Rename progress folder

alphadia/cli.py

@@ -96,7 +96,7 @@
     default="{}",
 )
 parser.add_argument(
-    "--custom-quant-dir",
+    "--quant-dir",
     type=str,
     help="Directory to save the quantification results (psm & frag parquet files) to be reused in a distributed search",
     nargs="?",
@@ -172,10 +172,10 @@ def parse_output_directory(args: argparse.Namespace, config: dict) -> str:
     return output_directory
 
 
-def parse_custom_quant_dir(args: argparse.Namespace, config: dict) -> str:
-    """Parse custom quant directory.
-    1. Use custom quant directory from config file if specified.
-    2. Use custom quant directory from command line if specified.
+def parse_quant_dir(args: argparse.Namespace, config: dict) -> str:
+    """Parse custom quant path.
+    1. Use custom quant path from config file if specified.
+    2. Use custom quant path from command line if specified.
 
     Parameters
     ----------
@@ -189,26 +189,22 @@ def parse_custom_quant_dir(args: argparse.Namespace, config: dict) -> str:
     Returns
     -------
 
-    custom_quant_dir : str
-        Custom quant directory.
+    quant_dir : str
+        path to quant directory.
     """
 
-    custom_quant_dir = None
-    if "custom_quant_dir" in config:
-        custom_quant_dir = (
-            utils.windows_to_wsl(config["custom_quant_dir"])
+    quant_dir = None
+    if "quant_dir" in config:
+        quant_dir = (
+            utils.windows_to_wsl(config["quant_dir"])
             if args.wsl
-            else config["custom_quant_dir"]
+            else config["quant_dir"]
         )
 
-    if args.custom_quant_dir is not None:
-        custom_quant_dir = (
-            utils.windows_to_wsl(args.custom_quant_dir)
-            if args.wsl
-            else args.custom_quant_dir
-        )
+    if args.quant_dir is not None:
+        quant_dir = utils.windows_to_wsl(args.quant_dir) if args.wsl else args.quant_dir
 
-    return custom_quant_dir
+    return quant_dir
 
 
 def parse_raw_path_list(args: argparse.Namespace, config: dict) -> list:
@@ -349,7 +345,7 @@ def run(*args, **kwargs):
         print("No output directory specified.")
         return
 
-    custom_quant_dir = parse_custom_quant_dir(args, config)
+    quant_dir = parse_quant_dir(args, config)
 
     reporting.init_logging(output_directory)
     raw_path_list = parse_raw_path_list(args, config)
@@ -367,11 +363,10 @@ def run(*args, **kwargs):
     for f in fasta_path_list:
         logger.progress(f"  {f}")
 
+    # TODO rename all output_directory, output_folder => output_path, quant_dir->quant_path (except cli parameter)
     logger.progress(f"Saving output to: {output_directory}")
-    if custom_quant_dir is not None:
-        logger.progress(
-            f"Saving quantification output to 'custom_quant_dir': {custom_quant_dir}"
-        )
+    if quant_dir is not None:
+        logger.progress(f"Saving quantification output to {quant_dir=}")
 
     try:
         import matplotlib
@@ -387,7 +382,7 @@ def run(*args, **kwargs):
             library_path=library_path,
             fasta_path_list=fasta_path_list,
             config=config,
-            custom_quant_dir=custom_quant_dir,
+            quant_path=quant_dir,
         )
 
         plan.run()

alphadia/planning.py

@@ -40,7 +40,7 @@ def __init__(
         fasta_path_list: list[str] | None = None,
         config: dict | None = None,
         config_base_path: str | None = None,
-        custom_quant_dir: str | None = None,
+        quant_path: str | None = None,
     ) -> None:
         """Highest level class to plan a DIA Search.
         Owns the input file list, speclib and the config.
@@ -60,8 +60,8 @@ def __init__(
         config_update : dict, optional
             dict to update the default config. Can be used for debugging purposes etc.
 
-        custom_quant_dir : str, optional
-            directory to save the quantification results (psm & frag parquet files). If not provided, the results are saved in the usual workflow folder
+        quant_path : str, optional
+            path to directory to save the quantification results (psm & frag parquet files). If not provided, the results are saved in the usual workflow folder
 
         """
         if config is None:
@@ -84,7 +84,7 @@ def __init__(
         self.raw_path_list = raw_path_list
         self.library_path = library_path
         self.fasta_path_list = fasta_path_list
-        self.custom_quant_dir = custom_quant_dir
+        self.quant_path = quant_path
 
         logger.progress(f"version: {alphadia.__version__}")
 
@@ -323,7 +323,7 @@ def run(
                 workflow = peptidecentric.PeptideCentricWorkflow(
                     raw_name,
                     self.config,
-                    custom_temp_folder=self.custom_quant_dir,
+                    quant_path=self.quant_path,
                 )
 
                 # check if the raw file is already processed

alphadia/workflow/base.py

@@ -14,7 +14,7 @@
 
 logger = logging.getLogger()
 
-TEMP_FOLDER = ".progress"
+QUANT_FOLDER_NAME = "quant"
 
 
 class WorkflowBase:
@@ -32,7 +32,7 @@ def __init__(
         self,
         instance_name: str,
         config: dict,
-        custom_temp_folder: str = None,
+        quant_path: str = None,
     ) -> None:
         """
         Parameters
@@ -44,13 +44,13 @@ def __init__(
         config: dict
             Configuration for the workflow. This will be used to initialize the calibration manager and fdr manager
 
-        custom_temp_folder: str
-            custom parent_path for workflow folders, relevant for distributed searches
+        quant_path: str
+            path to directory holding quant folders, relevant for distributed searches
 
         """
         self._instance_name: str = instance_name
-        self._parent_path: str = custom_temp_folder or os.path.join(
-            config["output"], TEMP_FOLDER
+        self._parent_path: str = quant_path or os.path.join(
+            config["output"], QUANT_FOLDER_NAME
         )
         self._config: dict = config
         self.reporter: reporting.Pipeline | None = None

alphadia/workflow/peptidecentric.py

@@ -103,12 +103,12 @@ def __init__(
         self,
         instance_name: str,
         config: dict,
-        custom_temp_folder: str = None,
+        quant_path: str = None,
     ) -> None:
         super().__init__(
             instance_name,
             config,
-            custom_temp_folder,
+            quant_path,
         )
         self.optlock = None
 

misc/distributed_search/inner.sh

@@ -16,10 +16,10 @@ cd $chunk_directory || exit
 config_filename="config.yaml"
 
 # run with or without custom quant_dir
-if [ -z "${custom_quant_dir}" ]; then
+if [ -z "${quant_dir}" ]; then
     alphadia --config ${config_filename}
 else
-    alphadia --config ${config_filename} --custom-quant-dir ${custom_quant_dir}
+    alphadia --config ${config_filename} --quant-dir ${quant_dir}
 fi
 
 echo "AlphaDIA completed successfully"

misc/distributed_search/outer.sh

@@ -76,7 +76,7 @@ mkdir -p ${first_search_directory}
 mbr_library_directory="${target_directory}/3_mbr_library"
 mkdir -p ${mbr_library_directory}
 
-mbr_progress_directory="${target_directory}/3_mbr_library/chunk_0/.progress"
+mbr_progress_directory="${target_directory}/3_mbr_library/chunk_0/quant"
 mkdir -p ${mbr_progress_directory}
 
 second_search_directory="${target_directory}/4_second_search"
@@ -85,7 +85,7 @@ mkdir -p ${second_search_directory}
 lfq_directory="${target_directory}/5_lfq"
 mkdir -p ${lfq_directory}
 
-lfq_progress_directory="${target_directory}/5_lfq/chunk_0/.progress"
+lfq_progress_directory="${target_directory}/5_lfq/chunk_0/quant"
 mkdir -p ${lfq_progress_directory}
 
 ### PREDICT LIBRARY ###
@@ -150,7 +150,7 @@ if [[ "$first_search" -eq 1 ]]; then
 	--ntasks-per-node=${ntasks_per_node} \
 	--cpus-per-task=${cpus} \
 	--mem=${mem} \
-	--export=ALL,target_directory=${first_search_directory},custom_quant_dir=${mbr_progress_directory} ./inner.sh
+	--export=ALL,target_directory=${first_search_directory},quant_dir=${mbr_progress_directory} ./inner.sh
 else
 	echo "Skipping first search"
 fi
@@ -211,7 +211,7 @@ if [[ "$second_search" -eq 1 ]]; then
 	--ntasks-per-node=${ntasks_per_node} \
 	--cpus-per-task=${cpus} \
 	--mem=${mem} \
-	--export=ALL,target_directory=${second_search_directory},custom_quant_dir=${lfq_progress_directory} ./inner.sh
+	--export=ALL,target_directory=${second_search_directory},quant_dir=${lfq_progress_directory} ./inner.sh
 else
 	echo "Skipping second search"
 fi

tests/unit_tests/test_outputaccumulator.py

@@ -8,6 +8,7 @@
 from conftest import mock_fragment_df, mock_precursor_df
 
 from alphadia import outputtransform
+from alphadia.workflow.base import QUANT_FOLDER_NAME
 
 
 def prepare_input_data():
@@ -64,11 +65,11 @@ def prepare_input_data():
     temp_folder = os.path.join(tempfile.gettempdir(), "alphadia")
     os.makedirs(temp_folder, exist_ok=True)
 
-    progress_folder = os.path.join(temp_folder, "progress")
-    os.makedirs(progress_folder, exist_ok=True)
+    quant_path = os.path.join(temp_folder, QUANT_FOLDER_NAME)
+    os.makedirs(quant_path, exist_ok=True)
 
     # setup raw folders
-    raw_folders = [os.path.join(progress_folder, run) for run in run_columns]
+    raw_folders = [os.path.join(quant_path, run) for run in run_columns]
 
     psm_base_df = mock_precursor_df(n_precursor=100, with_decoy=True)
     fragment_base_df = mock_fragment_df(n_precursor=200, n_fragments=10)

tests/unit_tests/test_outputtransform.py

@@ -8,6 +8,7 @@
 
 from alphadia import outputtransform
 from alphadia.workflow import manager, peptidecentric
+from alphadia.workflow.base import QUANT_FOLDER_NAME
 
 
 def test_output_transform():
@@ -54,11 +55,11 @@ def test_output_transform():
     temp_folder = os.path.join(tempfile.gettempdir(), "alphadia")
     os.makedirs(temp_folder, exist_ok=True)
 
-    progress_folder = os.path.join(temp_folder, "progress")
-    os.makedirs(progress_folder, exist_ok=True)
+    quant_path = os.path.join(temp_folder, QUANT_FOLDER_NAME)
+    os.makedirs(quant_path, exist_ok=True)
 
     # setup raw folders
-    raw_folders = [os.path.join(progress_folder, run) for run in run_columns]
+    raw_folders = [os.path.join(quant_path, run) for run in run_columns]
 
     psm_base_df = mock_precursor_df(n_precursor=100)
     fragment_base_df = mock_fragment_df(n_precursor=200)

tests/unit_tests/test_workflow.py

@@ -320,7 +320,7 @@ def test_workflow_base():
             assert my_workflow.config["output"] == config["output"]
             assert my_workflow.instance_name == workflow_name
             assert my_workflow.parent_path == os.path.join(
-                config["output"], base.TEMP_FOLDER
+                config["output"], base.QUANT_FOLDER_NAME
             )
             assert my_workflow.path == os.path.join(
                 my_workflow.parent_path, workflow_name