Introducing mypy.

Running it now produces no errors.

mypy.ini

@@ -0,0 +1,3 @@
+[mypy]
+ignore_missing_imports = True
+exclude = "^flycheck\."

pelicun/assessment.py

@@ -50,11 +50,12 @@
 """
 
 from __future__ import annotations
+from typing import Any
 import json
 from pelicun import base
 from pelicun import file_io
 from pelicun import model
-from pelicun.__init__ import __version__ as pelicun_version
+from pelicun.__init__ import __version__ as pelicun_version  # type: ignore
 
 
 class Assessment:
@@ -88,7 +89,7 @@ class Assessment:
         'loss',
     ]
 
-    def __init__(self, config_options=None):
+    def __init__(self, config_options: dict[str, Any] | None = None) -> None:
         """
         Initializes an Assessment object.
 

pelicun/base.py

@@ -77,7 +77,7 @@
 import argparse
 import pprint
 import numpy as np
-from scipy.interpolate import interp1d
+from scipy.interpolate import interp1d  # type: ignore
 import pandas as pd
 import colorama
 from colorama import Fore
@@ -90,7 +90,7 @@
 pp = pprint.PrettyPrinter(indent=2, width=80 - 24)
 
 pd.options.display.max_rows = 20
-pd.options.display.max_columns = None
+pd.options.display.max_columns = None  # type: ignore
 pd.options.display.expand_frame_repr = True
 pd.options.display.width = 300
 
@@ -1481,7 +1481,7 @@ def convert_units(
 
 def stringterpolation(
     arguments: str,
-) -> tuple[Callable[np.array, np.array]]:
+) -> Callable[[np.ndarray], np.ndarray]:
     """
     Turns a string of specially formatted arguments into a multilinear
     interpolating funciton.

pelicun/db.py

@@ -59,7 +59,7 @@
 from pathlib import Path
 from copy import deepcopy
 import numpy as np
-from scipy.stats import norm
+from scipy.stats import norm  # type: ignore
 import pandas as pd
 
 from pelicun import base
@@ -2648,6 +2648,7 @@ def find_class_type(entry: str) -> str | None:
 
     for fragility_id in fragility_data['ID'].to_list():
         class_type = find_class_type(fragility_id)
+        assert class_type is not None
 
         class_type_human_readable = class_types[class_type]
 

pelicun/model/damage_model.py

@@ -50,6 +50,7 @@
 """
 
 from __future__ import annotations
+from typing import TYPE_CHECKING
 import numpy as np
 import pandas as pd
 from pelicun.model.pelicun_model import PelicunModel
@@ -60,6 +61,9 @@
 from pelicun import uq
 from pelicun import file_io
 
+if TYPE_CHECKING:
+    from pelicun.assessment import Assessment
+
 idx = base.idx
 
 
@@ -71,7 +75,7 @@ class DamageModel(PelicunModel):
 
     __slots__ = ['ds_model', 'missing_components']
 
-    def __init__(self, assessment):
+    def __init__(self, assessment: Assessment) -> None:
         super().__init__(assessment)
 
         self.ds_model: DamageModel_DS = DamageModel_DS(assessment)

pelicun/model/loss_model.py

@@ -50,6 +50,7 @@
 """
 
 from __future__ import annotations
+from typing import TYPE_CHECKING
 import numpy as np
 import pandas as pd
 from pelicun.model.pelicun_model import PelicunModel
@@ -60,6 +61,8 @@
 from pelicun import uq
 from pelicun import file_io
 
+if TYPE_CHECKING:
+    from pelicun.assessment import Assessment
 
 idx = base.idx
 
@@ -75,17 +78,20 @@ class LossModel(PelicunModel):
 
     __slots__ = ['ds_model', 'lf_model']
 
+    # pylint: disable=dangerous-default-value
     def __init__(
-        self, assessment, decision_variables=('Carbon', 'Cost', 'Energy', 'Time')
-    ):
+        self,
+        assessment: Assessment,
+        decision_variables: list[str] = ['Carbon', 'Cost', 'Energy', 'Time'],
+    ) -> None:
         """
         Initializes LossModel objects.
 
         Parameters
         ----------
         assessment: pelicun.Assessment
             Parent assessment
-        decision_variables: tuple
+        decision_variables: list
             Defines the decision variables to be included in the loss
             calculations. Defaults to those supported, but fewer can be
             used if desired. When fewer are used, the loss parameters for
@@ -1289,7 +1295,7 @@ def _calc_median_consequence(self, eco_qnt):
             components and damage states, appropriately grouped to
             account for economies of scale.
 
-        decision_variables: tuple
+        decision_variables: list
             Defines the decision variables to be included in the loss
             calculations. Defaults to those supported, but fewer can be
             used if desired. When fewer are used, the loss parameters for

pelicun/model/pelicun_model.py

@@ -70,7 +70,7 @@ class PelicunModel:
 
     __slots__ = ['_asmnt', 'log']
 
-    def __init__(self, assessment):
+    def __init__(self, assessment: Assessment) -> None:
         # link the PelicunModel object to its Assessment object
         self._asmnt: Assessment = assessment
 

pelicun/tests/code_repetition_checker.py

@@ -43,7 +43,7 @@
 Python test files.
 """
 
-from glob2 import glob
+from glob2 import glob  # type: ignore
 
 # pylint: disable=missing-any-param-doc
 

pelicun/tests/test_uq.py

@@ -49,8 +49,8 @@
 import warnings
 import pytest
 import numpy as np
-from scipy.stats import norm
-from scipy.stats import lognorm
+from scipy.stats import norm  # type: ignore
+from scipy.stats import lognorm  # type: ignore
 from pelicun import uq
 from pelicun.tests.util import import_pickle
 from pelicun.tests.util import export_pickle

pelicun/tests/validation/1/test_1.py

@@ -49,7 +49,7 @@
 import pandas as pd
 import pelicun
 from pelicun import assessment
-from scipy.stats import norm
+from scipy.stats import norm  # type: ignore
 
 
 def test_validation_ds_probabilities():

pelicun/tools/HDF_to_CSV.py

@@ -64,6 +64,6 @@ def convert_HDF(HDF_path):
     parser = argparse.ArgumentParser()
     parser.add_argument('HDF_path')
 
-    args = parser.parse_args(args)
+    parser_args = parser.parse_args(args)
 
-    convert_HDF(args.HDF_path)
+    convert_HDF(parser_args.HDF_path)

pelicun/tools/export_DB.py

@@ -43,7 +43,7 @@
 from pathlib import Path
 import pandas as pd
 
-from pelicun.db import convert_Series_to_dict
+from pelicun.db import convert_Series_to_dict  # type: ignore
 
 
 def export_DB(data_path, target_dir):

pelicun/uq.py

@@ -59,11 +59,11 @@
 """
 
 from abc import ABC, abstractmethod
-from scipy.stats import uniform, norm
-from scipy.stats import multivariate_normal as mvn
-from scipy.stats._mvn import mvndst  # pylint: disable=no-name-in-module
-from scipy.linalg import cholesky, svd
-from scipy.optimize import minimize
+from scipy.stats import uniform, norm  # type: ignore
+from scipy.stats import multivariate_normal as mvn  # type: ignore
+from scipy.stats._mvn import mvndst  # type: ignore # pylint: disable=no-name-in-module # noqa # lol
+from scipy.linalg import cholesky, svd  # type: ignore
+from scipy.optimize import minimize  # type: ignore
 import numpy as np
 import pandas as pd
 import colorama
@@ -1226,7 +1226,7 @@ class RandomVariable(BaseRandomVariable):
     Random variable that needs `values` in `inverse_transform`
     """
 
-    __slots__ = []
+    __slots__: list[str] = []
 
     @abstractmethod
     def __init__(
@@ -1296,7 +1296,7 @@ class UtilityRandomVariable(BaseRandomVariable):
     Random variable that needs `sample_size` in `inverse_transform`
     """
 
-    __slots__ = []
+    __slots__: list[str] = []
 
     @abstractmethod
     def __init__(

setup.py

@@ -52,6 +52,7 @@ def read(*filenames, **kwargs):
         'development': [
             'flake8',
             'pylint',
+            'mypy',
             'black',
             'pytest',
             'pytest-cov',
@@ -63,6 +64,8 @@ def read(*filenames, **kwargs):
             'nbsphinx',
             'flake8-rst',
             'flake8-rst-docstrings',
+            'pandas-stubs',
+            'types-colorama',
         ],
     },
     classifiers=[