Trying this code from your notebooks as the standard autotune approach does not appear to give the best results. However, after only a minute and a few iterations, jupyter kernel restarts. Running on Windows 10, 32 core, 64 GB RAM.
I tried all options for fitting_mode, resize_mode, and max iterations, but still crashing. Can you help get this going or suggest a method to find optimal parameters?
flatfields_wsi = []
darkfields_wsi = []
records = []
Hi, for BaSiCPy, can you primarily try without darkfield now (so get_darkfield=False)? we will elaborate the effects of darkfield estimation in our later manuscript.
Hello,
Trying this code from your notebooks as the standard autotune approach does not appear to give the best results. However, after only a minute and a few iterations, jupyter kernel restarts. Running on Windows 10, 32 core, 64 GB RAM.
I tried all options for fitting_mode, resize_mode, and max iterations, but still crashing. Can you help get this going or suggest a method to find optimal parameters?
flatfields_wsi = [] darkfields_wsi = [] records = []
for smoothness_flatfield in [0.1, 0.5, 1, 5, 10]: for smoothness_darkfield in [0, 0.1, 0.5, 1, 5, 10]: for sparse_cost_darkfield in tqdm(np.array([0, 0.1, 0.5, 1, 5, 10]) * 0.01): basic = BaSiC( fitting_mode='approximate', max_iterations = 100, max_reweight_iterations = 5, max_reweight_iterations_baseline= 3, max_workers = 20, resize_mode = 'skimage_dask', smoothness_flatfield=smoothness_flatfield, smoothness_darkfield=smoothness_darkfield, sparse_cost_darkfield=sparse_cost_darkfield, get_darkfield=True, ) try: basic.fit(im_array) flatfields_wsi.append(basic.flatfield) darkfields_wsi.append(basic.darkfield) records.append( { "smoothness_flatfield": smoothness_flatfield, "smoothness_darkfield": smoothness_darkfield, "sparse_cost_darkfield": sparse_cost_darkfield, } ) except RuntimeError: pass conditions_df = pd.DataFrame.from_records(records)