feat: modernize all type

src/sp2xr/concentrations.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import pandas as pd
 
 

src/sp2xr/distribution.py

@@ -1,8 +1,7 @@
 from __future__ import annotations
+
 import numpy as np
 import pandas as pd
-from typing import Tuple
-from typing import Optional
 import gc
 import dask.dataframe as dd
 from dask.dataframe.utils import make_meta
@@ -17,7 +16,7 @@ def make_bin_arrays(
     ctrs: np.ndarray | None = None,
     n: int = 50,
     log: bool = True,
-) -> Tuple[np.ndarray, np.ndarray]:
+) -> tuple[np.ndarray, np.ndarray]:
     if lims is None and ctrs is None:
         lims = (
             np.logspace(np.log10(0.3), np.log10(400), n)
@@ -97,17 +96,17 @@ def calculate_histogram(series, bin_lims=np.logspace(np.log10(0.3), np.log10(400
 def process_hist_and_dist_partition(
     df: pd.DataFrame,
     col: str,
-    flag_col: Optional[str],
-    flag_value: Optional[int],
+    flag_col: str | None,
+    flag_value: int | None,
     bin_lims: np.ndarray,
     bin_ctrs: np.ndarray,
     dt,
     calculate_conc: bool = True,
     flow=None,  # kept for API compatibility; not used
-    rho_eff: Optional[float] = 1800,
-    BC_type: Optional[str] = "",
+    rho_eff: float = 1800,
+    BC_type: str = "",
     # t: float = 1.0,
-    name_prefix: Optional[str] = None,  # <-- NEW: force exact output name prefix
+    name_prefix: str | None = None,  # <-- NEW: force exact output name prefix
 ):
     # normalize dt -> "Xs"
     dt_str = f"{dt}s" if isinstance(dt, (int, float)) else str(dt)

src/sp2xr/flag_single_particle_data.py

@@ -1,8 +1,13 @@
+from __future__ import annotations
+
+from typing import Any
 import numpy as np
 import pandas as pd
 
 
-def define_flags(pdf_pbp, instr_config, run_config):
+def define_flags(
+    pdf_pbp: pd.DataFrame, instr_config: dict[str, Any], run_config: dict[str, Any]
+) -> pd.DataFrame:
     flag_inc_transit_time = (
         pdf_pbp["Incand Transit Time"]
         >= instr_config["instrument_parameters"]["IncTransitMin"]
@@ -71,7 +76,7 @@ def define_flags(pdf_pbp, instr_config, run_config):
     return pdf_pbp
 
 
-FLAG_COLS = [
+FLAG_COLS: list[str] = [
     "flag_valid_scatt_signal",
     "flag_scatt_not_sat",
     "flag_valid_inc_signal_in_range",

src/sp2xr/helpers.py

@@ -10,7 +10,6 @@ from dask_jobqueue import SLURMCluster
 import multiprocessing
 import psutil
 from dask.distributed import Client, LocalCluster
-from typing import Optional, List
 import shutil
 
 
@@ -295,7 +294,7 @@ def choose(cli_val, cfg, dotted, default=None):
     return cli_val if cli_val is not None else get(cfg, dotted, default)
 
 
-def find_matching_hk_file(pbp_path: str) -> Optional[str]:
+def find_matching_hk_file(pbp_path: str) -> str | None:
     """
     From a PbP filename, return a path to the corresponding hk file (.csv or .zip), if found.
     """
@@ -321,7 +320,7 @@ def extract_base_filename(file_path: str) -> str:
     return Path(file_path).stem
 
 
-def find_files(directory: str, pattern: str) -> List[str]:
+def find_files(directory: str, pattern: str) -> list[str]:
     """
     Recursively find all files in `directory` containing `pattern`.
     """
@@ -333,7 +332,7 @@ def find_files(directory: str, pattern: str) -> List[str]:
     return sorted(matches)
 
 
-def chunks(lst: List[str], n: int):
+def chunks(lst: list[str], n: int):
     """
     Yield successive `n`-sized chunks from list `lst`.
     """

src/sp2xr/io.py

@@ -1,3 +1,6 @@
+from __future__ import annotations
+
+from typing import Any
 import pandas as pd
 from pathlib import Path
 import os
@@ -15,7 +18,9 @@ logger = logging.getLogger(__name__)
 logger.setLevel(logging.INFO)
 
 
-def csv_to_parquet(csv_path: Path, parquet_path: Path, **read_csv_kwargs) -> None:
+def csv_to_parquet(
+    csv_path: Path | str, parquet_path: Path | str, **read_csv_kwargs: Any
+) -> None:
     """
     Read a CSV file, return an identical Parquet file.
 
@@ -35,7 +40,7 @@ def csv_to_parquet(csv_path: Path, parquet_path: Path, **read_csv_kwargs) -> Non
     df.to_parquet(parquet_path, index=False)
 
 
-def read_sp2xr_csv(file_path: str, schema: dict) -> dd.DataFrame:
+def read_sp2xr_csv(file_path: str, schema: dict[str, Any]) -> dd.DataFrame:
     """
     Read a SP2XR CSV/ZIP file with given schema from config.
 
@@ -54,7 +59,7 @@ def read_sp2xr_csv(file_path: str, schema: dict) -> dd.DataFrame:
     return df
 
 
-def load_matching_hk_file(file_path):
+def load_matching_hk_file(file_path: str | None) -> pd.DataFrame:
     expected_columns = ["Time Stamp", "Time (sec)"]
 
     if not file_path:

src/sp2xr/resample_pbp_hk.py

@@ -1,11 +1,13 @@
-from typing import List
+from __future__ import annotations
+
+from typing import Any
 import pandas as pd
 import numpy as np
 import dask.dataframe as dd
 from sp2xr.helpers import floor_index_to_dt
 
 
-_SUM_COLS: List[str] = [
+_SUM_COLS: list[str] = [
     "Dropped Records",
     "Incand Mass (fg)",
     "BC mass",
@@ -24,7 +26,7 @@ _SUM_COLS: List[str] = [
     "cnts_unclassified",
 ]
 
-_COUNT_SRC: List[str] = [
+_COUNT_SRC: list[str] = [
     "Incand Mass (fg)",
     "BC mass",
     "BC mass within range",
@@ -34,7 +36,7 @@ _COUNT_SRC: List[str] = [
     "temporary_col",
 ]
 
-_COUNT_DST: List[str] = [
+_COUNT_DST: list[str] = [
     "BC numb from file",
     "BC numb",
     "BC numb within range",
@@ -134,7 +136,9 @@ def resample_hk_partition(pdf: pd.DataFrame, dt="1s") -> pd.DataFrame:
     return out
 
 
-def join_pbp_with_flow(ddf_cal, flow_dt, run_config):
+def join_pbp_with_flow(
+    ddf_cal: dd.DataFrame, flow_dt: dd.DataFrame, run_config: dict[str, Any]
+) -> dd.DataFrame:
     inc_mass_bin_lims = np.logspace(
         np.log10(run_config["histo"]["inc"]["min_mass"]),
         np.log10(run_config["histo"]["inc"]["max_mass"]),