Remove old logging config, add CLI logging config

core/lls_core/__init__.py

@@ -2,57 +2,9 @@
 __version__ = "0.2.6"
 
 from strenum import StrEnum
-from enum import Enum
-from pyclesperanto_prototype._tier8._affine_transform_deskew_3d import DeskewDirection
-
-# Initialize configuration options
 
 #Choice of Deconvolution
 class DeconvolutionChoice(StrEnum):
     cuda_gpu = "cuda_gpu"
     opencl_gpu = "opencl_gpu"
     cpu = "cpu"
-
-#CONFIGURE LOGGING using a dictionary (can also be done with yaml file)
-import logging.config
-LOGGING_CONFIG = {
-    'version': 1,
-    'disable_existing_loggers': False,
-
-    'formatters': {
-        'default_formatter': {
-            'format': '[%(levelname)s:%(asctime)s] %(message)s'
-        },
-    },
-
-    'handlers': {
-        'stream_handler': {
-            'class': 'logging.StreamHandler',
-            'formatter': 'default_formatter',
-        },
-    },
-
-    'loggers': {
-        '': {
-            'handlers': ['stream_handler'],
-            'level': 'INFO',
-            'propagate': True
-        }
-    }
-}
-
-#Configuring logging level with empty string "" under the key loggers means its for root
-#This will override levels for all other python libraries as they haven't been imported yet
-#Specifying levels in each modules will override the root level
-
-# Specify during initialization:
-logging.config.dictConfig(LOGGING_CONFIG)
-logger = logging.getLogger(__name__)
-logger.debug("Logging is configured.")
-
-#specify an enum for log levels
-class Log_Levels(Enum):
-    DEBUG = 10
-    INFO = 20
-    WARNING = 30
-

core/lls_core/cmds/__main__.py

@@ -17,6 +17,8 @@
 from lls_core.models.crop import CropParams
 from lls_core import DeconvolutionChoice
 from typer import Typer, Argument, Option, Context, Exit
+import logging
+from rich.logging import RichHandler
 
 from lls_core.models.output import SaveFileType
 from pydantic import ValidationError
@@ -171,12 +173,14 @@ def process(
     json_config: Optional[Path] = Option(None, show_default=False, help="Path to a JSON file from which parameters will be read."),
     yaml_config: Optional[Path] = Option(None, show_default=False, help="Path to a YAML file from which parameters will be read."),
 
-    show_schema: bool = Option(default=False, help="If provided, image processing will not be performed, and instead a JSON document outlining the JSON/YAML options will be printed to stdout. This can be used to assist with writing a config file for use with the --json-config and --yaml-config options.")
+    show_schema: bool = Option(default=False, help="If provided, image processing will not be performed, and instead a JSON document outlining the JSON/YAML options will be printed to stdout. This can be used to assist with writing a config file for use with the --json-config and --yaml-config options."),
+    quiet: bool = Option(default=False, help="Silence all output.")
 ) -> None:
     from click.core import ParameterSource
-    from rich.console import Console
 
-    console = Console(stderr=True)
+    # The quiet flag informs the logging config
+    # RichHandler lets us do "pretty" logging
+    logging.basicConfig(level=logging.WARNING if quiet else logging.INFO, handlers=[RichHandler()])
 
     if show_schema:
         import json
@@ -221,11 +225,11 @@ def process(
             )
         )
     except ValidationError as e:
-        console.print(rich_validation(e))
+        logging.error(rich_validation(e), extra={"markup": True})
         raise Exit(code=1)
 
     lattice.save()
-    console.print(f"Processing successful. Results can be found in {lattice.save_dir.resolve()}")
+    logging.info(f"Processing successful. Results can be found in [bold]{lattice.save_dir.resolve()}[/]", extra={"markup": True})
 
 
 def main():

core/lls_core/models/lattice_data.py

@@ -23,7 +23,6 @@
     from xarray import DataArray
 
 import logging
-
 logger = logging.getLogger(__name__)
 
 class LatticeData(OutputParams, DeskewParams):
@@ -66,6 +65,15 @@ def read_image(cls, values: dict):
         # Use the Deskew version of this validator, to do the actual image loading
         return super().read_image(values)
 
+    @root_validator(pre=False)
+    def log_params(cls, values: dict):
+        """
+        Prints out the final parameter state
+        """
+        from rich.pretty import pretty_repr
+        logger.info(pretty_repr(values), extra={"markup": True})
+        return values
+
     @validator("workflow", pre=True)
     def parse_workflow(cls, v: Any):
         # Load the workflow from disk if it was provided as a path