chore: add type annotations for pandas and networkx

pyproject.toml

@@ -57,6 +57,8 @@ pytest-unordered = "^0.5.2"
 viztracer = "^0.16.1"
 ruff = ">=0.1.12,<0.4.0"
 pytest-sugar = ">=0.9.7,<1.1.0"
+pandas-stubs = "^2.2.1.240316"
+types-networkx = "^3.2.1.20240331"
 
 
 [tool.poetry.scripts]
@@ -130,7 +132,6 @@ pythonpath = [".", "src", "test"]
 
 
 [tool.mypy]
-ignore_missing_imports = true                                 # TODO: deactivate this
 show_column_numbers = true
 strict = true
 exclude = [
@@ -149,6 +150,11 @@ exclude = [
 ]
 
 
+[[tool.mypy.overrides]]
+module = ["jsonpath_ng.*", "viztracer.*"]  # For these packages, no type stubs are available yet
+ignore_missing_imports = true
+
+
 [tool.ruff]
 line-length = 120
 target-version = "py311"

src/dsp_tools/commands/excel2json/properties.py

@@ -5,6 +5,7 @@
 import warnings
 from typing import Any
 from typing import Optional
+from typing import cast
 
 import jsonpath_ng.ext
 import jsonschema
@@ -83,7 +84,7 @@ def excel2properties(
     return props, True
 
 
-def _read_check_property_df(excelfile: str) -> pd.DataFrame | None:
+def _read_check_property_df(excelfile: str) -> pd.DataFrame:
     sheets_df_dict = read_and_clean_all_sheets(excelfile=excelfile)
     if len(sheets_df_dict) != 1:
         msg = MoreThanOneSheetProblem("properties.xlsx", list(sheets_df_dict.keys())).execute_error_protocol()
@@ -166,7 +167,7 @@ def _add_optional_columns(df: pd.DataFrame) -> pd.DataFrame:
     in_df_cols = set(df.columns)
     if not optional_col_set.issubset(in_df_cols):
         additional_col = list(optional_col_set.difference(in_df_cols))
-        additional_df = pd.DataFrame(columns=additional_col, index=df.index, data=pd.NA)
+        additional_df = pd.DataFrame(columns=additional_col, index=df.index, data=None)
         df = pd.concat(objs=[df, additional_df], axis=1)
     return df
 
@@ -181,13 +182,13 @@ def _check_missing_values_in_row(df: pd.DataFrame) -> None | list[MissingValuesI
     if missing_gui_attributes is not None:
         missing_dict.update(missing_gui_attributes)
     if missing_dict:
-        missing_dict = get_wrong_row_numbers(wrong_row_dict=missing_dict, true_remains=True)
-        return [MissingValuesInRowProblem(column=col, row_numbers=row_nums) for col, row_nums in missing_dict.items()]
+        missing_dict_int = get_wrong_row_numbers(wrong_row_dict=missing_dict, true_remains=True)
+        return [MissingValuesInRowProblem(col, row_nums) for col, row_nums in missing_dict_int.items()]
     else:
         return None
 
 
-def _check_compliance_gui_attributes(df: pd.DataFrame) -> dict[str, pd.Series] | None:
+def _check_compliance_gui_attributes(df: pd.DataFrame) -> dict[str, pd.Series[bool]] | None:
     mandatory_attributes = ["Spinbox", "List"]
     mandatory_check = col_must_or_not_empty_based_on_other_col(
         df=df,
@@ -208,15 +209,17 @@ def _check_compliance_gui_attributes(df: pd.DataFrame) -> dict[str, pd.Series] |
         case None, None:
             return None
         case pd.Series(), pd.Series():
-            final_series = pd.Series(np.logical_or(mandatory_check, no_attribute_check))  # type: ignore[arg-type]
+            mandatory_check = cast("pd.Series[bool]", mandatory_check)
+            no_attribute_check = cast("pd.Series[bool]", no_attribute_check)
+            final_series: pd.Series[bool] = pd.Series(np.logical_or(mandatory_check, no_attribute_check))
         case pd.Series(), None:
-            final_series = mandatory_check
+            final_series = cast("pd.Series[bool]", mandatory_check)
         case None, pd.Series:
-            final_series = no_attribute_check
+            final_series = cast("pd.Series[bool]", no_attribute_check)
     return {"gui_attributes": final_series}
 
 
-def _row2prop(df_row: pd.Series, row_num: int, excelfile: str) -> dict[str, Any]:
+def _row2prop(df_row: pd.Series[Any], row_num: int, excelfile: str) -> dict[str, Any]:
     _property = {x: df_row[x] for x in mandatory_properties} | {
         "labels": get_labels(df_row=df_row),
         "super": [s.strip() for s in df_row["super"].split(",")],
@@ -239,7 +242,7 @@ def _row2prop(df_row: pd.Series, row_num: int, excelfile: str) -> dict[str, Any]
 
 
 def _get_gui_attribute(
-    df_row: pd.Series,
+    df_row: pd.Series[Any],
     row_num: int,
 ) -> dict[str, int | str | float] | InvalidExcelContentProblem | None:
     if pd.isnull(df_row["gui_attributes"]):

src/dsp_tools/commands/excel2json/resources.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import importlib.resources
 import json
 import warnings
@@ -96,7 +98,7 @@ def _find_validation_problem(
 
 
 def _row2resource(
-    class_info_row: pd.Series,
+    class_info_row: pd.Series[Any],
     class_df_with_cardinalities: pd.DataFrame,
 ) -> dict[str, Any]:
     """
@@ -270,7 +272,7 @@ def _validate_excel_file(classes_df: pd.DataFrame, df_dict: dict[str, pd.DataFra
             f"Please use {[f'label_{lang}' for lang in languages]}"
         )
     problems: list[Problem] = []
-    if missing_super_rows := [int(index) + 2 for index, row in classes_df.iterrows() if not check_notna(row["super"])]:
+    if missing_super_rows := [int(str(i)) + 2 for i, row in classes_df.iterrows() if not check_notna(row["super"])]:
         problems.append(MissingValuesInRowProblem(column="super", row_numbers=missing_super_rows))
     if duplicate_check := check_column_for_duplicate(classes_df, "name"):
         problems.append(duplicate_check)

src/dsp_tools/commands/excel2json/utils.py

@@ -1,6 +1,7 @@
 from __future__ import annotations
 
 from pathlib import Path
+from typing import Any
 from unittest import mock
 
 import numpy as np
@@ -117,7 +118,7 @@ def check_column_for_duplicate(df: pd.DataFrame, to_check_column: str) -> None |
         return None
 
 
-def check_required_values(df: pd.DataFrame, required_values_columns: list[str]) -> dict[str, pd.Series]:
+def check_required_values(df: pd.DataFrame, required_values_columns: list[str]) -> dict[str, pd.Series[bool]]:
     """
     If there are any empty cells in the column, it adds the column name and a boolean pd.Series to the dictionary.
     If there are no empty cells, then it is not included in the dictionary.
@@ -155,7 +156,9 @@ def turn_bool_array_into_index_numbers(series: pd.Series[bool], true_remains: bo
     return list(series[series].index)
 
 
-def get_wrong_row_numbers(wrong_row_dict: dict[str, pd.Series], true_remains: bool = True) -> dict[str, list[int]]:
+def get_wrong_row_numbers(
+    wrong_row_dict: dict[str, pd.Series[bool]], true_remains: bool = True
+) -> dict[str, list[int]]:
     """
     From the boolean pd.Series the index numbers of the True values are extracted.
     The resulting list is the new value of the dictionary.
@@ -168,13 +171,13 @@ def get_wrong_row_numbers(wrong_row_dict: dict[str, pd.Series], true_remains: bo
     Returns:
         Dictionary with the column name as key and the row number as a list.
     """
-    wrong_row_dict = {
+    wrong_row_dict_int = {
         k: turn_bool_array_into_index_numbers(series=v, true_remains=true_remains) for k, v in wrong_row_dict.items()
     }
-    return {k: [x + 2 for x in v] for k, v in wrong_row_dict.items()}
+    return {k: [x + 2 for x in v] for k, v in wrong_row_dict_int.items()}
 
 
-def get_labels(df_row: pd.Series) -> dict[str, str]:
+def get_labels(df_row: pd.Series[Any]) -> dict[str, str]:
     """
     This function takes a pd.Series which has "label_[language tag]" in the index.
     If the value of the index is not pd.NA, the language tag and the value are added to a dictionary.
@@ -190,7 +193,7 @@ def get_labels(df_row: pd.Series) -> dict[str, str]:
     return {lang: df_row[f"label_{lang}"] for lang in languages if df_row[f"label_{lang}"] is not pd.NA}
 
 
-def get_comments(df_row: pd.Series) -> dict[str, str] | None:
+def get_comments(df_row: pd.Series[Any]) -> dict[str, str] | None:
     """
     This function takes a pd.Series which has "comment_[language tag]" in the index.
     If the value of the index is not pd.NA, the language tag and the value are added to a dictionary.
@@ -207,7 +210,7 @@ def get_comments(df_row: pd.Series) -> dict[str, str] | None:
     return comments or None
 
 
-def find_one_full_cell_in_cols(df: pd.DataFrame, required_columns: list[str]) -> pd.Series | None:
+def find_one_full_cell_in_cols(df: pd.DataFrame, required_columns: list[str]) -> pd.Series[bool] | None:
     """
     This function takes a pd.DataFrame and a list of column names where at least one cell must have a value per row.
     A pd.Series with boolean values is returned, True if any rows do not have a value in at least one column
@@ -234,7 +237,7 @@ def col_must_or_not_empty_based_on_other_col(
     substring_colname: str,
     check_empty_colname: str,
     must_have_value: bool,
-) -> pd.Series | None:
+) -> pd.Series[bool] | None:
     """
     It is presumed that the column "substring_colname" has no empty cells.
     Based on the string content of the individual rows, which is specified in the "substring_list",

src/dsp_tools/commands/excel2xml/excel2xml_cli.py

@@ -2,6 +2,7 @@
 
 import warnings
 from pathlib import Path
+from typing import Any
 from typing import Callable
 from typing import Optional
 from typing import Union
@@ -166,7 +167,7 @@ def _convert_rows_to_xml(
 
 def _append_bitstream_to_resource(
     resource: etree._Element,
-    row: pd.Series,
+    row: pd.Series[Any],
     row_number: int,
 ) -> etree._Element:
     """
@@ -210,7 +211,7 @@ def _append_bitstream_to_resource(
 
 def _convert_resource_row_to_xml(
     row_number: int,
-    row: pd.Series,
+    row: pd.Series[Any],
 ) -> etree._Element:
     """
     Convert a resource-row to an XML resource element.
@@ -291,7 +292,7 @@ def _convert_resource_row_to_xml(
 
 
 def _get_prop_function(
-    row: pd.Series,
+    row: pd.Series[Any],
     resource_id: str,
 ) -> Callable[..., etree._Element]:
     """
@@ -328,7 +329,7 @@ def _get_prop_function(
 
 
 def _convert_row_to_property_elements(
-    row: pd.Series,
+    row: pd.Series[Any],
     max_num_of_props: int,
     row_number: int,
     resource_id: str,
@@ -399,7 +400,7 @@ def _convert_row_to_property_elements(
 
 def _convert_property_row_to_xml(
     row_number: int,
-    row: pd.Series,
+    row: pd.Series[Any],
     max_num_of_props: int,
     resource_id: str,
 ) -> etree._Element:
@@ -443,7 +444,7 @@ def _convert_property_row_to_xml(
 
 def _create_property(
     make_prop_function: Callable[..., etree._Element],
-    row: pd.Series,
+    row: pd.Series[Any],
     property_elements: list[PropertyElement],
     resource_id: str,
 ) -> etree._Element:

src/dsp_tools/commands/project/create/project_validate.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import importlib.resources
 import json
 from pathlib import Path
@@ -420,8 +422,8 @@ def _extract_cardinalities_from_project(
     return cardinalities, dependencies
 
 
-def _make_cardinality_dependency_graph(dependencies: dict[str, dict[str, list[str]]]) -> nx.MultiDiGraph:
-    graph = nx.MultiDiGraph()
+def _make_cardinality_dependency_graph(dependencies: dict[str, dict[str, list[str]]]) -> nx.MultiDiGraph[Any]:
+    graph: nx.MultiDiGraph[Any] = nx.MultiDiGraph()
     for start, cards in dependencies.items():
         for edge, targets in cards.items():
             for target in targets:
@@ -430,7 +432,7 @@ def _make_cardinality_dependency_graph(dependencies: dict[str, dict[str, list[st
 
 
 def _find_circles_with_min_one_cardinality(
-    graph: nx.MultiDiGraph, cardinalities: dict[str, dict[str, str]], dependencies: dict[str, dict[str, list[str]]]
+    graph: nx.MultiDiGraph[Any], cardinalities: dict[str, dict[str, str]], dependencies: dict[str, dict[str, list[str]]]
 ) -> set[tuple[str, str]]:
     errors: set[tuple[str, str]] = set()
     circles = list(nx.algorithms.cycles.simple_cycles(graph))