: float | int = 0.5, path_manager=None, visualise: bool = True):
     """
 

simpa_examples/perform_iterative_qPAI_reconstruction.py

@@ -22,7 +22,6 @@
 #  point to the correct file in the PathManager().
 
 
-@profile
 def run_perform_iterative_qPAI_reconstruction(spacing: float | int = 0.2, path_manager=None,
                                               visualise: bool = True):
     """