avoid prints, replace with logger.info

janelia-cellmap · Feb 18, 2025 · f5c26fd · f5c26fd
1 parent a9c6eda
commit f5c26fd
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Showing 18 changed files with 56 additions and 1,128 deletions.
diff --git a/dacapo/apply.py b/dacapo/apply.py
@@ -91,7 +91,7 @@ def apply(
     ), "Either validation_dataset and criterion, or iteration must be provided."
 
     # retrieving run
-    print(f"Loading run {run_name}")
+    logger.info(f"Loading run {run_name}")
     config_store = create_config_store()
     run_config = config_store.retrieve_run_config(run_name)
     run = Run(run_config)
@@ -102,7 +102,7 @@ def apply(
     # load weights
     if iteration is None:
         iteration = weights_store.retrieve_best(run_name, validation_dataset, criterion)  # type: ignore
-    print(f"Loading weights for iteration {iteration}")
+    logger.info(f"Loading weights for iteration {iteration}")
     weights_store.retrieve_weights(run_name, iteration)
 
     if parameters is None:
@@ -121,7 +121,7 @@ def apply(
             raise ValueError(
                 "validation_dataset must be a dataset name or a Dataset object, or parameters must be provided explicitly."
             )
-        print(f"Finding best parameters for validation dataset {_validation_dataset}")
+        logger.info(f"Finding best parameters for validation dataset {_validation_dataset}")
         parameters = run.task.evaluator.get_overall_best_parameters(
             _validation_dataset, criterion
         )
@@ -183,7 +183,7 @@ def apply(
         output_container, f"output_{run_name}_{iteration}_{parameters}"
     )
 
-    print(
+    logger.info(
         f"Applying best results from run {run.name} at iteration {iteration} to dataset {Path(input_container, input_dataset)}"
     )
     return apply_run(
@@ -243,7 +243,7 @@ def apply_run(
         ... )
     """
     # render prediction dataset
-    print(f"Predicting on dataset {prediction_array_identifier}")
+    logger.info(f"Predicting on dataset {prediction_array_identifier}")
     predict(
         run.name,
         iteration,
@@ -257,13 +257,13 @@ def apply_run(
     )
 
     # post-process the output
-    print(
+    logger.info(
         f"Post-processing output to dataset {output_array_identifier}",
         output_array_identifier,
     )
     post_processor = run.task.post_processor
     post_processor.set_prediction(prediction_array_identifier)
     post_processor.process(parameters, output_array_identifier, num_workers=num_workers)
 
-    print("Done")
+    logger.info("Done")
     return
diff --git a/dacapo/cli.py b/dacapo/cli.py
@@ -680,7 +680,7 @@ def segment_blockwise(
         overwrite=overwrite,
         write_size=write_roi.shape,
     )
-    print(
+    logger.info(
         f"Created output array {output_array_identifier.container}:{output_array_identifier.dataset} with ROI {_total_roi}."
     )
 
@@ -791,7 +791,7 @@ def config():
 def generate_dacapo_yaml(config):
     with open("dacapo.yaml", "w") as f:
         yaml.dump(config.serialize(), f, default_flow_style=False)
-    print("dacapo.yaml has been created.")
+    logger.info("dacapo.yaml has been created.")
 
 
 def generate_config(
@@ -832,7 +832,7 @@ def unpack_ctx(ctx):
     Example:
         >>> ctx = ...
         >>> kwargs = unpack_ctx(ctx)
-        >>> print(kwargs)
+        >>> logger.info(kwargs)
         {'arg1': value1, 'arg2': value2, ...}
     """
     kwargs = {
@@ -843,7 +843,7 @@ def unpack_ctx(ctx):
             kwargs[k] = int(v)
         elif v.replace(".", "").isnumeric():
             kwargs[k] = float(v)
-        print(f"{k}: {kwargs[k]}")
+        logger.info(f"{k}: {kwargs[k]}")
     return kwargs
 
 

diff --git a/dacapo/experiments/datasplits/datasplit.py b/dacapo/experiments/datasplits/datasplit.py
@@ -5,6 +5,9 @@
 import json
 import itertools
 
+import logging
+
+logger = logging.getLogger(__name__)
 
 class DataSplit(ABC):
     """
@@ -84,7 +87,7 @@ def _neuroglancer(self, embedded=False, bind_address="0.0.0.0", bind_port=0):
                     neuroglancer.LayerGroupViewer(layers=list(validate_layers.keys())),
                 ]
             )
-        print(f"Neuroglancer link: {viewer}")
+        logger.info(f"Neuroglancer link: {viewer}")
         if embedded:
             from IPython.display import IFrame