Performance analysis of FDTD

[gomezzz]

Feature

Desired Behavior / Functionality

Figure out what costs us most time, so we can optimize, ideally for

• 1 uniform
• 3 uniform
• 1 patterned layer

What Needs to Be Done

• run cprofile
• write output in this issue

How Can It Be Tested

N/A

[gomezzz]

Example for cprofile

    import cProfile, pstats
    # used to run this test individually
    profiler = cProfile.Profile()
    profiler.enable()
    start = timeit.default_timer()
    # (your code)
    profiler.disable()
    stats = pstats.Stats(profiler).sort_stats("tottime")
    stats.print_stats()
    stop = timeit.default_timer()
    print("Test ran for ", stop - start, " seconds.")

[torbjornstoro]

   Ordered by: internal time

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
       40   15.725    0.393   18.608    0.465 c:\Users\to-bo\OneDrive\Documents\ESA\NIDN\Developer\notebooks\..\nidn\fdtd_integration\compute_spectrum_fdtd.py:128(_average_along_detector)
     4000    2.537    0.001    2.537    0.001 c:\Users\to-bo\OneDrive\Documents\ESA\NIDN\Developer\notebooks\..\nidn\fdtd\boundaries.py:422(update_phi_E)
     4000    2.525    0.001    2.525    0.001 c:\Users\to-bo\OneDrive\Documents\ESA\NIDN\Developer\notebooks\..\nidn\fdtd\boundaries.py:450(update_phi_H)
     2000    1.256    0.001    1.376    0.001 c:\Users\to-bo\OneDrive\Documents\ESA\NIDN\Developer\notebooks\..\nidn\fdtd\sources.py:274(update_E)
   480001    1.155    0.000    1.952    0.000 C:\Users\to-bo\.conda\envs\nidn\lib\site-packages\torch\_tensor.py:620(__len__)
     2000    0.847    0.000    0.886    0.000 c:\Users\to-bo\OneDrive\Documents\ESA\NIDN\Developer\notebooks\..\nidn\fdtd\grid.py:55(curl_H)
     2000    0.844    0.000    0.885    0.000 c:\Users\to-bo\OneDrive\Documents\ESA\NIDN\Developer\notebooks\..\nidn\fdtd\grid.py:30(curl_E)
   490111    0.588    0.000    0.588    0.000 {built-in method torch._C._get_tracing_state}
   480690    0.494    0.000    2.445    0.000 {built-in method builtins.len}
    10110    0.340    0.000    0.340    0.000 {method 'unbind' of 'torch._C._TensorBase' objects}
     4000    0.271    0.000    0.271    0.000 c:\Users\to-bo\OneDrive\Documents\ESA\NIDN\Developer\notebooks\..\nidn\fdtd\boundaries.py:410(update_H)
     4000    0.271    0.000    0.271    0.000 c:\Users\to-bo\OneDrive\Documents\ESA\NIDN\Developer\notebooks\..\nidn\fdtd\boundaries.py:398(update_E)
     4000    0.253    0.000    0.256    0.000 c:\Users\to-bo\OneDrive\Documents\ESA\NIDN\Developer\notebooks\..\nidn\fdtd\detectors.py:114(detect_E)
     4000    0.244    0.000    0.246    0.000 c:\Users\to-bo\OneDrive\Documents\ESA\NIDN\Developer\notebooks\..\nidn\fdtd\detectors.py:120(detect_H)
       40    0.232    0.006   19.055    0.476 c:\Users\to-bo\OneDrive\Documents\ESA\NIDN\Developer\notebooks\..\nidn\fdtd_integration\compute_spectrum_fdtd.py:109(_get_abs_value_from_3D_signal)
    12220    0.188    0.000    0.188    0.000 {built-in method zeros}
   490131    0.187    0.000    0.187    0.000 {method 'dim' of 'torch._C._TensorBase' objects}
     2000    0.174    0.000    5.765    0.003 c:\Users\to-bo\OneDrive\Documents\ESA\NIDN\Developer\notebooks\..\nidn\fdtd\grid.py:276(update_E)
    28090    0.172    0.000    0.172    0.000 {method 'pow' of 'torch._C._TensorBase' objects}
     2000    0.162    0.000    4.156    0.002 c:\Users\to-bo\OneDrive\Documents\ESA\NIDN\Developer\notebooks\..\nidn\fdtd\grid.py:302(update_H)
     1000    0.152    0.000    0.198    0.000 c:\Users\to-bo\OneDrive\Documents\ESA\NIDN\Developer\notebooks\..\nidn\fdtd\objects.py:201(update_E)
   480021    0.083    0.000    0.083    0.000 {built-in method torch._C._has_torch_function_unary}
     2000    0.066    0.000    0.066    0.000 c:\Users\to-bo\OneDrive\Documents\ESA\NIDN\Developer\notebooks\..\nidn\fdtd\boundaries.py:187(update_E)
     2000    0.064    0.000    0.064    0.000 c:\Users\to-bo\OneDrive\Documents\ESA\NIDN\Developer\notebooks\..\nidn\fdtd\boundaries.py:192(update_H)
    10110    0.042    0.000    0.451    0.000 C:\Users\to-bo\.conda\envs\nidn\lib\site-packages\torch\_tensor.py:632(__iter__)
     8000    0.036    0.000    0.036    0.000 {built-in method sqrt}
     1000    0.036    0.000    0.036    0.000 {built-in method rsub}
    29090    0.029    0.000    0.248    0.000 C:\Users\to-bo\.conda\envs\nidn\lib\site-packages\torch\_tensor.py:25(wrapped)
      296    0.024    0.000    0.024    0.000 {method 'acquire' of '_thread.lock' objects}
     2000    0.023    0.000    9.944    0.005 c:\Users\to-bo\OneDrive\Documents\ESA\NIDN\Developer\notebooks\..\nidn\fdtd\grid.py:268(step)
       27    0.017    0.001    0.018    0.001 {built-in method builtins.sum}
    24125    0.016    0.000    0.016    0.000 {method 'append' of 'list' objects}
    29090    0.007    0.000    0.007    0.000 {built-in method torch._C._has_torch_function}
    10110    0.007    0.000    0.007    0.000 {built-in method builtins.iter}
        1    0.006    0.006   29.188   29.188 c:\Users\to-bo\OneDrive\Documents\ESA\NIDN\Developer\notebooks\..\nidn\fdtd_integration\compute_spectrum_fdtd.py:13(compute_spectrum_fdtd)
      131    0.006    0.000    0.006    0.000 C:\Users\to-bo\.conda\envs\nidn\lib\site-packages\zmq\sugar\socket.py:480(send)
       20    0.006    0.000    0.020    0.001 c:\Users\to-bo\OneDrive\Documents\ESA\NIDN\Developer\notebooks\..\nidn\fdtd\boundaries.py:356(_calculate_parameters)
       10    0.005    0.000   19.060    1.906 c:\Users\to-bo\OneDrive\Documents\ESA\NIDN\Developer\notebooks\..\nidn\fdtd_integration\compute_spectrum_fdtd.py:83(_get_detector_values)
        5    0.004    0.001    0.004    0.001 c:\Users\to-bo\OneDrive\Documents\ESA\NIDN\Developer\notebooks\..\nidn\fdtd_integration\calculate_transmission_reflection_coefficients.py:32(<listcomp>)
     1000    0.004    0.000    0.041    0.000 C:\Users\to-bo\.conda\envs\nidn\lib\site-packages\torch\_tensor.py:544(__rsub__)
     2000    0.004    0.000    0.004    0.000 {built-in method math.sin}
       70    0.004    0.000    0.004    0.000 {built-in method arange}
       10    0.004    0.000    9.948    0.995 c:\Users\to-bo\OneDrive\Documents\ESA\NIDN\Developer\notebooks\..\nidn\fdtd\grid.py:251(run)
       20    0.003    0.000    0.028    0.001 c:\Users\to-bo\OneDrive\Documents\ESA\NIDN\Developer\notebooks\..\nidn\fdtd_integration\calculate_transmission_reflection_coefficients.py:79(<listcomp>)
       10    0.003    0.000    0.010    0.001 c:\Users\to-bo\OneDrive\Documents\ESA\NIDN\Developer\notebooks\..\nidn\fdtd\grid.py:91(__init__)
       40    0.003    0.000    0.005    0.000 c:\Users\to-bo\OneDrive\Documents\ESA\NIDN\Developer\notebooks\..\nidn\fdtd\boundaries.py:280(_sigma)
       25    0.003    0.000    0.003    0.000 {built-in method ones}
      110    0.003    0.000    0.003    0.000 {built-in method tensor}
       10    0.002    0.000    0.009    0.001 c:\Users\to-bo\OneDrive\Documents\ESA\NIDN\Developer\notebooks\..\nidn\fdtd\sources.py:165(_register_grid)
       20    0.002    0.000    0.002    0.000 {method 'max' of 'torch._C._TensorBase' objects}
     1820    0.002    0.000    0.002    0.000 {method 'item' of 'torch._C._TensorBase' objects}
       20    0.002    0.000    0.049    0.002 c:\Users\to-bo\OneDrive\Documents\ESA\NIDN\Developer\notebooks\..\nidn\fdtd_integration\calculate_transmission_reflection_coefficients.py:71(_mean_square)
       10    0.002    0.000    0.075    0.007 c:\Users\to-bo\OneDrive\Documents\ESA\NIDN\Developer\notebooks\..\nidn\fdtd_integration\init_fdtd.py:18(init_fdtd)
       10    0.002    0.000    0.002    0.000 {built-in method isfinite}
       24    0.002    0.000    0.047    0.002 C:\Users\to-bo\.conda\envs\nidn\lib\site-packages\loguru\_logger.py:1841(_log)
       10    0.002    0.000    0.009    0.001 C:\Users\to-bo\.conda\envs\nidn\lib\site-packages\torch\_tensor_str.py:75(__init__)
      232    0.001    0.000    0.002    0.000 C:\Users\to-bo\.conda\envs\nidn\lib\site-packages\dotmap\__init__.py:103(__getattr__)
      100    0.001    0.000    0.001    0.000 {method 'clone' of 'torch._C._TensorBase' objects}
       24    0.001    0.000    0.004    0.000 C:\Users\to-bo\.conda\envs\nidn\lib\site-packages\loguru\_datetime.py:13(__format__)
       96    0.001    0.000    0.001    0.000 {method 'strftime' of 'datetime.date' objects}
       10    0.001    0.000    0.001    0.000 {method 'ne' of 'torch._C._TensorBase' objects}
        1    0.001    0.001    0.006    0.006 C:\Users\to-bo\.conda\envs\nidn\lib\site-packages\tqdm\std.py:846(__init__)
        5    0.001    0.000    0.054    0.011 c:\Users\to-bo\OneDrive\Documents\ESA\NIDN\Developer\notebooks\..\nidn\fdtd_integration\calculate_transmission_reflection_coefficients.py:12(calculate_transmission_reflection_coefficients)
       10    0.001    0.000    0.011    0.001 C:\Users\to-bo\.conda\envs\nidn\lib\site-packages\torch\_tensor_str.py:287(_str_intern)
     2000    0.001    0.000    0.001    0.000 c:\Users\to-bo\OneDrive\Documents\ESA\NIDN\Developer\notebooks\..\nidn\fdtd\boundaries.py:86(update_phi_H)
       20    0.001    0.000    0.011    0.001 c:\Users\to-bo\OneDrive\Documents\ESA\NIDN\Developer\notebooks\..\nidn\fdtd\detectors.py:63(_handle_slices)
      100    0.001    0.000    0.001    0.000 {method 'to' of 'torch._C._TensorBase' objects}
       10    0.001    0.000    0.006    0.001 c:\Users\to-bo\OneDrive\Documents\ESA\NIDN\Developer\notebooks\..\nidn\fdtd\boundaries.py:491(_set_sigmaE)
     2000    0.001    0.000    0.001    0.000 c:\Users\to-bo\OneDrive\Documents\ESA\NIDN\Developer\notebooks\..\nidn\fdtd\boundaries.py:79(update_phi_E)
       10    0.001    0.000    0.001    0.000 {method 'min' of 'torch._C._TensorBase' objects}
       50    0.001    0.000    0.001    0.000 {built-in method exp}
       10    0.001    0.000    0.003    0.000 c:\Users\to-bo\OneDrive\Documents\ESA\NIDN\Developer\notebooks\..\nidn\fdtd\boundaries.py:496(_set_sigmaH)
     2000    0.001    0.000    0.001    0.000 c:\Users\to-bo\OneDrive\Documents\ESA\NIDN\Developer\notebooks\..\nidn\fdtd\sources.py:295(update_H)
        5    0.001    0.000    0.001    0.000 c:\Users\to-bo\OneDrive\Documents\ESA\NIDN\Developer\notebooks\..\nidn\fdtd\objects.py:37(_register_grid)
       65    0.001    0.000    0.045    0.001 c:\Users\to-bo\OneDrive\Documents\ESA\NIDN\Developer\notebooks\..\nidn\fdtd\grid.py:354(__setitem__)
      190    0.001    0.000    0.005    0.000 c:\Users\to-bo\OneDrive\Documents\ESA\NIDN\Developer\notebooks\..\nidn\fdtd\backend.py:148(array)
     1000    0.001    0.000    0.001    0.000 {built-in method torch._C._has_torch_function_variadic}
       10    0.001    0.000    0.001    0.000 c:\Users\to-bo\OneDrive\Documents\ESA\NIDN\Developer\notebooks\..\nidn\fdtd\boundaries.py:519(_set_sigmaH)
       10    0.001    0.000    0.002    0.000 c:\Users\to-bo\OneDrive\Documents\ESA\NIDN\Developer\notebooks\..\nidn\fdtd\boundaries.py:514(_set_sigmaE)
      131    0.001    0.000    0.007    0.000 C:\Users\to-bo\.conda\envs\nidn\lib\site-packages\ipykernel\iostream.py:206(schedule)
       10    0.001    0.000    0.001    0.000 {built-in method masked_select}
      195    0.001    0.000    0.001    0.000 c:\Users\to-bo\OneDrive\Documents\ESA\NIDN\Developer\notebooks\..\nidn\fdtd\grid.py:214(_handle_single_key)
        7    0.001    0.000    0.001    0.000 C:\Users\to-bo\.conda\envs\nidn\lib\site-packages\tqdm\std.py:355(format_meter)
        1    0.001    0.001   29.191   29.191 c:\Users\to-bo\OneDrive\Documents\ESA\NIDN\Developer\notebooks\..\nidn\utils\compute_spectrum.py:6(compute_spectrum)
     1000    0.001    0.000    0.001    0.000 c:\Users\to-bo\OneDrive\Documents\ESA\NIDN\Developer\notebooks\..\nidn\fdtd\objects.py:217(update_H)
        1    0.001    0.001    0.001    0.001 C:\Users\to-bo\.conda\envs\nidn\lib\site-packages\tqdm\utils.py:246(_screen_shape_windows)
       24    0.000    0.000    0.031    0.001 C:\Users\to-bo\.conda\envs\nidn\lib\site-packages\loguru\_handler.py:98(emit)
     1668    0.000    0.000    0.000    0.000 {built-in method builtins.isinstance}
        5    0.000    0.000    0.002    0.000 c:\Users\to-bo\OneDrive\Documents\ESA\NIDN\Developer\notebooks\..\nidn\fdtd\objects.py:172(_register_grid)
      118    0.000    0.000    0.001    0.000 {method 'format' of 'str' objects}
      164    0.000    0.000    0.001    0.000 C:\Users\to-bo\.conda\envs\nidn\lib\threading.py:1126(is_alive)
       90    0.000    0.000    0.003    0.000 c:\Users\to-bo\OneDrive\Documents\ESA\NIDN\Developer\notebooks\..\nidn\fdtd\backend.py:163(linspace)
       20    0.000    0.000    0.001    0.000 c:\Users\to-bo\OneDrive\Documents\ESA\NIDN\Developer\notebooks\..\nidn\fdtd\detectors.py:109(<listcomp>)
       24    0.000    0.000    0.001    0.000 C:\Users\to-bo\.conda\envs\nidn\lib\site-packages\loguru\_datetime.py:81(aware_now)
       10    0.000    0.000    0.000    0.000 {method 'reshape' of 'torch._C._TensorBase' objects}
       33    0.000    0.000    0.025    0.001 C:\Users\to-bo\.conda\envs\nidn\lib\threading.py:556(wait)
       33    0.000    0.000    0.028    0.001 C:\Users\to-bo\.conda\envs\nidn\lib\site-packages\ipykernel\iostream.py:451(flush)
       24    0.000    0.000    0.001    0.000 C:\Users\to-bo\.conda\envs\nidn\lib\ntpath.py:180(split)
       10    0.000    0.000    0.023    0.002 c:\Users\to-bo\OneDrive\Documents\ESA\NIDN\Developer\notebooks\..\nidn\fdtd_integration\init_fdtd.py:110(_add_boundaries)
       33    0.000    0.000    0.000    0.000 C:\Users\to-bo\.conda\envs\nidn\lib\threading.py:228(__init__)
       10    0.000    0.000    0.004    0.000 c:\Users\to-bo\OneDrive\Documents\ESA\NIDN\Developer\notebooks\..\nidn\fdtd\sources.py:205(_handle_slices)
        5    0.000    0.000    0.002    0.000 c:\Users\to-bo\OneDrive\Documents\ESA\NIDN\Developer\notebooks\..\nidn\fdtd_integration\init_fdtd.py:180(_add_object)
       34    0.000    0.000    0.006    0.000 C:\Users\to-bo\.conda\envs\nidn\lib\site-packages\ipykernel\iostream.py:500(write)
      125    0.000    0.000    0.000    0.000 c:\Users\to-bo\OneDrive\Documents\ESA\NIDN\Developer\notebooks\..\nidn\fdtd\grid.py:199(_handle_slice)
       24    0.000    0.000    0.000    0.000 {built-in method time.localtime}
       31    0.000    0.000    0.000    0.000 {method 'sub' of 're.Pattern' objects}
       33    0.000    0.000    0.025    0.001 C:\Users\to-bo\.conda\envs\nidn\lib\threading.py:280(wait)
       10    0.000    0.000    0.012    0.001 c:\Users\to-bo\OneDrive\Documents\ESA\NIDN\Developer\notebooks\..\nidn\fdtd_integration\init_fdtd.py:161(_add_detectors)
       20    0.000    0.000    0.001    0.000 c:\Users\to-bo\OneDrive\Documents\ESA\NIDN\Developer\notebooks\..\nidn\fdtd\detectors.py:111(<listcomp>)
       20    0.000    0.000    0.001    0.000 c:\Users\to-bo\OneDrive\Documents\ESA\NIDN\Developer\notebooks\..\nidn\fdtd\detectors.py:110(<listcomp>)
       66    0.000    0.000    0.000    0.000 {built-in method _thread.allocate_lock}
       10    0.000    0.000    0.000    0.000 {built-in method ceil}
       24    0.000    0.000    0.004    0.000 {method 'format_map' of 'str' objects}
      232    0.000    0.000    0.000    0.000 C:\Users\to-bo\.conda\envs\nidn\lib\site-packages\dotmap\__init__.py:89(__getitem__)
       10    0.000    0.000    0.000    0.000 c:\Users\to-bo\OneDrive\Documents\ESA\NIDN\Developer\notebooks\..\nidn\fdtd\backend.py:243(set_backend)
       24    0.000    0.000    0.026    0.001 C:\Users\to-bo\.conda\envs\nidn\lib\site-packages\loguru\_simple_sinks.py:25(write)
       24    0.000    0.000    0.000    0.000 {method 'timetuple' of 'datetime.datetime' objects}
        5    0.000    0.000    0.000    0.000 {built-in method as_tensor}
       20    0.000    0.000    0.021    0.001 c:\Users\to-bo\OneDrive\Documents\ESA\NIDN\Developer\notebooks\..\nidn\fdtd\boundaries.py:284(_register_grid)
        5    0.000    0.000    0.006    0.001 C:\Users\to-bo\.conda\envs\nidn\lib\site-packages\tqdm\std.py:1197(update)
       30    0.000    0.000    0.000    0.000 c:\Users\to-bo\OneDrive\Documents\ESA\NIDN\Developer\notebooks\..\nidn\fdtd\boundaries.py:31(_register_grid)
      252    0.000    0.000    0.000    0.000 {method 'startswith' of 'str' objects}
       30    0.000    0.000    0.000    0.000 {built-in method now}
       25    0.000    0.000    0.000    0.000 c:\Users\to-bo\OneDrive\Documents\ESA\NIDN\Developer\notebooks\..\nidn\fdtd\backend.py:142(is_array)
       24    0.000    0.000    0.048    0.002 C:\Users\to-bo\.conda\envs\nidn\lib\site-packages\loguru\_logger.py:1965(debug)
       34    0.000    0.000    0.000    0.000 C:\Users\to-bo\.conda\envs\nidn\lib\site-packages\ipykernel\iostream.py:418(_is_master_process)
      131    0.000    0.000    0.000    0.000 C:\Users\to-bo\.conda\envs\nidn\lib\site-packages\ipykernel\iostream.py:96(_event_pipe)
      164    0.000    0.000    0.000    0.000 C:\Users\to-bo\.conda\envs\nidn\lib\threading.py:1059(_wait_for_tstate_lock)
      314    0.000    0.000    0.000    0.000 C:\Users\to-bo\.conda\envs\nidn\lib\site-packages\tqdm\utils.py:330(<genexpr>)
      215    0.000    0.000    0.000    0.000 C:\Users\to-bo\.conda\envs\nidn\lib\site-packages\torch\__init__.py:271(is_tensor)
       24    0.000    0.000    0.000    0.000 C:\Users\to-bo\.conda\envs\nidn\lib\ntpath.py:124(splitdrive)
       24    0.000    0.000    0.000    0.000 C:\Users\to-bo\.conda\envs\nidn\lib\ntpath.py:34(_get_bothseps)
       10    0.000    0.000    0.000    0.000 c:\Users\to-bo\OneDrive\Documents\ESA\NIDN\Developer\notebooks\..\nidn\fdtd\sources.py:270(<listcomp>)
       85    0.000    0.000    0.000    0.000 {built-in method builtins.max}
       20    0.000    0.000    0.011    0.001 c:\Users\to-bo\OneDrive\Documents\ESA\NIDN\Developer\notebooks\..\nidn\fdtd\detectors.py:35(_register_grid)
       10    0.000    0.000    0.010    0.001 C:\Users\to-bo\.conda\envs\nidn\lib\site-packages\torch\_tensor_str.py:223(_tensor_str)
       10    0.000    0.000    0.000    0.000 {method 'sum' of 'torch._C._TensorBase' objects}
       10    0.000    0.000    0.000    0.000 {method 'abs' of 'torch._C._TensorBase' objects}
        3    0.000    0.000    0.000    0.000 {built-in method builtins.compile}
        1    0.000    0.000   29.191   29.191 C:\Users\to-bo\AppData\Local\Temp\ipykernel_3112\2327297233.py:4(<module>)
        1    0.000    0.000    0.000    0.000 C:\Users\to-bo\AppData\Local\Temp\ipykernel_3112\2327297233.py:5(<module>)
       10    0.000    0.000    0.000    0.000 c:\Users\to-bo\OneDrive\Documents\ESA\NIDN\Developer\notebooks\..\nidn\fdtd\sources.py:268(<listcomp>)
       48    0.000    0.000    0.000    0.000 {method 'timestamp' of 'datetime.datetime' objects}
        5    0.000    0.000    0.000    0.000 {built-in method broadcast_to}
       10    0.000    0.000    0.010    0.001 c:\Users\to-bo\OneDrive\Documents\ESA\NIDN\Developer\notebooks\..\nidn\fdtd_integration\init_fdtd.py:131(_add_source)
       33    0.000    0.000    0.001    0.000 C:\Users\to-bo\.conda\envs\nidn\lib\threading.py:521(__init__)
       10    0.000    0.000    0.000    0.000 c:\Users\to-bo\OneDrive\Documents\ESA\NIDN\Developer\notebooks\..\nidn\fdtd\sources.py:269(<listcomp>)
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Test ran for  29.56145639999886  seconds.

[gomezzz]

   Ordered by: internal time

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       40    0.232    0.006   19.055    0.476 ..\nidn\fdtd_integration\compute_spectrum_fdtd.py:109(_get_abs_value_from_3D_signal)
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       40    0.003    0.000    0.005    0.000 ..\nidn\fdtd\boundaries.py:280(_sigma)
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Test ran for  29.56145639999886  seconds.

[gomezzz]

@gomezzz try out on GPU

[gomezzz]

For later: This may help optimize a lot by doing all frequencies in one run: flaport/fdtd#31 (might need ricker wavelet source)

[gomezzz]

Oh also, we overlooked this I think: https://fdtd.readthedocs.io/en/latest/examples/04-performance-profiling.html

[torbjornstoro]

If going with the RMS method for time tomain to frequency domain, I think we can assume that the rms of the free space simulation is the same for all frequencies, reducing the runtime by half
Update: Need the freespace simulation for each frequency to calculate the actual reflected signal

[torbjornstoro]

The simulation is currently 2D. Patterned layer requires 3D. Maybe uniform layer also can be 1D, which will impove runtime

[gomezzz]

Closed for now , will reopen when relevant