Parallelize over CPUs

pyproject.toml

@@ -26,6 +26,7 @@ dependencies = [
     "PyYAML",
     "click",
     "natsort",
+    "dandi",
 ]
 classifiers = [
     "Programming Language :: Python",

src/dandi_s3_log_parser/__init__.py

@@ -3,6 +3,7 @@
 from ._config import DANDI_S3_LOG_PARSER_BASE_FOLDER_PATH, IPINFO_CREDENTIALS, get_hash_salt
 from ._s3_log_file_parser import parse_dandi_raw_s3_log, parse_raw_s3_log, parse_all_dandi_raw_s3_logs
 from ._buffered_text_reader import BufferedTextReader
+from ._order_parsed_logs import order_parsed_logs
 
 __all__ = [
     "DANDI_S3_LOG_PARSER_BASE_FOLDER_PATH",
@@ -12,4 +13,5 @@
     "parse_raw_s3_log",
     "parse_dandi_raw_s3_log",
     "parse_all_dandi_raw_s3_logs",
+    "order_parsed_logs",
 ]

src/dandi_s3_log_parser/_buffered_text_reader.py

@@ -2,23 +2,23 @@
 
 
 class BufferedTextReader:
-    def __init__(self, *, file_path: str | pathlib.Path, maximum_ram_usage: int = 10**9):
+    def __init__(self, *, file_path: str | pathlib.Path, maximum_ram_usage_in_bytes: int = 10**9):
         """
         Lazily read a text file into RAM using buffers of a specified size.
 
         Parameters
         ----------
         file_path : string or pathlib.Path
             The path to the text file to be read.
-        maximum_ram_usage : int, default: 1 GB
-            The theoretical maximum amount of RAM to be used by the BufferedTextReader object.
+        maximum_ram_usage_in_bytes : int, default: 1 GB
+            The theoretical maximum amount of RAM (in bytes) to be used by the BufferedTextReader object.
         """
         self.file_path = file_path
-        self.maximum_ram_usage = maximum_ram_usage
+        self.maximum_ram_usage_in_bytes = maximum_ram_usage_in_bytes
 
         # The actual amount of bytes to read per iteration is 3x less than theoretical maximum usage
         # due to decoding and handling
-        self.buffer_size_in_bytes = int(maximum_ram_usage / 3)
+        self.buffer_size_in_bytes = int(maximum_ram_usage_in_bytes / 3)
 
         self.total_file_size = pathlib.Path(file_path).stat().st_size
         self.offset = 0

src/dandi_s3_log_parser/_order_parsed_logs.py

@@ -0,0 +1,19 @@
+import pathlib
+import pandas
+
+
+def order_parsed_logs(
+    unordered_parsed_s3_log_folder_path: pathlib.Path, ordered_parsed_s3_log_folder_path: pathlib.Path
+) -> None:
+    """Order the contents of all parsed log files chronologically."""
+    ordered_parsed_s3_log_folder_path.mkdir(exist_ok=True)
+
+    for unordered_parsed_s3_log_file_path in unordered_parsed_s3_log_folder_path.iterdir():
+        unordered_parsed_s3_log = pandas.read_table(filepath_or_buffer=unordered_parsed_s3_log_file_path, index_col=0)
+        ordered_parsed_s3_log = unordered_parsed_s3_log.sort_values(by="timestamp")
+
+        # correct index of first column
+        ordered_parsed_s3_log.index = range(len(ordered_parsed_s3_log))
+
+        ordered_parsed_s3_log_file_path = ordered_parsed_s3_log_folder_path / unordered_parsed_s3_log_file_path.name
+        ordered_parsed_s3_log.to_csv(path_or_buf=ordered_parsed_s3_log_file_path, sep="\t")