diff --git a/src/agora/abc.py b/src/agora/abc.py index c396b4b1503c7558dc51eaf9032b7cb14485bc38..916fd11c17b2d4d169ba50184ee1721191764919 100644 --- a/src/agora/abc.py +++ b/src/agora/abc.py @@ -249,5 +249,5 @@ class StepABC(ProcessABC): return self._run_tp(tp, **kwargs) def run(self): - # Replace run withn run_tp + # Replace run with run_tp raise Warning("Steps use run_tp instead of run") diff --git a/src/agora/io/writer.py b/src/agora/io/writer.py index a13828c795fe406423531ba9b11a3c2cac224881..ee337a09e950e8b90eaefa16b8be2ca317a91ed3 100644 --- a/src/agora/io/writer.py +++ b/src/agora/io/writer.py @@ -172,7 +172,6 @@ class DynamicWriter: # append or create new dataset self._append(value, key, hgroup) except Exception as e: - print(key, value) self._log( f"{key}:{value} could not be written: {e}", "error" ) diff --git a/src/extraction/core/extractor.py b/src/extraction/core/extractor.py index e254532faadf893a4d41254da6c925da9518bde2..8a29766f540423f022c99638459f79a9231f0058 100644 --- a/src/extraction/core/extractor.py +++ b/src/extraction/core/extractor.py @@ -407,8 +407,6 @@ class Extractor(StepABC): reduced = img if method is not None: reduced = reduce_z(img, method) - if reduced.shape[0] < 10: - print("ahoy") return reduced def extract_tp( @@ -484,12 +482,6 @@ class Extractor(StepABC): # generate boolean masks for background as a list with one mask per trap bgs = np.array([]) if self.params.sub_bg: - # bgs = [ - # ~np.sum(m, axis=0).astype(bool) - # if np.any(m) - # else np.zeros((tile_size, tile_size)).astype(bool) - # for m in masks - # ] bgs = ~np.array( list( map( diff --git a/src/extraction/core/functions/custom/localisation.py b/src/extraction/core/functions/custom/localisation.py index 85dd995d7793ba6821e92820d735963f965ac910..07c22587e0bb47d7d1c32a5b8c768278f53517b9 100644 --- a/src/extraction/core/functions/custom/localisation.py +++ b/src/extraction/core/functions/custom/localisation.py @@ -129,7 +129,6 @@ def nuc_est_conv( def nuc_conv_3d(cell_mask, trap_image, pixel_size=0.23, spacing=0.6): - print(cell_mask.shape, trap_image.shape) cell_mask = np.stack([cell_mask] * trap_image.shape[0]) ratio = spacing / pixel_size cell_fluo = trap_image[cell_mask]